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OSGeo-live/CesiumWidget | Examples/CesiumWidget Example KML.ipynb | 1 | 3142 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Cesium Widget Example KML\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If the installation of Cesiumjs is ok, it should be reachable here:\n",
"http://localhost:8888/nbextensions/CesiumWidget/cesium/index.html"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"from CesiumWidget import CesiumWidget\n",
"from IPython import display\n",
"import numpy as np"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Create widget object"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
"cesium = CesiumWidget()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Display the widget:"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
"cesium"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Cesium is packed with example data. Let's look at some GDP per captia data from 2008. "
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
"cesium.kml_url = '/nbextensions/CesiumWidget/cesium/Apps/SampleData/kml/gdpPerCapita2008.kmz'"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Example zoomto"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [],
"source": [
"for lon in np.arange(0, 360, 0.5):\n",
" cesium.zoom_to(lon, 0, 36000000, 0 ,-90, 0)"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[359.5, 0.0, 36000000.0, 0.0, -90.0, 0.0]"
]
},
"execution_count": 20,
"output_type": "execute_result",
"metadata": {}
}
],
"source": [
"cesium._zoomto"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Example flyto"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [],
"source": [
"cesium.fly_to(14, 90, 20000001)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[14.0, 90.0, 20000001.0, 0.0, -90.0, 0.0]"
]
},
"execution_count": 8,
"output_type": "execute_result",
"metadata": {}
}
],
"source": [
"cesium._flyto"
]
}
],
"metadata": {
"hide_input": false,
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3.0
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.0"
}
},
"nbformat": 4,
"nbformat_minor": 0
} | apache-2.0 |
mne-tools/mne-tools.github.io | 0.18/_downloads/db126f84a1b5439712a1d57b1be2255c/plot_time_frequency_global_field_power.ipynb | 1 | 6975 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n# Explore event-related dynamics for specific frequency bands\n\n\nThe objective is to show you how to explore spectrally localized\neffects. For this purpose we adapt the method described in [1]_ and use it on\nthe somato dataset. The idea is to track the band-limited temporal evolution\nof spatial patterns by using the Global Field Power (GFP).\n\nWe first bandpass filter the signals and then apply a Hilbert transform. To\nreveal oscillatory activity the evoked response is then subtracted from every\nsingle trial. Finally, we rectify the signals prior to averaging across trials\nby taking the magniude of the Hilbert.\nThen the GFP is computed as described in [2]_, using the sum of the squares\nbut without normalization by the rank.\nBaselining is subsequently applied to make the GFPs comparable between\nfrequencies.\nThe procedure is then repeated for each frequency band of interest and\nall GFPs are visualized. To estimate uncertainty, non-parametric confidence\nintervals are computed as described in [3]_ across channels.\n\nThe advantage of this method over summarizing the Space x Time x Frequency\noutput of a Morlet Wavelet in frequency bands is relative speed and, more\nimportantly, the clear-cut comparability of the spectral decomposition (the\nsame type of filter is used across all bands).\n\nReferences\n----------\n\n.. [1] Hari R. and Salmelin R. Human cortical oscillations: a neuromagnetic\n view through the skull (1997). Trends in Neuroscience 20 (1),\n pp. 44-49.\n.. [2] Engemann D. and Gramfort A. (2015) Automated model selection in\n covariance estimation and spatial whitening of MEG and EEG signals,\n vol. 108, 328-342, NeuroImage.\n.. [3] Efron B. and Hastie T. Computer Age Statistical Inference (2016).\n Cambrdige University Press, Chapter 11.2.\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Authors: Denis A. Engemann <[email protected]>\n#\n# License: BSD (3-clause)\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nimport mne\nfrom mne.datasets import somato\nfrom mne.baseline import rescale\nfrom mne.stats import _bootstrap_ci"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Set parameters\n\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"data_path = somato.data_path()\nraw_fname = data_path + '/MEG/somato/sef_raw_sss.fif'\n\n# let's explore some frequency bands\niter_freqs = [\n ('Theta', 4, 7),\n ('Alpha', 8, 12),\n ('Beta', 13, 25),\n ('Gamma', 30, 45)\n]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We create average power time courses for each frequency band\n\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# set epoching parameters\nevent_id, tmin, tmax = 1, -1., 3.\nbaseline = None\n\n# get the header to extract events\nraw = mne.io.read_raw_fif(raw_fname, preload=False)\nevents = mne.find_events(raw, stim_channel='STI 014')\n\nfrequency_map = list()\n\nfor band, fmin, fmax in iter_freqs:\n # (re)load the data to save memory\n raw = mne.io.read_raw_fif(raw_fname, preload=True)\n raw.pick_types(meg='grad', eog=True) # we just look at gradiometers\n\n # bandpass filter and compute Hilbert\n raw.filter(fmin, fmax, n_jobs=1, # use more jobs to speed up.\n l_trans_bandwidth=1, # make sure filter params are the same\n h_trans_bandwidth=1, # in each band and skip \"auto\" option.\n fir_design='firwin')\n raw.apply_hilbert(n_jobs=1, envelope=False)\n\n epochs = mne.Epochs(raw, events, event_id, tmin, tmax, baseline=baseline,\n reject=dict(grad=4000e-13, eog=350e-6), preload=True)\n # remove evoked response and get analytic signal (envelope)\n epochs.subtract_evoked() # for this we need to construct new epochs.\n epochs = mne.EpochsArray(\n data=np.abs(epochs.get_data()), info=epochs.info, tmin=epochs.tmin)\n # now average and move on\n frequency_map.append(((band, fmin, fmax), epochs.average()))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we can compute the Global Field Power\nWe can track the emergence of spatial patterns compared to baseline\nfor each frequency band, with a bootstrapped confidence interval.\n\nWe see dominant responses in the Alpha and Beta bands.\n\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"fig, axes = plt.subplots(4, 1, figsize=(10, 7), sharex=True, sharey=True)\ncolors = plt.get_cmap('winter_r')(np.linspace(0, 1, 4))\nfor ((freq_name, fmin, fmax), average), color, ax in zip(\n frequency_map, colors, axes.ravel()[::-1]):\n times = average.times * 1e3\n gfp = np.sum(average.data ** 2, axis=0)\n gfp = mne.baseline.rescale(gfp, times, baseline=(None, 0))\n ax.plot(times, gfp, label=freq_name, color=color, linewidth=2.5)\n ax.axhline(0, linestyle='--', color='grey', linewidth=2)\n ci_low, ci_up = _bootstrap_ci(average.data, random_state=0,\n stat_fun=lambda x: np.sum(x ** 2, axis=0))\n ci_low = rescale(ci_low, average.times, baseline=(None, 0))\n ci_up = rescale(ci_up, average.times, baseline=(None, 0))\n ax.fill_between(times, gfp + ci_up, gfp - ci_low, color=color, alpha=0.3)\n ax.grid(True)\n ax.set_ylabel('GFP')\n ax.annotate('%s (%d-%dHz)' % (freq_name, fmin, fmax),\n xy=(0.95, 0.8),\n horizontalalignment='right',\n xycoords='axes fraction')\n ax.set_xlim(-1000, 3000)\n\naxes.ravel()[-1].set_xlabel('Time [ms]')"
]
}
],
"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": 0
} | bsd-3-clause |
dinrker/PredictiveModeling | Session 1 - Linear_Regression.ipynb | 1 | 204715 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
""
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Goals of this Lesson\n",
" \n",
"- Present the fundamentals of Linear Regression for Prediction\n",
" - Notation and Framework\n",
" - Gradient Descent for Linear Regression\n",
" - Advantages and Issues\n",
" - Closed form Matrix Solutions for Linear Regression\n",
" - Advantages and Issues\n",
"- Demonstrate Python \n",
" - Exploratory Plotting\n",
" - Simple plotting with `pyplot` from `matplotlib`\n",
" - Code Gradient Descent\n",
" - Code Closed Form Matrix Solution\n",
" - Perform Linear Regression in scikit-learn\n",
"\n",
"\n",
"### References for Linear Regression\n",
"\n",
"\n",
"- Elements of Statistical Learning by Hastie, Tibshriani, Friedman - Chapter 3 \n",
"- Alex Ihler's Course Notes on Linear Models for Regression - http://sli.ics.uci.edu/Classes/2015W-273a\n",
"- scikit-learn Documentation - http://scikit-learn.org/stable/modules/linear_model.html#ordinary-least-squares\n",
"- Linear Regression Analysis By Seber and Lee - http://www.wiley.com/WileyCDA/WileyTitle/productCd-0471415405,subjectCd-ST24.html\n",
"- Applied Linear Regression by Weisberg - http://onlinelibrary.wiley.com/book/10.1002/0471704091\n",
"- Wikipedia - http://en.wikipedia.org/wiki/Linear_regression"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Linear Regression Notation and Framework\n",
"\n",
"Linear Regression is a supervised learning technique that is interested in predicting a response or target $\\mathbf{y}$, based on a linear combination of a set $D$ predictors or features, $\\mathbf{x}= (1, x_1,\\dots, x_D)$ such that,\n",
"\n",
"\\begin{equation*}\n",
"y = \\beta_0 + \\beta_1 x_1 + \\dots + \\beta_D x_D = \\mathbf{x_i}^T\\mathbf{\\beta}\n",
"\\end{equation*}\n",
"\n",
"_**Data We Observe**_\n",
"\n",
"\\begin{eqnarray*}\n",
"y &:& \\mbox{response or target variable} \\\\\n",
"\\mathbf{x} &:& \\mbox{set of $D$ predictor or explanatory variables } \\mathbf{x}^T = (1, x_1, \\dots, x_D) \n",
"\\end{eqnarray*}\n",
"\n",
"_** What We Are Trying to Learn**_\n",
"\n",
"\\begin{eqnarray*}\n",
"\\beta^T = (\\beta_0, \\beta_1, \\dots, \\beta_D) : \\mbox{Parameter values for a \"best\" prediction of } y \\rightarrow \\hat y\n",
"\\end{eqnarray*}\n",
"\n",
"_**Outcomes We are Trying to Predict**_\n",
"\n",
"\\begin{eqnarray*}\n",
"\\hat y : \\mbox{Prediction for the data that we observe}\n",
"\\end{eqnarray*}\n",
"\n",
"_**Matrix Notation**_\n",
"\n",
"\\begin{equation*}\n",
"\\mathbf{Y} = \\left( \\begin{array}{ccc}\n",
"y_1 \\\\\n",
"y_2 \\\\\n",
"\\vdots \\\\\n",
"y_i \\\\\n",
"\\vdots \\\\\n",
"y_N\n",
"\\end{array} \\right)\n",
"\\qquad\n",
"\\mathbf{X} = \\left( \\begin{array}{ccc}\n",
"1 & x_{1,1} & x_{1,2} & \\dots & x_{1,D} \\\\\n",
"1 & x_{2,1} & x_{2,2} & \\dots & x_{2,D} \\\\\n",
"\\vdots & \\vdots & \\vdots & \\ddots & \\vdots \\\\\n",
"1 & x_{i,1} & x_{i,2} & \\dots & x_{i,D} \\\\\n",
"\\vdots & \\vdots & \\vdots & \\ddots & \\vdots \\\\\n",
"1 & x_{N,1} & x_{N,2} & \\dots & x_{N,D} \\\\\n",
"\\end{array} \\right)\n",
"\\qquad\n",
"\\beta = \\left( \\begin{array}{ccc}\n",
"\\beta_0 \\\\\n",
"\\beta_1 \\\\\n",
"\\vdots \\\\\n",
"\\beta_j \\\\\n",
"\\vdots \\\\\n",
"\\beta_D\n",
"\\end{array} \\right)\n",
"\\end{equation*}\n",
"\n",
"\n",
"_Why is it called Linear Regression?_\n",
"\n",
"It is often asked, why is it called linear regression if we can use polynomial terms and other transformations as the predictors. That is \n",
"\n",
"\\begin{equation*}\n",
" y = \\beta_0 + \\beta_1 x_1 + \\beta_2 x_1^2 + \\beta_3 x_1^3 + \\beta_4 \\sin(x_1)\n",
"\\end{equation*}\n",
"\n",
"is still a linear regression, though it contains polynomial and trigonometric transformations of $x_1$. This is due to the fact that the term _linear_ applies to the learned coefficients $\\beta$ and not the input features $\\mathbf{x}$. \n",
"\n",
"\n",
"_** How can we Learn $\\beta$? **_\n",
"\n",
"Linear Regression can be thought of as an optimization problem where we want to minimize some loss function of the error between the prediction $\\hat y$ and the observed data $y$. \n",
"\n",
"\\begin{eqnarray*}\n",
" error_i &=& y_i - \\hat y_i \\\\\n",
" &=& y_i - \\mathbf{x_i^T}\\beta\n",
"\\end{eqnarray*}\n",
"\n",
"_Let's see what these errors look like..._\n",
"\n",
"Below we show a simulation where the observed $y$ was generated such that $y= 1 + 0.5 x + \\epsilon$ and $\\epsilon \\sim N(0,1)$. If we assume that know the truth that $y=1 + 0.5 x$, the red lines demonstrate the error (or residuals) between the observed and the truth. "
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false,
"scrolled": false
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"outputs": [
{
"data": {
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PAKzHahJ97W/1iU+YYKkyVdvxPTPXRsTNwOHAr0teegyYUHK8d/G5XhYvXtz9uL29nfb2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\nOf41MSSF75JUS33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"text/plain": [
"<matplotlib.figure.Figure at 0x10708b490>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#############################################################\n",
"# Demonstration - What do Residuals Look Like\n",
"#############################################################\n",
"\n",
"np.random.seed(33) # Setting a seed allows reproducability of experiments\n",
"\n",
"beta0 = 1 # Creating an intercept\n",
"beta1 = 0.5 # Creating a slope\n",
"\n",
"# Randomly sampling data points\n",
"x_example = np.random.uniform(0,5,10)\n",
"y_example = beta0 + beta1 * x_example + np.random.normal(0,1,10)\n",
"line1 = beta0 + beta1 * np.arange(-1, 6)\n",
"\n",
"f = plt.figure()\n",
"plt.scatter(x_example,y_example) # Plotting observed data\n",
"plt.plot(np.arange(-1,6), line1) # Plotting the true line\n",
"for i, xi in enumerate(x_example):\n",
" plt.vlines(xi, beta0 + beta1 * xi, y_example[i], colors='red') # Plotting Residual Lines\n",
"plt.annotate('Error or \"residual\"', xy = (x_example[5], 2), xytext = (-1.5,2.1),\n",
" arrowprops=dict(width=1,headwidth=7,facecolor='black', shrink=0.01))\n",
"f.set_size_inches(10,5)\n",
"plt.title('Errors in Linear Regression')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"_Choosing a Loss Function to Optimize_\n",
"\n",
"Historically Linear Regression has been solved using the method of Least Squares where we are interested in minimizing the mean squared error loss function of the form:\n",
"\n",
"\\begin{eqnarray*}\n",
" Loss(\\beta) = MSE &=& \\frac{1}{N} \\sum_{i=1}^{N} (y_i - \\hat y_i)^2 \\\\\n",
" &=& \\frac{1}{N} \\sum_{i=1}^{N} (y_i - \\mathbf{x_i^T}\\beta)^2 \\\\\n",
"\\end{eqnarray*}\n",
"\n",
"Where $N$ is the total number of observations. Other loss functions can be used, but using mean squared error (also referred to sum of the squared residuals in other text) has very nice properities for closed form solutions. We will use this loss function for both gradient descent and to create a closed form matrix solution."
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"### Before We Present Solutions for Linear Regression: Introducing a Baseball Dataset\n",
"\n",
"We'll use this dataset to investigate Linear Regression. The dataset consists of 337 observations and 18 variables from the set of Major League Baseball players who played at least one game in both the 1991 and 1992\n",
"seasons, excluding pitchers. The dataset contains the 1992 salaries for that population, along with performance measures for each player. Four categorical variables indicate how free each player was to move to other teams.\n",
"\n",
"** Reference **\n",
"\n",
"- Pay for Play: Are Baseball Salaries Based on Performance?\n",
" - http://www.amstat.org/publications/jse/v6n2/datasets.watnik.html\n",
"\n",
"**Filename**\n",
"\n",
"- 'baseball.dat.txt'.\n",
"\n",
"**Variables**\n",
"\n",
"- _Salary_: Thousands of dollars\n",
"- _AVG_: Batting average\n",
"- _OBP_: On-base percentage\n",
"- _Runs_: Number of runs\n",
"- _Hits_: Number of hits\n",
"- _Doubles_: Number of doubles\n",
"- _Triples_: Number of triples\n",
"- _HR_: Number of home runs\n",
"- _RBI_: Number of runs batted in\n",
"- _Walks_: Number of walks\n",
"- _SO_: Number of strike-outs\n",
"- _SB_: Number of stolen bases\n",
"- _Errs_: Number of errors\n",
"- _free agency eligibility_: Indicator of \"free agency eligibility\"\n",
"- _free agent in 1991/2_: Indicator of \"free agent in 1991/2\"\n",
"- _arbitration eligibility_: Indicator of \"arbitration eligibility\"\n",
"- _arbitration in 1991/2_: Indicator of \"arbitration in 1991/2\"\n",
"- _Name_: Player's name (in quotation marks)\n",
"\n",
"** What we will try to predict **\n",
"\n",
"We will attempt to predict the players salary based upon some predictor variables such as Hits, OBP, Walks, RBIs, etc. \n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Load The Data\n",
"\n",
"Loading data in python from csv files in python can be done by a few different ways. The numpy package has a function called 'genfromtxt' that can read csv files, while the pandas library has the 'read_csv' function. Remember that we have imported numpy and pandas as `np` and `pd` respectively at the top of this notebook. An example using pandas is as follows:\n",
"\n",
" pd.read_csv(filename, **args)\n",
"\n",
"http://pandas.pydata.org/pandas-docs/dev/generated/pandas.io.parsers.read_csv.html\n",
"\n",
"\n",
"###<span style=\"color:red\">STUDENT ACTIVITY (2 MINS)</span> \n",
"_**Student Action - Load the 'baseball.dat.txt' file into a variable called 'baseball'. Then use baseball.head() to view the first few entries**_"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#######################################################################\n",
"# Student Action - Load the file 'baseball.dat.txt' using pd.read_csv()\n",
"#######################################################################\n",
"baseball = pd.read_csv('data/baseball.dat.txt')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"_**Crash Course: Plotting with Matplotlib**_\n",
"\n",
"At the top of this notebook we have imported the the package `pyplot as plt` from the `matplotlib` library. `matplotlib` is a great package for creating simple plots in Python. Below is a link to their tutorial for basic plotting.\n",
"\n",
"_Tutorials_\n",
"\n",
"- http://matplotlib.org/users/pyplot_tutorial.html\n",
"- https://scipy-lectures.github.io/intro/matplotlib/matplotlib.html\n",
"\n",
"_Simple Plotting_\n",
"\n",
"- Step 0: Import the packge pyplot from matplotlib for plotting \n",
" - `import matplotlib.pyplot as plt`\n",
"- Step 1: Create a variable to store a new figure object\n",
" - `fig = plt.figure()`\n",
"- Step 2: Create the plot of your choice\n",
" - Common Plots\n",
" - `plt.plot(x,y)` - A line plot\n",
" - `plt.scatter(x,y)` - Scatter Plots\n",
" - `plt.hist(x)` - Histogram of a variable\n",
" - Example Plots: http://matplotlib.org/gallery.html\n",
"- Step 3: Create labels for your plot for better interpretability\n",
" - X Label\n",
" - `plt.xlabel('String')`\n",
" - Y Label\n",
" - `plt.ylabel('String')`\n",
" - Title\n",
" - `plt.title('String')`\n",
"- Step 4: Change the figure size for better viewing within the iPython Notebook\n",
" - `fig.set_size_inches(width, height)`\n",
"- Step 5: Show the plot\n",
" - `plt.show()`\n",
" - The above command allows the plot to be shown below the cell that you're currently in. This is made possible by the `magic` command `%matplotlib inline`. \n",
"- _NOTE: This may not always be the best way to create plots, but it is a quick template to get you started._\n",
" \n",
"_Transforming Variables_\n",
"\n",
"We'll talk more about numpy later, but to perform the logarithmic transformation use the command\n",
"\n",
"- `np.log(`$array$`)`"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
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"text/plain": [
"<matplotlib.figure.Figure at 0x10741b410>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#############################################################\n",
"# Demonstration - Plot a Histogram of Hits \n",
"#############################################################\n",
"f = plt.figure()\n",
"plt.hist(baseball['Hits'], bins=15)\n",
"plt.xlabel('Number of Hits')\n",
"plt.ylabel('Frequency')\n",
"plt.title('Histogram of Number of Hits')\n",
"f.set_size_inches(10, 5)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##<span style=\"color:red\">STUDENT ACTIVITY (7 MINS)</span> \n",
"\n",
"### Data Exploration - Investigating Variables\n",
"\n",
"Work in pairs to import the package `matplotlib.pyplot`, create the following two plots. \n",
"\n",
"- A histogram of the $log(Salary)$\n",
" - hint: `np.log()`\n",
"- a scatterplot of $log(Salary)$ vs $Hits$."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"data": {
"image/png": 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SxBpbEKuqc4EHLbL+h8BkjMlJkiQ15J31JUmSGjGISZIkNWIQkyRJasQgJkmS1IhBTJIk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\nH0xyfpJv9fMkF24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"text/plain": [
"<matplotlib.figure.Figure at 0x107437d90>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#############################################################\n",
"# Student Action - import matplotlib.pylot \n",
"# - Plot a Histogram of log(Salaries)\n",
"#############################################################\n",
"\n",
"f = plt.figure()\n",
"plt.hist(np.log(baseball['Salary']), bins = 15)\n",
"plt.xlabel('log(Salaries)')\n",
"plt.ylabel('Frequency')\n",
"plt.title('Histogram of log Salaries')\n",
"f.set_size_inches(10, 5)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"data": {
"image/png": 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1zM3lm1tTtOPZmHe0e/dvA4/n0KG3A9WrAa/uop37gQ3h96cn3dxSUX6hDKSo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\noEtEREQkAwq6RERERDLwfwF/HvQOYhJeMQAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1078a5f90>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#############################################################\n",
"# Studdent Action - Plot a Scatter Plot of Salarie vs. Hitting\n",
"#############################################################\n",
"\n",
"f = plt.figure()\n",
"plt.scatter(baseball['Hits'], np.log(baseball['Salary']))\n",
"plt.xlabel('Hits')\n",
"plt.ylabel('log(Salaries)')\n",
"plt.title('Scatter Plot of Salarie vs. Hitting')\n",
"f.set_size_inches(10, 5)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Gradient Descent for Linear Regression\n",
"\n",
"In Linear Regression we are interested in optimizing our loss function $Loss(\\beta)$ to find the optimatal $\\beta$ such that \n",
"\n",
"\\begin{eqnarray*}\n",
"\\hat \\beta &=& \\arg \\min_{\\beta} \\frac{1}{N} \\sum_{i=1}^{N} (y_i - \\mathbf{x_i^T}\\beta)^2 \\\\\n",
"&=& \\arg \\min_{\\beta} \\frac{1}{N} \\mathbf{(Y - X\\beta)^T (Y - X\\beta)} \\\\\n",
"\\end{eqnarray*}\n",
"\n",
"One optimization technique called 'Gradient Descent' is useful for finding an optimal solution to this problem. Gradient descent is a first order optimization technique that attempts to find a local minimum of a function by updating its position by taking steps proportional to the negative gradient of the function at its current point. The gradient at the point indicates the direction of steepest ascent and is the best guess for which direction the algorithm should go. \n",
"\n",
"If we consider $\\theta$ to be some parameters we are interested in optimizing, $L(\\theta)$ to be our loss function, and $\\alpha$ to be our step size proportionality, then we have the following algorithm:\n",
"\n",
"_________\n",
"\n",
"_**Algorithm - Gradient Descent**_\n",
"\n",
"- Initialize $\\theta$\n",
"- Until $\\alpha || \\nabla L(\\theta) || < tol $:\n",
" - $\\theta^{(t+1)} = \\theta^{(t)} - \\alpha \\nabla_{\\theta} L(\\theta^{(t)})$\n",
"__________\n",
"\n",
"For our problem at hand, we therefore need to find $\\nabla L(\\beta)$. The deriviative of $L(\\beta)$ due to the $j^{th}$ feature is:\n",
"\n",
"\\begin{eqnarray*}\n",
" \\frac{\\partial L(\\beta)}{\\partial \\beta_j} = -\\frac{2}{N}\\sum_{i=1}^{N} (y_i - \\mathbf{x_i^T}\\beta)\\cdot{x_{i,j}}\n",
"\\end{eqnarray*}\n",
"\n",
"In matrix notation this can be written:\n",
"\n",
"\\begin{eqnarray*}\n",
"Loss(\\beta) &=& \\frac{1}{N}\\mathbf{(Y - X\\beta)^T (Y - X\\beta)} \\\\\n",
"&=& \\frac{1}{N}\\mathbf{(Y^TY} - 2 \\mathbf{\\beta^T X^T Y + \\beta^T X^T X\\beta)} \\\\\n",
"\\nabla_{\\beta} L(\\beta) &=& \\frac{1}{N} (-2 \\mathbf{X^T Y} + 2 \\mathbf{X^T X \\beta)} \\\\\n",
"&=& -\\frac{2}{N} \\mathbf{X^T (Y - X \\beta)} \\\\\n",
"\\end{eqnarray*}"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"###<span style=\"color:red\">STUDENT ACTIVITY (7 MINS)</span> \n",
"### Create a function that returns the gradient of $L(\\beta)$"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Test Passed!\n"
]
}
],
"source": [
"###################################################################\n",
"# Student Action - Programming the Gradient\n",
"###################################################################\n",
"\n",
"def gradient(X, y, betas):\n",
" #****************************\n",
" # Your code here!\n",
" return -2.0/len(X)*np.dot(X.T, y - np.dot(X, betas))\n",
" #****************************\n",
" \n",
"\n",
"#########################################################\n",
"# Testing your gradient function\n",
"#########################################################\n",
"np.random.seed(33)\n",
"X = pd.DataFrame({'ones':1, \n",
" 'X1':np.random.uniform(0,1,50)})\n",
"y = np.random.normal(0,1,50)\n",
"betas = np.array([-1,4])\n",
"grad_expected = np.array([ 2.98018138, 7.09758971])\n",
"grad = gradient(X,y,betas)\n",
"try:\n",
" np.testing.assert_almost_equal(grad, grad_expected)\n",
" print \"Test Passed!\"\n",
"except AssertionError:\n",
" print \"*******************************************\"\n",
" print \"ERROR: Something isn't right... Try Again!\"\n",
" print \"*******************************************\"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"###<span style=\"color:red\">STUDENT ACTIVITY (15 MINS)</span> \n",
"\n",
"_** Student Action - Use your Gradient Function to complete the Gradient Descent for the Baseball Dataset**_\n",
"\n",
"#### Code Gradient Descent Here\n",
"\n",
"We have set-up the all necessary matrices and starting values. In the designated section below code the algorithm from the previous section above. "
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"33.0 [ 0.01513772 5.13000121]\n",
"Test Passed!\n"
]
}
],
"source": [
"# Setting up our matrices \n",
"Y = np.log(baseball['Salary'])\n",
"N = len(Y)\n",
"X = pd.DataFrame({'ones' : np.ones(N), \n",
" 'Hits' : baseball['Hits']})\n",
"p = len(X.columns)\n",
"\n",
"# Initializing the beta vector \n",
"betas = np.array([0.015,5.13])\n",
"\n",
"# Initializing Alpha\n",
"alph = 0.00001\n",
"\n",
"# Setting a tolerance \n",
"tol = 1e-8\n",
"\n",
"###################################################################\n",
"# Student Action - Programming the Gradient Descent Algorithm Below\n",
"###################################################################\n",
"\n",
"niter = 1.\n",
"while (alph*np.linalg.norm(gradient(X,Y,betas)) > tol) and (niter < 20000):\n",
" #****************************\n",
" # Your code here!\n",
" betas -= alph*gradient(X, Y, betas)\n",
" niter += 1\n",
" \n",
" #****************************\n",
"\n",
"print niter, betas\n",
"\n",
"try:\n",
" beta_expected = np.array([ 0.01513772, 5.13000121])\n",
" np.testing.assert_almost_equal(betas, beta_expected)\n",
" print \"Test Passed!\"\n",
"except AssertionError:\n",
" print \"*******************************************\"\n",
" print \"ERROR: Something isn't right... Try Again!\"\n",
" print \"*******************************************\"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"** Comments on Gradient Descent**\n",
"\n",
"- Advantage: Very General Algorithm $\\rightarrow$ Gradient Descent and its variants are used throughout Machine Learning and Statistics\n",
"- Disadvantage: Highly Sensitive to Initial Starting Conditions\n",
" - Not gauranteed to find the global optima\n",
"- Disadvantage: How do you choose step size $\\alpha$?\n",
" - Too small $\\rightarrow$ May never find the minima\n",
" - Too large $\\rightarrow$ May step past the minima\n",
" - Can we fix it?\n",
" - Adaptive step sizes\n",
" - Newton's Method for Optimization\n",
" - http://en.wikipedia.org/wiki/Newton%27s_method_in_optimization\n",
" - Each correction obviously comes with it's own computational considerations.\n",
"\n",
"See the Supplementary Material for any help necessary with scripting this in Python."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Visualizing Gradient Descent to Understand its Limitations \n",
"\n",
"Let's try to find the value of $X$ that maximizes the following function:\n",
"\n",
"\\begin{equation}\n",
" f(x) = w \\times \\frac{1}{\\sqrt{2\\pi \\sigma_1^2}} \\exp \\left( - \\frac{(x-\\mu_1)^2}{2\\sigma_1^2}\\right) + (1-w) \\times \\frac{1}{\\sqrt{2\\pi \\sigma_2^2}} \\exp \\left( - \\frac{(x-\\mu_2)^2}{2\\sigma_2^2}\\right)\n",
"\\end{equation}\n",
"\n",
"where $w=0.3$, $\\mu_1 = 3, \\sigma_1^2=1$ and $\\mu_2 = -1, \\sigma_2^2=0.5$\n",
"\n",
"Let's visualize this function"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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k9p9/61a45hpYsgRee81Pc0l8NGoEzzzjj/ESSYTS9tEqdkTLObfFzHoAk4As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\n8v8V6wfTEXqqvAAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x107d0ea90>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"x1 = np.arange(-10, 15, 0.05)\n",
"mu1 = 6.5 \n",
"var1 = 3\n",
"mu2 = -1\n",
"var2 = 10\n",
"weight = 0.3\n",
"def mixed_normal_distribution(x, mu1, var1, mu2, var2):\n",
" pdf1 = np.exp( - (x - mu1)**2 / (2*var1) ) / np.sqrt(2 * np.pi * var1)\n",
" pdf2 = np.exp( - (x - mu2)**2 / (2*var2) ) / np.sqrt(2 * np.pi * var2)\n",
" return weight * pdf1 + (1-weight )*pdf2\n",
"\n",
"pdf = mixed_normal_distribution(x1, mu1, var1, mu2, var2)\n",
"fig = plt.figure()\n",
"plt.plot(x1, pdf)\n",
"fig.set_size_inches([10,5])\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Now let's show visualize happens for different starting conditions and different step sizes\n"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"-0.998334050624 410.0\n"
]
},
{
"data": {
"image/png": 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0aNhkk9BpJEnrsyCSpAC++AIOPzy5/nezzUKnkUrfrOc/olb7vzD72Q/Yuv1+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\nSUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x107178090>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"def mixed_gradient(x, mu1, var1, mu2, var2):\n",
" grad_pdf1 = np.exp( - (x - mu1)**2 / (2*var1) ) * ((x-mu1)/var1) / np.sqrt(2 * np.pi * var1)\n",
" grad_pdf2 = np.exp( - (x - mu2)**2 / (2*var2) ) * ((x-mu2)/var2) / np.sqrt(2 * np.pi * var2)\n",
" return weight * grad_pdf1 + (1-weight)*grad_pdf2\n",
"\n",
"# Initialize X\n",
"x = 3.25\n",
"# Initializing Alpha\n",
"alph = 5\n",
"# Setting a tolerance \n",
"tol = 1e-8\n",
"niter = 1.\n",
"results = []\n",
"while (alph*np.linalg.norm(mixed_gradient(x, mu1, var1, mu2, var2)) > tol) and (niter < 500000):\n",
" #****************************\n",
" results.append(x)\n",
" x = x - alph * mixed_gradient(x, mu1, var1, mu2, var2)\n",
" niter += 1\n",
" \n",
" #****************************\n",
"print x, niter\n",
"\n",
"if niter < 500000:\n",
" exes = mixed_normal_distribution(np.array(results), mu1, var1, mu2, var2)\n",
" fig = plt.figure()\n",
" plt.plot(x1, pdf)\n",
" plt.plot(results, exes, color='red', marker='x')\n",
" plt.ylim([0,0.1])\n",
" fig.set_size_inches([20,10])\n",
" plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Linear Regression Matrix Solution\n",
"\n",
"From the last section, you may have recognized that we could actually solve for $\\beta$ directly. \n",
"\n",
"\\begin{eqnarray*}\n",
"Loss(\\beta) &=& \\frac{1}{N}\\mathbf{(Y - X\\beta)^T (Y - X\\beta)} \\\\\n",
"\\nabla_{\\beta} L(\\beta) &=& \\frac{1}{N} (-2 \\mathbf{X^T Y} + 2 \\mathbf{X^T X \\beta}) \\\\\n",
"\\end{eqnarray*}\n",
"\n",
"Setting to zero\n",
"\n",
"\\begin{eqnarray*}\n",
"-2 \\mathbf{X^T Y} + 2 \\mathbf{X^T X} \\beta &=& 0 \\\\\n",
"\\mathbf{X^T X \\beta} &=& \\mathbf{X^T Y} \\\\\n",
"\\end{eqnarray*}\n",
"\n",
"If we assume that the columns $X$ are linearly independent then\n",
"\n",
"\\begin{eqnarray*}\n",
" \\hat \\beta &=& \\mathbf{(X^T X)^{-1}X^T Y} \\\\\n",
"\\end{eqnarray*}\n",
"\n",
"This is called the _Ordinary Least Squares_ (OLS) Estimator \n",
"\n",
"###<span style=\"color:red\">STUDENT ACTIVITY (10 MINS)</span> \n",
"\n",
"_** Student Action - Solve for $\\hat \\beta$ directly using OLS on the Baseball Dataset - 10 mins** _\n",
" \n",
"- Review the Supplementary Materials for help with Linear Algebra"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Betas: [ 0.01513353 5.13051682]\n",
"Test Passed!\n"
]
}
],
"source": [
"# Setting up our matrices \n",
"y = np.log(baseball['Salary'])\n",
"N = len(Y)\n",
"X = pd.DataFrame({'ones' : np.ones(N), \n",
" 'Hits' : baseball['Hits']})\n",
"\n",
"#############################################################\n",
"# Student Action - Program a closed form solution for \n",
"# Linear Regression. Compare with Gradient\n",
"# Descent.\n",
"#############################################################\n",
"\n",
"def solve_linear_regression(X, y):\n",
" #****************************\n",
" return np.dot(np.linalg.inv(np.dot(X.T, X)), np.dot(X.T, y))\n",
" \n",
" #****************************\n",
"\n",
"betas = solve_linear_regression(X,y)\n",
"\n",
"try:\n",
" beta_expected = np.array([ 0.01513353, 5.13051682])\n",
" np.testing.assert_almost_equal(betas, beta_expected)\n",
" print \"Betas: \", betas\n",
" print \"Test Passed!\"\n",
"except AssertionError:\n",
" print \"*******************************************\"\n",
" print \"ERROR: Something isn't right... Try Again!\"\n",
" print \"*******************************************\""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"** Comments on solving the loss function directly **\n",
"\n",
"- Advantage: Simple solution to code \n",
"- Disadvantage: The Design Matrix must be Full Rank to invert\n",
" - Can be corrected with a Generalized Inverse Solution\n",
"- Disadvantage: Inverting a Matrix can be a computational expensive operation\n",
" - If we have a design matrix that has $N$ observations and $D$ predictors, then X is $(N\\times D)$ it follows then that\n",
" \n",
" \\begin{eqnarray*}\n",
" \\mathbf{X^TX} \\mbox{ is of size } (D \\times N) \\times (N \\times D) = (D \\times D) \\\\\n",
" \\end{eqnarray*}\n",
" \n",
" - If a matrix is of size $(D\\times D)$, the computational cost of inverting it is $O(D^3)$. \n",
" - Thus inverting a matrix is directly related to the number of predictors that are included in the analysis. \n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Sci-Kit Learn Linear Regression\n",
"\n",
"As we've shown in the previous two exercises, when coding these algorithms ourselves, we must consider many things such as selecting step sizes, considering the computational cost of inverting matrices. For many applications though, packages have been created that have taken into consideration many of these parameter selections. We now turn our attention to the Python package for Machine Learning called 'scikit-learn'. \n",
"\n",
"- http://scikit-learn.org/stable/\n",
"\n",
"Included is the documentation for the scikit-learn implementation of Ordinary Least Squares from their linear models package\n",
"\n",
"- _Generalized Linear Models Documentation:_ \n",
" - http://scikit-learn.org/stable/modules/linear_model.html#ordinary-least-squares\n",
"\n",
"- _LinearRegression Class Documentation:_ \n",
" - http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LinearRegression.html#sklearn.linear_model.LinearRegression\n",
"\n",
"From this we that we'll need to import the module `linear_model` using the following:\n",
"\n",
" from sklearn import linear_model\n",
" \n",
"Let's examine an example using the `LinearRegression` class from scikit-learn. We'll continue with the simulated data from the beginning of the exercise."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### _Example using the variables from the Residual Example_\n",
"\n",
"** Notes ** \n",
"\n",
"- Calling `linear_model.LinearRegression()` creates an object of class `sklearn.linear_model.base.LinearRegression`\n",
" - Defaults \n",
" - `fit_intercept = True`: automatically adds a column vector of ones for an intercept\n",
" - `normalize = False`: defaults to not normalizing the input predictors\n",
" - `copy_X = False`: defaults to not copying X\n",
" - `n_jobs = 1`: The number of jobs to use for the computation. If -1 all CPUs are used. This will only provide speedup for n_targets > 1 and sufficient large problems.\n",
" - Example\n",
" - `lmr = linear_model.LinearRegression()\n",
"- To fit a model, the method `.fit(X,y)` can be used\n",
" - X must be a column vector for scikit-learn\n",
" - This can be accomplished by creating a DataFrame using `pd.DataFrame()`\n",
" - Example\n",
" - lmr.fit(X,y)\n",
"- To predict out of sample values, the method `.predict(X)` can be used\n",
"- To see the $\\beta$ estimates use `.coef_` for the coefficients for the predictors and `.intercept` for $\\beta_0$"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": false
},
"outputs": [
{
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I6enp+sY3viFJuv3227V161b16NHjnJD+x59bewaqru3U1FQ9/vjjuuGGG9SzZ08tXbo0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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10ee3d950>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[ 0.26827489]] [ 1.23701501]\n"
]
}
],
"source": [
"#############################################################\n",
"# Demonstration - scikit-learn with Regression Example\n",
"#############################################################\n",
"\n",
"from sklearn import linear_model\n",
"\n",
"lmr = linear_model.LinearRegression()\n",
"lmr.fit(pd.DataFrame(x_example), pd.DataFrame(y_example))\n",
"\n",
"xTest = pd.DataFrame(np.arange(-1,6))\n",
"yHat = lmr.predict(xTest)\n",
"\n",
"f = plt.figure()\n",
"plt.scatter(x_example, y_example)\n",
"p1, = plt.plot(np.arange(-1,6), line1)\n",
"p2, = plt.plot(xTest, yHat)\n",
"plt.legend([p1, p2], ['y = 1 + 0.5x', 'OLS Estimate'], loc=2)\n",
"f.set_size_inches(10,5)\n",
"plt.show()\n",
"\n",
"print lmr.coef_, lmr.intercept_"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"###<span style=\"color:red\">STUDENT ACTIVITY (15 MINS)</span> \n",
"\n",
"### _**Final Student Task**_\n",
"\n",
"Programming Linear Regression using the scikit-learn method. For the ambitious students, plot all results on one plot. "
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"data": {
"image/png": 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TclmJRNhVRxRYet0S2iCG5rqE8uXS2NGNZyRHp1y6ttLZnZ/U0dEhodCItGB5lKQPWlmM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\nREREZBADJyIiIiKDGDgRERERGfT/AY16rpeNOtuuAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x107b8ecd0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[ 0.01513353] 5.13051681568\n"
]
}
],
"source": [
"#######################################################################\n",
"# Student Action - Use scikit-learn to calculate the beta coefficients\n",
"#\n",
"# Note: You no longer need the intercept column in your X matrix for \n",
"# sci-kit Learn. It will add that column automatically.\n",
"#######################################################################\n",
"\n",
"lmr2 = linear_model.LinearRegression(fit_intercept=True)\n",
"lmr2.fit(pd.DataFrame(baseball['Hits']), np.log(baseball['Salary']))\n",
"\n",
"xtest = np.arange(0,200)\n",
"ytest = lmr2.intercept_ + lmr2.coef_*xtest\n",
"\n",
"f = plt.figure()\n",
"plt.scatter(baseball['Hits'], np.log(baseball['Salary']))\n",
"plt.plot(xtest, ytest, color='r', linewidth=3)\n",
"f.set_size_inches(10,5)\n",
"plt.show()\n",
"print lmr2.coef_, lmr2.intercept_"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Linear Regression in the Real World\n",
"\n",
"In the real world, Linear Regression for predictive modeling doesn't end once you've fit the model. Models are often fit and used to predict user behavior, used to quantify business metrics, or sometimes used to identify cats faces for internet points. In that pursuit, it isn't really interesting to fit a model and assess its performance on data that has already been observed. The real interest lies in _**how it predicts future observations!**_\n",
"\n",
"Often times then, we may be susceptible to creating a model that is perfected for our observed data, but that does not generalize well to new data. In order to assess how we perform to new data, we can _score_ the model on both the old and new data, and compare the models performance with the hope that the it generalizes well to the new data. After lunch we'll introduce some techniques and other methods to better our chances of performing well on new data. \n",
"\n",
"Before we break for lunch though, let's take a look at a simulated dataset to see what we mean...\n",
"\n",
"_Situation_\n",
"\n",
"Imagine that last year a talent management company managed 400 celebrities and tracked how popular they were within the public eye, as well various predictors for that metric. The company is now interested in managing a few new celebrities, but wants to sign those stars that are above a certain 'popularity' threshold to maintain their image.\n",
"\n",
"Our job is to predict how popular each new celebrity will be over the course of the coming year so that we make that best decision about who to manage. For this analysis we'll use a function `l2_error` to compare our errors on a training set, and on a test set of celebrity data.\n",
"\n",
"The variable `celeb_data_old` represents things we know about the previous batch of celebrities. Each row represents one celeb. Each column represents some tangible measure about them -- their age at the time, number of Twitter followers, voice squeakiness, etc. The specifics of what each column represents aren't important.\n",
"\n",
"Similarly, `popularity_score_old` is a previous measure of the celebrities popularity.\n",
"\n",
"Finally, `celeb_data_new` represents the same information that we had from `celeb_data_old` but for the new batch of internet wonders that we're considering.\n",
"\n",
"How can we predict how popular the NEW batch of celebrities will be ahead of time so that we can decide who to sign? And are these estimates stable from year to year?"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Predicted L2 Error: 18.1262825607\n"
]
}
],
"source": [
"with np.load('data/mystery_data_old.npz') as data:\n",
" celeb_data_old = data['celeb_data_old']\n",
" popularity_old = data['popularity_old']\n",
" celeb_data_new = data['celeb_data_new']\n",
"\n",
"lmr3 = linear_model.LinearRegression()\n",
"lmr3.fit(celeb_data_old, popularity_old)\n",
"predicted_popularity_old = lmr3.predict(celeb_data_old)\n",
"predicted_popularity_new = lmr3.predict(celeb_data_new)\n",
"\n",
"def l2_error(y_true, y_pred):\n",
" \"\"\"\n",
" calculate the sum of squared errors (i.e. \"L2 error\") \n",
" given a vector of true ys and a vector of predicted ys\n",
" \"\"\"\n",
" diff = (y_true-y_pred)\n",
" return np.sqrt(np.dot(diff, diff))\n",
"\n",
"print \"Predicted L2 Error:\", l2_error(popularity_old, predicted_popularity_old)"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": false
},
"source": [
"### Checking How We Did\n",
"At the end of the year, we tally up the popularity numbers for each celeb and check how well we did on our predictions."
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Predicted L2 Error: 24.173135433\n"
]
}
],
"source": [
"with np.load('data/mystery_data_new.npz') as data:\n",
" popularity_new = data['popularity_new']\n",
"\n",
"print \"Predicted L2 Error:\", l2_error(popularity_new, predicted_popularity_new)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Something's not right... our model seems to be performing worse on this data! Our model performed so well on last year's data, why didn't it work on the data from this year?"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python [Root]",
"language": "python",
"name": "Python [Root]"
},
"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
}
| mit |
Diyago/Machine-Learning-scripts | classification/Kaggle: Malware Prediction/adjust differently disturbuted features.ipynb | 1 | 24274 | {
"cells": [
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
" def basic_fe(df):\n",
" df['EngineVersion_2'] = df['EngineVersion'].apply(lambda x: x.split('.')[2]).astype('category')\n",
" df['EngineVersion_3'] = df['EngineVersion'].apply(lambda x: x.split('.')[3]).astype('category')\n",
"\n",
" df['AppVersion_1'] = df['AppVersion'].apply(lambda x: x.split('.')[1]).astype('category')\n",
" df['AppVersion_2'] = df['AppVersion'].apply(lambda x: x.split('.')[2]).astype('category')\n",
" df['AppVersion_3'] = df['AppVersion'].apply(lambda x: x.split('.')[3]).astype('category')\n",
"\n",
" df['AvSigVersion_0'] = df['AvSigVersion'].apply(lambda x: x.split('.')[0]).astype('category')\n",
" df['AvSigVersion_1'] = df['AvSigVersion'].apply(lambda x: x.split('.')[1]).astype('category')\n",
" df['AvSigVersion_2'] = df['AvSigVersion'].apply(lambda x: x.split('.')[2]).astype('category')\n",
"\n",
" df['OsBuildLab_0'] = df['OsBuildLab'].apply(lambda x: x.split('.')[0]).astype('category')\n",
" df['OsBuildLab_1'] = df['OsBuildLab'].apply(lambda x: x.split('.')[1]).astype('category')\n",
" df['OsBuildLab_2'] = df['OsBuildLab'].apply(lambda x: x.split('.')[2]).astype('category')\n",
" df['OsBuildLab_3'] = df['OsBuildLab'].apply(lambda x: x.split('.')[3]).astype('category')\n",
"\n",
" df['Census_OSVersion_0'] = df['Census_OSVersion'].apply(lambda x: x.split('.')[0]).astype('category')\n",
" df['Census_OSVersion_1'] = df['Census_OSVersion'].apply(lambda x: x.split('.')[1]).astype('category')\n",
" df['Census_OSVersion_2'] = df['Census_OSVersion'].apply(lambda x: x.split('.')[2]).astype('category')\n",
" df['Census_OSVersion_3'] = df['Census_OSVersion'].apply(lambda x: x.split('.')[3]).astype('category')\n",
"\n",
"\n",
" df['primary_drive_c_ratio'] = df['Census_SystemVolumeTotalCapacity']/ df['Census_PrimaryDiskTotalCapacity']\n",
" df['non_primary_drive_MB'] = df['Census_PrimaryDiskTotalCapacity'] - df['Census_SystemVolumeTotalCapacity']\n",
"\n",
" df['aspect_ratio'] = df['Census_InternalPrimaryDisplayResolutionVertical']/ df['Census_InternalPrimaryDisplayResolutionHorizontal']\n",
"\n",
" df['monitor_dims'] = df['Census_InternalPrimaryDisplayResolutionHorizontal'].astype(str) + '*' + df['Census_InternalPrimaryDisplayResolutionVertical'].astype('str')\n",
" df['monitor_dims'] = df['monitor_dims'].astype('category')\n",
"\n",
" df['dpi'] = ((df['Census_InternalPrimaryDisplayResolutionHorizontal']**2 + df['Census_InternalPrimaryDisplayResolutionVertical']**2)**.5)/(df['Census_InternalPrimaryDiagonalDisplaySizeInInches'])\n",
"\n",
" df['dpi_square'] = df['dpi'] ** 2\n",
"\n",
" df['MegaPixels'] = (df['Census_InternalPrimaryDisplayResolutionHorizontal'] * df['Census_InternalPrimaryDisplayResolutionVertical'])/1e6\n",
"\n",
" df['Screen_Area'] = (df['aspect_ratio']* (df['Census_InternalPrimaryDiagonalDisplaySizeInInches']**2))/(df['aspect_ratio']**2 + 1)\n",
"\n",
" df['ram_per_processor'] = df['Census_TotalPhysicalRAM']/ df['Census_ProcessorCoreCount']\n",
"\n",
" df['new_num_0'] = df['Census_InternalPrimaryDiagonalDisplaySizeInInches'] / df['Census_ProcessorCoreCount']\n",
"\n",
" df['new_num_1'] = df['Census_ProcessorCoreCount'] * df['Census_InternalPrimaryDiagonalDisplaySizeInInches']\n",
"\n",
" df['Census_IsFlightingInternal'] = df['Census_IsFlightingInternal'].fillna(1)\n",
" df['Census_ThresholdOptIn'] = df['Census_ThresholdOptIn'].fillna(1)\n",
" df['Census_IsWIMBootEnabled'] = df['Census_IsWIMBootEnabled'].fillna(1)\n",
" df['Wdft_IsGamer'] = df['Wdft_IsGamer'].fillna(0)\n",
"\n",
" df.SmartScreen = df.SmartScreen.str.lower()\n",
" df.SmartScreen.replace({\"promt\":\"prompt\",\n",
" \"promprt\":\"prompt\",\n",
" \"00000000\":\"0\",\n",
" \"enabled\":\"on\",\n",
" \"of\":\"off\" ,\n",
" \"deny\":\"0\" , # just one\n",
" \"requiredadmin\":\"requireadmin\"\n",
" },inplace=True)\n",
"\n",
" df.SmartScreen = df.SmartScreen.astype(\"category\")\n",
"\n",
" def group_battery(x):\n",
" x = x.lower()\n",
" if 'li' in x: return 1\n",
" else: return 0\n",
"\n",
" df['isLithium_InternalBatteryType'] = df['Census_InternalBatteryType'].apply(group_battery) \n",
"\n",
" add_cat_feats = [\n",
" 'Census_OSBuildRevision',\n",
" 'OsBuildLab',\n",
" 'SmartScreen',\n",
" 'AVProductsInstalled']\n",
" for col1 in add_cat_feats:\n",
" for col2 in add_cat_feats:\n",
" if col1 != col2:\n",
" df[col1 + '__' + col2] = df[col1].astype(str) + df[col2].astype(str)\n",
" df[col1 + '__' + col2] = df[col1 + '__' + col2].astype('category')\n",
" \n",
" return df"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Download Train and Test Data.\n",
"\n"
]
},
{
"data": {
"text/plain": [
"201425"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import lightgbm as lgb\n",
"from scipy.sparse import vstack, csr_matrix, save_npz, load_npz\n",
"from sklearn.preprocessing import LabelEncoder, OneHotEncoder\n",
"from sklearn.model_selection import StratifiedKFold\n",
"import gc\n",
"gc.enable()\n",
"\n",
"dtypes = {\n",
" 'MachineIdentifier': 'category',\n",
" 'ProductName': 'category',\n",
" 'EngineVersion': 'category',\n",
" 'AppVersion': 'category',\n",
" 'AvSigVersion': 'category',\n",
" 'IsBeta': 'int8',\n",
" 'RtpStateBitfield': 'float16',\n",
" 'IsSxsPassiveMode': 'int8',\n",
" 'DefaultBrowsersIdentifier': 'float32',\n",
" 'AVProductStatesIdentifier': 'float32',\n",
" 'AVProductsInstalled': 'float16',\n",
" 'AVProductsEnabled': 'float16',\n",
" 'HasTpm': 'int8',\n",
" 'CountryIdentifier': 'int16',\n",
" 'CityIdentifier': 'float32',\n",
" 'OrganizationIdentifier': 'float16',\n",
" 'GeoNameIdentifier': 'float16',\n",
" 'LocaleEnglishNameIdentifier': 'int16',\n",
" 'Platform': 'category',\n",
" 'Processor': 'category',\n",
" 'OsVer': 'category',\n",
" 'OsBuild': 'int16',\n",
" 'OsSuite': 'int16',\n",
" 'OsPlatformSubRelease': 'category',\n",
" 'OsBuildLab': 'category',\n",
" 'SkuEdition': 'category',\n",
" 'IsProtected': 'float16',\n",
" 'AutoSampleOptIn': 'int8',\n",
" 'PuaMode': 'category',\n",
" 'SMode': 'float16',\n",
" 'IeVerIdentifier': 'float16',\n",
" 'SmartScreen': 'category',\n",
" 'Firewall': 'float16',\n",
" 'UacLuaenable': 'float64', # was 'float32'\n",
" 'Census_MDC2FormFactor': 'category',\n",
" 'Census_DeviceFamily': 'category',\n",
" 'Census_OEMNameIdentifier': 'float32', # was 'float16'\n",
" 'Census_OEMModelIdentifier': 'float32',\n",
" 'Census_ProcessorCoreCount': 'float16',\n",
" 'Census_ProcessorManufacturerIdentifier': 'float16',\n",
" 'Census_ProcessorModelIdentifier': 'float32', # was 'float16'\n",
" 'Census_ProcessorClass': 'category',\n",
" 'Census_PrimaryDiskTotalCapacity': 'float64', # was 'float32'\n",
" 'Census_PrimaryDiskTypeName': 'category',\n",
" 'Census_SystemVolumeTotalCapacity': 'float64', # was 'float32'\n",
" 'Census_HasOpticalDiskDrive': 'int8',\n",
" 'Census_TotalPhysicalRAM': 'float32',\n",
" 'Census_ChassisTypeName': 'category',\n",
" 'Census_InternalPrimaryDiagonalDisplaySizeInInches': 'float32', # was 'float16'\n",
" 'Census_InternalPrimaryDisplayResolutionHorizontal': 'float32', # was 'float16'\n",
" 'Census_InternalPrimaryDisplayResolutionVertical': 'float32', # was 'float16'\n",
" 'Census_PowerPlatformRoleName': 'category',\n",
" 'Census_InternalBatteryType': 'category',\n",
" 'Census_InternalBatteryNumberOfCharges': 'float64', # was 'float32'\n",
" 'Census_OSVersion': 'category',\n",
" 'Census_OSArchitecture': 'category',\n",
" 'Census_OSBranch': 'category',\n",
" 'Census_OSBuildNumber': 'int16',\n",
" 'Census_OSBuildRevision': 'int32',\n",
" 'Census_OSEdition': 'category',\n",
" 'Census_OSSkuName': 'category',\n",
" 'Census_OSInstallTypeName': 'category',\n",
" 'Census_OSInstallLanguageIdentifier': 'float16',\n",
" 'Census_OSUILocaleIdentifier': 'int16',\n",
" 'Census_OSWUAutoUpdateOptionsName': 'category',\n",
" 'Census_IsPortableOperatingSystem': 'int8',\n",
" 'Census_GenuineStateName': 'category',\n",
" 'Census_ActivationChannel': 'category',\n",
" 'Census_IsFlightingInternal': 'float16',\n",
" 'Census_IsFlightsDisabled': 'float16',\n",
" 'Census_FlightRing': 'category',\n",
" 'Census_ThresholdOptIn': 'float16',\n",
" 'Census_FirmwareManufacturerIdentifier': 'float16',\n",
" 'Census_FirmwareVersionIdentifier': 'float32',\n",
" 'Census_IsSecureBootEnabled': 'int8',\n",
" 'Census_IsWIMBootEnabled': 'float16',\n",
" 'Census_IsVirtualDevice': 'float16',\n",
" 'Census_IsTouchEnabled': 'int8',\n",
" 'Census_IsPenCapable': 'int8',\n",
" 'Census_IsAlwaysOnAlwaysConnectedCapable': 'float16',\n",
" 'Wdft_IsGamer': 'float16',\n",
" 'Wdft_RegionIdentifier': 'float16',\n",
" 'HasDetections': 'int8'\n",
" }\n",
"\n",
"print('Download Train and Test Data.\\n')\n",
"train = pd.read_csv('./data/train.csv', dtype=dtypes, low_memory=True)\n",
"train['MachineIdentifier'] = train.index.astype('uint32')\n",
"test = pd.read_csv('./data/test.csv', dtype=dtypes, low_memory=True)\n",
"test['MachineIdentifier'] = test.index.astype('uint32')\n",
"\n",
"#Add some new features\n",
"train, test = basic_fe(train), basic_fe(test)\n",
"gc.collect()\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Transform all features to category.\n",
"\n",
"If you don't want use Sparse Matrix choose Kernel Version 2 to get simple solution.\n",
"\n",
"--------------------------------------------------------------------------------------------------------\n",
"Transform Data to Sparse Matrix.\n",
"Sparse Matrix can be used to fit a lot of models, eg. XGBoost, LightGBM, Random Forest, K-Means and etc.\n",
"To concatenate Sparse Matrices by column use hstack()\n",
"Read more about Sparse Matrix https://docs.scipy.org/doc/scipy/reference/sparse.html\n",
"Good Luck!\n",
"--------------------------------------------------------------------------------------------------------\n",
"\n",
"LightGBM\n",
"\n",
"Fold 1\n",
"\n",
"Training until validation scores don't improve for 100 rounds.\n",
"[100]\tvalid_0's auc: 0.732405\tvalid_0's binary_logloss: 0.602641\n",
"[200]\tvalid_0's auc: 0.737891\tvalid_0's binary_logloss: 0.597396\n",
"[300]\tvalid_0's auc: 0.739226\tvalid_0's binary_logloss: 0.596077\n",
"[400]\tvalid_0's auc: 0.739204\tvalid_0's binary_logloss: 0.596025\n",
"Early stopping, best iteration is:\n",
"[344]\tvalid_0's auc: 0.739271\tvalid_0's binary_logloss: 0.595999\n",
"Fold 2\n",
"\n",
"Training until validation scores don't improve for 100 rounds.\n",
"[100]\tvalid_0's auc: 0.73243\tvalid_0's binary_logloss: 0.602742\n",
"[200]\tvalid_0's auc: 0.737995\tvalid_0's binary_logloss: 0.597388\n",
"[300]\tvalid_0's auc: 0.739323\tvalid_0's binary_logloss: 0.596089\n",
"[400]\tvalid_0's auc: 0.739384\tvalid_0's binary_logloss: 0.595959\n",
"Early stopping, best iteration is:\n",
"[353]\tvalid_0's auc: 0.739431\tvalid_0's binary_logloss: 0.595951\n",
"Fold 3\n",
"\n",
"Training until validation scores don't improve for 100 rounds.\n",
"[100]\tvalid_0's auc: 0.732533\tvalid_0's binary_logloss: 0.602728\n",
"[200]\tvalid_0's auc: 0.738215\tvalid_0's binary_logloss: 0.59731\n",
"[300]\tvalid_0's auc: 0.739656\tvalid_0's binary_logloss: 0.59591\n",
"[400]\tvalid_0's auc: 0.739678\tvalid_0's binary_logloss: 0.595791\n",
"Early stopping, best iteration is:\n",
"[324]\tvalid_0's auc: 0.739737\tvalid_0's binary_logloss: 0.595814\n",
"Fold 4\n",
"\n",
"Training until validation scores don't improve for 100 rounds.\n",
"[100]\tvalid_0's auc: 0.73331\tvalid_0's binary_logloss: 0.601981\n",
"[200]\tvalid_0's auc: 0.738913\tvalid_0's binary_logloss: 0.596599\n",
"[300]\tvalid_0's auc: 0.740184\tvalid_0's binary_logloss: 0.595352\n",
"[400]\tvalid_0's auc: 0.740225\tvalid_0's binary_logloss: 0.595246\n",
"Early stopping, best iteration is:\n",
"[380]\tvalid_0's auc: 0.74027\tvalid_0's binary_logloss: 0.595218\n",
"Fold 5\n",
"\n",
"Training until validation scores don't improve for 100 rounds.\n",
"[100]\tvalid_0's auc: 0.732395\tvalid_0's binary_logloss: 0.602545\n",
"[200]\tvalid_0's auc: 0.737864\tvalid_0's binary_logloss: 0.597333\n",
"[300]\tvalid_0's auc: 0.739254\tvalid_0's binary_logloss: 0.595984\n",
"[400]\tvalid_0's auc: 0.739308\tvalid_0's binary_logloss: 0.59588\n",
"Early stopping, best iteration is:\n",
"[364]\tvalid_0's auc: 0.73932\tvalid_0's binary_logloss: 0.595883\n",
"\n",
"Done.\n"
]
}
],
"source": [
"print('Transform all features to category.\\n')\n",
"for usecol in test.columns.tolist():\n",
" if usecol == 'MachineIdentifier':\n",
" continue\n",
" train[usecol] = train[usecol].astype('str')\n",
" test[usecol] = test[usecol].astype('str')\n",
" \n",
" #Fit LabelEncoder\n",
" le = LabelEncoder().fit(\n",
" np.unique(train[usecol].unique().tolist()+\n",
" test[usecol].unique().tolist()))\n",
"\n",
" #At the end 0 will be used for dropped values\n",
" train[usecol] = le.transform(train[usecol])+1\n",
" test[usecol] = le.transform(test[usecol])+1\n",
"\n",
" agg_tr = (train\n",
" .groupby([usecol])\n",
" .aggregate({'MachineIdentifier':'count'})\n",
" .reset_index()\n",
" .rename({'MachineIdentifier':'Train'}, axis=1))\n",
" agg_te = (test\n",
" .groupby([usecol])\n",
" .aggregate({'MachineIdentifier':'count'})\n",
" .reset_index()\n",
" .rename({'MachineIdentifier':'Test'}, axis=1))\n",
"\n",
" agg = pd.merge(agg_tr, agg_te, on=usecol, how='outer').replace(np.nan, 0)\n",
" #Select values with more than 1000 observations\n",
" agg = agg[(agg['Train'] > 1000)].reset_index(drop=True)\n",
" agg['Total'] = agg['Train'] + agg['Test']\n",
" #Drop unbalanced values\n",
" agg = agg[(agg['Train'] / agg['Total'] > 0.2) & (agg['Train'] / agg['Total'] < 0.8)]\n",
" agg[usecol+'Copy'] = agg[usecol]\n",
"\n",
" train[usecol] = (pd.merge(train[[usecol]], \n",
" agg[[usecol, usecol+'Copy']], \n",
" on=usecol, how='left')[usecol+'Copy']\n",
" .replace(np.nan, 0).astype('int').astype('category'))\n",
"\n",
" test[usecol] = (pd.merge(test[[usecol]], \n",
" agg[[usecol, usecol+'Copy']], \n",
" on=usecol, how='left')[usecol+'Copy']\n",
" .replace(np.nan, 0).astype('int').astype('category'))\n",
"\n",
" del le, agg_tr, agg_te, agg, usecol\n",
" gc.collect()\n",
" \n",
"y_train = np.array(train['HasDetections'])\n",
"train_ids = train.index\n",
"test_ids = test.index\n",
"\n",
"del train['HasDetections'], train['MachineIdentifier'], test['MachineIdentifier']\n",
"gc.collect()\n",
"\n",
"print(\"If you don't want use Sparse Matrix choose Kernel Version 2 to get simple solution.\\n\")\n",
"\n",
"print('--------------------------------------------------------------------------------------------------------')\n",
"print('Transform Data to Sparse Matrix.')\n",
"print('Sparse Matrix can be used to fit a lot of models, eg. XGBoost, LightGBM, Random Forest, K-Means and etc.')\n",
"print('To concatenate Sparse Matrices by column use hstack()')\n",
"print('Read more about Sparse Matrix https://docs.scipy.org/doc/scipy/reference/sparse.html')\n",
"print('Good Luck!')\n",
"print('--------------------------------------------------------------------------------------------------------')\n",
"\n",
"#Fit OneHotEncoder\n",
"ohe = OneHotEncoder(categories='auto', sparse=True, dtype='uint8').fit(train)\n",
"\n",
"#Transform data using small groups to reduce memory usage\n",
"m = 100000\n",
"train = vstack([ohe.transform(train[i*m:(i+1)*m]) for i in range(train.shape[0] // m + 1)])\n",
"test = vstack([ohe.transform(test[i*m:(i+1)*m]) for i in range(test.shape[0] // m + 1)])\n",
"save_npz('train.npz', train, compressed=True)\n",
"save_npz('test.npz', test, compressed=True)\n",
"\n",
"del ohe, train, test\n",
"gc.collect()\n",
"\n",
"skf = StratifiedKFold(n_splits=5, shuffle=True, random_state=42)\n",
"skf.get_n_splits(train_ids, y_train)\n",
"\n",
"lgb_test_result = np.zeros(test_ids.shape[0])\n",
"counter = 0\n",
"\n",
"print('\\nLightGBM\\n')\n",
"\n",
"for train_index, test_index in skf.split(train_ids, y_train):\n",
" \n",
" print('Fold {}\\n'.format(counter + 1))\n",
" \n",
" train = load_npz('train.npz')\n",
" X_fit = vstack([train[train_index[i*m:(i+1)*m]] for i in range(train_index.shape[0] // m + 1)])\n",
" X_val = vstack([train[test_index[i*m:(i+1)*m]] for i in range(test_index.shape[0] // m + 1)])\n",
" X_fit, X_val = csr_matrix(X_fit, dtype='float32'), csr_matrix(X_val, dtype='float32')\n",
" y_fit, y_val = y_train[train_index], y_train[test_index]\n",
" \n",
" del train\n",
" gc.collect()\n",
"\n",
" lgb_model = lgb.LGBMClassifier(max_depth=-1,\n",
" n_estimators=25000,\n",
" learning_rate=0.05,\n",
" num_leaves=2**12-1,\n",
" colsample_bytree=0.28,\n",
" objective='binary', \n",
" n_jobs=-1)\n",
" \n",
" \n",
" lgb_model.fit(X_fit, y_fit, eval_metric='auc', \n",
" eval_set=[(X_val, y_val)], \n",
" verbose=100, early_stopping_rounds=100)\n",
" \n",
" \n",
" del X_fit, X_val, y_fit, y_val, train_index, test_index\n",
" gc.collect()\n",
" \n",
" test = load_npz('test.npz')\n",
" test = csr_matrix(test, dtype='float32')\n",
" lgb_test_result += lgb_model.predict_proba(test)[:,1]\n",
" counter += 1\n",
" \n",
" del test, lgb_model\n",
" gc.collect()\n",
" \n",
"\n",
"submission = pd.read_csv('./data/sample_submission.csv')\n",
"submission['HasDetections'] = lgb_test_result / counter\n",
"submission.to_csv('lgb_submission.csv', index=False)\n",
"\n",
"print('\\nDone.')"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'MachineIdentifier'"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"usecol"
]
}
],
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"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
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"file_extension": ".py",
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| apache-2.0 |
adityagilra/from-papers | Deneve/Deneve_comparisons.ipynb | 1 | 16665 | {
"metadata": {
"name": ""
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": [
"Comparing different papers from Sophie Deneve's group"
]
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Key ideas"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Use a cost function that is the mod-squared difference of an external quantity vector and its prediction vector (+ spike cost terms). The difference vector pre-multiplied by the readout weight matrix^T (- rate penalty), gives the membrane potentials of a layer of spiking LIF output neurons that thus code predictively / fire spikes to greedy-minimize the cost function.\n",
"\n",
"The prediction spike train from output neurons is decoded by another virtual layer (may or may not be present) as a rate.\n",
"\n",
"The input layer encodes a 'stimulus' which is the input to the output layer with its feedforward weights. Currently only instantaneous response to stimulus seems allowed [Boerlin and Deneve 2011]?\n",
"\n",
"The external world quantity being predicted is not necessarily the stimulus i.e. network is not just an autoencoder. The quantity being predicted can be a log posterior probability of the stimulus [Deneve 2007, Boerlin and Deneve 2011] or dynamical variables that change with the stimulus [Boerlin et al 2013].\n",
"\n",
"The recurrent weights are to be learnt as some function of: (a) constants in the dynamical system, (b) input neuron tuning filters (no time dependence allowed), (b) readout weights, (c) spatial derivatives of readout weights (for log-posterior), and (c) constants in the cost fuction and time constants in neural dynamics. It is claimed that gradient descent on the spiking rule cost function with respect to the recurrent weights is sufficient to learn them. But currently only the fast recurrent connections are learnt for an autoencoder with further constraint on neural decoding time constant [Bourdoukan et al 2012]."
]
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Boerlin et al 2013"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Assume an external dynamical system $\\dot{\\mathbf{x}}(t)=\\mathbf{A}\\mathbf{x}(t)+\\mathbf{c}(t)$. An output readout layer rate codes the $J$ dynamical variables $\\mathbf{x}(t)$ as $\\dot{\\mathbf{\\hat{x}}}=-\\lambda_d \\mathbf{\\hat{x}} + \\mathbf{\\Gamma} \\mathbf{o}(t)$. The recurrent network weights are derived from the readout weights $\\mathbf{\\Gamma}$, the desired dynamics $\\mathbf{A}$ and the penalty scalar $\\mu$ as\n",
"\n",
"$\\mathbf{W}=\\mathbf{\\Omega}^s h_d^1(u) - \\mathbf{\\Omega}^f \\delta(u)$ ...Eqn(40)\n",
"\n",
"$\\mathbf{\\Omega}^s = \\mathbf{\\Gamma}^T(\\mathbf{A}+\\lambda_d\\mathbf{I})\\mathbf{\\Gamma}$ ...Eqn(38)\n",
"\n",
"$\\mathbf{\\Omega}^f = \\left(\\mathbf{\\Gamma}^T\\mathbf{\\Gamma}+\\mu\\lambda_d^2\\right)$ ...Eqn(39)\n",
"\n",
"The recurrent network neurons receive the same input $\\mathbf{c}(t)$ as the dynamical system, and spike only 'as much as is necessary' for the readout to track the dynamical variables $\\mathbf{x}(t)$.\n",
"\n",
"Cost function: $E(t) = \\int_0^t du \\left(|| \\mathbf{x}(u)-\\mathbf{\\hat{x}}(u)||_2^2 +\\nu ||\\mathbf{r}(u)||_1 +\\mu ||\\mathbf{r}(u)||_2^2 \\right)~~$ ...Eqn(4)\n",
"\n",
"$\\mathbf{V}(t) = \\mathbf{\\Gamma}^T(\\mathbf{x}(t)-\\mathbf{\\hat{x}}(t)) - \\mu \\mathbf{r}(t)\\lambda_d$\n",
"\n",
"$T_k = \\frac{1}{2} \\left( \\mathbf{\\Gamma}_k^T\\mathbf{\\Gamma}_k + \\nu \\lambda_d + \\mu \\lambda_d^2 \\right)$\n",
"\n",
"$\\dot{\\mathbf{V}}(t) = -\\lambda_V\\mathbf{V}(t) + \\frac{1}{\\lambda_d}\\mathbf{\\Omega}^s\\mathbf{r}(t) - \\mathbf{\\Omega}^f\\mathbf{o}(t) + \\mathbf{\\Gamma}^T\\mathbf{c}(t)$ ...Eqn(37)\n",
"\n",
"Noise model:??"
]
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Bourdoukan et al 2012, Learning optimal spike-based representations, NIPS"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Plasticity rule can be derived from the same cost function as the spiking rule."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Autoencoder here; not encoding a dynamical system (see just below its Eqn(4)):\n",
"\n",
"$\\dot{\\mathbf{x}}(t)=-\\mathbf{x}(t)+\\mathbf{c}(t)$\n",
"\n",
"Thus $A=-\\mathbf{I}$ in the formalism above.\n",
"\n",
"Also they've taken $\\nu=0$ in the cost function throughout."
]
},
{
"cell_type": "heading",
"level": 4,
"metadata": {},
"source": [
"Homogeneous LIF neurons representing vector $\\mathbf{x}$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$\\dot{\\mathbf{V}}(t) = -\\mathbf{V}(t) - \\mathbf{\\Omega}\\mathbf{o}(t) + \\mathbf{\\Gamma}^T\\mathbf{c}(t)$ ...Eqn(20,15,6)\n",
"\n",
"Different equations belong to different sections which build from one neuron scalar $x$, to multi-neuron scalar $x$, to multi-neuron vector $\\mathbf{x}$."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"*[* Insert $A=-\\mathbf{I}$ in Eqn(37-39) of above Boerlin et al 2013 paper to get:"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$\\dot{\\mathbf{V}}(t) = -\\lambda_V\\mathbf{V}(t) + (-\\frac{1}{\\lambda_d}+1)\\mathbf{\\Gamma}^T\\mathbf{\\Gamma} \\mathbf{r}(t) - \\left(\\mathbf{\\Gamma}^T\\mathbf{\\Gamma}+\\mu\\lambda_d^2\\mathbf{I}\\right)\\mathbf{o}(t) + \\mathbf{\\Gamma}^T\\mathbf{c}(t)$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Output read-out layer in this case has $\\lambda_d=1$, so the second term goes to zero.\n",
"Only the fast inhibition, here $\\mathbf{\\Omega}=\\left(\\mathbf{\\Gamma}^T\\mathbf{\\Gamma}+\\mu\\mathbf{I}\\right)$, remains. But I expect the slow inhibition to be important for learning?? *]*"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Here the readout weights $\\mathbf\\Gamma$ are constant, and $\\mathbf{\\Omega}$ will be learnt to be equal to its above final value.\n",
"\n",
"Assume $\\mathbf{\\Omega}$ changes much more slowly than $\\mathbf{V}$, and integrate and get the average membrane potentials:\n",
"\n",
"$\\mathbf{V}(t)-\\mathbf{V}(0)\\exp(-t) = \\mathbf{\\Gamma}^T\\mathbf{x}-\\mathbf{\\Omega}(h*\\mathbf{o})(t)$. Ignoring initial value which goes away at long time, we can write:\n",
"\n",
"$\\mathbf{V}(t) = \\mathbf{\\Gamma}^T\\mathbf{x}-\\mathbf{\\Omega}\\bar{\\mathbf{o}}(t)$ ...Eqn(6)\n",
"\n",
"$\\bar{\\mathbf{o}} \\equiv (h*\\mathbf{o})(t) \\equiv [h* o_i]$ where $h(\\tau)=\\theta(\\tau)\\exp (-\\tau)$. Note that this means $\\dot{\\bar{\\mathbf{o}}}=-\\bar{\\mathbf{o}}+\\mathbf{o}$."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Also, the readout $\\dot{\\hat{\\mathbf{x}}}=-\\hat{\\mathbf{x}}+\\mathbf{\\Gamma} \\mathbf{o}(t)$. Integrating, $\\hat{\\mathbf{x}}(t)-\\hat{\\mathbf{x}}(0)\\exp(-t) = \\mathbf{\\Gamma}(h*\\mathbf{o})(t) = \\mathbf{\\Gamma}\\bar{\\mathbf{o}}(t)$. Ignoring decaying initial value, we have $\\hat{\\mathbf{x}}(t) = \\mathbf{\\Gamma}\\bar{\\mathbf{o}}(t)$.\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"From Eqn(20),\n",
"\n",
"$\\mathbf{x}(t)=\\left(\\mathbf{\\Gamma}^T\\right)^{-1}\\left(\\mathbf{V}(t)+\\mathbf{\\Omega}\\bar{\\mathbf{o}}\\right)$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"\n",
"Use the same cost function as for spiking [since we use greedy minimization, it is done at each $\\Delta t$, so I guess the integration is not needed.]:\n",
"\n",
"$L = ||\\mathbf{x}(t)-\\mathbf{\\hat{x}}(t)||_2^2 +\\mu ||\\mathbf{r}(t)||_2^2$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Using above equations:\n",
"\n",
"$L = ||\\left(\\mathbf{\\Gamma}^T\\right)^{-1}\\left(\\mathbf{V}(t)+\\mathbf{\\Omega}\\bar{\\mathbf{o}}(t)\\right)-\\mathbf{\\Gamma}\\bar{\\mathbf{o}}(t)||_2^2 +\\mu ||\\mathbf{r}(t)||_2^2 = ||\\left(\\mathbf{\\Gamma}^T\\right)^{-1}\\left(\\mathbf{V}(t)+\\mathbf{\\Omega}\\bar{\\mathbf{o}}(t)-\\mathbf{\\Gamma}^T\\mathbf{\\Gamma}\\bar{\\mathbf{o}}(t)\\right)||_2^2 +\\mu ||\\mathbf{r}(t)||_2^2$\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"*[*\n",
"Will the minimum be reached when $\\mathbf{\\Omega}=\\left(\\mathbf{\\Gamma}^T\\mathbf{\\Gamma}+\\mu\\mathbf{I}\\right)$? Is this required by consistency? But using this $\\mathbf{\\Omega}$ in above $L$ does not make the $\\bar{\\mathbf{o}}$ terms go away except for the case when $\\mu=0$.\n",
"\n",
"Anyway, they've replaced the above cost function by $L_V = ||\\mathbf{V}||^2/2$ which they claim has the same minimum as $L$. Does it in the case when $\\mu\\ne 0$?\n",
"*]*"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Calculate gradient:\n",
"\n",
"$\\frac{\\partial L_V}{\\partial \\Omega_{ij}}=\\sum_k V_k\\frac{\\partial V_k}{\\partial \\Omega_{ij}} = -\\sum_{kl}V_k \\frac{\\partial\\Omega_{kl}}{\\partial\\Omega_{ij}}\\bar{o}_l - \\sum_{kl}V_k\\Omega_{kl}\\frac{\\partial\\bar{o}_l}{\\partial\\Omega_{ij}} = -V_i\\bar{o}_j - \\sum_{kl}V_k\\Omega_{kl}\\frac{\\partial\\bar{o}_l}{\\partial\\Omega_{ij}}$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The second summation can be neglected as it is averaged out under realistic conditions, and introducing a time constant of learning the $\\mathbf{\\Omega}$ via gradient descent:\n",
"\n",
"$\\tau\\dot{\\Omega}_{ij}=-\\frac{\\partial L_V}{\\partial \\Omega_{ij}}=V_i\\bar{o}_j$\n",
"\n",
"This is a Hebbian plasticity rule, whereby connectivity changes in proportion to presynaptic firing rate $\\bar{o}_j$ and post-synaptic membrane potential $V_i$ [note these are recurrent connections in the same population.]."
]
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Boerlin and Deneve 2011, Spike based population coding and working memory"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Assume stimulus evolves as as drift-diffusion process $dx_t=\\delta dt+ \\sigma dW_t$, where $W$ is a Wiener process."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Response likelihood of the input neurons is assumed to belong to an exponential family with sufficient linear statistics:"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$p(\\mathbf{S}^n_{t}|x_t)=\\Phi^n(\\mathbf{S}^n_{t})\\Psi^n(x_t)\\exp\\left(\\sum_j H^n_j(x_t)S^n_{t,j}\\right)$\n",
"\n",
"where $\\Phi^n(\\mathbf{S}^n_{t})$ and $\\Psi^n(x_t)$ are arbitrary functions, and\n",
"\n",
"$\\mathbf{H}^n(x_t)'=\\Sigma^{-1}(x_t)\\mathbf{f}^n(x_t)' = \\mathbf{\\Gamma}'(x_t)$ ...Eqn(14 & 30) [Weiji Ma, et al, Nat Neurosci, 2006],\n",
"\n",
"where $\\mathbf{f}^n(x_t)$ are the neurons' tuning curves, and $\\Sigma(x_t)$ is the spike count covariance matrix."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Figure 1 from Boerlin Deneve 2011, PLOS Comp Biol.\n",
"\n",
"<img src=\"files/images/BoerlinDeneve2011_network.png\">"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The output neurons above are online decoded by a set of decoder/readout neurons (not shown -- explicit neural layer may not be required for perceptual or motor tasks), each of which codes for the log posterior $L_i(t)\\equiv l(x_t,t)|_{x_t=x_i}$ of its preferred value $x_i$ in the discretized stimulus space $(x_1,x_2,...,x_N)$.\n",
"\n",
"$\\Gamma_{ij}=\\Gamma_j(x_i)$."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The decoder can combine spike trains from multiple sensory areas generating a sum of log-posteriors, in effect performing a product of posterior probabilities."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The decoder neurons approximate $L_i(t)$ by $G_i(t)$, and follow the dynamics:\n",
"\n",
"$\\dot{G_i}(t)=-\\lambda G_i +\\sum_{ij}\\Gamma_{ij}O_j(t)$ ...Eqn(20),\n",
"\n",
"where $O_j(t)$ is the spike train of the output layer."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The dynamics of $L_i(t)$ are calculated assuming an ideal observer and assuming responses of input neurons are independent of each other and depend only on current stimulus location *[*But the input may be an integral of locations, There is often delayed lateral inhibition too. The input is a filtered version of the stimulus over all time, as in figure above?!*]* as Eqn (19)."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"An approximation of the dynamics assuming $L_i(t)\\approx G_i(t)$ is:"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$\\dot{L_i}(t)=-\\lambda L_i(t)+Y_i(t)+Z_i^2(t)+I_i(t)$."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"where $I_i(t)$ is the input current to decoder neuron $i$ at time $t$. $Y_i(t)$ and $Z_i(t)$ defined below."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$Y_i(t)\\equiv \\lambda G_i(t) -\\delta \\partial_x G_i(t) +\\frac{\\sigma^2}{2} \\partial_{xx} G_i(t)$\n",
"\n",
"$Z_i(t) \\equiv \\frac{\\sigma}{\\sqrt{2}}\\partial_x G_i(t)$\n",
"\n",
"Using Eqn(20), we can get the dynamical equations for $Y_i(t)$ and $Z_i(t)$ involving the stimulus dynamics constants, and the output weights $\\Gamma_{ij}$ and their spatial derivatives. Integrating these dynamical equations, one should be able to get $Y_i(t)$ and $Z_i(t)$ as functions of above terms."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Cost function is $\\sum_j(L_j(t)-G_j(t))^2$. Only those output neurons fire whose spike causes a kernel that brings $\\mathbf{L}$ closer to $\\mathbf{G}$.\n",
"\n",
"This results in the firing condition:\n",
"\n",
"$\\sum_j \\Gamma_{ji} (L_j(t)-G_j(t)) > \\sum_j \\Gamma_{ji}^2/2$\n",
"\n",
"where the LHS can be construed as the membrane potential of an LIF neuron and the RHS as the threshold."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The membrane potential of the output neurons follows the dynamics:\n",
"\n",
"$\\dot{V_i}(t) = -\\lambda V_i(t) +\\sum_n\\sum_j\\{[\\mathbf{\\Gamma}^T \\mathbf{H}^n]_{ij} S^n_j(t)-\\Gamma_{ij}^T\\log\\Psi^n_i\\}-\\sum_{j\\ne i}[\\mathbf{\\Gamma}^T\\mathbf{\\Gamma}]_{ij}O_j(t)+U_i(\\mathbf{O},t)$\n",
"\n",
"where slow currents $U_i(\\mathbf{O},t) = Y_i(t)+\\sum_j \\Gamma^T_{ij}Z_j(t)^2$."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"*[*\n",
"The second term involving dual summation is the input to the output LIF neurons (see figure also). Third and fourth terms are recurrent connections.\n",
"The current $I_i(t)$ doesn't appear here, only in the decoder layer.\n",
"\n",
"What are the learned quantities? $\\Gamma$ is learnt from the input tunings (Eqn(30)). $U_i$ had $Y_i$ which involves $\\delta$ and $\\sigma$. Do they need to be learnt?\n",
"*]*"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
} | lgpl-3.0 |
SiggyF/notebooks | sfo.ipynb | 1 | 271882 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/baart_f/.virtualenvs/main/lib/python3.5/site-packages/matplotlib/__init__.py:913: UserWarning: axes.color_cycle is deprecated and replaced with axes.prop_cycle; please use the latter.\n",
" warnings.warn(self.msg_depr % (key, alt_key))\n"
]
}
],
"source": [
"import netCDF4\n",
"import matplotlib\n",
"import matplotlib.style\n",
"import matplotlib.pyplot as plt\n",
"import pandas\n",
"import numpy as np\n",
"import datetime\n",
"import pytz\n",
"\n",
"%matplotlib inline\n",
"matplotlib.style.use('grayscale')\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<class 'netCDF4._netCDF4.Variable'>\n",
"int16 sea_surface_height_above_reference_level(time, depth, latitude, longitude)\n",
" long_name: Sea Level (HOURLY)\n",
" units: millimeters\n",
" _FillValue: -32768\n",
" ancillary_variables: sensor_type_code\n",
"unlimited dimensions: time\n",
"current shape = (1034352, 1, 1, 1)\n",
"filling off"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# local copy\n",
"# ds = netCDF4.Dataset('/Users/baart_f/models/sfo/reconstruction/OS_UH-RQH551A_20140806_R.nc')\n",
"url = 'http://uhslc.soest.hawaii.edu/thredds/dodsC/uhslc/rqh/OS_UH-RQH551A_20150401_R'\n",
"url = 'http://uhslc.soest.hawaii.edu/thredds/dodsC/uhslc/rqh/OS_UH-RQH551A_20160323_R'\n",
"ds = netCDF4.Dataset(url)\n",
"# this is the variable we're interested in\n",
"ds.variables['sea_surface_height_above_reference_level']\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"# get all the data\n",
"z = ds.variables['sea_surface_height_above_reference_level'][:,0,0,0]\n",
"# get the times and convert to datetime objects\n",
"t = netCDF4.num2date(ds.variables['time'][:], ds.variables['time'].units)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'days since 1700-01-01T00:00:00Z'"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# this is in zulu time, aka as UTC\n",
"ds.variables['time'].units "
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"# make excplicit that time is in UTC\n",
"utc = pytz.timezone('UTC')\n",
"t_utc = np.array([utc.localize(t_i) for t_i in t])\n",
"# We also need the pacific timezone from back in 1962\n",
"pacific = pytz.timezone('US/Pacific')\n"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1962-06-12 00:00:00-07:00\n"
]
}
],
"source": [
"# lookup the times that we need\n",
"index = np.logical_and(\n",
" t_utc >= pacific.localize(datetime.datetime(1962, 6,10,23)), \n",
" t_utc < pacific.localize(datetime.datetime(1962, 6, 13,1))\n",
")\n",
"\n",
"d = pacific.localize(datetime.datetime(1962, 6, 12))\n",
"# this should be 7 hours before mid\n",
"print(d)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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JdiWTdr0XERGxHteuXWP8+PEAzJ49G3t7e4OL0gdXV1caN27Mpk2bWLduHYMH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"text/plain": [
"<matplotlib.figure.Figure at 0x118263eb8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig, ax = plt.subplots(figsize=(20,7))\n",
"ax.plot(t[index], z[index], linewidth=2)\n",
"ax.set_xlabel('Time in PDT (UTC-7)')\n",
"name = ds.variables['sea_surface_height_above_reference_level'].long_name\n",
"unit = ds.variables['sea_surface_height_above_reference_level'].units\n",
"ax.set_ylabel('%s [%s]' % (name, unit))\n",
"ax.xaxis.set_ticks([], minor=True)\n",
"\n",
"ax.xaxis.set_major_formatter(matplotlib.dates.DateFormatter('%D', tz=pacific))\n",
"ax.xaxis.set_major_locator(matplotlib.dates.DayLocator(tz=pacific))\n",
"ax.xaxis.set_minor_locator(matplotlib.dates.HourLocator(tz=pacific))\n",
"ax.fill_betweenx([1800, 3600], pacific.localize(datetime.datetime(1962,6,11,22,0)), pacific.localize(datetime.datetime(1962,6,12,0,0)), alpha=0.1)\n",
"plt.savefig('tide.pdf')"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.collections.PolyCollection at 0x10fad41d0>"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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jUezYsQN//vOf3Q6nD3rjMjWnlFGvJiH+UBnzjMQeSk6pWCyminhizhCwKGUO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\nEREREREREbE7TUqJiIiIiIiIiIjd/T+iXzTU2Kz5LAAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x118673cc0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# spring/neap\n",
"index = np.logical_and(\n",
" t_utc >= pacific.localize(datetime.datetime(1962, 6,4,23)), \n",
" t_utc < pacific.localize(datetime.datetime(1962, 6, 30,1))\n",
")\n",
"fig, ax = plt.subplots(figsize=(20,7))\n",
"ax.plot(t[index], z[index], linewidth=2)\n",
"ax.set_xlabel('Time in PDT (UTC-7)')\n",
"name = ds.variables['sea_surface_height_above_reference_level'].long_name\n",
"unit = ds.variables['sea_surface_height_above_reference_level'].units\n",
"ax.set_ylabel('%s [%s]' % (name, unit))\n",
"ax.xaxis.set_ticks([], minor=True)\n",
"\n",
"ax.xaxis.set_major_formatter(matplotlib.dates.DateFormatter('%D', tz=pacific))\n",
"ax.xaxis.set_major_locator(matplotlib.dates.DayLocator(tz=pacific))\n",
"ax.xaxis.set_minor_locator(matplotlib.dates.HourLocator(tz=pacific))\n",
"ax.fill_betweenx([1000, 4000], \n",
" pacific.localize(datetime.datetime(1962,6,12,22,0)), \n",
" pacific.localize(datetime.datetime(1962,6,13,0,0)), alpha=0.5)\n"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>diff</th>\n",
" <th>name</th>\n",
" <th>tz</th>\n",
" <th>utc</th>\n",
" <th>diff_minutes</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>-1 days +16:07:00</td>\n",
" <td>LMT</td>\n",
" <td>1699-12-31 16:07:00-07:53</td>\n",
" <td>1700-01-01 00:00:00+00:00</td>\n",
" <td>-473.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>-1 days +16:00:00</td>\n",
" <td>PST</td>\n",
" <td>1901-12-13 12:45:52-08:00</td>\n",
" <td>1901-12-13 20:45:52+00:00</td>\n",
" <td>-480.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>-1 days +17:00:00</td>\n",
" <td>PDT</td>\n",
" <td>1918-03-31 03:00:00-07:00</td>\n",
" <td>1918-03-31 10:00:00+00:00</td>\n",
" <td>-420.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>-1 days +16:00:00</td>\n",
" <td>PST</td>\n",
" <td>1918-10-27 01:00:00-08:00</td>\n",
" <td>1918-10-27 09:00:00+00:00</td>\n",
" <td>-480.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>-1 days +17:00:00</td>\n",
" <td>PDT</td>\n",
" <td>1919-03-30 03:00:00-07:00</td>\n",
" <td>1919-03-30 10:00:00+00:00</td>\n",
" <td>-420.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" diff name tz utc \\\n",
"0 -1 days +16:07:00 LMT 1699-12-31 16:07:00-07:53 1700-01-01 00:00:00+00:00 \n",
"1 -1 days +16:00:00 PST 1901-12-13 12:45:52-08:00 1901-12-13 20:45:52+00:00 \n",
"2 -1 days +17:00:00 PDT 1918-03-31 03:00:00-07:00 1918-03-31 10:00:00+00:00 \n",
"3 -1 days +16:00:00 PST 1918-10-27 01:00:00-08:00 1918-10-27 09:00:00+00:00 \n",
"4 -1 days +17:00:00 PDT 1919-03-30 03:00:00-07:00 1919-03-30 10:00:00+00:00 \n",
"\n",
" diff_minutes \n",
"0 -473.0 \n",
"1 -480.0 \n",
"2 -420.0 \n",
"3 -480.0 \n",
"4 -420.0 "
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Create an overview of timezones in 1962\n",
"\n",
"# Timezone information from the Olson timezone database\n",
"reference = 'US/Pacific'\n",
"tz = pytz.timezone(reference)\n",
"# all the details are in here:\n",
"tz._transition_info\n",
"\n",
"# We're showing times relative to UTC\n",
"utc = pytz.timezone('UTC')\n",
"\n",
"# Lookup all the transitions\n",
"rows = []\n",
"for date, transinfo in zip(tz._utc_transition_times, tz._transition_info):\n",
" dateutc = date.replace(tzinfo=utc)\n",
" if dateutc.year < 1700:\n",
" # replace year 1 by year 1700 to avoid having to deal with julian calendar\n",
" dateutc = dateutc.replace(year=1700)\n",
" # create a row with information\n",
" row = {'utc': dateutc,\n",
" 'tz': dateutc.astimezone(tz),\n",
" 'diff': dateutc.astimezone(tz).replace(tzinfo=utc) - dateutc,\n",
" 'name': transinfo[2]}\n",
" rows.append(row)\n",
"\n",
"# convert it to a data frame for easier querying\n",
"df = pandas.DataFrame.from_records(rows)\n",
"# compute the difference in minutes\n",
"# note that if you use apply the timedelta is converted to a numpy timedelta, why...?\n",
"df['diff_minutes'] = df['diff'].apply(lambda x:1/60.0*x.total_seconds())\n",
"# show the first timezones used, PST was actually introduced in 1883 by the railway, seems to be fixed in later versions\n",
"# LMT is local mean time\n",
"df.head()\n"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x11f4f38d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# plot the timezones in US/Pacific, which is equal to America/Los_angeles\n",
"fig, ax = plt.subplots(figsize=(20,5))\n",
"# plot the timezone using a post step plot\n",
"ax.plot_date(\n",
" df['utc'], \n",
" df['diff_minutes'], \n",
" drawstyle='steps-post', \n",
" linestyle='-', \n",
" marker='', \n",
" color='#009999', \n",
" linewidth=3\n",
")\n",
"# # zoom-in and enhance\n",
"ax.set_xlim(\n",
" datetime.datetime(1962,1,1), \n",
" datetime.datetime(1963,1,1)\n",
")\n",
"ax.set_ylim(-500, -400)\n",
"\n",
"# # labels\n",
"ax.set_xlabel('date')\n",
"ax.set_ylabel('minutes to UTC')\n",
"ax.set_title(reference)\n",
"\n",
"# # annotate\n",
"arrowprops=dict(facecolor='black', shrink=0.1)\n",
"for i, row in df.iterrows():\n",
" x = matplotlib.dates.date2num(row['tz'])\n",
" y = row['diff_minutes']\n",
" name = row['name']\n",
" if row['utc'] >= utc.localize(datetime.datetime(1962,1,1)) and row['utc'] < utc.localize(datetime.datetime(1963,1,1)):\n",
" ax.annotate(name, xy=(x,y), xytext=(x+10, y+10), arrowprops=arrowprops)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"ds = netCDF4.Dataset('/Users/baart_f/models/sfo/sfo-3di/subgrid_map_15min.nc')\n",
"# same thing for FM model\n",
"# ds = netCDF4.Dataset('/Users/baart_f/models/sfo/baydelta/dec1999_mar2000/DFM_OUTPUT_alcatraz/alcatraz_map.nc')"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'seconds since 1962-06-09 00:00:00'"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# get the coordinates\n",
"x = ds.variables['FlowElem_xcc'][:]\n",
"y = ds.variables['FlowElem_ycc'][:]\n",
"coords = np.c_[x,y]\n",
"# I assume this is in local time PDT?, perhaps PST, see later\n",
"ds.variables['time'].units "
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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upLS0lGHDhnHy5ElGjBjR5JgeOHCAjz76CHt7e44cOaL2Pg0vCDuTVofmQkND2bdvH/v2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\nPigAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11f9357b8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.plot(coords[:,0], coords[:,1], 'k.')\n",
"# points near fort point\n",
"xr = (542500, 543100)\n",
"yr = (4181000, 4182500)\n",
"plt.xlim(xr[0], xr[1])\n",
"plt.ylim(yr[0], yr[1])\n",
"for i, coord in enumerate(coords):\n",
" if (xr[0] <= coord[0] < xr[1] \n",
" and \n",
" yr[0] <= coord[1] < yr[1]):\n",
" plt.text(coord[0], coord[1], str(i))"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(716401.9583333334, 716404.0416666666)"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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SUuDp6clqHPr6+pg5cyb+/PNPVuNojczMTPTp00ctpyqamJggMzOTk3/8mnP9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\ncejQIeTk5GDDhg2IjIxEZmYmjh49iqN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"text/plain": [
"<matplotlib.figure.Figure at 0x11f384668>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"t = netCDF4.num2date(ds.variables['time'][:], ds.variables['time'].units)\n",
"# Plot the model water levels near fort Point\n",
"fig, ax = plt.subplots()\n",
"# it seems that the model is off by 1 hour, perhaps using PST instead of PDT?\n",
"ax.plot(t + datetime.timedelta(seconds=3600), ds.variables['s1'][:,5480])\n",
"# Format x axis\n",
"ax.xaxis.set_major_locator(matplotlib.dates.DayLocator())\n",
"ax.xaxis.set_major_formatter(matplotlib.dates.DateFormatter('%Y-%m-%d'))\n",
"ax.set_xlim(\n",
" datetime.datetime(1962, 6,10,23), \n",
" datetime.datetime(1962,6,13, 1)\n",
")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"hide_input": false,
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.3"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
| gpl-3.0 |
cathalmccabe/PYNQ | boards/Pynq-Z2/logictools/notebooks/pattern_generator_and_trace_analyzer.ipynb | 4 | 128597 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Pattern Generator and Trace Analyzer\n",
"\n",
"This notebook will show how to use the Pattern Generator to generate patterns on I/O pins. The pattern that will be generated is 3-bit up count performed 4 times. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Step 1: Download the `logictools` overlay"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"run_control": {
"frozen": false,
"read_only": false
}
},
"outputs": [
{
"data": {
"application/javascript": [
"\n",
"require(['notebook/js/codecell'], function(codecell) {\n",
" codecell.CodeCell.options_default.highlight_modes[\n",
" 'magic_text/x-csrc'] = {'reg':[/^%%microblaze/]};\n",
" Jupyter.notebook.events.one('kernel_ready.Kernel', function(){\n",
" Jupyter.notebook.get_cells().map(function(cell){\n",
" if (cell.cell_type == 'code'){ cell.auto_highlight(); } }) ;\n",
" });\n",
"});\n"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from pynq.overlays.logictools import LogicToolsOverlay\n",
"\n",
"logictools_olay = LogicToolsOverlay('logictools.bit')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Step 2: Create WaveJSON waveform\n",
"The pattern to be generated is specified in the waveJSON format \n",
"\n",
"The pattern is applied to the Arduino interface, pins **D0**, **D1** and **D2** are set to generate a 3-bit count. \n",
"To check the generated pattern we loop them back to pins **D19**, **D18** and **D17** respectively and use the the trace analyzer to view the loopback signals\n",
"\n",
"The Waveform class is used to display the specified waveform."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
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],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from pynq.lib.logictools import Waveform\n",
"\n",
"up_counter = {'signal': [\n",
" ['stimulus',\n",
" {'name': 'bit0', 'pin': 'D0', 'wave': 'lh' * 8},\n",
" {'name': 'bit1', 'pin': 'D1', 'wave': 'l.h.' * 4},\n",
" {'name': 'bit2', 'pin': 'D2', 'wave': 'l...h...' * 2}], \n",
" \n",
" ['analysis',\n",
" {'name': 'bit2_loopback', 'pin': 'D17'},\n",
" {'name': 'bit1_loopback', 'pin': 'D18'},\n",
" {'name': 'bit0_loopback', 'pin': 'D19'}]], \n",
"\n",
" 'foot': {'tock': 1},\n",
" 'head': {'text': 'up_counter'}}\n",
"\n",
"waveform = Waveform(up_counter)\n",
"waveform.display()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Note:** Since there are no captured samples at this moment, the analysis group will be empty."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Step 3: Instantiate the pattern generator and trace analyzer objects\n",
"Users can choose whether to use the trace analyzer by calling the `trace()` method. \n",
"The analyzer can be set to trace a specific number of samples using, `num_analyzer_samples` argument."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"run_control": {
"frozen": false,
"read_only": false
}
},
"outputs": [],
"source": [
"pattern_generator = logictools_olay.pattern_generator\n",
"pattern_generator.trace(num_analyzer_samples=16)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Step 4: Setup the pattern generator\n",
"The pattern generator will work at the default frequency of 10MHz. This can be modified using a `frequency` argument in the `setup()` method. "
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"pattern_generator.setup(up_counter,\n",
" stimulus_group_name='stimulus',\n",
" analysis_group_name='analysis')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"__Set the loopback connections using jumper wires on the Arduino Interface__\n",
"\n",
"\n",
"* __Output pins D0, D1 and D2 are connected to pins D19, D18 and D17 respectively__ \n",
"* __Loopback/Input pins D19, D18 and D17 are observed using the trace analyzer as shown below__\n",
"* __After setup, the pattern generator should be ready to run__\n",
"\n",
"**Note:** Make sure all other pins are disconnected."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Step 5: Run and display waveform\n",
"\n",
"The ` run()` method will execute all the samples, `show_waveform()` method is used to display the waveforms. \n",
"Alternatively, we can also use `step()` method to single step the pattern."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"scrolled": true
},
"outputs": [
{
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transform="translate(0,5)"><text class="info" text-anchor="end" x="-10" xml:space="preserve" y="15"><tspan>bit0</tspan></text><g id="wavelane_draw_0_0" transform="translate(0, 0)"><use transform="translate(0)" xlink:href="#000" xmlns:xlink="http://www.w3.org/1999/xlink"/><use transform="translate(20)" xlink:href="#000" xmlns:xlink="http://www.w3.org/1999/xlink"/><use transform="translate(40)" xlink:href="#pclk" xmlns:xlink="http://www.w3.org/1999/xlink"/><use transform="translate(60)" xlink:href="#111" xmlns:xlink="http://www.w3.org/1999/xlink"/><use transform="translate(80)" xlink:href="#nclk" xmlns:xlink="http://www.w3.org/1999/xlink"/><use transform="translate(100)" xlink:href="#000" xmlns:xlink="http://www.w3.org/1999/xlink"/><use transform="translate(120)" xlink:href="#pclk" xmlns:xlink="http://www.w3.org/1999/xlink"/><use transform="translate(140)" xlink:href="#111" xmlns:xlink="http://www.w3.org/1999/xlink"/><use transform="translate(160)" xlink:href="#nclk" 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transform="translate(380)" xlink:href="#111" xmlns:xlink="http://www.w3.org/1999/xlink"/><use transform="translate(400)" xlink:href="#nclk" xmlns:xlink="http://www.w3.org/1999/xlink"/><use transform="translate(420)" xlink:href="#000" xmlns:xlink="http://www.w3.org/1999/xlink"/><use transform="translate(440)" xlink:href="#pclk" xmlns:xlink="http://www.w3.org/1999/xlink"/><use transform="translate(460)" xlink:href="#111" xmlns:xlink="http://www.w3.org/1999/xlink"/><use transform="translate(480)" xlink:href="#nclk" xmlns:xlink="http://www.w3.org/1999/xlink"/><use transform="translate(500)" xlink:href="#000" xmlns:xlink="http://www.w3.org/1999/xlink"/><use transform="translate(520)" xlink:href="#pclk" xmlns:xlink="http://www.w3.org/1999/xlink"/><use transform="translate(540)" xlink:href="#111" xmlns:xlink="http://www.w3.org/1999/xlink"/><use transform="translate(560)" xlink:href="#nclk" xmlns:xlink="http://www.w3.org/1999/xlink"/><use transform="translate(580)" xlink:href="#000" xmlns:xlink="http://www.w3.org/1999/xlink"/><use transform="translate(600)" xlink:href="#pclk" xmlns:xlink="http://www.w3.org/1999/xlink"/><use transform="translate(620)" xlink:href="#111" xmlns:xlink="http://www.w3.org/1999/xlink"/></g></g><g id="wavelane_1_0" transform="translate(0,35)"><text class="info" text-anchor="end" x="-10" xml:space="preserve" y="15"><tspan>bit1</tspan></text><g id="wavelane_draw_1_0" transform="translate(0, 0)"><use transform="translate(0)" xlink:href="#000" xmlns:xlink="http://www.w3.org/1999/xlink"/><use transform="translate(20)" xlink:href="#000" xmlns:xlink="http://www.w3.org/1999/xlink"/><use transform="translate(40)" xlink:href="#000" xmlns:xlink="http://www.w3.org/1999/xlink"/><use transform="translate(60)" xlink:href="#000" xmlns:xlink="http://www.w3.org/1999/xlink"/><use transform="translate(80)" xlink:href="#pclk" xmlns:xlink="http://www.w3.org/1999/xlink"/><use transform="translate(100)" xlink:href="#111" 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transform="translate(320)" xlink:href="#nclk" xmlns:xlink="http://www.w3.org/1999/xlink"/><use transform="translate(340)" xlink:href="#000" xmlns:xlink="http://www.w3.org/1999/xlink"/><use transform="translate(360)" xlink:href="#000" xmlns:xlink="http://www.w3.org/1999/xlink"/><use transform="translate(380)" xlink:href="#000" xmlns:xlink="http://www.w3.org/1999/xlink"/><use transform="translate(400)" xlink:href="#pclk" xmlns:xlink="http://www.w3.org/1999/xlink"/><use transform="translate(420)" xlink:href="#111" xmlns:xlink="http://www.w3.org/1999/xlink"/><use transform="translate(440)" xlink:href="#111" xmlns:xlink="http://www.w3.org/1999/xlink"/><use transform="translate(460)" xlink:href="#111" xmlns:xlink="http://www.w3.org/1999/xlink"/><use transform="translate(480)" xlink:href="#nclk" xmlns:xlink="http://www.w3.org/1999/xlink"/><use transform="translate(500)" xlink:href="#000" xmlns:xlink="http://www.w3.org/1999/xlink"/><use transform="translate(520)" xlink:href="#000" xmlns:xlink="http://www.w3.org/1999/xlink"/><use transform="translate(540)" xlink:href="#000" xmlns:xlink="http://www.w3.org/1999/xlink"/><use transform="translate(560)" xlink:href="#pclk" xmlns:xlink="http://www.w3.org/1999/xlink"/><use transform="translate(580)" xlink:href="#111" xmlns:xlink="http://www.w3.org/1999/xlink"/><use transform="translate(600)" xlink:href="#111" xmlns:xlink="http://www.w3.org/1999/xlink"/><use transform="translate(620)" xlink:href="#111" xmlns:xlink="http://www.w3.org/1999/xlink"/></g></g><g id="wavelane_2_0" transform="translate(0,65)"><text class="info" text-anchor="end" x="-10" xml:space="preserve" y="15"><tspan>bit2</tspan></text><g id="wavelane_draw_2_0" transform="translate(0, 0)"><use transform="translate(0)" xlink:href="#000" xmlns:xlink="http://www.w3.org/1999/xlink"/><use transform="translate(20)" xlink:href="#000" xmlns:xlink="http://www.w3.org/1999/xlink"/><use transform="translate(40)" xlink:href="#000" 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transform="translate(260)" xlink:href="#111" xmlns:xlink="http://www.w3.org/1999/xlink"/><use transform="translate(280)" xlink:href="#111" xmlns:xlink="http://www.w3.org/1999/xlink"/><use transform="translate(300)" xlink:href="#111" xmlns:xlink="http://www.w3.org/1999/xlink"/><use transform="translate(320)" xlink:href="#nclk" xmlns:xlink="http://www.w3.org/1999/xlink"/><use transform="translate(340)" xlink:href="#000" xmlns:xlink="http://www.w3.org/1999/xlink"/><use transform="translate(360)" xlink:href="#000" xmlns:xlink="http://www.w3.org/1999/xlink"/><use transform="translate(380)" xlink:href="#000" xmlns:xlink="http://www.w3.org/1999/xlink"/><use transform="translate(400)" xlink:href="#000" xmlns:xlink="http://www.w3.org/1999/xlink"/><use transform="translate(420)" xlink:href="#000" xmlns:xlink="http://www.w3.org/1999/xlink"/><use transform="translate(440)" xlink:href="#000" xmlns:xlink="http://www.w3.org/1999/xlink"/><use transform="translate(460)" xlink:href="#000" xmlns:xlink="http://www.w3.org/1999/xlink"/><use transform="translate(480)" xlink:href="#pclk" xmlns:xlink="http://www.w3.org/1999/xlink"/><use transform="translate(500)" xlink:href="#111" xmlns:xlink="http://www.w3.org/1999/xlink"/><use transform="translate(520)" xlink:href="#111" xmlns:xlink="http://www.w3.org/1999/xlink"/><use transform="translate(540)" xlink:href="#111" xmlns:xlink="http://www.w3.org/1999/xlink"/><use transform="translate(560)" xlink:href="#111" xmlns:xlink="http://www.w3.org/1999/xlink"/><use transform="translate(580)" xlink:href="#111" xmlns:xlink="http://www.w3.org/1999/xlink"/><use transform="translate(600)" xlink:href="#111" xmlns:xlink="http://www.w3.org/1999/xlink"/><use transform="translate(620)" xlink:href="#111" xmlns:xlink="http://www.w3.org/1999/xlink"/></g></g><g id="wavelane_3_0" transform="translate(0,95)"><text class="info" text-anchor="end" x="-10" xml:space="preserve" y="15"><tspan>bit2_loopback</tspan></text><g id="wavelane_draw_3_0" transform="translate(0, 0)"><use transform="translate(0)" xlink:href="#000" xmlns:xlink="http://www.w3.org/1999/xlink"/><use transform="translate(20)" xlink:href="#000" xmlns:xlink="http://www.w3.org/1999/xlink"/><use transform="translate(40)" xlink:href="#000" xmlns:xlink="http://www.w3.org/1999/xlink"/><use transform="translate(60)" xlink:href="#000" xmlns:xlink="http://www.w3.org/1999/xlink"/><use transform="translate(80)" xlink:href="#000" xmlns:xlink="http://www.w3.org/1999/xlink"/><use transform="translate(100)" xlink:href="#000" xmlns:xlink="http://www.w3.org/1999/xlink"/><use transform="translate(120)" xlink:href="#000" xmlns:xlink="http://www.w3.org/1999/xlink"/><use transform="translate(140)" xlink:href="#000" xmlns:xlink="http://www.w3.org/1999/xlink"/><use transform="translate(160)" xlink:href="#pclk" xmlns:xlink="http://www.w3.org/1999/xlink"/><use transform="translate(180)" xlink:href="#111" xmlns:xlink="http://www.w3.org/1999/xlink"/><use transform="translate(200)" xlink:href="#111" xmlns:xlink="http://www.w3.org/1999/xlink"/><use transform="translate(220)" xlink:href="#111" xmlns:xlink="http://www.w3.org/1999/xlink"/><use transform="translate(240)" xlink:href="#111" xmlns:xlink="http://www.w3.org/1999/xlink"/><use transform="translate(260)" xlink:href="#111" xmlns:xlink="http://www.w3.org/1999/xlink"/><use transform="translate(280)" xlink:href="#111" xmlns:xlink="http://www.w3.org/1999/xlink"/><use transform="translate(300)" xlink:href="#111" xmlns:xlink="http://www.w3.org/1999/xlink"/><use transform="translate(320)" xlink:href="#nclk" xmlns:xlink="http://www.w3.org/1999/xlink"/><use transform="translate(340)" xlink:href="#000" xmlns:xlink="http://www.w3.org/1999/xlink"/><use transform="translate(360)" xlink:href="#000" xmlns:xlink="http://www.w3.org/1999/xlink"/><use transform="translate(380)" xlink:href="#000" xmlns:xlink="http://www.w3.org/1999/xlink"/><use transform="translate(400)" xlink:href="#000" xmlns:xlink="http://www.w3.org/1999/xlink"/><use transform="translate(420)" xlink:href="#000" xmlns:xlink="http://www.w3.org/1999/xlink"/><use transform="translate(440)" xlink:href="#000" xmlns:xlink="http://www.w3.org/1999/xlink"/><use transform="translate(460)" xlink:href="#000" xmlns:xlink="http://www.w3.org/1999/xlink"/><use transform="translate(480)" xlink:href="#pclk" xmlns:xlink="http://www.w3.org/1999/xlink"/><use transform="translate(500)" xlink:href="#111" xmlns:xlink="http://www.w3.org/1999/xlink"/><use transform="translate(520)" xlink:href="#111" xmlns:xlink="http://www.w3.org/1999/xlink"/><use transform="translate(540)" xlink:href="#111" xmlns:xlink="http://www.w3.org/1999/xlink"/><use transform="translate(560)" xlink:href="#111" xmlns:xlink="http://www.w3.org/1999/xlink"/><use transform="translate(580)" xlink:href="#111" xmlns:xlink="http://www.w3.org/1999/xlink"/><use transform="translate(600)" xlink:href="#111" xmlns:xlink="http://www.w3.org/1999/xlink"/><use transform="translate(620)" xlink:href="#111" xmlns:xlink="http://www.w3.org/1999/xlink"/></g></g><g id="wavelane_4_0" transform="translate(0,125)"><text class="info" text-anchor="end" x="-10" xml:space="preserve" y="15"><tspan>bit1_loopback</tspan></text><g id="wavelane_draw_4_0" transform="translate(0, 0)"><use transform="translate(0)" xlink:href="#000" xmlns:xlink="http://www.w3.org/1999/xlink"/><use transform="translate(20)" xlink:href="#000" xmlns:xlink="http://www.w3.org/1999/xlink"/><use transform="translate(40)" xlink:href="#000" xmlns:xlink="http://www.w3.org/1999/xlink"/><use transform="translate(60)" xlink:href="#000" xmlns:xlink="http://www.w3.org/1999/xlink"/><use transform="translate(80)" xlink:href="#pclk" xmlns:xlink="http://www.w3.org/1999/xlink"/><use transform="translate(100)" xlink:href="#111" xmlns:xlink="http://www.w3.org/1999/xlink"/><use transform="translate(120)" xlink:href="#111" xmlns:xlink="http://www.w3.org/1999/xlink"/><use transform="translate(140)" xlink:href="#111" xmlns:xlink="http://www.w3.org/1999/xlink"/><use transform="translate(160)" xlink:href="#nclk" xmlns:xlink="http://www.w3.org/1999/xlink"/><use transform="translate(180)" xlink:href="#000" xmlns:xlink="http://www.w3.org/1999/xlink"/><use transform="translate(200)" xlink:href="#000" xmlns:xlink="http://www.w3.org/1999/xlink"/><use transform="translate(220)" xlink:href="#000" xmlns:xlink="http://www.w3.org/1999/xlink"/><use transform="translate(240)" xlink:href="#pclk" xmlns:xlink="http://www.w3.org/1999/xlink"/><use transform="translate(260)" xlink:href="#111" xmlns:xlink="http://www.w3.org/1999/xlink"/><use transform="translate(280)" xlink:href="#111" xmlns:xlink="http://www.w3.org/1999/xlink"/><use transform="translate(300)" xlink:href="#111" xmlns:xlink="http://www.w3.org/1999/xlink"/><use transform="translate(320)" xlink:href="#nclk" xmlns:xlink="http://www.w3.org/1999/xlink"/><use transform="translate(340)" xlink:href="#000" xmlns:xlink="http://www.w3.org/1999/xlink"/><use transform="translate(360)" xlink:href="#000" xmlns:xlink="http://www.w3.org/1999/xlink"/><use transform="translate(380)" xlink:href="#000" xmlns:xlink="http://www.w3.org/1999/xlink"/><use transform="translate(400)" xlink:href="#pclk" xmlns:xlink="http://www.w3.org/1999/xlink"/><use transform="translate(420)" xlink:href="#111" xmlns:xlink="http://www.w3.org/1999/xlink"/><use transform="translate(440)" xlink:href="#111" xmlns:xlink="http://www.w3.org/1999/xlink"/><use transform="translate(460)" xlink:href="#111" xmlns:xlink="http://www.w3.org/1999/xlink"/><use transform="translate(480)" xlink:href="#nclk" xmlns:xlink="http://www.w3.org/1999/xlink"/><use transform="translate(500)" xlink:href="#000" xmlns:xlink="http://www.w3.org/1999/xlink"/><use transform="translate(520)" xlink:href="#000" xmlns:xlink="http://www.w3.org/1999/xlink"/><use transform="translate(540)" xlink:href="#000" xmlns:xlink="http://www.w3.org/1999/xlink"/><use 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| bsd-3-clause |
cydcowley/Imperial-Visualizations | visuals_maths/VC-Riley/pynb/riley.ipynb | 1 | 3316 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<img src=\"imperial_logo.png\" width=\"275\" align=\"left\">\n",
"<p style=\"text-align: right\">\n",
" Created by Dong-Woo (Dom) Ko<br>Email: [email protected]<br><a>HTML Version (This will be a link)</a>\n",
"</p>\n",
"<br>\n",
"# VC: Volume Elements in different Coordinate Systems\n",
"\n",
"## Learning Objectives\n",
"* To aid in visualisation of volume elements in different coordinate systems of $I\\!R^3$.\n",
"\n",
"## Table of Contents\n",
"1. Introduction\n",
"2. Cylindrical\n",
"3. Spherical"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 1. Introduction\n",
"We will explore volume elements in Cylindrical and Spherical Coordinates Systems. (In the form of Riley Diagrams) <br>\n",
"This visualisation is designed to help you with triple integration."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"A volume element is the differential element dV, and when integrated over specified range, the result will give you the volume of the solid.\n",
"\n",
"For Cartesian,\n",
"### Volume Element:\n",
"$$\n",
" dV = dx * dy * dz.\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Volume integral:\n",
"\\begin{align}\n",
" V &= \\iiint_V{dV}\\\\\n",
" &= \\iiint_V \\,dx\\,dy\\,dz\n",
"\\end{align}"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Note:\n",
"- These volume elements are <b>infinitesimally small</b>. Hence consider them as a <b>small cube</b>.\n",
"- Different coordinates systems may be used depending on the shape of the solid."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 2. Cylindrical"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Volume Element, dV:\n",
"\\begin{align}\n",
" dV &= d\\rho * \\rho d\\phi * dz\\\\\n",
" &= \\rho \\; d\\rho \\; d\\phi \\; dz.\n",
"\\end{align}"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Riley Diagram:\n",
"<img src=\"cylin.png\" width=\"650\" align=\"center\">"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 3. Spherical"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Volume Element, dV:\n",
"\\begin{align}\n",
" dV &= dr * rd\\theta * r\\sin\\theta d\\phi\\\\\n",
" &= r^2 \\; \\sin\\theta \\; dr \\; d\\theta \\; d\\phi.\n",
"\\end{align}"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Riley Diagram:\n",
"<img src=\"spher.png\" width=\"650\" align=\"center\">"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
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"version": 3
},
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"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.2"
}
},
"nbformat": 4,
"nbformat_minor": 2
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| mit |
batfish/pybatfish | jupyter_notebooks/Pandas Examples.ipynb | 1 | 114099 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Pandas Examples"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Batfish questions can return a huge amount of data, which you may want to filter in various ways based on your task. While most Batfish questions support basic filtering, they may not support your desired filtering criteria. Further, for performance, you may want to fetch the answer once and filter it using multiple different criteria. These scenarios are where Pandas-based filtering can help. \n",
"\n",
"Batfish answers can be easily turned into a [Pandas DataFrame](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html) (using `.frame()`), after which you can use the full power of Pandas to filter and manipulate data. This notebook provides a few examples of common manipulations for Batfish. It is not intended as a complete guide of Pandas data manipulation.\n",
"\n",
" "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's first initialize a snapshot that we will use in our examples."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Your snapshot was successfully initialized but Batfish failed to fully recognized some lines in one or more input files. Some unrecognized configuration lines are not uncommon for new networks, and it is often fine to proceed with further analysis. You can help the Batfish developers improve support for your network by running:\n",
"\n",
" bf.upload_diagnostics(dry_run=False, contact_info='<optional email address>')\n",
"\n",
"to share private, anonymized information. For more information, see the documentation with:\n",
"\n",
" help(bf.upload_diagnostics)\n"
]
},
{
"data": {
"text/plain": [
"'snapshot'"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Import packages\n",
"%run startup.py\n",
"bf = Session(host=\"localhost\")\n",
"\n",
"# Initialize a network and a snapshot\n",
"bf.set_network(\"pandas-example\")\n",
"\n",
"SNAPSHOT_NAME = \"snapshot\"\n",
"SNAPSHOT_PATH = \"networks/hybrid-cloud/\"\n",
"bf.init_snapshot(SNAPSHOT_PATH, name=SNAPSHOT_NAME, overwrite=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Filtering `initIssues`"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"After initializing the snapshot, you often want to look at the <code>[initIssues](https://pybatfish.readthedocs.io/en/latest/notebooks/snapshot.html#Snapshot-Initialization-Issues)</code> answer. If there are too many issues, you may want to ignore a particular class of issues. We show below how to do that. "
]
},
{
"cell_type": "code",
"execution_count": 2,
"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 tex2jax_ignore\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Nodes</th>\n",
" <th>Source_Lines</th>\n",
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" <th>Details</th>\n",
" <th>Line_Text</th>\n",
" <th>Parser_Context</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>['leaf1']</td>\n",
" <td>None</td>\n",
" <td>Convert warning (redflag)</td>\n",
" <td>Interface Ethernet12 has an undefined channel group Port-Channel20</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>None</td>\n",
" <td>[configs/Leaf2.cfg:[6], configs/Leaf4.cfg:[6], configs/Leaf1.cfg:[6], configs/Leaf3.cfg:[6], configs/Spine1.cfg:[6], configs/Spine2.cfg:[6]]</td>\n",
" <td>Parse warning</td>\n",
" <td>This syntax is unrecognized</td>\n",
" <td>transceiver qsfp default-mode 4x10G</td>\n",
" <td>[arista_configuration]</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>None</td>\n",
" <td>[aws_configs:[]]</td>\n",
" <td>Parse warning (unimplemented)</td>\n",
" <td>Unrecognized element 'ServiceDetails' in AWS file aws_configs/us-west-2/VpcEndpointServices.json</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>None</td>\n",
" <td>[aws_configs:[]]</td>\n",
" <td>Parse warning (unimplemented)</td>\n",
" <td>Unrecognized element 'ServiceDetails' in AWS file aws_configs/us-east-2/VpcEndpointServices.json</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Nodes \\\n",
"0 ['leaf1'] \n",
"1 None \n",
"2 None \n",
"3 None \n",
"\n",
" Source_Lines \\\n",
"0 None \n",
"1 [configs/Leaf2.cfg:[6], configs/Leaf4.cfg:[6], configs/Leaf1.cfg:[6], configs/Leaf3.cfg:[6], configs/Spine1.cfg:[6], configs/Spine2.cfg:[6]] \n",
"2 [aws_configs:[]] \n",
"3 [aws_configs:[]] \n",
"\n",
" Type \\\n",
"0 Convert warning (redflag) \n",
"1 Parse warning \n",
"2 Parse warning (unimplemented) \n",
"3 Parse warning (unimplemented) \n",
"\n",
" Details \\\n",
"0 Interface Ethernet12 has an undefined channel group Port-Channel20 \n",
"1 This syntax is unrecognized \n",
"2 Unrecognized element 'ServiceDetails' in AWS file aws_configs/us-west-2/VpcEndpointServices.json \n",
"3 Unrecognized element 'ServiceDetails' in AWS file aws_configs/us-east-2/VpcEndpointServices.json \n",
"\n",
" Line_Text Parser_Context \n",
"0 None None \n",
"1 transceiver qsfp default-mode 4x10G [arista_configuration] \n",
"2 None None \n",
"3 None None "
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Lets get the initIssues for our snapshot\n",
"issues = bf.q.initIssues().answer().frame()\n",
"issues"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
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" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
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" <td>None</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>None</td>\n",
" <td>[aws_configs:[]]</td>\n",
" <td>Parse warning (unimplemented)</td>\n",
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" <td>None</td>\n",
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" <tr>\n",
" <th>3</th>\n",
" <td>None</td>\n",
" <td>[aws_configs:[]]</td>\n",
" <td>Parse warning (unimplemented)</td>\n",
" <td>Unrecognized element 'ServiceDetails' in AWS file aws_configs/us-east-2/VpcEndpointServices.json</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Nodes Source_Lines Type \\\n",
"0 ['leaf1'] None Convert warning (redflag) \n",
"2 None [aws_configs:[]] Parse warning (unimplemented) \n",
"3 None [aws_configs:[]] Parse warning (unimplemented) \n",
"\n",
" Details \\\n",
"0 Interface Ethernet12 has an undefined channel group Port-Channel20 \n",
"2 Unrecognized element 'ServiceDetails' in AWS file aws_configs/us-west-2/VpcEndpointServices.json \n",
"3 Unrecognized element 'ServiceDetails' in AWS file aws_configs/us-east-2/VpcEndpointServices.json \n",
"\n",
" Line_Text Parser_Context \n",
"0 None None \n",
"2 None None \n",
"3 None None "
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Ignore all issues whose Line_Text contain one of these as a substring\n",
"line_texts_to_ignore = [\"transceiver\"]\n",
"\n",
"\n",
"def has_substring(text: Optional[str], substrings: List[str]) -> bool:\n",
" \"\"\"Returns True if 'text' is not None and contains one of the 'substrings'\"\"\"\n",
" return text is not None and any(substr in text for substr in substrings)\n",
"\n",
"\n",
"issues[\n",
" issues.apply(\n",
" lambda issue: not has_substring(issue[\"Line_Text\"], line_texts_to_ignore),\n",
" axis=1,\n",
" )\n",
"]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In the code above, we are using the Pandas method <code>[apply](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.apply.html)</code> to map `issues` to a binary array based on whether the issue has one of the substrings in `line_texts_to_ignore`. Passing `axis=1` makes `apply` iterate over rows instead of columns. The helper method `has_substring` makes this determination. It returns `True` if `text` is not `None` and has any of the substrings. The Python method <code>[any](https://docs.python.org/3/library/functions.html#any)</code> returns `True` if any element of the input iterable is `True`. Using the binary array as a filter for `issues` produces rows that match our criterion. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Instead of ignoring some issues, you may want to focus on issues that match a certain criteria. That too can be easily accomplished, as follows."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
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" vertical-align: middle;\n",
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" <td>[aws_configs:[]]</td>\n",
" <td>Parse warning (unimplemented)</td>\n",
" <td>Unrecognized element 'ServiceDetails' in AWS file aws_configs/us-west-2/VpcEndpointServices.json</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>None</td>\n",
" <td>[aws_configs:[]]</td>\n",
" <td>Parse warning (unimplemented)</td>\n",
" <td>Unrecognized element 'ServiceDetails' in AWS file aws_configs/us-east-2/VpcEndpointServices.json</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
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" </tbody>\n",
"</table>\n",
"</div>"
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" Nodes Source_Lines Type \\\n",
"2 None [aws_configs:[]] Parse warning (unimplemented) \n",
"3 None [aws_configs:[]] Parse warning (unimplemented) \n",
"\n",
" Details \\\n",
"2 Unrecognized element 'ServiceDetails' in AWS file aws_configs/us-west-2/VpcEndpointServices.json \n",
"3 Unrecognized element 'ServiceDetails' in AWS file aws_configs/us-east-2/VpcEndpointServices.json \n",
"\n",
" Line_Text Parser_Context \n",
"2 None None \n",
"3 None None "
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Only show issues whose details match these substrings\n",
"focus_details = [\"Unrecognized element 'ServiceDetails' in AWS\"]\n",
"\n",
"issues[\n",
" issues.apply(lambda issue: has_substring(issue[\"Details\"], focus_details), axis=1)\n",
"]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The code above is similar to the one we used earlier, with the only differences being that we use the `focus_details` list as the argument to the `has_substrings` helper and we do not invert its result."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Filtering objects"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"`Line_Text` and `Details` columns above have string values, but many Batfish answers contain other data types as well. We generalize the approach above to filter other data types and to filter based on multiple columns. We use the `interfaceProperties` question for this demonstrate."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
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" <th></th>\n",
" <th>Interface</th>\n",
" <th>Access_VLAN</th>\n",
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" <th>Admin_Up</th>\n",
" <th>All_Prefixes</th>\n",
" <th>Allowed_VLANs</th>\n",
" <th>Auto_State_VLAN</th>\n",
" <th>Bandwidth</th>\n",
" <th>Blacklisted</th>\n",
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" <th>Channel_Group_Members</th>\n",
" <th>DHCP_Relay_Addresses</th>\n",
" <th>Declared_Names</th>\n",
" <th>Description</th>\n",
" <th>Encapsulation_VLAN</th>\n",
" <th>HSRP_Groups</th>\n",
" <th>HSRP_Version</th>\n",
" <th>Inactive_Reason</th>\n",
" <th>Incoming_Filter_Name</th>\n",
" <th>MLAG_ID</th>\n",
" <th>MTU</th>\n",
" <th>Native_VLAN</th>\n",
" <th>Outgoing_Filter_Name</th>\n",
" <th>PBR_Policy_Name</th>\n",
" <th>Primary_Address</th>\n",
" <th>Primary_Network</th>\n",
" <th>Proxy_ARP</th>\n",
" <th>Rip_Enabled</th>\n",
" <th>Rip_Passive</th>\n",
" <th>Spanning_Tree_Portfast</th>\n",
" <th>Speed</th>\n",
" <th>Switchport</th>\n",
" <th>Switchport_Mode</th>\n",
" <th>Switchport_Trunk_Encapsulation</th>\n",
" <th>VRF</th>\n",
" <th>VRRP_Groups</th>\n",
" <th>Zone_Name</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>__aws-services-gateway__[aws-services]</td>\n",
" <td>None</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>[]</td>\n",
" <td></td>\n",
" <td>True</td>\n",
" <td>1e+12</td>\n",
" <td>False</td>\n",
" <td>None</td>\n",
" <td>[]</td>\n",
" <td>[]</td>\n",
" <td>[]</td>\n",
" <td>To AWS services</td>\n",
" <td>None</td>\n",
" <td>[]</td>\n",
" <td>None</td>\n",
" <td></td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>1500</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>link-local:169.254.0.1</td>\n",
" <td>None</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>None</td>\n",
" <td>False</td>\n",
" <td>NONE</td>\n",
" <td>DOT1Q</td>\n",
" <td>default</td>\n",
" <td>[]</td>\n",
" <td>None</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>__aws-services-gateway__[backbone]</td>\n",
" <td>None</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>[]</td>\n",
" <td></td>\n",
" <td>True</td>\n",
" <td>1e+12</td>\n",
" <td>False</td>\n",
" <td>None</td>\n",
" <td>[]</td>\n",
" <td>[]</td>\n",
" <td>[]</td>\n",
" <td>To AWS backbone</td>\n",
" <td>None</td>\n",
" <td>[]</td>\n",
" <td>None</td>\n",
" <td></td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>1500</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>link-local:169.254.0.1</td>\n",
" <td>None</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>None</td>\n",
" <td>False</td>\n",
" <td>NONE</td>\n",
" <td>DOT1Q</td>\n",
" <td>default</td>\n",
" <td>[]</td>\n",
" <td>None</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>exitgw[GigabitEthernet1]</td>\n",
" <td>None</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>['10.10.100.2/24']</td>\n",
" <td></td>\n",
" <td>True</td>\n",
" <td>1e+09</td>\n",
" <td>False</td>\n",
" <td>None</td>\n",
" <td>[]</td>\n",
" <td>[]</td>\n",
" <td>['GigabitEthernet1']</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>[]</td>\n",
" <td>None</td>\n",
" <td></td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>1500</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>10.10.100.2/24</td>\n",
" <td>10.10.100.0/24</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>1e+09</td>\n",
" <td>False</td>\n",
" <td>NONE</td>\n",
" <td>DOT1Q</td>\n",
" <td>default</td>\n",
" <td>[]</td>\n",
" <td>None</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>exitgw[GigabitEthernet2]</td>\n",
" <td>None</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>['10.10.101.2/24']</td>\n",
" <td></td>\n",
" <td>True</td>\n",
" <td>1e+09</td>\n",
" <td>False</td>\n",
" <td>None</td>\n",
" <td>[]</td>\n",
" <td>[]</td>\n",
" <td>['GigabitEthernet2']</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>[]</td>\n",
" <td>None</td>\n",
" <td></td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>1500</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>10.10.101.2/24</td>\n",
" <td>10.10.101.0/24</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>1e+09</td>\n",
" <td>False</td>\n",
" <td>NONE</td>\n",
" <td>DOT1Q</td>\n",
" <td>default</td>\n",
" <td>[]</td>\n",
" <td>None</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>exitgw[GigabitEthernet3]</td>\n",
" <td>None</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>['147.75.69.27/31']</td>\n",
" <td></td>\n",
" <td>True</td>\n",
" <td>1e+09</td>\n",
" <td>False</td>\n",
" <td>None</td>\n",
" <td>[]</td>\n",
" <td>[]</td>\n",
" <td>['GigabitEthernet3']</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>[]</td>\n",
" <td>None</td>\n",
" <td></td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>1500</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>147.75.69.27/31</td>\n",
" <td>147.75.69.26/31</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>1e+09</td>\n",
" <td>False</td>\n",
" <td>NONE</td>\n",
" <td>DOT1Q</td>\n",
" <td>default</td>\n",
" <td>[]</td>\n",
" <td>None</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Interface Access_VLAN Active Admin_Up \\\n",
"0 __aws-services-gateway__[aws-services] None True True \n",
"1 __aws-services-gateway__[backbone] None True True \n",
"2 exitgw[GigabitEthernet1] None True True \n",
"3 exitgw[GigabitEthernet2] None True True \n",
"4 exitgw[GigabitEthernet3] None True True \n",
"\n",
" All_Prefixes Allowed_VLANs Auto_State_VLAN Bandwidth Blacklisted \\\n",
"0 [] True 1e+12 False \n",
"1 [] True 1e+12 False \n",
"2 ['10.10.100.2/24'] True 1e+09 False \n",
"3 ['10.10.101.2/24'] True 1e+09 False \n",
"4 ['147.75.69.27/31'] True 1e+09 False \n",
"\n",
" Channel_Group Channel_Group_Members DHCP_Relay_Addresses \\\n",
"0 None [] [] \n",
"1 None [] [] \n",
"2 None [] [] \n",
"3 None [] [] \n",
"4 None [] [] \n",
"\n",
" Declared_Names Description Encapsulation_VLAN HSRP_Groups \\\n",
"0 [] To AWS services None [] \n",
"1 [] To AWS backbone None [] \n",
"2 ['GigabitEthernet1'] None None [] \n",
"3 ['GigabitEthernet2'] None None [] \n",
"4 ['GigabitEthernet3'] None None [] \n",
"\n",
" HSRP_Version Inactive_Reason Incoming_Filter_Name MLAG_ID MTU Native_VLAN \\\n",
"0 None None None 1500 None \n",
"1 None None None 1500 None \n",
"2 None None None 1500 None \n",
"3 None None None 1500 None \n",
"4 None None None 1500 None \n",
"\n",
" Outgoing_Filter_Name PBR_Policy_Name Primary_Address \\\n",
"0 None None link-local:169.254.0.1 \n",
"1 None None link-local:169.254.0.1 \n",
"2 None None 10.10.100.2/24 \n",
"3 None None 10.10.101.2/24 \n",
"4 None None 147.75.69.27/31 \n",
"\n",
" Primary_Network Proxy_ARP Rip_Enabled Rip_Passive Spanning_Tree_Portfast \\\n",
"0 None False False False False \n",
"1 None False False False False \n",
"2 10.10.100.0/24 True False False False \n",
"3 10.10.101.0/24 True False False False \n",
"4 147.75.69.26/31 True False False False \n",
"\n",
" Speed Switchport Switchport_Mode Switchport_Trunk_Encapsulation VRF \\\n",
"0 None False NONE DOT1Q default \n",
"1 None False NONE DOT1Q default \n",
"2 1e+09 False NONE DOT1Q default \n",
"3 1e+09 False NONE DOT1Q default \n",
"4 1e+09 False NONE DOT1Q default \n",
"\n",
" VRRP_Groups Zone_Name \n",
"0 [] None \n",
"1 [] None \n",
"2 [] None \n",
"3 [] None \n",
"4 [] None "
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Fetch interface properties and display its first five rows\n",
"interfaces = bf.q.interfaceProperties().answer().frame()\n",
"interfaces.head(5)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To filter based on a column, we need to know its data type. We can learn that in the [Batfish documentation](https://pybatfish.readthedocs.io/en/latest/questions.html) or by inspecting the answer we got from Batfish (e.g., using Python's `type()` method)."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We show three examples of filtering based on the `Interface` and `Active` columns, which are of type <code>[pybatfish.datamodel.primitives.Interface](https://pybatfish.readthedocs.io/en/latest/datamodel.html#pybatfish.datamodel.primitives.Interface)</code> and `bool`, respectively. The former has `hostname` and `interface` properties (which are strings). "
]
},
{
"cell_type": "code",
"execution_count": 6,
"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 tex2jax_ignore\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Interface</th>\n",
" <th>Access_VLAN</th>\n",
" <th>Active</th>\n",
" <th>Admin_Up</th>\n",
" <th>All_Prefixes</th>\n",
" <th>Allowed_VLANs</th>\n",
" <th>Auto_State_VLAN</th>\n",
" <th>Bandwidth</th>\n",
" <th>Blacklisted</th>\n",
" <th>Channel_Group</th>\n",
" <th>Channel_Group_Members</th>\n",
" <th>DHCP_Relay_Addresses</th>\n",
" <th>Declared_Names</th>\n",
" <th>Description</th>\n",
" <th>Encapsulation_VLAN</th>\n",
" <th>HSRP_Groups</th>\n",
" <th>HSRP_Version</th>\n",
" <th>Inactive_Reason</th>\n",
" <th>Incoming_Filter_Name</th>\n",
" <th>MLAG_ID</th>\n",
" <th>MTU</th>\n",
" <th>Native_VLAN</th>\n",
" <th>Outgoing_Filter_Name</th>\n",
" <th>PBR_Policy_Name</th>\n",
" <th>Primary_Address</th>\n",
" <th>Primary_Network</th>\n",
" <th>Proxy_ARP</th>\n",
" <th>Rip_Enabled</th>\n",
" <th>Rip_Passive</th>\n",
" <th>Spanning_Tree_Portfast</th>\n",
" <th>Speed</th>\n",
" <th>Switchport</th>\n",
" <th>Switchport_Mode</th>\n",
" <th>Switchport_Trunk_Encapsulation</th>\n",
" <th>VRF</th>\n",
" <th>VRRP_Groups</th>\n",
" <th>Zone_Name</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>exitgw[GigabitEthernet1]</td>\n",
" <td>None</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>['10.10.100.2/24']</td>\n",
" <td></td>\n",
" <td>True</td>\n",
" <td>1e+09</td>\n",
" <td>False</td>\n",
" <td>None</td>\n",
" <td>[]</td>\n",
" <td>[]</td>\n",
" <td>['GigabitEthernet1']</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>[]</td>\n",
" <td>None</td>\n",
" <td></td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>1500</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>10.10.100.2/24</td>\n",
" <td>10.10.100.0/24</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>1e+09</td>\n",
" <td>False</td>\n",
" <td>NONE</td>\n",
" <td>DOT1Q</td>\n",
" <td>default</td>\n",
" <td>[]</td>\n",
" <td>None</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>exitgw[GigabitEthernet2]</td>\n",
" <td>None</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>['10.10.101.2/24']</td>\n",
" <td></td>\n",
" <td>True</td>\n",
" <td>1e+09</td>\n",
" <td>False</td>\n",
" <td>None</td>\n",
" <td>[]</td>\n",
" <td>[]</td>\n",
" <td>['GigabitEthernet2']</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>[]</td>\n",
" <td>None</td>\n",
" <td></td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>1500</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>10.10.101.2/24</td>\n",
" <td>10.10.101.0/24</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>1e+09</td>\n",
" <td>False</td>\n",
" <td>NONE</td>\n",
" <td>DOT1Q</td>\n",
" <td>default</td>\n",
" <td>[]</td>\n",
" <td>None</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>exitgw[GigabitEthernet3]</td>\n",
" <td>None</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>['147.75.69.27/31']</td>\n",
" <td></td>\n",
" <td>True</td>\n",
" <td>1e+09</td>\n",
" <td>False</td>\n",
" <td>None</td>\n",
" <td>[]</td>\n",
" <td>[]</td>\n",
" <td>['GigabitEthernet3']</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>[]</td>\n",
" <td>None</td>\n",
" <td></td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>1500</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>147.75.69.27/31</td>\n",
" <td>147.75.69.26/31</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>1e+09</td>\n",
" <td>False</td>\n",
" <td>NONE</td>\n",
" <td>DOT1Q</td>\n",
" <td>default</td>\n",
" <td>[]</td>\n",
" <td>None</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>exitgw[GigabitEthernet4]</td>\n",
" <td>None</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>[]</td>\n",
" <td></td>\n",
" <td>True</td>\n",
" <td>1e+09</td>\n",
" <td>False</td>\n",
" <td>None</td>\n",
" <td>[]</td>\n",
" <td>[]</td>\n",
" <td>['GigabitEthernet4']</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>[]</td>\n",
" <td>None</td>\n",
" <td>Administratively down</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>1500</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>1e+09</td>\n",
" <td>False</td>\n",
" <td>NONE</td>\n",
" <td>DOT1Q</td>\n",
" <td>default</td>\n",
" <td>[]</td>\n",
" <td>None</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>exitgw[Loopback0]</td>\n",
" <td>None</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>['2.2.2.2/32']</td>\n",
" <td></td>\n",
" <td>True</td>\n",
" <td>8e+09</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>[]</td>\n",
" <td>[]</td>\n",
" <td>['Loopback0']</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>[]</td>\n",
" <td>None</td>\n",
" <td></td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>1500</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>2.2.2.2/32</td>\n",
" <td>2.2.2.2/32</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>None</td>\n",
" <td>False</td>\n",
" <td>NONE</td>\n",
" <td>DOT1Q</td>\n",
" <td>default</td>\n",
" <td>[]</td>\n",
" <td>None</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>exitgw[Loopback123]</td>\n",
" <td>None</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>['192.168.123.7/32']</td>\n",
" <td></td>\n",
" <td>True</td>\n",
" <td>8e+09</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>[]</td>\n",
" <td>[]</td>\n",
" <td>['Loopback123']</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>[]</td>\n",
" <td>None</td>\n",
" <td></td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>1500</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>192.168.123.7/32</td>\n",
" <td>192.168.123.7/32</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>None</td>\n",
" <td>False</td>\n",
" <td>NONE</td>\n",
" <td>DOT1Q</td>\n",
" <td>default</td>\n",
" <td>[]</td>\n",
" <td>None</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>exitgw[Tunnel1]</td>\n",
" <td>None</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>['169.254.25.162/30']</td>\n",
" <td></td>\n",
" <td>True</td>\n",
" <td>100000</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>[]</td>\n",
" <td>[]</td>\n",
" <td>['Tunnel1']</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>[]</td>\n",
" <td>None</td>\n",
" <td></td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>1500</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>169.254.25.162/30</td>\n",
" <td>169.254.25.160/30</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>None</td>\n",
" <td>False</td>\n",
" <td>NONE</td>\n",
" <td>DOT1Q</td>\n",
" <td>default</td>\n",
" <td>[]</td>\n",
" <td>None</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>exitgw[Tunnel2]</td>\n",
" <td>None</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>['169.254.172.2/30']</td>\n",
" <td></td>\n",
" <td>True</td>\n",
" <td>100000</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>[]</td>\n",
" <td>[]</td>\n",
" <td>['Tunnel2']</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>[]</td>\n",
" <td>None</td>\n",
" <td></td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>1500</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>169.254.172.2/30</td>\n",
" <td>169.254.172.0/30</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>None</td>\n",
" <td>False</td>\n",
" <td>NONE</td>\n",
" <td>DOT1Q</td>\n",
" <td>default</td>\n",
" <td>[]</td>\n",
" <td>None</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>exitgw[Tunnel3]</td>\n",
" <td>None</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>['169.254.252.78/30']</td>\n",
" <td></td>\n",
" <td>True</td>\n",
" <td>100000</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>[]</td>\n",
" <td>[]</td>\n",
" <td>['Tunnel3']</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>[]</td>\n",
" <td>None</td>\n",
" <td></td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>1500</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>169.254.252.78/30</td>\n",
" <td>169.254.252.76/30</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>None</td>\n",
" <td>False</td>\n",
" <td>NONE</td>\n",
" <td>DOT1Q</td>\n",
" <td>default</td>\n",
" <td>[]</td>\n",
" <td>None</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>exitgw[Tunnel4]</td>\n",
" <td>None</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>['169.254.215.82/30']</td>\n",
" <td></td>\n",
" <td>True</td>\n",
" <td>100000</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>[]</td>\n",
" <td>[]</td>\n",
" <td>['Tunnel4']</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>[]</td>\n",
" <td>None</td>\n",
" <td></td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>1500</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>169.254.215.82/30</td>\n",
" <td>169.254.215.80/30</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>None</td>\n",
" <td>False</td>\n",
" <td>NONE</td>\n",
" <td>DOT1Q</td>\n",
" <td>default</td>\n",
" <td>[]</td>\n",
" <td>None</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Interface Access_VLAN Active Admin_Up \\\n",
"2 exitgw[GigabitEthernet1] None True True \n",
"3 exitgw[GigabitEthernet2] None True True \n",
"4 exitgw[GigabitEthernet3] None True True \n",
"5 exitgw[GigabitEthernet4] None False False \n",
"6 exitgw[Loopback0] None True True \n",
"7 exitgw[Loopback123] None True True \n",
"8 exitgw[Tunnel1] None True True \n",
"9 exitgw[Tunnel2] None True True \n",
"10 exitgw[Tunnel3] None True True \n",
"11 exitgw[Tunnel4] None True True \n",
"\n",
" All_Prefixes Allowed_VLANs Auto_State_VLAN Bandwidth Blacklisted \\\n",
"2 ['10.10.100.2/24'] True 1e+09 False \n",
"3 ['10.10.101.2/24'] True 1e+09 False \n",
"4 ['147.75.69.27/31'] True 1e+09 False \n",
"5 [] True 1e+09 False \n",
"6 ['2.2.2.2/32'] True 8e+09 None \n",
"7 ['192.168.123.7/32'] True 8e+09 None \n",
"8 ['169.254.25.162/30'] True 100000 None \n",
"9 ['169.254.172.2/30'] True 100000 None \n",
"10 ['169.254.252.78/30'] True 100000 None \n",
"11 ['169.254.215.82/30'] True 100000 None \n",
"\n",
" Channel_Group Channel_Group_Members DHCP_Relay_Addresses \\\n",
"2 None [] [] \n",
"3 None [] [] \n",
"4 None [] [] \n",
"5 None [] [] \n",
"6 None [] [] \n",
"7 None [] [] \n",
"8 None [] [] \n",
"9 None [] [] \n",
"10 None [] [] \n",
"11 None [] [] \n",
"\n",
" Declared_Names Description Encapsulation_VLAN HSRP_Groups \\\n",
"2 ['GigabitEthernet1'] None None [] \n",
"3 ['GigabitEthernet2'] None None [] \n",
"4 ['GigabitEthernet3'] None None [] \n",
"5 ['GigabitEthernet4'] None None [] \n",
"6 ['Loopback0'] None None [] \n",
"7 ['Loopback123'] None None [] \n",
"8 ['Tunnel1'] None None [] \n",
"9 ['Tunnel2'] None None [] \n",
"10 ['Tunnel3'] None None [] \n",
"11 ['Tunnel4'] None None [] \n",
"\n",
" HSRP_Version Inactive_Reason Incoming_Filter_Name MLAG_ID MTU \\\n",
"2 None None None 1500 \n",
"3 None None None 1500 \n",
"4 None None None 1500 \n",
"5 None Administratively down None None 1500 \n",
"6 None None None 1500 \n",
"7 None None None 1500 \n",
"8 None None None 1500 \n",
"9 None None None 1500 \n",
"10 None None None 1500 \n",
"11 None None None 1500 \n",
"\n",
" Native_VLAN Outgoing_Filter_Name PBR_Policy_Name Primary_Address \\\n",
"2 None None None 10.10.100.2/24 \n",
"3 None None None 10.10.101.2/24 \n",
"4 None None None 147.75.69.27/31 \n",
"5 None None None None \n",
"6 None None None 2.2.2.2/32 \n",
"7 None None None 192.168.123.7/32 \n",
"8 None None None 169.254.25.162/30 \n",
"9 None None None 169.254.172.2/30 \n",
"10 None None None 169.254.252.78/30 \n",
"11 None None None 169.254.215.82/30 \n",
"\n",
" Primary_Network Proxy_ARP Rip_Enabled Rip_Passive \\\n",
"2 10.10.100.0/24 True False False \n",
"3 10.10.101.0/24 True False False \n",
"4 147.75.69.26/31 True False False \n",
"5 None True False False \n",
"6 2.2.2.2/32 True False False \n",
"7 192.168.123.7/32 True False False \n",
"8 169.254.25.160/30 True False False \n",
"9 169.254.172.0/30 True False False \n",
"10 169.254.252.76/30 True False False \n",
"11 169.254.215.80/30 True False False \n",
"\n",
" Spanning_Tree_Portfast Speed Switchport Switchport_Mode \\\n",
"2 False 1e+09 False NONE \n",
"3 False 1e+09 False NONE \n",
"4 False 1e+09 False NONE \n",
"5 False 1e+09 False NONE \n",
"6 False None False NONE \n",
"7 False None False NONE \n",
"8 False None False NONE \n",
"9 False None False NONE \n",
"10 False None False NONE \n",
"11 False None False NONE \n",
"\n",
" Switchport_Trunk_Encapsulation VRF VRRP_Groups Zone_Name \n",
"2 DOT1Q default [] None \n",
"3 DOT1Q default [] None \n",
"4 DOT1Q default [] None \n",
"5 DOT1Q default [] None \n",
"6 DOT1Q default [] None \n",
"7 DOT1Q default [] None \n",
"8 DOT1Q default [] None \n",
"9 DOT1Q default [] None \n",
"10 DOT1Q default [] None \n",
"11 DOT1Q default [] None "
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Display all interfaces on node 'exitgw'\n",
"interfaces[interfaces.apply(lambda row: row[\"Interface\"].hostname == \"exitgw\", axis=1)]"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
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"</style>\n",
"<table border=\"1\" class=\"dataframe tex2jax_ignore\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Interface</th>\n",
" <th>Access_VLAN</th>\n",
" <th>Active</th>\n",
" <th>Admin_Up</th>\n",
" <th>All_Prefixes</th>\n",
" <th>Allowed_VLANs</th>\n",
" <th>Auto_State_VLAN</th>\n",
" <th>Bandwidth</th>\n",
" <th>Blacklisted</th>\n",
" <th>Channel_Group</th>\n",
" <th>Channel_Group_Members</th>\n",
" <th>DHCP_Relay_Addresses</th>\n",
" <th>Declared_Names</th>\n",
" <th>Description</th>\n",
" <th>Encapsulation_VLAN</th>\n",
" <th>HSRP_Groups</th>\n",
" <th>HSRP_Version</th>\n",
" <th>Inactive_Reason</th>\n",
" <th>Incoming_Filter_Name</th>\n",
" <th>MLAG_ID</th>\n",
" <th>MTU</th>\n",
" <th>Native_VLAN</th>\n",
" <th>Outgoing_Filter_Name</th>\n",
" <th>PBR_Policy_Name</th>\n",
" <th>Primary_Address</th>\n",
" <th>Primary_Network</th>\n",
" <th>Proxy_ARP</th>\n",
" <th>Rip_Enabled</th>\n",
" <th>Rip_Passive</th>\n",
" <th>Spanning_Tree_Portfast</th>\n",
" <th>Speed</th>\n",
" <th>Switchport</th>\n",
" <th>Switchport_Mode</th>\n",
" <th>Switchport_Trunk_Encapsulation</th>\n",
" <th>VRF</th>\n",
" <th>VRRP_Groups</th>\n",
" <th>Zone_Name</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>exitgw[GigabitEthernet1]</td>\n",
" <td>None</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>['10.10.100.2/24']</td>\n",
" <td></td>\n",
" <td>True</td>\n",
" <td>1e+09</td>\n",
" <td>False</td>\n",
" <td>None</td>\n",
" <td>[]</td>\n",
" <td>[]</td>\n",
" <td>['GigabitEthernet1']</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>[]</td>\n",
" <td>None</td>\n",
" <td></td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>1500</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>10.10.100.2/24</td>\n",
" <td>10.10.100.0/24</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>1e+09</td>\n",
" <td>False</td>\n",
" <td>NONE</td>\n",
" <td>DOT1Q</td>\n",
" <td>default</td>\n",
" <td>[]</td>\n",
" <td>None</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>exitgw[GigabitEthernet2]</td>\n",
" <td>None</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>['10.10.101.2/24']</td>\n",
" <td></td>\n",
" <td>True</td>\n",
" <td>1e+09</td>\n",
" <td>False</td>\n",
" <td>None</td>\n",
" <td>[]</td>\n",
" <td>[]</td>\n",
" <td>['GigabitEthernet2']</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>[]</td>\n",
" <td>None</td>\n",
" <td></td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>1500</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>10.10.101.2/24</td>\n",
" <td>10.10.101.0/24</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>1e+09</td>\n",
" <td>False</td>\n",
" <td>NONE</td>\n",
" <td>DOT1Q</td>\n",
" <td>default</td>\n",
" <td>[]</td>\n",
" <td>None</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>exitgw[GigabitEthernet3]</td>\n",
" <td>None</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>['147.75.69.27/31']</td>\n",
" <td></td>\n",
" <td>True</td>\n",
" <td>1e+09</td>\n",
" <td>False</td>\n",
" <td>None</td>\n",
" <td>[]</td>\n",
" <td>[]</td>\n",
" <td>['GigabitEthernet3']</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>[]</td>\n",
" <td>None</td>\n",
" <td></td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>1500</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>147.75.69.27/31</td>\n",
" <td>147.75.69.26/31</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>1e+09</td>\n",
" <td>False</td>\n",
" <td>NONE</td>\n",
" <td>DOT1Q</td>\n",
" <td>default</td>\n",
" <td>[]</td>\n",
" <td>None</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>exitgw[GigabitEthernet4]</td>\n",
" <td>None</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>[]</td>\n",
" <td></td>\n",
" <td>True</td>\n",
" <td>1e+09</td>\n",
" <td>False</td>\n",
" <td>None</td>\n",
" <td>[]</td>\n",
" <td>[]</td>\n",
" <td>['GigabitEthernet4']</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>[]</td>\n",
" <td>None</td>\n",
" <td>Administratively down</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>1500</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>1e+09</td>\n",
" <td>False</td>\n",
" <td>NONE</td>\n",
" <td>DOT1Q</td>\n",
" <td>default</td>\n",
" <td>[]</td>\n",
" <td>None</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Interface Access_VLAN Active Admin_Up All_Prefixes \\\n",
"2 exitgw[GigabitEthernet1] None True True ['10.10.100.2/24'] \n",
"3 exitgw[GigabitEthernet2] None True True ['10.10.101.2/24'] \n",
"4 exitgw[GigabitEthernet3] None True True ['147.75.69.27/31'] \n",
"5 exitgw[GigabitEthernet4] None False False [] \n",
"\n",
" Allowed_VLANs Auto_State_VLAN Bandwidth Blacklisted Channel_Group \\\n",
"2 True 1e+09 False None \n",
"3 True 1e+09 False None \n",
"4 True 1e+09 False None \n",
"5 True 1e+09 False None \n",
"\n",
" Channel_Group_Members DHCP_Relay_Addresses Declared_Names \\\n",
"2 [] [] ['GigabitEthernet1'] \n",
"3 [] [] ['GigabitEthernet2'] \n",
"4 [] [] ['GigabitEthernet3'] \n",
"5 [] [] ['GigabitEthernet4'] \n",
"\n",
" Description Encapsulation_VLAN HSRP_Groups HSRP_Version \\\n",
"2 None None [] None \n",
"3 None None [] None \n",
"4 None None [] None \n",
"5 None None [] None \n",
"\n",
" Inactive_Reason Incoming_Filter_Name MLAG_ID MTU Native_VLAN \\\n",
"2 None None 1500 None \n",
"3 None None 1500 None \n",
"4 None None 1500 None \n",
"5 Administratively down None None 1500 None \n",
"\n",
" Outgoing_Filter_Name PBR_Policy_Name Primary_Address Primary_Network \\\n",
"2 None None 10.10.100.2/24 10.10.100.0/24 \n",
"3 None None 10.10.101.2/24 10.10.101.0/24 \n",
"4 None None 147.75.69.27/31 147.75.69.26/31 \n",
"5 None None None None \n",
"\n",
" Proxy_ARP Rip_Enabled Rip_Passive Spanning_Tree_Portfast Speed Switchport \\\n",
"2 True False False False 1e+09 False \n",
"3 True False False False 1e+09 False \n",
"4 True False False False 1e+09 False \n",
"5 True False False False 1e+09 False \n",
"\n",
" Switchport_Mode Switchport_Trunk_Encapsulation VRF VRRP_Groups \\\n",
"2 NONE DOT1Q default [] \n",
"3 NONE DOT1Q default [] \n",
"4 NONE DOT1Q default [] \n",
"5 NONE DOT1Q default [] \n",
"\n",
" Zone_Name \n",
"2 None \n",
"3 None \n",
"4 None \n",
"5 None "
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Display all GigabitEthernet interfaces on node 'exitgw'\n",
"interfaces[\n",
" interfaces.apply(\n",
" lambda row: row[\"Interface\"].hostname == \"exitgw\"\n",
" and row[\"Interface\"].interface.startswith(\"GigabitEthernet\"),\n",
" axis=1,\n",
" )\n",
"]"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
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" <tr>\n",
" <th>2</th>\n",
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" <td>True</td>\n",
" <td>1e+09</td>\n",
" <td>False</td>\n",
" <td>None</td>\n",
" <td>[]</td>\n",
" <td>[]</td>\n",
" <td>['GigabitEthernet1']</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>[]</td>\n",
" <td>None</td>\n",
" <td></td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>1500</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>10.10.100.2/24</td>\n",
" <td>10.10.100.0/24</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>1e+09</td>\n",
" <td>False</td>\n",
" <td>NONE</td>\n",
" <td>DOT1Q</td>\n",
" <td>default</td>\n",
" <td>[]</td>\n",
" <td>None</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>exitgw[GigabitEthernet2]</td>\n",
" <td>None</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>['10.10.101.2/24']</td>\n",
" <td></td>\n",
" <td>True</td>\n",
" <td>1e+09</td>\n",
" <td>False</td>\n",
" <td>None</td>\n",
" <td>[]</td>\n",
" <td>[]</td>\n",
" <td>['GigabitEthernet2']</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>[]</td>\n",
" <td>None</td>\n",
" <td></td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>1500</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>10.10.101.2/24</td>\n",
" <td>10.10.101.0/24</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>1e+09</td>\n",
" <td>False</td>\n",
" <td>NONE</td>\n",
" <td>DOT1Q</td>\n",
" <td>default</td>\n",
" <td>[]</td>\n",
" <td>None</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>exitgw[GigabitEthernet3]</td>\n",
" <td>None</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>['147.75.69.27/31']</td>\n",
" <td></td>\n",
" <td>True</td>\n",
" <td>1e+09</td>\n",
" <td>False</td>\n",
" <td>None</td>\n",
" <td>[]</td>\n",
" <td>[]</td>\n",
" <td>['GigabitEthernet3']</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>[]</td>\n",
" <td>None</td>\n",
" <td></td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>1500</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>147.75.69.27/31</td>\n",
" <td>147.75.69.26/31</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>1e+09</td>\n",
" <td>False</td>\n",
" <td>NONE</td>\n",
" <td>DOT1Q</td>\n",
" <td>default</td>\n",
" <td>[]</td>\n",
" <td>None</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Interface Access_VLAN Active Admin_Up All_Prefixes \\\n",
"2 exitgw[GigabitEthernet1] None True True ['10.10.100.2/24'] \n",
"3 exitgw[GigabitEthernet2] None True True ['10.10.101.2/24'] \n",
"4 exitgw[GigabitEthernet3] None True True ['147.75.69.27/31'] \n",
"\n",
" Allowed_VLANs Auto_State_VLAN Bandwidth Blacklisted Channel_Group \\\n",
"2 True 1e+09 False None \n",
"3 True 1e+09 False None \n",
"4 True 1e+09 False None \n",
"\n",
" Channel_Group_Members DHCP_Relay_Addresses Declared_Names \\\n",
"2 [] [] ['GigabitEthernet1'] \n",
"3 [] [] ['GigabitEthernet2'] \n",
"4 [] [] ['GigabitEthernet3'] \n",
"\n",
" Description Encapsulation_VLAN HSRP_Groups HSRP_Version Inactive_Reason \\\n",
"2 None None [] None \n",
"3 None None [] None \n",
"4 None None [] None \n",
"\n",
" Incoming_Filter_Name MLAG_ID MTU Native_VLAN Outgoing_Filter_Name \\\n",
"2 None None 1500 None None \n",
"3 None None 1500 None None \n",
"4 None None 1500 None None \n",
"\n",
" PBR_Policy_Name Primary_Address Primary_Network Proxy_ARP Rip_Enabled \\\n",
"2 None 10.10.100.2/24 10.10.100.0/24 True False \n",
"3 None 10.10.101.2/24 10.10.101.0/24 True False \n",
"4 None 147.75.69.27/31 147.75.69.26/31 True False \n",
"\n",
" Rip_Passive Spanning_Tree_Portfast Speed Switchport Switchport_Mode \\\n",
"2 False False 1e+09 False NONE \n",
"3 False False 1e+09 False NONE \n",
"4 False False 1e+09 False NONE \n",
"\n",
" Switchport_Trunk_Encapsulation VRF VRRP_Groups Zone_Name \n",
"2 DOT1Q default [] None \n",
"3 DOT1Q default [] None \n",
"4 DOT1Q default [] None "
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Display all active GigabitEthernet interfaces on node 'exitgw'\n",
"interfaces[\n",
" interfaces.apply(\n",
" lambda row: row[\"Interface\"].hostname == \"exitgw\"\n",
" and row[\"Interface\"].interface.startswith(\"GigabitEthernet\")\n",
" and row[\"Active\"],\n",
" axis=1,\n",
" )\n",
"]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Filtering columns"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"When viewing Batfish answers, you may want to view only some of the columns. Pandas makes that easy for both original answers and answers where some rows have been filtered, as both of them are just DataFrames."
]
},
{
"cell_type": "code",
"execution_count": 9,
"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 tex2jax_ignore\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Interface</th>\n",
" <th>All_Prefixes</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>exitgw[GigabitEthernet1]</td>\n",
" <td>['10.10.100.2/24']</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>exitgw[GigabitEthernet2]</td>\n",
" <td>['10.10.101.2/24']</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>exitgw[GigabitEthernet3]</td>\n",
" <td>['147.75.69.27/31']</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Interface All_Prefixes\n",
"2 exitgw[GigabitEthernet1] ['10.10.100.2/24']\n",
"3 exitgw[GigabitEthernet2] ['10.10.101.2/24']\n",
"4 exitgw[GigabitEthernet3] ['147.75.69.27/31']"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Filter interfaces to all active GigabitEthernet interfaces on node exitgw\n",
"exitgw_gige_active_interfaces = interfaces[\n",
" interfaces.apply(\n",
" lambda row: row[\"Interface\"].hostname == \"exitgw\"\n",
" and row[\"Interface\"].interface.startswith(\"GigabitEthernet\")\n",
" and row[\"Active\"],\n",
" axis=1,\n",
" )\n",
"]\n",
"# Display only the Interface and All_Prefixes columns of the filtered DataFrame\n",
"exitgw_gige_active_interfaces[[\"Interface\", \"All_Prefixes\"]]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Counting rows"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Often, you would be interested in counting the number of rows in the filtered answer. This is super easy because Python's `len()` method, which we use for iterables, can be used on DataFrames as well."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"3"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Show the number of rows in the filtered DataFrame that we obtained above\n",
"len(exitgw_gige_active_interfaces)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Grouping rows"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"For more advanced operations than filtering rows and columns, chances are that you will find Pandas <code>[groupyby](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.groupby.html)</code> pretty handy. This method enables you to group rows using a custom criteria and analyze those groups. For instance, if you wanted to group interfaces by nodes, you may do the following:"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"# Get interfaces grouped by node name\n",
"intefaces_by_hostname = interfaces.groupby(\n",
" lambda index: interfaces.loc[index][\"Interface\"].hostname\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We obtained a Pandas `DataFrameGroupBy` object above. The `groupby` method iterates over row indexes (`apply` iterated over rows), calls the lambda over each, and groups rows whose indices yield the same value. In our example, the lambda first gets the row using `interfaces.loc[index]`, then gets the interface (which is of type `pybatfish.datamodel.primitives.Interface`), and finally the hostname. \n",
"\n",
"`DataFrameGroupBy` objects offer many functions that are useful for analysis. We demonstrate two of them below."
]
},
{
"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 tex2jax_ignore\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Interface</th>\n",
" <th>Access_VLAN</th>\n",
" <th>Active</th>\n",
" <th>Admin_Up</th>\n",
" <th>All_Prefixes</th>\n",
" <th>Allowed_VLANs</th>\n",
" <th>Auto_State_VLAN</th>\n",
" <th>Bandwidth</th>\n",
" <th>Blacklisted</th>\n",
" <th>Channel_Group</th>\n",
" <th>Channel_Group_Members</th>\n",
" <th>DHCP_Relay_Addresses</th>\n",
" <th>Declared_Names</th>\n",
" <th>Description</th>\n",
" <th>Encapsulation_VLAN</th>\n",
" <th>HSRP_Groups</th>\n",
" <th>HSRP_Version</th>\n",
" <th>Inactive_Reason</th>\n",
" <th>Incoming_Filter_Name</th>\n",
" <th>MLAG_ID</th>\n",
" <th>MTU</th>\n",
" <th>Native_VLAN</th>\n",
" <th>Outgoing_Filter_Name</th>\n",
" <th>PBR_Policy_Name</th>\n",
" <th>Primary_Address</th>\n",
" <th>Primary_Network</th>\n",
" <th>Proxy_ARP</th>\n",
" <th>Rip_Enabled</th>\n",
" <th>Rip_Passive</th>\n",
" <th>Spanning_Tree_Portfast</th>\n",
" <th>Speed</th>\n",
" <th>Switchport</th>\n",
" <th>Switchport_Mode</th>\n",
" <th>Switchport_Trunk_Encapsulation</th>\n",
" <th>VRF</th>\n",
" <th>VRRP_Groups</th>\n",
" <th>Zone_Name</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>exitgw[GigabitEthernet1]</td>\n",
" <td>None</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>['10.10.100.2/24']</td>\n",
" <td></td>\n",
" <td>True</td>\n",
" <td>1e+09</td>\n",
" <td>False</td>\n",
" <td>None</td>\n",
" <td>[]</td>\n",
" <td>[]</td>\n",
" <td>['GigabitEthernet1']</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>[]</td>\n",
" <td>None</td>\n",
" <td></td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>1500</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>10.10.100.2/24</td>\n",
" <td>10.10.100.0/24</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>1e+09</td>\n",
" <td>False</td>\n",
" <td>NONE</td>\n",
" <td>DOT1Q</td>\n",
" <td>default</td>\n",
" <td>[]</td>\n",
" <td>None</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>exitgw[GigabitEthernet2]</td>\n",
" <td>None</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>['10.10.101.2/24']</td>\n",
" <td></td>\n",
" <td>True</td>\n",
" <td>1e+09</td>\n",
" <td>False</td>\n",
" <td>None</td>\n",
" <td>[]</td>\n",
" <td>[]</td>\n",
" <td>['GigabitEthernet2']</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>[]</td>\n",
" <td>None</td>\n",
" <td></td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>1500</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>10.10.101.2/24</td>\n",
" <td>10.10.101.0/24</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>1e+09</td>\n",
" <td>False</td>\n",
" <td>NONE</td>\n",
" <td>DOT1Q</td>\n",
" <td>default</td>\n",
" <td>[]</td>\n",
" <td>None</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>exitgw[GigabitEthernet3]</td>\n",
" <td>None</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>['147.75.69.27/31']</td>\n",
" <td></td>\n",
" <td>True</td>\n",
" <td>1e+09</td>\n",
" <td>False</td>\n",
" <td>None</td>\n",
" <td>[]</td>\n",
" <td>[]</td>\n",
" <td>['GigabitEthernet3']</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>[]</td>\n",
" <td>None</td>\n",
" <td></td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>1500</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>147.75.69.27/31</td>\n",
" <td>147.75.69.26/31</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>1e+09</td>\n",
" <td>False</td>\n",
" <td>NONE</td>\n",
" <td>DOT1Q</td>\n",
" <td>default</td>\n",
" <td>[]</td>\n",
" <td>None</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>exitgw[GigabitEthernet4]</td>\n",
" <td>None</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>[]</td>\n",
" <td></td>\n",
" <td>True</td>\n",
" <td>1e+09</td>\n",
" <td>False</td>\n",
" <td>None</td>\n",
" <td>[]</td>\n",
" <td>[]</td>\n",
" <td>['GigabitEthernet4']</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>[]</td>\n",
" <td>None</td>\n",
" <td>Administratively down</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>1500</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>1e+09</td>\n",
" <td>False</td>\n",
" <td>NONE</td>\n",
" <td>DOT1Q</td>\n",
" <td>default</td>\n",
" <td>[]</td>\n",
" <td>None</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>exitgw[Loopback0]</td>\n",
" <td>None</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>['2.2.2.2/32']</td>\n",
" <td></td>\n",
" <td>True</td>\n",
" <td>8e+09</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>[]</td>\n",
" <td>[]</td>\n",
" <td>['Loopback0']</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>[]</td>\n",
" <td>None</td>\n",
" <td></td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>1500</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>2.2.2.2/32</td>\n",
" <td>2.2.2.2/32</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>None</td>\n",
" <td>False</td>\n",
" <td>NONE</td>\n",
" <td>DOT1Q</td>\n",
" <td>default</td>\n",
" <td>[]</td>\n",
" <td>None</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>exitgw[Loopback123]</td>\n",
" <td>None</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>['192.168.123.7/32']</td>\n",
" <td></td>\n",
" <td>True</td>\n",
" <td>8e+09</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>[]</td>\n",
" <td>[]</td>\n",
" <td>['Loopback123']</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>[]</td>\n",
" <td>None</td>\n",
" <td></td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>1500</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>192.168.123.7/32</td>\n",
" <td>192.168.123.7/32</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>None</td>\n",
" <td>False</td>\n",
" <td>NONE</td>\n",
" <td>DOT1Q</td>\n",
" <td>default</td>\n",
" <td>[]</td>\n",
" <td>None</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>exitgw[Tunnel1]</td>\n",
" <td>None</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>['169.254.25.162/30']</td>\n",
" <td></td>\n",
" <td>True</td>\n",
" <td>100000</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>[]</td>\n",
" <td>[]</td>\n",
" <td>['Tunnel1']</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>[]</td>\n",
" <td>None</td>\n",
" <td></td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>1500</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>169.254.25.162/30</td>\n",
" <td>169.254.25.160/30</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>None</td>\n",
" <td>False</td>\n",
" <td>NONE</td>\n",
" <td>DOT1Q</td>\n",
" <td>default</td>\n",
" <td>[]</td>\n",
" <td>None</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>exitgw[Tunnel2]</td>\n",
" <td>None</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>['169.254.172.2/30']</td>\n",
" <td></td>\n",
" <td>True</td>\n",
" <td>100000</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>[]</td>\n",
" <td>[]</td>\n",
" <td>['Tunnel2']</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>[]</td>\n",
" <td>None</td>\n",
" <td></td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>1500</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>169.254.172.2/30</td>\n",
" <td>169.254.172.0/30</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>None</td>\n",
" <td>False</td>\n",
" <td>NONE</td>\n",
" <td>DOT1Q</td>\n",
" <td>default</td>\n",
" <td>[]</td>\n",
" <td>None</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>exitgw[Tunnel3]</td>\n",
" <td>None</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>['169.254.252.78/30']</td>\n",
" <td></td>\n",
" <td>True</td>\n",
" <td>100000</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>[]</td>\n",
" <td>[]</td>\n",
" <td>['Tunnel3']</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>[]</td>\n",
" <td>None</td>\n",
" <td></td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>1500</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>169.254.252.78/30</td>\n",
" <td>169.254.252.76/30</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>None</td>\n",
" <td>False</td>\n",
" <td>NONE</td>\n",
" <td>DOT1Q</td>\n",
" <td>default</td>\n",
" <td>[]</td>\n",
" <td>None</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>exitgw[Tunnel4]</td>\n",
" <td>None</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>['169.254.215.82/30']</td>\n",
" <td></td>\n",
" <td>True</td>\n",
" <td>100000</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>[]</td>\n",
" <td>[]</td>\n",
" <td>['Tunnel4']</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>[]</td>\n",
" <td>None</td>\n",
" <td></td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>1500</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>169.254.215.82/30</td>\n",
" <td>169.254.215.80/30</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>None</td>\n",
" <td>False</td>\n",
" <td>NONE</td>\n",
" <td>DOT1Q</td>\n",
" <td>default</td>\n",
" <td>[]</td>\n",
" <td>None</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Interface Access_VLAN Active Admin_Up \\\n",
"2 exitgw[GigabitEthernet1] None True True \n",
"3 exitgw[GigabitEthernet2] None True True \n",
"4 exitgw[GigabitEthernet3] None True True \n",
"5 exitgw[GigabitEthernet4] None False False \n",
"6 exitgw[Loopback0] None True True \n",
"7 exitgw[Loopback123] None True True \n",
"8 exitgw[Tunnel1] None True True \n",
"9 exitgw[Tunnel2] None True True \n",
"10 exitgw[Tunnel3] None True True \n",
"11 exitgw[Tunnel4] None True True \n",
"\n",
" All_Prefixes Allowed_VLANs Auto_State_VLAN Bandwidth Blacklisted \\\n",
"2 ['10.10.100.2/24'] True 1e+09 False \n",
"3 ['10.10.101.2/24'] True 1e+09 False \n",
"4 ['147.75.69.27/31'] True 1e+09 False \n",
"5 [] True 1e+09 False \n",
"6 ['2.2.2.2/32'] True 8e+09 None \n",
"7 ['192.168.123.7/32'] True 8e+09 None \n",
"8 ['169.254.25.162/30'] True 100000 None \n",
"9 ['169.254.172.2/30'] True 100000 None \n",
"10 ['169.254.252.78/30'] True 100000 None \n",
"11 ['169.254.215.82/30'] True 100000 None \n",
"\n",
" Channel_Group Channel_Group_Members DHCP_Relay_Addresses \\\n",
"2 None [] [] \n",
"3 None [] [] \n",
"4 None [] [] \n",
"5 None [] [] \n",
"6 None [] [] \n",
"7 None [] [] \n",
"8 None [] [] \n",
"9 None [] [] \n",
"10 None [] [] \n",
"11 None [] [] \n",
"\n",
" Declared_Names Description Encapsulation_VLAN HSRP_Groups \\\n",
"2 ['GigabitEthernet1'] None None [] \n",
"3 ['GigabitEthernet2'] None None [] \n",
"4 ['GigabitEthernet3'] None None [] \n",
"5 ['GigabitEthernet4'] None None [] \n",
"6 ['Loopback0'] None None [] \n",
"7 ['Loopback123'] None None [] \n",
"8 ['Tunnel1'] None None [] \n",
"9 ['Tunnel2'] None None [] \n",
"10 ['Tunnel3'] None None [] \n",
"11 ['Tunnel4'] None None [] \n",
"\n",
" HSRP_Version Inactive_Reason Incoming_Filter_Name MLAG_ID MTU \\\n",
"2 None None None 1500 \n",
"3 None None None 1500 \n",
"4 None None None 1500 \n",
"5 None Administratively down None None 1500 \n",
"6 None None None 1500 \n",
"7 None None None 1500 \n",
"8 None None None 1500 \n",
"9 None None None 1500 \n",
"10 None None None 1500 \n",
"11 None None None 1500 \n",
"\n",
" Native_VLAN Outgoing_Filter_Name PBR_Policy_Name Primary_Address \\\n",
"2 None None None 10.10.100.2/24 \n",
"3 None None None 10.10.101.2/24 \n",
"4 None None None 147.75.69.27/31 \n",
"5 None None None None \n",
"6 None None None 2.2.2.2/32 \n",
"7 None None None 192.168.123.7/32 \n",
"8 None None None 169.254.25.162/30 \n",
"9 None None None 169.254.172.2/30 \n",
"10 None None None 169.254.252.78/30 \n",
"11 None None None 169.254.215.82/30 \n",
"\n",
" Primary_Network Proxy_ARP Rip_Enabled Rip_Passive \\\n",
"2 10.10.100.0/24 True False False \n",
"3 10.10.101.0/24 True False False \n",
"4 147.75.69.26/31 True False False \n",
"5 None True False False \n",
"6 2.2.2.2/32 True False False \n",
"7 192.168.123.7/32 True False False \n",
"8 169.254.25.160/30 True False False \n",
"9 169.254.172.0/30 True False False \n",
"10 169.254.252.76/30 True False False \n",
"11 169.254.215.80/30 True False False \n",
"\n",
" Spanning_Tree_Portfast Speed Switchport Switchport_Mode \\\n",
"2 False 1e+09 False NONE \n",
"3 False 1e+09 False NONE \n",
"4 False 1e+09 False NONE \n",
"5 False 1e+09 False NONE \n",
"6 False None False NONE \n",
"7 False None False NONE \n",
"8 False None False NONE \n",
"9 False None False NONE \n",
"10 False None False NONE \n",
"11 False None False NONE \n",
"\n",
" Switchport_Trunk_Encapsulation VRF VRRP_Groups Zone_Name \n",
"2 DOT1Q default [] None \n",
"3 DOT1Q default [] None \n",
"4 DOT1Q default [] None \n",
"5 DOT1Q default [] None \n",
"6 DOT1Q default [] None \n",
"7 DOT1Q default [] None \n",
"8 DOT1Q default [] None \n",
"9 DOT1Q default [] None \n",
"10 DOT1Q default [] None \n",
"11 DOT1Q default [] None "
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Display the rows corresponding to node 'exitgw' group\n",
"intefaces_by_hostname.get_group(\"exitgw\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Here, we used the <code>[get_group](https://pandas.pydata.org/docs/reference/api/pandas.core.groupby.GroupBy.get_group.html)</code> method to get all information for 'exitgw', thus viewing all interfaces for that node. This is possible using row filtering as well, but we can do other things that are not, such as:"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"scrolled": false
},
"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 tex2jax_ignore\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Interface</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>__aws-services-gateway__</th>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>exitgw</th>\n",
" <td>10</td>\n",
" </tr>\n",
" <tr>\n",
" <th>i-01602d9efaed4409a</th>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>i-02cae6eaa9edeed70</th>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>i-04cd3db5124a05ee6</th>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>i-0a5d64b8b58c6dd09</th>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>igw-02fd68f94367a67c7</th>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>igw-0a8309f3192e7cea3</th>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>internet</th>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>isp_16509</th>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>isp_65200</th>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>leaf1</th>\n",
" <td>15</td>\n",
" </tr>\n",
" <tr>\n",
" <th>leaf2</th>\n",
" <td>13</td>\n",
" </tr>\n",
" <tr>\n",
" <th>leaf3</th>\n",
" <td>13</td>\n",
" </tr>\n",
" <tr>\n",
" <th>leaf4</th>\n",
" <td>14</td>\n",
" </tr>\n",
" <tr>\n",
" <th>spine1</th>\n",
" <td>18</td>\n",
" </tr>\n",
" <tr>\n",
" <th>spine2</th>\n",
" <td>18</td>\n",
" </tr>\n",
" <tr>\n",
" <th>srv-101</th>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>subnet-009d57c7f13813630</th>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>subnet-0333a0749ea4ce3df</th>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>subnet-03acae3b9a534fff9</th>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>subnet-06005943afe32f714</th>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>subnet-06a692ed4ef84368d</th>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>subnet-09b389def558a9c7d</th>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>subnet-0cb5f4c094bee5214</th>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>subnet-0f84a4be105f7aaef</th>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>tgw-06b348adabd13452d</th>\n",
" <td>8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>tgw-0888a76c8a371246d</th>\n",
" <td>8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>vpc-00157b5941bfd4959</th>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>vpc-00b65e98077106059</th>\n",
" <td>8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>vpc-0276455718806058a</th>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>vpc-0574d08f8d05917e4</th>\n",
" <td>8</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Interface\n",
"__aws-services-gateway__ 2\n",
"exitgw 10\n",
"i-01602d9efaed4409a 1\n",
"i-02cae6eaa9edeed70 1\n",
"i-04cd3db5124a05ee6 1\n",
"i-0a5d64b8b58c6dd09 1\n",
"igw-02fd68f94367a67c7 2\n",
"igw-0a8309f3192e7cea3 2\n",
"internet 3\n",
"isp_16509 6\n",
"isp_65200 2\n",
"leaf1 15\n",
"leaf2 13\n",
"leaf3 13\n",
"leaf4 14\n",
"spine1 18\n",
"spine2 18\n",
"srv-101 1\n",
"subnet-009d57c7f13813630 4\n",
"subnet-0333a0749ea4ce3df 4\n",
"subnet-03acae3b9a534fff9 3\n",
"subnet-06005943afe32f714 4\n",
"subnet-06a692ed4ef84368d 4\n",
"subnet-09b389def558a9c7d 3\n",
"subnet-0cb5f4c094bee5214 3\n",
"subnet-0f84a4be105f7aaef 3\n",
"tgw-06b348adabd13452d 8\n",
"tgw-0888a76c8a371246d 8\n",
"vpc-00157b5941bfd4959 5\n",
"vpc-00b65e98077106059 8\n",
"vpc-0276455718806058a 5\n",
"vpc-0574d08f8d05917e4 8"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Display the number of interfaces per node\n",
"intefaces_by_hostname.count()[[\"Interface\"]]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In this example, we used the <code>[count](https://pandas.pydata.org/docs/reference/api/pandas.core.groupby.DataFrameGroupBy.count.html)</code> method, which counts non-null entries for each column in the group. We then filtered by the `Interface` column to see interfaces per node."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Summary\n",
"\n",
"In this notebook, we showed how you can use Pandas methods to manipulate Batfish answers, including filtering rows, filtering columns, and grouping rows. Hopefully, these examples help you get started with your analyses. Find us on Slack (link below) if you have questions."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"***\n",
"### Get involved with the Batfish community\n",
"\n",
"Join our community on [Slack](https://join.slack.com/t/batfish-org/shared_invite/enQtMzA0Nzg2OTAzNzQ1LTcyYzY3M2Q0NWUyYTRhYjdlM2IzYzRhZGU1NWFlNGU2MzlhNDY3OTJmMDIyMjQzYmRlNjhkMTRjNWIwNTUwNTQ) and [GitHub](https://github.com/batfish/batfish). "
]
}
],
"metadata": {
"hide_input": false,
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.7"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| apache-2.0 |
sserrot/champion_relationships | venv/Lib/site-packages/nbconvert/preprocessors/tests/files/Parallel Execute A.ipynb | 1 | 2485 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Ensure notebooks can execute in parallel\n",
"\n",
"This notebook uses a file system based \"lock\" to assert that two instances of the notebook kernel will run in parallel. Each instance writes to a file in a temporary directory, and then tries to read the other file from\n",
"the temporary directory, so that running them in sequence will fail, but running them in parallel will succed.\n",
"\n",
"Two notebooks are launched, each which sets the `this_notebook` variable. One notebook is set to `this_notebook = 'A'` and the other `this_notebook = 'B'`."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"import os.path\n",
"import tempfile\n",
"import time"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# the variable this_notebook is injectected in a cell above by the test framework.\n",
"this_notebook = 'A'\n",
"other_notebook = 'B'\n",
"directory = os.environ['NBEXECUTE_TEST_PARALLEL_TMPDIR']\n",
"with open(os.path.join(directory, 'test_file_{}.txt'.format(this_notebook)), 'w') as f:\n",
" f.write('Hello from {}'.format(this_notebook))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"start = time.time()\n",
"timeout = 5\n",
"end = start + timeout\n",
"target_file = os.path.join(directory, 'test_file_{}.txt'.format(other_notebook))\n",
"while time.time() < end:\n",
" time.sleep(0.1)\n",
" if os.path.exists(target_file):\n",
" with open(target_file, 'r') as f:\n",
" text = f.read()\n",
" if text == 'Hello from {}'.format(other_notebook):\n",
" break\n",
"else:\n",
" assert False, \"Timed out – didn't get a message from {}\".format(other_notebook)"
]
}
],
"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": "3.6.7"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| mit |
phobson/statsmodels | examples/notebooks/glm_formula.ipynb | 1 | 3162 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Generalized Linear Models (Formula)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This notebook illustrates how you can use R-style formulas to fit Generalized Linear Models.\n",
"\n",
"To begin, we load the ``Star98`` dataset and we construct a formula and pre-process the data:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from __future__ import print_function\n",
"import statsmodels.api as sm\n",
"import statsmodels.formula.api as smf\n",
"star98 = sm.datasets.star98.load_pandas().data\n",
"formula = 'SUCCESS ~ LOWINC + PERASIAN + PERBLACK + PERHISP + PCTCHRT + \\\n",
" PCTYRRND + PERMINTE*AVYRSEXP*AVSALK + PERSPENK*PTRATIO*PCTAF'\n",
"dta = star98[['NABOVE', 'NBELOW', 'LOWINC', 'PERASIAN', 'PERBLACK', 'PERHISP',\n",
" 'PCTCHRT', 'PCTYRRND', 'PERMINTE', 'AVYRSEXP', 'AVSALK',\n",
" 'PERSPENK', 'PTRATIO', 'PCTAF']]\n",
"endog = dta['NABOVE'] / (dta['NABOVE'] + dta.pop('NBELOW'))\n",
"del dta['NABOVE']\n",
"dta['SUCCESS'] = endog"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Then, we fit the GLM model:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"mod1 = smf.glm(formula=formula, data=dta, family=sm.families.Binomial()).fit()\n",
"mod1.summary()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Finally, we define a function to operate customized data transformation using the formula framework:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def double_it(x):\n",
" return 2 * x\n",
"formula = 'SUCCESS ~ double_it(LOWINC) + PERASIAN + PERBLACK + PERHISP + PCTCHRT + \\\n",
" PCTYRRND + PERMINTE*AVYRSEXP*AVSALK + PERSPENK*PTRATIO*PCTAF'\n",
"mod2 = smf.glm(formula=formula, data=dta, family=sm.families.Binomial()).fit()\n",
"mod2.summary()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As expected, the coefficient for ``double_it(LOWINC)`` in the second model is half the size of the ``LOWINC`` coefficient from the first model:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"print(mod1.params[1])\n",
"print(mod2.params[1] * 2)"
]
}
],
"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
}
| bsd-3-clause |
numenta/nupic.research | projects/archive/dynamic_sparse/notebooks/ExperimentAnalysis-TestRestoration.ipynb | 3 | 24759 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Experiment: test_restoration\n",
"\n",
"Evaluate if restoration affected existing capabilities. Comparing two approaches to calculate coactivations to see if they are getting to the same values.\n",
"\n",
"#### Conclusion\n"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"base_exp_config = dict(\n",
" device=\"cuda\",\n",
" # dataset related\n",
" dataset_name=\"PreprocessedGSC\",\n",
" data_dir=\"~/nta/datasets/gsc\",\n",
" batch_size_train=(4, 16),\n",
" batch_size_test=1000,\n",
" # network related\n",
" network=tune.grid_search([\"GSCHeb_v0\", \"GSCHeb\"]),\n",
" optim_alg=\"SGD\",\n",
" momentum=0, # 0.9,\n",
" learning_rate=0.01, # 0.1,\n",
" weight_decay=0.01, # 1e-4,\n",
" lr_scheduler=\"MultiStepLR\",\n",
" lr_milestones=[30, 60, 90],\n",
" lr_gamma=0.9, # 0.1,\n",
" use_kwinners=True,\n",
" # sparse_linear_only=True, # False\n",
" # model related\n",
" model=\"DSNNWeightedMag\",\n",
" # on_perc=0.04,\n",
" # sparse related\n",
" on_perc=tune.grid_search([0.02, 0.03, 0.04]),\n",
" weight_prune_perc=0.3,\n",
" # weight_prune_perc=tune.grid_search(list(np.arange(0, 1.001, 0.05))),\n",
" # pruning_early_stop=2,\n",
" # additional validation\n",
" # test_noise=False,\n",
" # debugging\n",
" # debug_weights=True,\n",
" # debug_sparse=False,\n",
")\n"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"%load_ext autoreload\n",
"%autoreload 2"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"from __future__ import absolute_import\n",
"from __future__ import division\n",
"from __future__ import print_function\n",
"\n",
"import os\n",
"import glob\n",
"import tabulate\n",
"import pprint\n",
"import click\n",
"import numpy as np\n",
"import pandas as pd\n",
"from ray.tune.commands import *\n",
"from nupic.research.frameworks.dynamic_sparse.common.browser import *\n",
"\n",
"import matplotlib\n",
"import matplotlib.pyplot as plt\n",
"from matplotlib import rcParams\n",
"\n",
"%config InlineBackend.figure_format = 'retina'\n",
"\n",
"import seaborn as sns\n",
"sns.set(style=\"whitegrid\")\n",
"sns.set_palette(\"colorblind\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Load and check data"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"exps = ['test_restoration_5']\n",
"paths = [os.path.expanduser(\"~/nta/results/{}\".format(e)) for e in exps]\n",
"df = load_many(paths)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"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>Experiment Name</th>\n",
" <th>train_acc_max</th>\n",
" <th>train_acc_max_epoch</th>\n",
" <th>train_acc_min</th>\n",
" <th>train_acc_min_epoch</th>\n",
" <th>train_acc_median</th>\n",
" <th>train_acc_last</th>\n",
" <th>val_acc_max</th>\n",
" <th>val_acc_max_epoch</th>\n",
" <th>val_acc_min</th>\n",
" <th>...</th>\n",
" <th>lr_milestones</th>\n",
" <th>lr_scheduler</th>\n",
" <th>model</th>\n",
" <th>momentum</th>\n",
" <th>network</th>\n",
" <th>on_perc</th>\n",
" <th>optim_alg</th>\n",
" <th>use_kwinners</th>\n",
" <th>weight_decay</th>\n",
" <th>weight_prune_perc</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0_network=GSCHeb_v0,on_perc=0.02</td>\n",
" <td>0.780295</td>\n",
" <td>62</td>\n",
" <td>0.248023</td>\n",
" <td>0</td>\n",
" <td>0.749780</td>\n",
" <td>0.733913</td>\n",
" <td>0.865277</td>\n",
" <td>63</td>\n",
" <td>0.138332</td>\n",
" <td>...</td>\n",
" <td>60.0</td>\n",
" <td>MultiStepLR</td>\n",
" <td>DSNNWeightedMag</td>\n",
" <td>0</td>\n",
" <td>GSCHeb_v0</td>\n",
" <td>0.02</td>\n",
" <td>SGD</td>\n",
" <td>True</td>\n",
" <td>0.01</td>\n",
" <td>None-None-0.3-0.3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1_network=GSCHeb,on_perc=0.02</td>\n",
" <td>0.811298</td>\n",
" <td>46</td>\n",
" <td>0.241724</td>\n",
" <td>0</td>\n",
" <td>0.779709</td>\n",
" <td>0.766868</td>\n",
" <td>0.884523</td>\n",
" <td>19</td>\n",
" <td>0.309944</td>\n",
" <td>...</td>\n",
" <td>60.0</td>\n",
" <td>MultiStepLR</td>\n",
" <td>DSNNWeightedMag</td>\n",
" <td>0</td>\n",
" <td>GSCHeb</td>\n",
" <td>0.02</td>\n",
" <td>SGD</td>\n",
" <td>True</td>\n",
" <td>0.01</td>\n",
" <td>None-None-0.3-0.3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2_network=GSCHeb_v0,on_perc=0.03</td>\n",
" <td>0.854799</td>\n",
" <td>60</td>\n",
" <td>0.263548</td>\n",
" <td>0</td>\n",
" <td>0.842301</td>\n",
" <td>0.839566</td>\n",
" <td>0.921010</td>\n",
" <td>60</td>\n",
" <td>0.384122</td>\n",
" <td>...</td>\n",
" <td>60.0</td>\n",
" <td>MultiStepLR</td>\n",
" <td>DSNNWeightedMag</td>\n",
" <td>0</td>\n",
" <td>GSCHeb_v0</td>\n",
" <td>0.03</td>\n",
" <td>SGD</td>\n",
" <td>True</td>\n",
" <td>0.01</td>\n",
" <td>None-None-0.3-0.3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>3_network=GSCHeb,on_perc=0.03</td>\n",
" <td>0.855239</td>\n",
" <td>70</td>\n",
" <td>0.263939</td>\n",
" <td>0</td>\n",
" <td>0.837394</td>\n",
" <td>0.846988</td>\n",
" <td>0.923817</td>\n",
" <td>96</td>\n",
" <td>0.141540</td>\n",
" <td>...</td>\n",
" <td>60.0</td>\n",
" <td>MultiStepLR</td>\n",
" <td>DSNNWeightedMag</td>\n",
" <td>0</td>\n",
" <td>GSCHeb</td>\n",
" <td>0.03</td>\n",
" <td>SGD</td>\n",
" <td>True</td>\n",
" <td>0.01</td>\n",
" <td>None-None-0.3-0.3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>4_network=GSCHeb_v0,on_perc=0.04</td>\n",
" <td>0.864515</td>\n",
" <td>91</td>\n",
" <td>0.281564</td>\n",
" <td>0</td>\n",
" <td>0.851723</td>\n",
" <td>0.859438</td>\n",
" <td>0.925421</td>\n",
" <td>65</td>\n",
" <td>0.336808</td>\n",
" <td>...</td>\n",
" <td>60.0</td>\n",
" <td>MultiStepLR</td>\n",
" <td>DSNNWeightedMag</td>\n",
" <td>0</td>\n",
" <td>GSCHeb_v0</td>\n",
" <td>0.04</td>\n",
" <td>SGD</td>\n",
" <td>True</td>\n",
" <td>0.01</td>\n",
" <td>None-None-0.3-0.3</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 35 columns</p>\n",
"</div>"
],
"text/plain": [
" Experiment Name train_acc_max train_acc_max_epoch \\\n",
"0 0_network=GSCHeb_v0,on_perc=0.02 0.780295 62 \n",
"1 1_network=GSCHeb,on_perc=0.02 0.811298 46 \n",
"2 2_network=GSCHeb_v0,on_perc=0.03 0.854799 60 \n",
"3 3_network=GSCHeb,on_perc=0.03 0.855239 70 \n",
"4 4_network=GSCHeb_v0,on_perc=0.04 0.864515 91 \n",
"\n",
" train_acc_min train_acc_min_epoch train_acc_median train_acc_last \\\n",
"0 0.248023 0 0.749780 0.733913 \n",
"1 0.241724 0 0.779709 0.766868 \n",
"2 0.263548 0 0.842301 0.839566 \n",
"3 0.263939 0 0.837394 0.846988 \n",
"4 0.281564 0 0.851723 0.859438 \n",
"\n",
" val_acc_max val_acc_max_epoch val_acc_min ... lr_milestones \\\n",
"0 0.865277 63 0.138332 ... 60.0 \n",
"1 0.884523 19 0.309944 ... 60.0 \n",
"2 0.921010 60 0.384122 ... 60.0 \n",
"3 0.923817 96 0.141540 ... 60.0 \n",
"4 0.925421 65 0.336808 ... 60.0 \n",
"\n",
" lr_scheduler model momentum network on_perc optim_alg \\\n",
"0 MultiStepLR DSNNWeightedMag 0 GSCHeb_v0 0.02 SGD \n",
"1 MultiStepLR DSNNWeightedMag 0 GSCHeb 0.02 SGD \n",
"2 MultiStepLR DSNNWeightedMag 0 GSCHeb_v0 0.03 SGD \n",
"3 MultiStepLR DSNNWeightedMag 0 GSCHeb 0.03 SGD \n",
"4 MultiStepLR DSNNWeightedMag 0 GSCHeb_v0 0.04 SGD \n",
"\n",
" use_kwinners weight_decay weight_prune_perc \n",
"0 True 0.01 None-None-0.3-0.3 \n",
"1 True 0.01 None-None-0.3-0.3 \n",
"2 True 0.01 None-None-0.3-0.3 \n",
"3 True 0.01 None-None-0.3-0.3 \n",
"4 True 0.01 None-None-0.3-0.3 \n",
"\n",
"[5 rows x 35 columns]"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.head(5)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# replace hebbian prine\n",
"# df['hebbian_prune_perc'] = df['hebbian_prune_perc'].replace(np.nan, 0.0, regex=True)\n",
"# df['weight_prune_perc'] = df['weight_prune_perc'].replace(np.nan, 0.0, regex=True)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Index(['Experiment Name', 'train_acc_max', 'train_acc_max_epoch',\n",
" 'train_acc_min', 'train_acc_min_epoch', 'train_acc_median',\n",
" 'train_acc_last', 'val_acc_max', 'val_acc_max_epoch', 'val_acc_min',\n",
" 'val_acc_min_epoch', 'val_acc_median', 'val_acc_last', 'val_acc_all',\n",
" 'epochs', 'experiment_file_name', 'trial_time', 'mean_epoch_time',\n",
" 'batch_size_test', 'batch_size_train', 'data_dir', 'dataset_name',\n",
" 'device', 'learning_rate', 'lr_gamma', 'lr_milestones', 'lr_scheduler',\n",
" 'model', 'momentum', 'network', 'on_perc', 'optim_alg', 'use_kwinners',\n",
" 'weight_decay', 'weight_prune_perc'],\n",
" dtype='object')"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.columns"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(60, 35)"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.shape"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Experiment Name 1_network=GSCHeb,on_perc=0.02\n",
"train_acc_max 0.811298\n",
"train_acc_max_epoch 46\n",
"train_acc_min 0.241724\n",
"train_acc_min_epoch 0\n",
"train_acc_median 0.779709\n",
"train_acc_last 0.766868\n",
"val_acc_max 0.884523\n",
"val_acc_max_epoch 19\n",
"val_acc_min 0.309944\n",
"val_acc_min_epoch 81\n",
"val_acc_median 0.846231\n",
"val_acc_last 0.836006\n",
"val_acc_all 0 0.377706\n",
"1 0.630714\n",
"2 0.695269\n",
"3...\n",
"epochs 100\n",
"experiment_file_name /Users/lsouza/nta/results/test_restoration_5/e...\n",
"trial_time 33.2763\n",
"mean_epoch_time 0.332763\n",
"batch_size_test 1000\n",
"batch_size_train 10\n",
"data_dir ~/nta/datasets/gsc\n",
"dataset_name PreprocessedGSC\n",
"device cuda\n",
"learning_rate 0.01\n",
"lr_gamma 0.9\n",
"lr_milestones 60\n",
"lr_scheduler MultiStepLR\n",
"model DSNNWeightedMag\n",
"momentum 0\n",
"network GSCHeb\n",
"on_perc 0.02\n",
"optim_alg SGD\n",
"use_kwinners True\n",
"weight_decay 0.01\n",
"weight_prune_perc None-None-0.3-0.3\n",
"Name: 1, dtype: object"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.iloc[1]"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"model\n",
"DSNNWeightedMag 60\n",
"Name: model, dtype: int64"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.groupby('model')['model'].count()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
" ## Analysis"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Experiment Details"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"num_epochs=100"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Did any trials failed?\n",
"df[df[\"epochs\"]<num_epochs][\"epochs\"].count()"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(60, 35)"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Removing failed or incomplete trials\n",
"df_origin = df.copy()\n",
"df = df_origin[df_origin[\"epochs\"]>=num_epochs]\n",
"df.shape"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Series([], Name: epochs, dtype: int64)"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# which ones failed?\n",
"# failed, or still ongoing?\n",
"df_origin['failed'] = df_origin[\"epochs\"]<num_epochs\n",
"df_origin[df_origin['failed']]['epochs']"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [],
"source": [
"# helper functions\n",
"def mean_and_std(s):\n",
" return \"{:.3f} ± {:.3f}\".format(s.mean(), s.std())\n",
"\n",
"def round_mean(s):\n",
" return \"{:.0f}\".format(round(s.mean()))\n",
"\n",
"stats = ['min', 'max', 'mean', 'std']\n",
"\n",
"def agg(columns, filter=None, round=3):\n",
" if filter is None:\n",
" return (df.groupby(columns)\n",
" .agg({'val_acc_max_epoch': round_mean,\n",
" 'val_acc_max': stats, \n",
" 'model': ['count']})).round(round)\n",
" else:\n",
" return (df[filter].groupby(columns)\n",
" .agg({'val_acc_max_epoch': round_mean,\n",
" 'val_acc_max': stats, \n",
" 'model': ['count']})).round(round)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### Does improved weight pruning outperforms regular SET"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
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" <thead>\n",
" <tr>\n",
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" <th></th>\n",
" <th>val_acc_max_epoch</th>\n",
" <th colspan=\"4\" halign=\"left\">val_acc_max</th>\n",
" <th>model</th>\n",
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" <tr>\n",
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" <th></th>\n",
" <th>round_mean</th>\n",
" <th>min</th>\n",
" <th>max</th>\n",
" <th>mean</th>\n",
" <th>std</th>\n",
" <th>count</th>\n",
" </tr>\n",
" <tr>\n",
" <th>on_perc</th>\n",
" <th>network</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\">0.02</th>\n",
" <th>GSCHeb</th>\n",
" <td>37</td>\n",
" <td>0.864</td>\n",
" <td>0.889</td>\n",
" <td>0.875</td>\n",
" <td>0.008</td>\n",
" <td>10</td>\n",
" </tr>\n",
" <tr>\n",
" <th>GSCHeb_v0</th>\n",
" <td>42</td>\n",
" <td>0.865</td>\n",
" <td>0.887</td>\n",
" <td>0.876</td>\n",
" <td>0.006</td>\n",
" <td>10</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">0.03</th>\n",
" <th>GSCHeb</th>\n",
" <td>66</td>\n",
" <td>0.893</td>\n",
" <td>0.924</td>\n",
" <td>0.914</td>\n",
" <td>0.010</td>\n",
" <td>10</td>\n",
" </tr>\n",
" <tr>\n",
" <th>GSCHeb_v0</th>\n",
" <td>62</td>\n",
" <td>0.907</td>\n",
" <td>0.922</td>\n",
" <td>0.916</td>\n",
" <td>0.005</td>\n",
" <td>10</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">0.04</th>\n",
" <th>GSCHeb</th>\n",
" <td>81</td>\n",
" <td>0.923</td>\n",
" <td>0.937</td>\n",
" <td>0.933</td>\n",
" <td>0.004</td>\n",
" <td>10</td>\n",
" </tr>\n",
" <tr>\n",
" <th>GSCHeb_v0</th>\n",
" <td>73</td>\n",
" <td>0.923</td>\n",
" <td>0.938</td>\n",
" <td>0.930</td>\n",
" <td>0.004</td>\n",
" <td>10</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" val_acc_max_epoch val_acc_max model\n",
" round_mean min max mean std count\n",
"on_perc network \n",
"0.02 GSCHeb 37 0.864 0.889 0.875 0.008 10\n",
" GSCHeb_v0 42 0.865 0.887 0.876 0.006 10\n",
"0.03 GSCHeb 66 0.893 0.924 0.914 0.010 10\n",
" GSCHeb_v0 62 0.907 0.922 0.916 0.005 10\n",
"0.04 GSCHeb 81 0.923 0.937 0.933 0.004 10\n",
" GSCHeb_v0 73 0.923 0.938 0.930 0.004 10"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"agg(['on_perc', 'network'])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
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"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
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},
"nbformat": 4,
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| agpl-3.0 |
jupyter/docker-demo-images | notebooks/Welcome to Perl 6.ipynb | 1 | 20926 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<div class=\"clearfix\" style=\"padding: 10px; padding-left: 0px\">\n",
"<img src=\"https://raw.githubusercontent.com/jupyter/nature-demo/master/images/jupyter-logo.png\" width=\"150px\" style=\"display: inline-block; margin-top: 5px;\">\n",
"<img src=\"https://perl6.org/camelia-logo.png\">\n",
"</div>\n",
"\n",
"## Welcome to Perl 6 on the Temporary Notebook (tmpnb) service!\n",
"\n",
"This Notebook Server was **launched just for you**. It's a temporary way for you to try out a Perl 6 Jupyter notebook.\n",
"\n",
"<div class=\"alert alert-warning\" role=\"alert\" style=\"margin: 10px\">\n",
"<p>**WARNING**</p>\n",
"\n",
"<p>Don't rely on this server for anything you want to last - your server will be *deleted after 10 minutes of inactivity*.</p>\n",
"</div>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### To run Perl 6 code, type some code and type ctrl-enter or alt-enter. The value of the last expression will be placed into the corresponding `Out` cell."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"hello, world"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"\"hello, world\""
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(1a 1b 2a 2b 3a 3b 4a 4b 5a 5b)"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"1..5 X~ 'a'..'b'"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Type `[tab]` for autocompletion. Autocompleting a parenthesis will show you unicode set operators."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"(2..7) ⊂ (1..10)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### SVG::Plot can be used to generate plots."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
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"<text x=\"-6.5625\" y=\"-0\" font-size=\"12\" text-anchor=\"end\" dominant-baseline=\"middle\">-2</text>\n",
"<line x1=\"-2.1875\" x2=\"2.1875\" y1=\"-20.3125\" y2=\"-20.3125\" style=\"stroke:black; stroke-width: 1\" />\n",
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"<text x=\"-6.5625\" y=\"-121.875\" font-size=\"12\" text-anchor=\"end\" dominant-baseline=\"middle\">1</text>\n",
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"<line x1=\"-2.1875\" x2=\"2.1875\" y1=\"-162.5\" y2=\"-162.5\" style=\"stroke:black; stroke-width: 1\" />\n",
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"<line x1=\"0\" y1=\"0\" x2=\"320\" y2=\"0\" style=\"stroke:black; stroke-width: 2\" />\n",
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"<text x=\"200\" y=\"-171.25\" text-anchor=\"middle\">sin(x/10), cos(x/10)</text>\n",
"</g>\n",
"</svg>\n"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"use SVG;\n",
"use SVG::Plot;\n",
"\n",
"my $points = 50;\n",
"my @x = (0..$points).map: { sin(2 * π * $_ / $points) };\n",
"my @d1 = (0..$points).map: { 2 * cos(2 * π * $_ / $points) };\n",
"my @d2 = (0..$points).map: { cos(2 * π * $_ / $points) };\n",
"SVG.serialize: SVG::Plot.new(\n",
" width => 400,\n",
" height => 250,\n",
" :@x,\n",
" values => (@d1, @d2),\n",
" title => 'sin(x/10), cos(x/10)',\n",
").plot(:xy-lines);"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"For more examples, see https://github.com/bduggan/p6-jupyter-kernel/tree/master/eg"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Perl 6",
"language": "perl6",
"name": "perl6"
},
"language_info": {
"file_extension": ".p6",
"mimetype": "text/plain",
"name": "perl6",
"version": "6.c"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| bsd-3-clause |
ipa-lth/jupyter_nb | identification/polyfitting_step.ipynb | 1 | 17236 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"from scipy.optimize import curve_fit\n",
"%pylab inline"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def func(x, a, b, c, d):\n",
" res=np.array([])\n",
" for x_single in x:\n",
" if x_single <= d:\n",
" res= np.append(res, [c])\n",
" else:\n",
" res = np.append(res, [a *(1 - np.exp(-b * (x_single-d))) + c])\n",
" return res"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#Wikipedia\n",
"def tn_pt2(xdata, td, k, w0, d):\n",
" raise(NotImplementedError('This function does not work as expected, some mathematical error occures! use pt2 instead'))\n",
" def _a1_2(w0, d):\n",
" #print 'd_w0:',d,w0\n",
" return (-d * w0 + w0 * np.sqrt(np.abs(d**2 - 1)), \n",
" -d * w0 - w0 * np.sqrt(np.abs(d**2 - 1)))\n",
" def _T1_2(a1_2):\n",
" #print a1_2\n",
" a1, a2 = a1_2\n",
" return (-1.0/a1, -1.0/a2)\n",
" def T1_2(w0, d):\n",
" return _T1_2(_a1_2(w0, d))\n",
"\n",
" res = np.array([])\n",
" for t, x in xdata:\n",
" if t < td:\n",
" res = np.append(res, [0.0]) \n",
" else:\n",
" t = t - td\n",
" #print 'x,td,t,d:',x, td, t,d\n",
" T1, T2 = T1_2(w0, d)\n",
" #print 'T1, T2:',T1, T2\n",
" if d > 1: # Kriechfall\n",
" val = k * (1 - (T1 / (T1 - T2)) * np.exp(-t / T1) + (T2 / (T1 - T2)) * np.exp(-t / T2)) * x\n",
" res = np.append(res, [val]) \n",
" elif d == 1: #aperiodischer Grenzfall\n",
" #print \"aperio\"\n",
" #print 1.0/T1, np.exp(-t/T1)\n",
" val = k * ((1 - (1 + 1.0/T1 ) * np.exp(-t/T1))) * x\n",
" res = np.append(res, [val])\n",
" elif d <= -1: # instabiler Kriechfall\n",
" val = k * (1 - (T1 / (T1 - T2)) * np.exp(t / T1) + (T2 / (T1 - T2)) * np.exp(t / T2)) * x\n",
" res = np.append(res, [val])\n",
" else: #Schwingfall\n",
" #we = wo * np.sqrt(1-d**2)\n",
" val = k * (1 - 1/(np.sqrt(1-d**2)) * np.exp(-d*w0*t) * np.sin(w0*np.sqrt(1-d**2) * t + np.arccos(d))) * x\n",
" res = np.append(res, [val])\n",
" return res"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#t = np.arange(0, 10, 0.1)\n",
"#x = np.ones(len(t))\n",
"#tn_pt2(zip(t, x), td=0, k=2, w0=1, d=0.5)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#t = np.arange(0, 70, 0.1)\n",
"#x = np.ones(len(t))\n",
"#xdata = zip(t, x)\n",
"#y = tn_pt2(xdata, td=1, k=2, w0=2, d=0.9)\n",
"#y = func(xdata, 10, 5, 0, 1)\n",
"#print len(y), len(xdata)\n",
"#y_noise = 0.1 * np.random.normal(size=xdata.size)\n",
"#ydata = y + y_noise\n",
"#ydata = tn_pt2(xdata, 0, 2, 1, 5)\n",
"#plt.plot(xdata, y, 'g-', label='data')\n",
"#plt.plot(xdata, ydata, 'b-', label='data_noise')\n",
"#tn_pt2(zip([0, 1],[0, 0.3]), td=0, k=2, w0=1, d=1) ##TODO CORRECT !!= 0 -> https://de.wikipedia.org/wiki/Datei:Step-PT2.svg\n",
"#plt.plot(xdata, tn_pt2(xdata, td=0, k=2, w0=1, d=0.2), 'b-', label='data')\n",
"#plt.plot(xdata, tn_pt2(xdata, td=0, k=2, w0=1, d=1), 'g-', label='data')\n",
"#plt.plot(xdata, tn_pt2(xdata, td=0, k=2, w0=1, d=5), 'r-', label='data')\n",
"#tn_pt2(xdata, td=0, k=2, w0=1, d=1)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#http://www.eit.hs-karlsruhe.de/mesysto/teil-a-zeitkontinuierliche-signale-und-systeme/uebertragungsglieder-der-regelungstechnik/zusammengesetzte-uebertragungsglieder/pt2-glied.html\n",
"#xt is zip (xdata, tdata) \n",
"def pt2(xt, K, d, T, delay=0):\n",
" y = np.array([])\n",
" for x, tx in xt:\n",
" #For now no delay:\n",
" if tx < delay:\n",
" y = np.append(y, [0.0])\n",
" else:\n",
" t = tx - delay\n",
"\n",
" if d > 1: #aperiodischer Fall\n",
" T1, T2 = (T/(d+np.sqrt(d**2-1)), T/(d-np.sqrt(d**2-1)))\n",
" #print 'T1, T2: ', T1, T2\n",
" h = K * (1.0 - 1.0/(T1-T2) * (T1*np.exp(-t/T1)-T2*np.exp(-t/T2)))\n",
" y = np.append(y, [h*x])\n",
" elif d == 1: #aperiodischer Grenzfall\n",
" h = K * (1.0 - (1.0 + t/T)*np.exp(-t/T))\n",
" y = np.append(y, [h*x])\n",
" else: #periodischer Fall d<1 \n",
" h = K * (1.0 + 1.0/(np.sqrt(1-d**2)) * np.exp(-d*t/T) * np.cos(np.sqrt(1-d**2)/T * t - np.pi - np.arctan(-d/np.sqrt(1-d**2))))\n",
" y = np.append(y, [h*x])\n",
" return y"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"t = np.arange(0, 10, 1)\n",
"x = np.ones(len(t))\n",
"pt2(xt=zip(x, t), K=2, d=5, T=1)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"t = np.arange(0, 70, 0.1)\n",
"x = np.ones(len(t))\n",
"xdata = zip(t, x)\n",
"#y = tn_pt2(xdata, td=1, k=2, w0=2, d=0.9)\n",
"#y = func(xdata, 10, 5, 0, 1)\n",
"#print len(y), len(xdata)\n",
"#y_noise = 0.1 * np.random.normal(size=xdata.size)\n",
"#ydata = y + y_noise\n",
"#ydata = tn_pt2(xdata, 0, 2, 1, 5)\n",
"\n",
"plt.plot(t, x, 'g:', label='data')\n",
"plt.plot(t, pt2(xt=zip(x, t), K=1, d=2, T=1, delay=10), 'b-', label='d=2')\n",
"plt.plot(t, pt2(xt=zip(x, t), K=1, d=1.0, T=1, delay=10), 'g-', label='d=1')\n",
"plt.plot(t, pt2(xt=zip(x, t), K=1, d=3, T=1, delay=10), 'r-', label='d=3')\n",
"\n",
"plt.plot(t, pt2(xt=zip(x, t), K=2, d=0.25, T=1), 'b-', label='d=0.25')\n",
"plt.plot(t, pt2(xt=zip(x, t), K=2, d=1, T=1, delay=20), 'g-', label='d=1')\n",
"plt.plot(t, pt2(xt=zip(x, t), K=2, d=0.5, T=1), 'r-', label='d=0.5')\n",
"\n",
"plt.plot(t, pt2(xt=zip(x, t), K=0.2, d=0, T=1, delay=10), 'r:', label='d=0.2')\n",
"#plt.plot(xdata, ydata, 'b-', label='data_noise')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#http://www.eit.hs-karlsruhe.de/mesysto/teil-a-zeitkontinuierliche-signale-und-systeme/uebertragungsglieder-der-regelungstechnik/zusammengesetzte-uebertragungsglieder/pt2-glied.html\n",
"#xdata is normalized for x per delta-time-step \n",
"def pt2_func(soll, K, d, T, delay=0):\n",
" y = np.array([])\n",
" xt = zip(soll, np.arange(0, len(soll), 1))\n",
" for x, tx in xt:\n",
" #For now no delay:\n",
" if tx < delay:\n",
" y = np.append(y, [0.0]) # This can actually be not zero but the last value\n",
" else:\n",
" t = tx - delay\n",
"\n",
" if d > 1: #aperiodischer Fall\n",
" T1, T2 = (T/(d+np.sqrt(d**2-1)), T/(d-np.sqrt(d**2-1)))\n",
" #print 'T1, T2: ', T1, T2\n",
" h = K * (1.0 - 1.0/(T1-T2) * (T1*np.exp(-t/T1)-T2*np.exp(-t/T2)))\n",
" y = np.append(y, [h*x])\n",
" elif d == 1: #aperiodischer Grenzfall\n",
" h = K * (1.0 - (1.0 + t/T)*np.exp(-t/T))\n",
" y = np.append(y, [h*x])\n",
" else: #periodischer Fall d<1 \n",
" h = K * (1.0 + 1.0/(np.sqrt(1-d**2)) * np.exp(-d*t/T) * np.cos(np.sqrt(1-d**2)/T * t - np.pi - np.arctan(-d/np.sqrt(1-d**2))))\n",
" y = np.append(y, [h*x])\n",
" return y"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def fit(soll, ist, maxfev=1000):\n",
" #popt, pcov = curve_fit(pt2_func, xdata, ydata, method='dogbox')\n",
" try:\n",
" popt_damp, pcov_damp = curve_fit(pt2_func, soll, ist, \n",
" method='dogbox', \n",
" bounds=([-10, 0, 1.0, 0], [100, 5, 10, 80]))\n",
" #plt.plot(np.arange(0, len(xdata), 1), pt2_func(xdata, *popt), 'g-', label='fit')\n",
" print 'd>1:', np.sum(np.sqrt(np.diag(pcov_damp))), popt_damp\n",
" except RuntimeError as e:\n",
" print e\n",
" popt_damp = None\n",
" pcov_damp = None\n",
" \n",
" try:\n",
" popt_swing, pcov_swing = curve_fit(pt2_func, soll, ist, \n",
" method='dogbox', \n",
" bounds=([-10, 0, 0., 0], [100., 1.001, 10, 80]))\n",
" #plt.plot(np.arange(0, len(xdata), 1), pt2_func(xdata, *popt), 'r-', label='fit')\n",
" print 'd<1:', np.sum(np.sqrt(np.diag(pcov_swing))), popt_swing\n",
" except RuntimeError as e:\n",
" print e\n",
" popt_swing = None\n",
" pcov_swing = None\n",
" return popt_damp, pcov_damp, popt_swing, pcov_swing\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"test_soll = np.ones(1000)\n",
"test_ist = pt2_func(test_soll, K=20, d=0.8, T=10, delay=10)\n",
"test_noise = 0.01 * np.random.normal(size=test_ist.size)\n",
"\n",
"test_ist_noise = test_ist + test_noise\n",
" \n",
"popt, pcov, popt2, pcov2 = fit(test_soll, test_ist_noise)\n",
"plt.plot(np.arange(0, len(test_soll), 1), pt2_func(test_soll, *popt), 'b-', label='d>1')\n",
"plt.plot(np.arange(0, len(test_soll), 1), pt2_func(test_soll, *popt2), 'g-', label='d<1')\n",
"plt.plot(np.arange(0, len(test_soll), 1), test_ist, 'r:', label='ist')\n",
"plt.plot(np.arange(0, len(test_soll), 1), test_soll, 'y-', label='soll')\n",
"plt.legend()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"from pandas import Series, DataFrame, Panel\n",
"#%pylab inline\n",
"#plt.rcParams['figure.figsize'] = (15, 15)\n",
"\n",
"### get soll ###\n",
"df_soll = pd.read_csv('~/catkin_ws/src/pitasc/applications/sysident/step_log/2017-05-18/2017-05-18_11-45-37_control_output.log',\n",
" header=0,\n",
" names=['time', 'x_soll'])\n",
"\n",
"#remove trailing [\n",
"#df_soll['x_soll'] = df_soll['x_soll'].str[2:].astype(float)\n",
"df_soll = df_soll.set_index('time')\n",
"\n",
"### get ist ###\n",
"df_ist = pd.read_csv('~/catkin_ws/src/pitasc/applications/sysident/step_log/2017-05-18/2017-05-18_11-45-37_task_vel.log',\n",
" header=0,\n",
" names=['time', 'x_ist'])\n",
"#remove trailing [\n",
"#df_ist['x_ist'] = df_ist['x_ist'].str[2:].astype(float)\n",
"df_ist = df_ist.set_index('time')\n",
"\n",
"### make one df with ist and soll; indexed by time\n",
"# Concates both series to one and fills (unknown) data with last valid one\n",
"df_ist_soll = pd.concat([df_soll.x_soll, df_ist.x_ist], axis=1).fillna(method='pad')\n",
"# Fills first value with 0 (there is no valid before that one)\n",
"df_ist_soll = df_ist_soll.fillna(0)\n",
"df_ist_soll.plot(ylim=[-0.05, 0.21], style='.-', drawstyle=\"steps\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Make Timeseries and resample\n",
"\n",
"ts_ist_soll = df_ist_soll.set_index(pd.to_datetime(df_ist_soll.index, unit='s')) # TODO make start date usefull\n",
"ts_ist_soll_4ms = ts_ist_soll.resample('4ms').pad().fillna(0).astype(float)\n",
"ts_ist_soll_4ms.plot(ylim=[-0.05, 0.21], style='.-', drawstyle=\"steps\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"## Collect all results\n",
"frames = []"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"ts_ist_soll_4ms.plot(ylim=[-0.05, 0.21], style='.-', drawstyle=\"steps\")\n",
"# curvefit\n",
"ts_soll = ts_ist_soll_4ms.x_soll.as_matrix()\n",
"ts_ist = ts_ist_soll_4ms.x_ist.as_matrix()\n",
"popt_damp, pcov_damp, popt_swing, pcov_swing = fit(ts_soll, ts_ist)\n",
"\n",
"t = np.arange(0, len(ts_ist)*0.004, 0.004)\n",
"if popt_damp is not None:\n",
" ist_est_damp = pt2_func(ts_soll, *popt_damp)\n",
" ts_ist_curvefit1 = pd.Series(ist_est_damp, index=ts_ist_soll_4ms.index, name='x_ist_curvefit_damp')\n",
" ts_ist_curvefit1.plot()\n",
" frames.append(ts_ist_curvefit1.to_frame())\n",
" \n",
"if popt_swing is not None:\n",
" ist_est_swing = pt2_func(ts_soll, *popt_swing)\n",
" ts_ist_curvefit2 = pd.Series(ist_est_swing, index=ts_ist_soll_4ms.index, name='x_ist_curvefit_swing')\n",
" ts_ist_curvefit2.plot()\n",
" frames.append(ts_ist_curvefit2.to_frame())\n",
"\n",
"#plt.plot(t, ist_est_damp, 'b-', label='d>1')\n",
"#plt.plot(t, ist_est_swing, 'g-', label='d<1')\n",
"#plt.legend()\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#polyfit\n",
"z = np.polyfit(t, ts_ist, 5)\n",
"p = np.poly1d(z)\n",
"#print 'resampled p'\n",
"#print p\n",
"\n",
"ts_ist_soll_4ms.plot(ylim=[-0.05, 0.21], style='.-', drawstyle=\"steps\")\n",
"\n",
"ts_ist_polyfit1 = pd.Series(p(t), index=ts_ist_soll_4ms.index, name='x_ist_ployfit_5')\n",
"ts_ist_polyfit1.plot()\n",
"\n",
"frames.append(ts_ist_polyfit1.to_frame())"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#polyfit on nonresampled data\n",
"z2 = np.polyfit(df_ist_soll.index, df_ist_soll.x_ist.as_matrix(), 5)\n",
"p2 = np.poly1d(z2)\n",
"#print 'nonresampled p'\n",
"#print p\n",
"\n",
"ts_ist_polyfit2 = pd.Series(p2(t), index=ts_ist_soll_4ms.index, name='x_ist_ployfit2_5')\n",
"\n",
"#ts_ist_polyfit2= ts_ist_polyfit2.resample('4ms').pad().fillna(0)\n",
"#s3 = pd.concat([s_4ms, pl2], axis=1).fillna(method='pad').fillna(0)\n",
"#y_data_raw.time.as_matrix()\n",
"#plt.figure()\n",
"ts_ist_soll_4ms.plot(ylim=[-0.05, 0.21], style='.-', drawstyle=\"steps\")\n",
"ts_ist_polyfit2.plot()\n",
"\n",
"frames.append(ts_ist_polyfit2.to_frame())"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"## Merge all series together to one showable dataframe\n",
"\n",
"frames.append(ts_ist_soll_4ms)\n",
"all_results = pd.concat(frames, axis=1)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [],
"source": [
"all_results.plot(ylim=[-0.05, 0.21], style='', drawstyle=\"steps\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.12"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| mit |
kylepjohnson/notebooks | fluent_python/Chapter 2, An Array of Sequences.ipynb | 2 | 42100 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Generator expressions"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[36, 35, 37, 94, 38]"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"symbols = '$#%^&'\n",
"[ord(s) for s in symbols]"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(36, 35, 37, 94, 38)"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"tuple(ord(s) for s in symbols)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<generator object <genexpr> at 0x1048d7f78>"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"(ord(s) for s in symbols)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"36\n",
"35\n",
"37\n",
"94\n",
"38\n"
]
}
],
"source": [
"for x in (ord(s) for s in symbols):\n",
" print(x)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array('I', [36, 35, 37, 94, 38])"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import array\n",
"array.array('I', (ord(s) for s in symbols))"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"black S\n",
"black M\n",
"black L\n",
"white S\n",
"white M\n",
"white L\n"
]
}
],
"source": [
"colors = ['black', 'white']\n",
"sizes = ['S', 'M', 'L']\n",
"for tshirt in ((c, s) for c in colors for s in sizes):\n",
" print(tshirt)"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"black S\n",
"black M\n",
"black L\n",
"white S\n",
"white M\n",
"white L\n"
]
}
],
"source": [
"for tshirt in ('%s %s' % (c, s) for c in colors for s in sizes):\n",
" print(tshirt)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Tuples as Records"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"lax_coordinates = (33.9425, -118.408056)\n",
"city, year, pop, chg, area = ('Tokyo', 2003, 32450, 0.66, 8014)\n",
"traveler_ids = [('USA', '31195855'), ('BRA', 'CE342567'), ('ESP', 'XDA205856')]"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"BRA/CE342567\n",
"ESP/XDA205856\n",
"USA/31195855\n"
]
}
],
"source": [
"for passport in sorted(traveler_ids):\n",
" print('%s/%s' % passport)"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"USA\n",
"BRA\n",
"ESP\n"
]
}
],
"source": [
"for country, _ in traveler_ids:\n",
" print(country)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Tuple Unpacking"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"afile.txt\n"
]
}
],
"source": [
"import os\n",
"_, filename = os.path.split('/home/kyle/afile.txt')\n",
"print(filename)"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"a, b, *rest = range(5)"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(0, 1, [2, 3, 4])"
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a, b, rest"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(0, 1, [2])"
]
},
"execution_count": 37,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a, b, *rest = range(3)\n",
"a, b, rest"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(0, 1, [])"
]
},
"execution_count": 39,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a, b, *rest = range(2)\n",
"a, b, rest"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(0, [1, 2], 3, 4)"
]
},
"execution_count": 42,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a, *body, c, d = range(5)\n",
"a, body, c, d"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"([0, 1], 2, 3, 4)"
]
},
"execution_count": 43,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"*head, b, c, d = range(5)\n",
"head, b, c, d"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"metro_areas = [('Tokyo','JP',36.933,(35.689722,139.691667)),\n",
" ('Delhi NCR', 'IN', 21.935, (28.613889, 77.208889)),\n",
" ('Mexico City', 'MX', 20.142, (19.433333, -99.133333)),\n",
" ('New York-Newark', 'US', 20.104, (40.808611, -74.020386)),\n",
" ('Sao Paulo', 'BR', 19.649, (-23.547778, -46.635833)),\n",
" ]"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" | lat. | long. \n"
]
}
],
"source": [
"print('{:15} | {:^9} | {:^9}'.format('', 'lat.', 'long.'))"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"fmt = '{:15} | {:9.4f} | {:9.4f}'"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"'{:15} | {:9.4f} | {:9.4f}'"
]
},
"execution_count": 47,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"fmt"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Mexico City | 19.4333 | -99.1333\n",
"New York-Newark | 40.8086 | -74.0204\n",
"Sao Paulo | -23.5478 | -46.6358\n"
]
}
],
"source": [
"for name, cc, pop, (latitude, longitude) in metro_areas:\n",
" if longitude <= 0:\n",
" print(fmt.format(name, latitude, longitude))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Named tuples"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from collections import namedtuple"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"City = namedtuple('City', 'name country population coordinates')"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"tokyo = City('Tokyo', 'JP', 36.933, (35.689722, 139.691667))"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"City(name='Tokyo', country='JP', population=36.933, coordinates=(35.689722, 139.691667))"
]
},
"execution_count": 53,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"tokyo"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"36.933"
]
},
"execution_count": 54,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"tokyo.population"
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"'Tokyo'"
]
},
"execution_count": 56,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"tokyo.name"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(35.689722, 139.691667)"
]
},
"execution_count": 57,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"tokyo.coordinates"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"'JP'"
]
},
"execution_count": 58,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"tokyo[1]"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"('name', 'country', 'population', 'coordinates')"
]
},
"execution_count": 59,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# a few useful methods on namedtuple\n",
"City._fields"
]
},
{
"cell_type": "code",
"execution_count": 64,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"LatLong = namedtuple('LatLong', 'lat long')\n",
"delhi_data = ('Delhi NCR', 'IN', 21.935, LatLong(28.613889, 77.208889))\n",
"delhi = City._make(delhi_data) # instantiate a named tuple from an iterable"
]
},
{
"cell_type": "code",
"execution_count": 62,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"OrderedDict([('name', 'Delhi NCR'),\n",
" ('country', 'IN'),\n",
" ('population', 21.935),\n",
" ('coordinates', LatLong(lat=28.613889, long=77.208889))])"
]
},
"execution_count": 62,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"delhi._asdict()"
]
},
{
"cell_type": "code",
"execution_count": 63,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"name: Delhi NCR\n",
"country: IN\n",
"population: 21.935\n",
"coordinates: LatLong(lat=28.613889, long=77.208889)\n"
]
}
],
"source": [
"for key, value in delhi._asdict().items():\n",
" print(key + ':', value)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Slicing"
]
},
{
"cell_type": "code",
"execution_count": 65,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[10, 20]"
]
},
"execution_count": 65,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# why slices and range exclude the last item\n",
"\n",
"l = [10,20,30,40,50,60]\n",
"l[:2]"
]
},
{
"cell_type": "code",
"execution_count": 67,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[30, 40, 50, 60]"
]
},
"execution_count": 67,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"l[2:]"
]
},
{
"cell_type": "code",
"execution_count": 68,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"'bye'"
]
},
"execution_count": 68,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# slice objects\n",
"s = 'bicycle'\n",
"s[::3]"
]
},
{
"cell_type": "code",
"execution_count": 69,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"'elcycib'"
]
},
"execution_count": 69,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"s[::-1]"
]
},
{
"cell_type": "code",
"execution_count": 70,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"'eccb'"
]
},
"execution_count": 70,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"s[::-2]"
]
},
{
"cell_type": "code",
"execution_count": 81,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"invoice = \"\"\"\n",
"0.....6.................................40........52...55........\n",
"1909 Pimoroni PiBrella $17.50 3 $52.50\n",
"1489 6mm Tactile Switch x20 $4.95 2 $9.90\n",
"1510 Panavise Jr. - PV-201 $28.00 1 $28.00\n",
"1601 PiTFT Mini Kit 320x240 $34.95 1 $34.95\n",
"\"\"\""
]
},
{
"cell_type": "code",
"execution_count": 84,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"SKU = slice(0,6)\n",
"DESCRIPTION = slice(6, 40)\n",
"UNIT_PRICE = slice(40, 52)\n",
"QUANTITY = slice(52, 55)\n",
"ITEM_TOTAL = slice(55, None)"
]
},
{
"cell_type": "code",
"execution_count": 85,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" $17.50 Pimoroni PiBrella \n",
" $4.95 6mm Tactile Switch x20 \n",
" $28.00 Panavise Jr. - PV-201 \n",
" $34.95 PiTFT Mini Kit 320x240 \n",
" \n"
]
}
],
"source": [
"line_items = invoice.split('\\n')[2:]\n",
"for item in line_items:\n",
" print(item[UNIT_PRICE], item[DESCRIPTION])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Assigning to Slices"
]
},
{
"cell_type": "code",
"execution_count": 101,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]"
]
},
"execution_count": 101,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"l = list(range(10))\n",
"l"
]
},
{
"cell_type": "code",
"execution_count": 102,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[0, 1, 20, 30, 5, 6, 7, 8, 9]"
]
},
"execution_count": 102,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"l[2:5] = [20, 30]\n",
"l"
]
},
{
"cell_type": "code",
"execution_count": 103,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[0, 1, 20, 30, 5, 8, 9]"
]
},
"execution_count": 103,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"del l[5:7]\n",
"l"
]
},
{
"cell_type": "code",
"execution_count": 104,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[0, 1, 20, 11, 5, 22, 9]"
]
},
"execution_count": 104,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"l[3::2] = [11, 22]\n",
"l"
]
},
{
"cell_type": "code",
"execution_count": 105,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "TypeError",
"evalue": "can only assign an iterable",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-105-d29be80f6a36>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0ml\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m100\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0ml\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mTypeError\u001b[0m: can only assign an iterable"
]
}
],
"source": [
"l[2:5] = 100\n",
"l"
]
},
{
"cell_type": "code",
"execution_count": 106,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[0, 1, 100, 22, 9]"
]
},
"execution_count": 106,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"l[2:5] = [100]\n",
"l"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Using + and * with Sequences"
]
},
{
"cell_type": "code",
"execution_count": 108,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[1, 2, 3, 1, 2, 3, 1, 2, 3, 1, 2, 3, 1, 2, 3]"
]
},
"execution_count": 108,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"l = [1, 2, 3]\n",
"l * 5"
]
},
{
"cell_type": "code",
"execution_count": 109,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"'abcdabcdabcdabcdabcd'"
]
},
"execution_count": 109,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"5 * 'abcd'"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Building Lists of Lists"
]
},
{
"cell_type": "code",
"execution_count": 113,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[['_', '_', '_'], ['_', '_', '_'], ['_', '_', '_']]"
]
},
"execution_count": 113,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"board = [['_'] *3 for i in range(3)]\n",
"board"
]
},
{
"cell_type": "code",
"execution_count": 114,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[['_', '_', '_'], ['_', '_', 'X'], ['_', '_', '_']]"
]
},
"execution_count": 114,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"board[1][2] = 'X'\n",
"board"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Augmented Assignment with Sequences"
]
},
{
"cell_type": "code",
"execution_count": 116,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"4378448072"
]
},
"execution_count": 116,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"l = [1, 2, 3]\n",
"id(l)"
]
},
{
"cell_type": "code",
"execution_count": 117,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"4378448072"
]
},
"execution_count": 117,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"l *= 2\n",
"id(l) # same list"
]
},
{
"cell_type": "code",
"execution_count": 118,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"4371237552"
]
},
"execution_count": 118,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"t=(1,2,3)\n",
"id(t)"
]
},
{
"cell_type": "code",
"execution_count": 119,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"4370770824"
]
},
"execution_count": 119,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"t *= 2\n",
"id(t) # new tuple was created"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# A += Assignment Puzzler"
]
},
{
"cell_type": "code",
"execution_count": 122,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" 1 0 LOAD_NAME 0 (s)\n",
" 3 LOAD_NAME 1 (a)\n",
" 6 DUP_TOP_TWO\n",
" 7 BINARY_SUBSCR\n",
" 8 LOAD_NAME 2 (b)\n",
" 11 INPLACE_ADD\n",
" 12 ROT_THREE\n",
" 13 STORE_SUBSCR\n",
" 14 LOAD_CONST 0 (None)\n",
" 17 RETURN_VALUE\n"
]
}
],
"source": [
"import dis\n",
"dis.dis('s[a] += b')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"• Putting mutable items in tuples is not a good idea.\n",
"\n",
"• Augmented assignment is not an atomic operation—we just saw it throwing an exception after doing part of its job.\n",
"\n",
"• Inspecting Python bytecode is not too difficult, and is often helpful to see what is going on under the hood."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# list.sort and the sorted Built-In Function"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"`sorted()` makes a new list, doesn't touch the original.\n",
"\n",
"`sort()` changes list in place."
]
},
{
"cell_type": "code",
"execution_count": 126,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"['apple', 'banana', 'grape', 'raspberry']"
]
},
"execution_count": 126,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"fruits = ['grape', 'raspberry', 'apple', 'banana']\n",
"sorted(fruits)"
]
},
{
"cell_type": "code",
"execution_count": 127,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"['grape', 'raspberry', 'apple', 'banana']"
]
},
"execution_count": 127,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"fruits"
]
},
{
"cell_type": "code",
"execution_count": 128,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"['raspberry', 'grape', 'banana', 'apple']"
]
},
"execution_count": 128,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sorted(fruits, reverse=True)"
]
},
{
"cell_type": "code",
"execution_count": 129,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"['grape', 'apple', 'banana', 'raspberry']"
]
},
"execution_count": 129,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sorted(fruits, key=len)"
]
},
{
"cell_type": "code",
"execution_count": 130,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"['raspberry', 'banana', 'grape', 'apple']"
]
},
"execution_count": 130,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sorted(fruits, key=len, reverse=True)"
]
},
{
"cell_type": "code",
"execution_count": 131,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"['grape', 'raspberry', 'apple', 'banana']"
]
},
"execution_count": 131,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"fruits"
]
},
{
"cell_type": "code",
"execution_count": 134,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"fruits.sort() # note that sort() returns None"
]
},
{
"cell_type": "code",
"execution_count": 135,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"['apple', 'banana', 'grape', 'raspberry']"
]
},
"execution_count": 135,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"fruits"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Next: use `bisect` module to better search sorted lists."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Managing Ordered Sequences with bisect"
]
},
{
"cell_type": "code",
"execution_count": 163,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"4"
]
},
"execution_count": 163,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"breakpoints=[60, 70, 80, 90]\n",
"grades='FDCBA'\n",
"bisect.bisect(breakpoints, 99)"
]
},
{
"cell_type": "code",
"execution_count": 165,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0"
]
},
"execution_count": 165,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"bisect.bisect(breakpoints, 59)"
]
},
{
"cell_type": "code",
"execution_count": 166,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"2"
]
},
"execution_count": 166,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"bisect.bisect(breakpoints, 75)"
]
},
{
"cell_type": "code",
"execution_count": 155,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def grade(score, breakpoints=[60, 70, 80, 90], grades='FDCBA'):\n",
" i = bisect.bisect(breakpoints, score)\n",
" return grades[i]"
]
},
{
"cell_type": "code",
"execution_count": 157,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"['F', 'A', 'C', 'C', 'B', 'A', 'A']"
]
},
"execution_count": 157,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"[grade(score) for score in [33, 99, 77, 70, 89, 90, 100]]"
]
},
{
"cell_type": "code",
"execution_count": 158,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"'F'"
]
},
"execution_count": 158,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"grade(4)"
]
},
{
"cell_type": "code",
"execution_count": 159,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"'A'"
]
},
"execution_count": 159,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"grade(93)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Inserting with bisect.insort"
]
},
{
"cell_type": "code",
"execution_count": 169,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"10 -> [10]\n",
" 0 -> [0, 10]\n",
" 6 -> [0, 6, 10]\n",
" 8 -> [0, 6, 8, 10]\n",
" 7 -> [0, 6, 7, 8, 10]\n",
" 2 -> [0, 2, 6, 7, 8, 10]\n",
"10 -> [0, 2, 6, 7, 8, 10, 10]\n"
]
}
],
"source": [
"import bisect\n",
"import random\n",
"\n",
"SIZE = 7\n",
"\n",
"random.seed(1729)\n",
"\n",
"my_list = []\n",
"for i in range(SIZE):\n",
" new_item = random.randrange(SIZE*2)\n",
" bisect.insort(my_list, new_item)\n",
" print('%2d ->' % new_item, my_list)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Arrays"
]
},
{
"cell_type": "code",
"execution_count": 173,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0.051056611520245765"
]
},
"execution_count": 173,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from array import array\n",
"from random import random\n",
"\n",
"floats = array('d', (random() for i in range(10**7)))\n",
"floats[-1]"
]
},
{
"cell_type": "code",
"execution_count": 174,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"fp = open('floats.bin', 'wb')\n",
"floats.tofile(fp)\n",
"fp.close()"
]
},
{
"cell_type": "code",
"execution_count": 176,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 176,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"floats2 = array('d')\n",
"fp = open('floats.bin', 'rb')\n",
"floats2.fromfile(fp, 10**7)\n",
"fp.close()\n",
"floats2[-1]\n",
"floats2 == floats"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To sort an array, use `a = array.array(a.typecode, sorted(a))`. To keep it sorted while adding to it, use `bisect.insort`."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Memory Views"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The built-in memorview class is a shared-memory sequence type that lets you handle slices of arrays without copying bytes."
]
},
{
"cell_type": "code",
"execution_count": 184,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"5"
]
},
"execution_count": 184,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Changing the value of an array item by poking one of its bytes\n",
"import array\n",
"\n",
"numbers = array.array('h', [-2, -1, 0, 1, 2])\n",
"memv = memoryview(numbers)\n",
"len(memv)"
]
},
{
"cell_type": "code",
"execution_count": 185,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"-2"
]
},
"execution_count": 185,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"memv[0]"
]
},
{
"cell_type": "code",
"execution_count": 187,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[254, 255, 255, 255, 0, 0, 1, 0, 2, 0]"
]
},
"execution_count": 187,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"memv_oct = memv.cast('B') # ch type of array to unsigned char\n",
"memv_oct.tolist()"
]
},
{
"cell_type": "code",
"execution_count": 188,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"memv_oct[5] = 4"
]
},
{
"cell_type": "code",
"execution_count": 191,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array('h', [-2, -1, 1024, 1, 2])"
]
},
"execution_count": 191,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"numbers"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# NumPy and SciPy"
]
},
{
"cell_type": "code",
"execution_count": 194,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11])"
]
},
"execution_count": 194,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import numpy\n",
"\n",
"a = numpy.arange(12)\n",
"a"
]
},
{
"cell_type": "code",
"execution_count": 195,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"numpy.ndarray"
]
},
"execution_count": 195,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"type(a)"
]
},
{
"cell_type": "code",
"execution_count": 196,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(12,)"
]
},
"execution_count": 196,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a.shape"
]
},
{
"cell_type": "code",
"execution_count": 197,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 0, 1, 2, 3],\n",
" [ 4, 5, 6, 7],\n",
" [ 8, 9, 10, 11]])"
]
},
"execution_count": 197,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a.shape = 3, 4 # turn a into three units of 4\n",
"a"
]
},
{
"cell_type": "code",
"execution_count": 198,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([ 8, 9, 10, 11])"
]
},
"execution_count": 198,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a[2]"
]
},
{
"cell_type": "code",
"execution_count": 199,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"9"
]
},
"execution_count": 199,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a[2, 1]"
]
},
{
"cell_type": "code",
"execution_count": 200,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([1, 5, 9])"
]
},
"execution_count": 200,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a[:, 1]"
]
},
{
"cell_type": "code",
"execution_count": 201,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 0, 4, 8],\n",
" [ 1, 5, 9],\n",
" [ 2, 6, 10],\n",
" [ 3, 7, 11]])"
]
},
"execution_count": 201,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a.transpose()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Loading, saving, and operating:\n",
"\n",
"Use `numpy.loadtxt()`"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Deques and Other Queues"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Inserting and removing from the left of a list (the 0-index end) is costly. `collections.deque` is a thread-safe double-ended queue designed for fast inserting and removing from both ends."
]
},
{
"cell_type": "code",
"execution_count": 204,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"deque([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])"
]
},
"execution_count": 204,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from collections import deque\n",
"\n",
"dq = deque(range(10), maxlen=10)\n",
"dq"
]
},
{
"cell_type": "code",
"execution_count": 205,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"deque([7, 8, 9, 0, 1, 2, 3, 4, 5, 6])"
]
},
"execution_count": 205,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dq.rotate(3)\n",
"dq"
]
},
{
"cell_type": "code",
"execution_count": 206,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"deque([1, 2, 3, 4, 5, 6, 7, 8, 9, 0])"
]
},
"execution_count": 206,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dq.rotate(-4)\n",
"dq"
]
},
{
"cell_type": "code",
"execution_count": 207,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"deque([-1, 1, 2, 3, 4, 5, 6, 7, 8, 9])"
]
},
"execution_count": 207,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dq.appendleft(-1)\n",
"dq"
]
},
{
"cell_type": "code",
"execution_count": 208,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"deque([3, 4, 5, 6, 7, 8, 9, 11, 22, 33])"
]
},
"execution_count": 208,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dq.extend([11, 22, 33])\n",
"dq"
]
},
{
"cell_type": "code",
"execution_count": 210,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"deque([40, 30, 20, 10, 40, 30, 20, 10, 3, 4])"
]
},
"execution_count": 210,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dq.extendleft([10, 20, 30, 40])\n",
"dq"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"a hidden cost: removing items from the middle of a deque is not as fast"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"On using single type in list: \"we put items in a list to process them later, which implies that all items should support at least some operation in common\"."
]
},
{
"cell_type": "code",
"execution_count": 211,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[0, '1', 5, 6, '9', 14, 19, '23', 28, '28']"
]
},
"execution_count": 211,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# but a workaround with `key`\n",
"l = [28, 14, '28', 5, '9', '1', 0, 6, '23', 19]\n",
"sorted(l, key=int)"
]
},
{
"cell_type": "code",
"execution_count": 212,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[0, '1', 14, 19, '23', 28, '28', 5, 6, '9']"
]
},
"execution_count": 212,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sorted(l, key=str)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.4.3"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| mit |
Bio204-class/bio204-notebooks | Introduction-to-Simulation.ipynb | 1 | 211731 | {
"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": [
"import numpy as np\n",
"import pandas as pd\n",
"import matplotlib\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"matplotlib.style.use(\"bmh\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## A brief note about pseudo-random numbers\n",
"\n",
"When carrying out simulations, it is typical to use random number generators. Most computers can not generate true random numbers -- instead we use algorithms that approximate the generation of random numbers (pseudo-random number generators). One important difference between a true random number generator and a pseudo-random number generator is that a series of pseudo-random numbers can be regenerated if you know the \"seed\" value that initialized the algorithm. We can specifically set this seed value, so that we can guarantee that two different people evaluating this notebook get the same results, even though we're using (pseudo)random numbers in our simulation."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# set the seed for the pseudo-random number generator\n",
"# the seed is any 32 bit integer\n",
"# different seeds will generate different results for the \n",
"# simulations that follow\n",
"np.random.seed(20160208) "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Generating a population to sample from\n",
"\n",
"We'll start by simulating our \"population of interest\" -- i.e. the population we want to make inferences about. We'll assume that our variable of interest (e.g. circulating stress hormone levels) is normally distributed with a mean of 10 nM and a standard deviation of 1 nM."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"popn = np.random.normal(loc=10, scale=1, size=6500)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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rGGHFkjOGrQuIJ6fXMMKJJWcrom8wnHPOZSP6BsOPwwgrlpyxHN8QS04/DiOc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\nQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x108800f28>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.hist(popn,bins=50)\n",
"plt.xlabel(\"Glucorticoid concentration (nM)\")\n",
"plt.ylabel(\"Frequency\")\n",
"pass"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Mean glucorticoid concentration: 10.0257167668\n",
"Standard deviation of glucocorticoid concentration: 1.00065331822\n"
]
}
],
"source": [
"print(\"Mean glucorticoid concentration:\", np.mean(popn))\n",
"print(\"Standard deviation of glucocorticoid concentration:\", np.std(popn))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Take a random sample of the population of interest\n",
"\n",
"We'll use the [`np.random.choice`](http://docs.scipy.org/doc/numpy-1.10.0/reference/generated/numpy.random.choice.html) function to take a sample from our population of interest."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"sample1 = np.random.choice(popn, size=25)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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PA+flV78GuDQykHoIcZQzVlXmkpUzVlVyRujkHcL/BT7g7icAz+fX3Q7MDU8lIiKF66Qg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\nBID/D/q4r4Z/kz11AAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x108a8c358>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.hist(sample1)\n",
"plt.xlabel(\"Glucorticoid concentration (nM)\")\n",
"plt.ylabel(\"Frequency\")\n",
"pass"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(10.114658118125904, 0.95536108734474512)"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.mean(sample1), np.std(sample1,ddof=1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Take a second random sample of size 25"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"sample2 = np.random.choice(popn, size=25)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(9.8548319370046187, 0.76063274167275385)"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.mean(sample2), np.std(sample2,ddof=1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Compare the first and second samples"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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5FHhzPnUwHLgIqD3cZe2X7Z6l9V+2ayQnFD+eFUe7+2YAd98EHN3DOmX4EmMj\nOQEcuMfMHjKzD7csXXPKMJ6NKst4Xgn8uIfryzaW9XJCP8ay5V9MM7MxZBV1IvAicKeZXebu36ha\n7RGgzd13mdnbgO8BJ7Uyp7s/YWZfAO4BXgKWAY1/Q65FGsxZ+Hj2IpVPNdTLeba7bzSz15P953s8\nf1Un/VP4eJrZZ4C9NX+TSqeBnE2PZRFTRucDa9x9az7F8R3grOoV3P0ld9+VX/4xcKiZva7VQd39\nVnef4+7nAi8AT9Ws8iwHvxqfkF/XUn3lLMt45jZXptXM7FigpyPGPQu0VS0XMa6N5MTdN+b/bgG+\nSzalUDZlGM+GFD2eZvZBsnfZl9VZpRRj2UDOfo1lEQWhG5hrZsPMzMi+qHbQ9xGq5+HN7HSyj8du\nbW1MyCsrZtYG/DZQW4m/D1yerzOXbPprc0tD0nfOgsfTOHje+PvAB/PLVwD/3sN9HgKmmtlEyz6B\n9p78foOp6ZxmNtzMjswvjwDeSjaFN5hqc9be1pMyjGftba+9svXjeVBGM7sQuAa42N3rHcmw8LFs\nJGe/x3KwuuN9dM6vJSsCK4B/JuvW/z7wv/Lb/yAPvwz4OXBGQTnvq8pxbn7dgZz58pfIPnWwnF4+\nPVFkzqLGk6wwbQD2kL0Q+BAwFriX7FvrdwNj8nWPA35Ydd8L83VWAp8qY05gEtmnTJYBXQXlfBfZ\nnPbLZEcA+HFJx7PPnK0czzoZVwLrgI785+9LOpZ95uzvWOqLaSIiAugUmiIiklNBEBERQAVBRERy\nKggiIgKoIIiISE4FQUREABWEX1tmdquZ/UUJchxvZtvzLyEOZDs7zOyEOrddYWb/NZDt/zozs3lm\nNmgnozKzJWY2I3ibXzSzj0RuU/qmgpAoM3uPmS01s5fMbJOZ3W9mV5Ug11ozm19Zdvdn3H2UD/AL\nL+4+0t2f7m2VgWy/DCw7b8Uzfa/Z53ZeNbPJlWV3X+LuJw90u3X29Q5gu7sv73NlDhyjf3ftoVPy\nY/a/mn/bHuCLwKfzIyJLi6ggJMjMPg78Ddkx+49x92OBjwBnmdmhBWU6pIj9/pox+ihsDY5zK4vj\nR4CvNbG+A2uB91auMLNTgCOoyu3ZEWYfBy6OiSmNUEFITH52pOuAq9z9u+6+E8Ddl7v7Bzw7Rnvt\nfV4zpVL9KjI/rtSN+au3bWZ2n5kdnt92sWVnutpqZovMbFrVNtaa2SfNbDnwkpndQXbgrx/k00Sf\nyI/58qqWJ1AyAAAGvUlEQVSZDcnvM9bMbjGzZ83sl2b2nartfdiys889b2bfM7Pj6uR9nZl938xe\nNLOlwJQ+xmyemf0sf2zrzKxy/KlRZna7mT2XP5bP1I6Zmd2QP/bV+TFkKrf39jjekb/i3ZZPp0yv\nGbOPW3Ymq21m9i9mdphlhy6/CxifT49tN7NjzexaM/s3M/uamb0AXGFmbzKzn+f3f9bM/q7yStrM\nFpMVlhX5Nn639p2HmU0zs5/m9+8ys3dW3XarmX3JzH6Y3/9+M5tUZ1wPBeYDi6uuu9bMvmVmt+X3\n77LXniHta2THh6q4gl+de6LaYuDtPe1bBslgHodDP4NybJMLgFfIz5jUy3q3An+RX74CuK/m9v3k\nZ1YDvkx2Jqtjyf6YzCU7vtRJZIfUnk92UqNryI6jMjS/31qyY6mMBw6vuu68qv1MzPdVOcPTj4Bv\nAqPybb45v34+sAWYke/7b4HFdfL+S/4zjOyMcetrH1/V/dqA7cDv5fsbC5ya33Y72VEgh+c5nwQ+\nVDVme8iON29kr4SfrdpuvccxC9gMzMnv94F8TA6tGp+lwDHAGOAxfnXMqXOA7pr81+Y53pkvH57v\n4/R8+23AL4CPVd3nVWBS1fKB7ZId8n4l2VnghgLn5eNzYtXvzRbgNLIXjF8HvlFnbN8I7Ogh7y6y\n31MDPg/cX3X72vy5fpzsLGpDyI7Rc3yeu61q3d8GHi76/9x/px+9Q0jPOOB5/9UZk6h69bvLzOY1\nuB3L72tkB8z6mLtv8sxSz95p/B7ZwbIWeXao8i+SvbWvPlz5Anff4AcfdbHe0SyPI/tD8fvuvt3d\n97t75Z3LZcBXPXunsxf4P2Qn9anMKVfyDiE7I9xn3X23u/+Cnl9dVlwG3OPu/5rvb5u7r8i3826y\ng37tcvd1ZCdu+kDVfde5+y2e/XW6DTjOzI627HDY9R7Hh4F/cPeH87H8Gtkf9Lk1Y7bZ3V8AfkB2\nisTe3O/uPwDw7Mx4y9z9wXz73cBXyP7oHzTcdbZ1JjDC3b/g7vvc/afAD6mawgG+6+6P5L9jd/SS\nbwywo4frl7j7T/Jx+xpwag/rVN4lvIWsOPR01sQd+T6kRVQQ0vNLYFxlCgbA3c9297H5bc0+p+PI\nXnWu6eG28WRHVazsx8mOWFl9hqj1TexrArDV3bc3sK+dZI+n9mxUryd7RV6933XUdzywuofrx5G9\nQu6u2U71/jZV5Xk5v3hkvs16j2Mi8PF8mmmrmW0je9zjq9apPkT6rnybvTmo0WxmJ5rZD8xsYz6N\n9Ln88TTiuNrt0cvj7iPfNmBkD9fX3n9Y9e9r7utkxfqD5Keh7cFIsvN7SIuoIKTnfrJXnM2cx3Un\n2bQIcOCELxXPA7vpeR5+A9kfuGrHc/Af49oGZm8NzWeA11nWB+l1X5Ydw/0oXltwtgD7OPjERG3U\n9wwwtYfrnwf2cvDjm0hjJzvp7XE8A3zO3V+X/4x19yPd/VsNbLfe2NVefxPZq+op7j4G+AyNnzN7\nA689xWob/TvJyyqyN5nH9blmjfydzVrgbWQnyerJyWSHlZcWUUFIjLu/CPwF8Pdm9j/M7EjLzKTq\nj36N5cBvmtmpljWLryX/I5O/6r8V+GszO87MhpjZ3Lxh+K/A283sPDMbamafICse9/cScRMwueY6\ny/e1iez8r39vZmPybb45X+ebwIeqMn4eWOruB72azacxvgP8uZkdYdn5oasblLXuANrN7FIzO8Sy\nhvSMfDv/CnwuH8OJwB/TwCdm+ngc/wR8xLITEWFmI8zsorzA9WUzcFSdQlNtJNlHPXdZ1uSv/bhx\nT89BxQPALss+DDDUzM4F3kE2/k3Jp/bu5bXTVbXqFasrgflV775qnUP98wXLIFBBSJC73wD8CfBJ\nsv/8m8heNX6S7AQ4teuvJCsiC8lOr1n7Ja5PkJ1E4yGyaZrryZrATwHvJzsJ0BayT3y80933VTbd\nQ7zrgc/m0yV/0sN6HyB7hf8E2R/Aq/OMC4HPkv2xf5bsBB/vqX4YVZf/N9kfxY3ALflPj/KCclH+\nGLeSnTCkMqf9MbIpjTVkJxn6urvfWm9bDT6OR8j6CF8ys61k431FnW3UZn2S7A/zmnz8jq2z6ieA\n95nZduAfyRrs1f4cuD3fxqU1+9gLvJNsTJ4ne24/kP+O9Jqvjq+QnzWwF97TZXdf6+4dPd2Wv+s4\nmez839IiOkGOiAyIZR9p/kNv8MtpDW7zi8Aqd/+HqG1K31QQREQE0JSRiIjkVBBERARQQRARkZwK\ngoiIACoIIiKSU0EQERFABUFERHIqCCIiAsD/B3yOWnflBX5jAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x108e66630>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.hist(sample1)\n",
"plt.hist(sample2,alpha=0.5)\n",
"plt.xlabel(\"Glucorticoid concentration (nM)\")\n",
"plt.ylabel(\"Frequency\")\n",
"pass"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
" ## Generate a large number of samples of size 25 \n",
"\n",
"Every time we take a random sample from our population of interest we'll get a different estimate of the mean and standard deviation (or whatever other statistics we're interested in). To explore how well random samples of size 25 perform, generally, in terms of estimating the mean and standard deviation of the population of interest we need a large number of such samples. \n",
"\n",
"It's tedious to take one sample at a time, so we'll generate 100 samples of size 25, and calculate the mean and standard deviation for each of those samples (storing the means and standard deviations in lists)."
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"means25 = []\n",
"std25 = []\n",
"for i in range(100):\n",
" s = np.random.choice(popn, size=25)\n",
" means25.append(np.mean(s))\n",
" std25.append(np.std(s,ddof=1))"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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qqA6G66abbuKSSy7hj3/8I0899RS77LILn/vc2hB4u+66K/feey/PPPMMhx9+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\ncDCwA3CMpO3yy7355ps9La2mtL3t+tM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"text/plain": [
"<matplotlib.figure.Figure at 0x10899e358>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.hist(means25,bins=15)\n",
"plt.xlabel(\"Mean glucocorticoid concentration\")\n",
"plt.ylabel(\"Frequency\")\n",
"plt.title(\"Distribution of estimates of the\\n mean glucocorticoid concentration\\n for 100 samples of size 25\")\n",
"plt.vlines(np.mean(popn), 0, 18, linestyle='dashed', color='red',label=\"True Mean\")\n",
"plt.legend(loc=\"upper right\")\n",
"pass"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Relative Frequency Histogram\n",
"\n",
"A relative frequency histogram is like a frequency histogram, except the bin heights are given in fractions of the total sample size (relative frequency) rather than absolute frequency. This is equivalent to adding the constraint that the total height of all the bars in the histogram will add to 1.0."
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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GEk7s5/CVP6zYaxCx58+DjlBEJHd5df8zoHcP9uqzUw6JpB6arkFRX17hxN6X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\nKCIikgs1KCIikov/A3JWwi43RJqTAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10919d6d8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Relative Frequency Histogram\n",
"plt.hist(means25, bins=15, weights=np.ones_like(means25) * (1.0/len(means25)))\n",
"plt.xlabel(\"mean glucocorticoid concentration\")\n",
"plt.ylabel(\"Relative Frequency\")\n",
"plt.vlines(np.mean(popn), 0, 0.20, linestyle='dashed', color='red',label=\"True Mean\")\n",
"plt.legend(loc=\"upper right\")\n",
"pass"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Density histogram\n",
"If instead of constraining the total height of the bars, we constrain the **total area** of the bars to sum to one, we call this a density histogram. When comparing histograms based on different numbers of samples, with different bin width, etc. you should usually use the density histogram.\n",
"\n",
"The argument `normed=True` to the `pyplot.hist` function will this function calculate a density histogram instead of the default frequency histogram."
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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bgFFmtl3/BT333HODnXVQrX1e+VNS/nQiZ4f4+RcuXFjIclpZOMYBS8ruP5Y/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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x108967e10>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.hist(means25,bins=15,normed=True)\n",
"plt.xlabel(\"Mean glucocorticoid concentration\")\n",
"plt.ylabel(\"Density\")\n",
"plt.vlines(np.mean(popn), 0, 2.5, linestyle='dashed', color='red',label=\"True Mean\")\n",
"plt.legend(loc=\"upper right\")\n",
"pass"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## How does the spread of our estimates of the mean change as sample size increases?\n",
"\n",
"What happens as we increase the size of our samples? Let's draw 100 random samples of size 50, 100, and 200 observations to compare."
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"means50 = []\n",
"std50 = []\n",
"for i in range(100):\n",
" s = np.random.choice(popn, size=50)\n",
" means50.append(np.mean(s))\n",
" std50.append(np.std(s,ddof=1))\n",
" \n",
"means100 = []\n",
"std100 = []\n",
"for i in range(100):\n",
" s = np.random.choice(popn, size=100)\n",
" means100.append(np.mean(s))\n",
" std100.append(np.std(s,ddof=1))\n",
" \n",
"means200 = []\n",
"std200 = []\n",
"for i in range(100):\n",
" s = np.random.choice(popn, size=200)\n",
" means200.append(np.mean(s))\n",
" std200.append(np.std(s,ddof=1)) "
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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Lizl4NHk9itaX3aE7XbOcKWSvWLGCRx55hDVr1pCWlsbYsWN59NFH6d69e2gZHULT4rmE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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x108a59908>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# the label arguments get used when we create a legend\n",
"plt.hist(means25, normed=True, alpha=0.75, histtype=\"stepfilled\", label=\"n=25\")\n",
"plt.hist(means50, normed=True, alpha=0.75, histtype=\"stepfilled\", label=\"n=50\")\n",
"plt.hist(means100, normed=True, alpha=0.75, histtype=\"stepfilled\", label=\"n=100\")\n",
"plt.hist(means200, normed=True, alpha=0.75, histtype=\"stepfilled\", label=\"n=200\")\n",
"plt.xlabel(\"Mean glucocorticoid concentration\")\n",
"plt.ylabel(\"Density\")\n",
"plt.vlines(np.mean(popn), 0, 7, linestyle='dashed', color='black',label=\"True Mean\")\n",
"plt.legend()\n",
"pass"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Standard Error of the Mean\n",
"\n",
"We see from the graph above that our estimates of the mean cluster more tightly about the true mean as our sample size increases. Let's quantify that by calculating the standard deviation of our mean estimates as a function of sample size.\n",
"\n",
"The standard deviation of the sampling distribution of a statistic of interest is called the \"Standard Error\" of that statistic. Here, through simulation, we are estimating the \"Standard Error of the Mean\"."
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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FpJslrZH0/mJ93303RsnFycX66r4bETHlH6SiuAnYD9gZuAF4YafjanMObgVm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\nTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x108a8c630>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sm25 = np.std(means25,ddof=1)\n",
"sm50 = np.std(means50,ddof=1)\n",
"sm100 = np.std(means100,ddof=1)\n",
"sm200 = np.std(means200, ddof=1)\n",
"\n",
"x = [25,50,100,200]\n",
"y = [sm25,sm50,sm100,sm200]\n",
"plt.scatter(x,y)\n",
"plt.xlabel(\"Sample size\")\n",
"plt.ylabel(\"Std Dev of Mean Estimates\")\n",
"pass"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You can show mathematically for normally distributed data, that the expected Standard Error of the Mean as a function of sample size is:\n",
"\n",
"$$\n",
"\\mbox{Standard Error of Mean} = \\frac{\\sigma}{\\sqrt{n}}\n",
"$$\n",
"\n",
"where $\\sigma$ is the population standard deviation, and $n$ is the sample size.\n",
"\n",
"Let's compare that theoretical expectation to our simulated estimates."
]
},
{
"cell_type": "code",
"execution_count": 74,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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YrJWcfjrq8ThF+D17oH79WDfJGGPiVqhHJjcBJf7ntwI9gHOAq6tcI0ZqomYC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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10868cba8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"x = [25,50,100,200]\n",
"y = [sm25,sm50,sm100,sm200]\n",
"theory = [np.std(popn)/np.sqrt(i) for i in range(10,250)]\n",
"plt.scatter(x,y, label=\"Simulation estimates\")\n",
"plt.plot(range(10,250), theory, color='red', label=\"Theoretical expectation\")\n",
"plt.xlabel(\"Sample size\")\n",
"plt.ylabel(\"Std Error of Mean\")\n",
"plt.legend()\n",
"plt.xlim(0,300)\n",
"pass\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Standard Errors of the Standard Deviation\n",
"\n",
"Above we explored how the spread in our estimates of the mean changed with sample size. We can similarly explore how our estimates of the standard deviation of the population change as we vary our sample size. "
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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u6Py7q0dD8PKQ/nY//eXOYKXTxFVeqen1azfxzNPbOL3hzaHrE2Du7Ebg0PlN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\nNKAdOClYfwvwIG72synAavo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"text/plain": [
"<matplotlib.figure.Figure at 0x108a8cf60>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# the label arguments get used when we create a legend\n",
"plt.hist(std25, normed=True, alpha=0.75, histtype=\"stepfilled\", label=\"n=25\")\n",
"plt.hist(std50, normed=True, alpha=0.75, histtype=\"stepfilled\", label=\"n=50\")\n",
"plt.hist(std100, normed=True, alpha=0.75, histtype=\"stepfilled\", label=\"n=100\")\n",
"plt.hist(std200, normed=True, alpha=0.75, histtype=\"stepfilled\", label=\"n=200\")\n",
"plt.xlabel(\"Standard Deviation of Glucocorticoid Concentration\")\n",
"plt.ylabel(\"Density\")\n",
"plt.vlines(np.std(popn), 0, 9, linestyle='dashed', color='black',label=\"True Standard Deviation\")\n",
"#plt.legend()\n",
"pass"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You can show mathematically for normally distributed data, that the expected Standard Error of the Standard Deviation is approximately\n",
"\n",
"$$\n",
"\\mbox{Standard Error of Standard Deviation} \\approx \\frac{\\sigma}{\\sqrt{2(n-1)}}\n",
"$$\n",
"\n",
"where $\\sigma$ is the population standard deviation, and $n$ is the sample size.\n",
"\n",
"Let's compare that theoretical expectation to our simulated estimates."
]
},
{
"cell_type": "code",
"execution_count": 75,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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vc7f44pvFV7tVejWXiDwcyA5U9YHQNSc2FQ8ZgrrdJCxYgOzYgTZsGO0mGWNM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\nrkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x108431cc0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"x = [25,50,100,200]\n",
"y = [ss25,ss50,ss100,ss200]\n",
"plt.scatter(x,y, label=\"Simulation estimates\")\n",
"plt.xlabel(\"Sample size\")\n",
"plt.ylabel(\"Std Error of Std Dev\")\n",
"\n",
"theory = [np.std(popn)/(np.sqrt(2.0*(i-1))) for i in range(10,250)]\n",
"plt.plot(range(10,250), theory, color='red', label=\"Theoretical expectation\")\n",
"\n",
"plt.xlim(0,300)\n",
"plt.legend()\n",
"pass\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": "3.5.1"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| cc0-1.0 |
tensorflow/workshops | tfx_labs/Lab_6_Model_Analysis.ipynb | 1 | 27204 | {
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "TFX Lab 6 – TensorFlow Model Analysis",
"provenance": [],
"private_outputs": true,
"collapsed_sections": [
"tghWegsjhpkt"
]
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
}
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "tghWegsjhpkt"
},
"source": [
"##### Copyright © 2019 The TensorFlow Authors."
]
},
{
"cell_type": "code",
"metadata": {
"colab_type": "code",
"id": "rSGJWC5biBiG",
"colab": {}
},
"source": [
"#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n",
"# you may not use this file except in compliance with the License.\n",
"# You may obtain a copy of the License at\n",
"#\n",
"# https://www.apache.org/licenses/LICENSE-2.0\n",
"#\n",
"# Unless required by applicable law or agreed to in writing, software\n",
"# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
"# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
"# See the License for the specific language governing permissions and\n",
"# limitations under the License."
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "YuSYVbwEYNHw"
},
"source": [
"# TensorFlow Model Analysis\n",
"***An Example of a Key TFX Library***\n",
"\n",
"This example colab notebook illustrates how TensorFlow Model Analysis (TFMA) can be used to investigate and visualize the characteristics of a dataset and the performance of a model. We'll use a model that we trained previously, and now you get to play with the results!\n",
"\n",
"The model we trained was for the [Chicago Taxi Example](https://github.com/tensorflow/model-analysis/tree/master/examples/chicago_taxi), which uses the [Taxi Trips dataset](https://data.cityofchicago.org/Transportation/Taxi-Trips/wrvz-psew) released by the City of Chicago.\n",
"\n",
"Note: This site provides applications using data that has been modified for use from its original source, www.cityofchicago.org, the official website of the City of Chicago. The City of Chicago makes no claims as to the content, accuracy, timeliness, or completeness of any of the data provided at this site. The data provided at this site is subject to change at any time. It is understood that the data provided at this site is being used at one’s own risk.\n",
"\n",
"[Read more](https://cloud.google.com/bigquery/public-data/chicago-taxi) about the dataset in [Google BigQuery](https://cloud.google.com/bigquery/). Explore the full dataset in the [BigQuery UI](https://bigquery.cloud.google.com/dataset/bigquery-public-data:chicago_taxi_trips).\n",
"\n",
"Key Point: As a modeler and developer, think about how this data is used and the potential benefits and harm a model's predictions can cause. A model like this could reinforce societal biases and disparities. Is a feature relevant to the problem you want to solve or will it introduce bias? For more information, read about <a target='_blank' href='https://developers.google.com/machine-learning/fairness-overview/'>ML fairness</a>.\n",
"\n",
"Key Point: In order to understand `TFMA` and how it works with Apache Beam, you'll need to know a little bit about Apache Beam itself. The <a target='_blank' href='https://beam.apache.org/documentation/programming-guide/'>Beam Programming Guide</a> is a great place to start.\n",
"\n",
"The columns in the dataset are:\n",
"<table>\n",
"<tr><td>pickup_community_area</td><td>fare</td><td>trip_start_month</td></tr>\n",
"\n",
"<tr><td>trip_start_hour</td><td>trip_start_day</td><td>trip_start_timestamp</td></tr>\n",
"<tr><td>pickup_latitude</td><td>pickup_longitude</td><td>dropoff_latitude</td></tr>\n",
"<tr><td>dropoff_longitude</td><td>trip_miles</td><td>pickup_census_tract</td></tr>\n",
"<tr><td>dropoff_census_tract</td><td>payment_type</td><td>company</td></tr>\n",
"<tr><td>trip_seconds</td><td>dropoff_community_area</td><td>tips</td></tr>\n",
"</table>"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "q7-ouHFnWAsu"
},
"source": [
"## Install Jupyter Extensions\n",
"Note: If running TFMA in a local Jupyter notebook, then these Jupyter extensions must be installed in the environment before running Jupyter.\n",
"\n",
"```bash\n",
"jupyter nbextension enable --py widgetsnbextension\n",
"jupyter nbextension install --py --symlink tensorflow_model_analysis\n",
"jupyter nbextension enable --py tensorflow_model_analysis\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "LZj-impiAD_l"
},
"source": [
"## Setup\n",
"First, we install the necessary packages, download data, import modules and set up paths.\n",
"\n",
"### Install TensorFlow, TensorFlow Model Analysis (TFMA) and TensorFlow Data Validation (TFDV)"
]
},
{
"cell_type": "code",
"metadata": {
"colab_type": "code",
"id": "SA2E343NAMRF",
"colab": {}
},
"source": [
"!pip install -q -U \\\n",
" tensorflow==2.0.0 \\\n",
" tfx==0.15.0rc0"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "Fa3d_-FfGW4y",
"colab_type": "text"
},
"source": [
"### Import packages\n",
"We import necessary packages, including standard TFX component classes."
]
},
{
"cell_type": "code",
"metadata": {
"id": "lyVdkfDQVf3x",
"colab_type": "code",
"colab": {}
},
"source": [
"import csv\n",
"import io\n",
"import os\n",
"import requests\n",
"import tempfile\n",
"import zipfile\n",
"\n",
"from google.protobuf import text_format\n",
"\n",
"import tensorflow as tf\n",
"\n",
"import tensorflow_data_validation as tfdv\n",
"import tensorflow_model_analysis as tfma\n",
"from tensorflow_metadata.proto.v0 import schema_pb2"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "NhVqFXPDR6rq",
"colab_type": "code",
"colab": {}
},
"source": [
"tf.__version__"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "CR9ylx5YtyJq",
"colab_type": "code",
"colab": {}
},
"source": [
"tfma.version.VERSION_STRING"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "RptgLn2RYuK3"
},
"source": [
"## Load The Files\n",
"We'll download a zip file that has everything we need. That includes:\n",
"\n",
"* Training and evaluation datasets\n",
"* Data schema\n",
"* Training results as EvalSavedModels\n",
"\n",
"Note: We are downloading with HTTPS from a Google Cloud server."
]
},
{
"cell_type": "code",
"metadata": {
"id": "3jeaUrTpWdJy",
"colab_type": "code",
"colab": {}
},
"source": [
"# Download the zip file from GCP and unzip it\n",
"BASE_DIR = tempfile.mkdtemp()\n",
"TFMA_DIR = os.path.join(BASE_DIR, 'eval_saved_models-2.0')\n",
"DATA_DIR = os.path.join(TFMA_DIR, 'data')\n",
"OUTPUT_DIR = os.path.join(TFMA_DIR, 'output')\n",
"SCHEMA = os.path.join(TFMA_DIR, 'schema.pbtxt')\n",
"\n",
"response = requests.get('https://storage.googleapis.com/tfx-colab-datasets/eval_saved_models-2.0.zip', stream=True)\n",
"zipfile.ZipFile(io.BytesIO(response.content)).extractall(BASE_DIR)\n",
"\n",
"print(\"Here's what we downloaded:\")\n",
"!cd {TFMA_DIR} && find ."
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "_xa7ZDV1MycO"
},
"source": [
"## Parse the Schema\n",
"\n",
"Among the things we downloaded was a schema for our data that was created by [TensorFlow Data Validation](https://www.tensorflow.org/tfx/data_validation/). Let's parse that now so that we can use it with TFMA."
]
},
{
"cell_type": "code",
"metadata": {
"colab_type": "code",
"id": "uW5eB4TPcwFw",
"colab": {}
},
"source": [
"schema = schema_pb2.Schema()\n",
"contents = tf.io.read_file(SCHEMA).numpy()\n",
"schema = text_format.Parse(contents, schema)\n",
"\n",
"tfdv.display_schema(schema)"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "UP3yuJxfNXRL"
},
"source": [
"## Use the Schema to Create TFRecords\n",
"\n",
"We need to give TFMA access to our dataset, so let's create a TFRecords file. We can use our schema to create it, since it gives us the correct type for each feature."
]
},
{
"cell_type": "code",
"metadata": {
"colab_type": "code",
"id": "8-wud3fPczl6",
"colab": {}
},
"source": [
"datafile = os.path.join(DATA_DIR, 'eval', 'data.csv')\n",
"reader = csv.DictReader(open(datafile))\n",
"examples = []\n",
"for line in reader:\n",
" example = tf.train.Example()\n",
" for feature in schema.feature:\n",
" key = feature.name\n",
" if len(line[key]) > 0:\n",
" if feature.type == schema_pb2.FLOAT:\n",
" example.features.feature[key].float_list.value[:] = [float(line[key])]\n",
" elif feature.type == schema_pb2.INT:\n",
" example.features.feature[key].int64_list.value[:] = [int(line[key])]\n",
" elif feature.type == schema_pb2.BYTES:\n",
" example.features.feature[key].bytes_list.value[:] = [line[key].encode('utf8')]\n",
" else:\n",
" if feature.type == schema_pb2.FLOAT:\n",
" example.features.feature[key].float_list.value[:] = []\n",
" elif feature.type == schema_pb2.INT:\n",
" example.features.feature[key].int64_list.value[:] = []\n",
" elif feature.type == schema_pb2.BYTES:\n",
" example.features.feature[key].bytes_list.value[:] = []\n",
" examples.append(example)\n",
"\n",
"TFRecord_file = os.path.join(BASE_DIR, 'train_data.rio')\n",
"with tf.io.TFRecordWriter(TFRecord_file) as writer:\n",
" for example in examples:\n",
" writer.write(example.SerializeToString())\n",
" writer.flush()\n",
" writer.close()\n",
"\n",
"!ls {TFRecord_file}"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "Qm5luW1EN7g7"
},
"source": [
"## Run TFMA and Render Metrics\n",
"\n",
"Now we're ready to create a function that we'll use to run TFMA and render metrics. It requires an [`EvalSavedModel`](https://www.tensorflow.org/api_docs/python/tf/saved_model), a list of [`SliceSpecs`](https://www.tensorflow.org/tfx/model_analysis/api_docs/python/tfma/SingleSliceSpec), and an index into the SliceSpec list. It will create an EvalResult using [`tfma.run_model_analysis`](https://www.tensorflow.org/tfx/model_analysis/api_docs/python/tfma/run_model_analysis), and use it to create a `SlicingMetricsViewer` using [`tfma.view.render_slicing_metrics`](https://www.tensorflow.org/tfx/model_analysis/api_docs/python/tfma/view/render_slicing_metrics), which will render a visualization of our dataset using the slice we created."
]
},
{
"cell_type": "code",
"metadata": {
"colab_type": "code",
"id": "H2wANNF_2dCR",
"colab": {}
},
"source": [
"def run_and_render(eval_model=None, slice_list=None, slice_idx=0):\n",
" \"\"\"Runs the model analysis and renders the slicing metrics\n",
"\n",
" Args:\n",
" eval_model: An instance of tf.saved_model saved with evaluation data\n",
" slice_list: A list of tfma.slicer.SingleSliceSpec giving the slices\n",
" slice_idx: An integer index into slice_list specifying the slice to use\n",
"\n",
" Returns:\n",
" A SlicingMetricsViewer object if in Jupyter notebook; None if in Colab.\n",
" \"\"\"\n",
" eval_result = tfma.run_model_analysis(eval_shared_model=eval_model,\n",
" data_location=TFRecord_file,\n",
" file_format='tfrecords',\n",
" slice_spec=slice_list,\n",
" output_path='sample_data',\n",
" extractors=None)\n",
" return tfma.view.render_slicing_metrics(eval_result, slicing_spec=slice_list[slice_idx] if slice_list else None)"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "cSl9qyTCbBKR"
},
"source": [
"### Slicing and Dicing\n",
"\n",
"We previously trained a model, and now we've loaded the results. Let's take a look at our visualizations, starting with using TFMA to slice along particular features. But first we need to read in the EvalSavedModel from one of our previous training runs.\n",
"\n",
"* To define the slice you want to visualize you create a [`tfma.slicer.SingleSliceSpec`](https://www.tensorflow.org/tfx/model_analysis/api_docs/python/tfma/SingleSliceSpec)\n",
"\n",
"* To use [`tfma.view.render_slicing_metrics`](https://www.tensorflow.org/tfx/model_analysis/api_docs/python/tfma/view/render_slicing_metrics) you can either use the name of the column (by setting `slicing_column`) or provide a [`tfma.slicer.SingleSliceSpec`](https://www.tensorflow.org/tfx/model_analysis/api_docs/python/tfma/SingleSliceSpec) (by setting `slicing_spec`)\n",
"* If neither is provided, the overview will be displayed\n",
"\n",
"Plots are interactive:\n",
"\n",
"* Click and drag to pan\n",
"* Scroll to zoom\n",
"* Right click to reset the view\n",
"\n",
"Simply hover over the desired data point to see more details. Select from four different types of plots using the selections at the bottom.\n",
"\n",
"For example, we'll be setting `slicing_column` to look at the `trip_start_hour` feature in our `SliceSpec`."
]
},
{
"cell_type": "code",
"metadata": {
"colab_type": "code",
"id": "hJ5_UMnWYmaE",
"colab": {}
},
"source": [
"# Load the TFMA results for the first training run\n",
"# This will take a minute\n",
"eval_model_base_dir_0 = os.path.join(TFMA_DIR, 'run_0', 'eval_model_dir')\n",
"eval_model_dir_0 = os.path.join(eval_model_base_dir_0,\n",
" max(os.listdir(eval_model_base_dir_0)))\n",
"eval_shared_model_0 = tfma.default_eval_shared_model(\n",
" eval_saved_model_path=eval_model_dir_0)\n",
"\n",
"# Slice our data by the trip_start_hour feature\n",
"slices = [tfma.slicer.SingleSliceSpec(columns=['trip_start_hour'])]\n",
"\n",
"run_and_render(eval_model=eval_shared_model_0, slice_list=slices, slice_idx=0)"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "LJuxvGCpn4yF"
},
"source": [
"### Slices Overview\n",
"\n",
"The default visualization is the **Slices Overview** when the number of slices is small. It shows the values of metrics for each slice. Since we've selected `trip_start_hour` above, it's showing us metrics like accuracy and AUC for each hour, which allows us to look for issues that are specific to some hours and not others.\n",
"\n",
"In the visualization above:\n",
"\n",
"* Try sorting the feature column, which is our `trip_start_hours` feature, by clicking on the column header\n",
"* Try sorting by precision, and **notice that the precision for some of the hours with examples is 0, which may indicate a problem**\n",
"\n",
"The chart also allows us to select and display different metrics in our slices.\n",
"\n",
"* Try selecting different metrics from the \"Show\" menu\n",
"* Try selecting recall in the \"Show\" menu, and **notice that the recall for some of the hours with examples is 0, which may indicate a problem**\n",
"\n",
"It is also possible to set a threshold to filter out slices with smaller numbers of examples, or \"weights\". You can type a minimum number of examples, or use the slider."
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "cQT-1Ckcnd_7"
},
"source": [
"### Metrics Histogram\n",
"\n",
"This view also supports a **Metrics Histogram** as an alternative visualization, which is also the default view when the number of slices is large. The results will be divided into buckets and the number of slices / total weights / both can be visualized. Columns can be sorted by clicking on the column header. Slices with small weights can be filtered out by setting the threshold. Further filtering can be applied by dragging the grey band. To reset the range, double click the band. Filtering can also be used to remove outliers in the visualization and the metrics tables. Click the gear icon to switch to a logarithmic scale instead of a linear scale.\n",
"\n",
"* Try selecting \"Metrics Histogram\" in the Visualization menu\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "hSnqI6Esb1XM"
},
"source": [
"### More Slices\n",
"\n",
"Let's create a whole list of `SliceSpec`s, which will allow us to select any of the slices in the list. We'll select the `trip_start_day` slice (days of the week) by setting the `slice_idx` to `1`. **Try changing the `slice_idx` to `0` or `2` and running again to examine different slices.**"
]
},
{
"cell_type": "code",
"metadata": {
"colab_type": "code",
"id": "355wqvY3yBod",
"colab": {}
},
"source": [
"slices = [tfma.slicer.SingleSliceSpec(columns=['trip_start_hour']),\n",
" tfma.slicer.SingleSliceSpec(columns=['trip_start_day']),\n",
" tfma.slicer.SingleSliceSpec(columns=['trip_start_month'])]\n",
"run_and_render(eval_model=eval_shared_model_0, slice_list=slices, slice_idx=0)"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "PsXM0NYGeajg"
},
"source": [
"You can create feature crosses to analyze combinations of features. Let's create a `SliceSpec` to look at a cross of `trip_start_day` and `trip_start_hour`:"
]
},
{
"cell_type": "code",
"metadata": {
"colab_type": "code",
"id": "k7vbFS1Me1SH",
"colab": {}
},
"source": [
"slices = [tfma.slicer.SingleSliceSpec(columns=['trip_start_day', 'trip_start_hour'])]\n",
"run_and_render(eval_shared_model_0, slices, 0)"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "GmeODqrUfJw2"
},
"source": [
"Crossing the two columns creates a lot of combinations! Let's narrow down our cross to only look at **trips that start at noon**. Then let's select `accuracy` from the visualization:"
]
},
{
"cell_type": "code",
"metadata": {
"colab_type": "code",
"id": "kdvBNfcHfRWg",
"colab": {}
},
"source": [
"slices = [tfma.slicer.SingleSliceSpec(columns=['trip_start_day'], features=[('trip_start_hour', 12)])]\n",
"run_and_render(eval_shared_model_0, slices, 0)"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "meRvFkKcPbux"
},
"source": [
"## Tracking Model Performance Over Time\n",
"\n",
"Your training dataset will be used for training your model, and will hopefully be representative of your test dataset and the data that will be sent to your model in production. However, while the data in inference requests may remain the same as your training data, in many cases it will start to change enough so that the performance of your model will change.\n",
"\n",
"That means that you need to monitor and measure your model's performance on an ongoing basis, so that you can be aware of and react to changes. Let's take a look at how TFMA can help."
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "Vm2y2DqNF4HL"
},
"source": [
"### Measure Performance For New Data\n",
"We downloaded the results of three different training runs above, so let's load them now:"
]
},
{
"cell_type": "code",
"metadata": {
"colab_type": "code",
"id": "zJYUOjmFfuPy",
"colab": {}
},
"source": [
"def get_eval_result(base_dir, run_name, data_loc, slice_spec):\n",
" eval_model_base_dir = os.path.join(base_dir, run_name, \"eval_model_dir\")\n",
" versions = os.listdir(eval_model_base_dir)\n",
" eval_model_dir = os.path.join(eval_model_base_dir, max(versions))\n",
" output_dir = os.path.join(base_dir, \"output\", run_name)\n",
" eval_shared_model = tfma.default_eval_shared_model(eval_saved_model_path=eval_model_dir)\n",
"\n",
" return tfma.run_model_analysis(eval_shared_model=eval_shared_model,\n",
" data_location=data_loc,\n",
" file_format='tfrecords',\n",
" slice_spec=slice_spec,\n",
" output_path=output_dir,\n",
" extractors=None)\n",
"\n",
"slices = [tfma.slicer.SingleSliceSpec()]\n",
"result_ts0 = get_eval_result(TFMA_DIR, 'run_0', TFRecord_file, slices)\n",
"result_ts1 = get_eval_result(TFMA_DIR, 'run_1', TFRecord_file, slices)\n",
"result_ts2 = get_eval_result(TFMA_DIR, 'run_2', TFRecord_file, slices)"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "tkg63PKHwRcm",
"colab_type": "text"
},
"source": [
"Next, let's use TFMA to see how these runs compare using [`render_time_series`](https://www.tensorflow.org/tfx/model_analysis/api_docs/python/tfma/view/render_time_series)."
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "RsO-gqCRK0ar"
},
"source": [
"### How does it look today?\n",
"First, we'll imagine that we've trained and deployed our model yesterday, and now we want to see how it's doing on the new data coming in today. We can specify particular slices to look at. Let's compare our training runs for trips that started at noon.\n",
"\n",
"Note:\n",
"* The visualization will start by displaying accuracy. Add AUC and average loss by using the \"Add metric series\" menu.\n",
"* Hover over the curves to see the values.\n",
"* In the metric series charts the X axis is the model ID number of the model run that you're examining. The numbers themselves are not meaningful."
]
},
{
"cell_type": "code",
"metadata": {
"colab_type": "code",
"id": "KjEws8T0cDm9",
"colab": {}
},
"source": [
"output_dirs = [os.path.join(TFMA_DIR, \"output\", run_name)\n",
" for run_name in (\"run_0\", \"run_1\", \"run_2\")]\n",
"\n",
"eval_results_from_disk = tfma.load_eval_results(\n",
" output_dirs[:2], tfma.constants.MODEL_CENTRIC_MODE)\n",
"\n",
"tfma.view.render_time_series(eval_results_from_disk, slices[0])"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "EQ7kZxESN9Bx"
},
"source": [
"Now we'll imagine that another day has passed and we want to see how it's doing on the new data coming in today, compared to the previous two days. Again add AUC and average loss by using the \"Add metric series\" menu:"
]
},
{
"cell_type": "code",
"metadata": {
"colab_type": "code",
"id": "VjQmlXMmLwHf",
"colab": {}
},
"source": [
"eval_results_from_disk = tfma.load_eval_results(\n",
" output_dirs, tfma.constants.MODEL_CENTRIC_MODE)\n",
"\n",
"tfma.view.render_time_series(eval_results_from_disk, slices[0])"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "fqQY7KEJrda8",
"colab_type": "code",
"colab": {}
},
"source": [
""
],
"execution_count": 0,
"outputs": []
}
]
}
| apache-2.0 |
materialsinnovation/mks-tutorial | notebooks/part1-python-intro.ipynb | 1 | 112598 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Intro to Python"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"2"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# using the notebook\n",
"\n",
"1 + 1"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"6\n"
]
}
],
"source": [
"# variables\n",
"\n",
"a = 2\n",
"b = a * 3\n",
"print(b)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# lists\n",
"\n",
"my_list = [1, \"hi\", [2, 3]]"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1\n"
]
}
],
"source": [
"print(my_list[0])"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[2, 3]\n"
]
}
],
"source": [
"print(my_list[-1])"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1\n",
"hi\n",
"[2, 3]\n"
]
}
],
"source": [
"# loops\n",
"\n",
"for item in my_list:\n",
" print(item)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"5 > 3\n"
]
}
],
"source": [
"# conditionals\n",
"\n",
"value = 5\n",
"\n",
"if value > 3:\n",
" print(\"{0} > 3\".format(value))\n",
"else:\n",
" print(\"{0} <= 3\".format(value))"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# arrays\n",
"\n",
"import numpy as np\n",
"\n",
"my_array = np.array([1.0, 2.3, 3.5])"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[ 1. 2.3 3.5]\n"
]
}
],
"source": [
"print(my_array)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1.0\n"
]
}
],
"source": [
"print(my_array[0])"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[ 1. 2.3]\n"
]
}
],
"source": [
"print(my_array[:2])"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(3,)\n"
]
}
],
"source": [
"print(my_array.shape)"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[0 1 2 3 4 5 6 7 8 9]\n"
]
}
],
"source": [
"print(np.arange(10))"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"array_2d = np.reshape(np.arange(6), (2, 3))"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[0 1 2]\n",
" [3 4 5]]\n"
]
}
],
"source": [
"print(array_2d)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[0 1 2]\n"
]
}
],
"source": [
"print(array_2d[0])"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"4\n"
]
}
],
"source": [
"# NBVAL_IGNORE_OUTPUT\n",
"\n",
"print(array_2d[1, 1])"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(2, 3)\n"
]
}
],
"source": [
"print(array_2d.shape)"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[ 0. 2.3 7. ]\n",
" [ 3. 9.2 17.5]]\n"
]
}
],
"source": [
"print(array_2d * my_array)"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[ 1. 3.3 5.5]\n",
" [ 4. 6.3 8.5]]\n"
]
}
],
"source": [
"print(array_2d + my_array)"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"data": {
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0P8qSod/xxlvR8e2mlwPFlqxmDZ0h8F3rght1wAWQM/R+TWFRyorSQpymCDIN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\nQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f25d518eeb8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# plotting\n",
"\n",
"%matplotlib inline\n",
"\n",
"import matplotlib.pyplot as plt\n",
"\n",
"x = np.random.random(100)\n",
"\n",
"plt.plot(x);"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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VqNIZgunYq4JGn086YOJMieOefXNRvTTgFOp/lVbaRWiroGqnolW3lIec63OD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\nee0wSJl9D3slvDfP71vokNFnT1XeeeatfM/lpZBf6f8iVLjIQtxe9b0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IZP+UiecFVUJj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sv/3TM83P2t3xfqLg4PsDUy7fhkfHZzDjaLjUlOcVFBNRuwVayXONksB3UBia0hhEcWXA06u7yl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\nAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f25eb653898>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"data = np.random.random((100, 100))\n",
"plt.imshow(data);"
]
},
{
"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": "3.6.1"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| mit |
jbliss1234/ML | tf_kdd99.ipynb | 2 | 27404 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# T81-558: Applications of Deep Neural Networks\n",
"### TensorFlow (SKFLOW) Meets KDD-99\n",
"* Instructor: [Jeff Heaton](https://sites.wustl.edu/jeffheaton/), School of Engineering and Applied Science, [Washington University in St. Louis](https://engineering.wustl.edu/Programs/Pages/default.aspx)\n",
"* For more information visit the [class website](https://sites.wustl.edu/jeffheaton/t81-558/).\n",
"\n",
"This simple example shows how to load a non-trivial dataset from CSV and train a neural network. The dataset is the\n",
"[KDD99 dataset](http://kdd.ics.uci.edu/databases/kddcup99/kddcup99.html). This dataset is used to detect between normal and malicious network activity.\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Imports for this Notebook"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Imports\n",
"import pandas as pd\n",
"from sklearn import preprocessing\n",
"from sklearn.cross_validation import train_test_split\n",
"import tensorflow.contrib.learn as skflow\n",
"from sklearn import metrics"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Several Useful Functions\n",
"\n",
"These are functions that I reuse often to encode the feature vector (FV)."
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# These are several handy functions that I use in my class:\n",
"\n",
"# Encode a text field to dummy variables\n",
"def encode_text_dummy(df,name):\n",
" dummies = pd.get_dummies(df[name])\n",
" for x in dummies.columns:\n",
" dummy_name = \"{}-{}\".format(name,x)\n",
" df[dummy_name] = dummies[x]\n",
" df.drop(name, axis=1, inplace=True)\n",
" \n",
"# Encode a text field to a single index value\n",
"def encode_text_index(df,name): \n",
" le = preprocessing.LabelEncoder()\n",
" df[name] = le.fit_transform(df[name])\n",
" return le.classes_\n",
" \n",
"# Encode a numeric field to Z-Scores\n",
"def encode_numeric_zscore(df,name,mean=None,sd=None):\n",
" if mean is None:\n",
" mean = df[name].mean()\n",
" \n",
" if sd is None:\n",
" sd = df[name].std()\n",
" \n",
" df[name] = (df[name]-mean)/sd\n",
" \n",
"# Encode a numeric field to fill missing values with the median.\n",
"def missing_median(df, name):\n",
" med = df[name].median()\n",
" df[name] = df[name].fillna(med)\n",
"\n",
"# Convert a dataframe to x/y suitable for training.\n",
"def to_xy(df,target):\n",
" result = []\n",
" for x in df.columns:\n",
" if x != target:\n",
" result.append(x)\n",
" return df.as_matrix(result),df[target]\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Read in Raw KDD-99 Dataset"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Read 494021 rows.\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>duration</th>\n",
" <th>protocol_type</th>\n",
" <th>service</th>\n",
" <th>flag</th>\n",
" <th>src_bytes</th>\n",
" <th>dst_bytes</th>\n",
" <th>land</th>\n",
" <th>wrong_fragment</th>\n",
" <th>urgent</th>\n",
" <th>hot</th>\n",
" <th>...</th>\n",
" <th>dst_host_srv_count</th>\n",
" <th>dst_host_same_srv_rate</th>\n",
" <th>dst_host_diff_srv_rate</th>\n",
" <th>dst_host_same_src_port_rate</th>\n",
" <th>dst_host_srv_diff_host_rate</th>\n",
" <th>dst_host_serror_rate</th>\n",
" <th>dst_host_srv_serror_rate</th>\n",
" <th>dst_host_rerror_rate</th>\n",
" <th>dst_host_srv_rerror_rate</th>\n",
" <th>outcome</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>309974</th>\n",
" <td>0</td>\n",
" <td>icmp</td>\n",
" <td>ecr_i</td>\n",
" <td>SF</td>\n",
" <td>1032</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
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" <td>255</td>\n",
" <td>1.0</td>\n",
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" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>smurf.</td>\n",
" </tr>\n",
" <tr>\n",
" <th>293952</th>\n",
" <td>0</td>\n",
" <td>icmp</td>\n",
" <td>ecr_i</td>\n",
" <td>SF</td>\n",
" <td>1032</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>255</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>smurf.</td>\n",
" </tr>\n",
" <tr>\n",
" <th>211354</th>\n",
" <td>0</td>\n",
" <td>icmp</td>\n",
" <td>ecr_i</td>\n",
" <td>SF</td>\n",
" <td>1032</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
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" <td>255</td>\n",
" <td>1.0</td>\n",
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" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>smurf.</td>\n",
" </tr>\n",
" <tr>\n",
" <th>302812</th>\n",
" <td>0</td>\n",
" <td>icmp</td>\n",
" <td>ecr_i</td>\n",
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" <td>1032</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>255</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>smurf.</td>\n",
" </tr>\n",
" <tr>\n",
" <th>438732</th>\n",
" <td>0</td>\n",
" <td>icmp</td>\n",
" <td>ecr_i</td>\n",
" <td>SF</td>\n",
" <td>520</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>255</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>smurf.</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 42 columns</p>\n",
"</div>"
],
"text/plain": [
" duration protocol_type service flag src_bytes dst_bytes land \\\n",
"309974 0 icmp ecr_i SF 1032 0 0 \n",
"293952 0 icmp ecr_i SF 1032 0 0 \n",
"211354 0 icmp ecr_i SF 1032 0 0 \n",
"302812 0 icmp ecr_i SF 1032 0 0 \n",
"438732 0 icmp ecr_i SF 520 0 0 \n",
"\n",
" wrong_fragment urgent hot ... dst_host_srv_count \\\n",
"309974 0 0 0 ... 255 \n",
"293952 0 0 0 ... 255 \n",
"211354 0 0 0 ... 255 \n",
"302812 0 0 0 ... 255 \n",
"438732 0 0 0 ... 255 \n",
"\n",
" dst_host_same_srv_rate dst_host_diff_srv_rate \\\n",
"309974 1.0 0.0 \n",
"293952 1.0 0.0 \n",
"211354 1.0 0.0 \n",
"302812 1.0 0.0 \n",
"438732 1.0 0.0 \n",
"\n",
" dst_host_same_src_port_rate dst_host_srv_diff_host_rate \\\n",
"309974 1.0 0.0 \n",
"293952 1.0 0.0 \n",
"211354 1.0 0.0 \n",
"302812 1.0 0.0 \n",
"438732 1.0 0.0 \n",
"\n",
" dst_host_serror_rate dst_host_srv_serror_rate dst_host_rerror_rate \\\n",
"309974 0.0 0.0 0.0 \n",
"293952 0.0 0.0 0.0 \n",
"211354 0.0 0.0 0.0 \n",
"302812 0.0 0.0 0.0 \n",
"438732 0.0 0.0 0.0 \n",
"\n",
" dst_host_srv_rerror_rate outcome \n",
"309974 0.0 smurf. \n",
"293952 0.0 smurf. \n",
"211354 0.0 smurf. \n",
"302812 0.0 smurf. \n",
"438732 0.0 smurf. \n",
"\n",
"[5 rows x 42 columns]"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"\n",
"# This file is a CSV, just no CSV extension or headers\n",
"# Download from: http://kdd.ics.uci.edu/databases/kddcup99/kddcup99.html\n",
"df = pd.read_csv(\"/Users/jeff/Downloads/data/kddcup.data_10_percent\", header=None)\n",
"\n",
"print(\"Read {} rows.\".format(len(df)))\n",
"# df = df.sample(frac=0.1, replace=False) # Uncomment this line to sample only 10% of the dataset\n",
"df.dropna(inplace=True,axis=1) # For now, just drop NA's (rows with missing values)\n",
"\n",
"# The CSV file has no column heads, so add them\n",
"df.columns = [\n",
" 'duration',\n",
" 'protocol_type',\n",
" 'service',\n",
" 'flag',\n",
" 'src_bytes',\n",
" 'dst_bytes',\n",
" 'land',\n",
" 'wrong_fragment',\n",
" 'urgent',\n",
" 'hot',\n",
" 'num_failed_logins',\n",
" 'logged_in',\n",
" 'num_compromised',\n",
" 'root_shell',\n",
" 'su_attempted',\n",
" 'num_root',\n",
" 'num_file_creations',\n",
" 'num_shells',\n",
" 'num_access_files',\n",
" 'num_outbound_cmds',\n",
" 'is_host_login',\n",
" 'is_guest_login',\n",
" 'count',\n",
" 'srv_count',\n",
" 'serror_rate',\n",
" 'srv_serror_rate',\n",
" 'rerror_rate',\n",
" 'srv_rerror_rate',\n",
" 'same_srv_rate',\n",
" 'diff_srv_rate',\n",
" 'srv_diff_host_rate',\n",
" 'dst_host_count',\n",
" 'dst_host_srv_count',\n",
" 'dst_host_same_srv_rate',\n",
" 'dst_host_diff_srv_rate',\n",
" 'dst_host_same_src_port_rate',\n",
" 'dst_host_srv_diff_host_rate',\n",
" 'dst_host_serror_rate',\n",
" 'dst_host_srv_serror_rate',\n",
" 'dst_host_rerror_rate',\n",
" 'dst_host_srv_rerror_rate',\n",
" 'outcome'\n",
"]\n",
"\n",
"# display 5 rows\n",
"df[0:5]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Encode the feature vector\n",
"Encode every row in the database. This is not instant!"
]
},
{
"cell_type": "code",
"execution_count": 26,
"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>duration</th>\n",
" <th>src_bytes</th>\n",
" <th>dst_bytes</th>\n",
" <th>wrong_fragment</th>\n",
" <th>hot</th>\n",
" <th>num_failed_logins</th>\n",
" <th>num_compromised</th>\n",
" <th>root_shell</th>\n",
" <th>su_attempted</th>\n",
" <th>num_root</th>\n",
" <th>...</th>\n",
" <th>flag-S2</th>\n",
" <th>flag-SF</th>\n",
" <th>flag-SH</th>\n",
" <th>land-0</th>\n",
" <th>land-1</th>\n",
" <th>logged_in-0</th>\n",
" <th>logged_in-1</th>\n",
" <th>is_host_login-0</th>\n",
" <th>is_guest_login-0</th>\n",
" <th>is_guest_login-1</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>309974</th>\n",
" <td>-0.065862</td>\n",
" <td>-0.013753</td>\n",
" <td>-0.029016</td>\n",
" <td>-0.049704</td>\n",
" <td>-0.043171</td>\n",
" <td>-0.007793</td>\n",
" <td>-0.008656</td>\n",
" <td>-0.008999</td>\n",
" <td>-0.004499</td>\n",
" <td>-0.009776</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
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" <td>1.0</td>\n",
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" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>293952</th>\n",
" <td>-0.065862</td>\n",
" <td>-0.013753</td>\n",
" <td>-0.029016</td>\n",
" <td>-0.049704</td>\n",
" <td>-0.043171</td>\n",
" <td>-0.007793</td>\n",
" <td>-0.008656</td>\n",
" <td>-0.008999</td>\n",
" <td>-0.004499</td>\n",
" <td>-0.009776</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>211354</th>\n",
" <td>-0.065862</td>\n",
" <td>-0.013753</td>\n",
" <td>-0.029016</td>\n",
" <td>-0.049704</td>\n",
" <td>-0.043171</td>\n",
" <td>-0.007793</td>\n",
" <td>-0.008656</td>\n",
" <td>-0.008999</td>\n",
" <td>-0.004499</td>\n",
" <td>-0.009776</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>302812</th>\n",
" <td>-0.065862</td>\n",
" <td>-0.013753</td>\n",
" <td>-0.029016</td>\n",
" <td>-0.049704</td>\n",
" <td>-0.043171</td>\n",
" <td>-0.007793</td>\n",
" <td>-0.008656</td>\n",
" <td>-0.008999</td>\n",
" <td>-0.004499</td>\n",
" <td>-0.009776</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>438732</th>\n",
" <td>-0.065862</td>\n",
" <td>-0.020571</td>\n",
" <td>-0.029016</td>\n",
" <td>-0.049704</td>\n",
" <td>-0.043171</td>\n",
" <td>-0.007793</td>\n",
" <td>-0.008656</td>\n",
" <td>-0.008999</td>\n",
" <td>-0.004499</td>\n",
" <td>-0.009776</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 115 columns</p>\n",
"</div>"
],
"text/plain": [
" duration src_bytes dst_bytes wrong_fragment hot \\\n",
"309974 -0.065862 -0.013753 -0.029016 -0.049704 -0.043171 \n",
"293952 -0.065862 -0.013753 -0.029016 -0.049704 -0.043171 \n",
"211354 -0.065862 -0.013753 -0.029016 -0.049704 -0.043171 \n",
"302812 -0.065862 -0.013753 -0.029016 -0.049704 -0.043171 \n",
"438732 -0.065862 -0.020571 -0.029016 -0.049704 -0.043171 \n",
"\n",
" num_failed_logins num_compromised root_shell su_attempted \\\n",
"309974 -0.007793 -0.008656 -0.008999 -0.004499 \n",
"293952 -0.007793 -0.008656 -0.008999 -0.004499 \n",
"211354 -0.007793 -0.008656 -0.008999 -0.004499 \n",
"302812 -0.007793 -0.008656 -0.008999 -0.004499 \n",
"438732 -0.007793 -0.008656 -0.008999 -0.004499 \n",
"\n",
" num_root ... flag-S2 flag-SF flag-SH land-0 land-1 \\\n",
"309974 -0.009776 ... 0.0 1.0 0.0 1.0 0.0 \n",
"293952 -0.009776 ... 0.0 1.0 0.0 1.0 0.0 \n",
"211354 -0.009776 ... 0.0 1.0 0.0 1.0 0.0 \n",
"302812 -0.009776 ... 0.0 1.0 0.0 1.0 0.0 \n",
"438732 -0.009776 ... 0.0 1.0 0.0 1.0 0.0 \n",
"\n",
" logged_in-0 logged_in-1 is_host_login-0 is_guest_login-0 \\\n",
"309974 1.0 0.0 1.0 1.0 \n",
"293952 1.0 0.0 1.0 1.0 \n",
"211354 1.0 0.0 1.0 1.0 \n",
"302812 1.0 0.0 1.0 1.0 \n",
"438732 1.0 0.0 1.0 1.0 \n",
"\n",
" is_guest_login-1 \n",
"309974 0.0 \n",
"293952 0.0 \n",
"211354 0.0 \n",
"302812 0.0 \n",
"438732 0.0 \n",
"\n",
"[5 rows x 115 columns]"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Now encode the feature vector\n",
"\n",
"encode_numeric_zscore(df, 'duration')\n",
"encode_text_dummy(df, 'protocol_type')\n",
"encode_text_dummy(df, 'service')\n",
"encode_text_dummy(df, 'flag')\n",
"encode_numeric_zscore(df, 'src_bytes')\n",
"encode_numeric_zscore(df, 'dst_bytes')\n",
"encode_text_dummy(df, 'land')\n",
"encode_numeric_zscore(df, 'wrong_fragment')\n",
"encode_numeric_zscore(df, 'urgent')\n",
"encode_numeric_zscore(df, 'hot')\n",
"encode_numeric_zscore(df, 'num_failed_logins')\n",
"encode_text_dummy(df, 'logged_in')\n",
"encode_numeric_zscore(df, 'num_compromised')\n",
"encode_numeric_zscore(df, 'root_shell')\n",
"encode_numeric_zscore(df, 'su_attempted')\n",
"encode_numeric_zscore(df, 'num_root')\n",
"encode_numeric_zscore(df, 'num_file_creations')\n",
"encode_numeric_zscore(df, 'num_shells')\n",
"encode_numeric_zscore(df, 'num_access_files')\n",
"encode_numeric_zscore(df, 'num_outbound_cmds')\n",
"encode_text_dummy(df, 'is_host_login')\n",
"encode_text_dummy(df, 'is_guest_login')\n",
"encode_numeric_zscore(df, 'count')\n",
"encode_numeric_zscore(df, 'srv_count')\n",
"encode_numeric_zscore(df, 'serror_rate')\n",
"encode_numeric_zscore(df, 'srv_serror_rate')\n",
"encode_numeric_zscore(df, 'rerror_rate')\n",
"encode_numeric_zscore(df, 'srv_rerror_rate')\n",
"encode_numeric_zscore(df, 'same_srv_rate')\n",
"encode_numeric_zscore(df, 'diff_srv_rate')\n",
"encode_numeric_zscore(df, 'srv_diff_host_rate')\n",
"encode_numeric_zscore(df, 'dst_host_count')\n",
"encode_numeric_zscore(df, 'dst_host_srv_count')\n",
"encode_numeric_zscore(df, 'dst_host_same_srv_rate')\n",
"encode_numeric_zscore(df, 'dst_host_diff_srv_rate')\n",
"encode_numeric_zscore(df, 'dst_host_same_src_port_rate')\n",
"encode_numeric_zscore(df, 'dst_host_srv_diff_host_rate')\n",
"encode_numeric_zscore(df, 'dst_host_serror_rate')\n",
"encode_numeric_zscore(df, 'dst_host_srv_serror_rate')\n",
"encode_numeric_zscore(df, 'dst_host_rerror_rate')\n",
"encode_numeric_zscore(df, 'dst_host_srv_rerror_rate')\n",
"outcomes = encode_text_index(df, 'outcome')\n",
"num_classes = len(outcomes)\n",
"\n",
"# display 5 rows\n",
"\n",
"df.dropna(inplace=True,axis=1)\n",
"df[0:5]\n",
"# This is the numeric feature vector, as it goes to the neural net\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Train the Neural Network"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Step #49, avg. train loss: 0.42411, avg. val loss: 0.38433\n",
"Step #99, avg. train loss: 0.09951, avg. val loss: 0.10089\n",
"Step #149, avg. train loss: 0.09113, avg. val loss: 0.08418\n",
"Step #199, avg. train loss: 0.08270, avg. val loss: 0.07179\n",
"Step #249, avg. train loss: 0.05196, avg. val loss: 0.06019\n",
"Step #299, avg. train loss: 0.06040, avg. val loss: 0.05594\n",
"Step #349, avg. train loss: 0.06639, avg. val loss: 0.05264\n",
"Step #399, avg. train loss: 0.06915, avg. val loss: 0.05109\n",
"Step #449, avg. train loss: 0.03100, avg. val loss: 0.04427\n",
"Step #499, avg. train loss: 0.04303, avg. val loss: 0.04306\n"
]
},
{
"data": {
"text/plain": [
"TensorFlowDNNClassifier(batch_size=32, class_weight=None, clip_gradients=5.0,\n",
" config=None, continue_training=False, dropout=None,\n",
" hidden_units=[10, 20, 10], learning_rate=0.1, n_classes=19,\n",
" optimizer='Adagrad', steps=500, verbose=1)"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Break into X (predictors) & y (prediction)\n",
"x, y = to_xy(df,'outcome')\n",
"\n",
"# Create a test/train split. 25% test\n",
"# Split into train/test\n",
"x_train, x_test, y_train, y_test = train_test_split(\n",
" x, y, test_size=0.25, random_state=42)\n",
"\n",
"# Create a deep neural network with 3 hidden layers of 10, 20, 10\n",
"classifier = skflow.TensorFlowDNNClassifier(hidden_units=[10, 20, 10], \n",
" n_classes=num_classes, steps=500)\n",
"\n",
"# Early stopping\n",
"early_stop = skflow.monitors.ValidationMonitor(x_test, y_test,\n",
" early_stopping_rounds=200,\n",
" n_classes=num_classes,\n",
" print_steps=50)\n",
" \n",
"# Fit/train neural network\n",
"classifier.fit(x, y, early_stop)\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Validation score: 0.9872075135616549\n"
]
}
],
"source": [
"# Measure accuracy\n",
"pred = classifier.predict(x_test)\n",
"score = metrics.accuracy_score(y_test, pred)\n",
"print(\"Validation score: {}\".format(score))"
]
},
{
"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"
},
"widgets": {
"state": {},
"version": "1.1.2"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| apache-2.0 |
RPGroup-PBoC/gist_pboc_2017 | code/nb/setting_up_python.ipynb | 1 | 44383 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"# Setting Up Python For Scientific Computing"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"(c) 2016 Griffin Chure. This work is licensed under a [Creative Commons Attribution License CC-BY 4.0](https://creativecommons.org/licenses/by/4.0/). All code contained herein is licensed under an [MIT license](https://opensource.org/licenses/MIT) "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In this tutorial, we will set up a scientific Python computing environment using the [Anaconda python distribution by Continuum Analytics](https://www.continuum.io/downloads). "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Why Python?"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As is true in human language, there are [hundreds of computer programming languages](https://en.wikipedia.org/wiki/List_of_programming_languages). While each has their own merit, the major languages for scientific computing are C, C++, R, MATLAB, Python, Java, and Fortran. [MATLAB](https://www.mathworks.com) and [Python](https://www.python.org) are similar in syntax and typically read as if it were written in plain english. This makes both languages a useful tool for teaching but they are also very powerful languages and are **very** actively used in real-life research. MATLAB is proprietary while Python is open source. A benefit of being open source is that anyone can write and release Python packages. For science, there are many wonderful community-driven packages such as [NumPy](http://www.numpy.org), [SciPy](http://www.scipy.org), [scikit-image](http://scikit-image.org), and [Pandas](http://pandas.pydata.org) just to name a few. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Installing Python 3.5 with Anaconda"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Python 3.5 vs Python 2.7 "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"There are two dominant versions of Python used for scientific computing, Python 2.7.x and Python 3.5.x. We are at an interesting crossroads between these two versions. The most recent release (Python 3.5.0 as of December 2016) is not backwards compatible with previous versions of Python. While there are still some packages written for Python 2.7 that have not been modified for compatibility with Python 3.5, a large number have transitioned. As this will be the future for scientific computing with Python, we will use Python 3.5.0 for these tutorials."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Anaconda"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"There are several Python distributions available for MacOS, Windows, and Linux. The two most popular, [Enthought Canopy](https://www.enthought.com/products/canopy/) and [Anaconda](https://www.continuum.io/why-anaconda) are specifically designed for scientific computing and data science work. For this course, we will use the Anaconda Python 3.5 distribution. To install the correct version, follow the instructions below.\n",
"\n",
"1. Navigate to [the Anaconda download page](https://www.continuum.io/downloads) and download the Python 3.5 graphical installer. You will be asked for your email address which you should provide. If you are affiliated with a university, you should use your `.edu` address as you will have access to some useful goodies unavailable to the public.\n",
"2. Launch the installer and follow the onscreen instructions. \n",
"3. Open the newly installed **Anaconda Navigator** application.\n",
"\n",
"Congratulations! You now have the beginnings of a scientific Python distribution."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Launching an interpreter through Anaconda Navigator "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Unlike [MATLAB](https://www.mathworks.com), another popular scientific computing language, Python does not have an *official* graphical user interface (GUI). Rather, we will be writing Python scripts in a text editor and running them through the [IPython interpreter](https://ipython.org/project.html) (also referred to in Anaconda as the 'qtconsole'). Here we will be able to tell our computer to execute snippets of code and run Python scripts. To launch the IPython interpreter, open the Anaconda Navigator application and click on 'Launch' under the 'qtconsole', shown in the screenshot below. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You should now be greeted with a white window with some information about your IPython version and an input prompt reading `In[1]`. Before we begin coding in Python, we will need to install two packages."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Installing extra packages using Conda "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"With the Anaconda Python distribution, you can install verified packages (scientific and non-scientific) through the [Conda](http://conda.pydata.org/docs/) package manager. **Note that you do not have to download Conda separately. This comes packaged with Anaconda**. To install packages through Conda, we must manually enter their names on the command line. For the purposes of these tutorials, we will only need to install/upgrade two packages -- [Seaborn for plotting styling](http://seaborn.pydata.org) and an update IPython to [IPython 5.0](http://blog.jupyter.org/2016/07/08/ipython-5-0-released/). Rather than do this on the command line, we can install these directly from the IPython interpreter. In your IPython interpreter, type the following lines.\n",
"\n",
"```\n",
"In[1]: ! conda upgrade ipython --yes\n",
"In[2]: ! conda install seaborn --yes\n",
"```\n",
"\n",
"Note that the flag `--yes` is telling Conda that you agree to upgrade the packages on your computer that might not be compatible with other Python packages. You can remove the `--yes` tag, but you will have to approve them manually.\n",
"\n",
"Once you have executed these commands, close the IPython interpreter window and open a new one. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Installing Atom text editor"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"While we now have everything we need to execute Python scripts, we need an editor to write them with. A particularly useful one is [Atom](https://atom.io), but any text editor should work. To install Atom on your machine, follow the instructions below. \n",
"\n",
"1. [Navigate to the Atom](https://atom.io) homepage and follow the instructions for installation.\n",
"\n",
"2. Once installed, launch Atom and navigate to `Packages -> Settings View -> Open` and scroll to the bottom of the page. Make sure the setting `Tab Length` is set to 4. Below that, make sure `Tab Type` is set to `soft`. This is important as indentation and white space is interpreted in Python.\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Setting up the directory structure"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"For this course (and your coding in 'real life'), it will help if you follow a specific directory structure for your code and data. During this course, we will be writing a lot of Python scripts that will load in data. So you can directly follow along in class, it is important that you and the instructors have the same directory structure. To make this structure, open Atom and follow the instructions below.\n",
"\n",
"1. Navigate to `File -> Add Project Folder` and make a new folder in your home directory. On MacOS and Linux, this will be in `/Users/YOUR_USERNAME/`. On Windows, this will be XXX.\n",
"\n",
"2. Name this project `pboc`.\n",
"\n",
"3. Now `pboc` should appear on the left-hand side of your editor. Right-click on `pboc` and make a new folder called `data`. This is where all of our data from the class will live.\n",
"\n",
"Now, if everything went well, your Atom editor window should look like this on the left-hand side. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Your first script and reading these tutorials "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This tutorial (as all others in this course) are written as [Jupyter notebooks]() which are documents which contain cells for writing text and math as well as cells that contain and excute block of Python code. While we will be writing python code in our Atom text editor, these tutorials will serve as a useful reference that not only shows the code and output, but an explaination of the biological and physical principles behind it. For these tutorials, code and it's output are rendered in two boxes as is shown below. "
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Hello! This is the print function. Python will print this line below\n"
]
}
],
"source": [
"# This is a comment and is not read by Python\n",
"print('Hello! This is the print function. Python will print this line below')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The box with the gray background contains the python code while the output is in the box with the white background. When reading these tutorials, you may want to retype (or copy-and-paste) the code lines into Atom or in the IPython interpreter directly. \n",
"\n",
"If you have followed the steps above, we are finally ready to write our first Python script. In your Atom window, create a new file named `my_first_script.py` and save it within your `pboc` root directory (not in `data`). You can do this by going to `File -> New File` then `File -> Save` and navigate to your `pboc` folder. Now, in the `my_first_script.py` file, we'll generate a plot of one of my favorite functions. Type (or copy and paste) the following lines into you script file and save it. \n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we are finally ready to write our first Python script. In your Atom window, create a new file named `my_first_script.py` and save it within your `pboc` root directory (not in `data`). You can do this by going to `File -> New File` then `File -> Save` and navigate to your `pboc` folder. Now, in the `my_first_script.py` file, we'll generate a plot of one of my favorite functions. Type (or copy and paste) the following lines into you script file and save it by going to `File -> Save`. "
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x118e33ef0>"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Import Python packages necessary for this script\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"\n",
"# Generate a beautiful sinusoidal curve\n",
"x = np.linspace(0, 2*np.pi, 500)\n",
"y = np.sin(2 * np.sin(2 * np.sin(2 * x)))\n",
"plt.plot(x, y)\n",
"plt.xlabel('$x$')\n",
"plt.ylabel('$y$')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Once you have this file saved, open a new IPython interpreter through the Anaconda Navigator window and type the following commands.\n",
"```\n",
"In [1]: cd pboc\n",
"In [2]: %matplotlib\n",
"In [3]: %run my_first_script.py\n",
"```\n",
"\n",
"The first command navigates to the correct directory. The second command allows for us to keep typing while plots are being shown. The third command runs the script we just wrote through the IPython interpreter. The percentage signs for `In [2]:` and `In [3]:` are called Python magic fuctions and are [explained in the python syntax tutorial](). While just typing `matplotlib` and `run my_first_script.py` will work, it is better style to use these magic functions.\n",
"\n",
"\n",
"If everything works as expected, you should see the plot below."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x118eb6048>"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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2U2B2sepYpiGVMBgZBZeCt58xmo4ns+O8k5zIWqYJ8xqqOr/Pi7r82i4s05DT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\nRgCAVmIEAGj1Ha9P3hLBIe/jAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x118e51e48>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# These commands are for showing the plot in this notebook only.\n",
"%matplotlib inline\n",
"plt.plot(x, y)\n",
"plt.xlabel('$x$')\n",
"plt.ylabel('$y$')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"With that, you are now set up to do some scientific computing in Python! For the rest of the course, we will be going through this same procedure to computationally explore principles of physical biology. To this end, our computer screens will typically look something like this:\n",
"\n",
"\n",
"\n",
"although you can code however you feel comfortable!"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## What is Jupyter?"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"[Jupyter Notebooks](http://jupyter.org) are very useful tools for writing code, text, and math into a single document. In fact, this (and all other tutorials) were written in Jupyter noteooks. While we won't use them in this class, I strongly suggest you learn about them by following [this excellent tutorial](http://bebi103.caltech.edu/2016/tutorials/t0b_intro_to_jupyter_notebooks.html) written by a Caltech Professor of Biology and Biological Engineering, [Justin Bois](http://www.bois.caltech.edu)."
]
}
],
"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": "3.4.5"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
| mit |
bobflagg/sentiment-analysis | FastText.ipynb | 1 | 9321 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* [nzw0301/keras-examples](https://github.com/nzw0301/keras-examples)\n",
"* [fchollet/keras](https://github.com/fchollet/keras/blob/master/examples/imdb_fasttext.py)\n",
"* [kemaswill/fasttext_benchmark](https://github.com/kemaswill/fasttext_benchmark/blob/master/fasttext.py)\n",
"* [Keras Blog](https://blog.keras.io/index.html)"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1.1.1\n",
"0.18.1\n",
"0.7.0\n",
"0.18.1\n"
]
}
],
"source": [
"import keras \n",
"print(keras.__version__)\n",
"import scipy\n",
"print(scipy.__version__)\n",
"import theano\n",
"print(theano.__version__)\n",
"import sklearn\n",
"print(sklearn.__version__)"
]
},
{
"cell_type": "raw",
"metadata": {
"collapsed": false
},
"source": [
"from __future__ import print_function\n",
"import os\n",
"import numpy as np\n",
"\n",
"from keras.preprocessing.text import Tokenizer\n",
"from keras.preprocessing.sequence import pad_sequences\n",
"from keras.utils.np_utils import to_categorical\n",
"from keras.models import Sequential, Model\n",
"from keras.layers import Dense, Input, Flatten, Activation, Merge"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"np.random.seed(1337)\n",
"\n",
"EmbeddingDim = 50\n",
"MaxWords = 30000\n",
"SequenceLength = 50\n",
"Epochs = 5\n",
"SamplesPerEpoch = 1000\n",
"BatchSize = 64\n",
"Labels = 3\n",
"LabelMapping = {\n",
" 1: 0,\n",
" 2: 0,\n",
" 3: 1,\n",
" 4: 2,\n",
" 5: 2\n",
"}\n",
"\n",
"def oneHot(dictionarySize, wordIndex):\n",
"\tvect = np.zeros(dictionarySize)\n",
"\tif wordIndex > 0: vect[wordIndex] = 1\n",
"\treturn vect\n",
"\n",
"# From https://primes.utm.edu/lists/small/100ktwins.txt\n",
"Prime1 = 15327749\n",
"Prime2 = 18409199\n",
"\n",
"# `sequence` must refer to zero-padded sequence.\n",
"# From http://www.fit.vutbr.cz/~imikolov/rnnlm/thesis.pdf, equation 6.6\n",
"def biGramHash(sequence, t, buckets):\n",
"\tt1 = sequence[t - 1] if t - 1 >= 0 else 0\n",
"\treturn (t1 * Prime1) % buckets\n",
"\n",
"def triGramHash(sequence, t, buckets):\n",
"\tt1 = sequence[t - 1] if t - 1 >= 0 else 0\n",
"\tt2 = sequence[t - 2] if t - 2 >= 0 else 0\n",
"\n",
"\treturn (t2 * Prime1 * Prime2 + t1 * Prime1) % buckets\n",
"\n",
"def sentenceVector(tokeniser, dictionarySize, sentence, oneHotVectors, oneHotAveraged, contextHashes):\n",
"\tresult = np.array([])\n",
"\tsequences = tokeniser.texts_to_sequences([sentence])\n",
"\t# Zero-pad every string\n",
"\tpadded = pad_sequences(sequences, maxlen=SequenceLength)[0]\n",
"\n",
"\tif oneHotVectors:\n",
"\t\tiptOneHot = [oneHot(dictionarySize, i) for i in padded]\n",
"\t\tresult = np.append(\n",
"\t\t\tresult,\n",
"\t\t\tnp.mean(iptOneHot, axis=0) if oneHotAveraged else np.concatenate(iptOneHot)\n",
"\t\t)\n",
"\n",
"\tif contextHashes:\n",
"\t\tbuckets = np.zeros(dictionarySize * 2)\n",
"\t\tfor t in range(SequenceLength): buckets[biGramHash(padded, t, dictionarySize)] = 1\n",
"\t\tfor t in range(SequenceLength): buckets[dictionarySize + triGramHash(padded, t, dictionarySize)] = 1\n",
"\t\tresult = np.append(result, buckets)\n",
"\n",
"\treturn result\n",
"\n",
"\n",
"def mapGenerator(generator, tokeniser, dictionarySize, oneHot, oneHotAveraged, contextHashes):\n",
"\tfor row in generator:\n",
"\t\tsentence = row[0]\n",
"\t\tlabel = row[1]\n",
"\n",
"\t\tx = sentenceVector(tokeniser, dictionarySize, sentence, oneHot, oneHotAveraged, contextHashes)\n",
"\t\ty = np.zeros(Labels)\n",
"\t\ty[LabelMapping[label]] = 1\n",
"\t\tyield (x[np.newaxis], y[np.newaxis])\n",
"\n",
"def train(oneHot, oneHotAveraged, contextHashes):\n",
"\tn = (Epochs + 1) * SamplesPerEpoch # TODO + 1 should not be needed\n",
"\n",
"\ttokeniser = Tokenizer(nb_words=MaxWords)\n",
"\ttokeniser.fit_on_texts((row[0] for row in trainingData(n)))\n",
"\n",
"\t# `word_index` maps each word to its unique index\n",
"\tdictionarySize = len(tokeniser.word_index) + 1\n",
"\n",
"\toneHotDimension = (1 if oneHotAveraged else SequenceLength) * dictionarySize if oneHot else 0\n",
"\tcontextHashesDimension = dictionarySize * 2 if contextHashes else 0\n",
"\n",
"\tmodel = Sequential()\n",
"\tmodel.add(Dense(EmbeddingDim, input_dim=(oneHotDimension + contextHashesDimension)))\n",
"\tmodel.add(Dense(Labels, activation='softmax'))\n",
"\tmodel.compile(optimizer='rmsprop', loss='categorical_crossentropy', metrics=['accuracy'])\n",
"\n",
"\ttrainingGenerator = mapGenerator(trainingData(n), tokeniser, dictionarySize, oneHot, oneHotAveraged, contextHashes)\n",
"\tvalidationGenerator = mapGenerator(validationData(n), tokeniser, dictionarySize, oneHot, oneHotAveraged, contextHashes)\n",
"\n",
"\tmodel.fit_generator(trainingGenerator,\n",
"\t\tnb_epoch=Epochs,\n",
"\t\tsamples_per_epoch=SamplesPerEpoch,\n",
"\t\tvalidation_data=validationGenerator,\n",
"\t\tnb_val_samples=SamplesPerEpoch)\n",
"\n",
"\tmodel2 = Sequential()\n",
"\tmodel2.add(Dense(EmbeddingDim, input_dim=(oneHotDimension + contextHashesDimension), weights=model.layers[0].get_weights()))\n",
"\n",
"\treturn model, model2, tokeniser, dictionarySize\n",
"\n",
"# TODO Fix\n",
"def query(model, tokeniser, dictionarySize, sentence):\n",
"\tconcat = sentenceVector(tokeniser, dictionarySize, sentence)\n",
"\treturn model.predict(np.asarray(concat)[np.newaxis])"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import json\n",
"import codecs\n",
"\n",
"DataSetPath = '/home/data/sentiment-analysis-and-text-classification/yelp_dataset_challenge_academic_dataset/yelp_academic_dataset_review.json'\n",
"\n",
"\n",
"def processFile(n, validation):\n",
" with codecs.open(DataSetPath, encoding='iso-8859-1') as f:\n",
" if validation:\n",
" for _ in range(n): next(f)\n",
"\n",
" for _ in range(n):\n",
" line = next(f).strip()\n",
" review = json.loads(line)\n",
"\n",
" while len(review['text'].split()) > 50:\n",
" line = next(f).strip()\n",
" review = json.loads(line)\n",
"\n",
" yield (review['text'], int(review['stars']))\n",
"\n",
"def trainingData(n):\n",
" return processFile(n, validation = False)\n",
"\n",
"def validationData(n):\n",
" return processFile(n, validation = True)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import time\n",
"import six.moves.cPickle\n",
"from sklearn.manifold import TSNE\n",
"import numpy as np\n",
"\n",
"import csv\n"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 1/5\n",
"1000/1000 [==============================] - 24s - loss: 0.6913 - acc: 0.7510 - val_loss: 0.8134 - val_acc: 0.7270\n",
"Epoch 2/5\n",
"1000/1000 [==============================] - 24s - loss: 0.6052 - acc: 0.7840 - val_loss: 0.5645 - val_acc: 0.7980\n",
"Epoch 3/5\n",
"1000/1000 [==============================] - 25s - loss: 0.6514 - acc: 0.7590 - val_loss: 0.7004 - val_acc: 0.7820\n",
"Epoch 4/5\n",
"1000/1000 [==============================] - 25s - loss: 0.5726 - acc: 0.8090 - val_loss: 0.6256 - val_acc: 0.7840\n",
"Epoch 5/5\n",
"1000/1000 [==============================] - 26s - loss: 0.6181 - acc: 0.7660 - val_loss: 0.5753 - val_acc: 0.7740\n"
]
}
],
"source": [
"model, model2, tokeniser, dictionarySize = train(oneHot = True, oneHotAveraged = True, contextHashes=True)"
]
},
{
"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 |
happycube/kaggle2017 | renthop/model-xgb-r3.ipynb | 1 | 105184 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import os\n",
"import sys\n",
"import operator\n",
"import numpy as np\n",
"import pandas as pd\n",
"from scipy import sparse\n",
"import xgboost as xgb\n",
"import random\n",
"from sklearn import model_selection, preprocessing, ensemble\n",
"from sklearn.metrics import log_loss\n",
"from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer\n",
"\n",
"import pickle\n",
"\n",
"import sklearn.cluster\n",
"\n",
"import Levenshtein\n",
"\n",
"from multiprocessing import Pool"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"train_df = pd.read_pickle('fin-dprep-train.pkl')\n",
"test_df = pd.read_pickle('fin-dprep-test.pkl')\n",
"\n",
"features_to_use = pickle.load(open('fin-dprep-flist.pkl', 'rb'))\n",
"\n",
"medium_price = pd.read_pickle('fin-medium-price-r2.pkl')\n",
"\n",
"train_df = pd.merge(train_df, medium_price, left_on='listing_id', right_index=True)\n",
"test_df = pd.merge(test_df, medium_price, left_on='listing_id', right_index=True)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"adams = pd.read_pickle('features-adams.pkl')\n",
"\n",
"train_df = pd.merge(train_df, adams, left_on='listing_id', right_index=True)\n",
"test_df = pd.merge(test_df, adams, left_on='listing_id', right_index=True)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"train_df[\"predicted_price_diff\"] = np.log(train_df[\"price\"]) - np.log(train_df[\"predicted_price\"])\n",
"test_df[\"predicted_price_diff\"] = np.log(test_df[\"price\"]) - np.log(test_df[\"predicted_price\"])"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"class MeansProcessor:\n",
" def __init__(self, key, outkey = None, tgt = 'interest_cat'):\n",
" self.key = key\n",
" self.outkey = key if outkey is None else outkey\n",
" \n",
" self.count = {}\n",
" self.means = {}\n",
" self.std = {}\n",
" self.global_means = 0\n",
" \n",
" self.tgt = tgt\n",
" \n",
" self.outkeys = [self.outkey + '_level', self.outkey + '_level_std']\n",
" \n",
" def fit(self, df):\n",
" self.global_means = df[self.tgt].mean()\n",
" \n",
" for k in df.groupby(self.key, sort=False):\n",
" \n",
" self.count[k[0]] = len(k[1])\n",
"\n",
" if len(k[1]) < 0:\n",
" self.means[k[0]] = np.nan\n",
" self.std[k[0]] = np.nan\n",
" else:\n",
" self.means[k[0]] = np.mean(k[1][self.tgt])\n",
" self.std[k[0]] = np.std(k[1][self.tgt])\n",
" \n",
" def predict(self, df):\n",
" for l in self.outkeys:\n",
" df[l] = np.nan # self.global_means[l]\n",
" \n",
" df[self.outkey + '_count'] = 0\n",
" \n",
" for k in df.groupby(self.key, sort=False):\n",
" if k[0] == 0:\n",
" continue\n",
" \n",
" if k[0] in self.means:\n",
" df.loc[k[1].index, self.outkey + '_count'] = self.count[k[0]]\n",
" df.loc[k[1].index, self.outkey + '_level'] = self.means[k[0]]\n",
" df.loc[k[1].index, self.outkey + '_level_std'] = self.std[k[0]]\n",
" \n",
" return df\n",
" \n",
" def get_features(self):\n",
" return self.outkeys.copy() + [self.outkey + '_count']\n",
"\n",
"# i kept the same index randomization (with fixed seed) so I could validate this code against\n",
"# the original...\n",
"\n",
"target_num_map = {'low':0, 'medium':1, 'high':2}\n",
"train_y = np.array(train_df['interest_level'].apply(lambda x: target_num_map[x]))\n",
"\n",
"def proc_fold(fold):\n",
" train_index = fold[0]\n",
" test_index = fold[1]\n",
" \n",
" cv_train = train_df.iloc[train_index]\n",
" cv_valid = train_df.iloc[test_index][['interest_level', 'manager_id', 'building_id']]\n",
" cv_test = test_df.copy()\n",
" \n",
" m_build = MeansProcessor('building_id', 'building_sort')\n",
" m_build.fit(cv_train)\n",
" cv_valid = m_build.predict(cv_valid)\n",
" cv_test = m_build.predict(cv_test)\n",
"\n",
" m_mgr = MeansProcessor('manager_id', 'manager_sort')\n",
" m_mgr.fit(cv_train)\n",
" cv_valid = m_mgr.predict(cv_valid)\n",
" cv_test = m_mgr.predict(cv_test)\n",
"\n",
" m_comb = MeansProcessor(['building_id', 'manager_id'], 'mb_comb')\n",
" m_comb.fit(cv_train)\n",
" cv_valid = m_comb.predict(cv_valid)\n",
" cv_test = m_comb.predict(cv_test)\n",
"\n",
" return cv_train, cv_valid, cv_test\n",
"\n",
"kf = model_selection.StratifiedKFold(n_splits=5, shuffle=True, random_state=2016)\n",
"folds = [(k[0], k[1]) for k in kf.split(list(range(train_df.shape[0])), train_y)]\n",
"\n",
"#with Pool(5) as pool:\n",
"# rv = pool.map(proc_fold, folds)\n",
"\n",
"import pickle\n",
"\n",
"try:\n",
" rv = pickle.load(open('0420-model-groupfeatures.pkl', 'rb'))\n",
"except:\n",
" with Pool(5) as pool:\n",
" rv = pool.map(proc_fold, folds)\n",
"\n",
" pickle.dump(rv, open('0420-model-groupfeatures.pkl', 'wb'))\n",
"\n",
"# dummies to get feature id's\n",
"m_build = MeansProcessor('building_id', 'building_sort')\n",
"m_mgr = MeansProcessor('manager_id', 'manager_sort')\n",
"m_comb = MeansProcessor(['building_id', 'manager_id'], 'mb_comb')\n",
"\n",
"group_features = m_build.get_features() + m_mgr.get_features() + m_comb.get_features()\n",
"\n",
"cv_test = []\n",
"for r in rv:\n",
" cv_test.append(test_df.merge(r[2][group_features], left_index=True, right_index=True))\n",
"\n",
"cv_allvalid = pd.concat([r[1] for r in rv])\n",
"\n",
"train_df = train_df.merge(cv_allvalid[group_features], left_index=True, right_index=True)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"train_ids = []\n",
"val_ids = []\n",
"\n",
"for dev_index, val_index in kf.split(range(train_df.shape[0]), train_df.interest_cat):\n",
" train_ids.append(train_df.iloc[dev_index].listing_id.values)\n",
" val_ids.append(train_df.iloc[val_index].listing_id.values)\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"adams_features = ['num_rot15_X', 'num_rot15_Y', 'num_rot30_X', 'num_rot30_Y', 'num_rot45_X', 'num_rot45_Y', 'num_rot60_X', 'num_rot60_Y', 'num_rho', 'num_phi', 'num_cap_share', 'num_nr_of_lines', 'num_redacted', 'num_email', 'num_phone_nr']\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#fl = features_to_use + m_build.get_features() + m_mgr.get_features() + m_comb.get_features() + tfidf_fn\n",
"\n",
"fl = features_to_use.copy() + group_features + adams_features.copy()\n",
"\n",
"#fl.remove('price')\n",
"#fl.remove('price_t')\n",
"#fl.remove('price_per_room')\n",
"fl.append('predicted_price')\n",
"fl.append('predicted_price_diff')\n",
"\n",
"fl.append('manager_lazy_rate')\n",
"\n",
"fl.append('density_exp01')"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def run3_to_stackdf(run):\n",
" \n",
" df_testpreds3 = pd.DataFrame(run[2].mean(axis=0))\n",
" df_testpreds3.columns = ['low', 'medium', 'high']\n",
" df_testpreds3['listing_id'] = test_df.listing_id\n",
"\n",
" df_allpreds3 = pd.concat([run[1][['low', 'medium', 'high', 'listing_id']], df_testpreds3])\n",
"\n",
" df_allpreds3.sort_values('listing_id', inplace=True)\n",
" df_allpreds3.set_index('listing_id', inplace=True)\n",
" \n",
" df_fold = []\n",
" for f in range(run[2].shape[0]):\n",
" df_fold.append(pd.DataFrame(run[2][f]))\n",
" df_fold[-1]['listing_id'] = test_df.listing_id\n",
" df_fold[-1].sort_values('listing_id', inplace=True)\n",
" df_fold[-1].set_index('listing_id', inplace=True)\n",
"\n",
" return (df_allpreds3, df_fold)\n"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"_cell_guid": "15a46712-582b-28f7-1626-917cdab4f7e5",
"collapsed": true
},
"outputs": [],
"source": [
"def runXGB(train_X, train_y, test_X, test_y=None, feature_names=None, seed_val=0, num_rounds=4000):\n",
" param = {}\n",
" param['objective'] = 'multi:softprob'\n",
" #param['tree_method'] = 'hist'\n",
" param['eta'] = 0.02\n",
" param['max_depth'] = 6\n",
" param['silent'] = 1\n",
" param['num_class'] = 3\n",
" param['eval_metric'] = \"mlogloss\"\n",
" param['min_child_weight'] = 1\n",
" param['subsample'] = 0.7\n",
" param['colsample_bytree'] = 0.7\n",
" param['seed'] = seed_val\n",
" #param['base_score'] = [np.mean(train_y == i) for i in [0, 1, 2]]\n",
" num_rounds = num_rounds\n",
"\n",
" plst = list(param.items())\n",
" xgtrain = xgb.DMatrix(train_X, label=train_y)\n",
"\n",
" if test_y is not None:\n",
" xgtest = xgb.DMatrix(test_X, label=test_y)\n",
" watchlist = [ (xgtrain,'train'), (xgtest, 'test') ]\n",
" model = xgb.train(plst, xgtrain, num_rounds, watchlist, early_stopping_rounds=50, verbose_eval=10)\n",
" else:\n",
" xgtest = xgb.DMatrix(test_X)\n",
" model = xgb.train(plst, xgtrain, num_rounds)\n",
"\n",
" pred_test_y = model.predict(xgtest, ntree_limit=model.best_ntree_limit)\n",
" return pred_test_y, model"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def run_cv(train_df, cv_test, kf, features_to_use):\n",
" train_X = train_df[features_to_use]\n",
" train_y = np.array(train_df['interest_level'].apply(lambda x: target_num_map[x]))\n",
"\n",
" cv_preds = []\n",
" cv_scores = []\n",
" models = []\n",
" test_preds = []\n",
" \n",
" fold = 0\n",
"\n",
" for dev_index, val_index in kf.split(range(train_X.shape[0]), train_y):\n",
"\n",
" dev_X, val_X = train_X.iloc[dev_index], train_X.iloc[val_index]\n",
" dev_y, val_y = train_y[dev_index], train_y[val_index]\n",
" preds, model = runXGB(dev_X, dev_y, val_X, val_y)\n",
" models.append(model)\n",
"\n",
" cv_scores.append(log_loss(val_y, preds))\n",
" print(cv_scores)\n",
"\n",
" cut_df = train_df.iloc[val_index]\n",
" out_df = pd.DataFrame(preds)\n",
" out_df.columns = [\"low\", \"medium\", \"high\"]\n",
" out_df[\"listing_id\"] = cut_df.listing_id.values\n",
" interest = cut_df.interest_level.apply(lambda x: target_num_map[x])\n",
" out_df['interest_tgt'] = interest.values\n",
"\n",
" cv_preds.append(out_df)\n",
"\n",
" xgtest = xgb.DMatrix(cv_test[fold][features_to_use])\n",
" test_preds.append(model.predict(xgtest, ntree_limit=model.best_ntree_limit))\n",
"\n",
" df_cv = pd.concat(cv_preds)\n",
" print(log_loss(df_cv.interest_tgt, df_cv[['low', 'medium', 'high']]))\n",
"\n",
" apreds = np.array(test_preds)\n",
" \n",
" return models, df_cv, apreds"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[0]\ttrain-mlogloss:1.0843\ttest-mlogloss:1.08452\n",
"Multiple eval metrics have been passed: 'test-mlogloss' will be used for early stopping.\n",
"\n",
"Will train until test-mlogloss hasn't improved in 50 rounds.\n",
"[10]\ttrain-mlogloss:0.962632\ttest-mlogloss:0.965063\n",
"[20]\ttrain-mlogloss:0.871514\ttest-mlogloss:0.875962\n",
"[30]\ttrain-mlogloss:0.800614\ttest-mlogloss:0.80751\n",
"[40]\ttrain-mlogloss:0.745262\ttest-mlogloss:0.754446\n",
"[50]\ttrain-mlogloss:0.700616\ttest-mlogloss:0.712114\n",
"[60]\ttrain-mlogloss:0.665245\ttest-mlogloss:0.679158\n",
"[70]\ttrain-mlogloss:0.636389\ttest-mlogloss:0.652703\n",
"[80]\ttrain-mlogloss:0.612529\ttest-mlogloss:0.631245\n",
"[90]\ttrain-mlogloss:0.592969\ttest-mlogloss:0.613856\n",
"[100]\ttrain-mlogloss:0.576529\ttest-mlogloss:0.599517\n",
"[110]\ttrain-mlogloss:0.562609\ttest-mlogloss:0.587732\n",
"[120]\ttrain-mlogloss:0.550703\ttest-mlogloss:0.577969\n",
"[130]\ttrain-mlogloss:0.540578\ttest-mlogloss:0.569841\n",
"[140]\ttrain-mlogloss:0.531801\ttest-mlogloss:0.562976\n",
"[150]\ttrain-mlogloss:0.524123\ttest-mlogloss:0.557312\n",
"[160]\ttrain-mlogloss:0.517427\ttest-mlogloss:0.552572\n",
"[170]\ttrain-mlogloss:0.511243\ttest-mlogloss:0.548362\n",
"[180]\ttrain-mlogloss:0.505627\ttest-mlogloss:0.54462\n",
"[190]\ttrain-mlogloss:0.500577\ttest-mlogloss:0.541516\n",
"[200]\ttrain-mlogloss:0.496057\ttest-mlogloss:0.538659\n",
"[210]\ttrain-mlogloss:0.491808\ttest-mlogloss:0.536176\n",
"[220]\ttrain-mlogloss:0.487873\ttest-mlogloss:0.534084\n",
"[230]\ttrain-mlogloss:0.484331\ttest-mlogloss:0.532278\n",
"[240]\ttrain-mlogloss:0.480666\ttest-mlogloss:0.53036\n",
"[250]\ttrain-mlogloss:0.477285\ttest-mlogloss:0.528804\n",
"[260]\ttrain-mlogloss:0.47432\ttest-mlogloss:0.527401\n",
"[270]\ttrain-mlogloss:0.471564\ttest-mlogloss:0.526083\n",
"[280]\ttrain-mlogloss:0.468669\ttest-mlogloss:0.524847\n",
"[290]\ttrain-mlogloss:0.465895\ttest-mlogloss:0.523686\n",
"[300]\ttrain-mlogloss:0.463197\ttest-mlogloss:0.522611\n",
"[310]\ttrain-mlogloss:0.460487\ttest-mlogloss:0.521429\n",
"[320]\ttrain-mlogloss:0.457938\ttest-mlogloss:0.520591\n",
"[330]\ttrain-mlogloss:0.455568\ttest-mlogloss:0.519834\n",
"[340]\ttrain-mlogloss:0.453265\ttest-mlogloss:0.519078\n",
"[350]\ttrain-mlogloss:0.451087\ttest-mlogloss:0.518366\n",
"[360]\ttrain-mlogloss:0.448954\ttest-mlogloss:0.517783\n",
"[370]\ttrain-mlogloss:0.446849\ttest-mlogloss:0.517062\n",
"[380]\ttrain-mlogloss:0.444869\ttest-mlogloss:0.5165\n",
"[390]\ttrain-mlogloss:0.442804\ttest-mlogloss:0.515907\n",
"[400]\ttrain-mlogloss:0.440687\ttest-mlogloss:0.515336\n",
"[410]\ttrain-mlogloss:0.438678\ttest-mlogloss:0.514888\n",
"[420]\ttrain-mlogloss:0.436954\ttest-mlogloss:0.514387\n",
"[430]\ttrain-mlogloss:0.435228\ttest-mlogloss:0.513875\n",
"[440]\ttrain-mlogloss:0.433318\ttest-mlogloss:0.513475\n",
"[450]\ttrain-mlogloss:0.431625\ttest-mlogloss:0.513108\n",
"[460]\ttrain-mlogloss:0.429857\ttest-mlogloss:0.512725\n",
"[470]\ttrain-mlogloss:0.428154\ttest-mlogloss:0.512363\n",
"[480]\ttrain-mlogloss:0.426557\ttest-mlogloss:0.511999\n",
"[490]\ttrain-mlogloss:0.424958\ttest-mlogloss:0.511635\n",
"[500]\ttrain-mlogloss:0.423205\ttest-mlogloss:0.511248\n",
"[510]\ttrain-mlogloss:0.421512\ttest-mlogloss:0.510888\n",
"[520]\ttrain-mlogloss:0.419972\ttest-mlogloss:0.510564\n",
"[530]\ttrain-mlogloss:0.418096\ttest-mlogloss:0.510237\n",
"[540]\ttrain-mlogloss:0.41628\ttest-mlogloss:0.509821\n",
"[550]\ttrain-mlogloss:0.414723\ttest-mlogloss:0.50958\n",
"[560]\ttrain-mlogloss:0.412994\ttest-mlogloss:0.509304\n",
"[570]\ttrain-mlogloss:0.411486\ttest-mlogloss:0.509035\n",
"[580]\ttrain-mlogloss:0.409767\ttest-mlogloss:0.508842\n",
"[590]\ttrain-mlogloss:0.408127\ttest-mlogloss:0.508621\n",
"[600]\ttrain-mlogloss:0.406433\ttest-mlogloss:0.508357\n",
"[610]\ttrain-mlogloss:0.404847\ttest-mlogloss:0.508142\n",
"[620]\ttrain-mlogloss:0.40339\ttest-mlogloss:0.507952\n",
"[630]\ttrain-mlogloss:0.401846\ttest-mlogloss:0.507808\n",
"[640]\ttrain-mlogloss:0.400383\ttest-mlogloss:0.50764\n",
"[650]\ttrain-mlogloss:0.398718\ttest-mlogloss:0.507481\n",
"[660]\ttrain-mlogloss:0.397126\ttest-mlogloss:0.507334\n",
"[670]\ttrain-mlogloss:0.39575\ttest-mlogloss:0.507187\n",
"[680]\ttrain-mlogloss:0.394337\ttest-mlogloss:0.507069\n",
"[690]\ttrain-mlogloss:0.392872\ttest-mlogloss:0.506936\n",
"[700]\ttrain-mlogloss:0.391407\ttest-mlogloss:0.506771\n",
"[710]\ttrain-mlogloss:0.389959\ttest-mlogloss:0.506608\n",
"[720]\ttrain-mlogloss:0.388267\ttest-mlogloss:0.506379\n",
"[730]\ttrain-mlogloss:0.386816\ttest-mlogloss:0.506181\n",
"[740]\ttrain-mlogloss:0.38532\ttest-mlogloss:0.505946\n",
"[750]\ttrain-mlogloss:0.384012\ttest-mlogloss:0.505938\n",
"[760]\ttrain-mlogloss:0.382522\ttest-mlogloss:0.505894\n",
"[770]\ttrain-mlogloss:0.380949\ttest-mlogloss:0.505814\n",
"[780]\ttrain-mlogloss:0.379504\ttest-mlogloss:0.505735\n",
"[790]\ttrain-mlogloss:0.378122\ttest-mlogloss:0.505645\n",
"[800]\ttrain-mlogloss:0.376661\ttest-mlogloss:0.505621\n",
"[810]\ttrain-mlogloss:0.375335\ttest-mlogloss:0.505439\n",
"[820]\ttrain-mlogloss:0.373878\ttest-mlogloss:0.505299\n",
"[830]\ttrain-mlogloss:0.372356\ttest-mlogloss:0.50525\n",
"[840]\ttrain-mlogloss:0.371048\ttest-mlogloss:0.505213\n",
"[850]\ttrain-mlogloss:0.369632\ttest-mlogloss:0.505144\n",
"[860]\ttrain-mlogloss:0.36851\ttest-mlogloss:0.505121\n",
"[870]\ttrain-mlogloss:0.366969\ttest-mlogloss:0.505027\n",
"[880]\ttrain-mlogloss:0.365698\ttest-mlogloss:0.504924\n",
"[890]\ttrain-mlogloss:0.364287\ttest-mlogloss:0.504829\n",
"[900]\ttrain-mlogloss:0.362869\ttest-mlogloss:0.504778\n",
"[910]\ttrain-mlogloss:0.361409\ttest-mlogloss:0.504671\n",
"[920]\ttrain-mlogloss:0.359955\ttest-mlogloss:0.504559\n",
"[930]\ttrain-mlogloss:0.3586\ttest-mlogloss:0.504512\n",
"[940]\ttrain-mlogloss:0.357286\ttest-mlogloss:0.504529\n",
"[950]\ttrain-mlogloss:0.355944\ttest-mlogloss:0.50434\n",
"[960]\ttrain-mlogloss:0.354625\ttest-mlogloss:0.504263\n",
"[970]\ttrain-mlogloss:0.353351\ttest-mlogloss:0.5042\n",
"[980]\ttrain-mlogloss:0.352217\ttest-mlogloss:0.504188\n",
"[990]\ttrain-mlogloss:0.350983\ttest-mlogloss:0.504131\n",
"[1000]\ttrain-mlogloss:0.349605\ttest-mlogloss:0.504078\n",
"[1010]\ttrain-mlogloss:0.348465\ttest-mlogloss:0.504065\n",
"[1020]\ttrain-mlogloss:0.347195\ttest-mlogloss:0.50405\n",
"[1030]\ttrain-mlogloss:0.34616\ttest-mlogloss:0.504\n",
"[1040]\ttrain-mlogloss:0.344973\ttest-mlogloss:0.504012\n",
"[1050]\ttrain-mlogloss:0.343676\ttest-mlogloss:0.503997\n",
"[1060]\ttrain-mlogloss:0.342548\ttest-mlogloss:0.503949\n",
"[1070]\ttrain-mlogloss:0.341165\ttest-mlogloss:0.503902\n",
"[1080]\ttrain-mlogloss:0.340019\ttest-mlogloss:0.503825\n",
"[1090]\ttrain-mlogloss:0.338769\ttest-mlogloss:0.503852\n",
"[1100]\ttrain-mlogloss:0.337513\ttest-mlogloss:0.503765\n",
"[1110]\ttrain-mlogloss:0.33628\ttest-mlogloss:0.503702\n",
"[1120]\ttrain-mlogloss:0.335216\ttest-mlogloss:0.503638\n",
"[1130]\ttrain-mlogloss:0.333951\ttest-mlogloss:0.503662\n",
"[1140]\ttrain-mlogloss:0.33272\ttest-mlogloss:0.503663\n",
"[1150]\ttrain-mlogloss:0.331435\ttest-mlogloss:0.50358\n",
"[1160]\ttrain-mlogloss:0.330239\ttest-mlogloss:0.503633\n",
"[1170]\ttrain-mlogloss:0.328997\ttest-mlogloss:0.503613\n",
"[1180]\ttrain-mlogloss:0.327721\ttest-mlogloss:0.50357\n",
"[1190]\ttrain-mlogloss:0.326513\ttest-mlogloss:0.503569\n",
"Stopping. Best iteration:\n",
"[1148]\ttrain-mlogloss:0.331644\ttest-mlogloss:0.503548\n",
"\n",
"[0.50354797493140779]\n",
"[0]\ttrain-mlogloss:1.08441\ttest-mlogloss:1.08447\n",
"Multiple eval metrics have been passed: 'test-mlogloss' will be used for early stopping.\n",
"\n",
"Will train until test-mlogloss hasn't improved in 50 rounds.\n",
"[10]\ttrain-mlogloss:0.963338\ttest-mlogloss:0.96512\n",
"[20]\ttrain-mlogloss:0.872293\ttest-mlogloss:0.875789\n",
"[30]\ttrain-mlogloss:0.801673\ttest-mlogloss:0.80687\n",
"[40]\ttrain-mlogloss:0.746456\ttest-mlogloss:0.753397\n",
"[50]\ttrain-mlogloss:0.701918\ttest-mlogloss:0.710507\n",
"[60]\ttrain-mlogloss:0.666698\ttest-mlogloss:0.676959\n",
"[70]\ttrain-mlogloss:0.637917\ttest-mlogloss:0.65002\n",
"[80]\ttrain-mlogloss:0.614188\ttest-mlogloss:0.62805\n",
"[90]\ttrain-mlogloss:0.59472\ttest-mlogloss:0.610331\n",
"[100]\ttrain-mlogloss:0.578207\ttest-mlogloss:0.59564\n",
"[110]\ttrain-mlogloss:0.564296\ttest-mlogloss:0.583486\n",
"[120]\ttrain-mlogloss:0.552471\ttest-mlogloss:0.573411\n",
"[130]\ttrain-mlogloss:0.542331\ttest-mlogloss:0.565022\n",
"[140]\ttrain-mlogloss:0.533491\ttest-mlogloss:0.557949\n",
"[150]\ttrain-mlogloss:0.525761\ttest-mlogloss:0.55193\n",
"[160]\ttrain-mlogloss:0.518923\ttest-mlogloss:0.546847\n",
"[170]\ttrain-mlogloss:0.512907\ttest-mlogloss:0.542477\n",
"[180]\ttrain-mlogloss:0.507334\ttest-mlogloss:0.538635\n",
"[190]\ttrain-mlogloss:0.502388\ttest-mlogloss:0.535317\n",
"[200]\ttrain-mlogloss:0.497888\ttest-mlogloss:0.532502\n",
"[210]\ttrain-mlogloss:0.493461\ttest-mlogloss:0.530008\n",
"[220]\ttrain-mlogloss:0.489623\ttest-mlogloss:0.527737\n",
"[230]\ttrain-mlogloss:0.485919\ttest-mlogloss:0.525744\n",
"[240]\ttrain-mlogloss:0.482474\ttest-mlogloss:0.523962\n",
"[250]\ttrain-mlogloss:0.479292\ttest-mlogloss:0.522355\n",
"[260]\ttrain-mlogloss:0.476407\ttest-mlogloss:0.520875\n",
"[270]\ttrain-mlogloss:0.473338\ttest-mlogloss:0.51953\n",
"[280]\ttrain-mlogloss:0.470426\ttest-mlogloss:0.518263\n",
"[290]\ttrain-mlogloss:0.467696\ttest-mlogloss:0.517177\n",
"[300]\ttrain-mlogloss:0.465222\ttest-mlogloss:0.516321\n",
"[310]\ttrain-mlogloss:0.462826\ttest-mlogloss:0.51541\n",
"[320]\ttrain-mlogloss:0.460516\ttest-mlogloss:0.514552\n",
"[330]\ttrain-mlogloss:0.457944\ttest-mlogloss:0.513648\n",
"[340]\ttrain-mlogloss:0.455696\ttest-mlogloss:0.512938\n",
"[350]\ttrain-mlogloss:0.453398\ttest-mlogloss:0.512143\n",
"[360]\ttrain-mlogloss:0.451104\ttest-mlogloss:0.511343\n",
"[370]\ttrain-mlogloss:0.448978\ttest-mlogloss:0.510797\n",
"[380]\ttrain-mlogloss:0.446959\ttest-mlogloss:0.510284\n",
"[390]\ttrain-mlogloss:0.444992\ttest-mlogloss:0.509778\n",
"[400]\ttrain-mlogloss:0.443042\ttest-mlogloss:0.50922\n",
"[410]\ttrain-mlogloss:0.440953\ttest-mlogloss:0.508712\n",
"[420]\ttrain-mlogloss:0.438952\ttest-mlogloss:0.5083\n",
"[430]\ttrain-mlogloss:0.437078\ttest-mlogloss:0.507833\n",
"[440]\ttrain-mlogloss:0.435347\ttest-mlogloss:0.507542\n",
"[450]\ttrain-mlogloss:0.433531\ttest-mlogloss:0.507203\n",
"[460]\ttrain-mlogloss:0.431669\ttest-mlogloss:0.506852\n",
"[470]\ttrain-mlogloss:0.429938\ttest-mlogloss:0.506513\n",
"[480]\ttrain-mlogloss:0.428168\ttest-mlogloss:0.506195\n",
"[490]\ttrain-mlogloss:0.426265\ttest-mlogloss:0.505867\n",
"[500]\ttrain-mlogloss:0.42444\ttest-mlogloss:0.505696\n",
"[510]\ttrain-mlogloss:0.422833\ttest-mlogloss:0.50536\n",
"[520]\ttrain-mlogloss:0.42106\ttest-mlogloss:0.505053\n",
"[530]\ttrain-mlogloss:0.419275\ttest-mlogloss:0.504855\n",
"[540]\ttrain-mlogloss:0.417739\ttest-mlogloss:0.504622\n",
"[550]\ttrain-mlogloss:0.415985\ttest-mlogloss:0.504339\n",
"[560]\ttrain-mlogloss:0.41443\ttest-mlogloss:0.503984\n",
"[570]\ttrain-mlogloss:0.41288\ttest-mlogloss:0.503696\n",
"[580]\ttrain-mlogloss:0.411124\ttest-mlogloss:0.50348\n",
"[590]\ttrain-mlogloss:0.409498\ttest-mlogloss:0.503242\n",
"[600]\ttrain-mlogloss:0.407858\ttest-mlogloss:0.503054\n",
"[610]\ttrain-mlogloss:0.406342\ttest-mlogloss:0.5029\n",
"[620]\ttrain-mlogloss:0.404835\ttest-mlogloss:0.502742\n",
"[630]\ttrain-mlogloss:0.403322\ttest-mlogloss:0.502587\n",
"[640]\ttrain-mlogloss:0.401903\ttest-mlogloss:0.502477\n",
"[650]\ttrain-mlogloss:0.400476\ttest-mlogloss:0.502355\n",
"[660]\ttrain-mlogloss:0.39893\ttest-mlogloss:0.502176\n",
"[670]\ttrain-mlogloss:0.397316\ttest-mlogloss:0.502036\n",
"[680]\ttrain-mlogloss:0.395909\ttest-mlogloss:0.501942\n",
"[690]\ttrain-mlogloss:0.394373\ttest-mlogloss:0.501752\n",
"[700]\ttrain-mlogloss:0.392947\ttest-mlogloss:0.501647\n",
"[710]\ttrain-mlogloss:0.391624\ttest-mlogloss:0.501503\n",
"[720]\ttrain-mlogloss:0.390085\ttest-mlogloss:0.501304\n",
"[730]\ttrain-mlogloss:0.388485\ttest-mlogloss:0.501104\n",
"[740]\ttrain-mlogloss:0.387125\ttest-mlogloss:0.501058\n",
"[750]\ttrain-mlogloss:0.385653\ttest-mlogloss:0.500946\n",
"[760]\ttrain-mlogloss:0.38423\ttest-mlogloss:0.500878\n",
"[770]\ttrain-mlogloss:0.382705\ttest-mlogloss:0.500801\n",
"[780]\ttrain-mlogloss:0.381314\ttest-mlogloss:0.500695\n",
"[790]\ttrain-mlogloss:0.379896\ttest-mlogloss:0.500481\n",
"[800]\ttrain-mlogloss:0.378673\ttest-mlogloss:0.500342\n",
"[810]\ttrain-mlogloss:0.377267\ttest-mlogloss:0.500304\n",
"[820]\ttrain-mlogloss:0.375808\ttest-mlogloss:0.500208\n",
"[830]\ttrain-mlogloss:0.37454\ttest-mlogloss:0.500205\n",
"[840]\ttrain-mlogloss:0.373047\ttest-mlogloss:0.500074\n",
"[850]\ttrain-mlogloss:0.371705\ttest-mlogloss:0.499973\n",
"[860]\ttrain-mlogloss:0.370419\ttest-mlogloss:0.499936\n",
"[870]\ttrain-mlogloss:0.369036\ttest-mlogloss:0.499895\n",
"[880]\ttrain-mlogloss:0.3678\ttest-mlogloss:0.499854\n",
"[890]\ttrain-mlogloss:0.3665\ttest-mlogloss:0.499792\n",
"[900]\ttrain-mlogloss:0.365206\ttest-mlogloss:0.499683\n",
"[910]\ttrain-mlogloss:0.363882\ttest-mlogloss:0.499602\n",
"[920]\ttrain-mlogloss:0.362477\ttest-mlogloss:0.499523\n",
"[930]\ttrain-mlogloss:0.361074\ttest-mlogloss:0.499432\n",
"[940]\ttrain-mlogloss:0.359805\ttest-mlogloss:0.499419\n",
"[950]\ttrain-mlogloss:0.358464\ttest-mlogloss:0.499283\n",
"[960]\ttrain-mlogloss:0.357269\ttest-mlogloss:0.499233\n",
"[970]\ttrain-mlogloss:0.355916\ttest-mlogloss:0.499079\n",
"[980]\ttrain-mlogloss:0.35467\ttest-mlogloss:0.499032\n",
"[990]\ttrain-mlogloss:0.353303\ttest-mlogloss:0.49894\n",
"[1000]\ttrain-mlogloss:0.351995\ttest-mlogloss:0.498896\n",
"[1010]\ttrain-mlogloss:0.350829\ttest-mlogloss:0.498867\n",
"[1020]\ttrain-mlogloss:0.349608\ttest-mlogloss:0.498833\n",
"[1030]\ttrain-mlogloss:0.34845\ttest-mlogloss:0.498787\n",
"[1040]\ttrain-mlogloss:0.34714\ttest-mlogloss:0.498707\n",
"[1050]\ttrain-mlogloss:0.345986\ttest-mlogloss:0.498684\n",
"[1060]\ttrain-mlogloss:0.344971\ttest-mlogloss:0.498691\n",
"[1070]\ttrain-mlogloss:0.343566\ttest-mlogloss:0.498622\n",
"[1080]\ttrain-mlogloss:0.342284\ttest-mlogloss:0.498547\n",
"[1090]\ttrain-mlogloss:0.340959\ttest-mlogloss:0.49846\n",
"[1100]\ttrain-mlogloss:0.339795\ttest-mlogloss:0.498383\n",
"[1110]\ttrain-mlogloss:0.33853\ttest-mlogloss:0.498366\n",
"[1120]\ttrain-mlogloss:0.337245\ttest-mlogloss:0.498369\n",
"[1130]\ttrain-mlogloss:0.33597\ttest-mlogloss:0.4983\n",
"[1140]\ttrain-mlogloss:0.334827\ttest-mlogloss:0.498267\n",
"[1150]\ttrain-mlogloss:0.333687\ttest-mlogloss:0.498134\n",
"[1160]\ttrain-mlogloss:0.332545\ttest-mlogloss:0.498063\n",
"[1170]\ttrain-mlogloss:0.33133\ttest-mlogloss:0.497993\n",
"[1180]\ttrain-mlogloss:0.330098\ttest-mlogloss:0.497883\n",
"[1190]\ttrain-mlogloss:0.328995\ttest-mlogloss:0.49781\n",
"[1200]\ttrain-mlogloss:0.327856\ttest-mlogloss:0.497813\n",
"[1210]\ttrain-mlogloss:0.326682\ttest-mlogloss:0.497791\n",
"[1220]\ttrain-mlogloss:0.325566\ttest-mlogloss:0.497639\n",
"[1230]\ttrain-mlogloss:0.324294\ttest-mlogloss:0.497647\n",
"[1240]\ttrain-mlogloss:0.323246\ttest-mlogloss:0.49762\n",
"[1250]\ttrain-mlogloss:0.322107\ttest-mlogloss:0.497593\n",
"[1260]\ttrain-mlogloss:0.320932\ttest-mlogloss:0.497562\n",
"[1270]\ttrain-mlogloss:0.319832\ttest-mlogloss:0.497478\n",
"[1280]\ttrain-mlogloss:0.318678\ttest-mlogloss:0.497483\n",
"[1290]\ttrain-mlogloss:0.317526\ttest-mlogloss:0.497437\n",
"[1300]\ttrain-mlogloss:0.316343\ttest-mlogloss:0.497394\n",
"[1310]\ttrain-mlogloss:0.315194\ttest-mlogloss:0.497328\n",
"[1320]\ttrain-mlogloss:0.314014\ttest-mlogloss:0.497331\n",
"[1330]\ttrain-mlogloss:0.313055\ttest-mlogloss:0.497315\n",
"[1340]\ttrain-mlogloss:0.312034\ttest-mlogloss:0.497276\n",
"[1350]\ttrain-mlogloss:0.310972\ttest-mlogloss:0.497395\n",
"[1360]\ttrain-mlogloss:0.309897\ttest-mlogloss:0.497488\n",
"[1370]\ttrain-mlogloss:0.308827\ttest-mlogloss:0.497463\n",
"[1380]\ttrain-mlogloss:0.30767\ttest-mlogloss:0.497458\n",
"[1390]\ttrain-mlogloss:0.306563\ttest-mlogloss:0.497535\n",
"Stopping. Best iteration:\n",
"[1340]\ttrain-mlogloss:0.312034\ttest-mlogloss:0.497276\n",
"\n",
"[0.50354797493140779, 0.49727635213071869]\n",
"[0]\ttrain-mlogloss:1.08426\ttest-mlogloss:1.08473\n",
"Multiple eval metrics have been passed: 'test-mlogloss' will be used for early stopping.\n",
"\n",
"Will train until test-mlogloss hasn't improved in 50 rounds.\n",
"[10]\ttrain-mlogloss:0.962354\ttest-mlogloss:0.966784\n",
"[20]\ttrain-mlogloss:0.870781\ttest-mlogloss:0.87868\n",
"[30]\ttrain-mlogloss:0.799577\ttest-mlogloss:0.810948\n",
"[40]\ttrain-mlogloss:0.744205\ttest-mlogloss:0.758608\n",
"[50]\ttrain-mlogloss:0.699562\ttest-mlogloss:0.716695\n",
"[60]\ttrain-mlogloss:0.664085\ttest-mlogloss:0.683849\n",
"[70]\ttrain-mlogloss:0.635242\ttest-mlogloss:0.657538\n",
"[80]\ttrain-mlogloss:0.611469\ttest-mlogloss:0.636129\n",
"[90]\ttrain-mlogloss:0.591668\ttest-mlogloss:0.618659\n",
"[100]\ttrain-mlogloss:0.575058\ttest-mlogloss:0.60424\n",
"[110]\ttrain-mlogloss:0.561069\ttest-mlogloss:0.592427\n",
"[120]\ttrain-mlogloss:0.549162\ttest-mlogloss:0.582647\n",
"[130]\ttrain-mlogloss:0.538953\ttest-mlogloss:0.574408\n",
"[140]\ttrain-mlogloss:0.530174\ttest-mlogloss:0.567637\n",
"[150]\ttrain-mlogloss:0.522513\ttest-mlogloss:0.561946\n",
"[160]\ttrain-mlogloss:0.51571\ttest-mlogloss:0.557206\n",
"[170]\ttrain-mlogloss:0.509541\ttest-mlogloss:0.552922\n",
"[180]\ttrain-mlogloss:0.504099\ttest-mlogloss:0.549406\n",
"[190]\ttrain-mlogloss:0.499039\ttest-mlogloss:0.546253\n",
"[200]\ttrain-mlogloss:0.494542\ttest-mlogloss:0.543474\n",
"[210]\ttrain-mlogloss:0.490419\ttest-mlogloss:0.541056\n",
"[220]\ttrain-mlogloss:0.486534\ttest-mlogloss:0.538834\n",
"[230]\ttrain-mlogloss:0.482746\ttest-mlogloss:0.536719\n",
"[240]\ttrain-mlogloss:0.479371\ttest-mlogloss:0.534935\n",
"[250]\ttrain-mlogloss:0.476163\ttest-mlogloss:0.533408\n",
"[260]\ttrain-mlogloss:0.473017\ttest-mlogloss:0.53202\n",
"[270]\ttrain-mlogloss:0.470215\ttest-mlogloss:0.530825\n",
"[280]\ttrain-mlogloss:0.467495\ttest-mlogloss:0.529702\n",
"[290]\ttrain-mlogloss:0.464672\ttest-mlogloss:0.528519\n",
"[300]\ttrain-mlogloss:0.461912\ttest-mlogloss:0.527592\n",
"[310]\ttrain-mlogloss:0.459434\ttest-mlogloss:0.52673\n",
"[320]\ttrain-mlogloss:0.456968\ttest-mlogloss:0.525802\n",
"[330]\ttrain-mlogloss:0.454311\ttest-mlogloss:0.524903\n",
"[340]\ttrain-mlogloss:0.452007\ttest-mlogloss:0.52426\n",
"[350]\ttrain-mlogloss:0.449917\ttest-mlogloss:0.523643\n",
"[360]\ttrain-mlogloss:0.447734\ttest-mlogloss:0.523065\n",
"[370]\ttrain-mlogloss:0.445301\ttest-mlogloss:0.522406\n",
"[380]\ttrain-mlogloss:0.443257\ttest-mlogloss:0.521839\n",
"[390]\ttrain-mlogloss:0.441171\ttest-mlogloss:0.521375\n",
"[400]\ttrain-mlogloss:0.439199\ttest-mlogloss:0.520917\n",
"[410]\ttrain-mlogloss:0.437249\ttest-mlogloss:0.52042\n",
"[420]\ttrain-mlogloss:0.435247\ttest-mlogloss:0.519972\n",
"[430]\ttrain-mlogloss:0.433564\ttest-mlogloss:0.519614\n",
"[440]\ttrain-mlogloss:0.431711\ttest-mlogloss:0.519282\n",
"[450]\ttrain-mlogloss:0.429893\ttest-mlogloss:0.51893\n",
"[460]\ttrain-mlogloss:0.427968\ttest-mlogloss:0.518588\n",
"[470]\ttrain-mlogloss:0.426423\ttest-mlogloss:0.518248\n",
"[480]\ttrain-mlogloss:0.424701\ttest-mlogloss:0.51798\n",
"[490]\ttrain-mlogloss:0.422972\ttest-mlogloss:0.517625\n",
"[500]\ttrain-mlogloss:0.421223\ttest-mlogloss:0.517306\n",
"[510]\ttrain-mlogloss:0.41941\ttest-mlogloss:0.517073\n",
"[520]\ttrain-mlogloss:0.417667\ttest-mlogloss:0.516773\n",
"[530]\ttrain-mlogloss:0.416166\ttest-mlogloss:0.516626\n",
"[540]\ttrain-mlogloss:0.41441\ttest-mlogloss:0.516381\n",
"[550]\ttrain-mlogloss:0.412924\ttest-mlogloss:0.516156\n",
"[560]\ttrain-mlogloss:0.411245\ttest-mlogloss:0.515916\n",
"[570]\ttrain-mlogloss:0.409568\ttest-mlogloss:0.515626\n",
"[580]\ttrain-mlogloss:0.408187\ttest-mlogloss:0.51539\n",
"[590]\ttrain-mlogloss:0.406699\ttest-mlogloss:0.515215\n",
"[600]\ttrain-mlogloss:0.404991\ttest-mlogloss:0.515124\n",
"[610]\ttrain-mlogloss:0.403465\ttest-mlogloss:0.514865\n",
"[620]\ttrain-mlogloss:0.40177\ttest-mlogloss:0.514694\n",
"[630]\ttrain-mlogloss:0.400031\ttest-mlogloss:0.51442\n",
"[640]\ttrain-mlogloss:0.398467\ttest-mlogloss:0.514233\n",
"[650]\ttrain-mlogloss:0.397062\ttest-mlogloss:0.514072\n",
"[660]\ttrain-mlogloss:0.395534\ttest-mlogloss:0.513916\n",
"[670]\ttrain-mlogloss:0.393961\ttest-mlogloss:0.513689\n",
"[680]\ttrain-mlogloss:0.392397\ttest-mlogloss:0.513418\n",
"[690]\ttrain-mlogloss:0.390923\ttest-mlogloss:0.513178\n",
"[700]\ttrain-mlogloss:0.389441\ttest-mlogloss:0.513011\n",
"[710]\ttrain-mlogloss:0.387945\ttest-mlogloss:0.512965\n",
"[720]\ttrain-mlogloss:0.386502\ttest-mlogloss:0.512822\n",
"[730]\ttrain-mlogloss:0.385117\ttest-mlogloss:0.512737\n",
"[740]\ttrain-mlogloss:0.383892\ttest-mlogloss:0.512516\n",
"[750]\ttrain-mlogloss:0.382671\ttest-mlogloss:0.512389\n",
"[760]\ttrain-mlogloss:0.381222\ttest-mlogloss:0.512272\n",
"[770]\ttrain-mlogloss:0.379735\ttest-mlogloss:0.512119\n",
"[780]\ttrain-mlogloss:0.378362\ttest-mlogloss:0.512072\n",
"[790]\ttrain-mlogloss:0.376936\ttest-mlogloss:0.512047\n",
"[800]\ttrain-mlogloss:0.375588\ttest-mlogloss:0.511921\n",
"[810]\ttrain-mlogloss:0.374278\ttest-mlogloss:0.511752\n",
"[820]\ttrain-mlogloss:0.372873\ttest-mlogloss:0.511569\n",
"[830]\ttrain-mlogloss:0.371578\ttest-mlogloss:0.511432\n",
"[840]\ttrain-mlogloss:0.370315\ttest-mlogloss:0.511311\n",
"[850]\ttrain-mlogloss:0.369046\ttest-mlogloss:0.511137\n",
"[860]\ttrain-mlogloss:0.36767\ttest-mlogloss:0.511073\n",
"[870]\ttrain-mlogloss:0.366365\ttest-mlogloss:0.511045\n",
"[880]\ttrain-mlogloss:0.364996\ttest-mlogloss:0.510975\n",
"[890]\ttrain-mlogloss:0.363611\ttest-mlogloss:0.51087\n",
"[900]\ttrain-mlogloss:0.362221\ttest-mlogloss:0.51079\n",
"[910]\ttrain-mlogloss:0.360919\ttest-mlogloss:0.510684\n",
"[920]\ttrain-mlogloss:0.359511\ttest-mlogloss:0.510571\n",
"[930]\ttrain-mlogloss:0.35835\ttest-mlogloss:0.510513\n",
"[940]\ttrain-mlogloss:0.357178\ttest-mlogloss:0.510547\n",
"[950]\ttrain-mlogloss:0.355781\ttest-mlogloss:0.510484\n",
"[960]\ttrain-mlogloss:0.354429\ttest-mlogloss:0.510396\n",
"[970]\ttrain-mlogloss:0.353049\ttest-mlogloss:0.510353\n",
"[980]\ttrain-mlogloss:0.351611\ttest-mlogloss:0.510288\n",
"[990]\ttrain-mlogloss:0.350424\ttest-mlogloss:0.510237\n",
"[1000]\ttrain-mlogloss:0.349147\ttest-mlogloss:0.510164\n",
"[1010]\ttrain-mlogloss:0.347782\ttest-mlogloss:0.510067\n",
"[1020]\ttrain-mlogloss:0.346452\ttest-mlogloss:0.510097\n",
"[1030]\ttrain-mlogloss:0.345001\ttest-mlogloss:0.509948\n",
"[1040]\ttrain-mlogloss:0.343686\ttest-mlogloss:0.509896\n",
"[1050]\ttrain-mlogloss:0.342457\ttest-mlogloss:0.509862\n",
"[1060]\ttrain-mlogloss:0.34129\ttest-mlogloss:0.509761\n",
"[1070]\ttrain-mlogloss:0.340106\ttest-mlogloss:0.509657\n",
"[1080]\ttrain-mlogloss:0.33887\ttest-mlogloss:0.509685\n",
"[1090]\ttrain-mlogloss:0.337587\ttest-mlogloss:0.509662\n",
"[1100]\ttrain-mlogloss:0.336529\ttest-mlogloss:0.5097\n",
"[1110]\ttrain-mlogloss:0.335251\ttest-mlogloss:0.509607\n",
"[1120]\ttrain-mlogloss:0.334053\ttest-mlogloss:0.50952\n",
"[1130]\ttrain-mlogloss:0.332875\ttest-mlogloss:0.509482\n",
"[1140]\ttrain-mlogloss:0.331785\ttest-mlogloss:0.509379\n",
"[1150]\ttrain-mlogloss:0.33052\ttest-mlogloss:0.509339\n",
"[1160]\ttrain-mlogloss:0.329345\ttest-mlogloss:0.509313\n",
"[1170]\ttrain-mlogloss:0.3281\ttest-mlogloss:0.509281\n",
"[1180]\ttrain-mlogloss:0.326889\ttest-mlogloss:0.509157\n",
"[1190]\ttrain-mlogloss:0.325753\ttest-mlogloss:0.509109\n",
"[1200]\ttrain-mlogloss:0.32447\ttest-mlogloss:0.509071\n",
"[1210]\ttrain-mlogloss:0.323233\ttest-mlogloss:0.509133\n",
"[1220]\ttrain-mlogloss:0.322028\ttest-mlogloss:0.509153\n",
"[1230]\ttrain-mlogloss:0.320999\ttest-mlogloss:0.509164\n",
"[1240]\ttrain-mlogloss:0.319809\ttest-mlogloss:0.509093\n",
"Stopping. Best iteration:\n",
"[1198]\ttrain-mlogloss:0.324769\ttest-mlogloss:0.509041\n",
"\n",
"[0.50354797493140779, 0.49727635213071869, 0.50904081005502133]\n",
"[0]\ttrain-mlogloss:1.0843\ttest-mlogloss:1.0847\n",
"Multiple eval metrics have been passed: 'test-mlogloss' will be used for early stopping.\n",
"\n",
"Will train until test-mlogloss hasn't improved in 50 rounds.\n",
"[10]\ttrain-mlogloss:0.96257\ttest-mlogloss:0.966603\n",
"[20]\ttrain-mlogloss:0.871066\ttest-mlogloss:0.878328\n",
"[30]\ttrain-mlogloss:0.800099\ttest-mlogloss:0.810332\n",
"[40]\ttrain-mlogloss:0.744821\ttest-mlogloss:0.757618\n",
"[50]\ttrain-mlogloss:0.700201\ttest-mlogloss:0.715542\n",
"[60]\ttrain-mlogloss:0.664688\ttest-mlogloss:0.682406\n",
"[70]\ttrain-mlogloss:0.635823\ttest-mlogloss:0.655865\n",
"[80]\ttrain-mlogloss:0.611975\ttest-mlogloss:0.634174\n",
"[90]\ttrain-mlogloss:0.592279\ttest-mlogloss:0.616644\n",
"[100]\ttrain-mlogloss:0.575817\ttest-mlogloss:0.602275\n",
"[110]\ttrain-mlogloss:0.561844\ttest-mlogloss:0.590401\n",
"[120]\ttrain-mlogloss:0.54992\ttest-mlogloss:0.580621\n",
"[130]\ttrain-mlogloss:0.539747\ttest-mlogloss:0.572348\n",
"[140]\ttrain-mlogloss:0.530845\ttest-mlogloss:0.565423\n",
"[150]\ttrain-mlogloss:0.523199\ttest-mlogloss:0.559518\n",
"[160]\ttrain-mlogloss:0.516331\ttest-mlogloss:0.5544\n",
"[170]\ttrain-mlogloss:0.510257\ttest-mlogloss:0.550114\n",
"[180]\ttrain-mlogloss:0.504637\ttest-mlogloss:0.546228\n",
"[190]\ttrain-mlogloss:0.499619\ttest-mlogloss:0.542805\n",
"[200]\ttrain-mlogloss:0.495018\ttest-mlogloss:0.539955\n",
"[210]\ttrain-mlogloss:0.490889\ttest-mlogloss:0.537424\n",
"[220]\ttrain-mlogloss:0.48685\ttest-mlogloss:0.535091\n",
"[230]\ttrain-mlogloss:0.483195\ttest-mlogloss:0.5331\n",
"[240]\ttrain-mlogloss:0.479688\ttest-mlogloss:0.531309\n",
"[250]\ttrain-mlogloss:0.476225\ttest-mlogloss:0.529677\n",
"[260]\ttrain-mlogloss:0.473105\ttest-mlogloss:0.528236\n",
"[270]\ttrain-mlogloss:0.470114\ttest-mlogloss:0.526924\n",
"[280]\ttrain-mlogloss:0.467271\ttest-mlogloss:0.525814\n",
"[290]\ttrain-mlogloss:0.464591\ttest-mlogloss:0.524661\n",
"[300]\ttrain-mlogloss:0.462065\ttest-mlogloss:0.523795\n",
"[310]\ttrain-mlogloss:0.459373\ttest-mlogloss:0.522848\n",
"[320]\ttrain-mlogloss:0.456814\ttest-mlogloss:0.521983\n",
"[330]\ttrain-mlogloss:0.454387\ttest-mlogloss:0.521174\n",
"[340]\ttrain-mlogloss:0.452069\ttest-mlogloss:0.52035\n",
"[350]\ttrain-mlogloss:0.449827\ttest-mlogloss:0.519663\n",
"[360]\ttrain-mlogloss:0.447658\ttest-mlogloss:0.518948\n",
"[370]\ttrain-mlogloss:0.4456\ttest-mlogloss:0.518367\n",
"[380]\ttrain-mlogloss:0.443258\ttest-mlogloss:0.517796\n",
"[390]\ttrain-mlogloss:0.441213\ttest-mlogloss:0.51728\n",
"[400]\ttrain-mlogloss:0.439221\ttest-mlogloss:0.516685\n",
"[410]\ttrain-mlogloss:0.43726\ttest-mlogloss:0.516183\n",
"[420]\ttrain-mlogloss:0.435591\ttest-mlogloss:0.515699\n",
"[430]\ttrain-mlogloss:0.433517\ttest-mlogloss:0.515236\n",
"[440]\ttrain-mlogloss:0.431896\ttest-mlogloss:0.514953\n",
"[450]\ttrain-mlogloss:0.43002\ttest-mlogloss:0.514533\n",
"[460]\ttrain-mlogloss:0.428287\ttest-mlogloss:0.514197\n",
"[470]\ttrain-mlogloss:0.426531\ttest-mlogloss:0.513803\n",
"[480]\ttrain-mlogloss:0.424743\ttest-mlogloss:0.513481\n",
"[490]\ttrain-mlogloss:0.422938\ttest-mlogloss:0.513322\n",
"[500]\ttrain-mlogloss:0.421196\ttest-mlogloss:0.513009\n",
"[510]\ttrain-mlogloss:0.419446\ttest-mlogloss:0.512763\n",
"[520]\ttrain-mlogloss:0.418057\ttest-mlogloss:0.512542\n",
"[530]\ttrain-mlogloss:0.416336\ttest-mlogloss:0.512287\n",
"[540]\ttrain-mlogloss:0.414579\ttest-mlogloss:0.512071\n",
"[550]\ttrain-mlogloss:0.41291\ttest-mlogloss:0.511819\n",
"[560]\ttrain-mlogloss:0.411506\ttest-mlogloss:0.511657\n",
"[570]\ttrain-mlogloss:0.410062\ttest-mlogloss:0.511449\n",
"[580]\ttrain-mlogloss:0.408554\ttest-mlogloss:0.511278\n",
"[590]\ttrain-mlogloss:0.406948\ttest-mlogloss:0.511126\n",
"[600]\ttrain-mlogloss:0.405218\ttest-mlogloss:0.511036\n",
"[610]\ttrain-mlogloss:0.403522\ttest-mlogloss:0.510878\n",
"[620]\ttrain-mlogloss:0.40191\ttest-mlogloss:0.510636\n",
"[630]\ttrain-mlogloss:0.400306\ttest-mlogloss:0.510392\n",
"[640]\ttrain-mlogloss:0.398943\ttest-mlogloss:0.510222\n",
"[650]\ttrain-mlogloss:0.397394\ttest-mlogloss:0.510102\n",
"[660]\ttrain-mlogloss:0.395815\ttest-mlogloss:0.509959\n",
"[670]\ttrain-mlogloss:0.394188\ttest-mlogloss:0.509811\n",
"[680]\ttrain-mlogloss:0.392644\ttest-mlogloss:0.509661\n",
"[690]\ttrain-mlogloss:0.391195\ttest-mlogloss:0.509526\n",
"[700]\ttrain-mlogloss:0.389678\ttest-mlogloss:0.509392\n",
"[710]\ttrain-mlogloss:0.388326\ttest-mlogloss:0.509219\n",
"[720]\ttrain-mlogloss:0.386889\ttest-mlogloss:0.509137\n",
"[730]\ttrain-mlogloss:0.385465\ttest-mlogloss:0.50896\n",
"[740]\ttrain-mlogloss:0.383957\ttest-mlogloss:0.508974\n",
"[750]\ttrain-mlogloss:0.382314\ttest-mlogloss:0.50882\n",
"[760]\ttrain-mlogloss:0.380806\ttest-mlogloss:0.508721\n",
"[770]\ttrain-mlogloss:0.379301\ttest-mlogloss:0.508567\n",
"[780]\ttrain-mlogloss:0.377876\ttest-mlogloss:0.508469\n",
"[790]\ttrain-mlogloss:0.376585\ttest-mlogloss:0.508399\n",
"[800]\ttrain-mlogloss:0.375081\ttest-mlogloss:0.508344\n",
"[810]\ttrain-mlogloss:0.373704\ttest-mlogloss:0.508262\n",
"[820]\ttrain-mlogloss:0.372186\ttest-mlogloss:0.50814\n",
"[830]\ttrain-mlogloss:0.370851\ttest-mlogloss:0.508021\n",
"[840]\ttrain-mlogloss:0.369565\ttest-mlogloss:0.507855\n",
"[850]\ttrain-mlogloss:0.368268\ttest-mlogloss:0.507857\n",
"[860]\ttrain-mlogloss:0.366846\ttest-mlogloss:0.507776\n",
"[870]\ttrain-mlogloss:0.365489\ttest-mlogloss:0.507637\n",
"[880]\ttrain-mlogloss:0.363997\ttest-mlogloss:0.507527\n",
"[890]\ttrain-mlogloss:0.362688\ttest-mlogloss:0.507513\n",
"[900]\ttrain-mlogloss:0.361354\ttest-mlogloss:0.507454\n",
"[910]\ttrain-mlogloss:0.359985\ttest-mlogloss:0.507398\n",
"[920]\ttrain-mlogloss:0.358702\ttest-mlogloss:0.507347\n",
"[930]\ttrain-mlogloss:0.357415\ttest-mlogloss:0.507253\n",
"[940]\ttrain-mlogloss:0.356102\ttest-mlogloss:0.50712\n",
"[950]\ttrain-mlogloss:0.354722\ttest-mlogloss:0.507072\n",
"[960]\ttrain-mlogloss:0.353535\ttest-mlogloss:0.507031\n",
"[970]\ttrain-mlogloss:0.352123\ttest-mlogloss:0.507001\n",
"[980]\ttrain-mlogloss:0.350841\ttest-mlogloss:0.506907\n",
"[990]\ttrain-mlogloss:0.349527\ttest-mlogloss:0.50684\n",
"[1000]\ttrain-mlogloss:0.348065\ttest-mlogloss:0.506725\n",
"[1010]\ttrain-mlogloss:0.346883\ttest-mlogloss:0.506746\n",
"[1020]\ttrain-mlogloss:0.345745\ttest-mlogloss:0.506719\n",
"[1030]\ttrain-mlogloss:0.344487\ttest-mlogloss:0.506677\n",
"[1040]\ttrain-mlogloss:0.343146\ttest-mlogloss:0.506574\n",
"[1050]\ttrain-mlogloss:0.341875\ttest-mlogloss:0.506514\n",
"[1060]\ttrain-mlogloss:0.340505\ttest-mlogloss:0.506429\n",
"[1070]\ttrain-mlogloss:0.339232\ttest-mlogloss:0.50633\n",
"[1080]\ttrain-mlogloss:0.337991\ttest-mlogloss:0.506265\n",
"[1090]\ttrain-mlogloss:0.336845\ttest-mlogloss:0.506233\n",
"[1100]\ttrain-mlogloss:0.335681\ttest-mlogloss:0.506185\n",
"[1110]\ttrain-mlogloss:0.334446\ttest-mlogloss:0.506179\n",
"[1120]\ttrain-mlogloss:0.333324\ttest-mlogloss:0.506201\n",
"[1130]\ttrain-mlogloss:0.332089\ttest-mlogloss:0.506163\n",
"[1140]\ttrain-mlogloss:0.330955\ttest-mlogloss:0.506104\n",
"[1150]\ttrain-mlogloss:0.329747\ttest-mlogloss:0.506074\n",
"[1160]\ttrain-mlogloss:0.328571\ttest-mlogloss:0.505996\n",
"[1170]\ttrain-mlogloss:0.327361\ttest-mlogloss:0.506024\n",
"[1180]\ttrain-mlogloss:0.326179\ttest-mlogloss:0.505952\n",
"[1190]\ttrain-mlogloss:0.325048\ttest-mlogloss:0.505891\n",
"[1200]\ttrain-mlogloss:0.323968\ttest-mlogloss:0.505875\n",
"[1210]\ttrain-mlogloss:0.322867\ttest-mlogloss:0.505808\n",
"[1220]\ttrain-mlogloss:0.321694\ttest-mlogloss:0.505867\n",
"[1230]\ttrain-mlogloss:0.320454\ttest-mlogloss:0.505893\n",
"[1240]\ttrain-mlogloss:0.319324\ttest-mlogloss:0.505865\n",
"[1250]\ttrain-mlogloss:0.318108\ttest-mlogloss:0.505806\n",
"[1260]\ttrain-mlogloss:0.316875\ttest-mlogloss:0.505727\n",
"[1270]\ttrain-mlogloss:0.315737\ttest-mlogloss:0.505709\n",
"[1280]\ttrain-mlogloss:0.314662\ttest-mlogloss:0.505695\n",
"[1290]\ttrain-mlogloss:0.313601\ttest-mlogloss:0.50568\n",
"[1300]\ttrain-mlogloss:0.312365\ttest-mlogloss:0.505622\n",
"[1310]\ttrain-mlogloss:0.311312\ttest-mlogloss:0.505735\n",
"[1320]\ttrain-mlogloss:0.310273\ttest-mlogloss:0.505719\n",
"[1330]\ttrain-mlogloss:0.309157\ttest-mlogloss:0.505707\n",
"[1340]\ttrain-mlogloss:0.30811\ttest-mlogloss:0.505725\n",
"[1350]\ttrain-mlogloss:0.307024\ttest-mlogloss:0.505628\n",
"Stopping. Best iteration:\n",
"[1300]\ttrain-mlogloss:0.312365\ttest-mlogloss:0.505622\n",
"\n",
"[0.50354797493140779, 0.49727635213071869, 0.50904081005502133, 0.50562155057920022]\n",
"[0]\ttrain-mlogloss:1.08437\ttest-mlogloss:1.08469\n",
"Multiple eval metrics have been passed: 'test-mlogloss' will be used for early stopping.\n",
"\n",
"Will train until test-mlogloss hasn't improved in 50 rounds.\n",
"[10]\ttrain-mlogloss:0.961517\ttest-mlogloss:0.964832\n",
"[20]\ttrain-mlogloss:0.869499\ttest-mlogloss:0.875438\n",
"[30]\ttrain-mlogloss:0.798566\ttest-mlogloss:0.806999\n",
"[40]\ttrain-mlogloss:0.743055\ttest-mlogloss:0.753922\n",
"[50]\ttrain-mlogloss:0.699129\ttest-mlogloss:0.712352\n",
"[60]\ttrain-mlogloss:0.663576\ttest-mlogloss:0.679108\n",
"[70]\ttrain-mlogloss:0.634785\ttest-mlogloss:0.652494\n",
"[80]\ttrain-mlogloss:0.611254\ttest-mlogloss:0.631034\n",
"[90]\ttrain-mlogloss:0.591908\ttest-mlogloss:0.613743\n",
"[100]\ttrain-mlogloss:0.575665\ttest-mlogloss:0.59951\n",
"[110]\ttrain-mlogloss:0.562081\ttest-mlogloss:0.587843\n",
"[120]\ttrain-mlogloss:0.550551\ttest-mlogloss:0.578276\n",
"[130]\ttrain-mlogloss:0.540389\ttest-mlogloss:0.570079\n",
"[140]\ttrain-mlogloss:0.531721\ttest-mlogloss:0.563221\n",
"[150]\ttrain-mlogloss:0.523981\ttest-mlogloss:0.557387\n",
"[160]\ttrain-mlogloss:0.517068\ttest-mlogloss:0.552458\n",
"[170]\ttrain-mlogloss:0.510985\ttest-mlogloss:0.548231\n",
"[180]\ttrain-mlogloss:0.505312\ttest-mlogloss:0.544558\n",
"[190]\ttrain-mlogloss:0.500503\ttest-mlogloss:0.541199\n",
"[200]\ttrain-mlogloss:0.495856\ttest-mlogloss:0.538408\n",
"[210]\ttrain-mlogloss:0.491453\ttest-mlogloss:0.535809\n",
"[220]\ttrain-mlogloss:0.487475\ttest-mlogloss:0.533604\n",
"[230]\ttrain-mlogloss:0.483761\ttest-mlogloss:0.531505\n",
"[240]\ttrain-mlogloss:0.480191\ttest-mlogloss:0.529776\n",
"[250]\ttrain-mlogloss:0.476755\ttest-mlogloss:0.52803\n",
"[260]\ttrain-mlogloss:0.473638\ttest-mlogloss:0.526556\n",
"[270]\ttrain-mlogloss:0.470523\ttest-mlogloss:0.52521\n",
"[280]\ttrain-mlogloss:0.467623\ttest-mlogloss:0.523973\n",
"[290]\ttrain-mlogloss:0.464854\ttest-mlogloss:0.522805\n",
"[300]\ttrain-mlogloss:0.462263\ttest-mlogloss:0.521746\n",
"[310]\ttrain-mlogloss:0.459581\ttest-mlogloss:0.520755\n",
"[320]\ttrain-mlogloss:0.457196\ttest-mlogloss:0.519821\n",
"[330]\ttrain-mlogloss:0.454562\ttest-mlogloss:0.518978\n",
"[340]\ttrain-mlogloss:0.452289\ttest-mlogloss:0.518345\n",
"[350]\ttrain-mlogloss:0.450212\ttest-mlogloss:0.517635\n",
"[360]\ttrain-mlogloss:0.447927\ttest-mlogloss:0.516965\n",
"[370]\ttrain-mlogloss:0.445661\ttest-mlogloss:0.516356\n",
"[380]\ttrain-mlogloss:0.443638\ttest-mlogloss:0.515821\n",
"[390]\ttrain-mlogloss:0.441685\ttest-mlogloss:0.515256\n",
"[400]\ttrain-mlogloss:0.439611\ttest-mlogloss:0.514698\n",
"[410]\ttrain-mlogloss:0.437697\ttest-mlogloss:0.514221\n",
"[420]\ttrain-mlogloss:0.435804\ttest-mlogloss:0.513727\n",
"[430]\ttrain-mlogloss:0.433926\ttest-mlogloss:0.513322\n",
"[440]\ttrain-mlogloss:0.431995\ttest-mlogloss:0.512914\n",
"[450]\ttrain-mlogloss:0.430253\ttest-mlogloss:0.512556\n",
"[460]\ttrain-mlogloss:0.42848\ttest-mlogloss:0.512116\n",
"[470]\ttrain-mlogloss:0.426735\ttest-mlogloss:0.511707\n",
"[480]\ttrain-mlogloss:0.424914\ttest-mlogloss:0.511423\n",
"[490]\ttrain-mlogloss:0.423261\ttest-mlogloss:0.511043\n",
"[500]\ttrain-mlogloss:0.42161\ttest-mlogloss:0.510801\n",
"[510]\ttrain-mlogloss:0.419805\ttest-mlogloss:0.510524\n",
"[520]\ttrain-mlogloss:0.418161\ttest-mlogloss:0.510257\n",
"[530]\ttrain-mlogloss:0.416549\ttest-mlogloss:0.510007\n",
"[540]\ttrain-mlogloss:0.414969\ttest-mlogloss:0.509764\n",
"[550]\ttrain-mlogloss:0.413303\ttest-mlogloss:0.509539\n",
"[560]\ttrain-mlogloss:0.41152\ttest-mlogloss:0.50934\n",
"[570]\ttrain-mlogloss:0.41007\ttest-mlogloss:0.509105\n",
"[580]\ttrain-mlogloss:0.408359\ttest-mlogloss:0.508863\n",
"[590]\ttrain-mlogloss:0.406938\ttest-mlogloss:0.508725\n",
"[600]\ttrain-mlogloss:0.405358\ttest-mlogloss:0.508521\n",
"[610]\ttrain-mlogloss:0.403832\ttest-mlogloss:0.508249\n",
"[620]\ttrain-mlogloss:0.402341\ttest-mlogloss:0.508104\n",
"[630]\ttrain-mlogloss:0.400572\ttest-mlogloss:0.507892\n",
"[640]\ttrain-mlogloss:0.399136\ttest-mlogloss:0.507744\n",
"[650]\ttrain-mlogloss:0.397506\ttest-mlogloss:0.507566\n",
"[660]\ttrain-mlogloss:0.396105\ttest-mlogloss:0.50735\n",
"[670]\ttrain-mlogloss:0.394499\ttest-mlogloss:0.50718\n",
"[680]\ttrain-mlogloss:0.392887\ttest-mlogloss:0.506973\n",
"[690]\ttrain-mlogloss:0.391279\ttest-mlogloss:0.506815\n",
"[700]\ttrain-mlogloss:0.389798\ttest-mlogloss:0.506686\n",
"[710]\ttrain-mlogloss:0.388279\ttest-mlogloss:0.506577\n",
"[720]\ttrain-mlogloss:0.386946\ttest-mlogloss:0.506397\n",
"[730]\ttrain-mlogloss:0.385378\ttest-mlogloss:0.506309\n",
"[740]\ttrain-mlogloss:0.384035\ttest-mlogloss:0.506183\n",
"[750]\ttrain-mlogloss:0.382516\ttest-mlogloss:0.506095\n",
"[760]\ttrain-mlogloss:0.38108\ttest-mlogloss:0.505983\n",
"[770]\ttrain-mlogloss:0.37966\ttest-mlogloss:0.505891\n",
"[780]\ttrain-mlogloss:0.378238\ttest-mlogloss:0.505788\n",
"[790]\ttrain-mlogloss:0.3769\ttest-mlogloss:0.505683\n",
"[800]\ttrain-mlogloss:0.375646\ttest-mlogloss:0.505608\n",
"[810]\ttrain-mlogloss:0.374144\ttest-mlogloss:0.505501\n",
"[820]\ttrain-mlogloss:0.372702\ttest-mlogloss:0.505424\n",
"[830]\ttrain-mlogloss:0.371296\ttest-mlogloss:0.505411\n",
"[840]\ttrain-mlogloss:0.369804\ttest-mlogloss:0.505351\n",
"[850]\ttrain-mlogloss:0.368521\ttest-mlogloss:0.505282\n",
"[860]\ttrain-mlogloss:0.367096\ttest-mlogloss:0.505201\n",
"[870]\ttrain-mlogloss:0.365739\ttest-mlogloss:0.505056\n",
"[880]\ttrain-mlogloss:0.364203\ttest-mlogloss:0.504942\n",
"[890]\ttrain-mlogloss:0.36287\ttest-mlogloss:0.504868\n",
"[900]\ttrain-mlogloss:0.361514\ttest-mlogloss:0.504776\n",
"[910]\ttrain-mlogloss:0.3601\ttest-mlogloss:0.504664\n",
"[920]\ttrain-mlogloss:0.35877\ttest-mlogloss:0.504659\n",
"[930]\ttrain-mlogloss:0.3574\ttest-mlogloss:0.504576\n",
"[940]\ttrain-mlogloss:0.356138\ttest-mlogloss:0.504547\n",
"[950]\ttrain-mlogloss:0.354866\ttest-mlogloss:0.504486\n",
"[960]\ttrain-mlogloss:0.353533\ttest-mlogloss:0.504421\n",
"[970]\ttrain-mlogloss:0.352283\ttest-mlogloss:0.504366\n",
"[980]\ttrain-mlogloss:0.350951\ttest-mlogloss:0.504301\n",
"[990]\ttrain-mlogloss:0.349846\ttest-mlogloss:0.5042\n",
"[1000]\ttrain-mlogloss:0.348739\ttest-mlogloss:0.504165\n",
"[1010]\ttrain-mlogloss:0.347497\ttest-mlogloss:0.50409\n",
"[1020]\ttrain-mlogloss:0.346223\ttest-mlogloss:0.50396\n",
"[1030]\ttrain-mlogloss:0.34486\ttest-mlogloss:0.503909\n",
"[1040]\ttrain-mlogloss:0.343493\ttest-mlogloss:0.503808\n",
"[1050]\ttrain-mlogloss:0.342261\ttest-mlogloss:0.503758\n",
"[1060]\ttrain-mlogloss:0.341042\ttest-mlogloss:0.503685\n",
"[1070]\ttrain-mlogloss:0.339874\ttest-mlogloss:0.503614\n",
"[1080]\ttrain-mlogloss:0.338726\ttest-mlogloss:0.503542\n",
"[1090]\ttrain-mlogloss:0.337481\ttest-mlogloss:0.503559\n",
"[1100]\ttrain-mlogloss:0.336182\ttest-mlogloss:0.503523\n",
"[1110]\ttrain-mlogloss:0.335128\ttest-mlogloss:0.503474\n",
"[1120]\ttrain-mlogloss:0.333994\ttest-mlogloss:0.50349\n",
"[1130]\ttrain-mlogloss:0.332911\ttest-mlogloss:0.503507\n",
"[1140]\ttrain-mlogloss:0.331737\ttest-mlogloss:0.503426\n",
"[1150]\ttrain-mlogloss:0.330541\ttest-mlogloss:0.503429\n",
"[1160]\ttrain-mlogloss:0.329218\ttest-mlogloss:0.503446\n",
"[1170]\ttrain-mlogloss:0.328009\ttest-mlogloss:0.50342\n",
"[1180]\ttrain-mlogloss:0.32684\ttest-mlogloss:0.503287\n",
"[1190]\ttrain-mlogloss:0.3257\ttest-mlogloss:0.503249\n",
"[1200]\ttrain-mlogloss:0.324627\ttest-mlogloss:0.50324\n",
"[1210]\ttrain-mlogloss:0.323567\ttest-mlogloss:0.50319\n",
"[1220]\ttrain-mlogloss:0.322341\ttest-mlogloss:0.503154\n",
"[1230]\ttrain-mlogloss:0.32121\ttest-mlogloss:0.503181\n",
"[1240]\ttrain-mlogloss:0.319958\ttest-mlogloss:0.503165\n",
"[1250]\ttrain-mlogloss:0.318721\ttest-mlogloss:0.503188\n",
"[1260]\ttrain-mlogloss:0.317462\ttest-mlogloss:0.503167\n",
"[1270]\ttrain-mlogloss:0.316274\ttest-mlogloss:0.503114\n",
"[1280]\ttrain-mlogloss:0.315078\ttest-mlogloss:0.503138\n",
"[1290]\ttrain-mlogloss:0.313979\ttest-mlogloss:0.503143\n",
"[1300]\ttrain-mlogloss:0.312816\ttest-mlogloss:0.503163\n",
"[1310]\ttrain-mlogloss:0.31169\ttest-mlogloss:0.50316\n",
"[1320]\ttrain-mlogloss:0.310757\ttest-mlogloss:0.503137\n",
"Stopping. Best iteration:\n",
"[1273]\ttrain-mlogloss:0.315966\ttest-mlogloss:0.503096\n",
"\n",
"[0.50354797493140779, 0.49727635213071869, 0.50904081005502133, 0.50562155057920022, 0.50309564042943167]\n",
"0.503716503364\n"
]
}
],
"source": [
"rv3 = run_cv(train_df, cv_test, kf, fl) "
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [],
"source": [
"dfs3 = run3_to_stackdf(rv3)\n",
"pickle.dump(dfs3, open('modeloutput-xgb-clf-r3.pkl', 'wb'))"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def run_to_stackdf(run):\n",
" df_testpreds = pd.DataFrame(run[2].mean(axis=0))\n",
" df_testpreds.columns = ['level']\n",
" df_testpreds['listing_id'] = cv_test[0].listing_id\n",
" df_allpreds = pd.concat([run[1][['level', 'listing_id']], df_testpreds])\n",
"\n",
" df_allpreds.sort_values('listing_id', inplace=True)\n",
" df_allpreds.set_index('listing_id', inplace=True)\n",
"\n",
" df_fold = []\n",
" for f in range(run[2].shape[0]):\n",
" df_fold.append(pd.DataFrame(run[2][f]))\n",
" df_fold[-1]['listing_id'] = test_df.listing_id\n",
" df_fold[-1].sort_values('listing_id', inplace=True)\n",
" df_fold[-1].set_index('listing_id', inplace=True)\n",
"\n",
" return (df_allpreds, df_fold)\n"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def runXGB1(train_X, train_y, test_X, test_y=None, feature_names=None, seed_val=0, num_rounds=4000):\n",
" param = {}\n",
" param['objective'] = 'reg:logistic'\n",
" #param['tree_method'] = 'hist'\n",
" param['eta'] = 0.02\n",
" param['max_depth'] = 6\n",
" param['silent'] = 1\n",
" param['num_class'] = 1\n",
" param['eval_metric'] = \"rmse\"\n",
" param['min_child_weight'] = 1\n",
" param['subsample'] = 0.7\n",
" param['colsample_bytree'] = 0.7\n",
" param['seed'] = seed_val\n",
" param['base_score'] = train_y.mean()\n",
" num_rounds = num_rounds\n",
"\n",
" plst = list(param.items())\n",
" xgtrain = xgb.DMatrix(train_X, label=train_y)\n",
"\n",
" if test_y is not None:\n",
" xgtest = xgb.DMatrix(test_X, label=test_y)\n",
" watchlist = [ (xgtrain,'train'), (xgtest, 'test') ]\n",
" model = xgb.train(plst, xgtrain, num_rounds, watchlist, early_stopping_rounds=50, verbose_eval=10)\n",
" else:\n",
" xgtest = xgb.DMatrix(test_X)\n",
" model = xgb.train(plst, xgtrain, num_rounds)\n",
"\n",
" pred_test_y = model.predict(xgtest, ntree_limit=model.best_ntree_limit)\n",
" return pred_test_y, model"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"medium_regression_tgt = (.5 + (9/13)) / 2\n",
"\n",
"def run_cv1(train_df, cv_test, kf, features_to_use):\n",
" \n",
" train_X = train_df[features_to_use] #sparse.hstack([train_df[features_to_use], tr_sparse]).tocsr()\n",
" train_y3 = np.array(train_df['interest_level'].apply(lambda x: target_num_map[x]))\n",
" \n",
" train_y = np.zeros_like(train_y3, dtype=np.float32)\n",
" train_y[train_y3 == 1] = medium_regression_tgt\n",
" train_y[train_y3 == 2] = 1\n",
"\n",
" cv_preds = []\n",
" cv_scores = []\n",
" models = []\n",
" test_preds = []\n",
" \n",
" fold = 0\n",
"\n",
" for dev_index, val_index in kf.split(range(train_X.shape[0]), train_y):\n",
"\n",
" dev_X, val_X = train_X.iloc[dev_index], train_X.iloc[val_index]\n",
" dev_y, val_y = train_y[dev_index], train_y[val_index]\n",
" preds, model = runXGB1(dev_X, dev_y, val_X, val_y)\n",
" models.append(model)\n",
"\n",
" cv_scores.append(model.best_score)\n",
" print(cv_scores)\n",
"\n",
" cut_df = train_df.iloc[val_index]\n",
" \n",
" out_df = pd.DataFrame(preds)\n",
" out_df.columns = [\"level\"]\n",
" out_df[\"listing_id\"] = cut_df.listing_id.values\n",
" out_df['interest_tgt'] = val_y # cut_df.interest.values\n",
"\n",
" cv_preds.append(out_df)\n",
"\n",
" xgtest = xgb.DMatrix(cv_test[fold][features_to_use])\n",
" test_preds.append(model.predict(xgtest, ntree_limit=model.best_ntree_limit))\n",
"\n",
" df_cv = pd.concat(cv_preds)\n",
" print(np.sqrt(sklearn.metrics.mean_squared_error(df_cv.interest_tgt, df_cv.level)))\n",
" \n",
" apreds = np.array(test_preds)\n",
" \n",
" return models, df_cv, apreds"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[0]\ttrain-rmse:0.334207\ttest-rmse:0.334246\n",
"Multiple eval metrics have been passed: 'test-rmse' will be used for early stopping.\n",
"\n",
"Will train until test-rmse hasn't improved in 50 rounds.\n",
"[10]\ttrain-rmse:0.313838\ttest-rmse:0.314438\n",
"[20]\ttrain-rmse:0.29779\ttest-rmse:0.29909\n",
"[30]\ttrain-rmse:0.285302\ttest-rmse:0.287287\n",
"[40]\ttrain-rmse:0.27554\ttest-rmse:0.278241\n",
"[50]\ttrain-rmse:0.268255\ttest-rmse:0.271672\n",
"[60]\ttrain-rmse:0.262388\ttest-rmse:0.266489\n",
"[70]\ttrain-rmse:0.258037\ttest-rmse:0.262683\n",
"[80]\ttrain-rmse:0.2544\ttest-rmse:0.259523\n",
"[90]\ttrain-rmse:0.251435\ttest-rmse:0.257065\n",
"[100]\ttrain-rmse:0.248993\ttest-rmse:0.255148\n",
"[110]\ttrain-rmse:0.246917\ttest-rmse:0.253506\n",
"[120]\ttrain-rmse:0.245168\ttest-rmse:0.252138\n",
"[130]\ttrain-rmse:0.243581\ttest-rmse:0.250978\n",
"[140]\ttrain-rmse:0.242149\ttest-rmse:0.249958\n",
"[150]\ttrain-rmse:0.240888\ttest-rmse:0.24912\n",
"[160]\ttrain-rmse:0.239864\ttest-rmse:0.248472\n",
"[170]\ttrain-rmse:0.23884\ttest-rmse:0.247824\n",
"[180]\ttrain-rmse:0.237959\ttest-rmse:0.247244\n",
"[190]\ttrain-rmse:0.237147\ttest-rmse:0.246759\n",
"[200]\ttrain-rmse:0.236228\ttest-rmse:0.246335\n",
"[210]\ttrain-rmse:0.235378\ttest-rmse:0.24584\n",
"[220]\ttrain-rmse:0.234634\ttest-rmse:0.245451\n",
"[230]\ttrain-rmse:0.233899\ttest-rmse:0.24507\n",
"[240]\ttrain-rmse:0.233375\ttest-rmse:0.244773\n",
"[250]\ttrain-rmse:0.232802\ttest-rmse:0.244463\n",
"[260]\ttrain-rmse:0.232169\ttest-rmse:0.244183\n",
"[270]\ttrain-rmse:0.231624\ttest-rmse:0.243925\n",
"[280]\ttrain-rmse:0.231101\ttest-rmse:0.243663\n",
"[290]\ttrain-rmse:0.230498\ttest-rmse:0.243431\n",
"[300]\ttrain-rmse:0.230045\ttest-rmse:0.24323\n",
"[310]\ttrain-rmse:0.229596\ttest-rmse:0.243034\n",
"[320]\ttrain-rmse:0.22899\ttest-rmse:0.242834\n",
"[330]\ttrain-rmse:0.22857\ttest-rmse:0.242652\n",
"[340]\ttrain-rmse:0.228186\ttest-rmse:0.242506\n",
"[350]\ttrain-rmse:0.227671\ttest-rmse:0.242362\n",
"[360]\ttrain-rmse:0.227087\ttest-rmse:0.242227\n",
"[370]\ttrain-rmse:0.226571\ttest-rmse:0.242033\n",
"[380]\ttrain-rmse:0.226087\ttest-rmse:0.24189\n",
"[390]\ttrain-rmse:0.225687\ttest-rmse:0.241813\n",
"[400]\ttrain-rmse:0.225102\ttest-rmse:0.241633\n",
"[410]\ttrain-rmse:0.224542\ttest-rmse:0.24151\n",
"[420]\ttrain-rmse:0.223946\ttest-rmse:0.241387\n",
"[430]\ttrain-rmse:0.223446\ttest-rmse:0.241264\n",
"[440]\ttrain-rmse:0.222965\ttest-rmse:0.241135\n",
"[450]\ttrain-rmse:0.222482\ttest-rmse:0.241039\n",
"[460]\ttrain-rmse:0.222055\ttest-rmse:0.240929\n",
"[470]\ttrain-rmse:0.221512\ttest-rmse:0.240795\n",
"[480]\ttrain-rmse:0.221065\ttest-rmse:0.240694\n",
"[490]\ttrain-rmse:0.220728\ttest-rmse:0.240615\n",
"[500]\ttrain-rmse:0.220333\ttest-rmse:0.240523\n",
"[510]\ttrain-rmse:0.219999\ttest-rmse:0.240476\n",
"[520]\ttrain-rmse:0.219455\ttest-rmse:0.240416\n",
"[530]\ttrain-rmse:0.219018\ttest-rmse:0.240327\n",
"[540]\ttrain-rmse:0.218645\ttest-rmse:0.240249\n",
"[550]\ttrain-rmse:0.218106\ttest-rmse:0.240167\n",
"[560]\ttrain-rmse:0.217571\ttest-rmse:0.240131\n",
"[570]\ttrain-rmse:0.217186\ttest-rmse:0.24007\n",
"[580]\ttrain-rmse:0.216688\ttest-rmse:0.239979\n",
"[590]\ttrain-rmse:0.21621\ttest-rmse:0.239899\n",
"[600]\ttrain-rmse:0.215744\ttest-rmse:0.239857\n",
"[610]\ttrain-rmse:0.21541\ttest-rmse:0.239805\n",
"[620]\ttrain-rmse:0.214905\ttest-rmse:0.239755\n",
"[630]\ttrain-rmse:0.214556\ttest-rmse:0.239699\n",
"[640]\ttrain-rmse:0.214117\ttest-rmse:0.23964\n",
"[650]\ttrain-rmse:0.213696\ttest-rmse:0.239565\n",
"[660]\ttrain-rmse:0.213246\ttest-rmse:0.239531\n",
"[670]\ttrain-rmse:0.212853\ttest-rmse:0.239449\n",
"[680]\ttrain-rmse:0.212505\ttest-rmse:0.239414\n",
"[690]\ttrain-rmse:0.212031\ttest-rmse:0.239379\n",
"[700]\ttrain-rmse:0.211502\ttest-rmse:0.239345\n",
"[710]\ttrain-rmse:0.21113\ttest-rmse:0.23932\n",
"[720]\ttrain-rmse:0.21067\ttest-rmse:0.239253\n",
"[730]\ttrain-rmse:0.210238\ttest-rmse:0.239201\n",
"[740]\ttrain-rmse:0.209893\ttest-rmse:0.239154\n",
"[750]\ttrain-rmse:0.209434\ttest-rmse:0.239095\n",
"[760]\ttrain-rmse:0.208963\ttest-rmse:0.239039\n",
"[770]\ttrain-rmse:0.208544\ttest-rmse:0.238977\n",
"[780]\ttrain-rmse:0.208165\ttest-rmse:0.238905\n",
"[790]\ttrain-rmse:0.207726\ttest-rmse:0.238811\n",
"[800]\ttrain-rmse:0.207382\ttest-rmse:0.238765\n",
"[810]\ttrain-rmse:0.207012\ttest-rmse:0.238745\n",
"[820]\ttrain-rmse:0.206605\ttest-rmse:0.238713\n",
"[830]\ttrain-rmse:0.206202\ttest-rmse:0.23866\n",
"[840]\ttrain-rmse:0.205757\ttest-rmse:0.238657\n",
"[850]\ttrain-rmse:0.205373\ttest-rmse:0.238644\n",
"[860]\ttrain-rmse:0.204958\ttest-rmse:0.238607\n",
"[870]\ttrain-rmse:0.204606\ttest-rmse:0.238583\n",
"[880]\ttrain-rmse:0.204173\ttest-rmse:0.238536\n",
"[890]\ttrain-rmse:0.203884\ttest-rmse:0.238526\n",
"[900]\ttrain-rmse:0.203426\ttest-rmse:0.23848\n",
"[910]\ttrain-rmse:0.203046\ttest-rmse:0.238436\n",
"[920]\ttrain-rmse:0.202674\ttest-rmse:0.238413\n",
"[930]\ttrain-rmse:0.20221\ttest-rmse:0.238382\n",
"[940]\ttrain-rmse:0.20186\ttest-rmse:0.238375\n",
"[950]\ttrain-rmse:0.201444\ttest-rmse:0.238347\n",
"[960]\ttrain-rmse:0.20102\ttest-rmse:0.23833\n",
"[970]\ttrain-rmse:0.200692\ttest-rmse:0.238311\n",
"[980]\ttrain-rmse:0.20025\ttest-rmse:0.238301\n",
"[990]\ttrain-rmse:0.199801\ttest-rmse:0.238303\n",
"[1000]\ttrain-rmse:0.199396\ttest-rmse:0.238279\n",
"[1010]\ttrain-rmse:0.199022\ttest-rmse:0.238274\n",
"[1020]\ttrain-rmse:0.198643\ttest-rmse:0.238248\n",
"[1030]\ttrain-rmse:0.198348\ttest-rmse:0.238215\n",
"[1040]\ttrain-rmse:0.19799\ttest-rmse:0.238195\n",
"[1050]\ttrain-rmse:0.197678\ttest-rmse:0.2382\n",
"[1060]\ttrain-rmse:0.19728\ttest-rmse:0.238156\n",
"[1070]\ttrain-rmse:0.196929\ttest-rmse:0.23812\n",
"[1080]\ttrain-rmse:0.196534\ttest-rmse:0.23807\n",
"[1090]\ttrain-rmse:0.19622\ttest-rmse:0.238083\n",
"[1100]\ttrain-rmse:0.195799\ttest-rmse:0.238069\n",
"[1110]\ttrain-rmse:0.195425\ttest-rmse:0.238067\n",
"[1120]\ttrain-rmse:0.195083\ttest-rmse:0.238073\n",
"[1130]\ttrain-rmse:0.194663\ttest-rmse:0.23804\n",
"[1140]\ttrain-rmse:0.194367\ttest-rmse:0.23803\n",
"[1150]\ttrain-rmse:0.194004\ttest-rmse:0.238014\n",
"[1160]\ttrain-rmse:0.193643\ttest-rmse:0.238008\n",
"[1170]\ttrain-rmse:0.193261\ttest-rmse:0.237982\n",
"[1180]\ttrain-rmse:0.19286\ttest-rmse:0.237949\n",
"[1190]\ttrain-rmse:0.192459\ttest-rmse:0.23793\n",
"[1200]\ttrain-rmse:0.192057\ttest-rmse:0.23791\n",
"[1210]\ttrain-rmse:0.191714\ttest-rmse:0.237894\n",
"[1220]\ttrain-rmse:0.191347\ttest-rmse:0.237895\n",
"[1230]\ttrain-rmse:0.190948\ttest-rmse:0.237879\n",
"[1240]\ttrain-rmse:0.190604\ttest-rmse:0.237843\n",
"[1250]\ttrain-rmse:0.190214\ttest-rmse:0.237815\n",
"[1260]\ttrain-rmse:0.189863\ttest-rmse:0.237804\n",
"[1270]\ttrain-rmse:0.189436\ttest-rmse:0.237775\n",
"[1280]\ttrain-rmse:0.189103\ttest-rmse:0.23776\n",
"[1290]\ttrain-rmse:0.188756\ttest-rmse:0.237747\n",
"[1300]\ttrain-rmse:0.188435\ttest-rmse:0.237736\n",
"[1310]\ttrain-rmse:0.188003\ttest-rmse:0.237714\n",
"[1320]\ttrain-rmse:0.187613\ttest-rmse:0.237663\n",
"[1330]\ttrain-rmse:0.187307\ttest-rmse:0.237645\n",
"[1340]\ttrain-rmse:0.186947\ttest-rmse:0.237644\n",
"[1350]\ttrain-rmse:0.186576\ttest-rmse:0.237657\n",
"[1360]\ttrain-rmse:0.186228\ttest-rmse:0.237643\n",
"[1370]\ttrain-rmse:0.185895\ttest-rmse:0.237682\n",
"[1380]\ttrain-rmse:0.185459\ttest-rmse:0.237656\n",
"[1390]\ttrain-rmse:0.185138\ttest-rmse:0.237601\n",
"[1400]\ttrain-rmse:0.184826\ttest-rmse:0.237591\n",
"[1410]\ttrain-rmse:0.18449\ttest-rmse:0.237602\n",
"[1420]\ttrain-rmse:0.184151\ttest-rmse:0.237583\n",
"[1430]\ttrain-rmse:0.183794\ttest-rmse:0.237552\n",
"[1440]\ttrain-rmse:0.183433\ttest-rmse:0.237568\n",
"[1450]\ttrain-rmse:0.183089\ttest-rmse:0.23753\n",
"[1460]\ttrain-rmse:0.18277\ttest-rmse:0.237504\n",
"[1470]\ttrain-rmse:0.182445\ttest-rmse:0.237508\n",
"[1480]\ttrain-rmse:0.182045\ttest-rmse:0.237526\n",
"[1490]\ttrain-rmse:0.181734\ttest-rmse:0.237504\n",
"[1500]\ttrain-rmse:0.181468\ttest-rmse:0.2375\n",
"[1510]\ttrain-rmse:0.181231\ttest-rmse:0.237491\n",
"[1520]\ttrain-rmse:0.180947\ttest-rmse:0.237486\n",
"[1530]\ttrain-rmse:0.180595\ttest-rmse:0.237489\n",
"[1540]\ttrain-rmse:0.180192\ttest-rmse:0.237507\n",
"[1550]\ttrain-rmse:0.179865\ttest-rmse:0.237501\n",
"[1560]\ttrain-rmse:0.179501\ttest-rmse:0.237523\n",
"[1570]\ttrain-rmse:0.179137\ttest-rmse:0.237528\n",
"Stopping. Best iteration:\n",
"[1520]\ttrain-rmse:0.180947\ttest-rmse:0.237486\n",
"\n",
"[0.237486]\n",
"[0]\ttrain-rmse:0.334188\ttest-rmse:0.334237\n",
"Multiple eval metrics have been passed: 'test-rmse' will be used for early stopping.\n",
"\n",
"Will train until test-rmse hasn't improved in 50 rounds.\n",
"[10]\ttrain-rmse:0.313895\ttest-rmse:0.314328\n",
"[20]\ttrain-rmse:0.297897\ttest-rmse:0.298755\n",
"[30]\ttrain-rmse:0.285451\ttest-rmse:0.286825\n",
"[40]\ttrain-rmse:0.275749\ttest-rmse:0.277552\n",
"[50]\ttrain-rmse:0.268425\ttest-rmse:0.270724\n",
"[60]\ttrain-rmse:0.262726\ttest-rmse:0.265503\n",
"[70]\ttrain-rmse:0.258354\ttest-rmse:0.261597\n",
"[80]\ttrain-rmse:0.254709\ttest-rmse:0.258391\n",
"[90]\ttrain-rmse:0.251762\ttest-rmse:0.255882\n",
"[100]\ttrain-rmse:0.249267\ttest-rmse:0.253841\n",
"[110]\ttrain-rmse:0.247295\ttest-rmse:0.252208\n",
"[120]\ttrain-rmse:0.245461\ttest-rmse:0.250854\n",
"[130]\ttrain-rmse:0.243865\ttest-rmse:0.249683\n",
"[140]\ttrain-rmse:0.242477\ttest-rmse:0.248658\n",
"[150]\ttrain-rmse:0.241306\ttest-rmse:0.247816\n",
"[160]\ttrain-rmse:0.240241\ttest-rmse:0.247045\n",
"[170]\ttrain-rmse:0.239169\ttest-rmse:0.246338\n",
"[180]\ttrain-rmse:0.238234\ttest-rmse:0.24572\n",
"[190]\ttrain-rmse:0.23726\ttest-rmse:0.245181\n",
"[200]\ttrain-rmse:0.236489\ttest-rmse:0.244748\n",
"[210]\ttrain-rmse:0.235712\ttest-rmse:0.244356\n",
"[220]\ttrain-rmse:0.234954\ttest-rmse:0.243959\n",
"[230]\ttrain-rmse:0.234181\ttest-rmse:0.243612\n",
"[240]\ttrain-rmse:0.233532\ttest-rmse:0.243308\n",
"[250]\ttrain-rmse:0.232865\ttest-rmse:0.243013\n",
"[260]\ttrain-rmse:0.232242\ttest-rmse:0.242777\n",
"[270]\ttrain-rmse:0.231808\ttest-rmse:0.242556\n",
"[280]\ttrain-rmse:0.231295\ttest-rmse:0.242312\n",
"[290]\ttrain-rmse:0.230784\ttest-rmse:0.242165\n",
"[300]\ttrain-rmse:0.230312\ttest-rmse:0.242017\n",
"[310]\ttrain-rmse:0.229763\ttest-rmse:0.241784\n",
"[320]\ttrain-rmse:0.229197\ttest-rmse:0.241608\n",
"[330]\ttrain-rmse:0.228665\ttest-rmse:0.241466\n",
"[340]\ttrain-rmse:0.228102\ttest-rmse:0.241312\n",
"[350]\ttrain-rmse:0.227485\ttest-rmse:0.241108\n",
"[360]\ttrain-rmse:0.227034\ttest-rmse:0.24102\n",
"[370]\ttrain-rmse:0.226411\ttest-rmse:0.240862\n",
"[380]\ttrain-rmse:0.22607\ttest-rmse:0.240767\n",
"[390]\ttrain-rmse:0.225561\ttest-rmse:0.240671\n",
"[400]\ttrain-rmse:0.225139\ttest-rmse:0.240613\n",
"[410]\ttrain-rmse:0.22461\ttest-rmse:0.240502\n",
"[420]\ttrain-rmse:0.224074\ttest-rmse:0.240387\n",
"[430]\ttrain-rmse:0.223488\ttest-rmse:0.240299\n",
"[440]\ttrain-rmse:0.222977\ttest-rmse:0.240228\n",
"[450]\ttrain-rmse:0.222557\ttest-rmse:0.240163\n",
"[460]\ttrain-rmse:0.222071\ttest-rmse:0.240033\n",
"[470]\ttrain-rmse:0.221544\ttest-rmse:0.239939\n",
"[480]\ttrain-rmse:0.221029\ttest-rmse:0.23984\n",
"[490]\ttrain-rmse:0.220597\ttest-rmse:0.23974\n",
"[500]\ttrain-rmse:0.220075\ttest-rmse:0.239637\n",
"[510]\ttrain-rmse:0.21966\ttest-rmse:0.239564\n",
"[520]\ttrain-rmse:0.219253\ttest-rmse:0.239529\n",
"[530]\ttrain-rmse:0.2189\ttest-rmse:0.239477\n",
"[540]\ttrain-rmse:0.218449\ttest-rmse:0.239412\n",
"[550]\ttrain-rmse:0.217926\ttest-rmse:0.239336\n",
"[560]\ttrain-rmse:0.217507\ttest-rmse:0.23928\n",
"[570]\ttrain-rmse:0.217038\ttest-rmse:0.239233\n",
"[580]\ttrain-rmse:0.216744\ttest-rmse:0.239181\n",
"[590]\ttrain-rmse:0.216228\ttest-rmse:0.239112\n",
"[600]\ttrain-rmse:0.215787\ttest-rmse:0.239029\n",
"[610]\ttrain-rmse:0.215374\ttest-rmse:0.238946\n",
"[620]\ttrain-rmse:0.215007\ttest-rmse:0.23889\n",
"[630]\ttrain-rmse:0.214628\ttest-rmse:0.23886\n",
"[640]\ttrain-rmse:0.214119\ttest-rmse:0.238814\n",
"[650]\ttrain-rmse:0.213666\ttest-rmse:0.238769\n",
"[660]\ttrain-rmse:0.213174\ttest-rmse:0.238711\n",
"[670]\ttrain-rmse:0.212806\ttest-rmse:0.238657\n",
"[680]\ttrain-rmse:0.212346\ttest-rmse:0.238615\n",
"[690]\ttrain-rmse:0.211865\ttest-rmse:0.238569\n",
"[700]\ttrain-rmse:0.211485\ttest-rmse:0.238542\n",
"[710]\ttrain-rmse:0.211151\ttest-rmse:0.238457\n",
"[720]\ttrain-rmse:0.210728\ttest-rmse:0.238404\n",
"[730]\ttrain-rmse:0.210299\ttest-rmse:0.238411\n",
"[740]\ttrain-rmse:0.209903\ttest-rmse:0.238372\n",
"[750]\ttrain-rmse:0.209489\ttest-rmse:0.238361\n",
"[760]\ttrain-rmse:0.209184\ttest-rmse:0.238338\n",
"[770]\ttrain-rmse:0.208774\ttest-rmse:0.23826\n",
"[780]\ttrain-rmse:0.208341\ttest-rmse:0.238263\n",
"[790]\ttrain-rmse:0.207972\ttest-rmse:0.238259\n",
"[800]\ttrain-rmse:0.207556\ttest-rmse:0.238232\n",
"[810]\ttrain-rmse:0.207129\ttest-rmse:0.238191\n",
"[820]\ttrain-rmse:0.206727\ttest-rmse:0.238161\n",
"[830]\ttrain-rmse:0.206312\ttest-rmse:0.238113\n",
"[840]\ttrain-rmse:0.205931\ttest-rmse:0.238041\n",
"[850]\ttrain-rmse:0.205547\ttest-rmse:0.238037\n",
"[860]\ttrain-rmse:0.205118\ttest-rmse:0.237971\n",
"[870]\ttrain-rmse:0.204606\ttest-rmse:0.237935\n",
"[880]\ttrain-rmse:0.204263\ttest-rmse:0.237914\n",
"[890]\ttrain-rmse:0.203986\ttest-rmse:0.237907\n",
"[900]\ttrain-rmse:0.203592\ttest-rmse:0.237876\n",
"[910]\ttrain-rmse:0.203231\ttest-rmse:0.237838\n",
"[920]\ttrain-rmse:0.202749\ttest-rmse:0.23782\n",
"[930]\ttrain-rmse:0.202361\ttest-rmse:0.237829\n",
"[940]\ttrain-rmse:0.202002\ttest-rmse:0.237791\n",
"[950]\ttrain-rmse:0.201517\ttest-rmse:0.237804\n",
"[960]\ttrain-rmse:0.201111\ttest-rmse:0.23779\n",
"[970]\ttrain-rmse:0.200719\ttest-rmse:0.237792\n",
"[980]\ttrain-rmse:0.200296\ttest-rmse:0.237759\n",
"[990]\ttrain-rmse:0.199819\ttest-rmse:0.237721\n",
"[1000]\ttrain-rmse:0.199455\ttest-rmse:0.237705\n",
"[1010]\ttrain-rmse:0.199145\ttest-rmse:0.237727\n",
"[1020]\ttrain-rmse:0.19882\ttest-rmse:0.237714\n",
"[1030]\ttrain-rmse:0.198353\ttest-rmse:0.237707\n",
"[1040]\ttrain-rmse:0.197897\ttest-rmse:0.237695\n",
"[1050]\ttrain-rmse:0.197546\ttest-rmse:0.237701\n",
"[1060]\ttrain-rmse:0.197171\ttest-rmse:0.237689\n",
"[1070]\ttrain-rmse:0.196806\ttest-rmse:0.237633\n",
"[1080]\ttrain-rmse:0.196373\ttest-rmse:0.237662\n",
"[1090]\ttrain-rmse:0.196043\ttest-rmse:0.237674\n",
"[1100]\ttrain-rmse:0.195755\ttest-rmse:0.237667\n",
"[1110]\ttrain-rmse:0.195355\ttest-rmse:0.237675\n",
"[1120]\ttrain-rmse:0.194978\ttest-rmse:0.237699\n",
"Stopping. Best iteration:\n",
"[1075]\ttrain-rmse:0.196628\ttest-rmse:0.237626\n",
"\n",
"[0.237486, 0.237626]\n",
"[0]\ttrain-rmse:0.334147\ttest-rmse:0.334257\n",
"Multiple eval metrics have been passed: 'test-rmse' will be used for early stopping.\n",
"\n",
"Will train until test-rmse hasn't improved in 50 rounds.\n",
"[10]\ttrain-rmse:0.313507\ttest-rmse:0.315023\n",
"[20]\ttrain-rmse:0.297255\ttest-rmse:0.30019\n",
"[30]\ttrain-rmse:0.28468\ttest-rmse:0.288742\n",
"[40]\ttrain-rmse:0.27486\ttest-rmse:0.279944\n",
"[50]\ttrain-rmse:0.267487\ttest-rmse:0.273484\n",
"[60]\ttrain-rmse:0.261667\ttest-rmse:0.268495\n",
"[70]\ttrain-rmse:0.257249\ttest-rmse:0.264832\n",
"[80]\ttrain-rmse:0.253564\ttest-rmse:0.261776\n",
"[90]\ttrain-rmse:0.250589\ttest-rmse:0.259433\n",
"[100]\ttrain-rmse:0.248166\ttest-rmse:0.257565\n",
"[110]\ttrain-rmse:0.246152\ttest-rmse:0.25604\n",
"[120]\ttrain-rmse:0.244417\ttest-rmse:0.254818\n",
"[130]\ttrain-rmse:0.242875\ttest-rmse:0.253663\n",
"[140]\ttrain-rmse:0.241516\ttest-rmse:0.252783\n",
"[150]\ttrain-rmse:0.240343\ttest-rmse:0.25199\n",
"[160]\ttrain-rmse:0.239241\ttest-rmse:0.251289\n",
"[170]\ttrain-rmse:0.23827\ttest-rmse:0.250665\n",
"[180]\ttrain-rmse:0.237251\ttest-rmse:0.250054\n",
"[190]\ttrain-rmse:0.236361\ttest-rmse:0.249485\n",
"[200]\ttrain-rmse:0.235523\ttest-rmse:0.249039\n",
"[210]\ttrain-rmse:0.234875\ttest-rmse:0.248645\n",
"[220]\ttrain-rmse:0.234187\ttest-rmse:0.248266\n",
"[230]\ttrain-rmse:0.233435\ttest-rmse:0.247863\n",
"[240]\ttrain-rmse:0.232607\ttest-rmse:0.247441\n",
"[250]\ttrain-rmse:0.232051\ttest-rmse:0.247175\n",
"[260]\ttrain-rmse:0.231385\ttest-rmse:0.246906\n",
"[270]\ttrain-rmse:0.230791\ttest-rmse:0.246627\n",
"[280]\ttrain-rmse:0.23028\ttest-rmse:0.246423\n",
"[290]\ttrain-rmse:0.229765\ttest-rmse:0.246247\n",
"[300]\ttrain-rmse:0.229147\ttest-rmse:0.246021\n",
"[310]\ttrain-rmse:0.228754\ttest-rmse:0.245856\n",
"[320]\ttrain-rmse:0.22829\ttest-rmse:0.245687\n",
"[330]\ttrain-rmse:0.227828\ttest-rmse:0.245497\n",
"[340]\ttrain-rmse:0.227446\ttest-rmse:0.245346\n",
"[350]\ttrain-rmse:0.226878\ttest-rmse:0.245195\n",
"[360]\ttrain-rmse:0.226333\ttest-rmse:0.245074\n",
"[370]\ttrain-rmse:0.225761\ttest-rmse:0.244915\n",
"[380]\ttrain-rmse:0.225273\ttest-rmse:0.244764\n",
"[390]\ttrain-rmse:0.224687\ttest-rmse:0.244592\n",
"[400]\ttrain-rmse:0.22416\ttest-rmse:0.244418\n",
"[410]\ttrain-rmse:0.223628\ttest-rmse:0.244339\n",
"[420]\ttrain-rmse:0.223086\ttest-rmse:0.24426\n",
"[430]\ttrain-rmse:0.222548\ttest-rmse:0.244124\n",
"[440]\ttrain-rmse:0.222107\ttest-rmse:0.244007\n",
"[450]\ttrain-rmse:0.221668\ttest-rmse:0.243948\n",
"[460]\ttrain-rmse:0.221243\ttest-rmse:0.243821\n",
"[470]\ttrain-rmse:0.220681\ttest-rmse:0.243715\n",
"[480]\ttrain-rmse:0.220214\ttest-rmse:0.243614\n",
"[490]\ttrain-rmse:0.219749\ttest-rmse:0.243521\n",
"[500]\ttrain-rmse:0.219329\ttest-rmse:0.243481\n",
"[510]\ttrain-rmse:0.218891\ttest-rmse:0.243399\n",
"[520]\ttrain-rmse:0.218496\ttest-rmse:0.24333\n",
"[530]\ttrain-rmse:0.218092\ttest-rmse:0.243271\n",
"[540]\ttrain-rmse:0.217661\ttest-rmse:0.243199\n",
"[550]\ttrain-rmse:0.217254\ttest-rmse:0.243106\n",
"[560]\ttrain-rmse:0.216829\ttest-rmse:0.243049\n",
"[570]\ttrain-rmse:0.216392\ttest-rmse:0.242995\n",
"[580]\ttrain-rmse:0.215867\ttest-rmse:0.242929\n",
"[590]\ttrain-rmse:0.215477\ttest-rmse:0.242856\n",
"[600]\ttrain-rmse:0.215092\ttest-rmse:0.242767\n",
"[610]\ttrain-rmse:0.214651\ttest-rmse:0.242662\n",
"[620]\ttrain-rmse:0.214208\ttest-rmse:0.242652\n",
"[630]\ttrain-rmse:0.213748\ttest-rmse:0.24259\n",
"[640]\ttrain-rmse:0.213373\ttest-rmse:0.242508\n",
"[650]\ttrain-rmse:0.212934\ttest-rmse:0.24246\n",
"[660]\ttrain-rmse:0.212641\ttest-rmse:0.242439\n",
"[670]\ttrain-rmse:0.212242\ttest-rmse:0.242403\n",
"[680]\ttrain-rmse:0.211907\ttest-rmse:0.242327\n",
"[690]\ttrain-rmse:0.211341\ttest-rmse:0.242262\n",
"[700]\ttrain-rmse:0.210999\ttest-rmse:0.242228\n",
"[710]\ttrain-rmse:0.210479\ttest-rmse:0.242203\n",
"[720]\ttrain-rmse:0.210112\ttest-rmse:0.242165\n",
"[730]\ttrain-rmse:0.20973\ttest-rmse:0.242133\n",
"[740]\ttrain-rmse:0.209305\ttest-rmse:0.242098\n",
"[750]\ttrain-rmse:0.208915\ttest-rmse:0.242044\n",
"[760]\ttrain-rmse:0.208522\ttest-rmse:0.241989\n",
"[770]\ttrain-rmse:0.208091\ttest-rmse:0.241938\n",
"[780]\ttrain-rmse:0.207662\ttest-rmse:0.241867\n",
"[790]\ttrain-rmse:0.207204\ttest-rmse:0.241836\n",
"[800]\ttrain-rmse:0.206771\ttest-rmse:0.241812\n",
"[810]\ttrain-rmse:0.206285\ttest-rmse:0.241795\n",
"[820]\ttrain-rmse:0.205894\ttest-rmse:0.24173\n",
"[830]\ttrain-rmse:0.205437\ttest-rmse:0.241696\n",
"[840]\ttrain-rmse:0.204946\ttest-rmse:0.241683\n",
"[850]\ttrain-rmse:0.204467\ttest-rmse:0.241651\n",
"[860]\ttrain-rmse:0.204097\ttest-rmse:0.241607\n",
"[870]\ttrain-rmse:0.203732\ttest-rmse:0.241605\n",
"[880]\ttrain-rmse:0.203366\ttest-rmse:0.241583\n",
"[890]\ttrain-rmse:0.202945\ttest-rmse:0.241568\n",
"[900]\ttrain-rmse:0.202579\ttest-rmse:0.241545\n",
"[910]\ttrain-rmse:0.202171\ttest-rmse:0.241535\n",
"[920]\ttrain-rmse:0.201762\ttest-rmse:0.241521\n",
"[930]\ttrain-rmse:0.201335\ttest-rmse:0.241497\n",
"[940]\ttrain-rmse:0.200986\ttest-rmse:0.241469\n",
"[950]\ttrain-rmse:0.20062\ttest-rmse:0.241446\n",
"[960]\ttrain-rmse:0.200299\ttest-rmse:0.241438\n",
"[970]\ttrain-rmse:0.199929\ttest-rmse:0.241373\n",
"[980]\ttrain-rmse:0.199488\ttest-rmse:0.241346\n",
"[990]\ttrain-rmse:0.199085\ttest-rmse:0.241316\n",
"[1000]\ttrain-rmse:0.198721\ttest-rmse:0.241298\n",
"[1010]\ttrain-rmse:0.198419\ttest-rmse:0.241268\n",
"[1020]\ttrain-rmse:0.198033\ttest-rmse:0.241269\n",
"[1030]\ttrain-rmse:0.197702\ttest-rmse:0.241251\n",
"[1040]\ttrain-rmse:0.197323\ttest-rmse:0.241243\n",
"[1050]\ttrain-rmse:0.196983\ttest-rmse:0.24123\n",
"[1060]\ttrain-rmse:0.196506\ttest-rmse:0.241199\n",
"[1070]\ttrain-rmse:0.196158\ttest-rmse:0.24116\n",
"[1080]\ttrain-rmse:0.195813\ttest-rmse:0.241156\n",
"[1090]\ttrain-rmse:0.195477\ttest-rmse:0.241168\n",
"[1100]\ttrain-rmse:0.19514\ttest-rmse:0.241138\n",
"[1110]\ttrain-rmse:0.194768\ttest-rmse:0.241158\n",
"[1120]\ttrain-rmse:0.19433\ttest-rmse:0.241152\n",
"[1130]\ttrain-rmse:0.193963\ttest-rmse:0.241149\n",
"[1140]\ttrain-rmse:0.193657\ttest-rmse:0.241121\n",
"[1150]\ttrain-rmse:0.193241\ttest-rmse:0.241102\n",
"[1160]\ttrain-rmse:0.192806\ttest-rmse:0.241085\n",
"[1170]\ttrain-rmse:0.192406\ttest-rmse:0.241083\n",
"[1180]\ttrain-rmse:0.192027\ttest-rmse:0.241033\n",
"[1190]\ttrain-rmse:0.191615\ttest-rmse:0.240985\n",
"[1200]\ttrain-rmse:0.191181\ttest-rmse:0.240956\n",
"[1210]\ttrain-rmse:0.190807\ttest-rmse:0.240929\n",
"[1220]\ttrain-rmse:0.190467\ttest-rmse:0.240894\n",
"[1230]\ttrain-rmse:0.190171\ttest-rmse:0.240875\n",
"[1240]\ttrain-rmse:0.189822\ttest-rmse:0.24085\n",
"[1250]\ttrain-rmse:0.189382\ttest-rmse:0.24086\n",
"[1260]\ttrain-rmse:0.189042\ttest-rmse:0.240822\n",
"[1270]\ttrain-rmse:0.188645\ttest-rmse:0.24077\n",
"[1280]\ttrain-rmse:0.188351\ttest-rmse:0.240787\n",
"[1290]\ttrain-rmse:0.187988\ttest-rmse:0.240787\n",
"[1300]\ttrain-rmse:0.187698\ttest-rmse:0.240769\n",
"[1310]\ttrain-rmse:0.187318\ttest-rmse:0.240771\n",
"[1320]\ttrain-rmse:0.186978\ttest-rmse:0.240768\n",
"[1330]\ttrain-rmse:0.186713\ttest-rmse:0.240769\n",
"[1340]\ttrain-rmse:0.186314\ttest-rmse:0.240757\n",
"[1350]\ttrain-rmse:0.185984\ttest-rmse:0.240729\n",
"[1360]\ttrain-rmse:0.185622\ttest-rmse:0.240748\n",
"[1370]\ttrain-rmse:0.185297\ttest-rmse:0.240743\n",
"[1380]\ttrain-rmse:0.184947\ttest-rmse:0.240741\n",
"[1390]\ttrain-rmse:0.184615\ttest-rmse:0.240742\n",
"Stopping. Best iteration:\n",
"[1346]\ttrain-rmse:0.186137\ttest-rmse:0.240721\n",
"\n",
"[0.237486, 0.237626, 0.240721]\n",
"[0]\ttrain-rmse:0.334172\ttest-rmse:0.3343\n",
"Multiple eval metrics have been passed: 'test-rmse' will be used for early stopping.\n",
"\n",
"Will train until test-rmse hasn't improved in 50 rounds.\n",
"[10]\ttrain-rmse:0.313717\ttest-rmse:0.314793\n",
"[20]\ttrain-rmse:0.297577\ttest-rmse:0.299581\n",
"[30]\ttrain-rmse:0.284995\ttest-rmse:0.287832\n",
"[40]\ttrain-rmse:0.275272\ttest-rmse:0.278891\n",
"[50]\ttrain-rmse:0.26783\ttest-rmse:0.272154\n",
"[60]\ttrain-rmse:0.262065\ttest-rmse:0.267103\n",
"[70]\ttrain-rmse:0.257667\ttest-rmse:0.263269\n",
"[80]\ttrain-rmse:0.253994\ttest-rmse:0.260183\n",
"[90]\ttrain-rmse:0.251059\ttest-rmse:0.257769\n",
"[100]\ttrain-rmse:0.248575\ttest-rmse:0.255809\n",
"[110]\ttrain-rmse:0.246476\ttest-rmse:0.254194\n",
"[120]\ttrain-rmse:0.244665\ttest-rmse:0.252891\n",
"[130]\ttrain-rmse:0.24309\ttest-rmse:0.251793\n",
"[140]\ttrain-rmse:0.241712\ttest-rmse:0.250898\n",
"[150]\ttrain-rmse:0.240568\ttest-rmse:0.250139\n",
"[160]\ttrain-rmse:0.23942\ttest-rmse:0.249399\n",
"[170]\ttrain-rmse:0.23842\ttest-rmse:0.248757\n",
"[180]\ttrain-rmse:0.23754\ttest-rmse:0.248278\n",
"[190]\ttrain-rmse:0.236681\ttest-rmse:0.247821\n",
"[200]\ttrain-rmse:0.235779\ttest-rmse:0.247346\n",
"[210]\ttrain-rmse:0.235031\ttest-rmse:0.246942\n",
"[220]\ttrain-rmse:0.234333\ttest-rmse:0.246573\n",
"[230]\ttrain-rmse:0.233726\ttest-rmse:0.24627\n",
"[240]\ttrain-rmse:0.233019\ttest-rmse:0.245957\n",
"[250]\ttrain-rmse:0.232296\ttest-rmse:0.245675\n",
"[260]\ttrain-rmse:0.231638\ttest-rmse:0.245424\n",
"[270]\ttrain-rmse:0.231058\ttest-rmse:0.245135\n",
"[280]\ttrain-rmse:0.230466\ttest-rmse:0.244889\n",
"[290]\ttrain-rmse:0.230018\ttest-rmse:0.24465\n",
"[300]\ttrain-rmse:0.229493\ttest-rmse:0.244475\n",
"[310]\ttrain-rmse:0.228886\ttest-rmse:0.244266\n",
"[320]\ttrain-rmse:0.228373\ttest-rmse:0.244051\n",
"[330]\ttrain-rmse:0.227917\ttest-rmse:0.243856\n",
"[340]\ttrain-rmse:0.227328\ttest-rmse:0.243686\n",
"[350]\ttrain-rmse:0.226816\ttest-rmse:0.243514\n",
"[360]\ttrain-rmse:0.226299\ttest-rmse:0.243361\n",
"[370]\ttrain-rmse:0.225892\ttest-rmse:0.243227\n",
"[380]\ttrain-rmse:0.225283\ttest-rmse:0.243124\n",
"[390]\ttrain-rmse:0.224879\ttest-rmse:0.243003\n",
"[400]\ttrain-rmse:0.224339\ttest-rmse:0.242812\n",
"[410]\ttrain-rmse:0.223945\ttest-rmse:0.24271\n",
"[420]\ttrain-rmse:0.223566\ttest-rmse:0.242612\n",
"[430]\ttrain-rmse:0.222974\ttest-rmse:0.242495\n",
"[440]\ttrain-rmse:0.222538\ttest-rmse:0.242392\n",
"[450]\ttrain-rmse:0.222044\ttest-rmse:0.242299\n",
"[460]\ttrain-rmse:0.221537\ttest-rmse:0.24214\n",
"[470]\ttrain-rmse:0.22111\ttest-rmse:0.242006\n",
"[480]\ttrain-rmse:0.220682\ttest-rmse:0.241929\n",
"[490]\ttrain-rmse:0.22028\ttest-rmse:0.241829\n",
"[500]\ttrain-rmse:0.219875\ttest-rmse:0.241755\n",
"[510]\ttrain-rmse:0.2194\ttest-rmse:0.241688\n",
"[520]\ttrain-rmse:0.219064\ttest-rmse:0.241652\n",
"[530]\ttrain-rmse:0.218546\ttest-rmse:0.241553\n",
"[540]\ttrain-rmse:0.218132\ttest-rmse:0.241471\n",
"[550]\ttrain-rmse:0.217588\ttest-rmse:0.2414\n",
"[560]\ttrain-rmse:0.217169\ttest-rmse:0.241354\n",
"[570]\ttrain-rmse:0.216601\ttest-rmse:0.241273\n",
"[580]\ttrain-rmse:0.216196\ttest-rmse:0.241204\n",
"[590]\ttrain-rmse:0.215698\ttest-rmse:0.241131\n",
"[600]\ttrain-rmse:0.215225\ttest-rmse:0.241053\n",
"[610]\ttrain-rmse:0.21485\ttest-rmse:0.240989\n",
"[620]\ttrain-rmse:0.21442\ttest-rmse:0.240935\n",
"[630]\ttrain-rmse:0.21403\ttest-rmse:0.240876\n",
"[640]\ttrain-rmse:0.213557\ttest-rmse:0.240791\n",
"[650]\ttrain-rmse:0.213268\ttest-rmse:0.240759\n",
"[660]\ttrain-rmse:0.212812\ttest-rmse:0.240695\n",
"[670]\ttrain-rmse:0.212421\ttest-rmse:0.240649\n",
"[680]\ttrain-rmse:0.211953\ttest-rmse:0.240586\n",
"[690]\ttrain-rmse:0.211518\ttest-rmse:0.240575\n",
"[700]\ttrain-rmse:0.211107\ttest-rmse:0.24049\n",
"[710]\ttrain-rmse:0.210668\ttest-rmse:0.240452\n",
"[720]\ttrain-rmse:0.210193\ttest-rmse:0.240407\n",
"[730]\ttrain-rmse:0.209767\ttest-rmse:0.240405\n",
"[740]\ttrain-rmse:0.209315\ttest-rmse:0.240385\n",
"[750]\ttrain-rmse:0.208951\ttest-rmse:0.240325\n",
"[760]\ttrain-rmse:0.208527\ttest-rmse:0.240303\n",
"[770]\ttrain-rmse:0.208104\ttest-rmse:0.240268\n",
"[780]\ttrain-rmse:0.207715\ttest-rmse:0.240233\n",
"[790]\ttrain-rmse:0.207265\ttest-rmse:0.240171\n",
"[800]\ttrain-rmse:0.206933\ttest-rmse:0.24011\n",
"[810]\ttrain-rmse:0.206527\ttest-rmse:0.240075\n",
"[820]\ttrain-rmse:0.20613\ttest-rmse:0.240048\n",
"[830]\ttrain-rmse:0.205754\ttest-rmse:0.240005\n",
"[840]\ttrain-rmse:0.205405\ttest-rmse:0.23998\n",
"[850]\ttrain-rmse:0.205104\ttest-rmse:0.239971\n",
"[860]\ttrain-rmse:0.204683\ttest-rmse:0.239929\n",
"[870]\ttrain-rmse:0.20423\ttest-rmse:0.23994\n",
"[880]\ttrain-rmse:0.20388\ttest-rmse:0.239889\n",
"[890]\ttrain-rmse:0.203475\ttest-rmse:0.239877\n",
"[900]\ttrain-rmse:0.203126\ttest-rmse:0.239857\n",
"[910]\ttrain-rmse:0.202716\ttest-rmse:0.239827\n",
"[920]\ttrain-rmse:0.202274\ttest-rmse:0.239804\n",
"[930]\ttrain-rmse:0.201868\ttest-rmse:0.23979\n",
"[940]\ttrain-rmse:0.201511\ttest-rmse:0.239771\n",
"[950]\ttrain-rmse:0.201129\ttest-rmse:0.239742\n",
"[960]\ttrain-rmse:0.200758\ttest-rmse:0.239715\n",
"[970]\ttrain-rmse:0.200435\ttest-rmse:0.239696\n",
"[980]\ttrain-rmse:0.20009\ttest-rmse:0.239677\n",
"[990]\ttrain-rmse:0.1997\ttest-rmse:0.239658\n",
"[1000]\ttrain-rmse:0.19933\ttest-rmse:0.239625\n",
"[1010]\ttrain-rmse:0.198932\ttest-rmse:0.239603\n",
"[1020]\ttrain-rmse:0.198608\ttest-rmse:0.23958\n",
"[1030]\ttrain-rmse:0.198225\ttest-rmse:0.239572\n",
"[1040]\ttrain-rmse:0.197886\ttest-rmse:0.239515\n",
"[1050]\ttrain-rmse:0.197478\ttest-rmse:0.2395\n",
"[1060]\ttrain-rmse:0.197105\ttest-rmse:0.239436\n",
"[1070]\ttrain-rmse:0.196677\ttest-rmse:0.239439\n",
"[1080]\ttrain-rmse:0.196293\ttest-rmse:0.239399\n",
"[1090]\ttrain-rmse:0.195829\ttest-rmse:0.239337\n",
"[1100]\ttrain-rmse:0.195502\ttest-rmse:0.23934\n",
"[1110]\ttrain-rmse:0.195153\ttest-rmse:0.23931\n",
"[1120]\ttrain-rmse:0.194788\ttest-rmse:0.239265\n",
"[1130]\ttrain-rmse:0.1944\ttest-rmse:0.239263\n",
"[1140]\ttrain-rmse:0.194068\ttest-rmse:0.239235\n",
"[1150]\ttrain-rmse:0.193711\ttest-rmse:0.239256\n",
"[1160]\ttrain-rmse:0.193288\ttest-rmse:0.239277\n",
"[1170]\ttrain-rmse:0.192892\ttest-rmse:0.239259\n",
"[1180]\ttrain-rmse:0.192608\ttest-rmse:0.23928\n",
"[1190]\ttrain-rmse:0.192321\ttest-rmse:0.239252\n",
"[1200]\ttrain-rmse:0.192007\ttest-rmse:0.23925\n",
"[1210]\ttrain-rmse:0.191608\ttest-rmse:0.239256\n",
"[1220]\ttrain-rmse:0.19117\ttest-rmse:0.23924\n",
"[1230]\ttrain-rmse:0.190837\ttest-rmse:0.239239\n",
"[1240]\ttrain-rmse:0.190398\ttest-rmse:0.239224\n",
"[1250]\ttrain-rmse:0.190025\ttest-rmse:0.239219\n",
"[1260]\ttrain-rmse:0.189737\ttest-rmse:0.239231\n",
"[1270]\ttrain-rmse:0.189364\ttest-rmse:0.239235\n",
"[1280]\ttrain-rmse:0.189032\ttest-rmse:0.239241\n",
"[1290]\ttrain-rmse:0.188662\ttest-rmse:0.239201\n",
"[1300]\ttrain-rmse:0.188317\ttest-rmse:0.239183\n",
"[1310]\ttrain-rmse:0.187987\ttest-rmse:0.23923\n",
"[1320]\ttrain-rmse:0.187663\ttest-rmse:0.239253\n",
"[1330]\ttrain-rmse:0.187282\ttest-rmse:0.239233\n",
"[1340]\ttrain-rmse:0.186947\ttest-rmse:0.239242\n",
"Stopping. Best iteration:\n",
"[1297]\ttrain-rmse:0.18846\ttest-rmse:0.23917\n",
"\n",
"[0.237486, 0.237626, 0.240721, 0.23917]\n",
"[0]\ttrain-rmse:0.334199\ttest-rmse:0.334187\n",
"Multiple eval metrics have been passed: 'test-rmse' will be used for early stopping.\n",
"\n",
"Will train until test-rmse hasn't improved in 50 rounds.\n",
"[10]\ttrain-rmse:0.313606\ttest-rmse:0.314316\n",
"[20]\ttrain-rmse:0.297341\ttest-rmse:0.298725\n",
"[30]\ttrain-rmse:0.284749\ttest-rmse:0.286696\n",
"[40]\ttrain-rmse:0.275155\ttest-rmse:0.277816\n",
"[50]\ttrain-rmse:0.267837\ttest-rmse:0.271085\n",
"[60]\ttrain-rmse:0.261946\ttest-rmse:0.265793\n",
"[70]\ttrain-rmse:0.25745\ttest-rmse:0.261853\n",
"[80]\ttrain-rmse:0.253868\ttest-rmse:0.258793\n",
"[90]\ttrain-rmse:0.251041\ttest-rmse:0.256447\n",
"[100]\ttrain-rmse:0.248688\ttest-rmse:0.254571\n",
"[110]\ttrain-rmse:0.24672\ttest-rmse:0.253033\n",
"[120]\ttrain-rmse:0.244978\ttest-rmse:0.251724\n",
"[130]\ttrain-rmse:0.24341\ttest-rmse:0.250592\n",
"[140]\ttrain-rmse:0.241999\ttest-rmse:0.249584\n",
"[150]\ttrain-rmse:0.240746\ttest-rmse:0.248734\n",
"[160]\ttrain-rmse:0.239672\ttest-rmse:0.248023\n",
"[170]\ttrain-rmse:0.238739\ttest-rmse:0.247427\n",
"[180]\ttrain-rmse:0.237782\ttest-rmse:0.246855\n",
"[190]\ttrain-rmse:0.236904\ttest-rmse:0.2463\n",
"[200]\ttrain-rmse:0.236024\ttest-rmse:0.245773\n",
"[210]\ttrain-rmse:0.23533\ttest-rmse:0.24537\n",
"[220]\ttrain-rmse:0.234538\ttest-rmse:0.24494\n",
"[230]\ttrain-rmse:0.233816\ttest-rmse:0.244617\n",
"[240]\ttrain-rmse:0.23316\ttest-rmse:0.244293\n",
"[250]\ttrain-rmse:0.232604\ttest-rmse:0.244082\n",
"[260]\ttrain-rmse:0.23203\ttest-rmse:0.243818\n",
"[270]\ttrain-rmse:0.231417\ttest-rmse:0.243581\n",
"[280]\ttrain-rmse:0.230808\ttest-rmse:0.243296\n",
"[290]\ttrain-rmse:0.230311\ttest-rmse:0.243118\n",
"[300]\ttrain-rmse:0.229784\ttest-rmse:0.242925\n",
"[310]\ttrain-rmse:0.229183\ttest-rmse:0.242683\n",
"[320]\ttrain-rmse:0.228645\ttest-rmse:0.242526\n",
"[330]\ttrain-rmse:0.228081\ttest-rmse:0.242323\n",
"[340]\ttrain-rmse:0.227603\ttest-rmse:0.242164\n",
"[350]\ttrain-rmse:0.227101\ttest-rmse:0.242019\n",
"[360]\ttrain-rmse:0.22661\ttest-rmse:0.24188\n",
"[370]\ttrain-rmse:0.226155\ttest-rmse:0.241765\n",
"[380]\ttrain-rmse:0.225674\ttest-rmse:0.241643\n",
"[390]\ttrain-rmse:0.225169\ttest-rmse:0.241524\n",
"[400]\ttrain-rmse:0.224741\ttest-rmse:0.24141\n",
"[410]\ttrain-rmse:0.224221\ttest-rmse:0.241279\n",
"[420]\ttrain-rmse:0.223788\ttest-rmse:0.241197\n",
"[430]\ttrain-rmse:0.223331\ttest-rmse:0.24109\n",
"[440]\ttrain-rmse:0.222889\ttest-rmse:0.241004\n",
"[450]\ttrain-rmse:0.222371\ttest-rmse:0.240933\n",
"[460]\ttrain-rmse:0.221943\ttest-rmse:0.240838\n",
"[470]\ttrain-rmse:0.22148\ttest-rmse:0.240746\n",
"[480]\ttrain-rmse:0.220922\ttest-rmse:0.240652\n",
"[490]\ttrain-rmse:0.220568\ttest-rmse:0.24057\n",
"[500]\ttrain-rmse:0.220146\ttest-rmse:0.240505\n",
"[510]\ttrain-rmse:0.219699\ttest-rmse:0.240499\n",
"[520]\ttrain-rmse:0.21918\ttest-rmse:0.240458\n",
"[530]\ttrain-rmse:0.218723\ttest-rmse:0.240394\n",
"[540]\ttrain-rmse:0.218302\ttest-rmse:0.240306\n",
"[550]\ttrain-rmse:0.217813\ttest-rmse:0.240242\n",
"[560]\ttrain-rmse:0.217443\ttest-rmse:0.240152\n",
"[570]\ttrain-rmse:0.217047\ttest-rmse:0.240089\n",
"[580]\ttrain-rmse:0.216582\ttest-rmse:0.240107\n",
"[590]\ttrain-rmse:0.216208\ttest-rmse:0.240091\n",
"[600]\ttrain-rmse:0.215883\ttest-rmse:0.240067\n",
"[610]\ttrain-rmse:0.215318\ttest-rmse:0.239946\n",
"[620]\ttrain-rmse:0.214836\ttest-rmse:0.239905\n",
"[630]\ttrain-rmse:0.214393\ttest-rmse:0.239833\n",
"[640]\ttrain-rmse:0.21395\ttest-rmse:0.239784\n",
"[650]\ttrain-rmse:0.21359\ttest-rmse:0.239744\n",
"[660]\ttrain-rmse:0.213177\ttest-rmse:0.239715\n",
"[670]\ttrain-rmse:0.212833\ttest-rmse:0.239651\n",
"[680]\ttrain-rmse:0.212358\ttest-rmse:0.239581\n",
"[690]\ttrain-rmse:0.211876\ttest-rmse:0.239572\n",
"[700]\ttrain-rmse:0.211499\ttest-rmse:0.239494\n",
"[710]\ttrain-rmse:0.211019\ttest-rmse:0.239458\n",
"[720]\ttrain-rmse:0.210622\ttest-rmse:0.239422\n",
"[730]\ttrain-rmse:0.210189\ttest-rmse:0.239405\n",
"[740]\ttrain-rmse:0.20984\ttest-rmse:0.239365\n",
"[750]\ttrain-rmse:0.20949\ttest-rmse:0.239329\n",
"[760]\ttrain-rmse:0.209059\ttest-rmse:0.239311\n",
"[770]\ttrain-rmse:0.208536\ttest-rmse:0.23926\n",
"[780]\ttrain-rmse:0.208153\ttest-rmse:0.239231\n",
"[790]\ttrain-rmse:0.207725\ttest-rmse:0.239199\n",
"[800]\ttrain-rmse:0.207261\ttest-rmse:0.239157\n",
"[810]\ttrain-rmse:0.206921\ttest-rmse:0.239153\n",
"[820]\ttrain-rmse:0.206507\ttest-rmse:0.239147\n",
"[830]\ttrain-rmse:0.206104\ttest-rmse:0.239154\n",
"[840]\ttrain-rmse:0.205783\ttest-rmse:0.239113\n",
"[850]\ttrain-rmse:0.205374\ttest-rmse:0.239087\n",
"[860]\ttrain-rmse:0.205032\ttest-rmse:0.239038\n",
"[870]\ttrain-rmse:0.20463\ttest-rmse:0.238982\n",
"[880]\ttrain-rmse:0.20425\ttest-rmse:0.238977\n",
"[890]\ttrain-rmse:0.204013\ttest-rmse:0.238956\n",
"[900]\ttrain-rmse:0.203543\ttest-rmse:0.238914\n",
"[910]\ttrain-rmse:0.203162\ttest-rmse:0.238912\n",
"[920]\ttrain-rmse:0.20279\ttest-rmse:0.238857\n",
"[930]\ttrain-rmse:0.20238\ttest-rmse:0.238815\n",
"[940]\ttrain-rmse:0.201958\ttest-rmse:0.23879\n",
"[950]\ttrain-rmse:0.20162\ttest-rmse:0.238777\n",
"[960]\ttrain-rmse:0.201237\ttest-rmse:0.23877\n",
"[970]\ttrain-rmse:0.20083\ttest-rmse:0.238715\n",
"[980]\ttrain-rmse:0.200407\ttest-rmse:0.238701\n",
"[990]\ttrain-rmse:0.200056\ttest-rmse:0.238695\n",
"[1000]\ttrain-rmse:0.199647\ttest-rmse:0.238688\n",
"[1010]\ttrain-rmse:0.199312\ttest-rmse:0.238664\n",
"[1020]\ttrain-rmse:0.198949\ttest-rmse:0.238643\n",
"[1030]\ttrain-rmse:0.198562\ttest-rmse:0.238653\n",
"[1040]\ttrain-rmse:0.198151\ttest-rmse:0.23864\n",
"[1050]\ttrain-rmse:0.197744\ttest-rmse:0.238594\n",
"[1060]\ttrain-rmse:0.197473\ttest-rmse:0.238589\n",
"[1070]\ttrain-rmse:0.197102\ttest-rmse:0.238545\n",
"[1080]\ttrain-rmse:0.196735\ttest-rmse:0.238548\n",
"[1090]\ttrain-rmse:0.196317\ttest-rmse:0.238526\n",
"[1100]\ttrain-rmse:0.195967\ttest-rmse:0.238511\n",
"[1110]\ttrain-rmse:0.195552\ttest-rmse:0.238477\n",
"[1120]\ttrain-rmse:0.195151\ttest-rmse:0.238464\n",
"[1130]\ttrain-rmse:0.194797\ttest-rmse:0.238431\n",
"[1140]\ttrain-rmse:0.194417\ttest-rmse:0.238408\n",
"[1150]\ttrain-rmse:0.194012\ttest-rmse:0.23843\n",
"[1160]\ttrain-rmse:0.19364\ttest-rmse:0.238402\n",
"[1170]\ttrain-rmse:0.193284\ttest-rmse:0.238379\n",
"[1180]\ttrain-rmse:0.192892\ttest-rmse:0.238384\n",
"[1190]\ttrain-rmse:0.192505\ttest-rmse:0.238393\n",
"[1200]\ttrain-rmse:0.192143\ttest-rmse:0.238406\n",
"[1210]\ttrain-rmse:0.191809\ttest-rmse:0.238378\n",
"[1220]\ttrain-rmse:0.191433\ttest-rmse:0.238368\n",
"[1230]\ttrain-rmse:0.19106\ttest-rmse:0.238397\n",
"[1240]\ttrain-rmse:0.190678\ttest-rmse:0.238407\n",
"[1250]\ttrain-rmse:0.19031\ttest-rmse:0.238365\n",
"[1260]\ttrain-rmse:0.189949\ttest-rmse:0.238371\n",
"[1270]\ttrain-rmse:0.189674\ttest-rmse:0.238358\n",
"[1280]\ttrain-rmse:0.189368\ttest-rmse:0.238356\n",
"[1290]\ttrain-rmse:0.189\ttest-rmse:0.238338\n",
"[1300]\ttrain-rmse:0.18863\ttest-rmse:0.238333\n",
"[1310]\ttrain-rmse:0.188249\ttest-rmse:0.238355\n",
"[1320]\ttrain-rmse:0.187893\ttest-rmse:0.238351\n",
"[1330]\ttrain-rmse:0.187557\ttest-rmse:0.238325\n",
"[1340]\ttrain-rmse:0.187207\ttest-rmse:0.238317\n",
"[1350]\ttrain-rmse:0.18692\ttest-rmse:0.23832\n",
"[1360]\ttrain-rmse:0.18655\ttest-rmse:0.238324\n",
"[1370]\ttrain-rmse:0.186231\ttest-rmse:0.238346\n",
"[1380]\ttrain-rmse:0.185921\ttest-rmse:0.238348\n",
"Stopping. Best iteration:\n",
"[1339]\ttrain-rmse:0.187228\ttest-rmse:0.238312\n",
"\n",
"[0.237486, 0.237626, 0.240721, 0.23917, 0.238312]\n",
"0.238666\n"
]
}
],
"source": [
"rv1 = run_cv1(train_df, cv_test, kf, fl) "
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"dfs1 = run_to_stackdf(rv1)\n",
"pickle.dump(dfs1, open('modeloutput-xgb-reg-r3.pkl', 'wb'))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"_change_revision": 410,
"_is_fork": false,
"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
}
| apache-2.0 |
wikistat/Ateliers-Big-Data | Cdiscount/Part1-1-AIF-PythonNltk-Explore&CleanText-Cdiscount.ipynb | 1 | 26360 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# [Ateliers: Technologies de l'intelligence Artificielle](https://github.com/wikistat/AI-Frameworks)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<center>\n",
"<a href=\"http://www.insa-toulouse.fr/\" ><img src=\"http://www.math.univ-toulouse.fr/~besse/Wikistat/Images/logo-insa.jpg\" style=\"float:left; max-width: 120px; display: inline\" alt=\"INSA\"/></a> \n",
"<a href=\"http://wikistat.fr/\" ><img src=\"http://www.math.univ-toulouse.fr/~besse/Wikistat/Images/wikistat.jpg\" width=400, style=\"max-width: 150px; display: inline\" alt=\"Wikistat\"/></a>\n",
"<a href=\"http://www.math.univ-toulouse.fr/\" ><img src=\"http://www.math.univ-toulouse.fr/~besse/Wikistat/Images/logo_imt.jpg\" width=400, style=\"float:right; display: inline\" alt=\"IMT\"/> </a>\n",
" \n",
"</center>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Traitement Naturel du Langage (NLP) : Catégorisation de Produits Cdiscount\n",
"\n",
"Il s'agit d'une version simplifiée du concours proposé par Cdiscount et paru sur le site [datascience.net](https://www.datascience.net/fr/challenge). Les données d'apprentissage sont accessibles sur demande auprès de Cdiscount mais les solutions de l'échantillon test du concours ne sont pas et ne seront pas rendues publiques. Un échantillon test est donc construit pour l'usage de ce tutoriel. L'objectif est de prévoir la catégorie d'un produit à partir de son descriptif (*text mining*). Seule la catégorie principale (1er niveau, 47 classes) est prédite au lieu des trois niveaux demandés dans le concours. L'objectif est plutôt de comparer les performances des méthodes et technologies en fonction de la taille de la base d'apprentissage ainsi que d'illustrer sur un exemple complexe le prétraitement de données textuelles. \n",
"\n",
"Le jeux de données complet (15M produits) permet un test en vrai grandeur du **passage à l'échelle volume** des phases de préparation (*munging*), vectorisation (hashage, TF-IDF) et d'apprentissage en fonction de la technologie utilisée.\n",
"\n",
"La synthèse des résultats obtenus est développée par [Besse et al. 2016](https://hal.archives-ouvertes.fr/hal-01350099) (section 5)."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Partie 1-1 : Exploration et Nettoyage de données textuelles\n",
"\n",
"Dans ce premier notebook nous verrons différent traitements généralement opérés sur des données textuelles :\n",
"\n",
"* **Nettoyage** : Suppression des caractères mal codés et de ponctuation, transformation des majuscules en minuscules, en remarquant que ces transformations ne seraient pas pertinentes pour un objectif de détection de pourriels.\n",
"* **StopWord** : Suppression des mots inutiles ou mots de liaison, articles qui n'ont a priori pas de pouvoir discriminant.\n",
"* **Stemming** (ou Racinisation): Les mots sont réduits à leur seule racine afin de réduire la taille du dictionnaire."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Librairies"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"code_folding": []
},
"outputs": [],
"source": [
"#Importation des librairies utilisées\n",
"import unicodedata \n",
"import time\n",
"import pandas as pd\n",
"import numpy as np\n",
"import random\n",
"import nltk\n",
"import re \n",
"import collections\n",
"import itertools\n",
"import warnings\n",
"warnings.filterwarnings(\"ignore\")\n",
"\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sb\n",
"sb.set_style(\"whitegrid\")\n",
"\n",
"import sklearn.cross_validation as scv"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**nltk**\n",
"\n",
"Si vous utilisez la librairie `nltk` pour la première fois, il est nécessaire d'utiliser la commande suivante. Cette commande permet de télécharger de nombreux corpus de texte, mais également des informations grammaticales sur différentes langues. Information notamment nécessaire à l'étape de racinisation."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"code_folding": [],
"collapsed": true
},
"outputs": [],
"source": [
"# nltk.download(\"all\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Les données\n",
"\n",
"Dans le dossier *Cdiscount/data* de ce répértoire vous trouverez les fichiers suivants :\n",
"\n",
"* `cdiscount_test.csv.zip`: Fichier d'apprentissage constitué de 1.000.000 de lignes\n",
"* `cdisount_test`: Fichier test constitué de 50.000 lignes\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
" ### Read & Split Dataset\n",
" \n",
" On définit une fonction permettant de lire le fichier d'apprentissage et de créer deux DataFrame Pandas, un pour l'apprentissage, l'autre pour la validation.\n",
" La fonction créée un DataFrame en lisant entièrement le fichier. Puis elle scinde ce DataFrame en deux grâce à la fonction dédiée de sklearn. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"code_folding": [],
"collapsed": true
},
"outputs": [],
"source": [
"def split_dataset(input_path, nb_line, tauxValid):\n",
" data_all = pd.read_csv(input_path,sep=\",\", nrows=nb_line)\n",
" data_all = data_all.fillna(\"\")\n",
" data_train, data_valid = scv.train_test_split(data_all, test_size = tauxValid)\n",
" time_end = time.time()\n",
" return data_train, data_valid"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Bien que déjà réduit par rapport au fichier original du concours, contenant plus de 15M de lignes, le fichier cdiscount_test.csv.zip, contenant 1M de lignes est encore volumineux. \n",
"Nous allons charger en mémoire qu'une partie de ce fichier grace à l'argument `nb_line` afin d'éviter des temps de calcul trop couteux. \n",
"Nous allons extraire 5% de ces 1M de lignes commes échantillons de validation."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"input_path = \"data/cdiscount_train.csv.zip\"\n",
"nb_line=100000 # part totale extraite du fichier initial ici déjà réduit\n",
"tauxValid = 0.05\n",
"data_train, data_valid = split_dataset(input_path, nb_line, tauxValid)\n",
"# Cette ligne permet de visualiser les 5 premières lignes de la DataFrame \n",
"N_train = data_train.shape[0]\n",
"N_valid = data_valid.shape[0]\n",
"print(\"Train set : %d elements, Validation set : %d elements\" %(N_train, N_valid))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"La commande suivante permet d'afficher les premières lignes du fichiers. \n",
"\n",
"Vous pouvez observer que chaque produit possède 3 niveaux de Catégories, qui correspondent au différents niveaux de l'arborescence que vous retrouverez sur le site.\n",
"Il y a 44 catégories de niveau 1, 428 de niveau 2 et 3170 de niveau 3. \n",
"\n",
"Dans ce TP, nous nous interesserons uniquement à classer les produits dans la catégorie de niveau 1."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"data_train.head(5)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"La commande suivante permet d'afficher un exemple de produits pour chaque Catégorie de niveau 1."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"data_train.groupby(\"Categorie1\").first()[[\"Description\",\"Libelle\",\"Marque\"]]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Distribution des classes"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#Count occurence of each Categorie\n",
"data_count = data_train[\"Categorie1\"].value_counts()\n",
"#Rename index to add percentage\n",
"new_index = [k+ \": %.2f%%\" %(v*100/N_train) for k,v in data_count.iteritems()]\n",
"data_count.index = new_index\n",
"\n",
"fig=plt.figure(figsize= (10,10))\n",
"ax = fig.add_subplot(1,1,1)\n",
"data_count.plot.barh(logx = False)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Q** Que peut-on dire sur la distribution de ces classes?"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Sauvegarde des données\n",
"\n",
"On sauvegarde dans des csv les fichiers `train` et `validation` afin que ces mêmes fichiers soit ré-utilisés plus tard dans d'autre calepin"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"data_valid.to_csv(\"data/cdiscount_valid.csv\", index=False)\n",
"data_train.to_csv(\"data/cdiscount_train_subset.csv\", index=False)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Nettoyage des données\n",
"\n",
"Afin de limiter la dimension de l'espace des variables ou *features* (i.e les mots présents dans le document), tout en conservant les informations essentielles, il est nécessaire de nettoyer les données en appliquant plusieurs étapes:\n",
"\n",
"* Chaque mot est écrit en minuscule.\n",
"* Les termes numériques, de ponctuation et autres symboles sont supprimés.\n",
"* 155 mots-courants, et donc non informatifs, de la langue française sont supprimés (STOPWORDS). Ex: le, la, du, alors, etc...\n",
"* Chaque mot est \"racinisé\", via la fonction `STEMMER.stem` de la librairie nltk. La racinisation transforme un mot en son radical ou sa racine. Par exemple, les mots: cheval, chevaux, chevalier, chevalerie, chevaucher sont tous remplacés par \"cheva\"."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Exemple \n",
"\n",
"Observons dans un premier temps l'effet de ces différentes étapes sur un exemple. \n",
"\n",
"**Ligne Originale**"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"i = 0\n",
"description = data_train.Description.values[i]\n",
"print(\"Original Description : \" + description)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Suppression des posibles balises HTML dans la description**\n",
"\n",
"Les descriptions produits étant parfois extraites d'autres sites commerçant, des balises HTML peuvent être incluts dans la description. \n",
"La librairie 'BeautifulSoup' permet de supprimer ces balises\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from bs4 import BeautifulSoup #Nettoyage d'HTML\n",
"txt = BeautifulSoup(description,\"html.parser\",from_encoding='utf-8').get_text()\n",
"print(txt)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Conversion du texte en minuscule**\n",
"\n",
"Certaines mots peuvent être écrits en majuscule dans les descriptions textes, cela à pour conséquence de dupliquer le nombre de features et une perte d'information."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"txt = txt.lower()\n",
"print(txt)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Remplacement de caractères spéciaux**\n",
"\n",
"Certains caractères spéciaux sont supprimés comme par exemple :\n",
"\n",
"* `\\u2026`: `…`\n",
"* `\\u00a0`: `NO-BREAK SPACE`\n",
"\n",
"Cette liste est non exhaustive et peut être etayée en fonction du jeu de donées étudié, de l'objectif souhaité ou encore du résultat de l'étude explorative."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"txt = txt.replace(u'\\u2026','.') \n",
"txt = txt.replace(u'\\u00a0',' ')\n",
"print(txt)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Suppression des accents**"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"txt = unicodedata.normalize('NFD', txt).encode('ascii', 'ignore').decode(\"utf-8\")\n",
"print(txt)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Supprime les caractères qui ne sont ne sont pas des lettres minuscules**\n",
"\n",
"Une fois ces premières étapes passées, on supprime tous les caractères qui sont pas des lettres minusculres, c'est à dire les signes de ponctuation, les caractères numériques etc..."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"txt = re.sub('[^a-z_]', ' ', txt)\n",
"print(txt)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Remplace la description par une liste de mots (tokens), supprime les mots de moins de 2 lettres ainsi que les stopwords**\n",
"\n",
"On va supprimer maintenant tous les mots considérés comme \"non-informatif\". Par exemple : \"le\", \"la\", \"de\" ...\n",
"Des listes contenants ces mots sont proposés dans des libraires tels que *nltk* ou encore *lucène*."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"## listes de mots à supprimer dans la description des produits\n",
"## Depuis NLTK\n",
"nltk_stopwords = nltk.corpus.stopwords.words('french') \n",
"## Depuis Un fichier externe.\n",
"lucene_stopwords =open(\"data/lucene_stopwords.txt\",\"r\").read().split(\",\") #En local\n",
"## Union des deux fichiers de stopwords \n",
"stopwords = list(set(nltk_stopwords).union(set(lucene_stopwords)))\n",
"\n",
"stopwords[:10]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"On applique également la suppression des accents à cette liste"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"stopwords = [unicodedata.normalize('NFD', sw).encode('ascii', 'ignore').decode(\"utf-8\") for sw in stopwords]\n",
"stopwords[:10]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Enfin on crée des *tokens*, liste de mots dans la description produit, en supprimant les éléments de notre description produit qui sont présent dans la liste de stopword."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"tokens = [w for w in txt.split() if (len(w)>2) and (w not in stopwords)]\n",
"remove_words = [w for w in txt.split() if (len(w)<2) or (w in stopwords)]\n",
"\n",
"print(tokens)\n",
"print(remove_words)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Racinisation (Stem) chaque tokens**\n",
"\n",
"Pour chaque mot de notre liste de token, on va ramener ce mot à sa racine au sens de l'algorithme de Snowball présent dans la librairie **nltk**. \n",
"\n",
"Cette liste de mots néttoyé et racinisé va constitué les *features* de cette description produits."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"## Fonction de setmming de stemming permettant la racinisation\n",
"stemmer=nltk.stem.SnowballStemmer('french')\n",
"tokens_stem = [stemmer.stem(token) for token in tokens]\n",
"print(tokens_stem)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Fonction de nettoyage de texte\n",
"\n",
"On définit une fonction `clean-txt` qui prend en entrée un texte de description produit et qui retourne le texte nettoyé en appliquant successivement les étapes présentés précedemment. \n",
"\n",
"On définit également une fonction `clean_marque` qui contient signifcativement moins d'étape de nettoyage. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"code_folding": [],
"collapsed": true
},
"outputs": [],
"source": [
"# Fonction clean générale\n",
"def clean_txt(txt):\n",
" ### remove html stuff\n",
" txt = BeautifulSoup(txt,\"html.parser\",from_encoding='utf-8').get_text()\n",
" ### lower case\n",
" txt = txt.lower()\n",
" ### special escaping character '...'\n",
" txt = txt.replace(u'\\u2026','.')\n",
" txt = txt.replace(u'\\u00a0',' ')\n",
" ### remove accent btw\n",
" txt = unicodedata.normalize('NFD', txt).encode('ascii', 'ignore').decode(\"utf-8\")\n",
" ###txt = unidecode(txt)\n",
" ### remove non alphanumeric char\n",
" txt = re.sub('[^a-z_]', ' ', txt)\n",
" ### remove french stop words\n",
" tokens = [w for w in txt.split() if (len(w)>2) and (w not in stopwords)]\n",
" ### french stemming\n",
" tokens_stem = [stemmer.stem(token) for token in tokens]\n",
" ### tokens = stemmer.stemWords(tokens)\n",
" return ' '.join(tokens), \" \".join(tokens_stem)\n",
"\n",
"def clean_marque(txt):\n",
" txt = re.sub('[^a-zA-Z0-9]', '_', txt).lower()\n",
" return txt"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Applique le nettoyage sur toutes les lignes de la DataFrame et créé deux nouvelles Dataframe (avant et sans l'étape de racinisation)."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"code_folding": []
},
"outputs": [],
"source": [
"\n",
"# fonction de nettoyage du fichier(stemming et liste de mots à supprimer)\n",
"def clean_df(input_data, column_names= ['Description', 'Libelle', 'Marque']):\n",
"\n",
" nb_line = input_data.shape[0]\n",
" print(\"Start Clean %d lines\" %nb_line)\n",
" \n",
" # Cleaning start for each columns\n",
" time_start = time.time()\n",
" clean_list=[]\n",
" clean_stem_list=[]\n",
" for column_name in column_names:\n",
" column = input_data[column_name].values\n",
" if column_name == \"Marque\":\n",
" array_clean = np.array(list(map(clean_marque,column)))\n",
" clean_list.append(array_clean)\n",
" clean_stem_list.append(array_clean)\n",
" else:\n",
" A = np.array(list(map(clean_txt,column)))\n",
" array_clean = A[:,0]\n",
" array_clean_stem = A[:,1]\n",
" clean_list.append(array_clean)\n",
" clean_stem_list.append(array_clean_stem)\n",
" time_end = time.time()\n",
" print(\"Cleaning time: %d secondes\"%(time_end-time_start))\n",
" \n",
" #Convert list to DataFrame\n",
" array_clean = np.array(clean_list).T\n",
" data_clean = pd.DataFrame(array_clean, columns = column_names)\n",
" \n",
" array_clean_stem = np.array(clean_stem_list).T\n",
" data_clean_stem = pd.DataFrame(array_clean_stem, columns = column_names)\n",
" return data_clean, data_clean_stem"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Nettoyage des DataFrames"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Take approximately 2 minutes fors 100.000 rows\n",
"warnings.filterwarnings(\"ignore\")\n",
"data_valid_clean, data_valid_clean_stem = clean_df(data_valid)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"warnings.filterwarnings(\"ignore\")\n",
"data_train_clean, data_train_clean_stem = clean_df(data_train)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Affiche les 5 premières lignes de la DataFrame d'apprentissage après nettoyage."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"data_train_clean.head(5)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"data_train_clean_stem.head(5)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Taille du dictionnaire de mots pour le dataset avant et après la racinisation."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"concatenate_text = \" \".join(data_train[\"Description\"].values)\n",
"list_of_word = concatenate_text.split(\" \")\n",
"N = len(set(list_of_word))\n",
"print(N)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"concatenate_text = \" \".join(data_train_clean[\"Description\"].values)\n",
"list_of_word = concatenate_text.split(\" \")\n",
"N = len(set(list_of_word))\n",
"print(N)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"concatenate_text = \" \".join(data_train_clean_stem[\"Description\"].values)\n",
"list_of_word_stem = concatenate_text.split(\" \")\n",
"N = len(set(list_of_word_stem))\n",
"print(N)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Wordcloud"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Les représentations *Wordcloud* permettent des représentations de l'ensemble des mots d'un corpus de documents. Dans cette représentation plus un mot apparait de manière fréquent dans le corpus, plus sa taille sera grande dans la représentation du corpus.\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from wordcloud import WordCloud"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"A=WordCloud(background_color=\"black\")\n",
"A.generate_from_text?"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Wordcloud de l'ensemble des description à l'état brut."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"all_descr = \" \".join(data_valid.Description.values)\n",
"wordcloud_word = WordCloud(background_color=\"black\", collocations=False).generate_from_text(all_descr)\n",
"\n",
"plt.figure(figsize=(10,10))\n",
"plt.imshow(wordcloud_word,cmap=plt.cm.Paired)\n",
"plt.axis(\"off\")\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Wordcloud après racinisation et nettoyage"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"all_descr_clean_stem = \" \".join(data_valid_clean_stem.Description.values)\n",
"wordcloud_word = WordCloud(background_color=\"black\", collocations=False).generate_from_text(all_descr_clean_stem)\n",
"\n",
"plt.figure(figsize=(10,10))\n",
"plt.imshow(wordcloud_word,cmap=plt.cm.Paired)\n",
"plt.axis(\"off\")\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Vous pouvez observer que les mots \"voir et \"present\" sont les plus représentés. Cela est du au fait que la pluspart des descriptions se terminent par \"Voir la présentation\". C'est deux mots ne sont donc pas informatif car présent dans beaucoup de catégorie différente. C'est une bon exemple de *stopword* propre à un problème spécifique.\n",
"\n",
"**Exercice** Ajouter les mots `voir`et `présentation`à la liste des stopwords plus hauts et refaites tourner le nettoyage.\n",
"\n",
"**Exercice** Générer les wordcloud par catégorie pour 3 catégories de votre choix."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Sauvegarde des jeux de données nettoyés dans des fichiers csv."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"data_valid_clean.to_csv(\"data/cdiscount_valid_clean.csv\", index=False)\n",
"data_train_clean.to_csv(\"data/cdiscount_train_clean.csv\", index=False)\n",
"\n",
"data_valid_clean_stem.to_csv(\"data/cdiscount_valid_clean_stem.csv\", index=False)\n",
"data_train_clean_stem.to_csv(\"data/cdiscount_train_clean_stem.csv\", index=False)"
]
}
],
"metadata": {
"anaconda-cloud": {},
"hide_input": false,
"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"
},
"toc": {
"nav_menu": {
"height": "279px",
"width": "252px"
},
"number_sections": true,
"sideBar": true,
"skip_h1_title": false,
"toc_cell": false,
"toc_position": {},
"toc_section_display": "block",
"toc_window_display": true
},
"toc-autonumbering": true,
"toc-showtags": true
},
"nbformat": 4,
"nbformat_minor": 2
}
| mit |
algorithmicart/raytracing | 07_experiment_recursive_rays.ipynb | 1 | 1960 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# raytracing tutorial\n",
"# 07- experiment with recursive rays function"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# max depth for scene\n",
"max_depth = 3\n",
"\n",
"# recursive ray function\n",
"\n",
"def ray(depth):\n",
" print(\"depth = \", depth, \"max_depth = \", max_depth)\n",
" \n",
" # colour contribution\n",
" colour_contribution = 0.1/depth\n",
" \n",
" # fire off new ray\n",
" if (depth < max_depth):\n",
" colour_contribution += ray(depth+1)\n",
" pass\n",
" \n",
" return colour_contribution"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"depth = 1 max_depth = 3\n",
"depth = 2 max_depth = 3\n",
"depth = 3 max_depth = 3\n"
]
},
{
"data": {
"text/plain": [
"0.18333333333333335"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ray(1)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.2"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| gpl-2.0 |
stonebig/winpython_afterdoc | docs/Winpython_checker.ipynb | 2 | 29764 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Winpython Default checker"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import warnings\n",
"#warnings.filterwarnings(\"ignore\", category=DeprecationWarning)\n",
"#warnings.filterwarnings(\"ignore\", category=UserWarning)\n",
"#warnings.filterwarnings(\"ignore\", category=FutureWarning)\n",
"# warnings.filterwarnings(\"ignore\") # would silence all warnings"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib inline\n",
"# use %matplotlib widget for the adventurous"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Compilers: Numba and Cython\n",
"\n",
"##### Requirement\n",
"To get Cython working, Winpython 3.7+ users should install \"Microsoft Visual C++ Build Tools 2017\" (visualcppbuildtools_full.exe, a 4 Go installation) at https://beta.visualstudio.com/download-visual-studio-vs/\n",
"\n",
"To get Numba working, not-windows10 users may have to install \"Microsoft Visual C++ Redistributable pour Visual Studio 2017\" (vc_redist) at <https://beta.visualstudio.com/download-visual-studio-vs/>\n",
"\n",
"Thanks to recent progress, Visual Studio 2017/2018/2019 are cross-compatible now\n",
"\n",
"#### Compiler toolchains"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### Numba (a JIT Compiler)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# checking Numba JIT toolchain\n",
"import numpy as np\n",
"image = np.zeros((1024, 1536), dtype = np.uint8)\n",
"\n",
"#from pylab import imshow, show\n",
"import matplotlib.pyplot as plt\n",
"from timeit import default_timer as timer\n",
"\n",
"from numba import jit\n",
"\n",
"@jit\n",
"def create_fractal(min_x, max_x, min_y, max_y, image, iters , mandelx):\n",
" height = image.shape[0]\n",
" width = image.shape[1]\n",
" pixel_size_x = (max_x - min_x) / width\n",
" pixel_size_y = (max_y - min_y) / height\n",
" \n",
" for x in range(width):\n",
" real = min_x + x * pixel_size_x\n",
" for y in range(height):\n",
" imag = min_y + y * pixel_size_y\n",
" color = mandelx(real, imag, iters)\n",
" image[y, x] = color\n",
"\n",
"@jit\n",
"def mandel(x, y, max_iters):\n",
" c = complex(x, y)\n",
" z = 0.0j\n",
" for i in range(max_iters):\n",
" z = z*z + c\n",
" if (z.real*z.real + z.imag*z.imag) >= 4:\n",
" return i\n",
" return max_iters"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Numba speed\n",
"start = timer()\n",
"create_fractal(-2.0, 1.0, -1.0, 1.0, image, 20 , mandel) \n",
"dt = timer() - start\n",
"\n",
"fig = plt.figure()\n",
"print (\"Mandelbrot created by numba in %f s\" % dt)\n",
"plt.imshow(image)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### Cython (a compiler for writing C extensions for the Python language)\n",
"WinPython 3.5 and 3.6 users may not have mingwpy available, and so need \"VisualStudio C++ Community Edition 2015\" https://www.visualstudio.com/downloads/download-visual-studio-vs#d-visual-c "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Cython + Mingwpy compiler toolchain test\n",
"%load_ext Cython"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%%cython -a\n",
"# with %%cython -a , full C-speed lines are shown in white, slowest python-speed lines are shown in dark yellow lines \n",
"# ==> put your cython rewrite effort on dark yellow lines\n",
"def create_fractal_cython(min_x, max_x, min_y, max_y, image, iters , mandelx):\n",
" height = image.shape[0]\n",
" width = image.shape[1]\n",
" pixel_size_x = (max_x - min_x) / width\n",
" pixel_size_y = (max_y - min_y) / height\n",
" \n",
" for x in range(width):\n",
" real = min_x + x * pixel_size_x\n",
" for y in range(height):\n",
" imag = min_y + y * pixel_size_y\n",
" color = mandelx(real, imag, iters)\n",
" image[y, x] = color\n",
"\n",
"def mandel_cython(x, y, max_iters):\n",
" cdef int i \n",
" cdef double cx, cy , zx, zy\n",
" cx , cy = x, y \n",
" zx , zy =0 ,0 \n",
" for i in range(max_iters):\n",
" zx , zy = zx*zx - zy*zy + cx , zx*zy*2 + cy\n",
" if (zx*zx + zy*zy) >= 4:\n",
" return i\n",
" return max_iters"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#Cython speed\n",
"start = timer()\n",
"create_fractal_cython(-2.0, 1.0, -1.0, 1.0, image, 20 , mandel_cython) \n",
"dt = timer() - start\n",
"\n",
"fig = plt.figure()\n",
"print (\"Mandelbrot created by cython in %f s\" % dt)\n",
"plt.imshow(image)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Graphics: Matplotlib, Pandas, Seaborn, Holoviews, Bokeh, bqplot, ipyleaflet, plotnine"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Matplotlib 3.4.1\n",
"# for more examples, see: http://matplotlib.org/gallery.html\n",
"from mpl_toolkits.mplot3d import axes3d\n",
"import matplotlib.pyplot as plt\n",
"from matplotlib import cm\n",
"\n",
"ax = plt.figure().add_subplot(projection='3d')\n",
"X, Y, Z = axes3d.get_test_data(0.05)\n",
"\n",
"# Plot the 3D surface\n",
"ax.plot_surface(X, Y, Z, rstride=8, cstride=8, alpha=0.3)\n",
"\n",
"# Plot projections of the contours for each dimension. By choosing offsets\n",
"# that match the appropriate axes limits, the projected contours will sit on\n",
"# the 'walls' of the graph\n",
"cset = ax.contourf(X, Y, Z, zdir='z', offset=-100, cmap=cm.coolwarm)\n",
"cset = ax.contourf(X, Y, Z, zdir='x', offset=-40, cmap=cm.coolwarm)\n",
"cset = ax.contourf(X, Y, Z, zdir='y', offset=40, cmap=cm.coolwarm)\n",
"\n",
"ax.set_xlim(-40, 40)\n",
"ax.set_ylim(-40, 40)\n",
"ax.set_zlim(-100, 100)\n",
"\n",
"ax.set_xlabel('X')\n",
"ax.set_ylabel('Y')\n",
"ax.set_zlabel('Z')\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Seaborn\n",
"# for more examples, see http://stanford.edu/~mwaskom/software/seaborn/examples/index.html\n",
"import seaborn as sns\n",
"sns.set()\n",
"df = sns.load_dataset(\"iris\")\n",
"sns.pairplot(df, hue=\"species\", height=1.5)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# altair-example \n",
"import altair as alt\n",
"\n",
"alt.Chart(df).mark_bar().encode(\n",
" x=alt.X('sepal_length', bin=alt.Bin(maxbins=50)),\n",
" y='count(*):Q',\n",
" color='species:N',\n",
" #column='species',\n",
").interactive()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# temporary warning removal\n",
"import warnings\n",
"import matplotlib as mpl\n",
"warnings.filterwarnings(\"ignore\", category=mpl.cbook.MatplotlibDeprecationWarning)\n",
"# Holoviews\n",
"# for more example, see http://holoviews.org/Tutorials/index.html\n",
"import numpy as np\n",
"import holoviews as hv\n",
"hv.extension('matplotlib')\n",
"dots = np.linspace(-0.45, 0.45, 11)\n",
"fractal = hv.Image(image)\n",
"\n",
"layouts = {y: (fractal * hv.Points(fractal.sample([(i,y) for i in dots])) +\n",
" fractal.sample(y=y) )\n",
" for y in np.linspace(0, 0.45,11)}\n",
"\n",
"hv.HoloMap(layouts, kdims=['Y']).collate().cols(2)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Bokeh 0.12.5 \n",
"import numpy as np\n",
"from six.moves import zip\n",
"from bokeh.plotting import figure, show, output_notebook\n",
"N = 4000\n",
"x = np.random.random(size=N) * 100\n",
"y = np.random.random(size=N) * 100\n",
"radii = np.random.random(size=N) * 1.5\n",
"colors = [\"#%02x%02x%02x\" % (int(r), int(g), 150) for r, g in zip(50+2*x, 30+2*y)]\n",
"\n",
"output_notebook()\n",
"TOOLS=\"hover,crosshair,pan,wheel_zoom,box_zoom,reset,tap,save,box_select,poly_select,lasso_select\"\n",
"\n",
"p = figure(tools=TOOLS)\n",
"p.scatter(x,y, radius=radii, fill_color=colors, fill_alpha=0.6, line_color=None)\n",
"show(p)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Datashader (holoviews+Bokeh)\n",
"import datashader as ds\n",
"import numpy as np\n",
"import holoviews as hv\n",
"\n",
"from holoviews import opts\n",
"from holoviews.operation.datashader import datashade, shade, dynspread, spread, rasterize\n",
"from holoviews.operation import decimate\n",
"\n",
"hv.extension('bokeh')\n",
"\n",
"decimate.max_samples=1000\n",
"dynspread.max_px=20\n",
"dynspread.threshold=0.5\n",
"\n",
"def random_walk(n, f=5000):\n",
" \"\"\"Random walk in a 2D space, smoothed with a filter of length f\"\"\"\n",
" xs = np.convolve(np.random.normal(0, 0.1, size=n), np.ones(f)/f).cumsum()\n",
" ys = np.convolve(np.random.normal(0, 0.1, size=n), np.ones(f)/f).cumsum()\n",
" xs += 0.1*np.sin(0.1*np.array(range(n-1+f))) # add wobble on x axis\n",
" xs += np.random.normal(0, 0.005, size=n-1+f) # add measurement noise\n",
" ys += np.random.normal(0, 0.005, size=n-1+f)\n",
" return np.column_stack([xs, ys])\n",
"\n",
"def random_cov():\n",
" \"\"\"Random covariance for use in generating 2D Gaussian distributions\"\"\"\n",
" A = np.random.randn(2,2)\n",
" return np.dot(A, A.T)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"np.random.seed(1)\n",
"points = hv.Points(np.random.multivariate_normal((0,0), [[0.1, 0.1], [0.1, 1.0]], (50000,)),label=\"Points\")\n",
"paths = hv.Path([0.15*random_walk(10000) for i in range(10)], kdims=[\"u\",\"v\"], label=\"Paths\")\n",
"decimate(points) + rasterize(points) + rasterize(paths)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"ropts = dict(colorbar=True, tools=[\"hover\"], width=350)\n",
"rasterize( points).opts(cmap=\"kbc_r\", cnorm=\"linear\").relabel('rasterize()').opts(**ropts).hist() + \\\n",
"dynspread(datashade( points, cmap=\"kbc_r\", cnorm=\"linear\").relabel(\"datashade()\"))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#bqplot\n",
"from IPython.display import display\n",
"from bqplot import (Figure, Map, Mercator, Orthographic, ColorScale, ColorAxis,\n",
" AlbersUSA, topo_load, Tooltip)\n",
"def_tt = Tooltip(fields=['id', 'name'])\n",
"map_mark = Map(scales={'projection': Mercator()}, tooltip=def_tt)\n",
"map_mark.interactions = {'click': 'select', 'hover': 'tooltip'}\n",
"fig = Figure(marks=[map_mark], title='Interactions Example')\n",
"display(fig)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# ipyleaflet (javascript library usage)\n",
"from ipyleaflet import (\n",
" Map, Marker, TileLayer, ImageOverlay, Polyline, Polygon,\n",
" Rectangle, Circle, CircleMarker, GeoJSON, DrawControl\n",
")\n",
"from traitlets import link\n",
"center = [34.6252978589571, -77.34580993652344]\n",
"m = Map(center=[34.6252978589571, -77.34580993652344], zoom=10)\n",
"dc = DrawControl()\n",
"\n",
"def handle_draw(self, action, geo_json):\n",
" print(action)\n",
" print(geo_json)\n",
"m\n",
"m"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"dc.on_draw(handle_draw)\n",
"m.add_control(dc)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib widget\n",
"# Testing matplotlib interactions with a simple plot\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"# warning ; you need to launch a second time %matplotlib widget, if after a %matplotlib inline \n",
"%matplotlib widget\n",
"\n",
"fig = plt.figure() #plt.figure(1)\n",
"plt.plot(np.sin(np.linspace(0, 20, 100)))\n",
"plt.show()\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# plotnine: giving a taste of ggplot of R langage (formerly we were using ggpy)\n",
"from plotnine import ggplot, aes, geom_blank, geom_point, stat_smooth, facet_wrap, theme_bw\n",
"from plotnine.data import mtcars\n",
"ggplot(mtcars, aes(x='hp', y='wt', color='mpg')) + geom_point() +\\\n",
"facet_wrap(\"~cyl\") + theme_bw()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Ipython Notebook: Interactivity & other"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import IPython;IPython.__version__"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Audio Example : https://github.com/ipython/ipywidgets/blob/master/examples/Beat%20Frequencies.ipynb\n",
"%matplotlib inline\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"from ipywidgets import interactive\n",
"from IPython.display import Audio, display\n",
"def beat_freq(f1=220.0, f2=224.0):\n",
" max_time = 3\n",
" rate = 8000\n",
" times = np.linspace(0,max_time,rate*max_time)\n",
" signal = np.sin(2*np.pi*f1*times) + np.sin(2*np.pi*f2*times)\n",
" print(f1, f2, abs(f1-f2))\n",
" display(Audio(data=signal, rate=rate))\n",
" try:\n",
" plt.plot(signal); #plt.plot(v.result);\n",
" except:\n",
" pass\n",
" return signal\n",
"v = interactive(beat_freq, f1=(200.0,300.0), f2=(200.0,300.0))\n",
"display(v)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Networks graph Example : https://github.com/ipython/ipywidgets/blob/master/examples/Exploring%20Graphs.ipynb\n",
"%matplotlib inline\n",
"from ipywidgets import interact\n",
"import matplotlib.pyplot as plt\n",
"import networkx as nx\n",
"# wrap a few graph generation functions so they have the same signature\n",
"\n",
"def random_lobster(n, m, k, p):\n",
" return nx.random_lobster(n, p, p / m)\n",
"\n",
"def powerlaw_cluster(n, m, k, p):\n",
" return nx.powerlaw_cluster_graph(n, m, p)\n",
"\n",
"def erdos_renyi(n, m, k, p):\n",
" return nx.erdos_renyi_graph(n, p)\n",
"\n",
"def newman_watts_strogatz(n, m, k, p):\n",
" return nx.newman_watts_strogatz_graph(n, k, p)\n",
"\n",
"@interact(n=(2,30), m=(1,10), k=(1,10), p=(0.0, 1.0, 0.001),\n",
" generator={'lobster': random_lobster,\n",
" 'power law': powerlaw_cluster,\n",
" 'Newman-Watts-Strogatz': newman_watts_strogatz,\n",
" u'Erdős-Rényi': erdos_renyi,\n",
" })\n",
"def plot_random_graph(n, m, k, p, generator):\n",
" g = generator(n, m, k, p)\n",
" nx.draw(g)\n",
" plt.title(generator.__name__)\n",
" plt.show()\n",
" "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Mathematical: statsmodels, lmfit, "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# checking statsmodels\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"plt.style.use('ggplot')\n",
"import statsmodels.api as sm\n",
"data = sm.datasets.anes96.load_pandas()\n",
"party_ID = np.arange(7)\n",
"labels = [\"Strong Democrat\", \"Weak Democrat\", \"Independent-Democrat\",\n",
" \"Independent-Independent\", \"Independent-Republican\",\n",
" \"Weak Republican\", \"Strong Republican\"]\n",
"plt.rcParams['figure.subplot.bottom'] = 0.23 # keep labels visible\n",
"plt.rcParams['figure.figsize'] = (6.0, 4.0) # make plot larger in notebook\n",
"age = [data.exog['age'][data.endog == id] for id in party_ID]\n",
"fig = plt.figure()\n",
"ax = fig.add_subplot(111)\n",
"plot_opts={'cutoff_val':5, 'cutoff_type':'abs',\n",
" 'label_fontsize':'small',\n",
" 'label_rotation':30}\n",
"sm.graphics.beanplot(age, ax=ax, labels=labels,\n",
" plot_opts=plot_opts)\n",
"ax.set_xlabel(\"Party identification of respondent\")\n",
"ax.set_ylabel(\"Age\")\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# lmfit test (from http://nbviewer.ipython.org/github/lmfit/lmfit-py/blob/master/examples/lmfit-model.ipynb)\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"def decay(t, N, tau):\n",
" return N*np.exp(-t/tau)\n",
"t = np.linspace(0, 5, num=1000)\n",
"data = decay(t, 7, 3) + np.random.randn(*t.shape)\n",
"\n",
"from lmfit import Model\n",
"\n",
"model = Model(decay, independent_vars=['t'])\n",
"result = model.fit(data, t=t, N=10, tau=1)\n",
"fig = plt.figure() # necessary to separate from previous ploot with %matplotlib widget\n",
"plt.plot(t, data) # data\n",
"plt.plot(t, decay(t=t, **result.values), color='orange', linewidth=5) # best-fit model"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## DataFrames: Pandas, Dask"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#Pandas \n",
"import pandas as pd\n",
"import numpy as np\n",
"\n",
"idx = pd.date_range('2000', '2005', freq='d', closed='left')\n",
"datas = pd.DataFrame({'Color': [ 'green' if x> 1 else 'red' for x in np.random.randn(len(idx))], \n",
" 'Measure': np.random.randn(len(idx)), 'Year': idx.year},\n",
" index=idx.date)\n",
"datas.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Split / Apply / Combine \n",
" Split your data into multiple independent groups.\n",
" Apply some function to each group.\n",
" Combine your groups back into a single data object.\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"datas.query('Measure > 0').groupby(['Color','Year']).size().unstack()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Web Scraping: Beautifulsoup"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# checking Web Scraping: beautifulsoup and requests \n",
"import requests\n",
"from bs4 import BeautifulSoup\n",
"\n",
"URL = 'http://en.wikipedia.org/wiki/Franklin,_Tennessee'\n",
"\n",
"req = requests.get(URL, headers={'User-Agent' : \"Mining the Social Web\"})\n",
"soup = BeautifulSoup(req.text, \"lxml\")\n",
"\n",
"geoTag = soup.find(True, 'geo')\n",
"\n",
"if geoTag and len(geoTag) > 1:\n",
" lat = geoTag.find(True, 'latitude').string\n",
" lon = geoTag.find(True, 'longitude').string\n",
" print ('Location is at', lat, lon)\n",
"elif geoTag and len(geoTag) == 1:\n",
" (lat, lon) = geoTag.string.split(';')\n",
" (lat, lon) = (lat.strip(), lon.strip())\n",
" print ('Location is at', lat, lon)\n",
"else:\n",
" print ('No location found')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Operations Research: Pulp"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"# Pulp example : minimizing the weight to carry 99 pennies\n",
"# (from Philip I Thomas)\n",
"# see https://www.youtube.com/watch?v=UmMn-N5w-lI#t=995\n",
"# Import PuLP modeler functions\n",
"from pulp import *\n",
"# The prob variable is created to contain the problem data \n",
"prob = LpProblem(\"99_pennies_Problem\",LpMinimize)\n",
"\n",
"# Variables represent how many of each coin we want to carry\n",
"pennies = LpVariable(\"Number_of_pennies\",0,None,LpInteger)\n",
"nickels = LpVariable(\"Number_of_nickels\",0,None,LpInteger)\n",
"dimes = LpVariable(\"Number_of_dimes\",0,None,LpInteger)\n",
"quarters = LpVariable(\"Number_of_quarters\",0,None,LpInteger)\n",
"\n",
"# The objective function is added to 'prob' first\n",
"\n",
"# we want to minimize (LpMinimize) this \n",
"prob += 2.5 * pennies + 5 * nickels + 2.268 * dimes + 5.670 * quarters, \"Total_coins_Weight\"\n",
"\n",
"# We want exactly 99 cents\n",
"prob += 1 * pennies + 5 * nickels + 10 * dimes + 25 * quarters == 99, \"\"\n",
"\n",
"# The problem data is written to an .lp file\n",
"prob.writeLP(\"99cents.lp\")\n",
"prob.solve()\n",
"\n",
"# print (\"status\",LpStatus[prob.status] )\n",
"print (\"Minimal Weight to carry exactly 99 pennies is %s grams\" % value(prob.objective))\n",
"# Each of the variables is printed with it's resolved optimum value\n",
"for v in prob.variables():\n",
" print (v.name, \"=\", v.varValue)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Deep Learning: see tutorial-first-neural-network-python-keras"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Symbolic Calculation: sympy"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# checking sympy \n",
"import sympy\n",
"a, b =sympy.symbols('a b')\n",
"e=(a+b)**5\n",
"e.expand()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## SQL tools: sqlite, Ipython-sql, sqlite_bro, baresql, db.py"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# checking Ipython-sql, sqlparse, SQLalchemy\n",
"%load_ext sql"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%%sql sqlite:///.baresql.db\n",
"DROP TABLE IF EXISTS writer;\n",
"CREATE TABLE writer (first_name, last_name, year_of_death);\n",
"INSERT INTO writer VALUES ('William', 'Shakespeare', 1616);\n",
"INSERT INTO writer VALUES ('Bertold', 'Brecht', 1956);\n",
"SELECT * , sqlite_version() as sqlite_version from Writer order by Year_of_death"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# checking baresql\n",
"from __future__ import print_function, unicode_literals, division # line needed only if Python2.7\n",
"from baresql import baresql\n",
"bsql = baresql.baresql(connection=\"sqlite:///.baresql.db\")\n",
"bsqldf = lambda q: bsql.df(q, dict(globals(),**locals()))\n",
"\n",
"users = ['Alexander', 'Billy', 'Charles', 'Danielle', 'Esmeralda', 'Franz', 'Greg']\n",
"# We use the python 'users' list like a SQL table\n",
"sql = \"select 'Welcome ' || c0 || ' !' as say_hello, length(c0) as name_length from users$$ where c0 like '%a%' \"\n",
"bsqldf(sql)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Transfering Datas to sqlite, doing transformation in sql, going back to Pandas and Matplotlib\n",
"bsqldf('''\n",
"select Color, Year, count(*) as size \n",
"from datas$$ \n",
"where Measure > 0 \n",
"group by Color, Year'''\n",
" ).set_index(['Year', 'Color']).unstack().plot(kind='bar')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# checking db.py\n",
"from db import DB\n",
"db=DB(dbtype=\"sqlite\", filename=\".baresql.db\")\n",
"db.query(\"select sqlite_version() as sqlite_version ;\") "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"db.tables"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# checking sqlite_bro: this should lanch a separate non-browser window with sqlite_bro's welcome\n",
"!cmd start cmd /C sqlite_bro"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# pyodbc or pypyodbc or ceODBC\n",
"try:\n",
" import pyodbc\n",
"except ImportError:\n",
" import pypyodbc as pyodbc # on PyPy, there is no pyodbc currently\n",
" \n",
"# look for pyodbc providers\n",
"sources = pyodbc.dataSources()\n",
"dsns = list(sources.keys())\n",
"sl = [' %s [%s]' % (dsn, sources[dsn]) for dsn in dsns]\n",
"print(\"pyodbc Providers: (beware 32/64 bit driver and python version must match)\\n\", '\\n'.join(sl))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# pythonnet\n",
"import clr\n",
"clr.AddReference(\"System.Data\")\n",
"clr.AddReference('System.Data.Common')\n",
"import System.Data.OleDb as ADONET\n",
"import System.Data.Odbc as ODBCNET\n",
"import System.Data.Common as DATACOM\n",
"\n",
"table = DATACOM.DbProviderFactories.GetFactoryClasses()\n",
"print(\"\\n .NET Providers: (beware 32/64 bit driver and python version must match)\")\n",
"for row in table.Rows:\n",
" print(\" %s\" % row[table.Columns[0]])\n",
" print(\" \",[row[column] for column in table.Columns if column != table.Columns[0]])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Qt libraries Demo\n",
"\n",
" \n",
"#### See [Dedicated Qt Libraries Demo](Qt_libraries_demo.ipynb)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Wrap-up"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# optional scipy full test (takes up to 10 minutes)\n",
"#!cmd /C start cmd /k python.exe -c \"import scipy;scipy.test()\""
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%pip list"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"!jupyter labextension list"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"!pip check"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"!pipdeptree"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"!pipdeptree -p pip"
]
},
{
"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": "3.7.10"
},
"widgets": {
"state": {
"056d32c70f644417b86a152d3a2385bd": {
"views": [
{
"cell_index": 14
}
]
},
"2307e84bf81346d49818eef8862360ca": {
"views": [
{
"cell_index": 22
}
]
},
"4e7a6f5db8e74905a08d4636afa3b82f": {
"views": [
{
"cell_index": 15
}
]
},
"e762d7875083491eb2933958cc3331a9": {
"views": [
{
"cell_index": 21
}
]
}
},
"version": "1.2.0"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
| mit |
lee212/simpleazure | ipynb/Use Case - NIST Pedestrian and Face Detection on Simple Azure (under development).ipynb | 1 | 11798 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Pedestrian and Face Detection on Simple Azure"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Pedestrian and Face Detection uses OpenCV to identify people standing in a picture or a video and NIST use case in this document is built with Apache Spark and Mesos clusters on multiple compute nodes.\n",
"\n",
"Simple Azure supports deploying software stacks for the NIST Pedestrian and Face Detection use case on top of Azure compute resources with the templates."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Original | Pedestrian Detected\n",
":-----------------------------------:|:------------------------------------------------------:\n",
"|"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Original | Pedestrian and Face Detected\n",
":---------------------------------------:|:----------------------------------------------------------:\n",
"|"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Introduction"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Human (pedestrian) detection and face detection have been studied during the last several years and models for them have improved along with Histograms of Oriented Gradients (HOG) for Human Detection [1]. OpenCV is a Computer Vision library including the SVM classifier and the HOG object detector for pedestrian detection and INRIA Person Dataset [2] is one of popular samples for both training and testing purposes. In this document, we deploy Apache Spark on Mesos clusters to train and apply detection models from OpenCV using Python API."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Ansible Automation Tool"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Ansible is a python tool to install/configure/manage software on multiple machines with JSON files where system descriptions are defined. There are reasons why we use Ansible:\n",
"\n",
"- Expandable: Leverages Python (default) but modules can be written in any language\n",
"- Agentless: no setup required on managed node\n",
"- Security: Allows deployment from user space; uses ssh for authentication\n",
"- Flexibility: only requires ssh access to privileged user\n",
"- Transparency: YAML Based script files express the steps of installing and configuring software\n",
"- Modularity: Single Ansible Role (should) contain all required commands and variables to deploy software package independently\n",
"- Sharing and portability: roles are available from source (github, bitbucket, gitlab, etc) or the Ansible Galaxy portal\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### INRIA Person Dataset"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This dataset contains positive and negative images for training and test purposes with annotation files for upright persons in each image. 288 positive test images, 453 negative test images, 614 positive training images and 1218 negative training images are included along with normalized 64x128 pixel formats. 970MB dataset is available to download [3]."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### HOG with SVM model"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Histogram of Oriented Gradient (HOG) and Support Vector Machine (SVM) are used as object detectors and classifiers and built-in python libraries from OpenCV provide these models for human detection."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Deployment by Ansible"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"When it comes to deploy applications and build clusters for batch-processing large datasets, Ansible scripts play a big role such as installation and configuration towards available machines. Ansible provides abstractions by Playbook Roles and reusability by Include statements. We define X application in X Ansible Role, for example, and use include statements to combine with other applications e.g. Y or Z. Five Ansible roles are used in this use case to build clusters for Human and Face Detection with INRIA dataset. The main Ansible playbook runs Ansible roles in order which looks like:\n",
"\n",
"```\n",
"---\n",
"include: sched/00-mesos.yml\n",
"include: proc/01-spark.yml\n",
"include: apps/02-opencv.yml\n",
"include: data/03-inria-dataset.yml\n",
"Include: anlys/04-human-face-detection.yml\n",
"```\n",
"\n",
"Directory names e.g. sched, proc, data, or anlys indicate BDSS layers like:\n",
"- sched: scheduler layer\n",
"- proc: data processing layer\n",
"- apps: application layer\n",
"- data: dataset layer\n",
"- anlys: analytics layer\n",
"and two digits in the filename indicate an order of roles to be run. \n",
"\n",
"\n",
"It is assumed that virtual machines are created by virtual-cluster-libs, the command line tool to start VM instances. For example on OpenStack, `vcl boot -p openstack -P $USER-` command starts a set of virtual machine instances with a cluster definition file `.cluster.py`. The number of machines and groups for clusters e.g. namenodes and datanodes are specified in the file and Ansible inventory file, a list of target machines with groups, is generated once machines are ready to use. Ansible roles run to install applications on virtual clusters.\n",
"\n",
"\n",
"Mesos role is installed first with Ansible inventory groups for masters and slaves in which mesos-master runs on the masters group and mesos-slave runs on the slaves group. Apache Zookeeper is included in the mesos role so that mesos slaves find an elected mesos leader from the zookeeper. Spark, as a data processing layer, provides two options for distributed job processing, batch job processing via a cluster mode and real-time processing via a client mode. The Mesos dispatcher runs on a masters group to accept a batch job submission and Spark interactive shell, which is the client mode, provides real-time processing on any node in the cluster. Either way, Spark is installed after a scheduler layer i.e. mesos to identify a master host for a job submission. Installation of OpenCV, INRIA Person Dataset and Human and Face Detection Python applications are followed."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Software Stacks"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The following software are expected in the stacks according to the [github](https://github.com/futuresystems/pedestrian-and-face-detection):\n",
"\n",
"- mesos cluster (master, worker)\n",
"- spark (with dispatcher for mesos cluster mode)\n",
"- openCV\n",
"- zookeeper\n",
"- INRIA Person Dataset\n",
"- Detection Analytics in Python"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- [1] Dalal, Navneet, and Bill Triggs. \"Histograms of oriented gradients for human detection.\" 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05). Vol. 1. IEEE, 2005. [pdf]\n",
"- [2] http://pascal.inrialpes.fr/data/human/\n",
"- [3] ftp://ftp.inrialpes.fr/pub/lear/douze/data/INRIAPerson.tar\n",
"- [4] https://docs.python.org/2/library/configparser.html"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Simple Azure with Ansible"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Simple Azure supports Ansible to import and run Ansible scripts towards target machines i.e. Azure virtual machines. In the previous tutorial, we've learned how to deploy 3 VMs from the 101-vm-sshkey template and we are going to use the three virtual machines in this example."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Server groups (inventory)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We may separate compute nodes in two groups: masters and workers therefore Mesos masters and zookeeper quorums manage job requests and leaders and workers run actual tasks. Ansible needs group definitions in their inventory therefore software installation associated with a proper part is completed. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Quick Instructions (under development)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Load SimpleAzure"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from simpleazure import SimpleAzure\n",
"saz = SimpleAzure()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### IP Addresses of Compute Nodes"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"ips = saz.arm.view_info()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Load Ansible API with IPs"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from simpleazure.ansible_api import AnsibleAPI\n",
"ansible_client = AnsibleAPI(ips)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Download Ansible Playbooks from Github"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The ansible scripts for Pedestrian and Face Detection is here: https://github.com/futuresystems/pedestrian-and-face-detection.\n",
"We clone the repository using Github command line tools."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from simpleazure.github_cli import GithubCLI\n",
"git_client = GithubCLI()\n",
"git_client.set_repo('https://github.com/futuresystems/pedestrian-and-face-detection')\n",
"git_client.clone()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Install Software Stacks to Targeted VMs"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"ansible_client.playbook(git_client.path + \"/site.yml\")\n",
"ansible_client.run()"
]
}
],
"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
}
| gpl-3.0 |
probcomp/bdbcontrib | examples/Index.ipynb | 1 | 12711 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Introduction\n",
"\n",
"These example notebooks introduce probabilistic programming. We first look at applications, how generative models can be applied to populations in [BayesDB](http://probcomp.csail.mit.edu/bayesdb/). We then progress towards creating new generative models that better fit our intuitions about those populations in [Venture](http://probcomp.csail.mit.edu/venture). BayesDB and Venture are developed by the [MIT Probabilistic Computing Group](http://probcomp.csail.mit.edu/). \n",
"\n",
"\n",
"We invite you to watch a [presentation](http://video.media.mit.edu/media/play/mansinghka-2016-03-15_h264_512x288.mov) \n",
"([slides](https://docs.google.com/presentation/d/1LdO6SPAFyC99Gb2QHa8-ikLLYluOPOB9MTavuOLTY9I/edit))\n",
"on the subject of this tutorial by Vikash Mansingka.\n",
"\n",
"\n",
"Before we get started..."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## About you...\n",
"\n",
"Signing up with your name and email helps build a community of support and helps improve your user experience. When you sign up, we collect information including the commands you tried, how long they took, what errors they resulted in, any additional data that you import, etc. If you provide your email, we will invite you to a low-traffic announcements list. Please include the name and email you use below in any reports of bugs or surprises. Send those reports to [email protected] or via [GitHub](https://github.com/probcomp/bdbcontrib/issues/new).\n",
"\n",
"If security is a primary concern, then you should do a security audit (and share the results with us) before using the software. As this is alpha software, results may not be reliable. \n",
"DO NOT USE THIS SOFTWARE FOR HIPAA-COVERED, PERSONALLY IDENTIFIABLE, OR SIMILARLY SENSITIVE DATA!\n",
"\n",
"**Please fill in your name and email,** then use shift-return (or the play button above) to run the cell."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"name = \"\"\n",
"email = \"\"\n",
"\n",
"\n",
"with open('bayesdb-session-capture-opt.txt', 'w') as optfile:\n",
" optfile.write('%s <%s>\\n' % (name, email))\n",
"\n",
"# To opt out, use optfile.write('False') instead.\n",
"# Even opting out of sending details, you still allow us to count how often users opt out.\n",
"# You can opt-in or opt-out on a per-population basis using the session_capture_name option to Population.\n",
"# You must choose to either opt-in or opt-out."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Background\n",
"\n",
"For those unfamiliar with the software, languages, or concepts we will use in this tutorial, we recommend:\n",
"* External [Introduction to SQL](http://www.tutorialspoint.com/sql/sql-overview.htm) — we do not implement all of SQL, but the basics are the same.\n",
"* External [Introduction to Python](https://docs.python.org/3/tutorial/introduction.html). We will introduce the basics of [pandas](http://pandas.pydata.org/pandas-docs/stable/tutorials.html) and [seaborn](https://stanford.edu/~mwaskom/software/seaborn/tutorial.html) but getting familiar with them might be helpful too.\n",
"* External quick explanations of [statistical populations](https://en.wikipedia.org/wiki/Statistical_population), [probability models](http://www.stat.yale.edu/Courses/1997-98/101/probint.htm), [generative models](https://en.wikipedia.org/wiki/Generative_model), and [predictive probabilities](https://en.wikipedia.org/wiki/Posterior_predictive_distribution).\n",
"* This page is an IPython Notebook. Many people are happy clicking on cells and running them with shift-enter. If you want to learn more, start by pressing Escape then H for a quick reference. You can also dive deeper with an external [Introduction to iPython notebooks](https://ipython.org/ipython-doc/3/notebook/notebook.html#basic-workflow).\n",
"\n",
"You do not need extensive knowledge of any of these to read our examples, so **feel free to skip ahead**. But if you are not very familiar with one of the technologies, then doing initial learning will be very helpful to you in playing around confidently and doing the suggested exercises."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## BayesDB\n",
"\n",
"[BayesDB](http://probcomp.csail.mit.edu/) allows you to query your data as other SQL database systems do. It also allows you to query the **implications** of your data. We explore these capabilities using information about satellites orbiting our planet.\n",
"\n",
"* [Querying and Plotting the Satellites Data](satellites/querying-and-plotting.ipynb) without doing any probabilistic analysis. This is a good place to start to get used to the language, *before* learning to explore the implications of the data.\n",
"* [Satellites Exploration](satellites/Satellites.ipynb) — a bit of the above, plus a short exploration of the results of probabilistic analysis.\n",
"\n",
"\n",
"**TODO**: The same in smaller chunks, with those chunks expanded, promised here.\n",
"\n",
"<!--\n",
"Descriptive Analysis (quantitatively describe a population):\n",
"* [Basic BQL querying and plotting](satellites/querying-and-plotting.ipynb).\n",
"* [Population recipes](satellites/with-recipes.ipynb).\n",
"\n",
"Exploratory Analysis (find relationships between variables, and suggest areas for future study):\n",
"* [Simulate for \"What If?\" questions](satellites/what-if.ipynb),\n",
"* [Estimate predictive relationships](satellites/predictive-relationships.ipynb),\n",
"* [Inferring missing values](satellites/missing-values.ipynb),\n",
"* [Finding unlikely values](satellites/unlikely-values.ipynb),\n",
"* [Mathematics of inference quality analysis](lab3),\n",
"* [Limitations of the default metamodel](satellites/cc-limitations.ipynb),\n",
"* [Analysis with a foreign model](satellites/foreign-model.ipynb).\n",
"\n",
"And, more suitable for presentation than for learning:\n",
"* A [one-notebook summary](satellites/Satellites.ipynb) with fewer details.\n",
"-->"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"## Working with your own data\n",
"\n",
"Because a default BayesDB model is unlikely to model your data plausibly, and because we do not yet have the tools to be confident that any model has captured the relationships in a population well, BayesDB is not ready for use for [higher levels of analysis](http://datascientistinsights.com/2013/01/29/six-types-of-analyses-every-data-scientist-should-know/). \n",
"\n",
"As you work with your data, **do not attempt to use BayesDB for**:\n",
"* inferential analysis: drawing conclusions about a larger population from which the data you analyze are a sample,\n",
"* predictive analysis: using the population you have to make predictions outside of that population,\n",
"* causal analysis: understanding how interventions in one variable will affect other variables, or\n",
"* mechanistic analysis: understanding causal and structural relationships between variables.\n",
"\n",
"For somewhat temporary technical reasons, BayesDB is not ready to handle very large populations, except by sub-sampling them (violating the caveat against inferential analysis!).\n",
"\n",
"While the focus of the group is towards better model types and inference strategies, some of these limitations are still in view to grow past. If these interest you, please work with us towards those goals.\n",
"\n",
"With those caveats, we explore a \"new\" dataset using BayesDB:\n",
"\n",
"* [ma-school-districts](ma-school-districts/MASchoolDistricts.ipynb)\n",
"\n",
"**TODO**: the same in smaller chunks, with those chunks expanded, is promised here.\n",
"\n",
"<!--\n",
"* [Preparing the data for analysis, and making initial modeling choices](ma-school-districts/data-prep.ipynb),\n",
"* [Describing the population](ma-school-districts/descriptions.ipynb),\n",
"* [Creating derived variables](ma-school-districts/derived-variables.ipynb),\n",
"* [Exploring relationships in the population](ma-school-districts/exploring-relationships.ipynb),\n",
"* [Finding and patching implausible model assumptions](ma-school-districts/implausible-assumptions.ipynb).\n",
"-->\n",
"\n",
"To work with your own data, please [contact the group](mailto:[email protected]) to have a conversation about the population you want to explore, about appropriate types of analysis, and to learn how to unlock analysis. We lock this feature because users have frequently misunderstood the limitations of our software, drawing unwarranted inferences. The concepts are easy to misuse, the software is in an early alpha version, and working with our team will help keep egg off your face, or worse."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Venture\n",
"\n",
"[Venture](http://probcomp.csail.mit.edu/venture) is a prototype general-purpose probabilistic computing platform. In Venture, one can create novel probabilistic models, and inference strategies that allow efficient learning for those models. Venture is programmed primarily in VentureScript, but also supports applications written in other probabilistic or traditional programming languages. In this tutorial we will explore a mix of the VentureScript language and the Python API to Venture.\n",
"\n",
"**TODO**: Tutorial examples promised here.\n",
"\n",
"<!--\n",
"* A simple case: [Bayesian linear regression in Venture](venture/bayesian-linear-regression.ipynb)\n",
"* More generally: [Setting up a probabilistic model in Venture](venture/probabilistic-model.ipynb)\n",
"* [Probabilistic inference in Venture](venture/probabilistic-inference.ipynb)\n",
"* [Time-accuracy tradeoffs in inference](venture/time-accuracy-general.ipynb)\n",
"* [New techniques for quantifying time and accuracy tradeoffs of MCMC-based inference](venture/time-accuracy-marcoct.ipynb)\n",
"-->"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"## Notes\n",
"\n",
"As I work in these notebooks, where is my work saved? Execute the following cell to find out:"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"'/Users/probcomp/GoogleDrive/ProbComp/bdbcontrib/examples'"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import os\n",
"os.getcwd()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"--------------------------------------------\n",
"\n",
"Copyright (c) 2010-2016, MIT Probabilistic Computing Project\n",
"\n",
"Licensed under Apache 2.0 (edit cell for details).\n",
"\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",
" 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",
"-->"
]
}
],
"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
}
| apache-2.0 |
ky822/Data_Bootcamp | Code/IPython/bootcamp_test.ipynb | 1 | 2970 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"###Data Bootcamp test program (IPython notebook)\n",
"\n",
"**First, welcome.** \n",
"\n",
"Now that we're friends, click on \"Cell\" above and choose \"Run all.\" You should see * in square brackets, which means the program is running. When it's done, a number will replace the asterisk. \n",
"\n",
"Below you should see a welcome message and today's date. If it isn't today's date, you're probably looking at old output."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Welcome to Data Bootcamp!\n",
"Today is: 2015-06-23\n"
]
}
],
"source": [
"import datetime \n",
"print('\\nWelcome to Data Bootcamp!')\n",
"print('Today is: ', datetime.date.today())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Now the test:** \n",
"\n",
"* If you get the message below \"Program halted, old version of Python, etc,\" you need to go back and install Anaconda again, this time following [the instructions](https://docs.google.com/document/d/1kvZAEEh4MqpWfVuW1eW3lBvsS2yKEJXtzAkOZSrtd5s/edit?usp=sharing) **EXACTLY**! Yes, we know that's discouraging, but better to know now than run into problems later. \n",
"\n",
"* If you get the message \"Congratulations etc,\" you're all set. Pat yourself on the back. You can close out the program and go back to whatever you were doing. "
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"What version of Python are we running? \n",
"3.4.1 |Anaconda 2.1.0 (64-bit)| (default, Jun 11 2014, 17:27:11) [MSC v.1600 64 bit (AMD64)]\n",
"\n",
"Congratulations, Python is up to date!\n"
]
}
],
"source": [
"import sys \n",
"\n",
"print('What version of Python are we running? \\n', sys.version, '\\n', sep='') \n",
"\n",
"if float(sys.version_info[0]) < 3.0 : \n",
" raise Exception('Program halted, old version of Python. \\n', \n",
" 'Sorry, you need to install Anaconda again.')\n",
"else:\n",
" print('Congratulations, Python is up to date!')\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": "3.4.1"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| mit |
bcantarel/bcantarel.github.io | bicf_nanocourses/courses/ML_1/exercises/Dimensional_Reduction.ipynb | 1 | 1449425 | null | gpl-3.0 |
adamwang0705/cross_media_affect_analysis | develop/20171019-daheng-build_shed_words_freq_dicts.ipynb | 1 | 71737 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"run_control": {
"frozen": false,
"read_only": false
}
},
"source": [
"# Build selected Hedonometer words frequency dicts for topic_news and topic_tweets docs\n",
"\n",
"Last modified: 2017-10-23"
]
},
{
"cell_type": "markdown",
"metadata": {
"run_control": {
"frozen": false,
"read_only": false
}
},
"source": [
"# Roadmap\n",
"1. Check shed words pattern-matching requiremnts\n",
"2. Build shed words freq dicts for topic docs"
]
},
{
"cell_type": "markdown",
"metadata": {
"run_control": {
"frozen": false,
"read_only": false
}
},
"source": [
"# Steps"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true,
"run_control": {
"frozen": false,
"read_only": false
}
},
"outputs": [],
"source": [
"\"\"\"\n",
"Initialization\n",
"\"\"\"\n",
"\n",
"'''\n",
"Standard modules\n",
"'''\n",
"import os\n",
"import pickle\n",
"import csv\n",
"import time\n",
"from pprint import pprint\n",
"\n",
"'''\n",
"Analysis modules\n",
"'''\n",
"import pandas as pd\n",
"\n",
"\n",
"'''\n",
"Custom modules\n",
"'''\n",
"import config\n",
"import utilities\n",
"\n",
"'''\n",
"Misc\n",
"'''\n",
"nb_name = '20171019-daheng-build_shed_words_freq_dicts'"
]
},
{
"cell_type": "markdown",
"metadata": {
"run_control": {
"frozen": false,
"read_only": false
}
},
"source": [
"## Check shed words pattern-matching requiremnts"
]
},
{
"cell_type": "markdown",
"metadata": {
"run_control": {
"frozen": false,
"read_only": false
}
},
"source": [
"__Ref__:\n",
" - Dodds, P. S., Harris, K. D., Kloumann, I. M., Bliss, C. A., & Danforth, C. M. (2011). Temporal patterns of happiness and information in a global social network: Hedonometrics and Twitter. PloS one, 6(12), e26752.\n",
"\n",
"__Notes__:\n",
" - See _2.1 Algorithm for Hedonometer_ P3\n",
" - See _Methods_ P23"
]
},
{
"cell_type": "markdown",
"metadata": {
"run_control": {
"frozen": false,
"read_only": false
}
},
"source": [
"## Build shed words freq dicts for topic docs"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"run_control": {
"frozen": false,
"read_only": false
},
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"dict_values(['laughter', 'happiness', 'love', 'happy', 'laughed', 'laugh', 'laughing', 'excellent', 'laughs', 'joy', 'successful', 'win', 'rainbow', 'smile', 'won', 'pleasure', 'smiled', 'rainbows', 'winning', 'celebration', 'enjoyed', 'healthy', 'music', 'celebrating', 'congratulations', 'weekend', 'celebrate', 'comedy', 'jokes', 'rich', 'victory', 'christmas', 'free', 'friendship', 'fun', 'holidays', 'loved', 'loves', 'loving', 'beach', 'hahaha', 'kissing', 'sunshine', 'beautiful', 'delicious', 'friends', 'funny', 'outstanding', 'paradise', 'sweetest', 'vacation', 'butterflies', 'freedom', 'flower', 'great', 'sunlight', 'sweetheart', 'sweetness', 'award', 'chocolate', 'hahahaha', 'heaven', 'peace', 'splendid', 'success', 'enjoying', 'kissed', 'attraction', 'celebrated', 'hero', 'hugs', 'positive', 'sun', 'birthday', 'blessed', 'fantastic', 'winner', 'delight', 'beauty', 'butterfly', 'entertainment', 'funniest', 'honesty', 'sky', 'smiles', 'succeed', 'wonderful', 'glorious', 'kisses', 'promotion', 'family', 'gift', 'humor', 'romantic', 'cupcakes', 'festival', 'hahahahaha', 'honour', 'relax', 'weekends', 'angel', 'b-day', 'bonus', 'brilliant', 'diamonds', 'holiday', 'lucky', 'mother', 'super', 'amazing', 'angels', 'enjoy', 'friend', 'friendly', \"mother's\", 'profit', 'finest', 'bday', 'champion', 'grandmother', 'haha', 'kiss', 'kitten', 'miracle', 'mom', 'sweet', 'blessings', 'bright', 'cutest', 'entertaining', 'excited', 'excitement', 'joke', 'millionaire', 'prize', 'succeeded', 'successfully', 'winners', 'shines', 'awesome', 'genius', 'achievement', 'cake', 'cheers', 'exciting', 'goodness', 'hug', 'income', 'party', 'puppy', 'smiling', 'song', 'succeeding', 'tasty', 'victories', 'achieved', 'billion', 'cakes', 'easier', 'flowers', 'gifts', 'gold', 'merry', 'families', 'handsome', 'lovers', 'affection', 'candy', 'cute', 'diamond', 'earnings', 'interesting', 'peacefully', 'praise', 'relaxing', 'roses', 'saturdays', 'faithful', 'heavens', 'cherish', 'comfort', 'congrats', 'cupcake', 'earn', 'extraordinary', 'glory', 'hilarious', 'moonlight', 'optimistic', 'peaceful', 'romance', 'feast', 'attractive', 'glad', 'grandma', 'internet', 'pleasant', 'profits', 'smart', 'x-mas', 'babies', 'cheer', 'courage', 'enthusiasm', 'honest', 'loyal', 'opportunities', 'triumph', 'wow', 'jewels', 'forests', 'apple', 'dreams', 'fantasy', 'food', 'honey', 'miracles', 'sex', 'sing', 'starlight', 'thankful', 'wins', 'achieve', 'adored', 'cash', 'dances', 'gorgeous', 'grandchildren', 'incredible', 'lunch', 'mommy', 'parties', 'perfect', 'saturday', 'surprise', 'truth', 'blessing', 'creative', 'dinner', 'kindness', 'pleased', 'sexy', 'strength', 'thank', 'thanks', 'thanksgiving', 'treasure', 'valentine', 'riches', 'awarded', 'fabulous', 'grandfather', 'heavenly', 'hope', 'kids', 'magical', 'million', 'nice', 'sundays', 'wealth', 'fantasies', 'cares', 'dance', 'daughters', 'favorable', \"friend's\", 'generosity', 'grateful', 'inspired', 'mothers', 'parents', \"valentine's\", 'intelligent', 'liberation', 'melody', 'wonderland', 'beloved', 'caring', 'homemade', 'inspiring', 'movies', 'precious', 'respect', 'satisfaction', 'satisfy', 'wedding', 'accomplished', 'adorable', 'championship', 'comfortable', 'cuddle', 'games', 'grandson', 'life', 'lovely', 'pretty', 'proud', 'rose', 'united', 'fruits', 'adventure', 'couple', 'dollars', 'eating', 'fortune', 'generous', 'golden', 'hahah', 'hooray', 'intelligence', 'lover', 'luxury', 'money', 'passion', 'prosperity', 'remarkable', 'sweetie', 'valentines', 'educated', 'gently', 'baby', 'books', 'bride', 'cherished', 'cookies', 'dessert', 'employed', 'glow', 'god', 'great-grandchildren', 'helped', 'independence', 'likes', 'luckily', 'moon', 'perfectly', 'satisfied', 'sunday', 'juicy', 'championships', 'divine', 'dreaming', 'foods', 'fresh', 'gladly', 'greatest', 'hearts', 'luck', 'millions', 'musicians', 'play', 'progress', 'savings', 'appreciation', 'bliss', 'bloom', 'book', 'child', 'companion', 'computer', 'gardens', 'gentle', 'hahahah', 'helpful', 'impressed', 'kind', 'knowledge', 'liberty', 'mama', 'nature', 'pal', 'passionate', 'promoted', 'reward', 'warmth', 'xmas', 'danced', 'amazed', 'appreciate', 'brother', 'confidence', 'darling', 'encouraging', 'energy', 'films', 'garden', 'graduated', 'guitar', 'health', 'heart', 'honor', 'like', 'musical', 'pets', 'relaxed', 'salary', 'star', 'sweeter', 'trust', 'yummy', 'ecstasy', 'eternal', 'approved', 'benefits', 'cartoon', 'comforted', 'cool', 'discount', 'good', 'google', 'ladies', 'libraries', 'luv', 'perfection', 'presents', 'prizes', 'special', 'wishes', 'alive', 'awards', 'bed', 'best', 'coffee', 'comfy', 'fiesta', 'genuine', 'helping', 'imagine', 'leisure', 'meal', 'promise', 'respected', 'rest', 'travel', 'abundant', 'attracted', 'devoted', 'favourite', 'granddaughter', 'heroes', 'ideas', 'liked', 'oceans', 'pizza', 'skies', 'sleep', 'spring', 'sunset', 'welcome', '1st', 'adoring', 'brighter', \"children's\", 'cure', 'fireworks', 'home', 'honored', 'journey', 'lovin', 'opportunity', 'paid', 'parks', 'playing', 'shine', 'strawberry', 'summertime', 'wealthy', 'appreciated', 'artistic', 'birth', 'children', 'fruit', 'inspire', 'juice', 'laptop', 'partners', 'son', 'stronger', 'superman', 'tree', 'valuable', \"woman's\", 'women', 'glowing', 'admiration', 'carnival', 'computers', 'confident', 'cookie', 'cutie', 'dearest', 'dream', 'freely', 'fridays', 'plants', 'quality', 'rabbit', 'resort', 'shopping', 'sincere', 'snack', 'stars', 'toys', 'useful', 'wise', 'yum', 'desirable', 'sparkle', 'bless', 'comic', 'cooking', 'dancing', 'earned', 'equality', 'faith', 'graduate', 'improvements', 'memories', 'park', 'pet', 'powerful', 'princess', 'qualities', 'thrill', 'true', 'wonder', 'everlasting', 'mamma', 'caress', 'charm', 'clever', 'father', 'grand', 'hehehe', 'idea', 'pearl', 'pictures', 'restaurant', 'sandwich', 'sharing', 'strong', 'talent', 'talented', 'tenderness', 'weddings', 'dove', 'awsome', 'cherry', 'daughter', 'eat', 'favorite', 'girlfriend', 'hoping', 'impressive', 'loyalty', 'parent', 'relationship', 'safe', 'scholarship', 'shining', 'sunrise', 'yoga', 'respects', 'fairy', 'humanity', 'productivity', 'brave', 'colours', 'correct', 'dad', 'daddy', 'dollar', 'easily', 'fans', 'goal', 'hawaii', 'honestly', 'inspiration', 'olympics', 'saints', 'sleeping', 'wisdom', 'believed', 'better', 'color', 'colors', \"dad's\", 'determination', 'discovered', 'gentlemen', 'girl', 'harmony', 'hello', 'hopes', 'noble', 'praised', 'reliable', 'trip', 'agreed', 'approval', 'brothers', 'concerts', 'cooperation', 'encouraged', 'giving', 'goals', 'ideal', 'intellectual', 'invitation', 'marry', 'musician', 'outdoors', 'photography', 'plenty', 'rome', 'trees', 'trips', 'unique', 'wildlife', 'lullaby', 'thrills', 'abroad', 'bath', 'benefit', 'birds', 'dads', 'elegant', 'eternally', 'fair', 'fancy', 'great-grandfather', 'imagination', 'improving', 'mountains', 'ocean', 'pancakes', 'photograph', 'praying', 'present', 'reunion', 'safely', 'saving', 'singing', 'songs', 'sunny', 'terrific', 'theater', 'vanilla', 'adore', 'gentleman', 'autumn', 'cinema', 'college', 'concert', 'correctly', 'cozy', 'dear', 'earning', 'earns', 'gardening', 'girls', 'massage', 'outdoor', 'photos', 'piano', 'sea', 'trusted', 'albums', 'dignity', 'favored', 'fitness', 'game', 'healing', 'learned', 'learning', 'prayers', 'promote', 'secure', 'spa', 'unity', 'wish', 'youtube', 'favour', 'clean', 'dynamic', 'encourage', 'infant', 'jewelry', 'necklace', 'paintings', 'stability', 'voyage', 'worthy', 'fulfill', 'eternity', 'accuracy', 'bookstores', 'breeze', 'bunny', 'cheese', 'comics', 'donated', 'easter', 'education', 'email', 'farmer', 'female', 'flavor', 'friday', 'moms', 'photo', 'pillow', 'pure', 'saved', 'shakespeare', 'survived', 'taste', 'valued', 'vitamin', 'infants', 'silk', 'dreamed', '#music', 'acceptance', 'banana', 'breakfast', 'cooperative', 'dancer', 'grace', 'greatly', 'guarantee', 'improved', 'improvement', 'independent', 'liking', 'paris', 'pasta', 'photographs', 'recipes', 'relationships', 'relief', 'sailing', 'science', 'seas', 'toast', 'truly', 'platinum', 'superstar', 'understands', 'accurately', 'advantage', 'belonging', 'buddy', 'childhood', 'daylight', 'discover', 'forgiveness', 'great-grandmother', 'hopefully', 'horses', 'interested', 'kid', 'live', 'lol', 'movie', 'popularity', 'solution', 'swim', 'toy', 'understanding', 'universe', 'woman', 'woohoo', 'rivers', 'sail', 'cared', 'active', 'artists', 'babe', 'believes', 'born', 'champagne', 'compassion', 'completed', 'create', 'dedicated', 'experienced', 'fathers', 'first', 'gains', 'heal', 'new', 'significant', 'singer', 'surprisingly', 'young', 'mansion', 'prevail', 'qualified', 'air', 'amazon', 'animal', 'bedroom', 'camera', 'cream', 'dreamer', 'forgiven', 'highest', 'horse', 'magic', 'manners', 'naturally', 'novels', 'performers', 'pies', 'protect', 'santa', 'shared', 'smooth', 'together', 'uncle', 'efficient', 'elevated', 'cafe', 'coke', 'completion', 'coolest', 'creation', 'dogs', 'effectiveness', 'esteemed', 'finished', 'glee', 'green', 'heartbeat', 'island', 'jukebox', 'medal', \"mom's\", 'museums', 'painting', 'pie', 'pool', 'reading', 'real', 'ruby', 'share', 'sons', 'traveling', 'variety', 'wonders', 'worth', 'guaranteed', 'raindrops', 'visions', 'pearls', 'america', 'easy', 'effective', 'future', 'humans', 'intimate', 'married', 'muffin', 'papa', 'plus', 'popcorn', 'savior', 'seasons', 'shop', 'sister', 'style', 'supporter', 'switzerland', 'tenderly', 'top', 'oxygen', 'rhyme', 'allright', 'american', 'artist', 'capable', 'complete', 'convenient', 'courtesy', 'donate', 'drinks', \"father's\", 'fine', 'focused', 'guitars', 'hi', 'integrity', 'justice', 'lake', 'mankind', 'mentor', 'merit', 'performance', 'plant', 'prepared', 'raise', 'romeo', 'shiny', 'sugar', 'surprising', 'technology', 'treat', 'university', 'wishing', 'yes', 'desires', 'wished', '4-bedroom', 'attract', 'bike', 'car', 'civilization', 'classy', 'confirmed', 'costumes', 'creating', 'culture', 'finish', 'gallery', 'knowing', 'lifelong', 'momma', 'neat', 'niece', 'online', 'orchestra', 'plays', 'revenue', 'shower', 'spiritual', 'surprised', 'tremendous', 'values', 'villages', 'warm', 'doggy', 'hallelujah', 'candle', 'secured', 'valid', 'agree', 'anniversary', 'antiques', 'believe', 'bucks', 'cruise', 'dancers', 'dine', 'dog', 'florida', 'grandsons', 'grants', 'hired', 'learn', 'marriage', 'mum', 'partner', 'productive', 'rockin', 'teaches', 'treats', 'tv', 'water', 'grin', 'invention', 'virtues', 'brains', 'sensation', 'ability', 'ace', 'animals', 'bake', 'bridegroom', 'desire', 'famous', 'forest', 'fountain', 'goodmorning', 'greater', 'grow', 'heritage', 'landscape', 'liberties', 'living', 'lyrics', 'mercy', 'museum', 'novel', 'palace', 'pianist', 'potential', 'power', 'privilege', 'proceed', 'promised', 'river', 'scotland', 'shares', 'skating', 'thanx', 'theatre', 'tours', 'well', 'acceptable', 'possibilities', 'accurate', 'candles', 'approve', 'assets', 'aunt', 'career', 'charms', 'communicate', 'competent', 'currency', 'dedication', 'dvd', 'eligible', 'fan', 'firefighters', 'greet', 'motivation', 'nieces', 'personality', 'powers', 'raises', 'sculpture', 'survivors', 'tea', 'television', 'tour', 'pony', 'rhythm', 'bird', 'care', 'cat', 'cook', 'corn', 'deposits', 'expert', 'high', 'holy', 'invite', 'leading', 'photographer', 'picture', 'promising', 'recover', 'recovered', 'recovery', 'salad', 'shops', 'solutions', 'sparks', 'sport', 'supreme', 'theaters', 'tunes', 'unite', 'volunteers', 'simplicity', 'attained', \"book's\", 'cameras', 'chatting', 'crown', 'disney', 'dresses', 'heartfelt', 'homes', 'husband', 'immortal', 'invest', 'kitty', 'offer', 'organized', 'performances', 'perfume', 'pray', 'rescue', 'restaurants', 'salaries', 'sisters', 'slept', 'steak', 'stories', 'varieties', 'vision', 'wife', 'youth', 'zoo', 'stimulation', 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'weapon', 'emptiness', 'injection', 'burnt', 'complicated', 'crap', 'never', 'politicians', 'tired', 'traffic', 'unfair', 'vulnerable', 'warning', 'fucker', 'sinner', 'envy', 'whack', 'alone', 'bleeds', 'cannot', 'confusion', \"couldn't\", 'expenses', 'ignored', 'nigga', 'noose', 'opposed', 'restrictions', 'scars', 'shots', 'savage', 'choke', 'cigarettes', 'doubts', 'fool', 'fury', 'lowest', 'suckers', 'whip', 'helpless', 'rats', 'conspiracy', 'crashing', 'falling', 'fools', 'lazy', 'nuclear', 'scar', 'suspicious', 'scarred', 'screamed', 'cough', 'damned', 'frown', 'pimps', 'vengeance', 'canceled', 'cavity', 'delayed', 'dull', 'fat', 'jerk', 'missile', 'remorse', 'rot', 'screwed', 'gangstas', 'captured', 'critical', 'fell', 'forget', 'freezing', 'ignore', 'losers', 'lynch', 'wasting', 'defect', 'frightened', 'combat', 'convicted', 'defeat', 'dirty', 'dread', 'drug', 'inferior', 'screamin', 'cryin', 'liar', 'aching', 'difficult', 'faggot', 'false', 'forgotten', 'garbage', 'kicked', 'scandal', 'sinners', 'suspension', 'woe', 'accusations', 'complain', 'declined', 'disorders', 'doubt', 'forced', 'lack', 'severe', 'smoke', 'yuck', 'feared', 'gangster', 'argument', 'avoid', 'bitch', 'bruise', 'dismissed', 'disorder', 'exhausted', 'incorrectly', 'isolation', 'scream', 'slapped', 'spit', 'suck', 'sucks', 'suspect', 'whore', 'wrong', 'cursed', 'doom', 'desperate', 'lonesome', 'regret', 'rob', 'defects', 'ambulance', 'annoy', 'conflict', 'criticism', 'execution', 'fought', 'indictment', 'pity', 'smoking', 'stink', 'tear', 'unable', 'cigarette', 'beg', 'prejudice', 'bullshit', 'decay', 'decline', 'deficit', 'difficulties', 'graves', 'regrets', 'suspended', 'trapped', 'yelling', 'aging', 'arguing', 'bullets', 'dumb', 'emergency', 'greed', 'idiot', 'idiots', 'inadequate', 'refugees', 'turmoil', 'rotting', 'greedy', 'havoc', 'arguments', 'bled', 'bored', 'complaints', 'horror', 'insane', 'jealousy', 'lawsuits', 'rat', 'resignation', 'scare', 'anxiety', 'fiend', 'hostile', 'weeping', 'broken', 'criticized', 'offensive', 'trembling', 'argue', 'argues', 'bitter', 'condemned', 'fights', 'muthafuckin', 'vicious', 'battle', 'confused', 'crappy', 'damn', 'guns', 'ignorance', 'missing', 'niggaz', 'problem', 'worthless', 'insecure', 'coffin', 'conflicts', 'damages', 'lawsuit', 'niggas', 'screaming', 'wound', 'bloody', 'cemetery', 'choking', 'explosion', 'foul', 'nervous', 'sore', 'tension', 'thief', 'thug', 'unfortunate', 'weakness', 'breakdown', 'bury', 'accused', 'awful', 'burn', 'cries', 'hangover', 'mistakes', 'problems', 'riot', 'sleepless', 'demon', 'boring', 'bruised', 'burned', 'collapse', 'complained', 'debt', 'fake', 'frustrated', 'impossible', 'ouch', 'deadly', 'disrespect', 'drown', 'badly', 'banned', 'burning', 'cancelled', 'dislike', 'threats', 'sins', 'bombs', 'complaint', 'errors', 'illegal', 'lonely', 'mourns', 'prisoner', 'stress', 'tax', 'violations', 'widow', 'addict', 'buried', 'devils', 'dump', 'hater', 'incorrect', 'infection', 'neglected', 'penalty', 'terrible', 'unkind', 'weak', 'annoying', 'bills', 'blame', 'burden', 'complaining', 'danger', 'demise', 'despair', 'disabled', 'discrimination', 'filthy', 'gun', 'lied', 'missiles', 'mourners', 'obituary', 'prosecution', 'worry', 'mafia', 'wounds', 'burns', 'cowards', 'fever', 'mistake', 'trouble', 'troubled', 'wasted', 'bitches', 'bleeding', 'fighting', 'lose', 'lost', 'pathetic', 'unfortunately', 'neglect', 'defeated', 'loses', 'stressed', 'ugly', 'violation', 'unholy', 'addiction', 'arrests', 'disgrace', 'heartbreaker', 'mourn', 'struggling', 'desperation', 'distress', 'fight', 'spam', 'taxes', 'waste', 'worse', 'sorrows', 'bleed', 'ache', 'bastards', 'fears', 'injuries', 'jealous', 'misery', 'ruin', 'shame', 'stupid', 'trash', 'deaf', 'afraid', 'ban', 'drugs', 'loneliness', 'penalties', 'surgery', 'tensions', 'bad', 'curse', 'demons', 'enemy', 'guilty', 'inflation', 'motherfucking', 'sin', 'heartaches', '#fail', 'beaten', 'lies', 'losing', 'nasty', 'retarded', 'rude', 'threatened', 'violated', 'thugs', 'abortion', 'brutal', 'crash', 'error', 'lie', 'mad', 'selfish', 'stole', 'worries', 'ashamed', 'infections', 'annoyed', 'blind', 'cheated', 'damage', 'disgusting', 'guilt', 'lying', 'motherfuckin', 'rotten', 'scared', 'scary', 'shitty', 'starving', 'stroke', 'betrayed', 'nightmares', 'assault', 'beating', 'grave', 'hopeless', 'loss', 'rage', 'satan', 'upset', 'corpse', 'abandoned', 'broke', 'cocaine', 'denied', 'harm', 'hurricane', 'miserable', 'pissed', 'ruined', 'tumor', 'attacked', 'bastard', 'destroy', 'failing', 'shooting', 'useless', 'motherfuckers', 'betray', 'psycho', 'shit', 'shot', 'stolen', 'crisis', 'damaged', 'haters', 'recession', 'saddam', 'slap', 'attacks', 'crashed', 'losses', 'panic', 'steal', 'stealing', 'tears', 'burial', 'cheat', 'dangerous', 'drowning', 'enemies', 'hating', 'prisoners', 'saddened', 'arrest', 'attack', 'flood', 'ill', 'killer', 'negative', 'worried', 'wounded', 'nigger', 'slaughter', 'asshole', 'flu', 'weapons', 'graveside', 'sad', 'victim', 'hurting', 'threat', 'frustration', 'hate', 'tragic', 'grief', 'accident', 'angry', 'fear', 'nightmare', 'poor', 'victims', 'anger', 'fired', 'fraud', 'theft', 'thieves', 'heartache', 'sadly', 'cheating', 'destruction', 'disappointed', 'bombing', 'devil', 'horrible', 'suffered', 'hatred', 'weep', 'hell', 'holocaust', 'injured', 'suffering', 'cried', 'crime', 'loser', 'depressed', 'divorce', 'hurt', 'robbed', 'tsunami', 'agony', 'drowned', 'homeless', 'pollution', 'corruption', 'crimes', 'hated', 'hurts', 'painful', 'sorrow', 'unemployment', 'unhappy', 'heartbreak', 'dying', 'funeral', 'pain', 'worst', 'dies', 'racist', 'rejected', 'robbery', 'suffer', 'virus', 'bankruptcy', 'fails', 'failure', 'hates', 'prison', 'slave', 'slaves', 'tragedy', 'violent', 'crying', 'destroyed', 'injury', 'rejection', 'motherfucker', 'sick', 'slavery', 'dead', 'disease', 'illness', 'killers', 'punishment', 'criminal', 'depression', 'headache', 'poverty', 'tumors', 'bomb', 'disaster', 'fail', 'poison', 'depressing', 'earthquake', 'evil', 'wars', 'abuse', 'diseases', 'sadness', 'violence', 'cruel', 'cry', 'failed', 'sickness', 'abused', 'tortured', 'fatal', 'killings', 'murdered', 'war', 'kills', 'jail', 'terror', 'die', 'killing', 'arrested', 'deaths', 'raped', 'torture', 'died', 'kill', 'killed', 'cancer', 'death', 'murder', 'terrorism', 'rape', 'suicide', 'terrorist'])\n"
]
}
],
"source": [
"\"\"\"\n",
"Check all shed words\n",
"\"\"\"\n",
"if 1 == 1:\n",
" ind_shed_word_dict = pd.read_pickle(config.IND_SHED_WORD_DICT_PKL)\n",
" print(ind_shed_word_dict.values())"
]
},
{
"cell_type": "markdown",
"metadata": {
"run_control": {
"frozen": false,
"read_only": false
}
},
"source": [
"### Build single shed words freq dict for topic_news docs"
]
},
{
"cell_type": "markdown",
"metadata": {
"run_control": {
"frozen": false,
"read_only": false
}
},
"source": [
"#### Result single dict format (for all topic_news docs)"
]
},
{
"cell_type": "markdown",
"metadata": {
"run_control": {
"frozen": false,
"read_only": false
}
},
"source": [
"```\n",
"{topic_ind_0: {\n",
" news_native_id_0_0: {shed_word_0_ind: shed_word_0_freq,\n",
" shed_word_1_ind: shed_word_1_freq,\n",
" ...},\n",
" news_native_id_0_1: {shed_word_0_ind: shed_word_0_freq,\n",
" shed_word_1_ind: shed_word_1_freq,\n",
" ...},\n",
" ...},\n",
"topic_ind_1: {\n",
" news_native_id_1_0: {shed_word_0_ind: shed_word_0_freq,\n",
" shed_word_1_ind: shed_word_1_freq,\n",
" ...},\n",
" news_native_id_1_1: {shed_word_0_ind: shed_word_0_freq,\n",
" shed_word_1_ind: shed_word_1_freq,\n",
" ...},\n",
" ...},\n",
"...}\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {
"run_control": {
"frozen": false,
"read_only": false
}
},
"source": [
"#### Build single shed words freq dict for all topic_news docs"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"run_control": {
"frozen": true,
"read_only": true
},
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(1/51) processing topic: Hillary_Clinton_email_controversy ... Sun Oct 22 18:49:53 2017\n",
"(2/51) processing topic: Iran_nuclear_deal ... Sun Oct 22 18:49:53 2017\n",
"(3/51) processing topic: ISIS_Jihadi_John_identity_reveal ... Sun Oct 22 18:49:53 2017\n",
"(4/51) processing topic: Ukraine_cease_fire ... Sun Oct 22 18:49:54 2017\n",
"(5/51) processing topic: Egypt_free_Al_Jazeera_journalist ... Sun Oct 22 18:49:54 2017\n",
"(6/51) processing topic: Keystone_XL_Pipeline_bill ... Sun Oct 22 18:49:54 2017\n",
"(7/51) processing topic: CIA_Torture_Report ... Sun Oct 22 18:49:54 2017\n",
"(8/51) processing topic: Obama_cybersecurity_plan ... Sun Oct 22 18:49:54 2017\n",
"(9/51) processing topic: DHS_funding_issue ... Sun Oct 22 18:49:54 2017\n",
"(10/51) processing topic: US_Cuba_relationship ... Sun Oct 22 18:49:54 2017\n",
"(11/51) processing topic: 2015_CPAC ... Sun Oct 22 18:49:54 2017\n",
"(12/51) processing topic: Iraq_free_ISIS_Tikrit ... Sun Oct 22 18:49:54 2017\n",
"(13/51) processing topic: Nigeria_Boko_Haram_terrorists ... Sun Oct 22 18:49:54 2017\n",
"(14/51) processing topic: Ferguson_unrest ... Sun Oct 22 18:49:54 2017\n",
"(15/51) processing topic: Hong_Kong_protest ... Sun Oct 22 18:49:55 2017\n",
"(16/51) processing topic: Sony_cyberattack ... Sun Oct 22 18:49:55 2017\n",
"(17/51) processing topic: Bill_Cosby_sexual_assault_allegation ... Sun Oct 22 18:49:55 2017\n",
"(18/51) processing topic: SpaceX_rocket_landing ... Sun Oct 22 18:49:55 2017\n",
"(19/51) processing topic: Brian_Williams_fake_story ... Sun Oct 22 18:49:55 2017\n",
"(20/51) processing topic: HSBC_tax_scandal ... Sun Oct 22 18:49:55 2017\n",
"(21/51) processing topic: David_Carr_death ... Sun Oct 22 18:49:55 2017\n",
"(22/51) processing topic: Patriots_Deflategate ... Sun Oct 22 18:49:55 2017\n",
"(23/51) processing topic: Delhi_Uber_driver_rape ... Sun Oct 22 18:49:55 2017\n",
"(24/51) processing topic: Superbug_spread ... Sun Oct 22 18:49:55 2017\n",
"(25/51) processing topic: Rudy_Giuliani_Obama_critique ... Sun Oct 22 18:49:55 2017\n",
"(26/51) processing topic: Oscar ... Sun Oct 22 18:49:55 2017\n",
"(27/51) processing topic: Super_Bowl ... Sun Oct 22 18:49:55 2017\n",
"(28/51) processing topic: Grammy ... Sun Oct 22 18:49:56 2017\n",
"(29/51) processing topic: Golden_Globe ... Sun Oct 22 18:49:56 2017\n",
"(30/51) processing topic: 500_million_Powerball ... Sun Oct 22 18:49:56 2017\n",
"(31/51) processing topic: Thanksgiving ... Sun Oct 22 18:49:56 2017\n",
"(32/51) processing topic: Black_Friday_and_Cyber_Monday ... Sun Oct 22 18:49:56 2017\n",
"(33/51) processing topic: Christmas ... Sun Oct 22 18:49:56 2017\n",
"(34/51) processing topic: New_Year ... Sun Oct 22 18:49:56 2017\n",
"(35/51) processing topic: Apple_Watch ... Sun Oct 22 18:49:56 2017\n",
"(36/51) processing topic: Yosemite_historic_climb ... Sun Oct 22 18:49:56 2017\n",
"(37/51) processing topic: Jon_Stewart_Daily_Show ... Sun Oct 22 18:49:56 2017\n",
"(38/51) processing topic: success_of_American_Sniper ... Sun Oct 22 18:49:56 2017\n",
"(39/51) processing topic: Ebola_virus_spread ... Sun Oct 22 18:49:56 2017\n",
"(40/51) processing topic: Indonesia_AirAsia_Flight_QZ8501_crash ... Sun Oct 22 18:49:56 2017\n",
"(41/51) processing topic: Paris_attacks ... Sun Oct 22 18:49:56 2017\n",
"(42/51) processing topic: Vanuatu_Cyclone_Pam ... Sun Oct 22 18:49:57 2017\n",
"(43/51) processing topic: Malaysia_Airlines_Flight_MH370_crash ... Sun Oct 22 18:49:57 2017\n",
"(44/51) processing topic: Colorado_NAACP_bombing ... Sun Oct 22 18:49:57 2017\n",
"(45/51) processing topic: FSU_shooting ... Sun Oct 22 18:49:57 2017\n",
"(46/51) processing topic: Chapel_Hill_shooting ... Sun Oct 22 18:49:57 2017\n",
"(47/51) processing topic: Bobbi_Kristina_Brown_death ... Sun Oct 22 18:49:57 2017\n",
"(48/51) processing topic: Taliban_Pakistan_school_massacre ... Sun Oct 22 18:49:57 2017\n",
"(49/51) processing topic: American_ISIS_Hostage_Kayla_Mueller ... Sun Oct 22 18:49:57 2017\n",
"(50/51) processing topic: TransAsia_Airways_Flight_GE235_crash ... Sun Oct 22 18:49:57 2017\n",
"(51/51) processing topic: Germanwings_Flight_9525_crash ... Sun Oct 22 18:49:57 2017\n",
"CPU times: user 3.76 s, sys: 124 ms, total: 3.88 s\n",
"Wall time: 4.04 s\n"
]
}
],
"source": [
"%%time\n",
"\"\"\"\n",
"Build single shed words freq dict for all topic_news docs\n",
"\n",
"Register\n",
" TOPICS_NEWS_SHED_WORDS_FREQ_DICT_PKL = os.path.join(DATA_DIR, 'topics_news_shed_words_freq.dict.pkl')\n",
"in config\n",
"\"\"\"\n",
"if 0 == 1:\n",
" topics_news_shed_words_freq_dict = {}\n",
" \n",
" for topic_ind, topic in enumerate(config.MANUALLY_SELECTED_TOPICS_LST):\n",
" localtime = time.asctime(time.localtime(time.time()))\n",
" print('({}/{}) processing topic: {} ... {}'.format(topic_ind+1,\n",
" len(config.MANUALLY_SELECTED_TOPICS_LST),\n",
" topic['name'],\n",
" localtime))\n",
" \n",
" topic_shed_words_freq_dict = {}\n",
" \n",
" '''\n",
" Load shed_word and shed_word_ind mapping pkls\n",
" '''\n",
" ind_shed_word_dict = pd.read_pickle(config.IND_SHED_WORD_DICT_PKL)\n",
" shed_word_ind_dict = pd.read_pickle(config.SHED_WORD_IND_DICT_PKL)\n",
" shed_words_set = set(ind_shed_word_dict.values())\n",
" \n",
" '''\n",
" Load topic_news doc\n",
" '''\n",
" csv.register_dialect('topics_docs_line', delimiter='\\t', doublequote=True, quoting=csv.QUOTE_ALL)\n",
" topic_news_csv_file = os.path.join(config.TOPICS_DOCS_DIR, '{}-{}.news.csv'.format(topic_ind, topic['name']))\n",
" with open(topic_news_csv_file, 'r') as f:\n",
" reader = csv.DictReader(f, dialect='topics_docs_line')\n",
" '''\n",
" Count shed words freq for each tweet\n",
" '''\n",
" # lazy load\n",
" for row in reader:\n",
" news_native_id = int(row['news_native_id'])\n",
" news_doc = row['news_doc']\n",
" \n",
" news_doc_shed_words_freq_dict = utilities.count_news_doc_shed_words_freq(news_doc, ind_shed_word_dict, shed_word_ind_dict, shed_words_set)\n",
" \n",
" topic_shed_words_freq_dict[news_native_id] = news_doc_shed_words_freq_dict\n",
" \n",
" topics_news_shed_words_freq_dict[topic_ind] = topic_shed_words_freq_dict\n",
" \n",
" '''\n",
" Make pkl for result single dict\n",
" '''\n",
" with open(config.TOPICS_NEWS_SHED_WORDS_FREQ_DICT_PKL, 'wb') as f:\n",
" pickle.dump(topics_news_shed_words_freq_dict, f)"
]
},
{
"cell_type": "markdown",
"metadata": {
"run_control": {
"frozen": false,
"read_only": false
}
},
"source": [
"#### Check basic statistics"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"run_control": {
"frozen": false,
"read_only": false
},
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"news_native_id: 30275\n",
"\t{2228: 1, 1221: 15, 2773: 10, 2575: 9, 2111: 3, 704: 12, 2290: 1, 2451: 7, 1644: 1, 1497: 1, 1341: 3, 788: 1, 791: 1, 2387: 2, 2106: 1, 1361: 1, 1504: 1, 416: 1, 1656: 6, 3718: 1, 1294: 1, 3586: 1, 1986: 1, 2856: 2, 733: 1, 1884: 1, 1849: 1, 1326: 1, 1577: 2, 1767: 1, 2922: 2, 2324: 1, 3555: 1, 3685: 1, 1008: 1, 460: 1, 1198: 1, 1260: 1, 1678: 1, 3393: 1, 1949: 3, 52: 1, 2592: 1, 2416: 1, 2204: 1, 3069: 1, 1916: 1, 1828: 4, 996: 1, 2901: 1, 2637: 1, 2299: 1, 2330: 1, 758: 1, 3674: 1, 779: 1, 3196: 3, 2653: 1, 3032: 1, 1492: 1, 2597: 2, 2908: 1, 849: 1, 1116: 2, 2439: 1, 3122: 1, 3374: 1, 383: 1, 2308: 1, 2718: 1, 1991: 1, 1934: 1, 1195: 1, 2248: 1}\n",
"\tLEN: 144\n",
"news_native_id: 30282\n",
"\t{1690: 1, 2751: 1, 2451: 2, 704: 6, 791: 1, 3317: 1, 1341: 1, 539: 1, 1257: 1, 1283: 1, 2278: 1, 3217: 1, 3174: 1, 1607: 1, 2139: 1, 3069: 1, 2597: 1, 2575: 3, 2111: 2, 1127: 1, 3110: 1, 1504: 2, 1656: 1, 3570: 1, 1664: 1, 1221: 3, 301: 1, 788: 1, 1118: 1, 1849: 1, 2908: 1, 3134: 1, 2773: 2, 2332: 1, 2412: 1, 2245: 1, 930: 1, 2587: 1, 3233: 1, 2273: 1, 1916: 1, 2653: 1, 2490: 1, 1120: 1, 1892: 1, 2602: 1, 2394: 1}\n",
"\tLEN: 60\n",
"news_native_id: 30290\n",
"\t{2228: 1, 1221: 15, 2773: 10, 2575: 9, 2111: 3, 704: 12, 2290: 1, 2451: 7, 1644: 1, 1497: 1, 1341: 3, 788: 1, 791: 1, 2387: 2, 2106: 1, 1361: 1, 1504: 1, 416: 1, 1656: 6, 3718: 1, 1294: 1, 3586: 1, 1986: 1, 2856: 2, 733: 1, 1884: 1, 1849: 1, 1326: 1, 1577: 2, 1767: 1, 2922: 2, 2324: 1, 3555: 1, 3685: 1, 1008: 1, 460: 1, 1198: 1, 1260: 1, 1678: 1, 3393: 1, 1949: 3, 52: 1, 2592: 1, 2416: 1, 2204: 1, 3069: 1, 1916: 1, 1828: 4, 996: 1, 2901: 1, 2637: 1, 2299: 1, 2330: 1, 758: 1, 3674: 1, 779: 1, 3196: 3, 2653: 1, 3032: 1, 1492: 1, 2597: 2, 2908: 1, 849: 1, 1116: 2, 2439: 1, 3122: 1, 3374: 1, 383: 1, 2308: 1, 2718: 1, 1991: 1, 1934: 1, 1195: 1, 2248: 1}\n",
"\tLEN: 144\n",
"news_native_id: 30304\n",
"\t{2773: 1, 1940: 3, 2192: 1, 1249: 1, 455: 1, 2387: 1, 1618: 1, 2368: 1}\n",
"\tLEN: 10\n",
"news_native_id: 30310\n",
"\t{1336: 1, 2451: 5, 1184: 2, 3504: 1, 791: 1, 2601: 1, 2287: 2, 2773: 2, 704: 2, 3032: 1, 1221: 2, 2575: 2, 1644: 1, 1718: 1, 1171: 1, 2663: 1, 3084: 1, 1127: 1, 459: 1, 550: 1, 11: 1, 1690: 1, 2100: 1, 2111: 1, 1415: 1, 1940: 1, 2459: 1, 2440: 1, 1544: 1, 1031: 1, 1949: 1, 1668: 1, 2102: 1}\n",
"\tLEN: 43\n"
]
}
],
"source": [
"\"\"\"\n",
"Print out sample news shed_words_freq_dicts inside single topic\n",
"\"\"\"\n",
"if 0 == 1:\n",
" target_topic_ind = 0\n",
" \n",
" with open(config.TOPICS_NEWS_SHED_WORDS_FREQ_DICT_PKL, 'rb') as f:\n",
" topics_news_shed_words_freq_dict = pickle.load(f)\n",
" \n",
" count = 0\n",
" for news_native_id, news_doc_shed_words_freq_dict in topics_news_shed_words_freq_dict[target_topic_ind].items():\n",
" print('news_native_id: {}'.format(news_native_id))\n",
" print('\\t{}'.format(news_doc_shed_words_freq_dict))\n",
" news_doc_shed_words_len = sum(news_doc_shed_words_freq_dict.values())\n",
" print('\\tLEN: {}'.format(news_doc_shed_words_len))\n",
" count += 1\n",
" if count >= 5:\n",
" break\n"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"run_control": {
"frozen": false,
"read_only": false
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Total shed words length of this topic_news doc: 22325\n",
"CPU times: user 0 ns, sys: 0 ns, total: 0 ns\n",
"Wall time: 1.06 ms\n"
]
}
],
"source": [
"%%time\n",
"\"\"\"\n",
"Check total shed words length of this topic_news doc\n",
"\"\"\" \n",
"if 0 == 1:\n",
" topic_news_shed_words_len = sum([sum(news_doc_shed_words_freq_dict.values()) for news_doc_shed_words_freq_dict in topics_news_shed_words_freq_dict[target_topic_ind].values()])\n",
" print('Total shed words length of this topic_news doc: {}'.format(topic_news_shed_words_len))"
]
},
{
"cell_type": "markdown",
"metadata": {
"run_control": {
"frozen": false,
"read_only": false
}
},
"source": [
"### Build shed words freq dicts for each topic_tweets doc separately"
]
},
{
"cell_type": "markdown",
"metadata": {
"run_control": {
"frozen": false,
"read_only": false
}
},
"source": [
"#### Result dict format (for each given topic_tweets doc)"
]
},
{
"cell_type": "markdown",
"metadata": {
"run_control": {
"frozen": false,
"read_only": false
}
},
"source": [
"```\n",
"{tweet_id_0_0: {shed_word_0_ind: shed_word_0_freq,\n",
" shed_word_1_ind: shed_word_1_freq,\n",
" ...},\n",
"tweet_id_0_1: {shed_word_0_ind: shed_word_0_freq,\n",
" shed_word_1_ind: shed_word_1_freq,\n",
" ...},\n",
"...}\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {
"run_control": {
"frozen": false,
"read_only": false
}
},
"source": [
"#### Build shed words freq dict for each topic separately"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"run_control": {
"frozen": true,
"read_only": true
},
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(1/51) processing topic: Hillary_Clinton_email_controversy ... Mon Oct 23 16:00:01 2017\n",
"(2/51) processing topic: Iran_nuclear_deal ... Mon Oct 23 16:00:23 2017\n",
"(3/51) processing topic: ISIS_Jihadi_John_identity_reveal ... Mon Oct 23 16:01:36 2017\n",
"(4/51) processing topic: Ukraine_cease_fire ... Mon Oct 23 16:01:54 2017\n",
"(5/51) processing topic: Egypt_free_Al_Jazeera_journalist ... Mon Oct 23 16:02:08 2017\n",
"(6/51) processing topic: Keystone_XL_Pipeline_bill ... Mon Oct 23 16:02:10 2017\n",
"(7/51) processing topic: CIA_Torture_Report ... Mon Oct 23 16:02:12 2017\n",
"(8/51) processing topic: Obama_cybersecurity_plan ... Mon Oct 23 16:02:17 2017\n",
"(9/51) processing topic: DHS_funding_issue ... Mon Oct 23 16:02:31 2017\n",
"(10/51) processing topic: US_Cuba_relationship ... Mon Oct 23 16:02:35 2017\n",
"(11/51) processing topic: 2015_CPAC ... Mon Oct 23 16:03:19 2017\n",
"(12/51) processing topic: Iraq_free_ISIS_Tikrit ... Mon Oct 23 16:03:26 2017\n",
"(13/51) processing topic: Nigeria_Boko_Haram_terrorists ... Mon Oct 23 16:03:39 2017\n",
"(14/51) processing topic: Ferguson_unrest ... Mon Oct 23 16:03:50 2017\n",
"(15/51) processing topic: Hong_Kong_protest ... Mon Oct 23 16:05:25 2017\n",
"(16/51) processing topic: Sony_cyberattack ... Mon Oct 23 16:05:31 2017\n",
"(17/51) processing topic: Bill_Cosby_sexual_assault_allegation ... Mon Oct 23 16:06:24 2017\n",
"(18/51) processing topic: SpaceX_rocket_landing ... Mon Oct 23 16:06:38 2017\n",
"(19/51) processing topic: Brian_Williams_fake_story ... Mon Oct 23 16:06:47 2017\n",
"(20/51) processing topic: HSBC_tax_scandal ... Mon Oct 23 16:06:55 2017\n",
"(21/51) processing topic: David_Carr_death ... Mon Oct 23 16:06:57 2017\n",
"(22/51) processing topic: Patriots_Deflategate ... Mon Oct 23 16:06:59 2017\n",
"(23/51) processing topic: Delhi_Uber_driver_rape ... Mon Oct 23 16:07:08 2017\n",
"(24/51) processing topic: Superbug_spread ... Mon Oct 23 16:07:19 2017\n",
"(25/51) processing topic: Rudy_Giuliani_Obama_critique ... Mon Oct 23 16:07:28 2017\n",
"(26/51) processing topic: Oscar ... Mon Oct 23 16:07:40 2017\n",
"(27/51) processing topic: Super_Bowl ... Mon Oct 23 16:08:36 2017\n",
"(28/51) processing topic: Grammy ... Mon Oct 23 16:09:30 2017\n",
"(29/51) processing topic: Golden_Globe ... Mon Oct 23 16:09:52 2017\n",
"(30/51) processing topic: 500_million_Powerball ... Mon Oct 23 16:10:16 2017\n",
"(31/51) processing topic: Thanksgiving ... Mon Oct 23 16:10:29 2017\n",
"(32/51) processing topic: Black_Friday_and_Cyber_Monday ... Mon Oct 23 16:11:50 2017\n",
"(33/51) processing topic: Christmas ... Mon Oct 23 16:12:46 2017\n",
"(34/51) processing topic: New_Year ... Mon Oct 23 16:14:50 2017\n",
"(35/51) processing topic: Apple_Watch ... Mon Oct 23 16:15:32 2017\n",
"(36/51) processing topic: Yosemite_historic_climb ... Mon Oct 23 16:15:47 2017\n",
"(37/51) processing topic: Jon_Stewart_Daily_Show ... Mon Oct 23 16:15:48 2017\n",
"(38/51) processing topic: success_of_American_Sniper ... Mon Oct 23 16:15:52 2017\n",
"(39/51) processing topic: Ebola_virus_spread ... Mon Oct 23 16:16:15 2017\n",
"(40/51) processing topic: Indonesia_AirAsia_Flight_QZ8501_crash ... Mon Oct 23 16:16:41 2017\n",
"(41/51) processing topic: Paris_attacks ... Mon Oct 23 16:17:07 2017\n",
"(42/51) processing topic: Vanuatu_Cyclone_Pam ... Mon Oct 23 16:17:46 2017\n",
"(43/51) processing topic: Malaysia_Airlines_Flight_MH370_crash ... Mon Oct 23 16:17:51 2017\n",
"(44/51) processing topic: Colorado_NAACP_bombing ... Mon Oct 23 16:18:05 2017\n",
"(45/51) processing topic: FSU_shooting ... Mon Oct 23 16:18:12 2017\n",
"(46/51) processing topic: Chapel_Hill_shooting ... Mon Oct 23 16:18:18 2017\n",
"(47/51) processing topic: Bobbi_Kristina_Brown_death ... Mon Oct 23 16:18:20 2017\n",
"(48/51) processing topic: Taliban_Pakistan_school_massacre ... Mon Oct 23 16:18:23 2017\n",
"(49/51) processing topic: American_ISIS_Hostage_Kayla_Mueller ... Mon Oct 23 16:18:30 2017\n",
"(50/51) processing topic: TransAsia_Airways_Flight_GE235_crash ... Mon Oct 23 16:18:31 2017\n",
"(51/51) processing topic: Germanwings_Flight_9525_crash ... Mon Oct 23 16:18:36 2017\n",
"CPU times: user 18min 25s, sys: 18.1 s, total: 18min 43s\n",
"Wall time: 18min 47s\n"
]
}
],
"source": [
"%%time\n",
"\"\"\"\n",
"Build shed words freq dict for each topic separately\n",
"\n",
"Register\n",
" TOPICS_TWEETS_SHED_WORDS_FREQ_DICT_PKLS_DIR = os.path.join(DATA_DIR, 'topics_tweets_shed_words_freq_dict_pkls')\n",
"in config\n",
"\n",
"Note:\n",
" - Number of tweets is large. Process each topic_tweets doc individually to avoid crash\n",
" - Execute second time for updated topic_tweets docs\n",
"\"\"\"\n",
"if 0 == 1:\n",
" for topic_ind, topic in enumerate(config.MANUALLY_SELECTED_TOPICS_LST):\n",
" localtime = time.asctime(time.localtime(time.time()))\n",
" print('({}/{}) processing topic: {} ... {}'.format(topic_ind+1,\n",
" len(config.MANUALLY_SELECTED_TOPICS_LST),\n",
" topic['name'],\n",
" localtime))\n",
" \n",
" topic_shed_words_freq_dict = {}\n",
" \n",
" '''\n",
" Load shed_word and shed_word_ind mapping pkls\n",
" '''\n",
" ind_shed_word_dict = pd.read_pickle(config.IND_SHED_WORD_DICT_PKL)\n",
" shed_word_ind_dict = pd.read_pickle(config.SHED_WORD_IND_DICT_PKL)\n",
" shed_words_set = set(ind_shed_word_dict.values())\n",
" \n",
" '''\n",
" Load topic_tweets doc\n",
" '''\n",
" csv.register_dialect('topics_docs_line', delimiter='\\t', doublequote=True, quoting=csv.QUOTE_ALL)\n",
" topic_tweets_csv_file = os.path.join(config.TOPICS_DOCS_DIR, '{}-{}.updated.tweets.csv'.format(topic_ind, topic['name']))\n",
" with open(topic_tweets_csv_file, 'r') as f:\n",
" reader = csv.DictReader(f, dialect='topics_docs_line')\n",
" \n",
" '''\n",
" Count shed words freq for each tweet\n",
" '''\n",
" # lazy load\n",
" for row in reader:\n",
" tweet_id = int(row['tweet_id'])\n",
" tweet_text = row['tweet_text']\n",
" \n",
" tweet_shed_words_freq_dict = utilities.count_tweet_shed_words_freq(tweet_text, ind_shed_word_dict, shed_word_ind_dict, shed_words_set)\n",
" \n",
" topic_shed_words_freq_dict[tweet_id] = tweet_shed_words_freq_dict\n",
" \n",
" '''\n",
" Make pkl for result dict file\n",
" '''\n",
" topic_tweets_shed_words_freq_dict_pkl_file = os.path.join(config.TOPICS_TWEETS_SHED_WORDS_FREQ_DICT_PKLS_DIR,\n",
" '{}.updated.dict.pkl'.format(topic_ind))\n",
" with open(topic_tweets_shed_words_freq_dict_pkl_file, 'wb') as f:\n",
" pickle.dump(topic_shed_words_freq_dict, f)"
]
},
{
"cell_type": "markdown",
"metadata": {
"run_control": {
"frozen": false,
"read_only": false
}
},
"source": [
"#### Check basic statistics"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"run_control": {
"frozen": false,
"read_only": false
},
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"tweet_id: 128954438\n",
"\t{704: 1}\n",
"\tLEN: 1\n",
"tweet_id: 128954439\n",
"\t{1916: 1, 677: 1, 2451: 1, 704: 1}\n",
"\tLEN: 4\n",
"tweet_id: 128954440\n",
"\t{2451: 1, 704: 1}\n",
"\tLEN: 2\n",
"tweet_id: 128954441\n",
"\t{2451: 1, 704: 1}\n",
"\tLEN: 2\n",
"tweet_id: 128954442\n",
"\t{2451: 1, 704: 1}\n",
"\tLEN: 2\n",
"tweet_id: 128954443\n",
"\t{1940: 1, 2884: 1}\n",
"\tLEN: 2\n",
"tweet_id: 128954444\n",
"\t{2451: 1, 704: 1}\n",
"\tLEN: 2\n",
"tweet_id: 128954445\n",
"\t{2451: 1, 704: 1}\n",
"\tLEN: 2\n",
"tweet_id: 128954446\n",
"\t{680: 1, 2387: 1, 3366: 1, 2451: 1, 704: 1}\n",
"\tLEN: 5\n",
"tweet_id: 128954448\n",
"\t{1577: 1, 2451: 1, 704: 1}\n",
"\tLEN: 3\n",
"tweet_id: 128954451\n",
"\t{2451: 1, 704: 1}\n",
"\tLEN: 2\n",
"tweet_id: 128954452\n",
"\t{232: 1, 2451: 1, 704: 1}\n",
"\tLEN: 3\n",
"tweet_id: 128954454\n",
"\t{2451: 1, 704: 1}\n",
"\tLEN: 2\n",
"tweet_id: 128954459\n",
"\t{704: 1}\n",
"\tLEN: 1\n",
"tweet_id: 128954460\n",
"\t{704: 1}\n",
"\tLEN: 1\n",
"tweet_id: 128954461\n",
"\t{3437: 1, 2451: 1, 704: 1}\n",
"\tLEN: 3\n",
"tweet_id: 128954462\n",
"\t{2451: 1, 704: 1}\n",
"\tLEN: 2\n",
"tweet_id: 128954463\n",
"\t{704: 1}\n",
"\tLEN: 1\n",
"tweet_id: 128954464\n",
"\t{}\n",
"\tLEN: 0\n",
"tweet_id: 128954465\n",
"\t{2111: 2, 1219: 1, 704: 1}\n",
"\tLEN: 4\n",
"CPU times: user 408 ms, sys: 172 ms, total: 580 ms\n",
"Wall time: 576 ms\n"
]
}
],
"source": [
"%%time\n",
"\"\"\"\n",
"Print out sample tweet shed_words_freq_dicts inside single topic\n",
"\"\"\"\n",
"if 0 == 1:\n",
" target_topic_ind = 0\n",
" \n",
" topic_tweets_shed_words_freq_dict_pkl_file = os.path.join(config.TOPICS_TWEETS_SHED_WORDS_FREQ_DICT_PKLS_DIR, '{}.updated.dict.pkl'.format(target_topic_ind))\n",
" with open(topic_tweets_shed_words_freq_dict_pkl_file, 'rb') as f:\n",
" topic_tweets_shed_words_freq_dict_tmp = pickle.load(f)\n",
" \n",
" count = 0\n",
" for tweet_id, tweet_shed_words_freq_dict in topic_tweets_shed_words_freq_dict_tmp.items():\n",
" print('tweet_id: {}'.format(tweet_id))\n",
" print('\\t{}'.format(tweet_shed_words_freq_dict))\n",
" tweet_shed_words_len = sum(tweet_shed_words_freq_dict.values())\n",
" print('\\tLEN: {}'.format(tweet_shed_words_len))\n",
" count += 1\n",
" if count >= 20:\n",
" break"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"run_control": {
"frozen": false,
"read_only": false
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Total shed words length of this topic_tweets_doc: 1105282\n",
"CPU times: user 216 ms, sys: 8 ms, total: 224 ms\n",
"Wall time: 224 ms\n"
]
}
],
"source": [
"%%time\n",
"\"\"\"\n",
"Check total shed words length of a topic_tweets doc\n",
"\"\"\" \n",
"if 0 == 1:\n",
" topic_tweets_shed_words_len = sum([sum(tweet_shed_words_freq_dict.values()) for tweet_shed_words_freq_dict in topic_tweets_shed_words_freq_dict_tmp.values()])\n",
" print('Total shed words length of this topic_tweets_doc: {}'.format(topic_tweets_shed_words_len))"
]
},
{
"cell_type": "markdown",
"metadata": {
"run_control": {
"frozen": false,
"read_only": false
}
},
"source": [
"# Notes"
]
},
{
"cell_type": "markdown",
"metadata": {
"run_control": {
"frozen": false,
"read_only": false
}
},
"source": [
" - Do NOT try to merge all topic_tweets shed words freq dicts into a single huge dict. This is extremely time-consuming and would leave VM unresponsive."
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
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"name": "ipython",
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},
"file_extension": ".py",
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"name": "python",
"nbconvert_exporter": "python",
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"toc": {
"colors": {
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"nav_menu": {
"height": "12px",
"width": "252px"
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"navigate_menu": true,
"number_sections": true,
"sideBar": true,
"threshold": "3",
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"toc_position": {
"height": "917px",
"left": "0px",
"right": "1755px",
"top": "66px",
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},
"toc_section_display": "block",
"toc_window_display": false,
"widenNotebook": true
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| mit |
macohen2/MattsProject | A Different Moneyball.ipynb | 1 | 265389 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# A Different Moneyball\n",
"\n",
"* By: Matthew Cohen\n",
"* (Based on the concept of the movie Moneyball)\n",
"* Hypothesis: Can wins in the NBA be predicted by statistics other than those that are shown on the scoreboard? In other words, disregarding points, can a teams success over a season be accurately predicted?\n",
"* \n",
"* "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Bringing in Data and Necessary Packages"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"<function close>"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import sqlite3\n",
"import pandas\n",
"from sklearn import preprocessing\n",
"from sklearn.preprocessing import Imputer\n",
"from sklearn import ensemble\n",
"import numpy\n",
"from sklearn.feature_selection import RFECV\n",
"from sklearn.cross_validation import cross_val_score\n",
"from sklearn.grid_search import GridSearchCV\n",
"from sklearn import tree\n",
"import matplotlib.pyplot as plt\n",
"\n",
"%matplotlib inline\n",
"\n",
"conn = sqlite3.connect('/Users/MatthewCohen/Documents/SQLite/TeamSeason1.sqlite')\n",
"query = \"\"\"SELECT t.won as wins, g.good_team, t.o_fgm as field_goals_made, t.o_fga as field_goals_attempted,\n",
"t.o_ftm as free_throws_made, t.o_fta as free_throws_attempted, t.o_oreb as offensive_rebounds,\n",
"t.o_dreb as defensive_rebounds, t.o_reb as total_rebounds, t.o_asts as assists, t.o_pf as personal_fouls,\n",
"t.o_stl as steals, t.o_to as turnovers, t.o_3pm as three_pointers_made, t.o_3pa as three_pointers_attempted,\n",
"t.d_fgm as field_goals_allowed, t.d_fga as field_goal_attempts_allowed, t.d_reb as rebounds_allowed,\n",
"t.d_asts as assists_allowed, t.d_pf as fouls_against, t.d_3pm as three_point_makes_allowed,\n",
"((o_fgm / o_fga)*100) as field_goal_percentage, ((o_ftm / o_fta)*100) as free_throw_percentage,\n",
"((o_3pm / o_3pa)*100) as three_point_percentage, o_blk as blocks, o_pts as points, d_pts as points_against\n",
"FROM TeamSeason1 t\n",
"LEFT OUTER JOIN Good_Teams2 g ON t.team = g.team and t.year = g.year\n",
"WHERE t.year > 1980 and t.year <= 2009;\"\"\"\n",
"df = pandas.read_sql(query, conn)\n",
"conn.close"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Defining Explanatory Features and Response Series"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"explanatory_features = [col for col in df.columns if col not in ['field_goals_made', 'field_goals_allowed', 'good_team', 'wins', 'points', 'points_against', 'free_throws_made', 'three_pointers_made']]\n",
"explanatory_df = df[explanatory_features]\n",
"explanatory_colnames = explanatory_df.columns\n",
"\n",
"response_series = df.good_team"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Checking for Correlation Between Explanatory Variables"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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i7FvOJWUm6WMzaxMK0Mz2rqdNNdY9EPiBmWUKllSxPQG43Mw+bf+WOY7j5KTh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9U3ydSS14QzA6Us1KKL8ItjiRFpUU3pz7Z3H4P2tTxatwTWtb13mUd3pr80XT8db8wsj+4kB7sS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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1093b1bd0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"toChart = explanatory_df.corr()\n",
"plt.pcolor(toChart)\n",
"plt.yticks(numpy.arange(0.5, len(toChart.index), 1), toChart.index)\n",
"plt.xticks(numpy.arange(0.5, len(toChart.columns), 1), toChart.columns, rotation=-90)\n",
"plt.colorbar()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Removing any Explanatory Features that are Highly Correlated"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'corrGroupings': [['field_goal_attempts_allowed', 'field_goals_attempted'], ['fouls_against', 'free_throws_attempted']], 'toRemove': ['field_goals_attempted', 'free_throws_attempted']}\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/MatthewCohen/anaconda/lib/python2.7/site-packages/IPython/kernel/__main__.py:23: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame\n"
]
}
],
"source": [
"def find_perfect_corr(df):\n",
" \"\"\"finds columns that are eother positively or negatively perfectly correlated (with correlations of +1 or -1), and creates a dict \n",
" that includes which columns to drop so that each remaining column\n",
" is independent\n",
" \"\"\" \n",
" corrMatrix = df.corr()\n",
" corrMatrix.loc[:,:] = numpy.tril(corrMatrix.values, k = -1)\n",
" already_in = set()\n",
" result = []\n",
" for col in corrMatrix:\n",
" perfect_corr = corrMatrix[col][abs(numpy.round(corrMatrix[col],10)) >= 0.9].index.tolist()\n",
" if perfect_corr and col not in already_in:\n",
" already_in.update(set(perfect_corr))\n",
" perfect_corr.append(col)\n",
" result.append(perfect_corr)\n",
" toRemove = []\n",
" for item in result:\n",
" toRemove.append(item[1:(len(item)+1)])\n",
" toRemove = sum(toRemove, [])\n",
" return {'corrGroupings':result, 'toRemove':toRemove}\n",
"\n",
"no_correlation = find_perfect_corr(explanatory_df)\n",
"explanatory_df.drop(no_correlation['toRemove'], 1, inplace = True)\n",
"print no_correlation"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Scaling Data"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"scaler = preprocessing.StandardScaler()\n",
"scaler.fit(explanatory_df)\n",
"explanatory_df = pandas.DataFrame(scaler.transform(explanatory_df), columns = explanatory_df.columns)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Scanning for best # of features to use"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"RFECV(cv=10,\n",
" estimator=RandomForestsWithCoef(bootstrap=True, compute_importances=None,\n",
" criterion='gini', max_depth=None, max_features='auto',\n",
" max_leaf_nodes=None, min_density=None, min_samples_leaf=1,\n",
" min_samples_split=2, n_estimators=500, n_jobs=1,\n",
" oob_score=False, random_state=None, verbose=0),\n",
" estimator_params={}, loss_func=None, scoring='roc_auc', step=1,\n",
" verbose=0)"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from sklearn.feature_selection import RFE\n",
"from sklearn.feature_selection import RFECV\n",
"from sklearn import tree\n",
"\n",
"class RandomForestsWithCoef(ensemble.RandomForestClassifier):\n",
" def fit(self, *args, **kwargs):\n",
" super(ensemble.RandomForestClassifier, self).fit(*args, **kwargs)\n",
" self.coef_ = self.feature_importances_\n",
"\n",
"rfWithCoef = RandomForestsWithCoef(n_estimators= 500)\n",
"rfe_cv = RFECV(estimator=rfWithCoef, step=1, cv=10, scoring='roc_auc', verbose = 0)\n",
"rfe_cv.fit(explanatory_df, response_series)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Defining New Feature Set"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"explanatory_features = [col for col in df.columns if col not in ['field_goals_made', 'field_goals_allowed', 'good_team', 'wins', 'points', 'points_against', 'free_throws_made', 'three_pointers_made', 'offensive_rebounds']]\n",
"explanatory_df = df[explanatory_features]\n",
"explanatory_colnames = explanatory_df.columns"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Predicting Wins Using Random Forests"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.903573327258\n"
]
}
],
"source": [
"rf = ensemble.RandomForestClassifier(n_estimators= 500)\n",
"roc_scores_rf = cross_val_score(rf, explanatory_df, response_series, cv=10, scoring='roc_auc', n_jobs = -1)\n",
"print roc_scores_rf.mean()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Grid Search for Best Parameters"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x10978c150>]"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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6RRTa5ToC2ADsEf3pdRPftvekNqsmE6IQKRVXyGoZiFoysavhouB0Jb0iCu0K\nMhOrsK4ljE5phDcCL9Toh8+SKJR6+85SiYs8NWZiVyOTfXOcZqkqCpImS1ou6QlJ00tsHyFpjqR+\nSUslnVe0fZCkxZJuKVg3XNI8SSskzZU0LJHelKedlgI0F2yuZeRRjqyJQnEJiaxaCtBEHSSJYcAQ\nYGOiLXKcDFBRFCQNAq4BJgMHA2dLKi4RMBVYbGYTgT7gSkmDC7ZPAx6DAZO2fx6YZ2YHAvPjcisZ\nQXtFoZlgc63xBMiQKJjxLPB74K2QD+5n+W26mRFI44En40gmx+kqqlkKk4CVZrbGzLYANwOnF+2z\nHhgavw8FNprZVgBJ+wIfAK6HAUXiTgNujN9vBD7UcA9qY2/a5D6KtMtSeA7YUWJ4me3ttBRgYFwh\n16YX23j9evgV8FaJ3Rs41uMJTtdSTRTGAM8ULK9l+4nprwMOkbSOMDn6tIJtXwc+B9vNdjbSzDbE\n7xuordpnM7TbfdQWSyG+qVayFtotCoVxhUy/TZvxB+A3wLsaOPzNuCg4XcrgKttr+YeeAfSbWZ+k\nA4B5kg4H3g08b2aLJfWVvYCZSSp7HUkzCxYXmNmCGtpUzAjg0QaOa5RmhqWOJrjbauVxwoQ79xeu\nlBhKEP1WzkldzELgH+L3LMcTcuREbG6tB0i8DbgQ+EirGuU49RCfr31Jna+aKDzLwFE0pWbxOgb4\nEoCZrZK0muBXPgY4TdIHgJ2AoZK+Z2bnABskjTKz5ySNBp4v1wAzm1lPh8qQRqC5GUthfh37r6C0\npbAvsLbNb+qPA7tKjCXb8YQcC6kjnhVLY9wOfNaMe1rWKsepg/iivCC3LOmSZs5XzX20CJggaZyk\nIcBZwOyifZYDJ8XGjCQ8oFaZ2QwzG2tm44G/Au6KgkA8x7nx+7nAz5vpRA20WxSeAfZtcDKXemIK\nUN591G7XUc6dlYsrdIKl8D/A0RI7Vtsx5jTMBb5qxg9a3jLHSYmKD60YMJ4K3EFwafzIzJZJmiJp\nStztcuBoSUuAO4GLzGxTqdMVfL8COFnSCuA9cbmVtC1PAfKVQ1+ksVhJPaOPIEOiEMm5ZDJvKZjx\ne8Lv7+hK+8Vg9G3Af5rxzXa0zXHSQmaZjAMCIMnMrNmpLZHYDIyJD4G2IPEQ8GkzHqjjGAGvAnvV\nOtG9xK4EwdutMOFN4gvAEDP+sb6WN4fE24HvElyGp5qxvJ3XrxeJqwklsEu+mEQr4laCwE3JauDc\ncXI0+9wtEK0IAAARlElEQVTs+oxmiZ0IiUatrhRaTCPB5j0IVU1rEgQAM14huMaKYxhpWQqLgXGE\n9qxJ4fr1UrYOksQg4CZCsP6TLghOL9D1okB0HaXwD91IsHkU9cUTcpRyIaUiCmZsBR4klOp4vd3X\nb4B7gWOiAOSJVts3CfGov8lS+W/HaSW9IArtDjLnaMRSGE198YQcmRGFyEIyHk/IYcYGwui3txVt\n+idCDsPpHSJujpMIvSAKbQ0yF9BuS+HAonVpisIPge+kdO1GuIeCkhcSnwQ+CpzSzjiU42SBXhCF\nXrAUBuQqxODzLqRUsM2MFR02bDMfV5D4CCEB7/3RinCcnqIXRCFNS6FeUUgqpjCG9ieudTILgeMl\nTiIUgPyAWWe4vxwnaXpBFNKyFDYRitXVU3CtUUvhaWC4xG5xOU3XUSeyBvgT8GPgw2Y8nG5zHCc9\nXBRaRHxLr9eF1JClECf2Wcm2uIKLQh3Ee3U18FEzFqbdHsdJk14QhbTcR1B/sLneEheFFLqQXBTq\nxIyvmXFr2u1wnLTpBVFIy30EjVkKjbiPwEXBcZwE6AVRSNtSqEkUJIYQJylq8FouCo7jNE0viELa\nlkKt7qORwPMxPtAIhbkKLgqO4zREV4tCLF29J+lNsF6P+6iZeAKEXIUDY3kGFwXHcRqiq0WBIAib\nYz2eNKgn0NxMPAEzfkeosDoeGEaFiYscx3HK0e2ikKbrCMLMdaOkqjPcQfOWAgQX0nuAdU24oRzH\n6WG6XRTSDDJjxhZgAyHDuBpNWQqRx4H34q4jx3EapNtFIW1LAWqPKyRpKbgoOI7TEC4KradWUUjK\nUngjLgqO4zRIt4tCqu6jSK3B5qQsBXBRcBynQaqKgqTJkpZLekLS9BLbR0iaI6lf0lJJ58X1O0l6\nMK5/TNKXC46ZKWmtpMXxMznRXm2j1yyF1cBWXBQcx2mQiqIgaRChlPBk4GDgbEkHFe02FVhsZhOB\nPuBKSYPN7HXgxLj+MOBEScfGYwy4ysyOiJ85yXVpAHvTAZZCzC1otGx2nhjYfpzOmBvZcZwMUs1S\nmASsNLM1ZrYFuBk4vWif9YTyDMSfG81sK4CZ5SagHwIMAl4sOE7NNLxGRtAZlsKewKsJTfvYB/w6\ngfM4jtODVBOFMcAzBctr2X545XXAIZLWAUuAabkNknaQ1E8Ylnm3mT1WcNwFkpZImiVpWMM9qEwW\n3EdPAftFa6AcScQTADDjtz65juM4jVItqaqWh8sMoN/M+iQdAMyTdLiZbTazPwMTJe0B3CGpz8wW\nEObv/WI8/jLgSuD8UieXNLNgcUE8vlZSDzSb8XuJPxGsgU1ldksinuA4Tg8iqY/gIUiEaqLwLDC2\nYHks2wcxjwG+BGBmqyStJlTrXJTbwcxeknQrcDThwZ4vwSDpeuCWcg0ws5nVu1GWLFgKsM2FVE4U\nErMUHMfpLeKL8oLcsqRLmjlfNffRImCCpHGShgBnAbOL9lkOnBQbM5IgCE/GUUnD4vqdgZOBxXF5\ndMHxZwCPNNOJUsTJ6zHjlaTP3QDVgs1uKTiOkwkqWgpmtlXSVOAOQqB4lpktkzQlbr8WuBy4QdIS\ngshcZGabJB0K3Chph7j+JjObH0/9FUkTCe6p1cCUFvQtdddRAdWCzW4pOI6TCaoWajOz24Hbi9Zd\nW/D9t8CpJY57BDiyzDnPqbul9ZMV1xHUZiksblNbHMdxytLNGc1uKTiO49RJN4tCliyFaqLgMQXH\ncTKBi0J7qMV95JaC4zip082ikCX30XPAcIkdizdI7ATsSvnhqo7jOG2jm0UhM5aCGX9i+5yPHCOB\nDZ6F7DhOFuhmUciSpQDl4woeZHYcJzN0syhkxlKIPEVpUfAgs+M4mcFFoX08Telgs1sKjuNkhm4W\nhU5xH7ml4DhOZuhKUZAYDOzBwPkb0qbcsFS3FBzHyQxdKQrAXsCLcdRPVnBLwXGczNOtopCFGdeK\neRoYK233O3dLwXGczNCtopC1IDNmvAq8TGhbIW4pOI6TGbpVFLIWZM4xwIUUrYaRhOlKHcdxUqdb\nRSFzlkKkONg8HNhsxh9Sao/jOM4AulkUMm8p4PEEx3EyRreKQhYDzbC9peDxBMdxMkW3ikJW3Udu\nKTiOk2m6VRSyGmgurn/kloLjOJmiqihImixpuaQnJE0vsX2EpDmS+iUtlXReXL+TpAfj+sckfbng\nmOGS5klaIWmupGGJ9irblkKh+8gtBcdxMkVFUZA0CLgGmAwcDJwt6aCi3aYCi81sItAHXClpsJm9\nDpwY1x8GnCjp2HjM54F5ZnYgMD8uJ0lWA80vALtK7BqX3VJwHCdTVLMUJgErzWyNmW0BbgZOL9pn\nPTA0fh8KbDSzrQBm9mpcPwQYxLZaRKcBN8bvNwIfargHRUiIjAaa40Q6hXEFtxQcx8kU1URhDPBM\nwfLauK6Q64BDJK0DlgDTchsk7SCpn5CcdbeZPRY3jTSzXMLWBkICV1LsDvzRjNcTPGeSFIqCWwqO\n42SKwVW21zJF5Ayg38z6JB0AzJN0uJltNrM/AxMl7QHcIanPzBYMuICZSSp7HUkzCxYXFB9fgqwG\nmXMUDkt1S8FxnKaQ1Edw3SdCNVEonld4LMFaKOQY4EsAZrZK0mrgLcCi3A5m9pKkW4GjgAXABkmj\nzOw5SaOB58s1wMxm1taVPFkNMud4GthPYhdgR+B3KbfHcZwOJr4oL8gtS7qkmfNVcx8tAiZIGidp\nCHAWMLton+XASbExIwmC8GQclTQsrt8ZOBnoj8fMBs6N388Fft5MJ4rIapA5R859NAp4LsYZHMdx\nMkFFS8HMtkqaCtxBCBTPMrNlkqbE7dcClwM3SFpCEJmLzGyTpEOBGyXtENffZGbz46mvAH4s6Xxg\nDXBmgn3KZJC5gJz7yOMJjuNkDpll90VVkpmZ6juGzwEjzfj7FjWrKSQOAO4E/h74WzPOSLlJjuN0\nEY08NwvpxozmrAea1wL7EEZxuaXgOE6m6EZRyHSgOZbJ3ggciY88chwnY7gopMNThMRAtxQcx8kU\n3SgKWXcfQRiBdBBuKTiOkzG6URQ6wVJ4Ov50UXAcJ1N0qyhk3VJ4Kv5095HjOJmiq0RBYgiwC9nP\nEs5ZCmUzuR3HcdKgq0SBGE/ogCzhpwjt/GPaDXEcxymkK0Uh7UbUwKPAOWk3wnEcp5hqBfE6jU4I\nMmPGVuD2tNvhOI5TTLdZCp0QZHYcx8ks3SYKWS+G5ziOk2m6TRQ6wn3kOI6TVbpNFDol0Ow4jpNJ\nuk0U3FJwHMdpgm4UBbcUHMdxGqTbRMEDzY7jOE3QbaLg7iPHcZwm6BpRkBCwF2ECG8dxHKcBqoqC\npMmSlkt6QtL0EttHSJojqV/SUknnxfVjJd0t6dG4/tMFx8yUtFbS4viZnEBfhgGveD0hx3GcxpFZ\n+dpxkgYBjwMnAc8CvwLONrNlBfvMBHY0s4sljYj7jyT490eZWb+k3YBfA6eb2XJJlwCbzeyqio2r\nYwJqiQOB28x4cy37O47jdCP1PDdLUc1SmASsNLM1ZrYFuBk4vWif9cDQ+H0osNHMtprZc2bWD2Bm\nLwPLCJPV59veaKPL4EFmx3GcJqkmCmOAZwqW1zLwwQ5wHXCIpHXAEmBa8UkkjQOOAB4sWH2BpCWS\nZkkaVme7S+FBZsdxnCapViW1lnkJZgD9ZtYn6QBgnqTDzWwzQHQd/QSYFi0GgO8AX4zfLwOuBM4v\ndfLonsqxwMwWlGmHZzM7jtNzSOoD+pI6XzVReBYYW7A8lmAtFHIM8CUAM1slaTXwFmCRpDcAPwX+\nw8x+njvAzPIzjkm6HrilXAPMbGb1bgBuKTiO04PEF+UFueUYs22Yau6jRcAESeMkDQHOAmYX7bOc\nEIhG0kiCIDwpScAs4DEzu7rwAEmjCxbPAB5pvAt5PJvZcRynSSpaCma2VdJU4A5gEDDLzJZJmhK3\nXwtcDtwgaQlBZC4ys02SjgP+FnhY0uJ4yovNbA7wFUkTCe6p1cCUBPoygmTExXEcp2epOCQ1beoc\nknob8G0zbm1xsxzHcTJLq4ekdhLuPnIcx2mSbhIFz1NwHMdpkm4SBbcUHMdxmqQrREFiZ+ANwOa0\n2+I4jtPJdIUoEF1HZjUl2zmO4zhl6BZRcNeR4zhOAnSLKHiQ2XEcJwG6RRS8xIXjOE4CdIsoeDE8\nx3GcBOgWUXBLwXEcJwG6SRTcUnAcx2mSbhEFDzQ7juMkQLeIgruPHMdxEqArqqRKjAeeN+OVNjTL\ncRwnszRbJbUrRMFxHMcJeOlsx3EcJzFcFBzHcZw8LgqO4zhOnqqiIGmypOWSnpA0vcT2EZLmSOqX\ntFTSeXH9WEl3S3o0rv90wTHDJc2TtELSXEnDEu2V4ziO0xAVRUHSIOAaYDJwMHC2pIOKdpsKLDaz\niUAfcKWkwcAW4LNmdgjwTuBTkt4aj/k8MM/MDgTmx+WeQlJf2m1oJd6/zsb717tUsxQmASvNbI2Z\nbQFuBk4v2mc9MDR+HwpsNLOtZvacmfUDmNnLwDJgTNzvNODG+P1G4EPNdaMj6Uu7AS2mL+0GtJi+\ntBvQYvrSbkCL6Uu7AVllcJXtY4BnCpbXAu8o2uc64C5J64DdgTOLTyJpHHAE8GBcNdLMNsTvG4CR\ndbXacRzHaQnVLIVakhhmAP1mtg8wEfi2pN1zGyXtBvwEmBYthoEXCIkS2U2WcBzH6SXMrOyHEAuY\nU7B8MTC9aJ/bgGMLlucDR8fvbwDuAD5TdMxyYFT8PhpYXub65h//+Mc//qnvU+m5Xu1TzX20CJgQ\n3T/rgLOAs4v2WQ6cBNwnaSTwFuBJSQJmAY+Z2dVFx8wGzgW+En/+vNTFPZvZcRynvVQtcyHpFOBq\nYBAwy8y+LGkKgJldK2kEcAOwH8Ed9WUz+4Gk44B7gIcJ6gVwsZnNkTQc+HE8Zg1wppn9LvHeOY7j\nOHWR6dpHjuM4TnvJZEZztYS5TkTSGkkPS1os6aG4rmOT+CR9V9IGSY8UrCvbH0kXx/u5XNL70ml1\nbZTp20xJa+P9Wxwt6Ny2jukblE8s7aL7V65/XXEPJe0k6cGYMPyYpC/H9cncv2YCEq34ENxUK4Fx\nhEB1P3BQ2u1KoF+rgeFF6/4FuCh+nw5ckXY76+jP8YRhxo9U6w8h8bE/3s9x8f7ukHYf6uzbJcCF\nJfbtqL7FNo8CJsbvuwGPAwd10f0r179uuoe7xJ+DgQeA45K6f1m0FGpJmOtUigPnHZvEZ2YLgReL\nVpfrz+nAD81si5mtIfxRTmpHOxuhTN9g+/sHHdY3ACufWNot969S4my33MNX49chhBfpF0no/mVR\nFEolzI0ps28nYcCdkhZJ+kRc121JfOX6sw/hPubo1Ht6gaQlkmYVmOYd3beixNKuu38F/XsgruqK\neyhpB0n9hPt0t5k9SkL3L4ui0K2R72PN7AjgFEIdqOMLN1qw87qm7zX0p9P6+h1gPCFBcz1wZYV9\nO6JvMbH0p4TE0s2F27rh/pVInO2ae2hmf7ZQb25f4ARJJxZtb/j+ZVEUngXGFiyPZaDKdSRmtj7+\nfAH4GcF82yBpFICk0cDz6bUwEcr1p/ie7hvXdQxm9rxFgOvZZn53ZN8kvYEgCDeZWS5PqGvuX0H/\n/iPXv267hwBm9hJwK3AUCd2/LIpCPmFO0hBCwtzslNvUFJJ2yZX+kLQr8D7gEbYl8UGFJL4Oolx/\nZgN/JWmIpPHABOChFNrXMPGfLMcZhPsHHdi3ComlXXH/yvWvW+6hwnQFw+L3nYGTgcUkdf/SjqKX\niayfQhgxsJKQ8JZ6m5rsz3hC9L8fWJrrEzAcuBNYAcwFhqXd1jr69ENClvsfCTGgj1XqD6FG1kpC\nBvz7025/nX37OPA9QiLmkvjPNrIT+xbbexzw5/j3uDh+JnfR/SvVv1O65R4ChwK/if17GPhcXJ/I\n/fPkNcdxHCdPFt1HjuM4Tkq4KDiO4zh5XBQcx3GcPC4KjuM4Th4XBcdxHCePi4LjOI6Tx0XBcRzH\nyeOi4DiO4+T5/86AaPOdlOWfAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10a4b44d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"trees_range = range(10, 300, 10)\n",
"param_grid = dict(n_estimators = trees_range)\n",
"grid = GridSearchCV(rf, param_grid, cv=10, scoring='roc_auc', n_jobs = -1)\n",
"grid.fit(explanatory_df, response_series)\n",
"grid_mean_scores = [result[1] for result in grid.grid_scores_]\n",
"plt.figure()\n",
"plt.plot(trees_range, grid_mean_scores)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Displaying Best Score"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"160\n",
"0.855148875761\n"
]
}
],
"source": [
"best_rf_est = grid.best_estimator_\n",
"print best_rf_est.n_estimators\n",
"print grid.best_score_"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Plotting Sensitivity vs Specificity"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x10dbad8d0>"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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mZlakLDWGMzPuMzOzLtBnjUHSpsBmwLKSvk4ygR7AcOoY/2BmZp2lWlPSYiRJYFD6vcfr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\nnBjMzKyME4OZmZX5/wHZHFPV/JnKAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1094caa90>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from sklearn.cross_validation import train_test_split\n",
"from sklearn import metrics\n",
"\n",
"xTrain, xTest, yTrain, yTest = train_test_split(\n",
" explanatory_df, response_series, test_size = 0.3)\n",
"rf_probabilities = pandas.DataFrame(best_rf_est.fit(xTrain, yTrain).predict_proba(xTest))\n",
"rf_fpr, rf_tpr, thresholds = metrics.roc_curve(yTest, rf_probabilities[1])\n",
"\n",
"plt.figure()\n",
"plt.plot(rf_fpr, rf_tpr, color = 'b')\n",
"plt.xlabel('False Positive Rate (1 - Specificity)')\n",
"plt.ylabel('True Positive Rate (Sensitivity)')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Predict on the New Data"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [
{
"data": {
"image/png": 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fJX8zszo5+ZuZdaG2Tf4S6wCb4mofM7O6tW3yBzYBXoxgddmBmJm1m3ZO/m7s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\nYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10a41b210>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Making two different groups - TRAINING and HOLDOUT\n",
"# TRAINING\n",
"conn = sqlite3.connect('/Users/MatthewCohen/Documents/SQLite/TeamSeason1.sqlite')\n",
"query = \"\"\"SELECT t.won as wins, g.good_team, t.o_fgm as field_goals_made, t.o_fga as field_goals_attempted,\n",
"t.o_ftm as free_throws_made, t.o_fta as free_throws_attempted, t.o_oreb as offensive_rebounds,\n",
"t.o_dreb as defensive_rebounds, t.o_reb as total_rebounds, t.o_asts as assists, t.o_pf as personal_fouls,\n",
"t.o_stl as steals, t.o_to as turnovers, t.o_3pm as three_pointers_made, t.o_3pa as three_pointers_attempted,\n",
"t.d_fgm as field_goals_allowed, t.d_fga as field_goal_attempts_allowed, t.d_reb as rebounds_allowed,\n",
"t.d_asts as assists_allowed, t.d_pf as fouls_against, t.d_3pm as three_point_makes_allowed,\n",
"((o_fgm / o_fga)*100) as field_goal_percentage, ((o_ftm / o_fta)*100) as free_throw_percentage,\n",
"((o_3pm / o_3pa)*100) as three_point_percentage, o_blk as blocks, o_pts as points, d_pts as points_against\n",
"FROM TeamSeason1 t\n",
"LEFT OUTER JOIN Good_Teams2 g ON t.team = g.team and t.year = g.year\n",
"WHERE t.year > 1980 and t.year < 1999;\"\"\"\n",
"df = pandas.read_sql(query, conn)\n",
"conn.close\n",
"\n",
"# Defining Explanatory Features\n",
"#'field_goals_made', 'field_goals_allowed', 'good_team', 'wins', 'points', 'points_against', 'free_throws_made', 'three_pointers_made'\n",
"explanatory_features = [col for col in df.columns if col not in ['field_goals_made', 'field_goals_allowed', 'good_team', 'wins', 'points', 'points_against', 'free_throws_made', 'three_pointers_made', 'offensive_rebounds']]\n",
"explanatory_df = df[explanatory_features]\n",
"explanatory_colnames = explanatory_df.columns\n",
"\n",
"# Defining Response Series\n",
"response_series = df.good_team\n",
"\n",
"# Scaling data such that it is normally distributed in order to input into model and improve accuracy\n",
"scaler = preprocessing.StandardScaler()\n",
"scaler.fit(explanatory_df)\n",
"explanatory_df = pandas.DataFrame(scaler.transform(explanatory_df), columns = explanatory_df.columns)\n",
"\n",
"# Predicting wins using Random Forests \n",
"rf = ensemble.RandomForestClassifier(n_estimators= 500)\n",
"roc_scores_rf = cross_val_score(rf, explanatory_df, response_series, cv=10, scoring='roc_auc', n_jobs = -1)\n",
"\n",
"# Grid Search for best parameters\n",
"trees_range = range(10, 300, 10)\n",
"param_grid = dict(n_estimators = trees_range)\n",
"grid = GridSearchCV(rf, param_grid, cv=10, scoring='roc_auc', n_jobs = -1)\n",
"grid.fit(explanatory_df, response_series)\n",
"grid_mean_scores = [result[1] for result in grid.grid_scores_]\n",
"plt.figure()\n",
"plt.plot(trees_range, grid_mean_scores)\n",
"\n",
"\n",
"best_rf_est = grid.best_estimator_\n",
"\n",
"\n",
"# HOLDOUT\n",
"conn = sqlite3.connect('/Users/MatthewCohen/Documents/SQLite/TeamSeason1.sqlite')\n",
"query2 = \"\"\"SELECT t.won as wins, g.good_team, t.o_fgm as field_goals_made, t.o_fga as field_goals_attempted,\n",
"t.o_ftm as free_throws_made, t.o_fta as free_throws_attempted, t.o_oreb as offensive_rebounds,\n",
"t.o_dreb as defensive_rebounds, t.o_reb as total_rebounds, t.o_asts as assists, t.o_pf as personal_fouls,\n",
"t.o_stl as steals, t.o_to as turnovers, t.o_3pm as three_pointers_made, t.o_3pa as three_pointers_attempted,\n",
"t.d_fgm as field_goals_allowed, t.d_fga as field_goal_attempts_allowed, t.d_reb as rebounds_allowed,\n",
"t.d_asts as assists_allowed, t.d_pf as fouls_against, t.d_3pm as three_point_makes_allowed,\n",
"((o_fgm / o_fga)*100) as field_goal_percentage, ((o_ftm / o_fta)*100) as free_throw_percentage,\n",
"((o_3pm / o_3pa)*100) as three_point_percentage, o_blk as blocks, o_pts as points, d_pts as points_against\n",
"FROM TeamSeason1 t\n",
"LEFT OUTER JOIN Good_Teams2 g ON t.team = g.team and t.year = g.year\n",
"WHERE t.year >= 1999 and t.year <= 2009;\"\"\"\n",
"df2 = pandas.read_sql(query2, conn)\n",
"conn.close\n",
"\n",
"# Defining Explanatory Features\n",
"#'field_goals_made', 'field_goals_allowed', 'good_team', 'wins', 'points', 'points_against\"\n",
"explanatory_features2 = [col for col in df2.columns if col not in ['field_goals_made', 'field_goals_allowed', 'good_team', 'wins', 'points', 'points_against', 'free_throws_made', 'three_pointers_made', 'offensive_rebounds']]\n",
"explanatory_df2 = df2[explanatory_features2]\n",
"explanatory_colnames2 = explanatory_df2.columns\n",
"\n",
"# Defining Response Series\n",
"response_series2 = df2.good_team\n",
"\n",
"# Scaling data such that it is normally distributed in order to input into model and improve accuracy\n",
"scaler = preprocessing.StandardScaler()\n",
"scaler.fit(explanatory_df2)\n",
"explanatory_df2 = pandas.DataFrame(scaler.transform(explanatory_df2), columns = explanatory_df2.columns)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Creating Prediction Object"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"prediction = best_rf_est.predict(explanatory_df2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Visualize Results"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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5jc4dzkSMfxoSIyizBxF1W8M/yjkV9UEaVy/Ab334cAwmkq3ylciH/UkNL9fB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\nN34zs8S48ZuZJcaN38wsMf8CLhRdDR78UkkAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10dbad750>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import pylab\n",
"\n",
"y = response_series2.tolist()\n",
"yhat = prediction.tolist()\n",
"\n",
"def randomize(mean):\n",
" N = 5\n",
" cov = [[0.02, 0.02], [0, 0.02]]\n",
" x,y = np.random.multivariate_normal(mean, cov, N).T\n",
" plt.scatter(x, y, s=70, alpha=0.03)\n",
"\n",
"for i in range(1,325):\n",
" randomize((y[i], yhat[i]))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Checking for Accuracy"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0\n"
]
}
],
"source": [
"diff = (response_series2 - prediction).tolist()\n",
"accuracy = (diff.count(0)/len(diff))*100\n",
"print accuracy"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## List of feature importances"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" importance\n",
"field_goal_percentage 0.122795\n",
"defensive_rebounds 0.108721\n",
"rebounds_allowed 0.091865\n",
"assists 0.070202\n",
"turnovers 0.061630\n",
"three_point_percentage 0.061299\n",
"total_rebounds 0.051376\n",
"blocks 0.050103\n",
"assists_allowed 0.048359\n",
"personal_fouls 0.045823\n",
"steals 0.045672\n",
"free_throw_percentage 0.040892\n",
"free_throws_attempted 0.038098\n",
"three_pointers_attempted 0.036026\n",
"field_goals_attempted 0.033915\n",
"three_point_makes_allowed 0.033079\n",
"field_goal_attempts_allowed 0.030997\n",
"fouls_against 0.029148\n"
]
}
],
"source": [
"importances = pandas.DataFrame(grid.best_estimator_.feature_importances_, index = explanatory_df.columns, columns =['importance'])\n",
"importances.sort(columns = ['importance'], ascending = False, inplace = True)\n",
"print importances"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Recursive feature elimination"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Index([u'defensive_rebounds', u'rebounds_allowed', u'field_goal_percentage'], dtype='object')\n"
]
}
],
"source": [
"#rfWithCoef = RandomForestsWithCoef(n_estimators= 500)\n",
"rfe = RFE(estimator=rfWithCoef, n_features_to_select=3, step=1, verbose = 0)\n",
"rfe.fit(explanatory_df, response_series)\n",
"\n",
"features_used = explanatory_df.columns[rfe.get_support()]\n",
"print features_used"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Run Random Forests on 3 Best Features"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"conn = sqlite3.connect('/Users/MatthewCohen/Documents/SQLite/TeamSeason1.sqlite')\n",
"query = \"\"\"SELECT t.won as wins, g.good_team, t.o_fgm as field_goals_made, t.o_fga as field_goals_attempted,\n",
"t.o_ftm as free_throws_made, t.o_fta as free_throws_attempted, t.o_oreb as offensive_rebounds,\n",
"t.o_dreb as defensive_rebounds, t.o_reb as total_rebounds, t.o_asts as assists, t.o_pf as personal_fouls,\n",
"t.o_stl as steals, t.o_to as turnovers, t.o_3pm as three_pointers_made, t.o_3pa as three_pointers_attempted,\n",
"t.d_fgm as field_goals_allowed, t.d_fga as field_goal_attempts_allowed, t.d_reb as rebounds_allowed,\n",
"t.d_asts as assists_allowed, t.d_pf as fouls_against, t.d_3pm as three_point_makes_allowed,\n",
"((o_fgm / o_fga)*100) as field_goal_percentage, ((o_ftm / o_fta)*100) as free_throw_percentage,\n",
"((o_3pm / o_3pa)*100) as three_point_percentage, o_blk as blocks, o_pts as points, d_pts as points_against\n",
"FROM TeamSeason1 t\n",
"LEFT OUTER JOIN Good_Teams2 g ON t.team = g.team and t.year = g.year\n",
"WHERE t.year > 1980 and t.year <= 2009;\"\"\"\n",
"df = pandas.read_sql(query, conn)\n",
"conn.close\n",
"\n",
"# Defining Explanatory Features\n",
"#'field_goals_made', 'field_goals_allowed', 'good_team', 'wins', 'points', 'points_against', 'free_throws_made', 'three_pointers_made'\n",
"explanatory_features = [col for col in df.columns if col in ['defensive_rebounds', 'field_goal_percentage', 'assists_allowed']]\n",
"explanatory_df = df[explanatory_features]\n",
"explanatory_colnames = explanatory_df.columns\n",
"\n",
"# Defining Response Series\n",
"response_series = df.good_team\n",
"\n",
"# Scaling data such that it is normally distributed in order to input into model and improve accuracy\n",
"scaler = preprocessing.StandardScaler()\n",
"scaler.fit(explanatory_df)\n",
"explanatory_df = pandas.DataFrame(scaler.transform(explanatory_df), columns = explanatory_df.columns)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Predicting wins using Random Forests"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.851648059543\n"
]
}
],
"source": [
"rf = ensemble.RandomForestClassifier(n_estimators= 500)\n",
"roc_scores_rf = cross_val_score(rf, explanatory_df, response_series, cv=10, scoring='roc_auc', n_jobs = -1)\n",
"print roc_scores_rf.mean()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Grid Search for best parameters"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x10df3f210>]"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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9ucRkYAtgddmxmJm1Sq2S/xxgZUSsiYj1wOXA4VXHrAMm5p8nAg9FxAYASTsC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\nQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x109489150>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"trees_range = range(10, 300, 10)\n",
"param_grid = dict(n_estimators = trees_range)\n",
"grid = GridSearchCV(rf, param_grid, cv=10, scoring='roc_auc', n_jobs = -1)\n",
"grid.fit(explanatory_df, response_series)\n",
"grid_mean_scores = [result[1] for result in grid.grid_scores_]\n",
"plt.figure()\n",
"plt.plot(trees_range, grid_mean_scores)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Best Estimator"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"70\n",
"0.881351080039\n"
]
}
],
"source": [
"best_rf_est = grid.best_estimator_\n",
"print best_rf_est.n_estimators\n",
"print grid.best_score_"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## FINDING ACCURACY OF SUBSET OF FEATURES"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x10e006ad0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Making two different groups - TRAINING and HOLDOUT\n",
"# TRAINING\n",
"conn = sqlite3.connect('/Users/MatthewCohen/Documents/SQLite/TeamSeason1.sqlite')\n",
"query = \"\"\"SELECT t.won as wins, g.good_team, t.o_fgm as field_goals_made, t.o_fga as field_goals_attempted,\n",
"t.o_ftm as free_throws_made, t.o_fta as free_throws_attempted, t.o_oreb as offensive_rebounds,\n",
"t.o_dreb as defensive_rebounds, t.o_reb as total_rebounds, t.o_asts as assists, t.o_pf as personal_fouls,\n",
"t.o_stl as steals, t.o_to as turnovers, t.o_3pm as three_pointers_made, t.o_3pa as three_pointers_attempted,\n",
"t.d_fgm as field_goals_allowed, t.d_fga as field_goal_attempts_allowed, t.d_reb as rebounds_allowed,\n",
"t.d_asts as assists_allowed, t.d_pf as fouls_against, t.d_3pm as three_point_makes_allowed,\n",
"((o_fgm / o_fga)*100) as field_goal_percentage, ((o_ftm / o_fta)*100) as free_throw_percentage,\n",
"((o_3pm / o_3pa)*100) as three_point_percentage, o_blk as blocks, o_pts as points, d_pts as points_against\n",
"FROM TeamSeason1 t\n",
"LEFT OUTER JOIN Good_Teams2 g ON t.team = g.team and t.year = g.year\n",
"WHERE t.year > 1980 and t.year < 1999;\"\"\"\n",
"df = pandas.read_sql(query, conn)\n",
"conn.close\n",
"\n",
"# Defining Explanatory Features\n",
"#'field_goals_made', 'field_goals_allowed', 'good_team', 'wins', 'points', 'points_against', 'free_throws_made', 'three_pointers_made'\n",
"explanatory_features = [col for col in df.columns if col in ['defensive_rebounds', 'assists_allowed', 'field_goal_percentage']]\n",
"explanatory_df = df[explanatory_features]\n",
"explanatory_colnames = explanatory_df.columns\n",
"\n",
"# Defining Response Series\n",
"response_series = df.good_team\n",
"\n",
"# Scaling data such that it is normally distributed in order to input into model and improve accuracy\n",
"scaler = preprocessing.StandardScaler()\n",
"scaler.fit(explanatory_df)\n",
"explanatory_df = pandas.DataFrame(scaler.transform(explanatory_df), columns = explanatory_df.columns)\n",
"\n",
"# Predicting wins using Random Forests \n",
"rf = ensemble.RandomForestClassifier(n_estimators= 500)\n",
"roc_scores_rf = cross_val_score(rf, explanatory_df, response_series, cv=10, scoring='roc_auc', n_jobs = -1)\n",
"\n",
"# Grid Search for best parameters\n",
"trees_range = range(10, 300, 10)\n",
"param_grid = dict(n_estimators = trees_range)\n",
"grid = GridSearchCV(rf, param_grid, cv=10, scoring='roc_auc', n_jobs = -1)\n",
"grid.fit(explanatory_df, response_series)\n",
"grid_mean_scores = [result[1] for result in grid.grid_scores_]\n",
"plt.figure()\n",
"plt.plot(trees_range, grid_mean_scores)\n",
"\n",
"\n",
"best_rf_est = grid.best_estimator_\n",
"\n",
"\n",
"# HOLDOUT\n",
"conn = sqlite3.connect('/Users/MatthewCohen/Documents/SQLite/TeamSeason1.sqlite')\n",
"query2 = \"\"\"SELECT t.won as wins, g.good_team, t.o_fgm as field_goals_made, t.o_fga as field_goals_attempted,\n",
"t.o_ftm as free_throws_made, t.o_fta as free_throws_attempted, t.o_oreb as offensive_rebounds,\n",
"t.o_dreb as defensive_rebounds, t.o_reb as total_rebounds, t.o_asts as assists, t.o_pf as personal_fouls,\n",
"t.o_stl as steals, t.o_to as turnovers, t.o_3pm as three_pointers_made, t.o_3pa as three_pointers_attempted,\n",
"t.d_fgm as field_goals_allowed, t.d_fga as field_goal_attempts_allowed, t.d_reb as rebounds_allowed,\n",
"t.d_asts as assists_allowed, t.d_pf as fouls_against, t.d_3pm as three_point_makes_allowed,\n",
"((o_fgm / o_fga)*100) as field_goal_percentage, ((o_ftm / o_fta)*100) as free_throw_percentage,\n",
"((o_3pm / o_3pa)*100) as three_point_percentage, o_blk as blocks, o_pts as points, d_pts as points_against\n",
"FROM TeamSeason1 t\n",
"LEFT OUTER JOIN Good_Teams2 g ON t.team = g.team and t.year = g.year\n",
"WHERE t.year >= 1999 and t.year <= 2009;\"\"\"\n",
"df2 = pandas.read_sql(query2, conn)\n",
"conn.close\n",
"\n",
"# Defining Explanatory Features\n",
"#'field_goals_made', 'field_goals_allowed', 'good_team', 'wins', 'points', 'points_against\"\n",
"explanatory_features2 = [col for col in df2.columns if col in ['defensive_rebounds', 'assists_allowed', 'field_goal_percentage']]\n",
"explanatory_df2 = df2[explanatory_features2]\n",
"explanatory_colnames2 = explanatory_df2.columns\n",
"\n",
"# Defining Response Series\n",
"response_series2 = df2.good_team\n",
"\n",
"# Scaling data such that it is normally distributed in order to input into model and improve accuracy\n",
"scaler = preprocessing.StandardScaler()\n",
"scaler.fit(explanatory_df2)\n",
"explanatory_df2 = pandas.DataFrame(scaler.transform(explanatory_df2), columns = explanatory_df2.columns)\n",
"\n",
"\n",
"prediction = best_rf_est.predict(explanatory_df2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Visualize Results"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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S00ZHQ2TmZE3gf/chibPNItWp9J6077V6KnyPzizywH9KcCQ5/EMONLkKkr0x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\nD/wej8dTY/x/w4BZak+Pb6IAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10dc4b910>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import pylab\n",
"\n",
"y = response_series2.tolist()\n",
"yhat = prediction.tolist()\n",
"\n",
"def randomize(mean):\n",
" N = 5\n",
" cov = [[0.02, 0.02], [0, 0.02]]\n",
" x,y = np.random.multivariate_normal(mean, cov, N).T\n",
" plt.scatter(x, y, s=70, alpha=0.03)\n",
"# return x,y\n",
"\n",
"for i in range(1,325):\n",
" randomize((y[i], yhat[i]))\n",
" # plt.scatter(x, y, s=70, alpha=0.03)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Accuracy"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0\n"
]
}
],
"source": [
"diff = (response_series2 - prediction).tolist()\n",
"accuracy = (diff.count(0)/len(diff))*100\n",
"print accuracy"
]
},
{
"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
}
| gpl-3.0 |
mne-tools/mne-tools.github.io | 0.14/_downloads/plot_linear_model_patterns.ipynb | 1 | 5918 | {
"nbformat_minor": 0,
"nbformat": 4,
"cells": [
{
"execution_count": null,
"cell_type": "code",
"source": [
"%matplotlib inline"
],
"outputs": [],
"metadata": {
"collapsed": false
}
},
{
"source": [
"\n# Linear classifier on sensor data with plot patterns and filters\n\n\nDecoding, a.k.a MVPA or supervised machine learning applied to MEG and EEG\ndata in sensor space. Fit a linear classifier with the LinearModel object\nproviding topographical patterns which are more neurophysiologically\ninterpretable [1]_ than the classifier filters (weight vectors).\nThe patterns explain how the MEG and EEG data were generated from the\ndiscriminant neural sources which are extracted by the filters.\nNote patterns/filters in MEG data are more similar than EEG data\nbecause the noise is less spatially correlated in MEG than EEG.\n\nReferences\n----------\n\n.. [1] Haufe, S., Meinecke, F., G\u00f6rgen, K., D\u00e4hne, S., Haynes, J.-D.,\n Blankertz, B., & Bie\u00dfmann, F. (2014). On the interpretation of\n weight vectors of linear models in multivariate neuroimaging.\n NeuroImage, 87, 96\u2013110. doi:10.1016/j.neuroimage.2013.10.067\n\n"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# Authors: Alexandre Gramfort <[email protected]>\n# Romain Trachel <[email protected]>\n# Jean-Remi King <[email protected]>\n#\n# License: BSD (3-clause)\n\nimport mne\nfrom mne import io, EvokedArray\nfrom mne.datasets import sample\nfrom mne.decoding import Vectorizer, get_coef\n\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.pipeline import make_pipeline\n\n# import a linear classifier from mne.decoding\nfrom mne.decoding import LinearModel\n\nprint(__doc__)\n\ndata_path = sample.data_path()"
],
"outputs": [],
"metadata": {
"collapsed": false
}
},
{
"source": [
"Set parameters\n\n"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"raw_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'\ntmin, tmax = -0.1, 0.4\nevent_id = dict(aud_l=1, vis_l=3)\n\n# Setup for reading the raw data\nraw = io.read_raw_fif(raw_fname, preload=True)\nraw.filter(.5, 25)\nevents = mne.read_events(event_fname)\n\n# Read epochs\nepochs = mne.Epochs(raw, events, event_id, tmin, tmax, proj=True,\n decim=4, baseline=None, preload=True)\n\nlabels = epochs.events[:, -1]\n\n# get MEG and EEG data\nmeg_epochs = epochs.copy().pick_types(meg=True, eeg=False)\nmeg_data = meg_epochs.get_data().reshape(len(labels), -1)"
],
"outputs": [],
"metadata": {
"collapsed": false
}
},
{
"source": [
"Decoding in sensor space using a LogisticRegression classifier\n\n"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"clf = LogisticRegression()\nscaler = StandardScaler()\n\n# create a linear model with LogisticRegression\nmodel = LinearModel(clf)\n\n# fit the classifier on MEG data\nX = scaler.fit_transform(meg_data)\nmodel.fit(X, labels)\n\n# Extract and plot spatial filters and spatial patterns\nfor name, coef in (('patterns', model.patterns_), ('filters', model.filters_)):\n # We fitted the linear model onto Z-scored data. To make the filters\n # interpretable, we must reverse this normalization step\n coef = scaler.inverse_transform([coef])[0]\n\n # The data was vectorized to fit a single model across all time points and\n # all channels. We thus reshape it:\n coef = coef.reshape(len(meg_epochs.ch_names), -1)\n\n # Plot\n evoked = EvokedArray(coef, meg_epochs.info, tmin=epochs.tmin)\n evoked.plot_topomap(title='MEG %s' % name)"
],
"outputs": [],
"metadata": {
"collapsed": false
}
},
{
"source": [
"Let's do the same on EEG data using a scikit-learn pipeline\n\n"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"X = epochs.pick_types(meg=False, eeg=True)\ny = epochs.events[:, 2]\n\n# Define a unique pipeline to sequentially:\nclf = make_pipeline(\n Vectorizer(), # 1) vectorize across time and channels\n StandardScaler(), # 2) normalize features across trials\n LinearModel(LogisticRegression())) # 3) fits a logistic regression\nclf.fit(X, y)\n\n# Extract and plot patterns and filters\nfor name in ('patterns_', 'filters_'):\n # The `inverse_transform` parameter will call this method on any estimator\n # contained in the pipeline, in reverse order.\n coef = get_coef(clf, name, inverse_transform=True)\n evoked = EvokedArray(coef, epochs.info, tmin=epochs.tmin)\n evoked.plot_topomap(title='EEG %s' % name[:-1])"
],
"outputs": [],
"metadata": {
"collapsed": false
}
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"name": "python2",
"language": "python"
},
"language_info": {
"mimetype": "text/x-python",
"nbconvert_exporter": "python",
"name": "python",
"file_extension": ".py",
"version": "2.7.13",
"pygments_lexer": "ipython2",
"codemirror_mode": {
"version": 2,
"name": "ipython"
}
}
}
} | bsd-3-clause |
Rotvig/cs231n | Project/Deep Network Comparison - ResNet.ipynb | 1 | 10223747 | null | mit |
kemerelab/NeuroHMM | StateOrdering.ipynb | 1 | 1071056 | null | mit |
spacecowboy/article-annriskgroups-source | DataSetStratification.ipynb | 1 | 54283 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Data set stratification\n",
"\n",
"This script randomly assigns training/test labels to each entry in a data set.\n",
"\n",
"One quarter (1/4) of the data is assigned as test, and rest as training. The labeling\n",
"is stratified for censoring so that both testing and training pieces have about the same\n",
"amount of censoring."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import datasets as ds\n",
"import pandas as pd\n",
"import numpy as np"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This cell can be used for all data sets except *colon*. *colon* is special because it has 3 types of events instead of just 2. Just change the first line to run a different data set."
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(7871,)\n",
"(7871, 11)\n",
"Training size: 5903\n",
"Testing size: 1968\n"
]
}
],
"source": [
"#data = ds._pbc\n",
"#data = ds._lung\n",
"#data = ds._nwtco\n",
"data = ds._flchain\n",
"\n",
"df = pd.read_csv(data['filename'][:-4] + \"_org.csv\",\n",
" sep=None, engine='python')\n",
"k = 4\n",
"\n",
"# flchain has three guys at zero, remove them\n",
"if 'flchain' in data['filename']:\n",
" df = df[(df[data['timecol']] > 0)]\n",
"\n",
"# Need shape later\n",
"n, d = df.shape\n",
"\n",
"# Random reordering\n",
"df = df.reindex(np.random.permutation(df.index))\n",
"df.sort(data['eventcol'], inplace=True)\n",
"\n",
"assignments = np.array((n // k + 1) * list(range(0, k)))\n",
"assignments = assignments[:n]\n",
"\n",
"print(assignments.shape)\n",
"print(df.shape)\n",
"\n",
"# Create a new column that specifies set\n",
"df['set'] = 1\n",
"# 0 is testing\n",
"df.loc[assignments == 0, 'set'] = 'testing'\n",
"# rest is training\n",
"df.loc[assignments != 0, 'set'] = 'training'\n",
"\n",
"print(\"Training size:\", np.sum(df['set'] == 'training'))\n",
"print(\"Testing size:\", np.sum(df['set'] == 'testing'))\n",
"\n",
"df = df.reindex(np.sort(df.index))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Print the labeled to data to a new file."
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"data/flchain.csv\n"
]
}
],
"source": [
"fname = data['filename']\n",
"print(fname)\n",
"df.to_csv(fname, na_rep='NA', index=False)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Colon\n",
"\n",
"Is kind of special. It has 3 events where two must be combined before stratification is possible."
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0"
]
},
"execution_count": 48,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data = ds._colon\n",
"\n",
"df = pd.read_csv(data['filename'], sep=None, engine='python')\n",
"n, d = df.shape\n",
"k = 4\n",
"\n",
"# Construct lists of events, censored\n",
"events = []\n",
"censored = []\n",
"\n",
"for i in df['id'].unique():\n",
" x = ((df['id'] == i) & (df['etype'] == 1))\n",
" if df[x]['status'].sum() < 1:\n",
" censored.append(i)\n",
" else:\n",
" events.append(i)\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 64,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Training size: 1392\n",
"Testing size: 466\n"
]
},
{
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" <td> 64</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 1</td>\n",
" <td> 1</td>\n",
" <td> 2</td>\n",
" <td> 3</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 2085</td>\n",
" <td> 2</td>\n",
" <td> training</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25 </th>\n",
" <td> 13</td>\n",
" <td> 1</td>\n",
" <td> Obs</td>\n",
" <td> 1</td>\n",
" <td> 64</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 1</td>\n",
" <td> 1</td>\n",
" <td> 2</td>\n",
" <td> 3</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 1130</td>\n",
" <td> 1</td>\n",
" <td> training</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26 </th>\n",
" <td> 14</td>\n",
" <td> 1</td>\n",
" <td> Lev</td>\n",
" <td> 1</td>\n",
" <td> 68</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 3</td>\n",
" <td> 1</td>\n",
" <td> 2</td>\n",
" <td> 3</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 2910</td>\n",
" <td> 2</td>\n",
" <td> training</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27 </th>\n",
" <td> 14</td>\n",
" <td> 1</td>\n",
" <td> Lev</td>\n",
" <td> 1</td>\n",
" <td> 68</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 3</td>\n",
" <td> 1</td>\n",
" <td> 2</td>\n",
" <td> 3</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 2231</td>\n",
" <td> 1</td>\n",
" <td> training</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28 </th>\n",
" <td> 15</td>\n",
" <td> 1</td>\n",
" <td> Obs</td>\n",
" <td> 1</td>\n",
" <td> 46</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 4</td>\n",
" <td> 0</td>\n",
" <td> 2</td>\n",
" <td> 3</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 2754</td>\n",
" <td> 2</td>\n",
" <td> testing</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29 </th>\n",
" <td> 15</td>\n",
" <td> 1</td>\n",
" <td> Obs</td>\n",
" <td> 1</td>\n",
" <td> 46</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 4</td>\n",
" <td> 0</td>\n",
" <td> 2</td>\n",
" <td> 3</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 2754</td>\n",
" <td> 1</td>\n",
" <td> testing</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1828</th>\n",
" <td> 915</td>\n",
" <td> 1</td>\n",
" <td> Lev</td>\n",
" <td> 0</td>\n",
" <td> 42</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 6</td>\n",
" <td> 0</td>\n",
" <td> 1</td>\n",
" <td> 3</td>\n",
" <td> 0</td>\n",
" <td> 1</td>\n",
" <td> 2114</td>\n",
" <td> 2</td>\n",
" <td> training</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1829</th>\n",
" <td> 915</td>\n",
" <td> 1</td>\n",
" <td> Lev</td>\n",
" <td> 0</td>\n",
" <td> 42</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 6</td>\n",
" <td> 0</td>\n",
" <td> 1</td>\n",
" <td> 3</td>\n",
" <td> 0</td>\n",
" <td> 1</td>\n",
" <td> 2114</td>\n",
" <td> 1</td>\n",
" <td> training</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1830</th>\n",
" <td> 916</td>\n",
" <td> 1</td>\n",
" <td> Obs</td>\n",
" <td> 0</td>\n",
" <td> 37</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 8</td>\n",
" <td> 1</td>\n",
" <td> 2</td>\n",
" <td> 3</td>\n",
" <td> 1</td>\n",
" <td> 1</td>\n",
" <td> 1262</td>\n",
" <td> 2</td>\n",
" <td> training</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1831</th>\n",
" <td> 916</td>\n",
" <td> 1</td>\n",
" <td> Obs</td>\n",
" <td> 0</td>\n",
" <td> 37</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 8</td>\n",
" <td> 1</td>\n",
" <td> 2</td>\n",
" <td> 3</td>\n",
" <td> 1</td>\n",
" <td> 1</td>\n",
" <td> 438</td>\n",
" <td> 1</td>\n",
" <td> training</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1832</th>\n",
" <td> 917</td>\n",
" <td> 1</td>\n",
" <td> Obs</td>\n",
" <td> 0</td>\n",
" <td> 71</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 5</td>\n",
" <td> 1</td>\n",
" <td> 3</td>\n",
" <td> 3</td>\n",
" <td> 0</td>\n",
" <td> 1</td>\n",
" <td> 259</td>\n",
" <td> 2</td>\n",
" <td> testing</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1833</th>\n",
" <td> 917</td>\n",
" <td> 1</td>\n",
" <td> Obs</td>\n",
" <td> 0</td>\n",
" <td> 71</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 5</td>\n",
" <td> 1</td>\n",
" <td> 3</td>\n",
" <td> 3</td>\n",
" <td> 0</td>\n",
" <td> 1</td>\n",
" <td> 122</td>\n",
" <td> 1</td>\n",
" <td> testing</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1834</th>\n",
" <td> 918</td>\n",
" <td> 1</td>\n",
" <td> Lev</td>\n",
" <td> 1</td>\n",
" <td> 50</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 7</td>\n",
" <td> 1</td>\n",
" <td> 2</td>\n",
" <td> 2</td>\n",
" <td> 0</td>\n",
" <td> 1</td>\n",
" <td> 323</td>\n",
" <td> 2</td>\n",
" <td> testing</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1835</th>\n",
" <td> 918</td>\n",
" <td> 1</td>\n",
" <td> Lev</td>\n",
" <td> 1</td>\n",
" <td> 50</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 7</td>\n",
" <td> 1</td>\n",
" <td> 2</td>\n",
" <td> 2</td>\n",
" <td> 0</td>\n",
" <td> 1</td>\n",
" <td> 78</td>\n",
" <td> 1</td>\n",
" <td> testing</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1836</th>\n",
" <td> 919</td>\n",
" <td> 1</td>\n",
" <td> Lev</td>\n",
" <td> 0</td>\n",
" <td> 70</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 1</td>\n",
" <td> 1</td>\n",
" <td> 1</td>\n",
" <td> 2</td>\n",
" <td> 2</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 678</td>\n",
" <td> 2</td>\n",
" <td> training</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1837</th>\n",
" <td> 919</td>\n",
" <td> 1</td>\n",
" <td> Lev</td>\n",
" <td> 0</td>\n",
" <td> 70</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 1</td>\n",
" <td> 1</td>\n",
" <td> 1</td>\n",
" <td> 2</td>\n",
" <td> 2</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 532</td>\n",
" <td> 1</td>\n",
" <td> training</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1838</th>\n",
" <td> 920</td>\n",
" <td> 1</td>\n",
" <td> Lev+5FU</td>\n",
" <td> 0</td>\n",
" <td> 70</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 1</td>\n",
" <td> 5</td>\n",
" <td> 0</td>\n",
" <td> 2</td>\n",
" <td> 2</td>\n",
" <td> 0</td>\n",
" <td> 1</td>\n",
" <td> 1827</td>\n",
" <td> 2</td>\n",
" <td> testing</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1839</th>\n",
" <td> 920</td>\n",
" <td> 1</td>\n",
" <td> Lev+5FU</td>\n",
" <td> 0</td>\n",
" <td> 70</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 1</td>\n",
" <td> 5</td>\n",
" <td> 0</td>\n",
" <td> 2</td>\n",
" <td> 2</td>\n",
" <td> 0</td>\n",
" <td> 1</td>\n",
" <td> 1827</td>\n",
" <td> 1</td>\n",
" <td> testing</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1840</th>\n",
" <td> 921</td>\n",
" <td> 1</td>\n",
" <td> Lev</td>\n",
" <td> 1</td>\n",
" <td> 58</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 3</td>\n",
" <td> 1</td>\n",
" <td> 2</td>\n",
" <td> 4</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 1399</td>\n",
" <td> 2</td>\n",
" <td> testing</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1841</th>\n",
" <td> 921</td>\n",
" <td> 1</td>\n",
" <td> Lev</td>\n",
" <td> 1</td>\n",
" <td> 58</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 3</td>\n",
" <td> 1</td>\n",
" <td> 2</td>\n",
" <td> 4</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 476</td>\n",
" <td> 1</td>\n",
" <td> testing</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1842</th>\n",
" <td> 922</td>\n",
" <td> 1</td>\n",
" <td> Lev+5FU</td>\n",
" <td> 0</td>\n",
" <td> 70</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 1</td>\n",
" <td> 1</td>\n",
" <td> 2</td>\n",
" <td> 3</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 1022</td>\n",
" <td> 2</td>\n",
" <td> training</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1843</th>\n",
" <td> 922</td>\n",
" <td> 1</td>\n",
" <td> Lev+5FU</td>\n",
" <td> 0</td>\n",
" <td> 70</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 1</td>\n",
" <td> 1</td>\n",
" <td> 2</td>\n",
" <td> 3</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 329</td>\n",
" <td> 1</td>\n",
" <td> training</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1844</th>\n",
" <td> 923</td>\n",
" <td> 1</td>\n",
" <td> Lev+5FU</td>\n",
" <td> 1</td>\n",
" <td> 55</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 2</td>\n",
" <td> 0</td>\n",
" <td> 3</td>\n",
" <td> 3</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 2240</td>\n",
" <td> 2</td>\n",
" <td> training</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1845</th>\n",
" <td> 923</td>\n",
" <td> 1</td>\n",
" <td> Lev+5FU</td>\n",
" <td> 1</td>\n",
" <td> 55</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 2</td>\n",
" <td> 0</td>\n",
" <td> 3</td>\n",
" <td> 3</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 2240</td>\n",
" <td> 1</td>\n",
" <td> training</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1846</th>\n",
" <td> 924</td>\n",
" <td> 1</td>\n",
" <td> Obs</td>\n",
" <td> 1</td>\n",
" <td> 64</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 1</td>\n",
" <td> 2</td>\n",
" <td> 0</td>\n",
" <td> 3</td>\n",
" <td> 3</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 2158</td>\n",
" <td> 2</td>\n",
" <td> testing</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1847</th>\n",
" <td> 924</td>\n",
" <td> 1</td>\n",
" <td> Obs</td>\n",
" <td> 1</td>\n",
" <td> 64</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 1</td>\n",
" <td> 2</td>\n",
" <td> 0</td>\n",
" <td> 3</td>\n",
" <td> 3</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 2158</td>\n",
" <td> 1</td>\n",
" <td> testing</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1848</th>\n",
" <td> 925</td>\n",
" <td> 1</td>\n",
" <td> Lev+5FU</td>\n",
" <td> 1</td>\n",
" <td> 71</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 1</td>\n",
" <td> 4</td>\n",
" <td> 0</td>\n",
" <td> 2</td>\n",
" <td> 3</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 1875</td>\n",
" <td> 2</td>\n",
" <td> testing</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1849</th>\n",
" <td> 925</td>\n",
" <td> 1</td>\n",
" <td> Lev+5FU</td>\n",
" <td> 1</td>\n",
" <td> 71</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 1</td>\n",
" <td> 4</td>\n",
" <td> 0</td>\n",
" <td> 2</td>\n",
" <td> 3</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 1875</td>\n",
" <td> 1</td>\n",
" <td> testing</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1850</th>\n",
" <td> 926</td>\n",
" <td> 1</td>\n",
" <td> Lev</td>\n",
" <td> 0</td>\n",
" <td> 72</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 2</td>\n",
" <td> 3</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 2154</td>\n",
" <td> 2</td>\n",
" <td> training</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1851</th>\n",
" <td> 926</td>\n",
" <td> 1</td>\n",
" <td> Lev</td>\n",
" <td> 0</td>\n",
" <td> 72</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 2</td>\n",
" <td> 3</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 2154</td>\n",
" <td> 1</td>\n",
" <td> training</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1852</th>\n",
" <td> 927</td>\n",
" <td> 1</td>\n",
" <td> Lev</td>\n",
" <td> 1</td>\n",
" <td> 76</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 1</td>\n",
" <td> 1</td>\n",
" <td> 1</td>\n",
" <td> 3</td>\n",
" <td> 3</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 1018</td>\n",
" <td> 2</td>\n",
" <td> training</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1853</th>\n",
" <td> 927</td>\n",
" <td> 1</td>\n",
" <td> Lev</td>\n",
" <td> 1</td>\n",
" <td> 76</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 1</td>\n",
" <td> 1</td>\n",
" <td> 1</td>\n",
" <td> 3</td>\n",
" <td> 3</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 851</td>\n",
" <td> 1</td>\n",
" <td> training</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1854</th>\n",
" <td> 928</td>\n",
" <td> 1</td>\n",
" <td> Lev+5FU</td>\n",
" <td> 0</td>\n",
" <td> 48</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 4</td>\n",
" <td> 0</td>\n",
" <td> 2</td>\n",
" <td> 3</td>\n",
" <td> 1</td>\n",
" <td> 1</td>\n",
" <td> 2072</td>\n",
" <td> 2</td>\n",
" <td> testing</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1855</th>\n",
" <td> 928</td>\n",
" <td> 1</td>\n",
" <td> Lev+5FU</td>\n",
" <td> 0</td>\n",
" <td> 48</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 4</td>\n",
" <td> 0</td>\n",
" <td> 2</td>\n",
" <td> 3</td>\n",
" <td> 1</td>\n",
" <td> 1</td>\n",
" <td> 2072</td>\n",
" <td> 1</td>\n",
" <td> testing</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1856</th>\n",
" <td> 929</td>\n",
" <td> 1</td>\n",
" <td> Lev</td>\n",
" <td> 0</td>\n",
" <td> 66</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 2</td>\n",
" <td> 3</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 1820</td>\n",
" <td> 2</td>\n",
" <td> training</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1857</th>\n",
" <td> 929</td>\n",
" <td> 1</td>\n",
" <td> Lev</td>\n",
" <td> 0</td>\n",
" <td> 66</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 2</td>\n",
" <td> 3</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 1820</td>\n",
" <td> 1</td>\n",
" <td> training</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>1858 rows × 17 columns</p>\n",
"</div>"
],
"text/plain": [
" id study rx sex age obstruct perfor adhere nodes status \\\n",
"0 1 1 Lev+5FU 1 43 0 0 0 5 1 \n",
"1 1 1 Lev+5FU 1 43 0 0 0 5 1 \n",
"2 2 1 Lev+5FU 1 63 0 0 0 1 0 \n",
"3 2 1 Lev+5FU 1 63 0 0 0 1 0 \n",
"4 3 1 Obs 0 71 0 0 1 7 1 \n",
"5 3 1 Obs 0 71 0 0 1 7 1 \n",
"6 4 1 Lev+5FU 0 66 1 0 0 6 1 \n",
"7 4 1 Lev+5FU 0 66 1 0 0 6 1 \n",
"8 5 1 Obs 1 69 0 0 0 22 1 \n",
"9 5 1 Obs 1 69 0 0 0 22 1 \n",
"10 6 1 Lev+5FU 0 57 0 0 0 9 1 \n",
"11 6 1 Lev+5FU 0 57 0 0 0 9 1 \n",
"12 7 1 Lev 1 77 0 0 0 5 1 \n",
"13 7 1 Lev 1 77 0 0 0 5 1 \n",
"14 8 1 Obs 1 54 0 0 0 1 0 \n",
"15 8 1 Obs 1 54 0 0 0 1 0 \n",
"16 9 1 Lev 1 46 0 0 1 2 0 \n",
"17 9 1 Lev 1 46 0 0 1 2 0 \n",
"18 10 1 Lev+5FU 0 68 0 0 0 1 0 \n",
"19 10 1 Lev+5FU 0 68 0 0 0 1 0 \n",
"20 11 1 Lev 0 47 0 0 1 1 0 \n",
"21 11 1 Lev 0 47 0 0 1 1 0 \n",
"22 12 1 Lev+5FU 1 52 0 0 0 2 0 \n",
"23 12 1 Lev+5FU 1 52 0 0 0 2 0 \n",
"24 13 1 Obs 1 64 0 0 0 1 1 \n",
"25 13 1 Obs 1 64 0 0 0 1 1 \n",
"26 14 1 Lev 1 68 1 0 0 3 1 \n",
"27 14 1 Lev 1 68 1 0 0 3 1 \n",
"28 15 1 Obs 1 46 1 0 0 4 0 \n",
"29 15 1 Obs 1 46 1 0 0 4 0 \n",
"... ... ... ... ... ... ... ... ... ... ... \n",
"1828 915 1 Lev 0 42 0 0 0 6 0 \n",
"1829 915 1 Lev 0 42 0 0 0 6 0 \n",
"1830 916 1 Obs 0 37 0 0 0 8 1 \n",
"1831 916 1 Obs 0 37 0 0 0 8 1 \n",
"1832 917 1 Obs 0 71 0 0 0 5 1 \n",
"1833 917 1 Obs 0 71 0 0 0 5 1 \n",
"1834 918 1 Lev 1 50 0 0 0 7 1 \n",
"1835 918 1 Lev 1 50 0 0 0 7 1 \n",
"1836 919 1 Lev 0 70 1 0 1 1 1 \n",
"1837 919 1 Lev 0 70 1 0 1 1 1 \n",
"1838 920 1 Lev+5FU 0 70 0 0 1 5 0 \n",
"1839 920 1 Lev+5FU 0 70 0 0 1 5 0 \n",
"1840 921 1 Lev 1 58 1 0 0 3 1 \n",
"1841 921 1 Lev 1 58 1 0 0 3 1 \n",
"1842 922 1 Lev+5FU 0 70 0 0 0 1 1 \n",
"1843 922 1 Lev+5FU 0 70 0 0 0 1 1 \n",
"1844 923 1 Lev+5FU 1 55 1 0 0 2 0 \n",
"1845 923 1 Lev+5FU 1 55 1 0 0 2 0 \n",
"1846 924 1 Obs 1 64 1 0 1 2 0 \n",
"1847 924 1 Obs 1 64 1 0 1 2 0 \n",
"1848 925 1 Lev+5FU 1 71 0 0 1 4 0 \n",
"1849 925 1 Lev+5FU 1 71 0 0 1 4 0 \n",
"1850 926 1 Lev 0 72 0 0 0 1 0 \n",
"1851 926 1 Lev 0 72 0 0 0 1 0 \n",
"1852 927 1 Lev 1 76 0 0 1 1 1 \n",
"1853 927 1 Lev 1 76 0 0 1 1 1 \n",
"1854 928 1 Lev+5FU 0 48 1 0 0 4 0 \n",
"1855 928 1 Lev+5FU 0 48 1 0 0 4 0 \n",
"1856 929 1 Lev 0 66 1 0 0 1 0 \n",
"1857 929 1 Lev 0 66 1 0 0 1 0 \n",
"\n",
" differ extent surg node4 time etype set \n",
"0 2 3 0 1 1521 2 training \n",
"1 2 3 0 1 968 1 training \n",
"2 2 3 0 0 3087 2 training \n",
"3 2 3 0 0 3087 1 training \n",
"4 2 2 0 1 963 2 training \n",
"5 2 2 0 1 542 1 training \n",
"6 2 3 1 1 293 2 training \n",
"7 2 3 1 1 245 1 training \n",
"8 2 3 1 1 659 2 testing \n",
"9 2 3 1 1 523 1 testing \n",
"10 2 3 0 1 1767 2 training \n",
"11 2 3 0 1 904 1 training \n",
"12 2 3 1 1 420 2 testing \n",
"13 2 3 1 1 229 1 testing \n",
"14 2 3 0 0 3192 2 testing \n",
"15 2 3 0 0 3192 1 testing \n",
"16 2 3 0 0 3173 2 training \n",
"17 2 3 0 0 3173 1 training \n",
"18 2 3 1 0 3308 2 training \n",
"19 2 3 1 0 3308 1 training \n",
"20 2 3 0 0 2908 2 testing \n",
"21 2 3 0 0 2908 1 testing \n",
"22 3 3 1 0 3309 2 training \n",
"23 3 3 1 0 3309 1 training \n",
"24 2 3 0 0 2085 2 training \n",
"25 2 3 0 0 1130 1 training \n",
"26 2 3 0 0 2910 2 training \n",
"27 2 3 0 0 2231 1 training \n",
"28 2 3 0 0 2754 2 testing \n",
"29 2 3 0 0 2754 1 testing \n",
"... ... ... ... ... ... ... ... \n",
"1828 1 3 0 1 2114 2 training \n",
"1829 1 3 0 1 2114 1 training \n",
"1830 2 3 1 1 1262 2 training \n",
"1831 2 3 1 1 438 1 training \n",
"1832 3 3 0 1 259 2 testing \n",
"1833 3 3 0 1 122 1 testing \n",
"1834 2 2 0 1 323 2 testing \n",
"1835 2 2 0 1 78 1 testing \n",
"1836 2 2 0 0 678 2 training \n",
"1837 2 2 0 0 532 1 training \n",
"1838 2 2 0 1 1827 2 testing \n",
"1839 2 2 0 1 1827 1 testing \n",
"1840 2 4 0 0 1399 2 testing \n",
"1841 2 4 0 0 476 1 testing \n",
"1842 2 3 0 0 1022 2 training \n",
"1843 2 3 0 0 329 1 training \n",
"1844 3 3 0 0 2240 2 training \n",
"1845 3 3 0 0 2240 1 training \n",
"1846 3 3 0 0 2158 2 testing \n",
"1847 3 3 0 0 2158 1 testing \n",
"1848 2 3 0 0 1875 2 testing \n",
"1849 2 3 0 0 1875 1 testing \n",
"1850 2 3 0 0 2154 2 training \n",
"1851 2 3 0 0 2154 1 training \n",
"1852 3 3 0 0 1018 2 training \n",
"1853 3 3 0 0 851 1 training \n",
"1854 2 3 1 1 2072 2 testing \n",
"1855 2 3 1 1 2072 1 testing \n",
"1856 2 3 0 0 1820 2 training \n",
"1857 2 3 0 0 1820 1 training \n",
"\n",
"[1858 rows x 17 columns]"
]
},
"execution_count": 64,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"trainingids = []\n",
"testingids = []\n",
"for d in [events, censored]:\n",
" ids = np.random.permutation(d)\n",
"\n",
" n = len(ids)\n",
" k = 4\n",
" assignments = np.array((n // k + 1) * list(range(0, k)))\n",
" assignments = assignments[:n]\n",
"\n",
" testingids.extend(ids[assignments == 0])\n",
" trainingids.extend(ids[assignments != 0])\n",
" \n",
"df['set'] = 1\n",
"\n",
"for i in trainingids:\n",
" which = (df['id'] == i)\n",
" df.loc[which, 'set'] = 'training'\n",
" \n",
"for i in testingids:\n",
" which = (df['id'] == i)\n",
" df.loc[which, 'set'] = 'testing'\n",
" \n",
"print(\"Training size:\", np.sum(df['set'] == 'training'))\n",
"print(\"Testing size:\", np.sum(df['set'] == 'testing'))\n",
"df"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Print data to file."
]
},
{
"cell_type": "code",
"execution_count": 65,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"data/colon.csv\n"
]
}
],
"source": [
"fname = data['filename'][:-8] + '.csv'\n",
"print(fname)\n",
"df.to_csv(fname, na_rep='NA', index=False)"
]
}
],
"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
}
| gpl-3.0 |
sanger-pathogens/pathogen-informatics-training | Notebooks/PathFind/answers.ipynb | 1 | 33188 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Answers"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"***"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Here are the answers to the questions from each of the tutorial sections.\n",
"\n",
" * [Introduction](#Introduction)\n",
" * [Finding your data](#Finding-your-data)\n",
" * [Sample information and accessions](#Sample-information-and-accessions)\n",
" * [Analysis pipeline status](Analysis-pipeline-status)\n",
" * [QC pipeline results](QC-pipeline-results) \n",
" * [Mapping pipeline results](Mapping-pipeline-results) \n",
" * [SNP calling pipeline results](snp-pipeline-results) \n",
" * [Assembly pipeline results](assembly-pipeline-results) \n",
" * [Annotation pipeline results](annotation-pipeline-results)\n",
" * [RNA-Seq expression pipeline results](RNA-Seq-expression-pipeline-results)\n",
"\n",
"**First, let's tell the system the location of our tutorial configuration file.**"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"export PF_CONFIG_FILE=$PWD/data/pathfind.conf"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"***"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Introduction\n",
"\n",
"**Q1: How many lanes are associated with study 607?**\n",
"\n",
"**50**\n",
"\n",
"For this search, you need to set the type (`-t`) to study and the id (`-i`) to 607. You can then pipe the locations returned by `pf data` into `wc -l` to count the number of locations (lines) returned."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"pf data -t study -i 607 | wc -l"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Q2: How many lanes are returned if you search using the file \"data/lanes_to_search.txt\"?** \n",
"\n",
"**10**\n",
"\n",
"For this search, you need to set the type (`-t`) to file and the id (`-i`) to the location of the file, \"data/lanes_to_search.txt\". You can then pipe the locations returned by `pf data` into `wc -l` to count the number of locations (lines) returned."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"pf data -t file -i data/lanes_to_search.txt | wc -l"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You can check that all the lanes in the file have been found by counting the number of lanes in the file."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"wc -l data/lanes_to_search.txt"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"***"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"## Finding your data\n",
"\n",
"**Q1: What is the location of the top level directory for data and results associated with lane 10018_1#1?**\n",
"\n",
"The location of the top directory can be found with:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"pf data -t lane -i 10018_1#1"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Q2: What is the location of the FASTQ file(s) associated with lane 10018_1#1?**\n",
"\n",
"The location of the FASTQ file can be found by using the `-f` or `--filetype` option to get the location of the FASTQ files:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"pf data -t lane -i 10018_1#1 -f fastq"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Q3: Symlink the FASTQ files from study 607 into a directory called \"study_607_links\". How many FASTQ files were symlinked to \"study_607_links?**\n",
"\n",
"**50**\n",
"\n",
"First, we need to get the FASTQ files for study 607 using the `-f` or `--filetype` option in case there are any non-FASTQ files. We then add the `-l` or `--symlink` option with directory we want to symlink to \"study_607_links\"."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"pf data -t study -i 607 -f fastq -l study_607_links"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We then look at the contents of \"study_607_links\" with `ls` and count the number of files (lines) returned with `wc -l`."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"ls study_607_links | wc -l"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Q4: What reference was used to map lane 10018_1#1 during QC and what percentage of the reads were mapped to the reference?**\n",
"\n",
"**Streptococcus_suis_P1_7_v1** and **0.00%**\n",
"\n",
"First, we need to get the statistics for lane 10018_1#1 using the `-s` or `--stats` option."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"pf data -t lane -i 10018_1#1 -s"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Then, we need to find the \"Reference\" and \"Mapped %\" column in the statistics file (10018_1_1.pathfind_stats.csv)."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"cat 10018_1_1.pathfind_stats.csv"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"***"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Sample information and accessions"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Q1: What is the sample name that corresponds with lane 10018_1#1?**\n",
"\n",
"**APP_N2_OP1**\n",
"\n",
"We can use the default output from running `pf info` with the identifier type (`-t` or `--type`) set as \"lane\" and the identifier (`-i` or `--id`) as 10018_1#1 to get the sample name."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"pf info -t lane -i 10018_1#1"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We could also have used `pf accession`."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"pf accession -t lane -i 10018_1#1"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Q2: What lane name(s) correspond with sample APP_T1_OP2?**\n",
"\n",
"**10018_1#3** and **10018_1#34**\n",
"\n",
"We can use the default output from running `pf info` with the identifier type (`-t` or `--type`) set as \"sample\" and the identifier (`-i` or `--id`) as APP_T1_OP2 to get the sample name."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"pf info -t sample -i APP_T1_OP2"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Again, we could also have used `pf accession`."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"pf accession -t sample -i APP_T1_OP2"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Q3: What are the sample and lane names of the last lane in the file \"data/lanes_to_search.txt\"?**\n",
"\n",
"**10018_1#51** and **APP_T5_OP2**\n",
"\n",
"We can use the default output from running `pf info` with the identifier type (`-t` or `--type`) set as \"file\" and the identifier (`-i` or `--id`) as \"data/lanes_to_search.txt\" to get the lane and sample names. To get the last line output (analogous to the last line in the file) we can use `tail -1`."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"pf info -t file -i data/lanes_to_search.txt | tail -1"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Again, we could also have used `pf accession`."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"pf accession -t file -i data/lanes_to_search.txt | tail -1"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Q4: What are the sample and lane accessions for lane 5477_6#1?**\n",
"\n",
"**ERS015862** and **ERR028809**\n",
"\n",
"We can use the default output from running `pf accession` with the identifier type (-t or --type) set as \"lane\" and the identifier (-i or --id) as 5477_6#1 to get the lane and sample accessions."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"pf accession -t lane -i 5477_6#1"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Q5: What are the two URLs which can be used to download the FASTQ files for lane 5477_6#1from the ENA?**\n",
"\n",
"**ftp://ftp.sra.ebi.ac.uk/vol1/fastq/ERR028/ERR028809/ERR028809_1.fastq.gz**\n",
"\n",
"and \n",
"\n",
"**ftp://ftp.sra.ebi.ac.uk/vol1/fastq/ERR028/ERR028809/ERR028809_2.fastq.gz**\n",
"\n",
"We can get the ENA download URLs by running pf accession with the identifier type (-t or --type) set as \"lane\" and the identifier (-i or --id) as 5477_6#1 with the option `-f` or `--fastq`."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"pf accession -t lane -i 5477_6#1 -f"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This will generate \"fastq_urls.txt\" which contains the two URLS you're looking for."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"cat fastq_urls.txt"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"_Note: if the file \"fastq_urls.txt\" already exists you will need to remove it before you can use `pf accession` to create it again._"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"***"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Analysis pipeline status"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Q1: Has the assembly pipeline been run on lane 10018_1#1? If so, what is the status?**\n",
"\n",
"**No**. \n",
"\n",
"The status for the assembly pipeline for lane 10018_1#1 is '-' which means that the assembly pipeline has not been run for this data."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"pf status -t lane -i 10018_1#1"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Q2: Which lanes in study 607 has the assembly pipeline been run on?**\n",
"\n",
"**10018_1#2**, **10018_1#2**, **10018_1#2**, **10018_1#2** and **10018_1#51** \n",
"\n",
"We can pipe the output of `pf status` for study 607 into `awk`. The assembly pipeline status is found in column 9 and we want to filter for values which are \"Done\". This should return five lanes."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"pf status -t study -i 607 | awk '$9 == \"Done\"'"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Q3: How many lanes in study 607 has the mapping pipeline been run on?**\n",
"\n",
"**41**\n",
"\n",
"The command structure here is similar to before except we want to filter values for the mapping pipeline in column 4. We can then count the number of lines returned with `wc -l`."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"pf status -t study -i 607 | awk '$4 == \"Done\"' | wc -l"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"***"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## QC pipeline results\n",
"\n",
"**Q1: What percentage of the reads from lane 10018_1#1 were \"unclassified\" by Kraken?**\n",
"\n",
"**69.55**\n",
"\n",
"We can use the default output from running `pf qc` with the identifier type (`-t` or `--type`) set as \"lane\" and the identifier (`-i` or `--id`) as 10018_1#1 to get the location of the Kraken report. We then use `xargs` to pass this location to `head` so that we can see the first few lines of the report."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"pf qc -t lane -i 10018_1#1 | xargs head"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Q2: What percentage of the reads from the lane 10018_1#1 were classified to the genus _Actinobacillus_ by Kraken?**\n",
"\n",
"**28.77%**\n",
"\n",
"We can write a summary of the Kraken report using the `--summary` or `-s` option. Here we called this file \"qc_genus_summary.csv\". To set the taxonomic level for the summary we use the `--level` or `-L` option. Genus is represented by a \"G\"."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"pf qc -t lane -i 10018_1#1 -L G -s qc_genus_summary.csv"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We then look to the summary file to see what precentage of reads were classified to the genus _Actinobacillus_."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"head qc_genus_summary.csv"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"***"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Mapping pipeline results"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Q1: How many BAM files are returned by default for lane 5477_6#10?**\n",
"\n",
"**4** \n",
"\n",
"You can use `grep -c` to count the number of returned locations ending in .bam (\".bam$\"). Notice we use a dollar sign to signify the end as we don't want to count the index files (.bam.bai)."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"pf map -t lane -i 5477_6#10 | grep -c \".bam$\""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Q2: Which mappers have been used with the mapping pipeline for lane 5477_6#10?**\n",
"\n",
"**bowtie2**, **smalt** and **tophat**\n",
"\n",
"We can use the `--details` or `-d` option to get information about which mapper and reference were used to generate each of the BAM files. Then we can use `awk` to get the 3rd column which contains the mapper. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"pf map -t lane -i 5477_6#10 -d | awk '{print $3}' "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If you want you can also `sort` to find the unique mappers with `uniq`."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"pf map -t lane -i 5477_6#10 -d | awk '{print $3}' | sort | uniq"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Q3: Which references have been used with the mapping pipeline for lane 5477_6#10?**\n",
"\n",
"The references used were:\n",
"\n",
"**Streptococcus_pneumoniae_ATCC_700669_v1** \n",
"**Streptococcus_pneumoniae_OXC141_v1** \n",
"**Streptococcus_pneumoniae_Taiwan19F-14_v1** \n",
"\n",
"You can us the same command as before except this time we are looking for the references in column 2 with `awk`."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"pf map -t lane -i 5477_6#10 -d | awk '{print $2}' | sort | uniq"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Q4: What percentage of the reads from lane 5477_6#10 were mapped to \"Streptococcus_pneumoniae_OXC141_v1\"?**\n",
"\n",
"**97.5%**\n",
"\n",
"First, we need to filter our returned mapping pipeline results by reference using the `--reference` or `-R` option. Then we write the comma-delimited statistics for the returned BAM files to file using the `--stats` or `-s` option."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"pf map -t lane -i 5477_6#10 -R \"Streptococcus_pneumoniae_OXC141_v1\" -s"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"cat 5477_6_10.mapping_stats.csv"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This generates \"5477_6_10.mapping_stats.csv\" which we can filter by mapper (column 10) using `awk` and return only the mapping percentage (column 12)."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"awk -F',' '$8==\"Streptococcus_pneumoniae_OXC141_v1\" {print $12}' 5477_6_10.mapping_stats.csv"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"***"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## SNP pipeline results"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Q1: How many lanes from run 10018_1 has the SNP calling pipeline been completed on?** \n",
"\n",
"**3**\n",
"\n",
"You can use `pf status` to tell you which of the lanes in run 10018_1 the SNP calling pipeline has been completed on."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"pf status -t lane -i 10018_1"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To count these you can get all of the rows where the SNP calling is \"Done\" (column 7) with `awk` and then count the number of lines returned with `wc -l`."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"pf status -t lane -i 10018_1 | awk '$7==\"Done\"' | wc -l"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Q2: How many gzipped VCF files are returned by default for lane 10018_1#20?**\n",
"\n",
"**1**"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"pf snp -t lane -i 10018_1#20"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Q3: Which mapper and reference was used by the SNP calling pipeline for lane 10018_1#20?**\n",
"\n",
"**smalt** and ***Streptococcus_suis_P1_7_v1***\n",
"\n",
"You can get the mapper and reference information using the `--details` or `-d` option."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"pf snp -t lane -i 10018_1#20 -d"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Q4: Generate the pseudogenome for lane 10018_1#20 excluding the reference.**\n",
"\n",
"To generate the pseudogenome you can use the `--pseudogenome` or `-p` option and `--exclude-reference` or `-x` option to exclude the reference."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"pf snp -t lane -i 10018_1#20 -p -x"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Q5: Symlink the gzipped VCF files generated by the SNP calling pipeline for run 10018_1 to a new directory called \"10010_1_vcfs\".**\n",
"\n",
"You can symlink the VCF files using the `--symlink` or `-l` option, followed by the name of the directory you want to create."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"pf snp -t lane -i 10018_1#20 -l 10010_1_vcfs"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"***"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Assembly pipeline results\n",
"\n",
"**Q1: How many assembly files are returned by default for lane 10018_1#50?**\n",
"\n",
"**2**\n",
"\n",
"Assemblies have been generated using IVA and SPAdes (look at the result paths)."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"pf assembly -t lane -i 10018_1#50"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Q2: Which program was used to generate the assembly for lane 10018_1#51?** \n",
"\n",
"**velvet**\n",
"\n",
"Look at the end of the path - \"10018_1#51/**velvet**_assembly/contigs.fa\"."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"pf assembly -t lane -i 10018_1#51"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Q3: Symlink the assembly/assemblies generated by \"IVA\" for run 10018_1 into a new directory called \"iva_results\".** "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"pf assembly -t lane -i 10018_1 -P iva -l iva_results"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Q4: How many contigs were assembled by velvet for lane 5477_6#1 and what is the N50?** \n",
"\n",
"**66** contigs with an N50 of **61,250**\n",
"\n",
"First, you need to generate the statistics file using the `--stats` or `-s` option. We need to filter our results so that we only get the statistics for the velvet assembly. We can do this with the `--program` or `-P` option."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"pf assembly -t lane -i 5477_6#1 -s -P velvet"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Then, you need to look at the contents."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"cat 5477_6_1.assemblyfind_stats.csv"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"***"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"## Annotation pipeline results\n",
"\n",
"**Q1: How many GFF files are returned by default for lane 10018_1#1?**\n",
"\n",
"**2**\n",
"\n",
"There are two GFF files returned, one for an IVA assembly and one for a SPAdes assembly."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"pf annotation -t lane -i 10018_1#1"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Q2: What is the location of the annotation for the SPAdes assembly of lane 10018_1#1?**\n",
"\n",
"To get the location of the SPAdes annotation, you need to use the `--program` or `-P` option to filter the results by assembler:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"pf annotation -t lane -i 10018_1#1 -P spades"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Q3: What is the location of the translated CDS sequence file for the SPAdes assembly of lane 10018_1#1?**\n",
"\n",
"To get the translated CDS sequence file you need to use the `--filetype` or `-f` option:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"pf annotation -t lane -i 10018_1#1 -P spades -f faa"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Q4: How many of the assemblies for run 5477_6 contain the gene \"_dnaG_\"?** \n",
"\n",
"**3**\n",
"\n",
"You need to use the `--gene` or `-g` option to search for a gene name."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"pf annotation -t lane -i 5477_6 -g dnaG "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"***"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"## RNA-Seq expression pipeline results"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"**Q1: How many count files are returned by default for run 8479_4?**\n",
"\n",
"**5**"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"pf rnaseq -t lane -i 8479_4"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"**Q2: Which mappers have been used with the mapping pipeline for lane 8479_4#18?**\n",
"\n",
"**bwa**\n",
"\n",
"You can get the mapper details using the `--details` or `-d` option."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"pf rnaseq -t lane -i 8479_4#18 -d"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"**Q3: Which reference was used with the mapping pipeline for lane 8479_4#18?**\n",
"\n",
"**Mus_musculus_mm10**\n",
"\n",
"You can get the reference details using the `--details` or `-d` option."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"pf rnaseq -t lane -i 8479_4#18 -d"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"**Q4: What is the location or path of the featurecounts file for lane 8479_4#18?**\n",
"\n",
"You can get the location of the featurecounts file by using the `--filetype` or `-f` option:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"pf rnaseq -t lane -i 8479_4#18 -f featurecounts"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"**Q5: Which of the lanes in run 8479_4 has the lowest percentage of mapped reads?**\n",
"\n",
"**8479_4#17**\n",
"\n",
"You can get the mapping statistics for the run using the `--statistics` or `-s` option."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"pf rnaseq -t lane -i 8479_4 -s"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This generates a new file called \"8479_4.rnaseqfind_stats.csv\". You can get the lane name and mapping percentage using `awk` to print the third and twelfth columns."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"awk -F',' '{print $3\"\\t\"$12}' 8479_4.rnaseqfind_stats.csv"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Q6: What is the sample name and symlinked file name associated with lane 8479_4#18?**\n",
"\n",
"**WT1xCtrl_2** and **8479_4#18.390152.pe.markdup.bam.expression.csv**\n",
"\n",
"You can use the `--summary` or `-S` option to get the relationship between lane, sample and symlinked file names."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"pf rnaseq -t lane -i 8479_4#18 -S"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"cat 8479_4_18.rnaseqfind_summary.tsv"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"***"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"## Finding a reference \n",
"\n",
"**Q1: How many Streptococcus pneumoniae references are available?**\n",
"\n",
"**6**\n",
"\n",
"Don't forget that genus, species and strain should be separated by an underscore (not a space!) in your query."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"pf ref -i Streptococcus_pneumoniae -A | wc -l"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If you got more, that's because your search wasn't specific enough e.g.:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"pf ref -i Streptococcus -AR"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Q2: How many of the _Streptococcus pneumoniae_ references were imported from a public repository?** \n",
"\n",
"**5**\n",
"\n",
"One of the references, \"Streptococcus_pneumoniae_str_110.58_v0.4\", has a version < 1 which means it is an internal assembly and so hasn't been imported from a public repository."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Q3: What is the location of the annotation (GFF) file for _Streptococcus pneumoniae P1031_.**\n",
"\n",
"You need to use the `--filetype` or `-f` option to get the location."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"pf ref -i Streptococcus_pneumoniae_P1031 -f gff"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Q4: Symlink the annotation (GFF) file for _Streptococcus pneumoniae P1031_ to your current directory.**"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"pf ref -i Streptococcus_pneumoniae_P1031 -f gff -l"
]
}
],
"metadata": {
"anaconda-cloud": {},
"kernelspec": {
"display_name": "Bash",
"language": "bash",
"name": "bash"
},
"language_info": {
"codemirror_mode": "shell",
"file_extension": ".sh",
"mimetype": "text/x-sh",
"name": "bash"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| gpl-3.0 |
dpaniukov/RulesFPC | thresholded_results/RulesFPC/model_1_FPC/model_1_FPC.ipynb | 2 | 21949 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Model 1 with FPC mask\n",
"\n",
"## Level 1:\n",
"### EVs:\n",
"stimulus appication \n",
"stimulus learning \n",
"stimulus na \n",
"feedback correct \n",
"feedback incorrect \n",
"feedback na \n",
"\n",
"### Contrasts:\n",
"stimulus appication>0 \n",
"stimulus learning>0 \n",
"stimulus appication>stimulus learning \n",
"\n",
"## Level 2:\n",
"task001 task1 \n",
"task001 task2 \n",
"task001 task1>task2 \n",
"task001 task2>task1 \n",
"\n",
"## Level 3:\n",
"positive contrast \n",
"negative contrast \n",
"\n",
"## FPC mask\n",
"*Images from randomise (cluster mass with t=2.49 and v=8) are thresholded at .95 and overlaid with unthresholded t-maps."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Prepare stuff"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"from IPython.display import IFrame\n",
"from IPython.display import Image\n",
"\n",
"# This function renders interactive brain images\n",
"def render(name,brain_list):\n",
" \n",
" #prepare file paths\n",
" brain_files = []\n",
" for b in brain_list:\n",
" brain_files.append(os.path.join(\"data\",b))\n",
" \n",
" wdata = \"\"\"\n",
" <!DOCTYPE html>\n",
"\n",
"<html xmlns=\"http://www.w3.org/1999/xhtml\" lang=\"en\">\n",
"\t<head>\n",
" \t<meta http-equiv=\"Content-Type\" content=\"text/html; charset=utf-8\"/>\n",
" \n",
" \t<!-- iOS meta tags -->\n",
" \t<meta name=\"viewport\" content=\"width=device-width, initial-scale=1.0, user-scalable=no\"/>\n",
" \t<meta name=\"apple-mobile-web-app-capable\" content=\"yes\">\n",
" \t<meta name=\"apple-mobile-web-app-status-bar-style\" content=\"black-translucent\">\n",
" \n",
" \t<link rel=\"stylesheet\" type=\"text/css\" href=\"../papaya/papaya.css?build=1420\" />\n",
" \t<script type=\"text/javascript\" src=\"../papaya/papaya.js?build=1422\"></script>\n",
" \n",
" \t<title>Papaya Viewer</title>\n",
" \n",
"\t<script type=\"text/javascript\">\n",
" \n",
" var params = [];\n",
" params[\"worldSpace\"] = true;\n",
" params[\"atlas\"] = \"MNI (Nearest Grey Matter)\";\n",
" params[\"images\"] = %s;\n",
" \n",
" </script>\n",
"\n",
"\t</head>\n",
"\n",
"\t<body>\n",
"\t\t\n",
"\t\t<div class=\"papaya\" data-params=\"params\"></div>\n",
"\t\t\n",
"\t</body>\n",
"</html>\n",
" \"\"\" % str(brain_files)\n",
" \n",
" fname=name+\"index.html\"\n",
" with open (fname, 'w') as f: f.write (wdata)\n",
"\n",
" return IFrame(fname, width=800, height=600)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# variables\n",
"l1cope=\"0\"\n",
"l2cope=\"0\"\n",
"l3cope=\"0\"\n",
"def paths():\n",
" sliced_img = os.path.join(\"data\", \"img_\"+l1cope+\"_\"+l2cope+\"_\"+l3cope+\"_wb.png\")\n",
" wb_img = \"WB.nii.gz\"\n",
" cluster_corr = \"rand_\"+l1cope+\"_\"+l2cope+\"_\"+l3cope+\".nii.gz\"\n",
" tstat_img = os.path.join(\"data\", \"imgt_\"+l1cope+\"_\"+l2cope+\"_\"+l3cope+\"_wb.png\")\n",
" html_cl = l1cope+\"_\"+l2cope+\"_\"+l3cope\n",
" html_t = l1cope+\"_\"+l2cope+\"_\"+l3cope+\"t\"\n",
" return sliced_img,wb_img,cluster_corr,tstat_img,html_cl,html_t"
]
},
{
"cell_type": "markdown",
"metadata": {
"scrolled": false
},
"source": [
"# Model results"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Rule learning and rule application in the matching task"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Rule Learning > Rule Application"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"scrolled": false
},
"outputs": [],
"source": [
"l1cope=\"3\"\n",
"l2cope=\"1\"\n",
"l3cope=\"2\"\n",
"sliced_img,wb_img,cluster_corr,tstat_img,html_cl,html_t = paths()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
"text/html": [
"\n",
" <iframe\n",
" width=\"800\"\n",
" height=\"600\"\n",
" src=\"3_1_2index.html\"\n",
" frameborder=\"0\"\n",
" allowfullscreen\n",
" ></iframe>\n",
" "
],
"text/plain": [
"<IPython.lib.display.IFrame at 0x7f1a247207f0>"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"render(html_cl,[wb_img,cluster_corr])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Rule Application > Rule Learning "
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"scrolled": false
},
"outputs": [],
"source": [
"l1cope=\"3\"\n",
"l2cope=\"1\"\n",
"l3cope=\"1\"\n",
"sliced_img,wb_img,cluster_corr,tstat_img,html_cl,html_t = paths()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
"text/html": [
"\n",
" <iframe\n",
" width=\"800\"\n",
" height=\"600\"\n",
" src=\"3_1_1index.html\"\n",
" frameborder=\"0\"\n",
" allowfullscreen\n",
" ></iframe>\n",
" "
],
"text/plain": [
"<IPython.lib.display.IFrame at 0x7f1a24720b70>"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"render(html_cl,[wb_img,cluster_corr])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Rule Learning > Baseline"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"scrolled": false
},
"outputs": [],
"source": [
"l1cope=\"2\"\n",
"l2cope=\"1\"\n",
"l3cope=\"1\"\n",
"sliced_img,wb_img,cluster_corr,tstat_img,html_cl,html_t = paths()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
"text/html": [
"\n",
" <iframe\n",
" width=\"800\"\n",
" height=\"600\"\n",
" src=\"2_1_1index.html\"\n",
" frameborder=\"0\"\n",
" allowfullscreen\n",
" ></iframe>\n",
" "
],
"text/plain": [
"<IPython.lib.display.IFrame at 0x7f1a247206a0>"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"render(html_cl,[wb_img,cluster_corr])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Baseline > Rule Learning"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"scrolled": false
},
"outputs": [],
"source": [
"l1cope=\"2\"\n",
"l2cope=\"1\"\n",
"l3cope=\"2\"\n",
"sliced_img,wb_img,cluster_corr,tstat_img,html_cl,html_t = paths()"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
"text/html": [
"\n",
" <iframe\n",
" width=\"800\"\n",
" height=\"600\"\n",
" src=\"2_1_2index.html\"\n",
" frameborder=\"0\"\n",
" allowfullscreen\n",
" ></iframe>\n",
" "
],
"text/plain": [
"<IPython.lib.display.IFrame at 0x7f1a24720550>"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"render(html_cl,[wb_img,cluster_corr])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Rule Application > Baseline"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"scrolled": false
},
"outputs": [],
"source": [
"l1cope=\"1\"\n",
"l2cope=\"1\"\n",
"l3cope=\"1\"\n",
"sliced_img,wb_img,cluster_corr,tstat_img,html_cl,html_t = paths()"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
"text/html": [
"\n",
" <iframe\n",
" width=\"800\"\n",
" height=\"600\"\n",
" src=\"1_1_1index.html\"\n",
" frameborder=\"0\"\n",
" allowfullscreen\n",
" ></iframe>\n",
" "
],
"text/plain": [
"<IPython.lib.display.IFrame at 0x7f1a24720da0>"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"render(html_cl,[wb_img,cluster_corr])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Baseline > Rule Application "
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
"image/png": "data/img_1_1_2_wb.png",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"l1cope=\"1\"\n",
"l2cope=\"1\"\n",
"l3cope=\"2\"\n",
"sliced_img,wb_img,cluster_corr,tstat_img,html_cl,html_t = paths()\n",
"Image(sliced_img)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
"text/html": [
"\n",
" <iframe\n",
" width=\"800\"\n",
" height=\"600\"\n",
" src=\"1_1_2index.html\"\n",
" frameborder=\"0\"\n",
" allowfullscreen\n",
" ></iframe>\n",
" "
],
"text/plain": [
"<IPython.lib.display.IFrame at 0x7f1a2471b6a0>"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"render(html_cl,[wb_img,cluster_corr])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Rule learning and rule application in the classification task"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Rule Learning > Rule Application"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"scrolled": false
},
"outputs": [],
"source": [
"l1cope=\"3\"\n",
"l2cope=\"2\"\n",
"l3cope=\"2\"\n",
"sliced_img,wb_img,cluster_corr,tstat_img,html_cl,html_t = paths()"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
"text/html": [
"\n",
" <iframe\n",
" width=\"800\"\n",
" height=\"600\"\n",
" src=\"3_2_2index.html\"\n",
" frameborder=\"0\"\n",
" allowfullscreen\n",
" ></iframe>\n",
" "
],
"text/plain": [
"<IPython.lib.display.IFrame at 0x7f1a2471b550>"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"render(html_cl,[wb_img,cluster_corr])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Rule Learning > Baseline "
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"scrolled": false
},
"outputs": [],
"source": [
"l1cope=\"2\"\n",
"l2cope=\"2\"\n",
"l3cope=\"1\"\n",
"sliced_img,wb_img,cluster_corr,tstat_img,html_cl,html_t = paths()"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
"text/html": [
"\n",
" <iframe\n",
" width=\"800\"\n",
" height=\"600\"\n",
" src=\"2_2_1index.html\"\n",
" frameborder=\"0\"\n",
" allowfullscreen\n",
" ></iframe>\n",
" "
],
"text/plain": [
"<IPython.lib.display.IFrame at 0x7f1a2471b2b0>"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"render(html_cl,[wb_img,cluster_corr])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Baseline > Rule Learning "
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"scrolled": false
},
"outputs": [],
"source": [
"l1cope=\"2\"\n",
"l2cope=\"2\"\n",
"l3cope=\"2\"\n",
"sliced_img,wb_img,cluster_corr,tstat_img,html_cl,html_t = paths()"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
"text/html": [
"\n",
" <iframe\n",
" width=\"800\"\n",
" height=\"600\"\n",
" src=\"2_2_2index.html\"\n",
" frameborder=\"0\"\n",
" allowfullscreen\n",
" ></iframe>\n",
" "
],
"text/plain": [
"<IPython.lib.display.IFrame at 0x7f1a2471b828>"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"render(html_cl,[wb_img,cluster_corr])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Rule Application > Baseline"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"scrolled": false
},
"outputs": [],
"source": [
"l1cope=\"1\"\n",
"l2cope=\"2\"\n",
"l3cope=\"1\"\n",
"sliced_img,wb_img,cluster_corr,tstat_img,html_cl,html_t = paths()"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
"text/html": [
"\n",
" <iframe\n",
" width=\"800\"\n",
" height=\"600\"\n",
" src=\"1_2_1index.html\"\n",
" frameborder=\"0\"\n",
" allowfullscreen\n",
" ></iframe>\n",
" "
],
"text/plain": [
"<IPython.lib.display.IFrame at 0x7f1a2471ba90>"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"render(html_cl,[wb_img,cluster_corr])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Baseline > Rule Application"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"scrolled": false
},
"outputs": [],
"source": [
"l1cope=\"1\"\n",
"l2cope=\"2\"\n",
"l3cope=\"2\"\n",
"sliced_img,wb_img,cluster_corr,tstat_img,html_cl,html_t = paths()"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
"text/html": [
"\n",
" <iframe\n",
" width=\"800\"\n",
" height=\"600\"\n",
" src=\"1_2_2index.html\"\n",
" frameborder=\"0\"\n",
" allowfullscreen\n",
" ></iframe>\n",
" "
],
"text/plain": [
"<IPython.lib.display.IFrame at 0x7f1a2471b0b8>"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"render(html_cl,[wb_img,cluster_corr])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Rule learning in the matching and classification tasks"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Matching > Classification "
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"scrolled": false
},
"outputs": [],
"source": [
"l1cope=\"2\"\n",
"l2cope=\"3\"\n",
"l3cope=\"1\"\n",
"sliced_img,wb_img,cluster_corr,tstat_img,html_cl,html_t = paths()"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
"text/html": [
"\n",
" <iframe\n",
" width=\"800\"\n",
" height=\"600\"\n",
" src=\"2_3_1index.html\"\n",
" frameborder=\"0\"\n",
" allowfullscreen\n",
" ></iframe>\n",
" "
],
"text/plain": [
"<IPython.lib.display.IFrame at 0x7f1a2471b358>"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"render(html_cl,[wb_img,cluster_corr])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Classification > Matching"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"scrolled": false
},
"outputs": [],
"source": [
"l1cope=\"2\"\n",
"l2cope=\"3\"\n",
"l3cope=\"2\"\n",
"sliced_img,wb_img,cluster_corr,tstat_img,html_cl,html_t = paths()"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
"text/html": [
"\n",
" <iframe\n",
" width=\"800\"\n",
" height=\"600\"\n",
" src=\"2_3_2index.html\"\n",
" frameborder=\"0\"\n",
" allowfullscreen\n",
" ></iframe>\n",
" "
],
"text/plain": [
"<IPython.lib.display.IFrame at 0x7f1a2471bc50>"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"render(html_cl,[wb_img,cluster_corr])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Rule application in the matching and classification tasks"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Matching > Classification"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"scrolled": false
},
"outputs": [],
"source": [
"l1cope=\"1\"\n",
"l2cope=\"3\"\n",
"l3cope=\"1\"\n",
"sliced_img,wb_img,cluster_corr,tstat_img,html_cl,html_t = paths()"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
"text/html": [
"\n",
" <iframe\n",
" width=\"800\"\n",
" height=\"600\"\n",
" src=\"1_3_1index.html\"\n",
" frameborder=\"0\"\n",
" allowfullscreen\n",
" ></iframe>\n",
" "
],
"text/plain": [
"<IPython.lib.display.IFrame at 0x7f1a2471b438>"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"render(html_cl,[wb_img,cluster_corr])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Classification > Matching"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"scrolled": false
},
"outputs": [],
"source": [
"l1cope=\"1\"\n",
"l2cope=\"3\"\n",
"l3cope=\"2\"\n",
"sliced_img,wb_img,cluster_corr,tstat_img,html_cl,html_t = paths()"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
"text/html": [
"\n",
" <iframe\n",
" width=\"800\"\n",
" height=\"600\"\n",
" src=\"1_3_2index.html\"\n",
" frameborder=\"0\"\n",
" allowfullscreen\n",
" ></iframe>\n",
" "
],
"text/plain": [
"<IPython.lib.display.IFrame at 0x7f1a2471b908>"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"render(html_cl,[wb_img,cluster_corr])"
]
}
],
"metadata": {
"anaconda-cloud": {},
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.3"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
| mit |
vicgalle/machine-learning | Interactive.ipynb | 1 | 15769 | {
"metadata": {
"name": "",
"signature": "sha256:4f8835a2d57c734e5841069b15dc6d0c9c0f9358f09e58196038f755fc45b143"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"__author__ = \"V\u00edctor Adolfo Gallego Alcal\u00e1, Ana Mar\u00eda Mart\u00ednez G\u00f3mez\"\n",
"%matplotlib qt"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 1
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import matplotlib.pyplot as plt\n",
"from __future__ import division\n",
"import numpy as np"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 2
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"class Point:\n",
" \n",
" def __init__(self, x=None, y=None):\n",
" \"\"\" Initialize the point by giving it its x and y coordinates\n",
" \"\"\"\n",
" self.x = x\n",
" self.y = y\n",
" \n",
" def __repr__(self):\n",
" \"\"\" Returns the representation of the point\n",
" \n",
" Returns\n",
" -------\n",
" The string which represents the point: '(x,y)'\n",
" \n",
" \"\"\"\n",
" return 'Point(x=%s, y=%s)' % (self.x, self.y)\n",
" \n",
" def get_array(self):\n",
" \"\"\" Returns the array with represents the point\n",
" \n",
" Returns\n",
" -------\n",
" An array [x,y]\n",
" \n",
" \"\"\"\n",
" return np.asarray([self.x, self.y])\n",
" \n",
" def isLeft(self, p2, p3):\n",
" \"\"\" This function computes the sign of the determinant of\n",
" \n",
" self.x self.y 1\n",
" p2.x p2.y 1\n",
" p3.x p3.y 1\n",
" \n",
" which represents the sign of the signed area of the triangle selfp2p3\n",
" \n",
" Parameters\n",
" ----------\n",
" self: first point\n",
" p2: second point\n",
" p3: third point\n",
" \n",
" Returns\n",
" -------\n",
" The sign of the signed area of the triangle selfp2p3\n",
" \n",
" \"\"\" \n",
" return np.sign(-p2.x*self.y + p3.x*self.y + self.x*p2.y - p3.x*p2.y - self.x*p3.y + p2.x*p3.y)\n",
"\n",
"def segmentIntersection(a1, a2, b1, b2):\n",
" \"\"\" Calculate the intersection of two segments\n",
" \n",
" Parameters\n",
" ----------\n",
" a1: first end of the first segment\n",
" a2: second end of the first segment\n",
" b1: first end of the second segment\n",
" b2: second end of the second segment\n",
" \n",
" Returns\n",
" -------\n",
" The intersection Point or nothing if the two segments given do not intersect\n",
"\n",
" \"\"\"\n",
" # We embedded the points in P^2\n",
" u = np.cross([a1.x, a1.y, 1], [a2.x, a2.y, 1])\n",
" v = np.cross([b1.x, b1.y, 1], [b2.x, b2.y, 1])\n",
" \n",
" # w is the intersection in P^2 of the two lines\n",
" w = np.cross(u, v)\n",
" \n",
" c1 = a1.isLeft(b1, b2) * a2.isLeft(b2, b1)\n",
" c2 = b1.isLeft(a1, a2) * b2.isLeft(a2, a1)\n",
"\n",
" # iff a1 and a2 are in different side of the second segment and \n",
" # b1 and b2 are in different side of the first segment, the segments intersect\n",
" if c1 >= 0 and c2 >= 0:\n",
" p = w[:2]/w[2]\n",
" return Point(p[0], p[1])\n",
" else:\n",
" return\n",
" \n",
"def intersect(P, Q, eps):\n",
" \"\"\" Calculate the intersection of two B\u00e9zier curves\n",
" \n",
" Parameters\n",
" ----------\n",
" P: Array with the control points of the first B\u00e9zier curve\n",
" Q: Array with the control points of the second B\u00e9zier curve\n",
" eps: tolerance\n",
" \n",
" Returns\n",
" -------\n",
" An Array with the intersection Points, as there can be more than one. \n",
" It can also be empty is the curves do not intersect\n",
"\n",
" \"\"\"\n",
" m = P.shape[1] - 1 # number of control points in P\n",
" n = Q.shape[1] - 1 # number of control points in Q\n",
"\n",
" # We order de control points by the x coordinate\n",
" Pmin, Pmax = np.min(P, axis=1), np.max(P, axis=1)\n",
" Qmin, Qmax = np.min(Q, axis=1), np.max(Q, axis=1)\n",
" \n",
" #We check if it is still possible to calculate the intersection\n",
" if not (Pmin[0] > Qmax[0] or Pmin[1] > Qmax[1] or Qmin[0] > Pmax[0] or Qmin[1] > Pmax[1]) :\n",
" \n",
" if m*(m-1)*np.max(np.linalg.norm(np.diff(P, n=2, axis=1), axis=0)) > eps:\n",
" \n",
" # We compute the compound B\u00e9zier polygon of P\n",
" X = np.copy(P)\n",
" T = np.atleast_2d(X)\n",
" P_first = np.zeros([2, m + 1])\n",
" P_first[:, 0] = P[:, 0]\n",
" for i in xrange(m):\n",
" T[:, 0:(m - i)] = 0.5 * (T[:, 0:(m - i)] + T[:, 1:(m - i) + 1])\n",
" P_first[:, i + 1] = T[:, 0]\n",
" int1 = intersect(P_first, Q, eps)\n",
" int2 = intersect(T, Q, eps)\n",
" return int1 + int2\n",
" \n",
" else:\n",
" \n",
" if n*(n-1)*np.max(np.linalg.norm(np.diff(Q, n=2, axis=1), axis=0)) > eps:\n",
" \n",
" #We compute the compound B\u00e9zier polygon of Q\n",
" X = np.copy(Q)\n",
" T = np.atleast_2d(X)\n",
" Q_first = np.zeros([2, n + 1])\n",
" Q_first[:, 0] = Q[:, 0]\n",
" for i in xrange(n):\n",
" T[:, 0:(n - i)] = 0.5 * (T[:, 0:(n - i)] + T[:, 1:(n - i) + 1])\n",
" Q_first[:, i + 1] = T[:, 0]\n",
" int1 = intersect(P, Q_first, eps)\n",
" int2 = intersect(P, T, eps)\n",
" return int1 + int2\n",
" \n",
" else:\n",
" return [segmentIntersection(Point(P[0,0], P[1,0]), Point(P[0,-1], P[1,-1]), Point(Q[0,0], Q[1,0]), Point(Q[0,-1], Q[1,-1]))]\n",
" \n",
" \n",
" else: \n",
" return []\n",
" \n",
"def parsePoints(L):\n",
" \"\"\" Eliminate the Nones of an Array\n",
" \n",
" Parameters\n",
" ----------\n",
" L: Array\n",
" \n",
" Returns\n",
" -------\n",
" An Array which has the same elements that L has except the Nones\n",
"\n",
" \"\"\"\n",
" return [l for l in L if l != None]"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 3
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Ploting methods - The ones used in the bezier project\n",
"\n",
"def bezier_subdivision(P, k, epsilon, lines=False):\n",
" \"\"\"Wrapper method of bezier_subdivision_aux. \n",
" \n",
" Parameters\n",
" ----------\n",
" P : (n+1)xdim array of float. The control points of the curve.\n",
" k : integer. Number of iterations.\n",
" epsilon : float. Tolerance threshold.\n",
" lines : boolean. Iff True, just returns the extreme points.\n",
" \n",
" Returns\n",
" -------\n",
" Nxdim array of float. The curve points. N is variable.\n",
" \"\"\"\n",
" P = np.asarray(P, dtype=np.float64).T\n",
" solution = bezier_subdivision_aux(P, k, epsilon, lines)\n",
" return solution\n",
"\n",
"def bezier_subdivision_aux(P, k, epsilon, lines=False):\n",
" \"\"\"It approximates the curve by performing sucesive subdivisions\n",
" in the domain's interval. See Section 3.5 from Prautzsch H.,et al.\n",
" B\u00e9zier and B-Spline Techniques for more details.\n",
" \n",
" Parameters\n",
" ----------\n",
" P : dimx(n+1) array of float. The control points of the curve.\n",
" k : integer. Number of iterations.\n",
" epsilon : float. Tolerance threshold.\n",
" lines : boolean. Iff True, just returns the extreme points.\n",
" \n",
" Returns\n",
" -------\n",
" Nxdim array of float. The curve points. N is variable.\n",
" \"\"\"\n",
" dim, n = P.shape\n",
" n -= 1\n",
" if n == 1:\n",
" return P.T\n",
"\n",
" delta2_b = np.diff(P, n=2, axis=1)\n",
" norm_delta2 = np.max(np.linalg.norm(delta2_b, axis=0))\n",
"\n",
" if lines and n * (n - 1) / 8 * norm_delta2 < epsilon:\n",
" return np.array([P[0], P[-1]])\n",
"\n",
" if k == 0 or norm_delta2 < epsilon:\n",
" return P.T\n",
" b_first = np.zeros([dim, n + 1])\n",
" X = np.copy(P)\n",
" T = np.atleast_2d(X)\n",
" b_first[:, 0] = P[:, 0]\n",
" for i in xrange(n):\n",
" T[:, 0:(n - i)] = 0.5 * (T[:, 0:(n - i)] + T[:, 1:(n - i) + 1])\n",
" b_first[:, i + 1] = T[:, 0]\n",
" z1 = bezier_subdivision(b_first.T, k - 1, epsilon, lines)\n",
" z2 = bezier_subdivision(T[:, :].T, k - 1, epsilon, lines)\n",
" return np.vstack([z1[:-1, :], z2])"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 4
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"class DrawLine():\n",
" \"\"\" Based on Antonio Vald\u00e9s code for plotting and moving a line\n",
" \"\"\"\n",
" def __init__(self):\n",
" self.figure, self.axes = plt.subplots(figsize=(10, 10))\n",
" self.axes.set_xlim(-10, 10)\n",
" self.axes.set_ylim(-10, 10)\n",
" self.touched_circle = None\n",
" self.circles = [[],[]]\n",
" self.intersections = []\n",
" self.line = [None, None]\n",
"\n",
" #We define the actions to do for every event\n",
" self.cid_press = self.figure.canvas.mpl_connect('button_press_event', self.click_event)\n",
" self.cid_move = self.figure.canvas.mpl_connect('motion_notify_event', self.motion_event)\n",
" self.cid_release = self.figure.canvas.mpl_connect('button_release_event', self.release_event)\n",
" \n",
" def click_event(self, event):\n",
" \"\"\" Action to do when clicking. It paints red circles when clicking with the right \n",
" button and blue ones otherwhise.\n",
" \n",
" Parameters\n",
" ----------\n",
" self: Object of the class DrawLine\n",
" event: Event\n",
" \"\"\"\n",
" flag = 0\n",
" if event.button == 3: # right click\n",
" flag = 1\n",
" self.initial_event = event\n",
" \n",
" # Just do nothing if a control point is touched\n",
" for c in self.axes.artists:\n",
" if c.contains(event)[0]:\n",
" self.touched_circle = c\n",
" self.x0, self.y0 = c.center\n",
" return\n",
" \n",
" c = plt.Circle((event.xdata, event.ydata), radius=0.2, color=(flag, 0, 1-flag))\n",
" self.circles[flag].append(c)\n",
" if not self.line[flag]:\n",
" self.line[flag] = plt.Line2D(*zip(*map(lambda x: x.center, self.circles[flag])), color=(flag, 0, 1-flag))\n",
" self.axes.add_line(self.line[flag])\n",
" else:\n",
" P = []\n",
" for c in self.circles[flag]:\n",
" P.append([c.center[0], c.center[1]])\n",
" W = bezier_subdivision(P, 5, 0.1)\n",
" W = W.T.tolist()\n",
" self.line[flag].set_data(W)\n",
" \n",
" self.axes.add_artist(c)\n",
" self.figure.canvas.draw()\n",
" \n",
" def motion_event(self, event):\n",
" \"\"\" Action to do when moving. \n",
" It repaints the curve and paints the intersection between the two curves too.\n",
" \n",
" Parameters\n",
" ----------\n",
" self: Object of the class DrawLine\n",
" event: Event\n",
" \n",
" \"\"\"\n",
" flag = 0\n",
" if event.button == 3: # right click\n",
" flag = 1\n",
" \n",
" # Do nothing if nothing is touched\n",
" if self.touched_circle == None:\n",
" return\n",
" \n",
" if event.inaxes == self.axes:\n",
" dx = event.xdata - self.initial_event.xdata\n",
" dy = event.ydata - self.initial_event.ydata\n",
" self.touched_circle.center = self.x0 + dx, self.y0 + dy\n",
" P = []\n",
" for c in self.circles[flag]:\n",
" P.append([c.center[0], c.center[1]])\n",
" W = bezier_subdivision(P, 5, 0.1)\n",
" W = W.T.tolist()\n",
" self.line[flag].set_data(W)\n",
" \n",
" Q = []\n",
" for c in self.circles[1-flag]:\n",
" Q.append([c.center[0], c.center[1]])\n",
" points = parsePoints(intersect(np.asarray(P).T, np.asarray(Q).T, 0.01))\n",
"\n",
" for p in points:\n",
" self.intersections.append(plt.Circle((p.x, p.y), radius=0.1, color=(0, 1, 0)))\n",
" self.axes.add_artist(self.intersections[-1])\n",
" \n",
" \n",
" self.figure.canvas.draw()\n",
" \n",
" def release_event(self, event):\n",
" \"\"\" When release the touched circule is set to None\n",
" \n",
" Parameters\n",
" ----------\n",
" self: Object of the class DrawLine\n",
" event: Event\n",
" \n",
" \"\"\"\n",
" self.touched_circle = None\n",
" \n",
" \n",
"dl = DrawLine()"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 5
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
} | mit |
SimonBiggs/electronfactors | historical_exploration_and_measurement/scripts/Save All Cutouts Images.ipynb | 1 | 2211821 | null | agpl-3.0 |
QuantumTechDevStudio/RUDNEVGAUSS | rms/parser_approx.ipynb | 1 | 82718 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import pandas as pd\n",
"from pandas.io.json import json_normalize #package for flattening json in pandas df\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import matplotlib\n",
"import json\n",
"import os\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"os.chdir(\"./runs/approx/main/\")\n",
"origin_path = os.getcwd() \n",
"runs_id = os.listdir(\"./\")\n",
"runs_id = [int(item) for item in runs_id]\n",
"runs_id = sorted(runs_id)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"df_list = []\n",
"for run_id in runs_id:\n",
" os.chdir(\"./\"+str(run_id))\n",
" f_in = open('out.json', 'r')\n",
" run_info = json.load(f_in)\n",
" f_in.close()\n",
" a = json_normalize(run_info)\n",
" df_list.append(a)\n",
" os.chdir(origin_path)\n",
"res1 = pd.concat(df_list,ignore_index=True)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"res_correct = res1"
]
},
{
"cell_type": "code",
"execution_count": 26,
"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>Model info.a</th>\n",
" <th>Model info.b</th>\n",
" <th>Model info.dim</th>\n",
" <th>Model info.eps</th>\n",
" <th>Model info.iters</th>\n",
" <th>Model info.k</th>\n",
" <th>Model info.m</th>\n",
" <th>Model info.max_t</th>\n",
" <th>Model info.n_sig</th>\n",
" <th>Out info.J_fin</th>\n",
" <th>Out info.MSE</th>\n",
" <th>Out info.Std</th>\n",
" <th>Out info.Time</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>50</th>\n",
" <td>0</td>\n",
" <td>6.283185</td>\n",
" <td>1</td>\n",
" <td>1.000000e-09</td>\n",
" <td>100000</td>\n",
" <td>1</td>\n",
" <td>10</td>\n",
" <td>200</td>\n",
" <td>13</td>\n",
" <td>8.481635e-10</td>\n",
" <td>8.481635e-10</td>\n",
" <td>0.000031</td>\n",
" <td>87.193648</td>\n",
" </tr>\n",
" <tr>\n",
" <th>77</th>\n",
" <td>0</td>\n",
" <td>6.283185</td>\n",
" <td>1</td>\n",
" <td>1.000000e-09</td>\n",
" <td>100000</td>\n",
" <td>1</td>\n",
" <td>10</td>\n",
" <td>200</td>\n",
" <td>20</td>\n",
" <td>7.158401e-10</td>\n",
" <td>7.158401e-10</td>\n",
" <td>0.000028</td>\n",
" <td>135.381111</td>\n",
" </tr>\n",
" <tr>\n",
" <th>92</th>\n",
" <td>0</td>\n",
" <td>6.283185</td>\n",
" <td>1</td>\n",
" <td>1.000000e-09</td>\n",
" <td>100000</td>\n",
" <td>1</td>\n",
" <td>10</td>\n",
" <td>200</td>\n",
" <td>24</td>\n",
" <td>8.366615e-10</td>\n",
" <td>8.366615e-10</td>\n",
" <td>0.000030</td>\n",
" <td>88.851291</td>\n",
" </tr>\n",
" <tr>\n",
" <th>108</th>\n",
" <td>0</td>\n",
" <td>6.283185</td>\n",
" <td>1</td>\n",
" <td>1.000000e-09</td>\n",
" <td>100000</td>\n",
" <td>1</td>\n",
" <td>10</td>\n",
" <td>200</td>\n",
" <td>28</td>\n",
" <td>7.022259e-10</td>\n",
" <td>7.022259e-10</td>\n",
" <td>0.000028</td>\n",
" <td>116.871864</td>\n",
" </tr>\n",
" <tr>\n",
" <th>110</th>\n",
" <td>0</td>\n",
" <td>6.283185</td>\n",
" <td>1</td>\n",
" <td>1.000000e-09</td>\n",
" <td>100000</td>\n",
" <td>1</td>\n",
" <td>10</td>\n",
" <td>200</td>\n",
" <td>28</td>\n",
" <td>8.470088e-10</td>\n",
" <td>8.470088e-10</td>\n",
" <td>0.000031</td>\n",
" <td>85.176691</td>\n",
" </tr>\n",
" <tr>\n",
" <th>137</th>\n",
" <td>0</td>\n",
" <td>6.283185</td>\n",
" <td>1</td>\n",
" <td>1.000000e-09</td>\n",
" <td>100000</td>\n",
" <td>1</td>\n",
" <td>10</td>\n",
" <td>200</td>\n",
" <td>35</td>\n",
" <td>7.683114e-10</td>\n",
" <td>7.683114e-10</td>\n",
" <td>0.000029</td>\n",
" <td>125.560257</td>\n",
" </tr>\n",
" <tr>\n",
" <th>147</th>\n",
" <td>0</td>\n",
" <td>6.283185</td>\n",
" <td>1</td>\n",
" <td>1.000000e-09</td>\n",
" <td>100000</td>\n",
" <td>1</td>\n",
" <td>10</td>\n",
" <td>200</td>\n",
" <td>37</td>\n",
" <td>8.767481e-10</td>\n",
" <td>8.767481e-10</td>\n",
" <td>0.000031</td>\n",
" <td>159.793518</td>\n",
" </tr>\n",
" <tr>\n",
" <th>181</th>\n",
" <td>0</td>\n",
" <td>6.283185</td>\n",
" <td>1</td>\n",
" <td>1.000000e-09</td>\n",
" <td>100000</td>\n",
" <td>1</td>\n",
" <td>10</td>\n",
" <td>200</td>\n",
" <td>46</td>\n",
" <td>8.874150e-10</td>\n",
" <td>8.874150e-10</td>\n",
" <td>0.000031</td>\n",
" <td>118.845316</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Model info.a Model info.b Model info.dim Model info.eps \\\n",
"50 0 6.283185 1 1.000000e-09 \n",
"77 0 6.283185 1 1.000000e-09 \n",
"92 0 6.283185 1 1.000000e-09 \n",
"108 0 6.283185 1 1.000000e-09 \n",
"110 0 6.283185 1 1.000000e-09 \n",
"137 0 6.283185 1 1.000000e-09 \n",
"147 0 6.283185 1 1.000000e-09 \n",
"181 0 6.283185 1 1.000000e-09 \n",
"\n",
" Model info.iters Model info.k Model info.m Model info.max_t \\\n",
"50 100000 1 10 200 \n",
"77 100000 1 10 200 \n",
"92 100000 1 10 200 \n",
"108 100000 1 10 200 \n",
"110 100000 1 10 200 \n",
"137 100000 1 10 200 \n",
"147 100000 1 10 200 \n",
"181 100000 1 10 200 \n",
"\n",
" Model info.n_sig Out info.J_fin Out info.MSE Out info.Std \\\n",
"50 13 8.481635e-10 8.481635e-10 0.000031 \n",
"77 20 7.158401e-10 7.158401e-10 0.000028 \n",
"92 24 8.366615e-10 8.366615e-10 0.000030 \n",
"108 28 7.022259e-10 7.022259e-10 0.000028 \n",
"110 28 8.470088e-10 8.470088e-10 0.000031 \n",
"137 35 7.683114e-10 7.683114e-10 0.000029 \n",
"147 37 8.767481e-10 8.767481e-10 0.000031 \n",
"181 46 8.874150e-10 8.874150e-10 0.000031 \n",
"\n",
" Out info.Time \n",
"50 87.193648 \n",
"77 135.381111 \n",
"92 88.851291 \n",
"108 116.871864 \n",
"110 85.176691 \n",
"137 125.560257 \n",
"147 159.793518 \n",
"181 118.845316 "
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"res_correct[res_correct['Out info.MSE'] < 0.9e-9]"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"matplotlib.rcParams['text.usetex'] = True\n",
"matplotlib.rcParams['text.latex.unicode'] = True\n",
"matplotlib.rcParams['text.latex.preamble'] = [r'\\usepackage{amsmath}']"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.legend.Legend at 0x7ff61e9a90b8>"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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eiYkJhEIhg0tERESkz5EjR3DGGWdYXQwi2zvjjDNw5MgRQ6fZ8iHdbD09PRgYGKg4qE9M\nTGBgYAA9PT0mlYyIiKi8ubk5dHR0WF0MqqN4PI5wOIxQKASfz4dkMml1kRpCR0eH4ddssE26yXp7\nezE2NoaBgQGMjY2ht7d30fcoAV3v+ERERGZhE5fWkU6nMTk5iaGhIQBALBZDX18fEomExSWzPzM+\nJ6xJrwNtUNfWqB966xj6vvYEDr11TB22WED//YmTuPnR/8LvT5ysS9mJiIhaVTwehxACQgjEYrGy\n4yrjNXIz1WQyiWAwqD7v7u5GMplEOp22sFStiyG9TvKD+qG3jqHn9l148fW30HP7Lhx665iugP6+\n4AR+8O+v4H3BCQZ1IiKiOolEIiVfi0ajdSyJedxuN6amptTnk5OTcDgccDgcFpaqdTGk15ES1H2+\nAbxr692Q2eESwLu23g2fb/GA/sbREwCAN46eYFAnIiKqA6/Xi7GxsZKvj4yMwOv11rFE5tEG8pGR\nEYyOjlpYmtbW8iG93l0wXtxzGZb0fRoHH7sLx37zHADg6L6ncPCxu7Ck79O4uOeygvfkB3QFgzoR\nEZH5fD4f0ul00SYvynCfz2dBycwTDocxODiI/v5+q4vSslr+wlEp5Q4AO7q7u7dWPZH164Ennsj8\nu8ioKwH8r/LkR5/PffFHny8cBuA0AFMFQzW+oKOMiltvBbZvr+ANREREra2zsxNutxuRSKSgxlyp\nYW+WmnQgc8Goy+VqqmVqRC1fk05ERET10cj3DhkcHCza5EUJ7p2dnUXfF4vF4PF4IISAx+PJqY1P\nJpPw+XxwOp0QQqCvr6+gy8NAIACn04l4PI6+vj44nU54PB7E4/GKyp9MJtHX16de4Kp9OJ1Odbx4\nPI7Ozk41oDdLe/tGxJBOREREddHI9w7p7+8vaPKiPPf7/UXfE41G0dfXB6/Xi0gkgu7u7pwgPjIy\nAgAYHR3F+Pg4pqen4fF4CqaTTqfh8/ng9/sRDAbVcK+X0pWi3+9HIpFQ5zs1NYVUKoVUKgUgE+Q3\nbtyo/qgQQiAQCOieDxlMSsmHlPB4PNIIExMTZV8/eGRGrr72Dtm27Az5B4O3y6XvuEgic+2o7Fi9\nVr7+5u8L3nP0+Kx03/ZzeV5gp5SAlIA8L7BTnhfYKd23/VwePT5rSNmpcottb2ou3N6thdtbyuef\nf97wae7evVuuXLlS7t6925Txa/Hmm2/mPJ+ampIAZCQSkVJK6XK5ZH9/v/r6yMiIBCBTqZRMpVIS\ngAwGg+rrDodDDg8P50zT7XYXDFMkEomc+Ukp5fDwsAQgx8fHC4alUqlFl0kp19TUVM5wr9ebU9ZW\nlL+9a6X38wJgUurIpqxJr7P/2vs0To5/Dav+75uwbM0l6Oz7GCAym2H2tZfwra8Vnso77ZQl+LdA\nL848/ZSc4Weefgr+LdCL005p+UsLiIioQZS6d0gxdru5n9/vz2n+oTR1KdZFYTweRzqdRigUymla\nEo/HSzZVcblcAFD0Lp/d3d3q/11dXbrLvHXrVgwNDcHtdhe89sYbb+ieDtUfQ3odKQebSGQMvxr9\nfyAAnLLaheWXXqmOc9ttt+FnP/tZwXuVoK5gQCciokalJ6jbLaADUHs6icViizZ1UYL21NQUEolE\nzkPb53o0GoXP54PH48lpG56v2r7Ko9Fo0TImk8mamw85nU7e6MhEDOl1kn+wWfm2U7H3lo34P3/w\nNsR/MoqVK1cCAObn59Hf31/0oKUN5AzoRETUyMoFdTsGdCBT0+1yuRCJRNS26aW6KFRqxbXvUx5K\n4O7r60MgEEBfXx9GR0exa9cuQ8ur/FDIr0VPJpNIJpPsvcXmGNLroNTBZuXbTsX4p9+PtX94Fu66\n6y51+IkTJ/DhD3+47GlABnQiImp0xYK6XQO6wu/3IxaLYXx8vGwf4m63Gw6Ho+iFl+l0Wq2JDwaD\nanMUbbA3UzAYxPDwMO8kanMtH9LNvpmR3oPNli1b8Cd/8icAgJMnT+KCCy6o6gp4IiKietm+fXvR\nLv2KPYaGhgrePzQ0hA0bNuDQoUPYsGEDhBAFz5XH9iL3+Ni8eXPZeRZ7T636+/uRTCbVm/2Uo9S4\n+3w+xGIxtbeXQCAAh8OhhvhoNGrKDZFcLhfcbndOG/doNIpkMolgMGjovMh4LR/SpZQ7pJRDK1as\nMGX6e/fu1VUb0NbWhn/6p3+CEAIAcO655+L73/8+9u7da0q5iIiIqHJK8AVKN3VReL1eTE1NIZ1O\no6+vD1u3boXL5VID8ujoKKanp+Hz+RAIBEy5a+muXbsQDAYRDocRCoUwPT2N8fHxgvGUMjqdTjid\nToTDYQCZpjFKe3nlzqtUH2wzYbLh4WHd43Z3d+P222/Hu9/9brWd2BVXXGFW0YiIiKgMt9uNTI95\nuaamCu8D7nA4io7rdruLhmIgE/Lzg37+GYdgMFhQ6z00NFT0zEQxDodD7Re9nLGxsZyyKrXvHo9H\n7cUmHA5j48aNRZefjNfyNel28/nPf97QCzlm5+bx6LOvYnZu3rBpEhERAZnmLnr6e5ZSqjWzWuFw\nGLt378bKlSuxe/duSCkLniuPYk1XduzYUXaeZjR3aWaxWEztHtLlciEcDsPr9aq5ZGhoCOl0uuK7\nnVJ1GNKb2OzcPPwPTeFzkefgf2iKQZ2IiGyl2HVblfSjTsYZGhqC1+vFxo0b4XQ6EYvFkEgkCi5m\ndblcRftxJ+MxpNtQKBRSD0xvvfUWHnzwQd3vnZiYQCgUUgP604lDODkv8XTiEIM6ERHZRrmOFRjU\nrREMBpFKpTA6OopAIICurq6CQJ5MJuvWC02rY0i3oZ6eHgwMDOALX/gCLrzwQmzZsqXoDY7yKQe8\nS90eNaDPzGZC+czsPIM6ERHZgp6ezxjU60vb1EXpmnFgYACxWEztEz4UCsHhcBS9eykZjyHdhpQD\nUygUwoEDBwAAN954I44fP17yPcoB759/+CP88NUzcgK6gkGdiIisVkk/6Azq9bV161Y4nU74/X4E\ng0E4HA7s2rULgUAATqcT4+Pjht9wiUpjSLep3t5e/PM//7PaJeNLL72Er371q0XH1R7w3jrzQjy5\n72BBQFfMzM7jyX0HsfO5A6aVnYiIqJhqblTEoF4fSneRqVQKiURCvVjU7Xarw8fHx3NugJRKpXhD\nJBMxpNvYNddcg0984hPq89tvv71gnPwD3qZ1Z+PyC1ZhWUfxTbusow2XX7AKm9adbVq5iYiIitF7\n75B8SlDnvUOolTCk29zXvvY19QKNmZmZnNeK1Uh0tLdh5HoPLutaWRDUl3W04bKulRi53oOOdm56\nIiKqr+Hh4YoDuqK3t7eie48QNTomNZtbsmQJvvvd7xYML3fKsFhQZ0AnIiIiahwtn9aEEJuFEOHD\nhw9bXZSS3vve9+L666/PGbZYmz5tUF/SJhjQiYiIiBpIyyc2KeUOKeXQihUrrC5KWcFgEMuXL1ef\nX3nllYueMlSC+pd96xjQiYiIiBoIU1uDOOuss7Bhwwb1eSQS0XWVe0d7G66+9BwGdCIiIqIGwuTW\nICYmJrBnzx71+TXXXMPuqIiIiIiaFEN6A1AuEn3kkUfUYT/4wQ/YbywRERFRk2JItzltLy4bN27M\neY03eCAiIiJqTkusLgCVltPN4hNPAJo26cjeibQXwEEg9zUj3XorsH27OdMmIiJqMvF4HJOTk0in\n09i7dy+CwaB6vxOiSjCk21RBP+hPPGF1kYiIiKiMdDqNyclJDA0NAQBisRj6+vqQSCQsLhk1IjZ3\nsaFyNyoiIiIie0omkwgGg+rz7u5uJJNJpNNpQ6bv9/vhdDohhEA4HDZkmtUIBAJwOp0ln9dTKBRC\nX1+fJfM2G2vSbWjv3r2FAX379swj28xFZAcvX74cr7/+Ok499VQAmYC/d+/ehVsnZ8eHlHUpOxER\nUatyu92YmppSn09OTsLhcMDhcNQ8bZ/Ph3g8jtHRUQBgE5qsRCKBWCxmdTFMwZp0GxoeHl60Bn3t\n2rUAgCNHjuBnP/uZOry3t3choBMREVFdaQP5yMiIGqprFY1GEQwG0d/fj/7+frjdbkOm2+hGRkYg\nq6yI/PrXv45kMmlwiYzDkN6gBgcH1f9/9KMfWVgSIiIiyhcOhzE4OIj+/v6ap6UESSNq5CkjnU7j\nC1/4AkM6Ge/aa69V/9+5cyeOHj1qYWmIiIhIEYvF4HK5DAno1LoY0hvUu971Lrzzne8EAPz+97/H\nzp07LS4RERFR80omk+jr64MQouChvWgyHo+js7MTXq8XQKaZSi1CoRC6uroAQJ2/dprxeFwd7vF4\nCi4o1V7U6ff71WmVW06fz6deoNrX12dobXMgEIAQQi230+lEV1dXyfVUyfJpn2un7/F4EI/H1XGU\n5QMW1ql2GtFoFF1dXer2NXod6MWQ3sC0TV4efvhhC0tCRETUvJSuFP1+PxKJBEZGRgAAU1NTSKVS\nSKVSADIBd+PGjfB4PGrACwQCNc17aGgI4+PjAIBgMIhEIqHW0EejUXg8HrhcLoyPj2NwcBB+vx9+\nv79gOh6PB5OTk0Vf01KWbXR0FOPj45ienobH46lpGYrZunUrfD4ftm3bhunpafh8voIgXMnyaaXT\nafh8Pvj9fgSDQfWHh0JZNiCzvIlEQr3gVxm3v78fU1NTiEQicLlchvXQUxEpJR9SwuPxSCNMTEwY\nMp2SMv20SCml/J//+R8JQAKQS5culYcPHy47PhnP9O1NtsLt3Vq4vaV8/vnnrS5CXb355psFw1Kp\nlAQgp6amcoZ7vV4ZDAbrUq5EIiEByEgkkjPc4XDI4eHhnGHj4+MSgBwfH5dSSjk8PCwBSK/Xa9i8\nh4eHpcPhKPm8HKU8WlNTUxKAHBoayhmud/nyy6J9XTsslUoVLJd2PCmljEQiBePqpffzAmBS6sim\nTV+TLoRwCyG8VpfDDBdeeCEuueQSAMDx48fx2GOP1TzN2bl5PPrsq5idm695WkRERI1u69atGBoa\nKtqbyhtvvGFBiTJisRjS6XTOWXUA8Hq9cDgciEQiOcOVGvJKKV09mtncw+12w+12Y3JyUh1W6fLl\n6+7uVv9frIlP/vQBYOPGjQiFQjnNZOqtIUK6EMKd97xfCOEVQujpa3AQQNNeDm1kk5fZuXn4H5rC\n5yLPwf/QFIM6ERG1vGg0WrR5RTKZRE9PT03TdjqdVTejUEJzsf7SXS5XQaiupF/1aDQKn88Hj8dT\nt5sUKTd+UlS6fPmq7QnH4XCoTWECgQA8Ho9lN0uyfUjP1oKPap67AUBKGQOQzg/wRTR1Y20lpJ9y\nyilYtmxZ1X2FKgH96cQhnJyXeDpxiEGdiIjqxo5ncpUgmF+LnkwmkUwm1VpXK5Sr4U4mk1Xf7Kiv\nrw+BQAB9fX0YHR3Frl27aiqnXpOTkzllNmv59PB6ver1BsFgELFYDKFQyLT5lWL7kJ4N49OaQYMA\nlJ+dSQBeQK1d1z6atvZc6/zzz8djjz2G119/HZFIBEK5w2gFtAF9ZjZzcJyZnWdQJyKiumi0M7nB\nYBDDw8OW9lve3d0Nh8NR0IwlGo2qF05WKp1OIxaLIRgMqk18zArD2jMI8Xgc8Xg850ePGcun1dnZ\nWVCOfA6HA8PDw3C73di7d29N86vGkrrPsXYO5Ib2MwFASlmqj6NuAF1CiJiU0oJLc833Z3/2Z1W/\nt1hAV2iD+sj1HnS02/43HRERNZhSZ3Lt8L3jcrngdrtzam6j0SiSyaTaJMIqDocDo6Oj8Pl8mJ6e\nxuDgIJLJJAKBAPr7+6uq5Xc4HHA4HGqPNA6HA8Fg0OiiA8h0gxgIBNQyOxwObNu2LacsRi+flvID\nS/kR8PDDDyMSiSAcDiMYDMLv96vNauLx+KI9ypihEUN6RaSU4VKvCSGGAAwBwOrVq7Fnz56a5/fW\nW28ZMp1S1mf/6p3HYuOnZ2bh7pjBpX9UupmMwO/w+M93wbGsQ2cpW4fZ25vshdu7tXB7AytWrMCR\nI0dMm/7s3Dw+FX0ez7ycxrGTC2dyn3rpEP76gX/H1/vfWdegPjc3V7C8P/nJT3DrrbfikksuweHD\nh7FixQr8+Mc/LhgvnU7jhhtuwLPPPgsA+OIXv4gtW7Zg//79uOGGG/Dyyy9j/fr1uOeee3Jq4I8c\nOYL29vay5XrrrbcAADMzMznzveKKK/DYY4/h1ltvhc/nw5o1a3DbbbfhU5/6lDreiRMn1Pnocc89\n9+DGG2+Ez+fDJZdcgi1btiCEBKUXAAAgAElEQVQWi+H48eMlp1nJPJRx//7v/x6f+9zn8B//8R/o\n7e3F17/+dbS3t9e8fMXKcuzYMXWYdl3feOONuOeee/DSSy/hqquuwpEjR3DllVfihRdewL333ouX\nX34Za9aswac+9Sl85CMfWXT5jh07ZugxQ1TbhrmehBDjUsq+7P9BAONSypgQoh+AS0pZc0Oh7u5u\nqb2quFp79uzB+vXra55OSUpzFr3bbZHxy9WkA8CyjjZc1rXSFjUadmT69iZb4fZuLdzewAsvvICL\nLrrIlGnb8fvnyJEjWL58eVXvDYfDSCQSas2zUvvudDoRiUTg9XoRDocxMjKi9sntdDqxf/9+S5vN\n1FsgEEAoFKr6Gjoj1bK9i9H7eRFCTEkpuxcbrxFT18MAlAZSLgAxC8tiG3Nzc3jiiSfw8Y9/HAMD\nA7rf19HehpHrPbisayWWdeTuDgzoRERklp3PHcCT+w4WDehApkb9yX0HsfO5A3UuWfVisZjaZZ/L\n5UI4HIbX61WbZgwNDSGdTlvarR81Dtsnr2xteXf2L6SU8exwL4C08ryG6W8WQoQPHz5ce2EtdPDg\nQWzYsAH33nsvotEoDhzQf1ArFtQZ0ImIyEyb1p2Nyy9YVVBBpFjW0YbLL1iFTevOrnPJqjM0NASv\n14uNGzfC6XQiFoshkUgUXHipp/tAIqABQrqUMiqldGovDJVShqWUsXLtzSuY/g4p5dCKFStqnZSl\n3v72t+P9738/gMxdZKPRUtfRFqcN6kvaBAM6ERGZqhnP5AaDQaRSKYyOjiIQCKCrq6sgkJvdfSA1\nj8bZ82lRtd7YSDlgftm3ruEOjERE1Hia6UyutqmL0sZ8YGAAsVgMsVimZW4oFILD4Sh699JWEQwG\nbdEevRE0zt5Pi/rwhz+sXrX81FNP4be//W3F0+hob8PVl57TUAdGIiJqXM10Jnfr1q1wOp3w+/0I\nBoNwOBzYtWsXAoEAnE4nxsfH63ZzIGp8Td8F42KEEJsBbF67dq3VRanZypUrsXHjRvz85z8HAIyN\njeEzFpeJiIhoMUpQ3/ncAWxad3ZDBnTlLpX53G530eEAkEqlzC4WNbDG+xQYrFnapCuuvfZa9f9q\nmrwQERFZgWdyiXLxk9AEQqEQJiYmAABXXXUVOjoyNx3ScwvbiYkJhEI1dzNPRERERAZiSG8CPT09\nGBgYwMTEBJxOJ6644gpd75uYmMDAwAB6enpMLiERERERVaLlQ3oz9JPe29uLsbExNagPDg7iVgA5\n104LUfDo3bABBw8dQu+GDUVfr+qxfbs1K4GIiIioibR8SG+WNunaoO5wOLAk28sLERERETWelg/p\nzUQJ6lu2bMF73/teq4tDRERERFViSG8ySlD3/fd/Y2L37oUXpMTE7t1YtXJlZriUxR+a8at6sLkL\nERERUc1aPqQ3Q5v0fNqmLwrlItGxsTH09vZaWDoiIiIiWkzLh/RmaZOeTwnqCgZ0IiIiosbR8ncc\nbWbaQD4zM4OLLrrIwtIQERERmSsej2NychLpdBp79+5FMBiEy+WyulhVYUhvYhMTE1Bi+tGjR3Hj\njTfm1K4TERERNYt0Oo3JyUkMDQ0BAGKxGPr6+pBIJCwuWXVavrlLs1LaoGtFIhH8+Mc/tqhERERE\njSmdTsPn86GrqwtCCHg8Hvj9fiSTyaqnGY1GIYRAOp02sKSNw+/3w+l0QgiBcDhsyDSTySSCwaD6\nvLu7G8lksmHXMWvSm5D2IlFs2JDz2nXXXYfHH3+cbdOJiIh0SCaT6Orqgsvlgt/vh8PhQCKRQCgU\nAgCMjIxYXMLG4/P5EI/HMTo6CgCGNUdxu92YmppSn09OTsLhcMDhcBgy/Xpr+ZAuhNgMYPPatWut\nLoohFuvFRUoJn8+HSCTCoE5ERMbbvh344hfrP99bbzWlG2C/3w+Xy1XQZGLbtm0FNemhUAj9/f0N\n2wa6XqLRKCKRCPr7+w2ftjaQj4yMqD8EGlHLN3dppt5dygX0iy++GABw7NgxXHHFFRgYGMDExIQV\nxSQiImoYk5OT8Hq9BcMdDgfcbrf6PJ1OIxAI1NQEphUo68fs2u1wOIzBwUFTfgjUS8uH9GaxWA36\nP/zDP6j/79ixA9/+9rcZ1ImIiBbR2dmJyclJq4tBFYjFYnC5XA0d0AGG9Kag50ZFH/7wh/HOd74T\nAHDkyBHE43H1hkcM6kREZJjt26u7Y7XCZne8DgaDiMfj6OvrQywWKzqOz+eD0+kEAPT19UEIoT5X\nhEIhOJ1OdHV1VXzRaSwWg8fjUS9a1ZYjEAio8/L7/ejq6io7HIC6PMr0il24We795ZSbdigUUqej\njBONRnVNN5lMqu/Jf2jXdTweR2dnp3r2Q+/07YghvQns3bt30RsVtbW14ZZbblGff+Mb38Cll16K\nsbEx7N27tx7FJCIiajj9/f0YGRlRu/MTQsDn8+WE7NHRUYyPjwPItINOJBI5FzCGQiEEAgF0d3cj\nGAzC4/EgEAjomn80GkVfXx+8Xi8ikQi6u7vR19dXEPI9Hg8mJyfh9/vLDo9Go/B4PHC5XBgfH8fg\n4CD8fn/B+xabbqmylpv20NCQup6CwSASiYSu2m5l3fv9fiQSCfVi3ampKaRSKaRSKQCZIL9x40b1\nB40QQvd6tiUpJR9SwuPxSCNMTEwYMp2SlDqDKsY/efKkvPDCCyUACUCGQqHap9/iTN/eZCvc3q2F\n21vK559/vn4zs8H3z5tvvln29fHxcTk0NCQdDocEIMfHx9XXEolEwTCFw+GQXq83Z1gwGJQAZCqV\nKjtPh8Mhh4eHc4a53W512PDwsARQMP1Sw4tNb3x8vKDspd5faVnzp62sp0gkomuaqVRKApBTU1M5\nw71erwwGg7rLVsxi27tSej8vACaljmzKmvQW0t7ejltuuQXnnXceRkZGcOONN1pdJCIioobh9Xox\nMjKC/fv3q10yLiYejyOdTheMq+fCSeW9oVAop3lHPB5HPB7PGbdUV5Da4bFYDOl0GoODgwXL5XA4\nEIlEyr6/nGqmrcfWrVsxNDSUc5Gu4o033qhqmo2CXTA2WReMi/nIRz6CwcFBdHR0WF0UIiKihuRw\nOBAIBOD3+xGPx4sGSMX09DSA6voCV5q0TE1NFYT6zs7OnOelpq8drkyv2Lgul6toO3m95a5m2npE\no9GcpkPa+en5kVTOueeei/3799u2H/WWr0mXTdQFox7t7e0M6ERERDXSG16VMK2E9Wrn4XK5ch7V\nBEtlesUCczKZrKl/dzOmrUwr/0dQMplEMpks2jVmM2n5kE5ERERUSqna2mAwmNNXuhLG829B73a7\n4XA4CpqN5N8cqRjlvcUufqzmVvfd3d1FyxKNRpFOp+Hz+SqeZj2mnS8YDGJ4eNi2NeBGafnmLq3u\nF7/4Be655x488MADOP30060uDhERkW2k02mMjY0hHA6jv78fPT09AICHH34Y8Xg8p521EhiVkPrw\nww+rr2/btk1tHuPz+RCPxxEKhQrml0wmEQwG4ff71fAfiUTQ19cHn88Hv9+PdDqNkZERuFwu3e3F\ntWUcHR2Fz+fD9PQ0BgcHkUwmEQgE0N/fX1PNtBnTdrlccLvdOTXx0WgUyWRS7SWmmbEmvYXdcMMN\neN/73odIJIL77rvP6uIQERHZisPhwP79+xEMBpFMJnHnnXfizjvvRGdnJ6ampgq6DxweHkYsFkMg\nEMhp3jE8PIzh4WGMjY3B7/fjjTfeQDAYLJhfOp1GOBzOaTLi9XoxNTWFdDqNvr4+bN26FS6Xq+j7\n9ejv78f4+DiSySR8Ph9GRkYQDAarvrDT7Gnv2rULwWAQ4XAYoVAI09PTRQO6sn6cTiecTqfaP3sy\nmYTH44HT6YTP56vqDIRVhNTeQKCFdXd3SyPuKLZnzx6sX7++9gKVIkTmr97tVmb8++67Dx/72McA\nAKtXr0YymcRpSm069wtdTN/eZCvc3q2F2xt44YUXcNFFF9VnZpV+v5ngyJEjWL58uWXzp+qFw2Ek\nEgn1x4tS++50OhGJROD1ehEOhzEyMqJeiOp0Og29cFTv50UIMSWl7F5sPNakt6BQKISJiQls2bIF\n55xzDgDgtddew+jo6KLvnZiYKHqKjoiIiMhKsVhM7ZrS5XIhHA7D6/WqTW2GhoaQTqcLuq+0K4b0\nFtTT04OBgQE89dRTuOmmm9Thi506m5iYwMDAgNomj4iIiMgOhoaG4PV6sXHjRjidTsRiMSQSiYJe\nZWrpDrLeGNJbUG9vL8bGxjAwMIC1a9firLPOAgD87ne/K/keJaCPjY2ht7e3XkUlIiIi0iUYDCKV\nSmF0dBSBQABdXV0FgbzWribrqeVDuhBisxAifPjwYauLUldKUL/uuutwzTXXlB2XAZ2IiIjsTNvU\nRWljPjAwgFgshlgsBiDT3FfbbabdtXwXjFLKHQB2dHd3b7W6LPWmrVF3Op1IpVIF4zCgExERUSPY\nunUrkskkOjs7MTIyAofDgV27dqnDu7u7sWvXLquLqVvLh/RWpwT1zZs35ww/ceIEfvnLXzKgExER\nke0pXVXmc7vdRYcDwCuvvGLr3nxavrkLLQR1oXR/hcyNFxjQiYiIiKzBkE4AgA996EPYunWhxc8D\nDzzAgE5ERJXbvj3T53mlD0U17xUiM1+iJsKQTqqvfvWr6v+f+MQnGNCJiIiILMKQTqq9e/eq/997\n772YmJiwsDRERERErYshnQAs9OKiUHp9YVAnIqKKbN8OSFn/B5u7UJNhSKecbhYV2u4ZGdSJiIiI\n6oshvcWV6gf9pptuwllnncWgTkRERGQBhvQWVu5GRcFgEM8//zxr1ImIWpyU0uoiENmeGZ8ThvQW\npedOosodSBnUiYhaU3t7O2ZnZ60uBpHtzc7Oor293dBptnxIF0JsFkKEDx8+bHVR6kZPQAcWQjrA\noE5E1IqWL1+ON9980+piENnem2++afjdS1s+pEspd0gph1asWGF1Uepm7969um5UpA3pwEJQ13bV\nSEREzauzsxOpVAqHDh3CiRMn2PSFSENKiRMnTuDQoUNIpVLo7Ow0dPpLDJ0aNYTh4WFd4+WHdCAT\n1HmTIyKi1rB06VKce+65mJ6exssvv4y5uTmri2SqY8eO4dRTT7W6GFQnRmzv9vZ2LF++HOeeey6W\nLl1qUMkyGNKppGIhnYiIWsvSpUtx1lln4ayzzrK6KKbbs2cPLr30UquLQXVi9+3d8s1dqDSGdCIi\nIiJrMKRTSQzpRERERNZgSKeSGNKJiIiIrMGQTiUxpBMRERFZgxeOUlF+vx+rVq2yuhhERERELYkh\nnYq67777rC4CERERUcticxciIiIiIpthSCciIiIishmGdCIiIiIim2FIp6L8fj8GBgbwy1/+0uqi\nEBEREbUchnQqKhwOIxKJ4Ne//rXVRSEiIiJqOQzpVBb7SiciIiKqv6buglEI4QLgAOAFEJVSJi0u\nUsNhSCciIiKqv4aoSRdCuPOe9wshvEKI4UXe6pZSxgHEAPSbVsAmxpBOREREVH+2D+lCCC+AUc1z\nNwBIKWMA0vkBXktKGc3+6wUQLTUelcaQTkRERFR/tm/uIqWMCSGmNYMGAYxn/08iE8DjQoj8mvKY\nlDKdDfkxNnWpDkM6ERERUf3ZPqQX4QCgDe1nAjm15qpsQA8ASAohxouNQ+UxpBMRERHVXyOGdN2y\nTWJipV4XQgwBGAKA1atXY8+ePTXP86233jJkOqWsz/7VO49qx1ccOHDA1OVpdGZvb7IXbu/Wwu3d\nerjNW4vdt7eQUlpdhkVla8H7sv8HAYxnm8H0A3BJKUO1zqO7u1tOTk7WOhns2bMH69evr3k6JQmR\n+at3u1U5fvZdWLVqFV5//XX95Wsxpm9vshVu79bC7d16uM1bi1XbWwgxJaXsXmw82184WsTDAFzZ\n/10oU1NOtUulUmiEH3JEREREzcT2zV2yteXdQoh+KWVUShkXQnRn25uns10s1jL9zQA2r1271pDy\nNotbbrkFy5cvh9PpxPz8PNrb260uEhEREVHLsH1Iz17sGc0bFjZw+jsA7Oju7t5q1DSbwT/+4z9a\nXQQiIiKiltWIzV2IiIiIiJoaQzoRERERkc20fEgXQmwWQoQPHz5sdVGIiIiIiAAwpENKuUNKObRi\nxQqri1KR2bl5U6f/zW9+E319feju7sYjjzxi6ryIiIiIKFfLh/RGog3m/oemTA3qL774ImKxGKam\npvCb3/zGtPkQERERUSGG9AYxOzcP/0NT6vOnE4dMDepOp1P9P5VKmTIPIiIiIiqu5UN6I7RJVwL6\n04lD6rCZ2XlTgzpDOhEREZF1Wj6k271Nujagz8zmhnEzg7rD4VD/Z0gnIiIiqq+WD+l2t/O5A3hy\n38GCgK6YmZ3Hk/sOYudzBwydL2vSiYiIiKzDkG5zm9adjcsvWIVlHcU31bKONlx+wSpsWne2ofNl\nSCciIiKyDkO6zXW0t2Hkeg8u61pZENSXdbThsq6VGLneg452YzclQzoRERGRdVo+pDfChaPaoK4w\nM6ADDOlEREREVmr5kG73C0cVSlBXmBnQgdyQPj09DSmlKfMhIiIiokItH9IbiTaQmxnQAWDZsmVY\nunQpAODkyZM4evSoafMiIiIiolwM6VbZvh0QovJHVseS9orGV59v3667iKOjoxgbG8P4+DhOOeUU\n45adiIiIiMpaYnUByL6uv/56q4tARERE1JJavia9ES4cJSIiIqLW0vIh3bILR7dvB6Q0/7GwoJlH\nBc1diIiIiMgaLR/SiYiIiIjshiG9wczOzePRZ1/F7Ny86fP66le/irVr1+LMM8/EN77xDdPnR0RE\nREQZvHC0gczOzcP/0BSe3HcQO/7zd6Z3w3jkyBEkEgkAwBtvvGHafIiIiIgoF2vSG4QS0J9OHMLJ\neYmnE4fgf2jK1Bp13nWUiIiIyBoM6Q1AG9BnZjOhfGZ23vSgzpBOREREZI2WD+l274KxWEBXmB3U\nGdKJiIiIrNHyId2yLhh12vncATy572BBQFfMzM7jyX0HsfO5A4bPmyGdiIiIyBotH9LtbtO6s3H5\nBauwrKP4plrW0YbLL1iFTevONnzeDOlERERE1mBIt7mO9jaMXO/BZV0rC4L6so42XNa10rReXhjS\niYiIiKzBkN4AigV1swM6UBjSpfYOpkRERERkGob0BqEN6kvahOkBHQCWLVuGpUuXAgBOnDiBmZkZ\n0+ZFRERERAt4M6MGogT1nc8dwKZ1Z5sa0BVOpxP/+7//CyBTm37aaaeZPk8iIiKiVseQ3mA62ttw\n9aXn1G1+jzzyCJYuXQqn04nVq1fXbb5ERERErYwhncp6z3veY3URiIiIiFpOy7dJt/vNjIiIiIio\n9bR8SLf7zYyIiIiIqPW0fEinxc3PzyOdTuPIkSNWF4WIiIioJTCkU1l33XUXOjo64HQ68eUvf9nq\n4hARERG1BIZ0KuvUU0/F/Pw8ACCdTltcGiIiIqLWwJBOZeXfdZSIiIiIzMeQTmUxpBMRERHVH0M6\nlcWQTkRERFR/DOlUFkM6ERERUf0xpFNZDOlERERE9ceQTmUxpBMRERHVH0M6lbVs2TKccsopAIDj\nx49jZmbG4hIRERERNb+WD+lCiM1CiPDhw4etLootCSFYm05ERERUZy0f0qWUO6SUQytWrLC6KLal\nDen8MUNERERkviVWF4DsLxaLYdmyZVixYgXa29utLg4RERFR02NIp0W94x3vsLoIRERERC2l5Zu7\nEBERERHZDUM6EREREZHNsLlLi5idm0dHe3W/yWZmZvDaa68hlUrB4XDg/PPPN7h0RERERKTFmvQm\nNjs3r/7vf2gq53kl7rvvPpx//vlwu924++67jSoeEREREZXAkN6kZufm4X9oSn3+dOJQ1UGd/aQT\nERER1RdDehNSAvrTiUPqsJnZ+aqDOkM6ERERUX0xpDcZbUCfmc0N49UGdYZ0IiIiovpiSG8yO587\ngCf3HSwI6IqZ2Xk8ue8gdj53QPc0GdKJiIiI6oshvclsWnc2Lr9gFZZ1FN+0yzracPkFq7Bp3dm6\np8mQTkRERFRfDOlNpqO9DSPXe3BZ18qCoL6sow2Xda3EyPWeirpjZEgnIiIiqi+G9CakDeqKagM6\nAJx22mno6OgAABw/fhwzMzOGlpeIiIiIcjGkNyklqCuqDegAIIRgbToRERFRHTV1SBdCOIQQXiHE\nsBDCYXV56k0byKsN6AqGdCIiY83OzePRZ1+t+kZzRNTcGiKkCyHcec/7lfC9yFu7AUwCSANwmVW+\nRlBLQAeAM888EytWrMCaNWtw4sQJg0pFRNSalO5yPxd5rqY7QhNR87J9SBdCeAGMap67AUBKGQOQ\nzg/wWtlxAMAhpYybWtAm94tf/ALpdBr79+/HpZdeanVxiIgalvZ+FifnZU13hCai5mX7kJ4N2tOa\nQYPI1IwDQBKAF1Br17UPhxBiSEqZBhDXUetOZQghrC4CEVHDK3bDuVruCE1EzWuJ1QWoggO5of1M\nAJBSRvNHFELEsjXxLgAFrxMREdWL3jtC13oNERE1h0YM6bpJKZPI1LYXJYQYAjAEAKtXr8aePXtq\nnudbb71lyHSMsj77V2+ZKh2/1dlte5O5uL1bi9HbOz0zC3fHDC79I1lyHIHf4fGf74JjWYdh8yX9\n+BlvLXbf3o0Y0tMAOrP/OwC8Ue2EpJRhAGEA6O7uluvXr6+5cHv27IER0zFapWXKH/+1117Df//3\nfyOVSuEd73gH/vRP/9S4wjUwu25vMge3d2sxenuXq0kHFu5n4f8Aa9Ktws94a7H79jYkpAsh7q30\nPVLKj1U5u4eR6bUFyDRjiZUZlwzy05/+FB/96EcBAH/5l3/JkE5EVCHl/hXFgnotN5wjouZk1JGg\nq8KH7u4QhRD9ALqzf6H00pJta56utdcWIcRmIUT48OHDtUym6bGfdCKi2mnvCL2sI/MVzIBORMUY\nUpMupfyAEdMpMe0o8i76zDZTMWr6OwDs6O7u3mrUNJsRQzoRkTG0NepP7jvIgE5ERZneJl0IcQ2A\nvuzTSSnld8yeJxmPIZ2IyDhKUN/53AFsWnc2AzoRFTD1qCCEmATwbSw0c/myEGKfEOIMM+dLxmNI\nJyIyVkd7G66+9BwGdCIqyrQjQ/Zi0qSUslNK+YHsoxPAb5DtUcUO2CZdH4Z0IiIiovox8+f7AIBi\nd/kMYKH5i+WklDuklEMrVqywuii2dvrpp2PJkkzrqJmZGRw7dsziEhERERE1LzNDegpAseTrLDKM\nbE4Iwdp0IiIiojopCOlCiDOEEJcUazcuhFhTQXvyRwB8WwixXPt+APfBRs1dSD+GdGpls3PzSM/M\nYnau8CY01OK2bweEqP9j+3arl5yITJQT0oUQY8jc0TMOICWE+Fbe+CkAfiHEonf5lFIGALwMIC2E\neDH7ngSAZ6WU24wovBHYJl0/h8Oh/s+QTq1EuVPkq9Mz8D80xaBORESmU0O6EOIuAEkATillG4Az\nAbwphPh/ldpzKeVhAKPQ2WRFSukD0AMgBOAuAB4p5YCxi1AbtknX76KLLsIll1yC3t5enHrqqVYX\nh6gutLdyl5B4OnGIQZ2IiEyn7SfdIaX8W+WJlDIN4CYhhAvAqBBiq5TyTSllWgghF5twtn/0tJRy\nN4C4EOJzACJCiASAv5VSvmzsopDZHnzwQauLQFRX2oCu3MJ9ZnZeDeq8AQ0ByDQ7qabpiRCZv3LR\nr1QiakHab5dEsRGklEkp5SCAz2fblOsVVP4RQmxFpiY9lJ3nfRWXlIiojooFdIU2qLNGnYga2ezc\nPB599lUey2xIG9LT5UaUUt4EoE8IcanOaXcBmMz+7wMQlVKOArgJNuqCkYiomJ3PHcCT+w4WBHTF\nzOw8ntx3EDufO1DnkhERGUOpjPhc5DlWOtiQGtKllKNCiK1CiA8LIeaEEJfkj5wN2Z0AhI5ppwGc\nL4RYAcAL4GFlMljkB0E98cJRIipm07qzcfkFq7Cso3hzlmUdbbj8glXYtO7sOpeMiKh22rOFJ+d5\nvY0d5Xz7ZEN4DEC3lPI/ir1BSrkLmVryxYwCmEDmYtSklPLH2eGDAHZVXWKD8cJR/X7zm9/gO9/5\nDr7yla/gkUcesbo4RKbqaG/DyPUeXNa1siCoL+tow2VdK9kmnWyPTRmomMWut+H+Yg8F3y5SysNS\nymfLvUlKuX+xCWe7YPQBGALg0byUQOauo9Rg4vE4/uZv/gaf+9zn8L3vfc/q4hCZrlhQZ0CnRsGm\nDFQMr7dpHBV/wwghHtZ7AamUcpeU8pFs143KsFE9IZ/sR3szo+npaQtLQlQ/2qAuIBjQqSGwKQOV\nwuttGkc13zI+AG6jC0L2xzuOUqtSgvo5ncsY0Mn22JSByuH1No1jyeKjNDchxGYAm9euXWt1UWyP\nIZ1aWUd7GxzLOhjQydb0NmUo9WNzdm4eO587gE3rzua+3mi2bwe++MVFR+sAcL+e6W3ROd9bb63u\nPgG0qGo/gZ1CiL8RQtybbf5yhxDi/zK0ZHXCC0f1Y0gnIrK3WpoysA07kb1UG9JHAISR6e+8D5m+\nz+NCiB8JIZYbVTiyl7e97W1ob28HAMzMzOD48eMWl4iIiLSqbcrANuzUyJq1F6NqQ3oSgFNKuVZK\n2SmlbAPwMQBrAbwshDjPsBKSbQghWJtORGRj1XQdyjbsTWL7dkBKXY/Zk3P46APP4KJbfqq+fU1g\nJy665af46APPYPbknO5pLdbUxewA3cxngKoN6UFtjy0AIKUMSym7kekHPVpzyciWGNKp4W3fDghR\n1WN9b2/V72WbTaqXSroOZXd8raee29zsAN3sZ4CqCell7xYqpYwB8Ash7qyuSGRnDOlERPanDepL\n2kp3Hcru+FpPvba52QG6Fc4AVRPSI8h0w1iSlDIOwFluHGpMDOlERI1BCepf9q0r2ZsLu+OzN6Oa\nioRCIUxMTACofJtPTK77QLYAACAASURBVEwgFApVXG4zA3SrnAGqJqQHAfQJIa5eZLyGSHBCiM1C\niPDhw4cXH5nwvve9D9dccw3++q//GmeddZbVxSGqXAXtNnMeimreq6PdJtC8Fz+RdTra23D1peeU\n7E6xmjbsVB9GNhXp6enBwMAAJiYmKtrmExMTGBgYQE9PT8XlriVAL3YsbJUzQBV/6qSUSQADAB4R\nQnyrzEWiDVGTzi4YK3PzzTfjkUcewbe//W243bynFZFRmvniJ7K3StqwU30Y3VSkt7cXY2NjRYO6\nolRAHxsbQ29vr+551Rqg9RwLW+UMUFWfPCllFEA3gA8ASAoh9goh7sz2nf5ZIcSLAMaMLCgRUbNq\n9oufWpm2mUGlcpoZ1HDBs55Hx5J23L/l3Xjh9g/h5eAmvHD7h3D/lnejY0l7y1wgPTs3j/TMrOWf\nO7OaipQK6gojAjpQW4DWeyxslTNAVZdeShmXUq5FpuvFFAA/Mn2nDwL4WynlbmOKSETUvFrh4qdW\npm1mUIlqmhlQ9ZTP4avTM5Z+7sxua10sqCuMCOhA9QG60mNhK5wBqnkJsl0vfkDpL11K2SOl3GVE\n4YiImlmrXPzUyvJDkR7FQtLcvFzkXVQt7edQwtozWfVoa11qnzQioGunVUmArvZYqLcXo0a1xOoC\nUGN58cUX8eCDDyKVSqGrqwuf+cxnrC4SUcNSvpBPlghg2i/kqy89p86lI6NoQ9Fi4adYSJqdm4ff\ntQlP3/KnOQFGV62hEJm/UmfIr3T8BrdY7W29A9+mdWdjx3/+rmhYBRa2ea1trbX75MHsMKMCukIJ\n0P6HpvDkvoNl91XlWPiJJ7+PT/3yh6UnuqXIfADcv8g4paxX/rn1Vls21WqOnxpUN6+++iruuOMO\n3HvvvXjsscfqNl/2ekHNqFkvfuLntZCeGvWSAZ1nW0xhx3Vbz7bWvb292LRpk/rcyICu0NMNKLBw\nLFzSJgybdzNgSKeKWNFPOnu9oGbVjBc/8fNaWrmgXqoWs1W6mrOCXddtvdpaz83N4cEHH1Sff+AD\nHzA0oCsW6wZUGWfkeg/+sPM0w+ffyBrnyE+2UO+Qzl4vqNk108VP/LwuThvUFY8//njJWkyrzra0\nwjaz85mserS1jsfjOc8fffTRqnsiMkJHexs+9MgIPvrAM7jolp9iTWAnLrrlp/joA89g9uScKfex\n2DMxofs+FlZonKO/SXgzo8rUM6Sz1wtqFc1w8RM/r/r19vbiS1/6kvr8mmuuKdnMoJ5nW7TbqBW2\nmd3PZOltKlKt0dHRnOfnnntu+QucTe4GtFRXoKPJnQ11LDRSay61Bm9mVJnly5ejvb0dAPD73/8e\nJ06cMGU+dmwrSGQms7+QzcTPa+XeObZwK5HjJ06gd8MG3cFFeejqy1yxSD/myjZUtMo2s/uZLD1N\nRaoxMTGR09QFAPbt24f777+/qi5DzdT+j7ctHvAVTdBfv1bjfAuQLQgh4HA41Odm1abbta0gkZnM\n+kI2Gz+vlXv99detLoJK+yNL0dA/riqs8dX+CPr7664y50ZOdaiF1lsm5foHoQ23ABKJBDZv3lxx\nl6Fknsb6JiBb0DZ5SafTpszDzm0FiSgXP6+VmZiYwEsvvWR1MQDwLEirUQL6zTffXHAm/PzzzwdQ\n5gLn7dsBKTF7cg6Pxn+7eDtxKTNdG1qgWe4rwJDeIow8wNajXbrd2woS0QJ+XvVT7yT6+OPqMJF9\ntAmBHz/ySMnAM3tyTn2Provpil1Ql1ezyrMgrUPbg9Db3/52dHZ2lhy3VFCvuPcm7f5WyUWdOt4z\ne3JOvchUoVxsutW1qSl+WPKI2cRquQio3Lg1hfQKTvnV1A6zktOQZCr2md0a7N621w5KdbN44YUX\nAgCklPiLv/iLos0MjGwzHgqF1HlUehZkYmICoVCo4nnWVbbGt5JHVT+A8h+LNXepdHo19FqSX6b8\nfe/aa6/FwYMHMTU1VbS4QGFQt1PvTa1yBohHzSZVzQFdb6i3oq/0VtXoAbdl+8y2UfvTemqGXmrM\nUu5ujnfffbf6/7Fjx3DNNdcUrb0s1Wb8zruCFbUf7unpUYPXL558Av/nf3frOguingXo6al08W2t\nFS6a3bt3b8G+19bWBrfbrT6XUuJXv/oVfvWrX6nDlKD+//37M7bqvalVzgDxyNmEqrkIqJKD1KZN\nm/CZz3wGt99+Oy6++GJzFoIaPuDaqdaF6qeRe6kxS35Av//++xGNRtXXr7zySnzoQx9Sn2u7wtNT\nY/hk6oyKLvRTgtfVV1+Nq666Cn/6J+9Wf1wpSgV0o+9IqZdZFRZNd9FsCcPDw4tut7POOgsXX3wx\nbr/99pzh7738/Xjx7RtsVWvdKtfB8OjZZKo5BVTpQeq6667DV77yFdx8882Vh3SLT/k1ikYPuOwz\nu7U1ai81ZsgPt1JK3HTTTfD5fDnj3X333ejo6MCGDRvwgx/8QG1mcOd3oovWGL7Yfh7+/kvfrLhH\nDiml2sOH8uNKYbeAbkaFRas0mdDrtddeAwDs2rUL8/MLy2zHWutWuQ6msUtPBSr9MPEgZT+NHnCt\n2Kds1yyoWX6MtmizHSPlNzN45ZVXcPDgwYLxLrjgAjz77LOIxWJ417vepdZ2d0zv11VjuO2v+3V3\nnaeE7p/85Cd49NFH1fdoA43dAroZFRZ2DJ/1cMstt+B73/seXn311Zzhf/AHfwAAOHToEP7zP/9T\nHW7XWmttUNeWpVkCOsCQ3nQq/TC16kHKrprhR1O996lGbxZEzS2/mcEzzzxTctw//uM/zum7ure3\nF9tuCuiuMSzZdZ5Gfugu9R67BXQzKizsGj7NdOjQIXzpS1/CDTfcgPPPPx9Hjx5VX9u4caP6fywW\nU/83q9ZaewFzpeMrFzCXOwOk1RAXPBfBkN5kKv0w2ekgVekHVqtRP4D5muFHUz33qUZvFkStZ+/e\nvRW/p5Iaw3JBXRu6e3p6sHv3bnzve9/D008/jfe85z344Ac/qI5788032y6gK4wK6q3SZEJLu0+8\n+93vxumnn64+792wQf1fG9KBxXtvuvurX6n4+1u5gPlrX/uaru9v7fjaC5iLnQHSauQLnptnz6uS\nEGKzECJ8+PBhq4timEoO6NUcpF588UX8+Z//Oa688kp8/OMfN6zc2h4HSil2QG7kD2A+O/1oqla9\nvvjMqGXjD8U8zdJsx0b0hvQTJ07gy1/+Mv7qr/4KQPk24/m0QV0Ri8VyQvf+/fuxceNG3HDDDbj5\n5pvxL//yLzk3t7njjjvw/e9/35KADtSvwsIOTSbqedzZtWuX+v/GjRtzjpPjh1er///bv/0bjh07\nlvPecr036fn+ztfb24tt27bhs5/9LJYsWVLR+Nu2bSu6b5YK6Fb92KxVy4d0KeUOKeXQihUrrC6K\noSo5oFd6kDp69Ch++MMf4l//9V/xi1/8wrAyl7t5giI/fDX6BzBfs9TsmN1ntlm1bNV80QDN9UOx\nUdjuOgQd5ubmyvZLrTh69CjWrVuH4eFhfPe731WD1WI1hlqrVq3KuVnN4OBgznHynHPOWbQcHR0d\ni45jFrMqLIoFYqubTNTzuKMN6Ze/f31Or27/lV6CM95+LgBgZmYGTz31VMH7S/XepKepVbHy33nn\nnfjKV76CO++8s+T9ASoZP3/6jZ4P7P1NTzWp5IBeSag3s5/0UjdPUDz10kE1fDXDB7CYZrkpTC19\nZi8WwMyqZav2i6YZ90M7a9TrEH7961/jyJEjAIDVq1eXHO+b3/wmzjvvPPX5Jz/5SZw8eTJnnFKf\no1gshg9+8INwu93Yt2+fOvyqq67K2T8dDgc2bNiAwcFBfPazn8XXv/51bM87gzE6Oqp72YxmVoVF\nqUBsZZOJeh13XnnlFbz00ksAgGXLluGh5NKCXt3a3rHQY1t+kxdFqd6bKlkObfk//elPF3zvK4p9\n3+ePv9j0G/q4LKXkQ0p4PB5phImJCUOmYxjlBHKF4584OVdylMOHD0sAEoA8/fTTDShkod27d8uV\nK1fKDw5/S1548+Nquc4L7JQX3vy4/ODwt+TKlSvl7t27TZm/XmZu7xMn5+SWB56RXdsel1seeKbs\nNrGzEyfn5I/jv9Vdfj3LrYzzR7f8VJ4X2Fnw+KNbflrTOlP2v5z9q8hnqeh4taj082pXJi5H/rav\ndVuXY/Tn+8EHH1SPnZs3by65nnbv3i07Ozvlqaeeqo5/zz33ZF4ss26/853vyCVLlqjvgaYRUWdn\nZ9n9VNmXlfEByCVLllh+jDVje5f83JbZHvX4vjH7uPPAAw+o+8XZf/wn6jrVfr+uuvrz6jjd3T3G\nLEfeMpQqv/Z7X3nPH93y05Lf9znT0cyjkvVjVWYDMCl1ZFPLw7FdHgzp+sefn5+XbW1t6gf5xIkT\nNRay0ImTc/KDw9+S7aedIVdfe0fOQWT1tXfI9tPOkB8c/pblwdXs7V1pwG10lXwhlwrqRoW2ar9o\nasKQXpbZ2zyf0Z/vv/u7v1OPm7fddlvZ9bR79255+umnq+M7HA558ODBou+ZnZ2VH/3oR3PCuRKy\nlfHL7a/Fws6pp54qu7u7bVEZYkaFhWU/xCstl4HHneuuu07dNzrX36B+frTfr3/4yR9JiMz3uxBC\nTk9P174cOgO09ntfeY9j/V/JtqWnyUuv9ssfjY3JH/7wh/Khhx6S999/vwyHw/KTn/ykdDgcuvbz\nYhjSG+TBkF7Z+J2dneqH/bXXXquhgMX9OP5b6bopE8jbli18YJXnq6+9Q7pu2in/f/auOz6K4n0/\nd8klpIcUqpBAAOlIEQFBchBEkC5E6UUg1p8gzdCbNLugCAgICGgooQtJSGiiFCGAIF9qRDqEnnq5\nm98fyU5273Zvy+1dLsk9n89+bndvZqfPPPPOO/NuPvGf6mHLgdOVdzGGEgJmb6mqGpIaWXCRdEHY\ne/WED2q37xdffJH2m7/99ptoPu3evZsjEHnnnXcs/Jw5c4bUqlWLQ851Oh3x8fEhiYmJovVWiBRm\nZmYK+ikK2ENgUSQTcbnxUqnfMZlMpGLFirSOdJnyE68kPWzCDuL1XB0SWr0+iZ04idy9e9fmdDDf\nX7FiBQkICCATJ04kn3/+ORkzZgzp378/WbFiBad9swVz5hNPvmvgwIHUvdz8cZH0YnK5SLo89xER\nEbSBnD9/3oYI8iMjx0CazEwgYRN2EJ967Wi8GIIeNmEHaTIzgWTkGFQPWw6crryLKWwhYPZWCzIf\naH788Uf7Ddwuki6IzSf+IxGxO3nrB3NFxO5UdeKudvtevXo1iYmJIY0bNxaUiptj9uzZtK/VarUc\nP3l5eaRSpUocwlK7dm1StmxZScTTWaXJjoTDJ+Iy46VUQmyOc+fO0ToSEBBAsnJyyZbuwwvT7MBr\nmhnJHjZsmEX7lkPSJ06cSN1PmTJFVr44O0kvHjvQXHA62HPzKADsOXsbj7MMMDy6jYyzyfS9Z9UG\nKBPWEADwOMuAPWdvqx62XeCy3GgVtmwEFTptQC3o9XrExMTQ55iYmOK/GakYoiQcTzpw4ED88MMP\nOHHiBEJCQsQ9AJg4cSKaNWsGABxT7QBw4MABaozG09MTI0eOxP3797Fp0ybB+sls7uvRowd69uwp\nqS4r2dhYXMCkrXfv3vSdM2w61Ov1aNOmDX3u1auXpPgIbbpPTi4cRyMjI1HGQ+c0beX27duc9p39\n7+nCP7Xu0FWsBd9aLRDWVI/effqgf//+GDx4MN5++22888478PLyos4XL15couqo+MGULpRKGIwm\nq2THFpJuMJqw4/RNdGlYSTCMLg0rYfupW9i5fhvnfdalo8j+9zQCa7yAVhEhTtPJuGAbmPLmO1IR\nKDzNQai8mdMG7IW0tDR6bzQaERkZabewXOAHMxnjO3qzOJ5+JBUajQZr1qxBvXr1OCSdIZLx8fE4\nfvw4QkNDMW7cOMnEUqPR5C+nSwSbqJe0SWpkZCRCQ0OBBw8AwCnSeOHCBcTHx9PnsmXLSiLoMWv+\nwoEL97D91C1Oe+jXrx8qVKiA5ORktG7dGgDgptVY+5zdUK9uXfxfVBQqVqyIChUqoFatWrR9d5u4\nBBe3zaNuy0fPxP1t89B64ChsmxMjeOrO5ILn2bNno2fPnoiPjy8ZdVSKuL00XC51l3y1Aca9mNpA\ndHQ0XWpau3at5OjIUU1gNpFodJ40Xlqfsk6zaZQQGeU9bVqRLCuSadPUS2wRpWFhm35FfrpNx44d\naXx8fHzstwQuo706NeyYjuJ8uosFZOTT6NGjCVjtgq36YFUVgieM+fPni6q7PHnyhGzbto18+OGH\nZPXq1fTv5ORkMn/+fPlpdWKcPHmSk7fDhg0r6ijRMZat9rHyp1WC7m1qFwVhCLk1Go287sWwdOlS\nThpWrFhhVWWHqY81hyygfsIm7CA1hyyQfLoLAFKpUiXX6S4l7SrtJJ1p4Ix7sQYeExNDSfqiRYsk\nRUVuJ8I0wFfHfMtpgG1GznAa/UhnGsTtjiIi6Vu6Dy9Sgp6cnEx0Oh2Nz/z581066WKQmQ57HNOp\nBtRs3yaTyfKljHx6+PAhCQ0NpX7YurcM6eaFCAGzIN0F7pctW0b7+I4dO0qKoyQ4ocAiNjZWcAJU\nFPjrr79o3jNxKt/pfeLpG0gSEpMs3DPtYWGbfk6Tt8nJySQgIICEh4dz6qCU4xelHLsstOmXybef\nf/5ZUjk6O0kvWeuCLigC23ojAzGrjW+++Sa+++47rF+/Hq+99pqsMKSYcGfrBO6Y/z437EYhJVY/\n0gVLdN/6I3TubkWiu8/UQ7ZFYkY3tLTXP7VMmSsxTGTvfQhMvB5lGVQxlPTs2TNUqFABnTp1wsyZ\nM5E/RstDYGAgpk6dSp/Zurfjx4/nXdq3Zq2ZgV6vx/jx4y3ed+jQgd7v37/fwkR8SQEhBKtWreK8\ns1f7lmoll70HhkGZhp1QrscEdO3ZG4lJhVZD2WNrnkl+vbIH2KpY5y9cpO9j1vyF1q+0tchfxn2z\nYTOR5hGO7DxuOrLzCNI8wtFs2ExER0fjyy+/FFVJevLkSYnop10kvZRDqXl1vV6P9957D2+99RYi\nIiJUDcN80475ALw3KckuG5mKo5lxh2L6dIAQGPKMiD/xHwx5RmlyFgZS3E6bVmTJMwdTD9esWYP7\n9wsnsGFhYSV6I51USDFlzteW2JYbzQmGNcGAOYSsHqoBJl7XH2SpYtH0xIkTuHv3Lnbv3o0NGzZA\no5GvC5ySkoIZM2bQZ7H6Z26tWU7eAvn1/PnnnwcAZGdn49ChQ7LjXBywbNky3LzJ3ZBur/FFymT0\niy++xPHjx/MfNNy6ra3cwIKoi226dzTY43frV9ri3bUn6X9MHWQTdYZwf/jpd7joFmb18ICLbmHo\nMuhdjB07FrGxsVZ1znfv3l0i+mkXSS/lsJd5daVhpKSkoGfPnjAajTh37hxyc3Mt3CcnJyMvL0/V\nBlhczYw7GqUln9gDTdWqVTn/5eTkYO/evWjUqJGi+qeWBLqoIdT+rElvzQdwOatrjgJ74kAgb+Ig\nhGPHjtF7JWbl2fnGwFr/p2R1lA+vvvoqvU9ISJAdb14UTPZlXwwkuDXkGTFs5VHUiN2JYSuP5gsU\neFbLUlJSMGrUKN5o2mN8EZuMJicnY8KECfTZp147i29pKzdAcLfx6N0nP17sU1G+bt0f4RN2cK7a\nsVuh8ygDH29vvNaxIzIzMizyqc7kXQifsAN1Ju8qzC9C0K9vX2gAaAB8tmCBcFkU5K1Q+2bAroOt\nX2mL2NhYSrhj3+4tenpTTeO/2LF6MT7//HPMnTvXarkkJycjNze32BN1F0kv5bDpWDOJxwr2bFIF\nl+a+jrT5XQSvS3NfR88mVaBv1w6PHj/Gg4cP8f4HH8DD0zP/OwUgAEY9fkwHPTUaoC3SvNIEh+ST\nAwZw3os1gJuv5Fy+fJkTxcDAQERFRWHv3r2K6p8UCTQf2BJoZ4F5+q1Jb8UIOoOiJOpy1fKk4ujR\no/RebvlZOw6Qr/4pXR3lg11Iup0htZ9i8pV9UhmDhw8f4urVq6qPL9bqVEpKCnr16gWjMQ8AoHFz\nR2DrfpxvmXKzkXViG8pn/YuNG/LjdejAfiwZ2BQtI0IsxnEvnRbVyS3k5WYjMzMTFy9ehLe3t+R4\nRUVF0W8lJSVZTSe7nvoFBGLk6uOidTAn10AJt1g6WkaE4JWy+SosH3/8MeLi4jiTXwbh4eEA8lXM\n/vjjDwCF7YTPvbPDRdJLCYQ6ZEa301rDcLZjzerWqQMPDw/6bEsDtNegXNJQWvKJjxCZk3Sj0QgA\ndPlf7kCuZOB3hnObhcBOT7eJS3glZ90mLuHE3xEreHKhJrk1B7tvat68uWR/UsrdvD6pmbeRkZHQ\n6XQAgFOnTuH2bee2SyGHEEdHR+PTTz+lqi5+fn70O8HBwRg7diwA2wRBUutUYtJeREdHY/PmzUhI\nSECTpk1RW/8G/EIqUPd5j+/i5pJhuJu4FP/8tgoNG9Sn8eIjuMz43VRXWM7t27eXFa+2+kJJ/oED\nB6zuSzh27Bji4uIQFhaGl5q/iHUT+yH97CHe/RdMHazdsT+HcLP5CAM2D4n9ZAJtB0J7Kdh75Hbv\n3k3vhdw7O5yHedkRGo1mSVHHoSggZdMQANGGwUfQL1++jB9//FH9SEtAdHQ0mjZtynmnpAHac1Au\nSbApn9hLzPbc+GkehkIjTsxAwyZEQUFBaNmypYVbto6u3IminIHfmQk6g9avtEWzYTORuOgTPLyU\nyvnv4aVUJC76BM2GzUTrV9oC4K7gjTq01urqmqM2C9tr4nD//n1cvXoVAODh4YEGDRpI8ien3Nn1\nyTf9f6oZffL19UWrVq3os5A01Rn288glxHFxcbRcAKBbt270nhCCAwcOUIKplKhLrVNrticjLi4O\n7dq1Q4cOHXD82DH8Gb8cL1UPpm51AaEIDM0vs+zsbHz99decfoc9jrtrNXT83pdSaMSIIelS43X6\nkTtq1apFwzx8+LBgWpnNy3PnzoXJaETu7cvIOLmTd/+FeR1kj99MOhjIFRR27NiR3u/Zs0eSH2dG\nsTBmpNFomhBCTrCeewN4BKAJIcSqgqZGo2lS4LZUgemwVhQ8Mx2UUGWnDWNo/rNYw9BqtRhx4wZG\nAPAJKo8KMSvwSq1QXj/mnWfa/C4AgPAJOwAUTgjaaM5h2JAhAPIHhytXruQbmAAKiZiCUxGEwHRU\nQjvi2YOyPQ3lFBmmTwdYG9GEoANoPbKKoTbGxwnAN9EbNGgQBg0aROsgYwQmNTUVT58+pRI4vV4v\ni0RLMQ5THAg6kN+WLrqFIaTbJ7i3tdAQSfa/p3Fv6zyEdv8EF93CaFtiGyZy3180BlXMYatBLSGw\nJ24vvPACZxVQCErKnV2f1q3/BUAIDl+6x3FTxl0jmfQwBu1effVV7N+/HwCQmJiIAQMGWLgTMqDj\nSEjtz/NO/k7zdTprohYdHQ2sXUuf79+/j3/++Qd169YFwJ2ISy0TqXVqycBZnDzTaDTwWbAAP82a\nSd9dnd+V63nePGDePOgB6AFgwgTLvnoocJD9/NZbwFtvoSeAngJx/vrlvliiH0jrelJUFC5cuAAg\nf5LWrp2lnjyDa9eucU7KeeXNd5CmYIWe/V5ufWrXrh3c3d2Rl5eHkydP4s6dOyhfvrxk/84Gp5ek\nazSaKADLWM9NAIAQkgTgEfMs4DcQpZigy900JKdhsPX4sp49sar7J2XWvv+fm4idVHi82Mcff1xI\n0O2EkmBm3AXHo2HDhgDyTbT/+eefNn3LmoSuuBB0oLAtla3xAkK7f0LfMwS9bI0XLNoSQ9SrBHkX\nRZQtYC/VPyWbRvlWdKSAqU8nT/yFRf0aw8dTx/nfx1OHRf0aC6aBb/XVXC+drb7gTPt5pPbny7+c\nRfM1JSUFv//+O8aMGcNJJwNmcsJA7oqt0jrFWOUuCrCl8Do3rSy99Pnz58NgMAAAWrduja2zR8ha\noeeD3Pbm7+/PWf0pLnsphOD0JL2AjD9gvXoThcT7CoAoIF+6bnYFAmgGIBBAdY1GU92R8S4qqKXC\nIdYwvHx8qWTRlJsFYswT/L6UzrPcrcO4c+MagHz1gjFjxoim1VYUR338EoFp09TfCGruXqG6ixQw\nJrUBqHIsHZuoM0hOTi42BB3gtqWyNV4ofB9cBWVrvCDYlnRuWnTetMTyJA4VNwsLnmAxZSrMwdcn\n2NoXKNFHFzr3XAr0ej1GjxmLD9adREZuHue/jNw8fLDuJG//L7Tpt37DRggOzle7uH37Ns6fP89x\n7yz7VJT051qtFq1atcLnn3+OMmXKWHzzwIED/IFJPDQBGg107m5YMbQ5/pndmaPO9c/szlgxtDmv\nDQiduxu6by0addIqQd6cfNLr9dBq8++PHz+OBw8e8Pq7efMmli9fTp+nTJkCD3c3m1RXlKJz585o\n2bIlZsyYgZdeesmuYdkbxZF9BIJL2oMBgBCy0ex6VEDwrxT4KRVwxIYsg9GEd9eehNbTh74z5WTQ\n75t30mKdZ7PnfJCWuJq+i42Nhb+/P2/Y2dnZ+PnnnzFo0CBERkYqTgMDewzKxQYSzz2PP/EfasTu\npEd6MWAf81UjdifiT/xX5ATaEVCbpAP5A+Hw4cPpc/fu3YsNQWfAtCW/i4V6oDnXz6FK5gXRpW17\nGSZSQiTZfYIG0tVD+EAIsfn4RblgpznbbBzIFki7tdXX99alYuKkyVi2bBnS0tJQp04dp93Po3Z/\nztZLdwZ8/XJfhI3bCl1AeXo04ldffinY3zZp3Ji627F9u6TjKjtvWsLJp8DAQFpvy5cvj0uXLvHG\n7bPPPkNOTg4A4KWXXqLGsGxRXVGK8ePH4/Dhw5g6dSrVqS+uKBY66baAEPIIQAe+/zQazUgAI4H8\nyrdv3z6bw3v27Jkq31GKQABTmgLPcowwsRrsmAb5EhWtRgNfT3cEPr6EffssG1tkwW/Kvn0Q0hR9\nlGVAE10Wtvn7V2aeJQAAIABJREFUID37GQBgSNVHKFexkLRrcAs7E/Yi0KtwuXVgONDWrzBO4xoa\n4eupwe8Ja+mpASEhIWjQoIFFHjLxOnDgAN5++216fvovv/yCChUqcNyG//QTws0syFmDoM61BB3r\nSNZ92uDBSCvQqVcLzPftVacIgGvpmXianYd1V06jarC3RbmrVafkpEGuHyVhSMHVq1exd+9eVKpU\nCczmFzc3N/r/4cOHkZSUBHd327vSRYsWYW7BPSEEGo2mSPsSJTh58iQu7P6J9Ybg0OJYLHrOhMaN\nG1v1WxbA7wf5CQAfIgt+hfKIqdtNPfLQuLYl0dJqbmHdlt28dR7I768eP9Ggnn8Gfj8oIFEVwYMH\nD5Ceng4A8Pb2xq1bt3Dnzh3JaeCDmB+mfzZPM9NeAW7/LJRPhe37Fnyr1kLVYG9cvXoVV69eFQyD\nDb4xwBZEFvxKyStmrHmabYRfGQ2qBksrQyYMHx8fZGRk4ObNm1i3bh0qV67McReeloZw6VFXDS3L\nmaB7ATga3Rurl30HAJgzZw7q1atH9zpEFrjdunUrUlPzN3FrtVqr/cnAcKBbeR0CvPjzacCAAYiJ\niUF4eDgyMzPpe+Z7Dx8+xOLFi+n77t27c1SFmDjJaUeMH3uPAUXN2URBCHH6C0Ai634+gKiC+94A\nxqsRRtOmTYkaSElJUeU7tiA3z0iGrjxKak/eRefLYRN2kNqTd5GhK4+S3DyjoD/GvZi7oSuPkjIV\naxDkj4WkwsAvSNiEHaLhmIeRnWsgtWrVot9ZsmQJf6KYeT8h5NVXX6Xuly1bZul22jSpShLqXtOm\nSSkeC8yfP58kJyeLppsPycnJZP78+YrCZdcTKeWmpE5JSYMqfpSEIQHLli2jdY0dRlhYGH1/9OhR\nm8NJT0/nhBEUFCRcJ5wUycnJxN/fn8THx9N0ACAREREkJCRE/fSIlPnmE/+RiNid5KuX+xZpf5Cd\nnU2OHDlCNm3aJDsNStJt3q7Z7ZWvzTL5xPxv7j5swg4SEbuTbD7xn2AY5pekfkEuZOZVbp6RbD7x\nH28cVq5cSZYvX07S09N5w+jcuTNt38uXL7c15hbfZzB+/HgaTq1atYjBYKBxt9bfPnmWQSpWrMg/\nbha437BhA/2/RYsW6qVBJB1NmjQhJpPJqnslYajuvgBFxdkAHCcSuGlxXMv/FQCjX14dgPWdDKUQ\nSo5UlGNCmvl+UNkg+s5UIFFnwql5OxmHDuzn9ctgycCm8NS54/jx45g+fTqaNGmCoUOHilpVZG/w\nSUxMFHRXXFAUxm3kqgEoqVMlAeZnpDNQW+Xls88+4zxv3LixWFnIS0lJQZ8+fTD6yRP06Fl4bgQB\ncOnyZdy7fx/6du3sd+wmjwoVsxfGXVu0p8d4enqiefPm6NWrl0PCk6ubrWQDvUP38zD63wxk6IL3\nbFKFV+d7yNChGPb22wgKDuatUzt37cK0gnvzzaNq4caNG/j222/p8+zZs+mKnFh/6+fjzdm3NX/+\nfOTlcfcfBAYGolOnTvDx8aFHL9oD6enp+O677+jz5MmTeY9dLEo8elSMzw+RwuSL8kK+tPwhgN6s\ndyORv2F0pArf7wpgaY0aNWycF+XDGSTpDORKxhe26VekEiej0UiSk5P5pW6sWfKpU6forD0oKIjk\n5eWpk2EFYciR/qhV3lLSLcm9BNgiBZNap6SkQVU/dpKk9+nTh9Y1dhiLFy8m5cuXJ2+88QbZvn27\nTWEkJycTDw8PizBsKWNHgonn5MmTybSi6D9YfYg5cvOMZEv34U4VJw7s2DaYds64V2OlzGQykdTU\nVNrvyVmNU4wiWhmdVtDuw8PDZUVX6spoTEwM7VuaNGlCxz/2yqi1/vbp06ckKCiIhISEkNmzZ5PM\nzEyLMAghJCcnhzx+/FhWGkTBCmPTpk3Ezc2NACD169cnRiNP2RfBGGAymcjEiRNJ48aNiU6nE8wD\nZ5ekizooLVdJUnfhoKDiihH02pN3FfmysFVSwmqAJpOJlC9fnnZwx44dszmbFJFPom5586afp6Oy\nlbyZL2/zXebL2xyI1Ckh97JgYwetFpo0aUL4SLrBYLBc0uWBtSV3QgrLko+ks/93VqLOjt/bb7/t\ndCSdEBuIpII6JVbeaoQhx4+cfk2M1KemptJ+t0GDBhb+ImJ3qk/QCSkykj7b3Z1UqVKFDBgwgGRl\nZUmOrpRx7OLFi8Td3Z32Lbt37xYV1PDl659//kmePXvG695W8E02rly5QpYuXWoRxpUrV8jIkSPJ\nxo0bCSE8aphFNAY0btyY5nF8fLyFl4wcA1kbv4tk5BjkxU0FuEi6zKukk3QhsAlbUZJ0UTJilo4B\nAwbQxvfpp59aOJczWMqROJlD7fK2yAc7kDab9UlV6DxV96PSwMSGyWQiAQEBhI+kS4EYeWHKctas\nWRZh3L9/n/z000/kjz/+cFqizo6XyWQiVatWtUhHZGQkfTdp0iQLfxQOIKuyiaSN5X3t+g2ydetW\ncvPmTdXCUOTHCskzhzVS/+zZM6LT6Wh5stMle3KiBCr0CSaTiYSHh9M0WKyCsfzcuXNHcVTFCHff\nvn1pHCIjI8nevXslCagYOGIfk3ka/v77b0l9YVG0byH3sbGxNM4xMTGc/zJyDKTJzATy7c9bSJOZ\nCQ4n6i6SLvMqrSRd7iYjNs6dO0d+++03SZ0CH/Hs06cPOX/+vDQSYhbGqlWrOJ0cX5qkDMg2bYgk\n9ilvTn6wBlg1yZoQUbfLRtBiStLv379P65iPj4+sMMSkt+yyHDx4sMXgxywfDx482MK9s4BNFC5e\nvEjT4OfnR9OxadMm+j40NJRKJItC0mZPKTdfebcZMZ2mfeDAgTaHodiPiqRer9fTNK1atUr6N9WA\nCuk+duwYjX9AQADJzs62LQwrsEZWwboWLVokS0Al+G0JaZDbj7DdG41GUq5cOaskXa7aplWoUN77\n9u2j+RweHk5XPxmCHjZhB/n25y0kbMIOhxN1F0mXeJVknXRCiKSKbitRlRIGH/EEQDQaDSlbtqx4\np2EWxs2bN2nj0+l0dMlP6YklcicoDOxV3kx+MXF6bfz3qpM0h6kBOCMRkYAjR47QOtagQQPJYYhN\ngBISk2hZGgwGEhQUZEHS2XsumBMfnJGoM1i8eDGNc9euXWk6DAYDqVKlijixk1N+TnZ6k1B5l32x\nG0339OnTraZblgTanm3DyfJW7XSPGzeOlgkzAbYpDBGw2yx7lYKJQ6tWrWQLqPi+zeDs2bPUfUxM\nDImLiyM5OTmC7uWmgVkB4IuTlImDo+t5Tk4O8fPzo/l9/vx5DkFnk3RHE3WpJL1kHskgA4SQ7YSQ\nkQEBAUUdlSKDWid3WDNcwWdVEQDc3d2xadMm2UZbKlasiPr16+eHazBg//79sk8scYThJ6Vo/Upb\nlG/KMse9cAKaDZuJ1q+0FfYkwwoenyU8axbwBE/V4DlZQ+x0HqVYsGCB4tNOlMTpypUr9D4iIsLi\n/8zMTCxZsgSDBg2iprOlGHmZuHQL1q3/BXq9HqmpqdSCH/ssZub+wYMHOHjwIIDCNsQ2juMsYJ+y\nxDYj7u7ujvfee4++DwsLc3jc7Alr5Z1x/X/0vnGTpuZeOf1RURj+KW0ghCAuLo4+9+nTx+5hsse9\nLrE/0Pf+L/aA1l2Hc//8o9hoGfvbP//8M3r16oV69erR/5csWYLhw4dDq9XSk8CUhMUO57nnnuP8\nFxsbi3v37ln9vkPqOXsMYo1VHp6eePL0KZ0VPV+7Nrw9dfhr6qvU8uuHA3rQ+7+mvgpvT530cdQB\nhvlKPUl3IR8MUWcglaDLaYBNmjShxJrBwoULFVtVZCyaabVa/H32nGwLeEqOHnMEmIH/7J719J17\nYAWkeYQ7/WBuy7GQYnD0UZXs4xf5SLqbmxs++ugjrFmzBnv37sWdO3ckTfwe1uiEZ8HPAwCaNWuG\nmzdv4ocffsCkSZOoux49etD7LVu20Hu9Xo/x48fTZ4PRhPiT14u0ThiNRiQnJ9NnNkkHgJiYGJw9\nexaJiYlo29bKJLMYQqi8idGA3LuFk7wHPlU4/8s58tYFdXDs2DH8+++/AICAgAA6fljDhQsXsHDh\nQvTu3Rs7duwQdc+H1q+0RZOhM5C0KJa+84poDujKoPnw2dYFLyJgCPRHH32ErVu3Wvzftm1bHDx4\nUDFBNw9n+fLlnPfz5s1Ds2bNrBJ0Vz23DS6S7gKFXPO9chrgn3/+iRdeeIFj2at+/fqIiYlRHN+h\nQ4di48aNuH//PmpGvSVbKu7Qs34lgmuem9D3ubcv4+GlVKft5BYsWIAvv/xS0WAgVcrNluhIJeq2\nSJDESLqnpyeH+P/++++KJn4VK1ZETEwM3n33XfrOnKTnr45ywdSVcRtOF2mdOHHiBD2HuFKlSqhT\npw7n/7Jly6Ju3brqBTh9ev7CNgOpShW2urdyFrt5eefe+xcwGgAAviEV0b9tA/oft43nw9qKn0PB\nTqNAvvTr25eaml8wf76svGVM0NeZvAvhE3agzuRdGLbyKAxTpgKw72rZhg0b6H2PHj2ohU4+MGWw\nZs0a/N///R82bdqE3bt3y46TwWjCiNXHcdEtDKHdP6Hv722dh9DusbjoFoYRq4/bVOZ6vR4bN26E\nTmdp2bVi5edsJujm4Wi13Lr+4MEDqwTdKet5MUKpJ+kajaarRqNZ+vjx46KOilPBnJiad54GowkD\nvtmF1R8UqmTwNUCTyYQRI0agVatWSEtLowYiAODM338rM1xScDVo2BBv9O6NskFB6NmkCi7NfZ0u\nW5lfYw+v45WKO5uRHkYy9+DiX9w/tG64t3UeHl5KFVbBYciL3KsAhjyjxX8pyckIDQlBSnKyVfLi\n7u6OsWPHIjY2VjZBlyPllkPUbSHoQP5APmbMGPTo0QMvvPACrxtzo0ZqTfzatm2LwMBAAMC1a9eo\neW8G7AEwz0SKdOBLSiq0JxcVFeV0hkzsCaHyzr19kd53jHyZlrcUdShnJzBsY3IJCQmS/UlRR7TH\nahmTl7/99ht9Z652yXYHFK4Ks1d+lBg12pp6A/v/dw8mApQJa0jf+zXujDJhDWEiwP7/3cPW1Buy\nv82GXq/Hwu8WW7xf/tMarF673maCzg7HfKVs48aNVgm6Q+q5yOTy+Vq16MRyx67daDozAeETCldG\nwifsQPiEHWg6MwGZOQbpY6dL3cX+cOmkSwO782Qa4MmbmTBmcC15sRvgtes30KxZM/z4449UElhU\nw3eVIG9BcqRU1cce6NKwEmoa/0X69s9Q7s1PC/8wGhDQZiDub5uHmsZ/VVPBEVNXkkKKU1JSMHfu\nXHz++eeYO3eu5AE2JiYGPXr0kE2i9Xo9YmNj0bVrV6txslWC1K1bN3z++eeIj49Hy5Yted3wWR7l\nI25yJ346nQ5dunShz/Hx8fRe7t4LWyGmUtO8eXMMGTIElStXthjAeb9nMOCff/5RNY5KISi5FdBx\nlbK3I21+F6Tv+Y7qwW7cuFHQLQPGX0zKmiLbByMVbDWRgwcPIjMzU9SPVNLW+pW2qqyW8fVrx44d\nw6ZNmzB48GCLeiq0Ktys+UvUCujff/+N9PR0SXEyR97ju7i3dT59fnpyF7L/Pa3oW3zIzM3DDzef\ng2cVtjqpBsHdYzH5CEFmbp6gX7mYMWMGve/atSs6duxo4cbZ9nux47g/OQkHJ+gR7MNdSQn28cDB\nCXp4e7g7JE5SUepJugvSwCZrc5dvxIEL95Dj5gU27SYmI4DCBjjjmx9x8uRJ+r+7u7vDTGObo0vD\nSlbJkVxVH3vh0IH9OL5iKjp8MA9BNRtz/nM3ZqHDB/NwfMVUHDpgu6lqqepK1og6e4D8+OOPZUm5\nf/nlF0VSV2ZSMHPmTNE4qSVBEkKrVq3o/YkTJ5CRkQGAS9TdtRpegv7NN9/gxIkTvKosAL9euqMl\nVFJUatq3b4+VK1fiv//+Q79+/QS/lZmZiU8//RTVqlWDXq9Hbm6uKnG0BUolt/aCu1ZTJPtg5KBy\n5cp0g2Jubi4OHDgg6kcOabN1tUyoX3P38ESvXr3w008/cVRdrKllfLz5PJo2bUbfM5u42X6tTWC7\nv1AZkc+H4tG+lcg8X+g3tPsnuLd1HnKunUbk86Ho/kJlXv9SkJmbhzbzU5CekYugqEL1UY8KEfAK\nb4T0jFy0mZ+iGlHPysqi93/88QdvGTnbfq/XXnsNAPDcc8/B398f3h7uODihcGxwVoIOuEi6CzLA\ndJ4LJ72PmsZ/4e3hDq2nN/3flP0MQH4DrGn8F9t+WoRBgwYBALy9vfHbb7+hwaZNhR+0plLBXNOm\nQQ24zZopWaVG5+7mkGUsc7AHnG1zYjgqOADgcfcfbJsTI1vSxAe5+oJM2b/xxhucd127dsW0adMo\nWZUqeY+OjsaWLVsQHx+vWGrGnhTw/W9vgg7k61szm6GNRiP+/PNP+h9D1D/r09CCoF+6dAmjRo1C\n06ZNUadOHRiNRotvd+zYEZ6engCAM2fO4PLlyw6VUMlVqdFoNHBzcxP8nk6nw+LFi3Hjxg3cuXOH\noyNcVFCyz8GesLbi50zo1KkTWrRogalTp6JGjRqi7uWSNjn9CB9Bl9qvSZn0ZgTVpO/YExIpE1id\nmxY9Q+4i4zyX3JcJa4hyPT7Bw+0L0K/KU5vK+9Od/yA9I3/Ca8p6St/nPb5LpfXpGbn4dKftq1dM\nnjMQKiNn2++l1+tx9uxZXLt2DVOmTAEADiF3VoIOuEi6CzLBdJ7HV0xFeG4a3Lz86H/GzMfw0mkR\nnpuG4yumIi4uDgMHDkRgYCB27NghuBTubAOlEOx1tCDzbfaAY66CAwCPrpwGMebZnF9KpbEZGRnI\nycmhz/v27UNGRgY+/PBD/PVXoeRKquRdr9crlpo1b94ccXFxSE9P5xyr5kiCzoBP5YWBzk2Lno2f\nsxiM2Ccx1KpVi5fc+vr6cvR/t2zZ4jAJlT1UanQ6Hd555x36vHDhQpviqBb46iCzkRFA/sbGgv0a\nVgUKrBURRv+1Xt26ghsoh6w4Qt1X/2QHhqw4gs6bljg9QQfy1YT++OMPzJgxQxJJV0La5PQjgLJ+\nTcqk975v4aZxRi9d6gQ2JSUF/fv1tfiuVgO81qE9tsdvRL++b9k07k16vQ6CfTyQ/e9p3Ns6j75n\npPXZ/55GsI8HJr1ex8pXxMHOcwbWysiZ9nt5eXmhbt26giu3zkrQARdJd20cVQA2UffyLNxRfveX\niaiQnkoJOgD07dsXmzdvFiVNzk7U7Xm0IN+As337duzetZPjLiMjAydOnABgW37JlcYSQjBnzhx0\n69aNV/9Up9OhcWOuak79+vVhMBjoMiMAbNq0iZdEy5GaffDBB/jhhx9Qrlw5vPnmm5g2bRrnW+++\n+64qBH3lypXQ6/UYPny46KkO1ki6ENg65my1FnP0798fAwcOxKZNm/DOO+84REIlh+yYTPLI+siR\nI6mqwZEjR5zmzHd2HUxM2surLpGYtFfyJHDo0KGoV68eWrRoIejGnC4Up+22StTUlOzV4FvB27Nn\nD285CPVrGf8cQNblY8jMzrFYZZIy6W0f+Qo90SQ1NRX3HzyUNIFl+iy+/W5ta4Vi6aBm6BDV3uZx\nz9vDHbNf0iB923zOCTJlwhoitPsnSN82H7Nf0thERK2tUEoh6gxaVA8uFitFTgUpFo9Kw9W0aVN5\n5qIEUBwtjip1n5ycTHQ6HfUD5Fv/TExMVGx9TIlVtOXLlzN7tEiHDh1E0yHXdLG1OKlR3mzz6gzq\n169PwJK9LV++nFy5coU3bhzz6hIgx9Lqs2fPSHR0NM1fdpx8fX1Jhw4dSJ8+fSzC2L59u4V7AMTb\n25u8+OKLZMCAAWTWrFkkLi4u30oe4bdKm52dTWbPnk08PT1JmTJlOPFgruXLl1P3ISEhZPfu3bLy\ngw/vv/8+/f6CBQuczhKjYmuxErD5xH8kInYnx3qm+RURu5P8tPcUCQoKIr169SJLly7lfsRK+xs4\ncCDN24EDB1p1z9c2pIRBCE/bkNC3JSQmEU/fQFJlwFxOu6gyYC7x9A0kCYlJVv2bh8GYIWfDERae\n7eFerbJg0h8Ru1Nyna1Xrx4NQ6vVkjlz5ljESahfc/MLJQCItowv6T5zHQ2PiZOU8mjSpAmts1Gj\nvrKwLitkVXjOnDnUX0BAgODYJ2W8tJa3ISEhZNeeRNJkZgInDU1mJpBdexJtslTMGzeeOAmlgW1p\ndfCKI9L7KHvXc6V+VAIkWhwtcnLsLJeLpCtz37ZtW+oHAKlSpQq5ceOGYIfObrBCHbRc4nnt2jXa\nEXp6epLMzEzJHZvSjoeBPcrbZDIRb29vDsG1FeblIWVgunnzJmnUqBGHELMnZdbyZvr06bwkne9q\n0aIF9cd8k/Hj4+Mj6K9u3bpkyJAhJCgoiLrX6XTEz89P8YDEoFOnTjScTZs2FR1Jt/fFMwkQMnNv\nTkR+XreO5tHLL7/M/YiVenv06FHqz8PDg9y5c0fQvVLyYq19C5EEJt1VBswlWi9/6r78W3OI1suf\nVBkwV5xUSgxDygRZLAzJUMm9mmWRm2ckm0/8J4mwnTlzhrcf6dKlC1mzZg3n23z9Gq1rPv4kIyub\nN06MP8aPeTmMGjWKfiewRW+rE9hK/eYQ/8AgkpycTDp06ED9jRkzRn6dlZm3GTkG6p5t4l6J8EtJ\nnOTmq1XYoZ5fvXqVfP/996Rbt27k2LFjqo2vSuAi6TIvF0lX5n7hwoXUT+PGjcmzZ88E3drUYEVQ\nu3Zt2hkmJCRISodFB2TmR0rHZo/yvnXrFjEnuLZCaKC0Vh6ZmZmkVq1aNC5lypSxyFuhPDIajeSf\nf/4h48ePp+41Gg3hI9uDBw+2iKv5AMtcNWvWJJMnTyZnzpzhlbwDID179rRJckQI4aQ7NTW1VJF0\ndt0wJ+rsOjJ8+HCaR9PMv8NTb9kTxRYtWlC/s2bNslrPk5OTSUBAABk5cqSkML744gveus64F+pz\nZiz5lQTph5Kq47eTwMihhfXW3YOE9JxIVxA2n/hPMM/EwjBfpWDcm69SCIUhlG6rUNE9u81lZGSQ\niRMnkmbNmgn6UUoO2fjoo48IH0lnJnn9+/cnwcHBgsSQcTtk6FCrcbJWfvHx8fQ75Wo0tDqBbdbn\nA5KQmET+/vtv6ker1ZKrV6+KlgWvgEpu3ha4Zwi6qHsrcbFlUpaQmOR0K0ZDhw6lZTJ9+nTVxlcl\ncJF0mZeLpCtzn5iYSP1Y6wBsXuIVwYcffkgb39ixYyWng4/oWby3AnuU9++//07TomYnIkTUhQam\n5ORkEhQURAIDA4mvr6/syYy5VHzv3r0kKCiIfPnll+SHH34go0ePJp07dyZLliyxjCxrgK1WrRqZ\nMGECOXHiBFUfEJpgASCtWrWyiRzk5eURnU5Hy+DJkye86ZYEMz+PHz8mHh4e9NtUhcnJ1GkIsa5S\nYzKZSFhYGE3HwYMHraabEG6ZrV27lvqtVKmS1bxNTk4m/v7+JCAgQHTl64svviAajYZ88cUXFulg\n3PP1OUzcGvd6l5SpVMuCFGo9fUi5V0eSwcsOkzlz5/ESPCkCiITEJNKszwfFUpLOgMmrpKQkEhoa\nKthPqUHQjUajRRidO3cmbKIOgPTp08dCgssuPwBk9+7d4nEq8GOe//fv3yeTJk0iCQkJ5MGjx6IT\nWEIIGTFiBA37jTfe4HxfFuTmrQQCLaVMbFFvctZ6/ssvv9Ayadmyparjq1y4SLrEC0BXAEtr1Kih\nOLPZKE0k3ZyEvTb+e0EJhc1LvCJg60A3atRIdjr8/QuXt+V0ZPYo7zVr1hA+km40Gsnp06fJ77//\nrvjb1tQAMrKyLUgwn1RSyqBhy+SH7ScwMJDs3btXchoAEC8vL2IwGBSThLS0NJr/5cqVs5puUZj5\n+fXXX7n1VMQ9g8mTJxN/f38CgGzYsMHCvRqrUXwQ0h++dOkSTYevry/Jzc2VlA6mTPbs2UMqVKjA\nW8/53CcnJ0te+WLXWSnCgb179xI/Pz8SERHBIX7mJA8AqfX882THjh2iKhaBbQaSajE/cPLMmSWM\nct0zaWnfvj1v+alB0JnvBAcHk9GjR3PC+P7774m7uztBQR9x9+5dqxP3oKAgkpCQIB4niXkltifk\n3r17nD00dBKrEklXda+GEoiE4awrRvfv3ydarZYwqxsukl6MLpckXZ57pkN8bfz31E/tybt4iboq\nDVYET548oZ22XAn0okWLOH6CgoIkDS65eUYSvytBdYLEp8995MgRKlF66aWXbPq+0GDWsmVL8s03\n31gn6Cz3Qt+18CdjADef+Fkl/zxxYvLt9OnTomEJYe/evfQ7LVu2FE23VZj5eeutt+i3p0+fLuqe\nwbRp06i/fv36EUKkqVeoAT794cWLF9P4dOnSRXI6CCksk0GDBlltr3xlJ3Xyx+6frAkHmP6qYcOG\nHDLOjpObbzB99+abb3K+zybc7DAAkODOoyw2EgqpZDxfxLq6StwnJyeTOR4ehe4deU2bRpKSkoiv\nr2++Sh0rTiEhIWTbtm2csujUqZP1fsAJV7Lklodi90ogEoYz7b0wn9C89NJLVvsdNlSZ0AhG10XS\nZV0uki7dvfkAyG6AfETdEZJ0Qgh55ZVXJDc+Nu7cucPx06xZM1E/TJoW/rxVdYLER17S09OpTrdW\nqyWPHj0SjJeUDVlC+txarZYEBAQIE3RCrOYtn7oBn3slBExKnJh8W7lypdWwrGHp0qX0OwMGDJCU\nbgaZmZlk//795NSpUxZ+cnJyqDSc6roLpMMcJ0+epP4CAgLIs8wsu+3vkII33niDxufrr7+2dCBh\n8AsKCiKVK1fOPz3HzD2valZuLjl16hT55JNPeFe+fvvtN86emBlLfiVaL39S/q05vP0OsyF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Vic1DpbXVEalPixE0GyNsnq/33+BnUqdFi2TFKcLl26RCZNmsS/58EJ+p3cPCN5bfz3xM3Ln5R7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\n",
"text/plain": [
"<Figure size 864x576 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"m_sig_all = np.array(res_correct['Model info.n_sig'].tolist())\n",
"mse_all = np.array(res_correct['Out info.MSE'].tolist())\n",
"mse_means = []\n",
"std_errs = []\n",
"h = 4\n",
"for i in range(50):\n",
" ds = res_correct[0+h*i:h+h*i]\n",
" mse_l = np.array(ds['Out info.MSE'].tolist())\n",
" mse_means.append(np.mean(mse_l))\n",
" std_errs.append(np.std(mse_l))\n",
"\n",
"my_set = set(m_sig_all)\n",
"m_sig = sorted( list(my_set) ) \n",
"\n",
"figure = plt.figure(figsize=(12,8))\n",
"axes = figure.add_subplot (1, 1, 1)\n",
"plt.grid(True)\n",
"plt.title(r'$\\sigma_{\\text{sol}}^2$ vs number of neurons. One-dimensional task', fontsize=25)\n",
"plt.xlabel('Number of neurons', fontsize=25)\n",
"plt.ylabel(r'$\\sigma_{\\text{sol}}^2$', fontsize=25)\n",
"plt.scatter(m_sig_all, mse_all, label = r'$\\sigma_{\\text{sol}}^2$ for all points', marker = \"D\",s=40)\n",
"plt.plot(m_sig, mse_means, color='black', marker='x', linestyle='dashed', linewidth=3, markersize=16, label = r'Mean $\\sigma_{\\text{sol}}^2$')\n",
"plt.errorbar(m_sig, mse_means, yerr=std_errs, ecolor='r', lw=2, capsize=15, mew = 3, zorder=3, label = r'Std.error of $\\sigma_{\\text{sol}}^2$', linestyle='None')\n",
"\n",
"axes.set_yscale ('log')\n",
"plt.legend(loc=1, prop={'size': 20})"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"m_sig_all = np.array(res_correct['Model info.n_sig'].tolist())\n",
"J_all = np.array(res_correct['Out info.J_fin'].tolist())\n",
"J_means = []\n",
"std_errs = []\n",
"h = 4\n",
"for i in range(15):\n",
" ds = res_correct[0+h*i:h+h*i]\n",
" J_l = np.array(ds['Out info.J_fin'].tolist())\n",
" J_means.append(np.mean(J_l))\n",
" std_errs.append(np.std(mse_l))\n",
"\n",
"my_set = set(m_sig_all)\n",
"m_sig = sorted( list(my_set) ) \n",
"\n",
"\n",
"\n",
"figure = plt.figure(figsize=(12,8))\n",
"axes = figure.add_subplot (1, 1, 1)\n",
"plt.grid(True)\n",
"plt.title('J_fin vs number of neurons', fontsize=15)\n",
"plt.xlabel('Number of neurons', fontsize=15)\n",
"plt.ylabel('J_fin', fontsize=15)\n",
"plt.scatter(m_sig_all, mse_all, label = 'J_fin for all points', marker = \"D\",s=40)\n",
"plt.plot(m_sig, mse_means, color='black', marker='x', linestyle='dashed', linewidth=3, markersize=16, label = 'Mean J_fin')\n",
"plt.errorbar(m_sig, mse_means, yerr=std_errs, ecolor='r', lw=2, capsize=15, mew = 3, zorder=3, label = 'Std.error of J_fin', linestyle='None')\n",
"\n",
"axes.set_yscale ('log')\n",
"plt.legend(loc=1, prop={'size': 14})"
]
},
{
"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": "3.6.8"
},
"varInspector": {
"cols": {
"lenName": 16,
"lenType": 16,
"lenVar": "20"
},
"kernels_config": {
"python": {
"delete_cmd_postfix": "",
"delete_cmd_prefix": "del ",
"library": "var_list.py",
"varRefreshCmd": "print(var_dic_list())"
},
"r": {
"delete_cmd_postfix": ") ",
"delete_cmd_prefix": "rm(",
"library": "var_list.r",
"varRefreshCmd": "cat(var_dic_list()) "
}
},
"types_to_exclude": [
"module",
"function",
"builtin_function_or_method",
"instance",
"_Feature"
],
"window_display": true
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| gpl-3.0 |
sainathadapa/fastai-courses | deeplearning1/nbs/lesson1.ipynb | 1 | 907011 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Using Convolutional Neural Networks"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Welcome to the first week of the first deep learning certificate! We're going to use convolutional neural networks (CNNs) to allow our computer to see - something that is only possible thanks to deep learning."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Introduction to this week's task: 'Dogs vs Cats'"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We're going to try to create a model to enter the [Dogs vs Cats](https://www.kaggle.com/c/dogs-vs-cats) competition at Kaggle. There are 25,000 labelled dog and cat photos available for training, and 12,500 in the test set that we have to try to label for this competition. According to the Kaggle web-site, when this competition was launched (end of 2013): *\"**State of the art**: The current literature suggests machine classifiers can score above 80% accuracy on this task\"*. So if we can beat 80%, then we will be at the cutting edge as of 2013!"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Basic setup"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"There isn't too much to do to get started - just a few simple configuration steps.\n",
"\n",
"This shows plots in the web page itself - we always wants to use this when using jupyter notebook:"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Define path to data: (It's a good idea to put it in a subdirectory of your notebooks folder, and then exclude that directory from git control by adding it to .gitignore.)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"path = \"data/dogscats/\"\n",
"#path = \"data/dogscats/sample/\""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"A few basic libraries that we'll need for the initial exercises:"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from __future__ import division,print_function\n",
"\n",
"import os, json\n",
"from glob import glob\n",
"import numpy as np\n",
"np.set_printoptions(precision=4, linewidth=100)\n",
"from matplotlib import pyplot as plt"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We have created a file most imaginatively called 'utils.py' to store any little convenience functions we'll want to use. We will discuss these as we use them."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Using gpu device 0: GeForce GTX TITAN X (CNMeM is enabled with initial size: 90.0% of memory, cuDNN 4007)\n",
"Using Theano backend.\n"
]
}
],
"source": [
"import utils; reload(utils)\n",
"from utils import plots"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Use a pretrained VGG model with our **Vgg16** class"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Our first step is simply to use a model that has been fully created for us, which can recognise a wide variety (1,000 categories) of images. We will use 'VGG', which won the 2014 Imagenet competition, and is a very simple model to create and understand. The VGG Imagenet team created both a larger, slower, slightly more accurate model (*VGG 19*) and a smaller, faster model (*VGG 16*). We will be using VGG 16 since the much slower performance of VGG19 is generally not worth the very minor improvement in accuracy.\n",
"\n",
"We have created a python class, *Vgg16*, which makes using the VGG 16 model very straightforward. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## The punchline: state of the art custom model in 7 lines of code\n",
"\n",
"Here's everything you need to do to get >97% accuracy on the Dogs vs Cats dataset - we won't analyze how it works behind the scenes yet, since at this stage we're just going to focus on the minimum necessary to actually do useful work."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# As large as you can, but no larger than 64 is recommended. \n",
"# If you have an older or cheaper GPU, you'll run out of memory, so will have to decrease this.\n",
"batch_size=64"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Import our class, and instantiate\n",
"import vgg16; reload(vgg16)\n",
"from vgg16 import Vgg16"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Found 23000 images belonging to 2 classes.\n",
"Found 2000 images belonging to 2 classes.\n",
"Epoch 1/1\n",
"23000/23000 [==============================] - 252s - loss: 0.1263 - acc: 0.9683 - val_loss: 0.0734 - val_acc: 0.9810\n"
]
}
],
"source": [
"vgg = Vgg16()\n",
"# Grab a few images at a time for training and validation.\n",
"# NB: They must be in subdirectories named based on their category\n",
"batches = vgg.get_batches(path+'train', batch_size=batch_size)\n",
"val_batches = vgg.get_batches(path+'valid', batch_size=batch_size*2)\n",
"vgg.finetune(batches)\n",
"vgg.fit(batches, val_batches, nb_epoch=1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The code above will work for any image recognition task, with any number of categories! All you have to do is to put your images into one folder per category, and run the code above.\n",
"\n",
"Let's take a look at how this works, step by step..."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Use Vgg16 for basic image recognition\n",
"\n",
"Let's start off by using the *Vgg16* class to recognise the main imagenet category for each image.\n",
"\n",
"We won't be able to enter the Cats vs Dogs competition with an Imagenet model alone, since 'cat' and 'dog' are not categories in Imagenet - instead each individual breed is a separate category. However, we can use it to see how well it can recognise the images, which is a good first step.\n",
"\n",
"First, create a Vgg16 object:"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"vgg = Vgg16()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Vgg16 is built on top of *Keras* (which we will be learning much more about shortly!), a flexible, easy to use deep learning library that sits on top of Theano or Tensorflow. Keras reads groups of images and labels in *batches*, using a fixed directory structure, where images from each category for training must be placed in a separate folder.\n",
"\n",
"Let's grab batches of data from our training folder:"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Found 23000 images belonging to 2 classes.\n"
]
}
],
"source": [
"batches = vgg.get_batches(path+'train', batch_size=4)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"(BTW, when Keras refers to 'classes', it doesn't mean python classes - but rather it refers to the categories of the labels, such as 'pug', or 'tabby'.)\n",
"\n",
"*Batches* is just a regular python iterator. Each iteration returns both the images themselves, as well as the labels."
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"imgs,labels = next(batches)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As you can see, the labels for each image are an array, containing a 1 in the first position if it's a cat, and in the second position if it's a dog. This approach to encoding categorical variables, where an array containing just a single 1 in the position corresponding to the category, is very common in deep learning. It is called *one hot encoding*. \n",
"\n",
"The arrays contain two elements, because we have two categories (cat, and dog). If we had three categories (e.g. cats, dogs, and kangaroos), then the arrays would each contain two 0's, and one 1."
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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UdU0XGkxrqabTbBx2LdPK44xgbC/rRmjWK0LbUVUVMXQcHBywXh3TdYHJNBu7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\nvbvlchswMdHHO6IdGO0JNhZHwOQR0oix6kC0TUNjhU0D6YVH+oBNHU1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e9qeYUT8bbd23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\nB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17+BDfv3mS327BYnWKMYxg05BqyJdbN7+Yj+VseT94b1cQkgseB1lTeMkY+Rot+UGUt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MZGj0oEy2WNC4EkicqlEolWnkQHBI7z01N0iMrE1hikdbCCEjicM7HrrCxOGKSMleSymmNs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\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1c2fY3d2hzKYESqHznEKHXL32EkZOWVnqsL89QEk4s9TCoLFlm96pM+Aex3GAEN1qNngE\nli+A+ALZxsYfMJz+LCutn2FaSqSa4BTIwNJrN7m2Idndv8Hb3/IwggyIEZQI08LJCS4cc+Z0i0D0\nCOKP8NKVf0AjuYuJvVlNwCroxU+xo56nQyJxBPhMN0e4Fr1kjX62gS5+h3fc/5d59vkJFkEYOIQJ\nqIxUUEFAM/khJtn3Mrj8RW7sXuHu80toeZr8sODsyiK/9Uuf5pmrX+F9F74fCmDWMZ0rkEk3S4L+\nLI5X6hae9Iw+6TNcJ4TzxezX6wzWaJ36/wuCowSzjp9rTnUcxwyHQ6LIC8ke60bPJVQvS0wrdItz\njqCiiCEF2hoKXdIMY1rdDnffcx/jacHC4jK3bt0iTGIavaVjnVelFNbOJ9Z14uu4dv0yjz76BQZD\nT5vc3d3lIx/5mC82BhJrvfNAGEJaiavVLkN1ggtuRj2qr2V97aSUvjh54j7Vf1t7PLGui91KeVeh\nmuZVd7Hr6zfvTiSl8hSdQBEFgkkFD6+vac2pjqKIJAoZ7uzOXjda00wbRFHo1z4p0eK4irkDrPHQ\ndK3NDH0Wx/ERxWquIP16ifQrvT+fQM+/f7LI8lrH+OOOP1OJtRReCt7okqDqhAAznqIQgslkMutW\nFIVHyHjf5pIoChGBQEmJcpIgjkjTECkHPtkOqyTaaKzTKBnNFgoPtTJobY9VmXylxSfWZalxgX89\nzwuiSMwSdudc1XFxqEpR0OKFNpRShIEhz0uUDBDCe8CKQM0Sa+cgUAmxSoijAhMb5NQizMsXbelx\nKt7X0zioPe+cm63xxviCwwsvvMDb3v527rzzAo995cvc9+638twLTzLJ+ywtraBNCUJz370XeP7S\nM3zn/Z9geclL7jdSX+2TAkqncMovdIPDA3qtiF4zIQ0FynnoXT4Z0t/bZWPzFtPxhDgOOXf+HpI4\nYWVxCemYwW/29/d58aXLXLj7XsajAe1Oj4O9XZRwLC0t4KTC2JLLl68yHE9QysM7+wcDsizz4mTS\nw9Z90t3m9satWRes3W5z/tzdYCS3bm6yu32DtV5JJ4kpdO7nVekrkUnaRIh9hINIpUQNiRETZBD5\njd1OccrgiLCuFoAAQ46TJcooQhMhTQi2RJQBgU0JzBRRCGSpsGWAMwmOksJIyjJHoE5Uci3WGlRQ\nV0x9xydOFMJA1xpuvlkP39d5WOtwxhKqwFMXquKK1hqpfKczz3Oy6ZggCIiimIv3P0C/32d7d49S\na3ABovKmb0Qp3YUeh9msnegrrPhjKymRwtuoMVe1jaSs/GTbiF6P4Z7AuKOspixLRAVjDpOYVhgh\nAvX/c/emMb5m+V3f5yzP+t9qr1t1696+t7fp7mnPTM9me0Js4ww4DoQE4UVKghQrsmQiAq+IIl4l\nL1BCpCQ4FvGLABFOAkgEG8kitjAiDsYL9nhmeraeXm/frapu7f/t2c85eXGe51//qr4zZkIC0zyt\n21X1X5/lPOf8lu9CURZEytsHDYdDXnzxRb7w+7/LdHqOVII4jsmmGWdn55wen9Hv9QiCgK9+7XV+\n4Ad+oOUfeaGnhXaD8PzuTvPBmNonC1LS7/cpq5p+5x7QNJR1RRhHqKLCOIdUXjis4w6vr6/T6/Va\nCLRaiO9lmbf4CaPUV7HpurgAHqqdZdmiSBdG8ZWOrO90W+bzCWHi+W5CSuqyWiT0uoOOhyFNY4mi\nGK01Vdkwn+d+zg4Cavy8KJVGq8ALUwovkJbnOU1TkyRezb0sKqTwneoOYtZV3LtjXq7Yd7SgYCl4\nWYjVtHxtz//ueY2FVljNW5+o1p2BVrTSw759h95X0wWdX6gP4S3L2bAAnlItX+BXfQXcukvP1UU3\nAvi25LLvoi0WkGoPj2wa47uWLWrGokHLFpHr/zNtYXoR8HIZ9DYtFNxZL2gmwPtYWgiDEF+l6KCT\nBikVyoZtQuNpOc4K7AIFbLxKsHQEKminhZb/ai3CGoxtENLzwYWDQCuM8GumdAaJL2I3riaNU5TU\nREFEXTYkrUr5vC4wwqBdzWCQMugNeHCUkTmHdI71rR1G8ZB77z/g2Zdf5iKbQd3w/jvvjmiqrgAA\nIABJREFUsrq6wmwyRtmaIss5mxxy++YeD997j+HqBqZuOHxyQF5XJE4SKcmwl2JsQ+VqpPa2e10D\nYJLlbG1tIaRie/smt2/f4fXXv8qPfP5H+eWTX2R8dMhRNuN7PvMp7j9+REbN1tYGcqI4vv/AH7OY\nIMKcpoKT4wnf/6mX+ZV/+h8T8GVOj07opzOaeh8d1Dj3PFbWCEqgaH1IAjA9sNusjI5p5D/kbP6P\nCNWrzMawc7vP43sz3nyn5qWbf4Pc/EPK+u8SiW+AfQEh74MwIEsakVMhUHaL51Z/nN7wDhYJeb4A\nTAsUTpjLrLpNtr2H9B7oY2SQI5oDLoox/XjObPqX+eIbf5We+kmefe7jfPPhb6HtOdoIKBwqXaMp\nTsnslOGzFX/hb/wZfv6//As4tUVMyNlbGTd2n0Vv9cB5+Ty/M34setSON2r7sKTVfj7y89JCKblN\nbpbRRta6xeu9F3H37xK+7BM5uehU0iW5oktgrsO9uyJb+ztXk6pFIiQlZV6ghKSpak8VabxXeAdx\nXk6oO9rPohNuPPddCm/B6FEGEMQRjVonWblBIwcM1yNKq/j6177M7Vs7+KKRd40JQl9AUlrQNHVb\nSPFzU1kVvPfee1RViZSS2WxKEq0TBStIKakri+gJpLTowFLmmbfMcw4tvS2cxHlUzlPOweI4ZCvZ\n6Fho9lwm1ZeWWl2XuvsJlxofncZJ12k2xuCkh6G7VouKFtljnEFYBS70cQkSqTTWVjTteFnYeUmJ\nCDRWSSrhUMIh28S6MQYlIqx1Ps5yEiUTAhUQBTEC6ZFOzq8Vi0hOdIg+2R6f81RIHM61nfS2pAuX\n1M3umK92/kVbyLk8H52ntneccVdsyf7fJNz/vLoK3xXboirk3MJ+pUvEuuc72fZLmOHVqpu/uUWb\nJF9CI7okXLZiKd0NugyluByAV20CZKsY6wcvC5iiWKpkdZ8Bl5N+i2laTEhd9WhRbWvfJ5dgH4Cv\nguK7BYgPXsK2cEq30lxCHBwd9Qi8kl+WZezv75MkCXt7N/nlX/5llHbEiUZrQX+QMJ9P2b15g+OT\nJ+T5lCybUNc5+/uPiKKQNI2pygbVWu9MpxPSQLIySgm1JE40SaQ4OfL+uk8O9mnqnK9+5cu8++7b\n7O8/4s0336DXS9ja2iAMNWfnF6ytrbG7u+t50LbxFlmRRmkBeDXhk5MjLsbnbG5uszJapd/vk6Yp\neZ5RVjn9froIsuM4Zjbz6q7OOebznPmsYLiywc7OTXQQUhuLVfjKndM4F4Bo7XLwNmtKRwglcKJV\nCsZPAI1pkYhCtYkvWGF990wqpFPQdmG6zxOmwRnf6fHXSnm+ibgKFe3UILub3yMnWgswHIhmEdB+\nGLY6r6jyKf0kwJmGSMfMZwXWOOqWX1oVOXWZEyYxxjm++MW3qEvND/3gv818WmJrFgWrygn6o3WS\nXt9ze4T0/HftE7848tdEKOFtWKqKqiwIVQBYBoMBw5U14qRPUVlP+ZMOpR1hJMmyGXVV+cDAGM7P\nTnn8+BGPHj2kLHM++alP8NM//Z/w2muvUVcN9+/f5wv/7Mvce/s+uzd2ePnFF/iRf+uH2X9wj6OD\n+/T6IaOVni+KCIMQhjgOiaIAqWrm2QVl5dWxdRiwvXOb0/MZTliMqynqirIuCOIIIyFvSu/NniSt\nI0KFa+fJqqpaFIhZ8Mu9PVTjYd3G0Ov1Ft2ArkjpAypFmqaLanRVVYsCyPn5+aK63dR+3u14a36u\n8mNUKksQSIJAEsWaONFYV1OWOXVTYK1hPssYjzNfLHEBgU7QKqYsDY8e7XNyfM79+w85PDxpCwVy\ngW4Rwusi1HVNns+xtiFJIlyryNqJwCl1qcLd7WdVVQs+eje/1i1SqUt0g8Bz0/1ruPLaqqpwGIyt\nMbbGYRCym5Z9oUQqgdICqdrHhcW6hsbUC//Sbn1ZWIv8y7oR/0U34a8zeOFOIZzv4DpvbWOMoapr\nGmdxQiyKMIu3t3OaFLINlcAHjD64dK31masNrjHYukHiENYgnEHaVrSx6+YACs/PFm2HWzoPz/Q1\nNYsTmqYNYYUMkDIEpbAIhLEIZwiUIAo0YaCIw4hAt0WwpqIuCwIglAJHgxXekrE3SMmrnLKqqGno\njYY8d/cZnIOz8zEbG5fK9mdnZ2xvbjKfzSjynEgFzKdjer2E8/Nzf1pNw3R8zvnJE4p8Tl1lzCZj\nLD7QzauCylREoaZuPd+rsqaq/b1+cnLGgwePGA1XuX//Ps45+mmPNIy4f+99+oMeSZricMS9GIEj\nRGE4o6xhY8Ofx9e/8gZWfBMhQta3YoL+GTqw2OYOdv5DGLuDEQO/HooS8DZrNEO0bAgouHfv7xBG\nsLXe52S/AAQbqztobrGe/lFss+e7y6bvr6CcgpwjRY/jowBpb1M1A6bzC+aTfXDVZXeSANuOQotH\nLhg6KGkESE8zUI5hX5DXMBjm3H/nbxOFc95893fAecXwBo1pNsnLjEG8QShvYHlMdfElHp3nPL54\ng8GGYnM4Ij8t6ck1sqzyRcluvZYtJz8IFrHbh2Jrk45vBZF92u/LccrlY98C/n3l76sQ56uf+UGI\n97fsereJP9e6mtf348r3uEtEjWmTsCRNWF1dJU17HrrsHMPhkMPDQ3r9dLGeXRZuLy2guiSsLEvK\nsmQ8Hi9sXo0xzGYznHMLkdJlwbLrucliP59yPJc/uw52F2926Nvun75STFh2HVmGgy9/X/dY0TYQ\nRYuWvcL/FnJpP1qON1cpWJcCzx+41Jenf6mIvIx6UMrzrK+/9ml/+9LMVX751evd7YNY+im4ujNX\nz2mHaH7aef9Otw9Vx9p3By2q9agFFkFWWZZEUcRwOFwIyXhoAZycnPLycyuAX1hxDqU1TjYL4n4Q\nhORFTtVUxHGIcIayqNA6REpBUZStqADQcgurqkK1/sRJ4rs1dTPFOYEVFlc32Npf4E7FsKoCBJbK\nCkJl8SJFiiBQ5FlJWQao0EFrBxCkKU3jCENFnpckwxREiRM1QWTRzqGNpqovE/DGGC/8glcXXwyT\npYpuNyEZY/jKV77C//1bv8ErL3+Ef+P7Po7Tki999St84ns+RpFnPHz4TVZHI27vDtla19j6jN29\n51jp7+FMRp1DpRRroxUevPcmX/it32KYQj/I+dj3fJJAw3v33ubth4cMR6skachgkPIn/viP8t69\nA7Y31jk+PubR/fd5/Yu/zyuvvMKnvvdzTKdTDp4ccf6Nb+Cc42D/EYaSIq9QYcT2jZukaZ84FSgZ\nMplkzCdT3nvvHZqm4dm7t+n1IzY2Nri4OOPu3btMp2sURcHR0SFr69s0pSWfFZS1oypq+r0EkhGl\nTcjdOo9PTnn48B3GWcXuM7ep6xW+9M6U+0chxswIdMThaZ9qPsaSM54p1CAAFTA1MYPBAJXOUT1F\ncZpjnCEzBa4qKKhYiRtc4HBRDmmMK3JsCXGYomSAtfOW09lxcTV5XhIkXkk5DEOEMkCDcN+ReNm/\n0i2b5ZQXF2yOhrxTN8TRBkdHU5o37tGLUrb3niWfnTM5P6Q/2iDubzDLDH/zF36VUT/g0598FZSm\nKGqageP+yYw4SsgbkK4h6UGkBErr1gooRCmDaAWMyjJnPpmyLSX9NOY00lgC7nzkNeaT9/Eq1yH7\nT/ZZW90i0AGnx0cEYcxgtMZv/savI4gXSdfe3h63b+/xEz/xE6yvr1PXNYlaBxqaKufhg/f4n37+\nZ0mTgL//S/8b/+FP/WmCwFCVE8rSEOiIXm8ArsGYjDyfc3ZRcHL0hBu7e4xWb7C6fotpOcc5w2R6\nzun8nCdnR+R1gdAhqfZQsr29PQDOz8+p65rpdMr5+Tn5/IInT44JwojhYI2zswuSSDPspWQXRwv+\n72QyuVRk13pRmKqqiqIolugaNXHkIa3T6RTb1D6BFY66ztE6YXXFK9b7LrzFmpoodCgZsLW1QRAE\nnJ2dk8Qrfj+P5wxHQ5p6xunJpKX3JEhh0Eoyn10WVDc3NxeBTFdQTXtJy2MLiBNfJOhstYxtmGd2\nkUhbaxc8c/AJ9dnZma/6uwa5EOTykLfRaMT5+TlSeuV058RC9dQHKt4K0gc89hLKB1xW0n0hQkqJ\n1oJARQvqUtd1lFJ6QakPwbZAmUhJFIVUtUdHBIGizI3XPwiCxThyokVUdQrrXHbAdKixxqCCANcY\n3+tr/cOhU1x3GOMLH1jprSTbhLy2FmGFF6ayDhV2/raCi8kUqRTGwYzIFz9bGpiS2qM2YslA5dSV\nJGjh4QiJVhZQuKZmEMdexKxxUNeIGPo6pZKKvCqIRinHFwXR2iou6fHaM89wEA7JJyWTwzPOzs79\nXNSDe++/yyDt0U9izk9PSISnArjGo1Vmk1NUHNMf9KhtA02NsYbj00NKU1NWJVJr6rwkjSLyecHG\n1jZKx+goJr8oOMyfYIRART0+8dnP8Pu/+U8QoUQpyZOHD1FRSFCVPDrYZ3dvh9PHh6RxSK+3RuAU\nt+68wLv3/xGjsMDWFwh9Rm5CVPNHuDX678H8OlF/xHn2gHn5sxyc/xKCBwyiHG1nRL0QZ2NuPfv3\nuLf/p3nthf+VvbVP8O7jNzk7P8CxQVXc5PnbP8dvf+mPUNT32Nv9LMenXyOfO7Cv8cnXfoGm+s8J\n4lWEOu8Q5754QkwtRNud9iJTjTWtNkQD8gIhKoQB12gINlnp9ZmcfI47L7zJbz/U7I5+hp34rzFc\n3aY3TDg4OEPFM6bjKar+PkTwBZ409/jz/9VPshF/kr/y5/9b/t3nf4z6MCA/kSQrIbYTuWsRUJ6j\n6rUdPhx3MigpL6kbsCjwCWC5Y3212H+16fOddPeWE7Bl+6nlT7juhdz9XE5Ir3dzl5WoryZc0lM/\nlPQIrsYRRSFBGHt6p+oThwEKRxqFJJGizKfMxudIERHHMUoJj1qVvgzYzftC+H1777332N/f92Kd\nx1PCSPMn/vgfI45jxuMx/X6fLMsWkGfbcvC7Yq+Ul57Zy+ir5eOz1qNRl7VFrvOn4TJR79bubq42\nTb0Q11yG7AP00xadhsOaiqqaUxcFzhliFI2pcM4SxxFCRORFRlF4d6DOmrBDkF3C461vPgoQSIyx\nVKbBOkEQeHRfh3S7gtBsx98y6mtRhEAs0BVdh7o75ku02JKY9NMKPbAYW+ALHlVV42yzKFJ820LR\nt9k+VIl1XTeIJEJIubDr6E7KslH7cgVItlZXcewHTGGhqmsSfXkyO9uT7nN8Zca1XcaOO+KTY2dr\npPYBuzESgWgrURXGNEjh4QphGFE3l12cS3sdiZIWrRKkdERhDBTtTeH3pywqbCRxUpAGYWvdIEjT\nmDgJSFINWGpnKKxFVNf8sruZaXmGEl2Fp632OF9Uns1mvP/++0T9hBs3bnD79m3GZcNLLz2Pa49t\ne3OVm7vbnB0/QFDQVDPy7IJQ99Aq8B1GV1FMzihnEyIt2N7epJdCOhxS1galQ0YrqwxHq8RxzIP7\n7/O7v/eLfPITn+XR4wekacrZ4Qkvv/ISZ+cnRHXAG2+8gRSO8+NDhBCML85pnGN9fZ31zW3WN3Yw\njQOlKcua8cWU0+NjTk/PSWLFcNRnNBouboiLiwsePXrQdupnBFHYih8FKNVwcXpKYwN0uMKDwwsI\nV6jMgIwx4zrn4q2H7OzA9vYGtz/iFYpdXVHOp1SuYlxMqWrNG+8+Zm1lyG/8zrukPYmrLaflCeNp\ngwrg0VnGjdUVaiXJbEwJhGGCQVLUFcZIaBpcC2XpfFP92FQ4G9DUDoHCNO21NA7rPkQAFAPWNcRB\nv4VBB5SlQ6uIJ0+OiQfrDIc3Ec5iBVghqRvF+sY25yePUSpYnB8nBE9Oz9lYD8jykl7vclrTwvMu\nUa1Qkm2RHlicsYiW+ymlBKUJgj5MvSqllAJTF1R1RhD2cahFh1NIEM4H+dPpmNPTiCDw93gQBKRp\nShynhKGiqQJu3rzJ9tYWo5WUyfkZZT7HmRJrBFooAiW89y4a42oaUzGfTTHGkCQJQZQQxAnG1DSm\nJq9yiiID0QqMLHEsu0W7g2Z1XdGyanAIf+4wnv4iJE1dLcRIuqpzt1lrsUvV724R76rZeZ6zMhx5\n3YJKApfQM2MbnKmJgsgLxOQVTkmUCHEhJGlCFMXM5xng5948z0nTlNFolaKo2up/QVXVbfc4utLd\n7eb9bkHv7Dq6Y+9g9L5rb5aQTOrKghuGIfNq1sLFA7K8QUvhE2V8Atl18ztERVf1Xw7yoGvMeFRN\nBze7hD/6TbYqs6KF4sFTkFUfgs0hqQ1IZzFlhVIS6wRFZYk6dVfhUWYG5zvTbbFBtsGuli00rzG4\nugHdIlHorCdFS2P2QaWUAThvp2QaRwM4JTDCi5gJK8FYqsos9tFJhUHhpEDL1rvcOYSWCFpYufQq\nuEEoMaXncxpTYRuL1rKFsCuU0NStw0CkBbVUgPLBrpUEIkDhKUYv7tzkuede4OxkTjEukJHi8OiA\nIJT0BwOcMzgMcRx5zQGcp6NLMDSkkcK5GtVSfwyWos5pqoo4iLGN10To5ok46YMOcUKy3l+lcJZa\nK47OzgkjycrOFsHWCgcHB6wlfc7PT3j04D53n7nL44PHGGHZHmyy/+SMxvxfDNTHCIHxiWO0scls\nukd/8CNsrP0Y59UuOtqnHm+xNtwjCv4cYQgPHv85eukpR6e/xkb/OaT1c9zWquSrb/w3fPoT/wUB\nEi0URu5jRUhZ3+WlF/4WJ+c/hxC3mUyH/NHP/xWys98jDYaclwVNdU7YxTlWgtNYaUHMPoAk8QUT\nibHH/razAgiYZlMsgs3dv8zrb/5Z7t79XorJF5mZ32Al/Ayqv0m4UdCMEwZJRtk8JrMBt27+p2Tz\nd5HRR8nMiDIJmYmMrK4ZSoFzLe8Yh8Nr9wgcshVu+rBs1xPj67PQ9STjuijod7pdTYiWvvMpHfDl\npOsPOoblpBSWEndxST8xeDs00T7RIcaCIEAqvFDp5ibfeOPrfOaz61irkVJ7lKBQi5Oz4Jc7w2Qy\nafdCUhQFL774MXZ2dhBCXCladEgr0zRtwn5ZVP1Wx7z4x1Vxt6d16pevy3I32Tl3Ze3sPnO5ECEl\nXoxPBpSFVyG3pqZyDcZ4OqsQLCl+Xyb4y/t/5Zp0yS5tqamFb2sdfMsCzdOu8nKSfdndFgsEghDL\nHeir5+Zbra3XC0Z8i0T6X9vE2tlLD7bO+2x5MHY3UhdQ+dewqKhfqT5I7xPdvd9a6xVMaSHTroNN\ndxf88qJa6/kVHQfCv79N0qWFpYHiB/NlggQeDrKsJut/dvBviZSOMI594lAuV9wcUlmUdkjl0IFP\nFq7DXL71CWTpWDy/r8xz6vmcnf7NBSdjNFzHWsjyCi1qdm/eoOO3aeVI4pCora4pCbU1CFtjTIUz\npS8EJCHTbExZVzgVInSA0iHD1TWfnMQJSa/Pb/6T31gkAx/96Ecp5hl7O7u8sz9lNBphTQ3NKlLA\ncNCnrC2bm5tEyYB5VqK1V1Adjy8WkNfBYIDAQ797/YT9/UOaxlfskiQhiiLSNGZSTphOZggXMJ6M\nmWYZK8NVknRIaStqG7CytsXB6TkimGFqgQp7pMMVpBEESlNlYHNJf2UV2Qw4PT2lnMxBxEwLmJUW\naSCvCho0gXbsHxU0xZh8ZggGltoFGBNQN4q6cghx6a3bTX5NU/u+jOz4yQ5QLeJCQacC/yHZoqRH\nkeUM+iPSpM+Dh/v0woA4SkmSnKPDJ8QbdxHSIqQmSFLqc8verbskgS+SJIM+zvkp7Obt59ne3uTi\n4oKmqgi1JGi5n1I6GlOQpjFF1tDUHiZ9cPiY7wkjmmZKVuT0hytETZ8wTgniCFs6inLGulwn1Ipp\nVlHmJUIK+v0eReaYTqcIIUiShPX1dc7OzpjNZmxtbbH54l2EcOggYrQy4MUXnwdheGd8ysHBY5xz\nrK/t0EuHxHHq0QhNgVaCOAwYG0NRFQxHI4TSPrmUBXVdLhS7g0AhpPKuCG3C0EHNlivAZVmiW5XO\nOI5b+ktJGHpv6X6/v+hUL8PE6romEJrZbNba0OkFB3x1dZUk6VGWBUWZI5yhbkpWhv3FuPUJe4ZS\ng0Vnsq4rb5E1HJBlOWsrKzgHpqmIwpCmLslzjybyPtUnzOdzhsNhCzP33d2joyOiKCLLssV+dfvY\nwfKSJFkomQvBohK9/B6l1AKuV9c1/cGA2TwjyzLSXkxjapTyn7m1tcWjR/st6qFcwOY7yPjyeeuC\nluW1YHmudvg1reO0w+X+0XxHdlv/yjahtIcDAo2xWOfXMikEjWloGofQnrfXWIMKA4R1SLr1zqtx\nSymJhKIQFi1lC+t11NZgXAv9Ft6CsClN+x0KUxeoOAStqOqKUCgC2XZGtPQJeAvTxflOVShqtMJT\ncHSbhAmDNRaBQwiN0gGBoC0UWAQtvLKFYaoooraWsDSgFI0DUUIoBH0RcXu9T1VOUEJw6/Yz9NM5\nurKcnB1Rm4rD00MEllBIqsb4DqdyKB3RlDnCSZIopKgKppMMIy1pkAAtZcwJillGEiSUNJRlzfbu\nirfIS1Oms4JAheggwMSaRgvmpubOi89zeP9d5nnG7OgYaWpuxiP277+H7qc0zjGdv00UKY6qn2Vq\n/h63+3+K0coQy1c5fPIMn//oj3E+1Tgy1jb6PDl+i3HZw5o1RF7wzPrPMbV/na31U2rxBCHniPJ5\nTsZfJh2e8O6jX6Kv/iTSKY8YFxWz+gGmHHFj8JeYc8jKMGB6DrBGOrxJmjwhc0cERYQDDKFHKog5\nTjtcLS71C6Qfj8ZZQnxhRWqJUzm1zen1YTaDT776V8mOfgWb/S88fvIzXFR/F9X7ccLhiGaeIkVF\nnDzElndZUT/OefFrvLDzeX7glef4xvoxtbEYDEkoqNWwnWttS8/q4kGBUB+edRkuY1o/3i8T0etJ\ncJewLYr+LT2HpddXrZPD07YryfT1JOYpCebTutTLvz8tob7+WgAnJE4onBNUlaGx1gt3OoNWAq1B\n0DCf5Xzu+z/Lz/3cz/Papz6Ldcrzjp3EtQgp51yrQ+Q4PNzn+PgJ6+vrHBwcEEUJ3/d9n1uIrXVe\nz0mSLNYmiY8dlrvs3bnvCq5dnrAoEiBaGecllfClY4VL5fTl4rNv7NUL4bEuVujOsXe/aBBatWgh\n1yJqHY1tmLQ+1csJOU6gVbCgnnUUq6uJslgk1l5zQCKUQCpNFCcLK+IOObZ0MFd7g8vjRQoUqo0p\n2rHZUUMWhe2r5+R68n5lvLVjWGuNNVddS7rn/7VNrJX2A0QrhXJcuaG7Lk13wTv4g2xvyjzP0Vph\nhVlchPl8htbDxSB2gU+ml+1OLi1UugvilfKc6zgLPlgyprVn0d3rPTneWr9Qg6MsK6QcIqWHRgjX\nWcjI1uPYW2XFfb84VqYhO5+0AZnC0RCGAVEsKQpDFEuCslPqA/j2kvBu6flu/4MwJtSahw8fkWUZ\nJycnvPLax3n3nW8SB4Zs9oQg0IxPT9i5sQ62ZmXYwzZly6kuybOcupxgnfBBAZY7d57l/oO3KGqH\njASltYgg5MH+Abu7uzz/0ssEcUT8sve6W11d5c033+T3fu/3iOOYZP0Or77yMqapaIobHB7sY6qS\nRiSA4PDwmNXVTZQMmM/zVgV8yv3794migOeevUWSJEwmUy8q1/oFdsrJs9mEdBCRZSVN2XiuoNQ0\nRlA2iuH6De4fjjH7Y5L+gN5Kw+S84Pgs55VPr/Hl3/kdsumMnc1NNlaHxFJwuD/mdFKy0l/lbFwQ\npuv0+z1GvQHIHioMsMwZX7zP8cSQ6jXuPZpzPpVcFFOi8znOQD/sY1zBbDbDWteqmBuqOsc5Q4FB\nRg1V6X2H57McYSuK4kMiJQyMJwV3pEcLBHGfhw+OyCcNz3/kDlVp0VGDNSXZ5IzhjWcJ4wGlCHju\nubvc3B5yevgewzRhXAryouLuKx8nn09578HX6YeKfm/H8/vjCB2E5PWEtdUBj7Ij4shzn7LZnKbI\niYe++lqYiiBdJYh7BFFEY0vC0DGZnjIQml6yQhonhEqzs73B2XlOf5AskqrJdIyae8jryckZt268\nwnCQoAKYzXM++urHKMopDx68z3vvvs2gP+L5uy/T6w2RIkSgCVVKbsYI4ZW3y7qiNlDZmqwu0Epi\nmgqJpZcExMkGZeWF3E7PxotubJIkiwCp87kdz6bs7O6BcxwdHZFnM++brSRaaba2tha84zAMFxzq\nabugaq0XCqOnp6esra0RBjFah+jG0NR5CxEvGQz7pGlMXVY4J8DOUCqgqWp6SYqWmrOzE8qyZDgc\neni2GGCtpSxLptMxw+GQqiro91OyedHaa2mC0CMDisJTH4IgWHSom6by3O26pK5LqsoXVLSWTCY5\nTRO2BYGkpeZUC62JIAwwjS/MhmHIxckxUkIQBYvv6aD/s+ncqy/jFhobHSTQa3R09mbLHR2vgAoa\npQSBDrFLkLlu8dZa8zTtjO/G7VIwphN28+NDao2wl0q/1lqM9QI4chlU1YoYCuHhvK4xWOUT60Y4\nysbz0JXz0Hmch39LoVEqoG4qAqERKsAuq7FLEHjxv0Vi3RbUZZvwOGHQQuGE9z1WzmGdX7tjlSCF\nAycRTl52XCwYB8IZwiBG5BlgfbBrPZ8eY1EolJDkWUYUxWxvDynOxjw5PkBKSa/XQwpHPptTlyU0\nhjgJMc6j4cJQ47BUee45u0otOmVCeE56VVXouE/d1C0driCC9t5X9FVMOhwxlYZ0ZcjK1oDz/Yek\n/f4ikJZGMinGrCYjZtJg5gXhKgxWEnrROe/e+zUGK3B89JjV3SOeefZ7OTmBKE4I0yFPHh+hb8BA\nxdRn67i84WMfTXFuh7rW2NCCaBBuwMbmLjVT9h/9M1Z6MJ2DswGWGhvljNKXaPIU56aUecKoN2I2\ndxxPLugPJS4H8JoYosODC9ei8ZbGZHtspmnaxkOAUBYXwFvv/BKf/vSfJHcSshVzD05vAAAgAElE\nQVT2Nn6UR4//M/ZeeMy99/8xWKgu5gj6jHprXGTvsJJ+lu3hcwz7n+dGL6GqNFlZtePCgayvin3h\n0Y5eHMvDjz9MWzcfdQmZvNad7rbrENtl+G/3+mW495Uk5Q/47uXPXe6qdonkdbjzlf1dSsqvFDbb\nbq/GYozwCTaCOE4IowStJXEcIqWP1YWAvb09XvzIHTzAxSCEvw+tvRS2iuOQ6XTKeDxmMpksOrk7\nOzsLdNPKysoC9dRRk6y19PuDyzl/6Tw9rXDQjevGGASXelLXIePLj0kpyfN8kSjmebYo8HYOSsvf\nEccROEtdNyhnKIsSbENdXjr2dErsHZKy+96uWNDlZZfXEpzw651xFtMmwz6RDa4m6tfGwtMQE+0O\n+8Znm0T7x6+OtetJ8fJ4elqS3HW8l723l8f8v7aJdSMvUKMEKVcJmweERjMN56yY4aIrvSw4MxqN\nEPoMO10j6k/BjFBylVlVUpqUVGryMkNaw5yYDTFHC4OxAdOmIcCgTckosjRCgI2pzZw0jRdQS+9t\n3uCMQLoRKtyHeo3GHCKau8ApqXpAbGJss4JLIrSdEboBfVWhcIxoyOsGO29I1BDtBNV8isWQhjWx\ndihRUTcRxkmCMKFpJgir0UJjW26tcGBq7zBiHb6uJSy28YIuGLBSLtRrbQDONjQF9MIRv/oP/ik/\n9IN/jP17Bzx75zbjyRH7+5azc8CFHB09JHIfYxgmlFkBkUM7Q0CGISWOA46Pjxmuhow2Vnhl5fu5\nmB8ggpREDen1V5BhzNnkgvE0R/VX2BltcXpxxOtvv8WjJ6c8+9zL3L3zAmHQI+wlFNmcJ4enjDa9\nXc709AgAIy1G1fSHfaIk5u03vsrZ+QkvfeQOW9trDIYpw5UVprMxSk+RSuNcTJoMODk5IssKhmqV\nnbVdTk+fYJqMPGvIZmNGW+tsb91ibfOM9x/epz8YMFq7w+07PaQMGaQBW4M1zmuFqQRvvf2A7e0N\n0lSzc3ONfr/vBWiEoDcakOUXDBJfFTVGcHwSsr62yXw+p9e/wy09JsvnDIcpO1ubhFHAvbcyzvff\nJIgMOirpBzsYVpnPa3J5SEyEaQRZ5iitJc9rEvPh4VjPpiVCSWpj0WFKbSTzAubzouVU+uC1MV6U\nRwYhVVNSlhnWNTz/wnNM5zMQlqKuefzkmIf37lHWNUnQ6hpISRgG6CDEoAjClvIhvc9iXZRe/dJa\nD9lzIFSEUBFahRhZAA1NnaMERFHkq6oOwlC3CZumo3F0E3Y3MZdNSVkrb+0RaAajIeVpRq/vLaKG\ngxXv8yw0IHFO+O6zjAhDu1CtTpKEMNJelMSaVoHaV9eFddTiku4Sx/GiAt7ZDHYLX5L4osB4PF4k\ny2kU0jTNglPdVc67riv4jl6v1yOOY4qiWPhK+07vHD1SxHFKoxyIrgLuYfKaDmImECigJgh8J318\ndu4Dr36KbSBQAhlolGhQuu8Ti7pECEcYBSgtFkWDjpM8GAyo65qLi4vF3N91rrsCa8f96kQnlyv4\nC36zVggFTd36cyK9cnqoCSMvxtZV65MkoSwqjOkWbveBhbtLqp8G0+tes6AgLD0PbTH3w4IfNRbp\nQDqHtBYNKCy2zpHOc9Gt9X6lEoloaPl7BtXaXTigbgxF7fnQsrJU1nu1WqlBK5zW1IvgEpAWS4m0\nEotBVJaeBCW7ZL4hcUEnq+bHgq0W49OrCENhGrQKQAiMbWjaboULHbW11A0g1MLKCyWQbQJlRU0d\nxRhnsU2BCzS50LjIEdY5a2rK0alg6zlDrmvWd0e8Gtzh8QPH/Uc9ptMx4SAmV1PKfIpUIUMdEkRD\nnHOcnp8iFERKY52jbmoC7elnToBOY86yKbd2n2M6nhGYmL5KePaZ5wkGA2KZs3FjGysFZ/MJp8cn\naKU5m2Ywy7DO0QiLEpJKVNjplMjB7BSOzH8N7pd4fmfEN977jyDYB/E9IPdoHKSJZnJW0MOSHw44\nD05BnoIK+a2vCTb7/w7Hs79IdnKAWN+lkGMicmblGGfP2duDR8ebHJ9lCGEx2Zy5+zLChUiMLyqK\ni8Uwm00cAkHGzFvdUfruYSMQRhC6EVacIwXs3rzL6fSU6eQCaW8ya/4S5uIBqvcu2L/DwXs/ya2V\nX0St1zw+dgye/R+Zjf8Ud+98hcO3/yxB+FM89/yn+PJbf40o+AUonyFIDW6sOK8bfvsr7/Hq+m12\nBxErg5CZG5E0JbItykRp2iYf3g5Ooj5433w3bk/pSsPVJKVbS7rtOvT3SmeYb5+MXP+c6/ty9c8P\ndiC7x693vpcf6xCs4BMmoTw1Ben1LYwBIUO0DolCzTff+DpvvPENprMxVVWwt7dLHAa88c2vsbu7\ny9bmjVb3I0CpVtxL1mgtqaqCyWTSFmAjr88k1KIofV2wq9frLdbcTutpOSleTqyXj/2SX+yesuZc\nvVZd4bjLV7rv735GUXSlQSmFozEGU1fUjS9Me2sy79SgtKQoc0xjSdOUprGUZb1ALnTr73V+stSq\nFfttEbptTOeEunJsV37/Non1B5ZH1/3vqpXWt0qiu+eXr4dfWxSik9B07oPj8p9z+1Al1uC9ojt1\nPinVFeh3N6i6QdKJLyz8nFtPTCEVrlmSrhei5WZ7+fbF1WyDn+66eIsPeeV9LF7akepbxP8V8YFu\n4F8ehwCMAJzwIGshMU7Q4O1+rPTiG9J6rWfLpYG8kt7KoGm6aqJYfKbfmWsnrf3bS7Bcfbg7P1VZ\nEW1EKKXYWFvn7be/gQ4Mzzxzi4uTI+7ff7RQDfY8Ny97b2zT3rRe5bYoCqRyKB0yn2fcuXOHPGuY\nZgU6qBn0hwwGQ3qDHukwZfbolLI2rKyssnvreTa3bpL2V6irjhfiA/uD/SeMJ+do3XLVW/GGsio9\n50Q6Li4uGA57JMlNkiSmLEuv9i46TkvIZDJZJCxnZ2dMpmPm8zHj8dhDZ43v8HXHmiQxaZqQDjaw\nNmR1dZMkLonjmDStF9BapRRpkhDH8YKL+fDhQ87PzynnF6yurBHHvkvWdeWEkOTFHCcFe3t7rI6G\nDPoxZ+enlGWOMZZYKaLIe/wVxkMnbVOjEMRR5K1HHOhAch1J8928XcxLLIq8tvRHGxQ25GxW8d67\n93nh+VtoCQKDNSVITRD3cMowzSdsDUP+8A//IebzOX/9f//7yNGQh+/cZ3N1hVDD/OwJaRzT73mv\nc6lCBv01Rit9nHTeTk0IZrMJdVnRzHPCMGQqK5yMCJIecS8gn12gpMPYkvH4HGMSHDHWGPae2eHg\n6AIhJFmWt1ZeIdZ45IkxhocH91lfW2V1NKCsK9LhiBVnubH3DKYas7u76ydyocFpnPPa8JoIrSxp\nOkSpY+bFjMbWlHbOIBgRqoCyqalasYRAAlIxGK0vqr9lWS50HcrSW3/0+kOaumY6nS9oE1mWkSYx\nMvCCXlJK4jhecMU67vXq6ipSSsbj8QJe3ev1kCpESgVY5vNW5buBMOiTpD2a3KMqmrrBNKXvMlaG\nqmq8b6cWnJ0ds7Y+RErhj7POyXIfCKRpzNRM6fV8gaGufdBd1zXD4ZBer8dkMiHP8/Z+jej3+8zn\ncy4uztA6Ic/n1HVJHAfM5zCdTtvqfLxYJ4IgIG+yK3C8uq6ZTCak/YRQe49N57y669ramheCCwKs\n81BAh6f9WGewztBZTCG8FZrDF06cc20SJ0iS/hXhmQ/d1jaETdOAbgMrp/B3rweJW+ETQQBjHaIr\nOLQLooeLCurKtutbgHUW2zhUGKCXhYik9Gue52FdittI0XpjLweWXuVVBBqcXytr0xC2QawTou18\niEWw13XZ/bF1AXoLib0isOTjgm4NldLDHZ3w9pthGBJHEXWeoYVAI+j3B6TBbS+upqe8885b5NnM\nC6kKhQw8XUpISZHnbbNAY4333W7qagFBFVKhZUDZNJS2YW1rA1NbNm5ss7Vzg+3btymnp4xWVjiZ\nnjFIhzya3ef46BBT1jjpRRwFitHKgJOTQwCSQHFW/ANOJ3+bjWEJ+gRvfZXhnGP/8Ztg4OLiHEGC\noUIS+lDUZ1I8f/ejPH7/iwjxAun6Pd5689cZrN5ic1vRi2JWbq3xzrswbfaRcsV7m+PAtjQ8rgZK\nl4xKhXPGd6ivdbecKPwYE3D/wSMcJTduJWSzx3zhrf+BT3/k30PYC5ADFDmPL/4xSe/fZDAKyE5r\nLt5dY2XvEVu3ZoyLX+bx/n9HmHwZpV8G9xneOXjI6qaimjgqY3j/8ICANZL+KrapEa0CuBDLyV2L\n5vh2MMLvou063Pt6ovYHvQc+2JV21173NCj302C23+6MPS1RehpUvduuKFsHcgFt95ZRAq1DlA4p\nioz337/H8cmTdk1Q3Lt3j63tTX7xF/8PXn31Y3zuc59j7+ZtXEsNbZoGGTQ4PC2sS2K1DplN56Rp\n+gGId5fIdnFjB0F+2nb9PPlu8wc7s9eT0u5fBwfv6GHd3XQ9V/L/BFoLnGloTOORFtbihKMzTfQF\nc+OfX3THxSKx7pLRrru+nFh3X+8VwPWiAbG831fGHZdz7vXnP3Ce/AtYGnEfKLh8u/HTbVJ6uHpX\njFlWVP9298H17UOVWEvR8qbb4LGDABZlcaVb3SVGs9kMpWBeFN4OprUmcrazLKoXA6EoCojAObvo\n6HaTg0+8HUVRMhiGdFL3SrWdGOGh4E3TYJ3niDkpvUqp9AuBxVLXFcbGOOFonKVwCiUUhVOUQjCz\njtxpFBUCSS0DwD+fO42WXuDISUEviXDWEChzpYojBV6gwXX1m25rA4QOhreAhF96Bu4fPMI6w/HJ\nAUU5I1WK8eSCl15+noNHX8Wg6feHCFhI+tv2BqvrmkePH/ClL/8uP/z5H2Q2myGk4vjoAiFDRqMt\npPaBcNUYbGPJJhkqSllb3yJME3qjDfLCUNUGawXzueeTJmkfhKI6OyXujVjbHKK1Js+mVE3G7OKc\nPM957bWPs31jgzgOGQyGlKX3QPZdrQZTx8znOQ8fPuTg8IAnhyeEUcDm5ip1kyOlJIkHaJVgnCWb\nF2xvbxOEMfcePOKtN++xtb3D5773RQBeeuklAMaTc46ODngymbC5ubkIlAeDAUEQ0Is0Ozu3yLKM\nx4+ecH42IdApp6enVE3B7b1bC/5oltccHZ4TxAE7e5usrqXowHH05JxsbgiDHnd3blMbWFmJvG+3\nKQlDxbiIgOr/9/vw/4tNqZDGuAUdQcgQA5yeT3lxUW1uaJoKJ3ygqkPNydkJs5Mpv/IrY27deobB\nYMDjiwvmRcPW+ipBEFFVFUmSeC2AKMKhkNKr9htrQHg4aW0MTVmhEnNZMVaSIAiJohAhHFoLbAVV\nVSCkt+DoFoSVlRWyLKOuK5QM6PUS7/lo/Dx0enqCUpIoUp7S0FQgBEnap3IZURD7boa9VI62xnlH\ngUVy4BciHQYoqbDGokPtA20BSEmIQOkIFV52n8Hf091YDMMQGUacnpx4PpR1KOm9s8Et7uck8ara\nZ2dnrUCZaV0PLrvEHce6qirCKKTIC5RuO30ofCPSn1PZnquqrHHW0Bv1MdYRaI3QgropyYs5dR2h\nA4ExNdZVNMZ3q0NaQbYwYTgcMZ1OyTKPYugE1Dp7rCRJqF2zKCgIITwioF0bfLJ8lTe1oPq0sM0g\nUARBiKMrInr18yi4TMKBxXix1i4Cfecu7TqeFvAsV8jhW3d7jDELS77v9s0Jh5MCYx3WeH1u1VKf\nBAKEpLHGcyal9p3hqrW7bM+ltRIdaHTq11slFZg2AG3FzmjFy6RzVFW5QIaEYbRAzVvXcOlt6pNf\ngyPAO2VESYypikUgCSwC2u66RrpN6ms/jlxrySmD0Cf0PtpDSIFW2quXS7lIgH2CDmmasLo2gHuP\ncbMpw7UdLIposEYQ9Vm/mTNaX+H84pTpdOILqbbmbOwT2FCHMJ2RRDF5NiPUIWkYUTcW4xxRmKKC\nkOFAEK+v8corH+XZO8+xe+cuhbFE/YTR4C5ZPmMyzvn617/K0eP7nB8dkuoApPejlYGmqCuiJGRj\nc8S7798D8dOsDnM/N+iDNtUVPHz/m9x65oe9FZgAp3PPLTe1LzIIwCneuXeAYpWN5Kf44us/w3Mf\nuc3J8YxsFhKEgovT3+ULr/8HfOyTf4sgElQG8uJ62f9akiYuubYdN3TplVidL6zfB+Em69tzHj85\n52D+Siuct4GSn2GSCbRSpNHf5PHhn2Fr7+fZ3fx+Xnrxf+ZrD36KjZuPGMVf4PDsiIHcoaf/Invb\n/z6VgidZRVpKer2U37n/Dunmx3l5d5vYlXReun7fvLhUl1hf9bf/7t6uqHM/JZG4DtX+QEK8dM2W\nfy7/vpgblzQnriTnLMetH/zMp21P61x3idjynCtESwmhi+01uu0wz+YnHB3u4xpDoGUrvBmQzebU\ndc3rr3+Ji4sLPvuZ7+PmzZusr28smnJN07SOEj57bJqGqsy908e1fe9cSoIgQLVWWddh9E87/5cN\nvavw8OvH3BVpu4S6e6+9dr6v/+6LBR3U3mDNpV+0p9Qanxy349k3NP06WNf1lYS6+1wvTOtzD0t7\nX0iJDDS6LSJ2wmPL1/o73dq0f/GXEN2jS6+5ts4+7bnuXcsJ/HcCAe+2D1VirYOgbdVf3jRdh6ab\nbJcHFoDWXbfSV0xcYz38ox1EQnhFWJ8U46vpXdW7PZ/LquFSdgPMYYxFSt2qhdP6xBo0DoNFSQ2B\nQGoQ0lHUBY1L288VVNaipcIqhVOS2lY0zlK5GtMUBFGKDjVCRxhBK7CziRCOQEmSVJKVgPCcNONE\nm+wbn2Sz1JUWXn5MwWIBch1v3PmpLAgCfvVX/09++A//Id6//zY3dlZ59s7zaBynZ0+4e3vghV5U\nQFVder0WRUU+b3BNw4MH7/Pw4bN88lN7zLKSjbUt5nlOkVeEsWFyMUYrzWiwgnOOaNgnzPrIMASd\n4GRJVTXMJlOsMdRlxXSWMc/mvPDiK1RGUtU5ygriIOL8+Iiz0xPiOKAxJQvvWuHIsjnzbIZzhiCI\n2d/f5/DwCGNq+v0hwxdWWd9Yw7maJPVcD9MIvvnGu+zefg4VBqxvbBCGIV9+/Q0khgf332PUrxkN\nb3BycuIt0ULNdDpl2vpjHx8f8+qrr7K7u8t4PObh/gFvv3OPNE3pD1awTnMxniFkyGe/9xX29vbI\nZjknT04Yn42ZzXJOTh4yHPVI0hFxorh//yEX44KdGxF3n7mFcyHf/ObbnB0fkPRjVjdXKZsATqf/\n0u7Hf6EtGtAPVmmyc0aJpp9AEMDjEyAZMW/OkLIhzmYkzqAHvqD11a++xfHjx2ytrXNj+xGvfuqz\nPH7ra9zcvcsgXaHSJTfurNPfVBg7wZkK2whOS3huY8TvuwpHioqHlKWhzC5IVzaIegL0CeFwDWH2\nMDREyZxQbVE3E+JIYOaHNPOIkFXOz0LCXsDG1jpHJ8dYZ5iWpUdatF7c9vQEqUBrg8CgBRT5lJVB\nQqGGLaxctciPAkRNYzOEy1DO0pRzpJ2x3h8xDHfRuo+VExwQSIWQ3rpPhBInNTQ1eVVjrFcXr6wj\nt4oGy2DUI4li9h8/pCgykjQmSSLKIqOoCkKlSOKYfpJwXNf0kwTpHFkUM83mHJ9fEOmAvCzoJTH9\nXkI+GyMc6DTC1Y7/h7o3i5EsS+/7fuecu8aWEZlZudTetfQ2NdOzc8YkZY4XbqBEyjBgQd4eDBiG\nBT9ahmzwgQBhGILtBz0YsAwZtuQH04AMgjJkQIRoDjUz1HCml+nu6u7qrr1yz4yMPe56zvHDuTcy\nsrpmuBgwpw9QqMyIjIgb9557zvd9///3/2NK1lZ6KCXAlNhC0lxxGgh+JAjDGOkJBv1TiqKkEXc4\nPT1FKhgO5rTazr7MV4pGYNGZRecaJSXtdpMo9NjfHRBEEUEYojxDsxmTJAlYSSNuM50NsEVJI4iZ\n+xGdeAXP80lkQpJklHGOLgtszVAqU6wuaAYeW40mp+MpWTkgiNfY2LrA/s4hs6GzbyutoaiKp9po\nGisR49EYZSQSyWwyc4KLxrhEUFcoifXwhE9aofkg3L4hQ2qPbWPOXCnAYM1ng37igtaa/g9YWRVv\nHXIvhUFbVxSXVfBmCk1ZJaS+7+N7nhPAEbl7HqeU7SsfaV2qbCpvbKxrdQLreqCFXQSE7tG6aOE2\nb4UAaZ2fuoRQnU/eFsy2pbhhOa44C8LsWeIsnEAb1gXRNfBtjbNYMsaCcLY5OpuTjkaEvS0CP0L5\nAVFrBRmHXEg3CeKA1c11rNXMZjO8oxHNZpPj/T0OdvcxSKxRSFwR3nouZmi2u6RFyUqvx+0vfokb\nN29y9dIVwmaLwBq0lAwOx5wO+sxnKbPJlNloTJlkyKYgLzW6LPCFZZ4VtFsN9vaOufveO8jOm1y/\neIuGH1aFNwFEXL3+KvvPPmKef4f2ys8xL1xMEZQKScXAUxY0XNp+maOD73Pl9l9jUn6PzQtbgM/R\nyUM2tm5jN9fIcijMmNK8OAz9FMLJp4tV7sq4+KYi8NDtrrK/u0NS/D4PDn+dTvwLqJWfJex+BZuB\nZwTJ8BdY613l8eP/mpe2fosVscnaxV/nvY//C7ZvWLYufg64Ta/17yC8DfxOm6IYoAhISs3UwqOT\nI4ZpyvWVmLm2FfnhzGOYqvlb8Bejk/7/PRYCuuKst9RRj88nynXsvZyELyd1y8jl8pVavseeR2I/\nlez8mIT8J1G/l0WMl9+7btc6l7xKhRQeYdggjJqA4OnTp0wmI8deSmfEccx0OqLdblOWTndkb2+H\n3/snv4suLbdu3ebGjVt86Yu3mc8T9vZ38DznCJRnBUEQsbu7y40bNxaIcU0Bb7fbrmCuvHNo9vJ5\nqn+G86j7888/n2TXRe9aQBNY2GPW7/G8OFdd6MgKJ/qZZzlKaMIwQOcpcKZb5Zx0TGW96drjJpMJ\nzWZzUXB+/j412lR2eArfC/D9ED+oipWaT82d+pieFzRbXP8aIFw84ezizvqsnQbD81Tv5XNav9/y\nPDbGIjDPPWY+9X3+tPGZSqyhpnCcnfgaPapV8IQ4U9MDt5kCGG3RpUV4lTKdEC6RFi4Z1dol3FK8\n+ARaeyZWgHX9c2VZOhuOhcBZVSVCo4UmVGd2SLq+yEZgZN0j4ao8ClDSgtXu802GJEBad5GFBSUs\nee4ocwv6CAKpirOJ9mMvvCsnWwvOkame+FCLbdQ9mUHgcXi0h+8LNjbWKYqsKmjUVf2aGeAQe4Cy\nLCiLgvlswtraGs1mk2anTRB3yAtNlmmSdMJG3EUKQZ4XlHlOUZYUpcAPYsazOdazFIVlNk+xVjCZ\nTklnU6SUdDodZskcP1oBq8jKFIqMeTIjnc/orPe4ePEi7XazqrqVJEmK0RBGjpptLWxsbHB4uE+j\n0SAMIvI8ZzodsqDNlIJebxUpFFlWIFCUhaHTaeN7AcfHfXzfZzKZcHJ8ShAEXLy0RbfbxatQM2CB\nqAkhGI8nCLHPhfVNwtCJO8znc8Ig4vK1y2Tzs0VwlqRMpzMK4/pzsyKj2enQW+uCmLK+2UOKSunW\naMJA0I5DGpFH5H92KuO+75PMM4L4TE3U3cN1DxDVRuAWVikl2rKgTbVaLZ4+fYrxfA72j3nl9usI\no1ntdmk1WrRaLUIlkFqSp4bVZpO33v0IJRV6qZJpdOEst6q1IE0TfCUXllKdTod0NqcsCmalrqj8\nmjgMKSqhpm63S5ZrslQjlcSTyhXYfOmSTJzAlawQ1AmWJJlVa5lBCoUQTohHCEGZF1gBSp1tqEHg\n+rCsCpECKA3WFBRZjhQeQrpzWhpLVlryrCArHVvG85xPby2uIqUjdtWijmVZolUEnK2p9RoTBAGh\nruhkQmKtcSrizSZhGJ0LcoLgDEHGlGjtCnE1suyUuYtKxMXRycPQZ56kpKkrPq6t9fA8y3QyXay3\njlp3JrQilTsOKgparaidJMlC8TzLskpB3K3ndVtInuc0GxHz6QxjHKoeNWMX5EiB73tYFGlZYq2m\n0WhgtOtVM42GQyiqORlFkROG0XLR416Lu9Ub9rmE7fmOsSpwqDfu5b60zwZeXTGxqoTT99QiqXCs\nYOnUa6XE4PavNMtoiBDl+wRBQJ7n+NKxR2wonBaVcHuiMQaFRFnnWawNKCkIvIAszzBZgYgDpKwS\nZFs6yrd0dkdWnyUDcRw7hkXlb1xfk9pdBCpF9pr5ps+sMj2l0FW/olIKz/exxlKUGZ506LzbPwq8\nwMfDIfBCxHz+1jXuPX6A9Bv0bt0h9zyMCvFQbF+5xLWXX2I0GqE8j8F4xGtf6SIsfPuf/3Neef0L\nSGM53N3BlJrGSoN5mhK321y5eZOXP3+HUmsuXr/K+vq623PCkGQ2xRrD7sEhDz6+x2hwwmw4Ytrv\nMzzZp729jQG8IACpSdMhaTbn1Vcu84u/8ss8uv93EPoY5H2saOAoARIhpmxfgeHo/3LIVbGBDY4A\n/yz/FhlerOnPjhDBKt/65n/OP/lu18U7ucfG+m2S+VOiYML6BhweaTzf/4ki+HVSfY5qd24IrGmC\nmGKsz9HRASsrsH/wv7DW2MaM/m1aF3+NQgHZBpYmtnyFwdFbdDcOGZf/Iy31n9Hyf4PVVfjow/+N\nRyeP+fmv/iY6a5LMj9BAFLdRGKzvs5uM6MzmfHJ8zKtyDdFsI4SqAJ4SJ0RbBeafoR6tF/WUPo9O\nPx9nPo+0/jiUuv6/TuJ+UqLy/Hs+/9iPe82LkvTlpNtROt3jLkYLqmtmODk5OSsIWMtkMiaOQ6bT\nKV4QLvZLawVK+nzyySecnJzyudevoXVJnhcLgK/eD/b393nppZfOJWZ1bPinfa/nk+sf9/+LXndG\n/Xajzk2cnsyZV/byta2Zq9Y61wVPOMcR55Vwtl66/nHHQitLs2DJPT9HFsdnz4Tj5GJvVJWWyFnD\nwPL3WeRZL7jG7gc+tRQsWi+o58GLlbyfnyPn5mh9nNW5Wd6f/zzjM5VYC/puplAAACAASURBVCAM\nA9K0XExgKb0qabJVD16xmERCCNrtiKzTQYicLCuhKAmCrgtW/YAkdYHrZDLH2+jiCUFmLL4fkM3H\nFJX3qVJUAgSuUl735SnlVQGRYjpJ6PUisqREIEjLgqAREzYDRw/FeSRb41QnIw6QRUrHF2SeYD4v\nia0mtK7PLJtp8smMmIxuJJnMI4xYJS0lpRJODdgLSPUYpEQDWWmJwoh07uxl8hz8CEoDRrhKdW4t\njUCSpYZSlwRhSLvdQk/GvPX2D9i+2OFnvvFl9vaf0ru4Rqg8lAqZjGeMRxNW2nOkCBiPBuRFyuHh\nMaeHA548ecTGao/rV6/R6nSJm6voQpPnGc+ePSNJ+nRX1riwus5sluEbGCcT4oZiPtXEHZ/h4IT9\n/X2ageunyuYTpuMRUegTNxqMx0PyLGHn6RNWWw0m41Mubmzw6pdeWyxWxhgGpwNms4TV1W4lOmVJ\n04JknpLMCxAlp/0BypNk2YzNrTV3Y1rF4HTKH37njzAanj494NbtG+RJwoW1Dh4uEbl86SUeVCh0\nu91mc3MdATx9+nQhnnTt2jW2t7e5/fLLPH78hPF4TNSO+cKNl3it0PzBH/wBk+mcdrNFaSxRq8mV\n61f44Q/fotkJaa80uHjlMpcubXNwdMpXfuYWYRDiewatLV//ma8gpeTue28yOD3gxtoWjx/+Jd+k\nf8axfmGNt99+m6/+zCuE0ufChTUePDohnYO2PkVR0u/3aazMkLag026ysX2Zxw8eo7Xmwb0PCCT8\n8aMPWNu6yOdvdFhbb+P5miIfoouM4SQnDFqcno740d23+cM/+h6ICKl8jNUoLCdHRzS3N11PfRCw\nt7vPl165SDHZZXV9k/HWiP5wwmw8Qino9wfs7e2x4bWZpDOePX7mFImNZaW7jlQ+Wa7xleTS9ior\nnSZh4KqnusiI4pCV1R7z2T5Pnn3MpcsXXdELjRCGUqf4UjCaTBhP5+R5ytPdPb74ZUsQSKTfZD4b\nU6Q5vqfw4iYHR0ecjsd0e5ucnA4ZJTnWC4habeJmgzgMGA/77O/t8dHdD5ylVDMmjkLQhqBaw4qi\n4OTkhCRJFsJejUaDdneFRqNB/+jYFacuXCBNEoQUi8Q/igKUsPi+R1GkzjLJGOK4hec5X+ksdRtx\nTRuzpHi+ZXw4ptvtYqYZRT5gc3OTMNT4vrPSEsIyn7u+6s3NbeJWk5PjPp7nMRi4NhCLZjqdcO36\npWo9nzGbzUjmGVEUMxgMaLVXsNbS7/dpN91x6SJndXUVIQTJdEYQRCAkSjaZznJOjgbkSYkcajCG\nVqtFVPlkS6XY2thg+8JFHj9+zMnJyTnEp96DkiQBWDB86r0rTVMoXU+esBZVQZ+2Esj7LAzlCYRz\n08JY17doUK74R4moRPlcsVsS+RG+8pDCWa4EUYAUztIKrd376BKMRQmJrmiHSig8T6BNgfSccGBZ\naqysvUudcrYQsgoAczwVgHB4YZYlrge7YqnVQWddhA/D0CHMeYFX9cELXNHdGIeWN2LnKIHVWGPw\nPYUwHtoUi3ugtNq5lwiD51u2Vlu8e+8e+3lKY32NIm7htVsYFN2oQ2mg0wsIG01WNi+hjcKUBb/6\nG3+dbD5jfHrKs8ePWF9dQ3SbeEGIH0a0uh3CZgvlh7SVK1AMxwMODg6gLPnko3u8+6O7HO7uYLOc\n2eAEUc5p+h6ngyMEvuvXBgI/Apvz+MEOQggubfwWx+P/nXHyz0imlo2Ni2i7hzA3GU6/i/HbzCb/\nPRdX/x6HIw+DwJQR6BSkQcsj5qUDNT78cItY3mI8eECvtYo1KVG0hrYNTk7g5s3L3L9/6LQHcGrH\n54LzqqVAVnz/OsA/n5QIVLmOjA2FmWNNwof3f5tJ/veRssOr1/8j5sKy8SocPTzF5CNubfyffLz3\n2xTZ/0Gq/xlXX1bs73TYPx7z6o2/xc54HRoWpRuERIQMkMmUAo8it5RewEdPjujK9/i5X/oFOpFF\nSdfKo6RTTy7yEmq2xWdguEJAnYjUSVd1npfQYIQDhow+a2mpKb9qSYRLLBBEg7WugKXr9i5rUcJD\nVMXaZXkJl3Cdb6mpr3ld9P206NQyldpUa4JbGHxZt+1Y0twdayN0Nq2BH1CWBWjDShyy2nJiuOtr\nPWbJnEkyx48DtBELbQ8AITJKz2M6HvC7//if8vWvf5UsSUmSOa2WdQX8Mufxwyd02k3u3LlDnrt2\ntDDwKhBNIoUBW2K0WCTsDlwAlHf+HFiQ1Og755LnOpmuUWVd5sxns3M91lJY/MAVHZVyvt1Oa8Wp\nfEshCFBIGVIIx0BDO3aczg1+1XJFnhIEEaXvYawhLzO6cRNlIZAKYe0CN651D8DHrwCUMAhRApTW\nTpTRKicyucSKqHurpT0vMFZq45JyIZCc2dLW58C9HiqkBKlUldhX58m48+RVgIcQYE15VggQAApd\niUZYK0Ao9/Ofg3nymUqsLdVk0hrhn91Q9QRzdh8u6XbiUGfPu0VBuh5GfV6aXipFUQmg+Z4iLxyC\nW+LoMTWtzFHNlyo89kwAoN6s68qPVK5PrMjLCiFXCCR+ECLTAqOVy3hRpMWcrJAUFjQ+lhhUCyRo\nNFb5GGlBNRiMU8qsYDpNSTLNaKop8pLCGDwEhbFkaYq10IwhiqC0kGuDCnxWV9cxWYYucjodiZAR\nK90e7Xabmy/fRAWOTv/tb/8Lbt66QqPRYHQyxPc8jMkAKEsDNqcoluglVSQYxxWdM4gw1hUjPvnk\nY773x9/hy1/+YhX0Tgj8BlHcJgqcJY8uS/pHx0SNiFdfuc3H77+DJxUSy2Rwiuy2iTzF7rPHxEFI\n6Eu6vRaN2CNuxhVqAWnqjsv5385QymdtbY00zcFCu92m2Wwwm48YDDSngz6+LxYWQ4LKvi2OubR1\nhatXr9Jutnjn7R9idcZkMuT2xW06nQ7dbpdOp0NRFAwGc8JKEK329MvznPv372PQTCZTdnd3KIqC\n7e1tWq0Vrly5hJQeyg84OnqCqvpt19d7rPQiLl+6wvbWRSbjGdevvUSv18NYzdZGlyx1FNnaqsZd\nlx/vGfnTNsLQ4+EHT/nq119DSslKt43nC6wHs2mC1ppsPsOaHGs0oZSEcZswdurUeTblWz//DRpx\nQGY0mz2JJ+aMTvtIX6F9D2t9Pn7wlD/5/ts82TlBqgglQ8rK3k4Ky2wyAlPiSdfzNJzPQfiEURsp\nMrYuXydJCx58/CF5mjFL5ty7d49EK46HtYd1k3ZnhTSYoa1jKEihkJdX8VQDT7rinPUipBC0Wi22\ntzfZ39+n1PNFldhaSJIp0+GINCs46p+QZs4bVSnpbP6EJQpjpNGkyQhfSbrdHq3uKsILSUsNoSZo\ntLAVzSxLEk77feaTKUIIwiBAFyWj+ZxWq0HgOfsxz/MWtK76vgZI05TT01PSmUOG8zyn1+uxv/OM\nIHJ+0ltbG3RaDYLAJ01cE8p0OmX9QmfhbVoWrnWm3W47Fo9IsTKi3V2p/JAlWWFJUkt7xdmTTGZz\nrAWhPLzK87cYzShLZ0Hoee58ZllGlickifs3nU4dNU0FgEMsvWqdDoIApVyA5VVK775feSRr4xIF\nz9BqNdjYWOf46BQpPNIkR8mMKBKkab5AO4vE9eO+/PLLPH78uEI1zoI+bc4rsdaISFmW6JqlsMS2\nUkqhPiP0USEq4a5FIaCiewNe9bjTWVVVsCQWa7UL2l0PnxDGddhWfF73qHH9kEIglai0CzRCeIt9\nWS/69sSCymqtE7O0C0EzXC+jkCghKCrGwTK9dZn6t/yco/LWMYBd/O+ETN3P1nVSucSitl6Txtlu\nKUG3EXEyHVLMRlhfYbVfFVicB3oUuF5DbewikfE8iWzGSLmGUBAFIbLTcL3eyqPVaaOFdAG6BVu6\ntqmD/X1smjE5OWF4ekiajJBFiTCFuwpKIRVQOEYGFUUfW9FCrWAqYPvav8X+k28hxQBsyvFgl55c\nBSFYbWxxePI2w/FjJD6WAgFIFNa44oio3LD64wOUiOj2VrE6w4oMTYy2IW+8sc5bb+2gVBNjcxyx\n3LHizmbTc32Sz7M+qjl4eWODg+kzUKDznO7aZfxslWawSs4Drl5ss7NzhDAxQqQcZw1keBWTNmlF\nHZ4+HBK2IjwBJ+MRsAoR6LlFE9SzEdDOGtFKYj/m8HBAikd7ad7U93EdD/54c6mfriFwrEmEQArH\nILL2rHXwha9ZQjyfRwfrBNDamlJ71qrpEsWzbHohTvgc4lkXbpcTyPo+f9Hnu/dQ9RdanP8FQ3Pp\n8+r3qFlWQdTkwuZFvCCg1WnT7LR5/+5dhuMRpc4/hRQXhROvffbsGV/4wh2MqVFdTVmUtFqOBba3\nt8ft27c/hegufyc3V2pqds0m/cnMgOcLD3UCba1ZFABqptcZFf6MBl6frzMk2kNag5AWz5OYEqyo\n6dkGoVTVmiNBKozJsAh0kWOlAxeFUGAlBlntYe4z5XOf+0L6/3Nzp76Iz1/fF52PFw2x9D61ZZyu\nfCKMObtfzx1HzY75/zg+U4k1OIEUUfUI1J7EPoosyxYqe8tBitaaoszJMoMuFRqDLlOwGlUJfymp\nmGYFSjm0xfU9OmTBV4ogEFXQmxBV/rDuYpwl9QJJluVgHVXbaA0awrgBIsTYknlimE1KulFEoRWJ\naCJKwcgoxlYwTqZ8uDsjlAUpMJ6kFP1jjsaQKMnT3VM+eXBK4EEyAy90SXOhnUK49SyBB61WvBAV\na7ebpLmm12jzxa9+HWlKdh4/4PBgn/k8wZIyGo3Y2dnh2o1rfO0bb7C6usrRJ0/50Y/e49LmbTqd\nLtNpyno3JE3zBaW9LDXJPEUIhcTQiEO2trYW4nFIibCWUufcvHmV9bUme7sHrK1uUZSGB/c+4mRc\nIlWTaVry0q3beIlPv39CJw4Ynp5gtSFPxpzMxzycDNnY7mHKhNHpCePBAV//+tfddfeqnj4pyLKE\nLMvo9Xp4nsd8Pmc2y5jN5gwGAxeUKVcouXbtGicnB4zHYzqdDrp0KN0bX/oijajN5a0r7O3ugDas\nra5w+9YVVta36J8MuHPnDkopPvjwfQ4Odrl6xQmU3bx5k+3tbfr9PsPhkM3tdeejO2mw0m3R7bZp\ntdr8/F/5JvM04emTXe7e/ZDLly9y4/pVWu2bDEZDpKd45933ePpkh7/57/4Nvv/9P+bw8JBf/KV/\nHT8I0YVTZ9y8uM31l66wfzAGDv6S79E/25BSOfpvFdT6vufaDQQUuQVRqf1bXAW0KoAp31n0BMrj\n+PCQX/7lb9HqtBA6Q/q+Q0yNJktzhpOcDz+8z7OdA4rSKRWbGhGxde+SQ8eEBYmgqM5pJB0Mp4KQ\nsNkCFYB0c38yGnJyeECmnR+ttdYl3ZMJ0veRuMqw1Y4SbW2AqpJcqw1GnPVypqlLVuvkOkkSTk+H\nWCBJMopyqc8HVxSsN6g0zWn2VoibPsIPGMwS/DDAtxpkLaro3nM+mZIkCV6lRhoFPuPhgCgMXaFi\n7gTflkWd6kRQF5bRaIQnpKPSVh7SQgiiKGI0HixUwn2vqjZbl7ykaUoUNSo9ijOq/xnNNiCKZvhe\nQJ65xDtJUozNq+O3i/nitB1ysixbHINDvx0ikWXZ4rhqgTUl7aJo4HkezWaTwekJQRDi+wpPukRP\nl+5vlPCwUhI1m4wnxwghaDUbTngFsMZQ5DnJfI42hixNmU9mADQaDedYUBV3F61D1VgOphbFMOPa\nB0ypSctkkVj76rOxNRvhlLt1aSoPaCqwwBBYhzdKX4F09D8rwMocKyrkC4OsBMr86lRpURmeKCfU\nWWXvTvlbCqwUFNZgZRUwo6FS4pfSKf0qL8BU1ECrTdXf7eKHZaXXc3R97aynFGdU/DopKqq/qYv0\nUrrinNEaoRyiXpoc4bmkX0iXauZ5wfZ6Bz2cc7L7gNWGRzErabTXieIGVikKI5FRiBUKU6F5QrSQ\nCrKioFOJdXa6K+hyKQjXlvF4wiybMBgNOTo+5Iff+2Omxyfk4ym5GWKSMfk8IZYeVhhKrR3yYnMn\nQlZWYkLWORNgLVc+f4fDnfcp9Fcoyzc5Pj1ie/UrgCHi85ye7BOHc+bl36br/bdMxQxpAjwbI9HY\nMnFIkFBocUp/fMh6J8eqEZYUzSUKuvzonROiMCLJRJUMFSwn1i+mA79gElo4OHwLG2tOpz/gzrW/\nST6TnJ5KgisHaBKOdhMudLcYFi20us+EnEu3f4XdZ/8h2AAfyGYpQejjxyFhOqLUoEVMJkcYCaLw\niEWJLlKk8dA5FJHitLSsW4c4WkuVqJwlNy8+6J++Ia2zVBLV+myokNClNsPnE5sXJbb1sIuKU732\nvbiP9kXr4nIhsk4aoUqIpTr3+ctihM8nW4uiRvUdxFIfeVEUSOEtEutme4VWp0uhSxqtFltbW4ym\nM0bvv7847uVEVWuLUq6AnCTJ0jlwyXV97P1+n6IoFq2By6Ac1C2W2rWv1CvP0nlYPt/LyXQ9llW/\n61aceh+q16znr1d9vup9sk6upXUCz0iLUALhObcDI4xDboW7O92xOCS4bn9y12ZJ/G7pUggpUZ6H\n9KoCqxCuQ9Wefbfz86a+bGdz5vnCxPNtC8+f1+X3O1e4sbVY6aep6y9sNPkL3L+fjd27GlmWkaQJ\nngoX/Yp5MaQhnGJrbblirbNSGQ6dD6K1ltlsiil9vMAHPIx2Aiquz1dTCjfJsrTEC0MKIG40KOcO\nfSgKFyRK2ULrrArWFFqf9WnleemoXFnJbD5BlVOOhsf0+yPyLCZNM967+xF+DvlIMhQGX8JJtkIh\nV5jMLT98/4BAQiH7CL/BuudjYpgVks2e4uZL1/FVxspKm7ywaEL+5L2PydMM3/fotJr0VjpsXFhH\na83e3h43trfprm7wB9/+NjpLELYgCgOAhen76uoqR0dHvPnWD3jl5Rv82q/9VT755CMODo74+PSU\nRtxhMNg/q4yV7iaezxNM1W/eaDS4dOmSE2bwPHIN2WRCoxHx5OmYR48eEIYxVy5v8+jhLt1uh1av\nSaezRRB3aPdWmacz8mTGbHRMliR0Wg0+9/qrZMmcx48Nk+ExV69e4+XbL4FQ+H5Iq9MlyzL6/T6d\njvMATdOUdnsFpRTHx33G4ym7u7s0m21arSb90yOGwxMePX5IFHlcf+myQ8jGCWHYphHFTIYznmRP\nuPv+e1y8uMXW1hZxHBBFEe+88z2+/KWvLubb1atXmU2npGnKfD5nNBrR6XS4efMmQUMQBCFRI+SD\nDz5i43CT11Y/x71P7nH/kycoaWi2WmxsOEXz3f5R1d/bpbuyjhQBurTs7x9wdHTIs/0DPOlxfHDI\n04cPmYxH3L59EeGv/aXdm3/e0W7HNJuOCpamKUHg0e216U9GfPjBA7781UsVBbNE2gJfKJrNFp1O\nl97qKsfjY27fus6l7Q20NBS2IJ9nWAullrz/9j3u3X/G/uGAolSUWiA9D4ygNCXSC/AVTCdDrDHo\noqyEjMBaRZobehd6zPpDehsXWd86Yf/pI/I0dYGt1rSbKzQaTVdIs4bZZEyz2SQOA7a3N9m+sEpZ\nGqQAU2qiRoz1JFIZ2q01ppOcTz75ZJGUBkGE0RCFbY6Oj5lOMo5PTklyi++Hi6r7dDanFUVsbV4k\n9AWlhUwbhqMR02SO8hv4YUial4wHQ4aDPrPpjCxN6a10HWIrLFMEo9EInRfEUZPRaFQV49okSbLo\nVS7L0iXgQUipc8LAX4g+eZ7H2toaUkrXi1YVrAJPLlwbTk5O8H2f0XCCrthCKytdmq0us9mM6WxO\ns+nUpaRXFU+EIowieqs+g8EA5cWuSJDOOTw4rJLkNlHkO2GzKGAwGLje/SrIqVGJukXIWtdrfUah\nc711UrgCaZYVjs5cUZcbjQbT8Qy3E1ebvLZMZhPSNF1sxknmHAUePny4UCOHZSTiLCio0fs6GPI8\nzyX1dc+ZqXpJPyuq4HXQImvOnwXrlF6FdcwtYR0Fu1IHrSIzdz5FFWBhLbKKxBxaXQWCSjlkuqzO\nYxWULSNWLIIv0KZACVeEsZV4lLsGDjWp2Wf1a+sCfI2E1T2AC8XiKhiTtYpw9a+e/3UgbW1F8bcF\nwvpI4R5THsShRxhIEp1R5HOEL50IkHHlGimVC17NmQ+7UhJNSRB6FMa5FPjKw7PaWXMWhtl0TjIa\nk8w1w+GQ+/fvO7bLdEYymqCDBEmJsCVCVLoKuGKOL50dlCkBjUs4nAM5H/3oHaCNzW+Qzj4gjjcR\nYhtrDzk5OGV96w3gPoX4AdqaSjhM40zFJMKpESGtT7M7Z629zmz8Jo2OxlC4AgyKGzdW+OT+hHZr\nnens9IXz61wSxlkP5DkUWMDGSsxeMsGKGTt7OwTNq5RHDXz26XWglDAY9lG0QIHteZyMLfP+E8qB\nZa0LBwPIZwGefwHfnmBS0MQQ7FAIQLeJzQBhNb4IMLrAGB+9xJpcTgZdIqb+QoH5X8qwBmHrfvA6\n67GAOocmLyextar+8vPLo6ZgL/9NXZSqn4ezlgwp5UJsbBmtrtfaPM+XtJHUCxW1hVqyaDK1Xa67\nt1W1ztYJZ5IkVWIoWdu6iOd5rmfYaI5Ph7x+5w3iZof3fvQWJycnbm+rtHnA2UbiCWYzpxxet6W6\ntkSna9JbbS+Oswad6t/PEmVXlAx8VzSQQqCfKz7USfBy0aH+HrXNl5t3Z/T8LMsWrz2j0Z9pTNTn\nfFG48h3FQGoJSmGtRCqPslSURXC2twmJVIqsYofFvo9X+XFb6RTAZZWoSuFiLz8IF+Jm1HexYMEo\nODcVq3Oiz4l6nq279d8sj+cV7W1lBby8B7vn3Nr844pB9rnWjbP5/qnp/WPHZyqxbqp1VOFjMks7\nvE7gf4Q4/SUy/z1a7QAlWajhZalGl4p1r2RX9AlFg8BK8lkP0U7xY590rvBUTqM5p38cMpvMkGFC\nIcGoGGsKCA0zMyXsQj+c0vYV0mhKCcq3TOYpg2JCwjoP97YoAounjhnvXUeLE2bSo5/BTITYaIX7\nRwd4OTTwsaXERgldf0SZSoyWvLQVsxq0CcKEsJERNFe5d39MYx7QsXPeuBWjiwPmyT4qXOd0Kmk0\nHE300sWLXH/pOs+ePeO9j++itWbjwgZ5kvLd/+fbiDzF9wqkhDAsKSOL70vmU80Xv/IV3njjC/yv\n//Dvc++jR1xY3eTy+i08SvLhAIUiDDcR801OU4sIBfNSgdwmOU1Jcon1Ttk5/Yhb7Uso1SQQDQbT\nHYb9U/onY1rtVeLmJlH7Eldub7C/d4z1JG+/9Q5JktHrXsAYS7PZZDSY8rM/+6/Saq4wmxbs7Oyy\nkawxSt4naHYJ4hZRHFb90wVlMkPZgvmoT5rkRH7IdOB6HossYtCf0VttEASKvJiDVUivSXetRRj6\nFEWANSWnx7usrmumU5hOMvbGQ+aZQEtNGULqZaz465SFYTafEDd8ms2IdrtNv3+KkD77+316vWNu\nvRwRxgpBEykUz57uY0zJdDZkNpvQ758ym8y5en0N5RlaXQVhhow0GxeusHFxE18FbF7cphN3ibwe\nnplxsHOPRtChfzBgOp4j/SYnE59u+7NBHwWqolRFqSpLwjBECIuSAWleEIYhRTnCmBJhnO1VvZFv\nbm5ya7vHq6+/BtKp4BugMAJjFfv7B3z08RP6wznGepiKXQKqWsxdMC+FxJQ5pijwohhfOraLNq5H\n2liBlQo/atJZ6dIPQ2aVD/lwdMp6Y5UwcIWWoizRVG0h1lE5m3HDUZlLw6xIaDQ6COHhe5IobNNo\nTOmfHqCUoRErysL19GSpZTCYcHIy5Ph4gJEBSnoEQUSap4uAAulhjCbNUpKidIhtlkFukYUmL13b\nxXQ0pshzBI42bUuNHzhhMlOeWQ7Vo2b6hKGzLkvTlLwoaEQx0p71B9eb/JmCslmImUjcZu3e+wxR\nqBHZuudaSb8qZjg02FSQZ73pPR+sGmOIwsYiGAgC52/tfLvPgq26uBa0o+qzReWXvIx+OWS9/g5Z\nNieZz8GPaDQr5LOizhpzpra6bGcGnAvU6veqh5tr5607livoz1fjfxzy8tM6PELK1KKUo/wZWzpV\nVeHogBiN1RW9XVY+1iaiLAryMqHVbZMULgHPLCA0UII0eL6/6NFOipQwCBwTQ7lgD2tRplwK3F1h\nTKAwUqLtmQiZFRaNptAlLdnE2MpuDpw9pgGJRPhOqCwThiAK0GWJFQKLhzAW6/mIKlm3RmBCQ54V\naG2JowbaGGeLZxVCQKg8vNijMy8JPZ9WvM7MWyHQBTovsJ4HgcJkBaYswMsxVhJFrWqP8llvu7YI\na1KMEiSlBqkYTjPu3XvKeKSYT4Z8+N3v0wlnDEcHZGVONBMoFSP8mNLPKL0Zge8jJ4LSL5EixMiQ\nIGozGZ0SNQ1BJGn1PbQ/4PYrv8WTp++QjP8h1v4+s3mG3zzElhpshO8fMNG/y4b6DWCTEUCYEq/m\nDIfQ7RTM+wCPCZKfR4S7eME99voHbLZ+gwcPJghgPtsDa1lIzi5U5ut7oC61WCxrIGYgUifqai3K\nepTyc+zuKlr8l8jwJl78LfY++fcYiCM+HPwqFzv/gJQ1pJpijEfv5DK9a08RFwz5WoM8g5uXbrGz\ns0uWH4DMHZrPfcjAhf8jshAiBbpIwCpyStqTI4aty/SsB2VJIQsyryASmo5STNVnZ1+uh7W2WpTP\nEuLlNQzOkrdlFHW5qOj+lhf8XrFQlvKisyLVecRx+b2X0e3lwunzyGPt82yto7PXdHAhLEbIc8df\n5K6dMY6bWOVmWaPVrtgGThPi9Vde58EnH1EzwGoxzmWBK2tda08cO12QOGqjlHt/p8XkEuk6wV1G\n2c/Oz3k1c16ATv+ksZxE1oXfmsWz/LlaF4vnlpNrKQW+XyHYVjtGixRY49ZcpOS8pZVYfG8RhEip\nEPXeVxUNERIjXDlSKLVQk66m1qJAtkzLP7vun2ZK/KR98fnnni/M6hkL8wAAIABJREFUnCXenxYj\nW5znP6Xt4c86PlOJ9SSZYrWi3WyR6gQbaDKT0ApLknxOoxFgKVEBTIcz0rwg8xqMzIRUNBB+m0JE\n5FhW/BCv5VFahQl9MqkZFwqRb5NaySQ3SA2yhJNBg8lUs/9xh/33U/J5hlI+aTkh16BthB+VjEf7\nfPBBn5UVaMsMK0e0vRZWgUgyknnOG5+/RC8qCMwFZBDSXLHs7H3C8UGILjNee+0qLWIajQLpzyls\nxO5eTpIapPAR0seLYhoqZDITZLnmzp07bG1t8Z3vfIfBYECaprzxxhsMh0O+8513Weu4SrIVBs9T\nZLlGFiWd1S6f/9zXePjgGY8fP+Ldd3/k/Kh3dnj8+DG9boc3vvAab6x1ef/9d/Gl4MGT+7y8eovp\nwYQilygdMUsyTkd9ptkh8XoPq5vEUZt/+Sdv8fCDP+G1117hC3c+x+t3Ps9oPCdLU/ae7bOxuc3h\nYECn0yFJjtnb22M2m7O6ukqWnPDBh29zfHxCEET0umusb8Z885W/xtraGnmes7P7hMlk4nr2kPh+\nSJblBFFEq9lBCMXp6ZDxeMzDh48QKufKlSukaU7gJ3hRyIWNdR49esDDh4/otGJmsxnrGz3ihs+b\nb75DHLUZjUZstzoYLWjEbdI0ZTKZcO/ePaR8hSAImEwmXLlyBd8PSZOcu3fvIr2CbrdFFElOB1PC\nMORrX/sa169fZ+fZAbdu3eLales827nPtWvXEMKQpSUv336d9kqX7mqHKIgRSKaDhK9+7Q1+b/8p\n/ZMhZUuw2u0gDDze28MOYaXZ+8u+Rf/MQygoS810OqfR9mi1m/i+QsmIyXhKHDU4HRxTFhkSTRjE\nNOMGzbjBzc9/nou9Bs2WT240mdZkyqM/HPHo4VPev/uQLLfo0sNaD4nASl2JUFQJjAUvUGTpHF2W\nFFlRaSyElKXBExJtFSpoovyA3oUtdp48Qcgh0goOdp9iaCGM5fLly4TNJvMkAVNQ6hyhC6aTCZ1W\njyhucTqYkWeWZquJp0LaoSKZG958803KsuTC+gxrPIyRzIZjBqM++0dHWCTf/PlvAh5SBkQR6DIn\nTTPSZIqkZDpP0UKSFq6oWFhDMUsYjCbk8wSdZbSaTVSnw+6zp5RFQTIv8KUi7HQQFiZT13/darU4\nPj5Ga836umO9TOaO6lxWVlO+7xFHEXPfUbPDyCcMQzrtJu1WzPHxIclswmw24+q1LYRQTvlYKdLU\n9aVPpzPEOCGKAtY3tqoKvwtatDYUGmxWYnDe2HlpsVbS6vRQQpFUFn61l/h8PqNWba3FA+t+a4cG\nOE/OVqtFniWLQCNPC4o6SMLR64URNKhUqT1HFVdWUZQFWZGdUYgrdDmKz9TAa1SgDjgcHf0M4U/T\ns8KIlJIyc8IxWIeuULU6fVYSa4Eg9B37yVjjbOCqoDdNEsIgcGipEJg6lrYOcfQ8zxVE/AZSKJTv\ntEik8hDSkOeuLQsqpKcSp3TBogvxahTaYt35ExKJqBD/T/fjCSHQ1qDNmZr5siq7KR3qJCoUUhuD\nFa7Hu6YlGmNR1indmiJH4miT1pT4SlEWJcYUFfKd4/mCMJQc9I8ZPHnK6o07RK2g0nCRSM8jjGM8\nCbmZ0my2kJ4iLTWlts6rPgiYDMaUCPJMMxvPePboIT/8l3/M3sGMTiMiScbMB8cL4sCsnOPLGC00\npU2xWmB0TJH5xO0ZngrIsgwpNMpPSFNneWhwp+/ZTkKoLnL37n/FpgxJi2est15neLqHFz2hHVzh\nwYN/xJH+23Sjv4sqoK0+R0d8nknyQ5J0xrz8Wwwnv48qBdYcUqJ58sHrfOlf+83qelTJUDWblscy\neiSqP7aiD0iwEdY6twONoD9/ynhym421Oa3GLsOB5dWX/xF/9Oavcuf1NoPx/0Aj+E8x+gI+N2h3\nvsf7H/83ZOKf0pGfZ3X1PR48ecbGVos8zxkNs6qlYfm4HHOlPj8gSArN8XTGDU+SJhNkaRCRRxgF\nNJXE15AXJZ+FYQCEcuJXVQIJZ8XA+rFli9vlxK++78+rKC8zRNzvDm2WKOGdK0rCWbJet1zUQmV1\new24AmddvFwuTJ59ztkxuOcq4TUgqtDmPM/xPFcwDUMH0Pixv6B6F0XBeDzmzp07zOdzrl97ieFg\nhNEjsjQj8MMKDTZsblyoAB69YEvVbKSyTBePdTqdT1l/nR2nXLBHAVS13iyjqstJZ91mUHto16+r\nEfdayKx2S6qL5zVrqi4G1OeyPu9GlCjfw/MluiiYzyd4GJAeQSAW16lm+cxms8V+qJSjede/m2qN\nDb0QKT2k9BwJotoHrLWVO9Knk9yza3neAm65oFI/X5/D+vvWrFp7ru982YLZO9cOVH/mojgkvRce\nz5+1wAGfscRa+wEiCpjZgtFkSqY0Xlui/FUsCVZ62Ir2pIIApTPmXkARBzwZpsQmJ8tgbE+RhUAX\nQ0okB0OP/izg++/uQxqSWElqBT45PiVWNihFF5tYmlGL3moLIQRhs0MQNrBCcffhD1jt9fj6N16l\n0T4l0hfR9hibNNkdPuTd04QolLy0ERCbBFWmlA2D9OeEwRRPWjBzFDPCyOD5GmxCkQOmRGGYZSWz\nVFMaQ5poTk4nRK0tPv7kHh9//JELCITli1/8Ao8ePWJn5ylvvHGNg509J8qlBOvrHYwt+Na3fp57\n95/x5S9/mfF4xocf3KPVavGLv/RX2dnZpb3SY219jT/89nf4lV/+N/nmv/JX+Af/8//E5Y0NsvSY\nIk3ICkithyai02lhkXz1q/8GF7fewFcr3Hz5OmZ6wNraGmEYMp/MmI6nTCcZL12/Qb8/5Ic/eJPJ\nZMKdO18gjpvc/+ShEx6bJhz3n2IwBJEgKw+JWz3eerPP2toaSimSdMpXvvIVer0V/vCP/m/8MMYW\noJRPUsB0POLg8IgP7j5gOBywut7kw48+YHvrMq1WkwdPHvODH/4JWhesdjtYHGX06PCQzWurXL96\nke2tK0wmMw4OH7P75JC3fvA2q53L9Ho9Wq0GaZpWFPMmnc4KDx8+XgT8jx8/dkrOsoVUltc/9zKb\nm5tkWU6z2abXW+X9d3/E6toKSZJUCuMrfHzvCb3NUxptp8hcpBn3H3/E3bsf8vLrN7l98yrtRhOF\nIElmfCH/HE/3dtFT9afeQz89wy1o/X6f1sq2o0mFDt0sC4O19SZdbRjSIax+RTcSQpIVGjyBFpJZ\nZnj8bI97958yneUoFQNeRUNSaO1o4lKKc+qTRVFUm1COoPJ7LA1RJB0SJhRSWOJWm5WVFWaDI0qT\noZRiMhrjK49Op0Ov13MVce2C9izLGJz08VREZ2Wd1V6ExkOpAITClIKNCxd59PCZ62fWHs1GFyl8\nikKT5xZrFJvb27z26h2kcAh/oQuM1k45GgiCiFBbtJDkg4JCawrtvGnTNIXa6kUbgjheUF1rV4M4\njtFFSRw75exl24y62lsHWuPxmCSdcWFtlTiKFpv1soDk4uoavUh0Xe/h84iDxPd8pPDR1vVNgyEI\nPTwpKXOq4McFz/VnzWZO7TuKIi5cWKfTaTMaDRAClOcSEEcndMIyUeQEZGoEorFkJbYQcDGWoiir\nn8VC6KT+bnVgIxCL3iwlFdY4Wmq9cddzCs5vwsvIRP09XmTjsXgPnk8xfnpHTZl21Lvz/raqEgUN\nfP9cIIXRWKEXtO7Ak85uSRuEMk6N1jo1WmtVhVpbpAVj3P9WVyiEqlMyca7/TyAIlIeVdvG5Amfb\nVSf4xrp+0jq4k9UxK6VQ2iyQcFm/bhlBqtSMtXHqvgKBMeVizbJWVI8bfE/Q63Y4LFO8RoM4jCiK\nKbmR+Cpy6I51c0kpD4RAlxZPBWiTV7RZS15Y0jwnTzJmkymj/gn5fI7VGePRBAQkaY4feA6B9w1G\nVRRRLdG5R1lqrq7dZl4cufYDNWc0PAUkm2urjokiphgjAI9WW/Hrf/23ufvhb9KmYHRiiOMVonYK\neoWbNyU7B7/HKPvvsParvHbj1zg5+Yib1xXv3v1ddgd/h47v015PIZhxOlb83M/9x7SiA8Zzpwfy\nPB3z3PyqaN+uDUBgROU/T46wCk2EwEeoKTevfQNr32IyGiAN9Hpw57X/hDL7Awrv9zg6+rt8/bW/\nx+5RSeFBVjwk8NfY3bU8etIniprM0xmzNGVZQO1sCFxHg7ODtAJyaxlnBVk+QxmBrxS+r0hNjhWS\notB8Vu7md967S7fTptvt0mw2WVlZIQxDzFJCvbyW1WjoMuoJ59e+5X3BFQzP2kekOEOc64QTWLQY\nAUttGmdez2Lp5+XPq5NnY8FaZ6erZGX5Z3GWedMpxhiCIATrjjmKIjqdDviCKAwXe2P/+ARbaELl\n88orr2CM4dmzZ9y/f3+xztXFht3dXb75zW/yox+9vUikfd8VnNfW1mg0GudsLBcMJXHmUFAUBZ4K\n8Dy/on2fL8gs76G10GidWNcJdJ1QLz9e08/rcxaGDlWvk8sgCBaFAD8KkQiMLpzzQRg5YWLhIbCY\nisGWzBPmc+ce4iwIw4UAqbWmYvkIhBEUpcFvKISqRDurmGSRyNrlosj5e0WKT987zxdS6jmzzCTT\nWiNrKnhZuvahqtAil5gVz3+uEPJTnO8/D2ugHp+pxLq0EiN9jM3xowgVeozmM9RcIFWInFmkCkjT\nlOEgZTyeMfYjZmnJs8MpTW3Ji5CpGNJWDUxpKdCUJqAoDQgIY3fjdpotGr4lsAXzQnKwP2PzkuRC\nt4kvLNoa4qbGiIS8LAmfChqhRxxBq+lBkiFNgZUFkS+IFcxLgzApoadRlKAsnnJBhCcNcQhB4KFU\ngRQai8FXTpLfUmKAeZozTVxS2+5uIoMOk8ljvvSlL3F0dESr1aLf77O9vU2e59y9+yFrKy3KApIk\n5+TkhH//P/gbfPe7/4I7b3yD3/md32FnZ5/Aj5j8v9y9ebBk133f9znn7r29191vfzPzZsEAGIAY\nACRIkaC42KYoMlJJkSK6JKdcZVfkVFSp/Ben/IdTKW8pxynljyxWLDtKpSxLKcksR1lEkZQomSS4\ngQQIkAAGM8Ds8/bXe9/9nJM/zu1+7w3Biln5Q4ROVc/recvt26fvPee3fJfxmN/5nd/B9wMef/xx\nzm5u0mgu8Ob1t6g3W9SbbeoLLaQy+MZQX1jEb3WJlaTX67F14QLnzz8BNNC4dDqLXLp4mel0zJ07\ndzh3fov9/UP6/RGXLj5GkiQ88cQT3L+/zbe//W2uXn2Gj338Izy4v8N0YgPjBw/uEdVcC6sppvh+\nk06nQ5LEFKXLSy99G9/3ufzo43zve69RFCXnti4xGo3p9UeMpzH1eo0knaBUwZNPPkWruUC/N+XZ\nZ58lCH1cV+IIw6B/iFN5UQupqDd8+v0j+v0hxgiKXPPg/hHDqOD8+XOEkV9xbWzVMwwtJHw42EYp\nxdmtFcoyw/VqTCZjarWahctU1UnPDShVZkXTlO2UhEHE3TvbfPeNPYoSPvLhRZIk5fqNtzk4OOAT\nn/gkF7Y2CRxBOp3gOW0OBofUmiFf/eLLf673548y3Erk58aNG1y8fAYhDAsLC2ybPlbVMp9XVgXW\nEqoehdTrtiParq3TqIdMkhjtSr7yjZe4ffMOrowQbkSpXKT0MIWtsoqqkuvW/CqhUbjSCl7Zro2D\n79fY3d0lvnyGpWbT+hVLa323vr6Op65ScwU3b7xJmZfEcUKW7czhyPVmjdFkzO7OAwb9Iz71iZ/C\ndQNGwwnujPec5NQaEbXmEqP+Pp32Mm+9fQOMx5nNJqFfI/BrDPp3aTZbfPKnPsXly4/iehGlqmBX\nwrFCICJAoO316vnc/dZdJtOEQjtMk5w0zWlGNVwMOlcYKdja2mLQ77O/v0voeXTbHer1OrV6k9u3\nbzOpdAImkwnD4ZCjXo/RaESj0cDB2h2Nx2OC6j0XSlcw4ONAY9bVS9O08pauUavVqEVWvGw0GoEQ\n5Lmh0ahj0EwmMXEyQumcdnthnujPNv2Z53EYhphGC2MUQkim0ymj0Wiu6j+zG0nTnEajSb3WYDqN\nKcuSIIjwfP9UIQStyNKYLEvJMju/roiqIEjg+1YQLzflqaByXuHXxxobJyHosyBmFrw8DFmcfc8Y\n5qI2sw38YZjgj/MoyhxtygrapxBY6yxjDFpoHMdFixmvzSAdiYOFDLoec/Eyq/ZtWcAzDpxFGlhR\nUYO1mymV5b4bY/nyWh+L0hxTEiq4nz4hdHOimCYc2zEzlZAe2ibo0gFhDEWekhU5XhhY5wVmwksg\nTKWYTJXck2OkRAoXYyRlnoAxuI6H4wC6sAJd4ykqK2k4LsU0pt71wfPxAg/hOghhbJHCleS5DQhd\nH4qsRCnbeZmMCoajHsN+n0m/x4tf+Qqjo0PGkwRVZvja7kVFbuHrXtda6ehJDanauFoAB9Qbd9Dj\nQ5AQRNCKHPYPNKvhJcZZya78Plr7tFYlaxsrXP/eNp3WP+Br3/xJHr+yBeYW06EhnfaQCw/oNt4P\nfJUpv8e1W58gVimB41Dq/5J61qHV3CDLv00olllpfpha+GHKon4q33yngtTDwxYYPIRbYITG6KnV\nSDQRohwhxAcw5oid+3+HD33g/+TW3R1WnZ9nt/8/MfX+AD+a8L1rv0oZb5As/BuO+q9hDi9zpvMf\nsNL9Aru96zhuihtBkb0D9NSAMD6aDCPBuAElgjvDKY+bkrAWEipBXmQ4vgNKUuSnVc5/nEdRakaT\nKcOxRddtbm7SbrcJPLdyUnBOaQsAp4qDJxOQk0nynDIjRIUCP+5qzsasMz37m9m9DMd86nkyLU4n\n1Q8f6zSi91hQTlXCXvOfSInAcro9zwNvpmJu8F2H6WSEKyFJM1qtRdbXN8mygvv3t23MIF20hkaj\nQa/X48zZjVPd2iAIOHOmw+XLjxAEwbyr+3DH9ZiHPps/gzpRpD45t7P3O/v6MAf95JzPkvhZQn+y\n+32SkjQrYriui+ceW1gp6eO4IUIXaAf8wLH0uDQnyQrSJJkn747nHguXzYoFUmIqvrfVXrBuIRhd\noX/4odDr4/tOPPT/4/l4+PnJ989svmbz+A5d8B/yyj8amfqHjHdVYr29O+FQTijzBOUojqZwe/tN\nHm1u4HqgdIaQVikzihoMB5o06hMFLWouNFyJqNXotgReElILmuRGMVEtxq/t8KH3nkWqhMRISlcS\nOprI9RiMU273Rng+LK0ZRNWxMs6UotQ4hcLBoUgzHGNA5bgiw/EV00QhXUGpqWDjhrDmkw0LHK2R\nWhG6ILVDnkGRaoyXWlEdBMbMxAw0SanZPxoiPI/OyiY7OwkHR/dYX1+l1ztkfX2VP/uzP2V5eYlu\nt8udO3dYWKiTFRlSeARRyPkLZzh77jzpn36J62/exPcDOp1FPvrRj3Lv3j1effVVPvnJT/H7v/9Z\n4jjmJ3/ywwShx/e+9wq//t//U/67X/8HXNzapBwMyKVLXxccDIaEjUWee/5jBOEaQbCJcH329q/x\n+rUbBIHH7dv3ufLke9jaqvMTHzzHSy+9xPXrN+isX2BtbY3V1VXiOEZK2NhcYdgzaF2yv3eEKq1o\nRaezCEazs3cLIQS1Wkia5hhKXn7lNTqdDt3uEgutNjt7PUaTlJ29I9LxlHa7zTPPXmFxcZEi18Rx\nzptv3bKdx/GIMk85s7lGnsSMRiNWtyStZshXv/IiS901XNfD90IunLtEv9+n2+0ymY7Y2dlmfX2d\nIAhot9ucO3eevd0DvvOd77C8vEy73WQyLcjzNp7ncHCwz2g04T1PPgtIXE8wHFrv3vbiElq5TMYF\ntfoK/9cffIm9nTGqKDna63Fu6zJGu+zvHxK6kkZNcnh0xHg64Nadu/R79/98b9AfYQSRppSG8dSQ\nxR6uEKy2fL5dHFCPYNRPKdOUYnWKW6a4BmRnmTwIKUOXhIzD0QTP85mkmt3bB7hEFIVBGYN0NIIc\n3BJMhqCJ57rUg5A4jvG8EJ0rQjdgMhzRrNVxRY4wUybxATJsolyDH49wvAaisUy5HCK3PMJEYG58\nA+0mKAXj4RH3b1kxMTyXqNaiFAUrm1s4MsIRNbRycGSAG0iEKsizFn5zi6c++Cn6mcv9WzdYa3cp\n0gm546MFNBbX6W4+TsECuhQ4QhA4HrkuKY0hKwsKXVAoEFpwVEBvOMbNFKIo0OMxTrdLaRQyCqF0\n8Js+3aUWSsd4rkej3cRxBNLzCOt1tBC2cFdVef0wpNNqUpYl3W6HIk+ZTqcMRxNqtRr1moUBl2VJ\nEEXgSjKlqbeXYDqmUIZWGAKSJMkI6zVGkynGGAKpkTpnebFDzQuY+jXLYY4lhAXWv3qC5zlYyzEb\ngBSlrbaXKkNpYf2olcFzXVQxZTToA1ZgxfEMpSkQvqAgI08mGFVWx4Q0L5jEKXGckOoM7Wc0Wh10\nkeJWvFulDFLYgoYxGoyDUWCUg1ESx684e0KAnAnKWO7vTI9MCol0jjnmYJM4XIdCq9MBosB6Wr9b\nhjYVEuK42HBcILBWVVqLeYAstKhilwq9oLTtCkuJRtnAy2gMGqWYH3c2TgaXgmMIqqw6VjPDlJMB\n18nCxknhmpna/izAM6byHwdQes6PnwVoUnC6w2JAaIOQyu7X2IBdCpAokAphStqLTZaFoNZqEi51\n0M4UJSVaKXSRgyNwsKgLey4VcqdU6NJ2n9I05/Cgz8HufchzBocHFEmMVilGF5TVeRsMUjrEBfgO\neL6PTnyuXN5iYbHJt1++xr29v2cLS8YhiwOyaYcnH/1VIk+ACDFAmh/yxuvgOZBlNR6/8teBP6Y3\nuEeneR6jJkS1JmRdEHvUmXIw/QbL9adBKBy5TNDqcrS9w8JqA2Me5eZbbRotGE2m6PwHE86Hu9cz\nDuYxmiO0bguO7RjjTIEC4wtWV3+JO2/+Kr53k9t3r7PQehov/g6i9R4WeB7FA6T3Cm7tNpnWdBa3\nWKj9LDXngyhWcCMHBajih9x7AoSplPeQ1sJUwIOjIa7v4DqOtStSGkd46NJgtLWEezeMNC/mSUVe\nJtzf3uWoP2R9uUOz2aTRaFRaKMfzc1LN+52KIydRTKJKrEHMu9Wz8TCs/CT0+yQ0XJ7i+Z5OOKFC\nolRfEdJ2Tg0oA6XSlNW6Ua83qjXjONH1fZtUGqUJgwBPOmTThDxJIPJoNBrzLr5NrO35LS4uMpmM\n5o4VrmsLpfV6na2tS5w7d+4HuNUn58s+Z14AsAKYGh7i5s/WvRkK4CTqKc/zedJ+slt9MqG2r1dR\nWU4k+LPk2nVdpBtaATtjUNLDka7dvyS4fkBefQ5KKdJKCNTqkHiVE4K9Y5UxCG19qB3nmFIwf79U\n1JpKkPDkmCf/UszRSif//p3GD6z10joSIWcFOXt12Ovune9He3zxQ+/Wv7Ac6wf3J7RqkjB0Ceoe\ngadYbF9ksTbAkBOEDq4rOXO2QzwtOHdulcFUM9gb8fSjHRqmg6DBRB7iTAJcGVMiuLkzZDlU1Enw\nhMYXAuO5SJUQFJokLYhyUGkNWayBitHWchjH8zFFhsOQPIHAb6KyI2QpkIFLJiSFE5LJEh24yHCR\nXIxw6k0oFL4TgPZJEnBlDddbwIgMZAjCY9jLyUuDH4WcO3OO5bUWiZnw2ht3mcQuruvTCjzA8Nbb\nN9g6f5Z+v89g2CeqhfT7A+r1GlHY4OMf+yn+6PP/D3/25RdYWTvD+575GL/7v/9Lnn/+Q3zhC5+n\n1xvy9NNP8drr1zi3dYHtnQNu3XtAp9Ph/KUneOFbL/Hzn/kPefNP/ghHAUhe+u4rBN0Vnnr2o5y/\neIWg1qUoXQ739+lPxmSZYGVjgw9/tEuuJNM0pz+a0ljs8OTT7yWotbl5863KXzri+99/lYODA2pe\nk+ef/yBZCr3eAaXKkDTwghHj8YharUapS5AZGkHkN5kMxySTmF6thy5K3nPlCbI4YW93mytXHqPR\n9HBdSZZZAbe1tTV6/SOiKOBgb4eXX34ZTwqeunoF13HwXMETVx5hc+M83/rmSyRTwUJzgbW1Na5d\nu8Ziu0WtVuPo6IhnnnmGhYWFqoqpWV9fZ2dnh3a7yXg8pN1ZoFar0e/3GY/HjEdTRqMDbt++TRj6\nXL78GO12l4P9ASAY9wuuPHoVlQh2dg75mU99Gt/36R0c8m//7EssNn06zYBWM2RppcvW5jp1L+TO\nH77+53uT/juOLM7wPYfJJCPPCxoNC0s+s7nKwd4erpC4kQumxPcFUmcUWcLG+gp3+vfY29uj24zo\nDfa4dvMOSh976c6Uf/OyAK0IgpA0s52ulZUl7t69e0IZ05BlCXE8IZOS559/nju3X+d9zz5Br7/H\nSs0j0zbAXlpaohaEtGsuL979Pmma4WBVqydD26VcP7tOe7FFZ7XNUqeL49ZAB2RZievbJNQYgx8E\nDIc9Pvrxv8xTTz3BH3/u/+aFP/sTPAP9Scp7nnqWn/6Zn2N5ZQ2FS6lKCqVwZEGWZ2RFTJJNGE0n\nDEcTSu3QqNegaKMmCcVkQiEld+/eZbGzaCFeDhwcHBBFEZcvX553HkajEUeTozm0rCgKa8FVQcAX\nFhYYjUakacry8jL1ep3RaFRV+l0WF+3Ph8MhtXqEMdbBQVR8vRkMe8ZtmwVn9VpzDtsKw5Aoitjb\n22NnZwc3DGm323Nl8VkQM+tmzCBuM4id5cWVuNJaoMVxjDNXILX+9e12G4E7h4wdHBxUqueKvMjJ\n8mzOk1MEGHMshJYXufXENtZruCxKhHBxXIkWugqIqoBHFydgk6er6Q9z5Wx31cyDoHnV/SFl0h/X\nYekT+Tw4M6Zyy9CWJwiiKi6YKqGVIEyVNFv/aqMKjLLWXYaZv7L1NJbCRRiDX0EfPcdBVRxIm7Cf\nRhJU7D0wxv6tFBVM21puoQ3CA9AgNLpCIriOg3QFojQWIu2HpHkO2lLLDNaPe/YaRml0XiLDKrk3\nIITB9yzXEmMV5B0HfGlQShMPerijI/zWMkE3RHoB2nHJtBUWl5JoAAAgAElEQVRWmsFkZ8Gy6zro\nUqMKTZkr7t28w/bOPd584xUiY5j0DzBpSqMdcHBwRBhG9hoqS5J4iuPV7fVvBmyd17zwzX/BYPQC\nyxt30fJ3sPZWgmihCWaVw/g/xZQNFlf/G6IIktxOU2lCirJNy/lP2J38LZSRqLKk0dLs7WSsbLwN\nTBC0WA7PMj10qS916O1/jYWVXdrd96N1DfRf5X2P/UfEbBK1d5lmVVED5px7dQImOk/UToS6hgRH\nr+DpGqU8xGtOyLwcFdcY9SVCv5/m4v9Gt12y179P5EDkfIxEfwfM5znM/5BudESy+wxrq5+mG34a\nVzYoxSFlqaHwwXhgpj/Q3QMQwsOhRGPXzlIL7h4NORqOcfyClnAwEsbjgnZziVLnvFs61o2FNmVZ\nzK2akv4Qf5qwffeWtWgMQ+r1euWOEs0TbXhnITM7rLDhnFdc+VprTndZT3ZTgTny6WEY7gzlM9s3\nZhDm2TmAVaSevbbWZr6nZVnGJI7nyW8QaKKwPhfT9A1WL8L1UUVBp9VgcLRPo9mklJJut0uSJDQa\njbnThZSS3d1dGo0ae3t7+L6P7/t0O6s8+uijbG5ssbhoVcFnNKX5e9Xa0lHEDCJ/rI7uB1G115zu\nzM8es+R5tqfOkFJFURBX73GmUP7w/M3matZFBwgrapcWnqW/OQLHNThhgzJPUUiEdBHSRQsL757E\nKS4K16kKn8Im1a7rojCkeUZW5EjXxY1MVfQwlmNtmNtxHSe+x+c4+yyZC9H9cC72yetk9nBOXI+z\neHDm4e25LurEvM6KNrMCx0z87v/PeFcl1s89vcmZ1WUkhjibsDtMSTJBe2mbdqdBmk0xxqrjLjcb\n5HnJomoS6JK6yPHVFIUhYIx0E5o1Sa4hcBJEmeMbje81KZVEmQhX1xBaE3k+rrnHtBhRigmFGlIL\nAxAGryYQJkE7mgJNpjIMOZ7jo4SLViUODr4MKbQhmWaUnn0tjOVTlkgKYx0eE5VjkhYqNxztD7m1\nPaC1tMTW5hkWhctkOmKsCvLcZW93wOrqqoUVV0qF9Xqd5eVlDg8P7TwsL3H23AVWljf40p++wOXL\nT/P1r38Xz/N47FJGmub82y//KbW6T6lsUPDxj32Cz33h8/QGY159400+88u/wng85v0f+AA7+znr\n732er3zpCxzs7RIunuHyhSf48Md/Eb/RwrgCR2qW11appYqV7qPs7m4zHByACMmFoDctuL8/QAtJ\nMCrp9Qb4vk9roY7rOWR5jEoVb15/g/39fR69/DjtdpuDgwP2DkcEfhNjHFTpgPFxHY/hYY8oith+\ncMDbb99kNBpx4fwlut1l1p5+iueeey833v4+cRzj+wGj0YjBYMrh4SHj8ZA0th24S+e3LJx+EPPq\ny68Aku5im6VuA1UKtrd3GScxRZFxcLjH2toKi4sLvP7666yvD9jcPMvOzg6DwYD9w3vU6wGHvSPO\nnvsJHEfSai2wsrzB9etvce/uLlHU4urVJzmzeZYsK1Cq5CMfeR5MwbkzmxZqbh7nW9/4Es16nUaj\nwfqZFUInp93y2Fjpkk41KtdME/X/eQ/9uIzJOGPrzFmuXbttfYGnEHgR7Rp0Li5yfmuZD33wY3RW\nFiiyPYSr8ISHKaf4gUdvb58b124QT3NK6VHKyHaVRIkQGq1LXNcBXLKKM9VutzHGClhNJhNmnvcH\nh3u0OwtMkpSLZ9d58srPEk/7lpeVpyAs/9IIEEHIwvpFnv3pz/DW917kcP+QSX/MJBnzystH3LzZ\nYP3cOnmyxu1bb6GNi5Q1GvUORkjCmoUZZ/k+JYoi1ciwxSd/4a/x/o9/mhe/8S1e/NqLLG+9h6Mp\nZG/fp7m4QC2QqDJFFinalJSmJMlyjnoDDnp9ppMYr9lifXmJiTuknycE9YhGq8Hq2jKD8Yi7d2/T\naDTwPMfahFS6nFFUpyxi8jyf24/4vqXU9Pt9xlQ+nxXvrlar4TiOnUMJnhczHttA1AZfDcqyZDTq\nMxqlWFSYiyptR9bzjrsejYYVC5pMLPzQr4oPk8lkLgi2ubk5/6xmlorNZpNWqzVP6Ge86ziOWVzs\n0Gq10Aji1AqV1ZsN2u02RW553w8ePGA8HmOM9egW1Uae5wWt5iKDcU6ea0otqEUNdG43/FkQ4/pO\n5cl93LGZB5dF1ZGZiZJRnAog55xEIZHSORUwzbu675KOtSvBEbaTa9SM/2pAa6SxCakQgtCZWWAB\nvmetiozCEQKpTOVbXXkBI0Foi0QoK8ipsv6qQlgXgMB3569vh7VXsY1w24HWSiHNccCnlbKd9cJ2\n5qTW+KLyoy4zykTjykpYznPs+5JWXVwKZy6Go0tF4HrUoggjCrTRSFyCyi5PCssLFwLydIrOFJNB\nj1ArXvzqn3LxmYy1q49SeiFu1GJalCyGHVzXIdfMrUCTaYYnPYaHfdIk5XP/5ncQwnDv7k0CR1AW\nE4RRpL0RkRRoVZKZHFNmNOsBZvI4U/Ud3rr/tzHOFwhbf5eGm+HILk7yMXCGxw+5D+E9APbHnyQe\nbHF5/fdpL51FRgPuH96j09ri4uZvsXv0v/D2vf+WTrREGC2gxR4OPkIojDOgviQBiVa/gsOU82d+\ni4yYw95vEAQr6CJjPFBz8bnZmHW/TmoWGMzcIqkoC7rrJXdu/jqRP6KYrrK5/ksw3CLjDilNLl39\nNV7/zsf46tf/Yz7587/J3fs1VKyQ4hOABJHjOxmUXwZ+m+XWTzIavQi+RioHo2vYostpCOrsa4nl\n+EujrNgcmsNhQi/xCIxLvebj+Q6ogsNJTCtqIbx3h/bJo5e2aDUbaK0ZDAbcv3+f0WiMdjwGcYpX\nKOJCcTiazAWxHjmzyerqKs1mE406LkRoa/tkhEKZypbLGFzpIaWl7JQzcUBt51IajSNtASuTBqWK\nOfJHCDBopCMxSp66RmZr6gz67FWXlNIaU5QUeU6eZbiOQ+hFhF7EzZu3mUynvOfqUziBA4HETTRZ\nkrGw0LJUqMmQRy6fpywzlKmxtLREkhVobek7rnTIVYKSGuNKAr9Fe0myvr7J1aee4cyZs4RBjciH\n0A9sdxxZ0VIEWti1TpuKM51nGFF5iJsST3pABS1XGoxCK/t+ylJVxeSMsrCozTzPGA4HpNmUKGrj\nBS7SFRQqh9Lgez7KgPQqRI4H0vWsrogXogVIkwIGIR2U9BF+i1zl5EWOMUfEpaYoQWcackOpDPV6\nRInCddwKXg9SgysFcZyQjAULUYgTBqAMRVmCcDBCooz1apkNxzm2b7Ob58mY9rgoPf9OVXCYIcHy\nvKiKqB5aOBjHOkXMKAjScTHy2KmkLEu0VpVQ3Ey0XP3AHvwXmmNdixJCzwZDLU/TGyUMpgcEnCHp\nF4RRiziZIEuNVgEuAsfVOL6H8DwwHsZItHQQnqQQhkJAKSW5ERT4mGiXonQonAaFLHCVZFq4ZP6Y\naQIaF2Vc8gKUEDjaIVcGxdR+iDKnVCmFyVHZBJlJZJnhq4Is1wQqpy5AlTEFDUqdosiQno9xM0ZJ\nj95QsL19RJlBc2kTP6pjhINDSeS5KNdhY+MMYbTCYrNJvWkvhMXFFkVRVMqzguXlLktLS3z3u9c4\n84lLpEnBjes3efaZD/C5P/oiv/d7/5rzF7e4d/8t7tw54gMfeILn3vdh3n77bcbjMUK6nLt0gd/+\nV7/Lh37yw7z0yqt4JuLqhy7zn/29f8jf/zt/lw89+zwfft/zGFPjqDchUwOioMa4B6Uek2aKxsIS\nOZCVUzJtoXS3tncZDkc0pUecTLhy5THyPCeOJ1YkShyS5j0aTZfbd25wcNhga+sCZ89+nDeuvUYc\nxwSurZbGk4TJcEAyGVOkKZ2FJroo6C4uEPkOnU6HO3fusLS0xNtvv00JeJ5VFp5OpwyHQ8o85crj\nl3EcyYMHD1hZv8jjjz/B9evXiJMJcTxFCMnFi+c4Gk7QuuT+g7torVlaWmJ7e5tr164RBBFnzpwh\nDEO8QNFoNFheXWFtbbXyGrQV1oWFNtNOjhAZ57cu0mi06PfvI6UkzWIWGjn37n2Pu3du0W0v8vzz\nj6HyjJ3dXTrdFuc3O4SOouY6FL5ge6fH62/c/XO9P3+koW0XURisXVopK29ijyuPXeapp67QbDVA\nGlxpyLMphYiQwoAwTMYxWaFQRmLXaWvlYHQJRleNKw3SJiqRF2I5TFbV2i6itkKZ5+kcVnXr1i2W\nOu+xkOOixLhgLEDQBt2eQxgZFrobdJdWGQ4meEGBa6zCOFqRxhPGowGTyYj24jquH+BHPkpjkwep\nkSjKMqPMFJky+EHEOC1pLK3x2JNX8aI6SV4g8wIxTSiLkjybIsoUgFLl9Ad9hsMRSWJFRCgyhGcD\nmEIpXM+ludDACwM63iIHBzvMhE9Oqq2WpZpXbaMossc/ASWTJ/aUGeSs1WoRxzF5Vlb85WCuIiuF\nrDh5PmFYWZAJiXDl3ANUKYOSqhItY15dD8OQTqfDcDqdv6Yx5rTFCcdKsrMN1XVd6ymv3GOY8AmF\n0tm5Wc90fy66Mqtq52lKrkscx6Mo7JxocwzP+8GN1QaOCBCOQKvjyrlXqVcLrOCSOhELnBT7EdIm\n4A9X47XWVnDnXTKqW7Kaj0qVW1hWHVTJk7Zdas/zqEzPbLJblkhswAlUnsjV3GkLzBNG4DsORVkl\n18YG38YYVCU6Z5XVZ93PGSBx5j8tKl9Yy+c2Ws9/Rxl7PMOxKJKUEl2q4+PNWitC4AiJmN8QBmFN\nuuy/RuIIaVXChQ3MBnGP/lGM0CUCwUI9Igp9kmSKUhD5NXvdGoPSJQariq8KhcoVeZmRpRn7O7vs\n79zHCzyyNMEJXUqjcASookBIB2NcWq0Go0HCZJrw+KUxr1x/kcHkf6DdXAXGqLRE1AU4RyATkFMg\nAaYgJhijuXd7wOMXVwjDAcOeZJT1CBfh7oM7uCagHv0NpunfxuTb7Pe/RwMJRNh7QmGog3mKD139\nn7mf7LMSXUWZXZrtPoPBPrW6gx/BaP84aZ1xQmf3+ez7mBPJLYLekaFQt9Dp9wmcK+yNXuWRlV9k\n+wiUGDOIBc3lqzSWF7l3D1ShAY3n+9T9ZSYThdQa7Vyj0zjD9vCISxcucPv+NdA+NqlOgB+0lzKm\ngq4KKw0uMDgIyjynP85Yj5ogfZRRFKXCC2pI30W+S+y2/MCjVrdij+3OAssrXYqiYDyxLiizImZS\nFaW11ty8fYubty2lbmFhgU6nw+LiIo1Go0IgaALfs+uCsDBupcE6dFToBCq7LaPtvSZ0leHM5v8Y\nUl6WBVofC509bE8401OY0TeyLJsjr2wn0+59a8tL7AnDG69/n2+Nxzx19SrPXn2aoijY3d1lodnk\nmWee4etf/xo/8zOfZn9ckqcxy90uzz33Xr74+UNrrxXWSdOUCxcu4Do+m5tnefzxK3Q7y/h+gB94\nuNJSM2bwd6WOqTInh5QOxhyrdc9Uwk928i0yKieO00pPJKUsc5J0itZWhNX3Inw/xPOCSr+jsPB0\n6TCD19jCny3sCikRlfaPmCEFqrW7KBK0KXE9wWQaV8WOgsl0RJaleFJg5LHK+exrFEXzjniWZezv\n77PuBRikRd65VlTWFWDUO+918+T61P+Pnz/MOZ8ppQMUZWGFa6X12z6JEtPGzKjbADiueyJhP0Hz\neYiq8BcWCi6FRlBadIBQOFJTjwJqwRKT6RFBq8VokCOlgysjHOkzig9QRqCxKoFaa5TUuI6HpkRp\ngUaihEALSSky8ByE41EWGUiH0khKDLoEdAQ6oVTV8UqPMndsZIECoTFCo4wN1H3HITACzxEWJudI\nfFeQliValmhdIoTBSENZwnjSJx74FLlGEBCFdZQCpQwaTVgLyZXCSQW+J9nf3+eDjz/B9vb2HGri\neR7D4ZCNjQ3Onj1LHEO326XfH7K01OHg4ICtc5sk05xXX30V14P19SZ3795lbXWLb3/7JRa7S/zS\nZz7D5774x/i+z1e/+jWefPJJ/uq//9dQQvPmzZs8+fTTSC9gMBhh1JAgKoizIYutRQQtpHRxPQcj\nrOJhmuUcHBzh+lM2NjZ4883r+N0V8jzn3r17PHL5AnmeWkhLGVOvLzEeT/GDEN/3uH//LmpN8uwz\n7+erX/0KWapxPUmaFPiBS+BbP9sgCNjY2JjbpgwGPTzfxQuatpt/MKDf758KuPM8Zzgc0qrXqNfr\nONJjeXmZ0WhAGPosLrY4POyjlKJer5Mk01PiEI888gj7+wcEQUBZlFYQqnGOMHQJazZZmcF1up2Z\nbYzB88RcGGQ6nZCkKaPRiLqvufn2WywsNOkuLXB0sEM8nbCzvc1h6tNtPUV7vY3OckajKao0BEEN\nGyT9+I+yLAkD6xF8sL/NQuMsZ89s8swTj3L5kXM0Wi7KTChUiW80DprJ0QHXX3+NWzeuQ6HQpYOR\nEmE8DBKBQmqDMCWR5zCJp/j1Jo2FBTwTzj+rdrvNaDSa+xKr6QStCzzP4bXvvoEqYs5sdKg3AnSZ\nkZU5bUfiBR6F0VBv0Vy/wGNOiRcu8Ob3X2V8uE9UD4mTMaKncBxBMh1z8eKjCBGRlhmO6zOe9BHS\nim7leYlXWyRqLdIfxyjp01xd53LQojcckRQaWZToNCZJp6STEdNJryrQaOI45uDggDNnzpAmGaPD\nQ1J/ZGGyrk1rlpaXEWiCoM7W+bOMR1OyLCOOYzrtJft8OuDw8LDiUXfnx9da02g0oDz2bs7znPX1\nder1OpPJpEpcHLQqAYlSmrI0JMkU3/PxfYHrejZjkrOurmuLGaW9VqMootlszuF6rVaLXOt5kj8T\nQbPDzKvJk0rhtV6vI6W0YmrZqIKbh1DBk30/pN3pUJYlk4kt3M025Xa7PUf3GMchioKqQyLRZUmc\nFEwmMaPR2FI9PI8kia0HuLFq6uPx0HYwHKvc7khJWc4grcdCPLMg4J24hb7vz/l1xhicysLqx33M\nFJvhONixHSuDqJT/XenYZFkbdFFSSG2FzkyJLnMCL0QKh8JY+LcFDFqhIcuhdio/2mruRBV8Y6r9\n03a64STXU2AVpqp5F4KyohTImaUZ4Ah5KshSFQJBK1XBFmfuBLPgSlrupjEEbjCnKQghyBNbMIqn\ntmBbFAWBH9HpLDE42MURsLhQt/BJ16W7skIpPLTrkhcpugTtQJ7lZGmG1ILxcMLO/Qd896Xv4uqM\nbDJBUjCdxkSOR57H+FjbIMd36PUOWFtrs7W5xNe/9Tc5Gv9D0un36dRXMPk5aiT0Dm7RWdoDE1WP\nJWAJKBFCceXiOXb2vsQXv/IrfOi9v4uDRKQNcEacOX+GuvBp+/+Eg8mvsrq6RWmuY8onQbUw3l0m\n6gbT/AXujPYxxRJmbY/DO/sEwQau6xEnOwSRmTWFH0oaih8oNM3Up6V02DrXohVdJR6+ibt4G5X/\nE755/f10a/+Kzff8BLs7hhe+eMhzz++Sjf8lrvPXqdcjDFMGk30AFtodcgeGk0MMHjv7hxgNjmkh\nGINXoLR7CiJqz0egURa2D2iVAgFFUfLlr32Xlb/0USKlkW6Bt1DDDyOMeHfcx2AVtP2qY6iKEldI\npOvjdy1SrtPpMB6P6ff7TKdT4ji2hSkEqiw47B0xHI/oTrqsrKzQbrdxpQPCamNYO73KO951KUsr\ntocAU2kdSGNQGGZOEifHySTqWMPh9EM8VKjMsow0TeeUJM+1yWZhNKrI6bbb9A4Peek730YaeOyx\nx8jzDK/TwZV2j0mSBNeNKueWkM5iey4S3Gg08KRkpbtiGwO1Bs1aHd/1LJ1Nyvm6dUrXYbZO8s4e\n4Cch3yevwZP3yOx5nmdWELTq6IbhMbXIIqBcm9hX+h0Ci6hxhYsjHKRwbcEQgRE2uQfmnXNjSorS\n2ojmRVw1IkqUmiXspz+nWcFgVmAuimLexArC2ml0lzlJ9PhBeDecPvY7jdnvztaJGQdcyNP87JMC\nocdFm9OIlHcac0rIj1Dsflcl1o5sYMImukgIJCyGETf2J4zLgrAFsdqmtqhxRInQOUppoiDGl5Ki\nlDQjiYh1JfZT4DjWj7JZD5DOGEOJLleQysEtJK4IyE2OcDwiNWaiXOoyRzqKRHlo45EOEqKpQnuC\nEYLE+AQqQkoHJR0c3ydTbXrybeIm9LRD23gU7hGxcRBKEsl1dLGD1nDvToM876FxWFxc4N5wgBIl\ne3Gf28U+j1x6nM0zj7K85jGexDjuOpubDd66sUc8PSCeFjz7zPuZjmIWmm0mg4RpkfNP//lvsLq+\nQj1yiOMe/f4Og4lmaTlEKcXNO2N+7df+Bv/rb/02aVqyd3jEP/9n/4KFhQUavkcyOOL+jVf5o8/X\nuXr1KvL8ec6uXyFLU+5MxwT6GgssEBGSHcUYPeWod4T0Qo6ODthY6zIZ9njw9k08GfChD3yEN/xF\nXrt2jeeee47vv36D5dUVgijE913i0YDxKGV//4jNzU0MBWHkc/vu61x/67ucPXsWx3F48cUX6XQ6\nNNsbTKdTSuHgBT6+LxEyRZsUkedkhWQnbbB30Gd17Sz9cYoxM671BoeHPm7QYFJkHO7e57nOAkIW\nhPWQEsHFJ57k2h99kXuHN9la3WA4GuC7HuPhiPFohOd5LNQW8KWgUGNa7TqevwgIGot1JumELMvY\n3ttheW2VUozZvNBkeDDCC5vs7w/57suv0WnWWW167Ax2aXaXuXjhUe7fesDe7X3yNGbQi/FXV3Ck\nh+fDNC2ptzrcfnADnb87kmqw9jWeL1hcgF7/gHrjUZaWOqwtLVqPa13gehbGnZeGbDrlxvUbHB0c\nohWAW3HvrDe1hVgZpOtgCoFjStZWu4zTwm4UQtsNQOhKZMMG78KxHWmjzRxOtre3x1KnQRC6uJFb\nedaWqIpna4QDfoioLdJaWqG7sk6WxOSTIcrYLnCWZSTxhMlwgBYpk7TEi2r4QUChc7IyRWlDmUzJ\nNaRKkpZ2A44addqOixdGOJ6LHzoEIqJMJ2RFxnA0tGrLBoIgIggi6nXLrZqkY6K65b8ZFApFkWUo\nVdgu8zQliqL5RgRirrwNpzea2cYXT6dzq7MoivB9n0ajYSvEWlCWGqUsUsZ2KSovzqoCr9UMZmUT\nz7JUYCTSkfPEeJagOI5DWgnDxHE8P2atViPLMutzv9CYw7jSNK2CiRAhZl6htsCY5sW8E2aMVfwO\nw5DpdDqHXG9vb+N5tog2nE7xvXAe5GjNKZ75LJiZ2ZdoXTIeD2k0w6qDEBOFdTzvOLBIkvRUMPVw\n1f1hhdeT9l7vhvEwXG7+0CcEhaTGcR0EgiIvEFFou8jSwZMBRlecykqQyv7ZiYCIY/j8KYEkc9pL\n9+Hg6GQR4/h6pxJLO+5Cz7pbxhik79oOBgZVFvPjzq63Mi/QRQnGMC0sWsMWZyXxNLUodGPs+hwF\nBEGEUYZms0GcKlxtOeaO69pr19XVtaTRQpCrfO5UQGnmxbO93T2KPCXJU3TVT1K6wPc8fCUoEDhB\nQNQK2d09oBlJ9id/jB9M2GyehyyDcglMD9spdC2XWDdBNcBU+g9oeod9ltcarK92mRTf4eLaLzBO\nfWIx4sHOTdzpIpc2L+H6q2T5LRy/BFUDtYjwdmh64HrbjNJfpxP+Jvf33oQQsqlEsAiM6R/2cIVz\n6rNTWlknhhPXkz2n47X55ts9Lp39Bd6+8/cR+fdw/B4tf5fem2+yuw0I+PAnfxn4PxhP9qjXIJkm\nCEcgpd0x+sNDtAuBKAiDBabTKZ4AgYsQGilmvdTT1/lxt8uelysctHBwNRTKMJ3ExG5AuxPiRYEV\n0RK8WyjWVRHrmINrGwdQaEPgudTCgEYtYm1lmV6vx+7uLrt72wghqsKiS1rkHA36xFlKfzRkfWnF\nup2E4ak1rVTlnGPLDDFiKj69ERhxOvE5aeX1MGf4nQTBZmNxcXFOb0qShDRJSeKMnb1d/DCkKDJa\nCw329/f5kz/5EzzP48KF89aJonIS2d7eprtxkcloSLNeo9lscu7cOcbjMfV6ndLkNJvNyvmiXu0P\njtWYMBaZdLIYME/wHp7/h9bSGYf8pAPFrBlQlsdJdZZlCOy64nkOnhecmo+T9pJ23bRaF0I4FmXj\nyDkloyK9YSlOGq1zDDlFmaB0TpYkTKZDkmwK0uD5FsVnhI2opLDX/AwNFkQhYZGT59ZNxHF9ZOWC\nIalEzt4Bcv3DEut32hdP/v3J4sXJZPrk72itTyXd/87jL2rHWmtbgXKlFX3xPI/pdILWFj5llEKg\nLKxBmzk0QmlV+Y5qpPRwhWMV46phN03LKyjLHKktN84KyBzL0au8RJUFniPJtMHoHIwmDME1TXSW\nUWYCT7kYx0E5tqOtSonKbZG9zCQq88hTgd9yyZKS0TRlnACuVfWNQlhZWcZ1fGquQ6u1wNHRHo8/\n8TR5pnj55Ze59MhjxGlMGLl85/t9tNfGGKgvBXitLsvn4PnnP8xv/uZvkiUDlrotuu0Wo96QQX/I\n6tIyjUXrSee5PouLkp/92Z/jX//+H/DMs+f5zC/9Mv/oH/1jW5Vr1jg4OKI/EBz1U1aXu/QO91lf\nX2d5eZmjgz1anTZ5XlYdIsvPDIKA3uEek+mAtwb7RAFsrHZQueKVl17gp/7y87z0Gy/xtRdeYGNj\ng+vXr3Px4hauJ1laWuXBgwf4vlUUVsqQpjlSwsJCk37/iIODA/7KX/lLTCYT7t57QHOhxc7OA9ZW\nlkmTEXlhYavTPMORHpqEpe4qn/3sZ/nFX/plPv/5z6GUIYmnaCWI4xStM4yx3WOES1kWeHj0j46o\n+yFow+HhIQeHezSbdZssb28T+AF79w944slHGcd9LjxyHqUtT9CUVpypt9/j3OZZfMdFGqgHdXqq\nT57GvPX2ddJsirfUwPVK9LhAui6IglrdxQ8NApeo4fOe965x/lIHx8RIocDN2ThbZxJ3eWsn/nO5\nN3/U4Xku7UUfP4RzZ5Z57n3vYXV1FUSBlBrjSMaFtT0gavwAACAASURBVCq7c7/Hy995nQcHexSF\nsnxdYyq4bCVqVZRIUeCYHEnOuH/Ax37y5/jmK6+R5FOMH87ha2EYzi2BalHEME+RLtQjy9u9f/cm\nF7fOEAQuzmJEGLoIrfED8FyPNCsZxzHahNQ7G2xeVsTJlEOVUhYJSZajjvq88OUvc/fONtqJ6K5s\ncO7iRTa3zjIZjC0Vpcw42nmA44Y0Oys4nrVsa3geS7INrkdeKiSGwd4+B/v7jLMJ/cmITquNNpKl\n5VXSNGWh2SQdjjicTmmsLLFxdoNS5aRxgucIjg4OkY7i0cceIUsLtJYVdMpyhWfeoWVZzpPIGfc5\nz3MWFhZOdXyLwnKZHOHOk+LhYMx4PK7gZ7YLa5RCygIpXXxfUBQ2sPE8D9/357zpGe89iiLq9TpR\nZWM3g7nPrNfq9foclTLTlsjzfJ6I1yMfx3EZjkaMJ1P8MAAhGI/HaK3pdDrcuHEDz/NYWVmxWgj7\n+wghCPzI7imThLyCGzqORxQ5ZFmGqjbrosxRpiROJjSaEX5gWGx32dw8y6WLl7lz5z7f/Ma3SbMc\neUKQ7Ac7MMdiNbPEema99bDdyo/rEELYdQpwHYeyKBCVUE+uY3wDST7Gd0NKYTCBoGam9v7T4Loe\npbK+wRYKelzUEJhKJ0FZITAj0IVGlRUEWzqV77U9F9d17Z5dUWQ9z0cVJaZUOEISaYd0kjAWCdKA\nyK2SOdJFS0lWKEgdtCkJAgchrS1fXqRItz73ic2yjCiKrPetG1kJMCHwG+GpgE4IgfaGOHjIrKQh\nfdwhlKMEtwzIpEduXFqlpF56ZDInN/co8i46b/Lg7TcY9m7zrS99CVF4OIWibiTCkRQqJy9LjCfx\n6yWyWCfWOfHhAQ7w5lu/R8GngPejswvg7rDX+0PW13+Ke9/p0Gh/qJq0IYW+hWBATS6Dt87imX0E\nAePkOqPkv+Dm7j/Gd/4m0GJt8yIH6g1e2+3TdP5rXrn+DJ2VNeLhmzQXXyYSAyRLOPospfxDHjz4\nz1ld+VuUNMG9j+A+rhJIvYCSI6tfIW3RjdJFGIlLjUz0bXIlwdESR2tcVaJkwO29kGbnv0Jnmnj4\nP+Kb6zQ2vsa9a79Mp/lJHjv/s7x++9+jHg3YHX6Opvdpak3DdAiu2ULoO6AEJUfAUaUmLyixVBky\nAIVb7RfSsfxugcCRJYmxn60nJK5QTB2fBwdHvHz3Jo2lK7RqATUvIMdFBQGSdwfH2miFUsdwWkuV\nssTTGYVott5ubGywublJkl3h8PCQo6Mj+v2+RW4UBXGSMBqPuf32bVZWVlhaWmJ1ddWu7VFkE7my\nxFRChlK6YGa8aW0RKJYXghQOjuvMC5DqBD1gdr8dr63HVkvAPDmdJVtHBzs2yRSa0eCQbrdLPBri\nSUO0uMhnP/tZHnvsUT72kY9wYescZVny9a9/nZ/+mVUOdh9wZnONhVaDZ5+5OremvLR1kaXuMq1W\nizAMCYKoijVsId+YYyj7SR9rUxUSHoY0zxLBLMvmz2drT57nVvx2PJ5bS1o61P/L3ZvHWHql532/\nc863363q1l7dzW6yySE5HGqGGskaxSMrWiwpkmDLRhAnjm1BiREkTmIgBiIYSSDDFmIISGzAkYQA\ntmEbCSDZUhDHsWRpopFirbNpNs4MlybZW3Xtt+5+v/2ckz/Od29V91ATjxxIZg5RYKP71q1b33be\n93mfRRDFEb7n9tXAD/FUsEqgUEo1QLdtnq1uUu2On1qBulJ4aFuvzsM8OyfLFuRZxmR0TJamlFWG\nH0p85dPuJvihM1EWApTyVyC8o7OHjrmqIgaDAWWlaXW7JIkmSRKeRJ2ugrOXDf67r6sygCWAvvzz\nMiXiakLF49Fvl8DNMjL36hT93T/Tv/qe/J5qrKVoChGpEbZCa7k6KC42QzsDEwUKBTSoW0OBMO6K\naigR7sJxOoNGcykcKq2QYMAYi/Wag+ye8VRVTRg5K3cphTOx8ADjoleq0tL2HQqvggCDcBoyKwkU\nGCPQVmGNWrmM1gYKDVq4hI84boEVpGnK9f0txqMB6+0EXcP9+w9J85qbtyquXdsjK2aotW3WNneZ\nTCbMZgs+84Uv8NL7P8D/9n/8U2prAM18OmY+niCMIolihoMLKh+2Nrd59OiIF198kR/90R/FWsNg\nMOCnfuonieMIT3l0um08T1BWKYPBgN/4jd/glVdewVpLv993VGYhsdpQVDm6cii+qTWhrxBRwoMH\nb5GEhuv7u+SF02rs7vTor61zfHzKyy9/wLk2zud0uo6KHSdtbJoipIfyAvI8A+HcnqMoQnqKd+7d\npd/vN/m4E4RQnJycEMchSho8pbFGYhDcvX+f0/MB08mcn//H/4Tv+O7v4mMf+xh1ZZvc2wTPD+l2\n3KRZ1zVFlnE2HWHxqeuS0Lss5BeLBevra0gp2dvfY3IxIU0X1HXNeDzGWIiSFv1+B127B+fW1rab\nqAl36/mepCwL8nyBtRXaFNRGMBwOuH79Oq1WzMQzBIFEWkWrFdLfjtAmB1PR7Xa4GMyx5GxtrwEH\nf7A35e9zaV0RhDFP39zno9/2EXprHaSyFHVOGAQutV34GOlz5849jo6GKF+hJY4mZhw1r3n8OhMt\nqfBqgyctVb5wsXfSorRzchare959l2gQXtU8J5Yu0y7j2BXdtQGQ1GWJ51fU9YKiMFRljsZDS4+w\n1aHb7zMbnVFkqdN2VZrR+IIo6aGCFl7oHHon0wmz2RQVhmRlhjAG35NEYYAIQvAVspms17pASWfU\ncjEYMBmOKPyMIHDT7PkkJe63ODs9RgmD73l0ut0mfznAlz7G1CRhh8V8ynh8QbfbJfMK8rxu6HaG\n+SxdIcxpmq5ctpe6tE6ns3JAHQ6HDAYDPM9zlGtfrTY1rQVGW6x3qXkK1JLW5wyZqlI3JkXeqsgw\nxqye48vpXVEUK4T9qqGRtRZd1ytttWxcwIuicG7m64lz97WWIGw2+FpT6eqxvE5jDBcXzgm93XZm\na4t8Tl0b8rykJsBTDvkvywqEahoAiZABxtSsrbs87Gef3cPzAtbXNtjaXicMQw4ODhgNJ6RpsZqU\nXtVXg2v+dG0f+0yrAuz/A2fSP4glbIW0rukwZeH2ReH2Xil9dFERyhi0y46Wngd1icADLLYWSOsj\nhETXBeAaR7STGBRpw37wfHRdUxY1URxhjaGqaswVTeLya1Vsox473qZ2XiRBO3Za6ACqxj1XhT54\nJSpUeF6CMXpVbAYkWOWtmotloQ4QNK71S+8MHAkEN/s1GK9FVUMQSDwRIBeG6XxGMDoh7LcQxrBI\nDZHwyNFUxYdZzM4Yje7zyU//KvPxhIuLBboUaGHIyoIoSQhbPShLBJBVGZ7nQHvPh1t7IW+89s95\n7sXvANPG+g8x8m2mucce38/3fPTvopLfcmZFnuDw5Bc4PPxFFvGv0ll7lUC+hMBndDbi+s1rDCa/\nzfZWwfF5waPDt9na7jGanFEUIc/c/qsU/M+sdWLqooMn3o+191HyDtY8T2fzb9P2BWM9QyqwZRtt\nI6zIsKiGnuCmlI7mb6ntEGS40uG6p7aHJQTcXmxsB6Um3Hzqv2AxFJxXP821zt8HPssnPv8yyhyC\n7vL2w5/lhfeFvHGncJRjZiC8lUwBnJxhuY+4M9ecP3OZqwyX96g7wY4BE/g+xhqmZcbr9+7ygRdu\nsld36UkPZSWyMUZ6TyxL44XgJCyOVeJM+IyxCOtMr5IocA1dVWOlYHNnm+29XYbDIffv3wccIFpU\nFR6Cs7OzlVP1xsYGW1tbeL6HaXpnhHDyTuPOt2kaatffXE6s3fP/cnB4VUIAl822kv7q7xeLBfP5\nnKqqmvSOqWNFVSWdbpeiKNjc3OTw8Gjlv3FwcMDnP/95tjb6+L7PYHCG1TVlnmHqGl1VdDodtnc2\nuRiMuHXzJr1ulyCI8JSPsO5ZAyCvYCqrpq5pCJda6+W/Penl8Zgu+IrB5ZIRt3zWLSnwvhcShmHj\noeKt4r0cg+bSjOvSeQJWR9id9BUQABohDRcXZ1wMT8nzlOH5CcJC4jsvAuV7eIGPUMp9CYFQCul5\nKN9HW4tt/i6KIgywyHNUw/hyLDMemwQ/zuz62vfNk+y6qwDLKn7xCYbD1fPw5HF+XPbx+Ovf7X2+\n1npPNdYOddJoa4gDlym61Bvo2j0QojBESYGp3MUYxzFR5ArxSlduE1eOdlTXppnW+A0q5HLunIRL\nusZaC8q6xhjQFWCtm/g0BYAxBdpY0O5hXWQpxtPUtqbWJbpSaF0hhcHzPfI8oyglGg9qS1kLhrOc\nykANlNrgexGTyQxhNJQxXjVnf2MD/IS93WvE7Ra7e5tsbm+wSH1+9p/9S4wxfOd3fif/zve+AsLw\n9tvv8O3f/q3kec6dL30KjOD+24+4OB9xcT4jCGBegGDAn/gTP4S1lg998Bv5uZ//x3zf9303f/fv\n/gMEHjs7e9y7d89NiSJJkrRI0zkPH97n7OyEosjY3d1l1wtWlv1Seo7yWZXk8ynT8QU7G336vQBd\nZ0iTsbPR5vT4LX7oT/4AP/MzP8d0PGFz82mGwwlSKsbZmN3dXR4+fEhZVgRBiLWC8XiEUs54bBld\nNRqNSFrr7K+tcXh4xPbWJuPhOYEPi7rC91pMpyM++YnPcHR6wvXrT7HZ3+DjH/84//F/9Bf5+Z//\np8znKUnSIctm+EFEVWnyrGQ+XXB8cMjG5j7b62tMxjNq4Ob1G5ycHLG9uUkcR+xubTPeGlDVBe0k\n5sHde6xv7XD37kOuXdsly8Yum1xKxuMxYRgyGAzZ2uwzGJxxdn7EaHrOM7d3ePa5G7z0jS9TljnZ\nIuXF9z/LS889x2wyZzAY0l9X1FWGr0Imw4y6rFnrtijL4g/3Bv06VlHmvPDih/ieP/5HWe910aai\nKhao2KM0FbURZFXFo3vHfPn1+3g2QpuFK7aaplcvKd4YBzqEAdPJnDAyrCUhd177Iq2ghQp8NN6K\nTuhQYOdAGYYhFAV1XWKMowrv7e2xWCzY3OpRVBVISzkdk1iPygqkClnvtMhEiyqLiSOf2fiU0fkx\naTaHSlMUOfPpjDemr7G2ucc0L1nb2eTitTHtbgetBFVRkZgYEIwHx0TdNUQYUpiKstLMFjlhnJAv\nCtL5Aokgr3J63XVqo9HWcnp6RrqYM7o44dnrN+mtraEDQVZmdNd6mDqmyFL29vbI8hmTyZiN/jZh\naLgYjKkqvTLyquua8/NzgJVGtNVqEfU6qyl2mqZcXFysmm9PBiukOAxjjKkJQ1fY6CaGKQxDjHHG\nM65Bd+dgGdklpVx9BgcOzjBXzFBarRbASvu91FRXTewSOMB16VWQ5y7Syw8jvMAnTYuVZnk+nztw\nIcuc8+tkQrfbdZ4LXri6RubzgjD0yPOSPCvBk0SRe4/N9U2uXd/h2vXdRkebUhQVVb1gODqn21nn\nW77lm3j06JBPfOJTWLucfn+123cUhU00mViBFVprvCD8g7gN//WXaGzCVszqK/pxq2hytDCOWYjB\n4Ib0bnIiRWM45AZYOE26KzpLqwnCwJWAxqeuauraAUHW6se0hksn+8cKK08hxOWEqFaaIAgduC0E\nwloMkkrXVKVFSItVFi00ha7wlOeo4njUDbAjlMLACvASwmKaLyvAKtC6MVRTCi0U2tZQGypjQFiq\nMqNIp4gypcYnbJr2vNZkM0mezzk/v8PR6R3KmSEr52SLDKkMlTUobaA2KD92VFG11JYK+usJ81nK\nC++/gamOQWisWmAx7Gx/lI73Q8Rig8XsAiNACEsr+EEW5a9TcJ+QdxxFnICnbr4PGBNFKZOpKxZ7\naz3G4wuqGq5tP0sS/xnevvddXNvz8ULL/GxGayNBqAskFo+Ms9EvEne+H2EEmgpLgLExgtpdFDiN\n/DJf1ggQBFh8sAVG1AgUmhjEEKipa588h0DA3l7ArveX+NLpK6juMf31VxDlFOt1eeG2x717BRKP\nzrpgNsqdlOdfYfq0koS8y2vFY68DqxSVhcl8Qd34TihxNaLtvbGuNnGe5zmJhLikWi9fs6Jfcylf\n6ff7K5nRbDbj/Pycau48Y5bMvvPzcy4uLtzQoNtxLv2eRFclge+5erw2ID3cdUFzbbvmHsRXPWuW\ndOknfw+l1CridLFYsGga7LIs8ePISYOSmDzPsdalRTgQ1HJycsIbb7zBwcEB29ubnJ4ckRcpw8E5\nQrk9q9vtcu/uA3Z39gn8iMC/jNNagjCWGmPkyjB0Cc45QLnAmks5y9XPvlzLRvrSaLRmsVisorWE\nECRJskrUcNPiYAXiLmMpl0tKie95Tu/cxF4uo0c94WEanwtnoqb50pc/z2w+xvMktirxlYdv3bPS\n9xxzkEYTL6VLfpDSwzb51c4sNkRKn6SJ6ZzP5ytjNk9KjFCrZ/dyaHUJpDwuDVnlZjevuRrPd1W2\ns7wuroJjy39feu1cpYlf/b7H5Ea/z/WeaqzLsqLb76JETRRKgsCh2UJ4GJeHgucFmEYbVVcaLZbT\nAoEu3UVjjMVoyKsMKyN0bSlyS5EXBO0QatUY2CjnHl1ZwkCSVgaE0zYWVUWYBBibM19MqbWHFZqi\nziirFENETo7OC3LjfFSyoqasc0apQYmKoBBkpeJ0mOGFkrKAOAwocqfvSNoRSlRc299ho9fic28+\n4PDwkPWtHpXJOT5L6Pf7/Lvf952cnw/48Au3eG5/nTfffJ3YLLDzGde3ttj5to/yla+8xr23HhAE\nHq1u5Ywl0gVJ3OX46JTFIuWXf+lX+NArL/JPfu5n8DxHpVtSuqW0zshAGyajMaKhMQ7OztnZ2eH6\nU6eMx2OuXbvG2trayulUVilxEnJydMDuxgucnB7QX+swHBySLgaczgNeeN8zfOkrb7J//RqtTsKj\nw1N67RajyQykhxWKi9GkYSt4nJydY4whjFtoK5jMFjw6HrKx3ueFlz5ANp9x//5d9na2uf/gESdn\nE8IwZDQa8dS16xR5Rjof0263+Nj/9cvEcci1/Rt85jOfYTS+oL/RIRBdsJLID4iCAGUNvhCsd9rM\npxmhH9BfX6cuK2qlGA0uCHxFK2zT31jDCsuNm88wG2ccHx67Rm1zG6thOp6BlRw9esTmi8/x6hc/\nS15kvPzyy7zvfbe5cXOXqvCZTYbMqwmD81M++IFXKPUh52/eId7eJLc1qa0YnqUcHx2jwhxD/Id4\nd359SwjBzs6Wc7GdjVlf75HNF1BrkIIwTjgbjnn06Ig4apEtjIvm8SRGOES7GRwA0G63WOvEVFOf\nTuKBzplNJ/R2ewTSJ68udTdXH6ZLmtWSkguAtWSZm6BqrVHSa4zuFFgPzw8x+GTaA+WiZcqiRii3\nqRrpKOpSwmQ6J+k6fdd4PGY0HhHEAVlpMEVNXVQIocjKithaWr2eK7xLzWQ6J8xL54AvJDIImVsf\n6XvUuSYIAgYnZ4SBYjweEz37PMr30MEVow4pGI/H7G/vUNfVyiXV86KVG7jv++SFXdFcXSPc6L3j\nGF/Y1VRYCDcNXywWtFotAm9JsdePbWTWOueppdOv1rb5uR5K0cS1qNU5cHrkjDRNWTqJdrvdlbPo\n0oBmKTVZTrqXlDPP85ocZbsqXiq9dAT3UL4zmsrL6rFNeUlBL4qCTtxGa/BCRT3OUcrFN8VxTNxp\nsb+/65D22Mf3VVN8ugImSWI8FaJ1xWh0Qa2rK5PT3xv1XlIrlwUD8FWF1r/RSyt02RQmyhVHpnJF\njDaGwHiglpNJRZoXtILGkb5yx6/IS6ytkeoy23oJtFycD5zUQIRYq5ESwsgjCDw8pRxDLOlQB/VX\nOUprI6itRnpeIw/TVFIiraMtWqEJI4/aVhg0KIUXuPMlpUetXbEohUAKx8Co7ZK+alGBoiZHeM7p\nAVGhpADd6BilRckQ6ft41Chp2VmL8EtBEkm0zonCgCQMKHLDZDplMX+L8eyzvHbnFxicv06VJqTF\nHE1GILqErQRkQGlwueq+JGi1ydMaoSKyxQmvv/q3EPrzSDHDqjla5NT2JT704v+Ctbe4MKXzlrBg\nbQ/MJs9c/6+48+i/5HD8z9jvVUhRYplSVAfMpwfM0r/Jdu+/YTY+ZW1jm3FxytlkSlXBSzd+lXdG\n34rnPSDpvB9R7wGg1AFCvESe/LfEAdTVGkbMMIywtouyTVZt00BJVcNS41x7boIpAWq0FFiznK6V\nVIUAOgxTj7JoE5gBz21/nrH+FHX+0/j+I5DrlObXGQ5+ku3ejzGbjjCyQtIDW33VpbxcVw35jDUo\nqRqn72rl7PyYERWWzMI4L7h/cs6L77sNUhEIha+Uo7m/B5bAXbfWNIZZS5+DZnzs6uYrOePWEi71\nss0zy2+5Z/p6t8f1vX2qwnB8fMz5+Tmz2Yx5mnMxmvDWO/doddp0u212t3e4vr/dmAvTNK4KuAQi\nl0wBpZzz/tVn6WP6aiFWjJI0TXnnnXc4Ozu73OPrmo2NPmubG26PMC7yK2kVZOUccHvTcDjk4x//\nOLeeus5oNOK1V7+A8nzWugmLrCKtSoos57nn30en1UUgnQO9546RaHK6FZdmh08+05VSK0+Bq5Pa\nJ1+7ZOIsM6onkwllmSOlm/rGcUIcJ0RhspJHLWsRByo+fv1VRUHouz4pjHyMztz59ELKMuPOnTc4\nePSA+/ffodJzlLLkRY2sDUHkDIIjP0D5Icr3kb6P8j3CICBocs1r0xiJodC1xiLp9tYBGI/HDM/P\nKbOMjf4aXtxZNc1X7y1woMrVZ3oYho/Xajyuq75a2z3JEFge16sA94oCL78aPLr63u+m3/9a6z3V\nWNOg20IKqury4ErhgfRBNPRFAzQZmo7y2Qj4JUgkVgo0ogk8X7rnufcyxiCMBbGkFlz+eA0Utctu\nq6yHLQ1Kg8THUrqMPWkdfVRJaiS11WhbowLQBeRVSV4LAinIZgXDaU5tQBsJGDzpEXo+gS/A1LRa\nCUkSYYVibb3L2fkR169fY7GYkuZzRqMRf/oHX2J3s8N8dMabX57y5ptv8uDBPV566SV8DPHaOk/f\nus2Du0cUxSEtCZ4nufHUPv/9j/+P/Id/9kf4kR/5ixwcHPADP/j9/PiP/zhhEFJVC+f42zivSiGJ\nQh9raibjobuQTY2nBIPhOb7vu5swn3Pr1i3m8xmeyfGbQn8ydhOy5U0gpaTMU7rtDgrBcDjECuh2\nu+R5RhRVjEYTwjAmTXOstQShc2hVyicMG8ReKv7UD/0gP/lTf4cX3vccUeBTFBVffv01pIWz0wFx\nElKWJUWZsbu3S7/fo9XfoN/f5OT4nO2dTTY2NhDSMpsNqTZj97vXmrqsmE2mmPLSJGk+n69QwcVi\nQZZlRJ7Dp601tFtthLHcunULKQx5llFkOVZDnmZsbGwRhwln56cr7Yzv+6z1+wRhDJVicD7hzpt3\nCXx44bn3c3J8xv2HB/Sv99hZbxHLiDgUnJ0O6fY9+htbf0A34r/+eu72s7S7Em0nhAnkxQLhBZTW\nZzZJOXrtAfcOTkjTJhte1UhZozHU1kdLhRSKSElkkfLKXgax5GJo6d9+H9XdI47u3+fGjYjD2TlS\nBkjlgampC0Gn02O+WDCdpYTFglaZo0Zjfvdjv8LzLzxL0Jb0Yo/JydvsPXULP2wThhG+StCESOGT\nkDIpCtKywIvXaXX3uDgZ4DGjtx4zSiUVPrXWtELF+Pg+e1tbzA4f0upucHF+QqVnqKyHCnrMzlvs\n3niOuhqSFSVVmlGpEBXG0FpDRx7htCSSbUqRMs2nKK+mLHOiKKDV76yavbIsMdM53TjgrKo5PHzE\ni89/qMlhdmCCkJqqyglCQSJ7jA8P8KKQTrtNVecUaUpdLojaXRZZs6HXFVZA3Eocko4PSiG0Yj4f\nUhbOHM1UFVVlEB2/KQCClcGLa5IrVBSv6JR+GKKVxChJGEXE5SUVPUmS1cZb1zWYmDCKMHoK5BRl\nhu8HWFtR2DZaaXw/QJQlfhhjKNxxUYpe25lGZfOSdhyQpim2LljvtuhEgryG0kLYalOWBuEJtjc3\nefr5rQZwsBSVpigLJplHt7uGyVKUp9zUNPSYDEe0oi4b7RabnQ6DqYtIWxagSqnVtHU5cbfWriYT\n76VVV4IidxProGmYtXZFuFCGNCtdJJE14HvMswU6dNOKsqyRQrnJtfSo65y6rlfHRTRFchiGGAx+\noPD9JgJOeghhKauclvTwAwd+ORdbp9EMggDqCoSktgahJCoMsIVyBmC2prYGKwXSl0hPUtvKTV/U\nMqrGaRGFUtgmus8ag2wYHoVuYtuEoMZFgyFpmpAKH4ExEmFrqipDlzVVpplPurRv3CArcwoy6tq9\n3zy9z8HBVzh8eI/5JCdSbQJPMtegfN9RLGvPTfSEwlMBWV6TFSlSxly/7rN/cwvs1H2JBdZ6mPo6\n88kmSes6O/0Z94+PsBKwGf3WLgdHE1rxj3A2/lsY8SsIKupsSBiH7G0F3L/7WbIMru/skVmJUi5m\nZ3vvFre7XYS+Tbe9wDJEFJtQr2HVAdViEz8ZcTb6n9jq/mVE3QE5c9nZdQQrnwzj2EdL7aMVCDSW\nGoR2Uj67cACGNIStiGJSAhLhRwRJl0Q+w27rT/HWyV9A2/uEyZRQapQ/IY58/FAxXmg3Hf86rvF3\nLagbcNdY6/TCnqI0hrSuKbWh0g2oJq9wl/8NX1cbC7miDbtlrWsYl83P1enfMtbIWku1pDYbg5KS\nsJ1w7do1Op0O5+fnjMfjJspVrJyi59MZ7SSi226hfI+qKJ1sRAqWLt+XjbW3ilG82kitqMC4/Oql\n2eAS7F2CnO3eGp3eGr3eOtLzyMsCjcUIySKrG0lRE+Poe81+aZlOx+zu7juat65WDV53vU8UJavp\n6kqupAGcXBQeb5bfbUr6pMaaq8fdmFVTnabp6jVSsjKGW06qfd+ZLwp5aVz25PUrGqZKXmQoLyYI\nfLSpOR+ccHx6xGd+95Ocn5+hTUGcKLSp8bwmqY0M1AAAIABJREFUO9xYBzYp17yjlNv/cZJaKdRl\nD7YCp5w53dXPqsuSPFtQFDH48Qoof7yRhaUGe3nM3m39XhTuJ5viJ99/eWyfNL57t5/lgKb/nzbW\nVeWMbkSk8JUzlnF6g4oodFlsRVEhESRhgqdC5sWYMHR5eoHnocvlDRpibY0VPkI0WgQkdVEhrcLU\nlrou8FsBpqGyzS2Mc490rkhtgAw8QgI8SnIzZV4pjgcRg3pKnYeUUQffLEBGzIs1KgyPTj0mqds0\n5tMJs6xGmxDPQjeOSTwPKk2aZXTWQnp71zg7v+BLr76DYMGHv/lFdvZ3OD0fkKY1RoeM0zFeEPBz\nP/+/MxnPyfOSJO7y/EvfStK5Rh60ePr913nu5Vf40quf4dUvfN4ZAsmEP/fn/gIf+cgf4+//vX9E\n0gr46Z/+O6z3I4bDHF/5jMcjyrJq6C2KunSOvf2+c4acjIYEnqLTj4njLuvrEUpVzGZneJ7H2eAh\ns6LNerfPOw+O6LQS7h8M2Oj3OR8Oef6525wNUr7hG17mdDAlyyu+9JU3ePbWTYT0kSrAWEkQJgwG\nA/TY4ntO15wuNMY4jfKvfPz/xvdb/O5nv8TDB+9weHjA/u4Wf/Tf+ghv/vKvsb+/y1PPXOPpG/vs\n7m5x/foOcy3Z3t7g4cFdPvu536EoM17+hhcYj4fce/3LALSTNte2d/GFh65qIl+x1ukyHA1cpFDg\nirokSaCyTMYZ0+mIja1N7rzxNlvb+9y5e4fbt29zbfca2Tzj8OERn/305/C9kK0P7PCDP/B9vPn2\nfX79t36Te3eP+PN/9t/HTnx+7Zc+Dyql0guevjXg/oMx83mAzlps3F6jTM/Z3jV80zc9h7aWo5Px\nH+4N+nWs28/cxPNiNNoBUTJkMJzxzsEBg/MRtVEY7VFpgZUgVEBZG5AKKzyEtXjCUk0viG1KN9lk\nIVysXBCFbF/b4/7b75DEbdTkHBnIFYqrTU0ch8wXLvtR6JrXvvglDu8f89aX3uDbPvJNnA8P+OKn\nP8Vmp2D/qZsU2YK4W+GFzgRkNJoyKafkaUoYhmxubiLqjCqdcG1nnc//7ico0xHXbtxke7fPZDpi\ne3eHvNbklaGrWhgbgIwojCSbTSjMggxBHCpHfUMQxwlhFDOazRDKI88LFosT8oUDdjY3N2klMbZ2\nDdr29jZHR4+YTCa8/vpX2Nvf4fr16xjjprNJkvDw4SMWC+eUGkURo9GI84sRUina7TZFUTSO285U\nTErJ8fExp6en+L5PFDkaXafTYZHVpOmcVhSxttYD20UpRegrctdPukz3RqMNjWOo0ivk2eIiteq6\nJmm1XO546MxOVjTeul41o1k+QXnOj0Aqzf7+dkMLdxREN2lPUEoxnU5XBUZdu9gQZ7LjGAhxHNLt\ntl0md1aAUKyt9fnSm6/j+RHtpMXh4RHj9JBW0qHVatHurbG2vkGUtFyzr9ucHw64cWOfBwf32Oz3\nmeYpOQVJP0Etqq+K3FrSKYHHqINXzVfeC+v4ZE6Yu4lIHDtapWuGA4TK2Eg65HXt5JsN46ioHRga\nxD5SONqgtRC341UBtGQfXJrOCJZRXAgLUqJ8SaQ8KukizpwDs1rt5xoBykNbg/L8JgfAgC/cVElK\nlBRIP8BI01CjXVPhppYWgcXYEmFrgsB9JiNg6dXgK6dBlVKs6I2ODQHSU4SmZGpDamso68rRt+cl\n2STDjubosItNF9SF5Yuf+jSv3/kUoY04/UrMC9ee58137pD0dpkXI4gb93Tr4iuVZ9G6pMoFSdsj\nDDzuPqjIq18jkmcIswtNEmia7ZCXCbW5z/ziEZ5sUXsLrChZiE+TrG8xm1h2Nv4Kp2c/Qi9p48fn\nIKZY63HzqS2+8MUf40Mf+Btsbb+EJwSz8oDDUzi6v8bLNwc8Ov9bPBr+d/j+L7Pb/wEmY59+t8Iz\nH0DHf49Z+cNY+x8QR99HWYMhp5V0WKQlSiz1y5aAdVQ8ZD77Au32h8gKQGmIx+jUPTuKdOSqV7tg\nVl8wMwpMBnJKb+snCBUMRj9OK5AQnbF3Y8obbwukB8bkjzWMFruaRD+5fM8xYq4W1e57XOoB1mK1\nkxMudMXvfPYrhNKj9z3fznYnQXlX1dz/hi+jUcIilFg1X+4/N712js8CKUXD6ITAiMumw4IvfaSS\nKKmccaMf4fkBrXaH3tr6ipY8n8+ZzGcMBucMBwM++ZlPI4zFk4L19XV2trfp9Xr0er1LN2uczp3m\neb5cy+fEqrlqADnf9+l2uyRJQhzHJEmCbnwX1jb6JG231xRlydraGrbWDIdDzs5OefP115mMhozH\nY4SweLpEr6/x8V/5ZV75xj/CLHN75Pb2LrY21HXVMLCcMZbVtWu0hYUnPEKW4OqSpr00Al1mVgOP\ngQJVVa3o36PRCN/36HQ2iOOYdnup7Q4bancDOqp3z8oGENIBJGU+YzI64e69t7h//66TYHlunzXW\nEEY+UgrKSjOfz+m31jAW4vAyJ1t5MSgfL/BdrG6TCGKFiy5c3isaifIDOt01kjCkyFMWswmDsxOY\nLOh0Os3v036s8dXa9R5FUTAajaiqiuvXr6+AnOVrl9fIkhq+9Na6BDr0Y+kgwlyanl1tpK826F+l\ntf46vBLeU421lK65cyZjBVXFCtUWwl2s2tYYbR47WK5pfjx6Y1ngaFs/dkFroxHWYK1avXapAbIC\nykqzSGtKz6fIMnyTEoiaulZAyGxe4VUatET4MUW+wJoKRAzSYIwiL5xNvq4EiKCxvxd4QoE2CM+J\n/ZMkpqg0Dx8dczEa04kNWb6grHIePXrEfF7yzK2XKZB01tZY39nhfJSiVUhrfYu4t4lRCfOZi6wJ\nfMW1a9eYzydUVcVXvnCHJE545+17jCdjwmiD8+Mpe9dCokhQFtppy7XTnfu+99iUNoqihh4Ki/mU\nJA6ZTkb0+32Mrsiq3Jk3SIsXBOTljHbSYjqb0+nEaONyZNfW9lmkbzKZlxSlizRKs4JWUTlKkFBE\ncYAfzJnORmxvb5NlGbV2BViaz0iPTxiPx8znc/7t7/wO/snP/gxCCMq64oMf/CDHx4e0WjEIQ5rO\naMVPIWXocgn762RpxXQ65zd/8zd5/vnbdDodl3tqHfpqMFhjmM/mpKlDQMMwxNiasixptRKUErTa\nCVYY8jx3WcUHj5hOp5SFu87SNG/0PhmdjlpRX/f29nju2ec5PzmlqizKKNZ6fR4dDYnaCs8LSJI2\nICnyHM8HG9a0uy0uBnOQAUGTyf1eWEbXaA1COaR/Ps84PBkwGE7I6yYbUzsqM8KCsFjhpjQWiTUV\nVVVQzmcIu3COzZ50xYCxj2lolAqayQJY68xR3HRLY4ybjlVlSRSEVHmJZwXrvR6tlqLTCdB1CYGL\npfPEMh7JbXyecuZnnu8TRy3WN3dBGPLaZdi3E8dwGI+HSCmZzRcsshwpA8Ko7eY1gY8vDFKG+L5C\nNnFRpqEkauMaT9kUeStHbqUcXTrwqfJs1XxWlYu+KoqCyWTCrVu3iKJW4zcRrdDvoigguaSM6cbg\nSzbvfZWCBhCG4erL8zwXuaXnFLmkqmqEcGg+tmEEicuoD2DVIC+nx0VZrxqppX56uYn6je50SU9f\n5ljHcUwgpZtQKp8wcoi7u98yKu0KibJ0iQBLKvmS+q1XJjA13W7XnZPGYTVJWljlUzVT0vkicyZX\nUjCbLqgrN41FeSStDto2uu8yZDics7FZYnC6syzLEFKy1u9xfDh7zJjmMZMyeTnJvrqhv1dcwbub\nHbpr7dVEqdNqPeZlIH2F8ARCSawnqaxBYlb0WIHCGoE1Bjzhml9h0cK4YYWwDfXaNaxS+Q2YoSlL\njfUEVmiMdE3Qsv8x1mKkcTREa7GiRtFMKXyD9ATCuGdFrV3LXllL4MlGy1kRRY1sQdfYUju9tTFg\nDEIqlLDY2jURSiqkliijsNoijZuYjbOUo7winU/Jipoq8jidFojhgI3+mKgn6UU+b7/+Jp/8l7+O\nkDX333qNjqr57Fs/xf23f5dXXvlPUOxi6xwpQfmKQPkIBXEcofJbGDmi0CnHZ/+A0P8YuhZ4tg/M\nMGg830eGAyIBSgfMCx+pfbSqKOsK1BHdDixmsL/9Dzk6/8v04imO5G5AZuzcmLC37TEZ5bTW+yzy\nU4QBuT7maNTj2uZf4XDyP3Dn/t9gtz+lFT2DERWSOap6llH5WbS8T5b/JpH6YWp1l7KaARJrJcK6\nXN92DHcvfoLPffknCD3QWZfKBFhv4Bo+FHJJSV7KgerkMtoqX8OyyWbyYwxO/hqtrTf4/Bv/nJvb\n/xlnowdA4WKzaGjfX2MS9bjZ4Fe/zhmrGbANM8H3+Mpb9/jGF5+h9cIzUFlM9Pu/v/4gl24M+55s\nyJaHWDRN9ZOTVgDR1NRLAHW59y6fw8AqMrGq3D6ztrbG+voak81NXvvKF5FCUFUVZ2dnjIZDdnZ2\neOqpp+j1eo9/nnc5D4/Thy9dojc3N1dGX2VZIrwYZ2rs46kAPwyotcWPYvLplE6nQxgGpPM5pq7I\nFjM8TxJ0QtI0JZtMKYoccDKpOI6b3sPH9z3n02ArB+E1kYOPTf2b6+n30vNepYIv47WWTfeyuQ6C\nYLUPO5aocn1Rw+ITT/Q7V4/X6udgGI+HfOKTv818PnO1VJVDU9cu92PnHVKSxLH7zNb1WViBks4k\nzcpLH4ur52M1fRfOGJpmOqyCoAEfKqqqJm+czZ+kX189HmVZrkDyJ2nZV3/PJ/98dTK9rDW01itX\n8CeP/dW/u0oXf7eJ+Nda76nGOgpCesYjNFAZxcAIBB26rS2SpCRNzxC6IFA+nvWock2iLVpMMOUu\ndfmAykIptokl1JRYaYiUR1JpirhNkc8IA+GiPUpBrgMyGzKuzxCl4q1hQFVLbFmhraHd2+Lg4oLK\nTunENelI0e3uUdcZelyTZxpNY1ylPMbTHDNrfiHpO01LVdFZb1FLjZGGUGVsbGyCFCg8sJYkCnnm\nqQ3iqMUitWxvP8X7X9jm6ZvP8MJzH2A4GfA93/tdvPTKQ9737HOgPZ6+/jSeCCgflpyfvs3Yu01/\n/YN8y0dv8bM/+5Nc397kfU+/n1/6xV9jZ3MdXVa0kxhvnrEmJXNhKAOJto5uVVmBbZDAGlx+cxAw\nzwtUqdn9wA1GwylPP32bPE+5uDhH6Ij+9U1sKTB1ztnFQwJfcD45w5OSN+7cZ2vnGq1em/zeAVXT\n6JwdHxIGHnv7+xSVxtaaySJDKMtwMnAug6ImzXNqW1LM5sxHF5yeHPErj+7y3X/so4yGZ3TCgFs3\ntvme7/oob775Ovv7u2hTMSvdRGM0yji/mPL2gwN0VdFuJ9zc2eZ37x/hhwkFlkwaF8GTu6D7ebqg\n12mjpMSXAbPJlEAqpKnxVAtLwPHxhKTXZjA64anra3gyZzEd8s7bB9RFTq/jkSSW08GEwWhCZ22N\n7/7e72AyGiH9kjQ+5uU/9n4Of/GEi9kY5c1oJSWBX5Llj6C+TS95jqo8I0ksSoSI8L1BOQOYLxbM\nFyWLumaeljw6GXJ+McZYgbYeunZRPEbXWNNQAlXLRX8YqHSJLEs6ccz44T3WkoQqibl7MWZeZiQq\notPtEsctOq0241w7SuSSFqZ8jNEYa0jiCGs1O1tbiBp8AVu7O6TlgLVuiyJbsN7bxOoSo2tqnTk2\ni5IoL6QuNL3eOq0oZv/aLq9+7tPcvP0S5w/uEEYRizQlLyqyqmQ8Xbic9ajFxtY1ZFAShr5DeqWP\nkB5h3HPa28qBNlleuqKg0Z0KIYh819i2223CwGdhXOFwcXGx0kDv7e1RlBlKKTqdzqpZvnnzJnV9\nj9FoRJFX+EFAt9vl/DxzcVNZShRFzrgsihiPHRNi6Q6+nC5fXFwwnWc4I6raaUoFBEGMrWukvMzI\nXhobLgsFrTV7G1uuAW2a6dlsttq8lhprIRxlMIqilV46CX2s1czmC7RRKK+10o6fD2fUdc1sNiNJ\nktUEfkkvLtI5QcMwmU6nTvc3nzvGgVHgBdw7OKeqdCM1MdSlJe5FSBlgDFgjmEymFFXF5uYWRSkI\n43VOz8ds9NeZzqfUGDwM+9f3ee3VA5a67yVY8GThBI/nLr9XVm0KKpthrNO7+2FIXYMQmkBFeNKl\nQ0hPYZXE6aQbgwTrqJ7CCoRWWKlxM2LhpsICrFhOo+oVa9hFwrjsaeEJRxMWBiMNcqlnFBYjLdYD\naV1mqhTWuR7LGik8ZO2APW0t0vPwjHFUbmuBGqzGGg3GYHWT4WWaOB+WwIhqtIQCjMWT7h5zOdg1\naSV5cDZhPB5ycHyOjdcxYRe/yih3B/RtiEgUr3/5y+TzBUenb/Hi0y+xtTbh9PgfcfN2CxnBfi9m\nOq9RvmrAAxcvivZA++RFhvQ94kSRMyX2Iqc9w+kbPdUhjgLqKoVS4lOS1z7Uku56wOZmh8P7R/Q7\nPTrdklb8Etbeb65HjRCadrtmeFGT6hQ1TB1orn0qUwEz0rzF0zt/nnunf53T4c/Ti5/GiNRFmwnB\nevI8qS44PHiVP/k938y/+MRdPKFQ0qcotHOCBhbpiCQY8fIHOngKFC00CdoOkMI46Z1Z0sc1jn0/\nQ+s9rNV02gl2HhAlklb/jzAvP4O09zi5GCCFj6T6mlPkr5pUfa2C2joQWEhJVVu0NoynU8azOdoY\nl/H7+7u1/sDXfDol9hzzwlyJaRJ+0DSH9kpDYhufDHcvWFwji2i8eIxrTDxlwFZ4woHe0vfxhXDP\nVRy7pdeN2N/fdc/jaYUxllx7lMMpp1PHXnz6qesIq0EXlCSrRtJo7dz3kXjKc3R1W4N0cqiyrika\nINr3faxdIITC1AVCtDC1JvBCbCXor+8zHA6cxOHaDeZFwcndd/AQnA5nZJVEKI+LyRwvCKlqQ6vV\nRngaJRqAD4vyfKzR+F7oZCmiwljnaK7wneu5EAjhgagb6RboWhBFTvYYBm2qfEJdWarKmSNXhQEj\nCP0AD0ESxFRFTdLqoa3E+AEVzmfKLnzqOidueY6ZS4kQBkONijOK2YzPffmTjObOr8hgHEgpE/JK\nEIQJpirJ0ilSeKgwZi3oURQFRitE7KOC0HlXCJdBvmT6uAgx3L1hl8BMk94kLFZK8EO8Vg/yGr24\nYD4vEKKg22ljNWhj8b3APfeBsqxXQIWUzblvGC7u9nTMIaWWtG5n6AiAAD9wrW5d11gupQTLoerV\nph5rMOYyyQPh2Bji67iTv+7GWgjxbcB/DXwY2AN+yFr7f175938I/PAT3/bL1trvv/KaEPjbwJ8B\nQuBjwF+y1p59rZ9dFHPqyGBtjR96SE8jREZWLPBji1UupgVhiBSIQFDMIEwSauuDaiORaB0wzubg\nKyyKrAIjPea5wZQdFxtTVlSlYFHkXMwXyCDCM5Y0zai1pqhKgtgVnVmeo41lsZjTaXcYjCcURUUY\nBtSVM9YpdE2ZpUgh8cPAFVXGTdGiyG80Jc6xr9dro5Ti7v0Drt++wTd8w0tuksSUVnuN3/nUq2ih\neOrGcyhP8ODh2/hhyPbGNusbmwBsbfVJ0wWDs3ts7r6P1z/xGT7xO5/gr/31v8mjwwO2t3fJz7IV\n7e707Jxv+ZZv5s5bryHDCCE9yvkcL/IQZQnGFT3Gmmbymq7MYnzf59r+Pm+99Rbf/M0f5ujoiF6v\ngxCCZ555hixbNPE9NRanqdRa40nJzs42/c11rt+4xYOHRxyfnFOUGePJmFanQ9JqYUVKGEesra1z\nfDTBlnalcalqh36ejsfU1rC5uUlV5mxsbKBNgRCSJEmQUvLSSy+RZQvW+zuucej2yYqS5194jiBO\nuDg7x5YZxyeHfORb/wi/9Tu/TW1gkebEcYKuS+LEJ4zWyLIF1vMQVtDq9ZjmBZQFQQhVmZKlKVu7\nXTrtp+i1FdiAs9MB4/GYNE3p9kKUsnS6Lf7FL/4CH/noR9nZ2UGiefvOHdKiJg7XePrWbSbDM5Ty\n2d+/zvH2BWtrLYpc02slCNlyMUa64P7B5D1xH4OjSc7ziqyqOTg8YzhNqeolSng5wbPO7sAVu8qn\nqFxWem+tT3ZRIskRQrC23mVqXIFTljnrnR55XjCdzpkMJ+hwDT8U6PoyOsVa7TbGOkMR0O/0qSrI\ns4xuu89Gsos1M5QnWSzm9JJ1rHGxUYFU1MKNuqRyxiVBEOFJj63tfbSpOD/06Kz3mRwdczGegFLk\nZcH6xiYChZCKIG4RRAE+y5pdEMUJLtHANdqlcc6fZVkSR0Ej4xANWn6po/I8r9FgOeOQJInZ2Fxv\nDAhdFIhrkuVjVLrB+Tlxp9cguZdmblJKNjY2KEuHli/1rkniPl+e5xijm0xqzxVUdUWepyiEY5k0\n9/rV5na5lqZdV909l5PwPM1WVPEwdNMCl3utyW2Fc5GuiWOfdJFT14ayqFZ03PF4vDI7c7R2x9oJ\nw5A4jlfT/fF47EzYgoDN9V3mWUmWPXTTda0R0qVPlIUhDGL2967hhcEKfDg4eMj4QlKUKS994Hbz\nO0j3rKxdLuzSEA4um+cnjcqW/1/pwP7fbqAr6w/zXpaBJmwJpGyovKpECndOlYywloYO6SQd1nmY\nORbUcvolPKRysz8AU1uEao6NcQ2y5wvqWlPWNZ4KEJ7nTIzCajXVKEx5SQk0jm7uaiuJRKCEo5PX\nyoE+Ypn3s6ScC0WI56ZN1oJ27Aoll14rrvOz1mLr5ndUvpvG2MaF1mrKomzAnTnvDOd87q0B4/GI\nWV5znmdU3ph+YRjevU/YW8csCmRaMM/HPP/8PvfufIFPnfyv1N2/SiZyfu3T38UPfvuvks5j4nZM\nWVUkSQQS5umQLbnGrKxodUJm83NUOCfyfIQ6BWbALlXRpyoChJkiTQAyJbSbINbIJiknWQHVBvVi\ngweTjBu7/ymPjv49JBKEAWoEQ5I2yGmXeZ0hBQjdxoQjTDhkWhjK813W/R/j9cPvJootpXgdpT8C\n6i6L6RpK/3E+/MH/nI1tweHRqzz/7IdXkXNLJ8q9XZ9M/A4eAZ4ESQ9BF+w7qFBC5bnnJ4CtsFYT\nxBU6a6HNFshX0eKIRQov7v8AXzz50wTW4hNhrEDI1IE570L/fHItqeLA45PtJaVUCMdqQaGBurb4\nrZDt/V2sJ8H7f5h78yBJsvu+7/Pey7Ouvu+ZnnsvLIDFLk6CIAARgMCbAkWF/YfDlijaZIStYISv\nCJkK2UFFOPSHGbIUVMgyZdk0wyYpkSYt0oQIEiBEECAX5+5gr5nZ6emenp4+quvOO/M9//GyanoW\nALEkdwPKiIrZraqurMzKl+/9ft9L8noH83d6Tr5/cMBk2JvlVU8bq37zYR0zMBtbWj/QXBsMZfmw\nKZye3e+tyacQAlc5eI6wyRj164888ghFUcxSXoYj6ybeH/Z5bjjglZdeYK7d4onHH2NuuQE8oH27\njjejTgMzZHfKyprmKgsh0HXE5Gg0wg8btNpzgKEoMxQhSwtzRAcjXFdx7vwWd268iC8dgsAnThPy\nvOTk5JTDbpdz5y/hewF+8IAVNaUa23MkqSpjUwIcxdSIa/a+qoCqnLF3pvOjRW9jO8cVBUVeMBj2\nKYoMaiQ6bHcYTCIWFhbq+1C9ngkC0jxnNDpkbr6FozxOe8f0+yccHd/j6OiQKB5yfHw8k+2cRcgL\nbZlhrutiqpIwcGiETVzHZzweE4YhzWbTMhOUPSbDAwbDWT37Q9tZNFpZVH3aaHYdwWQypt8bMteJ\nCPwGQkjKKpu9p9FosLKyWjfYH0gDvqEonu5DiAfeBlPk/OxrPIxKP/i+VuLw2uft3327EfRg+4sg\n1k3ga8C/AH7jW7znd4H/hAe3lNfmAP0j4PuAHwNGwC8Avw584M/asTYlWlQoDFpO9XkZGtuVssWu\nixQuJRWlLknLiqoyJGmBJxUoh7Jy0cI6g5ZaUBoHHJfhpERWDkoIirS0zryTkmFkddCVyRlPYrKi\nprfIKZUhpUpSHNciT0HgQV5SaSiMjfWR2uD5dnC7nsd4NJqF3qt6YDjKdvWmNwDXFSRJRBB4SOXS\naNhisns6QLlNoiQjyzMaLY/9vTukpebKI08ymUxY6iww7J/y5S/9CT/842/hkUeusrubIYzVfqys\nrHC/d8BwOESbks5ck/e///3s7t2iNCk6r5CuRCgX2703tTGXmdFDm83mbJHY7XYJw5DhcMiFCxcI\nQndG+8zzHN9rkCQpytGY4AFyOOh36SwsMJ70WVldIk4ThhNL+Zg6IIbNOZTr1Bo3UVP1bLzI1NZf\nKUWRVwSewm006I+GXLlyhWF/QLtti/w8zwgC61bu+b6l7MYRk8mIPE9REppzbSa9lPv39ojGI0aT\nMUHYxPPbBKW9gVZFQRSNLXpqBGVlEI6YLQgrYwt/tKHRDJFCoByXJC5nRUiSJMzNh0SjMVcefZyG\n7+N5DoHnEzUCkjxiPBoRRzYiIp5EzLdWuHTpMs2FkiwtGE8SyiJBKZeiFGT5n8t99Ds2jgG+/sot\nUqk5HY4ZT2K0dJFC2UWMmeq2wJECozOkMIzzisXlVd761reSxhEvf+ULtIRHUF3izqu3aG+dZ319\nlVd2DllqrzG/tMhn/uAPOX/5HH5oTeVMLbtA6DrX2LJEhK6oaq3nCy+8wGNvWUOXCWFo6aAYi2BV\nurB0KMegakq5kZLKlEil0JXi3NVHwbco8MLCAt2o5NoTDbQRlLrC8RSu79DoNKhcgRNYkyQlJJ7r\nIrwGaZKgHAcnMPgNqwUzSObn2ha9rl03XUfOIqsODw/RWtNq2fiNdrvJ8orNmXdqTdfBwQF5XtbU\nNZfhYIzruoxGI5rNJspxMPoBqjotTqeTZbPZ5OjoaCYJ0VqjTYlUDmmcgqlYWlhEmOqhgjaOY7sg\nCEPabWuyNplMyPOcTqfzUFzWZDIhTzNc16XRaMxQa9d1cRyHNBohJRgNaVoyHA6ZjCN6vQlJkbK2\ntsby8vIsEmzadFBK4vuKdqfFzs4Oo/HLrCAPAAAgAElEQVSQ9Y01VlZW0FpzdHREbxSR58UMmdaY\n2X7StI/WkotXLjLXWSAtE7I8oyokVCVJNMHM+TQCn7KAyilwHDkzZgmCYGZc5vu+pTQX5WxRcvac\nKvXnmpq/Y2PZET5ShVTC4HouTqFxpcBBUBhtqd1KIAXW1R+JhyQ1BaWu6d66xBUSI+258RwHz/cp\nqhKjaiqfqQteSlA2BkgpAZlC1KZi1EBzVVa4yqXIczAgfYOYWkqLCnRAri2l2lEOEh6YyFFgTInQ\nGlNWKEcijSRyOviOQ5EluAKk0eg8YxCUhK5HFeU0tIOoHNCCopSMYs3xUcJp/5TMCbg3iTC4KJMz\n1JIqypnER6QmZ36rTdpNGB+4HPd+Ht//dVr6/ZAanjjncuPmz7Cy9D9TMk84v0yUdglEga88stJH\nOXNkhYvfbJLpMYUocbNLaNlHsMtic49y6BI2P8ZJ9TlbcpVdHPqWHSRD3DlDXO7QCJeJhi7CjDDJ\nM+jwBiXXGfffRVZA6h+RZQNU5TNHQtldoXBPKPwBuclJc8W1c7/Pyy9/hIXFdbz5LikQ5U+wuvzD\nrG//Xb7y3L9kvP85hkcFou2hgxwv3aQJUB4QmluUp/8DSe85cvdlMNDI2mi3YbXUaCoCNPMImuj0\nDqW4RQW47gUis0sYglcZ8t4J1eI+CTlwDun0IX+YAvrNmCLT8SgQPFxTGyphkTklJUVVQT5GOR5J\nVdHxmpSVwheKpjGM9OtekX9H52Rt7FptSt+e0qf96kG6w5R1NI1qqqqHi5XZZ03nhpomPy2qlXJx\nHEsdllQIZYueNIqRyvrXtMIGC/M59+/ft/N/Zcdmt9dn584uG5XVYXc6nZmh5fQ+b4yNcYzjeKar\nPfu9TW0klmUZk8kEP2jMfucqzyl1QVWvWz3Pww8CqiwnK2zhXmoYRxFFUbG4uIgjXTzPmx3/2eiv\ns88JUaHUg9aMLWRtStHZ91oqfmXNz4zBWGs18jwlTVOmJn95UZEXBX5tpmvBh4oizyjTlMVFH60z\nvvKVZ7lx42XiJCKpH1lu9zmZpGTZ1DzTPkpsPerKDG000gSgBVmS4BXVrIGqpFvv16LDZxvDr6W7\nT4/3tfRtKSVBEFCVLbKsmEVgCiHw63hdJawm33Xdmqbvzz7v7PZNZRpnxvfZYn82rs9IGs58EkJ8\nq2SO119Z/7kLa2PMJ4FPAohvzVvLjDEn3+wFIUQH+FvAf2CM+Wz93N8EXhJCvNsY8+y33LlwMEJS\nKUlWpGgjURiSSuMLD+m1MUJTCIWSHqXnokNNWoxJigBtGqR5xSBKKauMSuSkeUGSuESl4cWbx5DZ\ngqvMc0qtMUKhHZfcGDyhSLOcNM9ptloMRhM0dUaAlEjHoVIKJ2ywtrRmtST+Os1mk/FgSBRFALz3\nve9lZ2eHmy8+X6NLAY4UBL5LledQu/ZevLjNwb07dOYabG6uE+UVvV5CksLlrQtcuvwoc/PQPbrN\npz/zaXbvDnnk0X0+8r0fxHNbrG9UOG7Jb/zGr/Djf+NHaXz/OpPJIWHDp9WcozIHzM3Pc/nqJW69\nssNnPv0psjQl922G4PL6ur3g05KyrGojlwdmAFOHyMFgQLPhs7a2Qbs9Z7tuVWFvPMqx0V7KFspS\nVTRCl0oXSODwaJ/eoM9jb3k77U7AufNrpNmE/UlsXVInE5rthXqRPprpP/K8ppZMHRNrTWmzNc/l\ni+doBD6NsMXq6qrV7FUVW1vXuHt3F+UIijzHcSVpNuGlF69ze2cXRymeessTLC112L29y5NPXGMw\nGHD/+ITJ8NSaXQGdoMHi4gK5NrheyOHxCaI0yEwjsgkNv0FVpgy6J8w3z4NoECc5vf6I0WSCkCWG\nAi+c48qFCxRKsLK8SLMZ4LmKK5e22UgLPvV7f8SrN28R+IpG8DhKai5fPsfp4B69wZgsd/DdnCwz\nRP0JhW4BR//+j2MsQ2QUp2S5tmZkYppZWVNEseYpAoOUBmNKNMIaSLU6trh0fRwlcJpzjMcjFmvn\nStf3qTCEzQbHx8ec296YHjWGCimmESHTTju1sZFBOHBwcIDWJabKkU4dIVRZ9NE12k5CWiNFTX+T\nCipB4AdkWUwz6OD4Dc5degTXddmY5CRJhB82ZghunMa0Qw/leWip8JSHW+fmlpXN11XCxlj5vm+p\n1K5LONU6VeUs+mSqi5uOBWPMDG1o1prXJE1ot3wWFxc5PDwmCALm5+dJk9wi0JMY33fryZuZsYpF\nvhsMh8OadVLNxqCNCynwhDMzY1F1xWsLSX8WhTXVWheFnUCnJjZTUzFTN6POxk9Nu9XWoDKbFdlK\nOkhVuz4byNKc0ShmNBwzySYsLS09pD+bfkZVVYxGvZmmOgxDlpeXZ9/F6AhHuXVRbenKWtuGoqzN\nlcbjMcdHXc4HwcwHY9ydUBYJghJ0icSa2DhSYSr9EDtgqvOanluMvYdOtXNn3/d6t+/kWLaNKnA9\n19I/pQBtFzNKTY3ILE3P1BS7aqbzq02/pMKV1mvBdRUo638w4xGi0cbS+fzAjh/rSyYwWswchKeo\nspECJQWOF9YLKI0UtgE0NWHSxqCrkrSw+ddK2t+8qKDSgtAPKArrOq2NxKWizHM8pWgEAVVRkGuN\nk2nKJEcZh3FhyCrNyWTM3skJe/cOuHd3yCBNicnItI1vy8sST1lkqZKSxeUF+icn5JMhL57+S0r1\nf9MM9hH5IkbmSCdHMkBimIwUrdYyftCnzIfouIHfGtDwDL3xiLbYotTzeEoTJX2a8z6KJi+98Acs\nLUyIq0OoNKHTAaGo8pKm10AZRZEVkDvE7glO7iBkCs4xggLJOZpBG98DJT2ivL6Wa78LXddPWhu2\nzz2BY3bZ2vpBcP+IXvQiVO9kZelHWZ17kuEEiuSDzJ/7eV48+Ckeu/bPIINz52KODwbEcQA8w407\nXVKRU0h7sbgsUMo9jAZHAXpSN0skaM/iysLeU8JQMhrvMbn3P9FazDC8B/inILqve318dmH9Wg7J\nFKF9sGB/gILleY7ve/ROBwQmoJp7fX4J3+k52XFdkNQNv4pSV8RpgpdmM7bPNG+4qkJc18X3G2f3\n/9DnaaNtBnhtPGjzjW0GsvU4eaCDDYJgVpQ5jsNix6XdvEiSrLGzs2tNxJTisNtnMElYWVlhe3ub\nxcXFh2Q1WZZx7/4BcWwZRSsrK0hHURlNVuS4jk2VqbT1IcmLlEajhZQgKSmyBEcARjMaDWh22kTD\nAVleYLRtEAwHQwwOjUZrZpI2XRPDg6JsemxaT9kNlq5uz5OZ3a/s/I2l0espgmwlcPa4inq9XeC4\ndjAMxhOLxAfWuLRIsjpxI2LYOyFYCfmTL3yO69ev23VumlIUGUVZkqSmzuyeAckYq5KhrEAoZtnt\ncZxSZqX1dshKqsrg+B4CRcfzUVLO6OCz310/aBbM2Az1+TirIZc1au17DRqNnLKsGI/HTJ3ULfBh\n58QpeDBtUn+L6/8bnjtb7E8ZF1N2wdn3nPmLb/icB69/5zXWHxJCHAF94NPAzxpjevVrz9T7/YPp\nm40xrwgh9oD3Ad9y8GdFwDAO6uxJSVaCwOGwWzBK7cnLC0vXKIs+eV6SphHHJ3B8PEGIAl1BVXkI\n357wsrJ6rAKHJPJQ0p4S6Xq2INP2Lq6LAm0EzXabUAga7RZiPGJ7e5swDLl89Qr/6ld/lXc89TTN\nZpvt7Ys8++yzLK+uEwYBT79rjd/8zd8kjSOEoxhFE4yu8JSg1bAuu2VmcBxFVlQobVhqzJGXPZbm\nW7RCh34a43kOyvV48m1PIYVCygqtYxvtoXxu3bjDD378++l1+2yfD/nEJ76PnYOU7v0jVhbWuHNv\nh9t37yAdyfrmeX71V/4VrbDF2voi9w/vUuUF7aUV667sOPRPe7jKQZeWup2e6UZlWTa72B3VZv/u\nAUtLS1y+fJnBoMfi4jLjfkxRZNw52Lf0cW+qc7BZ3YtLS7Q7HUajHlk6QYqCJB7N6KhRFFGUBUWa\n1KiegxSaydhS0fPMFt/39u+zuDSPozy8oEFRFbh+gMZSf8qy5Pj4mIWFBaJ4jBBQ5Amths+jj1y2\nBhWuy9xck5vXb3LaPUJUBVQVV89vsX9yQqPlIR2HllQM04qDoz5Lq6ucu3SBV3fvEp0MiaOIINBo\nU9DwmpRpzmBSce/gLnE8RhtN2JC8+z1Ps7m5xuDkkLc8+RhHh3u89JmXePTaVZ58/ArD0R2M6XH1\n2hKba0uc325TZhpKzRe/tMPOzn2Uq3n/+9/O+bVNru+9yMnpG+4K/qaMYwC/PU+3n9TXko3sOHvb\nkhiENghTIMgxVWIjGwKfvDQ4bkB7fhVVjMkmA9777rfxyS88y61RRGf9MnlZ0Gy0aYRNxsMJzkqO\nY7y6y2oAg+975EVuTY4qTWFspM/+wSGFzgkcKLMcJ3BB2m6wCh5Qp5RrKaZoi4hGcYrjuBQGwtY8\ny+cvMOh3Wb3gUGUxycRmse/t7tBqLhF0fHy/g+uGhEETTyqyKCY3msqArM2x/Fbr4W6t1ji+jYrK\n0pg0rsdJUTCZTAhDn7W1VdrtFs1mc+bwfXp6Sq/Xm9G4G40GnU5nhqaura3Q7XY5PToCYaM8bCRK\nYg3Fajpdq9Xi6Mg2cPI8Z2F+DseRNJoBjlS2yKosyrww3yEIghlrZDq5jsdjSs1scl1aWqKqqlkE\ni1NrV6f7nLqnFkWB1AZKg6yp7b3eiDs79xgOIvByhsOhde9utfA8b5aBXRQFVR6zuLhIp9MBYGVl\nhcFgwP7+Plo7s/MlhI0Q0WVlmwUYXNdDScXx8TGn/S5PvfPtXLhwgatbPs8/92WqLELSoSpydJEh\nUaRJNjvGszT413bwpwvW10Z/vIHbmzKWhbSUO1NNo0zqv8fMZAJn0Qxjajq8nJpGWfmGXfAYS5cU\n1OY7trAWgDCG2rYQYzQYbVFjOaWUT4twi07PUGyYmY2hK+smbqlXtQZQ28/EUBlttYoIjPKoyikt\nX+AKTWEqdFWRZhqjBbk2eFqRpDlxkeM0O5ymEa+eDrh5/4i7x13iUcrYaArl1/RZhS41pakw0iCE\nod/vkVQxSAHey2h20fSRogEyQYgIRc7yMlwKHqc7ypC4VEahkYyz+widYkzA/d4JG4sXiJMXabY6\nQMnxyR5b59/J6WiI8g9w/BAyG03mywBpJEILXOMjpcNEDDEmAFGAHCBMiTALjEcx0RhKNaLdcZmM\nNKUQKKGZSRGNYnf/kKbIWV/9GV545W9QhR4rne+h5X2URstjeWmFRy7/AH/8Qoft9Xnu3vsHfPCp\nf8rx6QELa+CzQi894vGn300pMstqMKClRQztsruu1wQg9RT4A+D09BQqzc7OTebDlFs3hyyvNenM\nzTOafFtG9EP00m/3+lTzOSusMZSl5pUbN+k8uo3jzKHF61+Qv47tTZuTl1dWCT1n1ticTCaUVUkU\nP5CYTRmK02bpysIGUsqZmdb0ni2lxLGtxpqSK5AIawxcaYwokerBvW4q4ZkVPlWBMBB0Omy+771M\nsoy7+4e8enuHJEm4ceMGt27dYmFhgc3NzZkHR7fb5dat25aV6Hns7e2ztLREu91mfn4eYaZ0djOT\nDLVaLQSaLJ8QxWPiZMhx74jeYEhSxMjABS2o8gptDPv3D1ld26Is9Cz2d/q9X3vd2CLuAUNTeao2\ngDOUpZkxOoUQFEUdDWYsfTyLLKPrtHvMeNiz7CfhMOgd088Vzzz1DrIixVQlvpL0ugfs3dlh59Vb\n3D14gVarQ5HHnByPSFNb9AtgnILvO+iqZFqj1qcECSgkGo0S4GCZbUoI5lttpJT0TgeMhjFveWub\nsNFEef6Mbj19TEG3aWP7LGI8bYLY/Uqk9JjrrOC5DQ7u71IUKZPIY2FhAYN6yBDvbMzaa9HmBwkS\nD77DayPMpqa2Uxnra//GGAO1lO5sQx6gLKs/a/g8tL0ZhfXvYqknO8AV4H8E/j8hxPuMPbp1IDfG\njF7zd0f1a99yi1JJ6AeUQlAYxSQzlKXizt4Jo3HE4mJApQvKssJRPllW0FzwEbIgKQxSKfKiwlMe\n49GExaUF8iQjSXOUJwj8NqZ20NRAWaY0GyHKtaDUye4p28srbJzbQrkOBwcHrG9s4rou4yhCej79\n0ZjltXUODo+QjkdvNOKRlVVOTns88cQT7O/vMRgNOT09xXUditzSg3VhkaI4mtD02zb+pc42NrXL\narttO2vPvOMp2rXDtS5z2u02585t0R8cIWmgtaHVaiKEptlssrneIYtLXGUo8tguUBPNIxcvs7Ky\nwqg/4oMf/wC/8//+DuOeNfRZWVqyxkmJ1Tpames3t6WfGhStr6/bm3FN67RdsYqXX36Z3ukJqytz\nNBruDJ10HIc0SSjKkji5z/s++Fd45ZWbXL16iWj46gNkvHwwSEPfI89zoiiyeuqiYDQasbC0aHXP\nRuM4HhpNlucoJ6DRDBmPx7iuYupYCxDHYxCKubk2GxsbNr83mWBMRRSNKdOEzfUttrc2GSURUZay\nsLyMjCO0kjRbMa4jWFyc57TfoyEc7t2NSdIJm6tLgGY0HDIxiqLKSTJNqw3LqwusbayytLJM2xMc\nHuzz/Isvsb+/z5NvuUaz2WCxavD2t1/DFC7zc00aIWhHUeRWc1rpiizNyXNLg2q32xhz/40Yv9Pt\nTRvHAHGSWmlEvSAR2tSOmvYOL4xACmuWksYxUpZIV5JllqngeLZpkhcFym/ya7/yq1TtNucuXiNX\nAVVZopR11x8Oh8zphylXZ2lAdiE/1RlZ1KrUGjfw0EVSv9d+b200AonnKQoBuqoX+sohLUo6YUCS\nRji+TyUctBZI5SI869ieZSl5ljIY9PCbDTbWtnA9H4TCGIVyAhzsuJtOSHlNc4M6bocHNCtjDFk9\nTqfjzdLfvJl2earBPj4+qfPXnRkC3Gq1GAwG1niqfkgpkMrBERDH8cxHod22cVO9Xg+l1Iw14rjO\nzOwjDH2qokDXFL2pvu0semwnKluETSeuKdIz3Z+qv/90Ej47KZe5pdApJZlMJnWztAIkZckMuQDr\nRDs9l1JKUIL19dWZwdtg0KudajNCr0GJrCdfG8GjsVpd5TpoXdocVWHIspiDgwM2NzeZ9yRLywu4\njqEoMqSZFpqSKi8euubgAUpvJ3anRnfVjM747Rb2f4HtTRvLQtpYk2l0m+soyzCpKtxA1L4akspY\no0AjDEoFCGUNQlEWQbKmP3WUj7DoiRTCarM1SERtHGgQUiKFQUiNNKYuyM2D0qZ+ThqLTM8cjWsn\nbWGo85Cl1foJq6c3ukILB0fVvxfKGg0BgcnwpEJLQVpVFBqGacngXp+yMhyNRtw46XFaafZGI7pp\nTAHI3BBTO0crgRJYUzWR4wcBjrINlaVGg739O7T9jN1un3w8YaMTIshsk9FU/M4n/2N++Pv/D1yx\nTZa0Cbwm2o9x/ZQkS2l2trh47m9zevpTtMJNRqMdqqpgYX6V0eiQYf6/8ugjP8nwVUnIHFJpEBFe\nYIuaMrexZn7QQZQBQqQgI0ChimusrV5hvv1blK7Pfm+IlG00FbosKEsoBaBzhFggNwHl+C5Xtn+N\nveHf49FL/y3LS48wzPYZjfoo0eDc4l9jxC/Rbn6aF1/9XwjVDzFOIM9/iZz/mtF4D6OyGcKsnftQ\nAcbFhnDnPECRHjSipBS0Gg0+/KGP89kv/jRXrv4EjcaPkhUlqAiq18cI+VZ00+l9dYpazxA4YWYe\nFcNxgpGKpCh4qOr/y21v6pzsBwFh6OP6Vi5nhCDPc8oqnRk9Te/HM/8LNZrNBdN7APAgjcEGGtd7\n0AihsHnPyhbadcFtPRBsB8VUFeiKMAgwwianuEHAhQsXGIxjekf3ZoVRv9/n5OSEhYUF2u02vV4P\nWaOb02NIsowky5jEMZfOb59BkvWs6YqAqoyJ0ojeuMfx6Qlhs8FgPCL0fCvr1DZ9iLowmyKf0wJt\nWvydna/A3u+NFtYX4My6Qwh7DRVFUV9T5ZnmY0lRZFSVjbNM0ollbJWa8XhI5c7jhTaVx5MG5fvs\nvHqDr19/jsFpl6Dh0z06Ic9KHOkhtUWoK2MNvorC0s0d52Haeplbx2yjwag6J9sLKIqMsrQys6DR\noNlsY/SZhsIZV294mIb9rcaRfd7YX7+OKwRqhpohz1N8Xz/0Wa91o/+ztrNF+Dfb/1kE+2xhbV7j\nAv7vBWJtjPm1M//7ghDiOvAq8CHgM3+Zz/7iC4dIcVybi+has9CmP4ZKN9g7zAnCBs1mk6SoSIuU\nSVQitMTxO4yKDK8Zoo0k9D3C9gbSy2ktlPRG93nLW9+D18zIi5LmfIcoSWg0feY7DdoNl1/657/O\nu97zPjSGbu+UH/mxv87t27dZ39jgxiuv4Puhzc2VPm5D0WkvsHpu016UWqOF5rR7TDweossMqSsc\nCVWR2x+zNiaI05hoHOH5x1y7vImLIBsXiKbDXDPkkWuPUxmF75T4rosKO3zwA9/D8tJdhFzkwoUV\n0nhI0Qy48+qr7NwfsH/riMmwS2NxkXe8+yNcf3GHwXDCRz72Ub78xWeJoiGNhsfVK5e43+sR9Xv0\n+wPiOLUF9RRdOEPnmG55nmMqzc2bt7h69QrjccT581vs7u6wt7PPzZuv8L73vptm6KBNhqxdthfm\n5my8kePjeQH7e3scHR2xtLRk3YezjNF4zPziKo7nImVJNJ7U+5aMRhOyrGB//wDHkezv7fGudz3N\nwf37bKyvkhcVIs1BVDPkajDosbm1TlHkBG7AaBJT5RWrKyskk4hoFLGytEoeZVBpHG24feMG73r6\nGe71TtBKoBqKO197gbnWHKHMqeIeLVfT2lpme2uJ0+4h0XhEdxAhUeTSUFYJW+dbXL16kc2tNa5c\nuUyaZuzu3+HW3QOCVpv3f9e7eezqZfJ0gudWPPn4ZXynRZmnSLpkacmgH5PGE0aRJi5c/uBzd1BU\n6KKizN+4iJ43cxwDfO73/hTHC0DI2gDFcPHqFq3LSyw1OoS5xi+HXNr0+fL12zQ7azT0ASIKUQUo\np0l7YZHu+Ijzy2t0Fr+Le/de5emL53j26y+Tz2/BwhyZ46HHE4wUeBiqUmOkQIscL/TppRPCLMRV\n4HgZGRpXSTY3rjAY3kCqOTzh47gaU/VxzBxKtsilROUVgesSZykSjedopLINHW0EMo7xhAvSUFQl\nrgyZay7SG99lcHsXEWVsbT1BRUFhciQVoe/gG5dJvYhJ4wSyktDzqApDVk7s/S2KcJVkkMREUcQo\niqmKiEbg02p4zHcaGGOYDEeEYUg8toYuQRDQ6w1ot+doNps0G20rWZGg05j+0X0W5mwMlW2qTQh8\nRZZClk7olwUH9+7T6XRwlcdCyyMdZ7WRmE8S13TxwEO6miSJyPPUItp13Eqe5xhT2bhBBHlaWF1q\nURJPrIu58EP89hxaWzfodsMnjSIoYlSzw2m3izTWTTWLcwLlY1xDlKVMuiNWFlZIS3vvCpsBQcPH\nCMFSuE27NUeWJXhewMnJEXEcMzffQtHg/tERRZogkZRaYmQJnkQ4ClkKK+wuNa52OT7oc3DQYz6w\niPvc/ApCBYySCK0lShkK5UFVoKsCWU/iRVVQe3jRaDcYdAdEw2iGGhhztkz4y29v5lj+hc98iZZf\nG3hhmxEffeISH3n8AkZoq1FWEiqD0DXapT0KUyGkg3IdTK3zszJpg5SW3q0NUNPJfWVRn6IscZRC\na4njqNmi9izdviztfnRlC3JRSwqU4yIdB5OniFq6oGuXbykEgedh8gxPBWRFiqM1SLv0DxzF6XBC\ngSQ1kqiAl/eP6Q0Svvb819GOy3GUMTaKyvGItaLSgNdAUyGkQOvSNh2UrewdTyKEphW2WGiHZNGI\njZX/nhs7/yXR+DnQC7ZQRUG1yuOPDfnN33uG//Cvf5ns7haOc43CvUGWg++tU+Q+b3trC1eeo8pf\npdUJMKaF0Ypmq8LIf8crN/5Pttb+I+LRhFKUaGXItK1XHQ9cBTJ/BCn2wTgI4YCJQU1AXKAzt87L\nuwc05yCJFZU0NGggnRKpCkoNVdLHb6+QR22KMma59XNUpoOhCyoFpSm4yWLzH9CPfoWi+idI9Yu0\n/SUGWZ+j099HOGNWOhdpmICkzKASuJWm8MHkBQYPqAeKAIu12f/udNo4JJx2u7zvA/8Ikf9z/MY6\n2eg2GBdRboDY//bjhum65+Ft1iirHZC1qWwyHC4GQ6IrPvXl53luZ5e5VgPtvjF5W2/2nHz9lV08\n90FJUJYlK4sdFjrtmcwor6ORpkV2HNmIm6Smilt5TANTs4WEskkO1gunNjg0WHqzMTUbiFkxas0m\ni5kMaJo2oLEF6pUrV1hdaHN8fEy32501d/v9PpPJpNaFFw8VsJNJVGusNS+99DIbGxssr6xQlgWy\nNiZFCsa9Aw5PjjGuwm14xHnC4vIChwddQhUQxzmdToPJZML8fGfGirKU5Yd+p4ckPxTCRu7Wmy32\nLMW7LB+so8/mWSulcB3Jvf17ZGlCkaU0lua4e/cuaRqz9dh5smRMHI1ZnOvwpWef46tf/lOqMifL\nrcQuywx5pm0Br110VXunODVLwKmpIDz4J/CnjV6DxJqmTpvkcZ7Z7+WHNr7X8wDbZJJnUN/p/Xja\nwDhLjZ9u03NmGYRWKuS6LgsLCwwGvZrK79Fs6ocaF2f10a/dzjawp9frdF54qHCuAYupfG3aJJoV\n/MCtO3e5vbv/UK2T1+yE17O96XFbxpgdIUQXuIod/IeAJ4TovKaztla/9i036TWojIcWBcrP0KLC\nE03QFY4RPPH4EwjHodAlW+fPYUyF05pjdOsVFrbO0dWaleVFlj0XaQxNfw2M5Pj0Fnf2vsQnfvDH\n+dqLnyctKnIUUXFKkmoee/Qag8O7BM0O80urvHpnh+X1Lb7y1et0ez1OBxOunN+mTDVHB8csL6zw\n9rc9Q/ewh+sp1lY3MWXK77/wVbVTYG4AACAASURBVDxHkEZ9dJ5CBfPtAKNLqjxHO5LAU0ipWV1f\nYHlpiWEvoRmGtLwGTUJazSanhy/TmmsxMSFetYLfaKHIeOc7n0D5HqhTsnhMvxugTJNBb5cyhpN7\ndwmziP/9f/tl/vZ/+nfZ2f0S73jmaQQFd3du02p6bC5fYGu5hUGwV8Qc5SlxWiKcOnxePKBG1L+v\n1Zkoj6JICYKQ46MuzWbIC19/idFgxCc+8QmKPEVXCSBJ4iGNhtW/LnfWiSY93LDB2so6O7d3eenr\nL3B0dMQkjllZXWV3d5f1zQ18PySeJMSxLSaiKEIpRZrmDIenbJ2/AChGk4grzTalLqmynJOTk9rl\n0upT7927x/b2Nsl4RLvRQYqCo+Mu9w8Oubtzm3IyIE0TOl5IrjN0ofmt3/gt5jfX2LxwHhGNefLR\nS4SNYGagseSF3Lp7QF6m+K7hiXe/hc/+0XMM+hFXHl/i6rW3cenyeTa31vC9kN/+N5/i+vMv8vH3\nPs5P/+RPoPwA11MIYRgPTkgmDQ5ORpTpmKbvsLXRpEhiWt4KH/rgCo++NWF3/x7nLyzQJKLoTrix\nt8e//sPuGz6G69/5DRvHAO/+7rfRWdvAIGpKZoEuM4oqxs8LVF6SjIc8/uHv4forX8UYGzmXZgnj\n0YC55Q3a7TanSpJXJdceuca1y+fozIV0Wm0OByMWFhfZ2Nzk8KUXrN7VGJTrUNaxDfbmD67rUeax\nNdpSUBaaOzt7zC+C0NgOeq0llUIgHBcjwG9aTbJFIAs8z7VIqTHMzc2RlQUVBpQkaDVptUOyPCKK\nhty7t0eqSy71DxFREy9soKuScWU10oEfYAQMBj36vR6dsMnW1haZsVTp4XBIEAR0u13iOGYwGLDc\n8bl48SIbGxuEoZ38dnZ2uHv3rvUriFNWV9fY3NzEcTzKosQYwenpKVliFx/TzE+A+/fvU1Ul/SKd\n0bHnOg3e9a53Eccx43FEr9cjSZKaTm5ZKjPTMCmYn5+f6aunGabWTVzj+8363FUYU1GWOa2W1aEb\nUSGMRXs8TxG25+jpHG0c7u4fcP/gPlqDpxySvMBrtUhLQyAqjFS0mm28wCVKI46Pj2nPtSjKnKPo\ngIUFW7AXZY7rKqtDN4aqNLYw0BpH+hZl1ZqiLAgbAZWxxlgYi44GQYBRkiqP2d3d5c6dO8wvzNFu\nN1laWqIsNXGcEDQbs8k7DENUvThN05QkyXHDgAXft5ElZ5rj9/a/7TD6C21v5Fj+O9/3Hh5dmSd0\nPFzHsbFa2Lg0WS+k01pGIJXVcUrt4OGAqxBK4jjWXK/IxnbxbQ0WkI4ir0ocJQhd32aNB836eitp\nBE3GkwlBaHWeUkrKqsRzrbSq4bpM4hhQBO02SZbieCFKOeR5hlQK5UiKIq8jcjShACVKlHRIJSRZ\ngRGC/UQR5S5Hwwm9OOOwP+Rzz99gv8qt829hY3uKzOCm0BQuCkm/YfBNHY3lSJzAx5gKZawBUFVB\nkZf0xxHSC2mKp3nnUz/PZz/3s5TuPqgRk/Ex+ahgebPB41d9vvzVv0qaXebD3/1/cXdnHT9wKaMO\n7bklsqLL+tp/R3/wM5R81crFlMZxSjB36PZ/ja987e/x4Q/9M1559QRoggrBd1hcbnB0d4cPPrXN\n9ZdvAFuM+kfo8JSWF9MO/hYXVt6B5oCFOZ8yCtFVl7j0AB9dVQhylJOTphOL07ZAT1aR3jFV2YDq\nKtAFv8co1jx95R+yc+ff0Uv+Gz57PeZHPvop1pZ+EPhDjl/9Ob78/AdZv3yNtvMUJvV593f9HdKJ\n5NbtIRe3n+GVvUPWly7SP/4qCLso7vd7eELjug6HdxSUATi3wQGRLQDngW8srM+CBWcdsKfPV7rC\nUc5sLMMD7bWSlpVhUDS8kGsby/zYxz7IYxfWEXNb/OB/9l+8nqH559re6Dn56bc+xtLC3Ox4fd8D\nHhSFZ6mxRWHTDtLR0P6bpkTjlPGwD9gi2PM8/KaVG/l+SLPRrgstVbOCFNRFjus8OK+u69UBXsze\nrxAoT+G7bVxK5ubm2N7eptfrMRgMqCqrzwVBWeV87GMf5fOf/zxxHFGUFWQFkCBLGA0n3NndRXkO\nG1ubDAY9ltdWGDcSskXLhCkTjSmAieHc9ipHh6e0V+zYbYUdFpbbtNoeni++QfM7ZR8BeJ6P5zcZ\nDvuUZY6pdedVVZLnBVlaH78Lw2GfOJmglOTevX1Gx7aBvbe3x9a5Tb707B+TJAlbWxusL/i8+LU/\nIUki7t69a5lotS55aW2VGy/vAVCWdfFsrCs7ykNK+92mzNEzVxSYEiUlQeDRabbQVY7vuhSplb/o\nytK3w7CB5wU140pgzMM51GfRYHhYd302dnIKqghpcASsra3TbrcZDAaMxxPG49vMz8/TbrcJw7C+\nPtxvKJinlPppwXyW+j39DmeztqdrnGl06Fnqt0BzfmOV8xurD90TeoMRv/uZz3+7YWT3+7re9ZfY\nhBDngCVgylP9MtZ87nuB/6d+z6PANvCFP+uzOovzNOaW6Cy0WNtoo5yST/32n3Jhe4vHHn+Sj3z0\nr3Lc7bGytsr+/X2EgvbSJfTmJl+9dYP2wiJxlvP1Wy9zces8EyMQxmFze53P/+kdXnnlFYy2P5jr\nNNm+sEw0HPDFL3yN9YUWTu2e22g02N3dpdXpMBqNuHDpIqcnPVrNJoHn40iXyWjMO59+F930lPGw\nT5pEaF1a/V2ZU5V2ETVddJa6xFQFrt+g3bEL06qs2Lm5x8riHIHboDfsc/7CBsZNGA4m7A8iFjvn\n2L70CF9/6Tl2D3ZJygkriyusrWzyxLUnaPhtfuAHfoCN5mV+4Z/8fY6iMXGUEfhtrly5Qq/XY3t7\nm5euP0+v30PkHp0gQ+NwaXuLta1tXrpxmzjT5GVVIwr2Qp12Lae076le8+DggO0L50DAhz/8Yabm\nFGmSIYTC83ySZAKAUi4aYQdTf8h73vNefvmXf5lr167xJ88+S54XjCY9jIDLl6/OOnpTveiMIt60\nmb7jOOL81rpdvFcZUtlOYBzHrKwskecpcy2L0nluQJLljMcTsizHaLvwPT09AW0oswIlFNEkRkjJ\n6WmfcZbxtitr3N+/y8baCqIZoLOEaNCrzahcvCAgq1Ic3+PJp67xse97O0vLHVxPoGoTt/v377O4\nuMiVy5cwaJI0otdPCAOH7uERn/nslzjcP6XMFKbK+M9/6hMkY43nurx0+zmuv7LPUfeEP/pjww/9\nlWu0tEOzptK8GdsbOY6BqVpyZk5kqhJjNE2lcHTJpN9jdHzIvb27BEFAnGa05kCXRR07Mb1J2/F6\ndP+IC9vrjIdjyixH4dtr01EgLb1n+jdSCpASTwgcYfNQjanIsnymv5pMJswveHaxZuoMSiEwukRQ\nYQwURVXflNXsu1RZjhAG3/NIqgRTI14IgTAlnu/Tmp+nGA3RoqAocjwvRElBkVekcYoQFYHvkhcZ\naRKRZQnjqmQStUlKq0sfDAaEYTjTDgthi9ipnndKV5zS9rQ2tWSjJAwbOI7HeDymKs3MATbLLPLs\nuu6seTWdzKbauWl8lRCCyWRitdJlOSumjalmk55SjkUlHdc+lANCEcUp2gicmjbn1TT5qXbPdV38\npnXQTpMUUUeJlFUBSpJFGWVmF3jag1JDUZa4YUAcT4gmCcPhiNVwhSAImMRjqtJmgudxiuvW9N7Q\np9ls4jiS0Whco+kPGofTnrjQVkMqPWVZIVoglcT3XYTnoE0x03/bhoPV0ltUo8Haxqp1Lp9M7E2/\nfkhHzDrwWmuE5fg/KK7fpO2NHMtSClQdF1OWJbq0aHxWFrQ9G7fVaDQtK8JxbEKGUAhHYRxJqTV5\naRlbjuPh+z5FVVmky/eYnJ7SaDRwlEuRlzjKYa4zj9aaTqdDkma4rldTUeumVDPEGFhYWAApKXW9\nwModXM/HE06dAGCpvMrxUY5DkecY5ZNWdc69q8izCUZLepOczEhGmWHv8JSD3pBJUZIU1mFfG4Nx\nXEyek5cZVWVQCLTnoJSLEDY92VQaA1RViXQ0AhfXDTCUpGnJeHzE1lXDD/3oz/Fvf/cnGSenLC1K\nhEoRZglMiJRjdu99khdfHLK9us0onyAMOC7kuWBlBV566YRrj/tYGrKuiRYFy4tN8jzh5PiEldUG\nJ6eKta2LHO29ytHBEa2WJI6v0538EoOjX2R+ZQlkTkqL9XNw6/YhW5sbxOmhzfZWmkrklpJvHIyw\nCKIUIBwbs43M0BIQdUO+akHZR7oRX9/5GiuN8wjvuzl34Y/5+gs/x53rv8eFR++xeuUKqzTZ7X2G\nQg5Ymn+U3/63/xXR2PCBD/wst+9/mfacZJCMmSHW9goHYyUGVN6D7yPAUAHRQ8w7UzMtztI/zxbV\nZymoZ/0PHi4kBNUZaqzn+3iBlStJ8edK63jd2xs9J7/22MzMXGuqFRa4rkIpO1Y9LyCQgizLZpLB\nqaRn+kiLlCLL8byAtJXgOB6BH1rZiO/NzqHruiA0ldYIbaikY9cHxrJOlLLFdVnalIooimayw7m5\nuVnyxGQyQmtrELy7u8vNmzdnx4OxkhOENfNKxyWaikajQWVK5p7soIz1MXGUIBtOUI5CVIawHdqG\nQlrQaHjMz3fwAysP+maF9fR82hzkB2grQsyaMVqXCOHU560iiseMRkOEEBwdHdJUkv6gh+s5lGVe\nF+AKz3e4c+vlOuEjJ57ElspubG70YDim0BVSSNvYp0IIEPWx/1kyCCnBdRWuUpRVjjIVZWnN1IRw\nZ00RR02d0IW9Pl4zbs5eU/Zz5UPF9RTBtr9/NSvwlXQIgpBms6TINZPY/s7Tgnlq8Hm2gJ4W1mfH\n41m0/KxvyWv3fdbFfEZN/ybHMH3t9W5/kRzrJrZDNt37ZSHE24Fe/fj7WB3IYf2+fwjcwObpYYwZ\nCSH+BfDzQog+NmjxHwN/bL6Na+Hf/Imf5sojl/nKV76GI9t0uyeY4vchXuW73vu97N2/y8HRPZ5/\n6Tkun3uC+fYS1bDPaT7i0uZlvvjF61x85CK532QwiVDSsLG2SX8wwvfWrcbB5FR5zoX1DW7dusXS\n4gLu+hxXrp7jt3/jX6OFQPoNLlx9jL3dVzm/tcHRvT2unDvHZHBK03eYm28wEAknyYTV9jr97hGd\nxjxve8vTfPqT/wZZGQJpB6DUFTrPkI6hVIZuNMLIBdLJhJZUpAYGbptP3bxPbjSr9yY8sTHPKIq5\nfutVnnqXy/rja4StBqFZY7+r+NKLN2kFezz5XI/v/+DH2b5sGMs9vvcHPs4//sVf48oT1yAYk0eK\nwF1jY7lDW/0hZCW7vZi5FcNcJ+Cw22VpaQWtrWFQ2wuYRBMqISm1sW6pnofRLkpKygLGQ83ayjrD\n4ZDLV84RNn2Chk9Z5rh45GmF44boJEF5DomOcDyXSTJk/94u0pFsb58nHk1421uu8tzXb9BodkgG\nA9LeCUms0UVJPB7RO75Pqxmidcnq1jbz84t0Wg08JcnSGEHEqNcl7lUsryxzenTM/EKLdDKiHS7g\nlw6Hx6eMoxThNDgcThhHEeMqYrmzie+4mCwjVIJLKiQIFVVV0h3EnNtaYHGhiZIGx+/gNUJO7uyQ\n5gPOX1jGrfo88/gK73j7WymN5N7+TVbXOywvrePKNn/tR76PZssjTcY8+8XnOTy6S7d/n2fe/z18\n7aVDut2Kfm7QosBvCYwDW1shlBGj5yTH94aUJiTwQv7wC102t1ZYFK+fCv6dHMcAWtSWQVWBU2sI\nO4HPggO3b+5hMkPoOGyub/L8DavLkkJTVgVFnlkvBEeiXIfKWN+EaBhxcrxPIB1EUSF0ZY1JhKGI\nUwia9gYpbayXUg7KWDOzIPBJ4hghxf/P3ZvHWprm9X2fZ3m3s95z17q1dHVV9VY9K8wMmwExgcBg\nDHGUmDgRIMVR/okFUZa/kGILRcL+w7EUIidSQmQnyHGUBAiCxE4CxgMDszM9PdPd09Vd1bXdqrue\n/Zx3e5b88bzn1K2exh4syDB5pFvLvefc8573PMtv+S5IIRmPJ1x55kKA/obYdPVH41vrcKYm1gpH\nsBeSSlEUjlgFa7hlHrpuXugQeKooCAMuFyxOD1gUS+aTE1rOkcYJ1TJndDYiSSFLYxazOcZUSBwn\nJ0eYsmBWzNb84E6nEzzRez02+312dnYarr1fezSvhGjKsiTJ2muIVFkGz1DfeGQLb5lOp2itmUwm\n6y5Au92mjkK3ejAYMJ3MOTk5C0lNnq/mEkLAcrkgz8P1pC6m3W6vD9Sqqun3Q2dRNT6WnqBEFMWh\nEJBmyRoBUBZz4jgBF+zQTk5GLPOck5MTjPGh4+4My7zEIyGKETEoHeG84uR0zNb2NsuyIEtStjc3\nA4KlU9HpdEB4Op0OSZIwm01IkpqT4ZK8MNSm4cF6D40qeFVVZEmCjAW2DlA9HWlQCutD0UJrHaDP\nMubk5ITh8JQ0Tbn2/E12Lu+R5znD4ZDpdEqdB49sU9jgl91gwCUSlAzVgm+BtZykMe1OhisqBv0+\nk/kcFWtauk1dOpCW/UuXePjgAUjFfFmgXEXSyqhry7IoMIsc4aGTZrQ7gmVRYJ3DeoeKI/K8ppoE\nLmSZ1msYYbGoqGvPeDQnz/O1hVlVhjm+GE2oTI2IYnTSpnSW0vqAELOBHlSWVVA7RrOoCmT3AotF\nTmdzj+WsYKYz8tpwXE/5zBe+yNIYRvMlo7xkXEFqIqTXLEzFwoVisVc1aRxseGIM9Twn1hEqirDG\nsKxqekJQLAu6gw4CzbKuyZ0k7XgOHsHzybN890f+O/7Rr/4Iz15skW2O8U0jQEnH+2/0mc5+no3s\nF9ncaaPijCI/IM06HNxVfPQj/5Dx4i/jKFHUIYFX20BJ5T7H6eHPU/qXuHztZ6gWX+DiFc3elueL\nX/wnYD+Dr36bIrcge4zsAzrq+7h7/9foZFdZFO8wm3k6cY/CnVDqOfgW0mYIr4ElkYA4bjOrFwi1\nRNgUK0pQt8B3oN7DiUM8S06rhK3tv8zB/Z8gad3h2Q9EwC5CfAeeHvubzyNoMZl8ir29FLtn+L3P\n/htcv/Kv093/KYrJlITuk0npPcY7TGXQagGuhXIZUuRU6gzv8rXY0pOn+KcSy1WgvrJpEkKglX7q\nOecD8IYoB0Jg8cRZC+s9UZJS6G+s4P3NPpOFIFAffPNeZJNk1xLf8GC9840quyaOFIlwZFlGr9db\nF3OfoHFySlOyXIR/j0ajkFinKVnaIsnStfDZqigcbCXBRCnSrVxBQCuJsxUnRwccjRZrV4fd3d2A\nAmqSrs9+9gukrYRf/Nt/KxTzmt9HkxcprzG1QShHlsWhoFrlLIsFH/+RT2DbEctljssrNrOU0mrM\nsqRqhBhNZWl3M8azIWW1oJ213lOlepXQKSXDOpjN8L5GqWARu44lGijyfL7k+PiY6XQCOKazMa2N\nPmdnJ1y8eJHR6IyVZdejRwfY+h5JkuCEINICj2BZG+qqJM9LvHKNkIQLVly4J91pr9aK9quiaJgA\n4QHO10Ex3HlaSUxVLhDCk2UtNjY2GAwGjUOHCorn1hKvmBjn4NjnO9fvRrmuHhPg2DXWelY2q1na\nJo5aRLpNPAuw8OFwiLXB4mxVuFjFFitqwgoCvvrdqwbD+YLZeZ/t1Ro//7woitcaG6u5/CQB/7Pl\nWH+UADtZpfD/RfP9/wH4D4APAj8DbACPCIv+b3jvzzvH/0cEWs//RnBU/CfAX/8XvfDBwQGWcBNr\nm9Pt9uhsbbDMQ+dkNB6xWCxZLJahs1JXzOYThsMh9WyE957FYoG1jrIsee7Gc2gZMRmHYND5IPVf\nFFM2NjYA2NjYYDg6Zjqd09veRinFZDQiawdRnLid4Z1jOplTVxV1Zalry95gwPHojH6/z2R4SpY1\nFR4fJqxSYRIZ59DCo5UGS1A0b3gXTkC3E9HJIo7ORoisx9nZGepSH2OWSOHZHPQw1ZLLF3cZJLvs\nzBZsHbRxZcnL126wtRn4PfPFjNFkEqwMNjcxpkZpCw4MBWhP1kk4Gc0QcxBaMBoNQwDkDLU3+EaK\nXwmH1FDUFuMKjCvIojZ1XeIxPHj4gHYPnr22R21qUpK1AJuOImwVfFqVCgsrSRMgeNp579nb2+O4\ndkwXBmsdVVFSSMHZ6Qk+6uKMZTEPScd8XuN98MRVagUzaUSBnGI8noORGFdifURtyvBl2wgXutkn\nZ2forIetDRJBLBW2roIgnTWkOkBjXF0ymYw4zhUf/KHnyMshIl6ALOhvRVx2u9y/N2I8PGLvwhaX\nL++DtDy4f4oTE3Z2N8Fr6kqSpX0irbh3eJ+vvPoG08kxi8rw0nTO8PgEa0oSJWl3U569domqKhDd\nhPmsYDabgPC42pB1IxbLEY8e3Ke93/+WWMdAw15z4IMFTSQgSyIubmY8fuchde04PD3hl3/5l7n4\n3F7QIfABMrzqMK82WIfn7bff4cd++AdoJZJbv/f7uGwbhSCKVajamsCDc+cEZ4LqeIAhRSoiJwit\nrAKEVWU5bMoh4NBCoKVAuid2EcYY/KoiSghQFCFhFz4oxnrhgn911gqOA4D1hvl8io5bmLpc+3E6\nX1MWOWVVgAvXbcqSxWJOXuXra1odCt1ul16v19jMhA7zSphldZBUVUXW7hJFYR+KoqDquiwK8uWS\nPM8DiiNu1MbLct1BritYLBbBc7LxNl1d64p3F8RceEqJU0pJaVbcOYH1TUFFyLVq8+prFVStuu9K\nBr6XUeG9LIocLwSTyRRTZQih0BqMh9IY0jSjKAuMcSgFizxnWRaNsE7gdbfbLWQUutQQOqx5nlOW\nNVrHtFqSOCmQUmMbukD4PAO8MFh/OHzTB/DOggs2YXVdY51FKUlVFc3cUIH/Pp2wv79Pp9Pm4sV9\njo6OODw8ZDabsSga1XCl8KaxJpMKwzeuQMo3cS2bZj74uubk6JhFWSCSCKEk0icopbh39wFFUTRC\nnAFSW+Q5NY5Wq4WKU7x1uMpSV5aqNKhIoVREpJMGpmfROm6E9+q1zY3Qgd2apq0G6hfuW6RjEuXQ\ncYRKUmonkQStg7oM68ITkA7GBk/YoixZuiXOCyIfMTMlC6upvcLKmNzA6WiOUYrSOJxUtBzYGpT3\nlLXBS08UK5yGgqBs7K3BInBC4BvRo0hoTCPSWBlLURm8gP3L+7zx2iNOTzp0oit85Nv+Gs7/Kjoe\n4P0I4RTegyNCyCmz8n/l+jN/FWsKFnnOo8czXrz6EaLkc+BjEHXYuxB4Umx1SpaWUL+GNHD8+B9z\n4eKPkmjDF7/4Kzx++H+SpK+z0RFcePZ5joavsLf5Il9+Y8z1l97HO1+tqF1OpwNxpbFWUimHqD0Y\nBXgUadBQqDTeXMdxF2wLERV4Ddg5wrZIdZdaCGpzghEZaeuHmB//I/wumGnCy5f+FvNKcPvxr2Or\nCfsX5pA/QMiMyzsvsrf/UyQkdPduMr5/ez0nm7AdEEjh8L4C28KLHCnAqiWuQXOf51Cf54GuAvE0\nTVkul+v/v6cYEzSCRyGJW/n8WgdFWVK2vuEi2Tf1TGbV0YOvSxYDdNef+/7TiVM4U6K13WMcx8Rx\njPEGwaShAIWua57nlEVFVrfWe3673V6LXAalaA0rcUIB1tTUZcl8MsY5RZIk9Ho9Op3OWlG80+lg\nHbi6ptVqPcWfXQ3rLYmOsN6S5xXWQ9YSWFczWUyIWylZq83p8RnOVE1MX1HXFVmWEakg1Ht2dha0\nObr1182H8zxjeMInPm/Ndn6erRLPs7Mz5vMZQgQ7rDzR6yLF6ekpSjfK2NajIoWxNcY6kBFlbamN\nC8riTTFCKo9wTcGEoN0R6rcrrjFfJ+bhAhochSGJNUqLhqcdUAVxHFBFq/fknX9qTTxVbHqPf7+7\nMCVEQD0F0TDfzKmg3B1HCVmWrc/pPM/XcO/zgmPvVvV+d5Hs/Ody3nrr/DWvPhutVSjivEcC/u7P\n+Z83/mV8rD/J07ibd49PfAO/owR+tvn6hofWurGTyTh4MCTLUqwNifZyuSRNWmxtbaPVnLt379Jt\nddm5uM2lS5f48mff4OLFi9y+d5ub77+GMWGh3L/7gK3N4Mm3Odjk4GRMr9drNlPBrVtv0R+0Q8DY\nvE6SJJT5Em9Dtc5UJWVZc/HiFYbDMbtbu2RxRjGvmAxPmY6HpKLL8PR4HazVxtPqauI42BIkkcI4\nUAa8FHQ6XYSAy9ubpP2U6VCxzDYZj064enVAf8uxe6nFoA29dhtVSwZxxNaVHu//6BUiremLGL+Y\nImPNg/uH/O7v/T5/6Sd+nM3dZ9BJTL+V4GYCR8He5U2Ohh0eHg8xTjBbTNnaHtDqxtx8/3W63R7d\nzoCiGPLFL73CaFKSxWGzkFqgbImQmjvvvM7Vq1eJouvcunWHvd0L9Dd6bAwGmGrJZDyiypdhAusg\npZ+mrQYGmK03mb29Xb52+z7dTsbwbIL2lofzGWl/j83NjZBwCNFwfgy9ficUQyykaQtrPEePh9y9\nc8TuXp+7D+9zcOzZ2d1ksZjx6OQxqW/R7g54dHzCZH6f46PHbKQR/STF+4rlfIh2jlanRSo1lfK0\n2xl62WU8WrC11WE8PyJuJ2QdyXOtAYONa0ymp3QHHbb2NxmORrTTPi/cvEm3lzId5Rw9GvMbv/F/\n8MzVfYydcXDvlK3NDjde3OHa/mWe3b/J4vQAFWuiRNPZaKGrGePZnMViyXg2RIqKixe2ufHsFYYn\nEceHj3j7wTfmYd2swW/aOgbAgXAF0pXEUkBdUk8LHk6HpJGhnodO0vXrl3ju5Wf59Ge+hKuWxEmH\nxWyCqUp0JGl3OuRnx8SlRXvF+OiM8dGQwdVdXFWSpjGDrQ0i0XB9CKrC3hoiIekkGaWpqVy9hiFX\nRcHX3rjFxz52A+kt1pQIH+N9SCLSjkCUIIXHmuA7L/EBJSE87VZKUeQB5p20KeuQMJVVjYoSuv0t\nNrb3mD98yMH9O5iqRuEpXjBkWgAAIABJREFUrMDZin6/xXw+pS5KjKlCRz2JcLam122zvb0doHTn\n7K/6/T5p/CQ5XUHxhBDs7OzQ6/WojKPTaVOWNWdnZ0gpabdDNV0pFXyti2Id6GxsbGCtXdtX5Xke\n7Oya5HtVlV8JuDhnGAz6ax5eURRMJpM1R+rk9HTNz0uSGOcMWkva7bD/lmVJkoTuTr6wnB2foHWA\n+R4fnXF8fILznqIISu1R1qLT1nRjjUeiqjbVOHDlWxYEGq1AxjHlckkrjfAIFosZQqi1L/bO9i4H\nB4+oasdwPKe2gQeXxQnCObCGKMoAi44kztJA0yuUitaUmCROMHYFeQ56Ce12m0fHDzmbnNBqtRgM\nBly4cIEbL17j4cOHjB7NODs7YzGbU5cO26jB8o3rpHxT13I+L5h2SpSUeCGodUSig6qwFB7nDQiB\nThReBuqPMAK1srCsSoRS6EShm2JzNwrFIWstuBpnHFaG7kotanzkQYIRBuUEUbSClFqkCIUcISVO\nJCBtsMzEkUiNcIq8pVDO4Is5MRKLagTPuuhqTJ32eGQ1i6zPtKo4PLjPwXjKsTSc+IrpPGdZWpxM\nqBs+Yj2vSITG1SCNQsdtlsscn5RYgk+0q0q0D0KBp+YUafbRM9CxJUPj6g3eeutZuvtnnKqvMl1u\n0e//NSaT3yTnvyfGgTpGsESSQTUC84d85dVf5Pu/628ijn6C1rP/C+8MP0dUxQjzV5gVn6Td+xwR\nc8rhPtZepr27DXKBjl8D3uDRwU8iXYKo/pD9bclimOKWCWRfpr+5xcOHl/je9/0CSXuTjRaUpcEs\nu4joGCE7ZIBxBRVjWkmKNxbnDcZPKDsTmOxCdozSkLkBLXUdFY05y48xIgMNs2nOh3f+Np//4i9B\n/fM8nu1iBNR1xos3/gq1rMmnsLnR5uTkERutPt6DNSXevYIXokESeSAPPH0EOIHHYJkgbIAzC+/x\nuHVSLcXTSsPOO3SjD3A+eXwv0STvg02bFIqWkojK0fGS/ShjoFsU45qJnn9Da+mbfiY3sF7vwfuQ\n4FhrkFGwo/QuqPpLITHe4JwEqUPZRghKL5BRaI5IFZPGGd7WeAJMO07rdUHWWstscYQQgqLKiGJJ\np90P8PKkRScO3UIbSL2YumYynVFYQZYo0iylnUWksQrw9CzC1jnbg5SjacFyOUfrGCECTcs7gfMW\n0mCrKeoEnEHiMT7GVga/nGCFpt3ehFnOcrxk6SvitmKrs4/wFUUxIU0iEGccH99ms9snFn1arZTa\n1HgpsMbhlAdhiXTY16qiRAmNqxVSRtjKEMkOSjhmZU0xXzAZPkZpgXUF7cxSlBOSFM6Gj9Dao3SA\nsHvAqhhjDV4qlBJYY3GuhOAsT6QyrLF4U/HuMpCy4W8pPFiLX9MkwGhIYhBK4BTUlcUZTxYnxFlC\n0slI0hQUeOExrsYhcCtNAg/eO5TUSCTSrfZi2dS6miYF5yDbMkPJkPRKFTzHrXOIGGIniEpIM0We\nz5jOJK12SPCl3QFnEV7j3ALnCpQG68ugk6K2miK+xLvQKBEqCEZ6t7L7egINDw2VVdItQCg8rkHW\nKZrW/zc0/sw51n+a48033+SFm89zdHRCr7uH1po8z2nFoXv18P4jnn/peR4+OOLGlYtc3r/MrJgh\npV53Rm7evIlzy7UXnrcgRMa1a9fWXq0vvfgyjx8Hdep2O+Pk9DFnZyO2NwdEUjKaTbl16xa7u1sI\nHMv5lLgTE0UJznm88Zw8PsFXjjtv32Kr3yWNI6wNVl4WA9YSxRoVK0TtqCpDmrTothVxkqKlRUjH\n9qDNdDHi6k6LV08crSxjOn5Iu+3ZvrCNRCFNTC9JMD5HKku2sctyuUBJQ2sjRijFYGcXGSfs7++j\nk4zDoyN2LkQo1UYIyff/0A9z/eaLXHrmLkdHBxTlgq3tHpev7Abf1rMztBH0N7f47r/wfURRxHQ2\n5969e+R5zny6oKoMuxf67O71mc+nzOdzbtzoNIF0RJ4v19Unj8V7h+70SeKMuq4bfmeMVpokFgw2\nety5fY86z/GN5/B8cQ9FhcI0aruGOIrY2howGo3I4owyL5mMhzy8f5fHh6csygKEo65zvmfrApWN\nmc4tk3KEGC8wHqx19LIW/VSj5jPmZcH+oEMnjhGuJooEUZqxuT1A9WviyFKVmuWsxZ07U7qbLfq9\nB+hYs7m5DVFMb2efwW6KWWqECJ24JG7x5puvcnI85NKlPX7wX/k43/Ph76PXljg/Js0k25ub2B3C\nfRIW40t8mrCczqm8YGt3wNHhGVt9RexmXN7q0maTw6KGgz91L+s/kyEECF8BFdIJRqcnPDg+ZT45\n4sXnr1E7S5RGdHor1epGkMJbrKlwziK1RCqFc+FeFUXJ+GzIdDhi8Ixfi1JkWUKsI7xv/GxpoFpC\noJWiNIEXHeyUFIsSDg+PV/3qJxftA4zUu1D61UJS2hop1fphuuFTee9xxmK1YyXUoUQI4pIkIc06\nRFHMfDqm3x9QlTkWRasVB1s447EKkjiIOZlWSKSzXofNzU3SNCVNU1b6BVmWgavWSd6KL/1EvRMW\n+bT5vmu6zDG2CXRoDpcV3zpA6AWLxeIpm5VuJ6MoqnViLYRYw6Dr2p/jNIUKb1FWLJY5CMkyL9ZV\n4zgJPbTVtSVJ0qiFB853GmdMxzMWLqcqDbPZkkVeoFSElc1rphnGhoM/SiKyLGIZZ41FTIBxag1e\nRhhTspwviCK17m4qpdb8R2MMeWWC6JbU2LqBhzVx9PozbfyaV515nMM26qNaa6yr13xMKcPzVByK\nHbPZ7Cmoaa/Xox316fe73L3zDhNbY2oDOJT+1jmalRCoc6qt8apDU9boJAgRAQHVAYEPaC0rbOLT\n/NXmAZyHFYKSai0+s+qaSREsekLHYxX0PLkO62gcAEIQFZAGIvABmwK3FxKHbJAUCq9jpnmNEBFe\neUTUorIwX+TMFznLoqQoasqqxkhLalKMaZRqm0uo65pIB1RW7YqwPwixFmoSImh5eBmBCgrB6IAC\ni5OHzPPHSAH5sGRvZ4er13+Qt+78JwjxWhOYSoR3IOd4pWl3d5nNC46mX8V6uHZdc+9rho2Nn+Rr\nt/9jBv2r4N8ibefMZhXDsxM2B801oRFEIHNgjhDQ3mlTzIcon1LkLS5d+k66PVg0+gPWO4SXuDrw\nqYUOn432AaWhtMSjqcqSXlsxnVZokTDoeubDEePii3ztrV9j59KA6y98nFq00H6buw/vI7M2eX6F\n3b2EN2//t/zox/9r/uj1rxC1odNNGJ4t2NzcCl37ZgqtEa7NH+dz3/DzJ52pVdfuj+tUr8aqS/Ve\ndj3vNUxDXfA4jPAUjdbAPA/6LN8a4wkPdTXC/QqWks433Wq5+lmwSvLrZ4fPYNWtlVKiREocx42Y\no1mLV4ZO7LwRPit58OABSXxKkqR0Oh2ybqMq3pxBi8WCPM/Z3NwkTpNgA9UUl1f0psFgwNWrVxl9\n7XZALjqB9Tagj1woCFhvwIMWzf4qBXVdceHKRS4M9nj44JB7j+9RzA2mNAjh6fbbRKqNFJblwiMw\nEAnmxZzKlqjEM8snT+6dECgae0nrGY3Oggq6tRgT02q1iCJNkQeEVRRFHBw8YDabobXAE1CY+bkz\nWUQRtbUYD3VtWFTl2pPZmGINhU6SEBOUedUgB5K1cnrzCTfzMViRee8REpQOsHWROLQI9meJ0gjr\niZUmieK1xkpAcJmgb9MUtJRuOsgmFDIjqZoCTdhrEc2u7oHG/Uiu59qKBuXBu4ZuYBDeNfFbtr63\nx8fHRFHE1tYWXhYoLdAReKEwJsL7mroOjlHGm3PzOSAxVtfzXkJrqy9rwzxfnzNNzPgUbP5fML51\nTm9gc3OTjY0Nrl27wd07R4FbuLmFLnQT+HnG43GAX09HXL54kTwviKJqbWBf2gKh4cqVK9y5fUAa\nZwjB2n/PGt+o41bgJWVZB37gRm8NCwqWAik7W1skSei0LFTTKUlDkjiZ51RlSSuJuLC3Q7/bCVDm\nBm6e17Y5ARozc2MgCZYEeV5QmBzfUlzYSJlPJkSNamw7ayGlp9VKAEsaJQgb4QyMz4Z0di4SEYGP\nsLZmWZX46ZSqrun0NxqlR8/Z6RG4mERtEkURo8khnU6fNOvQ2+gjF+AFSK3Y2OwTpwlZ2sbZhGU+\nZu/CFS5fiRgMdnj8+DF333mLIg8J9K233mR3d5fLly9z+dJlPEEx2NY5ZVE08I+wkINnXUVRlEgd\nOHJxkqB8HaClrsbbGqxBOYe3HluUKCDSukmyQkI0X0wDUqAoOT4OasneeYbDCVmWYozjzTfukrVi\n5vMZz17Zo6gKrLVhwTpLTyRIa0kiTZZERCIETFIEVowXkr09zfjslCzdIFId5rMxj48ec/XZgv1L\nF4ijNippkSU9Hj0+5eT+AVev7bGz3SGfFThTkESwvdVl/8ImE1nizJQyX6JrQbclqEREWcyQWpDF\nCdN5RWkc03lBp9NhoydRVNTFjDRrkUWarSQGvjUS61hKMu24sLXJa1/4PN0oxciKv/hv/wCINp/6\n5CtkvRZv3HqT79/7KP1Wh3ldoGNHni+p6pIsiUjTlLF3PHpwwBc+8wVOH99nNloSC4X3QRl2kS84\nuHufZ24+j226vKKpgidSswjgt/UmW1Vw//4Z3gu8qQGDQGNtTUQ4ILSUeOERtQvJnq1RAtCCSCpq\na0mkxhobxFcQKDQSSJM2+xevUVWOh3duIX1FHDm67Q5ZfwOsC4lmUbJcLrF1zdZGP8DesgCPCirc\nYc/SWqMEGO8bSoRqoM/tNfRuVZmdTqfrQqMxNcOzMaenp/S77TXUS2tNVVVIKTk6OgIfEvXj42Pe\nKm7T7fbJsqCwv7m5uQ584lg3RbSEOAlHS6sV7A+ttU2ie56nGETBtA7waWtriiLso5vtPkVuGI1G\nnA6HVFYQxz2iOCbr9UNi6yUOKPKKwizRQrO1vUfVCJU5FwLpJM5IE43C4+UTKkGe5zjrqSoTKDhW\noKOU/iDi9GgMDrQIHOqyLImyFkorTAN3XFFbZGTWCVOSBM/PEBA19lKJxjUBYDmfMyxLpmdnzTyM\n2NnZ4aPf+WHOTk4p8pyzszPODof/3y7If8mhRYCxq6bwpaUM8OfmM1ZKIRpBGOtDsUudg+utHuMb\naPYqeA9drybhfheS9rxis/M+8LGbDoSUEh8aldSRD8m0CCq8+JBI+lojcDgR4aSmJKISCV6nkGyj\n0y6HJmVRlkymjrsnJcfHU8ajBWVuAEmSZDhrKco5iBhjg9+yUhHGVlT1EpAY64jUKrkTDcxVoOlg\npaOwZ0hpiLVCtSXG3sKPYX//JnkVEbsRqfd87P1/hy9/9S8QiJPdAPHWdxG6Biy3Hv5Ndnd+AaFv\nMH7saXdn2OqEj37k73L0+O+BiPDJ25STxxTLDlubMdAmoIXz4Fctj/DkHD/qkXVr7t35EDeu/jTP\nXPpJBheuMrt7ti7krdAwcRzjhEVEEo1ef5bOOCQxW+r9iOzLxOZZ6qO3WNT/gEX1K6Tx38DYTR7e\n/Tl2N36JTj/l9CJsfvgTvPJ//QpX3v/fQPSbfOYrf5+e+m7KRYRvS7a2BswXo3U2HRLkRq/h3eNc\nPvzuxFkp9QTiydfbaq2KMyto86pb/V6QVgiaDEtcsJiLoOqkfPadW1x79jK7afLHLZ8/V2N1/q3+\nvRrvFoZajVWS8m7I73nBKLxdJ4crqHdANwVP5DwvA41zNFpbMVZVSVZX66LrcrlcQ3OzLCNOk3Xi\nfp4nK4Rga2uLOL5PVdVEUYxSMdZ6jHFrnqzA4wRIpRHKUdsaITyP7x6xnC4oSsNyXhHrAPueL5dI\naUiikKxFymO8oTQl4/mYTmcSuq1C0+p0gs3f2kYMnAkFW2B93SEBh7IMCLHRaLSGZyutEEIBT4tz\nGQvWgbGEgoYLCLxQ/127gePde4lyNZ8ZvhF8XSW2DWy9EdLElQgZVkOsI3AGh0A1Bc3z3tDrdbMq\ngIYJ0CBA3jVPeBcknSeF1KeEwZoizmpIKUN3uqHnLZdLlssl3W4XndYI0fzMNWg9U4e4TeqnoOHn\nx/n/ne9Yv/teneeJhwLEu3Dz/5zxLZVYOxc4WcOzIcYYDg8P2dnZ5u5Xz+h2u1y5/Ax5VVAWNTsX\ndnj06CHD6YxuV3J8fIwxGplKJvMjwLGzs0OxLLl79x5bWxmT/Aideb72tTcpcsN8Pufbvv1DlOWC\ndivFmZqd7U0qLxgNNoiUZDmbstHv0+tuUO3v8dpXXqfIF+SLOWfDM6YYTh89YDw8ZTgcUpYVWoaJ\nY01F7QVYh/UC4z1ITVkWCGcoq2Bu30pjOp029njOjRcv480Jy0XJclKTJAqlLCMzpK0s9dKQzyOK\nZYpKQiB7984drt24wc2XXmY5n4NdsD/o8srnv8jnPn+bH/2xH2f34gZFYTg6nPLo6ICbLz8PosJ5\nhVQZ+/sXcM7z6U+9yfve9z6+/Mpb/PAP/atEqs/u9rNsb27xpS99icVixsWLu4zGQ65fv0Ge52wM\n+iyX87VK8WodBTXxEiFzlNIURcXBwWMiralNyTOX93nnnS1Gj06pFnOUEJiqIpIDlrMxGkesNdZ7\nlsslpoZETZlN5jjXcD3xSNGmLmE+t9ytDnnmmWd469ZjUi2Z5zm7F54JXn1VQRr36EcdSmlJE0Vi\ng/3AcD5llFeknQ5b2z1Oj0+58dw2cStmdz8jmi3ZvXCNKEpIoj7Pvfg+bt9+zKtfeYPtbk2UbCFV\nRZxaXnjpMhubmvd/8Bqt1LKIa3wMW90uaWxZLB6xnFmk9HRaGVJ53nzrPlm2wSI3IAU//pc+gVuM\nWZyeMpss0cqT/cl4md/UYW1NHCU8c+kCX/50ThTHbLRS9i7uMhpVZO0MITTjkyWdTgdjKmpviYVr\nxIcM3gelaikkrbTNxYsXefD26ygROG94T5qlJEnC6HTE5VWA1Ih2rLhQQoZOpFQ0NlUzrA1cUOld\nU2ldbdAOvINz4h/CrX4mQjFfOIT3KCFDsVZKJBIhJLjA/0+yNt3eIHSerEFLT7fbJk7iYCuUJSgJ\nEkddK5yOaLVS0gZ2XVXVU+qjQrDuKsMTm4lV93qVhAeLughj3Nr+KljRtddcNQhdtzQN984aH3yr\no4jDx8fr5D1JMrwPHOOg0GnXh9HqAF79zhX/7olgiQRfrQPXFV97JXKTj5eMRyPG4wnz2RIZZ8RJ\nQrvVw7Y7FEWBrQ0IFTqDjQdoGgdYeWErjLMkLiR+OkqIlEdoAV4ihApq0mXNYrFgmRcI0Qo6D+7J\n+wh+yAbnLAjfBAKhy6iUREcK0Xwe1ro1D241pAyPcU4ACiFDETcUPArqakld52jpSDNNmnVptWKy\nJOHerT8bu60/zSEDZiqsgYa7hg1FhnV3WYqQADdd/lUnAp7m3oX7+iQQejcPbvW48yNwQv2aN3c+\n+XHN6+ItQQ1a4qmRXuG9wMmYUmhKoalkhFcxJTFx3GM29kyXQahyOCtZLmrq2uMIPGLnLFiLUOBw\nCBXg6ciG/+gs1lZ49eR9PdEVkNSFx4sSS4lxFULEOBUhDezsXiURHbb2a4SYUJSQNEhKXAYuAlWC\nmOD9jKq+i/UnHJ/8Mns7f5eNjSvcefAHfOjbPszpyZt87Lv+Op/79H9G5b5M3PF02i0gQfg23iWB\nJK4rvGoKGnGBiwzPXfsE7fgn0XFCZQu8aBwHlMJVddj7lEYQHB1C8G9xxmIqg1Ka+w/u0YlrNjuO\nN06+Cvw9pPwaZTVDupKitgxH/zPt6K9SdAHn+fC3/xST6kdotUY8uvU/MS1eJ02vUy5iOnFKHGVB\nONHD15FEnxp+3bFewZW/Psh+wp1ezSfhnwTbIV55Ugg6Pz/PJ3ReeZwCvIRYczQdceW595NsbCDU\nNx6Q/3kY5wtXzXcAEGKVgKzWYtOlPndPz/vJe+9DIuhCTSuSAToe/NubZDspUVGCIyTQRV4wW85Y\nNoWbFdprhdCKk2RNgVolnKvXrOuabrfL1tZWI3YVBLukVESRoK6bIp0PVJ5Vt9Z7jxeO5WgRkuI6\nlMCdsYAnLxbUZkGWaoSvcLFCIpgVcx4e3idSgXdsjGdHQSvrImXYY6zxSCHWGhOra18VambLOZPJ\niMl8itQaLwM023mPMXYtzmVt0O+orW/sQkXgUze/R6mQylnrMCaogCu18nx+8kkKEeDQEHq4QoYz\nzTVoPJxHaUekI2Kl8dIHv3v3xMe8rmtUpJtiqETwxIdciuCYsapzrTvW6uk59e5C1/k58/Rc1Eip\ng1ipcQwGNFZcC5K4RyQCalGYVbFM471ECb3O5Fdx3lPXA0+dFefX+FMe2+fm2R93ze81vqUS61WQ\npnTgxx0eHnLjxg3ufvWLgQvg4OrVZxkMNllMJswmU0bTOcfHY4bDIZ3ONu12m5dffpnXXvsKH/vo\n9/LKH32Zfi9wutI0ZXO3x2g0IU1b9Hob3Ll9l+lsTrfXYmtrwNHjR3zgIx/jnbt3ENIznYxRSlCb\nnJPTxyjt+fxnP8WoKMmLglYE89mUKIqwVUnAnmukdETOIYXAC48XsKiqUPHEEkcRMo6Zl55W1mU0\nWyCcodu6gBApZS752p0Drj23w6CybGSaTjum3Wtx684hf/TVt7iwn3HpQpvv+Nh3g5IkOoGqpFyU\nJAgWJydMT4d88XNfovRznr12iReu3eD6i5fJ8xlZK+a5528wHo/RKqGuPD/6Yx/k/r07vPD8Bzh4\neMLZ8IQvf+kVdvd6KNmi25Xkec5gEHawsiyZTCYsFjNGZ0e0spTr169zenYc4C5lSW0mXLx4iWVR\noeuELE3wbsbe3g4f+fCHOLrzkHaUEHuL0oJYQj6fgNaoxmplOp1TlQZvPLPplMn4FFwOokT5AEuL\nJEyHQx4LwfPXrvHw7ttcuHiZu2+/w0svvsw7b7yKkGGeSR2TaUVkwTlLu9vjcHbC/bsnPDNJqWvH\n7duvEXcFpJbnPrDLoHsZay33Dx5z6eqLDI+PuPnidT7ygWeIYke+XDAZzen3WuzsXCVrGc6ODnn1\ntQOOTh+QJCXf8dGb7PYTfNxheHbEfFkgMcQy5bXX3uKrb7zFo9OSzsdrXnhmi8gEz1aZZBST2Td3\ngf4JhreGclFy5eIeu5sb+HlBN0u4euMq9a3HtPpdTO1pdzvUZUW/3eFsMqYvJKZJxJxVTVIn6ff7\n9LtdTFXTzmIk4TBLWhFpO0WeNbDFVUVdSaQV6ChGlpKqDJSCq1evcnZ0gveesqxJGgEVAOGCYFzo\nloaujI6CwIjzbg19CsFAjTc1QkniKKGoSlRz8LSSlNbWBbCOt7VmNDzlwvIilzsJtffEjW2aMSYo\npvb6ayXqqBEnW8G8V1XpVQC4Cor6/f7a4igU9AJ0LHCr9RrdE0cp3W533RVYLBZrUbZVoNJpb1OW\nJd1ul73dfRaLnPF4vL6O1aEThNEaCL7Sgfs92GyS+2CbtSyCT3a/3ydLkrUXvTGhO31wcMDh4SHF\nOMDGK+vodPsMBjvs7F2gqCvGCmQcIVUElNS1Qbig1l0LG4I3HeNd8GKNtEQrh5SOqq4wdUA3leWQ\nNGk8pj0YF9R8l4tpcx8DqgZvsDroOQgfAs+iKNaHcNUUDMO8sOfuS4C4R1JhCXBpLSStNAiyCOdZ\nimC9dO/eXeoyiMD1ez2yphDy530oQLpVkUkSqZUdm0Q28EBBuHd1M2efFKIaYbgGxiilDMHa+eBq\n1Q15Vwdt3e1eQeaFCOiBJoByPnC8RcP5kwTOoxUah8ZKiYlTjE4ovKJUCSJrU0V9Zkay9DXv3H3I\n6eEjHj4+ZXo8Il8G+KTxDhlL4khiLU3XRzRQc0MUaZwNwW+ctAAfgmBroQqwVCqJ0RNkArV3KN9B\nKc1HX/gZOq0Zv/tPf53l8r9C6Ak2f8zodAb2CFwHCJBHohpPQrF8zGhxyPZ+jTVv886dJRcuXePh\n+BGt7BJnpzU/+AP/gN/5Zz9L3PoDTo+/QpZ8OxCKHADYDTwJCMP2dpulg+2tn6PducKlqy9zNL5L\ny8l1kNnuhuKWcQYv6tApE6GQ4RBIHYMDJU6pa8Ebp7/EYvSf0x7MkcsPkfT+LSjeJO3e4q03/lPu\nP/xZ9l7+Lbx5wNwaTPVxZPU7XHrxVR48+Dd5PP4v2d74BcrS0ulssFyU0HTpgPfsWPtzf74X1Bto\nRN38OsFeDesswoo10maVOJzv3q7soFadLIlEKIGIYq7cuE462IB2hpLfGmG2kGK996+Ev8537eBJ\nh+/dxYXV/8+v5dX3o+Y8883zQ2FVIUREK0tpt3psbGwwHg8Zjc8YDk8ZjyfnBKX0mio4PDsjyQIE\nfCWidR62r7Xm5ksvMJsuODs7YzyeUFWGurZBx8GHdWlLGwREMcgEWhspxbDEOZjMFiAUlSmRbUcU\nC9rdoBbtvCPJMvrdHkooxuWQWXXCtBQU8xKkYXfnCrHyaJ3inad2OYhQuK9NsIC1rqYol+T5gjdv\nvdGg8AxVlSOEpaxyHMn6np+/19aBIgrir1i8g9oGHny4t08oXav7fX5Ua+U+hzQhsRYylE+6UTif\nsiSlk7WY5qO1Snxd10ynU/CCvtug05EIFSEbf8hQvFq9ytMFKwgNikC1Xr2fFdqg8drmSZedZv/W\njX7GCgWRpillWTIajajrOqCY+xtICd4rkqSNFAGuvqIAnZ+34cr8uhx3HvWwGu8u0v5Jk2r4Fkys\nX3/9dV588Sanp6f0el2W1RlRrHj99ddRrYSz0YRON6WTxuzuDuj0Bwg2efDWEc8++yzzas79+/fp\n9Xq88847pGmCUlDXOQcP72LlJd5++22K3FCVNVIJ2p2I3//UPyWqCpLDUz756c/inKEucyIdFGwL\ns0QZgZaKs9ERBkmkJKasSSNNXQdz+BWkQUpNpwlEiyrHyAjTUOV3k4gsy0hbLW49uMvz1y5RE5FG\nY1w1pTSKSSG5c9/TWYCXAAAgAElEQVQyd2f0B8e0WjWFHfJ7v/NZvnpb89bDMd/3/R8k7W/xhT/4\nNM+9732U84qvfuaPuP3GG5iiJFELdrpdtgeauLPN1Wd3uPnsi5xUnla7TafTYjgcspde5OR4zO07\nt/mDz/46//6/+zMIHP/4N3+DyXjISy9fI412iaMNXnn1D+h0I8oyZ7A5II5i6kYoLrlwgaPDx7z6\n6gPSLCbLEvb29jkdDjk6PKHT3wjKsR56/Q7Tac3lS/tkkcZXOYs8pxSONL7IoNuCKKW0gV9/dHiC\nlDoEsbYkL8ZsDmIuPbNDN9VMJrMm4YnI8wW2OuD6/lUODo9Bao4ePmJ3d5ekrTk4uIdMOmxc2KRY\nFCRxxMHRCXF/k5vXdxhwh3LRZpbXOBux2b3A1Ysv8fajA6bTKR/7ru9ksJvxYy98RxDZUFe4/c5X\n+eQnP8srr7zG8GzGv/PTf5GXti/wR394m1//rT/EK3jp/VeYlX1++1f/GXfvn7C92eVDN69x87ln\n2N5qsXzlHUwtee7aRXCOYjnHUhL3Eg4ejLBR55u9RL/h0drZIdKHdJjzHS/s8Mobd5nQ4u7X3uR9\nL36M17/0gNncoEWLD7zveYr5CbfHBl+MyGSCXRzhtm5gWwN83CPv9fjff+N3qd6csxlljO4/wl6/\niBI9draf4/Fbv48vc1pZhMMSbCKaancFMtZU0nL1whavLj06gclMsH0hJMem9sTaI1iCHeP9DlJV\nT7oWiOCVulK61oo4izHeYs0MJTwOgxMS4y1OW/R2h/5GxujoiLODA/Z3niHrb1E7T1FVtNIWUaRI\n4ghjCoQMnf7QSe42yZttFLkllatp93tB4ERJ8mVNWRaknTaHh4c4G7O7dyWIkcUxOkoYDoeBs5aG\nanppQjLdzlqho11VRD2FGdaYwlDnBcJAHLV5NJ2y02vRbVS2bVVRVyHhlEgiFVHVgRunROhqamex\nVcnBnWO8MMxmC5yxCKE4evS4oYaUtAc7dLt9ojSh3etD0qJKNZUCmdfEPsH6oM6dxJJIgzOOe4fv\nUC5n3HzuWXb3Omz2OnhT47xHRREdOWU8mXM2meFExMnwiEVR0W53WZYjTodnbO3sYGSX46Mh2qc4\nGUGRI7IY7yMi3SLSikwkbOiMaFOyXC6JIo2OQmHREny35/mcVrYZvKld4N1Px5OmEBHRjrtkWRYS\n9U7wuF8Wljr/1imSnVcQXgVBzrkgICVkgM7DmtfurHmqi7DuJggA2aACmoRTSqz3wXpn1Yl2K2uU\nCLdCpyiJ8Q5hG+EbBMqAxocOtYionKBykpnKKIWG1jYu61GgMCoKnSBrODk+5NUvf4U3vvQqxWLO\nZHSGiDt4JZAyIlURRb1EKSh9U3iTAuc8kdYkSYu68nTTFsu8oKpKWu2EIvdoHZHnBakoMKLCGUk9\ny+ioi5TL+3z+9H/k/uN/j63u9yL5h3i/h8oGnC0/jz0TDLY0iWoBMda2KZZd+t0r9Hov4GTB6ejn\nyHp/h72d92Nb346k5uzsEXcOJN//Pb/G2299mv/7t3+Wsfks1y8KJMeM8jMG2Ud5dHiDixe+De9f\nYK/9CbLOdXw849HoS0BGmrUwxqIbFd9OGmyOlDBNwS6mdsGaDu/QKFQv4Wr/g4z4D1G9a/Ti/4cL\nFxLmZ3c4qe6ztbHk1q2fRujfozt9Bxiw8/42Nwa/hfef4kvv/GtcubLF4fRt4njOculZLJeBF96I\nZwfo6tcHvqt9+d1oh/M8/Xc/5jz0c/WYFYpnlagIIZ7wjQmJQteJoGzvQPoKPzMMoj5b6YCUPxsf\n6z/tsboP5+8FAF6sBd5WCBHWEF731GNX92qVoHPueYhziZcQiCYBimJFUeVIrUAKkixlg6AbEsfx\nWhm7qiru3r2LjkN8vNIaWTlhSCkpy5LDw0Mmkwl4yWAwQAhBVRkmkwmuMuA9STvFeMNsWbOz22fv\n8i710YTZcon2CpRislgSa491giwKVmOisWFUSUxVVkRC8eDoNljodjYozIKT00dsdLdJkz5KaPKi\nETxumkzdbrCGWy6XLPI5b92+BXiGo1O0FkgFQb7jaQTiin60XOYoUa/fM4Ru/Yrbbq2jqspGiIsG\nedGgrQArm86yCN30VpYg8LSyjO0GpSabYmikNDoLDQyj4zWFoqqqgAayFi8kUhCE7WTQwbDe4m1A\nlEoZrFXPQ6nPQ8qFeJLIvhu+7h0IEWDvSmriSLB/4SJHR0eMJ6eU1QJwZGmHKArNPxXLgBg4V2wL\nOZdcr28lnri/rF5rrZvC013u1WP+f9uxvv/wmO7GJq+9cR+tNZ04QburVO7TjIdTXtx9mcrWtOM2\nUnguPXOJfDTm3r0lnU6LLPPcu/eA+XxOq50xKcY8vHeXzv/L3ZvHWJbd932fc87d71tq7626Z3qm\np4czQ3JIkSJpUSKpxXQgy0gARRsSB/nDgeMg+U9/GEkQxAGcf4Is/wjKYgGKEkTxlkBr5MBOIpmO\nuEjchrP3TO9de731vrudJX+c+15V94wsyoIlTg5QqOqq17denXvPOb/lu/R6DPo5b7z2Hd58/axT\nIIQ4gy8KQas1buqVop1zJEr6iqdxSBtj8R2RKIpQVhPIAIul1V5kC1ht2HEcsWi9l6ZxitBCpiBQ\noHXA6HTCtUzx3HZEWozYVAGiv0l1WpBtbLG1tU7o7vLg3ogvz1/jJ3/6R2iDdZ6+GbG503DzYcD2\nsKFfzdm5PODo/mvsPvsR6naXuT7m//nd3+dw1CMWp1y6UfG5z/0IUrWcFG8j1ICmkBydGP7ur/46\ndRtz7dkXeeHDn+DjH3ueOFGUZcGNFz/El/7Z7/HOw3uMj94hzSIu7V7i9PQR21sXuP32bT788kfJ\n8z6np8foxnLpoveHXl8fsrf/kChPOXjrkK2tLXr9mDB0JIkA3RAGjkZrSDPGhWZj/Sr9oCQMUpxK\nsDImikP0YoGr5sRpj8nRPsN+RhbAC89d5sKFIVmc+8NexTTaEMcxeweH3H53xNbuJRwtG2sDru2s\nUY6OyDcvc3J4n0U94P4i4u1bYz564xrXB4YNeUgbx7w1HhPmA0TSY/fZm1zcfY42W+fSpW16vQBp\nG4ytMFpy0izYn7a8szelFCH59hpxvs5kFPLu3h20bXjuuV1efPY6PQXPPZUzqBPKdsK9N15nO89Q\nccbNmxkXLm1wefM5cmupxgcs6oI7Y8cf7htubv45Lcx/gZH3huj6iEXZkvbXcCiyXo/9/UOeemqB\nVAJrPYR4NBp5n0zxwHPunUE3Hjqs4gipFCqImM4LlANloS0rIuPhqUEcnUF83Jlv4XJDDQi9erHz\nqJVIQathenLK9qUY2XWipfM2WtZaLIagE6F1ziHxIj7unHKkozs4WMkxLX+AcxCohDBO0a6zhyqn\nJMM1H5wpUCoh7MRCRHcqnkG/fJd3maw451Yc5mVw45xdHRZSSnRrV5XbJZ/NH9ae97lYLDDGsLm5\nSbXw1ha6aWnnlYde14ZI+Sp627bEUbSaR4+C9BVlseROd56okfSqsIGQlIuFR5iMxpRNwWw2wwtO\nBV7wxwm2L11mbWuHJM09V9Y40C2BS4iVIkhj5vM55bygbQ153kciaFtNIAIaFJEKyHs5SRLRVNor\nFduWqqlI05T1dUWtBUXZIuozO484jtC68T7di5qTkwlSeCgh7uze+go6COlI4wBJvBLbaavS2ytZ\ngdN+rpd8weWcrw7prmN7viOklPKd9w/CkBIVhd5CMvBBl+9aOsIgxPmIy9MUOlhnFIaPwe6W6zEI\nAgzGi1J13UC9hBieg6cun3egE5s7E0sCcMb4wB+FQ2CcoDWCmRG0+QZl1EOLkDYc0NgQF0YIFaBt\nxeTolKZacO/tN5hPjzBtDUIzb8ZY2+kEENDUnr4ipOq6nRakpNWWspqBCzDWe9bmPU+VKBcOIRRh\nEBMHmrhvKduY/vZVJgdvEsmag9nPM0hn4BzlzDCe3WP7yogbz4eMims8PLnD9Z2UUfmQ9fQFjo7e\nJnnqeaRNvZKD+CqvfOdf4S98/HeIo5hL1wYoVUPYcNpERMPrfOEv/vccH/5vVO5VElGQBQWP9nJ+\n4if+S05Pfw2HRKrnQAZYe4irNYEMIUkJo5iqKnHdXmRar8yuZAbSotu6Q20YNBrRRNw/+irjmeR6\n7z9B6lPeOfxDnHwDBBydwide/p/5zd/5q9y99yIvXPtV3v3OCMVVdvov8MKLv8a3Xv37vPTiT+FD\nqKDrUgs/59gzTPL7jCch3O9JGt/n9X/UWNo0SiGRQp4rqvrgO4lCrPGojUiGhETQgog/KOJlZ4gr\n5zrkkfDog/Pzdj75sKZZfX3+MyytLf2QwiN2lpxcKSVJ7JE7jW7YO9hnPp/TtDW93ga7l9c92qdD\nqRrjNTEePHiAtobT01NfFO7sn5ZQ8bIsOdw/oKq80GVdlsRRQpZl7F66CKFkOp1xcHiApmX76hrX\nnt0lyhTRWk7RLMizDGNhO9xkND8iChWmbpFRiDOOsmyYTwqE8HDv2WQERlA2NWmaE28moDRStljr\nLUKzLKGuS4RwFMWMtvXuD+PZKbNitrJoNNbQdgKZ6ixc8FQ0a2m0XsUEy/kWAtIs7pBzHjm17JAD\nWOuwHo3t76EPaEiCEOEsaRSRRBHDXk6/6xAv59w433yI04S14QZJlpLEKWme+eS1S6SFeHxN+eLq\nufV2PmEWwuswGI/iWRYMpJS4c/Zo/lnzdlzLM2Bpubi9fYEoESyKivv3H7I2XGdrawcpvPWlUoB6\nfM2fPbc8tl88SV9YniPn1wOcddq/m/GBSqyPj48Jk5zBYMh06lWn/U11NE3NrVu3yPs99g72uPn8\ndcbjUw4fPGD/YI/7D+7xnVdf4fT0lKIocEc+wfXG7Y5yMaWqKuQfo8a6CpTPVfaA99yMJ/H7y89n\nG49gSdJX0nunKbk0U7dY5wV4tnoDbFnRAiqAOIlwaIrFhDiEaVlRLBx13RD3HGGYMBz0SaJ1Tk5O\niNKE0WiMDfsM+hsM+utcvfo03//JBV/79im2avjIR14CfLUpDDKOxwumkymDNEcpxe233+ad+w9o\nneXlD99gTQ4IgoCXX/4Yb775JrP5lCzLaHXFo4e3uXHjGmmWkmYxk8mEwWAAeN5nHEerit36+voK\ndrQUNVpCfOzCojuvYGN1x+1oiITfkHTTUDeafgc1DcMIY/TqkMuyjJ2dbTY3+wjrocIiSJjMPD80\niiLatibvxRRFhXUZQkmMEwRRyObWOnEoUa5BCY3WtfdxFJbZokSokFprLq5tsrVzgZPxiMlkxMVL\nm17YprE0tfDwYTknFJZ+L+bB3RmXLu6QJxFYy4sfepH7D/d59vplnrq2Q57GXNq+yHqQ4KgoqhFR\nrGhNQ7+XY0UPa1omkwJd1IhIelXIskQw+FOvsT+rkfQ3qNqUb755m404ohGSrY0divk+zjn6g4jj\n0yN6vbyD+HifZF1XqMxSFwWmrgiTmCRNIe+z37T0wpDatpQnEy5oC7UmzTPiOEZ0FUoReGjaMuCK\nidFtjQsdgyxlcy2nXBQc3L/F8x/5GGBXEFVrDVZXBJFPHLXt+NTKPzdta3BGY7XuRM4ESIXuKsbG\neY62dAlKGtJsSN4bsKgKHu4/QMUhiIzhcOitvoyv2J5B8ljxqMuyXAUebdsig4AoDKm6IGZRFEwm\nkw7q7EjiCKMbwg6iqyTEUYg1msPDkxWnuq5r6rrGOW8XNZofARLrLEVZ0WjDoipIe4OuU6wxTpPG\nEa4TDIsCSa+XYZsatEa2Lcenp8ymU9q68R7OszGtNWRrG5hGM9zaYm19nWeeew4rwlVhsyhK5pM5\ns9MTojimMiF5lhFHEYcHxyzmBSCo65rt7UscaUMYRqwN1ghEg7OgTdN1lCxpEhMnPfYOR92eE3ix\nw7r1SKEkJlApr50eEicpYRBSl0v/TN8lUwrCCKyrKSvvLR6EMeDnTGtLWbaEoT8X6rp+TADJq4iH\nSOVtypbBjIfDaXRb/xmvyH/BIYWP2KRPpo1zWLy2RUhXKOiCL2u8RSI8LpS0EsXpEFuu++x5+LwH\nDr6EOWqjvX2ez93PuIPGYxs1CtFRR1ycMp83zBtwaUjtFK2BRd0itSMIDLppmM4rpuOZXwNtV1Ay\nBtsZwhpr0JVPJnRjEGG04m9LEeDB8ZYoSjpeoOcfF8UM53xHLQoz6rYmtBLnJKPjE25e7fH63d8C\nvsTJyetsbl4Ae8jFy0/xzt5r9Dd7WP0c13c+wbx+A9PAwfwuT1+/SUPpYdfAeHGPK1de5ujkf+Wz\nn/qvub93i4WuCHshIlwwrUvmVYLK/jIf/wiMj2GQQxBG3H84RAWbNHrO7vWnODp6l1Ck4By6tujQ\nIJ3z8cn5bq6LiJIcq2vqqkAqyXDQYzGbEIcNo8nvAH+dO+8+ZGvtGlu732Fa1VCEnO4L7j20XH/m\nr1DPf5GHzS8zHPzbNFXJo9khQQxbW1+kn+wSBQ84ObbdHIsO0Nk9Me6s23rWaWUVQP9Juk1PChit\nvkZ0lJ8n4jugAYQzRN2artvOVjBM+KNT9e+9cZ5P6q1Ug5V68/vFtOe/fr/vwXlBM7UqgvkOtkWb\nhrIsmM8XtK0hDCLyrNcpZ4erwvGy+Li1tUWjWyaTCWVZ0ratFw8uipWzxLJ46d+HRw4tdT7CIGBz\nc5PFYsFoPmJjc50ojXFOQ6CJ85AwTDkZjVCBZHN9nUU9R+KL6qCoioqZLBBCEMcxbbUgChOM0yzq\nBaPxMdIGiD5IGWE76st4PPYCx1mn5eHcigrl3Tu8tpBt/ft39qwZt9KdEN5reSnseNb1XaIHfAc4\nCNTjKAJDB5f2nHNnDVGgiIOYPI7JkoQsSgiU7Pjc/v/r5TPQWZEuxeeW0Gy7ei7OEs+l8rhbqU4+\nnv+45X6/0lxyjyGd4ByK6RzKZNVtVp4KbN0QZxUnJyfMZjOiKGE4XEeKCM51q58srrkusz5/7Sf1\nPJbr4LG1/v/XjvVaP2etlzE9PfZKt6ZlPB7T1gsQmqqeUdUFb7z1Ou/eeY3j40O2BmtMp1OKYsa3\nvvUN8rzHaHRKmuYr4YOmrXDW8+mW1YvHb4Ifywk/f5PPV0SUUtR1/Z6b+P7XOuNQIs5I8t6IvoeW\nEVVVcWIdaRgTyQhnJwSR55Rqs2Bj6Eh6ynvlJYLjoxFhkKFkj+2dIa2RxEmKKUN62UXeeP0hIsyZ\nTuDTn/5RPvzpiovrF0lUSi9dQwjFf/MLv8ijgzmz6Sk/9ZP/Gj/6Y5/nh37kB3nj1jt845Xf5a3X\nv8Hf+Pf+OlIGzGYTfuZnfpavfOUr/O7/9WWGw5wbN27w6NEdhv0BVy9fY7FYeD5lmmJab51Q1zVl\nqZEKptMply9fXlkn5HnueTPpgGp6jBOKKBRgKlxToAJLW8yQtiFLe8ShWi30VjfEYUjblnzyUy9z\nZXeHjfUeGH9IlI3lcr+HEIr5omRt7Zjp5JgXP/yCT9pwTMqSCMWVKxexzYzrOxHlwuDaMZWOWShH\nbSLCOOLZmy/x9HMvUlvJu7feZvNSDyXAtg7TZvzfv/MVynnLF77wFGtJyM/+lc/x8OXrgOPmlU16\nWcKlfIvPfOp5pvMDJqNTpO5zYXCVEYekac68aCiKY7QRNO2cRTEmSjKUirBR3xuf6xnbiWOQhH+m\n6/FPM4QMkEHGyXhKbyMDoQiikGrqD5PBMEFK5+FEYeI3WmExtgVnsa3nuwrrO7VWhSADXBBgygZd\nNyjtEM4SBNGK52Wth24K8JVTBxJvlyW7LsyglxGqgqor3J0tW7s6FFQgfWcWgI7LaXx1GqEQgg4e\n5f0zvQCa9JBgHDjvrRinfYIkpdEVJydHxGnEoHeJ9eHA7xlGg4pWB5mxZ92EZRCR53lX8PNCSVq3\nneJ+u1IdDUPvBrBMypVSvti07JC2LcPhkDiKOHi0h+o672VV4oxXCDXSUjUNdd3gpC9oeqgXONcJ\nfWG9f6WUOGNoytKv98WC+WzGYl6ga39/BII4CMnjBOKYMEm4ev06Ko6IIq987pMlydH+AfNpwcZw\nDcIcRUIQRqyt+f3dWmjaliwKVoGKkhLpHEp4JFCLIZSRL2h2cyiE6Dy7NeXJmMW84MUPvcTx0Yjt\nrQ10a/3fi3ls//bPkhdosq7FGocxNUmS4+zZfi6QHRTRJ2PnC7Dnq/xLEbfHVdO/94fB0hp9FmgZ\nrxeyhNkJAUhvdXIGrfNr/DyXbflMu3NdruXZap3FuvfCCLXWqCh4LPj2UFWfdp3oBdIqpEhZ1JpT\nETEixBYNToY0ytC0BmsKqEuaquCV1+9wsPeQom7RMsAlKbptiMIapx3W+L/He9IKWqewziFkQLDk\nlEtHkvawFnRb0+rCP8sk4Py8JOEQ52bEQUpve500eY3F4a/Q9t4gT9ahjVBSMxkteObCf8qg/9Ok\n+Te4sr3B3XcPOV38n2B/nXfe+sdceT7AyRzlBpzuV1x7eo6U3+CfffU/5vs/8bPs7z/H5CTlcPoN\ndm9sse6uM3p0yv5+jKqhoYfNI5rwNiIZcXHwIifTV8FBZHcJnKRxJ1TWgTXIIEQ3DVEYEiUpieij\npKBuZmjdUBSnjEYTELD/7t8G/hGH87s8dX2H/fEfsjYMyNQPM937CgEGkRZceuanOXn07yP4ZSbF\nLzBY/3miQcT8BOLsKugeuQRk5z/dCYy9n/jYagg8fPdcPLb60RNB8ne75qTwBcZVEiEEQgpaL+JM\npWteuHGDmx9+iQY4HI1Zv7T9XV37z3vYToDuSTi4FPI934P3JtLvuQfOrXjby3W+3Jt9A6mhqhZM\np1OPZIkSer2cLBuskuplsrVUh15fX/eilHHMbDZjNpt1VpvnimCmRSA7qHPnUmDPzu4kidna2kRE\njouXLiIzEKHEKEu+nhEGKXvHhyRhTJZlgPUuPcYL7la6wTTeXg3896IkwVjL8fERi7CkWbSYpiUJ\nUxzekago5oBD6xbfyBIcHOxhO7FMY5diZLJL4cVqP1zy04MgQEiBEwK6oiLQdcYtUiqCQOGcfKxw\nKTvtIKWU95W2ll6akMcxSRSTRaHvYIuzLq9QEm0NAQ6h5GP3cVlkPkP2n6NjiDNawfLZEGJlkO49\npB0IvJ+0W+7vTq4SbI/y84n76vrn8q8gCMgyv8fOpvMVzD5JMvI8QIhzNnDn3sf5ItDyeuch4uef\n5+XnlXWc+u6RJx+oxLotZ7zxyh+u+ARLPl6SBrz+6jdRUdxBswx7D2+TD3pMx0edEmmDcCFNNSUM\nfHUnCLznZl3X2KVBeZcsP7nRCnhskT95s85v3kvV3mWX68mPVQWPMx9Y6TxZ39uTGCTen/X+aMHO\ndszaWo9hosEZmmpKnEa8eH2HMEt5a++EYjHm9p1HDPJdfuAzn+Sd22/x6HBEOoxJ1RArEn7hF/8O\nX/yxH+ULn/8xrG0INiuOHhyysbnJt7/xCgf7Y6JwwLPXr/Hqq98gjVMu7GxC6Lh4bYuPfepF3n1t\nQtB1IPr5gKZuePFDL3Fx+yrT2Slf+r1/Ql1pDg4OeOWVb/MDP/h5lFJU1YK6LBkOel5dffsSe/sP\nEUKwtbVFURQ++TaGuq59AhXE9PKcQZZgt/r0VEgWtASuZnd7yMIFjGZj2tYrz6ap7zw+ff06H/7w\nC8RJzWDQw7Ywm83o9TOi2PN2qqahn2c+KJcCGQa0VcO81Zw+OmB74yqJ1awHDc9fzDgoDPPWUGtL\nbQWqN0BGQ/prF6CsubL7LFG2YJD1yOKMt969y5d+7w9oasGVnWM+9vGPsjXYoP/MBTbX15lMphST\nkjRaY2//IVbWLGZzimrOMNnhdHyX3d1d1tcGLIoJeRoRyIQkvEBxvCDMdgiHm5TtlGsX4ZmdmNNp\n895F8z06nAuRyYDDoz1cNSfK+8zLgj4RTVvx9DMXeOW1b5GlFxBC8cwzT5N9/ds0ZQ1G05QLZAdF\nzPoDFiJEphnTw1MSKZAaEg1mUePWfScW52G5cRiB8sm0EHj4uFS4AMJIcePZ64yOBMXJ0Uq4w2oP\nWxLCoduKTDrP+1ECWgNC4pxFCWh0g5J4z2N/xPukU4CxAmUEtku+hxtbjCfHHB8+5OT0CBVBPTNc\n2NnA1JZsOEBbjZChV+DVetWhXiwWKKXI89zvTa1GxQm21YznBcI60ijGtpqgO6yrYo7W2vtAOofL\n/Hp4+umnUUoxnUy8RzU+GXz31jtYp+n3+8zmC5CCJOuRphmhEsShQgWeLxdIiJd8N+GYF1NGR/sY\n43nFs8nE33vrUNLRz2PSvEc46DFvNC++/BH6a5sQ+L+1qSrSJKcKC9bXBpSTCetrPSpi2qZGhQHr\ng4w8jal1y2Ctz97DRygJWxsbtE1JnEqqRY1zGikMUgRdt17StC1RFCPDmGvXhly4eg3nvO9oFCs+\n/Znvo2ka7t65x+i04OjomH5/SBCCdQ3zYsbGZh+J7agEhigIfGdcG0IVIPDIgqZpHlNql1J2aIMG\nbXQHXfSV+MViQas/GGvZWEdrjLc/VMrzpIX0cE/nOxfG2ZWdlnyfAH05zkNMlx8rWyTkY2fyWaHJ\nruZ0yaNbvq5qNeiWKE2wMqA1gnsHR6ACgiillSXGCnRd08xHtIuCR3uHNIuKabGg6bo1Tnvrx9Zq\nhFQoKbDaAZIgDNHaYZ1Ba02SZAjhdQ+aRmOMJUo66xcjESKgaVpi4QU1kSF1NebeCLKdLhzTBpo5\nYT8jKPoM0p/hwx/6N9g7+Rbj44Znr/Z5YfeLvH7nP+Ta1Wdpecur4KJ5avdZlFS8e/d3efXVr/Pq\nqwUXNvsIN6C/scVoeoipa65fuc7k4ZzReMGgn7F/uMfW1ZS90wc82nuEikekcgNFRiBDAllRUa9Q\nZV6gcAkDVdjOvz1NU5zLWV9Lsa5isNkyP/0aOxsRj25/nY1+xGTcMJto+oQoJGVraKfQNAlhdJ+y\nfQPCkvlpiawsIl0AACAASURBVNwGe2BAC7RlZQX0vvZaPI4kXCbeq+eK99pq/XHjyThwiaJ4EjJK\nCCIIvEbOxQusbayjmxKhzgL87/VhjO+yLwXbfJPJw3Efb9h1PshPIDHf0xkEP+NC4YTyyC0LdVlT\nVRVlPWZ0OqGqagZrG8RxSpblxGGCUu0qgQNWFKckSUAKsixbdZ7L0otperXoWbcPONrWe53Hsac6\nCemvV1WlR0vmEiG8rV+jG/ZH931MOx9jlSHOIiajMYNBztqwz4MHD4jWNkjCDF0bDL5gXlYFcZr5\nOQskdVtxcnqIrmviIGI0sty8eZPxeIwxhqZpVuKXo+mEJE87lFjpUaNhiBSChDPbLOcgDIMVimvR\nepTU8lmXShEHEXHsodyz6QJjfAE9TT2kfjAYdBacXvYvlZJIKUIBceDF0LQE5fzvGc2n1LpFuhCr\nRNetDpHKu500TYNQISqUq2LAcn15D++Obucc1ukVLW1J+/F5UEeZ6gqry8amT2QDrAu6dXa+weGf\nPyUj8mzA5ctelLUoCh4+vM/m5qZvEqTZ6r2cd0zxz+jjDhTn53JZ4F12rJ+kGn034wOVWL/11uvs\n7FwkiiKOj4/Jsow0iZjPKwbDjLo1ZFmKtZqiEmBbHIY0S6ib0kvrY4jj0HfIgqBT1rNIGfogmffv\nVi/H+Q71kwnzchNYdsI9ZK1dvfbJ8ZjlSHejlZI+UBPCf608XC21gr4Isc5b8EgnPZ9p1nLp8g7G\n1lgD77xzn5s3phwdjukN1lBhTC/NORqfeL64dJycHIHQJKEiUBFVVbN79SJxnHDp2jO8/c4B9+7E\nPPvs0xTFjI2dTS5sbdDev0e/f8bTXPIwFkXFl7/8/5L3UtbX1znYf0BRFAjhVsJKYRgSBX3Kcr7a\nWIyxGOc9xpfzt/TP1U4guofZmJYkCgjw8+OcIY0jTk6nOBM8ph6p25KnntpFKu/XV1UV0gX+d6gA\nIVxXtUzQWUSSbDCeF7TWK74aBIenE/YPBlzvgzKWVAVYYyhLy0y32EDS7yuy3hqt9pYSx8cnnJzc\nYnO4SRqn3H9wB+tqdi5c4OLlS0RhSFtXWN0wnY6pqpZFVbGxMaRqA05OTnn08JjN4ZCtDcXu4CpZ\n2kM3hjjOwTbkSZ/ZtCIILK0RFGVnKRQozyfW7b+MZfcvZRhrUGHKabEg0hrnQDmBkjE4GK5lBMpD\nuXABxpQoJRBW45xBWm/rooRdBXdBGFN3NlrCGERrkNavO6W6ztIqOjj7rCRgHE446qb0h08zBFl0\nfL4OEuQAt+xcLvl9vhIrBd6mSSr85TqlYOc7dNo2yCBaHZxNI8A5VBh7KPh8wmIxZTaZErse0/Ep\na2trCAaeItJxlmApRnImpLPcf+qmRJZQNyWt9mt9MOyhAg9ZqxbuMRXuJdzLWksURUynU2az2cqX\nuGkapBCEKkbJTiDNGYTy6rdxGCJVdwhKD1P33OuAqlywmM85PT313cWmXXGQsQ5rQ+IQ4izBBQHD\nNCcKY2QQIJTC4uGpcRgxHp34uTCavJdC7TvRnvJsiJMAUxmM02RJxGLm6Ocp4GjqEnAESmCcIlIR\nVeO7/WmSYF0LHVIpwtE0Df08ZW/vAOl8lf/6U7vk+cTvxUXln0n8AbxYLDqBLW/P4qwmDiOyNEa3\nrf+ee68wi3MePeBE93zoFq1boihECDqv0e/9ETlInOeSO9MQBAptNI0xOBVQBzkOS6QaYmpCW9Kq\nDQ/j9pm270oI6AtNbTVWBjQWQhFCLQlJMPnMc35rhxSKgIBABgjXEokQbS0QYixYG1C3DaqyzETC\nyKaIbJO5rnn7D77CvA0YbG0wN96yTljH/HRMPSsYG9/9mi0KnG2wCw9XlDYljtd84mE9/FgLg9QF\ngZRI2dFLHERKIVyDaUusa8DFKKkwzhDHiiiWtGaOKHZw0QEtJYeL/wDKPVwqkHYNF7RYnXBx+++i\nVB+bTijclMCl3N3TzOaOnbX/nMP5b2LKv0Wc7KLnBUEuMKLm0rWY69de5Hj/L7B98T/jh37489x6\neJFRIemljqPDOSIU5Nd6PCz2UIGkOYrYFDcpEk3ELmFgaNx9RC+lahuCxhCEIa2tyFKFsQ1OaE5G\nU5pmE7H2OiqGanqF4uAhaInq/S3M/C8i3I9zOXmRQPwKs0kI4T+mbS+RZ09R1ifYFvoX/gu+9tan\nuBneYXr4bZ5+6l/l/qN977EdFBi9gbcY0yDcqqsFnKl6W4+YYNnAEGeFlvO86CchpvDeGO09zZXu\nnFHdjfY68BYnFYmO6ecDLj+9y9PXnqJcTBgkEWvJnywg//McS/9jOCsciK7r/57XPhEfv2/HGjqe\n7Zm9nk9sK2azGdP5qbdulCFJ4qkTUgQrLvvy9Y/F1YFaoV+WmiJLZfBlsVlo/2Tg6NwaPBS8bRqk\nhDzPMVYTxRF1UzIMB2jbesFJ21JVDXmeIQOP5JpP51y5/CwP7j9Ct75IGMYxsQQZCU4nI+bzOVo1\nxEHGIBlQ1gVoCIRiPleUZclsNmM+n5OmKcPhkNlstopdjWnJsqwrDBp023oHiq5d7ZwjDsKVmwSc\nIQu8CrjvqIahQmtDr9dbJbDLXCSOY1+QFAIlvIBXoBSB9J9x4NT5e9eReh4rnPj34zvkAiG8AaHv\nWLNEfa/em4CV1eL7iYMtX3deAPM87WCJljj/f87yLd8Ej6KIJElWHtdpGpPnKTHZqgB7Pm/z+dbj\nz/L5sfLpfqK7/ScZH6jEOokCqsWMuhREgcSZhlrXCGkQWKJA0jYLhBRecEDXpGGM0S3OaCQe32+M\nxuAPYMnSFqr0pH5YBZyPVTg44zaer36cr9Qtg926rlfJ4rLScf6B8B9+c2jbFiMcSRiyKGasr60h\npSUKA+IoYC5DplVLZgHWsabl7qP7vPDcBhJNGgiefuYq1lrWBj1e+cYt/uE/+HWmixFf/PFPUhQl\nyh4zKxYkiabfkxwd3WY+mxCdXOejL15H13PqZs72hZw7jx5y5WpKaw/Ic8t2eoHaCB7cHfPu2yf8\n2v/+D/m5n/s5ej1f/RLOcWF7m09+/8f4pV/6JeIwQLeOoii9pUBVAVAUM9YGWaf67ZhOp6yvr3Hv\nwT5CiA5y4+ek1+uRALduvc3B3h4XdzY4qBbUxZwsGqBb7T39ygIbpCRJ1s23YTBMuXR5i+FajtEF\nzhmCQBEEEcfjKVneZzw+Is5y0iygrFo2NgecjCaUzrJ79Sq337rDnUdzLlzvs530iR1MZidoCVJl\njBeH/Js//lNsXbjEvQeP+PZ3XuUb33qFqxeHTE+mXNjcYvfKkJ/82R9kY32L5198hrac0zQVVVsi\nQoEJEtpAsjd7yDv34P/4zbdomimf/WzGjZcaiiOFbgSnRyWzufFQxCZkMfdWbbcP9nh4WqACzadf\nuIStK4rZ/M9uMf4pR1s1DNd7bG7usNOzjE9HGNOQpgMm0xGffvkF0p6gmrWMRzNaPWFtvcfD00Ns\nU2NtTbMoCPs5KgywRpIP1xgrRdhqlAM9nqP6GVo4+v0+TV0T5B5ivBT2wDniXCAWBiMNGr/njo4m\n9IaWQAUYbcF1sFbnsKbFmdYHIjhwBvCqoYFSuEDhWg/36pCjZEmME9IDpR1oYTDW0FvbxLqWLE94\ncOcW5WLOxJ5w7/Y7qGduEMcx2aBPazovSxmu7F+WHdAlH3o+nVHMfOEqjmMGvb4/dCJ/oGJKz7FO\nEtIkIssypp09lrWW/f19AqVI0pQsSWmahs2tLYb5wItIBSFaOMLEQ9t6SUaWnuNHtQ0WzXy+YP/R\nA6/C2sH2eoMeQbcXht3kO2pkGGCDjMtPP0OSpoQqRMgAg0cEePgc1E3BtWefwmlNz3rP+7KpmZU1\nTVPStDXGem6+kkMuXFxHVxOKYox0GhF4GGI2yJgvRtQdNLssSxb1lDhOyYc9wJJlCc8//2zXARkT\nhiEXLg24dHmL+/cecfv2feqqJQhz2tYRBilGV2ht0doSx4LhWo9iMe2S8KXP95mX8xLVFMZe1Gz5\n82XH6IMCB/eBT+dl2nm+SyFRyheWpGkQSnp+ohNoq5BoVOcFj/UqwzJYCpL5s1J1XW6rNSJIkDby\nr7X+XjppcWisC7BCYoXEWG/55JSiaSy1ymmClEZlLCrDvHbsnUxAxbTCMaoKqqYmcIJYqDPbMKW6\nNQ3gkEqsfN19EIjvVBuDwHdWlFQdMs4HiW2jV9xyIc7Uz5XyhTVTViu/9KZckIoUog5PjELIBTJI\nePEFuH0341uv/T6DgS9SaOe4fOEaly5PWYt/gnH9V1nMTsn6Cc7N6OTVmY/3mM5PODz5b/noJ/5d\nLN+PXfQ5Pjli48LAB9cItFII55MgFSiSNEE2GmXBGkccJRgh0KZGBIpmVhOGAhlKojChzG+hpzFx\ntUWebtLvXWVaTH2RMR+zdzBE1l/jwtV/nZPFjCtPpRyfvMDGYMreyZv01yUikGxvSD66/fPo+VfI\ng49SuRyCpeISCNqua7VM4PwPngx8u9j/j11D302w/ORrxBPJpG90SCSwNhjy1FNPszYY0kt7bK7l\n9JKIhfyTBeV/nsNTgDxCC7VMcs9zec+SHqUEjVMrHvaKL4vDSQUyIOnsKJ1xNLpZFW/n8zkg6aXr\n5FlOFIaEoSIKQSlHayWy49HrrjvqO4+SJZ9eIhEyIkpCUq3IaojnLbPFXpfM+2tJZTF24c9bcmIb\nYJwktIp2UTF7OCfrJwyjCyyKgqyXo51GZRZOBXVr+YNXXieOM8pWY9uW8Xjqk3qpEGnCbNRQCs0g\nlygjsXXLzJzQ1jXTScXBwZuUZUO/t064fYnjw0Nuv3sHYUqk0aQBGF1h7BkcP86Gq6ZUEAQeXYnD\niYA8P9PT8eeKxhgoS4FSMTtb66u8Y5lYL5PqpGsOLAU1lVIgJc4FhCrCISlbi4g2iNWCqDWo2YR5\n1EdIRZJ0iFsBCoOyDabxYmtC+m5wo1uEc1ijO0ckg29eL5PWZXHLIoRbNTW9V7r/vrWt52pbT3ET\nQrASSncOgUFJb1/Y72XEkbcWHY9GNHXNczczhIx9YcgKsLLb1iUyOLNoO7/Gz5+954tu5ws83834\nQCXW5ysc50sOsjukBQ5nXMc/sL7yc05ljk7uwnVVHiFcBwf1h99yot8Pc2+64PN8Je18oryyGLFn\nHd3zXd33eqGd63JLOnEu570+04BQSoS2rPVzJouWo8MD9LEPaB8dlshwxNVLG0RRQDNrENIRYXnp\nQ08Rhut88zuP+PrXv87zL1wlljU3n7tJ036K6fgRb792h1e++Sr0v58P/Ud/gyTKSJKInQtrZOsx\nR4dj/tq/8zOMxo/I0pukcZ+19W2y/BK//Rv/gP/xl/8HPve5L/CDP/hDrA0H7D06YHNzg7/0l77I\nV3//K1y6/gx37ryB1g2j0Ygsy7hwYZsoEOi27LgecsUbOTk5YTKZ4JxjZ2eH2WxGGAe8e+stxuNT\nLm9ucHJ4gGsixjXEYUisArbW13h3/5Q2sNz80AuowLC5mbG1PUQFxgc8IkDiqJsaKQXa+KQjjkOU\n6BEnllm5IEkSZuOCYjLmqUuXmMxbgnyLSjbcebRHNNwhDiLW1zf40NYLPHPjWVptuPXWa7zyja/y\nhc9/ni/8wGfp9WOsqdnd3SJMLYvFhK9+8xYfuXndd6NUhQwitnd2yLXh9p1v8E//6auUVYZAMp1q\npsUJJyOvUn/0aE6/r7hxQ/K1L32HRF3i+z67S92cMJvOaU3B0YUeAxWh7QfnALfGomTA2sYmT1/M\nuGsMo6ImDKOOc9USRpIKwcnJGBlU9LIM56zHdzlH2zS+WywlbasJ4wTdCSiFDnRVI40P0JMkoWoa\nwl62gictgzKlJFZ6gSJjLVZIRtMJlo7rJKT3ZoUOWWJYAd3cWbXWCX/NM4qZBXwnV0qJceDMkpvl\nRY6CKCSKE8j6ZFmPqlhQlwWL2ZymLimKOVGWYv2ptZq/Jeds1fl0ruNHWeI4JM99VXyxWBBFwWpP\ni6LoMSXxJUqkLEu01qRJwnwyJZBeICrPcvIkZbooCMOQJIkQgfLXPNdUXZpbFEXB+PSY6XTifXsD\n/z5VGBAoz3sOggDpoBUanCDr90iSFCl8ciKFAO2dE6bjMVYbNjY3vb3iYoGynu5jkKRWIrSiNl5M\nSgrL9vY6As93D5TAtA5TG2QSdImT7wosi6FN0/hkKfYCLW3TECgfOPTyzEPZpyN6vT47Fzap65a7\nd+9T1y11pVFJ6IM8j/WnKRdgNIEAGYjV/YIz3+9loXV5787D1c5X9r/nxxI1IbxWgZDC77sEWN3S\nEzWm9YF2S0BrQwbUnQOxQyJpjCZQAu0irDMdRcoXsNI4QNBClSJjiZUVWhiEaNGipmaD2oVoB7NW\nIMMEbRwmiLhfNZQ2ojQSIyWvvnmXd/ZPse2CIIkZbG1QV57/n8cJtmlZWA3Wc7WjIEJ0HrqNNbSm\n9UiNKPac0CRCa0cQLhFXjrKsPFS6bjoYpqdGLIthnsYm6KUxi7kDaYhjePPOV3n+UoIQMbgIJ0pK\nU/LtV6E3HCKSWzgb4FQAAeyPalywhggqrmz/Ct969W+S9V9FOIt0ERGGJC/R4hELe8Drb/xXfPZT\nf4+T+xlXdj7Kwjyk1S1BZ2m07I7leY5NImq3IECRpCF13VAuKrStUMZQFCVxJBASsjygKAFqxLjP\ndjLg3slX2d2ds3/kEEfP8rGbf5+Dyb9Fy//C/clf4+L632EYjDg+2WdrM2ZWBOS9IScPcjbiv81p\n9T+h3X8H9IAZiK5zJ84UqP95SfH5Dtgf9bo/io6wvP77/sw5pFsK5gUIERAFEU5ILm2v86lPfIor\nV59mMOgz7K8hMdRlA70PhvaJFOKMenGuYbSMVc9THc/Pn5BnhaPzVMcln3rpQV1V1SqxBuj3+ytV\n7zAICIPAC3NJie6oI/YcEhSW9+Y8NNjfKy/+Fb1Ho+I8lNoXwPz5bI0/Z+q64WB/jxs3r7M+HDKb\nTyhLX7zTrSYMvNJ/Wzf0spy6qjsUmhf4apqGtfUuyTW+a97IGl01mFb7D7NENkWUZcnR8QFpsqCs\nFtQdL/y8iNdyHB+fPkYfWv5tzoHQZ7lNkiRkeZ+gExhTSpGEZwUnf/4rlPLxTCx82reEXZ+PB6SM\nUEEIgV0VAQPRnVt1vdrPzl7vk/Ul9N9aVuhbYQ3W6O7+PU4XeKwQ9sS/z6N/n9Q2ebyLDCBW71NK\n2QmH6hVFoKdCZNeN76AsIOy5a5yhV570s14+x++n0fDHjQ9UYu1dzCVO+Ooqq46+hA6u4nAo5wkh\n1oFwnU+h9RUu5zzPxofGPhkXYin8Yx67yec3mZVy6fvwwID3bEbn4SuPb1KrP+ZM7VAsSfT+J63V\nCAuhcAx7Awwl40XFpGjpOWitQDtJbaCxjmrWcGl3myhShJFjOoXhMGHYy7FtZ03UVORZwujogCs7\nF3lDvsrJdI6UIW07p9fLUcqrmmZZRts0bK1tsr/3kBc+fJmiboiU5Ac++yl++7d/h1aXNE3JwcGc\nR3sP6G8MsMby8Y9/nLXhkKoaU5YLDg4OeP755/1d6oLqKIqQ0nvo9ft95nMvPnBycsJgMGBzc5Oy\nmDDo5cRhgDQGi2BcTGmcII8km8OQKIpI44C2NfT7OXkvZGsrR3YQYtGlQz55qIGlvYsPpp1xaGfR\n2njVR+m9eAMsIhTUUhBFMZtXr2Ear3C+tT3k6nO7pHlG2NTsXrmI+PQneen5G2ys5VjXYvCCW/NZ\nw9tvv8u9uzNC3XJ5IwddsDEcYNqSIIjI4oQotOAqcCVhMMRU/h4fHk04PJlTVvD889u0jaNuC6pa\nk6YZaZwgmwZtJY1K0PaD07E2TUsQpjS1901+5vpVvvnKG/R7a+ztvc3bt14j7wVMjjVvvvk2u9ee\nZXNrHYRFOIuwhnpRgvOHat1okl7fw/2VITSatigJW0PT1gyHQ04P9+ltrGOsXTmLOhyEnpLgpGBW\nLLi4vk6gYsbjKVjXebUG54pu3aYu/CHtodoCbOCvaLT3be6SKm1dpwoNSIGUCqmWtkSQ5D2ktayv\nbTE5PqUu5+zvPcTgGAzXufbMM1y58RxCBp1tlCXvvKOjKOL09BQhBL0sp6oq4jgmkKpLXrzlVVPV\n6KZd/fv0+ISq8sJmZVkyb88O9qIouoDA0B/0MZXBakucJKgspm4af5A5QRwHVGVF0ximkxEPHtxD\nNzXOWPJeStLPV76YGLeaK4cXfGqahstbG15wLbDosiVMAozWLNqaYl4wn0/Z2LxKiybuJQS1odWQ\np5Hv0inFdFEync84frjP1d3LzIsxvUjhbEggBU3dEsiQuq7JsoyqntEUXlhtNpuhtWW2mHrKShQx\nHPZJkpiqXqBNy8XL68ymJU1bESchL730Enfv3OfRw0M21npEUUQYdfB5Ba2uUYHDtg1x3F+Jly3P\nC78HSrT186x1uzpffED6wYCCr0YH5/afxSqOCbEeTioCjIgwRBh7jFgGxkJ5b1gLVni6hu9M060V\nwGmEkygZYoRF2wqDw6BoZYQloMHRyADjAhyS2llGtWZSl4ybGWVrefvWu7Ta8yKrpobpFOi6YQhM\n09LQgrFI4QiSEEzXsXCSrJdTliVFuQC6RIIAY6Fp2o5eESJVl0gJiMKAoiiIOms6gLpuSBNHFIQ4\nJXj22lNsbQ0ReowLJDgJosU4yc2bcPthTF1Z0tQAfp9CClqXEWaXuXTpkK3tTzBefIm1WCGsBGkh\ndBwd3+Li7jUi8ZB3b32ZrY3PUBVTdNxgjCHt1IkF4qwJ0BqEkNStxQqPQFksFtS2IApTnBPe1q6p\naJQl7EFYWz5+c4NXv/0HzMzXeXj7H/GJT/1NBupDPJy9w1sPLLu7e9ggpyj/CevqR4lCMI3AmZTR\naIYzLQFbbPY+Spo+w6wqQerlVHqxo+/iUfxuoZv/PBj4+/8Huk4ZOCdorSFWAtNCEmcMBkOPEAo9\nDcVo1wk7fnDQJ8uE6f0SiSe/d757DWdIz5WG0Hm9g6paxXlCCNI0pd/vrxxaljShJxOd853yP2qI\nriDgGybx6vcuE9LzhRIrDG1bQxQTBAGj/QmT0wmzyZwLaxtEKqDSFbZtMaYljmNMW61UynXd4Lrf\nJRwYIAxjAiExrdc/WZgaUzcIBwrpFbqR/nyoved7FBY4J1ZuEcvYYvk+4Qwhu1Lo7hJp6xx6MV9p\nSijpNU2UTAiDuCtOnCFklh/LJD1yj6tg+8Q0IAhCED4+F7X2qNkkIbLLRuXZPJ4vogghVtZqq7vU\nFRr9/J8l1vC4gOf5v/fJew9gOUMNP0nBPf9elpSAXq9H23pR66IoCMOYOPYoB86JvT2ZyP9Rz/yT\ndLvvdnygEuuO1ehV/jqYiAOE82qkUvokWkqFkN5bU9qgmxgBznchEALD+Y6SWKm3xnH8vp1p6x7n\nUT852cuNZNmR8IvCvacKcn4sYdFB4PkOYeCDTuMM2ljCJCS0LXkkqGqDDgQqgRLH0WJMUAiCPKc/\ns8xPJhTNEZevXGI82iMJW15+6QVGsxMiFXFw/4i/96u/xV/+4o9STQsGccKdo322dwYcHRxR1wY3\nbjkewdHBiN3LF7l29Rkm41eYzPYI4h6j2T6f+OSHeeHFGwRBxJ27t/iN3/gtAD77+c/xaP8Bb756\niy987vNcvHiRYn7E7du3+eQnP8np6Slrg5woFN3fbTolSL+hLgWY7t27x2g04srWgO97+SWMtXzt\ny19jbWub2bRg/0Qwms6xwPogZXd3l7uHI4bDIVvbfQaDgKKYspX3wUqkUCzK0isxqoDpdEKaZqgg\noDECIRV53idUEYvTCZc31oj66xRrMc9c3WR7cwsd9rh/OOLk4V0++uKzrG1cYjhIcCbiM5/5Pnrp\nD3BycoKQU7I4I4qHlI3k23/4Zb76lbuYcoScnPDRm5d57to2dyaPuPzcDUoN1X7BD396h6/KrxMF\nAS8/s0FW5tx+dI+DyRShUipbYlTBUzcvcPhQUbmAKFSEzrC5vcGiheNJSTH/gFj0ALar+PpDxayo\nAEEQ0rYt0+l0VaH1/sIxYUcrWF3jibV43gZCWLESz1h2BM/TN5bDOa/i65zp4GveWMMrdS6DNNmJ\nk7kVLUQKt/KslsJzoN25a3prEdHBQ62Hf3YFIik9z3ipwEmn4JmmKWmaI5qKqmo4Pj6mrBr66+tc\nds7bFXXXX6qcr3hTUoI5Q8UsE2bnHPP5nPl8TttIqqr6/8h78xjJkvy+7xMR786zzq7qa7pnuufo\nmZ3Zk8vlistjCVKmZFqy5EuiZfmSbcCyAcm0BQOyDck2LPEPwwYE/SPDtkAbsGgZkmDdEi9xd5ac\nXXJndq6do6fvrqquvDPfHRH+I97Lyu5dUqQEgRr4ATVdlZNVmflevIj4/n7fgyiKKMuyoeNB0UTX\ntUfbkfA8z3X6sxXK81CB5yihzQLfyja01qwWKfO5MyfrdrsUWbbuTq81TY2BTPtjbWqKqoRmUx/Y\nttgJvlScno4o0gzPl2xtbZEX6doTA2FRnkCFIXldoVRjAqnA8yRau+zdrK5RogFBxlX7W9Dagmin\n0auZN0Bra2uLOA6J4oDl0m1ghqpPtxsT+AJBhO8ljEczRqNTiqJCSm89nsMwROsCISza1K7w09D1\n2zHUbpZ85SEEVNUZ8HZA5+OxGbe0EVmNlq46K077XkBdplQaKl9QqA6p6jjplhCuYyo8B5ZrTRIm\nlCZHahfFEvo+K10QeIqqWJAXMZk2WC+iKMEqBUkfKT1KY/n20QmnkwX9wRa3797n/mTGeL5gNJti\ngMloDLrE+oYkjql10cwhNWXl4rvCMMaPJLPJmFJayjwj6XTwPcViMWsyUlUDQmlkSQ6MDAaD9Xzl\nxoL7vi2itJu/+XyGrjIGQYdKCSbjR8SRD16IMCnIHGSFzw6jR1ClPcLdbUwGNSkWjYh9ZuWSoqx5\n+PAGeDR5nwAAIABJREFU15/9w3zj7R/HipFj1mjBcnGT6888S2Uqjie/yutv/xTPX/9pPnHjBe6e\nhpSNB4Cv3NidrFaYFQzDc24PYy2r2dLNXypEBBpTS6IgQKkQm2eU6YrqdJcr1475R7/+p7H5PrPT\nn2Tn4Bxvfvgf8qXL/wdREPP9X/wZvvHOH6fvlWTT/4f7D/8IT135yywXioqIKDLkeorlJpMsBqYg\nCqA4azJZDyH/8T4im5vu32jv9Y8D3t/V6Mye/WOkAivxwoTnrj/L9raPFB6eikAoVsuCXqSg1nxc\nIulbB2+viZx90qgXvtPAt12jN2nH7XlvGRpt5rQxhm6328yvMX7YGl0KvCBwz2/YOu082DI9N82j\nnmR+isaTotNJ2N3dYTxxOdbr35UhUgTUFazEAmNgsH3IdDLno5t36fQjju+PibuCnf6QtE4ZL0aY\nqubi5Suk05R7D06J/IDuXsLtW7c42D/HbDYjUB7jR2OiKKLX6eAFPnWWo42TDWhrMQKi0KeutfMF\nUVCagulkhmi8nuBs3Bpj0LVBSEnRsKmC4AyqJUnCbr+7jr9qH9u8VoF3BqQ390UgmlhA1/dXnlt/\nwsgVOqLI/R01WxB4kl6S4FkPicaLY1e097z1tVjnTzfMvVaHIaRs2tc0bGD92B6t/byPMR8akNx2\n7+V3KdS02OrJZmU75trzEAQBR0cnZFnB7u4uvt/6xKim9vudHfInWS5n54zvMu5+8+NjBazbLDkp\nJb7nUZQlunbxNi76ROBJiTKghIfVNbUuG6poQV2HBIHLGo0aCou1FgzOSKsWVEW6FttjLUp5CGuw\npsLYuqGqeYBweifdOMhZ5Tq+wjmEVrVea6w2jRvai1PXNb6U5LpEqxAjFVF/yGy2YGgMYRJy5+GU\nZ585hy5LYj/iobB0el22+3OGvs+5KCQuctLkhOxY04m7vPXehK2Dq3z56ascxnOeZsy96EXefuMN\nxirmb3ztm3z5M8/xpS99kX/t+S8xnaT0dvY5Gn2T7rDH//gnfpaFtPzJn/qTHC/7yM45qrpmOfuA\n5y8MKZPfz+07v8Dlpwbk1RG/91/8PnYGB0xXHZ760U/wyS/M+OVf/mU+88J13viVI555+hKL8Zzh\ncEC2WuIPEuJOl7JynaIo8ogTQV1mXLpwiFKuAwhLZrMFo9GE+fEj7LLAzyV+kTIcKHo7PiMRkFch\neNAdbjEYJERBia9q0IZaa3JtmMwr9vb2GE8esbe3x3K5IJAKvzMgzzOiQDIdj7h57w4P75+gjeAH\nf/QLiN4h33j/mPff+xVeufE03/+7PoekYmsn4E/8l/8D15OcT10YEnW7PP+lnyCrC6zIyWrB3/zF\nd/jqL/48Lx70+MxzXfr7IYU45s7NU7IiYDUtuXJlD3/g8Vx4wMWLX0R2faoMxHiI9u/gkRKR8/Th\nAVG0zdOXLM/sKr76/tscL2qiMOFgMMSWBXcfPCDUHx/zsqoco/zzWLXFeARXL21zeNhBxUOiwTl+\n6auv0+0M0Uag6yWDbsCndy/zdv/XWGan+L0Iv5hRzyb4W30KvyCOQwb7faqbd5BIZqNTOvoyVVZg\negnFeI46qNGRoqLEE4Kw0tQqoOf3yZY1nqyJewKpJBE9vvlrr/N9X/wspi5RyqDrJcZzBJmqcaYV\ngXJaMO10lY75ITBopBJ4QqGNWyhrbdF1hW9DPNEYN3mKoD+gGwVso1h+dB+5GFEXc9Lpgo/enZMV\nObuHT1HkKy5evMju7jZFlpPrktC3JEnIcmERQlGWjnY3Hk/XMSR5lqMrjTY1y2WJF4SEnZiyNvS7\nXQ7CllESkJ3bxWh49933+Oj2HcJAEkURVblC1pKDXpdu0mEymTCZWVarlEePRvhhyPb2NhKneRJS\nIoOIuq6wNFEleeZAVKdDt/McWzs+Re7oa1WuCQJFNptx7+jWGsBfu3YNXVQEwgGU6XLKztY5UDVF\nlSIF+CLEM7BzsMP5XsB0tmCxmCGlK8aG3WTNbrh3/z5Rr08/SSgxBEnkFuzKrAH25NEp+XJBHMcI\nL2BxPEdKSZIkDPcTer0ehztXOT3tce9hzvHxMVVVMRwOiaKIKI7AxkicBr6NYjRoMJY0WxLHIboS\naG0QVlJXFUp5REFElX08duOVMU4fLZuOAKztg7PCsFJ7JLvnEJ0BfjwgtopSXmc2nTKbz4j9kA/e\n/4CqqlHVPYSuGcQhF8/t0cEjq+ZEKqAOJih/wAoPbXvUIkHIDsfjuwhRog38v7/wVcbjCbPFysXR\nrWa4yHqDFRZPGxQeeZCgqxxdNAZ2TR6bkIa6yjCVYNDrkmdLfCXRZU5uXBE49Jw5kMLiBwEi8NG6\nRBjDcjZeF/GKwhXYw9glEiilWC6XZ0WrKCJd5RR1SZaWBN4uej5D9JdIdYpAEfIUdx/8Mlcu/vvU\n3jMU3jFe4PSihVmCmNFJFOnsWbr+i/T9P4i1f8mZfVmP7jCGuo+nd9nvT7Bbf5N37n2ei4P/jGvX\n/gxR5Ip9/W4P2xQBrbUcP7jPzt4uNRo/8qjzAk9Yxxbr9MgWNelyxaAbcnp6xJUL8Pq7/x5C/CIi\nOsdQfpoqvYgnvso7sy/Tif8a07tdLnd/jn7nLUaLf4fRo29wkv4U3ein2e/Do0kNQmK8MRiQKluP\nMWkAG2HwseaseLxJ1YTvpHK2z9nsdG1229rH/nHHunkiJMYKgqRDFQTceP4lnr32PFf2LhF1YDpf\nMctyRBgRJDGTdEXH90j+ie6s35mjLdpKKdfgtAXLT3ZOAWRTpGz/fwtS2sSeyWTC6ekp3W6XXq9H\np9M5ox5vUIjb12qv0Zm8SazvmRY8nYEdVyg9A9eSXq/DpUuXHOV8viLLMnw/XK+LGk0YRtR1zZtv\nvo0nfFaTnN2tPWxlSIuC/k6Pbjfh3sN7LOfOs2BnuIXWmuViQRiEZFlGHITOt0X6zEYzltMlw17f\nycyaon5VlI2ni4+1giAMmS3GaG1RgSAOuusx3JqZ1ZUmz0u0xjX9lKCoNVJIPN/jmWefQ9XlWko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71bPLXtcfWZA7Lao7A5lYp486P7HPYFnY7bbs20YXx/gRExav6Ifrf/pIfpP9eH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\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kpZde4rFHzlEVA7S0BEFMu91BqgA3GrLQXWJhaZFJnvHaK1vsbR/T74+pTYSWgvMXz1NU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\nEtO0xZWLD/Ds68837VJJzEakkWmLN65fZ29vjzhu1FG0Xg7q3mZcFr6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XJW+8+Rq37lVN\ndkuUBGFBIj2f+WuPErd7DDqDZnFxFYlO2L25w/Wr3yawEYPBgNHhAfc9sMHx+HnSTsVD71phuzhk\nfHhIb0UxyTu8uOuZrcWsRRGhGXI8mpJnJefX1xi0Im6/mXMwq/HWMVhdYXR8xLWbh/T6HQ6IiOqM\ne7oxLemZjUbcu3me3VGJqAMIIrLJhLwqaEUSV8+QYdMfhtQESZsgiqjnt591DaOrNRXGVGTTKStJ\na06+9cNhRZ7RaWmybEa32wUWEOUaXZe42iBkQ5QjpMB4h3MGUPMFT80hUBqlHXt7+0TdNkEvxZqK\nqihoxQnZbMbhaMyZJOHK5fvAC1544SXKasaf/N9fJi/GvO+J93L12ptIKXBG4IXFU897gAPAcHQ0\npN9LuefiZW7sXEP5CD/PbCZJi9FowsrKSsO4KyVCKoz3WOeRpgLn0RJsVbO7u8voeIcgUGi9IHRa\nwKgagqZmGfMoPLYqieOIujbMKkW7t0W3u4Y18v9p701jJMuuO7/fXd4Wa25VWUtXV/XCXimRmm6S\nZksiKWm0zViUZBkc2COPDAgYaEb6YH+RYQOGrPEHAzYGM4ZlGYIHFmDIo5FIjWTPUKIk0gONJO5k\nc2myF/ZW3bVXZmTG9vZ3rz/cF5GR1dVkt6qrq4q+PyCBiorIiBMv33nvnnvO+R+ytKKoKoYb6whh\nmc1mVHVNGIZIKdtyVrf5Nlwb0DQNo9FoOYKrLEuSJGFtc8BotkPcDcnnFqkERTojUIrLOztUtaUf\nJhgkaZozHK4zmafEnQglJXWd0+8PXVlb0TAcRPz9n/97/A///a/zG7/xG/zUT/0U73vf+5hOp3zo\nQx/i4Ycf5k8+/gmeeuopztxzgh/4wSe4cnmHTjd2om24m98v/uIv8uSTT/LRj36Un//5n6csS4a9\nYFkSppRa9qq5BcNB79zqzdEYQ5ZlZJnTpgjDmChyZfJlWfLOd34v/+yfvZ/RaMTu6CqPPPoARZEx\nGo1AGJJOQmMKptOr1FWKEvDqy1fZXNsiihSf+9znufeehzh65BT7e3MCHfHCC98iy+Y8/v7HuXDh\nAtvb28znc4bDIefOnXMbHrZaZlustUynU+bzOXHUvyV++WaxVmLaDcDClGgVIE2FndduTrKtMLlC\n99cJtCuns8JlUo10CtBBFGGoqNqSZNMunIwQGMBKhcC1GQkEjbXIpiJWNeN8RhIJVF1ji4x8sk8Q\n9gmCGCF0W0ooCJQkFBZlMqwo0Sh0nhOFMY0OyCRYadvryoGy7jKTUVs0olXtNqja0JGgrUHokrwB\nUZ9oBTolUGJtQF0XxLFgECc8E5WE0TamCTCDXYKyIqgzZk3Aei/iyMaQIZCmIfk8xVaW/d09RNwl\nDkK2j/boJYIktqytrXF5lJHokE43YHeSkQc95jZi0Is5ttnnmC6orCDqrdPdOMbWcddmIDHMy5Qy\nK5HBjKhnKQ1kdUMjBJ1eg52vQ2DZ2u4j1IB+5z4e/FsbnDp1ivWtLdbW1ki6MUIIKgvWLPpmNQGG\nmhqsxJiSMGyPqXVrmAZBVjbtuSAI4wQmGcYK8rqhNg2dOEFriW0qqiwln2jE+jpxELZzjEEHEqFC\nNAbRGIpsSk1Jp2uo2w2RWnUJNGRFTWMthZU0VlJlFUVekUQdutGAeb5POS7RlWvPQU8xdU7eGaKP\n9EnuWUOKiFomNIyImoLA1hRNTZmHgKtMslXOsNfl6mjiNlKVQtwhw3ds4yrClLJobVBKgIRAh8u1\n5jKzHOhDAfEiGDqspKyIkgDdBnhuMoRFS+Wioqpg0Q+s2xpkrQVKWuqmaadHiWX2WgeBS1osAyFX\nMq6ERFioitLZSOM0FcSKkrMA01iQCmulu241rt/ZlTkLlGpYVIlaawCBMZX77q04mK0XZeju3hJE\nkrCq2TqySVXk7BSXqZqCrKrRVvDsS9/g/pOPMJrOieOYupjS68WIOiee1aRpypXLGcYYHnzgeyiK\nkqYySBTj2RQWJey40aTGWrbXtul31yjzqj3eB9eqRda+yOaU6RyNJQwlkgbRJgwkBimVE3QzTav9\n7qhWdHrcJqJBS00jGnS4Uk7NQUDaNA1KRqhWK8YYA3bRyy5QgbsOBEGMrN0GgF2EaVK5ALnNI8pF\nC500GAxC1Qjp7herGWqsGzVqjKt8OAiGFyPKDI1RSKnQgaSp3fssz9XK/V0jHRzq3ZZCLFtal60F\n2PZce+O+dEcF1ovme7i+gNit5PXsqaqGIHhtdC+u+Z3VXaG6Llls81jbtBcHw/UCawBp3R/e/bok\nrxvmZUUlLAMh2O5vEtkCLUs6KsQKiVFuzuN6R5ONrqK7IarXRdFwaZSyFQtMU3Hx1StsHjlGJ4qQ\n1o186scauTVEmZCuMlSNIgljdJiwN84pzu2yPuwyiAR3Dws3Eirqs7Zxgtk4Z7JbMhyucfHcWXaf\nNrz06iUmM/jsl57h4WcNP/5jf5u1/lPkNqJsJHuTnCNbxwmpyaYX2Dy6QZR0ePSRk7x46QrPf+Np\nPm4NT3zfMcp8zMkTR0goOf2OLS5cuUo6UfR6XeZpymwyJp1N6CU9dl9NySZXGPY2OLJ5hO/73new\nfnSDggr17AiqOUptoMKEb56b8tK3rvKed5zh3uMBqk45OozY7EUoaTl1uk+TrreL/YC6mCNVwGh/\nwjk75EgUM7EJti7prW2SFlPMbIaI10jzmvE8IwhceU6iaoTUbkyagKAVytJAoi0j02CbCiUaokhD\nk2GKOeqOylg3BEG8vCgud7DtQgDFqVAqFbhew3Y+9cIH3EX7QBjDNJb5fE7SCbH6oL+mamrysiCv\nApIkYbixyebRTUa7I2pb89TTT/H4+oCqqggjjVRt2ZdwQbwxICV84Qsv8tMffi9nzpxhfzRivp9h\nTI2WbjRSkZWIDeV6tazLPxtrl7vMgZak8ymTyZh8NkYIi5IKYZvlTroULDPXbqNXILCUeeUCbiHo\nD4/S6w9J4j7j/RkGSxhHdLpJGyxPSDM3luyP/+yL/PAPP7Ys+1pcX/LcjTmbzWbLOdZ5OSeKQvb3\nR8RJiLQ12XzGbD5F6QRrBWleUteG3qDPc889z19/5rM88cQTPPDgO+jGA4RpmI3nbGxsUpY1SRxw\n//338/zzz/PpT3+ae++9l17PZbeOHz/Ohz/8YSaTCV/4whdY3xjyrne9k6LM+OSnvsyPfOh76fV6\n5HnK/fffy5Url3AjP+zy5r4oRcvzfJk1KEtX6rXYwV4d2eG+v12qPSvl+muLomA2mzEXGZ1uh7zo\nMhj0GE9qwiggjkO6cY8LF8/S1BlxpFAqpNcbsL55nHJa8uLLDeLlV6gqRb+3QZpnZEVOb9BHKeVE\nboDBYLC0q9vtsjfdI2nH/Gmt2ZleJQpjdnYnb5Mn3hhffPpFTh19D7WqQCkoU3SdI61EdJWTlWpy\n6nSK6PVIixKpGmpjUIFmVuWoUGOaBqu0q4qSEqEVVV2xsv6iaa8PiYLYVFTTHYLAoqqSkAYpArIi\nx6QzkrUOtdBtn6By4jzUSBSf+tzn+Mh7v59gOsMaQbfTBa0osG2RmAsKwlAtsz5hYwkQiNqgq4rQ\nlATphNo05PUeavMUqnmYV184y6mHNdbuUZkjxEHOfnGZWf01Bv33MxmtcTS5H9b+EmYpHWGZipg4\n6XJka5tZepUyL2iKGl2CsqcwZc3TFy7z4x/6ARJdorUryby0kVLdH7CX7bCzX3Iu3WJ48nvZ2Ai4\n+0QPMXoRo0N0f4NgcIRwuIVF0VQ14XqH0c4+o509zr50jqObRxgcPUZjLGtxRj86TX8j5OipPkE8\nZNA/TW87oixzhn23EWqpkQi0iJYtJU3R9omj+X8/8Ud84Kd/Bqk1VVkhdOiUonVEKTJEFFKnDSIO\nqZUgs6BMw85ol3tOnaIuS/pJwnw8pRhPGL36Kpun7qKs3UK4psEIgQwlQR6A1TRlSlkWoEKESNjX\nQxKbIVMgn1M1NWUFxY6kqSMIFHEisSNLkod8/cpZPnj8IRIxJSkk02df4uI5wYm73gv9kKCzyVBV\nbGYzIgpmVcFl2SFPDUFnjfn+HoOkR6BEq0bfEITrt8o93zQGt0ui3AIVKUEo/ZosHxxkhRf/Xg26\n3D3HqbYbczAv2LVWtFlALWkaixtJ4cqZpVZuM80e7pleqHGvZkatMbxw9lXuOXXyYC0tALso7T2Y\nwrFIyrnEnPNva9uKUHvwfVxAvVh7HwTx167vVx8vsvODwYB0MmWSu7nRVir29vYYDyd0kxiLK1s/\nP9ln+9jmoUy+MYY0Tel0ulRVsZIgdN9jkUyw1i7bNofDIYPBYPn5ixLmsiypipzFZBEngmgQdpH1\ndgHvag5yWWm3cr8UgG3XUV//xnM8+uB9y7LvRc/04hxwpeiLTYmDmMVl0V1wKoVCa0FVtWse0Wae\nWSRMF62vtv3O7d+nDXhXN3YOAt7X6tgsnn/hlQs8cO9pl8leEaxzNquVc+SgukG2wnKHqhoW58eb\nWF/fGVtpLTe1r/kmUVevs82xUtbxemi9KLH49t9d2MVulAFhFzp+5EXJZDZF6oAaSdpoaut28YSp\nqIqMUCukdL0idV1jgYvjymUVhCTNYTSZkBUVYRi7DGJTEYuKjV6XJOlQIwkCzcbGBusbRzBWUTUC\nHUVURUaRpoAkyw1ZaallgNEhRQ1f+9qrnDs3Zz6VzCZw+dw+VSrASIrS0KiIy7spaa3ZOHY3QXed\nMEmoKXnH/cd49KHTbK6HfPW5q5jGoISmbgx1UVGXNd24gxBQlxXT8QRT15imQSJRrSP3Ogn7+3s8\n+I57OHF0i/noKmVRIDFo1ZYtIRl0Eja6XYq6Zq0fsDEI6HVdRjXsD9heHzLsuVni68MBSeyyht1Q\n0usmqKBDo2LGacH+/j470xopLUVdUdYNQii0DglUgJXuGNY44TJrDdgaDSjhAi4pcGI0xlCXKeLN\nbKndYoQ0xElI3ZTLPiBjDE2VUZUpTVWDAa1ChJQuY73sIZdIEVDkNdaADjRKRcymGXXZ0JQV0jSU\ndYFFkFUG0BR5xWAw4JFHHuHosSMYW5EXMz7xiU+0N7MapQxCNlhbY6koCreb/MwzO+yNJly8eJVz\nr15gPNmjLOZOTE0H7O7sYY0E4xR6Td1QNg2NsWTpnIsXXuHcy8+zd/EsoSiIghBJ2zeFmyEqJWgl\nUNKJiSgBWiqSJKHX63P36TPcfd+7iTtHySuBCGIq07B9YhsZKsIwIIoClHZjT/7Nv/0MaZpSlpXr\nQ2xvukVR8OKLL7Kzs0PVzrW0qubsuRcIIkGvGxEoSS+OGHa7DDaOgA7oDzbYH0/53//Fb/Ovfvej\nTCYpv/M7/4qP/v6/Znd3TBx12dradiXOpiaKBb/0j/4hP/OzH2Y82ePX/8mv8Zd/9RfM0ylBqHjg\ngQf45V/5R2RZwR987A/5rd/6F1w4f4W/+stnAdjd3V1mm9/1rnctg5zFqJEsy9jZ2UEpxebmJkmS\nLAXvVoPv1bmdixaCLMuWs0CPHDlCkiQIYblw4RXiWDOZ7tE0FceObbGxsUYYzUBeZW2tZDgoGPQr\nHnjoPvbHU973vv+As2crHn30UXZ39/jmN7/JM888Q5JEHD26hZSSzc1N4jhma2uLqqqWj3u9HseO\nHVvOvu33+0RRRJqmt8gz3xxPvviiKxcUbj5yVVVgGzqhdht9piaQgqaqMI27v1RNQ1U5ESolnSIz\nSCdcpg9KAxGLsku53GgLpLtfBUpSZxm9JHabdEqAcf2DTdWqk9cNdW2p67bPs10o/8Xnv+oUwq2h\nyQtsVWKsobbVcoF1IJzosqyBUEgLsjaIqsLmJeVsSpbOGO9fxdqGPLPMZ2W7fLdY4zbUkygkDCwS\nw7Gt46x1t+l2EgQNUliSQNPvdVgb9tneGnLsyAYnjm6wvTXkwXtPcdfRTb5+/hzDfo84CgilIAw1\n95y+m5PHt1kbdnj00Ud45OHv4dSpu9ncPELUSTBKECUxnV6PoBMiwwgVhUTdHidPn+auM6c5ftcp\n3vnud/Ouxx7nwYce5sTdd3Hf6Yd597vfzTvf+U7uOnmao0eP0luP6fZ69AdrRElMlqcoKZyAZg1S\naLRy964giAh1xL/7sz9CaknVlBgstTGUxuKurIKirimbxql5I926pc02CSHoxAlRENLrdBj2+3Tj\nhLLKW6GndmEsXIWQ0q7FIw4iwM37juMOMuqi4j6EHayMaETA1dGUsnCTH4p5STrNMRWEIuKzF57F\nlg26LrHZjJ4KMHnh+rCVq85ACaIAQl3TCSAOIVCaSDuFdykhigLiOFpei+4Iri3nlS6YW1QGXTuy\naPH/q/++VpRstSR49fVCCJTUqFZ53xqQbXbfmsMiWKtBzrVl3C+dPXeozFeulJCvligvMujXKoIv\nWC0ZXv1ZvO71Sp0BpFAoqen3B/R7A5Kk05ayu7FZ5y9eJIhc1j8MY145f5mrV6/SVA3WLL6LZDzZ\nQ+vDNq6yyNYuBC/39vaYz+fLHmpgOSawruv2byVR8kAJXaqDioLV77b6vVZVsxfH4RvPPLcM3K/9\nm6y2Ia6WTy+ed1UJbtKIaVvtFgkHYPn7h0rv7eGKoUVAf8Dhv9HB7x309D9/9pVl8L/6/Va/4+pG\nweq5vfr8d4rTrscdlbH+ruLb7REIQ1XVdLvJskzBNm4MwmvexlqkrdyOk7BYYZcXl6qpmU4yXmou\nE4aaQRIxTCDUhqYuEUGCwBLJEGsM00bSixKQBbWKQIesnexz/sIVLk6ukMRdIiW5a1AQS0M1mVBH\nXdKi5oWLXyPq9Nk+cQ9iNOaZF19l8LfuQ85j5qkGHdNfW2NU7/PMS+cYjHbYPrbB+b/4JiIIGA6H\n9PScjpgwvfgMurNPHm7y6c9+kU9//mucPnGRn/7xH2HYPYayYwJKalXyPdt3MVDv52N/9jT9bow1\nIVcvjlnfOMruBYFVQ9L5PrvpDspCEsXoXsDli+cJwjG1McRJl2PbA7qDAfu7I6JCMrYJ1BXTfMZk\nMmNN9Xj38QFbxQ67nZDjPYFNQsJIkFaWqdFok2GrhsDWbK/3mRc1Wgo2mpIgismzlL20ZDYDReDG\nGpmKNK+w7XiXSMdoGgrr5m4qGdEIja1KhKlo8inYikDadmewJp/tsbdzmcnu7k06Wd96RBtIwmq1\nhiu9YjkmQrQlV8qNfbDG7bwKt3StqhohQrRWVFa6EiOA5mAn2EpFY13m29gaISQq0GwdOcLu3pU2\ngAmIoogsy+gNYqAB4W4AQRAse3quXt0lEJIrV3bYWl+jbgqscYFEXZawqBqxrqzU2gZrDVWesXPl\nIoG0xJFTxpVxCNYSKt3WGLmCwUXG2uKCFbcAUWwdPUa33+NSWlHVhsa47xfFMevrw7b02y0oo6qk\naYOJPM8RuL78slUKz9qmvziO3U55ljIZXyaKIsoiw+gO0/E+gZCcOLbN1cxy/MQJJpMZX3rySebz\nOe95z3tRKuBbQvKtZ57l2NEt+p0n6HRjkjBqey0rirLiscce4/Lly3z5y1/mU5/6FL1ej/e9733s\n7OzQ7/d54okn+Mxn/poLFy7wF3/x7ymrirIqiJOI8cSN9QqjgLIqGI12GY+uEscxYRiSZRmbm5to\nrZeZ69Xe60U/+eKxE3FR1LUhDEPm84yyrAkCTZJElGXXZRJEQ56nRFFAECp29y4gREq3C1VVYG1B\n2ZRM5vvsjccEYcT61hbHC7hyeZe9vT1UQMy9MgAAHVVJREFUGNDgbuyz2YzHH3+cK1euuAxoGC5F\nd+I4pi5dz/WZU6eZTQ7aI253GtPQ5LnLKEiFCBIqYdkXNUGRI0WCKUGqikhVaJVRCEXZaIRVyEbS\nNBWSClM3oK3zA2uIcKq1Aos0Ei1qRJ2jm5xsskNTZIRFTkcqTNBBmX0CUzGdgu4mVPMaIwOECkFr\njJY0SJSImAso9RWQAjuaosMh/c46le5iZIAxAWDRGLS01NIiZmPs5Ap2fIXA5sjE0I0GdOs+5TPn\n0EPNK+OQhx7/Sc5e/D/prz9Noh8mYgLmJY6duperl5+D3r3YzoPoZMhs7zJJt2BrvseWzKmSE1R1\nTlUVrmrGakQ4REcBax0w9CibDnEYIcKQsGkwm99Psr7BZrLJxVFOUNbY+QSbHCfePEqQ9BFhB0uI\nsG7DLj5+CtndoL99hsG6K+vOi6kT0NQ9Bt0eQQMy0FQYpFYkooOKFKYxrHe2nTJ7XSHUyiJXWCpT\nutGIwiKsQVqXcTLGIuuaEEmVzinyhrrJ0bpmsGaZzgxaaKocyrrhyMmjSCXoqzUQBiPde2gt6HYi\nBv0DnYtgO6YoCjbsiVa4yi2u47JECaj7Q5r5nI6A7rEUdvdIp1NsXqKrBp1YKGsaLdiPJcN6nTTK\nsc2EJFCYFMKNATQlWg2ZlDM2QkWk9rlXSV7tCaKNDkUKVTxg65RmfzxGpzOnJn4HoLVeKuEvghgh\nDwdd1wZOq2Jkq8rOxpilTsHidxdZ62VfrzDLvtiiKNBKL6/VUZQs37ssy2WZ+Wv6fcWBGvUiILIr\n01EWCv+L4B/pNl5WRasWgZ25JjGx2ku++v0Xx2Dx/1prhLUkUYTd3kZYuHD+PJUxCCkZz8dc2d3h\nxPY2QRKgX34ZW1sCFDI8mEJw6dIltAqRUlAUr23MX9XieO6Zp7hw7mU2NjbY3Nzk4YcfJgyd/s98\nNkMpSRQG2KZCKmffQZB68F6L9wU3/ndxnF+7+XC4N33x3Rf+11SVG4MoJbo9Noa2nNw6pfk8z9tK\noLi9XxgwAqFfe0wXAbJU8tB3X+iUGOOqHi2u7W4huOcet8cJt4lrW8E0a60r9RbCaXy09iHcaMdl\nAL26aSIP1OzVdTY7Xg8fWN8iLAcn5bU7Z3VVs7Y24AMf+ACf/OQnXRZgpWTl0PtYu+ytlcKVsRrb\nOHViJVFasJ/PaeaCielRIBlqqIoZlXWjS6wI0ZF2maZIY5GoeEhmGiqp6W+fZLQ7ZndWoJuCY1oT\nBq5XwqgIFXXRRcbOzoigc4xef52rV15kPM/plGsEUchnv/A0/SMzSluwN7nEYB6S6oIz92jWN9fp\ndjvke1PO3AXZ9CuIcgjDIa9c3KEiZGda8YWvf4sfet8DaFFQZCPCWFFme27sDw1bRza4fP4ynWQd\nzZBvPHmR9a0TqHjuRg5Zw3Rs6EQx0+mMfidnozdEBTlNnXL53BxhNM0cMjVEkqPiiCObMUqFDM0c\ne+UKk+gER6K0zUyGNFYwzgo26gItNYaai1d26a5vEQeaLrtkFezPBLsppAUMe12sSEnTGXnuhLbK\n2t2YqrKiDjQ17bxWi1PDbArKdEqRZSTK0lQusLty6TxF2S5M7xAGaz1ms0kbzJQHpcpNiZEFVZGj\ng44rhhYQxhHWzlCBU/TsdHrsj6c4jTNLGCbkRY6ta2QAdZ5jZBerBVdH+zz2ve/g4sVXUVpiLRzd\nPkEYx3zlK09iC0FZpERRcqhXx0XJrt9ZIJhOUp4893U6ccjGxhqjnfMU2ZR52nDk6ElsXbuZsNKg\nA9jfvcx0b0Q6n9ANlcvJNC4rnVd1m3Vx/cyLrLXAUjWN6/20YIVibX2LqLdOYQydzhAhNFU1wpga\na0q6SUwg3ExhpYUL1OoGi0XrgDQtKMqKeVbwyiuvsLPjhLnW1tbafuOQjXBIkWZEQcjOpcsMu126\nScJof8zw5BkuXbrE7/3+7zOfz/mPP/IRHnv3YzSNpSjn/N9/9G/4/Oe+yHPPPccHP/gBHnn0Abdp\nIhqa2omo/fAPf4jHHvs+Pvaxj/F7v/e7fPGLn+fD/+HPcs/gHn7sx36MJ554go9//ON8+tOf58qO\nW4yFYbgUc6nrmmeffZa1tTXuvecerLXs7e0tReT29vbaTGPUZgbC5SJMa71UeM7SnNTm9Ho9rDnI\nmNaVU8Ht9XpuU6ApCMOQIAiYTVOm2XmSTk7djOn2KroJvHphxtETWwihaeqGTtLjzJked5+6h93d\nXap2cyDL5vT7fS5dusSVK1fo9XoURUEQBJy++zR12XDpwmVOnjy5XFjO5/Nb55x/A6xdlHS6Tghr\n3OaPDARWKmoDgVSudFdKFG5UTxC41iTbljIusiLLMtDF4soa6rqCKoNqTjEd0xMgkctFFLiFoLE1\nVZnTBCGNEdjFZdFKpDTt7pVyAj2NoUpn5CUEMkTEAUqBsAKsRdgKaxt0q3WQT8eoqiAQTdtD7AJ/\n0zRkc0MwjEBssj48TpIooERQEcUdjsdb7I2MU7ZWEVF/yCydQWlQQQjWYBFIESJFg5tRbVBx4s5J\n06B0SNWUSB0iVABBhzge0OlvUOkuvZ5GpDnT/RGdfp8wTEAF7rWoVu15UQnTpdvRyFC3fuI2eaKo\nSxAEdKIAK4WriFPKSW+uZCAXir9GrIwSXJwQK5W5i3UJuCCpLEvqql4GYotgrNvpkGflcuEuVduj\nqQAhCAKFrfVypOBqe0sQRKi2wm9R/WStxVSxsypKEIM+qmmQm5ay16VIM+q8QKQFQV5CUSCiELnZ\np6ob8llFXDWEwmLHE+IjG9jAEGiBFgeLbCEVgRQooJvEZCpgYzggjkPm05CI2U3wuLcepdw4uUNi\nTriZzatr1IUYFhwEn9eyDNZWXgsHQldSSoyVLnNpJco0SK1cUCzFofdenA/LYH8liynbgP1wVlut\nfN4iO+n0FhZcm4F0rZSHv8fC7tV1+iLbfi0LW8Mkpt/v0+l0yGZzGtz1azKbcmRri7px/c2dsENa\nF4RJ0CpVu8+6dOnSsqJK6MMCllJKhHU6HElnSFEU5HlOmqZcvHiRzc3N5aZFtCJ+KWhb0hZ/U3uQ\n/T9c8myX/eyr+k+LxMJiw2ShNr74LGOMq540Fmucqvi12WMhbTtGshWxUw3CCoxtsE2DtO560phF\nK5dFqcNCY9dWE4iloNtBdnnBqlr59SoYjGn7ptvZ324t5q7n9nXO7zeTtb7jAuvVP/iC1/s3HDSc\nS3nNQbnOQPgbtct9oLsIdKMYYw2FdIvmuq6RSrmbtZBtK4htK5kUReFEbKqqZr0PR7cidi4/TScs\nmJfWFXrLg92/xWcKISjNysD0Rb7LwkLLSljX6N9kGWWyzm4tqY2mqGr6oqEbCoSp6cUhWknyxrIz\ny9gYdIhMTRiUmG7DqM4IBOz1z5A2GZsyJc1SRDdmuHmCcX6e/f1LHD9+HF0UhIVlsCGZFymDI5or\ns8tUQiF0n9HUcm98D/epbxLpgrxRbJ56lDSfMjR9ZHSUOKm5/1jI+Dx0q4qonlGPz1Ilm0yLU+jR\nDuthzl2dEGzA3nRGvNnlyqjkhYsjvnpxxLAWvPNUjYoius0Eq/cpbEncE+h6E80aVRGQFgmd9XUm\nkwm7WYaqupThUdK84HhckzPlgjLYIwPCoOAsFTaoCSJLY2P6/aOUwZobrdWkyL6ikBFEORfHCU1Z\nMClK+okktIau7DOyllR0sCJte2AMWVFRNBKJU2s2VYowMel0RjcOKaucngww+T6ymSFNhqgscdhB\nd++QkjMg0JqmLJYLYSFcppFALPusRVukIYRwGZCFWre1bR+Vwtp2RqKQ2EZgG4NYuaA3CC7vjjh/\n/jx5ntPpxK2yaINWMXedvIdvvfDy8maz6Ml0Pc3uuuHG7EiKouLKlR3uPXMXWms6WjNP9wnCAQLT\njgIxYBrqumE+3mM23ScOtSv1pl1vtgsCJRZZ6kWaW7QjhhRWQFkZhmvrbB07SdmAkRohLZ1uTF1F\nZNkeOmhLx4Ukr3KMqWma2vUaW6dYmqUVZVkvBWQWJa6LfufBYMA83aMbxiAsGxsbdOKYIstJ+gOC\nOKC2tZvzCvz5J/+MRx56iDjqsDYY8IEf/H6mk33On7/Ii8+/yN13nWL72CZZPmVReh1FESdPnuRH\nf/RH+djHPsbzzz/Pxz/+cX7iJ36CI0eO0Ov1eM973sNLL73EuQtXmc+nHDt2lMlkTBhqzp8/R56n\nbGycQUpJlmWEYYhSaimY4m6e7pbmRFwOK9AK4QLoqirbIM4sMxZNY51gnVJEUUAUJW1mWlDXrg3D\nmAqEy67qQNNQ01/bYm19w81zDQOEFBgjCOOATi92mx/WqYKPx2PyPOf48eOMRqOVUuODzAfNtWKW\ntzeHj3GDadyseWudSFEoXWCbCTBZw7wo0FGDERph3EaIUorZfEIQdg+VNS6zFtLdn0RTIuucer5P\nZCu0rdDKtSzVtkErgQ5jSt0hSjrkgds0aWyb9ZICZIMVmknVMMRlb40pUVVKtb+DSHLCuOs0AxDU\nVYEpC+bpmHJ0ma4ogQJlDbUJKbOKKKpoqKnygHCwwcc/9uf85H90hqyaEQQZljm1tUznL6PDDUQx\noWkGqLhPd+s4IutSXJwTh5KgLpzSvhAu82IMQXtfFoFmVleo/hrTRiHDIXFvQNDdJEVSipgoVMjG\nMjcNOupSSwlSoWVAIzUIS1rkzMsZOkjo93qESUwQaXRgEMqNy5NCEoYhZVMjcD5SVMWhMsllAHxN\nJud6vbhNY6jKejnuJggC8sJtYoZhTKfTY7TzqtMbCAN2d3fpdhNO3nWCONbUTYlWCkSw1EZYbJ41\njWnfJ1wG3cvFcG1pmhoSsyz/F0Da7xHZmlAoRFVh0hTZWKJPrbH9I99Plo8pz7/C2uURsRXsX71A\nQw73HCWWHYJQ0xhDY0ETQpVTZxN6gw6NbejEAb1OHzHsQHb1ZrvhW4JYKRNeBE/uvriiKr0S5Bxk\new+Cz9Vr7eL1q6OYDpX3Slcu7V5j2mt3dSigvrYs+2Dj+yBQWpSYLwPgFcXiQ+Xp7UbBIuC63nl6\nvVhidVzY9crIpXWbBNYatA5Juj2STo8ir6iKAmHcBlJeuIoqJSRRECN0QBNJwGVqu93uUoRTXBuv\n4LLvQTt2qixzyjJnPp9y4cI5dnaucN999y3vpVK4dZSLCxbrC4u0lvo6MdSC1R751e8pOAisFxti\nC/+31oI6uB4sjtXiuBnjKg1tW87nKvkkFlcKX9bl8tjWdX3dDYzV82D5novScKVc+ePKeSeV06RR\ngV6ea8jFJoFwmXThWk8a41pIpZJY0yw/Z/U+dFDy/sa4UwLrGNyu0kJuf/WcECtB8ustSqxdvfjb\ntzywXrytKymx5EWxXFRanIiKaQ5OLhdQuHEwdV0Shm4sznA4pJfUbGxsc+quk+zupkgxZTJJX/OH\nPbjYrD5+ve/lRohcuXK13ZWOAcHO7og81gx7HYQQnH3hJfJKcPHyVXauwPbRdWQQEMVd4tJQTOZM\n5ykigHHpvpspG8pyRhwlzNKUZ55+mvVhn9F4hlKG0sDx46e49M3nKauSIAzJ85xnvvENoiMV6+td\nskJQZPtoYdjbvcDWVkPUFxy/614eyjY5f/Ys2yfOcHFvj/Hlq0TJgGI0p+hFlHlG1cCLr+xTNgWd\nwTG+8ORX+darYyoML2wKQhHx7ofuYtDXdLt9JlnDOJ1yrNOhaATzSzPSl0dcvHSJVy9PkFXJdJZS\nW1zJUqKIOyHDtXV0GPP+x3+Ihx59lLvPPIASXfJKEAVDBHOgIq9BiZhANnzj61/gq1/5Gl/9yteY\njefMo4x8XlLVNYE5UFqs65qiqom0oChrDAZlStAp0lY0dU2W5YwnBZGqOLrZoykFDQ1hlFDnFZBB\n6y+3KTFAVhrq0gWVZVGRJCHMZti+QkhBOguZ93KEqJhMauq8hjoniQpM7fw5Ly2VLdE6pEwhrxrU\n1ZQgFURJxDhs3GzsdMYf/OEneOc7vwcpNXmWMRj0UUpz8WLB88/vYC10+h2kLoA2I9OKr1gDeV4z\nGlXM5pILl2Z0u1N0nFM3krBu2B9fptd7AR0qZvmE2Wyfej5ub+ouQySEXQqI2HbDTUm38bXYVUYI\nrAoBQW8wRCQxl546RyMCN9O32UEHkjyfMRuPyNI5ogoIVEzWpGilOX/hMsYIJtOUL33pGaQImEzn\njMYjqqoiCCKCoMfZs5cZDofs7r5EL3DXx6effpq7Tp+mqmqGa2tUZUp6/ir9/pCf/Ls/zZe/8GVe\nPvsy/81/++v8yi/9MmFkybOaD37ox/j8577Mpz/9NZ5/4QI/93M/QxBIyjpfltgJIZGqw0c+8p/x\nxS9+ia999Sm+9KV/yi/8wi9wdHub8bhitFdgreLVc3tMZ8+jtSZLM86e3eHe+x5kd1Tw0vMXWV9f\np9PpUJbpMhttjKHX67tysygE3DV3gRBQla7ssK53CMIArfQy8xW0N2GEae8Pble8qkv29w1aWSJl\nEKRIKXj+VcM99z3M889fZT4veOWVParSLRCLoqSqKrJ8ipaaut5nMpkyGPR54YVL7O3v0+l0+OY3\nXkEKhTUJO7s50grqUoFIDvnLbUjrxyUvXLiEMU5sprEN1rhSTCE1WmeU7GPiPmpaUIcJ0GClRoiA\nKE6gSJlN94mG+8sF1TIbaS1SSXSVIk2FKFPsfJ+gydGmJFBgREjZWEKTYcMpWTAlmtdkUlMZgUHi\nunghEIZZlvHcKxfoNxMCaWks1CIktwrblo6rxKmyN3VJXVZgcorJHh0BTTZzitVRSCNipJ5QNgkV\nEdXLOzT6IuFwn83NCMQI7Jy8zJilL5Fna+R5xrToE9iK0FQETc701UuIuiCyhetbtjVgMI0baZbm\nBc+8cp5xVhAONmh0B5EU6GhGLkegNLWKkU2DmY3ZP3+WuEgRQYgMO4ggwbQzaso8oyomBFHCoL+O\nUIog1AhVUzc1SdxHS0U/jCmbmroVH6Jy5ZCyVfK1lqUAk8Uuyz5tqzk0m4x55utPYo0TLKrrhjzL\nMcYynozZ298lihRRFCKlYvfSKwgkQrvNpjrbJ5+N6HVjalMhBSjp/Lrb7RJH0VKxd/H/Srm+VmgL\nE4wLDF0rnVkqIxc1WNM4HYDGIOoK0zTMyowX9i8yy8bYckJdT0kaGOc5TTYiLa6SyARdzAlFRmNS\nRJCT2Rob7iJ6U+YEFDJqJZkMohwf8pnbkBhgPJ0dEq8yrTK3xLoNw/ZaKtXBaKRrM3oHPc60i3S5\nHP+0WJov1uiNtW0W1Z0/UuYHLTsmo1ls1JkGayxSzZfXBykkQgrKqmJ/MgUOzkUpw2XFxAIpXH+x\nPbQ5wPI8dkHhwbzuRXC3WJetjqxyn7ESOGKdMrlYlI5DEHfQSYEII4RQNFoyKwsii6s8URGDKKCz\n7YTt8jznW88/SxC4OCAIJEXVVgHB8n4UhRGzeUaeZ5w5c4b5fE6Wpbz00gu8/PKLnDlzhvvvv59B\nu2m+0HGx1m0suTPSrmwqLwRhLcYeBJHL4yadaFyeF7x6/iKL+KZp6ja+ceeEoj2GbXUZbTLE1C64\nRxjXT75I/VnQOnTl2qZcbpRcq0lgjKsGkspdc9xscnMgDLssjREr3weUdpOVRuOJqyo6lNW2Tg8H\nQVEW7txos/KmccLAiyz2MpC3lv3xUlD0O/qxuF4Zx+2GEOI/Bf6vW22Hx3OH8Pettf/yVhtxPbwv\nezxvitvSl70fezxvGu/LHs+dz3f04zslsN4Efhx4Gbh2crrH43HEwBngT621t6WSmfdlj+cNcVv7\nsvdjj+cN433Z47nzecN+fEcE1h6Px+PxeDwej8fj8dyu3FFzrD0ej8fj8Xg8Ho/H47nd8IG1x+Px\neDwej8fj8Xg8N4APrD0ej8fj8Xg8Ho/H47kBfGDt8Xg8Ho/H4/F4PB7PDeADa4/H4/F4PB6Px+Px\neG6AOyKwFkL8shDiJSFEJoT4rBDiPbfapgVCiF8TQphrfr55zWv+iRDighAiFUL8uRDi/rfZxh8U\nQvw/QojzrX0fvs5rvq2NQohICPG/CiF2hBBTIcTHhBBHb5XNQojfvs5x/+NbbPN/LYT4vBBiIoS4\nLIT4QyHEA9d53W11rN9OvC/fsI3el2+yzd6PvzPej2/YRu/H/p58W+B9+YZt9L7sffkQt31gLYT4\ne8A/BX4N+D7gq8CfCiG2bqlhh3kK2AaOtT8/sHhCCPFfAb8C/EPgvcAcZ3/4NtrXBb4C/GPgNfPV\n3qCN/xz4u8DPAR8ATgB/cKtsbvkTDh/3/+Sa599um38Q+F+A9wF/GwiAPxNCJIsX3KbH+m3B+/Jb\ngvflm2+z9+Nvg/fjtwTvx/6efMvxvvyW4H3Z+/JhrLW39Q/wWeB/XnksgHPAr95q21p7fg348rd5\n/gLwX648HgAZ8JFbZK8BPvxmbGwfF8DPrrzmwfa93nuLbP5t4F9/m9+5pTa3n7fVft4P3CnH+iYf\nD+/Lb6293pffHpu9Hx8+Ht6P31p7vR/7e/It+fG+/Jbb633Z+/LtnbEWQgTAY8CnFv9n3ZH4JPD+\nW2XXdXhHW1LxghDid4QQpwCEEPfgdnpW7Z8An+M2sf8N2vg4oK95zbPAK9za7/GhtiTkGSHEbwoh\nNlaee4xbb/MabjdwBHf8sb4hvC/ffO7w8+t29mXvxy3ej28+d/j5dTv7MXhfXuJ9+eZzh59f3pf/\nhtzWgTVuR0IBl6/5/8u4A3g78FngPwd+HPgl4B7g3wshujgbLbe3/W/Exm2gbE/S13vN282fAP8A\n+GHgV4EPAn8shBDt88e4hTa3dvxz4K+stYueoDv1WL8VeF+++dyp59dt68vej1+D9+Obz516ft22\nfgzel6+D9+Wbz516fnlfvgH0W/VG/3/FWvunKw+fEkJ8HjgLfAR45tZY9d2Ptfb3Vx5+QwjxdeAF\n4EPAv7slRh3mN4FHgO+/1YZ43hjel28Nt7kvez++w/B+fGu4zf0YvC/fcXhfvjV4X74xbveM9Q7Q\n4HYZVtkGLr395nxnrLVj4DngfpyNgtvb/jdi4yUgFEIMvs1rbinW2pdw58tCAfCW2SyE+A3g7wAf\nstZeXHnqu+JY/w3xvnzz+a44v24XX/Z+fF28H998vivOr9vFj8H78uvgffnm811xfnlffnPc1oG1\ntbYCvgT8yOL/2hKAHwE+favs+nYIIXq4k+9CezJe4rD9A5yq3W1h/xu08UtAfc1rHgTuBj7zthn7\nbRBC3AVsAgtHuyU2t07/08APWWtfWX3uu+VY/03wvnzz+W45v24HX/Z+fH28H998vlvOr9vBj9vP\n8L58Hbwv33y+W84v78tvkrdKBe1m/eBKPlJcvf9DwG8Bu8CRW21ba9//hJNsPw08Afw5rl5/s33+\nV1t7fwr4HuCPgG8B4dtoYxd4F/BunPrdf9E+PvVGbcSVXryEKwV5DPhr4C9vhc3tc/8jzmFO45zk\ni8DTQHALbf5NYA83FmB75Sdeec1td6zfxvPQ+/KN2+h9+Sbb7P34Ox4f78c3bqP3Y39PvuU/3pdv\nrl/crueX9+Wba/ctd5w3eED/MfAyTjb9M8Djt9qmFdt+FzeeIMMpy/1L4J5rXvPf4WTgU+BPgfvf\nZhs/2DpPc83P//FGbQQi3Ay5HWAKfBQ4eitsBmLgE7jdqRx4EfjfuOZmcAtsvp69DfAP3sz58Hbb\n/Tafi96Xb8xG78s32Wbvx2/oGHk/vjEbvR/7e/Jt8eN9+YZt9L7sffnQj2g/yOPxeDwej8fj8Xg8\nHs/fgNu6x9rj8Xg8Ho/H4/F4PJ7bHR9Yezwej8fj8Xg8Ho/HcwP4wNrj8Xg8Ho/H4/F4PJ4bwAfW\nHo/H4/F4PB6Px+Px3AA+sPZ4PB6Px+PxeDwej+cG8IG1x+PxeDwej8fj8Xg8N4APrD0ej8fj8Xg8\nHo/H47kBfGDt8Xg8Ho/H4/F4PB7PDeADa4/H4/F4PB6Px+PxeG4AH1h7PB6Px+PxeDwej8dzA/jA\n2uPxeDwej8fj8Xg8nhvg/wOAnJoNYaIu4wAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f176ead0a10>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plots(imgs, titles=labels)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can now pass the images to Vgg16's predict() function to get back probabilities, category indexes, and category names for each image's VGG prediction."
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(array([ 0.9392, 0.3244, 0.1253, 0.1698], dtype=float32),\n",
" array([285, 285, 478, 173]),\n",
" [u'Egyptian_cat', u'Egyptian_cat', u'carton', u'Ibizan_hound'])"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"vgg.predict(imgs, True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The category indexes are based on the ordering of categories used in the VGG model - e.g here are the first four:"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[u'tench', u'goldfish', u'great_white_shark', u'tiger_shark']"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"vgg.classes[:4]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"(Note that, other than creating the Vgg16 object, none of these steps are necessary to build a model; they are just showing how to use the class to view imagenet predictions.)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Use our Vgg16 class to finetune a Dogs vs Cats model\n",
"\n",
"To change our model so that it outputs \"cat\" vs \"dog\", instead of one of 1,000 very specific categories, we need to use a process called \"finetuning\". Finetuning looks from the outside to be identical to normal machine learning training - we provide a training set with data and labels to learn from, and a validation set to test against. The model learns a set of parameters based on the data provided.\n",
"\n",
"However, the difference is that we start with a model that is already trained to solve a similar problem. The idea is that many of the parameters should be very similar, or the same, between the existing model, and the model we wish to create. Therefore, we only select a subset of parameters to train, and leave the rest untouched. This happens automatically when we call *fit()* after calling *finetune()*.\n",
"\n",
"We create our batches just like before, and making the validation set available as well. A 'batch' (or *mini-batch* as it is commonly known) is simply a subset of the training data - we use a subset at a time when training or predicting, in order to speed up training, and to avoid running out of memory."
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"batch_size=64"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Found 23000 images belonging to 2 classes.\n",
"Found 2000 images belonging to 2 classes.\n"
]
}
],
"source": [
"batches = vgg.get_batches(path+'train', batch_size=batch_size)\n",
"val_batches = vgg.get_batches(path+'valid', batch_size=batch_size)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Calling *finetune()* modifies the model such that it will be trained based on the data in the batches provided - in this case, to predict either 'dog' or 'cat'."
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"vgg.finetune(batches)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Finally, we *fit()* the parameters of the model using the training data, reporting the accuracy on the validation set after every epoch. (An *epoch* is one full pass through the training data.)"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 1/1\n",
"23000/23000 [==============================] - 587s - loss: 0.1116 - acc: 0.9696 - val_loss: 0.0528 - val_acc: 0.9835\n"
]
}
],
"source": [
"vgg.fit(batches, val_batches, nb_epoch=1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"That shows all of the steps involved in using the Vgg16 class to create an image recognition model using whatever labels you are interested in. For instance, this process could classify paintings by style, or leaves by type of disease, or satellite photos by type of crop, and so forth.\n",
"\n",
"Next up, we'll dig one level deeper to see what's going on in the Vgg16 class."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Create a VGG model from scratch in Keras\n",
"\n",
"For the rest of this tutorial, we will not be using the Vgg16 class at all. Instead, we will recreate from scratch the functionality we just used. This is not necessary if all you want to do is use the existing model - but if you want to create your own models, you'll need to understand these details. It will also help you in the future when you debug any problems with your models, since you'll understand what's going on behind the scenes."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Model setup\n",
"\n",
"We need to import all the modules we'll be using from numpy, scipy, and keras:"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from numpy.random import random, permutation\n",
"from scipy import misc, ndimage\n",
"from scipy.ndimage.interpolation import zoom\n",
"\n",
"import keras\n",
"from keras import backend as K\n",
"from keras.utils.data_utils import get_file\n",
"from keras.models import Sequential, Model\n",
"from keras.layers.core import Flatten, Dense, Dropout, Lambda\n",
"from keras.layers import Input\n",
"from keras.layers.convolutional import Convolution2D, MaxPooling2D, ZeroPadding2D\n",
"from keras.optimizers import SGD, RMSprop\n",
"from keras.preprocessing import image"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's import the mappings from VGG ids to imagenet category ids and descriptions, for display purposes later."
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"FILES_PATH = 'http://files.fast.ai/models/'; CLASS_FILE='imagenet_class_index.json'\n",
"# Keras' get_file() is a handy function that downloads files, and caches them for re-use later\n",
"fpath = get_file(CLASS_FILE, FILES_PATH+CLASS_FILE, cache_subdir='models')\n",
"with open(fpath) as f: class_dict = json.load(f)\n",
"# Convert dictionary with string indexes into an array\n",
"classes = [class_dict[str(i)][1] for i in range(len(class_dict))]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Here's a few examples of the categories we just imported:"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[u'tench', u'goldfish', u'great_white_shark', u'tiger_shark', u'hammerhead']"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"classes[:5]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Model creation\n",
"\n",
"Creating the model involves creating the model architecture, and then loading the model weights into that architecture. We will start by defining the basic pieces of the VGG architecture.\n",
"\n",
"VGG has just one type of convolutional block, and one type of fully connected ('dense') block. Here's the convolutional block definition:"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def ConvBlock(layers, model, filters):\n",
" for i in range(layers): \n",
" model.add(ZeroPadding2D((1,1)))\n",
" model.add(Convolution2D(filters, 3, 3, activation='relu'))\n",
" model.add(MaxPooling2D((2,2), strides=(2,2)))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"...and here's the fully-connected definition."
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def FCBlock(model):\n",
" model.add(Dense(4096, activation='relu'))\n",
" model.add(Dropout(0.5))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"When the VGG model was trained in 2014, the creators subtracted the average of each of the three (R,G,B) channels first, so that the data for each channel had a mean of zero. Furthermore, their software that expected the channels to be in B,G,R order, whereas Python by default uses R,G,B. We need to preprocess our data to make these two changes, so that it is compatible with the VGG model:"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Mean of each channel as provided by VGG researchers\n",
"vgg_mean = np.array([123.68, 116.779, 103.939]).reshape((3,1,1))\n",
"\n",
"def vgg_preprocess(x):\n",
" x = x - vgg_mean # subtract mean\n",
" return x[:, ::-1] # reverse axis bgr->rgb"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we're ready to define the VGG model architecture - look at how simple it is, now that we have the basic blocks defined!"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def VGG_16():\n",
" model = Sequential()\n",
" model.add(Lambda(vgg_preprocess, input_shape=(3,224,224)))\n",
"\n",
" ConvBlock(2, model, 64)\n",
" ConvBlock(2, model, 128)\n",
" ConvBlock(3, model, 256)\n",
" ConvBlock(3, model, 512)\n",
" ConvBlock(3, model, 512)\n",
"\n",
" model.add(Flatten())\n",
" FCBlock(model)\n",
" FCBlock(model)\n",
" model.add(Dense(1000, activation='softmax'))\n",
" return model"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We'll learn about what these different blocks do later in the course. For now, it's enough to know that:\n",
"\n",
"- Convolution layers are for finding patterns in images\n",
"- Dense (fully connected) layers are for combining patterns across an image\n",
"\n",
"Now that we've defined the architecture, we can create the model like any python object:"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"model = VGG_16()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As well as the architecture, we need the weights that the VGG creators trained. The weights are the part of the model that is learnt from the data, whereas the architecture is pre-defined based on the nature of the problem. \n",
"\n",
"Downloading pre-trained weights is much preferred to training the model ourselves, since otherwise we would have to download the entire Imagenet archive, and train the model for many days! It's very helpful when researchers release their weights, as they did here."
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"fpath = get_file('vgg16.h5', FILES_PATH+'vgg16.h5', cache_subdir='models')\n",
"model.load_weights(fpath)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Getting imagenet predictions\n",
"\n",
"The setup of the imagenet model is now complete, so all we have to do is grab a batch of images and call *predict()* on them."
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"batch_size = 4"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Keras provides functionality to create batches of data from directories containing images; all we have to do is to define the size to resize the images to, what type of labels to create, whether to randomly shuffle the images, and how many images to include in each batch. We use this little wrapper to define some helpful defaults appropriate for imagenet data:"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def get_batches(dirname, gen=image.ImageDataGenerator(), shuffle=True, \n",
" batch_size=batch_size, class_mode='categorical'):\n",
" return gen.flow_from_directory(path+dirname, target_size=(224,224), \n",
" class_mode=class_mode, shuffle=shuffle, batch_size=batch_size)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"From here we can use exactly the same steps as before to look at predictions from the model."
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Found 23000 images belonging to 2 classes.\n",
"Found 2000 images belonging to 2 classes.\n"
]
},
{
"data": {
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sbw2RIsD3AzASUxZIKfmvPvbj/NRP/wK+54EwaF2iPIU0AVIJ7Cnaylffl0gPqrys9Z3g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SRaaVKtEUYglSNKNUMXE0VhHe7ZQ6EQti8g1T21GG4QbCklwv2SXG8RvhekIjdF/tbt3bkX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\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7fcxg0OX8/Jwg8DBG4EuDrzTj0YAg6hNEEZQB6SoFP8GrVdXXyxVpEpPrLR+//0ekizn5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\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etNaYTiMDSRhFdE1LoBRJbCt1jKdTrz/hgEm3RkRRxCydEYUJSijCMGacZjRtxTgNKKsN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\nkiRJHbiwliRJkiSpg38Ai2/y3JDx1cEAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f1762512650>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"batches = get_batches('train', batch_size=batch_size)\n",
"val_batches = get_batches('valid', batch_size=batch_size)\n",
"imgs,labels = next(batches)\n",
"\n",
"# This shows the 'ground truth'\n",
"plots(imgs, titles=labels)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The VGG model returns 1,000 probabilities for each image, representing the probability that the model assigns to each possible imagenet category for each image. By finding the index with the largest probability (with *np.argmax()*) we can find the predicted label."
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def pred_batch(imgs):\n",
" preds = model.predict(imgs)\n",
" idxs = np.argmax(preds, axis=1)\n",
"\n",
" print('Shape: {}'.format(preds.shape))\n",
" print('First 5 classes: {}'.format(classes[:5]))\n",
" print('First 5 probabilities: {}\\n'.format(preds[0, :5]))\n",
" print('Predictions prob/class: ')\n",
" \n",
" for i in range(len(idxs)):\n",
" idx = idxs[i]\n",
" print (' {:.4f}/{}'.format(preds[i, idx], classes[idx]))"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Shape: (4, 1000)\n",
"First 5 classes: [u'tench', u'goldfish', u'great_white_shark', u'tiger_shark', u'hammerhead']\n",
"First 5 probabilities: [ 1.1169e-08 1.7160e-07 2.2501e-06 2.3426e-08 5.9417e-08]\n",
"\n",
"Predictions prob/class: \n",
" 0.2285/papillon\n",
" 0.2947/lynx\n",
" 0.6434/Egyptian_cat\n",
" 0.4845/Australian_terrier\n"
]
}
],
"source": [
"pred_batch(imgs)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
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| apache-2.0 |
ProfessorKazarinoff/staticsite | content/code/matplotlib_plots/plot_an_FET_transfer_curve.ipynb | 1 | 25067 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Plot a field-effect transistor transfer curve"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
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"execution_count": 3,
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"source": [
"df = pd.read_csv('FET_data.csv', header=None)\n",
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"V = df[0].values\n",
"I = df[1].values\n",
"logI = np.log10(I)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
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Gb6KqA39SkTeAjB423QKsBCoBBX4GjFHV63o4xvXA9QDjx4+fv2fPnsAFbMwgsrOigcdW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\ngg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x145a3f9b668>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.plot(V,logI)\n",
"plt.xlabel('Voltage (V)')\n",
"plt.ylabel('Log of Current (logA)')\n",
"plt.title('Voltage vs. Current of a FET')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Calculate the on/off ratio and threshold voltage"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"855045.87155963306"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"on_off_ratio = np.max(I)/np.min(I)\n",
"on_off_ratio"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
"loc = np.argwhere(I==min(I))"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [],
"source": [
"Vt_array = V[loc]"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"Vt_flt = float(Vt_array)"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"4.0"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Vt_flt"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"float"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"type(Vt_flt)"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The on/off ratio is 855045.9 and the threshold voltage is 4.0 V\n"
]
}
],
"source": [
"print('The on/off ratio is {0:4.1f} and the threshold voltage is {1:2.1f} V'.format(on_off_ratio,Vt_flt))"
]
}
],
"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
}
| gpl-3.0 |
cavestruz/MLPipeline | notebooks/anomaly_detection/anomaly_detection_zhu.ipynb | 1 | 90463 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let us first explore an example that falls under novelty detection. Here, we train a model on data with some distribution and no outliers. The test data, has some \"novel\" subset of data that does not follow that distribution."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"from sklearn import svm\n",
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Use the np.random module to generate a normal distribution of 1,000 data points in two dimensions (e.g. x, y) - choose whatever mean and sigma^2 you like. Generate another 1,000 data points with a normal distribution in two dimensions that are well separated from the first set. You now have two \"clusters\". Concatenate them so you have 2,000 data points in two dimensions. Plot the points. This will be the training set."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"X_train_normal = np.concatenate((np.random.randn(1000,2), 2*np.random.randn(1000,2)+8.))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
" Plot the points."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.collections.PathCollection at 0x10f11ae50>"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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bezl6/chrVvJe+JlhKjOMM5MVZImQIYZJn/CZyUpdBdGwzO/OYvuBYmy/iIlVsN9uJmdl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\nDkUUgCAIQofy/wFBWrbG9mh+TQAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x109b43e10>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.scatter(X_train_normal[:,0],X_train_normal[:,1])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Generate 100 data points with the same distribution as your first random normal 2-d set, and 100 data points with the same distribution as your second random normal 2-d set. This will be the test set labeled X_test_normal."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"X_test_normal = np.concatenate((np.random.randn(100,2), 3*np.random.randn(100,2)+10.))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Generate 100 data points with a random uniform distribution. This will be the test set labeled X_test_uniform."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"X_test_uniform = np.random.rand(100,2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Define a model classifier with the svm.OneClassSVM"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"model = svm.OneClassSVM()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Fit the model to the training data."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"OneClassSVM(cache_size=200, coef0=0.0, degree=3, gamma='auto', kernel='rbf',\n",
" max_iter=-1, nu=0.5, random_state=None, shrinking=True, tol=0.001,\n",
" verbose=False)"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model.fit(X_train_normal)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Use the trained model to predict whether X_test_normal data point are in the same distributions. Calculate the fraction of \"false\" predictions."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([ 1., -1., 1., 1., -1., 1., 1., 1., -1., -1., 1., -1., 1.,\n",
" 1., 1., 1., 1., 1., 1., 1., 1., -1., 1., 1., -1., 1.,\n",
" 1., 1., 1., -1., -1., 1., 1., 1., 1., 1., 1., -1., 1.,\n",
" 1., 1., 1., 1., -1., -1., -1., -1., 1., 1., -1., 1., 1.,\n",
" 1., 1., -1., -1., 1., 1., 1., 1., 1., -1., 1., 1., 1.,\n",
" 1., -1., 1., -1., 1., 1., -1., -1., 1., 1., 1., 1., 1.,\n",
" -1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., -1., 1.,\n",
" 1., 1., -1., 1., -1., -1., 1., 1., 1., 1., 1., 1., -1.,\n",
" -1., -1., -1., -1., -1., -1., -1., 1., -1., -1., -1., -1., -1.,\n",
" -1., -1., -1., -1., -1., -1., -1., -1., -1., -1., -1., 1., -1.,\n",
" -1., -1., 1., -1., -1., 1., -1., -1., -1., -1., -1., -1., -1.,\n",
" -1., -1., -1., -1., -1., -1., -1., -1., -1., -1., -1., -1., -1.,\n",
" -1., -1., -1., 1., -1., 1., -1., -1., -1., -1., -1., -1., -1.,\n",
" -1., -1., -1., -1., -1., -1., -1., -1., -1., -1., -1., -1., -1.,\n",
" -1., -1., -1., -1., 1., -1., -1., -1., -1., -1., -1., -1., -1.,\n",
" -1., -1., -1., -1., -1.])"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model.predict(X_test_normal)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Use the trained model to predict whether X_test_uniform is in the same distribution. Calculate the fraction of \"false\" predictions."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.0752688172043\n"
]
}
],
"source": [
"from collections import Counter\n",
"\n",
"L1=model.predict(X_test_uniform)\n",
"a = Counter(L1).values()[0] \n",
"b = Counter(L1).values()[1]\n",
"c = float(b)/a\n",
"print c"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Use the trained model to see how well it recovers the training data. (Predict on the training data, and calculate the fraction of \"false\" predictions.)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1.002002002\n"
]
}
],
"source": [
"L2=model.predict(X_train_normal)\n",
"a2 = Counter(L2).values()[0]\n",
"b2 = Counter(L2).values()[1]\n",
"c2 = float(b2)/a2\n",
"print c2"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Create another instance of the model classifier, but change the kwarg value for nu. Hint: Use help to figure out what the kwargs are."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"model2 = svm.OneClassSVM(nu=.2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Redo the prediction on the training set, prediction on X_test_random, and prediction on X_test."
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([ 1., 1., 1., 1., -1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
" 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
" 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
" 1., 1., 1., 1., 1., 1., 1., 1., -1., 1., 1., 1., 1.,\n",
" 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
" 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
" 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
" 1., 1., 1., 1., 1., 1., 1., 1., 1.])"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model2.fit(X_train_normal)\n",
"L3=model2.predict(X_train_normal)\n",
"Counter(L3)\n",
"model2.predict(X_test_normal)\n",
"model2.predict(X_test_uniform)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Plot in scatter points the X_train in blue, X_test_normal in red, and X_test_uniform in black. Overplot the trained model decision function boundary for the first instance of the model classifier."
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.contour.QuadContourSet at 0x10f2e05d0>"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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Szzt9fXDZZe4/UIGkzmlG+2HT1eV+wPE4DA6W6UvrBxF5UFXX590uLGFPvn9FVZd47LsF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\nGzcAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10f237310>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.scatter(X_train_normal[:,0],X_train_normal[:,1],color='b')\n",
"plt.scatter(X_test_normal[:,0],X_test_normal[:,1],color='r')\n",
"plt.scatter(X_test_uniform[:,0],X_test_uniform[:,1],color='k')\n",
"\n",
"xx1, yy1 = np.meshgrid(np.linspace(-5, 22, 1000), np.linspace(-5, 22,1000))\n",
"Z1 =model.decision_function(np.c_[xx1.ravel(), yy1.ravel()])\n",
"Z1 = Z1.reshape(xx1.shape)\n",
"plt.contour(xx1, yy1, Z1, levels=[0],\n",
" linewidths=2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Do the same for the second instance of the model classifier."
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.contour.QuadContourSet at 0x10f0a3a50>"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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TjY64lR9i6+C+MnYDpKwQlY19HE2XzqNM9jCXxzOWvZx6enEJ7ezthcZGXn9t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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10f164a10>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.scatter(X_train_normal[:,0],X_train_normal[:,1],color='b')\n",
"plt.scatter(X_test_normal[:,0],X_test_normal[:,1],color='r')\n",
"plt.scatter(X_test_uniform[:,0],X_test_uniform[:,1],color='k')\n",
"\n",
"xx1, yy1 = np.meshgrid(np.linspace(-5, 22, 1000), np.linspace(-5, 22,1000))\n",
"Z1 =model2.decision_function(np.c_[xx1.ravel(), yy1.ravel()])\n",
"Z1 = Z1.reshape(xx1.shape)\n",
"plt.contour(xx1, yy1, Z1, levels=[0],\n",
" linewidths=2)"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"For this second example, we will explore what is known as \"outlier\" detection, where you do not have a training set of unpolluted data, but must instead explore what the outliers might be. Note, the difference between novelty detection and outlier detection is analogous to the difference between supervised and unsupervised classification. We will use tools from sklearn.covariance to illustrate this. "
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from sklearn.covariance import EllipticEnvelope"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Test how well EllipticEnvelope predicts the outliers when you concatenate the training data with the X_test_uniform data."
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([1, 1, 1, ..., 1, 1, 1])"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train_uniform=np.concatenate((X_train_normal,X_test_uniform))\n",
"envelope=EllipticEnvelope()\n",
"envelope.fit(train_uniform)\n",
"envelope.predict(train_uniform)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Compute and plot the mahanalobis distances of X_test, X_train_normal, X_train_uniform"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.collections.PathCollection at 0x1157dd6d0>"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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opNrvq5p7xCz9vlnvR2T7NnYRqcduUgkul300Uh0Ozfpez7wOBuG/q3sZDrdu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\nQk8IIR2HQk8IIR2HQk8IIR3n/wNykNulfKntdAAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10f209350>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.scatter(range(100),envelope.mahalanobis(X_test_uniform),color='black') #idk why but on the graph it's red...\n",
"plt.scatter(range(2000),envelope.mahalanobis(X_train_normal),color='b')\n",
"plt.scatter(range(200),envelope.mahalanobis(X_test_normal),color='r')"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.13"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| mit |
RaspberryJamBe/ipython-notebooks | notebooks/nl-be/Input - Temperatuur.ipynb | 1 | 2228 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<img src=\"Temperature01.png\" height=\"300\" />"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## DS18B20 1-wire temperatuur sensor\n",
"\n",
"- VDD = 3v3\n",
"- signaalpin = GPIO4\n",
"- 4.7 KOhm pull-up op signaal\n",
"\n",
"Raspbery Pi installatie + detectie device ID:\n",
"```\n",
"> sudo modprobe w1-gpio\n",
"> sudo modprobe w1-therm\n",
"> cd /sys/bus/w1/devices/\n",
"> ls\n",
"```\n",
"\n",
"nota: opgelet met Raspberry Pi 2 (wegens DeviceTree): \n",
"\"dtoverlay=w1-gpio\" toevoegen aan /boot/config.txt \n",
"http://www.raspberrypi.org/forums/viewtopic.php?f=28&t=97314"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Info inlezen vanuit sensor in fileformaat\n",
"temp_file = open(\"/sys/bus/w1/devices/28-011465166dff/w1_slave\")\n",
"temp_tekst = temp_file.read() \n",
"temp_file.close()\n",
"\n",
"# De temperatuur is te vinden op de tweede lijn in de tiende kolom\n",
"tweede_lijn = temp_tekst.split(\"\\n\")[1] \n",
"temperatuur_tekst = tweede_lijn.split(\" \")[9]\n",
"\n",
"# De eerste twee karakters zijn \"t=\", dus die laten we vallen, zodat we de rest kunnen converteren naar een nummer.\n",
"temperatuur = float(temperatuur_tekst[2:]) \n",
"# Omzetten van milligraden naar graden. \n",
"temperatuur = temperatuur / 1000 \n",
"print(\"Gemeten temperatuur: {}\".format(temperatuur))"
]
},
{
"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": "2.7.3"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| cc0-1.0 |
jakevdp/ESAC-stats-2014 | notebooks/01.2-Model-Fitting-Breakout.ipynb | 1 | 58618 | {
"metadata": {
"name": "",
"signature": "sha256:133e791b47e184498d5bea51d0829c1dafa7ae5909abe09c4f170c9084951099"
},
"nbformat": 3,
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"worksheets": [
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Frequentist Model Fitting Breakout\n",
"\n",
"In this session, we're going to fit some **Generalized Linear Models** to astronomical data.\n",
"\n",
"As usual, we'll start with the standard imports and IPython notebook setup:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%matplotlib inline\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"\n",
"# use seaborn plotting defaults\n",
"# If this causes an error, you can comment it out.\n",
"import seaborn as sns\n",
"sns.set()"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 1
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Part I: Fourier Fit to RR Lyrae\n",
"\n",
"We'll start by doing a multi-term Fourier fit to an RR Lyrae star.\n",
"Note that downloading the data will require installation of [astroML](http://astroml.org), which can be done easily by running\n",
"\n",
"```\n",
"$ pip install astroML\n",
"```\n",
"\n",
"at the command-line."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from fig_code import sample_light_curve\n",
"t, y, dy = sample_light_curve()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"[10003298 10004892 10013411 ..., 9984569 9987252 999528]\n"
]
}
],
"prompt_number": 2
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Visualize the data:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"plt.errorbar(t, y, dy, fmt='o')\n",
"plt.gca().invert_yaxis();"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
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4Vet03c5Z0he4n3anvhN+vQtPmw7mvQtPy+QxzPJ3UyrXr1vneRLibqBEXZpW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\n",
"text": [
"<matplotlib.figure.Figure at 0x10a179690>"
]
}
],
"prompt_number": 3
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This data has already been phased, so if we just take the fractional part of *t* then we can see the folded light curve"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"plt.errorbar(t % 1, y, dy, fmt='o')\n",
"plt.gca().invert_yaxis();"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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9bsl8zc2Vqu/n9uyL9XXuBw/N6OYd/8u3FjxuC9dWu+2T50lKd2xJ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\nrkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x10a760090>"
]
}
],
"prompt_number": 4
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Here is your task: **Fit a Fourier model to this data** of the form\n",
"\n",
"$$\n",
"y = a_0 + a_1 \\sin(\\omega t) + b_1 \\cos(\\omega t) + a_2 \\sin(2 \\omega t) + b_2 \\cos(2\\omega t)\n",
"$$\n",
"\n",
"and note that we can go to as high an order as we like.\n",
"First we'll do this using ``scipy.optimize.fmin`` (i.e. an iterative solution), and then we'll do it using the closed-form linear algebra solution.\n",
"\n",
"You should strive to make these functions as general as possible, i.e."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 1. Iterative Solution\n",
"\n",
"We'll start with an iterative solution to the problem.\n",
"\n",
"1. Create a function which evaluates the model given an array of times *t*, a base frequency $\\omega$, and an array of coefficients $\\theta$. **For this section, we will not treat $\\omega$ as a model parameter, but as a constant.** For your model, you can use $\\omega = 2\\pi$ (we'll relax this assumption in part II).\n",
"\n",
"2. Create a function which evaluates the log-likelihood as a function of $\\theta$, using the above function. **Keep in mind that ``theta`` must be a one-dimensional array (this is what ``optimize.fmin()`` requires)**\n",
"\n",
"3. Use ``scipy.optimize.fmin`` to maximize your log-likelihood (i.e. minimize the negative log-likelihood) to find the optimal model.\n",
"\n",
"4. Plot this model over the data to see how it looks.\n",
"\n",
"5. Use new data, from ``fig_code.sample_light_curve_2()``, and apply your code again (hopefully you've written the functions so that they can easily be reused, right?"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 2. Direct Solution\n",
"\n",
"Now that you've done this iteratively, let's compute the direct solution using linear algebra.\n",
"\n",
"1. Create a function which will construct the design matrix ($X$) given *t*, $\\omega$, and a specified number of terms\n",
"\n",
"2. Create a function which, given this design matrix, will compute the maximum likelihood parameters using ``np.linalg.dot`` and ``np.linalg.solve``.\n",
"\n",
"3. Plot this answer over your previous answer. Hint: they should agree!"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Part II: Bonus \u2013 finding the optimal phase\n",
"\n",
"It's possible (though much more difficult) to use maximum-likelihood estimation to find the best phase. Loading the data this way gives the raw MJD values of the observations."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"t, y, dy = sample_light_curve(phased=False)\n",
"plt.errorbar(t, y, dy, fmt='o');"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"[10003298 10004892 10013411 ..., 9984569 9987252 999528]\n"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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iXNLpjWuHA/9G83pZheVxxcz+g6QfSloq6R8kVTWdl1J4fpLH4eLm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\nAAB4jmAOAIDnCOYAAHiOYA4AgOf+P/9HfQ8DtZHSAAAAAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x10a073c10>"
]
}
],
"prompt_number": 7
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The model we want to fit is:\n",
"\n",
"$$\n",
"y = a_0 + a_1\\sin(\\omega t) + b_1\\cos(\\omega t)\n",
"$$\n",
"\n",
"except this time our parameter vector is $\\theta = [a_0, a_1, a_2, \\omega]$. Since we're fitting for $\\omega$ itself, this is **not a linear model**, so the closed-form solution will not work. Furthermore, this is a **non-convex** problem, so standard optimization will not work either!\n",
"\n",
"Instead, we'll do a bit of a hack: for each value of $\\omega$, we find the best $(a_0, a_1, b_1)$, and then report the value of the log-likelihood as a function of $\\omega$.\n",
"\n",
"Your task is to plot $\\omega$ vs. $\\log L_{max}(\\omega)$ and find the $\\omega$ which best-fits the data. Once you've done this, divide *t* by the phase, and re-produce the plots you did above."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We'll see later in the week that there is a more efficient way of doing this procedure, called the *Lomb-Scargle Periodogram*. At its core, however, it's essentially doing exactly what we do here, just much more efficiently."
]
}
],
"metadata": {}
}
]
} | bsd-2-clause |
feststelltaste/software-analytics | prototypes/Quantifying Graph Data.ipynb | 1 | 43231 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Introduction\n",
"This is a simple helper notebook to quickly get some numbers about your graphs out of a Neo4j database. You just need to start your Neo4j database locally with the default values before running this notebook.\n",
"\n",
"# Setup\n",
"First, we fire up the connection to Neo4j that contains all the data. If needed, you could add some custom parameters like URL or port to adjust the setup to your settings."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(3, 1, 3)"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import py2neo\n",
"import pandas as pd\n",
"graph = py2neo.Graph()\n",
"graph.dbms.kernel_version"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's get some numbers!"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Nodes"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Number of all Nodes"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[{'NumberOfAllNodes': 23829}]"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"graph.data(\"MATCH (n) RETURN COUNT(n) AS NumberOfAllNodes\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Nodes and their Labels"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>LabelCount</th>\n",
" <th>Labels</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>14034</td>\n",
" <td>[Git, Change]</td>\n",
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" <td>2847</td>\n",
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" <td>2143</td>\n",
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" <th>3</th>\n",
" <td>1335</td>\n",
" <td>[Xml, Element]</td>\n",
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" <tr>\n",
" <th>4</th>\n",
" <td>665</td>\n",
" <td>[File, Artifact, Maven, Container]</td>\n",
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" <tr>\n",
" <th>5</th>\n",
" <td>639</td>\n",
" <td>[Git, Commit]</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>327</td>\n",
" <td>[Java, Member, Method]</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>231</td>\n",
" <td>[Value, Java, Annotation]</td>\n",
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" <th>8</th>\n",
" <td>215</td>\n",
" <td>[Xml, Text]</td>\n",
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" <tr>\n",
" <th>9</th>\n",
" <td>177</td>\n",
" <td>[Value, Property]</td>\n",
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" <tr>\n",
" <th>10</th>\n",
" <td>162</td>\n",
" <td>[Java, Parameter]</td>\n",
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" <tr>\n",
" <th>11</th>\n",
" <td>159</td>\n",
" <td>[File, Type, Java]</td>\n",
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" <tr>\n",
" <th>12</th>\n",
" <td>110</td>\n",
" <td>[Value, Array]</td>\n",
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" <tr>\n",
" <th>13</th>\n",
" <td>99</td>\n",
" <td>[Value, Primitive]</td>\n",
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" <th>14</th>\n",
" <td>87</td>\n",
" <td>[Java, Field, Member]</td>\n",
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" <td>76</td>\n",
" <td>[Java, Member, Constructor, Method]</td>\n",
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" <th>16</th>\n",
" <td>62</td>\n",
" <td>[JUnit, TestCase]</td>\n",
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" <th>17</th>\n",
" <td>57</td>\n",
" <td>[File]</td>\n",
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" <tr>\n",
" <th>18</th>\n",
" <td>45</td>\n",
" <td>[Author, Git]</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>40</td>\n",
" <td>[Java, Member, Method, Test, Junit4]</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>29</td>\n",
" <td>[Maven, Plugin]</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>29</td>\n",
" <td>[Concept]</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>28</td>\n",
" <td>[Maven, Configuration]</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>26</td>\n",
" <td>[File, Type, Java, Class]</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>23</td>\n",
" <td>[Maven, ExecutionGoal]</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>23</td>\n",
" <td>[File, Package, Container, Directory, Java]</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>19</td>\n",
" <td>[Maven, PluginExecution]</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>19</td>\n",
" <td>[Value, Enum]</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>15</td>\n",
" <td>[File, Container, Directory]</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>10</td>\n",
" <td>[Value, Class]</td>\n",
" </tr>\n",
" <tr>\n",
" <th>30</th>\n",
" <td>10</td>\n",
" <td>[Git, Branch]</td>\n",
" </tr>\n",
" <tr>\n",
" <th>31</th>\n",
" <td>10</td>\n",
" <td>[File, Xml, Document, JUnit, TestSuite]</td>\n",
" </tr>\n",
" <tr>\n",
" <th>32</th>\n",
" <td>8</td>\n",
" <td>[File, Type, Repository, Java, Class, Spring, ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>33</th>\n",
" <td>7</td>\n",
" <td>[Subdomain]</td>\n",
" </tr>\n",
" <tr>\n",
" <th>34</th>\n",
" <td>7</td>\n",
" <td>[Java, Member, Method, AssertJ, Assert]</td>\n",
" </tr>\n",
" <tr>\n",
" <th>35</th>\n",
" <td>6</td>\n",
" <td>[File, Type, Java, Class, Jpa, Entity]</td>\n",
" </tr>\n",
" <tr>\n",
" <th>36</th>\n",
" <td>5</td>\n",
" <td>[File, Type, Java, Interface]</td>\n",
" </tr>\n",
" <tr>\n",
" <th>37</th>\n",
" <td>5</td>\n",
" <td>[File, Type, Java, Class, Spring, Component, C...</td>\n",
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" <tr>\n",
" <th>38</th>\n",
" <td>5</td>\n",
" <td>[File, Java, Properties]</td>\n",
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" <th>39</th>\n",
" <td>4</td>\n",
" <td>[Maven, Profile]</td>\n",
" </tr>\n",
" <tr>\n",
" <th>40</th>\n",
" <td>4</td>\n",
" <td>[File, Type, Repository, Java, Interface, Spri...</td>\n",
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" <tr>\n",
" <th>41</th>\n",
" <td>4</td>\n",
" <td>[Java, Member, Method, Spring, ManagedAttribute]</td>\n",
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" <tr>\n",
" <th>42</th>\n",
" <td>4</td>\n",
" <td>[File, Package, Container, Directory, Java, La...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>43</th>\n",
" <td>3</td>\n",
" <td>[Xml, Namespace]</td>\n",
" </tr>\n",
" <tr>\n",
" <th>44</th>\n",
" <td>1</td>\n",
" <td>[File, Repository, Git]</td>\n",
" </tr>\n",
" <tr>\n",
" <th>45</th>\n",
" <td>1</td>\n",
" <td>[Git, Branch, Current]</td>\n",
" </tr>\n",
" <tr>\n",
" <th>46</th>\n",
" <td>1</td>\n",
" <td>[Pom, Maven]</td>\n",
" </tr>\n",
" <tr>\n",
" <th>47</th>\n",
" <td>1</td>\n",
" <td>[Repository, Maven]</td>\n",
" </tr>\n",
" <tr>\n",
" <th>48</th>\n",
" <td>1</td>\n",
" <td>[File, Type, Java, Class, Spring, ManagedResou...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>49</th>\n",
" <td>1</td>\n",
" <td>[File, Package, Container, Directory, Java, Root]</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50</th>\n",
" <td>1</td>\n",
" <td>[Java, Member, Method, Spring, Assert]</td>\n",
" </tr>\n",
" <tr>\n",
" <th>51</th>\n",
" <td>1</td>\n",
" <td>[File, Type, Java, Class, Spring, Component, S...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>52</th>\n",
" <td>1</td>\n",
" <td>[File, Project, Maven, Container, Directory]</td>\n",
" </tr>\n",
" <tr>\n",
" <th>53</th>\n",
" <td>1</td>\n",
" <td>[File, Artifact, Maven, Container, Directory, ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>54</th>\n",
" <td>1</td>\n",
" <td>[File, Pom, Maven, Xml, Document]</td>\n",
" </tr>\n",
" <tr>\n",
" <th>55</th>\n",
" <td>1</td>\n",
" <td>[Java, Member, Method, Spring, ManagedOperation]</td>\n",
" </tr>\n",
" <tr>\n",
" <th>56</th>\n",
" <td>1</td>\n",
" <td>[File, Xml, Document]</td>\n",
" </tr>\n",
" <tr>\n",
" <th>57</th>\n",
" <td>1</td>\n",
" <td>[File, Container, Directory, Dependent]</td>\n",
" </tr>\n",
" <tr>\n",
" <th>58</th>\n",
" <td>1</td>\n",
" <td>[File, Artifact, Maven, Container, Directory, ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>59</th>\n",
" <td>1</td>\n",
" <td>[File, Container, Directory, TestReport, JUnit]</td>\n",
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],
"text/plain": [
" LabelCount Labels\n",
"0 14034 [Git, Change]\n",
"1 2847 [File, Git]\n",
"2 2143 [Xml, Attribute]\n",
"3 1335 [Xml, Element]\n",
"4 665 [File, Artifact, Maven, Container]\n",
"5 639 [Git, Commit]\n",
"6 327 [Java, Member, Method]\n",
"7 231 [Value, Java, Annotation]\n",
"8 215 [Xml, Text]\n",
"9 177 [Value, Property]\n",
"10 162 [Java, Parameter]\n",
"11 159 [File, Type, Java]\n",
"12 110 [Value, Array]\n",
"13 99 [Value, Primitive]\n",
"14 87 [Java, Field, Member]\n",
"15 76 [Java, Member, Constructor, Method]\n",
"16 62 [JUnit, TestCase]\n",
"17 57 [File]\n",
"18 45 [Author, Git]\n",
"19 40 [Java, Member, Method, Test, Junit4]\n",
"20 29 [Maven, Plugin]\n",
"21 29 [Concept]\n",
"22 28 [Maven, Configuration]\n",
"23 26 [File, Type, Java, Class]\n",
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"35 6 [File, Type, Java, Class, Jpa, Entity]\n",
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"40 4 [File, Type, Repository, Java, Interface, Spri...\n",
"41 4 [Java, Member, Method, Spring, ManagedAttribute]\n",
"42 4 [File, Package, Container, Directory, Java, La...\n",
"43 3 [Xml, Namespace]\n",
"44 1 [File, Repository, Git]\n",
"45 1 [Git, Branch, Current]\n",
"46 1 [Pom, Maven]\n",
"47 1 [Repository, Maven]\n",
"48 1 [File, Type, Java, Class, Spring, ManagedResou...\n",
"49 1 [File, Package, Container, Directory, Java, Root]\n",
"50 1 [Java, Member, Method, Spring, Assert]\n",
"51 1 [File, Type, Java, Class, Spring, Component, S...\n",
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"53 1 [File, Artifact, Maven, Container, Directory, ...\n",
"54 1 [File, Pom, Maven, Xml, Document]\n",
"55 1 [Java, Member, Method, Spring, ManagedOperation]\n",
"56 1 [File, Xml, Document]\n",
"57 1 [File, Container, Directory, Dependent]\n",
"58 1 [File, Artifact, Maven, Container, Directory, ...\n",
"59 1 [File, Container, Directory, TestReport, JUnit]"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd.DataFrame(graph.data(\"MATCH (n) RETURN labels(n) AS Labels, COUNT(n) AS LabelCount ORDER BY LabelCount DESC\"))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Relationships"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Number of all Relationships"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[{'NumberOfAllRelationships': 89926}]"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"graph.data(\"MATCH ()-[r]-() RETURN COUNT(r) AS NumberOfAllRelationships\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Relationships and their Types"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
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" <th>TypeCount</th>\n",
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" <tr>\n",
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" <td>CONTAINS_CHANGE</td>\n",
" <td>28068</td>\n",
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" <td>HAS_ATTRIBUTE</td>\n",
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" <td>HAS_ELEMENT</td>\n",
" <td>2646</td>\n",
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" <th>5</th>\n",
" <td>INVOKES</td>\n",
" <td>2200</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>HAS_SIBLING</td>\n",
" <td>2192</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>DEPENDS_ON</td>\n",
" <td>1728</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>HAS_PARENT</td>\n",
" <td>1466</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>COMMITTED</td>\n",
" <td>1278</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>HAS_COMMIT</td>\n",
" <td>1278</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>CONTAINS</td>\n",
" <td>1218</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>MANAGES_DEPENDENCY</td>\n",
" <td>1194</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>OF_TYPE</td>\n",
" <td>1146</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>DECLARES</td>\n",
" <td>1098</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>HAS_LAST_CHILD</td>\n",
" <td>884</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>HAS_FIRST_CHILD</td>\n",
" <td>884</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>OF_NAMESPACE</td>\n",
" <td>624</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>HAS</td>\n",
" <td>622</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>RETURNS</td>\n",
" <td>494</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>ANNOTATED_BY</td>\n",
" <td>458</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>HAS_TEXT</td>\n",
" <td>430</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>READS</td>\n",
" <td>362</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>REQUIRES</td>\n",
" <td>318</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>THROWS</td>\n",
" <td>156</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>DECLARES_DEPENDENCY</td>\n",
" <td>148</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>WRITES</td>\n",
" <td>122</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>EXTENDS</td>\n",
" <td>112</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>BELONGS_TO</td>\n",
" <td>100</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>HAS_AUTHOR</td>\n",
" <td>90</td>\n",
" </tr>\n",
" <tr>\n",
" <th>30</th>\n",
" <td>HAS_PROPERTY</td>\n",
" <td>60</td>\n",
" </tr>\n",
" <tr>\n",
" <th>31</th>\n",
" <td>IS</td>\n",
" <td>58</td>\n",
" </tr>\n",
" <tr>\n",
" <th>32</th>\n",
" <td>IS_ARTIFACT</td>\n",
" <td>58</td>\n",
" </tr>\n",
" <tr>\n",
" <th>33</th>\n",
" <td>HAS_CONFIGURATION</td>\n",
" <td>56</td>\n",
" </tr>\n",
" <tr>\n",
" <th>34</th>\n",
" <td>USES_PLUGIN</td>\n",
" <td>50</td>\n",
" </tr>\n",
" <tr>\n",
" <th>35</th>\n",
" <td>HAS_GOAL</td>\n",
" <td>46</td>\n",
" </tr>\n",
" <tr>\n",
" <th>36</th>\n",
" <td>IMPLEMENTS</td>\n",
" <td>42</td>\n",
" </tr>\n",
" <tr>\n",
" <th>37</th>\n",
" <td>HAS_EXECUTION</td>\n",
" <td>38</td>\n",
" </tr>\n",
" <tr>\n",
" <th>38</th>\n",
" <td>USES</td>\n",
" <td>38</td>\n",
" </tr>\n",
" <tr>\n",
" <th>39</th>\n",
" <td>HAS_ROOT_ELEMENT</td>\n",
" <td>24</td>\n",
" </tr>\n",
" <tr>\n",
" <th>40</th>\n",
" <td>HAS_HEAD</td>\n",
" <td>24</td>\n",
" </tr>\n",
" <tr>\n",
" <th>41</th>\n",
" <td>HAS_BRANCH</td>\n",
" <td>22</td>\n",
" </tr>\n",
" <tr>\n",
" <th>42</th>\n",
" <td>DEFINES_DEPENDENCY</td>\n",
" <td>10</td>\n",
" </tr>\n",
" <tr>\n",
" <th>43</th>\n",
" <td>HAS_PROFILE</td>\n",
" <td>8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>44</th>\n",
" <td>MANAGES_PLUGIN</td>\n",
" <td>8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>45</th>\n",
" <td>DECLARES_NAMESPACE</td>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>46</th>\n",
" <td>CREATES</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>47</th>\n",
" <td>DESCRIBES</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>48</th>\n",
" <td>HAS_REPOSITORY</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>49</th>\n",
" <td>HAS_MODEL</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50</th>\n",
" <td>HAS_EFFECTIVE_MODEL</td>\n",
" <td>2</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Type TypeCount\n",
"0 CONTAINS_CHANGE 28068\n",
"1 MODIFIES 28068\n",
"2 HAS_FILE 5694\n",
"3 HAS_ATTRIBUTE 4286\n",
"4 HAS_ELEMENT 2646\n",
"5 INVOKES 2200\n",
"6 HAS_SIBLING 2192\n",
"7 DEPENDS_ON 1728\n",
"8 HAS_PARENT 1466\n",
"9 COMMITTED 1278\n",
"10 HAS_COMMIT 1278\n",
"11 CONTAINS 1218\n",
"12 MANAGES_DEPENDENCY 1194\n",
"13 OF_TYPE 1146\n",
"14 DECLARES 1098\n",
"15 HAS_LAST_CHILD 884\n",
"16 HAS_FIRST_CHILD 884\n",
"17 OF_NAMESPACE 624\n",
"18 HAS 622\n",
"19 RETURNS 494\n",
"20 ANNOTATED_BY 458\n",
"21 HAS_TEXT 430\n",
"22 READS 362\n",
"23 REQUIRES 318\n",
"24 THROWS 156\n",
"25 DECLARES_DEPENDENCY 148\n",
"26 WRITES 122\n",
"27 EXTENDS 112\n",
"28 BELONGS_TO 100\n",
"29 HAS_AUTHOR 90\n",
"30 HAS_PROPERTY 60\n",
"31 IS 58\n",
"32 IS_ARTIFACT 58\n",
"33 HAS_CONFIGURATION 56\n",
"34 USES_PLUGIN 50\n",
"35 HAS_GOAL 46\n",
"36 IMPLEMENTS 42\n",
"37 HAS_EXECUTION 38\n",
"38 USES 38\n",
"39 HAS_ROOT_ELEMENT 24\n",
"40 HAS_HEAD 24\n",
"41 HAS_BRANCH 22\n",
"42 DEFINES_DEPENDENCY 10\n",
"43 HAS_PROFILE 8\n",
"44 MANAGES_PLUGIN 8\n",
"45 DECLARES_NAMESPACE 6\n",
"46 CREATES 4\n",
"47 DESCRIBES 4\n",
"48 HAS_REPOSITORY 2\n",
"49 HAS_MODEL 2\n",
"50 HAS_EFFECTIVE_MODEL 2"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd.DataFrame(graph.data(\"MATCH ()-[r]-() RETURN type(r) AS Type, COUNT(r) AS TypeCount ORDER BY TypeCount DESC\"))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Properties"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Number of all properties"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[{'NumberOfAllProperties': 44680}]"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"graph.data(\"MATCH (n) RETURN SUM(SIZE(KEYS(n))) as NumberOfAllProperties\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Amount of specific Properties"
]
},
{
"cell_type": "code",
"execution_count": 15,
"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>Property</th>\n",
" <th>PropertyCount</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>modificationKind</td>\n",
" <td>14034</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>name</td>\n",
" <td>5288</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>relativePath</td>\n",
" <td>2847</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>createdAtEpoch</td>\n",
" <td>2701</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>createdAt</td>\n",
" <td>2701</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>value</td>\n",
" <td>2628</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>deletedAtEpoch</td>\n",
" <td>1859</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>deletedAt</td>\n",
" <td>1859</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>fqn</td>\n",
" <td>913</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>time</td>\n",
" <td>711</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>type</td>\n",
" <td>667</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>group</td>\n",
" <td>667</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>version</td>\n",
" <td>645</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>date</td>\n",
" <td>639</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>epoch</td>\n",
" <td>639</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>sha</td>\n",
" <td>639</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>message</td>\n",
" <td>639</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>author</td>\n",
" <td>639</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>signature</td>\n",
" <td>543</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>visibility</td>\n",
" <td>385</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>lastModificationAtEpoch</td>\n",
" <td>342</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>lastModificationAt</td>\n",
" <td>342</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>fileName</td>\n",
" <td>337</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>cyclomaticComplexity</td>\n",
" <td>253</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>effectiveLineCount</td>\n",
" <td>232</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>lastLineNumber</td>\n",
" <td>232</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>firstLineNumber</td>\n",
" <td>232</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>index</td>\n",
" <td>162</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>transient</td>\n",
" <td>76</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>volatile</td>\n",
" <td>76</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>37</th>\n",
" <td>inherited</td>\n",
" <td>48</td>\n",
" </tr>\n",
" <tr>\n",
" <th>38</th>\n",
" <td>identString</td>\n",
" <td>45</td>\n",
" </tr>\n",
" <tr>\n",
" <th>39</th>\n",
" <td>email</td>\n",
" <td>45</td>\n",
" </tr>\n",
" <tr>\n",
" <th>40</th>\n",
" <td>status</td>\n",
" <td>29</td>\n",
" </tr>\n",
" <tr>\n",
" <th>41</th>\n",
" <td>abstract</td>\n",
" <td>23</td>\n",
" </tr>\n",
" <tr>\n",
" <th>42</th>\n",
" <td>final</td>\n",
" <td>17</td>\n",
" </tr>\n",
" <tr>\n",
" <th>43</th>\n",
" <td>phase</td>\n",
" <td>13</td>\n",
" </tr>\n",
" <tr>\n",
" <th>44</th>\n",
" <td>standalone</td>\n",
" <td>12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>45</th>\n",
" <td>characterEncodingScheme</td>\n",
" <td>12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>46</th>\n",
" <td>xmlWellFormed</td>\n",
" <td>12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>47</th>\n",
" <td>xmlVersion</td>\n",
" <td>12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>48</th>\n",
" <td>synthetic</td>\n",
" <td>11</td>\n",
" </tr>\n",
" <tr>\n",
" <th>49</th>\n",
" <td>failures</td>\n",
" <td>10</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50</th>\n",
" <td>tests</td>\n",
" <td>10</td>\n",
" </tr>\n",
" <tr>\n",
" <th>51</th>\n",
" <td>static</td>\n",
" <td>10</td>\n",
" </tr>\n",
" <tr>\n",
" <th>52</th>\n",
" <td>skipped</td>\n",
" <td>10</td>\n",
" </tr>\n",
" <tr>\n",
" <th>53</th>\n",
" <td>errors</td>\n",
" <td>10</td>\n",
" </tr>\n",
" <tr>\n",
" <th>54</th>\n",
" <td>groupId</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>55</th>\n",
" <td>uri</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>56</th>\n",
" <td>artifactId</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>57</th>\n",
" <td>packaging</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>58</th>\n",
" <td>prefix</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>59</th>\n",
" <td>releasesChecksumPolicy</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>60</th>\n",
" <td>url</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>61</th>\n",
" <td>releasesUpdatePolicy</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>62</th>\n",
" <td>snapshotsUpdatePolicy</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>63</th>\n",
" <td>layout</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>64</th>\n",
" <td>snapshotsChecksumPolicy</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>65</th>\n",
" <td>releasesEnabled</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>66</th>\n",
" <td>snapshotsEnabled</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>67 rows × 2 columns</p>\n",
"</div>"
],
"text/plain": [
" Property PropertyCount\n",
"0 modificationKind 14034\n",
"1 name 5288\n",
"2 relativePath 2847\n",
"3 createdAtEpoch 2701\n",
"4 createdAt 2701\n",
"5 value 2628\n",
"6 deletedAtEpoch 1859\n",
"7 deletedAt 1859\n",
"8 fqn 913\n",
"9 time 711\n",
"10 type 667\n",
"11 group 667\n",
"12 version 645\n",
"13 date 639\n",
"14 epoch 639\n",
"15 sha 639\n",
"16 message 639\n",
"17 author 639\n",
"18 signature 543\n",
"19 visibility 385\n",
"20 lastModificationAtEpoch 342\n",
"21 lastModificationAt 342\n",
"22 fileName 337\n",
"23 cyclomaticComplexity 253\n",
"24 effectiveLineCount 232\n",
"25 lastLineNumber 232\n",
"26 firstLineNumber 232\n",
"27 index 162\n",
"28 transient 76\n",
"29 volatile 76\n",
".. ... ...\n",
"37 inherited 48\n",
"38 identString 45\n",
"39 email 45\n",
"40 status 29\n",
"41 abstract 23\n",
"42 final 17\n",
"43 phase 13\n",
"44 standalone 12\n",
"45 characterEncodingScheme 12\n",
"46 xmlWellFormed 12\n",
"47 xmlVersion 12\n",
"48 synthetic 11\n",
"49 failures 10\n",
"50 tests 10\n",
"51 static 10\n",
"52 skipped 10\n",
"53 errors 10\n",
"54 groupId 3\n",
"55 uri 3\n",
"56 artifactId 3\n",
"57 packaging 3\n",
"58 prefix 2\n",
"59 releasesChecksumPolicy 1\n",
"60 url 1\n",
"61 releasesUpdatePolicy 1\n",
"62 snapshotsUpdatePolicy 1\n",
"63 layout 1\n",
"64 snapshotsChecksumPolicy 1\n",
"65 releasesEnabled 1\n",
"66 snapshotsEnabled 1\n",
"\n",
"[67 rows x 2 columns]"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd.DataFrame(graph.data(\"\"\"\n",
"MATCH (n) WITH KEYS(n) as keys \n",
"UNWIND keys as properties \n",
"RETURN properties as Property, COUNT(properties) as PropertyCount\n",
"ORDER BY PropertyCount DESC\"\"\"))"
]
}
],
"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",
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| gpl-3.0 |
paulgb/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers | Chapter3_MCMC/IntroMCMC.ipynb | 1 | 1229249 | null | mit |
d00d/quantNotebooks | Notebooks/quantopian_research_public/notebooks/lectures/Introduction_to_Python/notebook.ipynb | 1 | 68309 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Introduction to Python\n",
"by Maxwell Margenot\n",
"\n",
"Part of the Quantopian Lecture Series:\n",
"\n",
"* [www.quantopian.com/lectures](https://www.quantopian.com/lectures)\n",
"* [github.com/quantopian/research_public](https://github.com/quantopian/research_public)\n",
"\n",
"Notebook released under the Creative Commons Attribution 4.0 License.\n",
"\n",
"---\n",
"\n",
"All of the coding that you will do on the Quantopian platform will be in Python. It is also just a good, jack-of-all-trades language to know! Here we will provide you with the basics so that you can feel confident going through our other lectures and understanding what is happening."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Code Comments\n",
"\n",
"A comment is a note made by a programmer in the source code of a program. Its purpose is to clarify the source code and make it easier for people to follow along with what is happening. Anything in a comment is generally ignored when the code is actually run, making comments useful for including explanations and reasoning as well as removing specific lines of code that you may be unsure about. Comments in Python are created by using the pound symbol (`# Insert Text Here`). Including a `#` in a line of code will comment out anything that follows it."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# This is a comment\n",
"# These lines of code will not change any values\n",
"# Anything following the first # is not run as code"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You may hear text enclosed in triple quotes (`\"\"\" Insert Text Here \"\"\"`) referred to as multi-line comments, but this is not entirely accurate. This is a special type of `string` (a data type we will cover), called a `docstring`, used to explain the purpose of a function."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"\"\"\" This is a special string \"\"\""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Make sure you read the comments within each code cell (if they are there). They will provide more real-time explanations of what is going on as you look at each line of code."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Variables\n",
"\n",
"Variables provide names for values in programming. If you want to save a value for later or repeated use, you give the value a name, storing the contents in a variable. Variables in programming work in a fundamentally similar way to variables in algebra, but in Python they can take on various different data types.\n",
"\n",
"The basic variable types that we will cover in this section are `integers`, `floating point numbers`, `booleans`, and `strings`. \n",
"\n",
"An `integer` in programming is the same as in mathematics, a round number with no values after the decimal point. We use the built-in `print` function here to display the values of our variables as well as their types!"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"my_integer = 50\n",
"print my_integer, type(my_integer)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Variables, regardless of type, are assigned by using a single equals sign (`=`). Variables are case-sensitive so any changes in variation in the capitals of a variable name will reference a different variable entirely."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"one = 1\n",
"print One"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"A `floating point` number, or a `float` is a fancy name for a real number (again as in mathematics). To define a `float`, we need to either include a decimal point or specify that the value is a float."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [],
"source": [
"my_float = 1.0\n",
"print my_float, type(my_float)\n",
"my_float = float(1)\n",
"print my_float, type(my_float)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"A variable of type `float` will not round the number that you store in it, while a variable of type `integer` will. This makes `floats` more suitable for mathematical calculations where you want more than just integers.\n",
"\n",
"Note that as we used the `float()` function to force an number to be considered a `float`, we can use the `int()` function to force a number to be considered an `int`."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"my_int = int(3.14159)\n",
"print my_int, type(my_int)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The `int()` function will also truncate any digits that a number may have after the decimal point!\n",
"\n",
"Strings allow you to include text as a variable to operate on. They are defined using either single quotes ('') or double quotes (\"\")."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"my_string = 'This is a string with single quotes'\n",
"print my_string\n",
"my_string = \"This is a string with double quotes\"\n",
"print my_string"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Both are allowed so that we can include apostrophes or quotation marks in a string if we so choose."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"my_string = '\"Jabberwocky\", by Lewis Carroll'\n",
"print my_string\n",
"my_string = \"'Twas brillig, and the slithy toves / Did gyre and gimble in the wabe;\"\n",
"print my_string"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Booleans, or `bools` are binary variable types. A `bool` can only take on one of two values, these being `True` or `False`. There is much more to this idea of truth values when it comes to programming, which we cover later in the [Logical Operators](#id-section5) of this notebook."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"my_bool = True\n",
"print my_bool, type(my_bool)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"There are many more data types that you can assign as variables in Python, but these are the basic ones! We will cover a few more later as we move through this tutorial."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Basic Math\n",
"\n",
"Python has a number of built-in math functions. These can be extended even further by importing the **math** package or by including any number of other calculation-based packages.\n",
"\n",
"All of the basic arithmetic operations are supported: `+`, `-`, `/`, and `*`. You can create exponents by using `**` and modular arithmetic is introduced with the mod operator, `%`."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"print 'Addition: ', 2 + 2\n",
"print 'Subtraction: ', 7 - 4\n",
"print 'Multiplication: ', 2 * 5\n",
"print 'Division: ', 10 / 2\n",
"print 'Exponentiation: ', 3**2"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If you are not familiar with the the mod operator, it operates like a remainder function. If we type $15 \\ \\% \\ 4$, it will return the remainder after dividing $15$ by $4$."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"print 'Modulo: ', 15 % 4"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Mathematical functions also work on variables!"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [],
"source": [
"first_integer = 4\n",
"second_integer = 5\n",
"print first_integer * second_integer"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Make sure that your variables are floats if you want to have decimal points in your answer. If you perform math exclusively with integers, you get an integer. Including any float in the calculation will make the result a float."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"first_integer = 11\n",
"second_integer = 3\n",
"print first_integer / second_integer"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"first_number = 11.0\n",
"second_number = 3.0\n",
"print first_number / second_number"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Python has a few built-in math functions. The most notable of these are:\n",
"\n",
"* `abs()`\n",
"* `round()`\n",
"* `max()`\n",
"* `min()`\n",
"* `sum()`\n",
"\n",
"These functions all act as you would expect, given their names. Calling `abs()` on a number will return its absolute value. The `round()` function will round a number to a specified number of the decimal points (the default is $0$). Calling `max()` or `min()` on a collection of numbers will return, respectively, the maximum or minimum value in the collection. Calling `sum()` on a collection of numbers will add them all up. If you're not familiar with how collections of values in Python work, don't worry! We will cover collections in-depth in the next section. \n",
"\n",
"Additional math functionality can be added in with the `math` package."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import math"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The math library adds a long list of new mathematical functions to Python. Feel free to check out the [documentation](https://docs.python.org/2/library/math.html) for the full list and details. It concludes some mathematical constants"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"print 'Pi: ', math.pi\n",
"print \"Euler's Constant: \", math.e"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As well as some commonly used math functions"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"print 'Cosine of pi: ', math.cos(math.pi)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Collections\n",
"### Lists\n",
"\n",
"A `list` in Python is an ordered collection of objects that can contain any data type. We define a `list` using brackets (`[]`)."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"my_list = [1, 2, 3]\n",
"print my_list"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can access and index the list by using brackets as well. In order to select an individual element, simply type the list name followed by the index of the item you are looking for in braces."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"print my_list[0]\n",
"print my_list[2]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Indexing in Python starts from $0$. If you have a list of length $n$, the first element of the list is at index $0$, the second element is at index $1$, and so on and so forth. The final element of the list will be at index $n-1$. Be careful! Trying to access a non-existent index will cause an error."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"print 'The first, second, and third list elements: ', my_list[0], my_list[1], my_list[2]\n",
"print 'Accessing outside the list bounds causes an error: ', my_list[3]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can see the number of elements in a list by calling the `len()` function."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"print len(my_list)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can update and change a list by accessing an index and assigning new value."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"print my_list\n",
"my_list[0] = 42\n",
"print my_list"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This is fundamentally different from how strings are handled. A `list` is mutable, meaning that you can change a `list`'s elements without changing the list itself. Some data types, like `strings`, are immutable, meaning you cannot change them at all. Once a `string` or other immutable data type has been created, it cannot be directly modified without creating an entirely new object."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"my_string = \"Strings never change\"\n",
"my_string[0] = 'Z'"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As we stated before, a list can contain any data type. Thus, lists can also contain strings."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"my_list_2 = ['one', 'two', 'three']\n",
"print my_list_2"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Lists can also contain multiple different data types at once!"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"my_list_3 = [True, 'False', 42]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If you want to put two lists together, they can be combined with a `+` symbol."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"my_list_4 = my_list + my_list_2 + my_list_3\n",
"print my_list_4"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In addition to accessing individual elements of a list, we can access groups of elements through slicing."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"my_list = ['friends', 'romans', 'countrymen', 'lend', 'me', 'your', 'ears']"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Slicing\n",
"\n",
"We use the colon (`:`) to slice lists. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [],
"source": [
"print my_list[2:4]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Using `:` we can select a group of elements in the list starting from the first element indicated and going up to (but not including) the last element indicated.\n",
"\n",
"We can also select everything after a certain point"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [],
"source": [
"print my_list[1:]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"And everything before a certain point"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [],
"source": [
"print my_list[:4]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Using negative numbers will count from the end of the indices instead of from the beginning. For example, an index of `-1` indicates the last element of the list."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"print my_list[-1]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You can also add a third component to slicing. Instead of simply indicating the first and final parts of your slice, you can specify the step size that you want to take. So instead of taking every single element, you can take every other element."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"print my_list[0:7:2]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Here we have selected the entire list (because `0:7` will yield elements `0` through `6`) and we have selected a step size of `2`. So this will spit out element `0` , element `2`, element `4`, and so on through the list element selected. We can skip indicated the beginning and end of our slice, only indicating the step, if we like."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"print my_list[::2]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Lists implictly select the beginning and end of the list when not otherwise specified."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"print my_list[:]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"With a negative step size we can even reverse the list!"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"print my_list[::-1]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Python does not have native matrices, but with lists we can produce a working fascimile. Other packages, such as `numpy`, add matrices as a separate data type, but in base Python the best way to create a matrix is to use a list of lists."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can also use built-in functions to generate lists. In particular we will look at `range()` (because we will be using it later!). Range can take several different inputs and will return a list."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"b = 10\n",
"my_list = range(b)\n",
"print my_list"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Similar to our list-slicing methods from before, we can define both a start and an end for our range. This will return a list that is includes the start and excludes the end, just like a slice."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"a = 0\n",
"b = 10\n",
"my_list = range(a, b)\n",
"print my_list"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can also specify a step size. This again has the same behavior as a slice."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [],
"source": [
"a = 0\n",
"b = 10\n",
"step = 2\n",
"my_list = range(a, b, step)\n",
"print my_list"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Tuples\n",
"\n",
"A `tuple` is a data type similar to a list in that it can hold different kinds of data types. The key difference here is that a `tuple` is immutable. We define a `tuple` by separating the elements we want to include by commas. It is conventional to surround a `tuple` with parentheses."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"my_tuple = 'I', 'have', 30, 'cats'\n",
"print my_tuple"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"my_tuple = ('I', 'have', 30, 'cats')\n",
"print my_tuple"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As mentioned before, tuples are immutable. You can't change any part of them without defining a new tuple."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"my_tuple[3] = 'dogs' # Attempts to change the 'cats' value stored in the the tuple to 'dogs'"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You can slice tuples the same way that you slice lists!"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"print my_tuple[1:3]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"And concatenate them the way that you would with strings!"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"my_other_tuple = ('make', 'that', 50)\n",
"print my_tuple + my_other_tuple"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can 'pack' values together, creating a tuple (as above), or we can 'unpack' values from a tuple, taking them out."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"str_1, str_2, int_1 = my_other_tuple\n",
"print str_1, str_2, int_1"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Unpacking assigns each value of the tuple in order to each variable on the left hand side of the equals sign. Some functions, including user-defined functions, may return tuples, so we can use this to directly unpack them and access the values that we want."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Sets\n",
"\n",
"A `set` is a collection of unordered, unique elements. It works almost exactly as you would expect a normal set of things in mathematics to work and is defined using braces (`{}`)."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"things_i_like = {'dogs', 7, 'the number 4', 4, 4, 4, 42, 'lizards', 'man I just LOVE the number 4'}\n",
"print things_i_like, type(things_i_like)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Note how any extra instances of the same item are removed in the final set. We can also create a `set` from a list, using the `set()` function."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"animal_list = ['cats', 'dogs', 'dogs', 'dogs', 'lizards', 'sponges', 'cows', 'bats', 'sponges']\n",
"animal_set = set(animal_list)\n",
"print animal_set # Removes all extra instances from the list"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Calling `len()` on a set will tell you how many elements are in it."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"print len(animal_set)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Because a `set` is unordered, we can't access individual elements using an index. We can, however, easily check for membership (to see if something is contained in a set) and take the unions and intersections of sets by using the built-in set functions."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"'cats' in animal_set # Here we check for membership using the `in` keyword."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Here we checked to see whether the string 'cats' was contained within our `animal_set` and it returned `True`, telling us that it is indeed in our set.\n",
"\n",
"We can connect sets by using typical mathematical set operators, namely `|`, for union, and `&`, for intersection. Using `|` or `&` will return exactly what you would expect if you are familiar with sets in mathematics."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"print animal_set | things_i_like # You can also write things_i_like | animal_set with no difference"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Pairing two sets together with `|` combines the sets, removing any repetitions to make every set element unique."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"print animal_set & things_i_like # You can also write things_i_like & animal_set with no difference"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Pairing two sets together with `&` will calculate the intersection of both sets, returning a set that only contains what they have in common.\n",
"\n",
"If you are interested in learning more about the built-in functions for sets, feel free to check out the [documentation](https://docs.python.org/2/library/sets.html)."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Dictionaries\n",
"\n",
"Another essential data structure in Python is the dictionary. Dictionaries are defined with a combination of curly braces (`{}`) and colons (`:`). The braces define the beginning and end of a dictionary and the colons indicate key-value pairs. A dictionary is essentially a set of key-value pairs. The key of any entry must be an immutable data type. This makes both strings and tuples candidates. Keys can be both added and deleted.\n",
"\n",
"In the following example, we have a dictionary composed of key-value pairs where the key is a genre of fiction (`string`) and the value is a list of books (`list`) within that genre. Since a collection is still considered a single entity, we can use one to collect multiple variables or values into one key-value pair."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"my_dict = {\"High Fantasy\": [\"Wheel of Time\", \"Lord of the Rings\"], \n",
" \"Sci-fi\": [\"Book of the New Sun\", \"Neuromancer\", \"Snow Crash\"],\n",
" \"Weird Fiction\": [\"At the Mountains of Madness\", \"The House on the Borderland\"]}"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"After defining a dictionary, we can access any individual value by indicating its key in brackets."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"print my_dict[\"Sci-fi\"]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can also change the value associated with a given key"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"my_dict[\"Sci-fi\"] = \"I can't read\"\n",
"print my_dict[\"Sci-fi\"]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Adding a new key-value pair is as simple as defining it."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"my_dict[\"Historical Fiction\"] = [\"Pillars of the Earth\"]\n",
"print my_dict[\"Historical Fiction\"]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"print my_dict"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## String Shenanigans\n",
"\n",
"We already know that strings are generally used for text. We can used built-in operations to combine, split, and format strings easily, depending on our needs.\n",
"\n",
"The `+` symbol indicates concatenation in string language. It will combine two strings into a longer string."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"first_string = '\"Beware the Jabberwock, my son! /The jaws that bite, the claws that catch! /'\n",
"second_string = 'Beware the Jubjub bird, and shun /The frumious Bandersnatch!\"/'\n",
"third_string = first_string + second_string\n",
"print third_string"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Strings are also indexed much in the same way that lists are."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"my_string = 'Supercalifragilisticexpialidocious'\n",
"print 'The first letter is: ', my_string[0] # Uppercase S\n",
"print 'The last letter is: ', my_string[-1] # lowercase s\n",
"print 'The second to last letter is: ', my_string[-2] # lowercase u\n",
"print 'The first five characters are: ', my_string[0:5] # Remember: slicing doesn't include the final element!\n",
"print 'Reverse it!: ', my_string[::-1]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Built-in objects and classes often have special functions associated with them that are called methods. We access these methods by using a period ('.'). We will cover objects and their associated methods more in another lecture!\n",
"\n",
"Using string methods we can count instances of a character or group of characters."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [],
"source": [
"print 'Count of the letter i in Supercalifragilisticexpialidocious: ', my_string.count('i')\n",
"print 'Count of \"li\" in the same word: ', my_string.count('li')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can also find the first instance of a character or group of characters in a string."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"print 'The first time i appears is at index: ', my_string.find('i')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As well as replace characters in a string."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"print \"All i's are now a's: \", my_string.replace('i', 'a')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"print \"It's raining cats and dogs\".replace('dogs', 'more cats')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"There are also some methods that are unique to strings. The function `upper()` will convert all characters in a string to uppercase, while `lower()` will convert all characters in a string to lowercase!"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"my_string = \"I can't hear you\"\n",
"print my_string.upper()\n",
"my_string = \"I said HELLO\"\n",
"print my_string.lower()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### String Formatting\n",
"\n",
"Using the `format()` method we can add in variable values and generally format our strings."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"my_string = \"{0} {1}\".format('Marco', 'Polo')\n",
"print my_string"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"my_string = \"{1} {0}\".format('Marco', 'Polo')\n",
"print my_string"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We use braces (`{}`) to indicate parts of the string that will be filled in later and we use the arguments of the `format()` function to provide the values to substitute. The numbers within the braces indicate the index of the value in the `format()` arguments."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"See the `format()` [documentation](https://docs.python.org/2/library/string.html#format-examples) for additional examples."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If you need some quick and dirty formatting, you can instead use the `%` symbol, called the string formatting operator. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"print 'insert %s here' % 'value'"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The `%` symbol basically cues Python to create a placeholder. Whatever character follows the `%` (in the string) indicates what sort of type the value put into the placeholder will have. This character is called a *conversion type*. Once the string has been closed, we need another `%` that will be followed by the values to insert. In the case of one value, you can just put it there. If you are inserting more than one value, they must be enclosed in a tuple."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"print 'There are %s cats in my %s' % (13, 'apartment')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In these examples, the `%s` indicates that Python should convert the values into strings. There are multiple conversion types that you can use to get more specific with the the formatting. See the string formatting [documentation](https://docs.python.org/2/library/stdtypes.html#string-formatting) for additional examples and more complete details on use."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Logical Operators\n",
"### Basic Logic\n",
"\n",
"Logical operators deal with `boolean` values, as we briefly covered before. If you recall, a `bool` takes on one of two values, `True` or `False` (or $1$ or $0$). The basic logical statements that we can make are defined using the built-in comparators. These are `==` (equal), `!=` (not equal), `<` (less than), `>` (greater than), `<=` (less than or equal to), and `>=` (greater than or equal to)."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"print 5 == 5"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"print 5 > 5"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"These comparators also work in conjunction with variables."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"m = 2\n",
"n = 23\n",
"print m < n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can string these comparators together to make more complex logical statements using the logical operators `or`, `and`, and `not`. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"statement_1 = 10 > 2\n",
"statement_2 = 4 <= 6\n",
"print \"Statement 1 truth value: {0}\".format(statement_1)\n",
"print \"Statement 2 truth value: {0}\".format(statement_2)\n",
"print \"Statement 1 and Statement 2: {0}\".format(statement_1 and statement_2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The `or` operator performs a logical `or` calculation. This is an inclusive `or`, so if either component paired together by `or` is `True`, the whole statement will be `True`. The `and` statement only outputs `True` if all components that are `and`ed together are True. Otherwise it will output `False`. The `not` statement simply inverts the truth value of whichever statement follows it. So a `True` statement will be evaluated as `False` when a `not` is placed in front of it. Similarly, a `False` statement will become `True` when a `not` is in front of it.\n",
"\n",
"Say that we have two logical statements, or assertions, $P$ and $Q$. The truth table for the basic logical operators is as follows:\n",
"\n",
"| P | Q | `not` P| P `and` Q | P `or` Q|\n",
"|:-----:|:-----:|:---:|:---:|:---:|\n",
"| `True` | `True` | `False` | `True` | `True` |\n",
"| `False` | `True` | `True` | `False` | `True` |\n",
"| `True` | `False` | `False` | `False` | `True` |\n",
"| `False` | `False` | `True` | `False` | `False` |\n",
"\n",
"We can string multiple logical statements together using the logical operators."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"print ((2 < 3) and (3 > 0)) or ((5 > 6) and not (4 < 2))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Logical statements can be as simple or complex as we like, depending on what we need to express. Evaluating the above logical statement step by step we see that we are evaluating (`True and True`) `or` (`False and not False`). This becomes `True or (False and True`), subsequently becoming `True or False`, ultimately being evaluated as `True`."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Truthiness\n",
"\n",
"Data types in Python have a fun characteristic called truthiness. What this means is that most built-in types will evaluate as either `True` or `False` when a boolean value is needed (such as with an if-statement). As a general rule, containers like strings, tuples, dictionaries, lists, and sets, will return `True` if they contain anything at all and `False` if they contain nothing."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Similar to how float() and int() work, bool() forces a value to be considered a boolean!\n",
"print bool('')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"print bool('I have character!')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"print bool([])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"print bool([1, 2, 3])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"And so on, for the other collections and containers. `None` also evaluates as `False`. The number `1` is equivalent to `True` and the number `0` is equivalent to `False` as well, in a boolean context."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### If-statements\n",
"\n",
"We can create segments of code that only execute if a set of conditions is met. We use if-statements in conjunction with logical statements in order to create branches in our code. \n",
"\n",
"An `if` block gets entered when the condition is considered to be `True`. If condition is evaluated as `False`, the `if` block will simply be skipped unless there is an `else` block to accompany it. Conditions are made using either logical operators or by using the truthiness of values in Python. An if-statement is defined with a colon and a block of indented text."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# This is the basic format of an if statement. This is a vacuous example. \n",
"# The string \"Condition\" will always evaluated as True because it is a\n",
"# non-empty string. he purpose of this code is to show the formatting of\n",
"# an if-statement.\n",
"if \"Condition\": \n",
" # This block of code will execute because the string is non-empty\n",
" # Everything on these indented lines\n",
" print True\n",
"else:\n",
" # So if the condition that we examined with if is in fact False\n",
" # This block of code will execute INSTEAD of the first block of code\n",
" # Everything on these indented lines\n",
" print False\n",
"# The else block here will never execute because \"Condition\" is a non-empty string."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"i = 4\n",
"if i == 5:\n",
" print 'The variable i has a value of 5'"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Because in this example `i = 4` and the if-statement is only looking for whether `i` is equal to `5`, the print statement will never be executed. We can add in an `else` statement to create a contingency block of code in case the condition in the if-statement is not evaluated as `True`."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"i = 4\n",
"if i == 5:\n",
" print \"All lines in this indented block are part of this block\"\n",
" print 'The variable i has a value of 5'\n",
"else:\n",
" print \"All lines in this indented block are part of this block\"\n",
" print 'The variable i is not equal to 5'"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can implement other branches off of the same if-statement by using `elif`, an abbreviation of \"else if\". We can include as many `elifs` as we like until we have exhausted all the logical branches of a condition."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"i = 1\n",
"if i == 1:\n",
" print 'The variable i has a value of 1'\n",
"elif i == 2:\n",
" print 'The variable i has a value of 2'\n",
"elif i == 3:\n",
" print 'The variable i has a value of 3'\n",
"else:\n",
" print \"I don't care what i is\""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You can also nest if-statements within if-statements to check for further conditions."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"i = 10\n",
"if i % 2 == 0:\n",
" if i % 3 == 0:\n",
" print 'i is divisible by both 2 and 3! Wow!'\n",
" elif i % 5 == 0:\n",
" print 'i is divisible by both 2 and 5! Wow!'\n",
" else:\n",
" print 'i is divisible by 2, but not 3 or 5. Meh.'\n",
"else:\n",
" print 'I guess that i is an odd number. Boring.'"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Remember that we can group multiple conditions together by using the logical operators!"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"i = 5\n",
"j = 12\n",
"if i < 10 and j > 11:\n",
" print '{0} is less than 10 and {1} is greater than 11! How novel and interesting!'.format(i, j)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You can use the logical comparators to compare strings!"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"my_string = \"Carthago delenda est\"\n",
"if my_string == \"Carthago delenda est\":\n",
" print 'And so it was! For the glory of Rome!'\n",
"else:\n",
" print 'War elephants are TERRIFYING. I am staying home.'"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As with other data types, `==` will check for whether the two things on either side of it have the same value. In this case, we compare whether the value of the strings are the same. Using `>` or `<` or any of the other comparators is not quite so intuitive, however, so we will stay from using comparators with strings in this lecture. Comparators will examine the [lexicographical order](https://en.wikipedia.org/wiki/Lexicographical_order) of the strings, which might be a bit more in-depth than you might like."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Some built-in functions return a boolean value, so they can be used as conditions in an if-statement. User-defined functions can also be constructed so that they return a boolean value. This will be covered later with function definition!\n",
"\n",
"The `in` keyword is generally used to check membership of a value within another value. We can check memebership in the context of an if-statement and use it to output a truth value."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"if 'a' in my_string or 'e' in my_string:\n",
" print 'Those are my favorite vowels!'"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Here we use `in` to check whether the variable `my_string` contains any particular letters. We will later use `in` to iterate through lists!"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Loop Structures\n",
"\n",
"Loop structures are one of the most important parts of programming. The `for` loop and the `while` loop provide a way to repeatedly run a block of code repeatedly. A `while` loop will iterate until a certain condition has been met. If at any point after an iteration that condition is no longer satisfied, the loop terminates. A `for` loop will iterate over a sequence of values and terminate when the sequence has ended. You can instead include conditions within the `for` loop to decide whether it should terminate early or you could simply let it run its course."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"i = 5\n",
"while i > 0: # We can write this as 'while i:' because 0 is False!\n",
" i -= 1\n",
" print 'I am looping! {0} more to go!'.format(i)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"With `while` loops we need to make sure that something actually changes from iteration to iteration so that that the loop actually terminates. In this case, we use the shorthand `i -= 1` (short for `i = i - 1`) so that the value of `i` gets smaller with each iteration. Eventually `i` will be reduced to `0`, rendering the condition `False` and exiting the loop."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"A `for` loop iterates a set number of times, determined when you state the entry into the loop. In this case we are iterating over the list returned from `range()`. The `for` loop selects a value from the list, in order, and temporarily assigns the value of `i` to it so that operations can be performed with the value."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"for i in range(5):\n",
" print 'I am looping! I have looped {0} times!'.format(i + 1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Note that in this `for` loop we use the `in` keyword. Use of the `in` keyword is not limited to checking for membership as in the if-statement example. You can iterate over any collection with a `for` loop by using the `in` keyword.\n",
"\n",
"In this next example, we will iterate over a `set` because we want to check for containment and add to a new set."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"my_list = {'cats', 'dogs', 'lizards', 'cows', 'bats', 'sponges', 'humans'} # Lists all the animals in the world\n",
"mammal_list = {'cats', 'dogs', 'cows', 'bats', 'humans'} # Lists all the mammals in the world\n",
"my_new_list = set()\n",
"for animal in my_list:\n",
" if animal in mammal_list:\n",
" # This adds any animal that is both in my_list and mammal_list to my_new_list\n",
" my_new_list.add(animal)\n",
" \n",
"print my_new_list"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"There are two statements that are very helpful in dealing with both `for` and `while` loops. These are `break` and `continue`. If `break` is encountered at any point while a loop is executing, the loop will immediately end."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"i = 10\n",
"while True:\n",
" if i == 14:\n",
" break\n",
" i += 1 # This is shorthand for i = i + 1. It increments i with each iteration.\n",
" print i"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"for i in range(5):\n",
" if i == 2:\n",
" break\n",
" print i"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The `continue` statement will tell the loop to immediately end this iteration and continue onto the next iteration of the loop."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"i = 0\n",
"while i < 5:\n",
" i += 1\n",
" if i == 3:\n",
" continue\n",
" print i"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This loop skips printing the number $3$ because of the `continue` statement that executes when we enter the if-statement. The code never sees the command to print the number $3$ because it has already moved to the next iteration. The `break` and `continue` statements are further tools to help you control the flow of your loops and, as a result, your code."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The variable that we use to iterate over a loop will retain its value when the loop exits. Similarly, any variables defined within the context of the loop will continue to exist outside of it."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"for i in range(5):\n",
" loop_string = 'I transcend the loop!'\n",
" print 'I am eternal! I am {0} and I exist everywhere!'.format(i)\n",
"\n",
"print 'I persist! My value is {0}'.format(i)\n",
"print loop_string"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can also iterate over a dictionary!"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"my_dict = {'firstname' : 'Inigo', 'lastname' : 'Montoya', 'nemesis' : 'Rugen'}"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"for key in my_dict:\n",
" print key"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If we just iterate over a dictionary without doing anything else, we will only get the keys. We can either use the keys to get the values, like so:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"for key in my_dict:\n",
" print my_dict[key]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Or we can use the `iteritems()` function to get both key and value at the same time."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"for key, value in my_dict.iteritems():\n",
" print key, ':', value"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The `iteritems()` function creates a tuple of each key-value pair and the for loop stores unpacks that tuple into `key, value` on each separate execution of the loop!"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Functions\n",
"\n",
"A function is a reusable block of code that you can call repeatedly to make calculations, output data, or really do anything that you want. This is one of the key aspects of using a programming language. To add to the built-in functions in Python, you can define your own!"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def hello_world():\n",
" \"\"\" Prints Hello, world! \"\"\"\n",
" print 'Hello, world!'\n",
"\n",
"hello_world()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"for i in range(5):\n",
" hello_world()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Functions are defined with `def`, a function name, a list of parameters, and a colon. Everything indented below the colon will be included in the definition of the function.\n",
"\n",
"We can have our functions do anything that you can do with a normal block of code. For example, our `hello_world()` function prints a string every time it is called. If we want to keep a value that a function calculates, we can define the function so that it will `return` the value we want. This is a very important feature of functions, as any variable defined purely within a function will not exist outside of it."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def see_the_scope():\n",
" in_function_string = \"I'm stuck in here!\"\n",
"\n",
"see_the_scope()\n",
"print in_function_string"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
" The **scope** of a variable is the part of a block of code where that variable is tied to a particular value. Functions in Python have an enclosed scope, making it so that variables defined within them can only be accessed directly within them. If we pass those values to a return statement we can get them out of the function. This makes it so that the function call returns values so that you can store them in variables that have a greater scope.\n",
" \n",
"In this case specifically,including a return statement allows us to keep the string value that we define in the function."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def free_the_scope():\n",
" in_function_string = \"Anything you can do I can do better!\"\n",
" return in_function_string\n",
"my_string = free_the_scope()\n",
"print my_string"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Just as we can get values out of a function, we can also put values into a function. We do this by defining our function with parameters."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def multiply_by_five(x):\n",
" \"\"\" Multiplies an input number by 5 \"\"\"\n",
" return x * 5\n",
"\n",
"n = 4\n",
"print n\n",
"print multiply_by_five(n)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In this example we only had one parameter for our function, `x`. We can easily add more parameters, separating everything with a comma."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def calculate_area(length, width):\n",
" \"\"\" Calculates the area of a rectangle \"\"\"\n",
" return length * width"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"l = 5\n",
"w = 10\n",
"print 'Area: ', calculate_area(l, w)\n",
"print 'Length: ', l\n",
"print 'Width: ', w"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def calculate_volume(length, width, depth):\n",
" \"\"\" Calculates the volume of a rectangular prism \"\"\"\n",
" return length * width * depth"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If we want to, we can define a function so that it takes an arbitrary number of parameters. We tell Python that we want this by using an asterisk (`*`)."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def sum_values(*args):\n",
" sum_val = 0\n",
" for i in args:\n",
" sum_val += i\n",
" return sum_val"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"print sum_values(1, 2, 3)\n",
"print sum_values(10, 20, 30, 40, 50)\n",
"print sum_values(4, 2, 5, 1, 10, 249, 25, 24, 13, 6, 4)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The time to use `*args` as a parameter for your function is when you do not know how many values may be passed to it, as in the case of our sum function. The asterisk in this case is the syntax that tells Python that you are going to pass an arbitrary number of parameters into your function. These parameters are stored in the form of a tuple."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def test_args(*args):\n",
" print type(args)\n",
"\n",
"test_args(1, 2, 3, 4, 5, 6)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can put as many elements into the `args` tuple as we want to when we call the function. However, because `args` is a tuple, we cannot modify it after it has been created.\n",
"\n",
"The `args` name of the variable is purely by convention. You could just as easily name your parameter `*vars` or `*things`. You can treat the `args` tuple like you would any other tuple, easily accessing `arg`'s values and iterating over it, as in the above `sum_values(*args)` function."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Our functions can return any data type. This makes it easy for us to create functions that check for conditions that we might want to monitor.\n",
"\n",
"Here we define a function that returns a boolean value. We can easily use this in conjunction with if-statements and other situations that require a boolean."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def has_a_vowel(word):\n",
" \"\"\" \n",
" Checks to see whether a word contains a vowel \n",
" If it doesn't contain a conventional vowel, it\n",
" will check for the presence of 'y' or 'w'. Does\n",
" not check to see whether those are in the word\n",
" in a vowel context.\n",
" \"\"\"\n",
" vowel_list = ['a', 'e', 'i', 'o', 'u']\n",
" \n",
" for vowel in vowel_list:\n",
" if vowel in word:\n",
" return True\n",
" # If there is a vowel in the word, the function returns, preventing anything after this loop from running\n",
" return False"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"my_word = 'catnapping'\n",
"if has_a_vowel(my_word):\n",
" print 'How surprising, an english word contains a vowel.'\n",
"else:\n",
" print 'This is actually surprising.'"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def point_maker(x, y):\n",
" \"\"\" Groups x and y values into a point, technically a tuple \"\"\"\n",
" return x, y"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This above function returns an ordered pair of the input parameters, stored as a tuple."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [],
"source": [
"a = point_maker(0, 10)\n",
"b = point_maker(5, 3)\n",
"def calculate_slope(point_a, point_b):\n",
" \"\"\" Calculates the linear slope between two points \"\"\"\n",
" return (point_b[1] - point_a[1])/(point_b[0] - point_a[0])\n",
"print \"The slope between a and b is {0}\".format(calculate_slope(a, b))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"And that one calculates the slope between two points!"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"print \"The slope-intercept form of the line between a and b, using point a, is: y - {0} = {2}(x - {1})\".format(a[1], a[0], calculate_slope(a, b))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"With the proper syntax, you can define functions to do whatever calculations you want. This makes them an indispensible part of programming in any language."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Next Steps\n",
"\n",
"This was a lot of material and there is still even more to cover! Make sure you play around with the cells in each notebook to accustom yourself to the syntax featured here and to figure out any limitations. If you want to delve even deeper into the material, the [documentation for Python](https://docs.python.org/2/) is all available online. We are in the process of developing a second part to this Python tutorial, designed to provide you with even more programming knowledge, so keep an eye on the [Quantopian Lectures Page](quantopian.com/lectures) and the [forums](quantopian.com/posts) for any new lectures."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"*This presentation is for informational purposes only and does not constitute an offer to sell, a solicitation to buy, or a recommendation for any security; nor does it constitute an offer to provide investment advisory or other services by Quantopian, Inc. (\"Quantopian\"). Nothing contained herein constitutes investment advice or offers any opinion with respect to the suitability of any security, and any views expressed herein should not be taken as advice to buy, sell, or hold any security or as an endorsement of any security or company. In preparing the information contained herein, Quantopian, Inc. has not taken into account the investment needs, objectives, and financial circumstances of any particular investor. Any views expressed and data illustrated herein were prepared based upon information, believed to be reliable, available to Quantopian, Inc. at the time of publication. Quantopian makes no guarantees as to their accuracy or completeness. All information is subject to change and may quickly become unreliable for various reasons, including changes in market conditions or economic circumstances.*"
]
}
],
"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
} | unlicense |
machinelearningnanodegree/stanford-cs231 | assignments/assignment1/softmax.ipynb | 1 | 11500 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Softmax exercise\n",
"\n",
"*Complete and hand in this completed worksheet (including its outputs and any supporting code outside of the worksheet) with your assignment submission. For more details see the [assignments page](http://vision.stanford.edu/teaching/cs231n/assignments.html) on the course website.*\n",
"\n",
"This exercise is analogous to the SVM exercise. You will:\n",
"\n",
"- implement a fully-vectorized **loss function** for the Softmax classifier\n",
"- implement the fully-vectorized expression for its **analytic gradient**\n",
"- **check your implementation** with numerical gradient\n",
"- use a validation set to **tune the learning rate and regularization** strength\n",
"- **optimize** the loss function with **SGD**\n",
"- **visualize** the final learned weights\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import random\n",
"import numpy as np\n",
"from cs231n.data_utils import load_CIFAR10\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
"plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots\n",
"plt.rcParams['image.interpolation'] = 'nearest'\n",
"plt.rcParams['image.cmap'] = 'gray'\n",
"\n",
"# for auto-reloading extenrnal modules\n",
"# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython\n",
"%load_ext autoreload\n",
"%autoreload 2"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def get_CIFAR10_data(num_training=49000, num_validation=1000, num_test=1000, num_dev=500):\n",
" \"\"\"\n",
" Load the CIFAR-10 dataset from disk and perform preprocessing to prepare\n",
" it for the linear classifier. These are the same steps as we used for the\n",
" SVM, but condensed to a single function. \n",
" \"\"\"\n",
" # Load the raw CIFAR-10 data\n",
" cifar10_dir = 'cs231n/datasets/cifar-10-batches-py'\n",
" X_train, y_train, X_test, y_test = load_CIFAR10(cifar10_dir)\n",
" \n",
" # subsample the data\n",
" mask = range(num_training, num_training + num_validation)\n",
" X_val = X_train[mask]\n",
" y_val = y_train[mask]\n",
" mask = range(num_training)\n",
" X_train = X_train[mask]\n",
" y_train = y_train[mask]\n",
" mask = range(num_test)\n",
" X_test = X_test[mask]\n",
" y_test = y_test[mask]\n",
" mask = np.random.choice(num_training, num_dev, replace=False)\n",
" X_dev = X_train[mask]\n",
" y_dev = y_train[mask]\n",
" \n",
" # Preprocessing: reshape the image data into rows\n",
" X_train = np.reshape(X_train, (X_train.shape[0], -1))\n",
" X_val = np.reshape(X_val, (X_val.shape[0], -1))\n",
" X_test = np.reshape(X_test, (X_test.shape[0], -1))\n",
" X_dev = np.reshape(X_dev, (X_dev.shape[0], -1))\n",
" \n",
" # Normalize the data: subtract the mean image\n",
" mean_image = np.mean(X_train, axis = 0)\n",
" X_train -= mean_image\n",
" X_val -= mean_image\n",
" X_test -= mean_image\n",
" X_dev -= mean_image\n",
" \n",
" # add bias dimension and transform into columns\n",
" X_train = np.hstack([X_train, np.ones((X_train.shape[0], 1))])\n",
" X_val = np.hstack([X_val, np.ones((X_val.shape[0], 1))])\n",
" X_test = np.hstack([X_test, np.ones((X_test.shape[0], 1))])\n",
" X_dev = np.hstack([X_dev, np.ones((X_dev.shape[0], 1))])\n",
" \n",
" return X_train, y_train, X_val, y_val, X_test, y_test, X_dev, y_dev\n",
"\n",
"\n",
"# Invoke the above function to get our data.\n",
"X_train, y_train, X_val, y_val, X_test, y_test, X_dev, y_dev = get_CIFAR10_data()\n",
"print 'Train data shape: ', X_train.shape\n",
"print 'Train labels shape: ', y_train.shape\n",
"print 'Validation data shape: ', X_val.shape\n",
"print 'Validation labels shape: ', y_val.shape\n",
"print 'Test data shape: ', X_test.shape\n",
"print 'Test labels shape: ', y_test.shape\n",
"print 'dev data shape: ', X_dev.shape\n",
"print 'dev labels shape: ', y_dev.shape"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Softmax Classifier\n",
"\n",
"Your code for this section will all be written inside **cs231n/classifiers/softmax.py**. \n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# First implement the naive softmax loss function with nested loops.\n",
"# Open the file cs231n/classifiers/softmax.py and implement the\n",
"# softmax_loss_naive function.\n",
"\n",
"from cs231n.classifiers.softmax import softmax_loss_naive\n",
"import time\n",
"\n",
"# Generate a random softmax weight matrix and use it to compute the loss.\n",
"W = np.random.randn(3073, 10) * 0.0001\n",
"loss, grad = softmax_loss_naive(W, X_dev, y_dev, 0.0)\n",
"\n",
"# As a rough sanity check, our loss should be something close to -log(0.1).\n",
"print 'loss: %f' % loss\n",
"print 'sanity check: %f' % (-np.log(0.1))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Inline Question 1:\n",
"Why do we expect our loss to be close to -log(0.1)? Explain briefly.**\n",
"\n",
"**Your answer:** *Fill this in*\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Complete the implementation of softmax_loss_naive and implement a (naive)\n",
"# version of the gradient that uses nested loops.\n",
"loss, grad = softmax_loss_naive(W, X_dev, y_dev, 0.0)\n",
"\n",
"# As we did for the SVM, use numeric gradient checking as a debugging tool.\n",
"# The numeric gradient should be close to the analytic gradient.\n",
"from cs231n.gradient_check import grad_check_sparse\n",
"f = lambda w: softmax_loss_naive(w, X_dev, y_dev, 0.0)[0]\n",
"grad_numerical = grad_check_sparse(f, W, grad, 10)\n",
"\n",
"# similar to SVM case, do another gradient check with regularization\n",
"loss, grad = softmax_loss_naive(W, X_dev, y_dev, 1e2)\n",
"f = lambda w: softmax_loss_naive(w, X_dev, y_dev, 1e2)[0]\n",
"grad_numerical = grad_check_sparse(f, W, grad, 10)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Now that we have a naive implementation of the softmax loss function and its gradient,\n",
"# implement a vectorized version in softmax_loss_vectorized.\n",
"# The two versions should compute the same results, but the vectorized version should be\n",
"# much faster.\n",
"tic = time.time()\n",
"loss_naive, grad_naive = softmax_loss_naive(W, X_dev, y_dev, 0.00001)\n",
"toc = time.time()\n",
"print 'naive loss: %e computed in %fs' % (loss_naive, toc - tic)\n",
"\n",
"from cs231n.classifiers.softmax import softmax_loss_vectorized\n",
"tic = time.time()\n",
"loss_vectorized, grad_vectorized = softmax_loss_vectorized(W, X_dev, y_dev, 0.00001)\n",
"toc = time.time()\n",
"print 'vectorized loss: %e computed in %fs' % (loss_vectorized, toc - tic)\n",
"\n",
"# As we did for the SVM, we use the Frobenius norm to compare the two versions\n",
"# of the gradient.\n",
"grad_difference = np.linalg.norm(grad_naive - grad_vectorized, ord='fro')\n",
"print 'Loss difference: %f' % np.abs(loss_naive - loss_vectorized)\n",
"print 'Gradient difference: %f' % grad_difference"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Use the validation set to tune hyperparameters (regularization strength and\n",
"# learning rate). You should experiment with different ranges for the learning\n",
"# rates and regularization strengths; if you are careful you should be able to\n",
"# get a classification accuracy of over 0.35 on the validation set.\n",
"from cs231n.classifiers import Softmax\n",
"results = {}\n",
"best_val = -1\n",
"best_softmax = None\n",
"learning_rates = [1e-7, 5e-7]\n",
"regularization_strengths = [5e4, 1e8]\n",
"\n",
"################################################################################\n",
"# TODO: #\n",
"# Use the validation set to set the learning rate and regularization strength. #\n",
"# This should be identical to the validation that you did for the SVM; save #\n",
"# the best trained softmax classifer in best_softmax. #\n",
"################################################################################\n",
"pass\n",
"################################################################################\n",
"# END OF YOUR CODE #\n",
"################################################################################\n",
" \n",
"# Print out results.\n",
"for lr, reg in sorted(results):\n",
" train_accuracy, val_accuracy = results[(lr, reg)]\n",
" print 'lr %e reg %e train accuracy: %f val accuracy: %f' % (\n",
" lr, reg, train_accuracy, val_accuracy)\n",
" \n",
"print 'best validation accuracy achieved during cross-validation: %f' % best_val"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# evaluate on test set\n",
"# Evaluate the best softmax on test set\n",
"y_test_pred = best_softmax.predict(X_test)\n",
"test_accuracy = np.mean(y_test == y_test_pred)\n",
"print 'softmax on raw pixels final test set accuracy: %f' % (test_accuracy, )"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Visualize the learned weights for each class\n",
"w = best_softmax.W[:-1,:] # strip out the bias\n",
"w = w.reshape(32, 32, 3, 10)\n",
"\n",
"w_min, w_max = np.min(w), np.max(w)\n",
"\n",
"classes = ['plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']\n",
"for i in xrange(10):\n",
" plt.subplot(2, 5, i + 1)\n",
" \n",
" # Rescale the weights to be between 0 and 255\n",
" wimg = 255.0 * (w[:, :, :, i].squeeze() - w_min) / (w_max - w_min)\n",
" plt.imshow(wimg.astype('uint8'))\n",
" plt.axis('off')\n",
" plt.title(classes[i])"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"name": "python2",
"language": "python"
},
"language_info": {
"mimetype": "text/x-python",
"nbconvert_exporter": "python",
"name": "python",
"file_extension": ".py",
"version": "2.7.9",
"pygments_lexer": "ipython2",
"codemirror_mode": {
"version": 2.0,
"name": "ipython"
}
}
},
"nbformat": 4,
"nbformat_minor": 0
} | mit |
grokkaine/biopycourse | day2/scicomp_numpy.ipynb | 2 | 33730 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Scientific computing\n",
"\n",
"- Numpy: advanced array operations, multidimensional arrays\n",
"- Scipy: scientific computing by examples\n",
" - singular value decomposition, with scipy.linalg\n",
" - scipy.signal and scipy.fftpack: Signal theory\n",
" - scipy.optimize: Local and global optimization, fitting and root finding\n",
" - scipy.interpolate: Cubic interpolation\n",
" - scipy.integrate: Integration and ODE solvers\n",
" - scipy.ndimage - Image processing\n",
"- Simpy: symbolic math"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Numpy\n",
"\n",
"- using numpy improves RAM space and speed\n",
"- numpy enforces strong typing, while Python is a dynamic typed language\n",
"- translates in Numpy using less heap space for representing data\n",
"- multidimensional array operations are the core of scientific computing\n",
"\n",
"Further reading:\n",
"- https://docs.scipy.org/doc/numpy/user/basics.html"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"293 µs ± 1.46 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n",
"1.46 µs ± 19.3 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)\n",
"<class 'int'>\n",
"int32\n"
]
}
],
"source": [
"import numpy as np\n",
"L = range(1000)\n",
"%timeit [i**2 for i in L]\n",
"a = np.arange(1000)\n",
"%timeit a**2\n",
"print(type(L[1]))\n",
"print(a.dtype)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"##Get help!\n",
"#np.lookfor('create array')\n",
"#np.array?\n",
"#np.arr*?"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"a:\n",
" [0 1 2 3]\n",
"b:\n",
" [[0 1 2]\n",
" [3 4 5]]\n",
"b.shape:\n",
" (2, 3)\n"
]
}
],
"source": [
"a = np.array([0, 1, 2, 3])\n",
"print(\"a:\\n\", a)\n",
"b = np.array([[0, 1, 2], [3, 4, 5]])\n",
"print(\"b:\\n\", b)\n",
"\n",
"print(\"b.shape:\\n\", b.shape)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Data types\n",
"\n",
"There are 5 basic numerical types representing booleans (bool), integers (int), unsigned integers (uint) floating point (float) and complex. Those with numbers in their name indicate the bitsize of the type (i.e. how many bits are needed to represent a single value in memory). "
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"np.float32(1.0) : 1.0\n",
"np.arange(3, dtype=np.uint8) : [0 1 2]\n"
]
}
],
"source": [
"print(\"np.float32(1.0) :\", np.float32(1.0))\n",
"print(\"np.arange(3, dtype=np.uint8) :\", np.arange(3, dtype=np.uint8))"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[1. 2. 3.]\n"
]
}
],
"source": [
"z = np.array([1, 2, 3], dtype='f')\n",
"print(z)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[0 1 2]\n",
"[0. 1. 2.]\n",
"uint8\n"
]
}
],
"source": [
"z = np.arange(3, dtype=np.uint8)\n",
"print(z)\n",
"print(z.astype(float))\n",
"print(z.dtype)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Array creation"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[2 3 1 0]\n",
"\n",
"[[1. 2.]\n",
" [0. 0.]\n",
" [1. 3.]]\n"
]
}
],
"source": [
"# extrinsic\n",
"x = np.array([2,3,1,0])\n",
"print(x)\n",
"print()\n",
"x = np.array([[ 1., 2.], [ 0., 0.], [ 1., 3.]])\n",
"print(x)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[1 3 5 7]\n",
"[0. 0.2 0.4 0.6 0.8 1. ]\n"
]
}
],
"source": [
"#intrinsic\n",
"b = np.arange(1, 9, 2)\n",
"print(b)\n",
"c = np.linspace(0, 1, 6)\n",
"print(c)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[ 0 1 2 3 4 5 6]\n",
" [ 7 8 9 10 11 12 13]\n",
" [14 15 16 17 18 19 20]\n",
" [21 22 23 24 25 26 27]\n",
" [28 29 30 31 32 33 34]]\n"
]
}
],
"source": [
"print(np.arange(35).reshape(5,7))"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[0.9670321 0.41898259 0.20363724 0.35449615 0.16035759 0.71796865\n",
" 0.91560078]\n",
" [0.7941912 0.11327121 0.39895543 0.1938332 0.25015112 0.7301424\n",
" 0.8902718 ]\n",
" [0.38559724 0.80936697 0.87522364 0.2348649 0.50799691 0.96275272\n",
" 0.25653361]\n",
" [0.11539546 0.29497229 0.82328139 0.47741048 0.08609102 0.39704512\n",
" 0.5578069 ]\n",
" [0.72175903 0.77159655 0.4940947 0.16196267 0.405733 0.1140635\n",
" 0.94903288]]\n"
]
}
],
"source": [
"x = np.random.rand(35).reshape(5,7)\n",
"print(x)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Populating the interactive namespace from numpy and matplotlib\n"
]
},
{
"data": {
"text/plain": [
"<matplotlib.colorbar.Colorbar at 0x13a6ab54b08>"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"%pylab inline\n",
"import matplotlib.pyplot as plt\n",
"\n",
"image = np.random.rand(30, 30)\n",
"plt.imshow(image, cmap=plt.cm.hot) \n",
"plt.colorbar() "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Indexing, slicing and selection"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[0 1 2 3 4 5 6 7 8 9]\n",
"0 2 9 7\n",
"[2 3 4] [2 3 4 5 6 7 8 9] [0 1 2 3 4 5 6 7] [0 2 4 6 8] [2 4 6 8]\n"
]
}
],
"source": [
"a = np.arange(10)\n",
"print(a)\n",
"print(a[0], a[2], a[-1], a[-3])\n",
"print(a[2:5], a[2:], a[:-2], a[::2], a[2::2])"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[ 0 0 0]\n",
" [ 0 1 0]\n",
" [ 0 10 2]]\n",
"1\n"
]
}
],
"source": [
"a = np.diag(np.arange(3))\n",
"a[2, 1] = 10 # !third line, !second column\n",
"\n",
"print(a)\n",
"print(a[1, 1])\n",
"#print(a[1])\n",
"#print(a[:,1], a[1,:])\n",
"#print(a[1:,1:])"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[10 9 8 7 6 5 4 3 2]\n",
"\n",
"[7 7 4 2]\n",
"\n",
"[[9 9]\n",
" [8 7]]\n"
]
}
],
"source": [
"# array indexes\n",
"x = np.arange(10,1,-1)\n",
"print(x)\n",
"print()\n",
"print(x[np.array([3,3,-3,8])])\n",
"print()\n",
"print(x[np.array([[1,1],[2,3]])])"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[18 10 3 15 12 1 6 2 3 4]\n",
"[ True False True True True False True False True False]\n",
"[18 3 15 12 6 3]\n",
"[-1 10 -1 -1 -1 1 -1 2 -1 4]\n"
]
}
],
"source": [
"# 10 random numbers 0 - 20\n",
"a = np.random.randint(0, 20, 10)\n",
"print(a)\n",
"print(a%3==0)\n",
"print(a[a%3==0])\n",
"\n",
"a[a % 3 == 0] = -1\n",
"print(a)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Task:\n",
"- What does this do:"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[False False True True False True False True False False False True\n",
" False True False False False True False True False False False True\n",
" False False False False False True False True False False False False\n",
" False True False False False True False True False False False True\n",
" False False False False False True False False False False False True\n",
" False True False False False False False True False False False True\n",
" False True False False False False False True False False False True\n",
" False False False False False True False False False False False False\n",
" False True False False]\n"
]
}
],
"source": [
"# How does it work?\n",
"# Print the primes!\n",
"def get_primes():\n",
" primes = np.ones((100,), dtype=bool)\n",
" primes[:2] = 0\n",
" N_max = int(np.sqrt(len(primes)))\n",
" for j in range(2, N_max):\n",
" primes[2*j::j] = 0\n",
" return primes\n",
"print(get_primes())\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Broadcasting, assignment, structured arrays"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"True\n",
"False\n"
]
}
],
"source": [
"a = np.arange(10)\n",
"b = a\n",
"print(np.may_share_memory(a, b))\n",
"\n",
"a = np.arange(10)\n",
"c = a.copy() # force a copy\n",
"print(np.may_share_memory(a, c))"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"a: [1 2 3 4]\n",
"a + 1, 2**a: [2 3 4 5] [ 2 4 8 16]\n",
"2**(a + 1) - a: [ 3 6 13 28]\n"
]
}
],
"source": [
"#Array operations\n",
"a = np.array([1, 2, 3, 4])\n",
"print(\"a: \", a)\n",
"print(\"a + 1, 2**a: \", a + 1, 2**a)\n",
"print (\"2**(a + 1) - a: \", 2**(a + 1) - a)"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"a: [1 2 3 4]\n",
"b: [2. 2. 2. 2.]\n",
"a - b, a * b: [-1. 0. 1. 2.] [2. 4. 6. 8.]\n"
]
}
],
"source": [
"a = np.array([1, 2, 3, 4])\n",
"b = np.ones(4)+1\n",
"print(\"a: \",a)\n",
"print(\"b: \",b)\n",
"print(\"a - b, a * b: \", a - b, a * b)"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[1. 1. 1.]\n",
" [1. 1. 1.]\n",
" [1. 1. 1.]]\n",
"[[3. 3. 3.]\n",
" [3. 3. 3.]\n",
" [3. 3. 3.]]\n"
]
}
],
"source": [
"c = np.ones((3, 3))\n",
"print(c)\n",
"print(2*c + 1)"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[2. 2.]\n",
" [2. 2.]\n",
" [2. 2.]]\n",
"\n",
"[[2. 2. 2.]\n",
" [2. 2. 2.]]\n",
"\n",
"[[8. 8. 8.]\n",
" [8. 8. 8.]\n",
" [8. 8. 8.]]\n"
]
}
],
"source": [
"# matrix multiplication\n",
"a = np.ones((3, 2)) + 1\n",
"b = np.ones((2, 3)) + 1\n",
"c = a.dot(b)\n",
"print(a, b, c, sep=\"\\n\\n\")"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[ 0. 0.84147098 0.90929743 0.14112001 -0.7568025 ]\n",
"[ -inf 0. 0.69314718 1.09861229 1.38629436]\n",
"[ 1. 2.71828183 7.3890561 20.08553692 54.59815003]\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"D:\\programs\\anaconda\\envs\\biopy37\\lib\\site-packages\\ipykernel_launcher.py:3: RuntimeWarning: divide by zero encountered in log\n",
" This is separate from the ipykernel package so we can avoid doing imports until\n"
]
}
],
"source": [
"a = np.arange(5)\n",
"print(np.sin(a))\n",
"print(np.log(a))\n",
"print(np.exp(a))"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[1 2 3]\n",
"\n",
"[[1]\n",
" [2]\n",
" [3]]\n",
"\n",
"[[1 2 3]]\n"
]
}
],
"source": [
"# shape manipulation\n",
"x = np.array([1, 2, 3])\n",
"y = x[:, np.newaxis]\n",
"z = x[np.newaxis, :]\n",
"print(x, y, z, sep='\\n\\n')"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[1 2 3]\n",
" [4 5 6]]\n",
"[1 2 3 4 5 6]\n"
]
}
],
"source": [
"# flatten\n",
"a = np.array([[1, 2, 3], [4, 5, 6]])\n",
"print(a)\n",
"print(a.ravel())"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[4 3 5]\n",
" [1 2 1]]\n",
"[[3 4 5]\n",
" [1 1 2]]\n"
]
}
],
"source": [
"# sorting matrices\n",
"a = np.array([[4, 3, 5], [1, 2, 1]])\n",
"b = np.sort(a, axis=1) #sorting per row\n",
"print(a)\n",
"print(b)"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[2 3 1 0] [1 2 3 4]\n"
]
}
],
"source": [
"# sorting arguments\n",
"a = np.array([4, 3, 1, 2])\n",
"j = np.argsort(a)\n",
"print(j, a[j])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Reductions"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"10 10 10\n"
]
}
],
"source": [
"# unidimensional\n",
"x = np.array([1, 2, 3, 4])\n",
"print(np.sum(x), x.sum(), x.sum(axis=0))"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[1 1]\n",
" [2 2]]\n",
"[3 3]\n",
"[2 4]\n"
]
}
],
"source": [
"# multidimensional\n",
"x = np.array([[1, 1], [2, 2]])\n",
"print(x)\n",
"print(x.sum(axis=0)) # rows (first dimension)\n",
"print(x.sum(axis=1)) # columns (second dimension)"
]
},
{
"cell_type": "code",
"execution_count": 109,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[1 3 2]\n",
"1 3 0 1\n",
"False True\n",
"1.75 0.82915619758885 1.5 [2. 5.]\n"
]
}
],
"source": [
"x = np.array([1, 3, 2])\n",
"print(x)\n",
"print(x.min(), x.max(), x.argmin(), x.argmax())\n",
"\n",
"print(np.all([True, True, False]), np.any([True, True, False]))\n",
"\n",
"x = np.array([1, 2, 3, 1])\n",
"y = np.array([[1, 2, 3], [5, 6, 1]])\n",
"print(x.mean(), x.std(), np.median(x), np.median(y, axis=-1))"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"False\n",
"True\n"
]
}
],
"source": [
"a = np.zeros((100, 100))\n",
"print(np.any(a != 0))\n",
"\n",
"a = np.array([1, 2, 3, 2])\n",
"b = np.array([2, 2, 3, 2])\n",
"c = np.array([6, 4, 4, 5])\n",
"print(((a <= b) & (b <= c)).all())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Tricksy task:**\n",
"- Replace all values greater than 25 with 9 and all values smaller than 10 with 29."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[ 3 9 33 19 46 34 39 39 32 27]\n"
]
}
],
"source": [
"a = np.random.randint(0, 50, 10)\n",
"print(a)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Sympy"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Symbolic math is sometimes important, especially if we are weak at calculus or if we need to perform automated calculus on long formulas. We are briefly going through a few test cases, to get the feel of it. Symbolic math is especially developed for [Mathematica](http://www.wolfram.com/mathematica/?source=nav), or [Sage](http://www.sagemath.org/) which is an open-source equivalent."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2*sqrt(2)\n",
"2.82842712475\n"
]
}
],
"source": [
"import sympy\n",
"print sympy.sqrt(8)\n",
"import math\n",
"print math.sqrt(8)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"x + 2*y\n",
"x*(x + 2*y)\n",
"x**2 + 2*x*y\n",
"x*(x + 2*y)\n",
"2*y*z**t + z**(2*t)\n",
"z**t*(2*y + z**t)\n"
]
}
],
"source": [
"from sympy import symbols\n",
"x, y, z, t = symbols('x y z t')\n",
"expr = x + 2*y\n",
"print expr\n",
"print x * expr\n",
"from sympy import expand, factor, simplify\n",
"expanded_expr = expand(x*expr)\n",
"print expanded_expr\n",
"print factor(expanded_expr)\n",
"exp = expanded_expr.subs(x, z**t)\n",
"print exp\n",
"print simplify(exp)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In the scipy.optimize paragraph we needed the Hessian matrix for a function f. Here is how you can obtain it in sympy:"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Matrix([[12*x**2 - 4*y + 1.0, -4*x], [-4*x, 2]])\n"
]
},
{
"data": {
"text/latex": [
"\\left[\\begin{matrix}12 x^{2} - 4 y + 1.0 & - 4 x\\\\- 4 x & 2\\end{matrix}\\right]"
],
"text/plain": [
"<IPython.core.display.Latex object>"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import sympy\n",
"x, y = sympy.symbols('x y')\n",
"f = .5*(1 - x)**2 + (y - x**2)**2\n",
"h = sympy.hessian(f, [x,y])\n",
"print(h)\n",
"from IPython.display import Latex\n",
"Latex(sympy.latex(h))\n"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<iframe src=http://en.wikipedia.org/wiki/Hessian_matrix width=700 height=350></iframe>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from IPython.display import HTML\n",
"HTML('<iframe src=http://en.wikipedia.org/wiki/Hessian_matrix width=700 height=350></iframe>')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"jupyter": {
"outputs_hidden": 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": "3.7.10"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
| cc0-1.0 |
bosscha/alma-calibrator | notebooks/selecting_source/alma_database_selection10.ipynb | 1 | 5225 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# What is the most important project that we need to download first?\n",
"\n",
"### What I did is simply counting the occurance of the word (project name) in the report file"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from collections import Counter"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"filename = \"report_8_nonALMACAL_priority.txt\""
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"with open(filename, 'r') as ifile:\n",
" wordcount = Counter(ifile.read().split())"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"list_of_project = []\n",
"\n",
"for item in wordcount:\n",
" if len(item) == 14 and item[-1] == 'S': # project_name\n",
" list_of_project.append([item, wordcount[item]])"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"sorted_project = sorted(list_of_project, key=lambda data: data[1])"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Number of project: 633\n"
]
}
],
"source": [
"print(\"Number of project: \", len(sorted_project))"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"sorted_from_large = list(reversed(sorted_project))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"due to the structure of the report this number can not be used directly as a reference\n",
"\n",
"e.g. maybe large occurance due to small integration and observed many time and also it is possible only for one object in one band (like the first project in here)\n",
"\n",
"I think the year of Cycle is more important due to number of antenna."
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['2012.1.00453.S', 33]\n",
"['2015.1.01289.S', 32]\n",
"['2012.1.00139.S', 20]\n",
"['2012.1.00377.S', 13]\n",
"['2015.1.00412.S', 13]\n",
"['2012.1.00729.S', 13]\n",
"['2013.1.00020.S', 13]\n",
"['2012.1.00317.S', 13]\n",
"['2013.1.00700.S', 13]\n",
"['2015.1.01352.S', 12]\n",
"['2015.1.00144.S', 12]\n",
"['2015.1.00027.S', 11]\n",
"['2015.1.00932.S', 11]\n",
"['2015.1.01454.S', 11]\n",
"['2016.1.00567.S', 10]\n"
]
}
],
"source": [
"# 15 first\n",
"for i in sorted_from_large[0:15]:\n",
" print(i)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Sorted based on year"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"sorted_project_year = sorted(list_of_project, key=lambda data: data[0])"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"sorted_from_new = list(reversed(sorted_project_year))"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['2016.A.00011.S', 2]\n",
"['2016.A.00010.S', 2]\n",
"['2016.1.01609.S', 2]\n",
"['2016.1.01604.S', 3]\n",
"['2016.1.01567.S', 4]\n",
"['2016.1.01559.S', 2]\n",
"['2016.1.01552.S', 3]\n",
"['2016.1.01546.S', 2]\n",
"['2016.1.01541.S', 2]\n",
"['2016.1.01520.S', 6]\n",
"['2016.1.01515.S', 4]\n",
"['2016.1.01512.S', 6]\n",
"['2016.1.01495.S', 2]\n",
"['2016.1.01493.S', 2]\n",
"['2016.1.01481.S', 2]\n"
]
}
],
"source": [
"# 15 first\n",
"for i in sorted_from_new[0:15]:\n",
" print(i)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"There is 'A' in project name e.g. 2016.A.00011.S, 2016.A.00010.S, what does it mean?"
]
},
{
"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": "3.6.5"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| gpl-2.0 |
DhavalThkkar/internship2017 | Challenges/MNIST with Multi-Layer Perceptron.ipynb | 1 | 70495 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"___\n",
"\n",
"<a href='http://www.pieriandata.com'> <img src='../Pierian_Data_Logo.png' /></a>\n",
"___\n",
"# MNIST Multi-Layer Perceptron\n",
"\n",
"In this lecture we will build out a Multi Layer Perceptron model to try to classify hand written digits using TensorFlow (a very famous example).\n",
"\n",
"Keep in mind that no single lecture (or course!) can cover the vastness that is Deep Learning, I would highly suggest reading MIT's [Deep Learning](http://www.deeplearningbook.org/) textbook for more information on these topics!"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Get the Data\n",
"\n",
"We will be using the famous MNIST data set of [handwritten digits](http://yann.lecun.com/exdb/mnist/). \n",
"\n",
"The images which we will be working with are black and white images of size 28 x 28 pixels, or 784 pixels total. Our features will be the pixel values for each pixel. Either the pixel is \"white\" (blank with a 0), or there is some pixel value. \n",
"\n",
"We will try to correctly predict what number is written down based solely on the image data in the form of an array. This type of problem (Image Recognition) is a great use case for Deep Learning Methods!\n",
"\n",
"This data is to Deep Learning what the iris data set is to typical machine learning algorithms. \n",
"\n",
"Let's get the data:"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Extracting /tmp/data/train-images-idx3-ubyte.gz\n",
"Extracting /tmp/data/train-labels-idx1-ubyte.gz\n",
"Extracting /tmp/data/t10k-images-idx3-ubyte.gz\n",
"Extracting /tmp/data/t10k-labels-idx1-ubyte.gz\n"
]
}
],
"source": [
"import tensorflow as tf\n",
"\n",
"# Import MINST data\n",
"from tensorflow.examples.tutorials.mnist import input_data\n",
"mnist = input_data.read_data_sets(\"/tmp/data/\", one_hot=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Data Format\n",
"\n",
"The data is stored in a vector format, although the original data was a 2-dimensional matirx with values representing how much pigment was at a certain location. Let's explore this:"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"tensorflow.contrib.learn.python.learn.datasets.base.Datasets"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"type(mnist)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"numpy.ndarray"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"type(mnist.train.images)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(784,)"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#mnist.train.images[0]\n",
"mnist.train.images[2].shape"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"sample = mnist.train.images[2].reshape(28,28)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.image.AxesImage at 0x7fc5f8ad0e48>"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x7fc628c7cac8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.imshow(sample)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Parameters\n",
"\n",
"We'll need to define 4 parameters, it is really (really) hard to know what good parameter values are on a data set for which you have no experience with, however since MNIST is pretty famous, we have some reasonable values for our data below. The parameters here are:\n",
"\n",
"* Learning Rate - How quickly to adjust the cost function.\n",
"* Training Epochs - How many training cycles to go through\n",
"* Batch Size - Size of the 'batches' of training data"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Parameters\n",
"learning_rate = 0.001\n",
"training_epochs = 150\n",
"batch_size = 100"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Network Parameters\n",
"\n",
"Here we have parameters which will directly define our Neural Network, these would be adjusted depending on what your data looked like and what kind of a net you would want to build. Basically just some numbers we will eventually use to define some variables later on in our model:"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"# Network Parameters\n",
"n_hidden_1 = 256 # 1st layer number of features\n",
"n_hidden_2 = 256 # 2nd layer number of features\n",
"n_input = 784 # MNIST data input (img shape: 28*28)\n",
"n_classes = 10 # MNIST total classes (0-9 digits)\n",
"n_samples = mnist.train.num_examples"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### TensorFlow Graph Input"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"x = tf.placeholder(\"float\", [None, n_input])\n",
"y = tf.placeholder(\"float\", [None, n_classes])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## MultiLayer Model\n",
"\n",
"It is time to create our model, let's review what we want to create here.\n",
"\n",
"First we receive the input data array and then to send it to the first hidden layer. Then the data will begin to have a weight attached to it between layers (remember this is initially a random value) and then sent to a node to undergo an activation function (along with a Bias as mentioned in the lecture). Then it will continue on to the next hidden layer, and so on until the final output layer. In our case, we will just use two hidden layers, the more you use the longer the model will take to run (but it has more of an opportunity to possibly be more accurate on the training data).\n",
"\n",
"Once the transformed \"data\" has reached the output layer we need to evaluate it. Here we will use a loss function (also called a cost function) to evaluate how far off we are from the desired result. In this case, how many of the classes we got correct. \n",
"\n",
"Then we will apply an optimization function to minimize the cost (lower the error). This is done by adjusting weight values accordingly across the network. In out example, we will use the [Adam Optimizer](http://arxiv.org/pdf/1412.6980v8.pdf), which keep in mind, relative to other mathematical concepts, is an extremely recent development.\n",
"\n",
"We can adjust how quickly to apply this optimization by changing our earlier learning rate parameter. The lower the rate the higher the possibility for accurate training results, but that comes at the cost of having to wait (physical time wise) for the results. Of course, after a certain point there is no benefit to lower the learning rate.\n",
"\n",
"Now we will create our model, we'll start with 2 hidden layers, which use the [RELU](https://en.wikipedia.org/wiki/Rectifier_(neural_networks) activation function, which is a very simple rectifier function which essentially either returns x or zero. For our final output layer we will use a linear activation with matrix multiplication:"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def multilayer_perceptron(x, weights, biases):\n",
" '''\n",
" x : Place Holder for Data Input\n",
" weights: Dictionary of weights\n",
" biases: Dicitionary of biases\n",
" '''\n",
" \n",
" # First Hidden layer with RELU activation\n",
" layer_1 = tf.add(tf.matmul(x, weights['h1']), biases['b1'])\n",
" layer_1 = tf.nn.relu(layer_1)\n",
" \n",
" # Second Hidden layer with RELU activation\n",
" layer_2 = tf.add(tf.matmul(layer_1, weights['h2']), biases['b2'])\n",
" layer_2 = tf.nn.relu(layer_2)\n",
" \n",
" # Last Output layer with linear activation\n",
" out_layer = tf.matmul(layer_2, weights['out']) + biases['out']\n",
" return out_layer"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Weights and Bias\n",
"\n",
"In order for our tensorflow model to work we need to create two dictionaries containing our weight and bias objects for the model. We can use the **tf.variable** object type. This is different from a constant because TensorFlow's Graph Object becomes aware of the states of all the variables. A Variable is a modifiable tensor that lives in TensorFlow's graph of interacting operations. It can be used and even modified by the computation. We will generally have the model parameters be Variables. From the documentation string:\n",
"\n",
" A variable maintains state in the graph across calls to `run()`. You add a variable to the graph by constructing an instance of the class `Variable`.\n",
"\n",
" The `Variable()` constructor requires an initial value for the variable, which can be a `Tensor` of any type and shape. The initial value defines the type and shape of the variable. After construction, the type and shape of the variable are fixed. The value can be changed using one of the assign methods.\n",
" \n",
"We'll use tf's built-in random_normal method to create the random values for our weights and biases (you could also just pass ones as the initial biases)."
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"weights = {\n",
" 'h1': tf.Variable(tf.random_normal([n_input, n_hidden_1])),\n",
" 'h2': tf.Variable(tf.random_normal([n_hidden_1, n_hidden_2])),\n",
" 'out': tf.Variable(tf.random_normal([n_hidden_2, n_classes]))\n",
"}"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"biases = {\n",
" 'b1': tf.Variable(tf.random_normal([n_hidden_1])),\n",
" 'b2': tf.Variable(tf.random_normal([n_hidden_2])),\n",
" 'out': tf.Variable(tf.random_normal([n_classes]))\n",
"}"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Construct model\n",
"pred = multilayer_perceptron(x, weights, biases)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Cost and Optimization Functions\n",
"\n",
"We'll use Tensorflow's built-in functions for this part (check out the documentation for a lot more options and discussion on this):"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"ename": "ValueError",
"evalue": "No gradients provided for any variable, check your graph for ops that do not support gradients, between variables ['Tensor(\"Variable/read:0\", shape=(784, 256), dtype=float32)', 'Tensor(\"Variable_1/read:0\", shape=(256, 256), dtype=float32)', 'Tensor(\"Variable_2/read:0\", shape=(256, 10), dtype=float32)', 'Tensor(\"Variable_3/read:0\", shape=(256,), dtype=float32)', 'Tensor(\"Variable_4/read:0\", shape=(256,), dtype=float32)', 'Tensor(\"Variable_5/read:0\", shape=(10,), dtype=float32)'] and loss Tensor(\"Mean:0\", shape=(), dtype=float32).",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-16-5c1c700d682d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# Define loss and optimizer\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0mcost\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreduce_mean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msoftmax_cross_entropy_with_logits\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlogits\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0moptimizer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mAdamOptimizer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlearning_rate\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlearning_rate\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mminimize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcost\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m/home/thakkar_/anaconda3/lib/python3.6/site-packages/tensorflow/python/training/optimizer.py\u001b[0m in \u001b[0;36mminimize\u001b[0;34m(self, loss, global_step, var_list, gate_gradients, aggregation_method, colocate_gradients_with_ops, name, grad_loss)\u001b[0m\n\u001b[1;32m 284\u001b[0m \u001b[0;34m\"No gradients provided for any variable, check your graph for ops\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 285\u001b[0m \u001b[0;34m\" that do not support gradients, between variables %s and loss %s.\"\u001b[0m \u001b[0;34m%\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 286\u001b[0;31m ([str(v) for _, v in grads_and_vars], loss))\n\u001b[0m\u001b[1;32m 287\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 288\u001b[0m return self.apply_gradients(grads_and_vars, global_step=global_step,\n",
"\u001b[0;31mValueError\u001b[0m: No gradients provided for any variable, check your graph for ops that do not support gradients, between variables ['Tensor(\"Variable/read:0\", shape=(784, 256), dtype=float32)', 'Tensor(\"Variable_1/read:0\", shape=(256, 256), dtype=float32)', 'Tensor(\"Variable_2/read:0\", shape=(256, 10), dtype=float32)', 'Tensor(\"Variable_3/read:0\", shape=(256,), dtype=float32)', 'Tensor(\"Variable_4/read:0\", shape=(256,), dtype=float32)', 'Tensor(\"Variable_5/read:0\", shape=(10,), dtype=float32)'] and loss Tensor(\"Mean:0\", shape=(), dtype=float32)."
]
}
],
"source": [
"# Define loss and optimizer\n",
"cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y, logits=x))\n",
"optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Initialization of Variables\n",
"\n",
"Now initialize all those tf.Variable objects we created earlier. This will be the first thing we run when training our model:"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
"# Initializing the variables\n",
"init = tf.initialize_all_variables()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Training the Model\n",
"\n",
"### next_batch()\n",
"\n",
"Before we get started I want to cover one more convenience function in our mnist data object called next_batch. This returns a tuple in the form (X,y) with an array of the data and a y array indicating the class in the form of a binary array. For example:"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"Xsamp,ysamp = mnist.train.next_batch(1)"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.image.AxesImage at 0x136152c88>"
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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CGnxukKGB0JBiQwwtfmyYYssQWoZBE3YZf1tIH3ae2GuSk4VxT/4CKcRvRSH7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\ny3wZ6TDTAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x12934a9e8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.imshow(Xsamp.reshape(28,28))"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[ 0. 0. 0. 0. 0. 0. 0. 0. 1. 0.]]\n"
]
}
],
"source": [
"# Remember indexing starts at zero!\n",
"print(ysamp)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Running the Session\n",
"Now it is time to run our session! Pay attention to how we have two loops, the outer loop which runs the epochs, and the inner loop which runs the batches for each epoch of training. Let's breakdown each step!"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch: 1 cost=156.1939\n",
"Epoch: 2 cost=38.7113\n",
"Epoch: 3 cost=24.6571\n",
"Epoch: 4 cost=17.1834\n",
"Epoch: 5 cost=12.6043\n",
"Epoch: 6 cost=9.4217\n",
"Epoch: 7 cost=7.1025\n",
"Epoch: 8 cost=5.3346\n",
"Epoch: 9 cost=3.9459\n",
"Epoch: 10 cost=3.0107\n",
"Epoch: 11 cost=2.2067\n",
"Epoch: 12 cost=1.6921\n",
"Epoch: 13 cost=1.3159\n",
"Epoch: 14 cost=0.9436\n",
"Epoch: 15 cost=0.7575\n",
"Model has completed 15 Epochs of Training\n"
]
}
],
"source": [
"# Launch the session\n",
"sess = tf.InteractiveSession()\n",
"\n",
"# Intialize all the variables\n",
"sess.run(init)\n",
"\n",
"# Training Epochs\n",
"# Essentially the max amount of loops possible before we stop\n",
"# May stop earlier if cost/loss limit was set\n",
"for epoch in range(training_epochs):\n",
"\n",
" # Start with cost = 0.0\n",
" avg_cost = 0.0\n",
"\n",
" # Convert total number of batches to integer\n",
" total_batch = int(n_samples/batch_size)\n",
"\n",
" # Loop over all batches\n",
" for i in range(total_batch):\n",
"\n",
" # Grab the next batch of training data and labels\n",
" batch_x, batch_y = mnist.train.next_batch(batch_size)\n",
"\n",
" # Feed dictionary for optimization and loss value\n",
" # Returns a tuple, but we only need 'c' the cost\n",
" # So we set an underscore as a \"throwaway\"\n",
" _, c = sess.run([optimizer, cost], feed_dict={x: batch_x, y: batch_y})\n",
"\n",
" # Compute average loss\n",
" avg_cost += c / total_batch\n",
"\n",
" print(\"Epoch: {} cost={:.4f}\".format(epoch+1,avg_cost))\n",
"\n",
"print(\"Model has completed {} Epochs of Training\".format(training_epochs))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Model Evaluations\n",
"\n",
"Tensorflow comes with some built-in functions to help evaluate our model, including tf.equal and tf.cast with tf.reduce_mean.\n",
"\n",
"**tf.equal()**\n",
"\n",
"This is essentially just a check of predictions == y_test. In our case since we know the format of the labels is a 1 in an array of zeroes, we can compare argmax() location of that 1. Remember that **y** here is still that placeholder we created at the very beginning, we will perform a series of operations to get a Tensor that we can eventually fill in the test data for with an evaluation method. What we are currently running will still be empty of test data:"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Test model\n",
"correct_predictions = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Tensor(\"Squeeze:0\", shape=(), dtype=bool)\n"
]
}
],
"source": [
"print(correct_predictions[0])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In order to get a numerical value for our predictions we will need to use tf.cast to cast the Tensor of booleans back into a Tensor of Floating point values in order to take the mean of it."
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"correct_predictions = tf.cast(correct_predictions, \"float\")"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Tensor(\"Squeeze_1:0\", shape=(), dtype=float32)\n"
]
}
],
"source": [
"print(correct_predictions[0])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we use the tf.reduce_mean function in order to grab the mean of the elements across the tensor."
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"accuracy = tf.reduce_mean(correct_predictions)"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"tensorflow.python.framework.ops.Tensor"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"type(accuracy)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This may seem a little strange, but this accuracy is still a Tensor object. Remember that we still need to pass in our actual test data! Now we can call the MNIST test labels and images and evaluate our accuracy!"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 0., 0., 0., ..., 1., 0., 0.],\n",
" [ 0., 0., 1., ..., 0., 0., 0.],\n",
" [ 0., 1., 0., ..., 0., 0., 0.],\n",
" ..., \n",
" [ 0., 0., 0., ..., 0., 0., 0.],\n",
" [ 0., 0., 0., ..., 0., 0., 0.],\n",
" [ 0., 0., 0., ..., 0., 0., 0.]])"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"mnist.test.labels"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 0., 0., 0., ..., 0., 0., 0.],\n",
" [ 0., 0., 0., ..., 0., 0., 0.],\n",
" [ 0., 0., 0., ..., 0., 0., 0.],\n",
" ..., \n",
" [ 0., 0., 0., ..., 0., 0., 0.],\n",
" [ 0., 0., 0., ..., 0., 0., 0.],\n",
" [ 0., 0., 0., ..., 0., 0., 0.]], dtype=float32)"
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"mnist.test.images"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The eval() method allows you to directly evaluates this tensor in a `Session` without needing to call tf.sess():mm"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Accuracy: 0.9436\n"
]
}
],
"source": [
"print(\"Accuracy:\", accuracy.eval({x: mnist.test.images, y: mnist.test.labels}))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"94% not too shabby! But this actually isn't anywhere near as good as it could be. Running for more training epochs with this data (around 20,000) can produce accuracy around 99%. But we won't do that here because that will take a very long time to run!"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Great Job!\n",
"\n",
"### Extra Credit: See what happens if you try to make this model again with more layers!"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.1"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
| apache-2.0 |
QuantumTechDevStudio/RUDNEVGAUSS | archive/Rodion/Dif-Eq-1d/TensorFlow/OscillNetWork.ipynb | 1 | 70050 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import tensorflow as tf\n",
"import numpy\n",
"import math\n",
"import matplotlib.pyplot as plt\n",
"import pickle\n",
"import os\n",
"from tqdm import tqdm\n",
"from IPython.display import clear_output\n",
"\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"sess = tf.Session()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"k_const = 1.0\n",
"m_const = 1.0\n",
"omega_sq_const = k_const / m_const\n",
"\n",
"x0_const = 0.0\n",
"speed0_const = 1.0\n"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"hidden_layer_units = 8\n",
"amount_of_points = 15\n",
"min_time = 0\n",
"max_time = 2 * math.pi "
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"min_element = -1\n",
"max_element = 1"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"alpha = tf.constant(0.007, dtype=tf.double)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"w1_matrix = numpy.random.uniform(min_element, max_element, (hidden_layer_units, 1))\n",
"offset1_matrix = numpy.random.uniform(min_element, max_element, (hidden_layer_units, 1))\n",
"w2_matrix = numpy.random.uniform(min_element, max_element, (1, hidden_layer_units))\n"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"t_list = numpy.linspace(min_time, max_time, amount_of_points).reshape((1, amount_of_points))\n",
"x_expected = numpy.sin(omega * t_list)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"t_cv = numpy.linspace(min_time, max_time, 1000).reshape((1, 1000))\n"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def numpy_sigmoid(x):\n",
" res = 1 / (1 + numpy.exp(-x))\n",
" return res"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<tf.Tensor 'gradients/MatMul_grad/MatMul_1:0' shape=(1, ?) dtype=float64>"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"speed"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"w1 = tf.Variable(w1_matrix, dtype=tf.double, name='w1')\n",
"offset1 = tf.Variable(offset1_matrix, dtype=tf.double, name='offset1')\n",
"w2 = tf.Variable(w2_matrix, dtype=tf.double, name='w2')\n",
"# -----------------------------------------------------------\n",
"t = tf.placeholder(tf.double)\n",
"# -----------------------------------------------------------\n",
"k = tf.constant(k_const, dtype=tf.double, name='k')\n",
"m = tf.constant(m_const, dtype=tf.double, name='m')\n",
"omega_sq = k / m\n",
"# -----------------------------------------------------------\n",
"x0 = tf.constant(x0_const, dtype=tf.double, name='x0')\n",
"speed0 = tf.constant(speed0_const, dtype=tf.double, name='speed0')\n",
"begin_hamiltonian = (k * (x0 ** 2) / 2) + (m * (speed0 ** 2) / 2)\n",
"# -----------------------------------------------------------\n",
"init = tf.global_variables_initializer()\n",
"sess.run(init)\n"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# forward prop \n",
"hidden_layer = tf.sigmoid(tf.matmul(w1, t) + offset1)\n",
"x = tf.matmul(w2, hidden_layer)\n",
"# calculate speed and acc\n",
"speed = tf.gradients(x, t)[0]\n",
"acc = tf.gradients(speed, t)[0]\n",
"# calculate cost\n",
"equation_part = tf.reduce_sum((acc + omega_sq * x) ** 2)\n",
"begin_part = ((x[0][0] - x0) ** 2) + ((speed[0][0] - speed0) ** 2)\n",
"current_hamiltonian = (k * (x ** 2) / 2) + (m * (speed ** 2) / 2)\n",
"hamilton_part = tf.reduce_sum((current_hamiltonian - begin_hamiltonian) ** 2)\n",
"cost = (1 / amount_of_points) * (equation_part + begin_part + hamilton_part)\n",
"# calculate_grads\n",
"grad = tf.gradients(cost, [w1, offset1, w2])\n",
"# gradient descent\n",
"w1_ass = w1.assign_sub(alpha * grad[0])\n",
"offset1_ass = offset1.assign_sub(alpha * grad[1])\n",
"w2_ass = w2.assign_sub(alpha * grad[2])"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<tf.Tensor 'MatMul_1:0' shape=(1, ?) dtype=float64>"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
" 97%|█████████▋| 290137/300000 [05:08<00:10, 915.87it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.000161313553887\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 300000/300000 [05:18<00:00, 941.63it/s] \n"
]
}
],
"source": [
"# gradient descent run\n",
"sess.run(init)\n",
"J_list = []\n",
"for i in tqdm(range(300000)):\n",
" _, _, _, cur_cost = sess.run([w1_ass, offset1_ass, w2_ass, cost], {t: t_list})\n",
" if (i%10000) == 0:\n",
" clear_output()\n",
" print(cur_cost)\n",
" if (cur_cost<1e-6):\n",
" break\n",
" J_list.append(cur_cost ** (-1))"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"6581.67434762\n"
]
},
{
"data": {
"image/png": 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OzIxrIyKH6G/vl/PiovVce86xDOzePupwJAUd6jmBnu5eGsobgJ6h3AdYF7dc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\nke04NAfkAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f6aacd6e898>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.title('Cost Function ^ -1')\n",
"plt.plot(J_list)\n",
"print(J_list[-1])"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x7f6ad4030978>]"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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0FZGGIhIB9AbG+44gIvWAL4G+qrrGp3+UiERnPQe6AcuKIZNfMjOVkTPW0vjU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\nrDAYY4zJxgqDMcaYbKwwGGOMyeb/Ab4fFrJh3m5fAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f6a98b2cc50>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"x_cv = sess.run(x, {t: t_cv})\n",
"plt.title('Expected and approximate functions')\n",
"plt.grid(True)\n",
"plt.plot(t_cv[0], x_cv[0])\n",
"plt.plot(t_list[0],x_expected[0], 'x', color='red')"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"w1_matrix = sess.run(w1)\n",
"offset1_matrix = sess.run(offset1)\n",
"w2_matrix = sess.run(w2)"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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fA+D+ReCdFfS+az4EJnskPadH8QUtPAEDb8DE9Ojqyy/KTQkh8Icj+MMRSq70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\njlGwi4jkGAW7iEiO+T8bhbyxv5SsEwAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f6a98ac6eb8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.grid(True)\n",
"plt.title(\"Spectrum\")\n",
"for i in range(hidden_layer_units):\n",
" cur_sigm = w2_matrix[0][i] * numpy_sigmoid((w1_matrix[i][0] * t_cv) + offset1_matrix[i][0])\n",
" plt.plot(t_cv[0], cur_sigm[0])"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#save_params('cos(x)')"
]
}
],
"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"
},
"varInspector": {
"cols": {
"lenName": 16,
"lenType": 16,
"lenVar": "20"
},
"kernels_config": {
"python": {
"delete_cmd_postfix": "",
"delete_cmd_prefix": "del ",
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"library": "var_list.r",
"varRefreshCmd": "cat(var_dic_list()) "
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| gpl-3.0 |
jldbc/pybaseball | EXAMPLES/strikeouts_on_the_rise.ipynb | 1 | 61450 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Strikeouts on the Rise: Using pybaseball to Explore Historical Team Pitching Performance\n",
"[This NY Times piece](http://www.nytimes.com/interactive/2013/03/29/sports/baseball/Strikeouts-Are-Still-Soaring.html) from 2013 showed that strikeouts are on the rise in Major League Baseball. In a simple infographic, it shows the gradual increase over time in strikeouts per game from 1900 to 2012. \n",
"\n",
"This is a brief example of how you can use pybaseball to replicate this graphic and answer other historical questions about team-level baseball statistics. "
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from pybaseball import team_pitching\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#collect historic team pitching data from pybaseball\n",
"pitching_data = team_pitching(1900,2016)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"data shape: (2460, 20)\n",
" Season Team W L SV G GS IP SO K/9 \\\n",
"1 1907.0 Cubs 107.0 44.0 8.0 200.0 155.0 1373.1 586.0 3.84 \n",
"2 1909.0 Cubs 104.0 49.0 11.0 212.0 155.0 1409.1 680.0 4.34 \n",
"3 1906.0 Cubs 115.0 36.0 10.0 187.0 154.0 1388.1 702.0 4.55 \n",
"4 1910.0 Athletics 102.0 48.0 5.0 200.0 155.0 1421.2 789.0 4.99 \n",
"5 1909.0 Athletics 95.0 58.0 3.0 214.0 153.0 1378.0 728.0 4.75 \n",
"\n",
" BB/9 HR/9 BABIP LOB% GB% HR/FB ERA FIP xFIP WAR \n",
"1 2.63 0.07 0.241 0.747 NaN NaN 1.73 2.31 NaN 17.8 \n",
"2 2.32 0.04 0.248 0.745 NaN NaN 1.74 2.08 NaN 22.4 \n",
"3 2.89 0.08 0.238 0.757 NaN NaN 1.75 2.43 NaN 17.6 \n",
"4 2.85 0.05 0.254 0.731 NaN NaN 1.79 2.23 NaN 16.4 \n",
"5 2.52 0.06 0.251 0.737 NaN NaN 1.93 2.08 NaN 15.1 \n"
]
}
],
"source": [
"#a quick look at the data\n",
"print(\"data shape: {}\").format(pitching_data.shape)\n",
"\n",
"print(pitching_data.head())"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
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" text-align: left;\n",
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" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Season</th>\n",
" <th>W</th>\n",
" <th>L</th>\n",
" <th>SV</th>\n",
" <th>G</th>\n",
" <th>GS</th>\n",
" <th>IP</th>\n",
" <th>SO</th>\n",
" <th>K/9</th>\n",
" <th>BB/9</th>\n",
" <th>HR/9</th>\n",
" <th>BABIP</th>\n",
" <th>LOB%</th>\n",
" <th>GB%</th>\n",
" <th>HR/FB</th>\n",
" <th>ERA</th>\n",
" <th>FIP</th>\n",
" <th>xFIP</th>\n",
" <th>WAR</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>2460.000000</td>\n",
" <td>2460.000000</td>\n",
" <td>2460.000000</td>\n",
" <td>2460.000000</td>\n",
" <td>2460.000000</td>\n",
" <td>2460.000000</td>\n",
" <td>2460.000000</td>\n",
" <td>2460.000000</td>\n",
" <td>2460.000000</td>\n",
" <td>2460.000000</td>\n",
" <td>2460.000000</td>\n",
" <td>2460.000000</td>\n",
" <td>2460.000000</td>\n",
" <td>450.000000</td>\n",
" <td>450.000000</td>\n",
" <td>2460.000000</td>\n",
" <td>2460.000000</td>\n",
" <td>450.000000</td>\n",
" <td>2460.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>1966.289431</td>\n",
" <td>78.135366</td>\n",
" <td>78.135366</td>\n",
" <td>27.294715</td>\n",
" <td>414.617073</td>\n",
" <td>156.939837</td>\n",
" <td>1402.272276</td>\n",
" <td>800.954472</td>\n",
" <td>5.104598</td>\n",
" <td>3.236748</td>\n",
" <td>0.725545</td>\n",
" <td>0.282952</td>\n",
" <td>0.703174</td>\n",
" <td>0.441962</td>\n",
" <td>0.105998</td>\n",
" <td>3.861809</td>\n",
" <td>3.849037</td>\n",
" <td>4.189156</td>\n",
" <td>13.845935</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>33.922249</td>\n",
" <td>13.540774</td>\n",
" <td>13.474811</td>\n",
" <td>14.757541</td>\n",
" <td>144.634809</td>\n",
" <td>8.992996</td>\n",
" <td>82.564637</td>\n",
" <td>261.353634</td>\n",
" <td>1.543454</td>\n",
" <td>0.531561</td>\n",
" <td>0.330695</td>\n",
" <td>0.015073</td>\n",
" <td>0.034746</td>\n",
" <td>0.022116</td>\n",
" <td>0.014720</td>\n",
" <td>0.698738</td>\n",
" <td>0.619960</td>\n",
" <td>0.348521</td>\n",
" <td>4.627145</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>1900.000000</td>\n",
" <td>36.000000</td>\n",
" <td>36.000000</td>\n",
" <td>0.000000</td>\n",
" <td>146.000000</td>\n",
" <td>103.000000</td>\n",
" <td>922.100000</td>\n",
" <td>245.000000</td>\n",
" <td>1.850000</td>\n",
" <td>1.490000</td>\n",
" <td>0.030000</td>\n",
" <td>0.238000</td>\n",
" <td>0.564000</td>\n",
" <td>0.382000</td>\n",
" <td>0.069000</td>\n",
" <td>1.730000</td>\n",
" <td>1.920000</td>\n",
" <td>3.330000</td>\n",
" <td>-0.500000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>1937.000000</td>\n",
" <td>69.000000</td>\n",
" <td>69.000000</td>\n",
" <td>14.000000</td>\n",
" <td>295.000000</td>\n",
" <td>154.000000</td>\n",
" <td>1378.000000</td>\n",
" <td>571.750000</td>\n",
" <td>3.780000</td>\n",
" <td>2.900000</td>\n",
" <td>0.500000</td>\n",
" <td>0.272000</td>\n",
" <td>0.684750</td>\n",
" <td>0.426250</td>\n",
" <td>0.096000</td>\n",
" <td>3.420000</td>\n",
" <td>3.500000</td>\n",
" <td>3.940000</td>\n",
" <td>10.700000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>1972.000000</td>\n",
" <td>79.000000</td>\n",
" <td>78.000000</td>\n",
" <td>29.000000</td>\n",
" <td>398.000000</td>\n",
" <td>161.000000</td>\n",
" <td>1430.050000</td>\n",
" <td>811.000000</td>\n",
" <td>5.155000</td>\n",
" <td>3.220000</td>\n",
" <td>0.770000</td>\n",
" <td>0.283000</td>\n",
" <td>0.708000</td>\n",
" <td>0.441000</td>\n",
" <td>0.104000</td>\n",
" <td>3.860000</td>\n",
" <td>3.900000</td>\n",
" <td>4.200000</td>\n",
" <td>13.900000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>1996.000000</td>\n",
" <td>88.000000</td>\n",
" <td>88.000000</td>\n",
" <td>39.000000</td>\n",
" <td>538.000000</td>\n",
" <td>162.000000</td>\n",
" <td>1450.000000</td>\n",
" <td>999.000000</td>\n",
" <td>6.260000</td>\n",
" <td>3.570000</td>\n",
" <td>0.980000</td>\n",
" <td>0.294000</td>\n",
" <td>0.727000</td>\n",
" <td>0.457000</td>\n",
" <td>0.116000</td>\n",
" <td>4.320000</td>\n",
" <td>4.260000</td>\n",
" <td>4.430000</td>\n",
" <td>16.900000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>2016.000000</td>\n",
" <td>116.000000</td>\n",
" <td>120.000000</td>\n",
" <td>68.000000</td>\n",
" <td>768.000000</td>\n",
" <td>165.000000</td>\n",
" <td>1506.200000</td>\n",
" <td>1510.000000</td>\n",
" <td>9.350000</td>\n",
" <td>5.520000</td>\n",
" <td>1.610000</td>\n",
" <td>0.340000</td>\n",
" <td>0.795000</td>\n",
" <td>0.525000</td>\n",
" <td>0.159000</td>\n",
" <td>6.710000</td>\n",
" <td>5.830000</td>\n",
" <td>5.130000</td>\n",
" <td>29.500000</td>\n",
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"</table>\n",
"</div>"
],
"text/plain": [
" Season W L SV G \\\n",
"count 2460.000000 2460.000000 2460.000000 2460.000000 2460.000000 \n",
"mean 1966.289431 78.135366 78.135366 27.294715 414.617073 \n",
"std 33.922249 13.540774 13.474811 14.757541 144.634809 \n",
"min 1900.000000 36.000000 36.000000 0.000000 146.000000 \n",
"25% 1937.000000 69.000000 69.000000 14.000000 295.000000 \n",
"50% 1972.000000 79.000000 78.000000 29.000000 398.000000 \n",
"75% 1996.000000 88.000000 88.000000 39.000000 538.000000 \n",
"max 2016.000000 116.000000 120.000000 68.000000 768.000000 \n",
"\n",
" GS IP SO K/9 BB/9 \\\n",
"count 2460.000000 2460.000000 2460.000000 2460.000000 2460.000000 \n",
"mean 156.939837 1402.272276 800.954472 5.104598 3.236748 \n",
"std 8.992996 82.564637 261.353634 1.543454 0.531561 \n",
"min 103.000000 922.100000 245.000000 1.850000 1.490000 \n",
"25% 154.000000 1378.000000 571.750000 3.780000 2.900000 \n",
"50% 161.000000 1430.050000 811.000000 5.155000 3.220000 \n",
"75% 162.000000 1450.000000 999.000000 6.260000 3.570000 \n",
"max 165.000000 1506.200000 1510.000000 9.350000 5.520000 \n",
"\n",
" HR/9 BABIP LOB% GB% HR/FB \\\n",
"count 2460.000000 2460.000000 2460.000000 450.000000 450.000000 \n",
"mean 0.725545 0.282952 0.703174 0.441962 0.105998 \n",
"std 0.330695 0.015073 0.034746 0.022116 0.014720 \n",
"min 0.030000 0.238000 0.564000 0.382000 0.069000 \n",
"25% 0.500000 0.272000 0.684750 0.426250 0.096000 \n",
"50% 0.770000 0.283000 0.708000 0.441000 0.104000 \n",
"75% 0.980000 0.294000 0.727000 0.457000 0.116000 \n",
"max 1.610000 0.340000 0.795000 0.525000 0.159000 \n",
"\n",
" ERA FIP xFIP WAR \n",
"count 2460.000000 2460.000000 450.000000 2460.000000 \n",
"mean 3.861809 3.849037 4.189156 13.845935 \n",
"std 0.698738 0.619960 0.348521 4.627145 \n",
"min 1.730000 1.920000 3.330000 -0.500000 \n",
"25% 3.420000 3.500000 3.940000 10.700000 \n",
"50% 3.860000 3.900000 4.200000 13.900000 \n",
"75% 4.320000 4.260000 4.430000 16.900000 \n",
"max 6.710000 5.830000 5.130000 29.500000 "
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# some summary stats\n",
"pitching_data.describe()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# get league-average SO/game by year\n",
"league_average = pitching_data.groupby('Season', as_index=False)['K/9'].mean()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Library/Python/2.7/site-packages/matplotlib-override/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": {
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qBWrM/ime/qJ8PDpffbNcw57yXWplHu+QIyhIM5NSxxUK0839LHXuYb7k85Ni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"text/plain": [
"<matplotlib.figure.Figure at 0x112ef7790>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# plot avg. SO/game over time\n",
"plt.scatter(pitching_data['Season'], pitching_data['K/9'])\n",
"plt.plot(league_average['Season'], league_average['K/9'], c='red')\n",
"plt.xlim(1900,2016)\n",
"plt.ylim(2,10)\n",
"plt.xlabel('Season')\n",
"plt.ylabel('Average Strikeouts per Game')\n",
"plt.title('Strikeouts per Game over Time');"
]
},
{
"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 |
FlyRanch/figurefirst | examples/figure_groups_and_templates/figure_templates_example.ipynb | 1 | 213108 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import figurefirst\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"%matplotlib inline\n",
"from IPython.display import display,SVG"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def make_plot(template_filename, output_filename):\n",
" \n",
" ## Define colors, spine locations, and notes for data ######################\n",
" colors = {'group1': 'green',\n",
" 'group2': 'blue',\n",
" 'group3': 'orange'}\n",
"\n",
" spines = {'ax1': ['left', 'top'],\n",
" 'ax2': ['left', 'bottom'],\n",
" 'ax3': ['right', 'bottom']}\n",
" \n",
" functions = { 'ax1': np.sin,\n",
" 'ax2': np.cos,\n",
" 'ax3': np.tan}\n",
"\n",
" notes = {'group1': 'random data about green fish',\n",
" 'group2': 'random data about blue squirrels',\n",
" 'group3': 'random data about orange bats'}\n",
" \n",
" ## Open layout and generate matplotlib axes ################################\n",
" layout = figurefirst.svg_to_axes.FigureLayout(template_filename)\n",
" layout.make_mplfigures()\n",
"\n",
" ## Iterate through groups and axes and plot data ###########################\n",
" for group in ['group1', 'group2', 'group3']:\n",
" for ax in ['ax1', 'ax2', 'ax3']:\n",
" # grab the axis in this group\n",
" mpl_axis = layout.axes[(group, ax)]\n",
"\n",
" # generate some data\n",
" x_data = np.arange(0,10,0.1)\n",
" y_data = functions[ax](x_data)\n",
"\n",
" # plot the data\n",
" mpl_axis.plot(x_data, y_data, color=colors[group])\n",
"\n",
" # optional: make the spines look nice\n",
" mpl_axis.set_ylim(-1.2,1.2)\n",
" mpl_axis.set_xlim(-1,11)\n",
" figurefirst.mpl_functions.adjust_spines( mpl_axis, spines[ax], \n",
" spine_location_offset=5,\n",
" xticks=[0,5,10], \n",
" yticks=[-1, 0, 1])\n",
" figurefirst.mpl_functions.set_fontsize(mpl_axis.figure,8)\n",
"\n",
" # add the figure (group) to the layout as a new layer\n",
" layout.append_figure_to_layer(layout.figures[group], group, \n",
" cleartarget=True, # clear out the layer\n",
" save_traceback=True, # save the function call traceback\n",
" notes=notes[group]) # save notes about the data into the svg\n",
"\n",
" ## Hide the design layer and save the new svg file ##########################\n",
" layout.set_layer_visibility(inkscape_label = 'layout_design',vis = False)\n",
" layout.write_svg(output_filename)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"image/svg+xml": [
"<svg height=\"2.5in\" id=\"svg3495\" inkscape:version=\"0.91 r13725\" sodipodi:docname=\"figure_groups_and_templates.svg\" version=\"1.1\" viewBox=\"0 0 675.00001 225.00001\" width=\"7.5in\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:cc=\"http://creativecommons.org/ns#\" xmlns:dc=\"http://purl.org/dc/elements/1.1/\" xmlns:figurefirst=\"http://flyranch.github.io/figurefirst/\" xmlns:inkscape=\"http://www.inkscape.org/namespaces/inkscape\" xmlns:rdf=\"http://www.w3.org/1999/02/22-rdf-syntax-ns#\" xmlns:sodipodi=\"http://sodipodi.sourceforge.net/DTD/sodipodi-0.dtd\" xmlns:svg=\"http://www.w3.org/2000/svg\">\n",
" <sodipodi:namedview bordercolor=\"#666666\" borderopacity=\"1.0\" id=\"base\" inkscape:current-layer=\"layer3\" inkscape:cx=\"323.30014\" inkscape:cy=\"199.54986\" inkscape:document-units=\"px\" inkscape:pageopacity=\"0.0\" inkscape:pageshadow=\"2\" inkscape:window-height=\"1026\" inkscape:window-maximized=\"1\" inkscape:window-width=\"1615\" inkscape:window-x=\"65\" inkscape:window-y=\"24\" inkscape:zoom=\"1\" pagecolor=\"#ffffff\" showgrid=\"false\" units=\"in\"/>\n",
" <defs id=\"defs3497\"/>\n",
" <metadata id=\"metadata3500\">\n",
" <rdf:RDF>\n",
" <cc:Work rdf:about=\"\">\n",
" <dc:format>image/svg+xml</dc:format>\n",
" <dc:type rdf:resource=\"http://purl.org/dc/dcmitype/StillImage\"/>\n",
" <dc:title/>\n",
" </cc:Work>\n",
" </rdf:RDF>\n",
" </metadata>\n",
" <g id=\"layer1\" inkscape:groupmode=\"layer\" inkscape:label=\"layout_design\" transform=\"translate(0,-827.36215)\">\n",
" <g id=\"g3400\" style=\"fill:#008000\" transform=\"matrix(0.70397398,0,0,1,45.799262,255)\">\n",
" <rect height=\"56.029701\" id=\"rect3507\" style=\"opacity:0.15;fill:#008000;fill-opacity:1;stroke:none\" width=\"200.2128\" x=\"21.428572\" y=\"634.2193\">\n",
" <figurefirst:axis figurefirst:name=\"ax1\"/>\n",
" </rect>\n",
" <rect height=\"72.091553\" id=\"rect3509\" style=\"opacity:0.15;fill:#008000;fill-opacity:1;stroke:none\" width=\"96.283539\" x=\"21.428572\" y=\"698.09314\">\n",
" <figurefirst:axis figurefirst:name=\"ax2\"/>\n",
" </rect>\n",
" <rect height=\"72.091553\" id=\"rect3509-3\" style=\"opacity:0.15;fill:#008000;fill-opacity:1;stroke:none\" width=\"96.283539\" x=\"125.35783\" y=\"698.09314\">\n",
" <figurefirst:axis figurefirst:name=\"ax3\"/>\n",
" </rect>\n",
" <figurefirst:figure figurefirst:name=\"group1\"/>\n",
" </g>\n",
" <rect height=\"135.875\" id=\"rect4433\" style=\"opacity:0.15;fill:#0000ff;fill-opacity:1;stroke:none\" width=\"141.49876\" x=\"276.58215\" y=\"889.22955\">\n",
" <figurefirst:figure figurefirst:name=\"group2\" figurefirst:template=\"group1\"/>\n",
" </rect>\n",
" <rect height=\"135.875\" id=\"rect4433-7\" style=\"opacity:0.15;fill:#ffcc00;fill-opacity:1;stroke:none\" width=\"141.49876\" x=\"497.00122\" y=\"889.22955\">\n",
" <figurefirst:figure figurefirst:name=\"group3\" figurefirst:template=\"group1\"/>\n",
" </rect>\n",
" <text id=\"text5401\" sodipodi:linespacing=\"125%\" style=\"font-style:normal;font-weight:normal;font-size:40px;line-height:125%;font-family:sans-serif;letter-spacing:0px;word-spacing:0px;fill:#000000;fill-opacity:1;stroke:none;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\" x=\"123.51979\" xml:space=\"preserve\" y=\"921.89929\"><tspan id=\"tspan5403\" sodipodi:role=\"line\" style=\"font-size:10px\" x=\"123.51979\" y=\"921.89929\">ax 1</tspan></text>\n",
" <text id=\"text5401-3\" sodipodi:linespacing=\"125%\" style=\"font-style:normal;font-weight:normal;font-size:40px;line-height:125%;font-family:sans-serif;letter-spacing:0px;word-spacing:0px;fill:#000000;fill-opacity:1;stroke:none;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\" x=\"85.382576\" xml:space=\"preserve\" y=\"993.47351\"><tspan id=\"tspan5403-5\" sodipodi:role=\"line\" style=\"font-size:10px\" x=\"85.382576\" y=\"993.47351\">ax 2</tspan></text>\n",
" <text id=\"text5401-6\" sodipodi:linespacing=\"125%\" style=\"font-style:normal;font-weight:normal;font-size:40px;line-height:125%;font-family:sans-serif;letter-spacing:0px;word-spacing:0px;fill:#000000;fill-opacity:1;stroke:none;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\" x=\"160.13258\" xml:space=\"preserve\" y=\"993.97351\"><tspan id=\"tspan5403-2\" sodipodi:role=\"line\" style=\"font-size:10px\" x=\"160.13258\" y=\"993.97351\">ax 3</tspan></text>\n",
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"template_filename = 'figure_groups_and_templates.svg'\n",
"output_filename = 'figure_groups_and_templates_output.svg'\n",
"make_plot(template_filename, output_filename)\n",
"\n",
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},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
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| mit |
ondrejiayc/StatisticalMethods | examples/XrayImage/FirstLook.ipynb | 1 | 158346 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# A First Look at an X-ray Image Dataset"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Images are data. They can be 2D, from cameras, or 1D, from spectrographs, or 3D, from IFUs (integral field units). In each case, the data come packaged as an *array* of numbers, which we can visualize, and do calculations with.\n",
"\n",
"Let's suppose we are interested in clusters of galaxies. We choose one, Abell 1835, and propose to observe it with the XMM-Newton space telescope. We are successful, we design the observations, and they are taken for us. Next: we download the data, and take a look at it."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Getting the Data \n",
"\n",
"We will download our images from HEASARC, the online archive where XMM data are stored. "
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from __future__ import print_function\n",
"import astropy.io.fits as pyfits\n",
"import numpy as np\n",
"import os\n",
"import urllib\n",
"import astropy.visualization as viz\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
"plt.rcParams['figure.figsize'] = (10.0, 10.0)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Download the example data files if we don't already have them."
]
},
{
"cell_type": "code",
"execution_count": 6,
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"text": [
" 470.63 KB P0098010101M2X000BKGMAP3000.FTZ\n",
" 45.65 KB P0098010101M2U009IMAGE_3000.FTZ\n",
" 461.45 KB P0098010101M2U009EXPMAP3000.FTZ\n"
]
}
],
"source": [
"targdir = 'a1835_xmm'\n",
"if not os.path.isdir(targdir):\n",
" os.mkdir(targdir)\n",
"\n",
"filenames = ('P0098010101M2U009IMAGE_3000.FTZ', \n",
" 'P0098010101M2U009EXPMAP3000.FTZ',\n",
" 'P0098010101M2X000BKGMAP3000.FTZ')\n",
"\n",
"remotedir = 'http://heasarc.gsfc.nasa.gov/FTP/xmm/data/rev0/0098010101/PPS/'\n",
"\n",
"for filename in filenames:\n",
" path = os.path.join(targdir, filename)\n",
" url = os.path.join(remotedir, filename)\n",
" if not os.path.isfile(path):\n",
" urllib.urlretrieve(url, path)\n",
"\n",
"imagefile, expmapfile, bkgmapfile = [os.path.join(targdir, filename) for filename in filenames]\n",
" \n",
"for filename in os.listdir(targdir):\n",
" print('{0:>10.2f} KB {1}'.format(os.path.getsize(os.path.join(targdir, filename))/1024.0, filename))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## The XMM MOS2 image\n",
"\n",
"Let's find the \"science\" image taken with the MOS2 camera, and display it."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Filename: a1835_xmm/P0098010101M2U009IMAGE_3000.FTZ\n",
"No. Name Type Cards Dimensions Format\n",
"0 PRIMARY PrimaryHDU 262 (648, 648) int32 \n",
"1 GTI00006 BinTableHDU 29 15R x 2C [D, D] \n",
"2 GTI00106 BinTableHDU 29 15R x 2C [D, D] \n",
"3 GTI00206 BinTableHDU 29 16R x 2C [D, D] \n",
"4 GTI00306 BinTableHDU 29 15R x 2C [D, D] \n",
"5 GTI00406 BinTableHDU 29 15R x 2C [D, D] \n",
"6 GTI00506 BinTableHDU 29 15R x 2C [D, D] \n",
"7 GTI00606 BinTableHDU 29 15R x 2C [D, D] \n"
]
}
],
"source": [
"imfits = pyfits.open(imagefile)\n",
"imfits.info()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"`imfits` is a FITS object, containing multiple data structures. The image itself is an array of integer type, and size 648x648 pixels, stored in the primary \"header data unit\" or HDU. \n",
"\n",
"> _If we need it to be floating point for some reason, we need to cast it:\n",
"im = imfits[0].data.astype('np.float32')\n",
"Note that this (probably?) prevents us from using the pyfits \"writeto\" method to save any changes. Assuming the integer type is ok, just get a pointer to the image data._\n",
"\n",
"Accessing the `.data` member of the FITS object returns the image data as a numpy ndarray."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"im = imfits[0].data"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"Let's look at this with `ds9`. "
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"!ds9 -log \"$imagefile\""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"> _If you don't have the image viewing tool `ds9`, you should install it - it's very useful astronomical software. You can download it (later!) from [this webpage](http://ds9.si.edu/site/Download.html)._"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can also display the image in the notebook: "
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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X8/k8jo+P44kTJzRGSrlcxuXlZd1GvXYliqLqvlhJz9CUri0SieDAwAAmk0nV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LOzk+rq6uiv/uqvaNWqVRQKhWjDhg20Zs0aWrx4MRmNRpo+fTpFIhH67ne/SyMjI7Rq1Spm+IVC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\nsCCq01YoFJDVasV//+x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WoVZ5dNra2oCiKN3ejGg0ih+02WxW4sXhOV9+v19CIBZ6UwKB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\nTSykiIiIiGz6/6aYuJybZnNcAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f46f65fbc50>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.imshow(viz.scale_image(im, scale='log', max_cut=40), cmap='gray', origin='lower');"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"-----\n",
"## Exercise\n",
"\n",
"What is going on in this image? \n",
"\n",
"Make a list of everything that is interesting about this image with your neighbor, and we'll discuss the features you identify in about 5 minutes time.\n",
"\n",
"-----"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"image dimensions: (648, 648)\n",
"location of maximum pixel value: (348, 328)\n",
"maximum pixel value: 223\n"
]
}
],
"source": [
"index = np.unravel_index(im.argmax(), im.shape)\n",
"print(\"image dimensions:\",im.shape)\n",
"print(\"location of maximum pixel value:\",index)\n",
"print(\"maximum pixel value: \",im[index])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"> _NB. Images read in with pyfits are indexed with eg `im[y,x]`: ds9 shows that the maximum pixel value is at \"image coordinates\" `x=328, y=348`. `pyplot` knows what to do, but sometimes we may need to take the transpose of the `im` array. What `pyplot` does need to be told is that in astronomy, the origin of the image is conventionally taken to be at the bottom left hand corner, not the top left hand corner. That's what the `origin=lower` in the `plt.imshow` command was about._\n",
"\n",
"> _We will work in image coordinates throughout this course, for simplicity. Aligning images on the sky via a \"World Coordinate System\" is something to be learned elsewhere._"
]
},
{
"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 |
datahac/jup | v01/mobile-analytics_JSON.ipynb | 2 | 18295 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Mobile insights"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"request = \"POST https://analyticsreporting.googleapis.com/v4/reports:batchGet?fields=reports(columnHeader%2Cdata(rows%2Ctotals))&key={YOUR_API_KEY}\""
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"request={\n",
" \"reportRequests\": [\n",
" {\n",
" \"viewId\": \"123303369\",\n",
" \"dateRanges\": [\n",
" {\n",
" \"startDate\": \"2017-01-01\",\n",
" \"endDate\": \"2017-04-30\"\n",
" }\n",
" ],\n",
" \"metrics\": [\n",
" {\n",
" \"expression\": \"ga:sessions\"\n",
" },\n",
" {\n",
" \"expression\": \"ga:bounces\"\n",
" },\n",
" {\n",
" \"expression\": \"ga:goal1Completions\" #instead of \"ga:goal1Completions\" use \"goal_to_use_in_request\" variable from tracking-tags code\n",
" }\n",
" ],\n",
" \"dimensions\": [\n",
" {\n",
" \"name\": \"ga:deviceCategory\"\n",
" }\n",
" ]\n",
" }\n",
" ]\n",
"}"
]
},
{
"cell_type": "code",
"execution_count": 105,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"{'reports': [{'columnHeader': {'dimensions': ['ga:deviceCategory'],\n",
" 'metricHeader': {'metricHeaderEntries': [{'name': 'ga:sessions',\n",
" 'type': 'INTEGER'},\n",
" {'name': 'ga:bounces', 'type': 'INTEGER'},\n",
" {'name': 'ga:goal1Completions', 'type': 'INTEGER'}]}},\n",
" 'data': {'rows': [{'dimensions': ['desktop'],\n",
" 'metrics': [{'values': ['4263', '2251', '117']}]},\n",
" {'dimensions': ['mobile'],\n",
" 'metrics': [{'values': ['2352', '1468', '26']}]},\n",
" {'dimensions': ['tablet'],\n",
" 'metrics': [{'values': ['278', '155', '3']}]}]}}]}"
]
},
"execution_count": 105,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import json\n",
"\n",
"with open('files/TMRW_mob.json') as file:\n",
" input_dev = json.load(file)\n",
" \n",
"input_dev"
]
},
{
"cell_type": "code",
"execution_count": 106,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"['ga:deviceCategory']"
]
},
"execution_count": 106,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Define dimensions list\n",
"input_dev_dimensions = input_dev['reports'][0]['columnHeader']['dimensions']\n",
"\n",
"input_dev_dimensions"
]
},
{
"cell_type": "code",
"execution_count": 107,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"['ga:sessions', 'ga:bounces', 'ga:goal1Completions']"
]
},
"execution_count": 107,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Define metrics list\n",
"input_dev_metrics = input_dev['reports'][0]['columnHeader']['metricHeader']['metricHeaderEntries']\n",
"\n",
"def create_metric_list(raw_data):\n",
" lst = []\n",
" for item in raw_data:\n",
" lst.append(item['name'])\n",
" return lst\n",
"\n",
"input_dev_metrics = create_metric_list(input_dev_metrics)\n",
"\n",
"input_dev_metrics"
]
},
{
"cell_type": "code",
"execution_count": 108,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[{'dimensions': ['desktop'], 'metrics': [{'values': ['4263', '2251', '117']}]},\n",
" {'dimensions': ['mobile'], 'metrics': [{'values': ['2352', '1468', '26']}]},\n",
" {'dimensions': ['tablet'], 'metrics': [{'values': ['278', '155', '3']}]}]"
]
},
"execution_count": 108,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Create input_dev_data\n",
"\n",
"input_dev_data = input_dev['reports'][0]['data']['rows']\n",
"\n",
"input_dev_data"
]
},
{
"cell_type": "code",
"execution_count": 109,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'desktop': 4263, 'mobile': 2352, 'tablet': 278}\n",
"{'desktop': 2251, 'mobile': 1468, 'tablet': 155}\n",
"{'desktop': 117, 'mobile': 26, 'tablet': 3}\n"
]
},
{
"data": {
"text/plain": [
"[{'desktop': 4263, 'mobile': 2352, 'tablet': 278},\n",
" {'desktop': 2251, 'mobile': 1468, 'tablet': 155},\n",
" {'desktop': 117, 'mobile': 26, 'tablet': 3}]"
]
},
"execution_count": 109,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Define each metric dict\n",
"\n",
"dev_sessions = {}\n",
"dev_bounces = {}\n",
"dev_conversions = {}\n",
"\n",
"\n",
"for device in input_dev_data:\n",
" \n",
" device_name = device['dimensions'][0]\n",
" \n",
" sessions_metric = int(device['metrics'][0]['values'][0])\n",
" bounces_metric = int(device['metrics'][0]['values'][1])\n",
" conv_metric = int(device['metrics'][0]['values'][2])\n",
" \n",
" dev_sessions[device_name] = sessions_metric\n",
" dev_bounces[device_name] = bounces_metric\n",
" dev_conversions[device_name] = conv_metric\n",
" \n",
"print(dev_sessions)\n",
"print(dev_bounces)\n",
"print(dev_conversions)\n",
" \n",
" \n",
"new_data = [dev_sessions, dev_bounces, dev_conversions]\n",
"new_data"
]
},
{
"cell_type": "code",
"execution_count": 110,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"{'desktop': {'bounces': 2251, 'conversions': 117, 'sessions': 4263},\n",
" 'mobile': {'bounces': 1468, 'conversions': 26, 'sessions': 2352},\n",
" 'tablet': {'bounces': 155, 'conversions': 3, 'sessions': 278}}"
]
},
"execution_count": 110,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Create dev_data\n",
"\n",
"dev_data = {} \n",
"\n",
"for metric_list in new_data:\n",
" #print(metric_list)\n",
" for device in metric_list:\n",
" #print (device)\n",
" dev_data[device] = {'sessions':0,\n",
" 'bounces':0,\n",
" 'conversions':0}\n",
" \n",
" \n",
" dev_data[device]['sessions'] = dev_sessions[device] \n",
" dev_data[device]['bounces'] = dev_bounces[device]\n",
" dev_data[device]['conversions'] = dev_conversions[device]\n",
" \n",
"dev_data"
]
},
{
"cell_type": "code",
"execution_count": 111,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"{'desktop': {'bounces': 2251, 'conversions': 117, 'sessions': 4263},\n",
" 'mobile devices': {'bounces': 1623, 'conversions': 29, 'sessions': 2630}}"
]
},
"execution_count": 111,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Group MOBILE and TABLET into MOBILE DEVICES\n",
"dev_data['mobile devices'] = {}\n",
"\n",
"for device in dev_data:\n",
" \n",
" if device == 'mobile' or device == 'tablet':\n",
" metrics = dev_data[device]\n",
" \n",
" for metric in metrics:\n",
" dev_data['mobile devices'][metric] = 0\n",
" dev_data['mobile devices'][metric] += dev_data['mobile'][metric]+dev_data['tablet'][metric]\n",
" \n",
"del dev_data['mobile']\n",
"del dev_data['tablet']\n",
" \n",
"dev_data"
]
},
{
"cell_type": "code",
"execution_count": 112,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"{'desktop': {'bounces': 2251,\n",
" 'conversion rate': 0.02744546094299789,\n",
" 'conversions': 117,\n",
" 'sessions': 4263},\n",
" 'mobile devices': {'bounces': 1623,\n",
" 'conversion rate': 0.011026615969581748,\n",
" 'conversions': 29,\n",
" 'sessions': 2630}}"
]
},
"execution_count": 112,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Calculate CONVERSION RATE\n",
"\n",
"for device in dev_data:\n",
" dev_data[device]['conversion rate'] = 0\n",
" dev_data[device]['conversion rate'] = dev_data[device]['conversions']/dev_data[device]['sessions']\n",
" \n",
"dev_data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Print"
]
},
{
"cell_type": "code",
"execution_count": 113,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"'38%'"
]
},
"execution_count": 113,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Calculate how conversions can increase in the next 4 monthes\n",
"\n",
"conv_increase = int(dev_data['mobile devices']['sessions'] * dev_data['desktop']['conversion rate']\\\n",
" - dev_data['mobile devices']['conversions'])\n",
"\n",
"mobile_CR = float(dev_data['mobile devices']['conversion rate'])\n",
"mobile_CR = \"{0:.2f}%\".format(mobile_CR * 100)\n",
"desktop_CR = float(dev_data['desktop']['conversion rate'])\n",
"desktop_CR = \"{0:.2f}%\".format(desktop_CR * 100)\n",
"\n",
"mobile_session_share = dev_data['mobile devices']['sessions'] / (dev_data['mobile devices']['sessions']\\\n",
" + dev_data['desktop']['sessions'])\n",
"mobile_session_share = \"{0:.0f}%\".format(mobile_session_share * 100)\n",
"\n",
"mobile_session_share"
]
},
{
"cell_type": "code",
"execution_count": 114,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Have 43 more conversions per month by optmising mobile UX\n",
"Only 1.10% of all mobile visits end up completing a conversion\n"
]
}
],
"source": [
"print (\"Have %s more conversions per month by optmising mobile UX\" % conv_increase)\n",
"\n",
"print (\"Only %s of all mobile visits end up completing a conversion\" % mobile_CR)"
]
},
{
"cell_type": "code",
"execution_count": 115,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Optimize Mobile UX to get additional 43 conversions per months from mobile devices. Mobile traffic is 38% of total however conversion rate is just 1.10% comparing to 2.74% of desktop.\n"
]
}
],
"source": [
"print(\"Optimize Mobile UX to get additional %s conversions per months from mobile devices. Mobile traffic is %s of total however conversion rate is just %s comparing to %s of desktop.\" % (conv_increase, mobile_session_share, mobile_CR, desktop_CR))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Visualisation"
]
},
{
"cell_type": "code",
"execution_count": 98,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import plotly"
]
},
{
"cell_type": "code",
"execution_count": 99,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import plotly.plotly as py\n",
"import plotly.figure_factory as ff\n",
"from plotly import graph_objs as go\n",
"from __future__ import division\n",
"\n",
"\n",
"py.sign_in('m-nudha', 'NlSXxWgqIy6tgiVQIi6e')"
]
},
{
"cell_type": "code",
"execution_count": 100,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[2630, 1623, 29]"
]
},
"execution_count": 100,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"mob_metrics = []\n",
"\n",
"for metric in dev_data['mobile devices']:\n",
" if metric == \"sessions\" or metric == \"bounces\" or metric == \"conversions\":\n",
" mob_metrics.append(dev_data['mobile devices'][metric])\n",
"\n",
"mob_metrics"
]
},
{
"cell_type": "code",
"execution_count": 101,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# chart stages data\n",
"values = mob_metrics\n",
"phases = ['All users', 'Engaged users', 'Converted users']\n",
"\n",
"# color of each funnel section\n",
"colors = ['rgb(32,155,160)', 'rgb(253,93,124)', 'rgb(182,231,235)']"
]
},
{
"cell_type": "code",
"execution_count": 102,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"n_phase = len(phases)\n",
"plot_width = 400\n",
"\n",
"# height of a section and difference between sections \n",
"section_h = 100\n",
"section_d = 10\n",
"\n",
"# multiplication factor to calculate the width of other sections\n",
"unit_width = plot_width / max(values)\n",
"\n",
"# width of each funnel section relative to the plot width\n",
"phase_w = [int(value * unit_width) for value in values]\n",
"\n",
"# plot height based on the number of sections and the gap in between them\n",
"height = section_h * n_phase + section_d * (n_phase - 1)"
]
},
{
"cell_type": "code",
"execution_count": 103,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# list containing all the plot shapes\n",
"shapes = []\n",
"\n",
"# list containing the Y-axis location for each section's name and value text\n",
"label_y = []\n",
"\n",
"for i in range(n_phase):\n",
" if (i == n_phase-1):\n",
" points = [phase_w[i] / 2, height, phase_w[i] / 2, height - section_h]\n",
" else:\n",
" points = [phase_w[i] / 2, height, phase_w[i+1] / 2, height - section_h]\n",
"\n",
" path = 'M {0} {1} L {2} {3} L -{2} {3} L -{0} {1} Z'.format(*points)\n",
"\n",
" shape = {\n",
" 'type': 'path',\n",
" 'path': path,\n",
" 'fillcolor': colors[i],\n",
" 'line': {\n",
" 'width': 1,\n",
" 'color': colors[i]\n",
" }\n",
" }\n",
" shapes.append(shape)\n",
" \n",
" # Y-axis location for this section's details (text)\n",
" label_y.append(height - (section_h / 2))\n",
"\n",
" height = height - (section_h + section_d)"
]
},
{
"cell_type": "code",
"execution_count": 104,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\" seamless=\"seamless\" src=\"https://plot.ly/~m-nudha/32.embed\" height=\"560px\" width=\"800px\"></iframe>"
],
"text/plain": [
"<plotly.tools.PlotlyDisplay object>"
]
},
"execution_count": 104,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# For phase names\n",
"label_trace = go.Scatter(\n",
" x=[-350]*n_phase,\n",
" y=label_y,\n",
" mode='text',\n",
" text=phases,\n",
" textfont=dict(\n",
" color='rgb(200,200,200)',\n",
" size=15\n",
" )\n",
")\n",
" \n",
"# For phase values\n",
"value_trace = go.Scatter(\n",
" x=[350]*n_phase,\n",
" y=label_y,\n",
" mode='text',\n",
" text=values,\n",
" textfont=dict(\n",
" color='rgb(200,200,200)',\n",
" size=15\n",
" )\n",
")\n",
"\n",
"data = [label_trace, value_trace]\n",
" \n",
"layout = go.Layout(\n",
" title=\"<b>Funnel Chart</b>\",\n",
" titlefont=dict(\n",
" size=20,\n",
" color='rgb(203,203,203)'\n",
" ),\n",
" shapes=shapes,\n",
" height=560,\n",
" width=800,\n",
" showlegend=False,\n",
" paper_bgcolor='rgba(44,58,71,1)',\n",
" plot_bgcolor='rgba(44,58,71,1)',\n",
" xaxis=dict(\n",
" showticklabels=False,\n",
" zeroline=False,\n",
" ),\n",
" yaxis=dict(\n",
" showticklabels=False,\n",
" zeroline=False\n",
" )\n",
")\n",
" \n",
"fig = go.Figure(data=data, layout=layout)\n",
"py.iplot(fig)"
]
},
{
"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",
"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
}
| apache-2.0 |
ledeprogram/algorithms | class4/homework/pc_radhika_4_2.ipynb | 1 | 31884 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Assignment 2 ###\n",
"Using the 2013_NYC_CD_MedianIncome_Recycle.xlsx file, calculate the correlation between the recycling rate and the median income. Discuss your findings in your PR."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
"plt.style.use('ggplot')\n",
"import statistics\n",
"from decimal import Decimal"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"df = pd.read_excel(\"2013_NYC_CD_MedianIncome_Recycle.xlsx\")"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>CD_Name</th>\n",
" <th>MdHHIncE</th>\n",
" <th>RecycleRate</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Battery Park City, Greenwich Village & Soho</td>\n",
" <td>119596</td>\n",
" <td>0.286771</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Battery Park City, Greenwich Village & Soho</td>\n",
" <td>119596</td>\n",
" <td>0.264074</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Chinatown & Lower East Side</td>\n",
" <td>40919</td>\n",
" <td>0.156485</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Chelsea, Clinton & Midtown Business Distric</td>\n",
" <td>92583</td>\n",
" <td>0.235125</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Chelsea, Clinton & Midtown Business Distric</td>\n",
" <td>92583</td>\n",
" <td>0.246725</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" CD_Name MdHHIncE RecycleRate\n",
"0 Battery Park City, Greenwich Village & Soho 119596 0.286771\n",
"1 Battery Park City, Greenwich Village & Soho 119596 0.264074\n",
"2 Chinatown & Lower East Side 40919 0.156485\n",
"3 Chelsea, Clinton & Midtown Business Distric 92583 0.235125\n",
"4 Chelsea, Clinton & Midtown Business Distric 92583 0.246725"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"count 59.000000\n",
"mean 53895.932203\n",
"std 24371.741796\n",
"min 21318.000000\n",
"25% 37950.000000\n",
"50% 48252.000000\n",
"75% 61967.000000\n",
"max 119596.000000\n",
"Name: MdHHIncE, dtype: float64"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df['MdHHIncE'].describe()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x6c95590>"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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GbxcNUWt2AHalJiJ/YAHSgNYC4LjUjFd+eiMuNDQH9HwKu1ITkT8E9FukpKQE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\nYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x6c84d50>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"df.plot(kind='scatter', x='MdHHIncE', y='RecycleRate')\n",
"plt.xlabel('Median Income')\n",
"plt.ylabel('Recycling Rate')"
]
},
{
"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>MdHHIncE</th>\n",
" <th>RecycleRate</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>MdHHIncE</th>\n",
" <td>1.000000</td>\n",
" <td>0.884783</td>\n",
" </tr>\n",
" <tr>\n",
" <th>RecycleRate</th>\n",
" <td>0.884783</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" MdHHIncE RecycleRate\n",
"MdHHIncE 1.000000 0.884783\n",
"RecycleRate 0.884783 1.000000"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.corr(method='pearson', min_periods=1)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.image.AxesImage at 0x7ed92f0>"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
},
{
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"text/plain": [
"<matplotlib.figure.Figure at 0x4f473d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"\n",
"plt.matshow(df.corr())\n"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"48252.0"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df['MdHHIncE'].median()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0 21318\n",
"1 22343\n",
"2 51251\n",
"3 92583\n",
"4 119596\n",
"dtype: int64"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df['MdHHIncE'].mode()"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"37950.0"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df['MdHHIncE'].quantile(q=0.25) #1st Quartile"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"48252.0"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df['MdHHIncE'].quantile(q=0.5) #2nd Quartile (Median)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"61967.0"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df['MdHHIncE'].quantile(q=0.75) #3rd Quartile"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"24017.0"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"IQR = df['MdHHIncE'].quantile(q=0.75) - df['MdHHIncE'].quantile(q=0.25)\n",
"IQR"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"36025.5"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"1.5 * IQR"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"lower_outliers = (df['MdHHIncE'].quantile(q=0.25))- (IQR * 1.5)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"upper_outliers = (df['MdHHIncE'].quantile(q=0.75)) + (IQR * 1.5)"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"1924.5"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"lower_outliers"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"97992.5"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"upper_outliers\n"
]
},
{
"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>CD_Name</th>\n",
" <th>MdHHIncE</th>\n",
" <th>RecycleRate</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Battery Park City, Greenwich Village & Soho</td>\n",
" <td>119596</td>\n",
" <td>0.286771</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Battery Park City, Greenwich Village & Soho</td>\n",
" <td>119596</td>\n",
" <td>0.264074</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>Murray Hill, Gramercy & Stuyvesant Town</td>\n",
" <td>101769</td>\n",
" <td>0.222046</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>Upper East Side</td>\n",
" <td>104602</td>\n",
" <td>0.253719</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" CD_Name MdHHIncE RecycleRate\n",
"0 Battery Park City, Greenwich Village & Soho 119596 0.286771\n",
"1 Battery Park City, Greenwich Village & Soho 119596 0.264074\n",
"5 Murray Hill, Gramercy & Stuyvesant Town 101769 0.222046\n",
"7 Upper East Side 104602 0.253719"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df[(df['MdHHIncE'] > upper_outliers)]"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
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" <th>CD_Name</th>\n",
" <th>MdHHIncE</th>\n",
" <th>RecycleRate</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
"Empty DataFrame\n",
"Columns: [CD_Name, MdHHIncE, RecycleRate]\n",
"Index: []"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df[(df['MdHHIncE'] < lower_outliers)]"
]
},
{
"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",
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| gpl-3.0 |
albahnsen/ML_SecurityInformatics | notebooks/03-Pandas.ipynb | 1 | 138788 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 03 - Introduction to Python for Data Analysis\n",
"by [Alejandro Correa Bahnsen](albahnsen.com/)\n",
"\n",
"version 0.2, May 2016\n",
"\n",
"## Part of the class [Machine Learning for Security Informatics](https://github.com/albahnsen/ML_SecurityInformatics)\n",
"\n",
"\n",
"\n",
"This notebook is licensed under a [Creative Commons Attribution-ShareAlike 3.0 Unported License](http://creativecommons.org/licenses/by-sa/3.0/deed.en_US). Special thanks goes to [Donne Martin](http://donnemartin.com) and Wes McKinney's [Python for Data Analysis](http://www.amazon.com/Python-Data-Analysis-Wrangling-IPython/dp/1449319793) book."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Series\n",
"\n",
"A Series is a one-dimensional array-like object containing an array of data and an associated array of data labels. The data can be any NumPy data type and the labels are the Series' index."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Create a Series:"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0 1\n",
"1 1\n",
"2 2\n",
"3 -3\n",
"4 -5\n",
"5 8\n",
"6 13\n",
"dtype: int64"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ser_1 = pd.Series([1, 1, 2, -3, -5, 8, 13])\n",
"ser_1"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Get the array representation of a Series:"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([ 1, 1, 2, -3, -5, 8, 13])"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ser_1.values"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Index objects are immutable and hold the axis labels and metadata such as names and axis names.\n",
"\n",
"Get the index of the Series:"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Int64Index([0, 1, 2, 3, 4, 5, 6], dtype='int64')"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ser_1.index"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Create a Series with a custom index:"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"a 1\n",
"b 1\n",
"c 2\n",
"d -3\n",
"e -5\n",
"dtype: int64"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ser_2 = pd.Series([1, 1, 2, -3, -5], index=['a', 'b', 'c', 'd', 'e'])\n",
"ser_2"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Get a value from a Series:"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ser_2[4] == ser_2['e']"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Get a set of values from a Series by passing in a list:"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"c 2\n",
"a 1\n",
"b 1\n",
"dtype: int64"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ser_2[['c', 'a', 'b']]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Get values great than 0:"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"a 1\n",
"b 1\n",
"c 2\n",
"dtype: int64"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ser_2[ser_2 > 0]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Scalar multiply:"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"a 2\n",
"b 2\n",
"c 4\n",
"d -6\n",
"e -10\n",
"dtype: int64"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ser_2 * 2"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Apply a numpy math function:"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"a 2.718282\n",
"b 2.718282\n",
"c 7.389056\n",
"d 0.049787\n",
"e 0.006738\n",
"dtype: float64"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import numpy as np\n",
"np.exp(ser_2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"A Series is like a fixed-length, ordered dict. \n",
"\n",
"Create a series by passing in a dict:"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"bar 200\n",
"baz 300\n",
"foo 100\n",
"dtype: int64"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dict_1 = {'foo' : 100, 'bar' : 200, 'baz' : 300}\n",
"ser_3 = pd.Series(dict_1)\n",
"ser_3"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Re-order a Series by passing in an index (indices not found are NaN):"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"foo 100\n",
"bar 200\n",
"baz 300\n",
"qux NaN\n",
"dtype: float64"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"index = ['foo', 'bar', 'baz', 'qux']\n",
"ser_4 = pd.Series(dict_1, index=index)\n",
"ser_4"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Check for NaN with the pandas method:"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"foo False\n",
"bar False\n",
"baz False\n",
"qux True\n",
"dtype: bool"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd.isnull(ser_4)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Check for NaN with the Series method:"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"foo False\n",
"bar False\n",
"baz False\n",
"qux True\n",
"dtype: bool"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ser_4.isnull()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Series automatically aligns differently indexed data in arithmetic operations:"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"bar 400\n",
"baz 600\n",
"foo 200\n",
"qux NaN\n",
"dtype: float64"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ser_3 + ser_4"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Name a Series:"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"ser_4.name = 'foobarbazqux'"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Name a Series index:"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"ser_4.index.name = 'label'"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"label\n",
"foo 100\n",
"bar 200\n",
"baz 300\n",
"qux NaN\n",
"Name: foobarbazqux, dtype: float64"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ser_4"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Rename a Series' index in place:"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"fo 100\n",
"br 200\n",
"bz 300\n",
"qx NaN\n",
"Name: foobarbazqux, dtype: float64"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ser_4.index = ['fo', 'br', 'bz', 'qx']\n",
"ser_4"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## DataFrame\n",
"\n",
"A DataFrame is a tabular data structure containing an ordered collection of columns. Each column can have a different type. DataFrames have both row and column indices and is analogous to a dict of Series. Row and column operations are treated roughly symmetrically. Columns returned when indexing a DataFrame are views of the underlying data, not a copy. To obtain a copy, use the Series' copy method.\n",
"\n",
"Create a DataFrame:"
]
},
{
"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>pop</th>\n",
" <th>state</th>\n",
" <th>year</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>5.0</td>\n",
" <td>VA</td>\n",
" <td>2012</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>5.1</td>\n",
" <td>VA</td>\n",
" <td>2013</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>5.2</td>\n",
" <td>VA</td>\n",
" <td>2014</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4.0</td>\n",
" <td>MD</td>\n",
" <td>2014</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>4.1</td>\n",
" <td>MD</td>\n",
" <td>2015</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" pop state year\n",
"0 5.0 VA 2012\n",
"1 5.1 VA 2013\n",
"2 5.2 VA 2014\n",
"3 4.0 MD 2014\n",
"4 4.1 MD 2015"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data_1 = {'state' : ['VA', 'VA', 'VA', 'MD', 'MD'],\n",
" 'year' : [2012, 2013, 2014, 2014, 2015],\n",
" 'pop' : [5.0, 5.1, 5.2, 4.0, 4.1]}\n",
"df_1 = pd.DataFrame(data_1)\n",
"df_1"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Create a DataFrame specifying a sequence of columns:"
]
},
{
"cell_type": "code",
"execution_count": 21,
"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>year</th>\n",
" <th>state</th>\n",
" <th>pop</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2012</td>\n",
" <td>VA</td>\n",
" <td>5.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2013</td>\n",
" <td>VA</td>\n",
" <td>5.1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2014</td>\n",
" <td>VA</td>\n",
" <td>5.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2014</td>\n",
" <td>MD</td>\n",
" <td>4.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2015</td>\n",
" <td>MD</td>\n",
" <td>4.1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" year state pop\n",
"0 2012 VA 5.0\n",
"1 2013 VA 5.1\n",
"2 2014 VA 5.2\n",
"3 2014 MD 4.0\n",
"4 2015 MD 4.1"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_2 = pd.DataFrame(data_1, columns=['year', 'state', 'pop'])\n",
"df_2"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Like Series, columns that are not present in the data are NaN:"
]
},
{
"cell_type": "code",
"execution_count": 22,
"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>year</th>\n",
" <th>state</th>\n",
" <th>pop</th>\n",
" <th>unempl</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2012</td>\n",
" <td>VA</td>\n",
" <td>5.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2013</td>\n",
" <td>VA</td>\n",
" <td>5.1</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2014</td>\n",
" <td>VA</td>\n",
" <td>5.2</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2014</td>\n",
" <td>MD</td>\n",
" <td>4.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2015</td>\n",
" <td>MD</td>\n",
" <td>4.1</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" year state pop unempl\n",
"0 2012 VA 5.0 NaN\n",
"1 2013 VA 5.1 NaN\n",
"2 2014 VA 5.2 NaN\n",
"3 2014 MD 4.0 NaN\n",
"4 2015 MD 4.1 NaN"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_3 = pd.DataFrame(data_1, columns=['year', 'state', 'pop', 'unempl'])\n",
"df_3"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Retrieve a column by key, returning a Series:\n"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0 VA\n",
"1 VA\n",
"2 VA\n",
"3 MD\n",
"4 MD\n",
"Name: state, dtype: object"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_3['state']"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Retrive a column by attribute, returning a Series:"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0 2012\n",
"1 2013\n",
"2 2014\n",
"3 2014\n",
"4 2015\n",
"Name: year, dtype: int64"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_3.year"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Retrieve a row by position:"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"year 2012\n",
"state VA\n",
"pop 5\n",
"unempl NaN\n",
"Name: 0, dtype: object"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_3.ix[0]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Update a column by assignment:"
]
},
{
"cell_type": "code",
"execution_count": 26,
"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>year</th>\n",
" <th>state</th>\n",
" <th>pop</th>\n",
" <th>unempl</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2012</td>\n",
" <td>VA</td>\n",
" <td>5.0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2013</td>\n",
" <td>VA</td>\n",
" <td>5.1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2014</td>\n",
" <td>VA</td>\n",
" <td>5.2</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2014</td>\n",
" <td>MD</td>\n",
" <td>4.0</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2015</td>\n",
" <td>MD</td>\n",
" <td>4.1</td>\n",
" <td>4</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" year state pop unempl\n",
"0 2012 VA 5.0 0\n",
"1 2013 VA 5.1 1\n",
"2 2014 VA 5.2 2\n",
"3 2014 MD 4.0 3\n",
"4 2015 MD 4.1 4"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_3['unempl'] = np.arange(5)\n",
"df_3"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Assign a Series to a column (note if assigning a list or array, the length must match the DataFrame, unlike a Series):"
]
},
{
"cell_type": "code",
"execution_count": 27,
"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>year</th>\n",
" <th>state</th>\n",
" <th>pop</th>\n",
" <th>unempl</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2012</td>\n",
" <td>VA</td>\n",
" <td>5.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2013</td>\n",
" <td>VA</td>\n",
" <td>5.1</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2014</td>\n",
" <td>VA</td>\n",
" <td>5.2</td>\n",
" <td>6.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2014</td>\n",
" <td>MD</td>\n",
" <td>4.0</td>\n",
" <td>6.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2015</td>\n",
" <td>MD</td>\n",
" <td>4.1</td>\n",
" <td>6.1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" year state pop unempl\n",
"0 2012 VA 5.0 NaN\n",
"1 2013 VA 5.1 NaN\n",
"2 2014 VA 5.2 6.0\n",
"3 2014 MD 4.0 6.0\n",
"4 2015 MD 4.1 6.1"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"unempl = pd.Series([6.0, 6.0, 6.1], index=[2, 3, 4])\n",
"df_3['unempl'] = unempl\n",
"df_3"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Assign a new column that doesn't exist to create a new column:"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>year</th>\n",
" <th>state</th>\n",
" <th>pop</th>\n",
" <th>unempl</th>\n",
" <th>state_dup</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2012</td>\n",
" <td>VA</td>\n",
" <td>5.0</td>\n",
" <td>NaN</td>\n",
" <td>VA</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2013</td>\n",
" <td>VA</td>\n",
" <td>5.1</td>\n",
" <td>NaN</td>\n",
" <td>VA</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2014</td>\n",
" <td>VA</td>\n",
" <td>5.2</td>\n",
" <td>6.0</td>\n",
" <td>VA</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2014</td>\n",
" <td>MD</td>\n",
" <td>4.0</td>\n",
" <td>6.0</td>\n",
" <td>MD</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2015</td>\n",
" <td>MD</td>\n",
" <td>4.1</td>\n",
" <td>6.1</td>\n",
" <td>MD</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" year state pop unempl state_dup\n",
"0 2012 VA 5.0 NaN VA\n",
"1 2013 VA 5.1 NaN VA\n",
"2 2014 VA 5.2 6.0 VA\n",
"3 2014 MD 4.0 6.0 MD\n",
"4 2015 MD 4.1 6.1 MD"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_3['state_dup'] = df_3['state']\n",
"df_3"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Delete a column:"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>year</th>\n",
" <th>state</th>\n",
" <th>pop</th>\n",
" <th>unempl</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2012</td>\n",
" <td>VA</td>\n",
" <td>5.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2013</td>\n",
" <td>VA</td>\n",
" <td>5.1</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2014</td>\n",
" <td>VA</td>\n",
" <td>5.2</td>\n",
" <td>6.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2014</td>\n",
" <td>MD</td>\n",
" <td>4.0</td>\n",
" <td>6.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2015</td>\n",
" <td>MD</td>\n",
" <td>4.1</td>\n",
" <td>6.1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" year state pop unempl\n",
"0 2012 VA 5.0 NaN\n",
"1 2013 VA 5.1 NaN\n",
"2 2014 VA 5.2 6.0\n",
"3 2014 MD 4.0 6.0\n",
"4 2015 MD 4.1 6.1"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"del df_3['state_dup']\n",
"df_3"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Create a DataFrame from a nested dict of dicts (the keys in the inner dicts are unioned and sorted to form the index in the result, unless an explicit index is specified):"
]
},
{
"cell_type": "code",
"execution_count": 30,
"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>MD</th>\n",
" <th>VA</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2013</th>\n",
" <td>NaN</td>\n",
" <td>5.1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2014</th>\n",
" <td>4.0</td>\n",
" <td>5.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015</th>\n",
" <td>4.1</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" MD VA\n",
"2013 NaN 5.1\n",
"2014 4.0 5.2\n",
"2015 4.1 NaN"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pop = {'VA' : {2013 : 5.1, 2014 : 5.2},\n",
" 'MD' : {2014 : 4.0, 2015 : 4.1}}\n",
"df_4 = pd.DataFrame(pop)\n",
"df_4"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Transpose the DataFrame:"
]
},
{
"cell_type": "code",
"execution_count": 31,
"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>2013</th>\n",
" <th>2014</th>\n",
" <th>2015</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>MD</th>\n",
" <td>NaN</td>\n",
" <td>4.0</td>\n",
" <td>4.1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>VA</th>\n",
" <td>5.1</td>\n",
" <td>5.2</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" 2013 2014 2015\n",
"MD NaN 4.0 4.1\n",
"VA 5.1 5.2 NaN"
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_4.T"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Create a DataFrame from a dict of Series:"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>MD</th>\n",
" <th>VA</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2014</th>\n",
" <td>NaN</td>\n",
" <td>5.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015</th>\n",
" <td>4.1</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" MD VA\n",
"2014 NaN 5.2\n",
"2015 4.1 NaN"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data_2 = {'VA' : df_4['VA'][1:],\n",
" 'MD' : df_4['MD'][2:]}\n",
"df_5 = pd.DataFrame(data_2)\n",
"df_5"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Set the DataFrame index name:"
]
},
{
"cell_type": "code",
"execution_count": 33,
"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>MD</th>\n",
" <th>VA</th>\n",
" </tr>\n",
" <tr>\n",
" <th>year</th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2014</th>\n",
" <td>NaN</td>\n",
" <td>5.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015</th>\n",
" <td>4.1</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" MD VA\n",
"year \n",
"2014 NaN 5.2\n",
"2015 4.1 NaN"
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_5.index.name = 'year'\n",
"df_5"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Set the DataFrame columns name:"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th>state</th>\n",
" <th>MD</th>\n",
" <th>VA</th>\n",
" </tr>\n",
" <tr>\n",
" <th>year</th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2014</th>\n",
" <td>NaN</td>\n",
" <td>5.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015</th>\n",
" <td>4.1</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
"state MD VA\n",
"year \n",
"2014 NaN 5.2\n",
"2015 4.1 NaN"
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_5.columns.name = 'state'\n",
"df_5"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Return the data contained in a DataFrame as a 2D ndarray:"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([[ nan, 5.2],\n",
" [ 4.1, nan]])"
]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_5.values"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If the columns are different dtypes, the 2D ndarray's dtype will accomodate all of the columns:"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([[2012, 'VA', 5.0, nan],\n",
" [2013, 'VA', 5.1, nan],\n",
" [2014, 'VA', 5.2, 6.0],\n",
" [2014, 'MD', 4.0, 6.0],\n",
" [2015, 'MD', 4.1, 6.1]], dtype=object)"
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_3.values"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Reindexing"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Create a new object with the data conformed to a new index. Any missing values are set to NaN."
]
},
{
"cell_type": "code",
"execution_count": 37,
"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>year</th>\n",
" <th>state</th>\n",
" <th>pop</th>\n",
" <th>unempl</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2012</td>\n",
" <td>VA</td>\n",
" <td>5.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2013</td>\n",
" <td>VA</td>\n",
" <td>5.1</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2014</td>\n",
" <td>VA</td>\n",
" <td>5.2</td>\n",
" <td>6.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2014</td>\n",
" <td>MD</td>\n",
" <td>4.0</td>\n",
" <td>6.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2015</td>\n",
" <td>MD</td>\n",
" <td>4.1</td>\n",
" <td>6.1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" year state pop unempl\n",
"0 2012 VA 5.0 NaN\n",
"1 2013 VA 5.1 NaN\n",
"2 2014 VA 5.2 6.0\n",
"3 2014 MD 4.0 6.0\n",
"4 2015 MD 4.1 6.1"
]
},
"execution_count": 37,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_3"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Reindexing rows returns a new frame with the specified index:"
]
},
{
"cell_type": "code",
"execution_count": 38,
"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>year</th>\n",
" <th>state</th>\n",
" <th>pop</th>\n",
" <th>unempl</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2015</td>\n",
" <td>MD</td>\n",
" <td>4.1</td>\n",
" <td>6.1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2014</td>\n",
" <td>MD</td>\n",
" <td>4.0</td>\n",
" <td>6.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2014</td>\n",
" <td>VA</td>\n",
" <td>5.2</td>\n",
" <td>6.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2013</td>\n",
" <td>VA</td>\n",
" <td>5.1</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2012</td>\n",
" <td>VA</td>\n",
" <td>5.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" year state pop unempl\n",
"5 NaN NaN NaN NaN\n",
"4 2015 MD 4.1 6.1\n",
"3 2014 MD 4.0 6.0\n",
"2 2014 VA 5.2 6.0\n",
"1 2013 VA 5.1 NaN\n",
"0 2012 VA 5.0 NaN"
]
},
"execution_count": 38,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_3.reindex(list(reversed(range(0, 6))))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Missing values can be set to something other than NaN:"
]
},
{
"cell_type": "code",
"execution_count": 39,
"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>year</th>\n",
" <th>state</th>\n",
" <th>pop</th>\n",
" <th>unempl</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
"Empty DataFrame\n",
"Columns: [year, state, pop, unempl]\n",
"Index: []"
]
},
"execution_count": 39,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_3.reindex(range(6, 0), fill_value=0)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Interpolate ordered data like a time series:"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"ser_5 = pd.Series(['foo', 'bar', 'baz'], index=[0, 2, 4])"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0 foo\n",
"1 foo\n",
"2 bar\n",
"3 bar\n",
"4 baz\n",
"dtype: object"
]
},
"execution_count": 41,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ser_5.reindex(range(5), method='ffill')"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0 foo\n",
"1 bar\n",
"2 bar\n",
"3 baz\n",
"4 baz\n",
"dtype: object"
]
},
"execution_count": 42,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ser_5.reindex(range(5), method='bfill')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Reindex columns:"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>state</th>\n",
" <th>pop</th>\n",
" <th>unempl</th>\n",
" <th>year</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>VA</td>\n",
" <td>5.0</td>\n",
" <td>NaN</td>\n",
" <td>2012</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>VA</td>\n",
" <td>5.1</td>\n",
" <td>NaN</td>\n",
" <td>2013</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>VA</td>\n",
" <td>5.2</td>\n",
" <td>6.0</td>\n",
" <td>2014</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>MD</td>\n",
" <td>4.0</td>\n",
" <td>6.0</td>\n",
" <td>2014</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>MD</td>\n",
" <td>4.1</td>\n",
" <td>6.1</td>\n",
" <td>2015</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" state pop unempl year\n",
"0 VA 5.0 NaN 2012\n",
"1 VA 5.1 NaN 2013\n",
"2 VA 5.2 6.0 2014\n",
"3 MD 4.0 6.0 2014\n",
"4 MD 4.1 6.1 2015"
]
},
"execution_count": 43,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_3.reindex(columns=['state', 'pop', 'unempl', 'year'])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Reindex rows and columns while filling rows:"
]
},
{
"cell_type": "code",
"execution_count": 44,
"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>state</th>\n",
" <th>pop</th>\n",
" <th>unempl</th>\n",
" <th>year</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>MD</td>\n",
" <td>4.1</td>\n",
" <td>6.1</td>\n",
" <td>2015</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>MD</td>\n",
" <td>4.0</td>\n",
" <td>6.0</td>\n",
" <td>2014</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>VA</td>\n",
" <td>5.2</td>\n",
" <td>6.0</td>\n",
" <td>2014</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>VA</td>\n",
" <td>5.1</td>\n",
" <td>NaN</td>\n",
" <td>2013</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>VA</td>\n",
" <td>5.0</td>\n",
" <td>NaN</td>\n",
" <td>2012</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" state pop unempl year\n",
"5 0 0.0 0.0 0\n",
"4 MD 4.1 6.1 2015\n",
"3 MD 4.0 6.0 2014\n",
"2 VA 5.2 6.0 2014\n",
"1 VA 5.1 NaN 2013\n",
"0 VA 5.0 NaN 2012"
]
},
"execution_count": 44,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_3.reindex(index=list(reversed(range(0, 6))),\n",
" fill_value=0,\n",
" columns=['state', 'pop', 'unempl', 'year'])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Reindex using ix:"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>state</th>\n",
" <th>pop</th>\n",
" <th>unempl</th>\n",
" <th>year</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>VA</td>\n",
" <td>5.0</td>\n",
" <td>NaN</td>\n",
" <td>2012</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>VA</td>\n",
" <td>5.1</td>\n",
" <td>NaN</td>\n",
" <td>2013</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>VA</td>\n",
" <td>5.2</td>\n",
" <td>6.0</td>\n",
" <td>2014</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>MD</td>\n",
" <td>4.0</td>\n",
" <td>6.0</td>\n",
" <td>2014</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>MD</td>\n",
" <td>4.1</td>\n",
" <td>6.1</td>\n",
" <td>2015</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\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": [
" state pop unempl year\n",
"0 VA 5.0 NaN 2012\n",
"1 VA 5.1 NaN 2013\n",
"2 VA 5.2 6.0 2014\n",
"3 MD 4.0 6.0 2014\n",
"4 MD 4.1 6.1 2015\n",
"5 NaN NaN NaN NaN\n",
"6 NaN NaN NaN NaN"
]
},
"execution_count": 45,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_6 = df_3.ix[range(0, 7), ['state', 'pop', 'unempl', 'year']]\n",
"df_6"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Dropping Entries"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Drop rows from a Series or DataFrame:"
]
},
{
"cell_type": "code",
"execution_count": 46,
"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>state</th>\n",
" <th>pop</th>\n",
" <th>unempl</th>\n",
" <th>year</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>VA</td>\n",
" <td>5.2</td>\n",
" <td>6.0</td>\n",
" <td>2014</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>MD</td>\n",
" <td>4.0</td>\n",
" <td>6.0</td>\n",
" <td>2014</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>MD</td>\n",
" <td>4.1</td>\n",
" <td>6.1</td>\n",
" <td>2015</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\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": [
" state pop unempl year\n",
"2 VA 5.2 6.0 2014\n",
"3 MD 4.0 6.0 2014\n",
"4 MD 4.1 6.1 2015\n",
"5 NaN NaN NaN NaN\n",
"6 NaN NaN NaN NaN"
]
},
"execution_count": 46,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_7 = df_6.drop([0, 1])\n",
"df_7"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Drop columns from a DataFrame:"
]
},
{
"cell_type": "code",
"execution_count": 47,
"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>state</th>\n",
" <th>pop</th>\n",
" <th>year</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>VA</td>\n",
" <td>5.2</td>\n",
" <td>2014</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>MD</td>\n",
" <td>4.0</td>\n",
" <td>2014</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>MD</td>\n",
" <td>4.1</td>\n",
" <td>2015</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" state pop year\n",
"2 VA 5.2 2014\n",
"3 MD 4.0 2014\n",
"4 MD 4.1 2015\n",
"5 NaN NaN NaN\n",
"6 NaN NaN NaN"
]
},
"execution_count": 47,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_7 = df_7.drop('unempl', axis=1)\n",
"df_7"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Indexing, Selecting, Filtering"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Series indexing is similar to NumPy array indexing with the added bonus of being able to use the Series' index values."
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"a 1\n",
"b 1\n",
"c 2\n",
"d -3\n",
"e -5\n",
"dtype: int64"
]
},
"execution_count": 48,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ser_2"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Select a value from a Series:"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 49,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ser_2[0] == ser_2['a']"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Select a slice from a Series:"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"b 1\n",
"c 2\n",
"d -3\n",
"dtype: int64"
]
},
"execution_count": 50,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ser_2[1:4]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Select specific values from a Series:"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"b 1\n",
"c 2\n",
"d -3\n",
"dtype: int64"
]
},
"execution_count": 51,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ser_2[['b', 'c', 'd']]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Select from a Series based on a filter:"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"a 1\n",
"b 1\n",
"c 2\n",
"dtype: int64"
]
},
"execution_count": 52,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ser_2[ser_2 > 0]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Select a slice from a Series with labels (note the end point is inclusive):"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"a 1\n",
"b 1\n",
"c 2\n",
"dtype: int64"
]
},
"execution_count": 53,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ser_2['a':'c']"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Assign to a Series slice (note the end point is inclusive):"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"a 0\n",
"b 0\n",
"c 2\n",
"d -3\n",
"e -5\n",
"dtype: int64"
]
},
"execution_count": 54,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ser_2['a':'b'] = 0\n",
"ser_2"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Pandas supports indexing into a DataFrame."
]
},
{
"cell_type": "code",
"execution_count": 55,
"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>state</th>\n",
" <th>pop</th>\n",
" <th>unempl</th>\n",
" <th>year</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>VA</td>\n",
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" <tr>\n",
" <th>1</th>\n",
" <td>VA</td>\n",
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" <td>NaN</td>\n",
" <td>2013</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>VA</td>\n",
" <td>5.2</td>\n",
" <td>6.0</td>\n",
" <td>2014</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>MD</td>\n",
" <td>4.0</td>\n",
" <td>6.0</td>\n",
" <td>2014</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>MD</td>\n",
" <td>4.1</td>\n",
" <td>6.1</td>\n",
" <td>2015</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\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": [
" state pop unempl year\n",
"0 VA 5.0 NaN 2012\n",
"1 VA 5.1 NaN 2013\n",
"2 VA 5.2 6.0 2014\n",
"3 MD 4.0 6.0 2014\n",
"4 MD 4.1 6.1 2015\n",
"5 NaN NaN NaN NaN\n",
"6 NaN NaN NaN NaN"
]
},
"execution_count": 55,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_6"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Select specified columns from a DataFrame:"
]
},
{
"cell_type": "code",
"execution_count": 56,
"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>pop</th>\n",
" <th>unempl</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>5.0</td>\n",
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" <tr>\n",
" <th>1</th>\n",
" <td>5.1</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>5.2</td>\n",
" <td>6.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4.0</td>\n",
" <td>6.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>4.1</td>\n",
" <td>6.1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" pop unempl\n",
"0 5.0 NaN\n",
"1 5.1 NaN\n",
"2 5.2 6.0\n",
"3 4.0 6.0\n",
"4 4.1 6.1\n",
"5 NaN NaN\n",
"6 NaN NaN"
]
},
"execution_count": 56,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_6[['pop', 'unempl']]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Select a slice from a DataFrame:"
]
},
{
"cell_type": "code",
"execution_count": 57,
"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>state</th>\n",
" <th>pop</th>\n",
" <th>unempl</th>\n",
" <th>year</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>VA</td>\n",
" <td>5.0</td>\n",
" <td>NaN</td>\n",
" <td>2012</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>VA</td>\n",
" <td>5.1</td>\n",
" <td>NaN</td>\n",
" <td>2013</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" state pop unempl year\n",
"0 VA 5.0 NaN 2012\n",
"1 VA 5.1 NaN 2013"
]
},
"execution_count": 57,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_6[:2]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Select from a DataFrame based on a filter:"
]
},
{
"cell_type": "code",
"execution_count": 58,
"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>state</th>\n",
" <th>pop</th>\n",
" <th>unempl</th>\n",
" <th>year</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>VA</td>\n",
" <td>5.1</td>\n",
" <td>NaN</td>\n",
" <td>2013</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>VA</td>\n",
" <td>5.2</td>\n",
" <td>6</td>\n",
" <td>2014</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" state pop unempl year\n",
"1 VA 5.1 NaN 2013\n",
"2 VA 5.2 6 2014"
]
},
"execution_count": 58,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_6[df_6['pop'] > 5]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Perform a scalar comparison on a DataFrame:"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
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" <th>state</th>\n",
" <th>pop</th>\n",
" <th>unempl</th>\n",
" <th>year</th>\n",
" </tr>\n",
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" <tr>\n",
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" <tr>\n",
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" <tr>\n",
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" <td>True</td>\n",
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" <tr>\n",
" <th>3</th>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" state pop unempl year\n",
"0 True False False True\n",
"1 True True False True\n",
"2 True True True True\n",
"3 True False True True\n",
"4 True False True True\n",
"5 False False False False\n",
"6 False False False False"
]
},
"execution_count": 59,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_6 > 5"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Perform a scalar comparison on a DataFrame, retain the values that pass the filter:"
]
},
{
"cell_type": "code",
"execution_count": 60,
"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>state</th>\n",
" <th>pop</th>\n",
" <th>unempl</th>\n",
" <th>year</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>VA</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>VA</td>\n",
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" <tr>\n",
" <th>2</th>\n",
" <td>VA</td>\n",
" <td>5.2</td>\n",
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" <td>2014</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>MD</td>\n",
" <td>NaN</td>\n",
" <td>6.0</td>\n",
" <td>2014</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>MD</td>\n",
" <td>NaN</td>\n",
" <td>6.1</td>\n",
" <td>2015</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\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": [
" state pop unempl year\n",
"0 VA NaN NaN 2012\n",
"1 VA 5.1 NaN 2013\n",
"2 VA 5.2 6.0 2014\n",
"3 MD NaN 6.0 2014\n",
"4 MD NaN 6.1 2015\n",
"5 NaN NaN NaN NaN\n",
"6 NaN NaN NaN NaN"
]
},
"execution_count": 60,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_6[df_6 > 5]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Select a slice of rows from a DataFrame (note the end point is inclusive):"
]
},
{
"cell_type": "code",
"execution_count": 61,
"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>state</th>\n",
" <th>pop</th>\n",
" <th>unempl</th>\n",
" <th>year</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>VA</td>\n",
" <td>5.2</td>\n",
" <td>6</td>\n",
" <td>2014</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>MD</td>\n",
" <td>4.0</td>\n",
" <td>6</td>\n",
" <td>2014</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" state pop unempl year\n",
"2 VA 5.2 6 2014\n",
"3 MD 4.0 6 2014"
]
},
"execution_count": 61,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_6.ix[2:3]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Select a slice of rows from a specific column of a DataFrame:"
]
},
{
"cell_type": "code",
"execution_count": 62,
"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>state</th>\n",
" <th>pop</th>\n",
" <th>unempl</th>\n",
" <th>year</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>VA</td>\n",
" <td>5.0</td>\n",
" <td>NaN</td>\n",
" <td>2012</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>VA</td>\n",
" <td>5.1</td>\n",
" <td>NaN</td>\n",
" <td>2013</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>VA</td>\n",
" <td>5.2</td>\n",
" <td>6.0</td>\n",
" <td>2014</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>MD</td>\n",
" <td>4.0</td>\n",
" <td>6.0</td>\n",
" <td>2014</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>MD</td>\n",
" <td>4.1</td>\n",
" <td>6.1</td>\n",
" <td>2015</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\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": [
" state pop unempl year\n",
"0 VA 5.0 NaN 2012\n",
"1 VA 5.1 NaN 2013\n",
"2 VA 5.2 6.0 2014\n",
"3 MD 4.0 6.0 2014\n",
"4 MD 4.1 6.1 2015\n",
"5 NaN NaN NaN NaN\n",
"6 NaN NaN NaN NaN"
]
},
"execution_count": 62,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_6.ix[0:2, 'pop']\n",
"df_6"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Select rows based on an arithmetic operation on a specific row:"
]
},
{
"cell_type": "code",
"execution_count": 63,
"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>state</th>\n",
" <th>pop</th>\n",
" <th>unempl</th>\n",
" <th>year</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>VA</td>\n",
" <td>5.2</td>\n",
" <td>6.0</td>\n",
" <td>2014</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>MD</td>\n",
" <td>4.0</td>\n",
" <td>6.0</td>\n",
" <td>2014</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>MD</td>\n",
" <td>4.1</td>\n",
" <td>6.1</td>\n",
" <td>2015</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" state pop unempl year\n",
"2 VA 5.2 6.0 2014\n",
"3 MD 4.0 6.0 2014\n",
"4 MD 4.1 6.1 2015"
]
},
"execution_count": 63,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_6.ix[df_6.unempl > 5.0]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Arithmetic and Data Alignment"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Adding Series objects results in the union of index pairs if the pairs are not the same, resulting in NaN for indices that do not overlap:"
]
},
{
"cell_type": "code",
"execution_count": 64,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"a 1.764052\n",
"b 0.400157\n",
"c 0.978738\n",
"d 2.240893\n",
"e 1.867558\n",
"dtype: float64"
]
},
"execution_count": 64,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.random.seed(0)\n",
"ser_6 = pd.Series(np.random.randn(5),\n",
" index=['a', 'b', 'c', 'd', 'e'])\n",
"ser_6"
]
},
{
"cell_type": "code",
"execution_count": 65,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"a 1.624345\n",
"c -0.611756\n",
"e -0.528172\n",
"f -1.072969\n",
"g 0.865408\n",
"dtype: float64"
]
},
"execution_count": 65,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.random.seed(1)\n",
"ser_7 = pd.Series(np.random.randn(5),\n",
" index=['a', 'c', 'e', 'f', 'g'])\n",
"ser_7"
]
},
{
"cell_type": "code",
"execution_count": 66,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"a 3.388398\n",
"b NaN\n",
"c 0.366982\n",
"d NaN\n",
"e 1.339386\n",
"f NaN\n",
"g NaN\n",
"dtype: float64"
]
},
"execution_count": 66,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ser_6 + ser_7"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Set a fill value instead of NaN for indices that do not overlap:"
]
},
{
"cell_type": "code",
"execution_count": 67,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"a 3.388398\n",
"b 0.400157\n",
"c 0.366982\n",
"d 2.240893\n",
"e 1.339386\n",
"f -1.072969\n",
"g 0.865408\n",
"dtype: float64"
]
},
"execution_count": 67,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ser_6.add(ser_7, fill_value=0)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Adding DataFrame objects results in the union of index pairs for rows and columns if the pairs are not the same, resulting in NaN for indices that do not overlap:"
]
},
{
"cell_type": "code",
"execution_count": 68,
"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>a</th>\n",
" <th>b</th>\n",
" <th>c</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0.548814</td>\n",
" <td>0.715189</td>\n",
" <td>0.602763</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0.544883</td>\n",
" <td>0.423655</td>\n",
" <td>0.645894</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0.437587</td>\n",
" <td>0.891773</td>\n",
" <td>0.963663</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" a b c\n",
"0 0.548814 0.715189 0.602763\n",
"1 0.544883 0.423655 0.645894\n",
"2 0.437587 0.891773 0.963663"
]
},
"execution_count": 68,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.random.seed(0)\n",
"df_8 = pd.DataFrame(np.random.rand(9).reshape((3, 3)),\n",
" columns=['a', 'b', 'c'])\n",
"df_8"
]
},
{
"cell_type": "code",
"execution_count": 69,
"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>b</th>\n",
" <th>c</th>\n",
" <th>d</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0.417022</td>\n",
" <td>0.720324</td>\n",
" <td>0.000114</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0.302333</td>\n",
" <td>0.146756</td>\n",
" <td>0.092339</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0.186260</td>\n",
" <td>0.345561</td>\n",
" <td>0.396767</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" b c d\n",
"0 0.417022 0.720324 0.000114\n",
"1 0.302333 0.146756 0.092339\n",
"2 0.186260 0.345561 0.396767"
]
},
"execution_count": 69,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.random.seed(1)\n",
"df_9 = pd.DataFrame(np.random.rand(9).reshape((3, 3)),\n",
" columns=['b', 'c', 'd'])\n",
"df_9"
]
},
{
"cell_type": "code",
"execution_count": 70,
"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>a</th>\n",
" <th>b</th>\n",
" <th>c</th>\n",
" <th>d</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>NaN</td>\n",
" <td>1.132211</td>\n",
" <td>1.323088</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>NaN</td>\n",
" <td>0.725987</td>\n",
" <td>0.792650</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>NaN</td>\n",
" <td>1.078033</td>\n",
" <td>1.309223</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" a b c d\n",
"0 NaN 1.132211 1.323088 NaN\n",
"1 NaN 0.725987 0.792650 NaN\n",
"2 NaN 1.078033 1.309223 NaN"
]
},
"execution_count": 70,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_8 + df_9"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Set a fill value instead of NaN for indices that do not overlap:"
]
},
{
"cell_type": "code",
"execution_count": 71,
"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>a</th>\n",
" <th>b</th>\n",
" <th>c</th>\n",
" <th>d</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0.548814</td>\n",
" <td>1.132211</td>\n",
" <td>1.323088</td>\n",
" <td>0.000114</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0.544883</td>\n",
" <td>0.725987</td>\n",
" <td>0.792650</td>\n",
" <td>0.092339</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0.437587</td>\n",
" <td>1.078033</td>\n",
" <td>1.309223</td>\n",
" <td>0.396767</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" a b c d\n",
"0 0.548814 1.132211 1.323088 0.000114\n",
"1 0.544883 0.725987 0.792650 0.092339\n",
"2 0.437587 1.078033 1.309223 0.396767"
]
},
"execution_count": 71,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_10 = df_8.add(df_9, fill_value=0)\n",
"df_10"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Like NumPy, pandas supports arithmetic operations between DataFrames and Series.\n",
"\n",
"Match the index of the Series on the DataFrame's columns, broadcasting down the rows:"
]
},
{
"cell_type": "code",
"execution_count": 72,
"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>a</th>\n",
" <th>b</th>\n",
" <th>c</th>\n",
" <th>d</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>-0.003930</td>\n",
" <td>-0.406224</td>\n",
" <td>-0.530438</td>\n",
" <td>0.092224</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>-0.111226</td>\n",
" <td>-0.054178</td>\n",
" <td>-0.013864</td>\n",
" <td>0.396653</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" a b c d\n",
"0 0.000000 0.000000 0.000000 0.000000\n",
"1 -0.003930 -0.406224 -0.530438 0.092224\n",
"2 -0.111226 -0.054178 -0.013864 0.396653"
]
},
"execution_count": 72,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ser_8 = df_10.ix[0]\n",
"df_11 = df_10 - ser_8\n",
"df_11"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Match the index of the Series on the DataFrame's columns, broadcasting down the rows and union the indices that do not match:"
]
},
{
"cell_type": "code",
"execution_count": 73,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"a 0\n",
"d 1\n",
"e 2\n",
"dtype: int64"
]
},
"execution_count": 73,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ser_9 = pd.Series(range(3), index=['a', 'd', 'e'])\n",
"ser_9"
]
},
{
"cell_type": "code",
"execution_count": 74,
"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>a</th>\n",
" <th>b</th>\n",
" <th>c</th>\n",
" <th>d</th>\n",
" <th>e</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0.000000</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>-1.000000</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>-0.003930</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>-0.907776</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>-0.111226</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>-0.603347</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" a b c d e\n",
"0 0.000000 NaN NaN -1.000000 NaN\n",
"1 -0.003930 NaN NaN -0.907776 NaN\n",
"2 -0.111226 NaN NaN -0.603347 NaN"
]
},
"execution_count": 74,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_11 - ser_9"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Broadcast over the columns and match the rows (axis=0) by using an arithmetic method:"
]
},
{
"cell_type": "code",
"execution_count": 75,
"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>a</th>\n",
" <th>b</th>\n",
" <th>c</th>\n",
" <th>d</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0.548814</td>\n",
" <td>1.132211</td>\n",
" <td>1.323088</td>\n",
" <td>0.000114</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0.544883</td>\n",
" <td>0.725987</td>\n",
" <td>0.792650</td>\n",
" <td>0.092339</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0.437587</td>\n",
" <td>1.078033</td>\n",
" <td>1.309223</td>\n",
" <td>0.396767</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" a b c d\n",
"0 0.548814 1.132211 1.323088 0.000114\n",
"1 0.544883 0.725987 0.792650 0.092339\n",
"2 0.437587 1.078033 1.309223 0.396767"
]
},
"execution_count": 75,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_10"
]
},
{
"cell_type": "code",
"execution_count": 76,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0 100\n",
"1 200\n",
"2 300\n",
"dtype: int64"
]
},
"execution_count": 76,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ser_10 = pd.Series([100, 200, 300])\n",
"ser_10"
]
},
{
"cell_type": "code",
"execution_count": 77,
"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>a</th>\n",
" <th>b</th>\n",
" <th>c</th>\n",
" <th>d</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>-99.451186</td>\n",
" <td>-98.867789</td>\n",
" <td>-98.676912</td>\n",
" <td>-99.999886</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>-199.455117</td>\n",
" <td>-199.274013</td>\n",
" <td>-199.207350</td>\n",
" <td>-199.907661</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>-299.562413</td>\n",
" <td>-298.921967</td>\n",
" <td>-298.690777</td>\n",
" <td>-299.603233</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" a b c d\n",
"0 -99.451186 -98.867789 -98.676912 -99.999886\n",
"1 -199.455117 -199.274013 -199.207350 -199.907661\n",
"2 -299.562413 -298.921967 -298.690777 -299.603233"
]
},
"execution_count": 77,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_10.sub(ser_10, axis=0)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Function Application and Mapping"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"NumPy ufuncs (element-wise array methods) operate on pandas objects:"
]
},
{
"cell_type": "code",
"execution_count": 78,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
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" <th>a</th>\n",
" <th>b</th>\n",
" <th>c</th>\n",
" <th>d</th>\n",
" </tr>\n",
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" <td>0.000000</td>\n",
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" <tr>\n",
" <th>1</th>\n",
" <td>0.003930</td>\n",
" <td>0.406224</td>\n",
" <td>0.530438</td>\n",
" <td>0.092224</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0.111226</td>\n",
" <td>0.054178</td>\n",
" <td>0.013864</td>\n",
" <td>0.396653</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" a b c d\n",
"0 0.000000 0.000000 0.000000 0.000000\n",
"1 0.003930 0.406224 0.530438 0.092224\n",
"2 0.111226 0.054178 0.013864 0.396653"
]
},
"execution_count": 78,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_11 = np.abs(df_11)\n",
"df_11"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Apply a function on 1D arrays to each column:"
]
},
{
"cell_type": "code",
"execution_count": 79,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"a 0.111226\n",
"b 0.406224\n",
"c 0.530438\n",
"d 0.396653\n",
"dtype: float64"
]
},
"execution_count": 79,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"func_1 = lambda x: x.max() - x.min()\n",
"df_11.apply(func_1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Apply a function on 1D arrays to each row:"
]
},
{
"cell_type": "code",
"execution_count": 80,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0 0.000000\n",
"1 0.526508\n",
"2 0.382789\n",
"dtype: float64"
]
},
"execution_count": 80,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_11.apply(func_1, axis=1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Apply a function and return a DataFrame:"
]
},
{
"cell_type": "code",
"execution_count": 81,
"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>a</th>\n",
" <th>b</th>\n",
" <th>c</th>\n",
" <th>d</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>0.111226</td>\n",
" <td>0.406224</td>\n",
" <td>0.530438</td>\n",
" <td>0.396653</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" a b c d\n",
"min 0.000000 0.000000 0.000000 0.000000\n",
"max 0.111226 0.406224 0.530438 0.396653"
]
},
"execution_count": 81,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"func_2 = lambda x: pd.Series([x.min(), x.max()], index=['min', 'max'])\n",
"df_11.apply(func_2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Apply an element-wise Python function to a DataFrame:"
]
},
{
"cell_type": "code",
"execution_count": 82,
"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>a</th>\n",
" <th>b</th>\n",
" <th>c</th>\n",
" <th>d</th>\n",
" </tr>\n",
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" <tr>\n",
" <th>0</th>\n",
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" <td>0.00</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0.00</td>\n",
" <td>0.41</td>\n",
" <td>0.53</td>\n",
" <td>0.09</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0.11</td>\n",
" <td>0.05</td>\n",
" <td>0.01</td>\n",
" <td>0.40</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" a b c d\n",
"0 0.00 0.00 0.00 0.00\n",
"1 0.00 0.41 0.53 0.09\n",
"2 0.11 0.05 0.01 0.40"
]
},
"execution_count": 82,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"func_3 = lambda x: '%.2f' %x\n",
"df_11.applymap(func_3)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Apply an element-wise Python function to a Series:"
]
},
{
"cell_type": "code",
"execution_count": 83,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0 0.00\n",
"1 0.00\n",
"2 0.11\n",
"Name: a, dtype: object"
]
},
"execution_count": 83,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_11['a'].map(func_3)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Sorting and Ranking"
]
},
{
"cell_type": "code",
"execution_count": 84,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"fo 100\n",
"br 200\n",
"bz 300\n",
"qx NaN\n",
"Name: foobarbazqux, dtype: float64"
]
},
"execution_count": 84,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ser_4"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Sort a Series by its index:"
]
},
{
"cell_type": "code",
"execution_count": 85,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"br 200\n",
"bz 300\n",
"fo 100\n",
"qx NaN\n",
"Name: foobarbazqux, dtype: float64"
]
},
"execution_count": 85,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ser_4.sort_index()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Sort a Series by its values:"
]
},
{
"cell_type": "code",
"execution_count": 86,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/al/anaconda3/envs/py27/lib/python2.7/site-packages/ipykernel/__main__.py:1: FutureWarning: order is deprecated, use sort_values(...)\n",
" if __name__ == '__main__':\n"
]
},
{
"data": {
"text/plain": [
"fo 100\n",
"br 200\n",
"bz 300\n",
"qx NaN\n",
"Name: foobarbazqux, dtype: float64"
]
},
"execution_count": 86,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ser_4.order()"
]
},
{
"cell_type": "code",
"execution_count": 87,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
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" <th>a</th>\n",
" <th>b</th>\n",
" <th>d</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>three</th>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>one</th>\n",
" <td>4</td>\n",
" <td>5</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>two</th>\n",
" <td>8</td>\n",
" <td>9</td>\n",
" <td>10</td>\n",
" <td>11</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" c a b d\n",
"three 0 1 2 3\n",
"one 4 5 6 7\n",
"two 8 9 10 11"
]
},
"execution_count": 87,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_12 = pd.DataFrame(np.arange(12).reshape((3, 4)),\n",
" index=['three', 'one', 'two'],\n",
" columns=['c', 'a', 'b', 'd'])\n",
"df_12"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Sort a DataFrame by its index:"
]
},
{
"cell_type": "code",
"execution_count": 88,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
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" <th>c</th>\n",
" <th>a</th>\n",
" <th>b</th>\n",
" <th>d</th>\n",
" </tr>\n",
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" <tr>\n",
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" <td>0</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
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" <tr>\n",
" <th>two</th>\n",
" <td>8</td>\n",
" <td>9</td>\n",
" <td>10</td>\n",
" <td>11</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" c a b d\n",
"one 4 5 6 7\n",
"three 0 1 2 3\n",
"two 8 9 10 11"
]
},
"execution_count": 88,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_12.sort_index()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Sort a DataFrame by columns in descending order:"
]
},
{
"cell_type": "code",
"execution_count": 89,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
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" <th>c</th>\n",
" <th>b</th>\n",
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" </tbody>\n",
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"text/plain": [
" d c b a\n",
"three 3 0 2 1\n",
"one 7 4 6 5\n",
"two 11 8 10 9"
]
},
"execution_count": 89,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_12.sort_index(axis=1, ascending=False)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Sort a DataFrame's values by column:"
]
},
{
"cell_type": "code",
"execution_count": 90,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/al/anaconda3/envs/py27/lib/python2.7/site-packages/ipykernel/__main__.py:1: FutureWarning: by argument to sort_index is deprecated, pls use .sort_values(by=...)\n",
" if __name__ == '__main__':\n"
]
},
{
"data": {
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"text/plain": [
" c a b d\n",
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"two 8 9 10 11"
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},
"execution_count": 90,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_12.sort_index(by=['d', 'c'])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Ranking is similar to numpy.argsort except that ties are broken by assigning each group the mean rank:"
]
},
{
"cell_type": "code",
"execution_count": 91,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/al/anaconda3/envs/py27/lib/python2.7/site-packages/ipykernel/__main__.py:2: FutureWarning: order is deprecated, use sort_values(...)\n",
" from ipykernel import kernelapp as app\n"
]
},
{
"data": {
"text/plain": [
"1 -5\n",
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"dtype: int64"
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},
"execution_count": 91,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ser_11 = pd.Series([7, -5, 7, 4, 2, 0, 4, 7])\n",
"ser_11 = ser_11.order()\n",
"ser_11"
]
},
{
"cell_type": "code",
"execution_count": 92,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"1 1.0\n",
"5 2.0\n",
"4 3.0\n",
"3 4.5\n",
"6 4.5\n",
"0 7.0\n",
"2 7.0\n",
"7 7.0\n",
"dtype: float64"
]
},
"execution_count": 92,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ser_11.rank()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Rank a Series according to when they appear in the data:"
]
},
{
"cell_type": "code",
"execution_count": 93,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"1 1\n",
"5 2\n",
"4 3\n",
"3 4\n",
"6 5\n",
"0 6\n",
"2 7\n",
"7 8\n",
"dtype: float64"
]
},
"execution_count": 93,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ser_11.rank(method='first')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Rank a Series in descending order, using the maximum rank for the group:"
]
},
{
"cell_type": "code",
"execution_count": 94,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"1 8\n",
"5 7\n",
"4 6\n",
"3 5\n",
"6 5\n",
"0 3\n",
"2 3\n",
"7 3\n",
"dtype: float64"
]
},
"execution_count": 94,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ser_11.rank(ascending=False, method='max')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"DataFrames can rank over rows or columns."
]
},
{
"cell_type": "code",
"execution_count": 95,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
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" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>8</td>\n",
" <td>5</td>\n",
" <td>7</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" bar baz foo\n",
"0 -5 -1 7\n",
"1 4 2 -5\n",
"2 2 3 7\n",
"3 0 0 4\n",
"4 4 5 2\n",
"5 7 9 0\n",
"6 7 9 4\n",
"7 8 5 7"
]
},
"execution_count": 95,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_13 = pd.DataFrame({'foo' : [7, -5, 7, 4, 2, 0, 4, 7],\n",
" 'bar' : [-5, 4, 2, 0, 4, 7, 7, 8],\n",
" 'baz' : [-1, 2, 3, 0, 5, 9, 9, 5]})\n",
"df_13"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Rank a DataFrame over rows:"
]
},
{
"cell_type": "code",
"execution_count": 96,
"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>bar</th>\n",
" <th>baz</th>\n",
" <th>foo</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>7.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>4.5</td>\n",
" <td>3.0</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3.0</td>\n",
" <td>4.0</td>\n",
" <td>7.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2.0</td>\n",
" <td>2.0</td>\n",
" <td>4.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>4.5</td>\n",
" <td>5.5</td>\n",
" <td>3.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>6.5</td>\n",
" <td>7.5</td>\n",
" <td>2.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>6.5</td>\n",
" <td>7.5</td>\n",
" <td>4.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>8.0</td>\n",
" <td>5.5</td>\n",
" <td>7.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" bar baz foo\n",
"0 1.0 1.0 7.0\n",
"1 4.5 3.0 1.0\n",
"2 3.0 4.0 7.0\n",
"3 2.0 2.0 4.5\n",
"4 4.5 5.5 3.0\n",
"5 6.5 7.5 2.0\n",
"6 6.5 7.5 4.5\n",
"7 8.0 5.5 7.0"
]
},
"execution_count": 96,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_13.rank()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Rank a DataFrame over columns:"
]
},
{
"cell_type": "code",
"execution_count": 97,
"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>bar</th>\n",
" <th>baz</th>\n",
" <th>foo</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1.0</td>\n",
" <td>2.0</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>3.0</td>\n",
" <td>2.0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1.0</td>\n",
" <td>2.0</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1.5</td>\n",
" <td>1.5</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2.0</td>\n",
" <td>3.0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>2.0</td>\n",
" <td>3.0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>2.0</td>\n",
" <td>3.0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>3.0</td>\n",
" <td>1.0</td>\n",
" <td>2</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" bar baz foo\n",
"0 1.0 2.0 3\n",
"1 3.0 2.0 1\n",
"2 1.0 2.0 3\n",
"3 1.5 1.5 3\n",
"4 2.0 3.0 1\n",
"5 2.0 3.0 1\n",
"6 2.0 3.0 1\n",
"7 3.0 1.0 2"
]
},
"execution_count": 97,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_13.rank(axis=1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Axis Indexes with Duplicate Values"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Labels do not have to be unique in Pandas:"
]
},
{
"cell_type": "code",
"execution_count": 98,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"foo 0\n",
"foo 1\n",
"bar 2\n",
"bar 3\n",
"baz 4\n",
"dtype: int64"
]
},
"execution_count": 98,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ser_12 = pd.Series(range(5), index=['foo', 'foo', 'bar', 'bar', 'baz'])\n",
"ser_12"
]
},
{
"cell_type": "code",
"execution_count": 99,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"False"
]
},
"execution_count": 99,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ser_12.index.is_unique"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Select Series elements:"
]
},
{
"cell_type": "code",
"execution_count": 100,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"foo 0\n",
"foo 1\n",
"dtype: int64"
]
},
"execution_count": 100,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ser_12['foo']"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Select DataFrame elements:"
]
},
{
"cell_type": "code",
"execution_count": 101,
"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",
" <th>3</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>foo</th>\n",
" <td>-2.363469</td>\n",
" <td>1.135345</td>\n",
" <td>-1.017014</td>\n",
" <td>0.637362</td>\n",
" </tr>\n",
" <tr>\n",
" <th>foo</th>\n",
" <td>-0.859907</td>\n",
" <td>1.772608</td>\n",
" <td>-1.110363</td>\n",
" <td>0.181214</td>\n",
" </tr>\n",
" <tr>\n",
" <th>bar</th>\n",
" <td>0.564345</td>\n",
" <td>-0.566510</td>\n",
" <td>0.729976</td>\n",
" <td>0.372994</td>\n",
" </tr>\n",
" <tr>\n",
" <th>bar</th>\n",
" <td>0.533811</td>\n",
" <td>-0.091973</td>\n",
" <td>1.913820</td>\n",
" <td>0.330797</td>\n",
" </tr>\n",
" <tr>\n",
" <th>baz</th>\n",
" <td>1.141943</td>\n",
" <td>-1.129595</td>\n",
" <td>-0.850052</td>\n",
" <td>0.960820</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" 0 1 2 3\n",
"foo -2.363469 1.135345 -1.017014 0.637362\n",
"foo -0.859907 1.772608 -1.110363 0.181214\n",
"bar 0.564345 -0.566510 0.729976 0.372994\n",
"bar 0.533811 -0.091973 1.913820 0.330797\n",
"baz 1.141943 -1.129595 -0.850052 0.960820"
]
},
"execution_count": 101,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_14 = pd.DataFrame(np.random.randn(5, 4),\n",
" index=['foo', 'foo', 'bar', 'bar', 'baz'])\n",
"df_14"
]
},
{
"cell_type": "code",
"execution_count": 102,
"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",
" <th>3</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>bar</th>\n",
" <td>0.564345</td>\n",
" <td>-0.566510</td>\n",
" <td>0.729976</td>\n",
" <td>0.372994</td>\n",
" </tr>\n",
" <tr>\n",
" <th>bar</th>\n",
" <td>0.533811</td>\n",
" <td>-0.091973</td>\n",
" <td>1.913820</td>\n",
" <td>0.330797</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" 0 1 2 3\n",
"bar 0.564345 -0.566510 0.729976 0.372994\n",
"bar 0.533811 -0.091973 1.913820 0.330797"
]
},
"execution_count": 102,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_14.ix['bar']"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Summarizing and Computing Descriptive Statistics"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Unlike NumPy arrays, Pandas descriptive statistics automatically exclude missing data. NaN values are excluded unless the entire row or column is NA."
]
},
{
"cell_type": "code",
"execution_count": 103,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"df_15 = pd.DataFrame(np.random.randn(10, 3),\n",
" columns=['a', 'b', 'c'])\n",
"df_15['cat1'] = (np.random.rand(10) * 3).round(0)\n",
"df_15['cat2'] = (np.random.rand(10)).round(0)"
]
},
{
"cell_type": "code",
"execution_count": 104,
"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>a</th>\n",
" <th>b</th>\n",
" <th>c</th>\n",
" <th>cat1</th>\n",
" <th>cat2</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>-0.217418</td>\n",
" <td>0.158515</td>\n",
" <td>0.873418</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>-0.111383</td>\n",
" <td>-1.038039</td>\n",
" <td>-1.009480</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>-1.058257</td>\n",
" <td>0.656284</td>\n",
" <td>-0.062492</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>-1.738654</td>\n",
" <td>0.103163</td>\n",
" <td>-0.621667</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0.275718</td>\n",
" <td>-1.090675</td>\n",
" <td>-0.609985</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>0.306412</td>\n",
" <td>1.691826</td>\n",
" <td>-0.747954</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>-0.580797</td>\n",
" <td>-0.110754</td>\n",
" <td>2.042029</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>0.447521</td>\n",
" <td>0.683384</td>\n",
" <td>0.022886</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>0.857234</td>\n",
" <td>0.183931</td>\n",
" <td>-0.416112</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>1.250050</td>\n",
" <td>1.248300</td>\n",
" <td>-0.757674</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" a b c cat1 cat2\n",
"0 -0.217418 0.158515 0.873418 2 1\n",
"1 -0.111383 -1.038039 -1.009480 3 0\n",
"2 -1.058257 0.656284 -0.062492 2 1\n",
"3 -1.738654 0.103163 -0.621667 2 0\n",
"4 0.275718 -1.090675 -0.609985 1 1\n",
"5 0.306412 1.691826 -0.747954 1 0\n",
"6 -0.580797 -0.110754 2.042029 3 0\n",
"7 0.447521 0.683384 0.022886 1 1\n",
"8 0.857234 0.183931 -0.416112 3 1\n",
"9 1.250050 1.248300 -0.757674 2 0"
]
},
"execution_count": 104,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_15"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Sum and Mean"
]
},
{
"cell_type": "code",
"execution_count": 105,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"a -0.569574\n",
"b 2.485935\n",
"c -1.287030\n",
"cat1 20.000000\n",
"cat2 5.000000\n",
"dtype: float64"
]
},
"execution_count": 105,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_15.sum()"
]
},
{
"cell_type": "code",
"execution_count": 106,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0 3.814515\n",
"1 0.841098\n",
"2 2.535536\n",
"3 -0.257158\n",
"4 0.575058\n",
"5 2.250285\n",
"6 4.350478\n",
"7 3.153791\n",
"8 4.625053\n",
"9 3.740676\n",
"dtype: float64"
]
},
"execution_count": 106,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_15.sum(axis=1)"
]
},
{
"cell_type": "code",
"execution_count": 107,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"a -0.056957\n",
"b 0.248594\n",
"c -0.128703\n",
"cat1 2.000000\n",
"cat2 0.500000\n",
"dtype: float64"
]
},
"execution_count": 107,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_15.mean(axis=0)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Max and max location"
]
},
{
"cell_type": "code",
"execution_count": 108,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"a 1.250050\n",
"b 1.691826\n",
"c 2.042029\n",
"cat1 3.000000\n",
"cat2 1.000000\n",
"dtype: float64"
]
},
"execution_count": 108,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_15.max()"
]
},
{
"cell_type": "code",
"execution_count": 109,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"a 9\n",
"b 5\n",
"c 6\n",
"cat1 1\n",
"cat2 0\n",
"dtype: int64"
]
},
"execution_count": 109,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_15.idxmax()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Descriptive analysis"
]
},
{
"cell_type": "code",
"execution_count": 110,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"count 10.000000\n",
"mean -0.056957\n",
"std 0.892876\n",
"min -1.738654\n",
"25% -0.489952\n",
"50% 0.082167\n",
"75% 0.412244\n",
"max 1.250050\n",
"Name: a, dtype: float64"
]
},
"execution_count": 110,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_15['a'].describe()"
]
},
{
"cell_type": "code",
"execution_count": 111,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"2 4\n",
"3 3\n",
"1 3\n",
"Name: cat1, dtype: int64"
]
},
"execution_count": 111,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_15['cat1'].value_counts()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Pivot tables"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### group by cat1 and calculate mean"
]
},
{
"cell_type": "code",
"execution_count": 112,
"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>a</th>\n",
" <th>b</th>\n",
" <th>c</th>\n",
" <th>cat2</th>\n",
" </tr>\n",
" <tr>\n",
" <th>cat1</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0.343217</td>\n",
" <td>0.428178</td>\n",
" <td>-0.445018</td>\n",
" <td>0.666667</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>-0.441070</td>\n",
" <td>0.541565</td>\n",
" <td>-0.142104</td>\n",
" <td>0.500000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0.055018</td>\n",
" <td>-0.321621</td>\n",
" <td>0.205479</td>\n",
" <td>0.333333</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" a b c cat2\n",
"cat1 \n",
"1 0.343217 0.428178 -0.445018 0.666667\n",
"2 -0.441070 0.541565 -0.142104 0.500000\n",
"3 0.055018 -0.321621 0.205479 0.333333"
]
},
"execution_count": 112,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd.pivot_table(df_15, index='cat1', aggfunc=np.mean)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### group by cat1 and cat2 calculate the sum of b"
]
},
{
"cell_type": "code",
"execution_count": 113,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th>cat2</th>\n",
" <th>0.0</th>\n",
" <th>1.0</th>\n",
" </tr>\n",
" <tr>\n",
" <th>cat1</th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1.691826</td>\n",
" <td>-0.407291</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1.351463</td>\n",
" <td>0.814799</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>-1.148793</td>\n",
" <td>0.183931</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
"cat2 0 1\n",
"cat1 \n",
"1 1.691826 -0.407291\n",
"2 1.351463 0.814799\n",
"3 -1.148793 0.183931"
]
},
"execution_count": 113,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd.pivot_table(df_15, index='cat1', columns='cat2', values='b', aggfunc=np.sum)"
]
},
{
"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 |
kingb12/languagemodelRNN | report_notebooks/encdec_noing_250_512_040dr.ipynb | 1 | 65414 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Encoder-Decoder Analysis"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Model Architecture"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Encoder: \n",
"\n",
"nn.Sequential {\n",
" [input -> (1) -> (2) -> (3) -> output]\n",
" (1): nn.LookupTable\n",
" (2): nn.LSTM(250 -> 512)\n",
" (3): nn.Dropout(0.400000)\n",
"}\n",
"Decoder: \n",
"\n",
"nn.gModule\n"
]
}
],
"source": [
"report_file = '/Users/bking/IdeaProjects/LanguageModelRNN/reports/encdec_noing_250_512_040dr_2.json'\n",
"log_file = '/Users/bking/IdeaProjects/LanguageModelRNN/logs/encdec_noing_250_512_040dr_2.json'\n",
"\n",
"import json\n",
"import matplotlib.pyplot as plt\n",
"with open(report_file) as f:\n",
" report = json.loads(f.read())\n",
"with open(log_file) as f:\n",
" logs = json.loads(f.read())\n",
"print'Encoder: \\n\\n', report['architecture']['encoder']\n",
"print'Decoder: \\n\\n', report['architecture']['decoder']"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Perplexity on Each Dataset"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"('Train Perplexity: ', 4.9067959644881)\n",
"('Valid Perplexity: ', 1231.5531880044)\n",
"('Test Perplexity: ', 2230.0855503029)\n"
]
}
],
"source": [
"print('Train Perplexity: ', report['train_perplexity'])\n",
"print('Valid Perplexity: ', report['valid_perplexity'])\n",
"print('Test Perplexity: ', report['test_perplexity'])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Loss vs. Epoch"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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q0RARkWP22GOPsWfPHrZs2QLAG2+8waZNmwC4+uqrSUlJITk5mSeeeIKLLrqIzMxMxowZ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\nkiRJkiRJkiRJkiR1yh1quevfKLtnyAAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10cf50ed0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"%matplotlib inline\n",
"for k in logs.keys():\n",
" plt.plot(logs[k][0], logs[k][1], label=str(k) + ' (train)')\n",
" plt.plot(logs[k][0], logs[k][2], label=str(k) + ' (valid)')\n",
"plt.title('Loss v. Epoch')\n",
"plt.xlabel('Epoch')\n",
"plt.ylabel('Loss')\n",
"plt.legend()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Perplexity vs. Epoch"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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eat26Nb169eLpp59m//79xdvfeecdVq9eHfE9BQUF5OTkcNppp1XpXH5Qj00M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\nAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10b4d6550>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"%matplotlib inline\n",
"for k in logs.keys():\n",
" plt.plot(logs[k][0], logs[k][3], label=str(k) + ' (train)')\n",
" plt.plot(logs[k][0], logs[k][4], label=str(k) + ' (valid)')\n",
"plt.title('Perplexity v. Epoch')\n",
"plt.xlabel('Epoch')\n",
"plt.ylabel('Perplexity')\n",
"plt.legend()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Generations"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def print_sample(sample):\n",
" enc_input = ' '.join([w for w in sample['encoder_input'].split(' ') if w != '<pad>'])\n",
" gold = ' '.join([w for w in sample['gold'].split(' ') if w != '<mask>'])\n",
" print('Input: '+ enc_input + '\\n')\n",
" print('Gend: ' + sample['generated'] + '\\n')\n",
" print('True: ' + gold + '\\n')\n",
" print('\\n')\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Input: fennel - and coriander - spiced salmon fillets\n",
"\n",
"Gend: <beg> 1 . preheat oven to 350 degrees f ( 175 degrees c ) . <step> in a large skillet over medium heat . <step> add the onion\n",
"\n",
"True: 1 to make the marinade , mix together the garlic , red miso , mirin , sake , tobanjan , and sugar in a bowl\n",
"\n",
"\n",
"\n",
"Input: chocolate - mousse - filled strawberries\n",
"\n",
"Gend: <beg> preheat oven to 350 degrees . <step> cut the first and remove in a large skillet over medium heat . add the onion , and cook and\n",
"\n",
"True: dice up leftover chicken and remove any bone . add to pot and add water , chicken stock / gravy , garlic , onions <end>\n",
"\n",
"\n",
"\n",
"Input: cheese crusted apple pie\n",
"\n",
"Gend: <beg> preheat oven to 350 degrees . <step> cut the first and place in a large bowl , and cook . <step> in a large bowl , combine\n",
"\n",
"True: preheat broiler . <step> sprinkle the salmon steaks generously with salt and pepper . <step> sprinkle with 1 tablespoon lemon <end>\n",
"\n",
"\n",
"\n",
"Input: masaledar salmon - indian spiced baked salmon\n",
"\n",
"Gend: <beg> in a large bowl , combine the , sugar , and salt , and salt . <step> in a large skillet over medium - high heat ,\n",
"\n",
"True: in a shallow bowl , whisk together beer , flour , and salt . <step> rinse <end>\n",
"\n",
"\n",
"\n",
"Input: basic guacamole\n",
"\n",
"Gend: <beg> in a large bowl , combine the , flour , and salt , and salt . <step> in a large skillet over medium - high heat ,\n",
"\n",
"True: lima bean <step> main dish <step> salad <end> <end>\n",
"\n",
"\n",
"\n",
"Input: honey bbq pork ribs\n",
"\n",
"Gend: <beg> preheat oven to 350 degrees . <step> cut the first and remove into . <step> in a large skillet over medium heat . add the onion and\n",
"\n",
"True: combine ground chicken , red gold diced tomatoes with lime juice & cilantro , egg , and bread crumbs <end>\n",
"\n",
"\n",
"\n"
]
}
],
"source": [
"for sample in report['train_samples']:\n",
" print_sample(sample)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Input: <UNK2> <mask> <UNK2> <mask> <UNK2> <mask> <mask> <mask> <beg> <mask>\n",
"\n",
"Gend: <beg> preheat oven to 350 degrees . <step> cut the first and remove into . <step> in a large skillet over medium heat . add the onion and\n",
"\n",
"True: beat eggs and water together in a bowl . <step> add salt ( i do n't use salt due to the <end>\n",
"\n",
"\n",
"\n",
"Input: <mask> <mask> <mask> <mask> <mask> <mask> <mask> <mask> <mask> <mask>\n",
"\n",
"Gend: <beg> preheat oven to 350 degrees . <step> cut the first and remove into . <step> in a large skillet over medium heat . add the onion and\n",
"\n",
"True: in a food processor , pulse 1 bun until fine crumbs form ( you should have about 1 / 4 cup ) <end>\n",
"\n",
"\n",
"\n",
"Input: <UNK2> <UNK2> <UNK2> <UNK2> <UNK2> <UNK2> <UNK2> <UNK2> <UNK2> <UNK2>\n",
"\n",
"Gend: <beg> preheat oven to 350 degrees . <step> cut the first and remove into . <step> in a large skillet over medium heat . add the onion and\n",
"\n",
"True: prepare barbecue ( high heat ) . coarsely grind fennel seeds and coriander seeds in spice grinder . brush salmon fillets generously with oil .\n",
"\n",
"\n",
"\n",
"Input: <UNK2> <mask> <mask> <mask> <mask> <mask> <UNK2> <mask> <UNK2> <mask>\n",
"\n",
"Gend: <beg> preheat oven to 350 degrees . <step> cut the first and remove into . <step> in a large skillet over medium heat . add the onion and\n",
"\n",
"True: 1 . cook chicken in a pan with a drizzle of olive oil and some s & p and <end>\n",
"\n",
"\n",
"\n",
"Input: <UNK2> <mask> <mask> <mask> <mask> <mask> <UNK2> <mask> <UNK2> <mask>\n",
"\n",
"Gend: <beg> preheat oven to 350 degrees . <step> cut the first and remove into . <step> in a large skillet over medium heat . add the onion and\n",
"\n",
"True: 1 preheat the oven to 400f ° ( 200c ° ) . season the duck legs <end>\n",
"\n",
"\n",
"\n",
"Input: <UNK2> <UNK2> <UNK2> <UNK2> <UNK2> <UNK2> <UNK2> <UNK2> <UNK2> <UNK2>\n",
"\n",
"Gend: <beg> preheat oven to 350 degrees . <step> cut the first and remove into . <step> in a large skillet over medium heat . add the onion and\n",
"\n",
"True: preheat oven to 350 degrees f . <step> place one vanilla wafer into the bottom of <end>\n",
"\n",
"\n",
"\n"
]
}
],
"source": [
"for sample in report['valid_samples']:\n",
" print_sample(sample)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Input: pacific rim glazed flank steak\n",
"\n",
"Gend: <beg> in a large bowl , combine the , sugar , and salt , and salt . <step> in a large skillet over medium - high heat ,\n",
"\n",
"True: place chocolate in a heatproof bowl and melt over a pot of simmering water . remove from heat and set aside . bring cream to\n",
"\n",
"\n",
"\n",
"Input: chocolate french silk pancakes\n",
"\n",
"Gend: <beg> preheat oven to 350 degrees . <step> cut the first and place in a large skillet over medium - high heat . add the to and cook\n",
"\n",
"True: in a bowl , combine the first eight ingredients . <step> combine the cheeses and stir half into <end>\n",
"\n",
"\n",
"\n",
"Input: coffee cake in a mug with cinnamon oatmeal struesel topping\n",
"\n",
"Gend: <beg> 1 . preheat oven to 350 degrees f ( 175 degrees c ) . <step> in a large skillet over medium heat . <step> add the onion\n",
"\n",
"True: <step> 1 melt butter in a large skillet ( cast iron works well for this purpose <end>\n",
"\n",
"\n",
"\n",
"Input: smoked mackerel kedgeree\n",
"\n",
"Gend: <beg> in a large bowl , combine the , flour , sugar , and salt , and salt . <step> in a large bowl , combine the the\n",
"\n",
"True: preheat oven to 350f ° . <step> cook ravioli according to pkg directions ; drain . <step> in skillet cook beef , sausage , <end>\n",
"\n",
"\n",
"\n",
"Input: perfect baked rice with herbs and veggies\n",
"\n",
"Gend: <beg> preheat oven to 350 degrees . <step> cut the first and place in a large skillet over medium - high heat . add the to and cook\n",
"\n",
"True: 1 . beat cream cheese and sugar until smooth . fold in whipped topping . spoon mixture into <end>\n",
"\n",
"\n",
"\n",
"Input: seeteufel mit basilikum und pinienkernen\n",
"\n",
"Gend: <beg> preheat oven to 350 degrees . <step> cut the first and place in a large skillet over medium - high heat . add the to and cook\n",
"\n",
"True: make noodles : beat eggs , add salt and as much flour as can be worked into the eggs to make <end>\n",
"\n",
"\n",
"\n"
]
}
],
"source": [
"for sample in report['test_samples']:\n",
" print_sample(sample)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### BLEU Analysis"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Overall Score: 4.06 \n",
"\n",
"1-gram Score: 20.6\n",
"2-gram Score: 6.2\n",
"3-gram Score: 1.9\n",
"4-gram Score: 1.1\n"
]
}
],
"source": [
"print 'Overall Score: ', report['bleu']['score'], '\\n'\n",
"print '1-gram Score: ', report['bleu']['components']['1']\n",
"print '2-gram Score: ', report['bleu']['components']['2']\n",
"print '3-gram Score: ', report['bleu']['components']['3']\n",
"print '4-gram Score: ', report['bleu']['components']['4']"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### N-pairs BLEU Analysis\n",
"\n",
"This analysis randomly samples 1000 pairs of generations/ground truths and treats them as translations, giving their BLEU score. We can expect very low scores in the ground truth and high scores can expose hyper-common generations"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Overall Score (Generated): 53.5 \n",
"\n",
"1-gram Score: 68.4\n",
"2-gram Score: 55.4\n",
"3-gram Score: 49\n",
"4-gram Score: 44.1\n",
"\n",
"\n",
"Overall Score: (Gold) 10.37 \n",
"\n",
"1-gram Score: 23.9\n",
"2-gram Score: 11.1\n",
"3-gram Score: 7.7\n",
"4-gram Score: 5.7\n"
]
}
],
"source": [
"npairs_generated = report['n_pairs_bleu_generated']\n",
"npairs_gold = report['n_pairs_bleu_gold']\n",
"print 'Overall Score (Generated): ', npairs_generated['score'], '\\n'\n",
"print '1-gram Score: ', npairs_generated['components']['1']\n",
"print '2-gram Score: ', npairs_generated['components']['2']\n",
"print '3-gram Score: ', npairs_generated['components']['3']\n",
"print '4-gram Score: ', npairs_generated['components']['4']\n",
"\n",
"print '\\n'\n",
"\n",
"print 'Overall Score: (Gold)', npairs_gold['score'], '\\n'\n",
"print '1-gram Score: ', npairs_gold['components']['1']\n",
"print '2-gram Score: ', npairs_gold['components']['2']\n",
"print '3-gram Score: ', npairs_gold['components']['3']\n",
"print '4-gram Score: ', npairs_gold['components']['4']"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Alignment Analysis\n",
"\n",
"This analysis computs the average Smith-Waterman alignment score for generations, with the same intuition as N-pairs BLEU, in that we expect low scores in the ground truth and hyper-common generations to raise the scores"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Average Generated Score: 71.0130718954\n",
"Average Gold Score: 22.477124183\n"
]
}
],
"source": [
"print 'Average Generated Score: ', report['average_alignment_generated']\n",
"print 'Average Gold Score: ', report['average_alignment_gold']"
]
}
],
"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": 1
}
| mit |
quaquel/EMAworkbench | ema_workbench/examples/scenario_discovery_resampling.ipynb | 1 | 736286 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Performing Scenario Discovery in Python\n",
"\n",
"The purpose of example is to demonstrate how one can do scenario discovery in python. I will demonstrate how we can perform both PRIM in an interactive way, as well as briefly show how to use CART, which is also available in the exploratory modeling workbench. There is ample literature on both CART and PRIM and their relative merits for use in scenario discovery. So I won't be discussing that here in any detail.\n",
"\n",
"In order to demonstrate the use of the exploratory modeling workbench for scenario discovery, I am using a published example. I am using the data used in the original article by Ben Bryant and Rob Lempert where they first introduced 2010. Ben Bryant kindly made this data available and allowed me to share it. The data comes as a csv file. We can import the data easily using pandas. columns 2 up to and including 10 contain the experimental design, while the classification is presented in column 15\n",
"\n",
"This example is a slightly updated version of a blog post on https://waterprogramming.wordpress.com/2015/08/05/scenario-discovery-in-python/"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import pandas as pd\n",
"\n",
"data = pd.read_csv(\"./data/bryant et al 2010 data.csv\", index_col=False)\n",
"x = data.iloc[:, 2:11]\n",
"y = data.iloc[:, 15].values"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"the exploratory modeling workbench comes with a seperate analysis package. This analysis package contains prim. So let's import prim. The workbench also has its own logging functionality. We can turn this on to get some more insight into prim while it is running."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from ema_workbench.analysis import prim\n",
"from ema_workbench.util import ema_logging\n",
"\n",
"ema_logging.log_to_stderr(ema_logging.INFO);"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Next, we need to instantiate the prim algorithm. To mimic the original work of Ben Bryant and Rob Lempert, we set the peeling alpha to 0.1. The peeling alpha determines how much data is peeled off in each iteration of the algorithm. The lower the value, the less data is removed in each iteration. The minimium coverage threshold that a box should meet is set to 0.8. Next, we can use the instantiated algorithm to find a first box. "
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"[MainProcess/INFO] 882 points remaining, containing 89 cases of interest\n",
"[MainProcess/INFO] mean: 1.0, mass: 0.05102040816326531, coverage: 0.5056179775280899, density: 1.0 restricted_dimensions: 6\n"
]
}
],
"source": [
"prim_alg = prim.Prim(x, y, threshold=0.8, peel_alpha=0.1)\n",
"box1 = prim_alg.find_box()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's investigate this first box is some detail. A first thing to look at is the trade off between coverage and density. The box has a convenience function for this called `show_tradeoff`. "
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"box1.show_tradeoff()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Since we are doing this analysis in a notebook, we can take advantage of the interactivity that the browser offers. A relatively recent addition to the python ecosystem is the library [altair](https://altair-viz.github.io/getting_started/overview.html). Altair can be used to create interactive plots for use in a browser. Altair is an optional dependency for the workbench. If available, we can create the following visual."
]
},
{
"cell_type": "code",
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",
"text/plain": [
"<VegaLite 2 object>\n",
"\n",
"If you see this message, it means the renderer has not been properly enabled\n",
"for the frontend that you are using. For more information, see\n",
"https://altair-viz.github.io/user_guide/troubleshooting.html\n"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"box1.inspect_tradeoff()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Here we can interactively explore the boxes associated with each point in the density coverage trade-off. It also offers mouse overs for the various points on the trade off curve. Given the id of each point, we can also use the workbench to manually inpect the peeling trajectory. Following Bryant & Lempert, we inspect box 21. "
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"[MainProcess/INFO] resample 0\n",
"[MainProcess/INFO] resample 1\n",
"[MainProcess/INFO] resample 2\n",
"[MainProcess/INFO] resample 3\n",
"[MainProcess/INFO] resample 4\n",
"[MainProcess/INFO] resample 5\n",
"[MainProcess/INFO] resample 6\n",
"[MainProcess/INFO] resample 7\n",
"[MainProcess/INFO] resample 8\n",
"[MainProcess/INFO] resample 9\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>reproduce coverage</th>\n",
" <th>reproduce density</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Total biomass</th>\n",
" <td>100.0</td>\n",
" <td>100.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Demand elasticity</th>\n",
" <td>100.0</td>\n",
" <td>100.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Biomass backstop price</th>\n",
" <td>100.0</td>\n",
" <td>100.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Cellulosic cost</th>\n",
" <td>90.0</td>\n",
" <td>90.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Cellulosic yield</th>\n",
" <td>20.0</td>\n",
" <td>20.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Electricity coproduction</th>\n",
" <td>20.0</td>\n",
" <td>20.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Feedstock distribution</th>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Oil elasticity</th>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>oil supply shift</th>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" reproduce coverage reproduce density\n",
"Total biomass 100.0 100.0\n",
"Demand elasticity 100.0 100.0\n",
"Biomass backstop price 100.0 100.0\n",
"Cellulosic cost 90.0 90.0\n",
"Cellulosic yield 20.0 20.0\n",
"Electricity coproduction 20.0 20.0\n",
"Feedstock distribution 0.0 0.0\n",
"Oil elasticity 0.0 0.0\n",
"oil supply shift 0.0 0.0"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"box1.resample(21)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"coverage 0.752809\n",
"density 0.770115\n",
"id 21\n",
"mass 0.0986395\n",
"mean 0.770115\n",
"res_dim 4\n",
"Name: 21, dtype: object\n",
"\n",
" box 21 \n",
" min max qp values\n",
"Total biomass 450.000000 755.799988 [-1.0, 4.716968553178765e-06]\n",
"Demand elasticity -0.422000 -0.202000 [1.1849299115762218e-16, -1.0]\n",
"Biomass backstop price 150.049995 199.600006 [3.515112530263049e-11, -1.0]\n",
"Cellulosic cost 72.650002 133.699997 [0.15741333528927348, -1.0]\n",
"\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"
}
],
"source": [
"box1.inspect(21)\n",
"box1.inspect(21, style=\"graph\")\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If one where to do a detailed comparison with the results reported in the original article, one would see small numerical differences. These differences arise out of subtle differences in implementation. The most important difference is that the exploratory modeling workbench uses a custom objective function inside prim which is different from the one used in the scenario discovery toolkit. Other differences have to do with details about the hill climbing optimization that is used in prim, and in particular how ties are handled in selected the next step. The differences between the two implementations are only numerical, and don't affect the overarching conclusions drawn from the analysis. \n",
"\n",
"Let's select this 21 box, and get a more detailed view of what the box looks like. Following Bryant et al., we can use scatter plots for this. "
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 762.375x720 with 20 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"box1.select(21)\n",
"fig = box1.show_pairs_scatter(21)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Because the last restriction is not significant, we can choose to drop this restriction from the box. "
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"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"
}
],
"source": [
"box1.drop_restriction(\"Cellulosic cost\")\n",
"box1.inspect(style=\"graph\")\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We have now found a first box that explains over 75% of the cases of interest. Let's see if we can find a second box that explains the remainder of the cases."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"[MainProcess/INFO] 787 points remaining, containing 21 cases of interest\n",
"[MainProcess/INFO] box does not meet threshold criteria, value is 0.3541666666666667, returning dump box\n"
]
}
],
"source": [
"box2 = prim_alg.find_box()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As we can see, we are unable to find a second box. The best coverage we can achieve is 0.35, which is well below the specified 0.8 threshold. Let's look at the final overal results from interactively fitting PRIM to the data. For this, we can use to convenience functions that transform the stats and boxes to pandas data frames."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"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>coverage</th>\n",
" <th>density</th>\n",
" <th>mass</th>\n",
" <th>res_dim</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>box 1</th>\n",
" <td>0.764045</td>\n",
" <td>0.715789</td>\n",
" <td>0.10771</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>box 2</th>\n",
" <td>0.235955</td>\n",
" <td>0.026684</td>\n",
" <td>0.89229</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" coverage density mass res_dim\n",
"box 1 0.764045 0.715789 0.10771 3\n",
"box 2 0.235955 0.026684 0.89229 0"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"prim_alg.stats_to_dataframe()"
]
},
{
"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 tr th {\n",
" text-align: left;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr>\n",
" <th></th>\n",
" <th colspan=\"2\" halign=\"left\">box 1</th>\n",
" <th colspan=\"2\" halign=\"left\">box 2</th>\n",
" </tr>\n",
" <tr>\n",
" <th></th>\n",
" <th>min</th>\n",
" <th>max</th>\n",
" <th>min</th>\n",
" <th>max</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Demand elasticity</th>\n",
" <td>-0.422000</td>\n",
" <td>-0.202000</td>\n",
" <td>-0.8</td>\n",
" <td>-0.202000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Biomass backstop price</th>\n",
" <td>150.049995</td>\n",
" <td>199.600006</td>\n",
" <td>90.0</td>\n",
" <td>199.600006</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Total biomass</th>\n",
" <td>450.000000</td>\n",
" <td>755.799988</td>\n",
" <td>450.0</td>\n",
" <td>997.799988</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" box 1 box 2 \n",
" min max min max\n",
"Demand elasticity -0.422000 -0.202000 -0.8 -0.202000\n",
"Biomass backstop price 150.049995 199.600006 90.0 199.600006\n",
"Total biomass 450.000000 755.799988 450.0 997.799988"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"prim_alg.boxes_to_dataframe()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# CART"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The way of interacting with CART is quite similar to how we setup the prim analysis. We import cart from the analysis package. We instantiate the algorithm, and next fit CART to the data. This is done via the `build_tree` method."
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/jhkwakkel/miniconda3/lib/python3.6/importlib/_bootstrap.py:219: ImportWarning: can't resolve package from __spec__ or __package__, falling back on __name__ and __path__\n",
" return f(*args, **kwds)\n",
"/Users/jhkwakkel/miniconda3/lib/python3.6/importlib/_bootstrap.py:219: ImportWarning: can't resolve package from __spec__ or __package__, falling back on __name__ and __path__\n",
" return f(*args, **kwds)\n",
"/Users/jhkwakkel/miniconda3/lib/python3.6/importlib/_bootstrap.py:219: ImportWarning: can't resolve package from __spec__ or __package__, falling back on __name__ and __path__\n",
" return f(*args, **kwds)\n"
]
}
],
"source": [
"from ema_workbench.analysis import cart\n",
"\n",
"cart_alg = cart.CART(x, y, 0.05)\n",
"cart_alg.build_tree()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now that we have trained CART on the data, we can investigate its results. Just like PRIM, we can use `stats_to_dataframe` and `boxes_to_dataframe` to get an overview. "
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
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" vertical-align: middle;\n",
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"\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>coverage</th>\n",
" <th>density</th>\n",
" <th>mass</th>\n",
" <th>res dim</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>box 1</th>\n",
" <td>0.011236</td>\n",
" <td>0.021739</td>\n",
" <td>0.052154</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>box 2</th>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.546485</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>box 3</th>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.103175</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>box 4</th>\n",
" <td>0.044944</td>\n",
" <td>0.090909</td>\n",
" <td>0.049887</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>box 5</th>\n",
" <td>0.224719</td>\n",
" <td>0.434783</td>\n",
" <td>0.052154</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>box 6</th>\n",
" <td>0.112360</td>\n",
" <td>0.227273</td>\n",
" <td>0.049887</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>box 7</th>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.051020</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>box 8</th>\n",
" <td>0.606742</td>\n",
" <td>0.642857</td>\n",
" <td>0.095238</td>\n",
" <td>2</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" coverage density mass res dim\n",
"box 1 0.011236 0.021739 0.052154 2\n",
"box 2 0.000000 0.000000 0.546485 2\n",
"box 3 0.000000 0.000000 0.103175 2\n",
"box 4 0.044944 0.090909 0.049887 2\n",
"box 5 0.224719 0.434783 0.052154 2\n",
"box 6 0.112360 0.227273 0.049887 3\n",
"box 7 0.000000 0.000000 0.051020 3\n",
"box 8 0.606742 0.642857 0.095238 2"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cart_alg.stats_to_dataframe()"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
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"\n",
" .dataframe thead tr th {\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr>\n",
" <th></th>\n",
" <th colspan=\"2\" halign=\"left\">box 1</th>\n",
" <th colspan=\"2\" halign=\"left\">box 2</th>\n",
" <th colspan=\"2\" halign=\"left\">box 3</th>\n",
" <th colspan=\"2\" halign=\"left\">box 4</th>\n",
" <th colspan=\"2\" halign=\"left\">box 5</th>\n",
" <th colspan=\"2\" halign=\"left\">box 6</th>\n",
" <th colspan=\"2\" halign=\"left\">box 7</th>\n",
" <th colspan=\"2\" halign=\"left\">box 8</th>\n",
" </tr>\n",
" <tr>\n",
" <th></th>\n",
" <th>min</th>\n",
" <th>max</th>\n",
" <th>min</th>\n",
" <th>max</th>\n",
" <th>min</th>\n",
" <th>max</th>\n",
" <th>min</th>\n",
" <th>max</th>\n",
" <th>min</th>\n",
" <th>max</th>\n",
" <th>min</th>\n",
" <th>max</th>\n",
" <th>min</th>\n",
" <th>max</th>\n",
" <th>min</th>\n",
" <th>max</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Cellulosic yield</th>\n",
" <td>80.0</td>\n",
" <td>81.649998</td>\n",
" <td>81.649998</td>\n",
" <td>99.900002</td>\n",
" <td>80.000</td>\n",
" <td>99.900002</td>\n",
" <td>80.000000</td>\n",
" <td>99.900002</td>\n",
" <td>80.000</td>\n",
" <td>99.900002</td>\n",
" <td>80.0000</td>\n",
" <td>89.049999</td>\n",
" <td>89.049999</td>\n",
" <td>99.900002</td>\n",
" <td>80.000000</td>\n",
" <td>99.900002</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Demand elasticity</th>\n",
" <td>-0.8</td>\n",
" <td>-0.439000</td>\n",
" <td>-0.800000</td>\n",
" <td>-0.439000</td>\n",
" <td>-0.439</td>\n",
" <td>-0.316500</td>\n",
" <td>-0.439000</td>\n",
" <td>-0.316500</td>\n",
" <td>-0.439</td>\n",
" <td>-0.316500</td>\n",
" <td>-0.3165</td>\n",
" <td>-0.202000</td>\n",
" <td>-0.316500</td>\n",
" <td>-0.202000</td>\n",
" <td>-0.316500</td>\n",
" <td>-0.202000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Biomass backstop price</th>\n",
" <td>90.0</td>\n",
" <td>199.600006</td>\n",
" <td>90.000000</td>\n",
" <td>199.600006</td>\n",
" <td>90.000</td>\n",
" <td>144.350006</td>\n",
" <td>144.350006</td>\n",
" <td>170.750000</td>\n",
" <td>170.750</td>\n",
" <td>199.600006</td>\n",
" <td>90.0000</td>\n",
" <td>148.300003</td>\n",
" <td>90.000000</td>\n",
" <td>148.300003</td>\n",
" <td>148.300003</td>\n",
" <td>199.600006</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" box 1 box 2 box 3 \\\n",
" min max min max min \n",
"Cellulosic yield 80.0 81.649998 81.649998 99.900002 80.000 \n",
"Demand elasticity -0.8 -0.439000 -0.800000 -0.439000 -0.439 \n",
"Biomass backstop price 90.0 199.600006 90.000000 199.600006 90.000 \n",
"\n",
" box 4 box 5 \\\n",
" max min max min \n",
"Cellulosic yield 99.900002 80.000000 99.900002 80.000 \n",
"Demand elasticity -0.316500 -0.439000 -0.316500 -0.439 \n",
"Biomass backstop price 144.350006 144.350006 170.750000 170.750 \n",
"\n",
" box 6 box 7 \\\n",
" max min max min \n",
"Cellulosic yield 99.900002 80.0000 89.049999 89.049999 \n",
"Demand elasticity -0.316500 -0.3165 -0.202000 -0.316500 \n",
"Biomass backstop price 199.600006 90.0000 148.300003 90.000000 \n",
"\n",
" box 8 \n",
" max min max \n",
"Cellulosic yield 99.900002 80.000000 99.900002 \n",
"Demand elasticity -0.202000 -0.316500 -0.202000 \n",
"Biomass backstop price 148.300003 148.300003 199.600006 "
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cart_alg.boxes_to_dataframe()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Alternatively, we might want to look at the classification tree directly. For this, we can use the `show_tree` method. "
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1296x864 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig = cart_alg.show_tree()\n",
"fig.set_size_inches((18, 12))\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.7"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| bsd-3-clause |
metpy/MetPy | v0.11/_downloads/5f6dfc4b913dc349eba9f04f6161b5f1/GINI_Water_Vapor.ipynb | 1 | 3247 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\nGINI Water Vapor Imagery\n========================\n\nUse MetPy's support for GINI files to read in a water vapor satellite image and plot the\ndata using CartoPy.\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import cartopy.feature as cfeature\nimport matplotlib.pyplot as plt\nimport xarray as xr\n\nfrom metpy.cbook import get_test_data\nfrom metpy.io import GiniFile\nfrom metpy.plots import add_metpy_logo, add_timestamp, colortables"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Open the GINI file from the test data\nf = GiniFile(get_test_data('WEST-CONUS_4km_WV_20151208_2200.gini'))\nprint(f)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Get a Dataset view of the data (essentially a NetCDF-like interface to the\nunderlying data). Pull out the data and (x, y) coordinates. We use `metpy.parse_cf` to\nhandle parsing some netCDF Climate and Forecasting (CF) metadata to simplify working with\nprojections.\n\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"ds = xr.open_dataset(f)\nx = ds.variables['x'][:]\ny = ds.variables['y'][:]\ndat = ds.metpy.parse_cf('WV')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Plot the image. We use MetPy's xarray/cartopy integration to automatically handle parsing\nthe projection information.\n\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"fig = plt.figure(figsize=(10, 12))\nadd_metpy_logo(fig, 125, 145)\nax = fig.add_subplot(1, 1, 1, projection=dat.metpy.cartopy_crs)\nwv_norm, wv_cmap = colortables.get_with_range('WVCIMSS', 100, 260)\nwv_cmap.set_under('k')\nim = ax.imshow(dat[:], cmap=wv_cmap, norm=wv_norm,\n extent=(x.min(), x.max(), y.min(), y.max()), origin='upper')\nax.add_feature(cfeature.COASTLINE.with_scale('50m'))\nadd_timestamp(ax, f.prod_desc.datetime, y=0.02, high_contrast=True)\n\nplt.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": "3.7.1"
}
},
"nbformat": 4,
"nbformat_minor": 0
} | bsd-3-clause |
avinisilva/machine-learning | ipynbs/Random Forest Regression.ipynb | 2 | 692 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Random Forest Regression"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.1"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| mit |
ekostat/ekostat_calculator | .ipynb_checkpoints/lv_notebook_workspace-checkpoint.ipynb | 1 | 53307 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"'D:\\\\Utveckling\\\\GitHub\\\\ekostat_calculator'"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Reload when code changed:\n",
"%load_ext autoreload\n",
"%autoreload 2\n",
"%pwd"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"'0.19.2'"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import os \n",
"import core\n",
"import importlib\n",
"importlib.reload(core) \n",
"import pandas as pd\n",
"pd.__version__"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Load directories"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"root_directory = os.getcwd()\n",
"workspace_directory = root_directory + '/workspaces' \n",
"resource_directory = root_directory + '/resources'"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# LOAD WORKSPACES"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Load default workspace"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"====================================================================================================\n",
"Initiating WorkSpace: D:/Utveckling/GitHub/ekostat_calculator/workspaces/default\n",
"----------------------------------------------------------------------------------------------------\n",
"Initiating Subset: D:/Utveckling/GitHub/ekostat_calculator/workspaces/default/subsets/A\n",
"step_list ['step_1']\n",
"Initiating WorkStep: D:/Utveckling/GitHub/ekostat_calculator/workspaces/default/subsets/A/step_1\n",
"Initiating WorkStep: D:/Utveckling/GitHub/ekostat_calculator/workspaces/default/step_0\n"
]
}
],
"source": [
"\n",
"\n",
"default_workspace = core.WorkSpace(name='default', \n",
" parent_directory=workspace_directory, \n",
" resource_directory=resource_directory) "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Add new workspace"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"====================================================================================================\n",
"Initiating WorkSpace: D:/Utveckling/GitHub/ekostat_calculator/workspaces/lv\n",
"----------------------------------------------------------------------------------------------------\n",
"Initiating Subset: D:/Utveckling/GitHub/ekostat_calculator/workspaces/lv/subsets/A\n",
"step_list []\n",
"Initiating WorkStep: D:/Utveckling/GitHub/ekostat_calculator/workspaces/lv/step_0\n"
]
}
],
"source": [
"lv_workspace = core.WorkSpace(name='lv', \n",
" parent_directory=workspace_directory, \n",
" resource_directory=resource_directory) "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Copy files from default workspace to make a clone"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Initiating WorkStep: D:/Utveckling/GitHub/ekostat_calculator/workspaces/lv/step_0\n",
"Given subset is already present!\n",
"step: step_1\n",
"Initiating WorkStep: D:/Utveckling/GitHub/ekostat_calculator/workspaces/lv/subsets/A/step_1\n"
]
}
],
"source": [
"lv_workspace.add_files_from_workspace(default_workspace, overwrite=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Load all data in lv_workspace"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Continuum\\Anaconda3\\lib\\site-packages\\IPython\\core\\interactiveshell.py:2881: DtypeWarning: Columns (8,34,35,37,42,44,45,49,51,52,54,55,56) have mixed types. Specify dtype option on import or set low_memory=False.\n",
" exec(code_obj, self.user_global_ns, self.user_ns)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Sorting..\n",
"Reseting and Droping INDEX\n",
"Saving data to: D:/Utveckling/GitHub/ekostat_calculator/workspaces/lv/input_data/exports/Column_format_PhysicalChemical_data.txt\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Continuum\\Anaconda3\\lib\\site-packages\\IPython\\core\\interactiveshell.py:2881: DtypeWarning: Columns (13,37,41,43,50,53,59,79,81,82,83) have mixed types. Specify dtype option on import or set low_memory=False.\n",
" exec(code_obj, self.user_global_ns, self.user_ns)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Saving data to: D:/Utveckling/GitHub/ekostat_calculator/workspaces/lv/input_data/exports/Column_format_Zoobenthos_data.txt\n",
"Saving data to: D:/Utveckling/GitHub/ekostat_calculator/workspaces/lv/input_data/exports/all_data.txt\n"
]
}
],
"source": [
"lv_workspace.load_all_data()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Set first filter and load filtered data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Set first data filter "
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Type Areas in dataset:\n",
"04 - Västkustens yttre kustvatten. Kattegatt\n",
"01s - Västkustens inre kustvatten\n",
"03 - Västkustens yttre kustvatten. Skagerrak\n",
"01n - Västkustens inre kustvatten\n",
"02 - Västkustens fjordar\n",
"25 - Göta älvs- och Nordre älvs estuarie\n",
"\n",
"21 - Norra Kvarkens yttre kustvatten\n",
"20 - Norra Kvarkens inre kustvatten\n",
"23 - Norra Bottenviken. Yttre kustvatten\n",
"22 - Norra Bottenviken. Inre kustvatten\n",
"05 - Södra Hallands och norra Öresunds kustvatten\n",
"12n - Östergötlands och Stockholms skärgård. Mellankustvatten\n",
"06 - Öresunds kustvatten\n",
"18 - Norra Bottenhavet. Höga kusten. Inre kustvatten\n",
"17 - Södra Bottenhavet. Yttre kustvatten\n",
"15 - Stockholms skärgård. Yttre kustvatten\n",
"16 - Södra Bottenhavet. Inre kustvatten\n",
"14 - Östergötlands yttre kustvatten\n",
"19 - Norra Bottenhavet. Höga kusten. Yttre kustvatten\n",
"10 - Ölands och Gotlands kustvatten\n",
"07 - Skånes kustvatten\n",
"11 - Gotlands nordvästra kustvatten\n"
]
}
],
"source": [
"# show available waterbodies\n",
"workspace_data = lv_workspace.data_handler.get_all_column_data_df()\n",
"lst = workspace_data.WATER_TYPE_AREA.unique()\n",
"print('Type Areas in dataset:\\n{}'.format('\\n'.join(lst)))"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Waterbodies in dataset:\n",
"Onsala kustvatten\n",
"Dana fjord\n",
"Marstrandsfjorden\n",
"Älgöfjorden\n",
"Yttre Brofjorden\n",
"Kungshamn s skärgård\n",
"S Kosterfjorden\n",
"Gullmarn centralbassäng\n",
"Koljö fjord\n",
"Havstensfjorden\n",
"Byfjorden\n",
"Halsefjorden\n",
"Rivö fjord\n",
"\n",
"Del av n Kattegatts utsjövatten\n",
"Del av Skagerraks utsjövatten\n",
"N m Hallands kustvatten\n",
"Del av s Kattegatts utsjövatten\n",
"Färlevfjorden\n",
"Kalvöfjorden\n",
"Stigfjorden\n",
"Askeröfjorden\n",
"Ellösefjorden\n",
"Snäckedjupet\n",
"Brofjorden\n",
"Fjällbacka inre skärgård\n",
"Hunnebostrand skärgård\n",
"Bottnefjorden\n",
"Trälebergskile\n",
"Åbyfjorden\n",
"Göteborgs n n skärgårds kustvatten\n",
"S n Kvarkens kustvatten\n",
"N n Kvarkens kustvatten\n",
"Saltkällefjorden\n",
"Yttre Täftefjärden\n",
"Täftefjärden\n",
"Skelleftebukten\n",
"Simpan\n",
"Singlefjorden\n",
"Lindöfjorden sek namn\n",
"Norrbottens skärgårds kustvatten\n",
"Kinnbäcksfjärden\n",
"Strömstadsfjorden\n",
"Sörbrändöfjärden\n",
"Laholmsbukten\n",
"Skälderviken\n",
"Gussöfjärden\n",
"Rånefjärden\n",
"Bodöfjärden\n",
"Dragviksfjärden\n",
"Seskaröfjärden\n",
"Kråkfjärden\n",
"Helsingborgsområdet\n",
"Gaviksfjärden\n",
"Öregrunds kustvatten\n",
"Björkskärsfjärden\n",
"Laholmsbuktens kustvatten\n",
"Balgöarkipelagen\n",
"Klosterfjorden\n",
"S m Hallands kustvatten\n",
"Kasfjärden sek namn\n",
"Ängsfjärden sek namn\n",
"Inre Kungsbackafjorden\n",
"Yttre Kungsbackafjorden\n",
"Långvindsfjärden\n",
"Varren\n",
"Vändelsöarkipelagen\n",
"Skärsåfjärden sek namn\n",
"Krabbfjärden\n",
"N Höga kustens kustvatten\n",
"Kräklingeområdet\n",
"Kyrkefjälls sund\n",
"Risö-Säröarkipelagen\n",
"Öckerösund\n",
"Mysingen\n",
"M Bohusläns skärgårds kustvatten\n",
"Ö Gotlands n kustvatten\n",
"V Hanöbuktens kustvatten\n",
"M n Bohusläns skärgårds kustvatten\n",
"Örefjärden\n",
"n Långebyområdet\n",
"Ö Gotlands s kustvatten\n",
"Sotefjorden\n",
"Fjällbacka yttre skärgård\n",
"Hake fjord\n",
"Göteborgs n skärgårds kustvatten\n",
"Göteborgs s skärgårds kustvatten\n",
"V Gotlands m kustvatten\n",
"Askims fjord\n",
"Klintehamnsviken sek namn\n",
"Bråvikens kustvatten\n"
]
}
],
"source": [
"lst = workspace_data.SEA_AREA_NAME.unique()\n",
"print('Waterbodies in dataset:\\n{}'.format('\\n'.join(lst)))"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"sdfs\n",
"dict_keys(['SEA_AREA_NAME', 'STATN', 'WATER_TYPE_AREA'])\n",
"Save: \"SEA_AREA_NAME\" to file: \"D:/github/ekostat_calculator/workspaces/lv/step_0/data_filters/list_exclude_sea_area_name.fil\"\n",
"Save: \"STATN\" to file: \"D:/github/ekostat_calculator/workspaces/lv/step_0/data_filters/list_exclude_statn.fil\"\n",
"Save: \"WATER_TYPE_AREA\" to file: \"D:/github/ekostat_calculator/workspaces/lv/step_0/data_filters/list_exclude_water_type_area.fil\"\n",
"dict_keys(['MYEAR', 'SEA_AREA_NAME', 'STATN', 'WATER_TYPE_AREA'])\n",
"Save: \"MYEAR\" to file: \"D:/github/ekostat_calculator/workspaces/lv/step_0/data_filters/list_include_myear.fil\"\n",
"2015\n",
"2016\n",
"Save: \"SEA_AREA_NAME\" to file: \"D:/github/ekostat_calculator/workspaces/lv/step_0/data_filters/list_include_sea_area_name.fil\"\n",
"Byfjorden\n",
"Gullmarn centralbassäng\n",
"Havstensfjorden\n",
"Rivö fjord\n",
"Save: \"STATN\" to file: \"D:/github/ekostat_calculator/workspaces/lv/step_0/data_filters/list_include_statn.fil\"\n",
"SK36\n",
"Save: \"WATER_TYPE_AREA\" to file: \"D:/github/ekostat_calculator/workspaces/lv/step_0/data_filters/list_include_water_type_area.fil\"\n",
"sdfs\n",
"dict_keys(['SEA_AREA_NAME', 'STATN', 'WATER_TYPE_AREA'])\n",
"Save: \"SEA_AREA_NAME\" to file: \"D:/github/ekostat_calculator/workspaces/lv/step_0/data_filters/list_exclude_sea_area_name.fil\"\n",
"Save: \"STATN\" to file: \"D:/github/ekostat_calculator/workspaces/lv/step_0/data_filters/list_exclude_statn.fil\"\n",
"Save: \"WATER_TYPE_AREA\" to file: \"D:/github/ekostat_calculator/workspaces/lv/step_0/data_filters/list_exclude_water_type_area.fil\"\n",
"dict_keys(['MYEAR', 'SEA_AREA_NAME', 'STATN', 'WATER_TYPE_AREA'])\n",
"Save: \"MYEAR\" to file: \"D:/github/ekostat_calculator/workspaces/lv/step_0/data_filters/list_include_myear.fil\"\n",
"2015\n",
"2016\n",
"Save: \"SEA_AREA_NAME\" to file: \"D:/github/ekostat_calculator/workspaces/lv/step_0/data_filters/list_include_sea_area_name.fil\"\n",
"Byfjorden\n",
"Gullmarn centralbassäng\n",
"Havstensfjorden\n",
"Rivö fjord\n",
"Save: \"STATN\" to file: \"D:/github/ekostat_calculator/workspaces/lv/step_0/data_filters/list_include_statn.fil\"\n",
"Save: \"WATER_TYPE_AREA\" to file: \"D:/github/ekostat_calculator/workspaces/lv/step_0/data_filters/list_include_water_type_area.fil\"\n",
"sdfs\n",
"dict_keys(['SEA_AREA_NAME', 'STATN', 'WATER_TYPE_AREA'])\n",
"Save: \"SEA_AREA_NAME\" to file: \"D:/github/ekostat_calculator/workspaces/lv/step_0/data_filters/list_exclude_sea_area_name.fil\"\n",
"Save: \"STATN\" to file: \"D:/github/ekostat_calculator/workspaces/lv/step_0/data_filters/list_exclude_statn.fil\"\n",
"Save: \"WATER_TYPE_AREA\" to file: \"D:/github/ekostat_calculator/workspaces/lv/step_0/data_filters/list_exclude_water_type_area.fil\"\n",
"dict_keys(['MYEAR', 'SEA_AREA_NAME', 'STATN', 'WATER_TYPE_AREA'])\n",
"Save: \"MYEAR\" to file: \"D:/github/ekostat_calculator/workspaces/lv/step_0/data_filters/list_include_myear.fil\"\n",
"2015\n",
"2016\n",
"Save: \"SEA_AREA_NAME\" to file: \"D:/github/ekostat_calculator/workspaces/lv/step_0/data_filters/list_include_sea_area_name.fil\"\n",
"Byfjorden\n",
"Gullmarn centralbassäng\n",
"Havstensfjorden\n",
"Rivö fjord\n",
"Save: \"STATN\" to file: \"D:/github/ekostat_calculator/workspaces/lv/step_0/data_filters/list_include_statn.fil\"\n",
"Save: \"WATER_TYPE_AREA\" to file: \"D:/github/ekostat_calculator/workspaces/lv/step_0/data_filters/list_include_water_type_area.fil\"\n",
"sdfs\n",
"dict_keys(['SEA_AREA_NAME', 'STATN', 'WATER_TYPE_AREA'])\n",
"Save: \"SEA_AREA_NAME\" to file: \"D:/github/ekostat_calculator/workspaces/lv/step_0/data_filters/list_exclude_sea_area_name.fil\"\n",
"Save: \"STATN\" to file: \"D:/github/ekostat_calculator/workspaces/lv/step_0/data_filters/list_exclude_statn.fil\"\n",
"Save: \"WATER_TYPE_AREA\" to file: \"D:/github/ekostat_calculator/workspaces/lv/step_0/data_filters/list_exclude_water_type_area.fil\"\n",
"dict_keys(['MYEAR', 'SEA_AREA_NAME', 'STATN', 'WATER_TYPE_AREA'])\n",
"Save: \"MYEAR\" to file: \"D:/github/ekostat_calculator/workspaces/lv/step_0/data_filters/list_include_myear.fil\"\n",
"2015\n",
"2017\n",
"Save: \"SEA_AREA_NAME\" to file: \"D:/github/ekostat_calculator/workspaces/lv/step_0/data_filters/list_include_sea_area_name.fil\"\n",
"Byfjorden\n",
"Gullmarn centralbassäng\n",
"Havstensfjorden\n",
"Rivö fjord\n",
"Save: \"STATN\" to file: \"D:/github/ekostat_calculator/workspaces/lv/step_0/data_filters/list_include_statn.fil\"\n",
"Save: \"WATER_TYPE_AREA\" to file: \"D:/github/ekostat_calculator/workspaces/lv/step_0/data_filters/list_include_water_type_area.fil\"\n"
]
}
],
"source": [
"include_WB = ['Gullmarn centralbassäng', 'Rivö fjord', 'Byfjorden', 'Havstensfjorden']\n",
"include_stations = [] \n",
"exclude_stations = []\n",
"include_years = ['2015', '2017'] \n",
"\n",
"lv_workspace.set_data_filter(step=0, filter_type='include_list', filter_name='SEA_AREA_NAME', data=include_WB)\n",
"lv_workspace.set_data_filter(step=0, filter_type='include_list', filter_name='STATN', data=include_stations) \n",
"lv_workspace.set_data_filter(step=0, filter_type='exclude_list', filter_name='STATN', data=exclude_stations) \n",
"lv_workspace.set_data_filter(step=0, filter_type='include_list', filter_name='MYEAR', data=include_years) "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Apply first data filter "
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"lv_workspace.apply_first_filter() # This sets the first level of data filter in the IndexHandler "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Extract filtered data "
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1055 rows mathing the filter criteria\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>AMON</th>\n",
" <th>CPHL</th>\n",
" <th>DEPH</th>\n",
" <th>DOXY_BTL</th>\n",
" <th>DOXY_CTD</th>\n",
" <th>LATIT_DD</th>\n",
" <th>LONGI_DD</th>\n",
" <th>MNDEP</th>\n",
" <th>MXDEP</th>\n",
" <th>MYEAR</th>\n",
" <th>...</th>\n",
" <th>SERNO</th>\n",
" <th>SHARKID_MD5</th>\n",
" <th>SHIPC</th>\n",
" <th>STATN</th>\n",
" <th>STIME</th>\n",
" <th>TEMP_BTL</th>\n",
" <th>TEMP_CTD</th>\n",
" <th>WATER_DISTRICT</th>\n",
" <th>WATER_TYPE_AREA</th>\n",
" <th>WLTYP</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>55</th>\n",
" <td>1.14</td>\n",
" <td>0.8</td>\n",
" <td>0.5</td>\n",
" <td>7.0</td>\n",
" <td></td>\n",
" <td>58.39333</td>\n",
" <td>11.62667</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2015</td>\n",
" <td>...</td>\n",
" <td>8.0</td>\n",
" <td>NaN</td>\n",
" <td>7719</td>\n",
" <td>BJÖRKHOLMEN</td>\n",
" <td>08:45</td>\n",
" <td></td>\n",
" <td>4.6</td>\n",
" <td>Västerhavets vattendistrikt</td>\n",
" <td>02 - Västkustens fjordar</td>\n",
" <td>2 - Havsområde innanför 1 NM</td>\n",
" </tr>\n",
" <tr>\n",
" <th>56</th>\n",
" <td>1.21</td>\n",
" <td>1.0</td>\n",
" <td>2.0</td>\n",
" <td>7.3</td>\n",
" <td></td>\n",
" <td>58.39333</td>\n",
" <td>11.62667</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2015</td>\n",
" <td>...</td>\n",
" <td>8.0</td>\n",
" <td>NaN</td>\n",
" <td>7719</td>\n",
" <td>BJÖRKHOLMEN</td>\n",
" <td>08:45</td>\n",
" <td></td>\n",
" <td>4.6</td>\n",
" <td>Västerhavets vattendistrikt</td>\n",
" <td>02 - Västkustens fjordar</td>\n",
" <td>2 - Havsområde innanför 1 NM</td>\n",
" </tr>\n",
" <tr>\n",
" <th>57</th>\n",
" <td>1.07</td>\n",
" <td>0.75</td>\n",
" <td>5.0</td>\n",
" <td>7.6</td>\n",
" <td></td>\n",
" <td>58.39333</td>\n",
" <td>11.62667</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2015</td>\n",
" <td>...</td>\n",
" <td>8.0</td>\n",
" <td>NaN</td>\n",
" <td>7719</td>\n",
" <td>BJÖRKHOLMEN</td>\n",
" <td>08:45</td>\n",
" <td></td>\n",
" <td>4.9</td>\n",
" <td>Västerhavets vattendistrikt</td>\n",
" <td>02 - Västkustens fjordar</td>\n",
" <td>2 - Havsområde innanför 1 NM</td>\n",
" </tr>\n",
" <tr>\n",
" <th>58</th>\n",
" <td>0.93</td>\n",
" <td>0.91</td>\n",
" <td>10.0</td>\n",
" <td>6.7</td>\n",
" <td></td>\n",
" <td>58.39333</td>\n",
" <td>11.62667</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2015</td>\n",
" <td>...</td>\n",
" <td>8.0</td>\n",
" <td>NaN</td>\n",
" <td>7719</td>\n",
" <td>BJÖRKHOLMEN</td>\n",
" <td>08:45</td>\n",
" <td></td>\n",
" <td>5.5</td>\n",
" <td>Västerhavets vattendistrikt</td>\n",
" <td>02 - Västkustens fjordar</td>\n",
" <td>2 - Havsområde innanför 1 NM</td>\n",
" </tr>\n",
" <tr>\n",
" <th>59</th>\n",
" <td>0.47</td>\n",
" <td>0.27</td>\n",
" <td>15.0</td>\n",
" <td>6.4</td>\n",
" <td></td>\n",
" <td>58.39333</td>\n",
" <td>11.62667</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2015</td>\n",
" <td>...</td>\n",
" <td>8.0</td>\n",
" <td>NaN</td>\n",
" <td>7719</td>\n",
" <td>BJÖRKHOLMEN</td>\n",
" <td>08:45</td>\n",
" <td></td>\n",
" <td>7.0</td>\n",
" <td>Västerhavets vattendistrikt</td>\n",
" <td>02 - Västkustens fjordar</td>\n",
" <td>2 - Havsområde innanför 1 NM</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 44 columns</p>\n",
"</div>"
],
"text/plain": [
" AMON CPHL DEPH DOXY_BTL DOXY_CTD LATIT_DD LONGI_DD MNDEP MXDEP MYEAR \\\n",
"55 1.14 0.8 0.5 7.0 58.39333 11.62667 NaN NaN 2015 \n",
"56 1.21 1.0 2.0 7.3 58.39333 11.62667 NaN NaN 2015 \n",
"57 1.07 0.75 5.0 7.6 58.39333 11.62667 NaN NaN 2015 \n",
"58 0.93 0.91 10.0 6.7 58.39333 11.62667 NaN NaN 2015 \n",
"59 0.47 0.27 15.0 6.4 58.39333 11.62667 NaN NaN 2015 \n",
"\n",
" ... SERNO SHARKID_MD5 SHIPC STATN STIME \\\n",
"55 ... 8.0 NaN 7719 BJÖRKHOLMEN 08:45 \n",
"56 ... 8.0 NaN 7719 BJÖRKHOLMEN 08:45 \n",
"57 ... 8.0 NaN 7719 BJÖRKHOLMEN 08:45 \n",
"58 ... 8.0 NaN 7719 BJÖRKHOLMEN 08:45 \n",
"59 ... 8.0 NaN 7719 BJÖRKHOLMEN 08:45 \n",
"\n",
" TEMP_BTL TEMP_CTD WATER_DISTRICT WATER_TYPE_AREA \\\n",
"55 4.6 Västerhavets vattendistrikt 02 - Västkustens fjordar \n",
"56 4.6 Västerhavets vattendistrikt 02 - Västkustens fjordar \n",
"57 4.9 Västerhavets vattendistrikt 02 - Västkustens fjordar \n",
"58 5.5 Västerhavets vattendistrikt 02 - Västkustens fjordar \n",
"59 7.0 Västerhavets vattendistrikt 02 - Västkustens fjordar \n",
"\n",
" WLTYP \n",
"55 2 - Havsområde innanför 1 NM \n",
"56 2 - Havsområde innanför 1 NM \n",
"57 2 - Havsområde innanför 1 NM \n",
"58 2 - Havsområde innanför 1 NM \n",
"59 2 - Havsområde innanför 1 NM \n",
"\n",
"[5 rows x 44 columns]"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data_after_first_filter = lv_workspace.get_filtered_data(level=0) # level=0 means first filter \n",
"print('{} rows mathing the filter criteria'.format(len(data_after_first_filter)))\n",
"data_after_first_filter.head()\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Set subset filter and load subset data "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Set subset filter "
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"sdfs\n",
"dict_keys(['SEA_AREA_NAME', 'STATN', 'WATER_TYPE_AREA'])\n",
"Save: \"SEA_AREA_NAME\" to file: \"D:/github/ekostat_calculator/workspaces/lv/subsets/A/step_1/data_filters/list_exclude_sea_area_name.fil\"\n",
"Save: \"STATN\" to file: \"D:/github/ekostat_calculator/workspaces/lv/subsets/A/step_1/data_filters/list_exclude_statn.fil\"\n",
"Save: \"WATER_TYPE_AREA\" to file: \"D:/github/ekostat_calculator/workspaces/lv/subsets/A/step_1/data_filters/list_exclude_water_type_area.fil\"\n",
"dict_keys(['MYEAR', 'SEA_AREA_NAME', 'STATN', 'WATER_TYPE_AREA'])\n",
"Save: \"MYEAR\" to file: \"D:/github/ekostat_calculator/workspaces/lv/subsets/A/step_1/data_filters/list_include_myear.fil\"\n",
"2015\n",
"2016\n",
"Save: \"SEA_AREA_NAME\" to file: \"D:/github/ekostat_calculator/workspaces/lv/subsets/A/step_1/data_filters/list_include_sea_area_name.fil\"\n",
"Gullmarn centralbassäng\n",
"Rivö fjord\n",
"Save: \"STATN\" to file: \"D:/github/ekostat_calculator/workspaces/lv/subsets/A/step_1/data_filters/list_include_statn.fil\"\n",
"BJÖRKHOLMEN\n",
"Save: \"WATER_TYPE_AREA\" to file: \"D:/github/ekostat_calculator/workspaces/lv/subsets/A/step_1/data_filters/list_include_water_type_area.fil\"\n",
"sdfs\n",
"dict_keys(['SEA_AREA_NAME', 'STATN', 'WATER_TYPE_AREA'])\n",
"Save: \"SEA_AREA_NAME\" to file: \"D:/github/ekostat_calculator/workspaces/lv/subsets/A/step_1/data_filters/list_exclude_sea_area_name.fil\"\n",
"Save: \"STATN\" to file: \"D:/github/ekostat_calculator/workspaces/lv/subsets/A/step_1/data_filters/list_exclude_statn.fil\"\n",
"Save: \"WATER_TYPE_AREA\" to file: \"D:/github/ekostat_calculator/workspaces/lv/subsets/A/step_1/data_filters/list_exclude_water_type_area.fil\"\n",
"dict_keys(['MYEAR', 'SEA_AREA_NAME', 'STATN', 'WATER_TYPE_AREA'])\n",
"Save: \"MYEAR\" to file: \"D:/github/ekostat_calculator/workspaces/lv/subsets/A/step_1/data_filters/list_include_myear.fil\"\n",
"2015\n",
"2016\n",
"Save: \"SEA_AREA_NAME\" to file: \"D:/github/ekostat_calculator/workspaces/lv/subsets/A/step_1/data_filters/list_include_sea_area_name.fil\"\n",
"Gullmarn centralbassäng\n",
"Rivö fjord\n",
"Save: \"STATN\" to file: \"D:/github/ekostat_calculator/workspaces/lv/subsets/A/step_1/data_filters/list_include_statn.fil\"\n",
"BJÖRKHOLMEN\n",
"Save: \"WATER_TYPE_AREA\" to file: \"D:/github/ekostat_calculator/workspaces/lv/subsets/A/step_1/data_filters/list_include_water_type_area.fil\"\n",
"sdfs\n",
"dict_keys(['SEA_AREA_NAME', 'STATN', 'WATER_TYPE_AREA'])\n",
"Save: \"SEA_AREA_NAME\" to file: \"D:/github/ekostat_calculator/workspaces/lv/subsets/A/step_1/data_filters/list_exclude_sea_area_name.fil\"\n",
"Save: \"STATN\" to file: \"D:/github/ekostat_calculator/workspaces/lv/subsets/A/step_1/data_filters/list_exclude_statn.fil\"\n",
"Save: \"WATER_TYPE_AREA\" to file: \"D:/github/ekostat_calculator/workspaces/lv/subsets/A/step_1/data_filters/list_exclude_water_type_area.fil\"\n",
"dict_keys(['MYEAR', 'SEA_AREA_NAME', 'STATN', 'WATER_TYPE_AREA'])\n",
"Save: \"MYEAR\" to file: \"D:/github/ekostat_calculator/workspaces/lv/subsets/A/step_1/data_filters/list_include_myear.fil\"\n",
"2015\n",
"2016\n",
"Save: \"SEA_AREA_NAME\" to file: \"D:/github/ekostat_calculator/workspaces/lv/subsets/A/step_1/data_filters/list_include_sea_area_name.fil\"\n",
"Gullmarn centralbassäng\n",
"Rivö fjord\n",
"Save: \"STATN\" to file: \"D:/github/ekostat_calculator/workspaces/lv/subsets/A/step_1/data_filters/list_include_statn.fil\"\n",
"BJÖRKHOLMEN\n",
"Save: \"WATER_TYPE_AREA\" to file: \"D:/github/ekostat_calculator/workspaces/lv/subsets/A/step_1/data_filters/list_include_water_type_area.fil\"\n",
"sdfs\n",
"dict_keys(['SEA_AREA_NAME', 'STATN', 'WATER_TYPE_AREA'])\n",
"Save: \"SEA_AREA_NAME\" to file: \"D:/github/ekostat_calculator/workspaces/lv/subsets/A/step_1/data_filters/list_exclude_sea_area_name.fil\"\n",
"Save: \"STATN\" to file: \"D:/github/ekostat_calculator/workspaces/lv/subsets/A/step_1/data_filters/list_exclude_statn.fil\"\n",
"Save: \"WATER_TYPE_AREA\" to file: \"D:/github/ekostat_calculator/workspaces/lv/subsets/A/step_1/data_filters/list_exclude_water_type_area.fil\"\n",
"dict_keys(['MYEAR', 'SEA_AREA_NAME', 'STATN', 'WATER_TYPE_AREA'])\n",
"Save: \"MYEAR\" to file: \"D:/github/ekostat_calculator/workspaces/lv/subsets/A/step_1/data_filters/list_include_myear.fil\"\n",
"2016\n",
"2017\n",
"Save: \"SEA_AREA_NAME\" to file: \"D:/github/ekostat_calculator/workspaces/lv/subsets/A/step_1/data_filters/list_include_sea_area_name.fil\"\n",
"Gullmarn centralbassäng\n",
"Rivö fjord\n",
"Save: \"STATN\" to file: \"D:/github/ekostat_calculator/workspaces/lv/subsets/A/step_1/data_filters/list_include_statn.fil\"\n",
"BJÖRKHOLMEN\n",
"Save: \"WATER_TYPE_AREA\" to file: \"D:/github/ekostat_calculator/workspaces/lv/subsets/A/step_1/data_filters/list_include_water_type_area.fil\"\n"
]
}
],
"source": [
"include_WB = ['Gullmarn centralbassäng', 'Rivö fjord']\n",
"include_stations = ['BJÖRKHOLMEN'] \n",
"# Lägg till något som kan plocka in stationer öven ifrån närliggande WB?\n",
"exclude_stations = ['SLÄGGÖ'] # Example that both include and exclude are possible \n",
"include_years = ['2016', '2017'] \n",
"\n",
"lv_workspace.set_data_filter(step=1, subset='A', filter_type='include_list', filter_name='SEA_AREA_NAME', data=include_WB)\n",
"lv_workspace.set_data_filter(step=1, subset='A', filter_type='include_list', filter_name='STATN', data=include_stations)\n",
"lv_workspace.set_data_filter(step=1, subset='A', filter_type='exclude_list', filter_name='STATN', data=exclude_stations)\n",
"lv_workspace.set_data_filter(step=1, subset='A', filter_type='include_list', filter_name='MYEAR', data=include_years)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Apply subset filter "
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"lv_workspace.apply_subset_filter(subset='A') # Not handled properly by the IndexHandler"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Extract filtered data "
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"198 rows mathing the filter criteria\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>AMON</th>\n",
" <th>CPHL</th>\n",
" <th>DEPH</th>\n",
" <th>DOXY_BTL</th>\n",
" <th>DOXY_CTD</th>\n",
" <th>LATIT_DD</th>\n",
" <th>LONGI_DD</th>\n",
" <th>MNDEP</th>\n",
" <th>MXDEP</th>\n",
" <th>MYEAR</th>\n",
" <th>...</th>\n",
" <th>SERNO</th>\n",
" <th>SHARKID_MD5</th>\n",
" <th>SHIPC</th>\n",
" <th>STATN</th>\n",
" <th>STIME</th>\n",
" <th>TEMP_BTL</th>\n",
" <th>TEMP_CTD</th>\n",
" <th>WATER_DISTRICT</th>\n",
" <th>WATER_TYPE_AREA</th>\n",
" <th>WLTYP</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2629</th>\n",
" <td>0.76</td>\n",
" <td>0.6</td>\n",
" <td>0.0</td>\n",
" <td>7.05</td>\n",
" <td></td>\n",
" <td>58.38767</td>\n",
" <td>11.62667</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2017</td>\n",
" <td>...</td>\n",
" <td>8.0</td>\n",
" <td>NaN</td>\n",
" <td>77SN</td>\n",
" <td>BJÖRKHOLMEN</td>\n",
" <td>17:30</td>\n",
" <td>4.96</td>\n",
" <td>4.84</td>\n",
" <td>Västerhavets vattendistrikt</td>\n",
" <td>02 - Västkustens fjordar</td>\n",
" <td>2 - Havsområde innanför 1 NM</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2630</th>\n",
" <td>0.72</td>\n",
" <td>0.5</td>\n",
" <td>2.0</td>\n",
" <td>7.12</td>\n",
" <td></td>\n",
" <td>58.38767</td>\n",
" <td>11.62667</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2017</td>\n",
" <td>...</td>\n",
" <td>8.0</td>\n",
" <td>NaN</td>\n",
" <td>77SN</td>\n",
" <td>BJÖRKHOLMEN</td>\n",
" <td>17:30</td>\n",
" <td>4.93</td>\n",
" <td>4.84</td>\n",
" <td>Västerhavets vattendistrikt</td>\n",
" <td>02 - Västkustens fjordar</td>\n",
" <td>2 - Havsområde innanför 1 NM</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2631</th>\n",
" <td>0.74</td>\n",
" <td>0.6</td>\n",
" <td>5.0</td>\n",
" <td>7.16</td>\n",
" <td></td>\n",
" <td>58.38767</td>\n",
" <td>11.62667</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2017</td>\n",
" <td>...</td>\n",
" <td>8.0</td>\n",
" <td>NaN</td>\n",
" <td>77SN</td>\n",
" <td>BJÖRKHOLMEN</td>\n",
" <td>17:30</td>\n",
" <td>4.88</td>\n",
" <td>4.84</td>\n",
" <td>Västerhavets vattendistrikt</td>\n",
" <td>02 - Västkustens fjordar</td>\n",
" <td>2 - Havsområde innanför 1 NM</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2632</th>\n",
" <td>0.65</td>\n",
" <td>0.5</td>\n",
" <td>10.0</td>\n",
" <td>7.11</td>\n",
" <td></td>\n",
" <td>58.38767</td>\n",
" <td>11.62667</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2017</td>\n",
" <td>...</td>\n",
" <td>8.0</td>\n",
" <td>NaN</td>\n",
" <td>77SN</td>\n",
" <td>BJÖRKHOLMEN</td>\n",
" <td>17:30</td>\n",
" <td>5.12</td>\n",
" <td>4.86</td>\n",
" <td>Västerhavets vattendistrikt</td>\n",
" <td>02 - Västkustens fjordar</td>\n",
" <td>2 - Havsområde innanför 1 NM</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2633</th>\n",
" <td>0.46</td>\n",
" <td>0.3</td>\n",
" <td>15.0</td>\n",
" <td>6.86</td>\n",
" <td></td>\n",
" <td>58.38767</td>\n",
" <td>11.62667</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2017</td>\n",
" <td>...</td>\n",
" <td>8.0</td>\n",
" <td>NaN</td>\n",
" <td>77SN</td>\n",
" <td>BJÖRKHOLMEN</td>\n",
" <td>17:30</td>\n",
" <td>5.52</td>\n",
" <td>5.1</td>\n",
" <td>Västerhavets vattendistrikt</td>\n",
" <td>02 - Västkustens fjordar</td>\n",
" <td>2 - Havsområde innanför 1 NM</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 44 columns</p>\n",
"</div>"
],
"text/plain": [
" AMON CPHL DEPH DOXY_BTL DOXY_CTD LATIT_DD LONGI_DD MNDEP MXDEP \\\n",
"2629 0.76 0.6 0.0 7.05 58.38767 11.62667 NaN NaN \n",
"2630 0.72 0.5 2.0 7.12 58.38767 11.62667 NaN NaN \n",
"2631 0.74 0.6 5.0 7.16 58.38767 11.62667 NaN NaN \n",
"2632 0.65 0.5 10.0 7.11 58.38767 11.62667 NaN NaN \n",
"2633 0.46 0.3 15.0 6.86 58.38767 11.62667 NaN NaN \n",
"\n",
" MYEAR ... SERNO SHARKID_MD5 SHIPC \\\n",
"2629 2017 ... 8.0 NaN 77SN \n",
"2630 2017 ... 8.0 NaN 77SN \n",
"2631 2017 ... 8.0 NaN 77SN \n",
"2632 2017 ... 8.0 NaN 77SN \n",
"2633 2017 ... 8.0 NaN 77SN \n",
"\n",
" STATN STIME TEMP_BTL TEMP_CTD WATER_DISTRICT \\\n",
"2629 BJÖRKHOLMEN 17:30 4.96 4.84 Västerhavets vattendistrikt \n",
"2630 BJÖRKHOLMEN 17:30 4.93 4.84 Västerhavets vattendistrikt \n",
"2631 BJÖRKHOLMEN 17:30 4.88 4.84 Västerhavets vattendistrikt \n",
"2632 BJÖRKHOLMEN 17:30 5.12 4.86 Västerhavets vattendistrikt \n",
"2633 BJÖRKHOLMEN 17:30 5.52 5.1 Västerhavets vattendistrikt \n",
"\n",
" WATER_TYPE_AREA WLTYP \n",
"2629 02 - Västkustens fjordar 2 - Havsområde innanför 1 NM \n",
"2630 02 - Västkustens fjordar 2 - Havsområde innanför 1 NM \n",
"2631 02 - Västkustens fjordar 2 - Havsområde innanför 1 NM \n",
"2632 02 - Västkustens fjordar 2 - Havsområde innanför 1 NM \n",
"2633 02 - Västkustens fjordar 2 - Havsområde innanför 1 NM \n",
"\n",
"[5 rows x 44 columns]"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data_after_subset_filter = lv_workspace.get_filtered_data(level=1, subset='A') # level=0 means first filter \n",
"print('{} rows mathing the filter criteria'.format(len(data_after_subset_filter)))\n",
"data_after_subset_filter.head()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(array([1378, 1379, 1380, 1381, 1382, 1383, 1384, 1385, 1386, 1387, 1388,\n",
" 1478, 1479, 1480, 1481, 1482, 1483, 1484, 1485, 1486, 1487, 1488,\n",
" 1707, 1708, 1709, 1710, 1711, 1712, 1713, 1714, 1715, 1716, 1717,\n",
" 1815, 1816, 1817, 1818, 1819, 1820, 1821, 1822, 1823, 1824, 1825,\n",
" 1895, 1896, 1897, 1898, 1899, 1900, 1901, 1902, 1903, 1904, 1905,\n",
" 1987, 1988, 1989, 1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997,\n",
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" 2311, 2312, 2313, 2314, 2315, 2316, 2317, 2318, 2319, 2320, 2321,\n",
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" 2952, 2953, 2954, 2955, 2956, 2957, 2958, 2959, 2960, 2961, 2962,\n",
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" 5599, 5600, 5601, 5602, 5603, 5604, 5605, 5606, 5607, 5608, 5609,\n",
" 5780, 5781, 5782, 5783, 5784, 5785, 5786, 5787, 5788, 5789, 5790,\n",
" 5958, 5959, 5960, 5961, 5962, 5963, 5964, 5965, 5966, 5967, 5968,\n",
" 6076, 6077, 6078, 6079, 6080, 6081, 6082, 6083, 6084, 6085, 6086,\n",
" 6216, 6217, 6218, 6219, 6220, 6221, 6222, 6223, 6224, 6225, 6226,\n",
" 6530, 6531, 6532, 6533, 6534, 6535, 6536, 6537, 6538, 6539, 6540,\n",
" 6781, 6782, 6783, 6784, 6785, 6786, 6787, 6788, 6789, 6790, 6791], dtype=int64),)"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import numpy as np\n",
"np.where(lv_workspace.index_handler.subset_filter)"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"f = lv_workspace.get_data_filter_object(step=1, subset='A') \n"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"{'exclude_list': ['SEA_AREA_NAME', 'STATN', 'WATER_TYPE_AREA'],\n",
" 'include_list': ['MYEAR', 'SEA_AREA_NAME', 'STATN', 'WATER_TYPE_AREA']}"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"f.all_filters"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"{'SEA_AREA_NAME': [], 'STATN': ['SLÄGGÖ'], 'WATER_TYPE_AREA': []}"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"f.exclude_list_filter"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"{'MYEAR': ['2016', '2017'],\n",
" 'SEA_AREA_NAME': ['Gullmarn centralbassäng', 'Rivö fjord'],\n",
" 'STATN': ['BJÖRKHOLMEN'],\n",
" 'WATER_TYPE_AREA': []}"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"f.include_list_filter"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"s = lv_workspace.get_step_1_object('A')"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"{'exclude_list': ['SEA_AREA_NAME', 'STATN', 'WATER_TYPE_AREA'],\n",
" 'include_list': ['MYEAR', 'SEA_AREA_NAME', 'STATN', 'WATER_TYPE_AREA']}"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"s.data_filter.all_filters"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"f0 = lv_workspace.get_data_filter_object(step=0) "
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"{'SEA_AREA_NAME': [], 'STATN': [], 'WATER_TYPE_AREA': []}"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"f0.exclude_list_filter"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"{'MYEAR': ['2015', '2017'],\n",
" 'SEA_AREA_NAME': ['Byfjorden',\n",
" 'Gullmarn centralbassäng',\n",
" 'Havstensfjorden',\n",
" 'Rivö fjord'],\n",
" 'STATN': [],\n",
" 'WATER_TYPE_AREA': []}"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"f0.include_list_filter"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
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"outputs": [],
"source": []
},
{
"cell_type": "code",
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"outputs": [],
"source": []
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{
"cell_type": "code",
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"source": []
},
{
"cell_type": "code",
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"outputs": [],
"source": []
},
{
"cell_type": "code",
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"outputs": [],
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{
"cell_type": "code",
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"outputs": [],
"source": []
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{
"cell_type": "code",
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"collapsed": true
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"outputs": [],
"source": []
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{
"cell_type": "code",
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"outputs": [],
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| mit |
sympy/scipy-2017-codegen-tutorial | notebooks/07-the-hard-way.ipynb | 1 | 30106 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# The Harder Way: C Code generation, Custom Printers, and CSE [1 hour]\n",
"\n",
"One of the most common low level programming languages in use is C. Compiled C code can be optimized for execution speed for many different computers. Python is written in C as well as many of the vectorized operations in NumPy and numerical algorithms in SciPy. It is often necessary to translate a complex mathematical expression into C for optimal execution speeds and memory management. In this notebook you will learn how to automatically translate a complex SymPy expression into C, compile the code, and run the program.\n",
"\n",
"We will continue examining the complex chemical kinetic reaction ordinary differential equation introduced in the previous lesson.\n",
"\n",
"## Learning Objectives\n",
"\n",
"After this lesson you will be able to:\n",
"\n",
"- use a code printer class to convert a SymPy expression to compilable C code\n",
"- use an array compatible assignment to print valid C array code\n",
"- subclass the printer class and modify it to provide custom behavior\n",
"- utilize common sub expression elimination to simplify and speed up the code execution"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Import SymPy and enable mathematical printing in the Jupyter notebook."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import sympy as sym"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"sym.init_printing()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Ordinary Differential Equations\n",
"\n",
"The previously generated ordinary differential equations that describe chemical kinetic reactions are loaded below. These expressions describe the right hand side of this mathematical equation:\n",
"\n",
"$$\\frac{d\\mathbf{y}}{dt} = \\mathbf{f}(\\mathbf{y}(t))$$\n",
"\n",
"where the state vector $\\mathbf{y}(t)$ is made up of 14 states, i.e. $\\mathbf{y}(t) \\in \\mathbb{R}^{14}$.\n",
"\n",
"Below the variable `rhs_of_odes` represents $\\mathbf{f}(\\mathbf{y}(t))$ and `states` represents $\\mathbf{y}(t)$.\n",
"\n",
"From now own we will simply use $\\mathbf{y}$ instead of $\\mathbf{y}(t)$ and assume an implicit function of $t$."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from scipy2017codegen.chem import load_large_ode"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"rhs_of_odes, states = load_large_ode()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Exercise [2 min]\n",
"\n",
"Display the expressions (`rhs_of_odes` and `states`), inspect them, and find out their types and dimensions. What are some of the characteristics of the equations (type of mathematical expressions, linear or non-linear, etc)?"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Double Click For Solution\n",
"\n",
"<!--\n",
"\n",
"rhs_of_odes\n",
"type(rhs_of_odes)\n",
"rhs_of_odes.shape\n",
"# rhs_of_odes is a 14 x 1 SymPy matrix of expressions. The expressions are\n",
"# long multivariate polynomials.\n",
"states\n",
"type(states)\n",
"states.shape\n",
"# states is a 14 x 1 SymPy matrix of symbols\n",
"\n",
"The equations are nonlinear equations of the states. There are 14 equations and 14 states. The coefficients in the equations are various floating point numbers.\n",
"\n",
"-->"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# write your solution here"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Compute the Jacobian\n",
"\n",
"As has been shown in the previous lesson the Jacobian of the right hand side of the differential equations is often very useful for computations, such as integration and optimization. With:\n",
"\n",
"$$\\frac{d\\mathbf{y}}{dt} = \\mathbf{f}(\\mathbf{y})$$\n",
"\n",
"the Jacobian is defined as:\n",
"\n",
"$$\\mathbf{J}(\\mathbf{y}) = \\frac{\\partial\\mathbf{f}(\\mathbf{y})}{\\partial\\mathbf{y}}$$\n",
"\n",
"SymPy can compute the Jacobian of matrix objects with the `Matrix.jacobian()` method."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Exercise [3 min]\n",
"\n",
"Look up the Jacobian in the SymPy documentation then compute the Jacobian and store the result in the variable `jac_of_odes`. Inspect the resulting Jacobian for dimensionality, type, and the symbolic form."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Double Click For Solution\n",
"\n",
"<!--\n",
"\n",
"jac_of_odes = rhs_of_odes.jacobian(states)\n",
"type(jac_of_odes)\n",
"jac_of_odes.shape\n",
"jac_of_odes\n",
"\n",
"The Jacobian is a 14 x 14 SymPy matrix and contains 196 expressions which are linear functions of the state variables.\n",
"\n",
"-->"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# write your answer here"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# C Code Printing\n",
"\n",
"The two expressions are large and will likely have to be excuted many thousands of times to compute the desired numerical values, so we want them to execute as fast as possible. We can use SymPy to print these expressions as C code.\n",
"\n",
"We will design a double precision C function that evaluates both $\\mathbf{f}(\\mathbf{y})$ and $\\mathbf{J}(\\mathbf{y})$ simultaneously given the values of the states $\\mathbf{y}$. Below is a basic template for a C program that includes such a function, `evaluate_odes()`. Our job is to populate the function with the C version of the SymPy expressions.\n",
"\n",
"```C\n",
"#include <math.h>\n",
"#include <stdio.h>\n",
"\n",
"void evaluate_odes(const double state_vals[14], double rhs_result[14], double jac_result[196])\n",
"{\n",
" // We need to fill in the code here using SymPy.\n",
"}\n",
"\n",
"int main() {\n",
"\n",
" // initialize the state vector with some values\n",
" double state_vals[14] = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14};\n",
" // create \"empty\" 1D arrays to hold the results of the computation\n",
" double rhs_result[14];\n",
" double jac_result[196];\n",
" \n",
" // call the function\n",
" evaluate_odes(state_vals, rhs_result, jac_result);\n",
" \n",
" // print the computed values to the terminal\n",
" int i;\n",
"\n",
" printf(\"The right hand side of the equations evaluates to:\\n\");\n",
" for (i=0; i < 14; i++) {\n",
" printf(\"%lf\\n\", rhs_result[i]);\n",
" }\n",
"\n",
" printf(\"\\nThe Jacobian evaluates to:\\n\");\n",
" for (i=0; i < 196; i++) {\n",
" printf(\"%lf\\n\", jac_result[i]);\n",
" }\n",
"\n",
" return 0;\n",
"}\n",
"\n",
"```\n",
"\n",
"Instead of using the `ccode` convenience function you learned earlier let's use the underlying code printer class to do the printing. This will allow us to modify the class to for custom printing further down."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from sympy.printing.ccode import C99CodePrinter"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"All printing classes have to be instantiated and then the `.doprint()` method can be used to print SymPy expressions. Let's try to print the right hand side of the differential equations."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"printer = C99CodePrinter()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(printer.doprint(rhs_of_odes))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In this case, the C code printer does not do what we desire. It does not support printing a SymPy Matrix (see the first line of the output). In C, on possible representation of a matrix is an array type. The array type in C stores contigous values, e.g. doubles, in a chunk of memory. You can declare an array of doubles in C like:\n",
"\n",
"```C\n",
"double my_array[10];\n",
"```\n",
"\n",
"The word `double` is the data type of the individual values in the array which must all be the same. The word `my_array` is the variable name we choose to name the array and the `[10]` is the syntax to declare that this array will have 10 values.\n",
"\n",
"The array is \"empty\" when first declared and can be filled with values like so:\n",
"\n",
"```C\n",
"my_array[0] = 5;\n",
"my_array[1] = 6.78;\n",
"my array[2] = my_array[0] * 12;\n",
"```\n",
"\n",
"or like:\n",
"\n",
"```C\n",
"my_array = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10};\n",
"```\n",
"\n",
"It is possible to declare multidimensional arrays in C that could map more directly to the indices of our two dimensional matrix, but in this case we will map our two dimensional matrix to a one dimenasional array using C contingous row ordering.\n",
"\n",
"The code printers are capable of dealing with this need through the `assign_to` keyword argument in the `.doprint()` method but we must define a SymPy object that is appropriate to be assigned to. In our case, since we want to assign a Matrix we need to use an appropriately sized Matrix symbol."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"rhs_result = sym.MatrixSymbol('rhs_result', 14, 1)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(rhs_result)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(rhs_result[0])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(printer.doprint(rhs_of_odes, assign_to=rhs_result))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Notice that we have proper array value assignment and valid lines of C code that can be used in our function."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Excercise [5 min]\n",
"\n",
"Print out valid C code for the Jacobian matrix."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Double Click For Solution\n",
"\n",
"<!---\n",
"jac_result = sym.MatrixSymbol('jac_result', 14, 14)\n",
"\n",
"print(jac_result)\n",
"\n",
"print(printer.doprint(jac_of_odes, assign_to=jac_result))\n",
"-->"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# write your answer here"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Changing the Behavior of the Printer\n",
"\n",
"The SymPy code printers are relatively easy to extend. They are designed such that if you want to change how a particularly SymPy object prints, for example a `Symbol`, then you only need to modify the `_print_Symbol` method of the printer. In general, the code printers have a method for every SymPy object and also many builtin types. Use tab completion with `C99CodePrinter._print_` to see all of the options."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Once you find the method you want to modify, it is often useful to look at the existing impelementation of the print method to see how the code is written."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"C99CodePrinter._print_Symbol??"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Below is a simple example of overiding the `Symbol` printer method. Note that you should use the `self._print()` method instead of simply returning the string so that the proper printer, `self._print_str()`, is dispatched. This is most important if you are printing non-singletons, i.e. expressions that are made up of multiple singletons. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"C99CodePrinter._print_str??"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"class MyCodePrinter(C99CodePrinter):\n",
" def _print_Symbol(self, expr):\n",
" return self._print(\"No matter what symbol you pass in I will always print:\\n\\nNi!\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"my_printer = MyCodePrinter()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"theta = sym.symbols('theta')\n",
"theta"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(my_printer.doprint(theta))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Exercise [10 min]\n",
"\n",
"One issue with our current code printer is that the expressions use the symbols `y0, y1, ..., y13` instead of accessing the values directly from the arrays with `state_vals[0], state_vals[1], ..., state_vals[13]`. We could go back and rename our SymPy symbols to use brackets, but another way would be to override the `_print_Symbol()` method to print these symbols as we desire. Modify the code printer so that it prints with the proper array access in the expression."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Double Click For Solution: Subclassing\n",
"\n",
"<!--\n",
"\n",
"The following solution examines the symbol and if it is a state variable it overrides the printer, otherwise it uses the parent class to print the symbol as a fall back.\n",
"\n",
"class MyCodePrinter(C99CodePrinter):\n",
" def _print_Symbol(self, symbol):\n",
" if symbol in states:\n",
" idx = list(states).index(symbol)\n",
" return self._print('state_vals[{}]'.format(idx))\n",
" else:\n",
" return super()._print_Symbol(symbol)\n",
"\n",
"my_printer = MyCodePrinter()\n",
"\n",
"print(my_printer.doprint(rhs_of_odes, assign_to=rhs_result))\n",
"\n",
"-->"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Double Click For Solution: Exact replacement\n",
"\n",
"<!--\n",
"Another option is to replace the symbols with `MatrixSymbol` elements. Notice that the C printer assumes that a 2D matrix will get mapped to a 1D C array.\n",
"\n",
"state_vals = sym.MatrixSymbol('state_vals', 14, 1)\n",
"state_array_map = dict(zip(states, state_vals))\n",
"print(state_array_map)\n",
"print(printer.doprint(rhs_of_odes.xreplace(state_array_map), assign_to=rhs_result))\n",
"\n",
"-->"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# write your answer here"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Bonus Exercise\n",
"\n",
"*Do this exercise if you finish the previous one quickly.*\n",
"\n",
"It turns out that calling `pow()` for low value integer exponents executes slower than simply expanding the multiplication. For example `pow(x, 2)` could be printed as `x*x`. Modify the CCodePrinter `._print_Pow` method to expand the multiplication if the exponent is less than or equal to 4. You may want to have a look at the source code with `printer._print_Pow??`\n",
"\n",
"Note that a `Pow` expression has an `.exp` for exponent and `.base` for the item being raised. For example $x^2$ would have:\n",
"\n",
"```python\n",
"expr = x**2\n",
"expr.base == x\n",
"expr.exp == 2\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"### Double Click for Solution\n",
"\n",
"<!--\n",
"\n",
"printer._print_Pow??\n",
"\n",
"class MyCodePrinter(C99CodePrinter):\n",
" def _print_Pow(self, expr):\n",
" if expr.exp.is_integer and expr.exp > 0 and expr.exp <= 4:\n",
" return '*'.join([self._print(expr.base) for i in range(expr.exp)])\n",
" else:\n",
" return super()._print_Pow(expr)\n",
"\n",
"my_printer = MyCodePrinter()\n",
"\n",
"x = sym.Symbol('x')\n",
"\n",
"my_printer.doprint(x)\n",
"\n",
"my_printer.doprint(x**2)\n",
"\n",
"my_printer.doprint(x**4)\n",
"\n",
"my_printer.doprint(x**5)\n",
"\n",
"my_printer.doprint(x**1.5)\n",
"\n",
"-->"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# write your answer here"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Common Subexpression Elimination\n",
"\n",
"If you look carefully at the expressions in the two matrices you'll see repeated expressions. These are not ideal in the sense that the computer has to repeat the exact same calculation multiple times. For large expressions this can be a major issue. Compilers, such as gcc, can often eliminate common subexpressions on their own when different optimization flags are invoked but for complex expressions the algorithms in some compilers do not do a thorough job or compilation can take an extremely long time. SymPy has tools to perform common subexpression elimination which is both thorough and reasonably efficient. In particular if gcc is run with the lowest optimization setting `-O0` `cse` can give large speedups.\n",
"\n",
"For example if you have two expressions:\n",
"\n",
"```python\n",
"a = x*y + 5\n",
"b = x*y + 6\n",
"```\n",
"\n",
"you can convert this to these three expressions:\n",
"\n",
"```python\n",
"z = x*y\n",
"a = z + 5\n",
"b = z + 6\n",
"```\n",
"\n",
"and `x*y` only has to be computed once.\n",
"\n",
"The `cse()` function in SymPy returns the subexpression, `z = x*y`, and the simplified expressions: `a = z + 5`, `b = z + 6`.\n",
"\n",
"Here is how it works:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"sm.cse?"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"sub_exprs, simplified_rhs = sym.cse(rhs_of_odes)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"for var, expr in sub_exprs:\n",
" sym.pprint(sym.Eq(var, expr))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"`cse()` can return a number of simplified expressions and to do this it returns a list. In our case we have 1 simplified expression that can be accessed as the first item of the list."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"type(simplified_rhs)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"len(simplified_rhs)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"simplified_rhs[0]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You can find common subexpressions among multiple objects also:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"jac_of_odes = rhs_of_odes.jacobian(states)\n",
"\n",
"sub_exprs, simplified_exprs = sym.cse((rhs_of_odes, jac_of_odes))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"for var, expr in sub_exprs:\n",
" sym.pprint(sym.Eq(var, expr))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"simplified_exprs[0]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"simplified_exprs[1]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Exercise [15min]\n",
"\n",
"Use common subexpression elimination to print out C code for your two arrays such that:\n",
"\n",
"```C\n",
"double x0 = first_sub_expression;\n",
"...\n",
"double xN = last_sub_expression;\n",
"\n",
"rhs_result[0] = expressions_containing_the_subexpressions;\n",
"...\n",
"rhs_result[13] = ...;\n",
"\n",
"jac_result[0] = ...;\n",
"...\n",
"jac_result[195] = ...;\n",
"```\n",
"\n",
"The code you create can be copied and pasted into the provided template above to make a C program. *Refer back to the introduction to C code printing above.*\n",
"\n",
"To give you a bit of help we will first introduce the `Assignment` class. The printers know how to print variable assignments that are defined by an `Assignment` instance."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from sympy.printing.codeprinter import Assignment\n",
"\n",
"print(printer.doprint(Assignment(theta, 5)))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The following code demonstrates a way to use `cse()` to simplify single matrix objects. Note that we use `ImmutableDenseMatrix` because all dense matrics are internally converted to this type in the printers. Check the type of your matrices to see."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"class CMatrixPrinter(C99CodePrinter):\n",
" def _print_ImmutableDenseMatrix(self, expr):\n",
" sub_exprs, simplified = sym.cse(expr)\n",
" lines = []\n",
" for var, sub_expr in sub_exprs:\n",
" lines.append('double ' + self._print(Assignment(var, sub_expr)))\n",
" M = sym.MatrixSymbol('M', *expr.shape)\n",
" return '\\n'.join(lines) + '\\n' + self._print(Assignment(M, expr))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"p = CMatrixPrinter()\n",
"print(p.doprint(jac_of_odes))"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"Now create a custom printer that uses `cse()` on the two matrices simulatneously so that subexpressions are not repeated. *Hint: think about how the list printer method, `_print_list(self, list_of_exprs)`, might help here.*"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"### Double Click For Solution\n",
"\n",
"<!--\n",
"\n",
"class CMatrixPrinter(C99CodePrinter):\n",
" \n",
" def _print_list(self, list_of_exprs):\n",
" # NOTE : The MutableDenseMatrix is turned in an ImmutableMatrix inside here.\n",
" if all(isinstance(x, sym.ImmutableMatrix) for x in list_of_exprs):\n",
" sub_exprs, simplified_exprs = sym.cse(list_of_exprs)\n",
" lines = []\n",
" for var, sub_expr in sub_exprs:\n",
" ass = Assignment(var, sub_expr.xreplace(state_array_map))\n",
" lines.append('double ' + self._print(ass))\n",
" for mat in simplified_exprs:\n",
" lines.append(self._print(mat.xreplace(state_array_map)))\n",
" return '\\n'.join(lines)\n",
" else:\n",
" return super()._print_list(list_of_exprs)\n",
" \n",
" def _print_ImmutableDenseMatrix(self, expr):\n",
" if expr.shape[1] > 1:\n",
" M = sym.MatrixSymbol('jac_result', *expr.shape)\n",
" else:\n",
" M = sym.MatrixSymbol('rhs_result', *expr.shape)\n",
" return self._print(Assignment(M, expr))\n",
"\n",
"p = CMatrixPrinter()\n",
"print(p.doprint([rhs_of_odes, jac_of_odes]))\n",
"\n",
"-->"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# write your answer here"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Bonus Exercise: Compile and Run the C Program\n",
"\n",
"Below we provide you with a template for the C program described above. You can use it by passing in a string like:\n",
"\n",
"```python\n",
"c_template.format(code='the holy grail')\n",
"```\n",
"\n",
"Use this template and your code printer to create a file called `run.c` in the working directory.\n",
"\n",
"To compile the code there are several options. The first is gcc (the GNU C Compiler). If you have Linux, Mac, or Windows (w/ mingw installed) you can use the Jupyter notebook `!` command to send your command to the terminal. For example:\n",
"\n",
"```ipython\n",
"!gcc run.c -lm -o run\n",
"```\n",
"\n",
"This will compile `run.c`, link against the C math library with `-lm` and output, `-o`, to a file `run` (Mac/Linux) or `run.exe` (Windows).\n",
"\n",
"On Mac and Linux the program can be executed with:\n",
"\n",
"```ipython\n",
"!./run\n",
"```\n",
"\n",
"and on Windows:\n",
"\n",
"```ipython\n",
"!run.exe\n",
"```\n",
"\n",
"Other options are using the clang compiler or Windows `cl` compiler command:\n",
"\n",
"```ipython\n",
"!clang run.c -lm -o run\n",
"!cl run.c -lm\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"### Double Click For Solution\n",
"\n",
"<!--\n",
"\n",
"c_program = c_template.format(code=p.doprint([rhs_of_odes, jac_of_odes]))\n",
"print(c_program)\n",
"\n",
"with open('run.c', 'w') as f:\n",
" f.write(c_program)\n",
"\n",
"-->"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"c_template = \"\"\"\\\n",
"#include <math.h>\n",
"#include <stdio.h>\n",
"\n",
"void evaluate_odes(const double state_vals[14], double rhs_result[14], double jac_result[196])\n",
"{{\n",
" // We need to fill in the code here using SymPy.\n",
"{code}\n",
"}}\n",
"\n",
"int main() {{\n",
"\n",
" // initialize the state vector with some values\n",
" double state_vals[14] = {{1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14}};\n",
" // create \"empty\" 1D arrays to hold the results of the computation\n",
" double rhs_result[14];\n",
" double jac_result[196];\n",
"\n",
" // call the function\n",
" evaluate_odes(state_vals, rhs_result, jac_result);\n",
"\n",
" // print the computed values to the terminal\n",
" int i;\n",
" printf(\"The right hand side of the equations evaluates to:\\\\n\");\n",
" for (i=0; i < 14; i++) {{\n",
" printf(\"%lf\\\\n\", rhs_result[i]);\n",
" }}\n",
" printf(\"\\\\nThe Jacobian evaluates to:\\\\n\");\n",
" for (i=0; i < 196; i++) {{\n",
" printf(\"%lf\\\\n\", jac_result[i]);\n",
" }}\n",
"\n",
" return 0;\n",
"}}\\\n",
"\"\"\""
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# write your answer here"
]
}
],
"metadata": {
"anaconda-cloud": {},
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.1"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| bsd-3-clause |
ddossett/python-tutorial | notebooks/5-classes.ipynb | 1 | 4388 | {
"metadata": {
"name": ""
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": [
"Classes"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* A `class` is a useful object that is essentially a variable type (Python object) that you can define yourself.\n",
"* C lets you define structs, which is a single object that can contain many other named variables. A `class` extends this concept in several ways, most obviously a `class` can contain both variables (properties) and functions (methods).\n",
"* In general a `class` will embody some object and the data + functions that make sense to bundle together. Let's see some very simple syntax examples before moving on to the scripts directory where more complete examples are given."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Define a class with the class keyword\n",
"class A:\n",
" \"\"\"Docstrings work here too\"\"\"\n",
" x = 5 # defining properties of the class\n",
" y = 7\n",
"\n",
"print(A)\n",
"print(A.x, A.y) # Access properties with `.` as with other Python objects\n",
"A.x = 1 # Can change the class properties from outside the definition\n",
"print(A.x, A.y)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"<class '__main__.A'>\n",
"5 7\n",
"1 7\n"
]
}
],
"prompt_number": 7
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* This seems useless as you can only have one copy of the `class` and one set of values for properties.\n",
"* What we want are different **instances** of the class that can contain different values. These can then be assigned to variables."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"class B:\n",
" x = 3 # Evaluated once during the definition (similar to functions)\n",
" y = [] # These will be initially the same for all instances\n",
" \n",
" def __init__(self, z):\n",
" \"\"\"This function is special. It's run when the instance of the class is created and\n",
" is useful to set up some initial state of the class. The argument 'self' isn't a\n",
" keyword but is the convention, it is basically the instance being passed into the\n",
" class.\"\"\"\n",
" self.z = z # Can be different for each instance\n",
" \n",
" def z_squared(self):\n",
" return self.z**2\n",
"\n",
"# Instances are created in a similar way to functions and use the arguments from the \n",
"# __init__ function if it exists.\n",
"f = B(3) # Note there is only the z argument because the 'self' is automatically passed in\n",
"g = B(4)\n",
"print(f.x, g.x) # Same values\n",
"B.x = 5\n",
"print(f.x, g.x) # Both changed\n",
"\n",
"print(f.y, g.y) # Same values\n",
"B.y.append(\"Hello\")\n",
"print(f.y, g.y) # Both changed\n",
"\n",
"print(f.z_squared(), g.z_squared()) # Call class methods in a usual way\n",
"\n",
"# The `self` argument becomes more obvious when you realise that these are\n",
"# equivalent statements\n",
"print(f.z_squared(), B.z_squared(f))\n",
"print(g.z_squared(), B.z_squared(g))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"3 3\n",
"5 5\n",
"[] []\n",
"['Hello'] ['Hello']\n",
"9 16\n",
"9 9\n",
"16 16\n"
]
}
],
"prompt_number": 3
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* Now move on to the scripts directory to see some other `class` features and some awesome Python examples using the Python standard library and some of its extensive third party libraries for scientists."
]
}
],
"metadata": {}
}
]
} | mit |
makeyourownneuralnetwork/complex_valued_neuralnetwork | network_mnist.ipynb | 1 | 10401 | {
"cells": [
{
"cell_type": "code",
"execution_count": 110,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# neural network of many complex valued neurons in 3 layers\n",
"# applied to the MNIST dataset\n",
"import numpy"
]
},
{
"cell_type": "code",
"execution_count": 111,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"class neuralNetwork:\n",
" \n",
" def __init__(self, inputnodes, hiddennodes, outputnodes, cats, periods):\n",
" # set number of nodes in each input, hidden, output layer\n",
" self.inodes = inputnodes +1 # increament for bias node\n",
" self.hnodes = hiddennodes\n",
" self.onodes = outputnodes\n",
" \n",
" # link weight matrices, wih and who\n",
" # weights inside the arrays are w_i_j, where link is from node i to node j in the next layer\n",
" # w11 w21\n",
" # w12 w22 etc \n",
" self.wih = numpy.random.uniform(-1.0, 1.0, (self.hnodes, self.inodes))\n",
" self.wih = numpy.array(self.wih, ndmin=2, dtype='complex128')\n",
" self.wih += 1j * numpy.random.uniform(-1.0, 1.0, (self.hnodes, self.inodes))\n",
" \n",
" self.who = numpy.random.uniform(-1.0, 1.0, (self.onodes, self.hnodes))\n",
" self.who = numpy.array(self.who, ndmin=2, dtype='complex128')\n",
" self.who += 1j * numpy.random.random((self.onodes, self.hnodes))\n",
" \n",
" # number of output class categories\n",
" self.categories = cats\n",
" \n",
" # todo periodicity\n",
" self.periodicity = periods\n",
" pass\n",
" \n",
" def z_to_class(self, z):\n",
" # first work out the angle, but shift angle from [-pi/2, +pi.2] to [0,2pi]\n",
" angle = numpy.mod(numpy.angle(z) + 2*numpy.pi, 2*numpy.pi)\n",
" # from angle to category\n",
" p = int(numpy.floor (self.categories * angle / (2*numpy.pi)))\n",
" return p\n",
"\n",
" def class_to_angle(self, p):\n",
" # class to angle, using bisector\n",
" angle = ((p + 0.5) / self.categories) * 2 * numpy.pi\n",
" return angle\n",
" \n",
" def status(self):\n",
" print (\"self.wih = \", self.wih)\n",
" print (\"self.who = \", self.who)\n",
" pass\n",
"\n",
" def query(self, inputs_list):\n",
" # add bias input\n",
" inputs_list.append(1.0)\n",
" \n",
" # convert input to complex\n",
" inputs = numpy.array(inputs_list, ndmin=2, dtype='complex128').T\n",
" #print(\"inputs = \\n\", inputs)\n",
" \n",
" # signal into hidden layer\n",
" hidden_inputs = numpy.dot(self.wih, inputs)\n",
" #print(\"hidden_inputs = \", hidden_inputs)\n",
" #signal out of hidden layer\n",
" hidden_outputs = numpy.exp(1j * numpy.angle(hidden_inputs))\n",
" #print(\"hidden_outputs = \", hidden_outputs)\n",
" \n",
" # signal into final output layer\n",
" final_inputs = numpy.dot(self.who, hidden_outputs)\n",
" #print(\"final_input = \", final_inputs)\n",
" #signal out of output layer\n",
" final_outputs = numpy.exp(1j * numpy.angle(final_inputs))\n",
" #print(\"final_outputs = \", final_outputs)\n",
" \n",
" # map to output classes\n",
" output_classes = self.z_to_class(final_outputs)\n",
" #print(\"output_classes = \", output_classes)\n",
" return output_classes\n",
" \n",
" def train(self, inputs_list, target_class_list):\n",
" # add bias input\n",
" inputs_list.append(1.0)\n",
" \n",
" # convert input to complex\n",
" inputs = numpy.array(inputs_list, ndmin=2, dtype='complex128').T\n",
" #print(\"inputs = \\n\", inputs)\n",
" \n",
" # map target classes to unit circle\n",
" targets = numpy.exp(1j * self.class_to_angle(numpy.array(target_class_list, ndmin=2).T))\n",
" #print(\"targets = \\n\", targets)\n",
"\n",
" # signal into hidden layer\n",
" hidden_inputs = numpy.dot(self.wih, inputs)\n",
" #print(\"hidden_inputs = \", hidden_inputs)\n",
" #signal out of hidden layer\n",
" hidden_outputs = numpy.exp(1j * numpy.angle(hidden_inputs))\n",
" #print(\"hidden_outputs = \", hidden_outputs)\n",
" \n",
" # signal into final output layer\n",
" final_inputs = numpy.dot(self.who, hidden_outputs)\n",
" #print(\"final_inputs = \", final_inputs)\n",
" #signal out of output layer\n",
" final_outputs = numpy.exp(1j * numpy.angle(final_inputs))\n",
" #print(\"final_outputs = \", final_outputs)\n",
" \n",
" # output layer error is the (target - actual)\n",
" output_errors = targets - final_outputs\n",
" #print(\"output_errors = \", output_errors)\n",
" # hidden layer error is the output_errors split simply (not by weights)\n",
" hidden_errors = numpy.dot(numpy.array(numpy.ones((self.hnodes)), ndmin=2).T / self.hnodes, output_errors)\n",
" #print(\"hidden_errors = \", hidden_errors)\n",
" \n",
" # dw = e * x.T / (x.x.T)\n",
" dwho = numpy.dot(output_errors, numpy.conj(hidden_outputs.T)) / (self.hnodes)\n",
" #print(\"dwho = \", dwho)\n",
" self.who += dwho\n",
" \n",
" dwih = numpy.dot(hidden_errors, numpy.conj(inputs.T)) / (self.inodes)\n",
" #print(\"dwih = \", dwih)\n",
" self.wih += dwih \n",
" pass"
]
},
{
"cell_type": "code",
"execution_count": 112,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# number of input, hidden and output nodes\n",
"input_nodes = 784\n",
"hidden_nodes = 100\n",
"output_nodes = 1\n",
"\n",
"# categories, periodicity\n",
"categories = 10\n",
"periodicity = 1\n",
"\n",
"# create instance of neural network\n",
"n = neuralNetwork(input_nodes,hidden_nodes,output_nodes, categories, periodicity)"
]
},
{
"cell_type": "code",
"execution_count": 113,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# load the mnist training data CSV file into a list\n",
"training_data_file = open(\"mnist_dataset/mnist_train.csv\", 'r')\n",
"training_data_list = training_data_file.readlines()\n",
"training_data_file.close()"
]
},
{
"cell_type": "code",
"execution_count": 114,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# train neural network - OR\n",
"\n",
"epochs = 1\n",
"\n",
"for e in range(epochs):\n",
" # go through all records in the training data set\n",
" for record in training_data_list:\n",
" # split the record by the ',' commas\n",
" all_values = record.split(',')\n",
" # scale and shift the inputs\n",
" inputs = numpy.exp(1j * (numpy.asfarray(all_values[1:]) / 255.0) * numpy.pi)\n",
" inputs_list = inputs.tolist()\n",
" # create the target class list\n",
" targets = [int(all_values[0])]\n",
" n.train(inputs_list, targets)\n",
" pass\n",
" pass"
]
},
{
"cell_type": "code",
"execution_count": 115,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# load the mnist test data CSV file into a list\n",
"test_data_file = open(\"mnist_dataset/mnist_test.csv\", 'r')\n",
"test_data_list = test_data_file.readlines()\n",
"test_data_file.close()"
]
},
{
"cell_type": "code",
"execution_count": 116,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# test the neural network\n",
"\n",
"# scorecard for how well the network performs, initially empty\n",
"scorecard = []\n",
"\n",
"# go through all the records in the test data set\n",
"for record in test_data_list:\n",
" # split the record by the ',' commas\n",
" all_values = record.split(',')\n",
" # correct answer is first value\n",
" correct_label = int(all_values[0])\n",
" # scale and shift the inputs\n",
" inputs = numpy.exp(1j * (numpy.asfarray(all_values[1:]) / 255.0) * numpy.pi)\n",
" inputs_list = inputs.tolist()\n",
" # query the network\n",
" output_label = n.query(inputs_list)\n",
" # append correct or incorrect to list\n",
" if (output_label == correct_label):\n",
" # network's answer matches correct answer, add 1 to scorecard\n",
" scorecard.append(1)\n",
" else:\n",
" # network's answer doesn't match correct answer, add 0 to scorecard\n",
" scorecard.append(0)\n",
" pass\n",
" \n",
" pass"
]
},
{
"cell_type": "code",
"execution_count": 117,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"performance = 0.1719\n"
]
}
],
"source": [
"# calculate the performance score, the fraction of correct answers\n",
"scorecard_array = numpy.asarray(scorecard)\n",
"print (\"performance = \", scorecard_array.sum() / scorecard_array.size)"
]
},
{
"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
}
| gpl-2.0 |
phoebe-project/phoebe2-docs | 2.3/tutorials/gravb_bol.ipynb | 1 | 249849 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Gravity Brightening/Darkening (gravb_bol)\n",
"============================\n",
"\n",
"Setup\n",
"-----------------------------"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's first make sure we have the latest version of PHOEBE 2.3 installed (uncomment this line if running in an online notebook session such as colab)."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"#!pip install -I \"phoebe>=2.3,<2.4\""
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"As always, let's do imports and initialize a logger and a new bundle."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"import phoebe\n",
"from phoebe import u # units\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"\n",
"logger = phoebe.logger()\n",
"\n",
"b = phoebe.default_binary()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Wed, 30 Sep 2020 12:04 BUNDLE WARNING mesh dataset uses 'compute_times' instead of 'times', applying value sent as 'times' to 'compute_times'.\n"
]
},
{
"data": {
"text/plain": [
"<ParameterSet: 85 parameters | contexts: compute, figure, dataset, constraint>"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"b.add_dataset('lc', dataset='lc01')\n",
"b.add_dataset('mesh', times=[0], columns=['intensities*'])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Relevant Parameters\n",
"--------------------\n",
"\n",
"The 'gravb_bol' parameter corresponds to the β coefficient for gravity darkening corrections."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"ParameterSet: 2 parameters\n",
" gravb_bol@primary@component: 0.32\n",
" gravb_bol@secondary@component: 0.32\n"
]
}
],
"source": [
"print(b['gravb_bol'])"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Parameter: gravb_bol@primary@component\n",
" Qualifier: gravb_bol\n",
" Description: Bolometric gravity brightening\n",
" Value: 0.32\n",
" Constrained by: \n",
" Constrains: None\n",
" Related to: None\n",
"\n"
]
}
],
"source": [
"print(b['gravb_bol@primary'])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If you have a logger enabled, PHOEBE will print a warning if the value of gravb_bol is outside the \"suggested\" ranges. Note that this is strictly a warning, and will never turn into an error at [b.run_compute()](../api/phoebe.frontend.bundle.Bundle.run_compute.md).\n",
"\n",
"You can also manually call [b.run_checks()](../api/phoebe.frontend.bundle.Bundle.run_checks.md). The first returned item tells whether the system has passed checks: True means it has, False means it has failed, and None means the tests pass but with a warning. The second argument tells the first warning/error message raised by the checks.\n",
"\n",
"The checks use the following \"suggested\" values:\n",
" * teff 8000+: gravb_bol >= 0.9 (suggest 1.0)\n",
" * teff 6600-8000: gravb_bol 0.32-1.0\n",
" * teff 6600-: grav_bol < 0.9 (suggest 0.32)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Run Checks Report: PASS\n",
"\n"
]
}
],
"source": [
"print(b.run_checks())"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Wed, 30 Sep 2020 12:04 BUNDLE WARNING 'primary' probably has a radiative atm (teff=8500K>8000K), for which gravb_bol>=0.9 might be a better approx than gravb_bol=0.32.\n",
"Wed, 30 Sep 2020 12:04 BUNDLE WARNING 'primary' probably has a radiative atm (teff=8500K>=8000K), for which irrad_frac_refl_bol>0.8 (suggestion: 1.0) might be a better approx than irrad_frac_refl_bol=0.60.\n",
"Wed, 30 Sep 2020 12:04 BUNDLE WARNING 'primary' probably has a radiative atm (teff=8500K>8000K), for which gravb_bol>=0.9 might be a better approx than gravb_bol=0.80.\n",
"Wed, 30 Sep 2020 12:04 BUNDLE WARNING 'primary' probably has a radiative atm (teff=8500K>=8000K), for which irrad_frac_refl_bol>0.8 (suggestion: 1.0) might be a better approx than irrad_frac_refl_bol=0.60.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Run Checks Report: WARNING\n",
"WARNING: 'primary' probably has a radiative atm (teff=8500K>8000K), for which gravb_bol>=0.9 might be a better approx than gravb_bol=0.80. (2 affected parameters, affecting system,run_compute)\n",
"WARNING: 'primary' probably has a radiative atm (teff=8500K>=8000K), for which irrad_frac_refl_bol>0.8 (suggestion: 1.0) might be a better approx than irrad_frac_refl_bol=0.60. (2 affected parameters, affecting system,run_compute)\n"
]
}
],
"source": [
"b['teff@primary'] = 8500\n",
"b['gravb_bol@primary'] = 0.8\n",
"print(b.run_checks())"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Wed, 30 Sep 2020 12:04 BUNDLE WARNING 'primary' has intermittent temperature (6600K<teff=7000K<8000K), gravb_bol might be better between 0.32-1.00 than gravb_bol=0.20.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Run Checks Report: WARNING\n",
"WARNING: 'primary' has intermittent temperature (6600K<teff=7000K<8000K), gravb_bol might be better between 0.32-1.00 than gravb_bol=0.20. (2 affected parameters, affecting system,run_compute)\n"
]
}
],
"source": [
"b['teff@primary'] = 7000\n",
"b['gravb_bol@primary'] = 0.2\n",
"print(b.run_checks())"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Wed, 30 Sep 2020 12:04 BUNDLE WARNING 'primary' probably has a convective atm (teff=6000K<6600K), for which gravb_bol<0.9 (suggestion: 0.32) might be a better approx than gravb_bol=1.00.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Run Checks Report: WARNING\n",
"WARNING: 'primary' probably has a convective atm (teff=6000K<6600K), for which gravb_bol<0.9 (suggestion: 0.32) might be a better approx than gravb_bol=1.00. (2 affected parameters, affecting system,run_compute)\n"
]
}
],
"source": [
"b['teff@primary'] = 6000\n",
"b['gravb_bol@primary'] = 1.0\n",
"print(b.run_checks())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Influence on Intensities\n",
"------------------"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Wed, 30 Sep 2020 12:04 BUNDLE WARNING 'primary' probably has a convective atm (teff=6000K<6600K), for which gravb_bol<0.9 (suggestion: 0.32) might be a better approx than gravb_bol=1.00.\n"
]
}
],
"source": [
"b['teff@primary'] = 6000\n",
"b['gravb_bol@primary'] = 0.32"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 1/1 [00:00<00:00, 12.51it/s]\n"
]
},
{
"data": {
"text/plain": [
"<ParameterSet: 13 parameters | datasets: mesh01, lc01>"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"b.run_compute(model='gravb_bol_32')"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 576x432 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"afig, mplfig = b['primary@mesh01@gravb_bol_32'].plot(fc='intensities', ec='None', show=True)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Wed, 30 Sep 2020 12:04 BUNDLE WARNING 'primary' probably has a convective atm (teff=6000K<6600K), for which gravb_bol<0.9 (suggestion: 0.32) might be a better approx than gravb_bol=1.00.\n"
]
}
],
"source": [
"b['gravb_bol@primary'] = 1.0"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Wed, 30 Sep 2020 12:04 BUNDLE WARNING 'primary' probably has a convective atm (teff=6000K<6600K), for which gravb_bol<0.9 (suggestion: 0.32) might be a better approx than gravb_bol=1.00.\n",
"100%|██████████| 1/1 [00:00<00:00, 11.68it/s]\n"
]
},
{
"data": {
"text/plain": [
"<ParameterSet: 13 parameters | datasets: mesh01, lc01>"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"b.run_compute(model='gravb_bol_10')"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 576x432 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"afig, mplfig = b['primary@mesh01@gravb_bol_10'].plot(fc='intensities', ec='None', show=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Comparing these two plots, it is essentially impossible to notice any difference between the two models. But if we compare the intensities directly, we can see that there is a subtle difference, with a maximum difference of about 3%."
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.032169811043561015"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.nanmax((b.get_value('intensities', component='primary', model='gravb_bol_32') - b.get_value('intensities', component='primary', model='gravb_bol_10'))/b.get_value('intensities', component='primary', model='gravb_bol_10'))"
]
},
{
"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": 1
}
| gpl-3.0 |
abeschneider/algorithm_notes | Heaps.ipynb | 1 | 179506 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Run time\n",
"\n",
"* Find min/max: $O(1)$\n",
"* Insert: $O(\\log n)$\n",
"* Delete: $O(\\log n)$"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"from __future__ import print_function\n",
"import numpy as np\n",
"\n",
"from IPython.display import clear_output\n",
"from pjdiagram import *\n",
"from ipywidgets import *\n",
"\n",
"from heap import binary_heap_allocation_example, insert_item_to_heap_example, percolate_down_example"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"# Description\n",
"\n",
"A heap is a type of binary tree that allows fast insertion and fast traversal. This makes them a good candidate for sorting large amounts of data. Heaps have the following properties:\n",
"- the root node has maximum value key\n",
"- the key stored at a non-root is at most the value of its parent\n",
"\n",
"Therefore:\n",
"- any path from the root node to a leaf node is in nonincreasing order\n",
"\n",
"However:\n",
"- the left and right sub-trees don't have a formal relationship\n",
"\n",
"\n",
"## Storage\n",
"\n",
"A binary tree can be represented using an array with the following indexing:\n",
"\n",
"$$\\texttt{parent}\\left(i\\right) = (i-1)/2$$\n",
"$$\\texttt{left}\\left(i\\right) = (2i)+1$$\n",
"$$\\texttt{right}\\left(i\\right) = (2i)+2$$\n",
"\n",
"### Example\n",
"- root node index: $0$\n",
" - left child: $1$\n",
" - left child: $2*1+1 = 3$\n",
" - right child: $2*1+2 = 4$\n",
" - right child: $2$\n",
" - left child: $2*2 + 1 = 5$\n",
" - right child: $2*2 + 2 = 6$\n",
"\n",
"\n",
"\n",
"The figure below provides a visual demonstration."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"image/svg+xml": [
"<svg height=\"250pt\" version=\"1.1\" viewBox=\"0 0 650 250\" width=\"650pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n",
"<g id=\"surface137\">\n",
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10.324219 252.679688 9.671875 252.484375 9.15625 C 252.289062 8.644531 251.90625 8.386719 251.34375 8.386719 C 250.828125 8.386719 250.449219 8.632812 250.210938 9.117188 C 249.96875 9.605469 249.851562 10.324219 249.851562 11.269531 C 249.851562 11.980469 249.925781 12.554688 250.078125 12.988281 C 250.3125 13.648438 250.714844 13.980469 251.28125 13.980469 C 251.738281 13.980469 252.101562 13.777344 252.371094 13.375 Z M 273.253906 8.714844 C 273.566406 9.292969 273.722656 10.082031 273.722656 11.082031 C 273.722656 12.035156 273.582031 12.820312 273.296875 13.441406 C 272.886719 14.332031 272.21875 14.78125 271.285156 14.78125 C 270.445312 14.78125 269.820312 14.414062 269.410156 13.6875 C 269.070312 13.078125 268.898438 12.261719 268.898438 11.234375 C 268.898438 10.441406 269 9.757812 269.207031 9.191406 C 269.589844 8.128906 270.285156 7.597656 271.289062 7.597656 C 272.195312 7.597656 272.851562 7.96875 273.253906 8.714844 Z M 272.371094 13.375 C 272.640625 12.972656 272.773438 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292.371094 13.375 C 292.640625 12.972656 292.773438 12.21875 292.773438 11.117188 C 292.773438 10.324219 292.679688 9.671875 292.484375 9.15625 C 292.289062 8.644531 291.90625 8.386719 291.34375 8.386719 C 290.828125 8.386719 290.449219 8.632812 290.210938 9.117188 C 289.96875 9.605469 289.851562 10.324219 289.851562 11.269531 C 289.851562 11.980469 289.925781 12.554688 290.078125 12.988281 C 290.3125 13.648438 290.714844 13.980469 291.28125 13.980469 C 291.738281 13.980469 292.101562 13.777344 292.371094 13.375 Z M 313.253906 8.714844 C 313.566406 9.292969 313.722656 10.082031 313.722656 11.082031 C 313.722656 12.035156 313.582031 12.820312 313.296875 13.441406 C 312.886719 14.332031 312.21875 14.78125 311.285156 14.78125 C 310.445312 14.78125 309.820312 14.414062 309.410156 13.6875 C 309.070312 13.078125 308.898438 12.261719 308.898438 11.234375 C 308.898438 10.441406 309 9.757812 309.207031 9.191406 C 309.589844 8.128906 310.285156 7.597656 311.289062 7.597656 C 312.195312 7.597656 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10.441406 369 9.757812 369.207031 9.191406 C 369.589844 8.128906 370.285156 7.597656 371.289062 7.597656 C 372.195312 7.597656 372.851562 7.96875 373.253906 8.714844 Z M 372.371094 13.375 C 372.640625 12.972656 372.773438 12.21875 372.773438 11.117188 C 372.773438 10.324219 372.679688 9.671875 372.484375 9.15625 C 372.289062 8.644531 371.90625 8.386719 371.34375 8.386719 C 370.828125 8.386719 370.449219 8.632812 370.210938 9.117188 C 369.96875 9.605469 369.851562 10.324219 369.851562 11.269531 C 369.851562 11.980469 369.925781 12.554688 370.078125 12.988281 C 370.3125 13.648438 370.714844 13.980469 371.28125 13.980469 C 371.738281 13.980469 372.101562 13.777344 372.371094 13.375 Z M 393.253906 8.714844 C 393.566406 9.292969 393.722656 10.082031 393.722656 11.082031 C 393.722656 12.035156 393.582031 12.820312 393.296875 13.441406 C 392.886719 14.332031 392.21875 14.78125 391.285156 14.78125 C 390.445312 14.78125 389.820312 14.414062 389.410156 13.6875 C 389.070312 13.078125 388.898438 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13.6875 C 409.070312 13.078125 408.898438 12.261719 408.898438 11.234375 C 408.898438 10.441406 409 9.757812 409.207031 9.191406 C 409.589844 8.128906 410.285156 7.597656 411.289062 7.597656 C 412.195312 7.597656 412.851562 7.96875 413.253906 8.714844 Z M 412.371094 13.375 C 412.640625 12.972656 412.773438 12.21875 412.773438 11.117188 C 412.773438 10.324219 412.679688 9.671875 412.484375 9.15625 C 412.289062 8.644531 411.90625 8.386719 411.34375 8.386719 C 410.828125 8.386719 410.449219 8.632812 410.210938 9.117188 C 409.96875 9.605469 409.851562 10.324219 409.851562 11.269531 C 409.851562 11.980469 409.925781 12.554688 410.078125 12.988281 C 410.3125 13.648438 410.714844 13.980469 411.28125 13.980469 C 411.738281 13.980469 412.101562 13.777344 412.371094 13.375 Z M 433.253906 8.714844 C 433.566406 9.292969 433.722656 10.082031 433.722656 11.082031 C 433.722656 12.035156 433.582031 12.820312 433.296875 13.441406 C 432.886719 14.332031 432.21875 14.78125 431.285156 14.78125 C 430.445312 14.78125 429.820312 14.414062 429.410156 13.6875 C 429.070312 13.078125 428.898438 12.261719 428.898438 11.234375 C 428.898438 10.441406 429 9.757812 429.207031 9.191406 C 429.589844 8.128906 430.285156 7.597656 431.289062 7.597656 C 432.195312 7.597656 432.851562 7.96875 433.253906 8.714844 Z M 432.371094 13.375 C 432.640625 12.972656 432.773438 12.21875 432.773438 11.117188 C 432.773438 10.324219 432.679688 9.671875 432.484375 9.15625 C 432.289062 8.644531 431.90625 8.386719 431.34375 8.386719 C 430.828125 8.386719 430.449219 8.632812 430.210938 9.117188 C 429.96875 9.605469 429.851562 10.324219 429.851562 11.269531 C 429.851562 11.980469 429.925781 12.554688 430.078125 12.988281 C 430.3125 13.648438 430.714844 13.980469 431.28125 13.980469 C 431.738281 13.980469 432.101562 13.777344 432.371094 13.375 Z M 453.253906 8.714844 C 453.566406 9.292969 453.722656 10.082031 453.722656 11.082031 C 453.722656 12.035156 453.582031 12.820312 453.296875 13.441406 C 452.886719 14.332031 452.21875 14.78125 451.285156 14.78125 C 450.445312 14.78125 449.820312 14.414062 449.410156 13.6875 C 449.070312 13.078125 448.898438 12.261719 448.898438 11.234375 C 448.898438 10.441406 449 9.757812 449.207031 9.191406 C 449.589844 8.128906 450.285156 7.597656 451.289062 7.597656 C 452.195312 7.597656 452.851562 7.96875 453.253906 8.714844 Z M 452.371094 13.375 C 452.640625 12.972656 452.773438 12.21875 452.773438 11.117188 C 452.773438 10.324219 452.679688 9.671875 452.484375 9.15625 C 452.289062 8.644531 451.90625 8.386719 451.34375 8.386719 C 450.828125 8.386719 450.449219 8.632812 450.210938 9.117188 C 449.96875 9.605469 449.851562 10.324219 449.851562 11.269531 C 449.851562 11.980469 449.925781 12.554688 450.078125 12.988281 C 450.3125 13.648438 450.714844 13.980469 451.28125 13.980469 C 451.738281 13.980469 452.101562 13.777344 452.371094 13.375 Z M 473.253906 8.714844 C 473.566406 9.292969 473.722656 10.082031 473.722656 11.082031 C 473.722656 12.035156 473.582031 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"source": [
"binary_heap_allocation_example()"
]
},
{
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"source": [
"# Operations\n",
"\n",
"## Inserting a new item into the heap\n",
"\n",
"To insert a new item into the heap, we start by first adding it to the end. Once added, we will percolate the item up until its parent is larger than the item."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
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"deletable": true,
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"source": [
"def parent(i): return (i-1)/2\n",
"def left(i): return 2*i+1\n",
"def right(i): return 2*i+2\n",
"\n",
"def percolate_up(heap, startpos, pos):\n",
" ppos = parent(pos)\n",
" while pos > startpos and heap[ppos] < heap[pos]:\n",
" # percolate value up by swapping current position with parent position\n",
" heap[pos], heap[ppos] = heap[ppos], heap[pos]\n",
" \n",
" # move up one node\n",
" pos = ppos\n",
" ppos = parent(pos)\n",
" \n",
"def heap_insert(heap, value):\n",
" # add value to end\n",
" heap.append(value)\n",
" \n",
" # move value up heap until the nodes below it are smaller\n",
" percolate_up(heap, 0, len(heap)-1)"
]
},
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"source": [
"To see why this works, we can visualize the algorithm. We start with a new value of `100` (highlighted with red). That is inserted into the bottom of the heap. We percoluate `100` up (each swap is highlighted) until it gets placed into the root note. Once finished, the heap's properties are now restored, and every child will have a smaller value than its parent.\n",
"\n",
"To get a good sense of how `percolute_up` works, try putting different values in for the heap. Note that, it won't work correctly if the initial value isn't a proper heap."
]
},
{
"cell_type": "code",
"execution_count": 6,
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"text/plain": [
"<IPython.core.display.SVG object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"heap = [16, 14, 10, 8, 7, 9, 3, 2, 4]\n",
"heap.append(100)\n",
"insert_item_to_heap_example(heap)"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"A quick example of using the code:"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"adding 20: [20]\n",
"adding 5: [20, 5]\n",
"adding 1: [20, 5, 1]\n",
"adding 50: [50, 20, 1, 5]\n",
"adding 6: [50, 20, 1, 5, 6]\n"
]
},
{
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"text/plain": [
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]
},
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],
"source": [
"heap = []\n",
"heap_insert(heap, 20)\n",
"print(\"adding 20: \", heap) # [20]\n",
"heap_insert(heap, 5)\n",
"print(\"adding 5: \", heap) # [5, 20]\n",
"heap_insert(heap, 1)\n",
"print(\"adding 1: \", heap) # [1, 20, 5]\n",
"heap_insert(heap, 50)\n",
"print(\"adding 50: \", heap) # [1, 20, 5, 50]\n",
"heap_insert(heap, 6)\n",
"print(\"adding 6: \", heap) # [1, 5, 6, 50, 20]\n",
"\n",
"with Canvas(400, 150) as ctx:\n",
" draw_binary_tree(ctx, (200, 50), heap)\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"## Removing an item from the heap\n",
"\n",
"Removing the root node from the heap gives the largest value. In place of the root node, the smallest (i.e. last value in the heap) can be placed at the root, and the heap properties are then restored.\n",
"\n",
"To restore the heap properties, the function `percolate_down` starts at the root node, and traverses down the tree. At every node it compares the current node's value with the left and right child. If the children are smaller than the current node, because of the heap properties, we know the rest of the tree is correctly ordered. If the current node is less than the left node or right node, it is swapped with the largest value.\n",
"\n",
"To understand why this works, consider the two possibilities:\n",
"\n",
"(1) The current node is largest. This meets the definition of a heap."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
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"</g>\n",
"</svg>"
],
"text/plain": [
"<IPython.core.display.SVG object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"heap = [10, 5, 3]\n",
"with Canvas(400, 80) as ctx:\n",
" draw_binary_tree(ctx, (200, 20), heap)"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"(2) The left child is largest. In the case if we swap the parent node with the child, the heap properties are restored (i.e. the top node is larger than either of its children)."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
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],
"text/plain": [
"<IPython.core.display.SVG object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"heap1 = [5, 10, 3]\n",
"heap2 = [10, 5, 3]\n",
"with Canvas(400, 80) as ctx:\n",
" draw_binary_tree(ctx, (100, 20), heap1)\n",
" draw_binary_tree(ctx, (300, 20), heap2)"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"We have to do this recursively down the tree, as every swap we make can potentially cause a violation of the heap below. The code for the algorithm is given below:"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"def percolate_down(heap, i, size):\n",
" l = left(i)\n",
" r = right(i)\n",
" if l < size and heap[l] > heap[i]:\n",
" max = l\n",
" else:\n",
" max = i\n",
" \n",
" if r < size and heap[r] > heap[l]:\n",
" max = r\n",
" \n",
" # if left or right is greater than current index\n",
" if max != i:\n",
" # swap values\n",
" heap[i], heap[max] = heap[max], heap[i] \n",
" \n",
" # continue downward\n",
" percolate_down(heap, max, len(heap))"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"To see this code in action, we'll start with a well-formed heap. Next, we'll take a value off of the heap by swapping the root node with the last node. Finally, we restore the heap with a call to `percolate_down`. In the demo below the highlighted nodes show the two nodes that will be swapped (i.e. parent node and the largest child)."
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
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"</g>\n",
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],
"text/plain": [
"<IPython.core.display.SVG object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"heap = [16, 14, 10, 8, 7, 9, 3, 2, 4]\n",
"\n",
"# swap root with last value (4 is now root, and 16 is at the bottom)\n",
"heap[0], heap[-1] = heap[-1], heap[0]\n",
"\n",
"# remove `16` from heap, and restore the heap properties\n",
"value = heap.pop()\n",
"\n",
"percolate_down_example(heap)"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"Finally, putting everything together, we have `heap_pop`:"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"def heap_pop(heap):\n",
" # swap root with last value\n",
" heap[0], heap[-1] = heap[-1], heap[0]\n",
" \n",
" # remove last value\n",
" result = heap.pop()\n",
" \n",
" # restore heap properties\n",
" for i in range(len(heap)):\n",
" percolate_down(heap, 0, len(heap))\n",
" \n",
" return result"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"To see `heap_pop` in action:"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[100, 5, 20, 1, 3]\n",
"100\n",
"20\n",
"5\n",
"3\n",
"1\n"
]
}
],
"source": [
"heap = []\n",
"heap_insert(heap, 1)\n",
"heap_insert(heap, 100)\n",
"heap_insert(heap, 20)\n",
"heap_insert(heap, 5)\n",
"heap_insert(heap, 3)\n",
"print(heap)\n",
"\n",
"print(heap_pop(heap))\n",
"print(heap_pop(heap))\n",
"print(heap_pop(heap))\n",
"print(heap_pop(heap))\n",
"print(heap_pop(heap))"
]
}
],
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"display_name": "Python 2",
"language": "python",
"name": "python2"
},
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"name": "ipython",
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},
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"name": "python",
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},
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}
| mit |
robertoalotufo/ia898 | src/hadamard.ipynb | 1 | 138978 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Function hadamard\n",
"\n",
"## Synopse\n",
"Hadamard Transform.\n",
"\n",
" - **F = iahadamard(f)**\n",
" - Output:\n",
" - **F**: Image.\n",
" - Input:\n",
" - **f**: Image."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Function code"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"ExecuteTime": {
"end_time": "2017-07-31T20:43:03.317417",
"start_time": "2017-07-31T20:43:02.940342"
},
"collapsed": false
},
"outputs": [],
"source": [
"import numpy as np\n",
"\n",
"def hadamard(f):\n",
" import ia898.src as ia\n",
" f = np.asarray(f).astype(np.float64)\n",
" if len(f.shape) == 1: f = f[:, newaxis]\n",
" (m, n) = f.shape\n",
" A = ia.hadamardmatrix(m)\n",
" if (n == 1):\n",
" F = np.dot(A, f)\n",
" else:\n",
" B = ia.hadamardmatrix(n)\n",
" F = np.dot(np.dot(A, f), np.transpose(B))\n",
" return F"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Examples\n",
"\n",
"### Example 1"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"ExecuteTime": {
"end_time": "2017-07-31T20:43:08.099924",
"start_time": "2017-07-31T20:43:05.275844"
},
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[NbConvertApp] Converting notebook hadamard.ipynb to python\n",
"[NbConvertApp] Writing 1348 bytes to hadamard.py\n"
]
}
],
"source": [
"testing = (__name__ == \"__main__\")\n",
"\n",
"if testing:\n",
" ! jupyter nbconvert --to python hadamard.ipynb\n",
" import numpy as np\n",
" import sys,os\n",
" import matplotlib.image as mpimg\n",
" ia898path = os.path.abspath('../../')\n",
" if ia898path not in sys.path:\n",
" sys.path.append(ia898path)\n",
" import ia898.src as ia\n"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"ExecuteTime": {
"end_time": "2017-07-31T20:43:08.258308",
"start_time": "2017-07-31T20:43:08.102042"
},
"collapsed": false
},
"outputs": [
{
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qxgu+68Yhmq5QCjrJY52mucR/myb9kWBYFIgdo8BZNo02do7TRqDknWwFB5AzttHTRqG64a05jTIUamB3pNG8K2aO4mXVq3QdtONgU6yEAky7xAh8XZBSK2nQTuDvBtt/f2aSTFIgrzhG/7lD0FUSDlDYBUA0DAl9vvuPvxn/yk+3+JYOwe/uG/vv3unU9/7ltf81mEqm0pv9ocGipW7/bRU2h0qqF9mXKQZXMopiU9Hb3Ra3BjWU1uOxWt4mRrdh7vXJnvTzKxm2CUQ4PLIY1dwZ+uhQkLY6ANpOegfGiGrIm1je3pPh2tnU/fX9x2KsBCOab7lHnVmgEICtnyh34btwoABTv91NtedMUgkeDY3fH5Hz/uvj1+/W/sTp765vuedfk9Y73t8sOf+6d/4IsHrKe1i+meTPrsMPB8OKzTCla5lqvRmrscklANGWd0nO+ynbFnGVvMmA4WpnGuM6ptUmQXZDEN8KTNtha3cuv0nAbESIKGmh6tYV6Dcri35hBKsmXzTjZ7MnK12o3geqJh8vZOo71Rtx45QVz+1gtv+RJXixTsnhd851d+4vMfd5Gf9sm3f8onvPvet791h/nwAxd+8s89598HVdYm9qJCS7IBKwV8dlnTamiCvXf5utXcV6KWWM1OaFqTHlnG6n25G4nOKAt4dvdQtE/u3c3Nm12Wk8WfcFdYLQhsboO5GLZd25hmMOag6NXMAZOGoszTA8Va0CZSfumqqwHrufSohR92Zv3B9sf+068+9r/Ms4es4cWnfsvzteO4eTN3b/jPH/9NZpdgly5o6Ys1v/a+P1uHgcMQ2jkZ6qJqWMYMlJjmbBW8fAJNYwkyHacuqx5nJMFuWkXlsENhmSwry/ZuZy7dudRcqteKAmRrb1GyaVlDNtsqMdIyR6SmrEsqmQ9DczSbgll2x4ABSAA3u2aRGCcincZFACEz4klP+9rPufn+cwIUnvD9n+xDfe3+l7z+vU98zgvLL9Mvni4+bX/v8h23XXVvrbJamoZumI9Tr2MtYS0hwl2dGxRbY7qMMkkLYPQKb4al1RTgs/e+wJCgDXdfNNXgaHjZqG3D7A7uFCwnqVxL7KWmm5NpPgBUNMvA0BC8Y7OCEQz0bqPVomaZzKzE8i4txHqATGzQP3171JW/9F9v1GG0HvMdT5jj5tXfff/7XvfWz/ns05c/r3y3cNldP5k7v3zn2679GZfK0PtBUI7NcsiA7mYRKLTkCNOsk0ZMWHtioLyrvE0De3MECt7Wi3WiFtqSBSsMJeH7dbc1MKSRGR2klvY2E0hKCHrB51ITo6kw9lBhLp6RNsyqLQ4Wk2XpvWxGjKqRGIDZ+M+1A9XWMFw+t3jpz33BU+svY80XPGvJm69/5bsefN99D/7wR/3i3T/zPvgTrj4CCnIquPmFDVwe/WBOgs0hJXrpcMBUaxeiUa2QNdhD9PQ6LPIybkvnqt05B9qrypcq65U9pk4TkMuSY4s0jiP9PhRCIaAakreropomsZEupi1ZYGSEHBptIZe7cmmTDa2lIhltyCYkU35zQXbsd23ef3jfQ8/44gvbM96+9RO8UpoPPHjt+qUPr1ctP/KP7bmf8NF/EVYsI9Vl3/hdN7A8+hfubyu5YKQwF5X12Hp6cj8ExxaUoD1XDkRvZLHCFJC0QwNmzRLMEl6Z2RRQ22CjWa2swXXLtq5FDarsoIaNOcIEUG6TRlm1mo5CqKZzM5ixtJmMLWj1MZq+AQ5293LfdTVFa4D0q+ep//G4uT5L7ReNduGZn/yZu2tP+5a/93lPyjtifcPN31k9Y5ixZUN//DHsw9t+h04GzNvL3fZphQ7PilixT98WtzUaF1zFuT84ZVrVyqrezc4JphksDm5kxQxDetqJlBxsdQ9Yma3BDQguW2TIGuZ7t/SlSAspspy0HjkOzmDRE8FkFOUNmzENNNUSUvWYjnej98uBTVC+u5Lz7PBa0wOH5ZSbj7j0DL7tUd/41Nl/+atfeeEx7/uulWi1qG7esb7lkQ3V73oUaPBymlVLXvKetuY4GFZvImpzaQs2RtkcDc5lchhK0SQkR7OXCXeRB6mMvrmW85OGwUZCQx7GiuTmRe4XuqXTfLSpC1FYtmGAk4xpc7A8qiwIJ5oqi0mX2YycUavtlutfL9SFgyACJ3V19+v/6Kd++aPmu5aLT7jt6unSy4WPvHDjKeuCP/qX/uf/++Hbr+bU+W0mV57XF9717i/drr715a/GJ79svfoF//qxn9oFcWxRvZhJ56HDLslueWGn5vQZYBxcNgGlN7GtLZ1kxSEUOQ5MBWqnmka0pR0G6txqQAqvtd3aTQYztZfLYIRBucwc2FaaunqIcwnNdYZBoGKGSQETtAo7hdGufsEbIQUAiB6ol35Gf/HQ1ZP1zg9/2vWLuarmbY8dIE6/4UO/CuuFvT7keV+NGxd4H997Nz7kv3d/84U//vV/bL3J8aR7NYoyrIouzrBIrqwIljUrvWyhN8cBzaGzYWr0PCkSewIOFPa1U7Z3pG/u+8Wx0bCU3bggboE+2MixhRbJDLWrSROjhrAxTGuNqV01ykyOLkpaDF3Dy8jNTda0sup441f/roC2Ixq8E/Bf/8LyJ1+ji1/zyI3HLUvVw6gxxrVLTnzFi2qpfX92n+IRPnzXpdPxs2/78gdr9jP1H/A5AL4Q77IiI9GmaE9YRmNJNi2XVskxQ6DMc9OgfIu5ipGIQ2RjFJwmm4xpDKuYLnOU+XaRqF0Uhm/RLjKwLQjVrtED3rEtG1IMhDb3wrJFlxmB4d166PxKa3fx0loPnR1O/Pydb/mVS6+5IQi9AaDdMwfmw9/yLXfcOP3NT3/aWA5L3bz4yOXF88phvex36qQ8v+KBV/8pvK9HX7h6+Q/h75d6PZnXH32e293XwRxMKdA4GEKWHpsso+mIhG9tKnE6lmI3q4saOWlaioCWCWTTeluUmLBlC6E1hyuNiGklQs5ER7SXBQxiG+e6idjtWd4mFyZ7agBjffePviz9nYeWd6zL3E+KWQnCGoA62u942oXfHqb66ad9zI3TJ+twkgM8PT9dL5wf3vHUi9N4ozxxx48+8pybX2Gf97e+7Rftp3Y/x0ms9YUv+cF33vyev3Lb/yOZt3HShzSZ2AJs9DLVbU7fm9bzIW6bqbQcgpbTYJQMjEIrELC6kL3OkeoVQJkbGTEzFmqO9rl6BgF6w9KCgnlFHeWX5aIIkT7AJX7l/36YDONka24C0PDZFAQ2iOZdT34mT2++nUvN73zUZ1y83Vw+ddvww41x8w3nfmLEx3234/Rl5yfoq/WxV772v975X9YnviU2xksfftNrrt64/uY7I1Vz7RpzbEDMYaDPsenMXTF7yYEMltEWVe9yPV8PAwLKq0ehA0OUR07lOmHJlFlGwXtpWLi00twxDkaFp1s7pAVd4UwZUWFtN9cgmtbo3/myPejyMBM5XSCqDVCagU3o2bhjs8MnPnDgSl146FLcoUOerB1neXr1nddf/+Yd7/zw1wW1Xy/t//lnjN7yHePwoq94Z7tF/tBjfuoTLr/r17/sYqJs2BbJbEN5jiwGYUujt/VwHvJRsnJWGyewFthwAM2qYUKuB2nPsQ/tkF7ktjpCbZvGuWWaFbBnLy0ww7o3M8zm6BLpPiix1ujUCthSX7GXsVUWRYDVANlQBZvekPCPchy200uf7O72hIff94EHrt3YO/K8dqf7K+9+9he87F9/y9d/5f2suf/COn3JXs//nEvfNfL9v/KHR626pHue+MbX/ca7XvJQl4TNyjxpBiPDIkGXwynb9ThM9dTmshzgNqXqCqNI7OgZUUf56RoTTdmwEHqTLH2eRhETRLSxohGAabTcBHmau1IdWtKa0RJ8/MMHh+jdnG4NRFINqIQmekyJvO+rfjZi2T3/xivyrsvvisu3PXB+5111dvGwXr9548Nhf/O/fforfuke3Xvlzjd/4u/tR/1Srp/3O/yYb7wtX/jPt2V57Mtefv7Uz/0sq+zhkSbkBaTLJtu0kdVzgRmwdzNCh6gEWFZaylDwGbJD9LJfKPgWqhmRLS+rSY3J8s41vOWjNW6JJJvlYdMF0mupqgUMknGcxqyW+34A4pE4YIsQOqYaBvatFoD+m//2ZXbRltv+4FvyOW/OD1y03CwuHeK9dx6ujbPrcf89f+yhl965PLh+69/5+j/+ksPh3u/+6r9zmuf/+I534o7Z/MLP/XE+9q5xBbj81hMSknUGu1gKVaF80CoDwU5Oi41RxYyY7XPMQWuUI1fvtiVp7YZsoICBYpnWLUfeUiMlMWehvDXYB5izQbHMuruDNUuiI3bjb1crCRzR0UY5E5C3mmInAB+vwV+7cOn2xS998Wednmi+5773fODBBx88e+jGw339l198NsYfnLlcyvkXzz/1b3734ZvjS7/rvtNeXnf4c7cvl9YdHnndW95x/e0vvML3/kg54BYU8pDA4vsiaeyWRa+p7qKZVDRbB91jKbeGA8zRMPXeoiQ1Fy1LcbOFMCXNO5oCdYK5RpXPkCoW6DCoSdGKcrRvnvIKub3kfzLQyTiiXEElIINiEwE2gD7cODz3FQ/FDo+s48aIuX9n5sD1u1eczp99GR984uMerVg48u2f8w/8t1+Nm09/6s8O68Ov/+6nXb7jfOIF7/uO7d03P2q9fuenLgZt1gGZ+RYH96SjzL2reFg3Kg6SMwlT05Rctgm3w6CVLzUqVKPbUeiWtyg/j2CrI3YzesbmK2Q2V0FrC8MsJmRWNGdnD9sVQ+b19Yv1RINGlVnJwaRAeAvsplA3D8KX/MdxUF+5ngXOfueGx98Zd9R73/38L/uVn/3FP/H57ajhP/8bN4OaZ//PvT+COLtw/r4v+d4f+eQL/kuf4Xef3n/j3vcsZ+9dSo96snVbIoyjSy4FUjXGue+HLOWJDMBTQLMnPX3bNQwFpmR2iALnMrCdVuTgSmMbMraGDbUMVgqbS6PpdK/lEPL2BrwiXSQ5/F9e02yRao8uiVIGBKhlhTqOCXW4vjv5+Nfq5o2rebhGsvbvO7kUfX27csc9+vSn3P/FTZOdX5oPu/fGfuj8Iq/V5e3T88qLPvNK/8+zXyPuO3v2ww8/8hWEnv2Te9ZwNrhhTBbavALb2EcZFA2b8prONIqKZntnWFu3FStGmhQ5Mjgdmu6F5cCKGmTxdEuXmeaQdbAbtBoNRe7aeqkhUy+yl/3HLV00lhqMElA+6TaPxJfjyAhfu3oPPuaNfr5d+8DmoLX27zldedBtT96/4bff8SXpFTo3QVNG/6wH7Xzqzz7r3nt/IO6G/dnnfdg9wKcAz0I9d1y4/DFltaSsBFOyDO3y/eJyjRtLuylEwqAiWVbebW2NZR9Qy/cLYaWeUrKsmoFqh9kikrFHhBm1lJnDYhjJ4YssZEkhmFi1+72/dp5UuIOoYlPqLgNFMwotawDA+a828AcvXrh823LxshmAvPp7v/dA3vOUi/uXvPCj/4CB0EXWuZ2a6cajHrjN7EPv/amzn6l7vsDtnuff80FxiX/GZ37+F37Mgp4dbaYenT3FQltPgr3YAHE4woW96KgnGio26sAS3WuZPVtc6AYtBrPDZuZUhKZhDOt0gzNoMGeJvpXBwbSliYrNMF79VVesTWMzQwNoUwgtR4MtCPgDvxAJoP7jH7sLFy/4hdvvuv/C6/eoUl3/vTO/eHfc/dGP/5uZRtqGvdVht2Hb7ng47Sn/7PJv7/7wpyznrm9cPvHweT928riLb7n5z0Tc+QpoFAFrWKA9aU03IDpVsUWFPEOmHqpy2eZtbUTH7EAL0ckuGadtFmWeFS6P6QuhsqBRRC4NJmlqh6Eoq4A7Mhjv+uoHKaM2k7U3ioBippk6imJM/bIfdTFvuH7p/u/6xivvPcF+ues+wtA4vM1399wVH/LH1MtsS81dtmVlve/2h8f2ntu+7ifjB2+/8f2H8aPr67fP+0/2f733lYcEcGX00ibfL/Dz3dYkyqQJoRB9PrhATpToifbpbU64iCCixqglxxbbYGfkuoVy0djKLJBYhiLZ0Q4jKwBZL3OYzAe7y2Vtzqtf9QE022FlpNiOVopmHSzJ2ow6NCDi/Oee/V3/ep5+6DvvwINPvK+OUs3tLft59a73PRdWfdJ94qPNqnXqq2z3tr/6o3c/77f268v+7bOurvfP/3A/v++8NsG0qJJttUiAuE7K525jQ7AZa1MEykFatTOtig0FElpU2V3CwGKpkT4jK2rMsP0SMFd5D6EI1VKkTEJHRiAmY3izaH7jy98iAiYUmoSsQUBq2AxriZJVAyCg//LU5/3mZ7/r3pvLXfmMX7cDJJq2tz/w+Nv7ywppmZpr+nqwOZ/27kedj/bv+ZhH/tUFbe9+9Kfl2ww/9CH7+wYfHADuSYKU0tTevll5+iTNp6b3KE62vEn1mHYYBTP5BA2d5hqV9Gk6D8uR3C5dQy4yjwoeO2iEuRoVzrl2r7y5jCLpahnBWK59ze80Wx5CyxJEOilKREOAsSy6eUQD44Fve+O/euuf+vWD687pZ/ujNEZ8xp13fvlug1vvC7BlI8dZn195ytPXrJ/1BcV/un3v9z+oG7sL9c576koW8vaDH8nlySGQMgGsDGlA1WjQLRkpHAnVMbNhlOAHLzR2Wy/Zw9oFLudWC0YVEXOXEfAg26II7zFptVzsXomOBnooxrWv+Q1ZQ+jYiDRRRjbENrRLBAzA0RcAjnrqR77/rpe/54bX7m1nZ4WjZIhP+A/Z8O5ccb67eRG7a9rO77x5x8e+6Wtf9Vvf/k0Z1/ntL3/tF9ymyyg81fv2hSVtMmvr4nKw9qYCBuw2Q1LR3WzkusEajWKrcmnpiO9jrpaH6I1ocht5gjaqYCyYNwxki84qmUoRYIkx1UxWuo946Et/TSTp9HajmwFUw3g0izbRKZEwc5A4ye+98afunvme1//2K37tBnFLLYL/adFF+OJ2vo+5P1/d6unPvet3X/Lkl//Px53ndL7utx+81j0PabmVDofKyZbU0T6jSh0sdEM0R1s3XcSY3M/JBrm4j7YgiW6VjUQURjOKAyOnSRggTebDDG3eW6E5DjBvMy8K0QTdl8D6qj/5O4Jb0LuahnHUD6qbbSojYTQRWMfpCSFemD/yxofUFy6+94GP+VwQEEEQVw+NTJPyKVdftBhNNR/1xvte8fl/5f7nfvUvmtvY5ZX5+kDYcU0RYoV38yiXkSmyWUZMqokaLKjV7VhGDpvI3EArt55seJRXsg5uWe301YSle5FhNZuGcrUvarqWcgRRIJWhYK8+8L1/8b5ltwJyGkm5/ChxZzenpJKOfJgsyFhtt+jhn7sZr3/dW+/7G/a9uwlQFIB+FdLU1fbUsx/7izP3yvGc6+87/PMX/6Vf+oXvmb3ZdiP5DtkkiyplzRrscDdHhpYg6NldYwS9FZvMLBio9YDMdpY7WuxDrAfr40rFMigZa8mD3Df39L0OUYONclYFAurBcoMXXUtRTnr/sx9um5RMoEmkBEeTt2R/kosA0SJALdLdV1Vv+MTbX/OeR/7ND/ws90fFLADyhZ8QsDbi8m3Xrj3uYUzwOf/rrX59nw8Au578hFc+ebzdZjGH1AGYzXLMpQV1pGsD17KDYHO1ykirjrKOshkyokYWouQbAw5LuWm/9Fym1cFN0cEJjG0JzWhXRQSnaCr33hZaGZtsj6hv/S9ThWjbzNuTZfAsQpQAa6DhbKDBdhRbdrq3+bbbb9z37i/4S1d1AHGrCMJe/I+tZQfX+ve/7tu+5xqyrh1u+6vXLyy2nDVsfeSV7/64fNdrnzNpBRiRwcKYfliwNbNZFgVWyMz7EFbecC29FID9KWaP8vT9Ikiwzh4bgcFeRdC20znVRqrZ/AVq6V0vhR5mXg44zUUMyHQq/ZP/miWJouCGQsNMDTaPQjARpsHNJGiZ9G3iXqvD+IXxt87e8DAdM497GYiT8dYDNqrd7ePev0jd/kX7Hx9+kHdC7Dp9Ai489nu2aWomWY62bZDNIlXLfhTZ7HZVpLdlJFlDyWTkmNbWOarNpxogj9qFNE+YT0M15FR3eXittcgTg82jLpbGNiLN6Mh/+N+6rZQmA5tEAu0AGkfRc1MUm2ZdO9sIVOPB29nz4od80d94hBe2ceFKSYBgRP/8Z04QTNl//2xcGnX1/P1v2bcd9AUf2z/wJoHbe/d2/ys+Qsm55rrfba4aSCooUOUIsL2PMJcl1Y5CCWlwwQnh9NCWSSPBnCwDDVWhaSXBEDWPMjzbxuyymgdDzp7mcrEIQ5nf+IYfnoC5WeMo3qWjpaP6ETwuDUgQGxcf97yLoaaQs7vvfc83XcEJYqvHj1slIG5c//LvB8meNZ/wH5drfcft9etvied8TvCPP/8lbwWIulnb+37GCCBHOkhmC3BQrd1UVSc2+hQ2L68To6wlhQbUlQA2ooMmUSWI7qWuEXL4Ikwdmk4iCiyKOUARlCd8o4OF1Xj+t36p0SazRrMFwtlUGXDUwBBUE5TI7I8db4yragBnl6Erf+acq2Wc9I1HPbiHiAa0/5Yv2N18/SfF4aJ97m880KEz38ZH7152/aPe+Ideyqdef1+T8g9UiXRpwgteI7boIjrBSGxubUfqB1ZzSGHlKZWKXqx1rhthBVAuCp40UwLNNLUkqcAwOYw9ILEtqkM4Sgqi7fC3f1Giy9gOsKBOd8ySjCBEHucQCJA1fv7yqT2EgliU/uJdT/kpszzsTvbnj71vT4BtvvDff+m/euXL8OLTRz/8zR+Bm2d3X/zAQ3/ombffNZ558myM7UStefGvo826wVHtbcuEzk1mSueUDQcMgsbmlZG0NmUPkFbo4XVEDFqAykss5wHDZ6INTWgOjInwbGqdfWziij29DJKBhm94cdPYaAcNcpXotECro6GjBvaolSXLzm48vDzBC4Q6UR/xjf/GPbTtuTvf7nwvsNz5uZ/0IXdwufhJrwVe/6Tlxus+9DFv+sCdfOvv4eKFh58Ff8rt773tsYjTl/35J56RGposa7ARaaAc0eWC2tEUZBXWTJpldtQcZS4BaYIAk+dITQNBRCcUZQUD3NCiAkEh4dswUVqnix0UzeLv/HwZAZThljOkVXM9XjisCZgoUU1PBye0f9dynAdn81vmv3Mz3x22GHYALv/dL7rzPY9sy7zy1hPgDX960U/0A8/cTq9+mB56x9n2Yaf2+osTrWX92C+a1kXOHtYEiOx1czXh1AeJykYNy27EsU9WDRRQu8NmaqMMMJ8uSobNvEzVLoFAGgUxkmsTrIVCLl1tICFn+Lf/RLvKrIm2Cqqo6nQ3FNGEYBKsAeH4z4oqk6mJbYzxdw9OkjvD4frE03/oib/yq7/1QD/K4k3PBm7+zGn5/vPvPb5QmqDxXbhtqf3pY+pDr2eowaXEBjrADWhLM8o9SbFBUxbWQwdKKkBpJT/kIgpU0rrG5j202bIPVCC4LVPKggnIWFoVze2ENWyynE0QHvHD/1FoBxreBjOJJPq4Ho7bgsLxQzVFmBWqo9KabXb/Dzm5gdE3D4nHvvQD3/Rb77jSeAce//T3vPG//saLiMbbv+kCYIADeN2zcjsjcfbGS4WRxHZkm+J8ZJzv8girzFi20bJmgzNDtC6me46jQjzhUrU3Yi7JRCPpVmNalk0eiYH2ZEUGy+Bevq02h8soQE3ild8GsZMne5AmycQ5kK1mE2CTTes2iEfF6nHek1ECNJZvFa2cOKSk0x99+G+/8oYD+tqP3b3gR5784yeX9/38X7vtpX/kg/gnXn1b08fp2c23fqpoZMmzQ3Zg7NMt0GngUI8pszJDhkdmm7WaLBoOotkUFQIVWWbGzeUsQaPLDyFAcCG9RkzCtR19PT0kjPSyJd7zdftj5MMBQExjusHKBYgEBSvSjmKAozvm6JLzueholbvnp4D22PLGqsHveOpX/upRQfCOZZ6/NfCcffVXvPxPPvn3rx//Fz759tPt5Orv3Pf1haItUzV0a2R3s7QKKxbkmaLKXa1yagYln3CM9uq59LKNpNRocIvJouGYbBGIbi+XRs0lTO1w0kFK7WxLuW9f9yDokiiYeikiIFO7GE2AbNIB3lLFN3m8MWN/+Tq8pcM7h3Ona4eth9eX//mve/F+lVnipxBve+WvPfefgvq9u54M3LxwvP5/86fvPdluxBPq6Y96epV3wrQWujCmwN6P8DoKUCCWF0u0TYQ6BRBeS8IqaV1RFEmWaB05Skr2ciC70YEpE5eydGPZEdKJdisgYsmvf1NTBqpJp5vBqyQ6yZYffbHELVdcH2Ewk6iFe5oETR/u5w+f7Qs5P/pf/auf3o4mGah1219/wj8MEA++EMCrbi2Av/vTeM1b3nPlnVfOnuKAMlFVXeA4QkDmXCAl2qaJlNyADskjDJ7DlIZULNLRsdGdBaKBbDQXTiuB6e1ekawKT/aOkDwHvUUagv/sl0RAYDTNmkUrSBWSQWqJLUfbMTvMm8ZmCb7aaTobjcWtzvfZJOoxL3zhdz/sDqKPPpzVq9FTHwlgvuYjDAAOj8IDN+zqB26c3Zm0jlZ7UnIdJWGW1se3n5Y0biFZu47t2RHAWDZWRZkAm1STMk3FtLFvsmFsE0b62W6kYdlCLmvGthyvR11Y/Pt/tI4Grz4pQZagWVNKGPuWK5YlI2XH/JAmIcx7btwsFQ1ahu3P5nFtnL74jd/6XoTKeayUN5ZWq9/+JwDg048L4AH/tNomHmHcuSkTMpNlSOhWC61dWlaQQBroZZ7YSHa5TDOq4OW2WZd3e8PLcretZ8buRY1mtEndPhdZo0DrYrQ6DE5UIgb56n87mwQZC6CmEV2zVd5ZE9VoAjDT3LKOW59Hl8x7rp7r1J1w1/76oXGJ1O6/X/jO1+Rs6tifqb+PAjEf5/+7AuIvP/b0JyhBS1wPWEtpGVYUECCC1d1mAtobmQYS7i4zUHk0dpcqDKNE0AVx5rZMstXaYPsi6JYu7xk928K6w9RVRxwMsGvfuMmMFkE0AmqaGtUoITsJSm2EmbHruP9FQ2dLmteyGoPbzdSKw722fMsnf/ePlTBlVaBAfU8Y0C/9P94A+F+/8X34Bonqmx94Zs8cICtyO5qNqtU+gYrCLUmSWx1bkpaVazW19dyFxDi4tVvCMZLH3t84zUXbJB7N5GNbzK1rdGEwvEij+4hven+J7V5bz7Ob1/eHQ0oSjm96tZhdJgI0iziasA064uLHbC/z2gq8hIzlC//vH/7OCaoAIQgB792TeOStwHe8HgUAeM/n/IVP+r37aCLx2L+dHJtMKLaOBGQNCOqlAI6lQDDZrTnpmkx0oau5x3DmSbVXh2a3j5E2qAKiY21SaPQBKju0LKwDMykRogvxU79VMIueWXO/P2zbfjscskulvmWEurXkj7g7HNvxxWg0EyCIVjMBGpb9R3zfL/6ja8uyOAq3/kD5Igte+Qs38Od/7n8ct8ELHv/N+KMEfVkv/zt2AFEKNY4MpA2mWJxhVtksE2RsLVY+EEQj3Q2xqaYV29uq0N5qd6fbjGZpDB+wtmitbKTB2AJC6p7ZHNf+xYFwoNSZsTrQNQ85u9UtGABJDsogQTJ44FBoypYIgGaO2Ryhs0u49BOv/Na3r2N4YPaGWx6n/xx98QVP+P9w51fO4w74opfyz71XHKcXTr766WmmFTgCEMhKdk1rrfIuFKYoh1HWXd4V3VIYt7QpGAQaoAiPNkPnvmcNc2o/q6dcCHbXErSJotMPamK0sP7z8y50zc6q4rhw6XQ4OjNTLVByCk3oWPhlOqpKp5pGM/dgIIvD2896973Xv+PlaxwFuYc8P8ZK6BVx4RTxKf9Q65uON+BbL3zZj/vtt1/Y9cd+dWZuqazeuuEsOQ2SlDAZTprNORtqualbTBk2CEz3Lk7RsnQotDkLIlTdVT7MuqWFasvIHuGMpsvM4NSwd7+o4EWUGtLmQIFutNkyNEWQTRwzgY7yKAAwlI5BhwVDlQcavu2/8yP+xgvD7RiGSvZBIoD9K17w4E3edfdX3vGOh+8E0PjMV95+6Vyz8A943u3oPkaJkbL9ACKPc7cglLe7ZoB1xGbNcni1YGVlGuziSBeUMEONdkVVyKB1j2QRCIu5hknwhgGaAYx/NsWSTCUJKIpcTxrSniKKbCRIkg0I9kFIiOBkNECY0kcDUHzWF33jjzKOUbDLlHAUkKB/+g3/a71td+1OXPpr328AvvwNF+qum/fH4TMet1kZexgYMqLjsMgbJjKj4XMkoJQaMtTSfdR5yyxlaofsqILbHA3DjNi7g8E2sKJjixJ8HwR/iTGH1sPJFt7LcvaZ3YJ3dR83q5n7mtNrLxy5KgkBo+lWNoRkJkoi1MaG2RFUVPun/fB3/ovDMKFFWM8CBIqwe+44i4XXHnfP4f3bJ1147rfed3ve4Kmf3/mzJ+emfQjWINg1jqkfBkSXHEdm03KZhopusgBvtnqZaE+Tb84EuA0RB4fpuGVluW42DUUx/eCHCyHWUKd3m4L88fpg6gVJHvvi7rMij/jX8amD6OP82wDFtibRRPSk+6Q1S40n/OB/+rfnK4EWhIwogMfvBx4QYd/69/cg3mK96NpuPb/O+IHTLYFdQu3dFIdNW7d2AYVy1WAuJYwtmjaNmpECcQzmBGvkNMirzQxIJ0BuvtxYSJfZcu61oKyGN8yLbQHZSof4061utflYd7uT3RiDdO5iOb14cQFuxXcCMsEAgWzKdJQLq+FhBRNb0r0ve8t3PLQYj6kkmPsPDs1CCxDGX/tYNzTSp+f01u7OZ6CtPY3RNa2bSqB6kUv0IAbEgzd6qIxBYJVKasiLPuBtAUxDmLLKwqxDo3JwitVVw0ehSSulMTaBUiZJ5TvR6sYY4QTaZWROu7RQ6ctKkDKKlKzVIGBU83/vbgSDBGiXfvHsq+4fdkwTEzz6OA/qFpEAzo//TRiMUG+oWnXtvhertaqrimkFm+j2JlRmSTdWo/0AcTIAorhZ7nXMVDKUQ07ZKO/yMo6WlhIBm17VdKuEIwAxaC13Nd2gQm9bt6A+PjK6UNsh83Bzf741l+X0yIv6cSjnEc4jKRDsFgRrAWbG9T9/2F9+06AMKSPB2C23WHII9qUG4jUkSCF3mXk+Xf1P1xMvtTXoJoLtnLSOwRbTaMDSw0DP0ZhtCsCHsC2zD7JsCdbdMvcwp0HzGBk+SrLcRFgXQUO7pYbKALHrWEog1dZNQFVzy9mVM+UOhosw4ZhDRFD64BXJSBK0mjW7Svwbf+Rv/UqM00Ae35qFY3IIBQFP/3uCaE5xPQEOp7VlHRpvvS/N4XDCEGSEaAhss2O3t26qKlIUOfODNIjz5PSk6WGtRaw2jJY4a54XgLlyQxF2tMIjzaCBjLKwPFY8M4o7ANXqOuTsTmS1ulo2VjdXCzpGg9BACcfxSQDufgwJCWgbQ2r90b/zr3/woOiaaUaYOwAHAZJc/ksLIMKgy3cRDa+5NTT/7WgqcoEJDXU7hJwWrM0lE6xTjkbAw7zk9Lnc++h7B+AeUG1m2YmyXjooo3Rmow5arJIBs5BldLDM0IE4Etyjc3naUfFadez8YrjRfFl34VTQTmK3kICqjwPSURukJ5+/50gQiaiE8BH/6Wf+5XXtb14/P0ACu8Tg7ogWAn/kw5IkwwVdewSm/eIEDPlCDo/ETDSTt7CHOXxOeBEJp0fUcBYqzQbUm8bFk9vutjocCvIWGCEW4WW2mS2j09ZuHWOwAZPBNVbaUVtqZjnL0V8GQLfKQAG7ZV1Pd+swg7k7eHJ6cnIauMVM4BiURfRbr+atzChWZfOun3nrN79/obYS5tzmrMxM6VayDL8Wr0DTxuNMvhCw2k4EUbr+k5EpNjeHMbLKWdZu0amCWarLRjYomFgtJQzApYURQsPQhrIAMFxtYpYF+9i+xhEhdmZrT3NVBFWpRdPyU1ykCYK6qybcR8BZjGFUzZ4bIy6Gjv6QW1W9U5gBQ6u7LU5fZF//huBqtNWHhweD1nN/LADQS+ZXgdK40tRtOwk4Enus7f8bDTTZ1a7NvabhMiaMB6K6zcwTzRREptnws6sF4Gg393AvbxmE1Ja5OguylqmL4kSSpZ7hvetghSoX0ApuWr/iuwEBbJOIbWFMuVwoaB5ZcaZj7S0hq1tZKWwAfnooohjOb//Ir3pZh+jqOoIEtDar2m5hYr/wadvpyeMeM+wdNw59g4D14eI1SMLbr8Q0ycIkRMNL3Lc36N3rpE2s0T2qY4BxrS6tuv/2Cyfr9YOijuLeObIMGoewTBblZaXRxGG0q8YkImPdB18iBq0CUWHs0Z9xVbwFfpD0QBCWkgzTyGr3uaPI3PKo+iBsFsDFsUf7bTf8L/zLf/FPr/stIZ2xwXYX0bUdwzUu3nnPJz7v2U9813c879NuG+evf+2PvFqNddwwL4yfePoZp3lz2TuFdJRPZzKSPofUQ2o7sh0333nt7nuv/q9Lj3/8Yp3gRICTMAhzbaghTxNt8zTwfImysvasKK+jtE0kDUokMb6dx2uHpO7atv1h22+ZNecxFSUIsoW4cPlWaiRlBHTIuGv3pK8ZdfHbX/TvrlENtcIoSWZ0quEEoVD7H/zIp/zkn/mZ5cs/45due9pnfg0Q3LDKQL1S4Yu3aXNll7e86HTUCAnpUokREV1+5a33/+5r3rg9+Ko3XTuDkTFaBfOktCsxwnwfUhUnm73rYunANjPrY0drkokMhxn7BV9KHfVckLqra25bVfVx7GNYL31kwuyi+xE+N4ekxM1v/P6fePjS6Rf+/XdRbRCGm0TR0ejuY5wS2eJDV85/4V3XvuMTfu6tN979lqtCU+cnIcimslhWRAlVHQSCdYwYi3IZun1ab+GIefPwhJObV+qO3dBkhZw2Ur6Rc0hbQ0u6aBZucIR3WJghwKYf4cGygRryWcb91/7am/tWtwL0UZnLI/INkd6KbqtQiasmCoQERuqefzj/zANL50sSICXbASkh3I+xaseoNGCX844LF25uPb6VX/GzTz/dAxD77OT8dJ2fa6PLiHSAMVugb7sh+f54YIuloq3E7GV9+rzw6Dvu2D/q0e49ECkUF18Ou43hhOUx0s3FciMl77Se0aj1wEMQFF0ZKoDsjviBz7kC8fjBsWsXeBwSiQaLOGSgxmaILjRaxrbnfubP/8R+hD2sWKpLOLl4ZQsc6SSU1NIx9+fb/sEdF3fxzJeeP4+481c/fDkBQPTs3chPv3ffAIuLWLUWytJCTMvuRZtD42DVkFcuH/p46+CH7C+2H/K609NlxYkazeywMgpJWJlVGzMEWVfb0m7OlzhA02j3DGh0YLz5T9wCSW+NNzwWRBrMDPSDZfuQdZMY277YNOlDHv/queTgzL6XqELjZrbTzYTSMW8VAGCXtpN7Tm976KHlWX/2Jx/1y//y2w+/1Wprif5J380u0rZAKtJzzKEywlgyl6p5zH4R4oG7HqeW23RU8fr19jiSp16q8C5Tl2UvM8beDw4gLfpgNLAtLWNBWw6INoOuFkpP/UffeEyEFCjqmIYggyi1FTCbprZiB9svXG/AXGe/UZ272LLqfjoNhg3NYcYUYAXVLTzoqm/XjD7OXvWB31l2D73qJmLdb6DwjH9XwWt3dAm97NFuRyecV5pboaLgrGXvkGbfRHOtSVhTI0oJ+DQVMXwzV7lqFAzTFAR65WzvodKw43k/XEMmNQc3M5V1fv7nHcMgSTPasXun0N2VG6BPfdJJtx0Ag5J+eVmf9LTnf8j2xDvGic/ZCndo5ty6jezc+lbfhmP7DAgCOrlcuPOA3/p4qPMUEO79oR1nXqq0KFbU0izbSgLXspIpnSoehsHdD2LYVham8KDoI7YUSLgO3WlOsrussjsINsRwlGHUIVsWjg0tM4xpKAakxj95MgCaG83syBId5a6qzIHXbEd/nFRyFy48+ol33rj8pMfXGrZVyfzIEbQZrdV1zNfsFkCRAO/5q17zcPOBV92o6y/5+joyzFp+8vQAj5AD3SlHyDTCQipT+lwinYuWAjONS7HYKqIbIVPmSkKBpId6r0wqXDZoWwLBbkNYFsodaaqyo+ezRi6WZYVI+8HLtyab42x/pHWkzq79tfH+d59RGZCFNSidX9edF2+7buH7Lrm6j9B8H/NXSAsDbgXMEYB94aessMrzqzd7e/PHf8GbP+6ej6Rf+s7H5WR3eYsO2xoTqrbGweC5gN3DMNWmdBU2L6OMallKJWgybNkOVAadorMMxBZm1sikT4nuPSSGoeq4ANSlhg+Y5ew7/sfpsf4fi9YRylI3IGznpwyQ5m6Sur1rzpsH3vbRd55Xyo5Pk6IHGxJJv/WcCKElxhfdlsc7IuDw3J98+svffeOzOP4gtLZ19bqx3VabTTdr7xVz0uSTqi4iDZNk1AB15EfXogfjhGRjJfzACPMOSECl0IK1eprU4NbwNgtHNKnhH5wCyUA/7r/vcExHLx3NJ0dxTAuYh4tLwEWDka0E3XI7q+UjnnRRZuQHka9jl2QRQKv7WFZBMj7iV+poNiHD3rydd7/6Tc84/EyxWTCrtdlCadeFLqvGGNXRo1rmLSPDgexK8lApZVV3ewmsltkGtiRZ7+KoYqNRjOJOhrKIGAeG6KCVeRkWSE2PMqCe+d/+5P5I/ByT3G6VRYfQ8+qFQU9MmBnQbC3a3extfc6z3p3zZj54djQ2gTRGH70Sv+8bEnnHyf+6pTaCCk9+28lycu+S/A+fF905Wg2zJnJNkxnh1p4LuhldLbZt3nQZ0ye9RGOHWye30Wolh2O2bYsn5dIQClZ+BO85sPdtOE1dLlkLjY4eaM6eW+Ls2f/9Am9pwiHdagVgYzGob9zonLOyJMmCJ+s4rDa4H086GZfOL0KmBs0H3cxg6t9HTUHwbry9Dbj70ikpe2C3Dz71GffGWx5UIdLNiBjgUm4G5Nbmjeyj8+x4/FhSs92Hj8GBsNgZuxuRCIkZJK2j04UChSLpvYGSuaM7yDYF9u5GiPJzm4giSE7kM3/p8R9Uvt3SQh2pkiUCqrnPWQDUrR4FTT8dfuGRm/P62M5ByEiXwo7wI3RET4695QM/fR9O9NiXvupXn7K7dOnC0+vhD7zkdevl+f0hc6+KRm6dpm60Rlgdz9VDFelHNW83+8CGkjyqqKGyDcPhMNQsFtmDx9P+Guato9Y2p8kjMmGWw5Pt1lXpZU70ETM62ON/gbrFgh8hEkhSlZkZqltuplZL926bL1WHc5vvu3HPew57N/qtPoJt1lMSfp9SB5/14OfVo0++6El3PvPjl7N9v8N6PvQ7v3knXzTY8oVic3FPNOQqA9oQjaQS2UKbMTx0OFTb3CqBvUDjytpqCVsHJDdHZzWkQsQmFMZioE14NocFvK3LIJpLwzZnOVnzZDnY2QehgSag4904PkRKdJody5rd8fD5nnEe1/PG1bl7aFsNDZoQIoQ0QgXxKDIW8Jxf+hU84dGf7SfnT3jU1X6wIQofELaMg1gdxzGEMO9SFNSuQiStKXmxKTT8xuVRHeUNQdtibZsby5LJtvTJeeSVotDBDBw1drDNyqssjyNQipOwmmyGFWKVHK85on4SJbXUOhJHrYabseuokBs3S/Pa1bMb29Wr59decwiaOQi7JZ1j1VFbfoRDRHveB67ffudtt4E3DqdAkwZAT/+QB98WZDe7SZsmd4hW7TbOHdgiWrUQTYBqxW0j4d0tKDokDYNLqZ7tB5bXCX0sPpTZZjsQakINN4ctNqznMah+2Y5N3xRWMhaSP31UhR71s78/GwmgubvxeD3g8kjfOEvw0l133JVXppwuOaGjMrv7qK7jrRpAxke8PgdODbi5nIJsffkfMeHVT7f/19xmVEdth4XMw2wzN3gv+qAYq6qcISlsU2/cCmrrKoPHAQLp4eMYGk7M6O6tR5o00SZvO7rjZ80QRt+yAgw0qSCSsmwu9ooPHg1gx3PSbrm/SJoZhFDDSJ7kctivY7vRyyPLvQ+WWNaUEd5oSn2MGb4VtgVcvHGw3tkKxJlB4LP/3tnPAOcXTn91AsthqSC47cqktoY42wlZT60Fn9a5CBVOLpNyUmau2B68l9qimxs5HfuFabke4GULPOVxsC2K9GaOpPlW3TgqpCRBBwEwtRP1/mNDA8lirOsYEcPHMsZwO1pHSVgzVWdb7jO3B88u3/OsZ1xuwGgk6pjTSlnRb0GNAh/9uxtmPMoAfydIPPGH+/TDqXrVh5z9IhcbtqRljZosR2dXheuo9KCJHV1xkELNlospkA7ucS/EqLLu4YZp3QBz2LC9yiy6zQaHDA0rLgbHUSoEdrfRA6TIMTU6jwo4oLth5u4ea5jfIs+DBrdknvvh7OQu73FRt/FsPvlRtx1b4OMpSmUQGnbLVkmAH/0TYNmFFbh5XcQTfnrJ+Q2E3vU59j1rddaEGbJdwVBYIKfCMFU9IW+OWht9OHTOTe3ZTXmvI9DyABBt4A5Bq+Sc7SMcFHOED1pbiL7KBAS9QyoerYoos5J56D4ALUItVVUd2387evmPNDLJTMe8cvvtV+y2wwN58fxw/uqz8RgH6RCpuoWbHgnh4+dJr4V1Xp+AR+tpP79sc/9xDm3vufyma7RwVHPSPee5IB5Jpe6go8Mmjyb2Rph52DB3VZVmbwickQJiM8jTS9Z+0tUdNQjb1+xZbLnEsLKWCvN4CgZkkwY6gOJv44O9CxpdXV1QyY7VjGgQ2kPKh2M9qz4/7O+98r6Hrl2/duN8B7EESE1JR80RbmEB/mnXEYYxgNNl/6wfgyox/jCgl3xY/ZhzJNa2Vephp+myPnTilkmcqlDSQporYWs4xXYOdzl7aqcoxRlq1uRBDvSBblbeKjhcMdFIYNKYJloxypFNZhxHmSLsV3VkCnVs+DqrMoVGtzm7RXJ28/x6xgNX0NuDp8uVs/nQtevnh3aiKNpRWXLx5NZdO0pwb8tucGzAwuf9yJoV7P7LRD/w9PFDbmVDRBrQPm0LmtNcQTo2JudmmAYwZ2+HbTscChWzYDlGhFW6yjnopiCKtWi6Q4hlGG3lKZi+0TcTbckwh4uLSLgElRvCfvfY8nywH1ZX16yuhtTtdBpSvVXGtf12sGt67Ds/4hlPOa0tb2wiDeiCROFzf8o/WAEBnr554sNovgA7fRQPkIp8yiUg33n5vvtMmK1cKAELI9Vgl80jN8MCreaR1IbL4AZZ+qCNHoYtDAHCnaiuaXub+8Gu2vowLTWtaIHVum0Q5VWUZmuobc8kQwWz+36/aPEWPNylzq7sbsmssT/GmhwmsDx049Gv4RuefOFDPzQyu3lMVuhqEl/1pP/Eo8aUEh//OulNcAsgsrqhsln8Wqpf85z+HgNEeDbk3GjMY0pRCtaj20FoNSvX4sYgPJD0rbnp0EW6NWUHwG67/a7b7zwZpcTG9g5K8JAaaMLtOAha8kggqReG/FCraX923AHHCfbYyR9LgXTMComzI1TQMj+5dv3OD+RDD/zmuw/rs+8dQkspHYX2Fx8z/8A/urWSaPaCl/fYGKcBsJ1dirZDfaGoK48/+RnaUXDTZUw7nljUEdwRQDe9HV7SGoujLDPPRWM7MRpCpFxwI+T33nXp3ttPs0Yr2kDOuVQDw2iLbDOIND8e2idDmHooAwfykQaIY6m71ZmjP/hK6OzSVsfWVlzzkbNxVYetHjp76KHDUxaD8lhBJfQLvPKLv8dBUIB9wpvsVNSyAH5HNQxb9Zq3f5SQv3nXjdf4iCMcyaL8eJBE1bJnwxRWZo3eVLWdy+vA9llKxh7T5FWVhy4C7aeIEeNkueAnkQZmrNVoi+7oKY5bWmGjRZQoeSmbHUN687H1tA8OxLf0LUfSsLM6t+ONUc8bB7n3Pc+P1HL9+oP5hL/0cSfH+UGC8LWsrM/+yVMiQPrTbvQeqBMD+CF3s2m9qLP/qkFv/kT7ntHIgrrb2S7SybJzptqYNE7X2GnKDfSBxUip5q4kzRAbZlZ5xz13DMyJy4997L2PuezuoLa1V6DasuX7tiAMOqqyBzBvmMNlQsev3mp9jzKgW1KY4y/QrcpswgHEilZzzzvvePrlfHAeHn7du190/gnHQQIUT59y3tr3M152ty0kTy4X9sByaEAnBjRjb8l6gUOH9cJv7s0D3dbqYrM7HSqs6ab2zAhDZ7roVQcwD/MgtUxE89xMkrfHyaWLPt/8Np5cPLnt0iWWKtqMOTbRxd5ClilSPsC2mRwn7QYNM+rXdXSG3KLA+b9/BoDuKr9w4fQEeP4Cde17e/vL+ahnDnX7Rz1p/2snQqMA6tNhBMsvvfjZYPPuV7barCWgr5mx357Wiyf+GFG/8Iyzn1v2N857d/uli8tOB9Fc5ea1o2Rm1l6lQuKsbHop/SzaGsleELtNxt5Ycop9NbdiaorQuNXDwZtLGRzBUAa1DUO7RSHKBsw6lrfyqL6DSH3w3JzfvwUibEe0ES8/qj62+d6Ta4zHvkfCa6kUxKKp+SVb6nw5HOLiC58O4ye8vLV85q+aEuBQpT8BqLlWfM2PUe/9kld935/a9YVTH46qPt9vakMcckGNFradsg0GcR3a2ewxb6fRZwcnoQVACM4Hz7eL+0fFw6paD8NE4bDSthPfx2Zrp2BZMIYtjighYUcKOxd/8KZAt+GxeBg++Oj/N0jGxQTh1O++eOpOUjp/9zW//MQL42y7wduLhKOF049qGDIG8nAQ7NN/TY95zZ+XzQDyVOhpKPYB9WF3CvnW07ffuOPxt5+MATu9fMfd99wV3tzTE3WYrZgZMLCcw09YcKVYJPyGpVd3bz5bU/OtV+5/5LC/+s5H3vu+DyQtk4shdoccA/Uw4WFGC5BEcemOkkSOQq/ftu7WYQQJ83Ux/B/2UBLAaiIMzY7lwoXLCw3aHnr9lZXR537+AaKPuTIfR5SWtabiHQbac96Nj1/EZa2jw2aMzFaEU18O6MWf0T+4eBPbNDHHpdsfu6rZKPkOfk5nevPA5MxZXX1YzKwo29En2BxzwMfQqps8PcUat11enTZa2UrUjZsFu/2YAcNZueUxjNCS9G6crRbbT27dDLcjT+zLzv+PFQByN8xI9lE4SZzcdgooD+97R+l0Eclbilj8+clRRpyG/5yAeErqBX1AmgNItFKKwbb9/NOAHn7S8gMKam66kZvPHid3X2pQQc/o0QdlAeSSJ5izWLRim3Jfe1phBZCjlHEAtvN5YO83uFXvOmqgbESFzVsN4c5GWGoBRXM1Ob2W+D4sjppli99a8+tuGG4F5wO+yGDoD6rKYeLpcNSEO2g0os0onHzizNLc+pB8KcFLH2g82wdOPAG0D4bYVajAhScC/ct3fuBVpUJ3zGPW9e7Ou2PCtZ8uIGz1TrMNhxxOjoWhBQjSjp5iGZTVdoEX3GfPwz5XNLFF6JAtlmi7JZ22qcqgE9t3drbMF/fRwL8YYWN1zU0R3k2TrevxBCHjWE+P10012wiGYH5ysotdHNnZEEww4qMa2QSRrjcCfMJLZU84sGcEwFFsszC5itff+cehfu3z/N/b4DjZLTsbKIZO77jb35e7O+64cOni6cXd2N1+29JWUlb2jZy9bRO0jgC9bp014fCA+VIDO79Zhl67rUQ/RDcnqmdEtCU5g2Wib1Fe0Wv/7GEFHdZd1WZ+xLTMIlsQHG0UKsomMAchTzZtONmRnroF4AFfKbltDKadnzn4yS/rC2vCTmcDlUgUoSHnA++6/1lWPJvLLx9kjMZczqOdrYHb9+NysAoHmDdrogZVGL3r6CG0lqUPpxMas01chO1k82ljizNOr0Y1XT5jSUtsu9GMBtq2E6shlLUJZMP8m47HbXKkObrSzMWGfFSz0H4M0y41aWw/2nLZMoBpxT4iQdDuY1SRrvbg29lmn/JN+tCtQWYDVlaoJW0/8r1vuvro2z/2txq/8/g3/9inL7MXmv3/Xf15lGZZVt0JnnPuvW/8ZpvdzWf38IjIiMwYMyNnMpUpEGRKQlBIYpLUEpKq1LUaNVAqVJqqa5VA0FW91BpAFKqSVFJRgBCJECQkCTmQ8xiRMYeHe/hgbrN985vucE7/8SzQqv7WsmW27D979t59956992/bCEWJ8iZecyfIJBAYnVEBlYDtqRZsQxJi0eAgDgDgCZRngSgB44JJtSYjgVvIZtCxB08RxKJZtGEmTAkCCsQigiYo0OqFEyXEzAqQGLWwsFdIGESQhPybzhERCuRBArFTEFrgMDCTMJ8yFp6IGsXkPYnG30cB88EZPi1e2CsAEA3Gi4+tlsM/nKSJ+pNfAdn5S2/84p9YBlOnxBqCKCIMeniwV0dtAtIGyjo6BqwRGnSkHCtnoCGbMTEyxY1hlyqrGePgOCgN4DFrXMyJNHGIuAGOA8eagwYf10RI7DQSArBB85NtKB4CKDYsQCjomZGQgogoAK9RiQgIo3bELNKSRYhZCAMhIDIJwQ+7AOgwIafUH6BApAp+RxCrYq0BQBvPGp0oqeZApvtgp2B3O38dtrwCA6igZkMaGLRdLJETr+vrr4CPKSpiFZwl0SJGOQNElACUMbJ4hRz54AIomzegWKwoH2zWaHIpB6SA6JE8+YgRPZBiQSYBAqaAey8AAJMgBE+kCJEVmliD9x6VQhEhZmEmFKFAjAwEFECQW+SugAArFkg+SGIkAkB08CoArj7v5QEykWAWAwA0Bj0Y7VU3y9a2N/LvF5QvvyX8vJCz4kIdCAAOdma26V577Mxqd72/OltdO9ujRitSKjcKSYmKFCBC8F6hMIIPgUFlvcxEOjgUn5AGoCAQxAdmpwGZAmrFCkk0ArLSTMIKvVH/g6gWFUUBnVEUWq+cIvFiERQxi4IAROyFEFu0HoIT1IAqIBI4BEGQdzj0rAFYUBc1AT7yOTarojwpJwAO04YpYODD+yZVzVw++gsCi83ol37Qo0TgYxRlIgYU1t1kaLkCJjY2FCJV3HhruHVpKvBYY+opKI2YWETQJS00WCSJaglNcAiaRQIhU0BRTVprhADGaSOI5IkJQXMkv4mtP5IEWFppEFonslIhBIcReGkz5RSMV+QBARmQMID2iokR28K1H+aKIRBbZdRLIIjf8+9kIGJZMNgcFBAxK+DbX5pJXNkoWlk7An6hf/KFqzopJSk1xhHksUcQNiqxvFwACivFSlWatQROhSyRqiFhC4ZVYwKBCeQ1G5BKW6g8NyTGNUoF5VEFBGZtXCAGQyES58EDC6EwJtF/bL0NCgAUktgAWhOw0gAkqCMNjY2SEEQxIDOEgK1jXEjRqaWAWsZ18oxDSagoYqX1p0lQffB5uAoBlej2eO1FlNRGHcwKF6GO6ftA+OY7wm9tb62e3dw+u97JIISSQgAAdCaP5vfv7R2cWLASMJCONZJWCmziQ6IRERIjDTFo1CoxaQ5xUP1BDNqRiUMEykIEQMIcoyZhUZ6YkEQBk5ag4WcEQYRb6ViEgnjRQBioJaIpAt+QImEKIgQMiAGQgYABAwQSCAjAiI+B95GJI5erDL4oCHpYyNNAvkIwGgCcEgMS+f5w2l9ZX1GYfN8/V6HsZF/MdTA6uEaDTyQF0hKQUcx6sj9hyZSYNs3A7INolUQNqUZJIA5AXBvy0dI4IhMEOdS6QQqmActaYSD0kZAlQg1GQDASaCdFwdC9Q0S2tq6cZURSqDFIAAkgopEECSVKgAWFQZAFmQEVAGH7pVBYAJCQ/6rzFKfdUa8/yLIXUDD3zI+B1wqpYYDGI1BRGsJu3B1186yXDd4dIHz6UvVSmkV2crx0lj2f4noZBX2+aY6XIgAYbIwaQByguCDgoBHvPFqGGEgFVQtCacha51TMAgEVKNIBQFC0IyQI7AkVeEFkCYSG9T9sHb3Ivq6WtZOASiMLY8tvQibSGkSxRChw+u5jYQgiwi11GhklAGRvF93PUaepBi5KANz4OtN1ARcoxAygjAM7OVQx5Hke51EitvpRANl9N/5/tAk3P/Xq4biqlnVjxbpQNBxqx7K+gsIeAmWgKeqiAjKxUdqQBqU8x8IA1qLOkyQeqjjP00gBYawBiBtWCkBQDEZaUJGgVyAYFAIgG/d7gKJAWFAgBF8rrUgpYY5c0L7d/otFgTzfJSDWIEAgGJRAi6o7TYZieIZcaaOowEZT8qoAqMd+TfQgCApnKgIIkaqKabdBCiIxxbAE/1BWQnMn/XIRnXxjus2V5tQIUI1NUAEisNEonAgr8a2rXCho8EQOKo1imhhCSOrICTulA5DzkWOyTnuHXnlNUBtgjZ4jq4XRt3goEmAxgPTxANBKuy0wGaQBpWOKfBB0INw6BxGpd/64UcowCwqhBwYEJMFApxXr+Nd5OS7E+KjJ9OB3QFC+96dk5FWkDSAoACXIPou1QyvgameDK7vf/e9AvvTg137lz5uH415m0YhRzltgIEoaYa06fi4UIiwsehABRO+UgDYBAklAdHGTEiuwknhC1BJsrUiCBiPECWMAUtqxBhLj3kTkA7LVyc9I6w4WFFQGvBeE4J2JSEmbl2YEBlD14llPPhIB8i1tHwk8oCjh1my99kwwa8YBJ3XHpP8dCtD7/kt5atNYGmHwBACocHRcNt5zFNfpwqsk8n/l/xA5/sFnf+EH1rvzNFsEJQ65FK0EsExtmRqX1uJQIkajPIQUAI22Dq222se6MsTktGIUJVSWeaqtLbsoBj0JA3okEGWjTLNSNu6ACUXawYj1LBzsQavmEyMKgzbBBUBurI61YhZkFUQAWY8DUU6OWGmPTKC4Ze1Ki4AF+clenQuGSMCmBM8BoIkr+WhsNZggykcgTWKK2dr6eL9QbCJvQAV9fnNP/M3OGztrhQNmFo1FQiqywS8VzCpwKj7j68a7DIXa46vm0JAX1qQbUmAlqLIf1ZEFdFabCIsmSwSQQgIoGAwK6zjSEoD1KMW6TnoSiQH6WRA4Tfyfzr9M2jgvAM6bWKPidkNJSAJOOVKiKJyiZdshujCRAIXkuyYhMAkoKHqGpgC4svByrS4lXRApAkDdh2glWPLMRR686PFmEv5v/xDkM+/7xP/233qfsNcqrkQGQugTJDVMYjA6rQVwtam1R68dCwVhDd4wcmzVzCM0UsWZRp32OJGmq/u5AfJA2sUONIOSAKjHZqB0sFRUszeGOs2N+rXW4AnY9qaCMAUdOc9BxNo4JmDFwFoAEOKwTNHYIELgTj0jAgishAC+Z+gdE4kJSud0E1Dw+pdEPRhponWRWABQdN0dse6ef7zJ+7kltbdXn/9T/xBw+sCnfvnvDrwIEDeUKNEYxz6NAqamdlXtjBqs1EGhEBhllWiVNjPyceO5IqFMuZhUEKV8HHNkI0iMZw2gMBBq9uC9Eu0UdZK6aI6i/Revqc7GlTeWLCgkQixIQQAEBT1qYBbPjTMaFAVEBJVKzeK0oIAExNYUwgoYMLCO/NORioIGJgxJDJ8CRvzOj0s/BUCwZGwA8IbiKNZRlJ2tBbkJpvid3T93/emvinxiY+frT3rljQR0GonRe6U8CjXALJQIYmdZW+547SlQwVTX7CqJiSihJBNRCVsOaCEQlDxHjMKSPSh2SeKlcMlSR1BxPasP9q6rTc12f+3vcKvgYssMZQASYRIlokAFz43VxlgRFADBrMwLltQrK0IiDAKGPeo6k1K+CwAwIDAZD/AxAVTv/BdyHYCCYmQFAELIZdM3RFEu1ideXf1Q3ZO/+1HkV3/gl37qlxyAUONswuQErBZhoiYwzEylbQ1jnulVI6rp+QaRFXdYNAF6pUWBCTGLEV2DWLLTI5NIITokLnAzZHSl01uJ2b17f8e7Ku8cBb3Y/cyp/oUeoVVCGLkV8AVAKR/A+SZiAJQAxCiqOyiObJqw4sAqACGjo5pUbx1Ue6gGiACeQwH14LE8BYIK2hMJ+IBpJ0QaGDjisqBQdobh3npkoanTb7xGDUfMbEXEQjABqgS1dcs66rgorifzkrMTSeKo7jiJDYnrSOSNaoxosiGCjJoE2HDuQ/eM2/fns0h0DZPZUA2VanSRpiZel3vNjk4RjX7RIwPhKRcLuI16gLRuXwQxij2TKARB8ggcD6cz4xm9CuwxSGTrgBRpCNcBAOhNPUVmJJgpJx+wyxGTQdAIYKJEZ04xoeEAxzvkba2m9c5jXwH+wyvP//Z3FBEFxAQ118b1HQaCRsmizNZA5W7v9cb18r4yCjFBoznFbp2Kp6xJtQOjlE2MgA45SOgo123E1WKqcpaZpawq0Td2NwDMdm93v6mIgvpam+uHNzNjyCCIAp7aMKkSoMQrBgSUgBBkcsLoOG0stUdB5QMTu66Vh+A/fwjuowBs3WN5MgKY9/0p9rbRHYkFIESsw8iIFa/TMvqOr4rsvvel3/rRZaI9WqLIeRatpaKoAaiYUoi4jgGw0x15Mki5VZJAl3PlTWIhkoiNRMrHyjCjSxwUsF64yb1psLbeno/rt5471KY+WO/vr10aZncqYgcHDIgIBEgsKCr8kU2ICSgQCwESsQBh6393AKIzJMeAijEEFkDlA6vqVEtGQRH164KIz3w6mLRuyqN6zFoCANOYfRRTGhF56mUMfpz0aC3JCvBv9G/duuobIRVVFsj4RZPHsu94q7dwVmaT2d5RfGYzAZOyAiKFHabEBU1iYgbFqJAjzY3ioBuspMZYrXi6X0t+eKfRm9v7uoR5mbt+N1udey/mgE89scRCQl6UeCVvPg6tgUioTTqwEKPF3EpIQDgVIc06VICkRILwazeqGoGtlBr86F+DEH3wP0L0K/OiU8K32sZIjD6v6o0RZKNREgsuKhur4xOU7kd/heX5h8Y//Y/rr0yTt668+oI8cvXuZ48efRx//0b+g5cgBCx3bs/PnhtEXucGPImoWItx1hjw7JRorYHBC1BNqjYggRow3M/XZ8fVco824hDr1zmQpsngVe85xHQDT9WvAIIAipk0CwYFKMDUCuWi2nYVLwCisogDAgYdEBiCJJ4VBhEJ6S9V3oonqVPsbN8CEHznT/Gw8lE3jgYEhABID/FxdvTyzK+dO38t3315evah5Y05PvwjvwJSdPXnb5hfem3tB5787d/BD/7ws/++eHV97Rtv9JZxtyOGDtbObHbIZBqjZWqsSSPw3ksTeyBCDESCaJgxwqDIimmCLOomUlprgChpDhf6QICN7CoMmsRG+/CmQ55A8FQnkLbjq83+BQWkghYkRoGABB61P90Ktjc7UUsy/fFr4Jl1UFoin/43KBiv78mf/v4wH9VV9nECAkB9TXOwV5d39l+8QW/dOLSqpw+WYM72ZxxurO5/7k87mCwTLXAS0iZeRNuPLyTCjFFVC5tMTpJ80JGQJZKIUeiRAMuIWKmggpQqBnKCQcA7U9r5HhdzP81V0tnCnj1eavaGvcJgwEcqOnKhXbFOndmIrf4BolCAWDErRA8A2F4PEGGBACBAAd70kogEYjz/wdMXKgCA3Akk0AlOPpCwJ9JVy/ELqkqV1hf0425iu52GJdOadazxXR8HOXj/4W//lbU32K1ts6GQJ7XYwXctX0BU4o6//pX9hlQ22NpeXe9XUcIkjUQeGURLKLsILoKgUDMjCS8mJ8XkGLE5eQ3StbW8I0W10BgLKcIAAELhphIBQAoI7cuwNZCiaGBUTIBKgNt5PyAABWlf8+34AwWIW6gYKn7myDrxPgBorPn/BEC48qrg7ifmJ/m8egkVMoCGL3LYypfD1c5IwXS5tW6tesJPA33wEyIW8tfCKIAyfYqjpcobRlzfvIuC5bc+9Zn9BjKIVNw78+AjWxtGwGtSJIKBTIhcxAlCrTyIqlR5vH90P4kh0mbOwc1P1hTN4qVmCFpsW1JKySEgtNx4UdLS0xFEtZxaVNwWarTONQzAzKcGMgIBJhblkVVAEqXhlb+mgmu89QDKyxEAqu/6A5Zfv7fYmnE+UQE1gJj9N+4+OPzS1SdXdaTE9Uy5iFReW+l++HeYXzjz6sdjoyISE+sa+6VysrdDpW++8K++5ZgNQaBlsfPiF64/9ehaLIJBoM1RQoQqGKsaImqKg90dRG1sDCw6FcvloR/GKtOxj5zWEbACnczdaURECXCbahMSbE82qpU7UHlgBEYhpgBADAGFgZRtlw8UJhLB+M4bSRZ8w4yiSU9BEP/Uj7EmZA9kWgcgoFF+WUQzHS3uFflq0JT3ikpPz3j7F38H4eCpm7/w7SyGSXcNQ6dDWo5upyeT137hRQHU4GPwmlV99+43Hnj7Y+s5AEkwaaCgkQC9rsiZ6vZLhwSxSgBFiaoIIrR8aFbQaOr4HNksuuWAR98Cbv//3C6CDArazCErCRQEgSCIwJujInwzUMQUGFu+LQIJIlPaaN2tbAufE1MDoLr4Cq+uFlMO2gZmIAAlutS+SfC42Wl6a/UiKcLiQGCjqVb6Ewnj/PWPimkk6q92bWP6jIBpvPjyJ14x4hhIGAWJlIfjg28+/L63rWYmKEHNSlTbIU3V/nNfubd1JfYBnEZBMgSaAWnc64Le2K4i5ZLx4Gi72v5FPB2OiSAyAUJQgCxt6pqAlIPTUgFgJe3u2CsMrWektc8jqwCA5BExaXx/ozysgYwgYgpz+a537O5a5dVDbn0Z1CQ+8H8tuwkibjqWXFWLGhrmhVp6W/31f8T8/CNff40VOOn2jYPcu2ZWJPHJ157NktITEqJCAkGD3Jx89sW3v/vpAfvMEQATo27ETe5++bkyiiIKBsEzJB48QKxDBSV39PltF7J5lpyls/u2ABXgtDiHWYGgktNHHkECtGXMusXKCwUgAYK28VcHQGQmBvKiILocWCVXZjcvvXXxYhWGdwRBztrAfyIeDiwL/P39p37zcPP/+6M383fCI8/ZXZ3ZeihyDOW6ni+GgV35/f8IYD4wnzOeeyk23JjYUDWZC+zfIQgByBAwGtZIpB0HmHz8pfvvP2OCN548upBgsfPy52/p9VqCUuyyRiOhkEhATJpFvNALX5ZSxihNMf0SCkNr5gQCBW3YBVqcHbbhy8iHlqJN3MJQULvT7S6AIAqxIAOlP+4cyejoJXO5GKZ2+ycAAN/xHONK3Tm7ZH/hxsMv6TvPne3rymsV35yLL5vi6GQZsJBZGReOy/6FO4FvrRxc5OADWN9MKApuNkPc3w8NC6JIZBwTgig0QRksbv6bV//kQyOYCxKwWribn//aAeG8XFOeI1kiSTw+9lqz8eIbd6xnUzsJy37wwTWfacfZp+3Rqg36o2JAQeDWN+8ZGUFE82kMskUuiXKiGRSIEIMCs/3UVJh7/Wx/qS6uNavHAEB/8hOSZbNs6ySoC6995OdHv4nnX9raI2euvgTsWDW7Myu6CXWwvm7q6gf/R5RbH/m4I5kuGrFlGYydL12AWZvsYCQQpZDV2liCQCDF5ecO/vj7zkaljz37yY3PveK2/PyILRGu4o7z8WLndUzJn02PUY1v64e3/LBTrq8O443RzwqSE2W49XYgIwqS1V4LMDCJCAZQ4T8HAQAExSMABsS2kUMAA5H+EwU0MdW0mrIbZVlpCVG952d4C7TTYuxv/J3ky/cPP/x8IYnfW+n8mU+IW0m6vY6tWSTt5vVkOS/egwCNT463Ebyb9yyzs/NJ/fASVzqTA1CSanFKhNYffmGsPIsjjfDivbvf+aAWdjB+7ut34jOwDKWIbtDVpD2We3O11NjplmgP53olznSSdCsdm28IKhFIg9NMp5ROCKT5lKQpQnzKGJJ26UcUUm0UBgECobRY48jqd973ZhqpBiQ/enHzgU+jgETpG/xoVQ4fxHh69K795/JzNwY660UXPvZt3xPu0uVOt3t3MJ37WRKzrUKzGy6+wfLVrVvDlA1S48vawrzSI6w2VnXF5ExeE3qGqqiDoJAJotEf/Ye7f/KJWMHxs7877yQELOSCU7wTUkGZHlnFivaX07yonL7dq6edycit3Lr+S8AIAVjAKkEliEqgbdZEOZ0NCSAQe2w7V0Cg7boGBOTW5d8Ohrubt5ohItVGeH4/rr4BBNCHmh+qlhFp/+yfGv2crzf4xuS7+NB9/nf/2ff9mrsQQ8CVlWpxpyCwR7ORs9/+8wIHb71zZ5VDuUyiuEEXInyuDixVzJX1LgpBK55+OWgAaruXKGq+Mrn3vtXj11/FEfVTHqvhxNcojkBIZkec2Eh8xQqXQWMaYkGmpJvtkgQK2Og4hNYcBKJDuw6CAEhb+cOA1LKVEQAUBUAAaXNygsjIEuDR6BxgE2eVmN7w/GDrGyCIV+YBei/WO6/O3P632S++++73/dqfntT2X9qnfuOn//aH/uc38sm8P6jT6KKKb588f/+Mx7coBr+fTV78QIxVvBLR1PYu9I6SuLl7gmoJ6CXHAoWRGZh1ANLMmNrXJ3efnrze3/RLITGp66iAhgQA7HFNVSAnKnHGB51lmGmjo35oPIFSIuJ8rJxTBADikcSr0wEZK0GhVvemlqkNbQyORTEyIqCooJjV339IvAqaTkcJcBcQ8MNf4ujbfnM33MfJ2x669bU/Vefvf+L+8WfC/s3O7537ob/w1+oMzlybzy9sD7ZWPnmv5HLSfeh5kVvbb7y8KEz/0sZqPj73wPo2vPGGc7rjDBpz8d3qd5dSd+NiGZRSLloxJ2tnl1X5cteWF4aNjb0aLc3ZYeR7q4cVNeM7jQKyPhgFZUU6xjRQEilRn4VAwAAKQ61NZD0ioAiJEhQmQj7Fp2JbOtlmB4lAgJBDC9kVFmTF0dugBbeeGmuhREH1kZ8Pb3nwjZuzSt3/keSz41jDWb12dLJ09+P8f3/oqb/zcf3skQSabCGsbzzSXF7zg4++ILLsx8/97jeTra3m/MVqpOqmOzgzl83Ibw1P4Pp590zJB2bQ7FRVP2/4TK/uXWsmR/DIbeheOWziWRis+dXOnJM1PpFFMWMBLY1G64KP9apWKImO6+gTQCiNAGgKwWvNwTMStXe/QoHWRSWq5UIitxUsQeg0X4qnlElBugb/188YkZEe+KJ8AAbk0K++nT8JJqdu3nsOELgZ5P+vf/z+jTvmzlFzIVi7uxhdLbu+SB5bPxA/Hu3vXduO4ZBx1kziPBxlXQEdraqXYjO7uD55Q0gPR0dH6aZIZGJcUrqSDTYOrZiRWdgAnaiBeoxVI5MTQBRiQKMoKNQ5a8IeqKjaF0BxABCECH2tVeR9aFFKiIBCQiLkkUmQgAna0Ho4nR8xtYUjKCjf+f93AX5FUHMKu/I+KV1O3/hzF185UD51Zy7tamB2hp84+In/5cH5I2957e5qFO4mqmlm9d5R/OBf/BmEu+8/ePlv7JZRszgsQtkRWyU9N6MQc6KrMI7qSgcVJY1LM5LUO73AOAp33Vp9g9XCWxeFwIJTS/V46TZWImXIxh2dsATtgiMFFCYfc4h8+n8UJMNs0cQcWICUmNDGnQIgk5BgaAOUgKJ8i1pvN4FtD+PVY5ZmqSS21mmJ7T9BFNwAK2duHnMy9R+KPrlUa+sPngdvvQso2LxNfuLn3v580ku9beCaTvdOmqx7v7mCIFURf+l7G2yqKvQC5kiZ0dRpVIBR7O0Op8OBkMT5VcqFg26UgDa+idcDeZsnQgZBeVVRNtpqGkUpcEvdgazSi4iP4mHIX/9PCExWoMVDCCpSwYuKwUlgZEQSJRAMgICQUAAEAtIBFLBojyBtx52g0Cd/34lz5i0dt7xzebWudhiBntgDmFf1RvjKex5cnHSXg3QFgFSfItL9SfL4x/7uT/8X/2Yx0GG5tUnUGVIcW8gffjHIKxtvPP9UA0lqtB34SKMREzQZGWih4LXBwKC0BlbBqMorQiSHwIqxGqCNvAkSegjRkENinEdPRkTA9HQRhfHQzpumREEIAC0xjwmREAJXqJRmH4iYAqFqKyYYGbklQ51SskiEMIgAIqF5wjEpMpcy3zzQ68LEIxN89x96rbJL28f/6r/d/MMnd/YkZQCRp3UnJBjH2ff+n7/wV7/vs6DJx4Oya4ZiWHP2l/+fKJMn737yu0oISjw4xVp5b5BBgQPyOm1UYgWMBYuorJaYSTESupjJKIRIN44xZlTglGGLsQg3WsSzaIz1Rq8TdBmEWLSXFvzkIWgWipkDO0VGBDyj0gxCIARIATBwO/hHQRDywqKZkQkGz5ROg4eIVXZRg3lVQIF6/38lI4U5/5vLj/D9VaIgCiCWSzExUAhF5zv//ZmPPPKtwA7qxtfKU0wkj0SN+N3uG+XAOvYpkLI6Siwhg3BGLiMXZRBhUBEmrIR07sgqwzGyKAYIcaOpG8AzkgrkIq9CHLhCZIOiF2LQzDW8wsAIkW8lIYdMLNi2SzCzU0Rx8GKUIxRqq/6AApCQgCB5aDlTTCwCF9nqkEKw7YnZ/KEAo+58Q66Lp+L3/qvLu90IIcQawAdtWFPA2EeX/sQ/fejBxWGIiCPNmjVEbOSPfRzk9sMv/vpfCsyBKQkarBCBQ50GjEhrFBBCr4hRkRBCEG80ewXgFQkoFYBIE0KIJZWkVqg4SgMyIZVHJ2HZ2PhTgu0whxARCYHbVnUiIW0MuKaBSIsWCQCg2jIdAWxvA/6jhCESKnpUDGPVoCKExln5ghBiByZ8vSzcb5tn0q/m7LRiATCKAEnFGhMNTz39k7OnM20MOysOSLyv+M+CyHKNPu5ZJ1GcACod606Udrq9JOunSZqmJsp0kiZxrLUymtM0zWMxkdE+8qi1lE1duqUlFle5YDEoXzASCIBeIx2DjpcnhNDOAQQIUYQQiNsiGRQikuCQ2Lcz84DcYtYRFUv7DUCERAAgXBuzQ+cTE8B4l8z2QBDO1SyXSyx+9am31ItYLU2jBKBCpwMJoLhEufdO/vY/e+/nPKW+0tBCzuIH1w8lvDQ42n1kYY3WLjSx6BgVKxCAOHjFpy0hDBrEVmpUQlDesSaxiK4Wcd4Hqqzn2CGQDo0kfNTDhoSy3EQRJL8mbWVLS3s4hR4xAgu0HHlEpZEDEojgqeQNAsAAbfGuCGGrHwt1lgtbOwll5RutVeMAEN/9RaZH8sGry2/furHdSZ/Y/+Vf/h149X9/PukM8rw3HK3mWT76wfl/r58yMUR5r2ciF+lMRz9IIrtvwX/ty2K5mC0KtVxaV5fF8ng+Wy6LqiwcVCLiwAsLmlxQKTFJLybCBONhujrsr3bSDqV96uZxnCSg0zg6HV/VvnY8/W0AEGn3PC09SlpGBIkgIBERgjKmrTI2LQacWRBDK4G39RuADCS0vbGyMtpYW+8PuoPBaOs2ACJ9z2+Ezkqv868vPQE3V/Lsw5V65D1w7rFe1yM0tirLReWX8+/+8r/rXB1P5mXly0Uo2Hn/7QDQQPK1Iu90o6QzSBQGlICuKIulDSQgwVlL3rF4Dzk5IDHCilFrj2zFuxB0KFHbEBZVtbACjUohgPV6qjOH+vUlMrbozxYY0CI/EFAITh9yklYhbR+K9u0HoqDd/rXAXAQEHbKUSZRSknsRAvyMoLB5/G/CWYLbN//y9UnkIa3NaCWDbCNVKDaQaFJMmRr85f/1wSeX9yJjXCoUFKEbPvyS51fPvva73y1OxKMHFMviXaJ1EwBIgmWBpXjj2XuvxSk9J/GNUGhcpkITbIZAZJmW2utgEhtQBCAJOtFEkH4duS1nUq49/SMgI2IrjLQJcQQhYWq5DYAQEAGBHHDLDSQGESBGuNq3nhMhq9AbRP0lQMRE74VHIfyLwdu7X1jxnho0hKf3TKzYOQJhq6szH/0HP3e9nJCwE1yiYq1++G8D33vvrY9911yclaYm0YGzgL6BSOHUADKyYYlUTT5QKShKLKTKNk7Aai+M4h1x0xiupRZTwpKUaPReRyIQ0RfapoyATAjEyCAIrAQZgTi0WgkDqPb+CIICihkAFQIIYXsXIBAQwJ9rHFBNwWukGqN6XwhkG2p+mzv4w3ddh/01DWUD6AWcD5WyWjkwjpm5w4uzj/7Yv3r8M6VQIKsEHauHjWd7PLx7s6NwYUiLrkFbi0sj6F3QETguI+Uce1X7lB2hQKk8O2+cOO1tiIgCKG4S0nXwiWeHwomrIp2D0kYWIAjgsTU5t4MPIf4jrY+RqYWCIKMwAbeqMQALCWEACnK6fODm20rfCFUJeUiqJN33IASPnTBcKP5t9Lbrt9XMtBVOBCI+INWRDY2WxlBg8k/t/sQ/fe9/qjMCU+c+Yau+7RMst66f/KfvbxoJXleUciW+EmVdkDLpMlBopE4L1K6YkdWhU3orRmoVVYZaRz1DlEKTUcxVE4fADgOiaKVUUM877doSTaB2GtiqIcinBXrtLKTdcABqEcTQVkWgAsFTwwAwsmDvx7B2NRGWXcVBg35eAEV991eDHtpPbj0efaMTLLNhcARWkWITZsYidsAHT8LFB375Z/6b93xsRjbHykeB+MO/C1L0zGc+WopTgSlCto4XcVxiE2GzhARrETPniIIVs4xMKezJhcDWVMFR7j3pSCMFR+JNLajYRCRRomvyBJ9FFhLB1hP3ZmUeAxGwanGS8CZPhFoymDr1DmALtBLENq+G8hcfqT0AGVXYLFgTw6cBANQH/pb0o/8YHnhP3cQBDAtK5CNk9KKQvRdcWOUSFvHR9/67S//FB3/Pe2waSxmF1dHYh9sr+zsPlp4YNQsnUpkcTEACh+SYO4HACgfpVwYdS0HMQQoVs1eyNIh108tRkVgdZ6yVb5qIItEs3ug/lKCDsAJsj7ko7euPWxAMCLQiAQgJC+GbWP727BuI2joGAmLAVU9WGuSYhb1SLN8EBIiSr/nz8LG1J9df6ElSa6pRMUECSjXex14hIWlR4lUm/e/8hfPvePpWHWfcCBmlv/cXQXYeO/j4O7BiTRHWFjsqJ6qw662XLFhdUCJ1Y4l0Pe7TOLXGNXHCNmQ+yhyzcZiQDx0jGiApIGZcRkDaAaqTOUOgFgcDpwu/tDNQBhBsf0QhEEBR0haun3L1WkDiaQcvAaghiPZZwjGRNh4idcIKYQAn8LYvzK6/G27FjSeQLkYdAoyiGJxR2qNOaoSaDVCzuPjM3/3Xj81OtPYUQKG8838BsC771t0mbmrKvLc4aZghpGWzVFRBocUPCscsvcrHrHu1ghgy6lmDxK5JxJuODsDegi9MuuReU3E999pr0L8d2uBby77i07kWnIJU2zIRaYs7GUlQmARABwYg32IFiInb2difvVZGFjWQJY8RkuwEBJSrUMhbf7H7tkdPRC/FoHZChIDOLK1eqKCMRmW1FoIqovDo/Ef/5fv/g2uAJbiQ8MVbzDubb3z+YWqCnWDASLwDaRZJ8KrjnQGnFgiSZuSVVkH1ap2QD1HEmNQlOtYYvEdyTAS1I5QmQEqiPUj5acYAXoMXVqcmF8RTQBAItlPQtmkOT9sYsE0FtepQ+8Joz4X4jmWwVpzDGMskctT5GiAKvveOE3jj/Ic6X0ssaaw4RsMIQUrnBW0nkCuQnTaBu1J26j/+Sz/xc9/xsSBYdbkh+Mg/CzB9/PYfvEMHDbFoDAojUXPSwUHSMITIFCycKAc6Bo5QK/JGCde6kUyzRrSNJmr6i6w/4ahuKHUhEiJAZzEQGAZEZGY5fQwQTm0y3NJgTjvVCdvy+VNzAJ7+llEhAqFKjk8Ky1YYfQU2VOWnAQDwj/8Bx/+pc+0pOWCII9Nbt4As4DmKo0T8cllZy9rXTePL6bIyzZ+98z9nHwTPhhqAzts1gTtMDtkUFtOsB2VFeRq6vWGEJKKTKFGBvWFVW4kM+Kp2bNjWUYY+YYvasUoVRhpCVVmxofCN2JMDItfIRxGZGUXaBhDC1ulB0srvCIiCWpAIsX3uVSsTQlujwgKMAgQkksGyrkrWTMVUJlUd4DUBEf22T3L0ld67Nm7LEpFdMWkweACNtY/EBu+qxlfLygWTWopDJekP/O5/2Hy6E2V9TnyB74tFbq77T2WdldTYRXdzJSmaCibzkt1iVhQWKI5HRqJhbkIcYVyX5XwegmAnFVMHgNKnKZeNM0kcGUbpdTuZyXSo4vDer32DjbS7n1PsJzAiIxPS6YonntonQUhA+dPO+XZXfGqPI1YIl3KJRGWeOIrFaBv0DBAhVi8yRt23qRc686WOrR80LWNKyHPQcVCRt6CjiLDpmFB6Pel8+z+//Nji0DcpZJx8z6cAikx/5ntXQl1iLAZqq4mNMXohOg+NjwaNUbGPcaYYI5dBWmTaO+4ZzV2v67hutPZgcrK5dPU84UAq0UyWwl/YOQiRBfRAIG2pZHsRAITVaZkc8Ol5CFVbtiwCKB4wIDG2VCCEpzINPkalJIIsQJdOPCDCGsw5bF6/bos8MWQo5BUEJUCiNQ9rH2LNgKADUtz0jE9Lrh6c/e3/7elPu3pQRsas9X3wB4Pxi5erJo40F5HxetBEccbpsBQKNkfnGLquzHTPsE18Vns04kmQdIN9WGjusU2sEhCNcRMr8Q0pkxvf+fE+OWQ+LZVEVG/+rW/2S5yygwQRWiI/IgOgnDKpRYkQKhB5QBrIIu0buzya+YXlbwIIwJPWct+8Y+1lQ7kOISgfQyAFIEFkKT7lhTXKGjAaaLY3KadlM7m+9V/b9x7f3Dk5WM6b74wADzbh95TJsjgxg7iTZdnKMFGUUAKU9KzzEpJIslx7ibPExNFKL4qN82G6dI41WUmSpGmaNMmYOx0FeZLqSJBcvPmT/30lJEwATMiIeOptwlZGgdYyBzooCRgoiAgwCAUSAmIUEU9CYFZq18QxV5FXiTQW+l8QAcQPfT3g6MzT8ELdk6jGeVxUYpDAEVUQXFzJMk7UElCXIVSgl06yZf3E7//YP/nIPwesJKhnfhnF2vhFiFxFec2x074JrkmrZFpFRawmMO8mdVx6dpRmJ92YqxknqWkKAC3VcqSpnGIIohpRutZ1kqky1aiMV4Ye/tH/yZ06QoQRA9DpBrh1Rrdg8Rarhu3yR8woQKJEM5xCwwSHCiOngkvBQwd0BPgiEHL04X/hjX70wWkZCbIRikMgtkyAKiQUiYYonXIvYB1xnOgmthTlM/3h3/qpn/yhz7MDm8aXX2/4/srOs+/fTYg7RyHViitCSqx3UnewF0TXebLlvFfA+QBVb1zDLGooSsPM8NjFi5q0i0zjqVdMAza1VVoFZYG4ePy//Hl7ugFGppYNJNSOvAEECJBRIZ1aB6XNiFMrh1NbxgoI13Mfcw88lUlsEDCREwAAs/5lidInoy+cS9hxbEwHlAREAKGOAnIUB9ApmsilNaW0pJzSMvOjH/yFcz/41A1SPvV//qcbmZ5VH/vgqmkgl3Khc84ki7MFAM5Rh37SaUBDPlcUupQsVJQo7aYpGOvRqEqbNLISpUWT1uFEjUpxjdUDywYkbeidqz9bhnCaFRFoa1RVewiS00MhEwIABOTWHUHAJgCKCIFXjKyeiY2NBWxgCrqGuAoeCSGD2zxYeRB2N1npJgdDOsPlVKDx2mAcdAUII0UxN1bZZAExJS4kKlr9C//y2jsmh0ACT6MKbpHv7G8Jk9eDNKRJjQDKDwrWFRrHTVTYYAIFV86a5WKkOfLaCAUXNcnoAFSelnZmq+AnY6k5ToDICQGphDk88BNnlQIg/WY9CCrxQfD0RABCiAhBBBBO+yZPmWJErbsagN4R5TzINKytdbUL6OVZAUA8DwX3rl68PYqjRCfCSiVK5k0JtehOB2NFjbUhjoqTpm6QTKKggWFERl38yN9744lMMtH6AxnKwcj/mhGrKDXacFPLfLK3tzNtnIKqrJelODev2QQf6X6saDQ6m2OpImPSXtzXoziRJBQyt1WyorOezjdosih9CGNpHF79W+9R6jT8jkoBC7Ulr6gQiRCQAyG1fGdofSHIICAtcx0BOl3Nhhs1MCsrw8HKoN/9vIgAPlM43ny7upExFEoKVH5RYn+goWMj65umpCwOgjLIMp924wi0quacQjmu3vnkjzfvjbCy8sMiUsT6i5xDubBLv5ovZ2US6V6nE+skUdihua3EsJY0Xc9WV41ygSnORacZaEfERvU3V7ce2F7dODMcoI+g0RknydJhQpWa5//11V+u0AOQMACBCBIJAhMSBBLitgNNTj8AiOTbWrX2CLEtTjBwxEtoEnKQwzcFUegjXw107WnXDKIiqhCRQHmuj23mTshxqNHHJjETl3A1VWuOG0yVLWqVgPJ/7Pbf/OdPfDbKlqN+FfiwN/3GE8lMUswbBwo6lvvgrE21Mpo6RdNNghLNZdCduXZYEmSEQh0d90ttplEyzLMVKaHQvdCxCaUDxcFnLKlZG3W+72ef0ZpQgE7VHpaWIRNYCyEqQmB5M0oAQO3cXKh9gcDbtSejszTtBF9VoSfuEABFv/P3JN5ef10WxwtellFRLFznzz+pvnHvJbUmik0n9qlpOM1V70pHFKxSnXS1JtQg7od2frr7tDccfiADOF7FT0R6mFNOpawPSdTKEOuQKujkhDZZjyQsYBkqzCCPIlsHIqmXZdIUTaYAw7zSwFbSTHWyWCrtwVnUTdBVPBBR1/+7r/19UkEAVOv8BfAKUYmwbr3QyoMQeMTQzsVPhwftovjIHKi2K4taNWZuw7T3LYeIGJvP+s3H4YtwZKCoY591YNn7aBPZV+2qqZOIPNecMgJEYhY+54ULkck8F164yX74585//5X7SN/2CwBW4m9UkUkYMZ7VA0orFXWb1EjTdINihaYBkxEtFATlSetgFinGNbimSUAx3+/7CYuRhguKplZPIvFR1YfSMxobqfgBTwqVMINCFlZIyAxGWISRQEgxALTl8wSaAZTTigVRwGTjaLwfCoMlyxyFy5cAAGAIu3z+7TOfFXOc++FBP3VpBkuM06ppiibtODdgXJlWR31ulkMPbmZcp5FaEF2v7n7/v7z+5Mk4ia7emMnhcP/3v62Mgk0HMOHQ+OXIspxYBBSmfHGUJ/WEIpeNa/aaoB+0WIeFFGpuU6pnxixNrw421pNUi+YqcrWyiYtChaGJ1D6JOKV1ECYkDqCAACwSaGecJk8B+bQw5rQmh4SBCRjObp2wXq16phqohjqLdfVZFgJ6HDw/k3z1iXpRpbXGq52k7Jtp3+ICMJcolEmQOhJHOpsHw2ai13uhKmscIUIE5up3/L1/+s5Pzbp/9v89l5NN+vX3S4jKqWAyrcGg61ieJsmckmLtOA0NF1llOjVB7MNMTQ0uG1dfaRadpqxGnXSt6QkY63000rrRidN6QQSU+sh3OOVjBOgsnYlQggghcSCNhOyR0LBXAYlP2+cFkVVbd0EQEN8TnRVd6EAroYwoTvLN5wUB4cP7Hj4M462aYLVf+rRZ2OD86sJFhnSInWIUmjWYpNYwpnNRNl54W58NEURkuP+Wkx/7P977q/6CRwnz9HCyUumVZtkBr3rceJ3IWqfoqLE6lDCqAUvyqZ5kaTRPF8s+Vr1ZvKhMQtLvgyu4CIrZh26Z1IGMXjaisSEnKk57ogMiDp6Epqh8ZEggkFanAEVxARRzaE200tZvtLtjECGBt5PyBYRKz7GC9SDhy2MEEPVtn3LR03fVZFZicW8+P9lrcHFS7DYam2yGxYJqb8r5DrPC0lR3m0V5+OLCCAQ7d2Fc+mN8Z/dv6D/dJG/vAY//H7+76vmg8pExa6NORIIRubIuCB0vyvKEVLWYliDlwf2FDFeT5MrZi5vdlYHlYV02k7mb+MWsbsKck35ErImj4DQ2oo2yiLcAwW1dAXbNElC1lZQiEAIqAmBNxgUBjYIiSBCYEAQIAdLri+Pgl7DqrI56CZF5th9Hq5eevPBJPqe/FhRh6k8Kma+MqEFaorMauoPKxNIPvPVgp25CyianAStX3pppGu/sFBIKG8GfGf+t9IPw52Lzrt/40bdcnmpeLuFkbmE2yzaW1uQQDcto1XlwK2nUvZA6p5tpmmcdLro0j7o27qdDJc3YpNAJoxXNRsbUMbqMSFRU6zoPdlAuYLCHgrZ59F4NLAs0kQYAjxoMe1BE6JkotONTYkEmBt2maOCMITRWOw3ZMlDJI3eP1h/5zrdfh7+397inVfBiOujGzEPli77uT6u+6+i1kJyTcmMEc9tJeL5CIehJXQ76x2Xh4pON7pInSfq9v/iLf+XKC3/1xQ9fA9hKV7vzSkeAyliaV/0m5I0jm883fcggBK0ZE50NLNvQ916FJi876LM0aD5aGS0kV1r6PmbQ1nNkjY2kF8ZGOnV0KCLe9R96FgBErFNGG3SB0SgCFtAMgprQMgBIYEDhNjJAD0z10oY8zCvk2vWzMjmyo/zalRgu/OPJKwNxQ12ZROuIuduXsDzJOzkr6Rbad6ucvVqvbZ4hcVDd5NLhVjTQGFUrkaY5+9F3/9KD77n84b+1ezyAvd/4S3OVSqZDoQ0exJNej5T1gxBnyybj/t5SYzxm9GM/KRqJ8qRYrU/e6BjCOFDfVuIYS9io6q7G2GXoTL9sOLLxSbocA2Ko/bWX3Gk1VEAdk4IQAEBL8KjZOhJTgyCyMIggCQrRIyaR4zRa9IqEojKKJv6WO7OZJgAANwZxmUDl7SKOtYpstwhdDjKddJvlUBVsay+92Zh6jZJF1mQI6GUJviON3e+Ix/D4/MvvgTQe3xg/+0+mP9TNptXkbD3pqGkjRokrOuNQmZNA8+BXyv1YV7RYg7KHeTFdPxm6TrF2cDkp4sjfPQdbS5/0l7O+1DpKI9bQMblngySzELJSwmxNXXsJsbX++NDo2CjNyEzEHFhhaxEHRCUA3I5M4IkgalQvQmXqJq2O7Wh56F/ye+/7qwAnB0OiJRiDaXoSa1uGymU6rifg3CaHBQ+qWWnLJqlcv/ZFo9W43h9MUoMmOZKmzLJxdWW8vwlv/0f/fpqtN1/+Yypk9RR1utxXyDZIR4rEUpFjfshOBodK+rquuFT1Guh4Zb5M8lGuVGTTod3WEmm1cnD2wOo4L3XcIDnIQukGbgFxCVCJuvxqy5FBRhDnScVxzN4CaQmgUQVog9QIQBSEAmSZqWp0lqdrcd4BxeQCT74In/ieT39l/WnTKG3q1OfpxPrELOY9gPHMxJhHt1fMtDeTRJyjIl7DReqsamDRnRydkTBvPMRrtlPnxcHw7usvC/Bl+tWnlYx1vnZfh7xxR7qzgGzkfEQgExwtjbIATa6XrIb3zrlxj5vjC3PayfNoQvHqYE4hQrdFPtGh0uxcfZJEqUHqceKUYimdNqtHLUewDQNDcGWURlEIQkoYgpzKxhSAxZCQX+/5ogikswKCh1Vv6ZuAHjla+dXfj35vMe1VNQjXfnDc7x86vt/rTBU7n42bg2E6tbZDAKWa5YlIpGyIR2XuZaqcWik6YcoOrxefhadj73nZf6NYb1bi8n5NVQxOXN00aZ75fh3HXppkSVcnOOZta+6tnyvZsiQDNZh3V6p+1ckot1hn1sK40xDKIJJBNx+uKD1UlKZCRqAugB46Jeq3+pighHo6rsTEIqgIA6AAo9LaKCXAAR4W6mlByhOvDc5oK3xRBJD68FLo/K83Op2khkUSfNRb7h6jHkS95FKCUU6jLieNCgdH026UdWxIMF9McRYKVS+ndwKXKl6+qiE1Lk7tdzmR3ZH/rdBMw6J2e1IQZgf35zt3b33jKOE6PhOz07Myg15+o0NEChbe7vfO+tDrxOb1enFc7swYnF16v2cJI2+KIwpFKJF7ER4BJgi8bKS7SvSfdfF2FOSKWcEmUh7BtLNzYWEAEaXUh1wdVlZVGnqjpKOlHG6+IMiC16Didz8xyHRpxFghbhbHcUgjyDNM1irTSf1kMV9OTM/Pt2I1PvYm2jjbfTjrmFFPRmrvbnM/e2sfwz0TLYsPAEiJ8SeqanovKutevD/ebk4Oby9gWiyfn+9/+bkTDgH2JtOa/QpvdTvDsCLFrTts16PlrR2bTF65cVTuvd7pb2WzUs/9rGdOukpSq8jb6ZhYa+/nPvFP/V7AN5MAzIConQjWVqUx+SAtXQWRuG0WU5vg67LQgpPi+rJHs9c2xgAI9HRTy3vWXbVYndZqsNNRQBAVdJjtL1y1p05O1MWJG2S41g2QZv1yqZ1SXsF0bbbBURJgTGeUJNnREXbu5Oc2DiQcdk9uDPzexaYz1JzZhy+7vcWF2UG+DuNoMdyJkjQPNp2bwHAQdXp20auSY3vGqsvDxXQ6TI7Huj6aSToAbXREdK7Q0mTiFilOhVgxV01O5syO4KnxBYAEfLsoslM6VR4FEJQAoMReC+R4cBTSkOz5mG4MME1PVNs3/O3f8nhmfYK8rFFRdkKwsTaNYkjGNtrYU/fUatQvejiSOqFOzcPoBNVhE5ZabRys62wazlbQPXhD621ePZj3vv3fAE4eGv/aT/BG5ANW53aT++emV8rIXmCONvP7lR81urfYvB+VX5wbWM2O8qjQtwfZcrezsWPtIt+/t5H5cTOfTo51IlRCOQ0rC0ykHlonHgXBFX10b5sUp/Xbf1Sx0cog4q3WACgsEQuLDySy2aVur7PgzhnbabzTvfQzAEhs3vazIb8wG836hBkeRtmlSWG6wW2PrUzWgulWxbQoV2dl0j25OGnwiO6sJVnt0v7kQlNNTkxS88lExt1Of7yM0/n7/i1L46Jvjbu+GN5x8Y2VdHHHm73lhVIHdpSM12+v9eeVDtWyWukfTXfhmt4ttudHi+jkdjKB3n49XE7re/ksgmNdZVGNUdTt0dyK8xSICBFs4dJKP/3lqqUIwelicOoFQAnBATGEKqu0JkQPfGa2LPvjk019qMI0kuRu8lsCgBibr/HZ5mTWz6MyWui5PxzPan3+uIj8yWj/ILs0vf/qJD1eydS97tgs4ZbIbpw1yTBb3Jx0miJOF5F2pgPLrUYS0qNHnxO+d/bub337UvVlcj7afCNZNrNoZI+y4dKF6SDdiE3iw+Vld9EdYn4sfnnGzwbb5fDWI2WnPNffvb738tA2i2p+opfZQjlHMh71ZXHFzl8iDYgSZiXFvvPAq1UrDLQ6yZtxijcxQyhVxW1+O330kW+cm1VN8vL2eNLNJ0dbvfENQRQcwuvh6r37oq68dN68dPbQTJeT2eoJq/tFT1SVS4ivHsWDRoeRmvQ0Jny2Otp2Oo0Hx91ekd9bj/c3DJBW92DSV5v5//1HUOZX1Nc+0qx5u3Z72CzH54TUiHNXZXMXrUQ8qOqQQsgH02q43Q2RmtJKUJpxP+vkNG4OF1uDCY86+UIzzly0GEC9qDMox74AjpNQoa1S3WRrzf0lt2aR03goUMsFAkQGQUZBZgB14a/j3X1zsFotaaoaZJhWq0sQAHgLzPjs9JVQ32xu9kJ6OMRSFs3FxtLmRTfZTHc6WxsP7D4wRTJNPNUnHbefJGe2n580R/myOiMjt8Tj/NLuJu8tYe9cp3omboDngyNeD0Ucq+xutRZyOyddlwO5py6e+KZePBwdXz7euRB1tl7CM7kvIS+3xtNz9yfV2s65znS/l2w0+7pvdC/qYGi0rCkXZKb9EpSgNuSLTqRClPeTwzYrTnyKFgM4TQmchkQFBYFGkl8YHXRWZlh3DDZrKkRNAETRf3wRZP2FSjutt6c0v6yWK37UpPE3L62l2cJhoHSPz93pd+WNtcv3yq3gPCg9N8kQ4w3Lhw2HNCpm3RDzW+AoubeZfufHRA6f/up/+FvPNr3+0Xgz9+X64Zk9u+wuwqBxpjumM2MzKer1Sp3MbkrvqFg1x/z8ZW23qyIdER9Q+saltWRiifLKxWk/jqg7zNZWzHDGXpxHBFfptE5T7J7V0LbsyKl55k3KFqEiJCQkQlpdOamX+twwGa709dVzaw9cP/esCCLG7/scm+yZ8zGuDqm73luFmqO9sMD3j4Yj7AbpZOdVM/f+6Hhl4rPeK8WFpOCvNhjvR4N5l3vRxuXh4w/283271ZUH8z7N/gwALPbNFyZn+lHZG/UFZJdHj4zWBrHpxX42170hVqMbs7mC+g2dHd8pq2M49CbGotcfGe4+uD6AQ0WXZkRDveISlRRNGZxJEygAPDrxXDdVUvtBHG090KXWD9FehdNOcWzDRa1yTtGZztl0GdbC9opEK4u7N6fz+ZdbH/mVj7tMn5hOL+5gnJ/TbkAfevriOTrUB88+HzfpSq9Zu5Amy07TNMeSrUXFnsm2qnh/Ye8ewXGnis4/dKDsvdSOQlouDcyHGwC81yu+dS7rGdxQA9cMfJ1sS7cOja+LZm2lbqDG2YVO3Lvy0PYk7O/rrlo76Z051++ujCLJ8unZDfK940QvSSd5eewNN1D6plej8UQsABbDYNy9MJbs+sGuRyCGN7uG8JS3BtIWqINejXb6Oe50TK2rz/dzA0eDa/cEESTHZ8MQi/nwQmd7tgeui75zf3aXZbU8Ou7c3k8PzPDmlQU8tLz92PFyxNfvF1fudeVg1N0vl1zF38ITu1md+f1r5xo14SjX0/uXf+R/AJlt4q9/8OJOdzG6U/aD7WXo1tazw/F2utDFuEp4vdkshxgNFmmHwWXhipRHqV3vzdIRoo97C76+vDTRhQ7ed09WVmc+43TifcvX1RigwDhdjGg8RbV5UsFpHORNQbD1SEGbkojSZDnvz1buxK9vzfdKvdGdn/gFAAhswwFfSZqNx7hTdrLh8a3cdfZCUviv5kHBjWZyfnV4uD9QE5PNz720vgxWinHnDOzkZ9YOXN6o1BpaO15Nq0XVO9f448GB/dBPOQwuffn5LA1J2XBXh+XOAOKq6KWxv+AXYs6UVar8QmX6KNGhm9rU8O26d7WknYuTbTu0u0Nu7kChvVM6mmLlq7ruDbqFlQBAyIgAjN3GJyt1iWa0WAomVRsUgVPf1OlcHJHWoDxcFrOHT27Ur12+mly9uTfsHbcS0lshyFvTR4r8brTfdcfLysyq5bCMyiKsGpmmaYWTi2Z6/Xmd7NJKmRZZPqh80Qcop0Xo3BmcPHn7jUv3rvjDyWC+1czzNPi1t3xLeLb6xpe/c3unevncCchIxgVs3D0+u7+WzydwYpy6u5kXg6P72+UgL28NU3ez6d1b1bdWi1vDvWOdLc/tlGeV1xjLiebcvxTJAKqcGCEqQCAEpwNTv9QDNa4blS+PnTu1yiDIqZsKEUhEbUR4Nr8expF/XMHFtf6969gxf8iACOqjtyxsZ5N6frQaL/3l4/sqmQ1Pjtbr68Puzt52Z+Ne88iyI0dFc/Nsnpg7yfnxAqMywmQO1XB60UdSL/bL7X2/cunrC5D5YZzP/tLfBDjajn7nu+8+H519fbhvYfa+58r6nrU+i++NTmB0Mji/smfQrp/05SbNlyNZbiTXVu9M93Xi0a2sw6GsyHKqR5VfQJ7OFgO9p5ZQMInEVSt6kFexXtRmy5bVoptNTv6IsvhmYAQQEOiCVmptcG5H2e3RjIahHpZlmf6BCCKbd/+Gj6/vpxWOjta+Vj2iqitxTVevzrrznV78oR2YXjy6bSOehdF6Oumr87f29taV62VdK7LFq1WVSJrlnZkUg2Lzxna6X5QnV96flsBH6cmNXqfHk5O4Gmevvnju2TzQYP/+5ivn+Pb1Thm22Y38SnEC/aW2w8neoLcoLx9R0md87TjP7sFy6HQT7Iok+w1kd/M8LpgNCmkvIqy0B4jxMB0olRmwo/EpbfLNtHjrkosvaI2mZ95owlLf6+7OJz3crOfqBiAi9LOvuf7RcOqF/KJ7zq422Vo/0TO3Fd0fnclp+3AmJpRUrx5eODfGWz2C83DWz6bd3QuHOl/GdycnIX/1wko12HoxP8t6nl5Y6sEHfkvw8NzsN/9G36aqqnlQfW11XY9zo6M1m167Fe2soqr1fMk7dCkccLTh9Eq5f2k527gP9zY9lVl+xvacEN3TG8nJIO4eDs+N4nNrIAo0KUEEEYO+ifrFOKhsfX3l3JsnglMzISAh6q1HO8mgP3p9Juuhg6O1VTlf6kVzM1oIItB5eDY8EL3Csy1M7MS8ujw/f+PIht75pX3AmmblNi73j6TH91dHy5d7Y0zS4QrZ8XO0Z+8eRjB51vtJM1zf2zsuvrK7twC//thKb3z/BwCl+ZvJNxPX6VUPrfST7QvmBbzw5Ln7iZlHuHFxincjGR1w78r6+HbYvDB//dKJ2tgfVslbz+ztTzcup2Ed3OISNWwPDDVX4vW+4xqsICgyiF48W1Zkq86IS4+xqGfgzSfgTSu17l195nKadfrxucfjdHk1nyfPV6DPaH7lHdMggADnYDdcTWf34pcPF5Py4AYfHa6Ma65vZ5tTvD8/hKPq3GSnqK9PISm2ek5W1KemJjp7ZIYzomzR6T7cLL4YtvFbebb9Fl5mo5OZPgNnHwCAv9crf3/fJ2cCD0dr61euGxgPrw3u7qSvV8XGyUBofsaMpXsp2vBNdjkbrmH//Nqy9puD7bUK/VEwyTc1m2CWppi7WGdL7eYIiC4gcACXKTaBbUpBHHI3xv98+wMCyMpbU6VCkMi7x3CtvmerASRAnIbeBzZ/QxCR9buWS7m6d+VmlXb9ahie39/adsOZ5WrD9zj4M2W0uuiHewcPHp+V/pG9Pp0uLuH9xXZ8OA8LOJrlVF1Yuf3gSfdd61V5r6fdcccVk4dXfvjvAI+3j3/jx3F+byOH11dPzh+c2Z+e78bhclUspvWTNpre2eq4k970THk5Mt/0zXH11ubit5Q6NwvT/bW42sUN1AGT3qIqD5Nsn5rVcQ2IIgEAIbBTxlLJScZeS/lhh9Jitk6dk/11lVGl8toW1bmFOrO0fYlW9ere/Qvz/v74/fPipuiHPm7VY3or32uqpNiRbGWyrSDsJufCV1eXuHZjxAvu7Yd31qqZxzjFLNqD3sVZyJKz45NcRvtbORdos8V61b0duZvD9fLmWiIvbCmPYf/yP3jmd6SozMkUJqsZc2daPNW/+9DLMOyqY0UXVppm9XiWXQE6zht3pYjuHueT5VpveQ9vPjEf3rySU7xB/a1CXTir+qurwZWApq0cDR5BYRz5pqwj4Jk8PG3NQa2ZENTmkAsXYgloFyEadQ6vUYnjWdbr9hewt8Dsyf8pQfOur/u+hnJleyO+ph4dz9I03r1/e6HcXj5YP7OYxCPsP7tY2a6zcu1CN10uw9pOPMmi45Nucv5oTFej5v4+nkzu2uXL/fTM2fp+Ft/uTItzzyB0f/Kz3zGgzvb57OyZ4dO3d19XaWI722f23e694vl5Mp69csfe4Nd2J19+BdamZi3fOYrSonSUT0bOUHNttqppUNb5dYyOk1Bkx9eORFDiUkSCL5IQoYSmVj6ub/+DKgBAnh1jGyVczWxkQxRMcEp10ybd7fcc9JtyvFi7szl6AZrqH1rW6iXYmIwmX33y/GsB8dEQxfe3urI4OXT5Ndp+aX3r/mj+6MDT++qPd62GNBmf3xr7gwauv7LtCrxYn+jhYtMPovKY4+7hziPTg7Sf713s/NBrf+FH1iOAq7fkxXOLc2Xn/JkvQ3y0+oLs4/ly92oxSVdjDNVG+OBNzqdh+UC3lt3+A3tnvSmrcw9uuNvqmS7r28NsuS9n4rkthlbeWBKRiFYBW4aispELhXHLsxcOBoKrwyW0/SOmzmMJjYEgkdHbheZst1zBm1EThgedeH6LI3UkdA5e4wuxqG1zY7GQ8UacTutZ2vGbq6o5+607F7jHSzZhv+zglc6E0G8BHWculjCvjvfO7b062OB6pZPdoOni0fjgIHaZX7PxlTMn9PaP9pYJwNuUWybNc4/d6MZnL43j8bVmrfzmgz01C1VSwXBv7NfDwq/OttZe6fLGucpuCNx9FO9h93w+qku9tbjXScrmaHuaN8V49yYCGcCaMYgLDKyVcJNb/+7nBil1Oxi1u5+uuGJA2rFhMiwrGifQuWrD8FbfN1Y6z91VhA/+j9+4ApYvnsx0OHrxwoNht3fhxjmPi+3fv3Y3r+thXuzfS8536eiJPZZszY11et/fu/voDsadZqni51eX0TGPCr+mOynXWX+3k1cbJ3Q8s7No8dmH5kygVCir/EG6dGa3mMLgKs+2xulx9ppMLo9uPmDTRdi+Mk4WdOCgVy506CZ383jy0mUqM/XNlX09DbSmV5dKEqUnA3hdNCAlUS3sPAOC4jhkyPodOwBkRBkEAXUZJ1VtMxaxSjnl1qebyR735W6WXbl/ksLd7OGvdvW/NR8B+NZvzibX9LkY85dXe+tjxaOaXn+4Hph8X0OThKd26p1wf5YZe/v26LmVJ/eHtT/OOntd2/nyVnd7Yo63OLu/3qy+djTfOLPI1fHc9nqm17n+9ZO8zgBUrwoLmsjtjYdulY/dBj+74m888GJ09nz+zeN8bzm9fmfcO9r26uhgoF5bwuToYV1ezQ7smeXd7j7lEGYwK5Y379y+c1CN5hBEuqkGBvAGnLNiqHZN5vKVISlkBQh46YFUGyiUCRURaNYPPNqNBzLlxavlTnF57Vzn7Q9Wd3MDAND58w/SUVExbV0bPuLjzkIFKmvB9clapnubV8bzrdE7xrGG+2a7cy7bfXXgjh5c3Vb55lF3kJt0tXtSZHFx9/UHtvrmLd3Xm/7b1/rnaa/4jpMXMgG4/fVbz5ere7B/vtnRveePXz6+8+rO/c/t0fiFL27SUfXoAyo7MFvJ7o5uvlnvD7E76ano/lfC5aib1pcpPtPRN+9lGjMKZ3K9FKUIs0hhCFJhFMXARjVw9YEab4tCrUGguwVr/ZTs1BhDyFBvpzdncj8+xv7aaOSyaGmLsVNnT5MG3be/vZm8MtBLvp3tdPq+Fw96g/4Yrwzt+alA94ULk/XlvroaKTkP7uxh79GHH4hq1X9o1JeoVD6c3byUrJzIrH+U7PfsqNs7OgwfTLaHX7Hf+OR+nV3qvbT/vi421c3D+mL+yNqFpvfw2luff5XK+eSb4yZ97vkzd+rq3kli1+Pt5dX426IVzt53yb5RRqNUG7NZJWHsjpa0MWq0JkELlMSOG9c0ypQSJIiHG6P8MoSUAEH1yzzOrHfNZCuQEt88lcQ4btLolYSa8WCGe+fVsrzw2OkFeOPh46PVkZvt8sbuveHifr25ojqJV7c7/Fw6KBYPBS7PzG8Ys7VcD+uLl+lkcGbwSOm7tqdvdNODx4y5HffF1G79+JF8cWfnrZwch0n2gV99ER/60GZp6fxwp/Po+rcuDf2L2cEDZmN0Ai+nj9t4PMbzdf9O368veo9079q83owPh9GLXX0m5ZsP37vCZ0kRH5dXzOCJt1xhqcbHAiCuLdIKQRsmpRA4nI8XZoZCuoO4UY/3XSc1xNOxttb5MNxdVhbGAzzobk5JmXUXZqV+k6e1fPlkUPbL5dH+/uu7X1mka51bm6kvEsimd8Zf+uzdJW+NlzN3JRzvH5T7t/vnrsX4+TvN/MXm8Nb7H5mf88XndupjODK7frrievzEodw4M4s2L77rfoQAl780iFfLuy+9FvTrm6Po9n2ZfA7OPVTvuUHpssnhwblk1qw+P7wHG2tnmrDyxv3LWN/81Ccf8M0RPKsB08HtGWb9OfNyNfIIoFyeJXHt2QYFsFCCovrNZQAkMfqa8vXEZ5jV4v1i2MOkvlr2TXCX4xP9tsLHB36Fcn8CJj69ABt5h4d3r3/mXTeuPrY4Udfs/uX9zrGLqoPz115NHr+xN1DJwXJ409Ajd1durRm21fNZfIeGo/3j9Zzm0UJlLw/f0tyE3Qjna9sF14+zJbf/3i+68effPdi+PfpNNX98/sDxYvxNeKyBOwd773z9Qq4cPrrsnGQvXQoHi8k966vQufPwbtPZicr1+5dXDtX0zCqlYdc/hjuvPVtWG8OUatLsNJBRKC74AI0ChsSvdnhnAxA1bKyoHMtjMEYhhEqUwDk4m5qRVCFyy93D3FrGvePE/s6zDgBgyvGRCyG5srde1bafnxz1jv0r4wUs9GT5sP7mVY5fi9bftlt109zvq/GjG7S1cjKDpVv6K9P7n72ZDNYGbx1/69WXh9jfebik2zcSuRG8v9594qj+KsCV9IjUY+m9L+um3uRxZjtpfQe37t6+u/qymO18kMruq08v8Ey40Dy8d+vcSufSJNWjO7fP6nCox6gu+E1Qve3PLx6+60sFUQGU5HnDwVsfERGYgodYNDlhZAhCWI2P5mYlYXbQlKkxb71fT4dvbF38j51wR5PprUb1+LgDLwwfMPDp8/sX7fDGIysv6s7aTpDt1146qy+ce6BK92BnWMPyzEl5S01TfUbx+pEOof/5ZtDE6vwrbnfr0sr+ZHOZ759dH84nD3eoOnjtOtw5t7n9+dX95ZMv0EpT7k+GF9+4n+jycHz5oJOO8Nt/o7f/0LfurKkjVfXcfOMwOk4X23u3uLpJl1684JLJ4o13bhw+sbyfvLzS7/7/AMPnNtX/XQNAAAAAAElFTkSuQmCC'/></td></tr> <tr><td align='center'></td></tr></table></td><td> <table><tr><td><img 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'/></td></tr> <tr><td align='center'></td></tr></table></td><tr></tr></table></body>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"if testing:\n",
" f = mpimg.imread('../data/cameraman.tif')\n",
" F = ia.hadamard(f)\n",
" nb = ia.nbshow(2)\n",
" nb.nbshow(f)\n",
" nb.nbshow(ia.normalize(np.log(abs(F)+1)))\n",
" nb.nbshow()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Measuring time:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"ExecuteTime": {
"start_time": "2017-07-31T23:43:10.629Z"
},
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Computational time is:\n"
]
}
],
"source": [
"if testing:\n",
" f = mpimg.imread('../data/cameraman.tif')\n",
" print('Computational time is:')\n",
" %timeit ia.hadamard(f)"
]
},
{
"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"
},
"latex_envs": {
"LaTeX_envs_menu_present": true,
"autocomplete": true,
"bibliofile": "biblio.bib",
"cite_by": "apalike",
"current_citInitial": 1,
"eqLabelWithNumbers": true,
"eqNumInitial": 1,
"hotkeys": {
"equation": "Ctrl-E",
"itemize": "Ctrl-I"
},
"labels_anchors": false,
"latex_user_defs": false,
"report_style_numbering": false,
"user_envs_cfg": false
},
"toc": {
"colors": {
"hover_highlight": "#DAA520",
"running_highlight": "#FF0000",
"selected_highlight": "#FFD700"
},
"moveMenuLeft": true,
"nav_menu": {
"height": "123px",
"width": "252px"
},
"navigate_menu": true,
"number_sections": true,
"sideBar": true,
"threshold": 4,
"toc_cell": false,
"toc_section_display": "block",
"toc_window_display": false,
"widenNotebook": false
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| mit |
mzszym/oedes | examples/egdm/egdm-g1-g2-g3.ipynb | 1 | 275158 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Evaluation of transport models\n",
"\n",
"This example shows how to evaluate functions of the [Extended Gaussian Disorder Model](https://doi.org/10.1103/PhysRevB.78.085207) without running device simulation."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Below, $\\hat{\\sigma}$ denotes normalized Gaussian disorder $\\frac{\\sigma}{k T}$. $c$ denotes relative charge carrier concentration $\\frac{N}{N_0}$."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import matplotlib.pylab as plt\n",
"import numpy as np\n",
"from oedes.functions import egdm\n",
"from scipy.optimize import brentq\n",
"from oedes import *"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Enhancement factor of mobility depending on charge carrier concentration $g_1$"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
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"source": [
"c = 10**np.linspace(-10, 0., 101)\n",
"for nsigma in [3, 4, 5, 6][::-1]:\n",
" g1 = egdm.g1(nsigma, np.where(c < egdm.g1_max_c, c, egdm.g1_max_c))\n",
" plt.plot(c, g1, label='$\\hat{\\sigma}$ = %s'%nsigma)\n",
" testing.store(g1, rtol=1e-7)\n",
" # solve for g1(x)=2\n",
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" plt.plot(c2, 2, 'o', color='black')\n",
"plt.yscale('log')\n",
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"plt.legend(loc=0, frameon=False);"
]
},
{
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"metadata": {},
"source": [
"## Enhancement factor of mobility depending on electric field $g_2$"
]
},
{
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"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"En = np.linspace(0., 2.5, 101)"
]
},
{
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"execution_count": 4,
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"source": [
"for nsigma in [3, 4, 5, 6][::-1]:\n",
" g2 = egdm.g2(nsigma, np.where(En < egdm.g2_max_En, En, egdm.g2_max_En))\n",
" testing.store(g2, rtol=1e-7)\n",
" plt.plot(En, g2, label='$\\hat{\\sigma}$ = %s'%nsigma)\n",
"plt.yscale('log')\n",
"plt.ylim([1., 1e3])\n",
"plt.xlabel('normalized electric field, $E_n=eaF/\\sigma$')\n",
"plt.ylabel('mobility enhancement $g_2$')\n",
"plt.legend(loc=0, frameon=False);"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Enhancement factor of diffusion $g_3$"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
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",
"dtype": "float64",
"shape": "1001"
}
}
},
"metadata": {
"atol": 0,
"format": "oedes.testing.nb_store_array",
"label": null,
"rtol": 1e-07
},
"output_type": "display_data"
},
{
"data": {
"application/json": {
"data": {
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",
"dtype": "float64",
"shape": "1001"
}
}
},
"metadata": {
"atol": 0,
"format": "oedes.testing.nb_store_array",
"label": null,
"rtol": 1e-07
},
"output_type": "display_data"
},
{
"data": {
"application/json": {
"data": {
"__ndarray__": 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4+T/Z2gWAWv/5P2trnocfBvo/cStyPOoM+j/fiJOluhP6P8IQP8qQGvo/aT/csWwh+j9aLv5jTij6P+odZOg1L/o/I9r5RiM2+j+o+deHFj36P731Q7MPRPo/Chuw0Q5L+j+4UrvrE1L6PyjDMAofWfo/PkkHNjBg+j9zyWB4R2f6P5pZidpkbvo/+kP2ZYh1+j9B5EQksnz6P9xfOR/ig/o/Fjq9YBiL+j/Hxt3yVJL6P+N7yt+Xmfo/lSTTMeGg+j/P92XzMKj6P5eUDS+Hr/o/VOZu7+O2+j8F9EY/R776P9GcaCmxxfo/dEW6uCHN+j/AeDP4mNT6P1R/2vIW3Po/VfHBs5vj+j9dRQZGJ+v6Px9hy7S58vo/5S06C1P6+j/oNX5U8wH7PypNw5uaCfs/DEoz7EgR+z/q0PNQ/hj7P7Y2JNW6IPs/8n3bg34o+z9ecSZoSTD7PyPgBY0bOPs/sv5s/fQ/+z8r7z/E1Uf7P8t0Uuy9T/s/BtVmgK1X+z/q6CyLpF/7P1NhQRejZ/s/DkAtL6lv+z9aiGXdtnf7P7gnSyzMf/s/NxkrJumH+z/5wz7VDZD7PwqWrEM6mPs/yduIe26g+z9t1NaGqqj7P3cDim/usPs//r2HPzq5+z9e9KgAjsH7P7E1vLzpyfs/GuyHfU3S+z+o0MxMudr7P5iTSDQt4/s/sra4Panr+z+Vl91yLfT7P5Olfd25/Ps/V8Boh04F/D9Hu3t66w38P60BpMCQFvw/c1bjYz4f/D8lrFNu9Cf8P+4OK+qyMPw/Ypy/4Xk5/D9kg4tfSUL8P9gGMW4hS/w/d31+GAJU/D/mSHJp61z8P/++PmzdZfw/qf5NLNhu/D8Mq0W123f8PwaHChPogPw/t+vDUf2J/D/PE999G5P8P882EqRCnPw/B29f0XKl/D+DZBcTrK78Pxa523but/w/gDChCjrB/D9vkLHcjsr8P/I0rfvs0/w/S1WMdlTd/D8i959cxeb8P1mMkr0/8Pw/NjpoqcP5/D8kyH4wUQP9P940jWPoDP0/evGiU4kW/T9mwiYSNCD9PzxH1bDoKf0/Nyq/Qacz/T/l+EbXbz39P4upHoRCR/0/2s5EWx9R/T8tfgFwBlv9P7rs4tX3ZP0/E8i5oPNu/T/LTpXk+Xj9P0owv7UKg/0/wTi3KCaN/T8q0S5STJf9P9BZBEd9of0/fWc+HLmr/T9E6wbn/7X9PyVNprxRwP0/eoJ+sq7K/T/kKAbeFtX9Pwyvw1SK3/0/EpRILAnq/T+vxyx6k/T9P4ozClQp//0/23d4z8oJ/j/b4wgCeBT+P7yzQgExH/4/Jpyf4vUp/j91rIi7xjT+P3WPU6GjP/4/PDJAqYxK/j+/2HbogVX+P1GmBnSDYP4/Q6DkYJFr/j8qMOvDq3b+P1sq2rHSgf4/DFxXPwaN/j/2pO+ARpj+P92dGIuTo/4/kM0ycu2u/j/ObIxKVLr+PzK4ZCjIxf4/a87vH0nR/j+AGFtF19z+P9o30qxy6P4/A3aEahv0/j+jr6qS0f/+P+G0jTmVC/8/jRaNc2YX/z8XWCZVRSP/P0l8/PIxL/8/t+LfYSw7/z/sada2NEf/PynLIwdLU/8/ASJSaG9f/z8jkzrwoWv/P9wDDrXid/8/WNRdzTGE/z80jSRQj5D/PzhyzlT7nP8/uelB83Wp/z8Cq+dD/7X/P+Sisl+Xwv8/YIAnYD7P/z8w2mNf9Nv/P2rfJHi56P8/f4TNxY31/z8kEDayOAEAQC+2XziyBwBAem4dhDMOAEBeI4WkvBQAQANkCqlNGwBAM3d/oeYhAECFCBaehygAQIdtX68wLwBAxIBM5uE1AECtES1UmzwAQO7orgpdQwBAzmHcGydKAEAomhqa+VAAQOg6J5jUVwBA39wVKbheAEBeDU1gpGUAQCn4glGZbABAYLy5EJdzAEA3czuynXoAQAHwlUqtgQBAM0KW7sWIAEBQAkSz548AQKJz3K0SlwBAfYXN80aeAED0vrCahKUAQLQgRrjLrABAPglvYhy0AECNJymvdrsAQFiJibTawgBAT9G3iEjKAECGoulBwNEAQNBNXvZB2QBAz81avM3gAEDuHSaqY+gAQL/4BdYD8ABA+Qg8Vq73AEAPmANBY/8AQPLEj6wiBwFA5ksKr+wOAUBq6JJewRYBQDNYP9GgHgFAvwUcHYsmAUCOXy1YgC4BQJ3fcZiANgFAjMXk84s+AUBOhIGAokYBQMbiR1TETgFA58xAhfFWAUDp0YMpKl8BQJNJPVduZwFAHRq1JL5vAUB6FVaoGXgBQCbktfiAgAFANnGdLPSIAUC/yhFbc5EBQF1nXZv+mQFACcEZBZaiAUCmMzmwOasBQOsMEbXpswFA+bljLKa8AUB9/movb8UBQCQg4tdEzgFA/fAPQCfXAUDgotCCFuABQPNNn7sS6QFAVhSfBhzyAUCKzaOAMvsBQG8kOkdWBAJAnxOveIcNAkD+rBY0xhYCQBQaUpkSIAJAC8UUyWwpAkDRmujk1DICQMxZMQ9LPAJAheEua89FAkDxev4cYk8CQNwSm0kDWQJAkmLcFrNiAkD3BXWrcWwCQJqA7y4/dgJA5zaqyRuAAkC2YtKkB4oCQHMOXuoClAJAcyQFxQ2eAkBdozlgKKgCQHYLH+hSsgJAghuBiY28AkC99slx2MYCQJzP984z0QJAPTaSz5/bAkDKKp+iHOYCQE0WmHeq8AJAY85efkn7AkDXyDLn+QUDQPqlpuK7EANATDiWoY8bA0ASMR1VdSYDQFSYji5tMQNA+jZtX3c8A0AbGGUZlEcDQABFRo7DUgNAsN0A8AVeA0AYraJwW2kDQEJVVkLEdANAHyxkl0CAA0Cu3jSi0IsDQOTqVZV0lwNATf1/oyyjA0CgOZ//+K4DQDFx3dzZugNA6UOubs/GA0B/I93o2dIDQLwqnX/53gNAprSaZy7rA0DDmQ7WePcDQOnz0gDZAwRAN0N5Hk8QBEDcyWFm2xwEQHHu0xB+KQRA1XAXVzc2BEBROI5zB0MEQFR+zqHuTwRAARa9Hu1cBEC+jacoA2oEQNXmXf8wdwRAHp1L5HaEBEC3uY8a1ZEEQEmrE+dLnwRA5KCgkNusBEBuJvNfhLoEQGXFzJ9GyARAWHEDnSLWBECgjI6mGOQEQMZYkQ0p8gRAsqxiJVQABUCe05FDmg4FQHGA6L/7HAVAscpp9HgrBUD4NE49EjoFQIjH/PjHSAVAbFQBiJpXBUAzBwBNimYFQGVspqyXdQVAAiqaDcOEBUCOrGXYDJQFQOwXY3d1owVAktWlVv2yBUA3I+PjpMIFQI4OWo5s0gVAx1G7xlTiBUC0ixH/XfIFQHdTqqqIAgZACKsAPtUSBkA2VqkuRCMGQJCbQfPVMwZAEfNgA4tEBkCrIo7XY1UGQElCOOlgZgZA2xm0soJ3BkAsQT6vyYgGQDVdAls2mgZAvcgnM8mrBkBS5eO1gr0GQI9AkmJjzwZAMaXSuWvhBkBFG609nPMGQNnDu3H1BQdAfGZa23cYB0BdbtsBJCsHQF79wW76PQdALqQArvtQB0B8NjxOKGQHQEMdEuGAdwdAh3Nh+wWLB0DhJ5Y1uJ4HQAlM9SuYsgdAU67pfqbGB0BRzk/T49oHQMU2wNJQ7wdAUETXK+4DCEBLYXmSvBgIQHDFEsC8LQhA79HRc+9CCEDeMttyVVgIQJYCd4jvbQhAeUU2hr6DCECkMhBEw5kIQGTgdqD+rwhAJRNjgHHGCEBdF1fPHN0IQNq9WH8B9AhAMMLiiCALCUAPFs7qeiIJQGy/M6oROglAcyxI0uVRCUBmEjF0+GkJQGYe16ZKgglA+O20ht2aCUAg7qQ1srMJQFHnr9rJzAlANhneoSXmCUAt4gy8xv8JQH79yl6uGQpAlmg9xN0zCkCd+w4rVk4KQNKybNYYaQpAU4sQDieECkB+r1wegp8KQBN+iFgruwpAc7jgEiTXCkC13BupbfMKQLdbw3wJEAtAZf+x9fgsC0Dgb6eCPUoLQH5a8JnYZwtAkk0iusuFC0CW6OlqGKQLQIeO6T3AwgtADVimz8ThC0AqmYDIJwEMQJ7ttN3qIAxAZ2pi0g9BDECKTZJ4mGEMQNhSPbKGggxAG7dKctyjDEDj7oS9m8UMQBwsgKvG5wxAHgBvZ18KDUCtueEwaC0NQAGNbVzjUA1AoiM5VNN0DUCT3WyYOpkNQGjWhb8bvg1AU6OLdnnjDUC7timBVgkOQDVgrrm1Lw5AanTyEJpWDkB1wi2OBn4OQGuVvU7+pQ5AhY3jhYTODkAPGYR8nPcOQGez7JBJIQ9A/suqNo9LD0BLw3z2cHYPQL/AZm7yoQ9Afjz1URfOD0AD57Zq4/oPQMWOe0wtFBBAKKdgakArEEAeSZoXrUIQQJ+oFmp1WhBAeIQkhptyEECUI6OfIYsQQDpSYPsJpBBAsgqj8Fa9EEAVKeDqCtcQQME4lWso8RBAqh1EDLILEUABI4mAqiYRQAvgQ5gUQhFAZXzKQfNdEUBuNB6MSXoRQKakF6kalxFAK2aB72m0EUBs8hfdOtIRQPSjZhiR8BFAYQR7cnAPEkAjbmno3C4SQHFboKTaThJAi4IKAG5vEkAjDQODm5ASQCG7IuZnshJAdX3wEtjUEkBE7YMk8fcSQHDPK2i4GxNAiGsuXjNAE0A4rby6Z2UTQHuhM2dbixNAkK/JgxSyE0AIwsVpmdkTQBY+Xq7wARRASgNcJiErFEAqnprqMVUUQP9Rel0qgBRAYaVSMRKsFEAezuxv8dgUQHboBIPQBhVA/aLHPbg1FUBxYjjnsWUVQMdgYkXHlhVANpYuqQLJFUDYb+VmE+8VQNhv5WYT7xVA2G/lZhPvFUDYb+VmE+8VQNhv5WYT7xVA2G/lZhPvFUDYb+VmE+8VQNhv5WYT7xVA2G/lZhPvFUDYb+VmE+8VQNhv5WYT7xVA2G/lZhPvFUDYb+VmE+8VQNhv5WYT7xVA2G/lZhPvFUDYb+VmE+8VQNhv5WYT7xVA2G/lZhPvFUDYb+VmE+8VQNhv5WYT7xVA2G/lZhPvFUDYb+VmE+8VQNhv5WYT7xVA2G/lZhPvFUDYb+VmE+8VQNhv5WYT7xVA2G/lZhPvFUDYb+VmE+8VQNhv5WYT7xVA2G/lZhPvFUDYb+VmE+8VQNhv5WYT7xVA2G/lZhPvFUDYb+VmE+8VQNhv5WYT7xVA2G/lZhPvFUDYb+VmE+8VQNhv5WYT7xVA2G/lZhPvFUDYb+VmE+8VQNhv5WYT7xVA2G/lZhPvFUDYb+VmE+8VQNhv5WYT7xVA2G/lZhPvFUDYb+VmE+8VQNhv5WYT7xVA2G/lZhPvFUDYb+VmE+8VQNhv5WYT7xVA2G/lZhPvFUDYb+VmE+8VQNhv5WYT7xVA2G/lZhPvFUDYb+VmE+8VQNhv5WYT7xVA2G/lZhPvFUDYb+VmE+8VQNhv5WYT7xVA2G/lZhPvFUDYb+VmE+8VQNhv5WYT7xVA2G/lZhPvFUDYb+VmE+8VQNhv5WYT7xVA2G/lZhPvFUDYb+VmE+8VQNhv5WYT7xVA2G/lZhPvFUDYb+VmE+8VQNhv5WYT7xVA2G/lZhPvFUDYb+VmE+8VQNhv5WYT7xVA2G/lZhPvFUDYb+VmE+8VQA==",
"dtype": "float64",
"shape": "1001"
}
}
},
"metadata": {
"atol": 0,
"format": "oedes.testing.nb_store_array",
"label": null,
"rtol": 1e-07
},
"output_type": "display_data"
},
{
"data": {
"application/json": {
"data": {
"__ndarray__": 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"<matplotlib.figure.Figure at 0x7f9907c8f438>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"c = 10**np.linspace(-4, 0., 1001)\n",
"for nsigma in [3, 4, 5, 6][::-1]:\n",
" g3 = egdm.g3(nsigma, np.where(c < egdm.g3_max_c, c, egdm.g3_max_c))\n",
" plt.plot(c, g3, label='$\\hat{\\sigma}$ = %s'%nsigma)\n",
" testing.store(g3, rtol=1e-7)\n",
" # solve for g3(x)=2\n",
" c2 = brentq(lambda x: egdm.g3(nsigma, x) - 2., 1e-4, 0.5)\n",
" plt.plot(c2, 2, 'o', color='black')\n",
"plt.xscale('log')\n",
"plt.ylim([1., 8.])\n",
"plt.xlabel('carrier concentration')\n",
"plt.ylabel('diffusion enhancement $g_3$')\n",
"plt.legend(loc=0, frameon=False);"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Reference\n",
"\n",
"S. L. M. van Mensfoort and R. Coehoorn [Effect of Gaussian disorder on the voltage dependence of the current density in sandwich-type devices based on organic semiconductors](https://doi.org/10.1103/PhysRevB.78.085207), Phys Rev B 78, 085207 (2008)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"---\n",
"This file is a part of __oedes__, an open source organic electronic device \n",
"simulator. For more information, see <https://www.github.com/mzszym/oedes>.\n"
]
}
],
"metadata": {
"anaconda-cloud": {},
"hide_input": false,
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
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"language_info": {
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"varRefreshCmd": "cat(var_dic_list()) "
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| agpl-3.0 |
wathen/PhD | MHD/FEniCS/MHD/Stabilised/SaddlePointForm/Test/SplitMatrix/ScottTest/Untitled3.ipynb | 1 | 6899 | {
"metadata": {
"name": "",
"signature": "sha256:17db0ebaacb8c9da8aad5a7a4000e7775113b482d642290759770638fd976087"
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{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"#!/usr/bin/python\n",
"\n",
"# interpolate scalar gradient onto nedelec space\n",
"\n",
"import petsc4py\n",
"import sys\n",
"\n",
"petsc4py.init(sys.argv)\n",
"\n",
"from petsc4py import PETSc\n",
"from dolfin import *\n",
"Print = PETSc.Sys.Print\n",
"# from MatrixOperations import *\n",
"import numpy as np\n",
"import PETScIO as IO\n",
"import common\n",
"import scipy\n",
"import scipy.io\n",
"import time\n",
"\n",
"import BiLinear as forms\n",
"import IterOperations as Iter\n",
"import MatrixOperations as MO\n",
"import CheckPetsc4py as CP\n",
"import ExactSol\n",
"import Solver as S\n",
"import MHDmatrixPrecondSetup as PrecondSetup\n",
"import NSprecondSetup\n",
"import MHDprec as MHDpreconditioner\n",
"import memory_profiler\n",
"import gc\n",
"import MHDmulti\n",
"import MHDmatrixSetup as MHDsetup\n",
"#@profile\n",
"m = 2\n",
"\n",
"\n",
"errL2u =np.zeros((m-1,1))\n",
"errH1u =np.zeros((m-1,1))\n",
"errL2p =np.zeros((m-1,1))\n",
"errL2b =np.zeros((m-1,1))\n",
"errCurlb =np.zeros((m-1,1))\n",
"errL2r =np.zeros((m-1,1))\n",
"errH1r =np.zeros((m-1,1))\n",
"\n",
"\n",
"\n",
"l2uorder = np.zeros((m-1,1))\n",
"H1uorder =np.zeros((m-1,1))\n",
"l2porder = np.zeros((m-1,1))\n",
"l2border = np.zeros((m-1,1))\n",
"Curlborder =np.zeros((m-1,1))\n",
"l2rorder = np.zeros((m-1,1))\n",
"H1rorder = np.zeros((m-1,1))\n",
"\n",
"NN = np.zeros((m-1,1))\n",
"DoF = np.zeros((m-1,1))\n",
"Velocitydim = np.zeros((m-1,1))\n",
"Magneticdim = np.zeros((m-1,1))\n",
"Pressuredim = np.zeros((m-1,1))\n",
"Lagrangedim = np.zeros((m-1,1))\n",
"Wdim = np.zeros((m-1,1))\n",
"iterations = np.zeros((m-1,1))\n",
"SolTime = np.zeros((m-1,1))\n",
"udiv = np.zeros((m-1,1))\n",
"MU = np.zeros((m-1,1))\n",
"level = np.zeros((m-1,1))\n",
"NSave = np.zeros((m-1,1))\n",
"Mave = np.zeros((m-1,1))\n",
"TotalTime = np.zeros((m-1,1))\n",
"\n",
"nn = 2\n",
"\n",
"dim = 2\n",
"ShowResultPlots = 'yes'\n",
"split = 'Linear'\n",
"\n",
"MU[0]= 1e0\n",
"for xx in xrange(1,m):\n",
" print xx\n",
" level[xx-1] = xx+ 2\n",
" nn = 2**(level[xx-1])\n",
"\n",
"\n",
"\n",
" # Create mesh and define function space\n",
" nn = int(nn)\n",
" NN[xx-1] = nn/2\n",
" # parameters[\"form_compiler\"][\"quadrature_degree\"] = 6\n",
" # parameters = CP.ParameterSetup()\n",
" mesh = UnitSquareMesh(nn,nn)\n",
"\n",
" order = 2\n",
" parameters['reorder_dofs_serial'] = False\n",
" Velocity = VectorFunctionSpace(mesh, \"CG\", order)\n",
" Pressure = FunctionSpace(mesh, \"CG\", order-1)\n",
" Magnetic = FunctionSpace(mesh, \"N1curl\", order-1)\n",
" Lagrange = FunctionSpace(mesh, \"CG\", order-1)\n",
" W = MixedFunctionSpace([Velocity, Pressure, Magnetic,Lagrange])\n",
" # W = Velocity*Pressure*Magnetic*Lagrange\n",
" Velocitydim[xx-1] = Velocity.dim()\n",
" Pressuredim[xx-1] = Pressure.dim()\n",
" Magneticdim[xx-1] = Magnetic.dim()\n",
" Lagrangedim[xx-1] = Lagrange.dim()\n",
" Wdim[xx-1] = W.dim()\n",
" print \"\\n\\nW: \",Wdim[xx-1],\"Velocity: \",Velocitydim[xx-1],\"Pressure: \",Pressuredim[xx-1],\"Magnetic: \",Magneticdim[xx-1],\"Lagrange: \",Lagrangedim[xx-1],\"\\n\\n\"\n",
" dim = [Velocity.dim(), Pressure.dim(), Magnetic.dim(), Lagrange.dim()]\n",
"\n",
"\n",
" def boundary(x, on_boundary):\n",
" return on_boundary\n",
"\n",
" u0, p0,b0, r0, Laplacian, Advection, gradPres,CurlCurl, gradR, NS_Couple, M_Couple = ExactSol.MHD2D(4,1, mesh)\n",
"\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"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: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:FFC:Reusing form from cache.\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"1\n",
"\n",
"\n",
"W: [ 948.] Velocity: [ 578.] Pressure: [ 81.] Magnetic: [ 208.] Lagrange: [ 81.] \n",
"\n",
"\n",
"\n",
" >>>>>>>>>>>>>>>>>>>>>>>>>>\n",
" MHD 2D Exact Solution:\n",
" >>>>>>>>>>>>>>>>>>>>>>>>>>\n",
"\n",
" ----------------------\n",
" NS Exact Solution:\n",
" ----------------------\n",
" u = ("
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" x*y*exp(x + y) + x*exp(x + y) , -x*y*exp(x + y) - y*exp(x + y) )\n",
"\n",
" p = ( exp(y)*sin(x) )\n",
"\n",
" ---------------------------\n",
" Maxwell Exact Solution:\n",
" ---------------------------\n",
" b = ("
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" exp(x + y)*cos(x) , exp(x + y)*sin(x) - exp(x + y)*cos(x) )\n",
"\n",
" p = ( x*sin(2*pi*x)*sin(2*pi*y) )\n",
"\n"
]
}
],
"prompt_number": 23
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"p = plot(interpolate(b0,Magnetic), prefix='magnetic2d')\n",
"p.write_png()"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 25
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
} | mit |
atulsingh0/MachineLearning | python-machine-learning/Assignment 1.ipynb | 1 | 233501 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---\n",
"\n",
"_You are currently looking at **version 1.1** of this notebook. To download notebooks and datafiles, as well as get help on Jupyter notebooks in the Coursera platform, visit the [Jupyter Notebook FAQ](https://www.coursera.org/learn/python-machine-learning/resources/bANLa) course resource._\n",
"\n",
"---"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Assignment 1 - Introduction to Machine Learning"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"For this assignment, you will be using the Breast Cancer Wisconsin (Diagnostic) Database to create a classifier that can help diagnose patients. First, read through the description of the dataset (below)."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"from sklearn.datasets import load_breast_cancer\n",
"\n",
"cancer = load_breast_cancer()\n",
"\n",
"#print(cancer.DESCR) # Print the data set description\n",
"cancer"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The object returned by `load_breast_cancer()` is a scikit-learn Bunch object, which is similar to a dictionary."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"dict_keys(['DESCR', 'target_names', 'feature_names', 'target', 'data'])"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cancer.keys()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Question 0 (Example)\n",
"\n",
"How many features does the breast cancer dataset have?\n",
"\n",
"*This function should return an integer.*"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"30"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# You should write your whole answer within the function provided. The autograder will call\n",
"# this function and compare the return value against the correct solution value\n",
"def answer_zero():\n",
" # This function returns the number of features of the breast cancer dataset, which is an integer. \n",
" # The assignment question description will tell you the general format the autograder is expecting\n",
" return len(cancer['feature_names'])\n",
"\n",
"# You can examine what your function returns by calling it in the cell. If you have questions\n",
"# about the assignment formats, check out the discussion forums for any FAQs\n",
"answer_zero() "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Question 1\n",
"\n",
"Scikit-learn works with lists, numpy arrays, scipy-sparse matrices, and pandas DataFrames, so converting the dataset to a DataFrame is not necessary for training this model. Using a DataFrame does however help make many things easier such as munging data, so let's practice creating a classifier with a pandas DataFrame. \n",
"\n",
"\n",
"\n",
"Convert the sklearn.dataset `cancer` to a DataFrame. \n",
"\n",
"*This function should return a `(569, 31)` DataFrame with * \n",
"\n",
"*columns = *\n",
"\n",
" ['mean radius', 'mean texture', 'mean perimeter', 'mean area',\n",
" 'mean smoothness', 'mean compactness', 'mean concavity',\n",
" 'mean concave points', 'mean symmetry', 'mean fractal dimension',\n",
" 'radius error', 'texture error', 'perimeter error', 'area error',\n",
" 'smoothness error', 'compactness error', 'concavity error',\n",
" 'concave points error', 'symmetry error', 'fractal dimension error',\n",
" 'worst radius', 'worst texture', 'worst perimeter', 'worst area',\n",
" 'worst smoothness', 'worst compactness', 'worst concavity',\n",
" 'worst concave points', 'worst symmetry', 'worst fractal dimension',\n",
" 'target']\n",
"\n",
"*and index = *\n",
"\n",
" RangeIndex(start=0, stop=569, step=1)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
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"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>mean radius</th>\n",
" <th>mean texture</th>\n",
" <th>mean perimeter</th>\n",
" <th>mean area</th>\n",
" <th>mean smoothness</th>\n",
" <th>mean compactness</th>\n",
" <th>mean concavity</th>\n",
" <th>mean concave points</th>\n",
" <th>mean symmetry</th>\n",
" <th>mean fractal dimension</th>\n",
" <th>...</th>\n",
" <th>worst texture</th>\n",
" <th>worst perimeter</th>\n",
" <th>worst area</th>\n",
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" <th>target</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>17.990</td>\n",
" <td>10.38</td>\n",
" <td>122.80</td>\n",
" <td>1001.0</td>\n",
" <td>0.11840</td>\n",
" <td>0.27760</td>\n",
" <td>0.300100</td>\n",
" <td>0.147100</td>\n",
" <td>0.2419</td>\n",
" <td>0.07871</td>\n",
" <td>...</td>\n",
" <td>17.33</td>\n",
" <td>184.60</td>\n",
" <td>2019.0</td>\n",
" <td>0.16220</td>\n",
" <td>0.66560</td>\n",
" <td>0.71190</td>\n",
" <td>0.26540</td>\n",
" <td>0.4601</td>\n",
" <td>0.11890</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>20.570</td>\n",
" <td>17.77</td>\n",
" <td>132.90</td>\n",
" <td>1326.0</td>\n",
" <td>0.08474</td>\n",
" <td>0.07864</td>\n",
" <td>0.086900</td>\n",
" <td>0.070170</td>\n",
" <td>0.1812</td>\n",
" <td>0.05667</td>\n",
" <td>...</td>\n",
" <td>23.41</td>\n",
" <td>158.80</td>\n",
" <td>1956.0</td>\n",
" <td>0.12380</td>\n",
" <td>0.18660</td>\n",
" <td>0.24160</td>\n",
" <td>0.18600</td>\n",
" <td>0.2750</td>\n",
" <td>0.08902</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>19.690</td>\n",
" <td>21.25</td>\n",
" <td>130.00</td>\n",
" <td>1203.0</td>\n",
" <td>0.10960</td>\n",
" <td>0.15990</td>\n",
" <td>0.197400</td>\n",
" <td>0.127900</td>\n",
" <td>0.2069</td>\n",
" <td>0.05999</td>\n",
" <td>...</td>\n",
" <td>25.53</td>\n",
" <td>152.50</td>\n",
" <td>1709.0</td>\n",
" <td>0.14440</td>\n",
" <td>0.42450</td>\n",
" <td>0.45040</td>\n",
" <td>0.24300</td>\n",
" <td>0.3613</td>\n",
" <td>0.08758</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>11.420</td>\n",
" <td>20.38</td>\n",
" <td>77.58</td>\n",
" <td>386.1</td>\n",
" <td>0.14250</td>\n",
" <td>0.28390</td>\n",
" <td>0.241400</td>\n",
" <td>0.105200</td>\n",
" <td>0.2597</td>\n",
" <td>0.09744</td>\n",
" <td>...</td>\n",
" <td>26.50</td>\n",
" <td>98.87</td>\n",
" <td>567.7</td>\n",
" <td>0.20980</td>\n",
" <td>0.86630</td>\n",
" <td>0.68690</td>\n",
" <td>0.25750</td>\n",
" <td>0.6638</td>\n",
" <td>0.17300</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>20.290</td>\n",
" <td>14.34</td>\n",
" <td>135.10</td>\n",
" <td>1297.0</td>\n",
" <td>0.10030</td>\n",
" <td>0.13280</td>\n",
" <td>0.198000</td>\n",
" <td>0.104300</td>\n",
" <td>0.1809</td>\n",
" <td>0.05883</td>\n",
" <td>...</td>\n",
" <td>16.67</td>\n",
" <td>152.20</td>\n",
" <td>1575.0</td>\n",
" <td>0.13740</td>\n",
" <td>0.20500</td>\n",
" <td>0.40000</td>\n",
" <td>0.16250</td>\n",
" <td>0.2364</td>\n",
" <td>0.07678</td>\n",
" <td>0</td>\n",
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" <tr>\n",
" <th>5</th>\n",
" <td>12.450</td>\n",
" <td>15.70</td>\n",
" <td>82.57</td>\n",
" <td>477.1</td>\n",
" <td>0.12780</td>\n",
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" <td>0.157800</td>\n",
" <td>0.080890</td>\n",
" <td>0.2087</td>\n",
" <td>0.07613</td>\n",
" <td>...</td>\n",
" <td>23.75</td>\n",
" <td>103.40</td>\n",
" <td>741.6</td>\n",
" <td>0.17910</td>\n",
" <td>0.52490</td>\n",
" <td>0.53550</td>\n",
" <td>0.17410</td>\n",
" <td>0.3985</td>\n",
" <td>0.12440</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>18.250</td>\n",
" <td>19.98</td>\n",
" <td>119.60</td>\n",
" <td>1040.0</td>\n",
" <td>0.09463</td>\n",
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" <td>0.112700</td>\n",
" <td>0.074000</td>\n",
" <td>0.1794</td>\n",
" <td>0.05742</td>\n",
" <td>...</td>\n",
" <td>27.66</td>\n",
" <td>153.20</td>\n",
" <td>1606.0</td>\n",
" <td>0.14420</td>\n",
" <td>0.25760</td>\n",
" <td>0.37840</td>\n",
" <td>0.19320</td>\n",
" <td>0.3063</td>\n",
" <td>0.08368</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>13.710</td>\n",
" <td>20.83</td>\n",
" <td>90.20</td>\n",
" <td>577.9</td>\n",
" <td>0.11890</td>\n",
" <td>0.16450</td>\n",
" <td>0.093660</td>\n",
" <td>0.059850</td>\n",
" <td>0.2196</td>\n",
" <td>0.07451</td>\n",
" <td>...</td>\n",
" <td>28.14</td>\n",
" <td>110.60</td>\n",
" <td>897.0</td>\n",
" <td>0.16540</td>\n",
" <td>0.36820</td>\n",
" <td>0.26780</td>\n",
" <td>0.15560</td>\n",
" <td>0.3196</td>\n",
" <td>0.11510</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>13.000</td>\n",
" <td>21.82</td>\n",
" <td>87.50</td>\n",
" <td>519.8</td>\n",
" <td>0.12730</td>\n",
" <td>0.19320</td>\n",
" <td>0.185900</td>\n",
" <td>0.093530</td>\n",
" <td>0.2350</td>\n",
" <td>0.07389</td>\n",
" <td>...</td>\n",
" <td>30.73</td>\n",
" <td>106.20</td>\n",
" <td>739.3</td>\n",
" <td>0.17030</td>\n",
" <td>0.54010</td>\n",
" <td>0.53900</td>\n",
" <td>0.20600</td>\n",
" <td>0.4378</td>\n",
" <td>0.10720</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>12.460</td>\n",
" <td>24.04</td>\n",
" <td>83.97</td>\n",
" <td>475.9</td>\n",
" <td>0.11860</td>\n",
" <td>0.23960</td>\n",
" <td>0.227300</td>\n",
" <td>0.085430</td>\n",
" <td>0.2030</td>\n",
" <td>0.08243</td>\n",
" <td>...</td>\n",
" <td>40.68</td>\n",
" <td>97.65</td>\n",
" <td>711.4</td>\n",
" <td>0.18530</td>\n",
" <td>1.05800</td>\n",
" <td>1.10500</td>\n",
" <td>0.22100</td>\n",
" <td>0.4366</td>\n",
" <td>0.20750</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>16.020</td>\n",
" <td>23.24</td>\n",
" <td>102.70</td>\n",
" <td>797.8</td>\n",
" <td>0.08206</td>\n",
" <td>0.06669</td>\n",
" <td>0.032990</td>\n",
" <td>0.033230</td>\n",
" <td>0.1528</td>\n",
" <td>0.05697</td>\n",
" <td>...</td>\n",
" <td>33.88</td>\n",
" <td>123.80</td>\n",
" <td>1150.0</td>\n",
" <td>0.11810</td>\n",
" <td>0.15510</td>\n",
" <td>0.14590</td>\n",
" <td>0.09975</td>\n",
" <td>0.2948</td>\n",
" <td>0.08452</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>15.780</td>\n",
" <td>17.89</td>\n",
" <td>103.60</td>\n",
" <td>781.0</td>\n",
" <td>0.09710</td>\n",
" <td>0.12920</td>\n",
" <td>0.099540</td>\n",
" <td>0.066060</td>\n",
" <td>0.1842</td>\n",
" <td>0.06082</td>\n",
" <td>...</td>\n",
" <td>27.28</td>\n",
" <td>136.50</td>\n",
" <td>1299.0</td>\n",
" <td>0.13960</td>\n",
" <td>0.56090</td>\n",
" <td>0.39650</td>\n",
" <td>0.18100</td>\n",
" <td>0.3792</td>\n",
" <td>0.10480</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>19.170</td>\n",
" <td>24.80</td>\n",
" <td>132.40</td>\n",
" <td>1123.0</td>\n",
" <td>0.09740</td>\n",
" <td>0.24580</td>\n",
" <td>0.206500</td>\n",
" <td>0.111800</td>\n",
" <td>0.2397</td>\n",
" <td>0.07800</td>\n",
" <td>...</td>\n",
" <td>29.94</td>\n",
" <td>151.70</td>\n",
" <td>1332.0</td>\n",
" <td>0.10370</td>\n",
" <td>0.39030</td>\n",
" <td>0.36390</td>\n",
" <td>0.17670</td>\n",
" <td>0.3176</td>\n",
" <td>0.10230</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>15.850</td>\n",
" <td>23.95</td>\n",
" <td>103.70</td>\n",
" <td>782.7</td>\n",
" <td>0.08401</td>\n",
" <td>0.10020</td>\n",
" <td>0.099380</td>\n",
" <td>0.053640</td>\n",
" <td>0.1847</td>\n",
" <td>0.05338</td>\n",
" <td>...</td>\n",
" <td>27.66</td>\n",
" <td>112.00</td>\n",
" <td>876.5</td>\n",
" <td>0.11310</td>\n",
" <td>0.19240</td>\n",
" <td>0.23220</td>\n",
" <td>0.11190</td>\n",
" <td>0.2809</td>\n",
" <td>0.06287</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>13.730</td>\n",
" <td>22.61</td>\n",
" <td>93.60</td>\n",
" <td>578.3</td>\n",
" <td>0.11310</td>\n",
" <td>0.22930</td>\n",
" <td>0.212800</td>\n",
" <td>0.080250</td>\n",
" <td>0.2069</td>\n",
" <td>0.07682</td>\n",
" <td>...</td>\n",
" <td>32.01</td>\n",
" <td>108.80</td>\n",
" <td>697.7</td>\n",
" <td>0.16510</td>\n",
" <td>0.77250</td>\n",
" <td>0.69430</td>\n",
" <td>0.22080</td>\n",
" <td>0.3596</td>\n",
" <td>0.14310</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>14.540</td>\n",
" <td>27.54</td>\n",
" <td>96.73</td>\n",
" <td>658.8</td>\n",
" <td>0.11390</td>\n",
" <td>0.15950</td>\n",
" <td>0.163900</td>\n",
" <td>0.073640</td>\n",
" <td>0.2303</td>\n",
" <td>0.07077</td>\n",
" <td>...</td>\n",
" <td>37.13</td>\n",
" <td>124.10</td>\n",
" <td>943.2</td>\n",
" <td>0.16780</td>\n",
" <td>0.65770</td>\n",
" <td>0.70260</td>\n",
" <td>0.17120</td>\n",
" <td>0.4218</td>\n",
" <td>0.13410</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>14.680</td>\n",
" <td>20.13</td>\n",
" <td>94.74</td>\n",
" <td>684.5</td>\n",
" <td>0.09867</td>\n",
" <td>0.07200</td>\n",
" <td>0.073950</td>\n",
" <td>0.052590</td>\n",
" <td>0.1586</td>\n",
" <td>0.05922</td>\n",
" <td>...</td>\n",
" <td>30.88</td>\n",
" <td>123.40</td>\n",
" <td>1138.0</td>\n",
" <td>0.14640</td>\n",
" <td>0.18710</td>\n",
" <td>0.29140</td>\n",
" <td>0.16090</td>\n",
" <td>0.3029</td>\n",
" <td>0.08216</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>16.130</td>\n",
" <td>20.68</td>\n",
" <td>108.10</td>\n",
" <td>798.8</td>\n",
" <td>0.11700</td>\n",
" <td>0.20220</td>\n",
" <td>0.172200</td>\n",
" <td>0.102800</td>\n",
" <td>0.2164</td>\n",
" <td>0.07356</td>\n",
" <td>...</td>\n",
" <td>31.48</td>\n",
" <td>136.80</td>\n",
" <td>1315.0</td>\n",
" <td>0.17890</td>\n",
" <td>0.42330</td>\n",
" <td>0.47840</td>\n",
" <td>0.20730</td>\n",
" <td>0.3706</td>\n",
" <td>0.11420</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>19.810</td>\n",
" <td>22.15</td>\n",
" <td>130.00</td>\n",
" <td>1260.0</td>\n",
" <td>0.09831</td>\n",
" <td>0.10270</td>\n",
" <td>0.147900</td>\n",
" <td>0.094980</td>\n",
" <td>0.1582</td>\n",
" <td>0.05395</td>\n",
" <td>...</td>\n",
" <td>30.88</td>\n",
" <td>186.80</td>\n",
" <td>2398.0</td>\n",
" <td>0.15120</td>\n",
" <td>0.31500</td>\n",
" <td>0.53720</td>\n",
" <td>0.23880</td>\n",
" <td>0.2768</td>\n",
" <td>0.07615</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>13.540</td>\n",
" <td>14.36</td>\n",
" <td>87.46</td>\n",
" <td>566.3</td>\n",
" <td>0.09779</td>\n",
" <td>0.08129</td>\n",
" <td>0.066640</td>\n",
" <td>0.047810</td>\n",
" <td>0.1885</td>\n",
" <td>0.05766</td>\n",
" <td>...</td>\n",
" <td>19.26</td>\n",
" <td>99.70</td>\n",
" <td>711.2</td>\n",
" <td>0.14400</td>\n",
" <td>0.17730</td>\n",
" <td>0.23900</td>\n",
" <td>0.12880</td>\n",
" <td>0.2977</td>\n",
" <td>0.07259</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>13.080</td>\n",
" <td>15.71</td>\n",
" <td>85.63</td>\n",
" <td>520.0</td>\n",
" <td>0.10750</td>\n",
" <td>0.12700</td>\n",
" <td>0.045680</td>\n",
" <td>0.031100</td>\n",
" <td>0.1967</td>\n",
" <td>0.06811</td>\n",
" <td>...</td>\n",
" <td>20.49</td>\n",
" <td>96.09</td>\n",
" <td>630.5</td>\n",
" <td>0.13120</td>\n",
" <td>0.27760</td>\n",
" <td>0.18900</td>\n",
" <td>0.07283</td>\n",
" <td>0.3184</td>\n",
" <td>0.08183</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>9.504</td>\n",
" <td>12.44</td>\n",
" <td>60.34</td>\n",
" <td>273.9</td>\n",
" <td>0.10240</td>\n",
" <td>0.06492</td>\n",
" <td>0.029560</td>\n",
" <td>0.020760</td>\n",
" <td>0.1815</td>\n",
" <td>0.06905</td>\n",
" <td>...</td>\n",
" <td>15.66</td>\n",
" <td>65.13</td>\n",
" <td>314.9</td>\n",
" <td>0.13240</td>\n",
" <td>0.11480</td>\n",
" <td>0.08867</td>\n",
" <td>0.06227</td>\n",
" <td>0.2450</td>\n",
" <td>0.07773</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>15.340</td>\n",
" <td>14.26</td>\n",
" <td>102.50</td>\n",
" <td>704.4</td>\n",
" <td>0.10730</td>\n",
" <td>0.21350</td>\n",
" <td>0.207700</td>\n",
" <td>0.097560</td>\n",
" <td>0.2521</td>\n",
" <td>0.07032</td>\n",
" <td>...</td>\n",
" <td>19.08</td>\n",
" <td>125.10</td>\n",
" <td>980.9</td>\n",
" <td>0.13900</td>\n",
" <td>0.59540</td>\n",
" <td>0.63050</td>\n",
" <td>0.23930</td>\n",
" <td>0.4667</td>\n",
" <td>0.09946</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>21.160</td>\n",
" <td>23.04</td>\n",
" <td>137.20</td>\n",
" <td>1404.0</td>\n",
" <td>0.09428</td>\n",
" <td>0.10220</td>\n",
" <td>0.109700</td>\n",
" <td>0.086320</td>\n",
" <td>0.1769</td>\n",
" <td>0.05278</td>\n",
" <td>...</td>\n",
" <td>35.59</td>\n",
" <td>188.00</td>\n",
" <td>2615.0</td>\n",
" <td>0.14010</td>\n",
" <td>0.26000</td>\n",
" <td>0.31550</td>\n",
" <td>0.20090</td>\n",
" <td>0.2822</td>\n",
" <td>0.07526</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>16.650</td>\n",
" <td>21.38</td>\n",
" <td>110.00</td>\n",
" <td>904.6</td>\n",
" <td>0.11210</td>\n",
" <td>0.14570</td>\n",
" <td>0.152500</td>\n",
" <td>0.091700</td>\n",
" <td>0.1995</td>\n",
" <td>0.06330</td>\n",
" <td>...</td>\n",
" <td>31.56</td>\n",
" <td>177.00</td>\n",
" <td>2215.0</td>\n",
" <td>0.18050</td>\n",
" <td>0.35780</td>\n",
" <td>0.46950</td>\n",
" <td>0.20950</td>\n",
" <td>0.3613</td>\n",
" <td>0.09564</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>17.140</td>\n",
" <td>16.40</td>\n",
" <td>116.00</td>\n",
" <td>912.7</td>\n",
" <td>0.11860</td>\n",
" <td>0.22760</td>\n",
" <td>0.222900</td>\n",
" <td>0.140100</td>\n",
" <td>0.3040</td>\n",
" <td>0.07413</td>\n",
" <td>...</td>\n",
" <td>21.40</td>\n",
" <td>152.40</td>\n",
" <td>1461.0</td>\n",
" <td>0.15450</td>\n",
" <td>0.39490</td>\n",
" <td>0.38530</td>\n",
" <td>0.25500</td>\n",
" <td>0.4066</td>\n",
" <td>0.10590</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>14.580</td>\n",
" <td>21.53</td>\n",
" <td>97.41</td>\n",
" <td>644.8</td>\n",
" <td>0.10540</td>\n",
" <td>0.18680</td>\n",
" <td>0.142500</td>\n",
" <td>0.087830</td>\n",
" <td>0.2252</td>\n",
" <td>0.06924</td>\n",
" <td>...</td>\n",
" <td>33.21</td>\n",
" <td>122.40</td>\n",
" <td>896.9</td>\n",
" <td>0.15250</td>\n",
" <td>0.66430</td>\n",
" <td>0.55390</td>\n",
" <td>0.27010</td>\n",
" <td>0.4264</td>\n",
" <td>0.12750</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>18.610</td>\n",
" <td>20.25</td>\n",
" <td>122.10</td>\n",
" <td>1094.0</td>\n",
" <td>0.09440</td>\n",
" <td>0.10660</td>\n",
" <td>0.149000</td>\n",
" <td>0.077310</td>\n",
" <td>0.1697</td>\n",
" <td>0.05699</td>\n",
" <td>...</td>\n",
" <td>27.26</td>\n",
" <td>139.90</td>\n",
" <td>1403.0</td>\n",
" <td>0.13380</td>\n",
" <td>0.21170</td>\n",
" <td>0.34460</td>\n",
" <td>0.14900</td>\n",
" <td>0.2341</td>\n",
" <td>0.07421</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>15.300</td>\n",
" <td>25.27</td>\n",
" <td>102.40</td>\n",
" <td>732.4</td>\n",
" <td>0.10820</td>\n",
" <td>0.16970</td>\n",
" <td>0.168300</td>\n",
" <td>0.087510</td>\n",
" <td>0.1926</td>\n",
" <td>0.06540</td>\n",
" <td>...</td>\n",
" <td>36.71</td>\n",
" <td>149.30</td>\n",
" <td>1269.0</td>\n",
" <td>0.16410</td>\n",
" <td>0.61100</td>\n",
" <td>0.63350</td>\n",
" <td>0.20240</td>\n",
" <td>0.4027</td>\n",
" <td>0.09876</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>17.570</td>\n",
" <td>15.05</td>\n",
" <td>115.00</td>\n",
" <td>955.1</td>\n",
" <td>0.09847</td>\n",
" <td>0.11570</td>\n",
" <td>0.098750</td>\n",
" <td>0.079530</td>\n",
" <td>0.1739</td>\n",
" <td>0.06149</td>\n",
" <td>...</td>\n",
" <td>19.52</td>\n",
" <td>134.90</td>\n",
" <td>1227.0</td>\n",
" <td>0.12550</td>\n",
" <td>0.28120</td>\n",
" <td>0.24890</td>\n",
" <td>0.14560</td>\n",
" <td>0.2756</td>\n",
" <td>0.07919</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <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>539</th>\n",
" <td>7.691</td>\n",
" <td>25.44</td>\n",
" <td>48.34</td>\n",
" <td>170.4</td>\n",
" <td>0.08668</td>\n",
" <td>0.11990</td>\n",
" <td>0.092520</td>\n",
" <td>0.013640</td>\n",
" <td>0.2037</td>\n",
" <td>0.07751</td>\n",
" <td>...</td>\n",
" <td>31.89</td>\n",
" <td>54.49</td>\n",
" <td>223.6</td>\n",
" <td>0.15960</td>\n",
" <td>0.30640</td>\n",
" <td>0.33930</td>\n",
" <td>0.05000</td>\n",
" <td>0.2790</td>\n",
" <td>0.10660</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>540</th>\n",
" <td>11.540</td>\n",
" <td>14.44</td>\n",
" <td>74.65</td>\n",
" <td>402.9</td>\n",
" <td>0.09984</td>\n",
" <td>0.11200</td>\n",
" <td>0.067370</td>\n",
" <td>0.025940</td>\n",
" <td>0.1818</td>\n",
" <td>0.06782</td>\n",
" <td>...</td>\n",
" <td>19.68</td>\n",
" <td>78.78</td>\n",
" <td>457.8</td>\n",
" <td>0.13450</td>\n",
" <td>0.21180</td>\n",
" <td>0.17970</td>\n",
" <td>0.06918</td>\n",
" <td>0.2329</td>\n",
" <td>0.08134</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>541</th>\n",
" <td>14.470</td>\n",
" <td>24.99</td>\n",
" <td>95.81</td>\n",
" <td>656.4</td>\n",
" <td>0.08837</td>\n",
" <td>0.12300</td>\n",
" <td>0.100900</td>\n",
" <td>0.038900</td>\n",
" <td>0.1872</td>\n",
" <td>0.06341</td>\n",
" <td>...</td>\n",
" <td>31.73</td>\n",
" <td>113.50</td>\n",
" <td>808.9</td>\n",
" <td>0.13400</td>\n",
" <td>0.42020</td>\n",
" <td>0.40400</td>\n",
" <td>0.12050</td>\n",
" <td>0.3187</td>\n",
" <td>0.10230</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>542</th>\n",
" <td>14.740</td>\n",
" <td>25.42</td>\n",
" <td>94.70</td>\n",
" <td>668.6</td>\n",
" <td>0.08275</td>\n",
" <td>0.07214</td>\n",
" <td>0.041050</td>\n",
" <td>0.030270</td>\n",
" <td>0.1840</td>\n",
" <td>0.05680</td>\n",
" <td>...</td>\n",
" <td>32.29</td>\n",
" <td>107.40</td>\n",
" <td>826.4</td>\n",
" <td>0.10600</td>\n",
" <td>0.13760</td>\n",
" <td>0.16110</td>\n",
" <td>0.10950</td>\n",
" <td>0.2722</td>\n",
" <td>0.06956</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>543</th>\n",
" <td>13.210</td>\n",
" <td>28.06</td>\n",
" <td>84.88</td>\n",
" <td>538.4</td>\n",
" <td>0.08671</td>\n",
" <td>0.06877</td>\n",
" <td>0.029870</td>\n",
" <td>0.032750</td>\n",
" <td>0.1628</td>\n",
" <td>0.05781</td>\n",
" <td>...</td>\n",
" <td>37.17</td>\n",
" <td>92.48</td>\n",
" <td>629.6</td>\n",
" <td>0.10720</td>\n",
" <td>0.13810</td>\n",
" <td>0.10620</td>\n",
" <td>0.07958</td>\n",
" <td>0.2473</td>\n",
" <td>0.06443</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>544</th>\n",
" <td>13.870</td>\n",
" <td>20.70</td>\n",
" <td>89.77</td>\n",
" <td>584.8</td>\n",
" <td>0.09578</td>\n",
" <td>0.10180</td>\n",
" <td>0.036880</td>\n",
" <td>0.023690</td>\n",
" <td>0.1620</td>\n",
" <td>0.06688</td>\n",
" <td>...</td>\n",
" <td>24.75</td>\n",
" <td>99.17</td>\n",
" <td>688.6</td>\n",
" <td>0.12640</td>\n",
" <td>0.20370</td>\n",
" <td>0.13770</td>\n",
" <td>0.06845</td>\n",
" <td>0.2249</td>\n",
" <td>0.08492</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>545</th>\n",
" <td>13.620</td>\n",
" <td>23.23</td>\n",
" <td>87.19</td>\n",
" <td>573.2</td>\n",
" <td>0.09246</td>\n",
" <td>0.06747</td>\n",
" <td>0.029740</td>\n",
" <td>0.024430</td>\n",
" <td>0.1664</td>\n",
" <td>0.05801</td>\n",
" <td>...</td>\n",
" <td>29.09</td>\n",
" <td>97.58</td>\n",
" <td>729.8</td>\n",
" <td>0.12160</td>\n",
" <td>0.15170</td>\n",
" <td>0.10490</td>\n",
" <td>0.07174</td>\n",
" <td>0.2642</td>\n",
" <td>0.06953</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>546</th>\n",
" <td>10.320</td>\n",
" <td>16.35</td>\n",
" <td>65.31</td>\n",
" <td>324.9</td>\n",
" <td>0.09434</td>\n",
" <td>0.04994</td>\n",
" <td>0.010120</td>\n",
" <td>0.005495</td>\n",
" <td>0.1885</td>\n",
" <td>0.06201</td>\n",
" <td>...</td>\n",
" <td>21.77</td>\n",
" <td>71.12</td>\n",
" <td>384.9</td>\n",
" <td>0.12850</td>\n",
" <td>0.08842</td>\n",
" <td>0.04384</td>\n",
" <td>0.02381</td>\n",
" <td>0.2681</td>\n",
" <td>0.07399</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>547</th>\n",
" <td>10.260</td>\n",
" <td>16.58</td>\n",
" <td>65.85</td>\n",
" <td>320.8</td>\n",
" <td>0.08877</td>\n",
" <td>0.08066</td>\n",
" <td>0.043580</td>\n",
" <td>0.024380</td>\n",
" <td>0.1669</td>\n",
" <td>0.06714</td>\n",
" <td>...</td>\n",
" <td>22.04</td>\n",
" <td>71.08</td>\n",
" <td>357.4</td>\n",
" <td>0.14610</td>\n",
" <td>0.22460</td>\n",
" <td>0.17830</td>\n",
" <td>0.08333</td>\n",
" <td>0.2691</td>\n",
" <td>0.09479</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>548</th>\n",
" <td>9.683</td>\n",
" <td>19.34</td>\n",
" <td>61.05</td>\n",
" <td>285.7</td>\n",
" <td>0.08491</td>\n",
" <td>0.05030</td>\n",
" <td>0.023370</td>\n",
" <td>0.009615</td>\n",
" <td>0.1580</td>\n",
" <td>0.06235</td>\n",
" <td>...</td>\n",
" <td>25.59</td>\n",
" <td>69.10</td>\n",
" <td>364.2</td>\n",
" <td>0.11990</td>\n",
" <td>0.09546</td>\n",
" <td>0.09350</td>\n",
" <td>0.03846</td>\n",
" <td>0.2552</td>\n",
" <td>0.07920</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>549</th>\n",
" <td>10.820</td>\n",
" <td>24.21</td>\n",
" <td>68.89</td>\n",
" <td>361.6</td>\n",
" <td>0.08192</td>\n",
" <td>0.06602</td>\n",
" <td>0.015480</td>\n",
" <td>0.008160</td>\n",
" <td>0.1976</td>\n",
" <td>0.06328</td>\n",
" <td>...</td>\n",
" <td>31.45</td>\n",
" <td>83.90</td>\n",
" <td>505.6</td>\n",
" <td>0.12040</td>\n",
" <td>0.16330</td>\n",
" <td>0.06194</td>\n",
" <td>0.03264</td>\n",
" <td>0.3059</td>\n",
" <td>0.07626</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>550</th>\n",
" <td>10.860</td>\n",
" <td>21.48</td>\n",
" <td>68.51</td>\n",
" <td>360.5</td>\n",
" <td>0.07431</td>\n",
" <td>0.04227</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.1661</td>\n",
" <td>0.05948</td>\n",
" <td>...</td>\n",
" <td>24.77</td>\n",
" <td>74.08</td>\n",
" <td>412.3</td>\n",
" <td>0.10010</td>\n",
" <td>0.07348</td>\n",
" <td>0.00000</td>\n",
" <td>0.00000</td>\n",
" <td>0.2458</td>\n",
" <td>0.06592</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>551</th>\n",
" <td>11.130</td>\n",
" <td>22.44</td>\n",
" <td>71.49</td>\n",
" <td>378.4</td>\n",
" <td>0.09566</td>\n",
" <td>0.08194</td>\n",
" <td>0.048240</td>\n",
" <td>0.022570</td>\n",
" <td>0.2030</td>\n",
" <td>0.06552</td>\n",
" <td>...</td>\n",
" <td>28.26</td>\n",
" <td>77.80</td>\n",
" <td>436.6</td>\n",
" <td>0.10870</td>\n",
" <td>0.17820</td>\n",
" <td>0.15640</td>\n",
" <td>0.06413</td>\n",
" <td>0.3169</td>\n",
" <td>0.08032</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>552</th>\n",
" <td>12.770</td>\n",
" <td>29.43</td>\n",
" <td>81.35</td>\n",
" <td>507.9</td>\n",
" <td>0.08276</td>\n",
" <td>0.04234</td>\n",
" <td>0.019970</td>\n",
" <td>0.014990</td>\n",
" <td>0.1539</td>\n",
" <td>0.05637</td>\n",
" <td>...</td>\n",
" <td>36.00</td>\n",
" <td>88.10</td>\n",
" <td>594.7</td>\n",
" <td>0.12340</td>\n",
" <td>0.10640</td>\n",
" <td>0.08653</td>\n",
" <td>0.06498</td>\n",
" <td>0.2407</td>\n",
" <td>0.06484</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>553</th>\n",
" <td>9.333</td>\n",
" <td>21.94</td>\n",
" <td>59.01</td>\n",
" <td>264.0</td>\n",
" <td>0.09240</td>\n",
" <td>0.05605</td>\n",
" <td>0.039960</td>\n",
" <td>0.012820</td>\n",
" <td>0.1692</td>\n",
" <td>0.06576</td>\n",
" <td>...</td>\n",
" <td>25.05</td>\n",
" <td>62.86</td>\n",
" <td>295.8</td>\n",
" <td>0.11030</td>\n",
" <td>0.08298</td>\n",
" <td>0.07993</td>\n",
" <td>0.02564</td>\n",
" <td>0.2435</td>\n",
" <td>0.07393</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>554</th>\n",
" <td>12.880</td>\n",
" <td>28.92</td>\n",
" <td>82.50</td>\n",
" <td>514.3</td>\n",
" <td>0.08123</td>\n",
" <td>0.05824</td>\n",
" <td>0.061950</td>\n",
" <td>0.023430</td>\n",
" <td>0.1566</td>\n",
" <td>0.05708</td>\n",
" <td>...</td>\n",
" <td>35.74</td>\n",
" <td>88.84</td>\n",
" <td>595.7</td>\n",
" <td>0.12270</td>\n",
" <td>0.16200</td>\n",
" <td>0.24390</td>\n",
" <td>0.06493</td>\n",
" <td>0.2372</td>\n",
" <td>0.07242</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>555</th>\n",
" <td>10.290</td>\n",
" <td>27.61</td>\n",
" <td>65.67</td>\n",
" <td>321.4</td>\n",
" <td>0.09030</td>\n",
" <td>0.07658</td>\n",
" <td>0.059990</td>\n",
" <td>0.027380</td>\n",
" <td>0.1593</td>\n",
" <td>0.06127</td>\n",
" <td>...</td>\n",
" <td>34.91</td>\n",
" <td>69.57</td>\n",
" <td>357.6</td>\n",
" <td>0.13840</td>\n",
" <td>0.17100</td>\n",
" <td>0.20000</td>\n",
" <td>0.09127</td>\n",
" <td>0.2226</td>\n",
" <td>0.08283</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>556</th>\n",
" <td>10.160</td>\n",
" <td>19.59</td>\n",
" <td>64.73</td>\n",
" <td>311.7</td>\n",
" <td>0.10030</td>\n",
" <td>0.07504</td>\n",
" <td>0.005025</td>\n",
" <td>0.011160</td>\n",
" <td>0.1791</td>\n",
" <td>0.06331</td>\n",
" <td>...</td>\n",
" <td>22.88</td>\n",
" <td>67.88</td>\n",
" <td>347.3</td>\n",
" <td>0.12650</td>\n",
" <td>0.12000</td>\n",
" <td>0.01005</td>\n",
" <td>0.02232</td>\n",
" <td>0.2262</td>\n",
" <td>0.06742</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>557</th>\n",
" <td>9.423</td>\n",
" <td>27.88</td>\n",
" <td>59.26</td>\n",
" <td>271.3</td>\n",
" <td>0.08123</td>\n",
" <td>0.04971</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.1742</td>\n",
" <td>0.06059</td>\n",
" <td>...</td>\n",
" <td>34.24</td>\n",
" <td>66.50</td>\n",
" <td>330.6</td>\n",
" <td>0.10730</td>\n",
" <td>0.07158</td>\n",
" <td>0.00000</td>\n",
" <td>0.00000</td>\n",
" <td>0.2475</td>\n",
" <td>0.06969</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>558</th>\n",
" <td>14.590</td>\n",
" <td>22.68</td>\n",
" <td>96.39</td>\n",
" <td>657.1</td>\n",
" <td>0.08473</td>\n",
" <td>0.13300</td>\n",
" <td>0.102900</td>\n",
" <td>0.037360</td>\n",
" <td>0.1454</td>\n",
" <td>0.06147</td>\n",
" <td>...</td>\n",
" <td>27.27</td>\n",
" <td>105.90</td>\n",
" <td>733.5</td>\n",
" <td>0.10260</td>\n",
" <td>0.31710</td>\n",
" <td>0.36620</td>\n",
" <td>0.11050</td>\n",
" <td>0.2258</td>\n",
" <td>0.08004</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>559</th>\n",
" <td>11.510</td>\n",
" <td>23.93</td>\n",
" <td>74.52</td>\n",
" <td>403.5</td>\n",
" <td>0.09261</td>\n",
" <td>0.10210</td>\n",
" <td>0.111200</td>\n",
" <td>0.041050</td>\n",
" <td>0.1388</td>\n",
" <td>0.06570</td>\n",
" <td>...</td>\n",
" <td>37.16</td>\n",
" <td>82.28</td>\n",
" <td>474.2</td>\n",
" <td>0.12980</td>\n",
" <td>0.25170</td>\n",
" <td>0.36300</td>\n",
" <td>0.09653</td>\n",
" <td>0.2112</td>\n",
" <td>0.08732</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>560</th>\n",
" <td>14.050</td>\n",
" <td>27.15</td>\n",
" <td>91.38</td>\n",
" <td>600.4</td>\n",
" <td>0.09929</td>\n",
" <td>0.11260</td>\n",
" <td>0.044620</td>\n",
" <td>0.043040</td>\n",
" <td>0.1537</td>\n",
" <td>0.06171</td>\n",
" <td>...</td>\n",
" <td>33.17</td>\n",
" <td>100.20</td>\n",
" <td>706.7</td>\n",
" <td>0.12410</td>\n",
" <td>0.22640</td>\n",
" <td>0.13260</td>\n",
" <td>0.10480</td>\n",
" <td>0.2250</td>\n",
" <td>0.08321</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>561</th>\n",
" <td>11.200</td>\n",
" <td>29.37</td>\n",
" <td>70.67</td>\n",
" <td>386.0</td>\n",
" <td>0.07449</td>\n",
" <td>0.03558</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.1060</td>\n",
" <td>0.05502</td>\n",
" <td>...</td>\n",
" <td>38.30</td>\n",
" <td>75.19</td>\n",
" <td>439.6</td>\n",
" <td>0.09267</td>\n",
" <td>0.05494</td>\n",
" <td>0.00000</td>\n",
" <td>0.00000</td>\n",
" <td>0.1566</td>\n",
" <td>0.05905</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>562</th>\n",
" <td>15.220</td>\n",
" <td>30.62</td>\n",
" <td>103.40</td>\n",
" <td>716.9</td>\n",
" <td>0.10480</td>\n",
" <td>0.20870</td>\n",
" <td>0.255000</td>\n",
" <td>0.094290</td>\n",
" <td>0.2128</td>\n",
" <td>0.07152</td>\n",
" <td>...</td>\n",
" <td>42.79</td>\n",
" <td>128.70</td>\n",
" <td>915.0</td>\n",
" <td>0.14170</td>\n",
" <td>0.79170</td>\n",
" <td>1.17000</td>\n",
" <td>0.23560</td>\n",
" <td>0.4089</td>\n",
" <td>0.14090</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>563</th>\n",
" <td>20.920</td>\n",
" <td>25.09</td>\n",
" <td>143.00</td>\n",
" <td>1347.0</td>\n",
" <td>0.10990</td>\n",
" <td>0.22360</td>\n",
" <td>0.317400</td>\n",
" <td>0.147400</td>\n",
" <td>0.2149</td>\n",
" <td>0.06879</td>\n",
" <td>...</td>\n",
" <td>29.41</td>\n",
" <td>179.10</td>\n",
" <td>1819.0</td>\n",
" <td>0.14070</td>\n",
" <td>0.41860</td>\n",
" <td>0.65990</td>\n",
" <td>0.25420</td>\n",
" <td>0.2929</td>\n",
" <td>0.09873</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>564</th>\n",
" <td>21.560</td>\n",
" <td>22.39</td>\n",
" <td>142.00</td>\n",
" <td>1479.0</td>\n",
" <td>0.11100</td>\n",
" <td>0.11590</td>\n",
" <td>0.243900</td>\n",
" <td>0.138900</td>\n",
" <td>0.1726</td>\n",
" <td>0.05623</td>\n",
" <td>...</td>\n",
" <td>26.40</td>\n",
" <td>166.10</td>\n",
" <td>2027.0</td>\n",
" <td>0.14100</td>\n",
" <td>0.21130</td>\n",
" <td>0.41070</td>\n",
" <td>0.22160</td>\n",
" <td>0.2060</td>\n",
" <td>0.07115</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>565</th>\n",
" <td>20.130</td>\n",
" <td>28.25</td>\n",
" <td>131.20</td>\n",
" <td>1261.0</td>\n",
" <td>0.09780</td>\n",
" <td>0.10340</td>\n",
" <td>0.144000</td>\n",
" <td>0.097910</td>\n",
" <td>0.1752</td>\n",
" <td>0.05533</td>\n",
" <td>...</td>\n",
" <td>38.25</td>\n",
" <td>155.00</td>\n",
" <td>1731.0</td>\n",
" <td>0.11660</td>\n",
" <td>0.19220</td>\n",
" <td>0.32150</td>\n",
" <td>0.16280</td>\n",
" <td>0.2572</td>\n",
" <td>0.06637</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>566</th>\n",
" <td>16.600</td>\n",
" <td>28.08</td>\n",
" <td>108.30</td>\n",
" <td>858.1</td>\n",
" <td>0.08455</td>\n",
" <td>0.10230</td>\n",
" <td>0.092510</td>\n",
" <td>0.053020</td>\n",
" <td>0.1590</td>\n",
" <td>0.05648</td>\n",
" <td>...</td>\n",
" <td>34.12</td>\n",
" <td>126.70</td>\n",
" <td>1124.0</td>\n",
" <td>0.11390</td>\n",
" <td>0.30940</td>\n",
" <td>0.34030</td>\n",
" <td>0.14180</td>\n",
" <td>0.2218</td>\n",
" <td>0.07820</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>567</th>\n",
" <td>20.600</td>\n",
" <td>29.33</td>\n",
" <td>140.10</td>\n",
" <td>1265.0</td>\n",
" <td>0.11780</td>\n",
" <td>0.27700</td>\n",
" <td>0.351400</td>\n",
" <td>0.152000</td>\n",
" <td>0.2397</td>\n",
" <td>0.07016</td>\n",
" <td>...</td>\n",
" <td>39.42</td>\n",
" <td>184.60</td>\n",
" <td>1821.0</td>\n",
" <td>0.16500</td>\n",
" <td>0.86810</td>\n",
" <td>0.93870</td>\n",
" <td>0.26500</td>\n",
" <td>0.4087</td>\n",
" <td>0.12400</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>568</th>\n",
" <td>7.760</td>\n",
" <td>24.54</td>\n",
" <td>47.92</td>\n",
" <td>181.0</td>\n",
" <td>0.05263</td>\n",
" <td>0.04362</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.1587</td>\n",
" <td>0.05884</td>\n",
" <td>...</td>\n",
" <td>30.37</td>\n",
" <td>59.16</td>\n",
" <td>268.6</td>\n",
" <td>0.08996</td>\n",
" <td>0.06444</td>\n",
" <td>0.00000</td>\n",
" <td>0.00000</td>\n",
" <td>0.2871</td>\n",
" <td>0.07039</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>569 rows × 31 columns</p>\n",
"</div>"
],
"text/plain": [
" mean radius mean texture mean perimeter mean area mean smoothness \\\n",
"0 17.990 10.38 122.80 1001.0 0.11840 \n",
"1 20.570 17.77 132.90 1326.0 0.08474 \n",
"2 19.690 21.25 130.00 1203.0 0.10960 \n",
"3 11.420 20.38 77.58 386.1 0.14250 \n",
"4 20.290 14.34 135.10 1297.0 0.10030 \n",
"5 12.450 15.70 82.57 477.1 0.12780 \n",
"6 18.250 19.98 119.60 1040.0 0.09463 \n",
"7 13.710 20.83 90.20 577.9 0.11890 \n",
"8 13.000 21.82 87.50 519.8 0.12730 \n",
"9 12.460 24.04 83.97 475.9 0.11860 \n",
"10 16.020 23.24 102.70 797.8 0.08206 \n",
"11 15.780 17.89 103.60 781.0 0.09710 \n",
"12 19.170 24.80 132.40 1123.0 0.09740 \n",
"13 15.850 23.95 103.70 782.7 0.08401 \n",
"14 13.730 22.61 93.60 578.3 0.11310 \n",
"15 14.540 27.54 96.73 658.8 0.11390 \n",
"16 14.680 20.13 94.74 684.5 0.09867 \n",
"17 16.130 20.68 108.10 798.8 0.11700 \n",
"18 19.810 22.15 130.00 1260.0 0.09831 \n",
"19 13.540 14.36 87.46 566.3 0.09779 \n",
"20 13.080 15.71 85.63 520.0 0.10750 \n",
"21 9.504 12.44 60.34 273.9 0.10240 \n",
"22 15.340 14.26 102.50 704.4 0.10730 \n",
"23 21.160 23.04 137.20 1404.0 0.09428 \n",
"24 16.650 21.38 110.00 904.6 0.11210 \n",
"25 17.140 16.40 116.00 912.7 0.11860 \n",
"26 14.580 21.53 97.41 644.8 0.10540 \n",
"27 18.610 20.25 122.10 1094.0 0.09440 \n",
"28 15.300 25.27 102.40 732.4 0.10820 \n",
"29 17.570 15.05 115.00 955.1 0.09847 \n",
".. ... ... ... ... ... \n",
"539 7.691 25.44 48.34 170.4 0.08668 \n",
"540 11.540 14.44 74.65 402.9 0.09984 \n",
"541 14.470 24.99 95.81 656.4 0.08837 \n",
"542 14.740 25.42 94.70 668.6 0.08275 \n",
"543 13.210 28.06 84.88 538.4 0.08671 \n",
"544 13.870 20.70 89.77 584.8 0.09578 \n",
"545 13.620 23.23 87.19 573.2 0.09246 \n",
"546 10.320 16.35 65.31 324.9 0.09434 \n",
"547 10.260 16.58 65.85 320.8 0.08877 \n",
"548 9.683 19.34 61.05 285.7 0.08491 \n",
"549 10.820 24.21 68.89 361.6 0.08192 \n",
"550 10.860 21.48 68.51 360.5 0.07431 \n",
"551 11.130 22.44 71.49 378.4 0.09566 \n",
"552 12.770 29.43 81.35 507.9 0.08276 \n",
"553 9.333 21.94 59.01 264.0 0.09240 \n",
"554 12.880 28.92 82.50 514.3 0.08123 \n",
"555 10.290 27.61 65.67 321.4 0.09030 \n",
"556 10.160 19.59 64.73 311.7 0.10030 \n",
"557 9.423 27.88 59.26 271.3 0.08123 \n",
"558 14.590 22.68 96.39 657.1 0.08473 \n",
"559 11.510 23.93 74.52 403.5 0.09261 \n",
"560 14.050 27.15 91.38 600.4 0.09929 \n",
"561 11.200 29.37 70.67 386.0 0.07449 \n",
"562 15.220 30.62 103.40 716.9 0.10480 \n",
"563 20.920 25.09 143.00 1347.0 0.10990 \n",
"564 21.560 22.39 142.00 1479.0 0.11100 \n",
"565 20.130 28.25 131.20 1261.0 0.09780 \n",
"566 16.600 28.08 108.30 858.1 0.08455 \n",
"567 20.600 29.33 140.10 1265.0 0.11780 \n",
"568 7.760 24.54 47.92 181.0 0.05263 \n",
"\n",
" mean compactness mean concavity mean concave points mean symmetry \\\n",
"0 0.27760 0.300100 0.147100 0.2419 \n",
"1 0.07864 0.086900 0.070170 0.1812 \n",
"2 0.15990 0.197400 0.127900 0.2069 \n",
"3 0.28390 0.241400 0.105200 0.2597 \n",
"4 0.13280 0.198000 0.104300 0.1809 \n",
"5 0.17000 0.157800 0.080890 0.2087 \n",
"6 0.10900 0.112700 0.074000 0.1794 \n",
"7 0.16450 0.093660 0.059850 0.2196 \n",
"8 0.19320 0.185900 0.093530 0.2350 \n",
"9 0.23960 0.227300 0.085430 0.2030 \n",
"10 0.06669 0.032990 0.033230 0.1528 \n",
"11 0.12920 0.099540 0.066060 0.1842 \n",
"12 0.24580 0.206500 0.111800 0.2397 \n",
"13 0.10020 0.099380 0.053640 0.1847 \n",
"14 0.22930 0.212800 0.080250 0.2069 \n",
"15 0.15950 0.163900 0.073640 0.2303 \n",
"16 0.07200 0.073950 0.052590 0.1586 \n",
"17 0.20220 0.172200 0.102800 0.2164 \n",
"18 0.10270 0.147900 0.094980 0.1582 \n",
"19 0.08129 0.066640 0.047810 0.1885 \n",
"20 0.12700 0.045680 0.031100 0.1967 \n",
"21 0.06492 0.029560 0.020760 0.1815 \n",
"22 0.21350 0.207700 0.097560 0.2521 \n",
"23 0.10220 0.109700 0.086320 0.1769 \n",
"24 0.14570 0.152500 0.091700 0.1995 \n",
"25 0.22760 0.222900 0.140100 0.3040 \n",
"26 0.18680 0.142500 0.087830 0.2252 \n",
"27 0.10660 0.149000 0.077310 0.1697 \n",
"28 0.16970 0.168300 0.087510 0.1926 \n",
"29 0.11570 0.098750 0.079530 0.1739 \n",
".. ... ... ... ... \n",
"539 0.11990 0.092520 0.013640 0.2037 \n",
"540 0.11200 0.067370 0.025940 0.1818 \n",
"541 0.12300 0.100900 0.038900 0.1872 \n",
"542 0.07214 0.041050 0.030270 0.1840 \n",
"543 0.06877 0.029870 0.032750 0.1628 \n",
"544 0.10180 0.036880 0.023690 0.1620 \n",
"545 0.06747 0.029740 0.024430 0.1664 \n",
"546 0.04994 0.010120 0.005495 0.1885 \n",
"547 0.08066 0.043580 0.024380 0.1669 \n",
"548 0.05030 0.023370 0.009615 0.1580 \n",
"549 0.06602 0.015480 0.008160 0.1976 \n",
"550 0.04227 0.000000 0.000000 0.1661 \n",
"551 0.08194 0.048240 0.022570 0.2030 \n",
"552 0.04234 0.019970 0.014990 0.1539 \n",
"553 0.05605 0.039960 0.012820 0.1692 \n",
"554 0.05824 0.061950 0.023430 0.1566 \n",
"555 0.07658 0.059990 0.027380 0.1593 \n",
"556 0.07504 0.005025 0.011160 0.1791 \n",
"557 0.04971 0.000000 0.000000 0.1742 \n",
"558 0.13300 0.102900 0.037360 0.1454 \n",
"559 0.10210 0.111200 0.041050 0.1388 \n",
"560 0.11260 0.044620 0.043040 0.1537 \n",
"561 0.03558 0.000000 0.000000 0.1060 \n",
"562 0.20870 0.255000 0.094290 0.2128 \n",
"563 0.22360 0.317400 0.147400 0.2149 \n",
"564 0.11590 0.243900 0.138900 0.1726 \n",
"565 0.10340 0.144000 0.097910 0.1752 \n",
"566 0.10230 0.092510 0.053020 0.1590 \n",
"567 0.27700 0.351400 0.152000 0.2397 \n",
"568 0.04362 0.000000 0.000000 0.1587 \n",
"\n",
" mean fractal dimension ... worst texture worst perimeter \\\n",
"0 0.07871 ... 17.33 184.60 \n",
"1 0.05667 ... 23.41 158.80 \n",
"2 0.05999 ... 25.53 152.50 \n",
"3 0.09744 ... 26.50 98.87 \n",
"4 0.05883 ... 16.67 152.20 \n",
"5 0.07613 ... 23.75 103.40 \n",
"6 0.05742 ... 27.66 153.20 \n",
"7 0.07451 ... 28.14 110.60 \n",
"8 0.07389 ... 30.73 106.20 \n",
"9 0.08243 ... 40.68 97.65 \n",
"10 0.05697 ... 33.88 123.80 \n",
"11 0.06082 ... 27.28 136.50 \n",
"12 0.07800 ... 29.94 151.70 \n",
"13 0.05338 ... 27.66 112.00 \n",
"14 0.07682 ... 32.01 108.80 \n",
"15 0.07077 ... 37.13 124.10 \n",
"16 0.05922 ... 30.88 123.40 \n",
"17 0.07356 ... 31.48 136.80 \n",
"18 0.05395 ... 30.88 186.80 \n",
"19 0.05766 ... 19.26 99.70 \n",
"20 0.06811 ... 20.49 96.09 \n",
"21 0.06905 ... 15.66 65.13 \n",
"22 0.07032 ... 19.08 125.10 \n",
"23 0.05278 ... 35.59 188.00 \n",
"24 0.06330 ... 31.56 177.00 \n",
"25 0.07413 ... 21.40 152.40 \n",
"26 0.06924 ... 33.21 122.40 \n",
"27 0.05699 ... 27.26 139.90 \n",
"28 0.06540 ... 36.71 149.30 \n",
"29 0.06149 ... 19.52 134.90 \n",
".. ... ... ... ... \n",
"539 0.07751 ... 31.89 54.49 \n",
"540 0.06782 ... 19.68 78.78 \n",
"541 0.06341 ... 31.73 113.50 \n",
"542 0.05680 ... 32.29 107.40 \n",
"543 0.05781 ... 37.17 92.48 \n",
"544 0.06688 ... 24.75 99.17 \n",
"545 0.05801 ... 29.09 97.58 \n",
"546 0.06201 ... 21.77 71.12 \n",
"547 0.06714 ... 22.04 71.08 \n",
"548 0.06235 ... 25.59 69.10 \n",
"549 0.06328 ... 31.45 83.90 \n",
"550 0.05948 ... 24.77 74.08 \n",
"551 0.06552 ... 28.26 77.80 \n",
"552 0.05637 ... 36.00 88.10 \n",
"553 0.06576 ... 25.05 62.86 \n",
"554 0.05708 ... 35.74 88.84 \n",
"555 0.06127 ... 34.91 69.57 \n",
"556 0.06331 ... 22.88 67.88 \n",
"557 0.06059 ... 34.24 66.50 \n",
"558 0.06147 ... 27.27 105.90 \n",
"559 0.06570 ... 37.16 82.28 \n",
"560 0.06171 ... 33.17 100.20 \n",
"561 0.05502 ... 38.30 75.19 \n",
"562 0.07152 ... 42.79 128.70 \n",
"563 0.06879 ... 29.41 179.10 \n",
"564 0.05623 ... 26.40 166.10 \n",
"565 0.05533 ... 38.25 155.00 \n",
"566 0.05648 ... 34.12 126.70 \n",
"567 0.07016 ... 39.42 184.60 \n",
"568 0.05884 ... 30.37 59.16 \n",
"\n",
" worst area worst smoothness worst compactness worst concavity \\\n",
"0 2019.0 0.16220 0.66560 0.71190 \n",
"1 1956.0 0.12380 0.18660 0.24160 \n",
"2 1709.0 0.14440 0.42450 0.45040 \n",
"3 567.7 0.20980 0.86630 0.68690 \n",
"4 1575.0 0.13740 0.20500 0.40000 \n",
"5 741.6 0.17910 0.52490 0.53550 \n",
"6 1606.0 0.14420 0.25760 0.37840 \n",
"7 897.0 0.16540 0.36820 0.26780 \n",
"8 739.3 0.17030 0.54010 0.53900 \n",
"9 711.4 0.18530 1.05800 1.10500 \n",
"10 1150.0 0.11810 0.15510 0.14590 \n",
"11 1299.0 0.13960 0.56090 0.39650 \n",
"12 1332.0 0.10370 0.39030 0.36390 \n",
"13 876.5 0.11310 0.19240 0.23220 \n",
"14 697.7 0.16510 0.77250 0.69430 \n",
"15 943.2 0.16780 0.65770 0.70260 \n",
"16 1138.0 0.14640 0.18710 0.29140 \n",
"17 1315.0 0.17890 0.42330 0.47840 \n",
"18 2398.0 0.15120 0.31500 0.53720 \n",
"19 711.2 0.14400 0.17730 0.23900 \n",
"20 630.5 0.13120 0.27760 0.18900 \n",
"21 314.9 0.13240 0.11480 0.08867 \n",
"22 980.9 0.13900 0.59540 0.63050 \n",
"23 2615.0 0.14010 0.26000 0.31550 \n",
"24 2215.0 0.18050 0.35780 0.46950 \n",
"25 1461.0 0.15450 0.39490 0.38530 \n",
"26 896.9 0.15250 0.66430 0.55390 \n",
"27 1403.0 0.13380 0.21170 0.34460 \n",
"28 1269.0 0.16410 0.61100 0.63350 \n",
"29 1227.0 0.12550 0.28120 0.24890 \n",
".. ... ... ... ... \n",
"539 223.6 0.15960 0.30640 0.33930 \n",
"540 457.8 0.13450 0.21180 0.17970 \n",
"541 808.9 0.13400 0.42020 0.40400 \n",
"542 826.4 0.10600 0.13760 0.16110 \n",
"543 629.6 0.10720 0.13810 0.10620 \n",
"544 688.6 0.12640 0.20370 0.13770 \n",
"545 729.8 0.12160 0.15170 0.10490 \n",
"546 384.9 0.12850 0.08842 0.04384 \n",
"547 357.4 0.14610 0.22460 0.17830 \n",
"548 364.2 0.11990 0.09546 0.09350 \n",
"549 505.6 0.12040 0.16330 0.06194 \n",
"550 412.3 0.10010 0.07348 0.00000 \n",
"551 436.6 0.10870 0.17820 0.15640 \n",
"552 594.7 0.12340 0.10640 0.08653 \n",
"553 295.8 0.11030 0.08298 0.07993 \n",
"554 595.7 0.12270 0.16200 0.24390 \n",
"555 357.6 0.13840 0.17100 0.20000 \n",
"556 347.3 0.12650 0.12000 0.01005 \n",
"557 330.6 0.10730 0.07158 0.00000 \n",
"558 733.5 0.10260 0.31710 0.36620 \n",
"559 474.2 0.12980 0.25170 0.36300 \n",
"560 706.7 0.12410 0.22640 0.13260 \n",
"561 439.6 0.09267 0.05494 0.00000 \n",
"562 915.0 0.14170 0.79170 1.17000 \n",
"563 1819.0 0.14070 0.41860 0.65990 \n",
"564 2027.0 0.14100 0.21130 0.41070 \n",
"565 1731.0 0.11660 0.19220 0.32150 \n",
"566 1124.0 0.11390 0.30940 0.34030 \n",
"567 1821.0 0.16500 0.86810 0.93870 \n",
"568 268.6 0.08996 0.06444 0.00000 \n",
"\n",
" worst concave points worst symmetry worst fractal dimension target \n",
"0 0.26540 0.4601 0.11890 0 \n",
"1 0.18600 0.2750 0.08902 0 \n",
"2 0.24300 0.3613 0.08758 0 \n",
"3 0.25750 0.6638 0.17300 0 \n",
"4 0.16250 0.2364 0.07678 0 \n",
"5 0.17410 0.3985 0.12440 0 \n",
"6 0.19320 0.3063 0.08368 0 \n",
"7 0.15560 0.3196 0.11510 0 \n",
"8 0.20600 0.4378 0.10720 0 \n",
"9 0.22100 0.4366 0.20750 0 \n",
"10 0.09975 0.2948 0.08452 0 \n",
"11 0.18100 0.3792 0.10480 0 \n",
"12 0.17670 0.3176 0.10230 0 \n",
"13 0.11190 0.2809 0.06287 0 \n",
"14 0.22080 0.3596 0.14310 0 \n",
"15 0.17120 0.4218 0.13410 0 \n",
"16 0.16090 0.3029 0.08216 0 \n",
"17 0.20730 0.3706 0.11420 0 \n",
"18 0.23880 0.2768 0.07615 0 \n",
"19 0.12880 0.2977 0.07259 1 \n",
"20 0.07283 0.3184 0.08183 1 \n",
"21 0.06227 0.2450 0.07773 1 \n",
"22 0.23930 0.4667 0.09946 0 \n",
"23 0.20090 0.2822 0.07526 0 \n",
"24 0.20950 0.3613 0.09564 0 \n",
"25 0.25500 0.4066 0.10590 0 \n",
"26 0.27010 0.4264 0.12750 0 \n",
"27 0.14900 0.2341 0.07421 0 \n",
"28 0.20240 0.4027 0.09876 0 \n",
"29 0.14560 0.2756 0.07919 0 \n",
".. ... ... ... ... \n",
"539 0.05000 0.2790 0.10660 1 \n",
"540 0.06918 0.2329 0.08134 1 \n",
"541 0.12050 0.3187 0.10230 1 \n",
"542 0.10950 0.2722 0.06956 1 \n",
"543 0.07958 0.2473 0.06443 1 \n",
"544 0.06845 0.2249 0.08492 1 \n",
"545 0.07174 0.2642 0.06953 1 \n",
"546 0.02381 0.2681 0.07399 1 \n",
"547 0.08333 0.2691 0.09479 1 \n",
"548 0.03846 0.2552 0.07920 1 \n",
"549 0.03264 0.3059 0.07626 1 \n",
"550 0.00000 0.2458 0.06592 1 \n",
"551 0.06413 0.3169 0.08032 1 \n",
"552 0.06498 0.2407 0.06484 1 \n",
"553 0.02564 0.2435 0.07393 1 \n",
"554 0.06493 0.2372 0.07242 1 \n",
"555 0.09127 0.2226 0.08283 1 \n",
"556 0.02232 0.2262 0.06742 1 \n",
"557 0.00000 0.2475 0.06969 1 \n",
"558 0.11050 0.2258 0.08004 1 \n",
"559 0.09653 0.2112 0.08732 1 \n",
"560 0.10480 0.2250 0.08321 1 \n",
"561 0.00000 0.1566 0.05905 1 \n",
"562 0.23560 0.4089 0.14090 0 \n",
"563 0.25420 0.2929 0.09873 0 \n",
"564 0.22160 0.2060 0.07115 0 \n",
"565 0.16280 0.2572 0.06637 0 \n",
"566 0.14180 0.2218 0.07820 0 \n",
"567 0.26500 0.4087 0.12400 0 \n",
"568 0.00000 0.2871 0.07039 1 \n",
"\n",
"[569 rows x 31 columns]"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"def answer_one():\n",
" \n",
" df = pd.DataFrame(data=cancer['data'], columns=cancer['feature_names'])\n",
" df['target'] = cancer['target']\n",
" \n",
" return df\n",
"\n",
"\n",
"answer_one()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Question 2\n",
"What is the class distribution? (i.e. how many instances of `malignant` (encoded 0) and how many `benign` (encoded 1)?)\n",
"\n",
"*This function should return a Series named `target` of length 2 with integer values and index =* `['malignant', 'benign']`"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[212, 357]"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"def answer_two():\n",
" cancerdf = answer_one()\n",
" \n",
" malignant = (cancerdf['target']==0).sum()\n",
" benign = (cancerdf['target']==1).sum()\n",
" ans = [malignant, benign]\n",
" \n",
" return ans\n",
"\n",
"\n",
"answer_two()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Question 3\n",
"Split the DataFrame into `X` (the data) and `y` (the labels).\n",
"\n",
"*This function should return a tuple of length 2:* `(X, y)`*, where* \n",
"* `X` *has shape* `(569, 30)`\n",
"* `y` *has shape* `(569,)`."
]
},
{
"cell_type": "code",
"execution_count": 25,
"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>mean radius</th>\n",
" <th>mean texture</th>\n",
" <th>mean perimeter</th>\n",
" <th>mean area</th>\n",
" <th>mean smoothness</th>\n",
" <th>mean compactness</th>\n",
" <th>mean concavity</th>\n",
" <th>mean concave points</th>\n",
" <th>mean symmetry</th>\n",
" <th>mean fractal dimension</th>\n",
" <th>...</th>\n",
" <th>worst radius</th>\n",
" <th>worst texture</th>\n",
" <th>worst perimeter</th>\n",
" <th>worst area</th>\n",
" <th>worst smoothness</th>\n",
" <th>worst compactness</th>\n",
" <th>worst concavity</th>\n",
" <th>worst concave points</th>\n",
" <th>worst symmetry</th>\n",
" <th>worst fractal dimension</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>17.990</td>\n",
" <td>10.38</td>\n",
" <td>122.80</td>\n",
" <td>1001.0</td>\n",
" <td>0.11840</td>\n",
" <td>0.27760</td>\n",
" <td>0.300100</td>\n",
" <td>0.147100</td>\n",
" <td>0.2419</td>\n",
" <td>0.07871</td>\n",
" <td>...</td>\n",
" <td>25.380</td>\n",
" <td>17.33</td>\n",
" <td>184.60</td>\n",
" <td>2019.0</td>\n",
" <td>0.16220</td>\n",
" <td>0.66560</td>\n",
" <td>0.71190</td>\n",
" <td>0.26540</td>\n",
" <td>0.4601</td>\n",
" <td>0.11890</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>20.570</td>\n",
" <td>17.77</td>\n",
" <td>132.90</td>\n",
" <td>1326.0</td>\n",
" <td>0.08474</td>\n",
" <td>0.07864</td>\n",
" <td>0.086900</td>\n",
" <td>0.070170</td>\n",
" <td>0.1812</td>\n",
" <td>0.05667</td>\n",
" <td>...</td>\n",
" <td>24.990</td>\n",
" <td>23.41</td>\n",
" <td>158.80</td>\n",
" <td>1956.0</td>\n",
" <td>0.12380</td>\n",
" <td>0.18660</td>\n",
" <td>0.24160</td>\n",
" <td>0.18600</td>\n",
" <td>0.2750</td>\n",
" <td>0.08902</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>19.690</td>\n",
" <td>21.25</td>\n",
" <td>130.00</td>\n",
" <td>1203.0</td>\n",
" <td>0.10960</td>\n",
" <td>0.15990</td>\n",
" <td>0.197400</td>\n",
" <td>0.127900</td>\n",
" <td>0.2069</td>\n",
" <td>0.05999</td>\n",
" <td>...</td>\n",
" <td>23.570</td>\n",
" <td>25.53</td>\n",
" <td>152.50</td>\n",
" <td>1709.0</td>\n",
" <td>0.14440</td>\n",
" <td>0.42450</td>\n",
" <td>0.45040</td>\n",
" <td>0.24300</td>\n",
" <td>0.3613</td>\n",
" <td>0.08758</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>11.420</td>\n",
" <td>20.38</td>\n",
" <td>77.58</td>\n",
" <td>386.1</td>\n",
" <td>0.14250</td>\n",
" <td>0.28390</td>\n",
" <td>0.241400</td>\n",
" <td>0.105200</td>\n",
" <td>0.2597</td>\n",
" <td>0.09744</td>\n",
" <td>...</td>\n",
" <td>14.910</td>\n",
" <td>26.50</td>\n",
" <td>98.87</td>\n",
" <td>567.7</td>\n",
" <td>0.20980</td>\n",
" <td>0.86630</td>\n",
" <td>0.68690</td>\n",
" <td>0.25750</td>\n",
" <td>0.6638</td>\n",
" <td>0.17300</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>20.290</td>\n",
" <td>14.34</td>\n",
" <td>135.10</td>\n",
" <td>1297.0</td>\n",
" <td>0.10030</td>\n",
" <td>0.13280</td>\n",
" <td>0.198000</td>\n",
" <td>0.104300</td>\n",
" <td>0.1809</td>\n",
" <td>0.05883</td>\n",
" <td>...</td>\n",
" <td>22.540</td>\n",
" <td>16.67</td>\n",
" <td>152.20</td>\n",
" <td>1575.0</td>\n",
" <td>0.13740</td>\n",
" <td>0.20500</td>\n",
" <td>0.40000</td>\n",
" <td>0.16250</td>\n",
" <td>0.2364</td>\n",
" <td>0.07678</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>12.450</td>\n",
" <td>15.70</td>\n",
" <td>82.57</td>\n",
" <td>477.1</td>\n",
" <td>0.12780</td>\n",
" <td>0.17000</td>\n",
" <td>0.157800</td>\n",
" <td>0.080890</td>\n",
" <td>0.2087</td>\n",
" <td>0.07613</td>\n",
" <td>...</td>\n",
" <td>15.470</td>\n",
" <td>23.75</td>\n",
" <td>103.40</td>\n",
" <td>741.6</td>\n",
" <td>0.17910</td>\n",
" <td>0.52490</td>\n",
" <td>0.53550</td>\n",
" <td>0.17410</td>\n",
" <td>0.3985</td>\n",
" <td>0.12440</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>18.250</td>\n",
" <td>19.98</td>\n",
" <td>119.60</td>\n",
" <td>1040.0</td>\n",
" <td>0.09463</td>\n",
" <td>0.10900</td>\n",
" <td>0.112700</td>\n",
" <td>0.074000</td>\n",
" <td>0.1794</td>\n",
" <td>0.05742</td>\n",
" <td>...</td>\n",
" <td>22.880</td>\n",
" <td>27.66</td>\n",
" <td>153.20</td>\n",
" <td>1606.0</td>\n",
" <td>0.14420</td>\n",
" <td>0.25760</td>\n",
" <td>0.37840</td>\n",
" <td>0.19320</td>\n",
" <td>0.3063</td>\n",
" <td>0.08368</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>13.710</td>\n",
" <td>20.83</td>\n",
" <td>90.20</td>\n",
" <td>577.9</td>\n",
" <td>0.11890</td>\n",
" <td>0.16450</td>\n",
" <td>0.093660</td>\n",
" <td>0.059850</td>\n",
" <td>0.2196</td>\n",
" <td>0.07451</td>\n",
" <td>...</td>\n",
" <td>17.060</td>\n",
" <td>28.14</td>\n",
" <td>110.60</td>\n",
" <td>897.0</td>\n",
" <td>0.16540</td>\n",
" <td>0.36820</td>\n",
" <td>0.26780</td>\n",
" <td>0.15560</td>\n",
" <td>0.3196</td>\n",
" <td>0.11510</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>13.000</td>\n",
" <td>21.82</td>\n",
" <td>87.50</td>\n",
" <td>519.8</td>\n",
" <td>0.12730</td>\n",
" <td>0.19320</td>\n",
" <td>0.185900</td>\n",
" <td>0.093530</td>\n",
" <td>0.2350</td>\n",
" <td>0.07389</td>\n",
" <td>...</td>\n",
" <td>15.490</td>\n",
" <td>30.73</td>\n",
" <td>106.20</td>\n",
" <td>739.3</td>\n",
" <td>0.17030</td>\n",
" <td>0.54010</td>\n",
" <td>0.53900</td>\n",
" <td>0.20600</td>\n",
" <td>0.4378</td>\n",
" <td>0.10720</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>12.460</td>\n",
" <td>24.04</td>\n",
" <td>83.97</td>\n",
" <td>475.9</td>\n",
" <td>0.11860</td>\n",
" <td>0.23960</td>\n",
" <td>0.227300</td>\n",
" <td>0.085430</td>\n",
" <td>0.2030</td>\n",
" <td>0.08243</td>\n",
" <td>...</td>\n",
" <td>15.090</td>\n",
" <td>40.68</td>\n",
" <td>97.65</td>\n",
" <td>711.4</td>\n",
" <td>0.18530</td>\n",
" <td>1.05800</td>\n",
" <td>1.10500</td>\n",
" <td>0.22100</td>\n",
" <td>0.4366</td>\n",
" <td>0.20750</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>16.020</td>\n",
" <td>23.24</td>\n",
" <td>102.70</td>\n",
" <td>797.8</td>\n",
" <td>0.08206</td>\n",
" <td>0.06669</td>\n",
" <td>0.032990</td>\n",
" <td>0.033230</td>\n",
" <td>0.1528</td>\n",
" <td>0.05697</td>\n",
" <td>...</td>\n",
" <td>19.190</td>\n",
" <td>33.88</td>\n",
" <td>123.80</td>\n",
" <td>1150.0</td>\n",
" <td>0.11810</td>\n",
" <td>0.15510</td>\n",
" <td>0.14590</td>\n",
" <td>0.09975</td>\n",
" <td>0.2948</td>\n",
" <td>0.08452</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>15.780</td>\n",
" <td>17.89</td>\n",
" <td>103.60</td>\n",
" <td>781.0</td>\n",
" <td>0.09710</td>\n",
" <td>0.12920</td>\n",
" <td>0.099540</td>\n",
" <td>0.066060</td>\n",
" <td>0.1842</td>\n",
" <td>0.06082</td>\n",
" <td>...</td>\n",
" <td>20.420</td>\n",
" <td>27.28</td>\n",
" <td>136.50</td>\n",
" <td>1299.0</td>\n",
" <td>0.13960</td>\n",
" <td>0.56090</td>\n",
" <td>0.39650</td>\n",
" <td>0.18100</td>\n",
" <td>0.3792</td>\n",
" <td>0.10480</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>19.170</td>\n",
" <td>24.80</td>\n",
" <td>132.40</td>\n",
" <td>1123.0</td>\n",
" <td>0.09740</td>\n",
" <td>0.24580</td>\n",
" <td>0.206500</td>\n",
" <td>0.111800</td>\n",
" <td>0.2397</td>\n",
" <td>0.07800</td>\n",
" <td>...</td>\n",
" <td>20.960</td>\n",
" <td>29.94</td>\n",
" <td>151.70</td>\n",
" <td>1332.0</td>\n",
" <td>0.10370</td>\n",
" <td>0.39030</td>\n",
" <td>0.36390</td>\n",
" <td>0.17670</td>\n",
" <td>0.3176</td>\n",
" <td>0.10230</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>15.850</td>\n",
" <td>23.95</td>\n",
" <td>103.70</td>\n",
" <td>782.7</td>\n",
" <td>0.08401</td>\n",
" <td>0.10020</td>\n",
" <td>0.099380</td>\n",
" <td>0.053640</td>\n",
" <td>0.1847</td>\n",
" <td>0.05338</td>\n",
" <td>...</td>\n",
" <td>16.840</td>\n",
" <td>27.66</td>\n",
" <td>112.00</td>\n",
" <td>876.5</td>\n",
" <td>0.11310</td>\n",
" <td>0.19240</td>\n",
" <td>0.23220</td>\n",
" <td>0.11190</td>\n",
" <td>0.2809</td>\n",
" <td>0.06287</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>13.730</td>\n",
" <td>22.61</td>\n",
" <td>93.60</td>\n",
" <td>578.3</td>\n",
" <td>0.11310</td>\n",
" <td>0.22930</td>\n",
" <td>0.212800</td>\n",
" <td>0.080250</td>\n",
" <td>0.2069</td>\n",
" <td>0.07682</td>\n",
" <td>...</td>\n",
" <td>15.030</td>\n",
" <td>32.01</td>\n",
" <td>108.80</td>\n",
" <td>697.7</td>\n",
" <td>0.16510</td>\n",
" <td>0.77250</td>\n",
" <td>0.69430</td>\n",
" <td>0.22080</td>\n",
" <td>0.3596</td>\n",
" <td>0.14310</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>14.540</td>\n",
" <td>27.54</td>\n",
" <td>96.73</td>\n",
" <td>658.8</td>\n",
" <td>0.11390</td>\n",
" <td>0.15950</td>\n",
" <td>0.163900</td>\n",
" <td>0.073640</td>\n",
" <td>0.2303</td>\n",
" <td>0.07077</td>\n",
" <td>...</td>\n",
" <td>17.460</td>\n",
" <td>37.13</td>\n",
" <td>124.10</td>\n",
" <td>943.2</td>\n",
" <td>0.16780</td>\n",
" <td>0.65770</td>\n",
" <td>0.70260</td>\n",
" <td>0.17120</td>\n",
" <td>0.4218</td>\n",
" <td>0.13410</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>14.680</td>\n",
" <td>20.13</td>\n",
" <td>94.74</td>\n",
" <td>684.5</td>\n",
" <td>0.09867</td>\n",
" <td>0.07200</td>\n",
" <td>0.073950</td>\n",
" <td>0.052590</td>\n",
" <td>0.1586</td>\n",
" <td>0.05922</td>\n",
" <td>...</td>\n",
" <td>19.070</td>\n",
" <td>30.88</td>\n",
" <td>123.40</td>\n",
" <td>1138.0</td>\n",
" <td>0.14640</td>\n",
" <td>0.18710</td>\n",
" <td>0.29140</td>\n",
" <td>0.16090</td>\n",
" <td>0.3029</td>\n",
" <td>0.08216</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>16.130</td>\n",
" <td>20.68</td>\n",
" <td>108.10</td>\n",
" <td>798.8</td>\n",
" <td>0.11700</td>\n",
" <td>0.20220</td>\n",
" <td>0.172200</td>\n",
" <td>0.102800</td>\n",
" <td>0.2164</td>\n",
" <td>0.07356</td>\n",
" <td>...</td>\n",
" <td>20.960</td>\n",
" <td>31.48</td>\n",
" <td>136.80</td>\n",
" <td>1315.0</td>\n",
" <td>0.17890</td>\n",
" <td>0.42330</td>\n",
" <td>0.47840</td>\n",
" <td>0.20730</td>\n",
" <td>0.3706</td>\n",
" <td>0.11420</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>19.810</td>\n",
" <td>22.15</td>\n",
" <td>130.00</td>\n",
" <td>1260.0</td>\n",
" <td>0.09831</td>\n",
" <td>0.10270</td>\n",
" <td>0.147900</td>\n",
" <td>0.094980</td>\n",
" <td>0.1582</td>\n",
" <td>0.05395</td>\n",
" <td>...</td>\n",
" <td>27.320</td>\n",
" <td>30.88</td>\n",
" <td>186.80</td>\n",
" <td>2398.0</td>\n",
" <td>0.15120</td>\n",
" <td>0.31500</td>\n",
" <td>0.53720</td>\n",
" <td>0.23880</td>\n",
" <td>0.2768</td>\n",
" <td>0.07615</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>13.540</td>\n",
" <td>14.36</td>\n",
" <td>87.46</td>\n",
" <td>566.3</td>\n",
" <td>0.09779</td>\n",
" <td>0.08129</td>\n",
" <td>0.066640</td>\n",
" <td>0.047810</td>\n",
" <td>0.1885</td>\n",
" <td>0.05766</td>\n",
" <td>...</td>\n",
" <td>15.110</td>\n",
" <td>19.26</td>\n",
" <td>99.70</td>\n",
" <td>711.2</td>\n",
" <td>0.14400</td>\n",
" <td>0.17730</td>\n",
" <td>0.23900</td>\n",
" <td>0.12880</td>\n",
" <td>0.2977</td>\n",
" <td>0.07259</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>13.080</td>\n",
" <td>15.71</td>\n",
" <td>85.63</td>\n",
" <td>520.0</td>\n",
" <td>0.10750</td>\n",
" <td>0.12700</td>\n",
" <td>0.045680</td>\n",
" <td>0.031100</td>\n",
" <td>0.1967</td>\n",
" <td>0.06811</td>\n",
" <td>...</td>\n",
" <td>14.500</td>\n",
" <td>20.49</td>\n",
" <td>96.09</td>\n",
" <td>630.5</td>\n",
" <td>0.13120</td>\n",
" <td>0.27760</td>\n",
" <td>0.18900</td>\n",
" <td>0.07283</td>\n",
" <td>0.3184</td>\n",
" <td>0.08183</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>9.504</td>\n",
" <td>12.44</td>\n",
" <td>60.34</td>\n",
" <td>273.9</td>\n",
" <td>0.10240</td>\n",
" <td>0.06492</td>\n",
" <td>0.029560</td>\n",
" <td>0.020760</td>\n",
" <td>0.1815</td>\n",
" <td>0.06905</td>\n",
" <td>...</td>\n",
" <td>10.230</td>\n",
" <td>15.66</td>\n",
" <td>65.13</td>\n",
" <td>314.9</td>\n",
" <td>0.13240</td>\n",
" <td>0.11480</td>\n",
" <td>0.08867</td>\n",
" <td>0.06227</td>\n",
" <td>0.2450</td>\n",
" <td>0.07773</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>15.340</td>\n",
" <td>14.26</td>\n",
" <td>102.50</td>\n",
" <td>704.4</td>\n",
" <td>0.10730</td>\n",
" <td>0.21350</td>\n",
" <td>0.207700</td>\n",
" <td>0.097560</td>\n",
" <td>0.2521</td>\n",
" <td>0.07032</td>\n",
" <td>...</td>\n",
" <td>18.070</td>\n",
" <td>19.08</td>\n",
" <td>125.10</td>\n",
" <td>980.9</td>\n",
" <td>0.13900</td>\n",
" <td>0.59540</td>\n",
" <td>0.63050</td>\n",
" <td>0.23930</td>\n",
" <td>0.4667</td>\n",
" <td>0.09946</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>21.160</td>\n",
" <td>23.04</td>\n",
" <td>137.20</td>\n",
" <td>1404.0</td>\n",
" <td>0.09428</td>\n",
" <td>0.10220</td>\n",
" <td>0.109700</td>\n",
" <td>0.086320</td>\n",
" <td>0.1769</td>\n",
" <td>0.05278</td>\n",
" <td>...</td>\n",
" <td>29.170</td>\n",
" <td>35.59</td>\n",
" <td>188.00</td>\n",
" <td>2615.0</td>\n",
" <td>0.14010</td>\n",
" <td>0.26000</td>\n",
" <td>0.31550</td>\n",
" <td>0.20090</td>\n",
" <td>0.2822</td>\n",
" <td>0.07526</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>16.650</td>\n",
" <td>21.38</td>\n",
" <td>110.00</td>\n",
" <td>904.6</td>\n",
" <td>0.11210</td>\n",
" <td>0.14570</td>\n",
" <td>0.152500</td>\n",
" <td>0.091700</td>\n",
" <td>0.1995</td>\n",
" <td>0.06330</td>\n",
" <td>...</td>\n",
" <td>26.460</td>\n",
" <td>31.56</td>\n",
" <td>177.00</td>\n",
" <td>2215.0</td>\n",
" <td>0.18050</td>\n",
" <td>0.35780</td>\n",
" <td>0.46950</td>\n",
" <td>0.20950</td>\n",
" <td>0.3613</td>\n",
" <td>0.09564</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>17.140</td>\n",
" <td>16.40</td>\n",
" <td>116.00</td>\n",
" <td>912.7</td>\n",
" <td>0.11860</td>\n",
" <td>0.22760</td>\n",
" <td>0.222900</td>\n",
" <td>0.140100</td>\n",
" <td>0.3040</td>\n",
" <td>0.07413</td>\n",
" <td>...</td>\n",
" <td>22.250</td>\n",
" <td>21.40</td>\n",
" <td>152.40</td>\n",
" <td>1461.0</td>\n",
" <td>0.15450</td>\n",
" <td>0.39490</td>\n",
" <td>0.38530</td>\n",
" <td>0.25500</td>\n",
" <td>0.4066</td>\n",
" <td>0.10590</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>14.580</td>\n",
" <td>21.53</td>\n",
" <td>97.41</td>\n",
" <td>644.8</td>\n",
" <td>0.10540</td>\n",
" <td>0.18680</td>\n",
" <td>0.142500</td>\n",
" <td>0.087830</td>\n",
" <td>0.2252</td>\n",
" <td>0.06924</td>\n",
" <td>...</td>\n",
" <td>17.620</td>\n",
" <td>33.21</td>\n",
" <td>122.40</td>\n",
" <td>896.9</td>\n",
" <td>0.15250</td>\n",
" <td>0.66430</td>\n",
" <td>0.55390</td>\n",
" <td>0.27010</td>\n",
" <td>0.4264</td>\n",
" <td>0.12750</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>18.610</td>\n",
" <td>20.25</td>\n",
" <td>122.10</td>\n",
" <td>1094.0</td>\n",
" <td>0.09440</td>\n",
" <td>0.10660</td>\n",
" <td>0.149000</td>\n",
" <td>0.077310</td>\n",
" <td>0.1697</td>\n",
" <td>0.05699</td>\n",
" <td>...</td>\n",
" <td>21.310</td>\n",
" <td>27.26</td>\n",
" <td>139.90</td>\n",
" <td>1403.0</td>\n",
" <td>0.13380</td>\n",
" <td>0.21170</td>\n",
" <td>0.34460</td>\n",
" <td>0.14900</td>\n",
" <td>0.2341</td>\n",
" <td>0.07421</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>15.300</td>\n",
" <td>25.27</td>\n",
" <td>102.40</td>\n",
" <td>732.4</td>\n",
" <td>0.10820</td>\n",
" <td>0.16970</td>\n",
" <td>0.168300</td>\n",
" <td>0.087510</td>\n",
" <td>0.1926</td>\n",
" <td>0.06540</td>\n",
" <td>...</td>\n",
" <td>20.270</td>\n",
" <td>36.71</td>\n",
" <td>149.30</td>\n",
" <td>1269.0</td>\n",
" <td>0.16410</td>\n",
" <td>0.61100</td>\n",
" <td>0.63350</td>\n",
" <td>0.20240</td>\n",
" <td>0.4027</td>\n",
" <td>0.09876</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>17.570</td>\n",
" <td>15.05</td>\n",
" <td>115.00</td>\n",
" <td>955.1</td>\n",
" <td>0.09847</td>\n",
" <td>0.11570</td>\n",
" <td>0.098750</td>\n",
" <td>0.079530</td>\n",
" <td>0.1739</td>\n",
" <td>0.06149</td>\n",
" <td>...</td>\n",
" <td>20.010</td>\n",
" <td>19.52</td>\n",
" <td>134.90</td>\n",
" <td>1227.0</td>\n",
" <td>0.12550</td>\n",
" <td>0.28120</td>\n",
" <td>0.24890</td>\n",
" <td>0.14560</td>\n",
" <td>0.2756</td>\n",
" <td>0.07919</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <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>539</th>\n",
" <td>7.691</td>\n",
" <td>25.44</td>\n",
" <td>48.34</td>\n",
" <td>170.4</td>\n",
" <td>0.08668</td>\n",
" <td>0.11990</td>\n",
" <td>0.092520</td>\n",
" <td>0.013640</td>\n",
" <td>0.2037</td>\n",
" <td>0.07751</td>\n",
" <td>...</td>\n",
" <td>8.678</td>\n",
" <td>31.89</td>\n",
" <td>54.49</td>\n",
" <td>223.6</td>\n",
" <td>0.15960</td>\n",
" <td>0.30640</td>\n",
" <td>0.33930</td>\n",
" <td>0.05000</td>\n",
" <td>0.2790</td>\n",
" <td>0.10660</td>\n",
" </tr>\n",
" <tr>\n",
" <th>540</th>\n",
" <td>11.540</td>\n",
" <td>14.44</td>\n",
" <td>74.65</td>\n",
" <td>402.9</td>\n",
" <td>0.09984</td>\n",
" <td>0.11200</td>\n",
" <td>0.067370</td>\n",
" <td>0.025940</td>\n",
" <td>0.1818</td>\n",
" <td>0.06782</td>\n",
" <td>...</td>\n",
" <td>12.260</td>\n",
" <td>19.68</td>\n",
" <td>78.78</td>\n",
" <td>457.8</td>\n",
" <td>0.13450</td>\n",
" <td>0.21180</td>\n",
" <td>0.17970</td>\n",
" <td>0.06918</td>\n",
" <td>0.2329</td>\n",
" <td>0.08134</td>\n",
" </tr>\n",
" <tr>\n",
" <th>541</th>\n",
" <td>14.470</td>\n",
" <td>24.99</td>\n",
" <td>95.81</td>\n",
" <td>656.4</td>\n",
" <td>0.08837</td>\n",
" <td>0.12300</td>\n",
" <td>0.100900</td>\n",
" <td>0.038900</td>\n",
" <td>0.1872</td>\n",
" <td>0.06341</td>\n",
" <td>...</td>\n",
" <td>16.220</td>\n",
" <td>31.73</td>\n",
" <td>113.50</td>\n",
" <td>808.9</td>\n",
" <td>0.13400</td>\n",
" <td>0.42020</td>\n",
" <td>0.40400</td>\n",
" <td>0.12050</td>\n",
" <td>0.3187</td>\n",
" <td>0.10230</td>\n",
" </tr>\n",
" <tr>\n",
" <th>542</th>\n",
" <td>14.740</td>\n",
" <td>25.42</td>\n",
" <td>94.70</td>\n",
" <td>668.6</td>\n",
" <td>0.08275</td>\n",
" <td>0.07214</td>\n",
" <td>0.041050</td>\n",
" <td>0.030270</td>\n",
" <td>0.1840</td>\n",
" <td>0.05680</td>\n",
" <td>...</td>\n",
" <td>16.510</td>\n",
" <td>32.29</td>\n",
" <td>107.40</td>\n",
" <td>826.4</td>\n",
" <td>0.10600</td>\n",
" <td>0.13760</td>\n",
" <td>0.16110</td>\n",
" <td>0.10950</td>\n",
" <td>0.2722</td>\n",
" <td>0.06956</td>\n",
" </tr>\n",
" <tr>\n",
" <th>543</th>\n",
" <td>13.210</td>\n",
" <td>28.06</td>\n",
" <td>84.88</td>\n",
" <td>538.4</td>\n",
" <td>0.08671</td>\n",
" <td>0.06877</td>\n",
" <td>0.029870</td>\n",
" <td>0.032750</td>\n",
" <td>0.1628</td>\n",
" <td>0.05781</td>\n",
" <td>...</td>\n",
" <td>14.370</td>\n",
" <td>37.17</td>\n",
" <td>92.48</td>\n",
" <td>629.6</td>\n",
" <td>0.10720</td>\n",
" <td>0.13810</td>\n",
" <td>0.10620</td>\n",
" <td>0.07958</td>\n",
" <td>0.2473</td>\n",
" <td>0.06443</td>\n",
" </tr>\n",
" <tr>\n",
" <th>544</th>\n",
" <td>13.870</td>\n",
" <td>20.70</td>\n",
" <td>89.77</td>\n",
" <td>584.8</td>\n",
" <td>0.09578</td>\n",
" <td>0.10180</td>\n",
" <td>0.036880</td>\n",
" <td>0.023690</td>\n",
" <td>0.1620</td>\n",
" <td>0.06688</td>\n",
" <td>...</td>\n",
" <td>15.050</td>\n",
" <td>24.75</td>\n",
" <td>99.17</td>\n",
" <td>688.6</td>\n",
" <td>0.12640</td>\n",
" <td>0.20370</td>\n",
" <td>0.13770</td>\n",
" <td>0.06845</td>\n",
" <td>0.2249</td>\n",
" <td>0.08492</td>\n",
" </tr>\n",
" <tr>\n",
" <th>545</th>\n",
" <td>13.620</td>\n",
" <td>23.23</td>\n",
" <td>87.19</td>\n",
" <td>573.2</td>\n",
" <td>0.09246</td>\n",
" <td>0.06747</td>\n",
" <td>0.029740</td>\n",
" <td>0.024430</td>\n",
" <td>0.1664</td>\n",
" <td>0.05801</td>\n",
" <td>...</td>\n",
" <td>15.350</td>\n",
" <td>29.09</td>\n",
" <td>97.58</td>\n",
" <td>729.8</td>\n",
" <td>0.12160</td>\n",
" <td>0.15170</td>\n",
" <td>0.10490</td>\n",
" <td>0.07174</td>\n",
" <td>0.2642</td>\n",
" <td>0.06953</td>\n",
" </tr>\n",
" <tr>\n",
" <th>546</th>\n",
" <td>10.320</td>\n",
" <td>16.35</td>\n",
" <td>65.31</td>\n",
" <td>324.9</td>\n",
" <td>0.09434</td>\n",
" <td>0.04994</td>\n",
" <td>0.010120</td>\n",
" <td>0.005495</td>\n",
" <td>0.1885</td>\n",
" <td>0.06201</td>\n",
" <td>...</td>\n",
" <td>11.250</td>\n",
" <td>21.77</td>\n",
" <td>71.12</td>\n",
" <td>384.9</td>\n",
" <td>0.12850</td>\n",
" <td>0.08842</td>\n",
" <td>0.04384</td>\n",
" <td>0.02381</td>\n",
" <td>0.2681</td>\n",
" <td>0.07399</td>\n",
" </tr>\n",
" <tr>\n",
" <th>547</th>\n",
" <td>10.260</td>\n",
" <td>16.58</td>\n",
" <td>65.85</td>\n",
" <td>320.8</td>\n",
" <td>0.08877</td>\n",
" <td>0.08066</td>\n",
" <td>0.043580</td>\n",
" <td>0.024380</td>\n",
" <td>0.1669</td>\n",
" <td>0.06714</td>\n",
" <td>...</td>\n",
" <td>10.830</td>\n",
" <td>22.04</td>\n",
" <td>71.08</td>\n",
" <td>357.4</td>\n",
" <td>0.14610</td>\n",
" <td>0.22460</td>\n",
" <td>0.17830</td>\n",
" <td>0.08333</td>\n",
" <td>0.2691</td>\n",
" <td>0.09479</td>\n",
" </tr>\n",
" <tr>\n",
" <th>548</th>\n",
" <td>9.683</td>\n",
" <td>19.34</td>\n",
" <td>61.05</td>\n",
" <td>285.7</td>\n",
" <td>0.08491</td>\n",
" <td>0.05030</td>\n",
" <td>0.023370</td>\n",
" <td>0.009615</td>\n",
" <td>0.1580</td>\n",
" <td>0.06235</td>\n",
" <td>...</td>\n",
" <td>10.930</td>\n",
" <td>25.59</td>\n",
" <td>69.10</td>\n",
" <td>364.2</td>\n",
" <td>0.11990</td>\n",
" <td>0.09546</td>\n",
" <td>0.09350</td>\n",
" <td>0.03846</td>\n",
" <td>0.2552</td>\n",
" <td>0.07920</td>\n",
" </tr>\n",
" <tr>\n",
" <th>549</th>\n",
" <td>10.820</td>\n",
" <td>24.21</td>\n",
" <td>68.89</td>\n",
" <td>361.6</td>\n",
" <td>0.08192</td>\n",
" <td>0.06602</td>\n",
" <td>0.015480</td>\n",
" <td>0.008160</td>\n",
" <td>0.1976</td>\n",
" <td>0.06328</td>\n",
" <td>...</td>\n",
" <td>13.030</td>\n",
" <td>31.45</td>\n",
" <td>83.90</td>\n",
" <td>505.6</td>\n",
" <td>0.12040</td>\n",
" <td>0.16330</td>\n",
" <td>0.06194</td>\n",
" <td>0.03264</td>\n",
" <td>0.3059</td>\n",
" <td>0.07626</td>\n",
" </tr>\n",
" <tr>\n",
" <th>550</th>\n",
" <td>10.860</td>\n",
" <td>21.48</td>\n",
" <td>68.51</td>\n",
" <td>360.5</td>\n",
" <td>0.07431</td>\n",
" <td>0.04227</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.1661</td>\n",
" <td>0.05948</td>\n",
" <td>...</td>\n",
" <td>11.660</td>\n",
" <td>24.77</td>\n",
" <td>74.08</td>\n",
" <td>412.3</td>\n",
" <td>0.10010</td>\n",
" <td>0.07348</td>\n",
" <td>0.00000</td>\n",
" <td>0.00000</td>\n",
" <td>0.2458</td>\n",
" <td>0.06592</td>\n",
" </tr>\n",
" <tr>\n",
" <th>551</th>\n",
" <td>11.130</td>\n",
" <td>22.44</td>\n",
" <td>71.49</td>\n",
" <td>378.4</td>\n",
" <td>0.09566</td>\n",
" <td>0.08194</td>\n",
" <td>0.048240</td>\n",
" <td>0.022570</td>\n",
" <td>0.2030</td>\n",
" <td>0.06552</td>\n",
" <td>...</td>\n",
" <td>12.020</td>\n",
" <td>28.26</td>\n",
" <td>77.80</td>\n",
" <td>436.6</td>\n",
" <td>0.10870</td>\n",
" <td>0.17820</td>\n",
" <td>0.15640</td>\n",
" <td>0.06413</td>\n",
" <td>0.3169</td>\n",
" <td>0.08032</td>\n",
" </tr>\n",
" <tr>\n",
" <th>552</th>\n",
" <td>12.770</td>\n",
" <td>29.43</td>\n",
" <td>81.35</td>\n",
" <td>507.9</td>\n",
" <td>0.08276</td>\n",
" <td>0.04234</td>\n",
" <td>0.019970</td>\n",
" <td>0.014990</td>\n",
" <td>0.1539</td>\n",
" <td>0.05637</td>\n",
" <td>...</td>\n",
" <td>13.870</td>\n",
" <td>36.00</td>\n",
" <td>88.10</td>\n",
" <td>594.7</td>\n",
" <td>0.12340</td>\n",
" <td>0.10640</td>\n",
" <td>0.08653</td>\n",
" <td>0.06498</td>\n",
" <td>0.2407</td>\n",
" <td>0.06484</td>\n",
" </tr>\n",
" <tr>\n",
" <th>553</th>\n",
" <td>9.333</td>\n",
" <td>21.94</td>\n",
" <td>59.01</td>\n",
" <td>264.0</td>\n",
" <td>0.09240</td>\n",
" <td>0.05605</td>\n",
" <td>0.039960</td>\n",
" <td>0.012820</td>\n",
" <td>0.1692</td>\n",
" <td>0.06576</td>\n",
" <td>...</td>\n",
" <td>9.845</td>\n",
" <td>25.05</td>\n",
" <td>62.86</td>\n",
" <td>295.8</td>\n",
" <td>0.11030</td>\n",
" <td>0.08298</td>\n",
" <td>0.07993</td>\n",
" <td>0.02564</td>\n",
" <td>0.2435</td>\n",
" <td>0.07393</td>\n",
" </tr>\n",
" <tr>\n",
" <th>554</th>\n",
" <td>12.880</td>\n",
" <td>28.92</td>\n",
" <td>82.50</td>\n",
" <td>514.3</td>\n",
" <td>0.08123</td>\n",
" <td>0.05824</td>\n",
" <td>0.061950</td>\n",
" <td>0.023430</td>\n",
" <td>0.1566</td>\n",
" <td>0.05708</td>\n",
" <td>...</td>\n",
" <td>13.890</td>\n",
" <td>35.74</td>\n",
" <td>88.84</td>\n",
" <td>595.7</td>\n",
" <td>0.12270</td>\n",
" <td>0.16200</td>\n",
" <td>0.24390</td>\n",
" <td>0.06493</td>\n",
" <td>0.2372</td>\n",
" <td>0.07242</td>\n",
" </tr>\n",
" <tr>\n",
" <th>555</th>\n",
" <td>10.290</td>\n",
" <td>27.61</td>\n",
" <td>65.67</td>\n",
" <td>321.4</td>\n",
" <td>0.09030</td>\n",
" <td>0.07658</td>\n",
" <td>0.059990</td>\n",
" <td>0.027380</td>\n",
" <td>0.1593</td>\n",
" <td>0.06127</td>\n",
" <td>...</td>\n",
" <td>10.840</td>\n",
" <td>34.91</td>\n",
" <td>69.57</td>\n",
" <td>357.6</td>\n",
" <td>0.13840</td>\n",
" <td>0.17100</td>\n",
" <td>0.20000</td>\n",
" <td>0.09127</td>\n",
" <td>0.2226</td>\n",
" <td>0.08283</td>\n",
" </tr>\n",
" <tr>\n",
" <th>556</th>\n",
" <td>10.160</td>\n",
" <td>19.59</td>\n",
" <td>64.73</td>\n",
" <td>311.7</td>\n",
" <td>0.10030</td>\n",
" <td>0.07504</td>\n",
" <td>0.005025</td>\n",
" <td>0.011160</td>\n",
" <td>0.1791</td>\n",
" <td>0.06331</td>\n",
" <td>...</td>\n",
" <td>10.650</td>\n",
" <td>22.88</td>\n",
" <td>67.88</td>\n",
" <td>347.3</td>\n",
" <td>0.12650</td>\n",
" <td>0.12000</td>\n",
" <td>0.01005</td>\n",
" <td>0.02232</td>\n",
" <td>0.2262</td>\n",
" <td>0.06742</td>\n",
" </tr>\n",
" <tr>\n",
" <th>557</th>\n",
" <td>9.423</td>\n",
" <td>27.88</td>\n",
" <td>59.26</td>\n",
" <td>271.3</td>\n",
" <td>0.08123</td>\n",
" <td>0.04971</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.1742</td>\n",
" <td>0.06059</td>\n",
" <td>...</td>\n",
" <td>10.490</td>\n",
" <td>34.24</td>\n",
" <td>66.50</td>\n",
" <td>330.6</td>\n",
" <td>0.10730</td>\n",
" <td>0.07158</td>\n",
" <td>0.00000</td>\n",
" <td>0.00000</td>\n",
" <td>0.2475</td>\n",
" <td>0.06969</td>\n",
" </tr>\n",
" <tr>\n",
" <th>558</th>\n",
" <td>14.590</td>\n",
" <td>22.68</td>\n",
" <td>96.39</td>\n",
" <td>657.1</td>\n",
" <td>0.08473</td>\n",
" <td>0.13300</td>\n",
" <td>0.102900</td>\n",
" <td>0.037360</td>\n",
" <td>0.1454</td>\n",
" <td>0.06147</td>\n",
" <td>...</td>\n",
" <td>15.480</td>\n",
" <td>27.27</td>\n",
" <td>105.90</td>\n",
" <td>733.5</td>\n",
" <td>0.10260</td>\n",
" <td>0.31710</td>\n",
" <td>0.36620</td>\n",
" <td>0.11050</td>\n",
" <td>0.2258</td>\n",
" <td>0.08004</td>\n",
" </tr>\n",
" <tr>\n",
" <th>559</th>\n",
" <td>11.510</td>\n",
" <td>23.93</td>\n",
" <td>74.52</td>\n",
" <td>403.5</td>\n",
" <td>0.09261</td>\n",
" <td>0.10210</td>\n",
" <td>0.111200</td>\n",
" <td>0.041050</td>\n",
" <td>0.1388</td>\n",
" <td>0.06570</td>\n",
" <td>...</td>\n",
" <td>12.480</td>\n",
" <td>37.16</td>\n",
" <td>82.28</td>\n",
" <td>474.2</td>\n",
" <td>0.12980</td>\n",
" <td>0.25170</td>\n",
" <td>0.36300</td>\n",
" <td>0.09653</td>\n",
" <td>0.2112</td>\n",
" <td>0.08732</td>\n",
" </tr>\n",
" <tr>\n",
" <th>560</th>\n",
" <td>14.050</td>\n",
" <td>27.15</td>\n",
" <td>91.38</td>\n",
" <td>600.4</td>\n",
" <td>0.09929</td>\n",
" <td>0.11260</td>\n",
" <td>0.044620</td>\n",
" <td>0.043040</td>\n",
" <td>0.1537</td>\n",
" <td>0.06171</td>\n",
" <td>...</td>\n",
" <td>15.300</td>\n",
" <td>33.17</td>\n",
" <td>100.20</td>\n",
" <td>706.7</td>\n",
" <td>0.12410</td>\n",
" <td>0.22640</td>\n",
" <td>0.13260</td>\n",
" <td>0.10480</td>\n",
" <td>0.2250</td>\n",
" <td>0.08321</td>\n",
" </tr>\n",
" <tr>\n",
" <th>561</th>\n",
" <td>11.200</td>\n",
" <td>29.37</td>\n",
" <td>70.67</td>\n",
" <td>386.0</td>\n",
" <td>0.07449</td>\n",
" <td>0.03558</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.1060</td>\n",
" <td>0.05502</td>\n",
" <td>...</td>\n",
" <td>11.920</td>\n",
" <td>38.30</td>\n",
" <td>75.19</td>\n",
" <td>439.6</td>\n",
" <td>0.09267</td>\n",
" <td>0.05494</td>\n",
" <td>0.00000</td>\n",
" <td>0.00000</td>\n",
" <td>0.1566</td>\n",
" <td>0.05905</td>\n",
" </tr>\n",
" <tr>\n",
" <th>562</th>\n",
" <td>15.220</td>\n",
" <td>30.62</td>\n",
" <td>103.40</td>\n",
" <td>716.9</td>\n",
" <td>0.10480</td>\n",
" <td>0.20870</td>\n",
" <td>0.255000</td>\n",
" <td>0.094290</td>\n",
" <td>0.2128</td>\n",
" <td>0.07152</td>\n",
" <td>...</td>\n",
" <td>17.520</td>\n",
" <td>42.79</td>\n",
" <td>128.70</td>\n",
" <td>915.0</td>\n",
" <td>0.14170</td>\n",
" <td>0.79170</td>\n",
" <td>1.17000</td>\n",
" <td>0.23560</td>\n",
" <td>0.4089</td>\n",
" <td>0.14090</td>\n",
" </tr>\n",
" <tr>\n",
" <th>563</th>\n",
" <td>20.920</td>\n",
" <td>25.09</td>\n",
" <td>143.00</td>\n",
" <td>1347.0</td>\n",
" <td>0.10990</td>\n",
" <td>0.22360</td>\n",
" <td>0.317400</td>\n",
" <td>0.147400</td>\n",
" <td>0.2149</td>\n",
" <td>0.06879</td>\n",
" <td>...</td>\n",
" <td>24.290</td>\n",
" <td>29.41</td>\n",
" <td>179.10</td>\n",
" <td>1819.0</td>\n",
" <td>0.14070</td>\n",
" <td>0.41860</td>\n",
" <td>0.65990</td>\n",
" <td>0.25420</td>\n",
" <td>0.2929</td>\n",
" <td>0.09873</td>\n",
" </tr>\n",
" <tr>\n",
" <th>564</th>\n",
" <td>21.560</td>\n",
" <td>22.39</td>\n",
" <td>142.00</td>\n",
" <td>1479.0</td>\n",
" <td>0.11100</td>\n",
" <td>0.11590</td>\n",
" <td>0.243900</td>\n",
" <td>0.138900</td>\n",
" <td>0.1726</td>\n",
" <td>0.05623</td>\n",
" <td>...</td>\n",
" <td>25.450</td>\n",
" <td>26.40</td>\n",
" <td>166.10</td>\n",
" <td>2027.0</td>\n",
" <td>0.14100</td>\n",
" <td>0.21130</td>\n",
" <td>0.41070</td>\n",
" <td>0.22160</td>\n",
" <td>0.2060</td>\n",
" <td>0.07115</td>\n",
" </tr>\n",
" <tr>\n",
" <th>565</th>\n",
" <td>20.130</td>\n",
" <td>28.25</td>\n",
" <td>131.20</td>\n",
" <td>1261.0</td>\n",
" <td>0.09780</td>\n",
" <td>0.10340</td>\n",
" <td>0.144000</td>\n",
" <td>0.097910</td>\n",
" <td>0.1752</td>\n",
" <td>0.05533</td>\n",
" <td>...</td>\n",
" <td>23.690</td>\n",
" <td>38.25</td>\n",
" <td>155.00</td>\n",
" <td>1731.0</td>\n",
" <td>0.11660</td>\n",
" <td>0.19220</td>\n",
" <td>0.32150</td>\n",
" <td>0.16280</td>\n",
" <td>0.2572</td>\n",
" <td>0.06637</td>\n",
" </tr>\n",
" <tr>\n",
" <th>566</th>\n",
" <td>16.600</td>\n",
" <td>28.08</td>\n",
" <td>108.30</td>\n",
" <td>858.1</td>\n",
" <td>0.08455</td>\n",
" <td>0.10230</td>\n",
" <td>0.092510</td>\n",
" <td>0.053020</td>\n",
" <td>0.1590</td>\n",
" <td>0.05648</td>\n",
" <td>...</td>\n",
" <td>18.980</td>\n",
" <td>34.12</td>\n",
" <td>126.70</td>\n",
" <td>1124.0</td>\n",
" <td>0.11390</td>\n",
" <td>0.30940</td>\n",
" <td>0.34030</td>\n",
" <td>0.14180</td>\n",
" <td>0.2218</td>\n",
" <td>0.07820</td>\n",
" </tr>\n",
" <tr>\n",
" <th>567</th>\n",
" <td>20.600</td>\n",
" <td>29.33</td>\n",
" <td>140.10</td>\n",
" <td>1265.0</td>\n",
" <td>0.11780</td>\n",
" <td>0.27700</td>\n",
" <td>0.351400</td>\n",
" <td>0.152000</td>\n",
" <td>0.2397</td>\n",
" <td>0.07016</td>\n",
" <td>...</td>\n",
" <td>25.740</td>\n",
" <td>39.42</td>\n",
" <td>184.60</td>\n",
" <td>1821.0</td>\n",
" <td>0.16500</td>\n",
" <td>0.86810</td>\n",
" <td>0.93870</td>\n",
" <td>0.26500</td>\n",
" <td>0.4087</td>\n",
" <td>0.12400</td>\n",
" </tr>\n",
" <tr>\n",
" <th>568</th>\n",
" <td>7.760</td>\n",
" <td>24.54</td>\n",
" <td>47.92</td>\n",
" <td>181.0</td>\n",
" <td>0.05263</td>\n",
" <td>0.04362</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.1587</td>\n",
" <td>0.05884</td>\n",
" <td>...</td>\n",
" <td>9.456</td>\n",
" <td>30.37</td>\n",
" <td>59.16</td>\n",
" <td>268.6</td>\n",
" <td>0.08996</td>\n",
" <td>0.06444</td>\n",
" <td>0.00000</td>\n",
" <td>0.00000</td>\n",
" <td>0.2871</td>\n",
" <td>0.07039</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>569 rows × 30 columns</p>\n",
"</div>"
],
"text/plain": [
" mean radius mean texture mean perimeter mean area mean smoothness \\\n",
"0 17.990 10.38 122.80 1001.0 0.11840 \n",
"1 20.570 17.77 132.90 1326.0 0.08474 \n",
"2 19.690 21.25 130.00 1203.0 0.10960 \n",
"3 11.420 20.38 77.58 386.1 0.14250 \n",
"4 20.290 14.34 135.10 1297.0 0.10030 \n",
"5 12.450 15.70 82.57 477.1 0.12780 \n",
"6 18.250 19.98 119.60 1040.0 0.09463 \n",
"7 13.710 20.83 90.20 577.9 0.11890 \n",
"8 13.000 21.82 87.50 519.8 0.12730 \n",
"9 12.460 24.04 83.97 475.9 0.11860 \n",
"10 16.020 23.24 102.70 797.8 0.08206 \n",
"11 15.780 17.89 103.60 781.0 0.09710 \n",
"12 19.170 24.80 132.40 1123.0 0.09740 \n",
"13 15.850 23.95 103.70 782.7 0.08401 \n",
"14 13.730 22.61 93.60 578.3 0.11310 \n",
"15 14.540 27.54 96.73 658.8 0.11390 \n",
"16 14.680 20.13 94.74 684.5 0.09867 \n",
"17 16.130 20.68 108.10 798.8 0.11700 \n",
"18 19.810 22.15 130.00 1260.0 0.09831 \n",
"19 13.540 14.36 87.46 566.3 0.09779 \n",
"20 13.080 15.71 85.63 520.0 0.10750 \n",
"21 9.504 12.44 60.34 273.9 0.10240 \n",
"22 15.340 14.26 102.50 704.4 0.10730 \n",
"23 21.160 23.04 137.20 1404.0 0.09428 \n",
"24 16.650 21.38 110.00 904.6 0.11210 \n",
"25 17.140 16.40 116.00 912.7 0.11860 \n",
"26 14.580 21.53 97.41 644.8 0.10540 \n",
"27 18.610 20.25 122.10 1094.0 0.09440 \n",
"28 15.300 25.27 102.40 732.4 0.10820 \n",
"29 17.570 15.05 115.00 955.1 0.09847 \n",
".. ... ... ... ... ... \n",
"539 7.691 25.44 48.34 170.4 0.08668 \n",
"540 11.540 14.44 74.65 402.9 0.09984 \n",
"541 14.470 24.99 95.81 656.4 0.08837 \n",
"542 14.740 25.42 94.70 668.6 0.08275 \n",
"543 13.210 28.06 84.88 538.4 0.08671 \n",
"544 13.870 20.70 89.77 584.8 0.09578 \n",
"545 13.620 23.23 87.19 573.2 0.09246 \n",
"546 10.320 16.35 65.31 324.9 0.09434 \n",
"547 10.260 16.58 65.85 320.8 0.08877 \n",
"548 9.683 19.34 61.05 285.7 0.08491 \n",
"549 10.820 24.21 68.89 361.6 0.08192 \n",
"550 10.860 21.48 68.51 360.5 0.07431 \n",
"551 11.130 22.44 71.49 378.4 0.09566 \n",
"552 12.770 29.43 81.35 507.9 0.08276 \n",
"553 9.333 21.94 59.01 264.0 0.09240 \n",
"554 12.880 28.92 82.50 514.3 0.08123 \n",
"555 10.290 27.61 65.67 321.4 0.09030 \n",
"556 10.160 19.59 64.73 311.7 0.10030 \n",
"557 9.423 27.88 59.26 271.3 0.08123 \n",
"558 14.590 22.68 96.39 657.1 0.08473 \n",
"559 11.510 23.93 74.52 403.5 0.09261 \n",
"560 14.050 27.15 91.38 600.4 0.09929 \n",
"561 11.200 29.37 70.67 386.0 0.07449 \n",
"562 15.220 30.62 103.40 716.9 0.10480 \n",
"563 20.920 25.09 143.00 1347.0 0.10990 \n",
"564 21.560 22.39 142.00 1479.0 0.11100 \n",
"565 20.130 28.25 131.20 1261.0 0.09780 \n",
"566 16.600 28.08 108.30 858.1 0.08455 \n",
"567 20.600 29.33 140.10 1265.0 0.11780 \n",
"568 7.760 24.54 47.92 181.0 0.05263 \n",
"\n",
" mean compactness mean concavity mean concave points mean symmetry \\\n",
"0 0.27760 0.300100 0.147100 0.2419 \n",
"1 0.07864 0.086900 0.070170 0.1812 \n",
"2 0.15990 0.197400 0.127900 0.2069 \n",
"3 0.28390 0.241400 0.105200 0.2597 \n",
"4 0.13280 0.198000 0.104300 0.1809 \n",
"5 0.17000 0.157800 0.080890 0.2087 \n",
"6 0.10900 0.112700 0.074000 0.1794 \n",
"7 0.16450 0.093660 0.059850 0.2196 \n",
"8 0.19320 0.185900 0.093530 0.2350 \n",
"9 0.23960 0.227300 0.085430 0.2030 \n",
"10 0.06669 0.032990 0.033230 0.1528 \n",
"11 0.12920 0.099540 0.066060 0.1842 \n",
"12 0.24580 0.206500 0.111800 0.2397 \n",
"13 0.10020 0.099380 0.053640 0.1847 \n",
"14 0.22930 0.212800 0.080250 0.2069 \n",
"15 0.15950 0.163900 0.073640 0.2303 \n",
"16 0.07200 0.073950 0.052590 0.1586 \n",
"17 0.20220 0.172200 0.102800 0.2164 \n",
"18 0.10270 0.147900 0.094980 0.1582 \n",
"19 0.08129 0.066640 0.047810 0.1885 \n",
"20 0.12700 0.045680 0.031100 0.1967 \n",
"21 0.06492 0.029560 0.020760 0.1815 \n",
"22 0.21350 0.207700 0.097560 0.2521 \n",
"23 0.10220 0.109700 0.086320 0.1769 \n",
"24 0.14570 0.152500 0.091700 0.1995 \n",
"25 0.22760 0.222900 0.140100 0.3040 \n",
"26 0.18680 0.142500 0.087830 0.2252 \n",
"27 0.10660 0.149000 0.077310 0.1697 \n",
"28 0.16970 0.168300 0.087510 0.1926 \n",
"29 0.11570 0.098750 0.079530 0.1739 \n",
".. ... ... ... ... \n",
"539 0.11990 0.092520 0.013640 0.2037 \n",
"540 0.11200 0.067370 0.025940 0.1818 \n",
"541 0.12300 0.100900 0.038900 0.1872 \n",
"542 0.07214 0.041050 0.030270 0.1840 \n",
"543 0.06877 0.029870 0.032750 0.1628 \n",
"544 0.10180 0.036880 0.023690 0.1620 \n",
"545 0.06747 0.029740 0.024430 0.1664 \n",
"546 0.04994 0.010120 0.005495 0.1885 \n",
"547 0.08066 0.043580 0.024380 0.1669 \n",
"548 0.05030 0.023370 0.009615 0.1580 \n",
"549 0.06602 0.015480 0.008160 0.1976 \n",
"550 0.04227 0.000000 0.000000 0.1661 \n",
"551 0.08194 0.048240 0.022570 0.2030 \n",
"552 0.04234 0.019970 0.014990 0.1539 \n",
"553 0.05605 0.039960 0.012820 0.1692 \n",
"554 0.05824 0.061950 0.023430 0.1566 \n",
"555 0.07658 0.059990 0.027380 0.1593 \n",
"556 0.07504 0.005025 0.011160 0.1791 \n",
"557 0.04971 0.000000 0.000000 0.1742 \n",
"558 0.13300 0.102900 0.037360 0.1454 \n",
"559 0.10210 0.111200 0.041050 0.1388 \n",
"560 0.11260 0.044620 0.043040 0.1537 \n",
"561 0.03558 0.000000 0.000000 0.1060 \n",
"562 0.20870 0.255000 0.094290 0.2128 \n",
"563 0.22360 0.317400 0.147400 0.2149 \n",
"564 0.11590 0.243900 0.138900 0.1726 \n",
"565 0.10340 0.144000 0.097910 0.1752 \n",
"566 0.10230 0.092510 0.053020 0.1590 \n",
"567 0.27700 0.351400 0.152000 0.2397 \n",
"568 0.04362 0.000000 0.000000 0.1587 \n",
"\n",
" mean fractal dimension ... worst radius \\\n",
"0 0.07871 ... 25.380 \n",
"1 0.05667 ... 24.990 \n",
"2 0.05999 ... 23.570 \n",
"3 0.09744 ... 14.910 \n",
"4 0.05883 ... 22.540 \n",
"5 0.07613 ... 15.470 \n",
"6 0.05742 ... 22.880 \n",
"7 0.07451 ... 17.060 \n",
"8 0.07389 ... 15.490 \n",
"9 0.08243 ... 15.090 \n",
"10 0.05697 ... 19.190 \n",
"11 0.06082 ... 20.420 \n",
"12 0.07800 ... 20.960 \n",
"13 0.05338 ... 16.840 \n",
"14 0.07682 ... 15.030 \n",
"15 0.07077 ... 17.460 \n",
"16 0.05922 ... 19.070 \n",
"17 0.07356 ... 20.960 \n",
"18 0.05395 ... 27.320 \n",
"19 0.05766 ... 15.110 \n",
"20 0.06811 ... 14.500 \n",
"21 0.06905 ... 10.230 \n",
"22 0.07032 ... 18.070 \n",
"23 0.05278 ... 29.170 \n",
"24 0.06330 ... 26.460 \n",
"25 0.07413 ... 22.250 \n",
"26 0.06924 ... 17.620 \n",
"27 0.05699 ... 21.310 \n",
"28 0.06540 ... 20.270 \n",
"29 0.06149 ... 20.010 \n",
".. ... ... ... \n",
"539 0.07751 ... 8.678 \n",
"540 0.06782 ... 12.260 \n",
"541 0.06341 ... 16.220 \n",
"542 0.05680 ... 16.510 \n",
"543 0.05781 ... 14.370 \n",
"544 0.06688 ... 15.050 \n",
"545 0.05801 ... 15.350 \n",
"546 0.06201 ... 11.250 \n",
"547 0.06714 ... 10.830 \n",
"548 0.06235 ... 10.930 \n",
"549 0.06328 ... 13.030 \n",
"550 0.05948 ... 11.660 \n",
"551 0.06552 ... 12.020 \n",
"552 0.05637 ... 13.870 \n",
"553 0.06576 ... 9.845 \n",
"554 0.05708 ... 13.890 \n",
"555 0.06127 ... 10.840 \n",
"556 0.06331 ... 10.650 \n",
"557 0.06059 ... 10.490 \n",
"558 0.06147 ... 15.480 \n",
"559 0.06570 ... 12.480 \n",
"560 0.06171 ... 15.300 \n",
"561 0.05502 ... 11.920 \n",
"562 0.07152 ... 17.520 \n",
"563 0.06879 ... 24.290 \n",
"564 0.05623 ... 25.450 \n",
"565 0.05533 ... 23.690 \n",
"566 0.05648 ... 18.980 \n",
"567 0.07016 ... 25.740 \n",
"568 0.05884 ... 9.456 \n",
"\n",
" worst texture worst perimeter worst area worst smoothness \\\n",
"0 17.33 184.60 2019.0 0.16220 \n",
"1 23.41 158.80 1956.0 0.12380 \n",
"2 25.53 152.50 1709.0 0.14440 \n",
"3 26.50 98.87 567.7 0.20980 \n",
"4 16.67 152.20 1575.0 0.13740 \n",
"5 23.75 103.40 741.6 0.17910 \n",
"6 27.66 153.20 1606.0 0.14420 \n",
"7 28.14 110.60 897.0 0.16540 \n",
"8 30.73 106.20 739.3 0.17030 \n",
"9 40.68 97.65 711.4 0.18530 \n",
"10 33.88 123.80 1150.0 0.11810 \n",
"11 27.28 136.50 1299.0 0.13960 \n",
"12 29.94 151.70 1332.0 0.10370 \n",
"13 27.66 112.00 876.5 0.11310 \n",
"14 32.01 108.80 697.7 0.16510 \n",
"15 37.13 124.10 943.2 0.16780 \n",
"16 30.88 123.40 1138.0 0.14640 \n",
"17 31.48 136.80 1315.0 0.17890 \n",
"18 30.88 186.80 2398.0 0.15120 \n",
"19 19.26 99.70 711.2 0.14400 \n",
"20 20.49 96.09 630.5 0.13120 \n",
"21 15.66 65.13 314.9 0.13240 \n",
"22 19.08 125.10 980.9 0.13900 \n",
"23 35.59 188.00 2615.0 0.14010 \n",
"24 31.56 177.00 2215.0 0.18050 \n",
"25 21.40 152.40 1461.0 0.15450 \n",
"26 33.21 122.40 896.9 0.15250 \n",
"27 27.26 139.90 1403.0 0.13380 \n",
"28 36.71 149.30 1269.0 0.16410 \n",
"29 19.52 134.90 1227.0 0.12550 \n",
".. ... ... ... ... \n",
"539 31.89 54.49 223.6 0.15960 \n",
"540 19.68 78.78 457.8 0.13450 \n",
"541 31.73 113.50 808.9 0.13400 \n",
"542 32.29 107.40 826.4 0.10600 \n",
"543 37.17 92.48 629.6 0.10720 \n",
"544 24.75 99.17 688.6 0.12640 \n",
"545 29.09 97.58 729.8 0.12160 \n",
"546 21.77 71.12 384.9 0.12850 \n",
"547 22.04 71.08 357.4 0.14610 \n",
"548 25.59 69.10 364.2 0.11990 \n",
"549 31.45 83.90 505.6 0.12040 \n",
"550 24.77 74.08 412.3 0.10010 \n",
"551 28.26 77.80 436.6 0.10870 \n",
"552 36.00 88.10 594.7 0.12340 \n",
"553 25.05 62.86 295.8 0.11030 \n",
"554 35.74 88.84 595.7 0.12270 \n",
"555 34.91 69.57 357.6 0.13840 \n",
"556 22.88 67.88 347.3 0.12650 \n",
"557 34.24 66.50 330.6 0.10730 \n",
"558 27.27 105.90 733.5 0.10260 \n",
"559 37.16 82.28 474.2 0.12980 \n",
"560 33.17 100.20 706.7 0.12410 \n",
"561 38.30 75.19 439.6 0.09267 \n",
"562 42.79 128.70 915.0 0.14170 \n",
"563 29.41 179.10 1819.0 0.14070 \n",
"564 26.40 166.10 2027.0 0.14100 \n",
"565 38.25 155.00 1731.0 0.11660 \n",
"566 34.12 126.70 1124.0 0.11390 \n",
"567 39.42 184.60 1821.0 0.16500 \n",
"568 30.37 59.16 268.6 0.08996 \n",
"\n",
" worst compactness worst concavity worst concave points worst symmetry \\\n",
"0 0.66560 0.71190 0.26540 0.4601 \n",
"1 0.18660 0.24160 0.18600 0.2750 \n",
"2 0.42450 0.45040 0.24300 0.3613 \n",
"3 0.86630 0.68690 0.25750 0.6638 \n",
"4 0.20500 0.40000 0.16250 0.2364 \n",
"5 0.52490 0.53550 0.17410 0.3985 \n",
"6 0.25760 0.37840 0.19320 0.3063 \n",
"7 0.36820 0.26780 0.15560 0.3196 \n",
"8 0.54010 0.53900 0.20600 0.4378 \n",
"9 1.05800 1.10500 0.22100 0.4366 \n",
"10 0.15510 0.14590 0.09975 0.2948 \n",
"11 0.56090 0.39650 0.18100 0.3792 \n",
"12 0.39030 0.36390 0.17670 0.3176 \n",
"13 0.19240 0.23220 0.11190 0.2809 \n",
"14 0.77250 0.69430 0.22080 0.3596 \n",
"15 0.65770 0.70260 0.17120 0.4218 \n",
"16 0.18710 0.29140 0.16090 0.3029 \n",
"17 0.42330 0.47840 0.20730 0.3706 \n",
"18 0.31500 0.53720 0.23880 0.2768 \n",
"19 0.17730 0.23900 0.12880 0.2977 \n",
"20 0.27760 0.18900 0.07283 0.3184 \n",
"21 0.11480 0.08867 0.06227 0.2450 \n",
"22 0.59540 0.63050 0.23930 0.4667 \n",
"23 0.26000 0.31550 0.20090 0.2822 \n",
"24 0.35780 0.46950 0.20950 0.3613 \n",
"25 0.39490 0.38530 0.25500 0.4066 \n",
"26 0.66430 0.55390 0.27010 0.4264 \n",
"27 0.21170 0.34460 0.14900 0.2341 \n",
"28 0.61100 0.63350 0.20240 0.4027 \n",
"29 0.28120 0.24890 0.14560 0.2756 \n",
".. ... ... ... ... \n",
"539 0.30640 0.33930 0.05000 0.2790 \n",
"540 0.21180 0.17970 0.06918 0.2329 \n",
"541 0.42020 0.40400 0.12050 0.3187 \n",
"542 0.13760 0.16110 0.10950 0.2722 \n",
"543 0.13810 0.10620 0.07958 0.2473 \n",
"544 0.20370 0.13770 0.06845 0.2249 \n",
"545 0.15170 0.10490 0.07174 0.2642 \n",
"546 0.08842 0.04384 0.02381 0.2681 \n",
"547 0.22460 0.17830 0.08333 0.2691 \n",
"548 0.09546 0.09350 0.03846 0.2552 \n",
"549 0.16330 0.06194 0.03264 0.3059 \n",
"550 0.07348 0.00000 0.00000 0.2458 \n",
"551 0.17820 0.15640 0.06413 0.3169 \n",
"552 0.10640 0.08653 0.06498 0.2407 \n",
"553 0.08298 0.07993 0.02564 0.2435 \n",
"554 0.16200 0.24390 0.06493 0.2372 \n",
"555 0.17100 0.20000 0.09127 0.2226 \n",
"556 0.12000 0.01005 0.02232 0.2262 \n",
"557 0.07158 0.00000 0.00000 0.2475 \n",
"558 0.31710 0.36620 0.11050 0.2258 \n",
"559 0.25170 0.36300 0.09653 0.2112 \n",
"560 0.22640 0.13260 0.10480 0.2250 \n",
"561 0.05494 0.00000 0.00000 0.1566 \n",
"562 0.79170 1.17000 0.23560 0.4089 \n",
"563 0.41860 0.65990 0.25420 0.2929 \n",
"564 0.21130 0.41070 0.22160 0.2060 \n",
"565 0.19220 0.32150 0.16280 0.2572 \n",
"566 0.30940 0.34030 0.14180 0.2218 \n",
"567 0.86810 0.93870 0.26500 0.4087 \n",
"568 0.06444 0.00000 0.00000 0.2871 \n",
"\n",
" worst fractal dimension \n",
"0 0.11890 \n",
"1 0.08902 \n",
"2 0.08758 \n",
"3 0.17300 \n",
"4 0.07678 \n",
"5 0.12440 \n",
"6 0.08368 \n",
"7 0.11510 \n",
"8 0.10720 \n",
"9 0.20750 \n",
"10 0.08452 \n",
"11 0.10480 \n",
"12 0.10230 \n",
"13 0.06287 \n",
"14 0.14310 \n",
"15 0.13410 \n",
"16 0.08216 \n",
"17 0.11420 \n",
"18 0.07615 \n",
"19 0.07259 \n",
"20 0.08183 \n",
"21 0.07773 \n",
"22 0.09946 \n",
"23 0.07526 \n",
"24 0.09564 \n",
"25 0.10590 \n",
"26 0.12750 \n",
"27 0.07421 \n",
"28 0.09876 \n",
"29 0.07919 \n",
".. ... \n",
"539 0.10660 \n",
"540 0.08134 \n",
"541 0.10230 \n",
"542 0.06956 \n",
"543 0.06443 \n",
"544 0.08492 \n",
"545 0.06953 \n",
"546 0.07399 \n",
"547 0.09479 \n",
"548 0.07920 \n",
"549 0.07626 \n",
"550 0.06592 \n",
"551 0.08032 \n",
"552 0.06484 \n",
"553 0.07393 \n",
"554 0.07242 \n",
"555 0.08283 \n",
"556 0.06742 \n",
"557 0.06969 \n",
"558 0.08004 \n",
"559 0.08732 \n",
"560 0.08321 \n",
"561 0.05905 \n",
"562 0.14090 \n",
"563 0.09873 \n",
"564 0.07115 \n",
"565 0.06637 \n",
"566 0.07820 \n",
"567 0.12400 \n",
"568 0.07039 \n",
"\n",
"[569 rows x 30 columns]"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cancerdf = answer_one()\n",
"cancerdf.iloc[:, :-1]"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def answer_three():\n",
" cancerdf = answer_one()\n",
" \n",
" X= cancerdf.iloc[:, :-1]\n",
" y= cancerdf['target']\n",
" \n",
" return X, y"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Question 4\n",
"Using `train_test_split`, split `X` and `y` into training and test sets `(X_train, X_test, y_train, and y_test)`.\n",
"\n",
"**Set the random number generator state to 0 using `random_state=0` to make sure your results match the autograder!**\n",
"\n",
"*This function should return a tuple of length 4:* `(X_train, X_test, y_train, y_test)`*, where* \n",
"* `X_train` *has shape* `(426, 30)`\n",
"* `X_test` *has shape* `(143, 30)`\n",
"* `y_train` *has shape* `(426,)`\n",
"* `y_test` *has shape* `(143,)`"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"( mean radius mean texture mean perimeter mean area mean smoothness \\\n",
" 293 11.850 17.46 75.54 432.7 0.08372 \n",
" 332 11.220 19.86 71.94 387.3 0.10540 \n",
" 565 20.130 28.25 131.20 1261.0 0.09780 \n",
" 278 13.590 17.84 86.24 572.3 0.07948 \n",
" 489 16.690 20.20 107.10 857.6 0.07497 \n",
" 346 12.060 18.90 76.66 445.3 0.08386 \n",
" 357 13.870 16.21 88.52 593.7 0.08743 \n",
" 355 12.560 19.07 81.92 485.8 0.08760 \n",
" 112 14.260 19.65 97.83 629.9 0.07837 \n",
" 68 9.029 17.33 58.79 250.5 0.10660 \n",
" 526 13.460 18.75 87.44 551.1 0.10750 \n",
" 206 9.876 17.27 62.92 295.4 0.10890 \n",
" 65 14.780 23.94 97.40 668.3 0.11720 \n",
" 437 14.040 15.98 89.78 611.2 0.08458 \n",
" 126 13.610 24.69 87.76 572.6 0.09258 \n",
" 429 12.720 17.67 80.98 501.3 0.07896 \n",
" 392 15.490 19.97 102.40 744.7 0.11600 \n",
" 343 19.680 21.68 129.90 1194.0 0.09797 \n",
" 334 12.300 19.02 77.88 464.4 0.08313 \n",
" 440 10.970 17.20 71.73 371.5 0.08915 \n",
" 441 17.270 25.42 112.40 928.8 0.08331 \n",
" 137 11.430 15.39 73.06 399.8 0.09639 \n",
" 230 17.050 19.08 113.40 895.0 0.11410 \n",
" 7 13.710 20.83 90.20 577.9 0.11890 \n",
" 408 17.990 20.66 117.80 991.7 0.10360 \n",
" 523 13.710 18.68 88.73 571.0 0.09916 \n",
" 361 13.300 21.57 85.24 546.1 0.08582 \n",
" 553 9.333 21.94 59.01 264.0 0.09240 \n",
" 478 11.490 14.59 73.99 404.9 0.10460 \n",
" 303 10.490 18.61 66.86 334.3 0.10680 \n",
" .. ... ... ... ... ... \n",
" 459 9.755 28.20 61.68 290.9 0.07984 \n",
" 510 11.740 14.69 76.31 426.0 0.08099 \n",
" 151 8.219 20.70 53.27 203.9 0.09405 \n",
" 244 19.400 23.50 129.10 1155.0 0.10270 \n",
" 543 13.210 28.06 84.88 538.4 0.08671 \n",
" 544 13.870 20.70 89.77 584.8 0.09578 \n",
" 265 20.730 31.12 135.70 1419.0 0.09469 \n",
" 288 11.260 19.96 73.72 394.1 0.08020 \n",
" 423 13.660 19.13 89.46 575.3 0.09057 \n",
" 147 14.950 18.77 97.84 689.5 0.08138 \n",
" 177 16.460 20.11 109.30 832.9 0.09831 \n",
" 99 14.420 19.77 94.48 642.5 0.09752 \n",
" 448 14.530 19.34 94.25 659.7 0.08388 \n",
" 431 12.400 17.68 81.47 467.8 0.10540 \n",
" 115 11.930 21.53 76.53 438.6 0.09768 \n",
" 72 17.200 24.52 114.20 929.4 0.10710 \n",
" 537 11.690 24.44 76.37 406.4 0.12360 \n",
" 174 10.660 15.15 67.49 349.6 0.08792 \n",
" 87 19.020 24.59 122.00 1076.0 0.09029 \n",
" 551 11.130 22.44 71.49 378.4 0.09566 \n",
" 486 14.640 16.85 94.21 666.0 0.08641 \n",
" 314 8.597 18.60 54.09 221.2 0.10740 \n",
" 396 13.510 18.89 88.10 558.1 0.10590 \n",
" 472 14.920 14.93 96.45 686.9 0.08098 \n",
" 70 18.940 21.31 123.60 1130.0 0.09009 \n",
" 277 18.810 19.98 120.90 1102.0 0.08923 \n",
" 9 12.460 24.04 83.97 475.9 0.11860 \n",
" 359 9.436 18.32 59.82 278.6 0.10090 \n",
" 192 9.720 18.22 60.73 288.1 0.06950 \n",
" 559 11.510 23.93 74.52 403.5 0.09261 \n",
" \n",
" mean compactness mean concavity mean concave points mean symmetry \\\n",
" 293 0.05642 0.026880 0.022800 0.1875 \n",
" 332 0.06779 0.005006 0.007583 0.1940 \n",
" 565 0.10340 0.144000 0.097910 0.1752 \n",
" 278 0.04052 0.019970 0.012380 0.1573 \n",
" 489 0.07112 0.036490 0.023070 0.1846 \n",
" 346 0.05794 0.007510 0.008488 0.1555 \n",
" 357 0.05492 0.015020 0.020880 0.1424 \n",
" 355 0.10380 0.103000 0.043910 0.1533 \n",
" 112 0.22330 0.300300 0.077980 0.1704 \n",
" 68 0.14130 0.313000 0.043750 0.2111 \n",
" 526 0.11380 0.042010 0.031520 0.1723 \n",
" 206 0.07232 0.017560 0.019520 0.1934 \n",
" 65 0.14790 0.126700 0.090290 0.1953 \n",
" 437 0.05895 0.035340 0.029440 0.1714 \n",
" 126 0.07862 0.052850 0.030850 0.1761 \n",
" 429 0.04522 0.014020 0.018350 0.1459 \n",
" 392 0.15620 0.189100 0.091130 0.1929 \n",
" 343 0.13390 0.186300 0.110300 0.2082 \n",
" 334 0.04202 0.007756 0.008535 0.1539 \n",
" 440 0.11130 0.094570 0.036130 0.1489 \n",
" 441 0.11090 0.120400 0.057360 0.1467 \n",
" 137 0.06889 0.035030 0.028750 0.1734 \n",
" 230 0.15720 0.191000 0.109000 0.2131 \n",
" 7 0.16450 0.093660 0.059850 0.2196 \n",
" 408 0.13040 0.120100 0.088240 0.1992 \n",
" 523 0.10700 0.053850 0.037830 0.1714 \n",
" 361 0.06373 0.033440 0.024240 0.1815 \n",
" 553 0.05605 0.039960 0.012820 0.1692 \n",
" 478 0.08228 0.053080 0.019690 0.1779 \n",
" 303 0.06678 0.022970 0.017800 0.1482 \n",
" .. ... ... ... ... \n",
" 459 0.04626 0.015410 0.010430 0.1621 \n",
" 510 0.09661 0.067260 0.026390 0.1499 \n",
" 151 0.13050 0.132100 0.021680 0.2222 \n",
" 244 0.15580 0.204900 0.088860 0.1978 \n",
" 543 0.06877 0.029870 0.032750 0.1628 \n",
" 544 0.10180 0.036880 0.023690 0.1620 \n",
" 265 0.11430 0.136700 0.086460 0.1769 \n",
" 288 0.11810 0.092740 0.055880 0.2595 \n",
" 423 0.11470 0.096570 0.048120 0.1848 \n",
" 147 0.11670 0.090500 0.035620 0.1744 \n",
" 177 0.15560 0.179300 0.088660 0.1794 \n",
" 99 0.11410 0.093880 0.058390 0.1879 \n",
" 448 0.07800 0.088170 0.029250 0.1473 \n",
" 431 0.13160 0.077410 0.027990 0.1811 \n",
" 115 0.07849 0.033280 0.020080 0.1688 \n",
" 72 0.18300 0.169200 0.079440 0.1927 \n",
" 537 0.15520 0.045150 0.045310 0.2131 \n",
" 174 0.04302 0.000000 0.000000 0.1928 \n",
" 87 0.12060 0.146800 0.082710 0.1953 \n",
" 551 0.08194 0.048240 0.022570 0.2030 \n",
" 486 0.06698 0.051920 0.027910 0.1409 \n",
" 314 0.05847 0.000000 0.000000 0.2163 \n",
" 396 0.11470 0.085800 0.053810 0.1806 \n",
" 472 0.08549 0.055390 0.032210 0.1687 \n",
" 70 0.10290 0.108000 0.079510 0.1582 \n",
" 277 0.05884 0.080200 0.058430 0.1550 \n",
" 9 0.23960 0.227300 0.085430 0.2030 \n",
" 359 0.05956 0.027100 0.014060 0.1506 \n",
" 192 0.02344 0.000000 0.000000 0.1653 \n",
" 559 0.10210 0.111200 0.041050 0.1388 \n",
" \n",
" mean fractal dimension ... worst radius \\\n",
" 293 0.05715 ... 13.060 \n",
" 332 0.06028 ... 11.980 \n",
" 565 0.05533 ... 23.690 \n",
" 278 0.05520 ... 15.500 \n",
" 489 0.05325 ... 19.180 \n",
" 346 0.06048 ... 13.640 \n",
" 357 0.05883 ... 15.110 \n",
" 355 0.06184 ... 13.370 \n",
" 112 0.07769 ... 15.300 \n",
" 68 0.08046 ... 10.310 \n",
" 526 0.06317 ... 15.350 \n",
" 206 0.06285 ... 10.420 \n",
" 65 0.06654 ... 17.310 \n",
" 437 0.05898 ... 15.660 \n",
" 126 0.06130 ... 16.890 \n",
" 429 0.05544 ... 13.820 \n",
" 392 0.06744 ... 21.200 \n",
" 343 0.05715 ... 22.750 \n",
" 334 0.05945 ... 13.350 \n",
" 440 0.06640 ... 12.360 \n",
" 441 0.05407 ... 20.380 \n",
" 137 0.05865 ... 12.320 \n",
" 230 0.06325 ... 19.590 \n",
" 7 0.07451 ... 17.060 \n",
" 408 0.06069 ... 21.080 \n",
" 523 0.06843 ... 15.110 \n",
" 361 0.05696 ... 14.200 \n",
" 553 0.06576 ... 9.845 \n",
" 478 0.06574 ... 12.400 \n",
" 303 0.06600 ... 11.060 \n",
" .. ... ... ... \n",
" 459 0.05952 ... 10.670 \n",
" 510 0.06758 ... 12.450 \n",
" 151 0.08261 ... 9.092 \n",
" 244 0.06000 ... 21.650 \n",
" 543 0.05781 ... 14.370 \n",
" 544 0.06688 ... 15.050 \n",
" 265 0.05674 ... 32.490 \n",
" 288 0.06233 ... 11.860 \n",
" 423 0.06181 ... 15.140 \n",
" 147 0.06493 ... 16.250 \n",
" 177 0.06323 ... 17.790 \n",
" 99 0.06390 ... 16.330 \n",
" 448 0.05746 ... 16.300 \n",
" 431 0.07102 ... 12.880 \n",
" 115 0.06194 ... 13.670 \n",
" 72 0.06487 ... 23.320 \n",
" 537 0.07405 ... 12.980 \n",
" 174 0.05975 ... 11.540 \n",
" 87 0.05629 ... 24.560 \n",
" 551 0.06552 ... 12.020 \n",
" 486 0.05355 ... 16.460 \n",
" 314 0.07359 ... 8.952 \n",
" 396 0.06079 ... 14.800 \n",
" 472 0.05669 ... 17.180 \n",
" 70 0.05461 ... 24.860 \n",
" 277 0.04996 ... 19.960 \n",
" 9 0.08243 ... 15.090 \n",
" 359 0.06959 ... 12.020 \n",
" 192 0.06447 ... 9.968 \n",
" 559 0.06570 ... 12.480 \n",
" \n",
" worst texture worst perimeter worst area worst smoothness \\\n",
" 293 25.75 84.35 517.8 0.13690 \n",
" 332 25.78 76.91 436.1 0.14240 \n",
" 565 38.25 155.00 1731.0 0.11660 \n",
" 278 26.10 98.91 739.1 0.10500 \n",
" 489 26.56 127.30 1084.0 0.10090 \n",
" 346 27.06 86.54 562.6 0.12890 \n",
" 357 25.58 96.74 694.4 0.11530 \n",
" 355 22.43 89.02 547.4 0.10960 \n",
" 112 23.73 107.00 709.0 0.08949 \n",
" 68 22.65 65.50 324.7 0.14820 \n",
" 526 25.16 101.90 719.8 0.16240 \n",
" 206 23.22 67.08 331.6 0.14150 \n",
" 65 33.39 114.60 925.1 0.16480 \n",
" 437 21.58 101.20 750.0 0.11950 \n",
" 126 35.64 113.20 848.7 0.14710 \n",
" 429 20.96 88.87 586.8 0.10680 \n",
" 392 29.41 142.10 1359.0 0.16810 \n",
" 343 34.66 157.60 1540.0 0.12180 \n",
" 334 28.46 84.53 544.3 0.12220 \n",
" 440 26.87 90.14 476.4 0.13910 \n",
" 441 35.46 132.80 1284.0 0.14360 \n",
" 137 22.02 79.93 462.0 0.11900 \n",
" 230 24.89 133.50 1189.0 0.17030 \n",
" 7 28.14 110.60 897.0 0.16540 \n",
" 408 25.41 138.10 1349.0 0.14820 \n",
" 523 25.63 99.43 701.9 0.14250 \n",
" 361 29.20 92.94 621.2 0.11400 \n",
" 553 25.05 62.86 295.8 0.11030 \n",
" 478 21.90 82.04 467.6 0.13520 \n",
" 303 24.54 70.76 375.4 0.14130 \n",
" .. ... ... ... ... \n",
" 459 36.92 68.03 349.9 0.11100 \n",
" 510 17.60 81.25 473.8 0.10730 \n",
" 151 29.72 58.08 249.8 0.16300 \n",
" 244 30.53 144.90 1417.0 0.14630 \n",
" 543 37.17 92.48 629.6 0.10720 \n",
" 544 24.75 99.17 688.6 0.12640 \n",
" 265 47.16 214.00 3432.0 0.14010 \n",
" 288 22.33 78.27 437.6 0.10280 \n",
" 423 25.50 101.40 708.8 0.11470 \n",
" 147 25.47 107.10 809.7 0.09970 \n",
" 177 28.45 123.50 981.2 0.14150 \n",
" 99 30.86 109.50 826.4 0.14310 \n",
" 448 28.39 108.10 830.5 0.10890 \n",
" 431 22.91 89.61 515.8 0.14500 \n",
" 115 26.15 87.54 583.0 0.15000 \n",
" 72 33.82 151.60 1681.0 0.15850 \n",
" 537 32.19 86.12 487.7 0.17680 \n",
" 174 19.20 73.20 408.3 0.10760 \n",
" 87 30.41 152.90 1623.0 0.12490 \n",
" 551 28.26 77.80 436.6 0.10870 \n",
" 486 25.44 106.00 831.0 0.11420 \n",
" 314 22.44 56.65 240.1 0.13470 \n",
" 396 27.20 97.33 675.2 0.14280 \n",
" 472 18.22 112.00 906.6 0.10650 \n",
" 70 26.58 165.90 1866.0 0.11930 \n",
" 277 24.30 129.00 1236.0 0.12430 \n",
" 9 40.68 97.65 711.4 0.18530 \n",
" 359 25.02 75.79 439.6 0.13330 \n",
" 192 20.83 62.25 303.8 0.07117 \n",
" 559 37.16 82.28 474.2 0.12980 \n",
" \n",
" worst compactness worst concavity worst concave points worst symmetry \\\n",
" 293 0.17580 0.13160 0.09140 0.3101 \n",
" 332 0.09669 0.01335 0.02022 0.3292 \n",
" 565 0.19220 0.32150 0.16280 0.2572 \n",
" 278 0.07622 0.10600 0.05185 0.2335 \n",
" 489 0.29200 0.24770 0.08737 0.4677 \n",
" 346 0.13520 0.04506 0.05093 0.2880 \n",
" 357 0.10080 0.05285 0.05556 0.2362 \n",
" 355 0.20020 0.23880 0.09265 0.2121 \n",
" 112 0.41930 0.67830 0.15050 0.2398 \n",
" 68 0.43650 1.25200 0.17500 0.4228 \n",
" 526 0.31240 0.26540 0.14270 0.3518 \n",
" 206 0.12470 0.06213 0.05588 0.2989 \n",
" 65 0.34160 0.30240 0.16140 0.3321 \n",
" 437 0.12520 0.11170 0.07453 0.2725 \n",
" 126 0.28840 0.37960 0.13290 0.3470 \n",
" 429 0.09605 0.03469 0.03612 0.2165 \n",
" 392 0.39130 0.55530 0.21210 0.3187 \n",
" 343 0.34580 0.47340 0.22550 0.4045 \n",
" 334 0.09052 0.03619 0.03983 0.2554 \n",
" 440 0.40820 0.47790 0.15550 0.2540 \n",
" 441 0.41220 0.50360 0.17390 0.2500 \n",
" 137 0.16480 0.13990 0.08476 0.2676 \n",
" 230 0.39340 0.50180 0.25430 0.3109 \n",
" 7 0.36820 0.26780 0.15560 0.3196 \n",
" 408 0.37350 0.33010 0.19740 0.3060 \n",
" 523 0.25660 0.19350 0.12840 0.2849 \n",
" 361 0.16670 0.12120 0.05614 0.2637 \n",
" 553 0.08298 0.07993 0.02564 0.2435 \n",
" 478 0.20100 0.25960 0.07431 0.2941 \n",
" 303 0.10440 0.08423 0.06528 0.2213 \n",
" .. ... ... ... ... \n",
" 459 0.11090 0.07190 0.04866 0.2321 \n",
" 510 0.27930 0.26900 0.10560 0.2604 \n",
" 151 0.43100 0.53810 0.07879 0.3322 \n",
" 244 0.29680 0.34580 0.15640 0.2920 \n",
" 543 0.13810 0.10620 0.07958 0.2473 \n",
" 544 0.20370 0.13770 0.06845 0.2249 \n",
" 265 0.26440 0.34420 0.16590 0.2868 \n",
" 288 0.18430 0.15460 0.09314 0.2955 \n",
" 423 0.31670 0.36600 0.14070 0.2744 \n",
" 147 0.25210 0.25000 0.08405 0.2852 \n",
" 177 0.46670 0.58620 0.20350 0.3054 \n",
" 99 0.30260 0.31940 0.15650 0.2718 \n",
" 448 0.26490 0.37790 0.09594 0.2471 \n",
" 431 0.26290 0.24030 0.07370 0.2556 \n",
" 115 0.23990 0.15030 0.07247 0.2438 \n",
" 72 0.73940 0.65660 0.18990 0.3313 \n",
" 537 0.32510 0.13950 0.13080 0.2803 \n",
" 174 0.06791 0.00000 0.00000 0.2710 \n",
" 87 0.32060 0.57550 0.19560 0.3956 \n",
" 551 0.17820 0.15640 0.06413 0.3169 \n",
" 486 0.20700 0.24370 0.07828 0.2455 \n",
" 314 0.07767 0.00000 0.00000 0.3142 \n",
" 396 0.25700 0.34380 0.14530 0.2666 \n",
" 472 0.27910 0.31510 0.11470 0.2688 \n",
" 70 0.23360 0.26870 0.17890 0.2551 \n",
" 277 0.11600 0.22100 0.12940 0.2567 \n",
" 9 1.05800 1.10500 0.22100 0.4366 \n",
" 359 0.10490 0.11440 0.05052 0.2454 \n",
" 192 0.02729 0.00000 0.00000 0.1909 \n",
" 559 0.25170 0.36300 0.09653 0.2112 \n",
" \n",
" worst fractal dimension \n",
" 293 0.07007 \n",
" 332 0.06522 \n",
" 565 0.06637 \n",
" 278 0.06263 \n",
" 489 0.07623 \n",
" 346 0.08083 \n",
" 357 0.07113 \n",
" 355 0.07188 \n",
" 112 0.10820 \n",
" 68 0.11750 \n",
" 526 0.08665 \n",
" 206 0.07380 \n",
" 65 0.08911 \n",
" 437 0.07234 \n",
" 126 0.07900 \n",
" 429 0.06025 \n",
" 392 0.10190 \n",
" 343 0.07918 \n",
" 334 0.07207 \n",
" 440 0.09532 \n",
" 441 0.07944 \n",
" 137 0.06765 \n",
" 230 0.09061 \n",
" 7 0.11510 \n",
" 408 0.08503 \n",
" 523 0.09031 \n",
" 361 0.06658 \n",
" 553 0.07393 \n",
" 478 0.09180 \n",
" 303 0.07842 \n",
" .. ... \n",
" 459 0.07211 \n",
" 510 0.09879 \n",
" 151 0.14860 \n",
" 244 0.07614 \n",
" 543 0.06443 \n",
" 544 0.08492 \n",
" 265 0.08218 \n",
" 288 0.07009 \n",
" 423 0.08839 \n",
" 147 0.09218 \n",
" 177 0.09519 \n",
" 99 0.09353 \n",
" 448 0.07463 \n",
" 431 0.09359 \n",
" 115 0.08541 \n",
" 72 0.13390 \n",
" 537 0.09970 \n",
" 174 0.06164 \n",
" 87 0.09288 \n",
" 551 0.08032 \n",
" 486 0.06596 \n",
" 314 0.08116 \n",
" 396 0.07686 \n",
" 472 0.08273 \n",
" 70 0.06589 \n",
" 277 0.05737 \n",
" 9 0.20750 \n",
" 359 0.08136 \n",
" 192 0.06559 \n",
" 559 0.08732 \n",
" \n",
" [426 rows x 30 columns],\n",
" mean radius mean texture mean perimeter mean area mean smoothness \\\n",
" 512 13.400 20.52 88.64 556.7 0.11060 \n",
" 457 13.210 25.25 84.10 537.9 0.08791 \n",
" 439 14.020 15.66 89.59 606.5 0.07966 \n",
" 298 14.260 18.17 91.22 633.1 0.06576 \n",
" 37 13.030 18.42 82.61 523.8 0.08983 \n",
" 515 11.340 18.61 72.76 391.2 0.10490 \n",
" 382 12.050 22.72 78.75 447.8 0.06935 \n",
" 310 11.700 19.11 74.33 418.7 0.08814 \n",
" 538 7.729 25.49 47.98 178.8 0.08098 \n",
" 345 10.260 14.71 66.20 321.6 0.09882 \n",
" 421 14.690 13.98 98.22 656.1 0.10310 \n",
" 90 14.620 24.02 94.57 662.7 0.08974 \n",
" 412 9.397 21.68 59.75 268.8 0.07969 \n",
" 157 16.840 19.46 108.40 880.2 0.07445 \n",
" 89 14.640 15.24 95.77 651.9 0.11320 \n",
" 172 15.460 11.89 102.50 736.9 0.12570 \n",
" 318 9.042 18.90 60.07 244.5 0.09968 \n",
" 233 20.510 27.81 134.40 1319.0 0.09159 \n",
" 389 19.550 23.21 128.90 1174.0 0.10100 \n",
" 250 20.940 23.56 138.90 1364.0 0.10070 \n",
" 31 11.840 18.70 77.93 440.6 0.11090 \n",
" 283 16.240 18.77 108.80 805.1 0.10660 \n",
" 482 13.470 14.06 87.32 546.3 0.10710 \n",
" 211 11.840 18.94 75.51 428.0 0.08871 \n",
" 372 21.370 15.10 141.30 1386.0 0.10010 \n",
" 401 11.930 10.91 76.14 442.7 0.08872 \n",
" 159 10.900 12.96 68.69 366.8 0.07515 \n",
" 14 13.730 22.61 93.60 578.3 0.11310 \n",
" 364 13.400 16.95 85.48 552.4 0.07937 \n",
" 337 18.770 21.43 122.90 1092.0 0.09116 \n",
" .. ... ... ... ... ... \n",
" 500 15.040 16.74 98.73 689.4 0.09883 \n",
" 338 10.050 17.53 64.41 310.8 0.10070 \n",
" 427 10.800 21.98 68.79 359.9 0.08801 \n",
" 406 16.140 14.86 104.30 800.0 0.09495 \n",
" 96 12.180 17.84 77.79 451.1 0.10450 \n",
" 490 12.250 22.44 78.18 466.5 0.08192 \n",
" 384 13.280 13.72 85.79 541.8 0.08363 \n",
" 281 11.740 14.02 74.24 427.3 0.07813 \n",
" 325 12.670 17.30 81.25 489.9 0.10280 \n",
" 190 14.220 23.12 94.37 609.9 0.10750 \n",
" 380 11.270 12.96 73.16 386.3 0.12370 \n",
" 366 20.200 26.83 133.70 1234.0 0.09905 \n",
" 469 11.620 18.18 76.38 408.8 0.11750 \n",
" 225 14.340 13.47 92.51 641.2 0.09906 \n",
" 271 11.290 13.04 72.23 388.0 0.09834 \n",
" 547 10.260 16.58 65.85 320.8 0.08877 \n",
" 550 10.860 21.48 68.51 360.5 0.07431 \n",
" 492 18.010 20.56 118.40 1007.0 0.10010 \n",
" 185 10.080 15.11 63.76 317.5 0.09267 \n",
" 306 13.200 15.82 84.07 537.3 0.08511 \n",
" 208 13.110 22.54 87.02 529.4 0.10020 \n",
" 242 11.300 18.19 73.93 389.4 0.09592 \n",
" 313 11.540 10.72 73.73 409.1 0.08597 \n",
" 542 14.740 25.42 94.70 668.6 0.08275 \n",
" 514 15.050 19.07 97.26 701.9 0.09215 \n",
" 236 23.210 26.97 153.50 1670.0 0.09509 \n",
" 113 10.510 20.19 68.64 334.2 0.11220 \n",
" 527 12.340 12.27 78.94 468.5 0.09003 \n",
" 76 13.530 10.94 87.91 559.2 0.12910 \n",
" 162 19.590 18.15 130.70 1214.0 0.11200 \n",
" \n",
" mean compactness mean concavity mean concave points mean symmetry \\\n",
" 512 0.14690 0.144500 0.081720 0.2116 \n",
" 457 0.05205 0.027720 0.020680 0.1619 \n",
" 439 0.05581 0.020870 0.026520 0.1589 \n",
" 298 0.05220 0.024750 0.013740 0.1635 \n",
" 37 0.03766 0.025620 0.029230 0.1467 \n",
" 515 0.08499 0.043020 0.025940 0.1927 \n",
" 382 0.10730 0.079430 0.029780 0.1203 \n",
" 310 0.05253 0.015830 0.011480 0.1936 \n",
" 538 0.04878 0.000000 0.000000 0.1870 \n",
" 345 0.09159 0.035810 0.020370 0.1633 \n",
" 421 0.18360 0.145000 0.063000 0.2086 \n",
" 90 0.08606 0.031020 0.029570 0.1685 \n",
" 412 0.06053 0.037350 0.005128 0.1274 \n",
" 157 0.07223 0.051500 0.027710 0.1844 \n",
" 89 0.13390 0.099660 0.070640 0.2116 \n",
" 172 0.15550 0.203200 0.109700 0.1966 \n",
" 318 0.19720 0.197500 0.049080 0.2330 \n",
" 233 0.10740 0.155400 0.083400 0.1448 \n",
" 389 0.13180 0.185600 0.102100 0.1989 \n",
" 250 0.16060 0.271200 0.131000 0.2205 \n",
" 31 0.15160 0.121800 0.051820 0.2301 \n",
" 283 0.18020 0.194800 0.090520 0.1876 \n",
" 482 0.11550 0.057860 0.052660 0.1779 \n",
" 211 0.06900 0.026690 0.013930 0.1533 \n",
" 372 0.15150 0.193200 0.125500 0.1973 \n",
" 401 0.05242 0.026060 0.017960 0.1601 \n",
" 159 0.03718 0.003090 0.006588 0.1442 \n",
" 14 0.22930 0.212800 0.080250 0.2069 \n",
" 364 0.05696 0.021810 0.014730 0.1650 \n",
" 337 0.14020 0.106000 0.060900 0.1953 \n",
" .. ... ... ... ... \n",
" 500 0.13640 0.077210 0.061420 0.1668 \n",
" 338 0.07326 0.025110 0.017750 0.1890 \n",
" 427 0.05743 0.036140 0.014040 0.2016 \n",
" 406 0.08501 0.055000 0.045280 0.1735 \n",
" 96 0.07057 0.024900 0.029410 0.1900 \n",
" 490 0.05200 0.017140 0.012610 0.1544 \n",
" 384 0.08575 0.050770 0.028640 0.1617 \n",
" 281 0.04340 0.022450 0.027630 0.2101 \n",
" 325 0.07664 0.031930 0.021070 0.1707 \n",
" 190 0.24130 0.198100 0.066180 0.2384 \n",
" 380 0.11110 0.079000 0.055500 0.2018 \n",
" 366 0.16690 0.164100 0.126500 0.1875 \n",
" 469 0.14830 0.102000 0.055640 0.1957 \n",
" 225 0.07624 0.057240 0.046030 0.2075 \n",
" 271 0.07608 0.032650 0.027550 0.1769 \n",
" 547 0.08066 0.043580 0.024380 0.1669 \n",
" 550 0.04227 0.000000 0.000000 0.1661 \n",
" 492 0.12890 0.117000 0.077620 0.2116 \n",
" 185 0.04695 0.001597 0.002404 0.1703 \n",
" 306 0.05251 0.001461 0.003261 0.1632 \n",
" 208 0.14830 0.087050 0.051020 0.1850 \n",
" 242 0.13250 0.154800 0.028540 0.2054 \n",
" 313 0.05969 0.013670 0.008907 0.1833 \n",
" 542 0.07214 0.041050 0.030270 0.1840 \n",
" 514 0.08597 0.074860 0.043350 0.1561 \n",
" 236 0.16820 0.195000 0.123700 0.1909 \n",
" 113 0.13030 0.064760 0.030680 0.1922 \n",
" 527 0.06307 0.029580 0.026470 0.1689 \n",
" 76 0.10470 0.068770 0.065560 0.2403 \n",
" 162 0.16660 0.250800 0.128600 0.2027 \n",
" \n",
" mean fractal dimension ... worst radius \\\n",
" 512 0.07325 ... 16.410 \n",
" 457 0.05584 ... 14.350 \n",
" 439 0.05586 ... 14.910 \n",
" 298 0.05586 ... 16.220 \n",
" 37 0.05863 ... 13.300 \n",
" 515 0.06211 ... 12.470 \n",
" 382 0.06659 ... 12.570 \n",
" 310 0.06128 ... 12.610 \n",
" 538 0.07285 ... 9.077 \n",
" 345 0.07005 ... 10.880 \n",
" 421 0.07406 ... 16.460 \n",
" 90 0.05866 ... 16.110 \n",
" 412 0.06724 ... 9.965 \n",
" 157 0.05268 ... 18.220 \n",
" 89 0.06346 ... 16.340 \n",
" 172 0.07069 ... 18.790 \n",
" 318 0.08743 ... 10.060 \n",
" 233 0.05592 ... 24.470 \n",
" 389 0.05884 ... 20.820 \n",
" 250 0.05898 ... 25.580 \n",
" 31 0.07799 ... 16.820 \n",
" 283 0.06684 ... 18.550 \n",
" 482 0.06639 ... 14.830 \n",
" 211 0.06057 ... 13.300 \n",
" 372 0.06183 ... 22.690 \n",
" 401 0.05541 ... 13.800 \n",
" 159 0.05743 ... 12.360 \n",
" 14 0.07682 ... 15.030 \n",
" 364 0.05701 ... 14.730 \n",
" 337 0.06083 ... 24.540 \n",
" .. ... ... ... \n",
" 500 0.06869 ... 16.760 \n",
" 338 0.06331 ... 11.160 \n",
" 427 0.05977 ... 12.760 \n",
" 406 0.05875 ... 17.710 \n",
" 96 0.06635 ... 12.830 \n",
" 490 0.05976 ... 14.170 \n",
" 384 0.05594 ... 14.240 \n",
" 281 0.06113 ... 13.310 \n",
" 325 0.05984 ... 13.710 \n",
" 190 0.07542 ... 15.740 \n",
" 380 0.06914 ... 12.840 \n",
" 366 0.06020 ... 24.190 \n",
" 469 0.07255 ... 13.360 \n",
" 225 0.05448 ... 16.770 \n",
" 271 0.06270 ... 12.320 \n",
" 547 0.06714 ... 10.830 \n",
" 550 0.05948 ... 11.660 \n",
" 492 0.06077 ... 21.530 \n",
" 185 0.06048 ... 11.870 \n",
" 306 0.05894 ... 14.410 \n",
" 208 0.07310 ... 14.550 \n",
" 242 0.07669 ... 12.580 \n",
" 313 0.06100 ... 12.340 \n",
" 542 0.05680 ... 16.510 \n",
" 514 0.05915 ... 17.580 \n",
" 236 0.06309 ... 31.010 \n",
" 113 0.07782 ... 11.160 \n",
" 527 0.05808 ... 13.610 \n",
" 76 0.06641 ... 14.080 \n",
" 162 0.06082 ... 26.730 \n",
" \n",
" worst texture worst perimeter worst area worst smoothness \\\n",
" 512 29.66 113.30 844.4 0.15740 \n",
" 457 34.23 91.29 632.9 0.12890 \n",
" 439 19.31 96.53 688.9 0.10340 \n",
" 298 25.26 105.80 819.7 0.09445 \n",
" 37 22.81 84.46 545.9 0.09701 \n",
" 515 23.03 79.15 478.6 0.14830 \n",
" 382 28.71 87.36 488.4 0.08799 \n",
" 310 26.55 80.92 483.1 0.12230 \n",
" 538 30.92 57.17 248.0 0.12560 \n",
" 345 19.48 70.89 357.1 0.13600 \n",
" 421 18.34 114.10 809.2 0.13120 \n",
" 90 29.11 102.90 803.7 0.11150 \n",
" 412 27.99 66.61 301.0 0.10860 \n",
" 157 28.07 120.30 1032.0 0.08774 \n",
" 89 18.24 109.40 803.6 0.12770 \n",
" 172 17.04 125.00 1102.0 0.15310 \n",
" 318 23.40 68.62 297.1 0.12210 \n",
" 233 37.38 162.70 1872.0 0.12230 \n",
" 389 30.44 142.00 1313.0 0.12510 \n",
" 250 27.00 165.30 2010.0 0.12110 \n",
" 31 28.12 119.40 888.7 0.16370 \n",
" 283 25.09 126.90 1031.0 0.13650 \n",
" 482 18.32 94.94 660.2 0.13930 \n",
" 211 24.99 85.22 546.3 0.12800 \n",
" 372 21.84 152.10 1535.0 0.11920 \n",
" 401 20.14 87.64 589.5 0.13740 \n",
" 159 18.20 78.07 470.0 0.11710 \n",
" 14 32.01 108.80 697.7 0.16510 \n",
" 364 21.70 93.76 663.5 0.12130 \n",
" 337 34.37 161.10 1873.0 0.14980 \n",
" .. ... ... ... ... \n",
" 500 20.43 109.70 856.9 0.11350 \n",
" 338 26.84 71.98 384.0 0.14020 \n",
" 427 32.04 83.69 489.5 0.13030 \n",
" 406 19.58 115.90 947.9 0.12060 \n",
" 96 20.92 82.14 495.2 0.11400 \n",
" 490 31.99 92.74 622.9 0.12560 \n",
" 384 17.37 96.59 623.7 0.11660 \n",
" 281 18.26 84.70 533.7 0.10360 \n",
" 325 21.10 88.70 574.4 0.13840 \n",
" 190 37.18 106.40 762.4 0.15330 \n",
" 380 20.53 84.93 476.1 0.16100 \n",
" 366 33.81 160.00 1671.0 0.12780 \n",
" 469 25.40 88.14 528.1 0.17800 \n",
" 225 16.90 110.40 873.2 0.12970 \n",
" 271 16.18 78.27 457.5 0.13580 \n",
" 547 22.04 71.08 357.4 0.14610 \n",
" 550 24.77 74.08 412.3 0.10010 \n",
" 492 26.06 143.40 1426.0 0.13090 \n",
" 185 21.18 75.39 437.0 0.15210 \n",
" 306 20.45 92.00 636.9 0.11280 \n",
" 208 29.16 99.48 639.3 0.13490 \n",
" 242 27.96 87.16 472.9 0.13470 \n",
" 313 12.87 81.23 467.8 0.10920 \n",
" 542 32.29 107.40 826.4 0.10600 \n",
" 514 28.06 113.80 967.0 0.12460 \n",
" 236 34.51 206.00 2944.0 0.14810 \n",
" 113 22.75 72.62 374.4 0.13000 \n",
" 527 19.27 87.22 564.9 0.12920 \n",
" 76 12.49 91.36 605.5 0.14510 \n",
" 162 26.39 174.90 2232.0 0.14380 \n",
" \n",
" worst compactness worst concavity worst concave points worst symmetry \\\n",
" 512 0.38560 0.51060 0.20510 0.3585 \n",
" 457 0.10630 0.13900 0.06005 0.2444 \n",
" 439 0.10170 0.06260 0.08216 0.2136 \n",
" 298 0.21670 0.15650 0.07530 0.2636 \n",
" 37 0.04619 0.04833 0.05013 0.1987 \n",
" 515 0.15740 0.16240 0.08542 0.3060 \n",
" 382 0.32140 0.29120 0.10920 0.2191 \n",
" 310 0.10870 0.07915 0.05741 0.3487 \n",
" 538 0.08340 0.00000 0.00000 0.3058 \n",
" 345 0.16360 0.07162 0.04074 0.2434 \n",
" 421 0.36350 0.32190 0.11080 0.2827 \n",
" 90 0.17660 0.09189 0.06946 0.2522 \n",
" 412 0.18870 0.18680 0.02564 0.2376 \n",
" 157 0.17100 0.18820 0.08436 0.2527 \n",
" 89 0.30890 0.26040 0.13970 0.3151 \n",
" 172 0.35830 0.58300 0.18270 0.3216 \n",
" 318 0.37480 0.46090 0.11450 0.3135 \n",
" 233 0.27610 0.41460 0.15630 0.2437 \n",
" 389 0.24140 0.38290 0.18250 0.2576 \n",
" 250 0.31720 0.69910 0.21050 0.3126 \n",
" 31 0.57750 0.69560 0.15460 0.4761 \n",
" 283 0.47060 0.50260 0.17320 0.2770 \n",
" 482 0.24990 0.18480 0.13350 0.3227 \n",
" 211 0.18800 0.14710 0.06913 0.2535 \n",
" 372 0.28400 0.40240 0.19660 0.2730 \n",
" 401 0.15750 0.15140 0.06876 0.2460 \n",
" 159 0.08294 0.01854 0.03953 0.2738 \n",
" 14 0.77250 0.69430 0.22080 0.3596 \n",
" 364 0.16760 0.13640 0.06987 0.2741 \n",
" 337 0.48270 0.46340 0.20480 0.3679 \n",
" .. ... ... ... ... \n",
" 500 0.21760 0.18560 0.10180 0.2177 \n",
" 338 0.14020 0.10550 0.06499 0.2894 \n",
" 427 0.16960 0.19270 0.07485 0.2965 \n",
" 406 0.17220 0.23100 0.11290 0.2778 \n",
" 96 0.09358 0.04980 0.05882 0.2227 \n",
" 490 0.18040 0.12300 0.06335 0.3100 \n",
" 384 0.26850 0.28660 0.09173 0.2736 \n",
" 281 0.08500 0.06735 0.08290 0.3101 \n",
" 325 0.12120 0.10200 0.05602 0.2688 \n",
" 190 0.93270 0.84880 0.17720 0.5166 \n",
" 380 0.24290 0.22470 0.13180 0.3343 \n",
" 366 0.34160 0.37030 0.21520 0.3271 \n",
" 469 0.28780 0.31860 0.14160 0.2660 \n",
" 225 0.15250 0.16320 0.10870 0.3062 \n",
" 271 0.15070 0.12750 0.08750 0.2733 \n",
" 547 0.22460 0.17830 0.08333 0.2691 \n",
" 550 0.07348 0.00000 0.00000 0.2458 \n",
" 492 0.23270 0.25440 0.14890 0.3251 \n",
" 185 0.10190 0.00692 0.01042 0.2933 \n",
" 306 0.13460 0.01120 0.02500 0.2651 \n",
" 208 0.44020 0.31620 0.11260 0.4128 \n",
" 242 0.48480 0.74360 0.12180 0.3308 \n",
" 313 0.16260 0.08324 0.04715 0.3390 \n",
" 542 0.13760 0.16110 0.10950 0.2722 \n",
" 514 0.21010 0.28660 0.11200 0.2282 \n",
" 236 0.41260 0.58200 0.25930 0.3103 \n",
" 113 0.20490 0.12950 0.06136 0.2383 \n",
" 527 0.20740 0.17910 0.10700 0.3110 \n",
" 76 0.13790 0.08539 0.07407 0.2710 \n",
" 162 0.38460 0.68100 0.22470 0.3643 \n",
" \n",
" worst fractal dimension \n",
" 512 0.11090 \n",
" 457 0.06788 \n",
" 439 0.06710 \n",
" 298 0.07676 \n",
" 37 0.06169 \n",
" 515 0.06783 \n",
" 382 0.09349 \n",
" 310 0.06958 \n",
" 538 0.09938 \n",
" 345 0.08488 \n",
" 421 0.09208 \n",
" 90 0.07246 \n",
" 412 0.09206 \n",
" 157 0.05972 \n",
" 89 0.08473 \n",
" 172 0.10100 \n",
" 318 0.10550 \n",
" 233 0.08328 \n",
" 389 0.07602 \n",
" 250 0.07849 \n",
" 31 0.14020 \n",
" 283 0.10630 \n",
" 482 0.09326 \n",
" 211 0.07993 \n",
" 372 0.08666 \n",
" 401 0.07262 \n",
" 159 0.07685 \n",
" 14 0.14310 \n",
" 364 0.07582 \n",
" 337 0.09870 \n",
" .. ... \n",
" 500 0.08549 \n",
" 338 0.07664 \n",
" 427 0.07662 \n",
" 406 0.07012 \n",
" 96 0.07376 \n",
" 490 0.08203 \n",
" 384 0.07320 \n",
" 281 0.06688 \n",
" 325 0.06888 \n",
" 190 0.14460 \n",
" 380 0.09215 \n",
" 366 0.07632 \n",
" 469 0.09270 \n",
" 225 0.06072 \n",
" 271 0.08022 \n",
" 547 0.09479 \n",
" 550 0.06592 \n",
" 492 0.07625 \n",
" 185 0.07697 \n",
" 306 0.08385 \n",
" 208 0.10760 \n",
" 242 0.12970 \n",
" 313 0.07434 \n",
" 542 0.06956 \n",
" 514 0.06954 \n",
" 236 0.08677 \n",
" 113 0.09026 \n",
" 527 0.07592 \n",
" 76 0.07191 \n",
" 162 0.09223 \n",
" \n",
" [143 rows x 30 columns],\n",
" 293 1\n",
" 332 1\n",
" 565 0\n",
" 278 1\n",
" 489 0\n",
" 346 1\n",
" 357 1\n",
" 355 1\n",
" 112 1\n",
" 68 1\n",
" 526 1\n",
" 206 1\n",
" 65 0\n",
" 437 1\n",
" 126 0\n",
" 429 1\n",
" 392 0\n",
" 343 0\n",
" 334 1\n",
" 440 1\n",
" 441 0\n",
" 137 1\n",
" 230 0\n",
" 7 0\n",
" 408 0\n",
" 523 1\n",
" 361 1\n",
" 553 1\n",
" 478 1\n",
" 303 1\n",
" ..\n",
" 459 1\n",
" 510 1\n",
" 151 1\n",
" 244 0\n",
" 543 1\n",
" 544 1\n",
" 265 0\n",
" 288 1\n",
" 423 1\n",
" 147 1\n",
" 177 0\n",
" 99 0\n",
" 448 1\n",
" 431 1\n",
" 115 1\n",
" 72 0\n",
" 537 1\n",
" 174 1\n",
" 87 0\n",
" 551 1\n",
" 486 1\n",
" 314 1\n",
" 396 1\n",
" 472 1\n",
" 70 0\n",
" 277 0\n",
" 9 0\n",
" 359 1\n",
" 192 1\n",
" 559 1\n",
" Name: target, dtype: int32,\n",
" 512 0\n",
" 457 1\n",
" 439 1\n",
" 298 1\n",
" 37 1\n",
" 515 1\n",
" 382 1\n",
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" 538 1\n",
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" 421 1\n",
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" 157 1\n",
" 89 1\n",
" 172 0\n",
" 318 1\n",
" 233 0\n",
" 389 0\n",
" 250 0\n",
" 31 0\n",
" 283 0\n",
" 482 1\n",
" 211 1\n",
" 372 0\n",
" 401 1\n",
" 159 1\n",
" 14 0\n",
" 364 1\n",
" 337 0\n",
" ..\n",
" 500 1\n",
" 338 1\n",
" 427 1\n",
" 406 1\n",
" 96 1\n",
" 490 1\n",
" 384 1\n",
" 281 1\n",
" 325 1\n",
" 190 0\n",
" 380 1\n",
" 366 0\n",
" 469 1\n",
" 225 1\n",
" 271 1\n",
" 547 1\n",
" 550 1\n",
" 492 0\n",
" 185 1\n",
" 306 1\n",
" 208 1\n",
" 242 1\n",
" 313 1\n",
" 542 1\n",
" 514 0\n",
" 236 0\n",
" 113 1\n",
" 527 1\n",
" 76 1\n",
" 162 0\n",
" Name: target, dtype: int32)"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from sklearn.model_selection import train_test_split\n",
"\n",
"def answer_four():\n",
" X, y = answer_three()\n",
" \n",
" X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25 , random_state=0)\n",
" \n",
" return X_train, X_test, y_train, y_test\n",
"\n",
"answer_four()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Question 5\n",
"Using KNeighborsClassifier, fit a k-nearest neighbors (knn) classifier with `X_train`, `y_train` and using one nearest neighbor (`n_neighbors = 1`).\n",
"\n",
"*This function should return a * `sklearn.neighbors.classification.KNeighborsClassifier`."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from sklearn.neighbors import KNeighborsClassifier\n",
"\n",
"def answer_five():\n",
" X_train, X_test, y_train, y_test = answer_four()\n",
" \n",
" # Your code here\n",
" \n",
" return # Return your answer"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Question 6\n",
"Using your knn classifier, predict the class label using the mean value for each feature.\n",
"\n",
"Hint: You can use `cancerdf.mean()[:-1].values.reshape(1, -1)` which gets the mean value for each feature, ignores the target column, and reshapes the data from 1 dimension to 2 (necessary for the precict method of KNeighborsClassifier).\n",
"\n",
"*This function should return a numpy array either `array([ 0.])` or `array([ 1.])`*"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def answer_six():\n",
" cancerdf = answer_one()\n",
" means = cancerdf.mean()[:-1].values.reshape(1, -1)\n",
" \n",
" # Your code here\n",
" \n",
" return # Return your answer"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Question 7\n",
"Using your knn classifier, predict the class labels for the test set `X_test`.\n",
"\n",
"*This function should return a numpy array with shape `(143,)` and values either `0.0` or `1.0`.*"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def answer_seven():\n",
" X_train, X_test, y_train, y_test = answer_four()\n",
" knn = answer_five()\n",
" \n",
" # Your code here\n",
" \n",
" return # Return your answer"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Question 8\n",
"Find the score (mean accuracy) of your knn classifier using `X_test` and `y_test`.\n",
"\n",
"*This function should return a float between 0 and 1*"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def answer_eight():\n",
" X_train, X_test, y_train, y_test = answer_four()\n",
" knn = answer_five()\n",
" \n",
" # Your code here\n",
" \n",
" return # Return your answer"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Optional plot\n",
"\n",
"Try using the plotting function below to visualize the differet predicition scores between training and test sets, as well as malignant and benign cells."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def accuracy_plot():\n",
" import matplotlib.pyplot as plt\n",
"\n",
" %matplotlib notebook\n",
"\n",
" X_train, X_test, y_train, y_test = answer_four()\n",
"\n",
" # Find the training and testing accuracies by target value (i.e. malignant, benign)\n",
" mal_train_X = X_train[y_train==0]\n",
" mal_train_y = y_train[y_train==0]\n",
" ben_train_X = X_train[y_train==1]\n",
" ben_train_y = y_train[y_train==1]\n",
"\n",
" mal_test_X = X_test[y_test==0]\n",
" mal_test_y = y_test[y_test==0]\n",
" ben_test_X = X_test[y_test==1]\n",
" ben_test_y = y_test[y_test==1]\n",
"\n",
" knn = answer_five()\n",
"\n",
" scores = [knn.score(mal_train_X, mal_train_y), knn.score(ben_train_X, ben_train_y), \n",
" knn.score(mal_test_X, mal_test_y), knn.score(ben_test_X, ben_test_y)]\n",
"\n",
"\n",
" plt.figure()\n",
"\n",
" # Plot the scores as a bar chart\n",
" bars = plt.bar(np.arange(4), scores, color=['#4c72b0','#4c72b0','#55a868','#55a868'])\n",
"\n",
" # directly label the score onto the bars\n",
" for bar in bars:\n",
" height = bar.get_height()\n",
" plt.gca().text(bar.get_x() + bar.get_width()/2, height*.90, '{0:.{1}f}'.format(height, 2), \n",
" ha='center', color='w', fontsize=11)\n",
"\n",
" # remove all the ticks (both axes), and tick labels on the Y axis\n",
" plt.tick_params(top='off', bottom='off', left='off', right='off', labelleft='off', labelbottom='on')\n",
"\n",
" # remove the frame of the chart\n",
" for spine in plt.gca().spines.values():\n",
" spine.set_visible(False)\n",
"\n",
" plt.xticks([0,1,2,3], ['Malignant\\nTraining', 'Benign\\nTraining', 'Malignant\\nTest', 'Benign\\nTest'], alpha=0.8);\n",
" plt.title('Training and Test Accuracies for Malignant and Benign Cells', alpha=0.8)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Uncomment the plotting function to see the visualization, \n",
"# Comment out the plotting function when submitting your notebook for grading\n",
"\n",
"#accuracy_plot() "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"anaconda-cloud": {},
"coursera": {
"course_slug": "python-machine-learning",
"graded_item_id": "f9SY5",
"launcher_item_id": "oxndk",
"part_id": "mh1Vo"
},
"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
}
| gpl-3.0 |
saketkc/motif-logos-matplotlib | Sequence logo using matplotlib.ipynb | 1 | 112760 | {
"cells": [
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import seaborn\n",
"import matplotlib.pyplot as plt\n",
"plt.style.use('seaborn-ticks')\n",
"from matplotlib import transforms\n",
"import matplotlib.patheffects\n",
"import numpy as np\n",
"\n",
"COLOR_SCHEME = {'G': 'orange', \n",
" 'A': 'red', \n",
" 'C': 'blue', \n",
" 'T': 'darkgreen'}\n",
"BASES = list(COLOR_SCHEME.keys())\n",
"\n",
"ALL_SCORES1 = [[('C', 0.02247014831444764),\n",
" ('T', 0.057903843733384308),\n",
" ('A', 0.10370837683591219),\n",
" ('G', 0.24803586793255664)],\n",
" [('T', 0.046608227674354567),\n",
" ('G', 0.048827667087419063),\n",
" ('A', 0.084338697696451109),\n",
" ('C', 0.92994511407402669)],\n",
" [('G', 0.0),\n",
" ('T', 0.011098351287382456),\n",
" ('A', 0.022196702574764911),\n",
" ('C', 1.8164301607015951)],\n",
" [('C', 0.020803153636453006),\n",
" ('T', 0.078011826136698756),\n",
" ('G', 0.11268374886412044),\n",
" ('A', 0.65529933954826969)],\n",
" [('T', 0.017393530660176126),\n",
" ('A', 0.030438678655308221),\n",
" ('G', 0.22611589858228964),\n",
" ('C', 0.45078233627623127)],\n",
" [('G', 0.022364103549245576),\n",
" ('A', 0.043412671595594352),\n",
" ('T', 0.097349627214363091),\n",
" ('C', 0.1657574733649966)],\n",
" [('C', 0.03264675899941203),\n",
" ('T', 0.045203204768416654),\n",
" ('G', 0.082872542075430544),\n",
" ('A', 1.0949220710572034)],\n",
" [('C', 0.0),\n",
" ('T', 0.0076232429756614498),\n",
" ('A', 0.011434864463492175),\n",
" ('G', 1.8867526364762088)],\n",
" [('C', 0.0018955903000026028),\n",
" ('T', 0.0094779515000130137),\n",
" ('A', 0.35637097640048931),\n",
" ('G', 0.58005063180079641)],\n",
" [('A', 0.01594690817903021),\n",
" ('C', 0.017541598996933229),\n",
" ('T', 0.2774762023151256),\n",
" ('G', 0.48638069946042134)],\n",
" [('A', 0.003770051401807444),\n",
" ('C', 0.0075401028036148881),\n",
" ('T', 0.011310154205422331),\n",
" ('G', 1.8624053924928772)],\n",
" [('C', 0.036479877757360731),\n",
" ('A', 0.041691288865555121),\n",
" ('T', 0.072959755514721461),\n",
" ('G', 1.1517218549109602)],\n",
" [('G', 0.011831087684038642),\n",
" ('T', 0.068620308567424126),\n",
" ('A', 0.10174735408273231),\n",
" ('C', 1.0009100180696691)],\n",
" [('C', 0.015871770937774379),\n",
" ('T', 0.018757547471915176),\n",
" ('A', 0.32176408355669878),\n",
" ('G', 0.36505073156881074)],\n",
" [('A', 0.022798100897300954),\n",
" ('T', 0.024064662058262118),\n",
" ('G', 0.24571286522646588),\n",
" ('C', 0.34070495229855319)]]\n",
"\n",
"ALL_SCORES2 = [[('A', 0.01653482213365913),\n",
" ('G', 0.026710097292833978),\n",
" ('C', 0.035613463057111966),\n",
" ('T', 0.057235922770358522)],\n",
" [('C', 0.020055669245080433),\n",
" ('G', 0.023816107228533015),\n",
" ('A', 0.031336983195438178),\n",
" ('T', 0.058913528407423782)],\n",
" [('T', 0.018666958185377256),\n",
" ('G', 0.084001311834197651),\n",
" ('A', 0.093334790926886277),\n",
" ('C', 0.30333807051238043)],\n",
" [('C', 0.0),\n",
" ('G', 0.0),\n",
" ('A', 0.32027512306044359),\n",
" ('T', 0.82203948252180525)],\n",
" [('C', 0.012698627658037786),\n",
" ('A', 0.053334236163758708),\n",
" ('T', 0.096509570201087178),\n",
" ('G', 0.10920819785912497)],\n",
" [('C', 0.0),\n",
" ('G', 0.089472611853783468),\n",
" ('A', 0.1930724782107959),\n",
" ('T', 0.22132698721725386)],\n",
" [('C', 0.020962390607965918),\n",
" ('A', 0.026202988259957396),\n",
" ('G', 0.066380903591892068),\n",
" ('T', 0.07336836712788071)],\n",
" [('G', 0.0),\n",
" ('A', 0.10236420974570831),\n",
" ('C', 0.15354631461856247),\n",
" ('T', 0.29173799777526871)],\n",
" [('G', 0.027681850851852024),\n",
" ('C', 0.089966015268519078),\n",
" ('A', 0.089966015268519078),\n",
" ('T', 0.53287562889815143)],\n",
" [('A', 0.034165612000664765),\n",
" ('C', 0.06833122400132953),\n",
" ('G', 0.072601925501412631),\n",
" ('T', 0.28186629900548432)],\n",
" [('G', 0.0),\n",
" ('A', 0.037325935579058833),\n",
" ('C', 0.23328709736911771),\n",
" ('T', 0.72785574379164719)],\n",
" [('A', 0.017470244196759552),\n",
" ('C', 0.062892879108334396),\n",
" ('G', 0.094339318662501587),\n",
" ('T', 0.19916078384305891)],\n",
" [('G', 0.0),\n",
" ('A', 0.096447131567581681),\n",
" ('C', 0.15844885900388422),\n",
" ('T', 0.48223565783790845)],\n",
" [('G', 0.0),\n",
" ('A', 0.069291952024925829),\n",
" ('C', 0.20787585607477749),\n",
" ('T', 0.46425607856700307)],\n",
" [('G', 0.0),\n",
" ('A', 0.0),\n",
" ('C', 0.21713201856318373),\n",
" ('T', 1.1495224512168551)],\n",
" [('G', 0.0),\n",
" ('A', 0.048934292002649343),\n",
" ('T', 0.27263391258618919),\n",
" ('C', 0.42642740173737281)],\n",
" [('A', 0.0),\n",
" ('G', 0.053607190685875404),\n",
" ('C', 0.2054942309625224),\n",
" ('T', 0.69689347891638032)],\n",
" [('G', 0.0),\n",
" ('A', 0.0),\n",
" ('C', 0.31312908494534769),\n",
" ('T', 0.84220926295645249)],\n",
" [('G', 0.0),\n",
" ('C', 0.068079835765814778),\n",
" ('A', 0.068079835765814778),\n",
" ('T', 1.3207488138568066)],\n",
" [('G', 0.020257705570431345),\n",
" ('A', 0.020257705570431345),\n",
" ('C', 0.048618493369035232),\n",
" ('T', 0.055371061892512348)],\n",
" [('G', 0.0),\n",
" ('A', 0.076286510680262556),\n",
" ('C', 0.20538675952378382),\n",
" ('T', 0.34622339462580698)]]"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"class Scale(matplotlib.patheffects.RendererBase):\n",
" def __init__(self, sx, sy=None):\n",
" self._sx = sx\n",
" self._sy = sy\n",
"\n",
" def draw_path(self, renderer, gc, tpath, affine, rgbFace):\n",
" affine = affine.identity().scale(self._sx, self._sy)+affine\n",
" renderer.draw_path(gc, tpath, affine, rgbFace)\n",
"\n",
"def draw_logo(all_scores):\n",
" fig = plt.figure()\n",
" fig.set_size_inches(len(all_scores),2.5)\n",
" ax = fig.add_subplot(111)\n",
" ax.set_xticks(range(len(all_scores)))\n",
"\n",
" xshift = 0\n",
" trans_offset = transforms.offset_copy(ax.transAxes, \n",
" fig=fig, \n",
" x=0, \n",
" y=0, \n",
" units='points')\n",
"\n",
" \n",
" for scores in all_scores:\n",
" yshift = 0\n",
" for base, score in scores:\n",
" txt = ax.text(0, \n",
" 0, \n",
" base, \n",
" transform=trans_offset,\n",
" fontsize=80, \n",
" color=COLOR_SCHEME[base],\n",
" weight='bold',\n",
" ha='center',\n",
" family='sans-serif'\n",
" )\n",
" txt.set_clip_on(False) \n",
" txt.set_path_effects([Scale(1.0, score)])\n",
" fig.canvas.draw()\n",
" window_ext = txt.get_window_extent(txt._renderer)\n",
" yshift = window_ext.height*score\n",
" trans_offset = transforms.offset_copy(txt._transform, fig=fig, y=yshift, units='points')\n",
" xshift += window_ext.width\n",
" trans_offset = transforms.offset_copy(ax.transAxes, fig=fig, x=xshift, units='points')\n",
"\n",
"\n",
" ax.set_yticks(range(0,3))\n",
"\n",
"\n",
" seaborn.despine(ax=ax, offset=30, trim=True)\n",
" ax.set_xticklabels(range(1,len(all_scores)+1), rotation=90)\n",
" ax.set_yticklabels(np.arange(0,3,1))\n",
" plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Okayish logo\n",
"The ticks are off by a bit.\n"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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mm9xPXA63vDw47DAzl+300/3ORsRfmmIiTv30E/ztb9CxIwwfDm+8EZ4CYk2+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\nrkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f0f568db2b0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"draw_logo(ALL_SCORES1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Poorly aligned ticks\n"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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GvZZtarJzfl8F7ffaS/rJT2wKhVyMn+T7FXyOY31zv/ud9Ic/SCtX+tveuHFW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\no+wQ4JY2Se3oKypKXI8kX5u46+5AnuTW0WGdDW7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VxJNczOQCAAAA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\nAAAAAAAAAIg5JLkAAAAAAAAAAAAQc/4fKWv5s3ClJjIAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f0f567c8a58>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"draw_logo(ALL_SCORES2)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## How they should ideally look like:\n",
"Plotted using [meme-suite](http://meme-suite.org/)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
""
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python [conda env:clipseq]",
"language": "python",
"name": "conda-env-clipseq-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
}
| lgpl-2.1 |
OpenBookProjects/ipynb | R2Py/xccds4zoomq.ipynb | 1 | 8665 | {
"cells": [
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# 假设产生一个行为序列\n",
"import numpy as np\n",
"x = np.random.choice([0,1],size=20)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[1 1 0 1 0 1 0 0 1 1 1 1 1 1 1 0 0 0 0]\n",
"[1 0 1 0 1 0 0 1 1 1 1 1 1 1 0 0 0 0 0]\n",
"[1 1 0 1 0 1 0 0 1 1 1 1 1 1 1 0 0 0 0 0] <type 'numpy.ndarray'>\n"
]
}
],
"source": [
"# 如果我们想根据序列中前一个值来预测后一个值， 当然在更复杂的版本里可以用前2个值来预测后一个值\n",
"x_t = x[:-1]\n",
"x_t1 = x[1:]\n",
"print x_t\n",
"print x_t1\n",
"print x, type(x)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# 写成一个预测函数，预测当前一个取xt时，后一个取1的概率\n",
"from __future__ import division\n",
"def predict_func(x, xt):\n",
" x_t = x[:-1]\n",
" x_t1 = x[1:]\n",
" # p_11：表示前一个取1时，后一个取1的概率\n",
" # p_01：表示前一个取0时，后一个取1的概率\n",
" p_11 = np.sum((x_t==1) & (x_t1==1))/np.sum(x_t==1) \n",
" p_01 = np.sum((x_t==0) & (x_t1==1))/np.sum(x_t==0) \n",
" res = [p_01,p_11]\n",
" return res[xt]"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0.090909090909090912"
]
},
"execution_count": 46,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x= np.array([1,0,0,0,0,0,0,0,0,0,0,1,0,0])\n",
"predict_func(x,0)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0.375"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"predict_func(x,0) # 前一个值为0的条件下，后一个值为1的概率"
]
},
{
"cell_type": "code",
"execution_count": 73,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# 有时候单个样本的数量比较少，此时可以加上全局的概率做加权修正\n",
"def predict_func2(x, xt, prior):\n",
" x_t = x[:-1]\n",
" x_t1 = x[1:]\n",
" n = len(x)\n",
" # p_11：表示前一个取1时，后一个取1的概率\n",
" # p_01：表示前一个取0时，后一个取1的概率\n",
" p_11 = np.sum((x_t==1) & (x_t1==1))/np.sum(x_t==1) \n",
" p_01 = np.sum((x_t==0) & (x_t1==1))/np.sum(x_t==0) \n",
" p_11 = n/(n+1) *p_11 + 1/(n+1) * prior[1]\n",
" p_01 = n/(n+1) *p_01 + 1/(n+1) * prior[0]\n",
" res = [p_01,p_11]\n",
" return res[xt]"
]
},
{
"cell_type": "code",
"execution_count": 74,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0.88333333333333341"
]
},
"execution_count": 74,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# 如果根据所有人的数据，算出p11=0.6, p01= 0.3\n",
"prior = [0.3, 0.6]\n",
"predict_func2(x,0,prior)"
]
},
{
"cell_type": "code",
"execution_count": 79,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[1 0 0 1 1]\n"
]
},
{
"data": {
"text/plain": [
"0.46666666666666667"
]
},
"execution_count": 79,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x = np.random.choice([0,1],size=5)\n",
"print x\n",
"predict_func2(x,0,prior)"
]
},
{
"cell_type": "code",
"execution_count": 66,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[1 0 1 1 0 0 0 1 0 0 0 0 1 0 1 1 0 0 1 1 0 1 0 1 0 0 0 1 0 0]\n"
]
}
],
"source": [
"# 更复杂的版本，根据前两个值来预测后一个值\n",
"x = np.random.choice([0,1],size=30)\n",
"print x"
]
},
{
"cell_type": "code",
"execution_count": 67,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[(1, 0), (0, 1), (1, 1), (1, 0), (0, 0), (0, 0), (0, 1), (1, 0), (0, 0), (0, 0), (0, 0), (0, 1), (1, 0), (0, 1), (1, 1), (1, 0), (0, 0), (0, 1), (1, 1), (1, 0), (0, 1), (1, 0), (0, 1), (1, 0), (0, 0), (0, 0), (0, 1), (1, 0)]\n",
"[1 1 0 0 0 1 0 0 0 0 1 0 1 1 0 0 1 1 0 1 0 1 0 0 0 1 0 0]\n"
]
}
],
"source": [
"x_t = [tuple(x[i:i+2]) for i in range(len(x)-2)]\n",
"x_t1 = x[2:]\n",
"print x_t\n",
"print x_t1"
]
},
{
"cell_type": "code",
"execution_count": 71,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def predict_func_dpre(x, xt):\n",
" x_t = [tuple(x[i:i+2]) for i in range(len(x)-2)]\n",
" x_t1 = x[2:]\n",
" xt_11 = np.sum(np.array(x_t) == (1,1),axis=1) == 2\n",
" p_111 = np.sum(xt_11 & (x_t1==1))/np.sum(xt_11) \n",
" xt_10 = np.sum(np.array(x_t) == (1,0),axis=1) == 2\n",
" p_101 = np.sum(xt_10 & (x_t1==1))/np.sum(xt_10) \n",
" xt_01 = np.sum(np.array(x_t) == (0,1),axis=1) == 2\n",
" p_011 = np.sum(xt_01 & (x_t1==1))/np.sum(xt_01) \n",
" xt_00 = np.sum(np.array(x_t) == (0,0),axis=1) == 2\n",
" print xt_00\n",
" p_001 = np.sum(xt_00 & (x_t1==1))/np.sum(xt_00) \n",
" res = np.array([p_001,p_011,p_101, p_111]).reshape(2,2)\n",
" return res[xt]"
]
},
{
"cell_type": "code",
"execution_count": 72,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[False True True True True True True True True True True True\n",
" True True True]\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/opt/data/.pyenv/versions/279iPy/lib/python2.7/site-packages/IPython/kernel/__main__.py:5: RuntimeWarning: invalid value encountered in long_scalars\n",
"/opt/data/.pyenv/versions/279iPy/lib/python2.7/site-packages/IPython/kernel/__main__.py:9: RuntimeWarning: invalid value encountered in long_scalars\n"
]
},
{
"data": {
"text/plain": [
"nan"
]
},
"execution_count": 72,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"predict_func_dpre(x,(0,1)) # 当前两个值是(0,1)条件时，后面取值为1的概率"
]
},
{
"cell_type": "code",
"execution_count": 70,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/opt/data/.pyenv/versions/279iPy/lib/python2.7/site-packages/IPython/kernel/__main__.py:5: RuntimeWarning: invalid value encountered in long_scalars\n",
"/opt/data/.pyenv/versions/279iPy/lib/python2.7/site-packages/IPython/kernel/__main__.py:9: RuntimeWarning: invalid value encountered in long_scalars\n"
]
},
{
"data": {
"text/plain": [
"nan"
]
},
"execution_count": 70,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x= np.array([1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0])\n",
"predict_func_dpre(x,(0,1))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# 上述方法是讲顺序的，也可以不讲顺序，即认为 (0,1) = (1,0)"
]
}
],
"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 |
jbwhit/jupyter-best-practices | notebooks/07-Some_basics.ipynb | 2 | 13449 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from __future__ import absolute_import, division, print_function"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Github"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"https://github.com/jbwhit/OSCON-2015/commit/6750b962606db27f69162b802b5de4f84ac916d5"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## A few Python Basics"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Create a [list] \n",
"days = ['Monday', # multiple lines \n",
" 'Tuesday', # acceptable \n",
" 'Wednesday',\n",
" 'Thursday',\n",
" 'Friday',\n",
" 'Saturday',\n",
" 'Sunday', # trailing comma is fine!\n",
" ] "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"days"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Simple for-loop\n",
"for day in days:\n",
" print(day)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Double for-loop\n",
"for day in days:\n",
" for letter in day:\n",
" print(letter)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"print(days)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"print(*days)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Double for-loop\n",
"for day in days:\n",
" for letter in day:\n",
" print(letter)\n",
" print()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"for day in days:\n",
" for letter in day:\n",
" print(letter.lower())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## List Comprehensions"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"length_of_days = [len(day) for day in days]\n",
"length_of_days"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"letters = [letter for day in days\n",
" for letter in day]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"print(letters)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"letters = [letter for day in days for letter in day]\n",
"print(letters)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"[num for num in xrange(10) if num % 2]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"[num for num in xrange(10) if num % 2 else \"doesn't work\"]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"[num if num % 2 else \"works\" for num in xrange(10)]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"[num for num in xrange(10)]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"sorted_letters = sorted([x.lower() for x in letters])\n",
"print(sorted_letters)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"unique_sorted_letters = sorted(set(sorted_letters))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"print(\"There are\", len(unique_sorted_letters), \"unique letters in the days of the week.\")\n",
"print(\"They are:\", ''.join(unique_sorted_letters))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"print(\"They are:\", '; '.join(unique_sorted_letters))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def first_three(input_string):\n",
" \"\"\"Takes an input string and returns the first 3 characters.\"\"\"\n",
" return input_string[:3] "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# tab\n",
"np.linspace()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"[first_three(day) for day in days]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def last_N(input_string, number=2):\n",
" \"\"\"Takes an input string and returns the last N characters.\"\"\"\n",
" return input_string[-number:] "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"[last_N(day, 4) for day in days if len(day) > 6]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from math import pi\n",
"\n",
"print([str(round(pi, i)) for i in xrange(2, 9)])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"list_of_lists = [[i, round(pi, i)] for i in xrange(2, 9)]\n",
"print(list_of_lists)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"for sublist in list_of_lists:\n",
" print(sublist)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Let this be a warning to you!\n",
"\n",
"# If you see python code like the following in your work:\n",
"\n",
"for x in range(len(list_of_lists)):\n",
" print(\"Decimals:\", list_of_lists[x][0], \"expression:\", list_of_lists[x][1])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"print(list_of_lists)\n",
"\n",
"# Change it to look more like this: \n",
"\n",
"for decimal, rounded_pi in list_of_lists:\n",
" print(\"Decimals:\", decimal, \"expression:\", rounded_pi)\n",
" \n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# enumerate if you really need the index\n",
"\n",
"for index, day in enumerate(days):\n",
" print(index, day)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Dictionaries\n",
"\n",
"Python dictionaries are awesome. They are [hash tables](https://en.wikipedia.org/wiki/Hash_table) and have a lot of neat CS properties. Learn and use them well."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from IPython.display import IFrame, HTML\n",
"HTML('<iframe src=https://en.wikipedia.org/wiki/Hash_table width=100% height=550></iframe>')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"fellows = [\"Jonathan\", \"Alice\", \"Bob\"]\n",
"universities = [\"UCSD\", \"UCSD\", \"Vanderbilt\"]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"for x, y in zip(fellows, universities):\n",
" print(x, y)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Don't do this\n",
"{x: y for x, y in zip(fellows, universities)}"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Doesn't work like you might expect\n",
"{zip(fellows, universities)}"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"dict(zip(fellows, universities))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"fellows"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"fellow_dict = {fellow.lower(): university \n",
" for fellow, university in zip(fellows, universities)}"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"fellow_dict"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"fellow_dict['bob']"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"rounded_pi = {i:round(pi, i) for i in xrange(2, 9)}"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"rounded_pi[5]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"sum([i ** 2 for i in range(10)])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"sum(i ** 2 for i in range(10))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"huh = (i ** 2 for i in range(10))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"huh.next()"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"## Participate in StackOverflow\n",
"\n",
"An example: http://stackoverflow.com/questions/6605006/convert-pdf-to-image-with-high-resolution"
]
}
],
"metadata": {
"anaconda-cloud": {},
"hide_input": false,
"kernelspec": {
"display_name": "dspy3",
"language": "python",
"name": "dspy3"
},
"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"
},
"toc": {
"nav_menu": {
"height": "102px",
"width": "252px"
},
"number_sections": true,
"sideBar": true,
"skip_h1_title": false,
"title_cell": "Table of Contents",
"title_sidebar": "Contents",
"toc_cell": false,
"toc_position": {},
"toc_section_display": "block",
"toc_window_display": false
},
"varInspector": {
"cols": {
"lenName": 16,
"lenType": 16,
"lenVar": 40
},
"kernels_config": {
"python": {
"delete_cmd_postfix": "",
"delete_cmd_prefix": "del ",
"library": "var_list.py",
"varRefreshCmd": "print(var_dic_list())"
},
"r": {
"delete_cmd_postfix": ") ",
"delete_cmd_prefix": "rm(",
"library": "var_list.r",
"varRefreshCmd": "cat(var_dic_list()) "
}
},
"types_to_exclude": [
"module",
"function",
"builtin_function_or_method",
"instance",
"_Feature"
],
"window_display": false
}
},
"nbformat": 4,
"nbformat_minor": 1
}
| mit |
uber/pyro | tutorial/source/csis.ipynb | 1 | 150772 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Compiled Sequential Importance Sampling\n",
"\n",
"Compiled sequential importance sampling [1], or inference compilation, is a technique to amortize the computational cost of inference by learning a proposal distribution for importance sampling.\n",
"\n",
"The proposal distribution is learned to minimise the KL divergence between the model and the guide, $\\rm{KL}\\!\\left( p({\\bf z} | {\\bf x}) \\lVert q_{\\phi, x}({\\bf z}) \\right)$. This differs from variational inference, which would minimise $\\rm{KL}\\!\\left( q_{\\phi, x}({\\bf z}) \\lVert p({\\bf z} | {\\bf x}) \\right)$. Using this loss encourages the approximate proposal distribution to be broader than the true posterior (mass covering), whereas variational inference typically learns a narrower approximation (mode seeking). Guides for importance sampling are usually desired to have heavier tails than the model (see this [stackexchange question](https://stats.stackexchange.com/questions/76798/in-importance-sampling-why-should-the-importance-density-have-heavier-tails)). Therefore, the inference compilation loss is usually more suited to compiling a guide for importance sampling.\n",
"\n",
"Another benefit of CSIS is that, unlike many types of variational inference, it has no requirement that the model is differentiable. This allows it to be used for inference on arbitrarily complex programs (e.g. a Captcha renderer [1]).\n",
"\n",
"This example shows CSIS being used to speed up inference on a simple problem with a known analytic solution."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import torch\n",
"import torch.nn as nn\n",
"import torch.functional as F\n",
"\n",
"import pyro\n",
"import pyro.distributions as dist\n",
"import pyro.infer\n",
"import pyro.optim\n",
"\n",
"import os\n",
"smoke_test = ('CI' in os.environ)\n",
"n_steps = 2 if smoke_test else 2000"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Specify the model:\n",
"\n",
"The model is specified in the same way as any Pyro model, except that a keyword argument, `observations`, must be used to input a dictionary with each observation as a key. Since inference compilation involves learning to perform inference for any observed values, it is not important what the values in the dictionary are. `0` is used here."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"def model(prior_mean, observations={\"x1\": 0, \"x2\": 0}):\n",
" x = pyro.sample(\"z\", dist.Normal(prior_mean, torch.tensor(5**0.5)))\n",
" y1 = pyro.sample(\"x1\", dist.Normal(x, torch.tensor(2**0.5)), obs=observations[\"x1\"])\n",
" y2 = pyro.sample(\"x2\", dist.Normal(x, torch.tensor(2**0.5)), obs=observations[\"x2\"])\n",
" return x"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### And the guide:\n",
"\n",
"The guide will be trained (a.k.a. compiled) to use the observed values to make proposal distributions for each unconditioned `sample` statement. In the paper [1], a neural network architecture is automatically generated for any model. However, for the implementation in Pyro the user must specify a task-specific guide program structure. As with any Pyro guide function, this should have the same call signature as the model. It must also encounter the same unobserved `sample` statements as the model. So that the guide program can be trained to make good proposal distributions, the distributions at `sample` statements should depend on the values in `observations`. In this example, a feed-forward neural network is used to map the observations to a proposal distribution for the latent variable.\n",
"\n",
"`pyro.module` is called when the guide function is run so that the guide parameters can be found by the optimiser during training."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"class Guide(nn.Module):\n",
" def __init__(self):\n",
" super().__init__()\n",
" self.neural_net = nn.Sequential(\n",
" nn.Linear(2, 10),\n",
" nn.ReLU(),\n",
" nn.Linear(10, 20),\n",
" nn.ReLU(),\n",
" nn.Linear(20, 10),\n",
" nn.ReLU(),\n",
" nn.Linear(10, 5),\n",
" nn.ReLU(),\n",
" nn.Linear(5, 2))\n",
"\n",
" def forward(self, prior_mean, observations={\"x1\": 0, \"x2\": 0}):\n",
" pyro.module(\"guide\", self)\n",
" x1 = observations[\"x1\"]\n",
" x2 = observations[\"x2\"]\n",
" v = torch.cat((x1.view(1, 1), x2.view(1, 1)), 1)\n",
" v = self.neural_net(v)\n",
" mean = v[0, 0]\n",
" std = v[0, 1].exp()\n",
" pyro.sample(\"z\", dist.Normal(mean, std))\n",
"\n",
"guide = Guide()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Now create a `CSIS` instance:\n",
"The object is initialised with the model; the guide; a PyTorch optimiser for training the guide; and the number of importance-weighted samples to draw when performing inference. The guide will be optimised for a particular value of the model/guide argument, `prior_mean`, so we use the value set here throughout training and inference."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"optimiser = pyro.optim.Adam({'lr': 1e-3})\n",
"csis = pyro.infer.CSIS(model, guide, optimiser, num_inference_samples=50)\n",
"prior_mean = torch.tensor(1.)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Now we 'compile' the instance to perform inference on this model:\n",
"The arguments given to `csis.step` are passed to the model and guide when they are run to evaluate the loss."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"for step in range(n_steps):\n",
" csis.step(prior_mean)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### And now perform inference by importance sampling:\n",
"\n",
"The compiled guide program should now be able to propose a distribution for `z` that approximates the posterior, $p(z | x_1, x_2)$, for any $x_1, x_2$. The same `prior_mean` is entered again, as well as the observed values inside `observations`."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"posterior = csis.run(prior_mean,\n",
" observations={\"x1\": torch.tensor(8.),\n",
" \"x2\": torch.tensor(9.)})\n",
"marginal = pyro.infer.EmpiricalMarginal(posterior, \"z\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### We now plot the results and compare with importance sampling:\n",
"\n",
"We observe $x_1 = 8$ and $x_2 = 9$. Inference is performed by taking 50 samples using CSIS, and 50 using importance sampling from the prior. We then plot the resulting approximations to the posterior distributions, along with the analytic posterior."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"scrolled": false
},
"outputs": [
{
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\n",
"text/plain": [
"<Figure size 3000x1500 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import numpy as np\n",
"import scipy.stats\n",
"import matplotlib.pyplot as plt\n",
"\n",
"with torch.no_grad():\n",
" # Draw samples from empirical marginal for plotting\n",
" csis_samples = torch.stack([marginal() for _ in range(1000)])\n",
"\n",
" # Calculate empirical marginal with importance sampling\n",
" is_posterior = pyro.infer.Importance(model, num_samples=50).run(\n",
" prior_mean, observations={\"x1\": torch.tensor(8.),\n",
" \"x2\": torch.tensor(9.)})\n",
" is_marginal = pyro.infer.EmpiricalMarginal(is_posterior, \"z\")\n",
" is_samples = torch.stack([is_marginal() for _ in range(1000)])\n",
"\n",
"# Calculate true prior and posterior over z\n",
"true_posterior_z = torch.arange(-10, 10, 0.05)\n",
"true_posterior_p = dist.Normal(7.25, (5/6)**0.5).log_prob(true_posterior_z).exp()\n",
"prior_z = true_posterior_z\n",
"prior_p = dist.Normal(1., 5**0.5).log_prob(true_posterior_z).exp()\n",
"\n",
"plt.rcParams['figure.figsize'] = [30, 15]\n",
"plt.rcParams.update({'font.size': 30})\n",
"fig, ax = plt.subplots()\n",
"plt.plot(prior_z, prior_p, 'k--', label='Prior')\n",
"plt.plot(true_posterior_z, true_posterior_p, color='k', label='Analytic Posterior')\n",
"plt.hist(csis_samples.numpy(), range=(-10, 10), bins=100, color='r', density=1,\n",
" label=\"Inference Compilation\")\n",
"plt.hist(is_samples.numpy(), range=(-10, 10), bins=100, color='b', density=1,\n",
" label=\"Importance Sampling\")\n",
"plt.xlim(-8, 10)\n",
"plt.ylim(0, 5)\n",
"plt.xlabel(\"z\")\n",
"plt.ylabel(\"Estimated Posterior Probability Density\")\n",
"plt.legend()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Using $x_1 = 8$ and $x_2 = 9$ gives a posterior far from the prior, and so using the prior as a guide for importance sampling is inefficient, giving a very small effective sample size. By first learning a suitable guide function, CSIS has a proposal distribution much more closely matched to the true posterior. This allows samples to be drawn with far better coverage of the true posterior, and greater effective sample size, as shown in the graph above.\n",
"\n",
"For other examples of inference compilation, see [1] or <https://github.com/probprog/anglican-infcomp-examples>.\n",
"\n",
"## References\n",
"\n",
"[1] `Inference compilation and universal probabilistic programming`,<br /> \n",
"Tuan Anh Le, Atilim Gunes Baydin, and Frank Wood"
]
}
],
"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
}
| apache-2.0 |
lilleswing/deepchem | examples/tutorials/25_Uncertainty_In_Deep_Learning.ipynb | 1 | 74170 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "Gn1RVu2xkMdA"
},
"source": [
"# Tutorial Part 25: Uncertainty in Deep Learning\n",
"\n",
"A common criticism of deep learning models is that they tend to act as black boxes. A model produces outputs, but doesn't given enough context to interpret them properly. How reliable are the model's predictions? Are some predictions more reliable than others? If a model predicts a value of 5.372 for some quantity, should you assume the true value is between 5.371 and 5.373? Or that it's between 2 and 8? In some fields this situation might be good enough, but not in science. For every value predicted by a model, we also want an estimate of the uncertainty in that value so we can know what conclusions to draw based on it.\n",
"\n",
"DeepChem makes it very easy to estimate the uncertainty of predicted outputs (at least for the models that support it—not all of them do). Let's start by seeing an example of how to generate uncertainty estimates. We load a dataset, create a model, train it on the training set, predict the output on the test set, and then derive some uncertainty estimates.\n",
"\n",
"## Colab\n",
"\n",
"This tutorial and the rest in this sequence are designed to be done in Google colab. If you'd like to open this notebook in colab, you can use the following link.\n",
"\n",
"[](https://colab.research.google.com/github/deepchem/deepchem/blob/master/examples/tutorials/25_Uncertainty_In_Deep_Learning.ipynb)\n",
"\n",
"## Setup\n",
"\n",
"To run DeepChem within Colab, you'll need to run the following installation commands. This will take about 5 minutes to run to completion and install your environment. You can of course run this tutorial locally if you prefer. In that case, don't run these cells since they will download and install Anaconda on your local machine."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 323
},
"colab_type": "code",
"id": "p0MdAUAvkMdD",
"outputId": "e73f824a-cd0b-4c73-d2e7-ef70df9e4baf"
},
"outputs": [],
"source": [
"!curl -Lo conda_installer.py https://raw.githubusercontent.com/deepchem/deepchem/master/scripts/colab_install.py\n",
"import conda_installer\n",
"conda_installer.install()\n",
"!/root/miniconda/bin/conda info -e"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 361
},
"colab_type": "code",
"id": "hlLFgrdrAc-J",
"outputId": "16522993-056f-493e-9c62-6b74829d12d6"
},
"outputs": [],
"source": [
"!pip install --pre deepchem\n",
"import deepchem\n",
"deepchem.__version__"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "BUFgitSSkMdG"
},
"source": [
"We'll use the Delaney dataset from the MoleculeNet suite to run our experiments in this tutorial. Let's load up our dataset for our experiments, and then make some uncertainty predictions."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 88
},
"colab_type": "code",
"id": "4mHPuoOPkMdH",
"outputId": "43685a7b-d247-4fc2-a929-015e798f9ebb"
},
"outputs": [],
"source": [
"import deepchem as dc\n",
"import numpy as np\n",
"import matplotlib.pyplot as plot\n",
"\n",
"tasks, datasets, transformers = dc.molnet.load_delaney()\n",
"train_dataset, valid_dataset, test_dataset = datasets\n",
"\n",
"model = dc.models.MultitaskRegressor(len(tasks), 1024, uncertainty=True)\n",
"model.fit(train_dataset, nb_epoch=20)\n",
"y_pred, y_std = model.predict_uncertainty(test_dataset)"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "_DlPZsaekMdL"
},
"source": [
"All of this looks exactly like any other example, with just two differences. First, we add the option `uncertainty=True` when creating the model. This instructs it to add features to the model that are needed for estimating uncertainty. Second, we call `predict_uncertainty()` instead of `predict()` to produce the output. `y_pred` is the predicted outputs. `y_std` is another array of the same shape, where each element is an estimate of the uncertainty (standard deviation) of the corresponding element in `y_pred`. And that's all there is to it! Simple, right?\n",
"\n",
"Of course, it isn't really that simple at all. DeepChem is doing a lot of work to come up with those uncertainties. So now let's pull back the curtain and see what is really happening. (For the full mathematical details of calculating uncertainty, see https://arxiv.org/abs/1703.04977)\n",
"\n",
"To begin with, what does \"uncertainty\" mean? Intuitively, it is a measure of how much we can trust the predictions. More formally, we expect that the true value of whatever we are trying to predict should usually be within a few standard deviations of the predicted value. But uncertainty comes from many sources, ranging from noisy training data to bad modelling choices, and different sources behave in different ways. It turns out there are two fundamental types of uncertainty we need to take into account.\n",
"\n",
"### Aleatoric Uncertainty\n",
"\n",
"Consider the following graph. It shows the best fit linear regression to a set of ten data points."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 265
},
"colab_type": "code",
"id": "iLgia0GVkMdM",
"outputId": "30208f8a-d76c-43da-9030-40d7529246fe"
},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# Generate some fake data and plot a regression line.\n",
"x = np.linspace(0, 5, 10)\n",
"y = 0.15*x + np.random.random(10)\n",
"plot.scatter(x, y)\n",
"fit = np.polyfit(x, y, 1)\n",
"line_x = np.linspace(-1, 6, 2)\n",
"plot.plot(line_x, np.poly1d(fit)(line_x))\n",
"plot.show()"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "7fTPkHSakMdP"
},
"source": [
"The line clearly does not do a great job of fitting the data. There are many possible reasons for this. Perhaps the measuring device used to capture the data was not very accurate. Perhaps `y` depends on some other factor in addition to `x`, and if we knew the value of that factor for each data point we could predict `y` more accurately. Maybe the relationship between `x` and `y` simply isn't linear, and we need a more complicated model to capture it. Regardless of the cause, the model clearly does a poor job of predicting the training data, and we need to keep that in mind. We cannot expect it to be any more accurate on test data than on training data. This is known as *aleatoric uncertainty*.\n",
"\n",
"How can we estimate the size of this uncertainty? By training a model to do it, of course! At the same time it is learning to predict the outputs, it is also learning to predict how accurately each output matches the training data. For every output of the model, we add a second output that produces the corresponding uncertainty. Then we modify the loss function to make it learn both outputs at the same time.\n",
"\n",
"### Epistemic Uncertainty\n",
"\n",
"Now consider these three curves. They are fit to the same data points as before, but this time we are using 10th degree polynomials."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 214
},
"colab_type": "code",
"id": "hVoRaGn6kMdQ",
"outputId": "e25598cd-bcf3-4076-e7f5-43727dfa561a"
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 864x216 with 3 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plot.figure(figsize=(12, 3))\n",
"line_x = np.linspace(0, 5, 50)\n",
"for i in range(3):\n",
" plot.subplot(1, 3, i+1)\n",
" plot.scatter(x, y)\n",
" fit = np.polyfit(np.concatenate([x, [3]]), np.concatenate([y, [i]]), 10)\n",
" plot.plot(line_x, np.poly1d(fit)(line_x))\n",
"plot.show()"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "P_1Ag-VPkMdT"
},
"source": [
"Each of them perfectly interpolates the data points, yet they clearly are different models. (In fact, there are infinitely many 10th degree polynomials that exactly interpolate any ten data points.) They make identical predictions for the data we fit them to, but for any other value of `x` they produce different predictions. This is called *epistemic uncertainty*. It means the data does not fully constrain the model. Given the training data, there are many different models we could have found, and those models make different predictions.\n",
"\n",
"The ideal way to measure epistemic uncertainty is to train many different models, each time using a different random seed and possibly varying hyperparameters. Then use all of them for each input and see how much the predictions vary. This is very expensive to do, since it involves repeating the whole training process many times. Fortunately, we can approximate the same effect in a less expensive way: by using dropout.\n",
"\n",
"Recall that when you train a model with dropout, you are effectively training a huge ensemble of different models all at once. Each training sample is evaluated with a different dropout mask, corresponding to a different random subset of the connections in the full model. Usually we only perform dropout during training and use a single averaged mask for prediction. But instead, let's use dropout for prediction too. We can compute the output for lots of different dropout masks, then see how much the predictions vary. This turns out to give a reasonable estimate of the epistemic uncertainty in the outputs.\n",
"\n",
"### Uncertain Uncertainty?\n",
"\n",
"Now we can combine the two types of uncertainty to compute an overall estimate of the error in each output:\n",
"\n",
"$$\\sigma_\\text{total} = \\sqrt{\\sigma_\\text{aleatoric}^2 + \\sigma_\\text{epistemic}^2}$$\n",
"\n",
"This is the value DeepChem reports. But how much can you trust it? Remember how I started this tutorial: deep learning models should not be used as black boxes. We want to know how reliable the outputs are. Adding uncertainty estimates does not completely eliminate the problem; it just adds a layer of indirection. Now we have estimates of how reliable the outputs are, but no guarantees that those estimates are themselves reliable.\n",
"\n",
"Let's go back to the example we started with. We trained a model on the SAMPL training set, then generated predictions and uncertainties for the test set. Since we know the correct outputs for all the test samples, we can evaluate how well we did. Here is a plot of the absolute error in the predicted output versus the predicted uncertainty."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 279
},
"colab_type": "code",
"id": "r3jD4V4rkMdU",
"outputId": "c50122f9-e178-4f3e-ac74-760ddf338bc1"
},
"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"
}
],
"source": [
"abs_error = np.abs(y_pred.flatten()-test_dataset.y.flatten())\n",
"plot.scatter(y_std.flatten(), abs_error)\n",
"plot.xlabel('Standard Deviation')\n",
"plot.ylabel('Absolute Error')\n",
"plot.show()"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "rdGOqq_DkMdX"
},
"source": [
"The first thing we notice is that the axes have similar ranges. The model clearly has learned the overall magnitude of errors in the predictions. There also is clearly a correlation between the axes. Values with larger uncertainties tend on average to have larger errors. (Strictly speaking, we expect the absolute error to be *less than* the predicted uncertainty. Even a very uncertain number could still happen to be close to the correct value by chance. If the model is working well, there should be more points below the diagonal than above it.)\n",
"\n",
"Now let's see how well the values satisfy the expected distribution. If the standard deviations are correct, and if the errors are normally distributed (which is certainly not guaranteed to be true!), we expect 95% of the values to be within two standard deviations, and 99% to be within three standard deviations. Here is a histogram of errors as measured in standard deviations."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 265
},
"colab_type": "code",
"id": "IrD6swafkMdY",
"outputId": "55d11687-7d35-4a2c-d9d7-2410cea156d1",
"scrolled": true
},
"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"
}
],
"source": [
"plot.hist(abs_error/y_std.flatten(), 20)\n",
"plot.show()"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "bucmsdGSkMda"
},
"source": [
"All the values are in the expected range, and the distribution looks roughly Gaussian although not exactly. Perhaps this indicates the errors are not normally distributed, but it may also reflect inaccuracies in the uncertainties. This is an important reminder: the uncertainties are just estimates, not rigorous measurements. Most of them are pretty good, but you should not put too much confidence in any single value."
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "4NwKVrwCkMdb"
},
"source": [
"# Congratulations! Time to join the Community!\n",
"\n",
"Congratulations on completing this tutorial notebook! If you enjoyed working through the tutorial, and want to continue working with DeepChem, we encourage you to finish the rest of the tutorials in this series. You can also help the DeepChem community in the following ways:\n",
"\n",
"## Star DeepChem on GitHub\n",
"Starring DeepChem on GitHub helps build awareness of the DeepChem project and the tools for open source drug discovery that we're trying to build.\n",
"\n",
"## Join the DeepChem Gitter\n",
"The DeepChem [Gitter](https://gitter.im/deepchem/Lobby) hosts a number of scientists, developers, and enthusiasts interested in deep learning for the life sciences. Join the conversation!"
]
}
],
"metadata": {
"accelerator": "GPU",
"colab": {
"name": "07_Uncertainty_In_Deep_Learning.ipynb",
"provenance": []
},
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.6"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
| mit |
jmeyers314/batoid | notebook/PH != Pupil.ipynb | 2 | 10269 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"ExecuteTime": {
"end_time": "2019-09-22T21:44:06.807887Z",
"start_time": "2019-09-22T21:44:06.490976Z"
}
},
"outputs": [],
"source": [
"import batoid\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"ExecuteTime": {
"end_time": "2019-09-22T21:44:06.937321Z",
"start_time": "2019-09-22T21:44:06.823159Z"
}
},
"outputs": [],
"source": [
"telescope = batoid.Optic.fromYaml(\"HSC.yaml\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"ExecuteTime": {
"end_time": "2019-09-22T21:45:37.057223Z",
"start_time": "2019-09-22T21:45:37.052363Z"
}
},
"outputs": [],
"source": [
"def pupil(thx, thy, nside=512):\n",
" rays = batoid.RayVector.asGrid(\n",
" optic=telescope, wavelength=600e-9,\n",
" theta_x=thx, theta_y=thy,\n",
" nx=nside, ny=nside\n",
" )\n",
" rays2 = rays.copy()\n",
" telescope.stopSurface.interact(rays2)\n",
" telescope.trace(rays)\n",
" w = ~rays.vignetted\n",
" return rays2.x[w], rays2.y[w]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"ExecuteTime": {
"end_time": "2019-09-22T21:45:40.444732Z",
"start_time": "2019-09-22T21:45:38.811487Z"
}
},
"outputs": [],
"source": [
"fig = plt.figure(figsize=(12, 12))\n",
"ax = fig.add_subplot(111)\n",
"ax.scatter(*pupil(np.deg2rad(0.75),0), s=0.1)\n",
"ax.set_xlim(-4.2, 4.2)\n",
"ax.set_ylim(-4.2, 4.2)\n",
"ax.set_aspect(1)\n",
"fig.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"ExecuteTime": {
"end_time": "2019-09-22T21:45:43.983992Z",
"start_time": "2019-09-22T21:45:43.980595Z"
}
},
"outputs": [],
"source": [
"def spanRange(x, nside=512):\n",
" xmin, xmax = np.min(x), np.max(x)\n",
" xspan = xmax - xmin\n",
" xmin = xmin - 0.8*xspan\n",
" xmax = xmax + 0.8*xspan\n",
" return np.linspace(xmin, xmax, nside)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"ExecuteTime": {
"end_time": "2019-09-22T21:54:12.340579Z",
"start_time": "2019-09-22T21:54:12.330951Z"
}
},
"outputs": [],
"source": [
"def pinhole(thx, thy, nside=256):\n",
" # reset skips\n",
" for item in telescope.itemDict:\n",
" telescope[item].skip = False\n",
"\n",
" # First, need to determine where on the filter to constrain rays. We'll use the average position of the \n",
" # pupil beam that would have intersected the filter.\n",
" rays = batoid.RayVector.asGrid(\n",
" optic=telescope, lx=10, theta_x=thx, theta_y=thy,\n",
" nx=nside, wavelength=600e-9\n",
" )\n",
" tf = telescope.traceFull(rays)\n",
" surface = tf['F_entrance']\n",
" w = ~surface['out'].vignetted\n",
" rs = surface['out'][w]\n",
" xmean, ymean = np.mean(rs.x), np.mean(rs.y)\n",
" # Now we need to generate a bunch of rays that all pass through the above part of the filter, but over \n",
" # a range of angles.\n",
" # What is the range of angles for the pupil beam? \n",
" vx = spanRange(rs.vx, nside=nside)\n",
" vy = spanRange(rs.vy, nside=nside)\n",
" vx, vy = np.meshgrid(vx, vy)\n",
" vz = np.sqrt(1-vx*vx+vy*vy)\n",
" # Now need to make a RayVector with appropriate x,y,vx,vy,...\n",
"# rv = batoid.RayVector([\n",
"# batoid.Ray([xmean, ymean, 0], [vx_, vy_, vz_], 0, 600e-9)\n",
"# for vx_, vy_, vz_ in zip(vx.ravel(), vy.ravel(), vz.ravel())])\n",
" rv = batoid.RayVector(\n",
" xmean*np.ones(nside*nside, dtype=float),\n",
" ymean*np.ones(nside*nside, dtype=float),\n",
" np.zeros(nside*nside, dtype=float),\n",
" vx.ravel(), vy.ravel(), vz.ravel(),\n",
" np.zeros(nside*nside, dtype=float),\n",
" 600e-9*np.ones(nside*nside, dtype=float),\n",
" coordSys = surface['out'].coordSys\n",
" )\n",
" # trace forward from filter. So temporarily skip everything before the filter.\n",
" before_items = ['SubaruHSC.POPT2', \n",
" 'SubaruHSC.FEU',\n",
" 'SubaruHSC.TopRing',\n",
" 'SubaruHSC.BottomRing',\n",
" 'SubaruHSC.TertiarySpiderFirstPass',\n",
" 'SubaruHSC.PM',\n",
" 'SubaruHSC.TertiarySpiderSecondPass',\n",
" 'SubaruHSC.HSC.WFC.G1',\n",
" 'SubaruHSC.HSC.WFC.G2',\n",
" 'SubaruHSC.HSC.WFC.ADC',\n",
" 'SubaruHSC.HSC.WFC.G3',\n",
" 'SubaruHSC.HSC.WFC.G4',\n",
" 'SubaruHSC.HSC.WFC.G5',\n",
" ]\n",
" for item in before_items:\n",
" telescope[item].skip = True\n",
" forward_rays = telescope.trace(rv.copy())\n",
" # reset skips\n",
" for item in telescope.itemDict:\n",
" telescope[item].skip = False\n",
" # Now skip everything that happens *after* and including the filter\n",
" after_items = ['SubaruHSC.HSC.CAM.F',\n",
" 'SubaruHSC.HSC.CAM.W',\n",
" 'SubaruHSC.HSC.CAM.D',\n",
" ]\n",
" for item in after_items:\n",
" telescope[item].skip = True\n",
" rv = batoid.RayVector(\n",
" rv.x, rv.y, rv.z,\n",
" -rv.vx, -rv.vy, -rv.vz,\n",
" rv.t, rv.wavelength,\n",
" coordSys = rv.coordSys\n",
" )\n",
" reverse_rays = telescope.trace(rv.copy(), reverse=True)\n",
"\n",
" # reset skips\n",
" for item in telescope.itemDict:\n",
" telescope[item].skip = False\n",
" \n",
" w = ~reverse_rays.vignetted\n",
" return forward_rays.x[w], forward_rays.y[w]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"ExecuteTime": {
"end_time": "2019-09-22T21:54:13.774290Z",
"start_time": "2019-09-22T21:54:13.001905Z"
}
},
"outputs": [],
"source": [
"fig = plt.figure(figsize=(12, 12))\n",
"ax = fig.add_subplot(111)\n",
"ax.scatter(*pinhole(0,0), s=1)\n",
"fig.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"ExecuteTime": {
"end_time": "2019-09-22T21:54:21.391367Z",
"start_time": "2019-09-22T21:54:21.385603Z"
}
},
"outputs": [],
"source": [
"def plot(thx, thy):\n",
" fig = plt.figure(figsize=(6, 6))\n",
" ax = fig.add_subplot(111)\n",
"\n",
" pux, puy = pupil(thx, thy)\n",
" xspan = np.max(pux) - np.min(pux)\n",
" yspan = np.max(puy) - np.min(puy)\n",
" span = max(xspan, yspan)\n",
" pux = (pux - np.mean(pux))/span\n",
" puy = (puy - np.mean(puy))/span\n",
" \n",
" phx, phy = pinhole(thx, thy)\n",
" xspan = np.max(phx) - np.min(phx)\n",
" yspan = np.max(phy) - np.min(phy)\n",
" span = max(xspan, yspan)\n",
"\n",
" phx = -(phx - np.mean(phx))/span\n",
" phy = -(phy - np.mean(phy))/span\n",
"\n",
" ax.scatter(pux, puy, s=2, alpha=0.1, c='r', label='pupil')\n",
" ax.scatter(phx, phy, s=2, alpha=0.2, c='b', label='pinhole')\n",
" ax.legend()\n",
" fig.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"ExecuteTime": {
"end_time": "2019-09-22T21:54:26.486667Z",
"start_time": "2019-09-22T21:54:23.689643Z"
}
},
"outputs": [],
"source": [
"plot(0, np.deg2rad(0.75))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"ExecuteTime": {
"end_time": "2019-09-22T21:54:29.710612Z",
"start_time": "2019-09-22T21:54:29.705415Z"
}
},
"outputs": [],
"source": [
"def both(thx, thy):\n",
" pux, puy = pupil(thx, thy)\n",
" xspan = np.max(pux) - np.min(pux)\n",
" yspan = np.max(puy) - np.min(puy)\n",
" span = max(xspan, yspan)\n",
" pux = (pux - np.mean(pux))/span\n",
" puy = (puy - np.mean(puy))/span\n",
"\n",
" phx, phy = pinhole(thx, thy)\n",
" xspan = np.max(phx) - np.min(phx)\n",
" yspan = np.max(phy) - np.min(phy)\n",
" span = max(xspan, yspan)\n",
" phx = -(phx - np.mean(phx))/span\n",
" phy = -(phy - np.mean(phy))/span\n",
"\n",
" return pux, puy, phx, phy"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"ExecuteTime": {
"end_time": "2019-09-22T21:54:40.232328Z",
"start_time": "2019-09-22T21:54:31.673884Z"
}
},
"outputs": [],
"source": [
"fig, axes = plt.subplots(nrows=2, ncols=2, figsize=(8,8))\n",
"for ax, thy in zip(axes.ravel(), [0.0, 0.25, 0.5, 0.75]):\n",
" pux, puy, phx, phy = both(0.0, np.deg2rad(thy))\n",
"\n",
" ax.scatter(pux, puy, s=2, alpha=0.1, c='r', label='pupil')\n",
" ax.scatter(phx, phy, s=2, alpha=0.2, c='b', label='pinhole')\n",
" ax.set_title(r\"$\\theta_y$ = {:5.2f}\".format(thy))\n",
" ax.legend(loc=\"upper right\")\n",
"fig.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.8-final"
}
},
"nbformat": 4,
"nbformat_minor": 4
} | bsd-2-clause |
phoebe-project/phoebe2-docs | 2.0/tutorials/ORB.ipynb | 1 | 148228 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"'orb' Datasets and Options\n",
"============================\n",
"\n",
"Setup\n",
"-----------------------------"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's first make sure we have the latest version of PHOEBE 2.0 installed. (You can comment out this line if you don't use pip for your installation or don't want to update to the latest release)."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"!pip install -I \"phoebe>=2.0,<2.1\""
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"As always, let's do imports and initialize a logger and a new Bundle. See [Building a System](building_a_system.html) for more details."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"WARNING: Constant u'Gravitational constant' is already has a definition in the u'si' system [astropy.constants.constant]\n",
"WARNING: Constant u'Solar mass' is already has a definition in the u'si' system [astropy.constants.constant]\n",
"WARNING: Constant u'Solar radius' is already has a definition in the u'si' system [astropy.constants.constant]\n",
"WARNING: Constant u'Solar luminosity' is already has a definition in the u'si' system [astropy.constants.constant]\n",
"/usr/local/lib/python2.7/dist-packages/astropy/units/quantity.py:782: FutureWarning: comparison to `None` will result in an elementwise object comparison in the future.\n",
" return super(Quantity, self).__eq__(other)\n"
]
}
],
"source": [
"import phoebe\n",
"from phoebe import u # units\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"\n",
"logger = phoebe.logger()\n",
"\n",
"b = phoebe.default_binary()"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"Dataset Parameters\n",
"--------------------------\n",
"\n",
"Let's create the ParameterSet which would be added to the Bundle when calling add_dataset. Later we'll call add_dataset, which will create and attach this ParameterSet for us."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"ParameterSet: 1 parameters\n",
" times@_default: [] d\n"
]
}
],
"source": [
"ps, constraints = phoebe.dataset.orb()\n",
"print ps"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### times"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Parameter: times@_default\n",
" Qualifier: times\n",
" Description: Observed times\n",
" Value: [] d\n",
" Constrained by: \n",
" Constrains: None\n",
" Related to: None\n",
"\n"
]
}
],
"source": [
"print ps['times']"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Compute Options\n",
"------------------\n",
"\n",
"Let's look at the compute options (for the default PHOEBE 2 backend) that relate to dynamics and the ORB dataset"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"ParameterSet: 19 parameters\n",
" enabled@_default: True\n",
" dynamics_method: keplerian\n",
" ltte: False\n",
" irrad_method: wilson\n",
" boosting_method: none\n",
" protomesh: False\n",
" pbmesh: False\n",
" horizon: False\n",
" mesh_method@_default: marching\n",
" ntriangles@_default: 1000\n",
" distortion_method@_default: roche\n",
" eclipse_method: native\n",
" horizon_method: boolean\n",
" atm@_default: ck2004\n",
" lc_method@_default: numerical\n",
" fti_method@_default: none\n",
" fti_oversample@_default: 5\n",
" rv_method@_default@_default: flux-weighted\n",
" rv_grav@_default@_default: False\n"
]
}
],
"source": [
"ps_compute = phoebe.compute.phoebe()\n",
"print ps_compute"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### dynamics_method"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Parameter: dynamics_method\n",
" Qualifier: dynamics_method\n",
" Description: Which method to use to determine the dynamics of components\n",
" Value: keplerian\n",
" Choices: keplerian\n",
"\n"
]
}
],
"source": [
"print ps_compute['dynamics_method']"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The 'dynamics_method' parameter controls how stars and components are placed in the coordinate system as a function of time and has several choices:\n",
" * keplerian (default): Use Kepler's laws to determine positions. If the system has more than two components, then each orbit is treated independently and nested (ie there are no dynamical/tidal effects - the inner orbit is treated as a single point mass in the outer orbit).\n",
" * more coming soon"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### ltte"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Parameter: ltte\n",
" Qualifier: ltte\n",
" Description: Correct for light travel time effects\n",
" Value: False\n",
"\n"
]
}
],
"source": [
"print ps_compute['ltte']"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The 'ltte' parameter sets whether light travel time effects (Roemer delay) are included. If set to False, the positions and velocities are returned as they actually are for that given object at that given time. If set to True, they are instead returned as they were or will be when their light reaches the origin of the coordinate system.\n",
"\n",
"See the [Systemic Velocity Example Script](../examples/vgamma) for an example of how 'ltte' and 'vgamma' (systemic velocity) interplay."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Synthetics\n",
"------------------"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<ParameterSet: 3 parameters | contexts: compute, dataset>"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"b.add_dataset('orb', times=np.linspace(0,3,201))"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<ParameterSet: 14 parameters | components: primary, secondary>"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"b.run_compute()"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"['times@primary@orb01@phoebe01@latest@orb@model',\n",
" 'xs@primary@orb01@phoebe01@latest@orb@model',\n",
" 'ys@primary@orb01@phoebe01@latest@orb@model',\n",
" 'zs@primary@orb01@phoebe01@latest@orb@model',\n",
" 'vxs@primary@orb01@phoebe01@latest@orb@model',\n",
" 'vys@primary@orb01@phoebe01@latest@orb@model',\n",
" 'vzs@primary@orb01@phoebe01@latest@orb@model',\n",
" 'times@secondary@orb01@phoebe01@latest@orb@model',\n",
" 'xs@secondary@orb01@phoebe01@latest@orb@model',\n",
" 'ys@secondary@orb01@phoebe01@latest@orb@model',\n",
" 'zs@secondary@orb01@phoebe01@latest@orb@model',\n",
" 'vxs@secondary@orb01@phoebe01@latest@orb@model',\n",
" 'vys@secondary@orb01@phoebe01@latest@orb@model',\n",
" 'vzs@secondary@orb01@phoebe01@latest@orb@model']"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"b['orb@model'].twigs"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Parameter: times@primary@latest@model\n",
" Qualifier: times\n",
" Description: Synthetic times\n",
" Value: [ 0. 0.015 0.03 ..., 2.97 2.985 3. ] d\n",
" Constrained by: \n",
" Constrains: None\n",
" Related to: None\n",
"\n"
]
}
],
"source": [
"print b['times@primary@orb01@orb@model']"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Parameter: xs@primary@latest@model\n",
" Qualifier: xs\n",
" Description: X position\n",
" Value: [ 1.62265701e-16 -2.49387030e-01 -4.96560484e-01 ...,\n",
" 4.96560484e-01 2.49387030e-01 -2.19140711e-15] solRad\n",
" Constrained by: \n",
" Constrains: None\n",
" Related to: None\n",
"\n"
]
}
],
"source": [
"print b['xs@primary@orb01@orb@model']"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Parameter: vxs@primary@latest@model\n",
" Qualifier: vxs\n",
" Description: X velocity\n",
" Value: [-16.65044106 -16.57654582 -16.35551598 ..., -16.35551598\n",
" -16.57654582 -16.65044106] solRad / d\n",
" Constrained by: \n",
" Constrains: None\n",
" Related to: None\n",
"\n"
]
}
],
"source": [
"print b['vxs@primary@orb01@orb@model']"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Plotting\n",
"---------------\n",
"\n",
"By default, orb datasets plot as 'ys' vx 'xs' (plane of sky). Notice the y-scale here with inclination set to 90."
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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AI4Xn8dZnNc75FUoqhiS03ZAB0WVdlioSf2ccsjv7rI3MhX7gc9tSuqbMc6LGpWJIMnlv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\nYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f2a94390f90>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"axs, artists = b['orb@model'].plot()"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"As always, you have access to any of the arrays for either axes, so if you want to plot 'vxs' vs 'times'"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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xLZgKB/GuyXdhd8lu9FrVfdLwnj189UaFQ4g77gA++0z927M/LvkY6yatg6+P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\n5zVkMzJk77raE1sUlhj3T+hOL0rXZGKLYtYs2aNk9nbIgZJJ1NcDx45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wS37Ili0yCd1u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\njDEHOFBijDHGGHOAAyXGGGOMMQf+P9D8QEb8o+sUAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f2a8223e450>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"axs, artists = b['orb@model'].plot(x='times', y='vxs')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"3d axes are not yet supported for orbits, but hopefully will be soon.\n",
"\n",
"Once they are supported, they will default to x, y, and z positions plotted on their respective axes."
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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LWCqVUKlUkE6nPSmTinp5edfRwMBAJG03rajX6ygWizAMA4VCoWUsK/jFLZvNIp/Ps8WN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\nN+1jj/CTJWRNRQ36IHldrK3xBACB3BV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N+upStYd2LgBgqwXrup6frCrqFwCALYoAHDts\nTRQ/PO2W3XzvTpk83eZ+igJu94f3FND9ieKZ6bdGPX1PDLwQmh+5CcA/MdADaJomYrGYcGVUHnz7\nzVQqFYm4H6XW0ovkZwxFUfCHU77L/v2rvz095jN8jwba9ZKvmE/l5P3wdv16J1rPZzvfu1MmT1TE\nGbXERBjw94e3LOj+iIjd8BaDYRioVCp9FWPoW2IwTRMjIyNst0s7iKjAN9UJ4u7wUzGtqirrlxzU\nhdBuHD61NoyJ/L2Nmi0/j3ps15bfW7OnFEVh7iTS1aH0TT5LR1EUW5G7idrz2Wm3TFXG5FYReU+6\nrTbCDRS7ofvTThgxCJHyFnW/oG+JgR4IWtSCqnt6eWBpkSNXVVQERBaJqqrIZDKBK7TdjrESD+8K\nC/LynnXIPuznPS44B0DTiqPsKRIoJGuBFvZ6vc5iDjQ+L9JGi5zdztlJlmIigHbHlAFHFnK3SnVE\nDev1UezGKdPJqzXBWwz0PvaTNlhfB58LhQIz88KmnTrBrhEN7X79jgU4L77ki7cGz8NmTvHgs5us\nfZ/D4IUj3sEWN83Ak+mf4L0PT0dCMdiunyQS0uk0UqlUS39moGktJJNJ5mqixcw0zZYgI2WgpdNp\n157PACZMz2ciXLL6nKQ6CKIDtEHQyfGtmU70TNEGxGvTpn6rYQD62GIAwvdkcIOTlEYUsBaUhc08\nsrsfRDxUBR62vzQ/xszpkzF11b4AgA2um45sNotcLgfDMBiBksVArriBgQHkcjnWRpFMfk3TWrSJ\nKMDIv8zkgrLr+QyA7Zy7VXYhqtRSp+I6gp+gftTaQyLhda68NeEWz6K4JaFcLgvbRLnhjTfewNFH\nH41Zs2Yhl8thk002wfnnn88ITCT62mIgiChU48GL4NmliAYZz8lioGAiLZZW8hFBeny9hV2XurBV\n2YZh4IVzbsbaV68NADjjlrvw06MObalPoBoJcoPQbpdeVNrtEkkAo24T2v0T0fDSEhR8JK0iXdeR\nyWRa2lWOlyT0eILINRYbJYZUKgVd18d0rrPGzLqNSN0QdK68NUHkwNeVAE1r9sc//jESiQSmTZsm\nctq2ePHFF2GaJq677jpstNFGePbZZ3H00UejUqngkksuETrWhCCGoLBbdPm+Bk5ZTiIWa/L188qo\nUSxWmqahVCohFou59n0OCp50bv/Uk/jyIzvhug+PxPfLX0BhjbIn7VLJDWS9Tv4l5d1EFG8gF0gi\nkWABeSIJ3uVEu2AKYKdSKdcGM73UVyEofv3IUvYzFWDyFpidS6WXiEEU+LoSck/H43E8+uijWLhw\nIQzDwLbbbov99tsP+++/P/baay/hz82+++6Lfffdl/17ww03xGmnnYb58+cLJ4a+diURRLmSqK9B\nLBZzldIICl7ewquMRlDrhJfQoC51UZACX+T3hV23Qn7VzgCA6VdPZe44TdM8F84Boy6RXC6HgYEB\n5PN5JJNJFrwulUpskSey4OM+fAAbQEuwtl1fhSgxHgvuBQ9eBgDYXj2J/c4q1WF1qei63tKoSGSm\nUxQuqqiIPZlM4p577sGCBQuw+eabY6eddsItt9yCo446KpLx7LB69WpMmeJcPBoUfW0xhI0x8Au1\nn917GCKixdQwjLYyGkHBF+H5kdDwW9Oh6zoLygPNa3vn+w9i8LJms6DdLzgbfzn17JbsGb/g4w0A\n2HXxonT0fSiKwvSIyKLgYxy828kpgE3ulV7aObt9t+9MvgsAMO//neB4rNWloqoqTNO0laIY7xae\nnQRfx7DuuuvixhtvhGEYeOeddzpyD1599VXMmzcPl112mfBzTwiLAQi2G6Mvd2RkREhfAy8g14hX\nZVS/JESuE8MwIrkeymzSdZ0V+RFJUOHf4196DQDwr8KVePyVd4VaXpSzns/nmbQ5L4ugqioajQaz\nOvgANu86cQpgx2IxNBoNdh5aIHuFJJyw7cx1PH2Osr8oFdxJ2C5IwWFUAe2oA+V8VlIsFsN6663n\n63xnnXVWy8bE+l88HsfLL7/ccszbb7+N/fffH1/+8pcjsVD62mIgBN3B026Smup4rfwNMl61WmW7\n2qhqIXRdR6lUYgs07Z5FgVxgmqa1pO3SvaBCtPWnZPFZ8we4XzkX//3XHTCyQwmxmPiXt1arQdM0\npFKpMbEJCmDzsQQ6zi2ATZYCLYT0/24NYEed7WMnbBckXtOLxErXUqlUQhWBnnbaaTjyyCNdPzNr\n1iz28/LlyzF79mzsscce+PnPfx54XDf0NTGEcSVRJS3QrIfwKwcBePNv8rUDFODySwpei/BKpRIb\nI6gMgBP4OotCocAWTZK7AFozj+48/WQUftzUUhr4SQHl08X1qyXLQNM0ZDIZpFIptsvlXU5kHVAW\nDt0bXpnTGsDmawPi8ThM00Q2m+3JAPaCBxcLPZ9bC09y6xGRkMupG+9LO9hZDGGqnqdOnYqpU6e2\n/yCalsLs2bOx884744Ybbgg8Zjv0NTEQ/BADH0+gFMeo6hOsvZ/D9H12A5n45O8nN4ioMRqNBrNE\nqFKaApOqqrJgLhWyUeZR+fQy8pc2d1rTz/4s3p17v+c5OYEC2rquszoIO5AriU9FJKKwBq6JAKzW\nBB+M5pVQ+X7GdJ/bFUmNB05YuhcA4NtTf+n72HbPhF3shydPKq6LMl4TlSuJwFc+d6LAbfny5dhz\nzz0xc+ZMXHLJJXjvvffY39ZZx5sr0CsmBDEA3nbVvCskm80iHo+jVCr5HsuLxWBXOxCEGNzAk5yX\nIHMQWNNdqVCN/Pf8LhoYDQ6Tb3/Z0Ssw6/p1MDL5n/jaFT/HrSd9O/BcDMNgIoZ+Atp8KqLXmgkK\nStPP9Bne5WTNf7fWB9A9GO9d86WHH+Tr80EyfZysCZ48gaalTvelW+EWY4gS999/P5YtW4Zly5Zh\n/fXXZ3NRFKXF9SkC3Xv3BcLLQ6zrOoaHh1mf5Gw2yx5O0bsZp97PYVJPreBTXklcTVQlM8Ga7koP\nqGEYbOGjBzaTyTA/v6qqGBkZQalUwqRcEr/4+D8AAHfVT8Ev7ns80Pz4+EnYLCeqlygUChgYGGCb\nBGuP5lgsxvSZ+AC2tTUnH8B2E3CLaodrd763PygKHcMPyJrghf/oXbPeYxHCf1ETb6d6MRx++OEt\n7jl616LoFd7XFoPXGEO9Xke5XGYLNfnEg1Z6Oh1HtQOqqiKZTAYWwWsHWiTJ32/Nbgpb12G1RLLZ\nLHOzAKPyFtRgp1AotNRI8DvyarWK/befiYOfvhy/176L7yydjW02eAkf3/wjnuejaRorOOIXGRGw\nupxI4I+ug3SyrG4TpwB2u+Y7dD1RWxPbzPsUMAhsXT4+kvN7BZ/lBKAlXuNHr8gOUQW0rQTej1pJ\nfU0MBCfXjnWhzufzQhYVO2Ig33e9XncUqOOLsPyMxY9DldlEcqKL8Hh3G0k882mJtBt2W6jt/PvX\nfvsbWPyDJ/HGpFux1z2bYWn+dWywzqS2bgVKEkgkEpG4yvjrJt84FcRRqipfM2HNTKLgtbVmghZE\nklMhFxv/H+2sw3Stc1ocG4PLAAB/P2dukNsRyX2me2LN/iKi6FZXnCSGPoLXhZo+GwZOyqiiYQ0y\nOy2oQV1WfHtUytSiBY/GqtVqqFarLZpH7c5Lu+jnfvBzTD33X6hNegbb/XomXvjaMgzlm0JvyWSy\nZSGgdFTqQe21ajoIeEuBD57Twp5OpxnB0aJOVgXNm7cSeOvKmuVE8S1eliLMrtkOV9z9CPs5SJpw\nlIFiHnbFdXbqp059sDtVTd1vbT2BCUIMVouBd7W4LdQiXEnWjB23HXzQBRsA62MQla4SLcS8u418\nnLwQnnXx9HstK3/4GPI/mgIkatji1ll445vvIqEoY3bk5GPl01GjAKUTNxoNFidxmrvVTUTWhF3N\nBBGtXZaTaZotAWzRu+b/ffW/AADX7PBw4PsyHrC7x3Z9njtdmS4thh4DH2MAwEx/yuePwtXCg3zo\n8Xg8sqI1evhJVymKnTMvVDc0NNQSZKZrosWTdHXCoHzWSpbG+tHrpuPtYz/E0GBuTDopAKZ2GUUm\ni9fUVyvsCr/caiaIZGkHTPeWtyasrjf6PLmcvNZM/PP5N9nPX9trh6C3JhL4eW6diuv4+0Kg7DFR\n74WdxSCJoQdBXyK5Odq5WqzHBd15+N3B+7UYDMNgO+lcLue5ktnPONSICBjt+czLW9DiaRhGqGwg\nK/gah/WumYoXj1iO9aYOsOvNZDJskeR35FaXU1AETX21g5eaCQLJbrhVYDvtmu2KyOh7Iux379YA\ngG8MzA98PUD3id1Z02ENw2CyHFFXpkti6EHwi2CQfH6/izW5HgCwgHYUbg7eRQVAuNgeX5GdyWTY\nAkYLFVkNdK3WzCMRKJ9eRn7uVCBZxeY3rYt7Zi/GTpus2zbLCRh1OfG1B16h6zrK5WYldj6fF3pd\n1pqJRqPB7iHQfEad+kzYif5ZA9jWIjJg1KV512MvsHGu+ub/E3ZN3QbegtJ1Hdlslj0joirT+U1j\npVJhQpH9gr6vY6CUQgCsz2tU/mhSRiUz1q/v2ysJ8XUQtFPxQ17tLCHKPKrVakx2GUDLDrfRaKBc\nLjOhvKj63ZbP/hDp1c1d7uf/+jHc/s/nx4xFO3ISzsvlckgkEtA0DeVyGcVikQm7tcv6IsK1S7MV\nDaoMp+JAu5oJctFRALad6J9VQhwYDZ4ftWh3AMCZH/ltKAnxqHz3Ub2XZEl6lVZvd32iJTG6EX1N\nDIZhYHh4mL0EQdwBXhdrTdNQLBZZkBkQ/wJR/UCpVEIymYwkbkGZR5qmoVAosKK0VCqFWCyGWq2G\nkZERVCoVJsQXZbpgvV7H62fchw1Xfx0AcPIzn8FXf3qN4+dpR57NZjEwMNByDXxhnV0vASKSeDzu\nydUYBjyxUpq0XZ8J6qrm1GeCiIu3FKgojEgkHo/jv664lI190ud299z0vlPoZKYTFddRC1uytnky\nbldcJ7OSehixWAy5XA7xeJz1TPYLL8TgNU00zFjt0mtFvFjWGgg+84h85CRORzn6VBhIvn1RevzW\ndNRnfzAf35y/K24rHY+7tdOw9tm34725f3M9h1sQmO/VQH5pTdM8p9mGgZfaCz6fn2Q62tVM8Omw\nlKVjmibeWVnGU9nLAQBLvvpv5PP5UAFsml8voN08iYyB9p3r+Pvc78TQ1xYDMBrMiwK0WJfLZaTT\n6ZaezGGqi63HUR1EvV5nuxxrxpUf2LmSyD0Vi8Uc01ErlQrLsx8YGMDg4CDrnkZFbcVikRFYUHcF\nWUa1Wg3pdJpZJdcfewRu3OWfAIDy5CeQvzSPhu59DCeXE0lY0Ni8nIVo1Go1VlDpJ9Zl7TNBGx5e\nWoMviiPr2DRN7PCbjQAAGw0fjo+uPQTTNFmMg3oqJBIJlszg1rUuSssiioC23/H5znW8NVGr1ZhU\nB5EvEUmniOHAAw/ERz/6UWSzWay77ro47LDD8M4770QyVl9bDIQw2UVOCzztlvkKYP7BDlOTwMNO\nbE8k7Kq/aVy7zCM+bdNtV2vN3Sc58XYvP183YJf6euintsOe276HmdetDQAYumwAd+z5FPbfeTNf\n101zt8aDwsy93XWJKsjzUjNBFt3MS/YBJjWPW3TuZbZZTnZWFd+1jq/AjnKT1U2g9FYna0LXdZxx\nxhl46623sMkmm2DlypUYGBiIdE6zZ8/GOeecgxkzZuDtt9/GqaeeikMOOQR///vfhY+l+PhCuuub\n8wja/a1atYq5YPxgZGQEAFq+dH6xdurVsHr1auYv9gpySU2ePBmKorQolzoFQkn8zw9p1Ot1lEol\nDA4OssUqk8mwFFC+KpcIEPCXocO7bfjiI7dMIRrLa+pr/uJBINZc6DYvfROLL/ipp7kB7gRkN/cw\n7jK+cjrqgjyKpTQaDXz7+ltxj3E6AODVI/6DaZMKjDD4XS9dH5EfLx5Ji6E1DkHFd6KIolQqMXel\nKFD8RHQqaalUQiKRwE033YTbb78dixc3+1rssMMO+NznPoczzjiDxRmjxD333IODDjoItVrNT5KE\npwev711JvMtFxK6EgswAWpRR7cYNUzHNK5fywn4iwFdL12o15k6wpkNShk6QzCM/mUK0AFH6baFQ\n8JQoUD6ziIMzTTJ4sXAd8pfmsXJEbXscEVCj0WAB3nZzD+ouIwKq1+vIZrOBKsK9gvScGo0Gbnnk\nX4wUrtzmQeTT8ZYANlkBXgLY5FqhNp4AWJpt0DaedhhvV5IfxGIxHHPMMbjhhhswefJkLFiwAFts\nsQVuu+22jmQorVy5Erfeeit23333SDLn+t5ioN1OkB080NwdGIaBgYEB5mf0Irg3PDyMRCLhy/dI\nO/lUKsWkJdr5oQ3DwOrVq21VVJ1AlgnQtITIv0x9mhVFaSkGFClOx7s+rL58Sr/1mz32+rursPXN\no2qs+8cvwh2nnGj7WcrwocI1Py+V1W1Dbhknl1PQyukg4K2SB/71bxz22G4AgIPSl+OW73yrpRiO\nT2+1q5ngrUb6DLlWFEVhMTVglEyA4LUBdJ/S6bTQe1StVpn7UyRKpRKb63PPPYdDDjkEb775JtsM\nRhmYnzNnDubNm4dKpYJdd90V//d//4fJkyf7OYW0GHgEtRjoZaHdUSaT8Vw1HXTHUq/XkcvlIqm5\n4AuqyF1DUgJ0TSSpTUQqcg60eGQyGQwMDLAFhu5XuVzGyMgIqtWqZy3+mdMno3x6GWutOgAA8Gd9\nDvKX5vHW+8Njrp0IMUiNAj93vlcDEWmpVMLIyAgL3PLCiVGTAo35f4tfZaSwbeVE3PKdb7XMnfpM\nDA4OOtZMkMXE10xYhesAtASw29UGjAeiWKStz6O16tnveGeddRaL9dj9F4/H8fLLL7PPn3HGGViy\nZAnuv/9+xONxfOMb3wh3QQ6YMBYDZdz4rVDkc8f9KKNSAZrXgBTFLWiH41XewjRNrFq1ytPcyCKh\njCNqrmOneeQmGCcCfDCWUkQBtPj26cWm3bgX3/7jL/4Hs+8ZDUQPrdoTy+f+MdKeDXQ9fJojvVfW\nwjTR4GMlP/rDQ7hyxdcAAFuWj8ET5//E8zn4hZ8WcioMo90/BaR1XWfXw/9H4APYfKU8xWesgXza\nEBC5iAIF4kW6dqzWzSOPPIJzzz0XTz/9dKDzffjhh/jwww9dPzNr1izb+/L2229j/fXXx6OPPopd\ndtnF65CemKvvs5LCxBj4Enq/GUF+dg68sB8QrBDPDXzmUSqVQjqdZrtZPjfbLvMoCtAOV9O0MUqs\n1t7JRBK8xDLvtrFil80/gvLmZex2wVlYmvsZhif/DflL8zg0cyXmHfXlyHo2UHA6FosxH34ymWTp\njIDYLCeg1VW1+6Vn4vWhmwEAs3Eh7jn/VF9z91IzQeRAJE3Ba96VxIv+0RztagPI3RS1CmpUFgMf\npwvjqpo6dSqmTp0a6FgiXRJmFIm+txjoobTLLnID7a6JUKZMmeJrXIpNtMtO4IvjcrkcisWiLxIi\ni8HJyrBqHmUyGWadAKMvKElqi9YGcpqPXyVWfkfbzrdPKKt1rD2v1f96yZb34fgDdhdzMRbYVTMD\n4rOc6JyVSgW1uoYNfjEaX/nfmX/AnC/tLeyaqK6jVqu1uIR4sUIiCKuchNWaoM/Qd8knOlARZTKZ\nFLaY89X5okD3nayb3//+97j55pvxwAMPCBvDDosWLcITTzyBPfbYA5MnT8arr76K8847D++//z6e\nffZZPxs5aTHwIJdJO1h31yQDIRq0a+aF/YL4YtsFpkulUkv2DS2q+XyeuQb4xYp2dFF0xvKbjsqD\ncu1J3oJ2oOSOsi60ABCDjuXfWo7Fr72Hzz+4PQDgjOf3wRnPAxdv8Rec8F97CLs23lVljQ15UVfl\n3WXtXE50Hy++8yFcvuKr7PdPf/UNbPqRacKuiUCLOLlPvPaZsBP94xvv8CQBgBUahhG3ixpWi6FT\nxW25XA6///3vcf7556NcLmPGjBnYf//9cc4550Ri3fc9MfhxJfGyE9TboFarCZfSIJE6a3FcmEI8\n6zHWwjjKPKJ4AgUKDcNgvmR+oQ2jTmoHXrE0rDiddaHlpR149UzTNJHJZDB7x01Q3rGMmx54Asc/\nvScA4MwX9sWZLwBfyv4Mvzzhf0Jdm5/2onxxWpCiQF3X8cby97HNrzcavR/l6Rg5/7VQ12AH3uXH\nW3de+kwQwfE1E3YuJ7pG2ojxxMnfiyDFdVFnCAGda9Kz9dZb48EHH4x8HELfEwOhHTHw7Tf51E9+\nsRbxkPHd4/ykmLrBOi9rM6JYLDZG3oIyRvgqXL5FJb+j9RsAtsLJxSICVv84n44KNFMWSQPpG3vt\niMM/U8ItDz2FYxZ/CgBwh/od3HHpd5Ac3gxvnvUoBvP+Au6U1hukmpkyhZwWWqslpDUMrH3JFjBz\nK9g5fr7TQnx9rx19zdkLeJefU8zJyRLiY3P03ZAVZ+1ax8OpjWc39Xq2izH0m7IqMIGIwQ1W8TgR\nwV87Imo3ThiLgWAV9KOAodfMI+uOlvftew0A8/Czmw4L2uECYARk16fhS7tuia9+ahjP/Pt938K0\nawAAIABJREFU7HHnxgAAbeglzLi6GUfaR/kh7jzt5LZjhW1laoXTQvvmilXY8bebND+0ZnO6cfFI\nLP3+vFDjOSFI/YWdTIfdvbermaDnis9E8xvAdrr3UZNHPzbpASYQMThZDO2UUYNaDNbxRCmwOo1F\nUghkktPDystR0C7Q6wvvlK1CLzu1LXUq7rKmo0b5kjo11/n/7V15eBPV+n4n3UsXCspSKMjiFbwg\ne9kEZJHF0gW8oveigIqyCKKAgHjFy3UDQXFhFX4gIFzFLixlq6WFIhZZW6gsWihlaSkt3WnTNMn5\n/VHPMBkmySSZmaTtvM/Do02TnDNN5rznfN/7vR9/oeWGnNo1aYD8N/Kh0bih64fTcaPhDwCABPIe\nGix7DwDwtztzcXLJB3B3M20yT0Mscsl6GYbBhFUbsLt6rsnjDQp74/K8WGg0DGvNIJXKCTAlBXs7\n13FPQvQUSsNl3Dax9L258le+JQu9Lvo95JIOtw+2UHc2JdROFRUVdc5ZFagHxGAux8BN/lpqv+no\nLl7sOI6MRW88o9HI2i5Y8jyyN8ZPHT75N7tQ2IMmtaXaTVuCmFAVf0fLPwn9Nu8LuLt/jUs3CzEw\nphvgUXPy+KPJcgR+sZx9n+d9V2LFS/8AiFGS/tZc5N4tw+AvZuFGwx8f+F3Xe7Pwywcfs5+lpZCT\nvdbnjogDLIHu/rk5IS5JAPc3XtyaCXOtTYV6PfO7s3HdZaUE//0qKirQsGFDScdwBdR5YqDgL7pC\nyV+pxxNKZsuh9KFyQa7yiF6nRqNhFTNSxvj5N7uQ0kZON04KS2ogS3M3p9tv3ywAOW9cgUajwfmr\npRix9XkYm94vXvqxYgZ+XDfD5P1aFP8Dk7tPwPRnBsDPxzpRGI0E0b+cw9JDm3DJb73pL7lrjMED\ne4f/htAOISYnLqlVTjVzut/jWs7OdfRvT8NGVPkn1GeCSlfFJLC5hM9VOtENk1zusGooqY6Ab9Zm\nbcfnyC4eqImx25JktuUIzK1H8PDwYCWANJ/AMIwiMX56c9L+AABYFRQNb9mSlxALmvh1NFTFPwnR\nRbZTG3/cfH8vu5h9l3gKC05NgcH/usnrbzWMxuKr0Vhsa8hfoAg/pPh5JM/+Co38PEUltR1VOQGm\npKBEHQs95XBPk/y/PZfkKMFxTwnmThPc7xndMNAx6fMcSWA7S66qNOoNMdAPkt+hTOzrbCEG7u5H\njh4KgKnyiH/8pguvVquVxP9fDMzJUflVtNxm9/bGxrmLi9TXJrQDpfr68YO6YPyg4ya78eRzWfjP\nnu9wFptAvItsGqtd6UR8MGIann2y8wPXRutbbDnN2qpyouRNu/DJ3c6UTwr8wjOhBLa9NRP0b8at\n0uZ3rQPEJbD54D7v3r176omhNoJ+iNw2fXLeALRimibS7DFqs0ZC/EQ2lb9SiwvAVHkkp/8/YDnG\nb0teQszNqUTil4LOhRrL+fj4sAsLXah6t2+Kn995Dx4e/3E4AcxVOklxbWJCTvQ6fXx8ZCcFW67N\nGsnR51irmdDr9azKSUg1JZTApt9Doc+Sf2+qoaRaChrnp18mW28AsScGfsW0h4cHu4OWCvxENv1C\nUn8e2sie3hxyex4B9+WoYmL8QklIc2EDIWsEe+007IWQ8Z67u7vo6mtbSMJcMZlU4IecuORG7xGp\nvZwopCA8W2omuH5VtIaH+z7WEtj8rnX8Ak/u/6vEUEtBj6Pe3t7QarU2f9nFEAM3yUy7xNEdi625\nCXMnBn4im6s88vLygqenp8luCqgJJVHTM6kLggipaQpjb4zfUgKY5iVoRTYlNzkUM+ZgLTcjZjfO\nJQlLmxExxWRSghr7UTLnzl9KlRMgD+GJqZkAwLra0nvKlgQ2v2aCkibftkbNMdRSuLm5oWHDhqxP\nvNTyNb4fkRyhDe4YtJUoX3nEPQ5T3yWh2LIUFhdSF3cJhQ34eQkK2kNATtia1BabAOYnUQFlm/kA\nwioucyc5/m7cFpUTIP8pCDD97ri7u5vk3YQq92nIiRIF1xbcXM0EN9xEcfHiRbaTn5LEoNPpEBoa\ninPnziEtLQ1PPPGELOPUeWIA7jf2BuzbwZt7nV6vZxVO/EpmR8bjvobreUQT5mKUR3QhshSyscfi\nQolwDjcvQRcyChpKk2I3y4cUSW1LsXG+HNPNzY3drChxCqJ/S0sKNXPFZNzQk5iQkxKkwAUVP3Dz\nXEL1KnT+3O++mJoJSpr0u/jdd99hzZo1CAgIwCeffIIXX3wRw4cPl73X87x589CyZUucP39e1nHq\nvO02ANaqWWxDGz6KiorYEBEFt+mNkO7bYDCgpKTEZlUSt6EQVR7Rn4U8j2xZyLhKD25DFrG7QWo5\n7IzdLV3IuCRHv7v27Gb5kDrxa24MfiMioGb+3LCHHJBCtszNq9BFVijk5IzQGFVWWarT4Z7kuI4A\n5momuNJUSoD01ODu7o7jx49j2rRp8Pb2xh9//AEPDw8cO3YMvXr1kuU69+/fj7lz5yImJgaPP/64\nvScG1XZbDtBdJe39TP2IpAK9sfjKIwCCRng0fyJGecTdzfItLoR2g1yyk9IdVQzMLWSW8hJ0/lwp\nrBgoldSmiygtOgTAhgXpTlSOeg/6t5Si3kNMyIkurq5ECnT+QvUqttRMGI1G9j568sknkZeXh+zs\nbFRWVmLv3r2yhXby8vLw+uuvY/fu3YqY9tUbYrA3tENfS3cQ3KY3lm4yRwrj6A3n5eXFfgm4zdvp\nPBzduXNvFCHNO91NaTQatv5AandUPsSGc8TkJWi9BFf3LjSekjF+oZCHI/O3BkfcXy1BKOTEFz9U\nVVXJJn4AbCMFofnbWjNBLTxoTo/mHRo0aICmTZvijTfekPT6uHj55Zcxffp0dOvWDdnZ2bKNQ1Fv\niAGwr70nfR215ZYzyUxJgRBi0fOI2kpLGZcWUtnw9e60V7Q5jbejcCSpbW43qNPpzKps5PIGMgda\n7yHUd9pcvYel+VuDUgWOfFmor68v+xlIrXKicIQUhOYvpmaCfj99fX3h7u6OJUuWwMvLS9BCXAze\nffddLF261OK8Ll68iAMHDqC8vBzz588HIL3/k+DY9SHHQAtZioqK2G5ptqC4uJj9MKgqyBoIITbl\nNLj9IDQaDQIDAwWVRxUVFZLcDGLmT+Wo9KahnbyA+3F9qVoxyhXO4Yc8uPOnf18lQmP2eDoBludv\nLq9iznZCLnBPXXyCtWf+1iAlKVgDd7MCABs3bsRHH32ELl264M8//8Tu3bvRv799rWLv3r2Lu3fv\nWnxOmzZtMG7cOMTHx5s8Th1px48fj02bNtkyrKgvQr0ihuLiYpPCMDGorq5m/YgCAwNFLyCUGMz1\nYubPj6qbaIzWz8+PJQmu8sjWhcUemNu584/c3N7Ltsb1uVAqqU3nT4uYKMxJSaWCVH5VfJWQUO9r\nmiCVM4nOn5NYm24x87cWclKSFADT/Iy3tzeuXr2KFStWIDExEbm5uTAYDOjSpQu+/fZbhIaGyjKH\nmzdvorS0lP05JycHI0aMQExMDEJDQxEcHGzL26nJZz5sDSVptVp2h849aoodSwy4yiN/f39otVr2\nxqE3GQ0JKNHXwNLO3Za4vti4Mt/ATc5wDv38dToda3FBr4HfTMaRuD4XUhn90fkL9b7m5oUoaFhQ\nTthCCpbmLzbkpDQpUFEG/eyAGmVQQkICkpOT0aRJExw8eBDx8fFo1qyZbPNo2bKlyc+0KLFt27a2\nkoJo1IsTA9Uyc6WglsBNMtMYIsMw8Pf3t2lcIZkrF5R4qLqJhgBo8oubmFTCCM+RnTs3Ls6XMpor\nqjPXXEcuWArnCElJuYVR9tR7yGX0JwTuZ8fdAFmyGHF0PCkdWa2FnBiGQWVlpaKkQO9Nev+uW7cO\ny5YtQ1JSEjp27Cjr+JaQnZ2Ntm3b4uzZs7LJVVVi4IEQYtKrwdvbG+Xl5TAajTYXr5gjBq7nkZDy\nCAC7C+RqqaWqXBYCt1eyozc63yJCaJE1p86RC7aEcxyNi0tdGW4N/LoBGo40F/JzNGQmt023uZAT\nANaHTI57gEKIFGhuITExEZ07d7byDi4NNZTEh7VQEu3VYDQaTQrTHFEz8V/HJx56pOYrj2gMnC5i\nXL21IztZIXCT2lI4z/ItIoSqTwGw9h1KJdHF7ty5UkwAJvMXY3GhlPsrHU9Ibmsp5AeYFnbZssgq\n0bvBks0F3+ROqnuAglsdTklh69at+O9//4uff/65tpOCaNQ7YuCbYFFw+xuI7dUgZjwuMXCVR5Y8\nj7jKIzoPflEX31PeXoWHvWoZseAust7e3qisrGRvbOoBJVfyV6qdu7m4Pn+RdXd3Z7X7SthAiI3x\nWyvsErvIKtnQB7Bsc8EPW0pRGMi3DAGA7du349///jcOHDiArl27SnZtro56QQz0i25u58+vMuZ/\nsew9MXDB91Wy5HlkbpHmVy5b2smKuUGkTIxaA3eRpjt37iIldfJXrp27uXoPbs0HnTcNockBe5P2\n/MIuodOc0CJLSRyA7A19APOJZkunOUcKA+mGjEsKP/30ExYsWIB9+/ahZ8+e8l2sC6Je5BjoLvve\nvXvQ6/UIDAwE8GB/A3M7ZvqcoKAgm8alOQ1PT092Z+zv7w+GebDbGk1U2rtIm/OBEVIIOSMGbm2R\nNpf8tacoSimLC+54NARJT4FS6PXNQa6dO3eR5VbaU6dSqUKNYubBPSnYEuYS8nKydhqihYdcUti5\ncydmzpyJ+Ph49OvXT7qLcz7U5DMFXTQrKiqg0+nQsGFDk1i/j4+PxdizI8RAd2Vc5RH3pEDfn1pg\nSLFICymEhMIdSsXAbV2kzSV/xShsuOocJaqZuYs0TfwCwousFI1wlArn0O8Q/QwAYcM8qWEvKfBh\nSUDA/Qy41ejUPjs+Ph5Tp07Frl27MHDgQMmuzUWgJp/54BZpcWP91hYre0JJXALw9vZmQyfcnRgA\nNkYs5c6WYRhR4Q4as5U73GGr5QQ3XCDkY0PVRULhDqVqIoTG4y7S1uoN7En+csMrcu/cadiSNrfi\nhi4d6dFgCVKRAp2/mJATdVKlG8N9+/Zh6tSpiImJqYukIBr14sTAVabQxC69ucQsHjQHERQUJOrL\nyj2NUHsLvvKIJg75O025QG86bnW1LTtxe8cDpK1RsGSdTBcsJSwu7C22EiPlFfoMlC7uMjeepep3\nR05DUpKCNXBrTADg0KFDmDNnDvr374+EhARs374dERERso3vZKihJApKDOXl5dDpdGysX+zNZQsx\ncE8jdLGnpmLAg8ojX19f2RcxIY8l7g1uT28GW8eTAzTcodPpTDpxyVnvAUh3ffzkr6Vwh1IeWcD9\n6xMjJ+aehoR6HIj5DKhMXAm7F/54vr6+uHz5Mr766iscOHAA+fn58Pb2xpAhQ7BgwQIMGDBA1rk4\nAWooiYImP+mO0hZSAEwttC19afV6PcrKyljJK7W3MBgMJta9VHkkt4YfEG5oT69JTG8GW2Wk5saT\nA/TkRz2lvL29TVxV5dC682PSUoU7+CozfrhDqUJAW69PSKXFd+a1dCKV8u8pBvRkwh0vPz8fe/bs\nwebNm9GxY0fEx8djz549Ji1l6xvqxYmhuroaBQUF8PT0ZJPPttxg1EjPkoket6MbVR5RWw0ArDxQ\nr9crIg8F7JejmtsFWvNAksosTizMjWdpJ+7IaYivc5fb4oKa4VHIfRqScpE2dyLlyqlpjkZpUuCG\nq3799Vc899xz2LBhA8aOHSv7HFwAaiiJi4qKCjbMY4tLKnCfGPh9nQHhjm70pqDhDX5fAzkkjPw5\n8WsGHFF38HXu/AUKsK3FqBSwhfTMKYRsOQ1J1QVNLPgVuNxrcNTHSQhy79yFckNAzXfJx8dHNpUT\nhVDO5MSJExg7dizWrFmDcePG1QdSAFRiMAUtpbeHGKjPEp8YuGZ7VHkEwCTeDdxXHlEpKneBkqJi\nkwuuPFRqOSpXAshdoGgVsxKk4Kg5naXTkLmCKCULAQHLJCQ2L2ELlDwJccfjh2jlsLgAhEnh9OnT\niIqKwtdff41//etf9YUUAJUYTKHT6VBdXS24wFuDwWBASUmJiX+SGM8jS8ojoR2Uo9YQSvU1AO4v\nUNR4j0LO05DU1czWiuqoN4+SJyFbTybccI09CiGlSUGobkBqouNCiBTS09MRHh6OZcuWYdKkSfWJ\nFACVGEyh0+mg1+sfWODFgBIDrXngmu1RyaslzyNryiOhYiJb+xpwF2kl5K98EqLXLBSukeI0JHc1\ns9BpiEKpRdPR/sy2Wp87mxTMyXKlKgwUIoWMjAyMHj0aH3/8MSZPnlzfSAFQicEUVNJYXFwsqqiN\nC6PRyL5Oo9GwyiOqbqL5BPqldUSZIyamL6TsUFL+aq7al/t7odOQvTp3cw6icsFoNKKyshJ6vd6k\nuFHqsB8FNzwmZfU7v14CuK8QonkopcJj9uQwzFlciKm+pt5OXFK4cOECwsLC8MEHH2DatGn1kRQA\nlRhMQRcqe4iBEML2i66qqmLN9qhUkmtvQQvppLjhzBVDcW8MSgpKyV+5uzAxJGTuNCRW525v9bS9\nEDqZOBqusTae3L5V5hRCDMPAy8tLcqLjQ4rEtrkTnVDoUogULl++jGeeeQbvvvsuZs6cWV9JAVCJ\nwRT0pi4qKkKDBg1sik/TEwMA1mxPyPNIzhvcUqhDaY27vePZapQnZfMgsfOzdjJxlOj470W/M0r4\nVgH3cxiUzLgWLbaELsVCDrWTpepravhHCGFP9JmZmRg1ahRmz56N2bNnuwQpLFmyBAsXLsRbb72F\nL774Qsmh1QI3PrgqCLGgCU/gfqxZyPNIbjdPbjGUl5eXSaiDynDp4iR1G0dAmhoFbkiMT3T8YiiN\nRiNp8yBrEGu+xzCWfajEqmu4iXQlHGAB4RwGNy8hpu+yLZBLAsstzgRMlWb0e5SamopDhw6hT58+\nWLhwIWbMmOEypHDy5El8++236NKli7OnYhby3m0uBPqF4MaMrYEuuPwiNa4claqT9Ho9GjRooIjF\nM62o9vb2RkBAAPz8/FiTvsrKSpSWluLevXuoqqoy25jIFtA+1B4eHpIlKekC6uPjA39/f/j5+bH9\ntSsrK9kchtz5BMA0XCXWPwu4T3S+vr7w9/dnP3960qGfg06nM/kcaLjK2aRAr8HT0xO+vr4ICAhA\ngwYN4OHhwebJSktLWVdiWzZUSlY00xMb/Rv7+PggLy8PMTExmDBhAu7evYvff/8dP/30E0pLS2Wb\nhxiUl5fjxRdfxIYNG9CwYUOnzsUS6g0x2AqDwYDS0lIYDIYHeigAYJPOtPmOLQuKvaALGFciC4B1\n8fTz84O/vz9LElqtFmVlZSgvL2ettm0BJRpub2o5bnC6A+TWgtDHqqqqTK5BCqLjgirMHA1XUaLz\n9vZmiY5L1vQatFot2xfE19dXEVKg9hpeXl4W1U7myJom40tLS0V9DkrbXHDFEDR/OHToUPj6+mLK\nlCmYPXs2zp8/j+effx5JSUmyzsUa3njjDYSHh2PIkCFOnYc11KtQEiDuxFBdbdrmk6s2ortEpT2P\nxFpKc9s4mmtDKSaWLHXNgBgIafj518Dt0OVoPFxOx1JLttsA2I2GRqORNKbPhSNqJ6FwDQ05WeqU\n5kxSoMSek5ODsLAwjBs3Dp988gk0Gg0++ugjZGdno0mTJrLOxxJ++OEHpKWl4dSpU06bg1ioxMAD\nv80nvYG9vLxM/PTpeylR9GSuD7Q1mGtDaa0ngNLyUEvVzNauwZqU1xwcTaTbArqA0vg3JQtHr8ES\n+KRAT2KOXINQ32haAEiJRK/XK2KdDQiTQl5eHkaPHo3w8HCWFChat24t63ws4ebNm3jrrbeQmJio\nSHjUUdQbVRItnCkpKYGbmxv8/PxMfk9DL5WVlRaVR3QXTa2rucoaOczN5HArtSSDpc1ZrJ1MpIK9\nck17+xoApn9TpRcwbt2HOaWZo/0xlFQ70Wuoqqoy8UBSwg+MHwLMz89HWFgYBg8ejK+++kr2U7wt\n2LVrF8aOHQs3Nzf2czYYDCYhU4US46pclQtKDLQPM5cY6A5Zp9PBx8eHvZH4nkd85ZE57yCp2h/S\nhKGclamW9OGenp6ymptJFa6y5qbKXWCVrvYV24rTXK2BrUV1zpDAcsNH1PqcLyN1xOqFD3q/0toW\nNzc33L17F2FhYejXrx9Wr17tUqQA1PilZWdnmzw2adIkdOzYEQsWLEDHjh2VmooqVxUCP5REE8hc\nVZE5zyOj0WgSWuFKSKmfvj3SRT4cNYqzBXSOANg5e3h4sOErQHiBdRTcQjJHw1XmPgd+K1CaJ1Kq\n2teW/szcmD6/PwaN6VsrquOSglJqJ0oKXKKl3ydLOS5zhoXWIEQKRUVFiIiIQK9evbBq1SqXIwWg\npovh448//sBjjRs3VpIURKNeEgNd9A0GA8rKythiGCHPI26LSkstI/mLE/fGpuX8YkIE/F20p6en\n7AuYudAKdxduqdeyrRBbM2AvhJKmXMkoDX1wbcOlhqP9mc2JCMzlhwAoXhchRAr8axDKD3HzErZs\nnLi5L3ovlpSUICoqCp07d8a3334rexGklHCFmgpzqDehJLpAcKWCVHkkh+cRF9yThKUwh9JJX0B8\n4Zo5WwhbTfJs2UVLAf7pi5r9ibXctgdyqp3MVY/T3ykdPrInJMcNX+r1elGNlLj3Bt1MlJWVISoq\nCm3atMGWLVtkz4fVEag5Bi64xECLdcx5HjEMI1t8X2iB5Vo8A1DEHdWRcJW9Hd64py+lSMFcvN1W\new6xULI/M70GrVZrUlcgd+JXyjyNJXsL7jXwSaG8vBxjx45FcHAwtm3bViuUPi4ClRi4MBqN0Ol0\nbNGaGM8jueP7QiRBj99yGptxw1WO+joJucEKyWCVbmhvi+WEJXWQLfkhpTX8fMM/6hMktMBK9X2S\nO3kvtOmgKCsrQ/PmzVFVVYVnn30WjRo1wo8//qhI2Mwc1q5dizVr1uDatWsAgL///e9YtGgRRo4c\n6bQ5WYFKDFwYjUYUFRWxu3Jajm5OeaTUkZzeaJQQpGzcIwQh91CpYG4XztW30xOanHAksW1JHWRp\nF660BNbaNQpZnztaGOgsRRf9DJ577jlcvnwZLVu2hLu7OxISEhAUFCTrHKxh7969cHNzw6OPPgpC\nCL777jssW7YMaWlpLplUhkoMptBqtSgqKoKnpyd0Oh0CAwNFKY/khKVm9o427hGCMzq8abVaEysO\nW3fh9owrZZ7GUr9o+lkovWBySUFM8p5rlCe2xwcfzr5GNzc3pKSk4KuvvsJvv/2G4uJi+Pj44Omn\nn8Z3333ndILgonHjxli+fDlefvllZ09FCKpclQsvLy80bNiQ7f1Mi0tsUR5JBWvxfWpsZqli2daE\nqVhLDalA8zYGg4F1hOWHnKSWwcrRu8GctQWVkNJNBfUYUmLB5MfbrUHo+8RVCFlTBzmTFGi+TafT\nYdWqVdDr9bh16xZu3LiB3bt349ixYwgMDJR1PmJhNBqxY8cOVFRUoG/fvs6ejkOoNycGuhjrdDqW\nBLjadqV6GjgiR7WUMLW087O1uY6jsEZ8XEUKNwHvSMLUGWonmouikLMCno5pKylYez9rYTPqFOss\nUqBOrxMmTEBJSQn27t3L9op2FWRkZKBv377QarXw9/fH9u3b1RxDbcGff/6JkpIS/O1vf2OToUVF\nRfDx8QEgfZJOCNwb29H4vtiqa25CVAmzP1urmaWQwSqtdgJMbaypXbg99hxiITUpCEHoswBqwpg+\nPj6KKOXod4dLCq+++iry8vKwb98++Pv7yzoHe6DX63H9+nWUlJQgOjoa69evR0pKCjp06ODsqQlB\nJQYutmzZgrfffhtNmjRBeHg48vPz8f333+PYsWN49NFHBZO+UpKEtT7JjsCcqsbNzQ0Gg8FpKhlb\nic8eGaycNQNCsOZYak2jb0/YTOnTEHA//8V1CpCj5oNCiBT0ej2mTJmCa9eu4cCBAy4TMrKGp59+\nGu3bt8eaNWucPRUhqMTAh06nw/79+/HWW28hOzsbQ4YMQZcuXTBmzBh07dqVDStxFyYplEFK7mgt\nJX0dMWYTM66USV+hhCl/YaJ/VyVPQ1zDP2uOpeY0+rZUjzuDFPg5BQCy1HxQCJGCwWDA9OnTcfny\nZSQkJLh0Uxs+hg4ditatW2Pjxo3OnooQ1OQzH2VlZfj4449x584dxMTEICAgANHR0Rg3bhy8vLwQ\nERGBqKgo9OrVy4QkhHoZiL1B5XBHFTMmtQp3d3c3SfpSFZSrJ33NJeC5dgqEEGg0GsVJQayUmet/\nBJiGarj+R+Y2Hq5ACvT7IdSSVa/XP9CS1dbvFL/exMPDA0ajEbNmzcLvv/+OxMRElyaFhQsXYtSo\nUWjVqhXKysqwbds2HDlyBAkJCc6emkOodyeGl19+GXPmzEH37t3Zxw0GA44dO4bo6GjExcUBAEsS\nffr0YW0U+EVc1uSjUvRJtgXW4vvmqq4d8T6iyUlnJ32ljOcLjSm1D5GlsBm9BiX/roDt6iNrVcvW\nvlNCf1ej0YjZs2fj+PHjSEpKwkMPPSTpNUqNyZMnIykpCbm5uQgMDMQTTzyBBQsWuHKHNjWUZA+M\nRiOOHz+OmJgYxMXFobKyEuHh4RgzZgz69+/PFmtZIgkAirmjUtgaypEy6atUfB+4n/T18PBgzQrF\n2G3bCzkLArlj8EM1FHQXrZSRoiObGFuK6oScYI1GI+bPn4/Dhw8jKSkJTZs2lfQaVQBQicFxGI1G\nnD59mj1JFBcXIzw8HBERERg0aBCbIOPHXqmDq6N2E7bM05FQjtDulWvxLLRbVVrtBNQUKVoiW6ll\nsFJag4sF/bsC9wsv5T4RUVKQ0o7cUlGdu7s7uynhksJ7772HgwcPIjk5Gc2bN3d4Dvbi008/RVxc\nHC5dugQfHx/069cPS5cuxd/+9jenzUlCqMQgJYxGI86dO4fo6GjExsbi9u3bGD16NCKvUKEjAAAg\nAElEQVQjIzFkyBB4enqitLQUOTk5CA4OBiC/rh2QPrEtpuqa+h4pGSKztcuboyciJeShfPBPYHSD\nYas9hy2QgxT44BfV0TWnoqICqampGDZsGD7//HPs2rULSUlJaNmypeRzsAXPPPMM/vnPf6Jnz57Q\n6/V49913kZGRgYsXL7Ly9loMlRjkAiEEv//+O2JiYhAbG4vr16/jqaeewsWLF1FQUID09HT4+/sL\n1hhISRJyG9MJGeTRXayr1kUIwVYZrCuQgtDfVYw9hy1QghS44H6Wbm5uOHjwICZMmMCKJD744ANM\nnDgRTZo0kXUetqKgoABNmjRBSkoKnnzySWdPx1GoxKAECCHYvXs3JkyYAEIIWrRogU6dOiEyMhIj\nRoyAj4+Pya5PqtCA0qZtRqMRWq2WJQhAmUpfqZO+1mSwzkj62pOrsdf6nMIZpEDzbpTgCSH4+OOP\nkZCQAIZhcPr0aQDAokWL8J///EfW+diCzMxMPPbYYzh//vwDXdhqIVRiUALp6ekYMGAA2rdvj/j4\neFRWVrIniYyMDAwbNgxRUVEYOXIk/Pz8BJOlthrLcROwSt3U3FAOjQlLTXb8MeWO71tK+irVPY+e\n+hyR3ZojO771OYXSpADczw9xSWHFihVYt24dkpOT0b59e9y5cwfx8fF49NFHMWDAANnnJAaEEISH\nh6OsrAxHjhxx9nSkgEoMSkCn0+HDDz/E/Pnz4efnxz5OCMH169dZkjh79iwGDx6MyMhIhIWFISAg\nwCSeL4YkrHkQyQFroRxzlb6OuKg6K5RTXl4OQLmkrxz9G4Ti+dzroCc/Z5ACLQokhGDlypX4+uuv\nkZSUhMcee0z2OdiLadOm4eDBgzh27JhTE+ISQiUGVwEhBDk5OYiNjUVMTAxOnDiBgQMHsiTRuHFj\ni0lGultWuhe0rbt2c6ZsthQ/yWkdYg5CSV9uPN+e67AGJZr6mCNthmHg7e2tiAyWnm65pLBu3Tos\nW7YMiYmJ+Pvf/y7r+I5gxowZ2LNnD44ePYpWrVo5ezpSQSUGVwQhBHl5edi5cydiYmJw7Ngx9O3b\nF1FRURg9ejSaNGkiWDREd7FKNRDi9m6wd9duq3zUGZW+YpO+Uspgle70BtxfoOlcuZsPRwocxYzJ\nJYWNGzfio48+ws8//4wnnnhC0vGkxIwZM7Br1y4cOXIEbdu2dfZ0pIRKDK4OQggKCgqwa9cuxMbG\n4vDhw+jZsyeioqIQERGB5s2bIz8/H+fPn0fPnj3Z18nd01eOBdpa1TUNHylZLGdPLYajMlilexsA\n9yvwueEjS9chRcdALinQjczWrVvx/vvvIyEhAd26dXP4uuTC9OnT8b///Q+7d+82qV0IDAy06o9V\nC6ASQ20CIQRFRUXYs2cPYmJicOjQIXTo0AEFBQWorKzE8ePH0aRJE8ktLfhQwvBPqEIWANsvQonw\nkRS7dlsLA12FFMRchyMdA7mW5HQh/d///ocFCxbgwIEDJpscV4S56920aRMmTJjghBlJCpUYaisI\nIUhJSUFERAQ0Gg0aNWqExo0bIyoqCpGRkXjkkUdMEtdSNX6Xuy5CCHSx5MJa1bVUY0q5QFuTwRqN\nRkV9swBxpMCHvc2g+GNySeGnn37C7NmzsX//fvTu3Vuy61NhF1RiqK24cuUKunbtio4dOyI+Ph6+\nvr7Yt28fYmNjsW/fPrRr144lCdqEXGjnagtJKF0XwR2Ta+9sbnG1Z+cqZkw5K335MliNRgNvb2/Z\n+l1zYQ8p8GGuGZQ5pZYQKezcuRMzZ87Enj170L9/f8muT4XdUImhtoIQgrVr12LChAkPtDGsqKjA\nwYMHERMTg7179yIkJIR1gu3YsSMA2NxTQmkXWDFjClVdW9Lm2zKmklJNbtKXEKKIDFaO66QkYc6w\nkNa6cEkhPj4eU6dOxc6dOzFo0CCH5+AIjh49imXLluH06dPIzc3Fzp07ERER4dQ5OQkqMdR1aLVa\nJCYmIjo6Grt370bTpk0RGRmJMWPG4O9//7tg4yF+Twmli+UA2wv0zGnzbam6dsZ18hdoALLLYJUi\nPyF7DoZhsG/fPvTr1w8ZGRl47bXXEBMT4xIW1AcOHMCvv/6KHj16YOzYsYiLi1OJwdKTVGKoG6iq\nqkJycjJiY2Oxc+dOBAQEsCRBu9OZ8z2iFtZKtsW0t0DP1vAG8GAyVEnyMzemuZoPRxRnzjgR0TE1\nGg3KysrQqVMnVFVVwd/fH2PHjsU777yDxx9/XJG5iIVGo1FPDNaepBJD3UN1dTVSUlIQHR2NnTt3\nwtvbm+0p0atXL+j1evz0008YMWIEW7QmdSyfD3scUsW8p7kdOD1J0I5vSlmgA/YRkaMyWH58X8kc\nEZeI9u7dixUrVsDd3R1nz55FeXk5evfujdTUVJchB5UYRDxJJYa6DYPBgF9++YVtPEQIQfPmzZGW\nloYffvgBI0eOdEiFIgZSOKSKGUNoBw5A0dwJX7+vhAzWVUghJSUF//znP7F582ZERERAq9UiKSkJ\nt2/fxiuvvCL7nMRCJQYRT1KJof6gsLAQQ4YMwYULFxAaGoorV6480J1OKEzjCEko0QFNaExKRDRc\nBshfGMj1BJLqdGKtP4Zer1c8TCZECr/++iuee+45rF+/Hs8++6zLnA6EoBKDdchfSaTCJUAIwejR\no3Hjxg2kpKQgNDSU7U43a9asB7rTeXt7m5CETqezmSRsbTcq1XUK9RGm11FZWQnA8ZoP/pg0d0Lt\nH6QCwzDw9PSEp6eniVKLhsiA+2otJcCV+1JSOHHiBMaNG4e1a9e6PCmoEAf1xFCPkJCQgJYtWz7g\nKW+uO11ERASGDh0KT09PmxO+Ungt2QoxpxOhqmtHSIKbO1HKxwpwjgxWqAbk9OnTiIqKwpdffokX\nX3yxVpCCemIQ8SSVGFRwQQhBRkYGm5PIzs7GqFGjEBUVhWHDhrEnCUs9JehJQUkzPHv6N1izgrA2\nb2eTArdmQGrrcz6ohQiXFNLT0xEeHo5ly5Zh0qRJLk0K9+7dQ2ZmJggh6N69O7744gsMHjwYjRo1\nQkhIiLOnpyRUYuBCp9MhNDQU586dQ1pamks7O7oKCCG4fPky21Pizz//xPDhwxEVFcV2pxPqKUGh\n5EnB0f4NliwtzLX/dAVSUEIGK0QKGRkZCAsLw8cff4zXXnvNpUkBAI4cOYLBgwc/MM+JEydi48aN\nTpqVU6ASAxdvvfUWMjMzsX//fpw9e1YlBhtBCMHVq1fNdqfz9/fHpUuXUF5ebuJIye0pIcfiIUfI\nylrVtUajYducKpVQB5wjgxUyG7xw4QLCwsKwaNEiTJ8+3eVJQYUJVGKg2L9/P+bOnYuYmBg8/vjj\n6onBQXC708XFxeHMmTPo3bs3zpw5g7Zt2yIpKQlubm5mexhIRRJK9G8QqrqmcKWCOTEwl18xJ4MV\nIoXLly/jmWeewYIFC/Dmm2+qpFD7oBIDAOTl5aFnz57YvXs3GjVqhDZt2qjEICEIIfjxxx8xceJE\nPPzww6iqqkKPHj1MutMJOcE6Kh11RlMf7ulEqfafgDyV29ZksPRauaSQmZmJUaNG4e2338acOXNc\nghRWrVqF5cuX4/bt2+jSpQu++eYb9OrVS7bx6Gdu7udaAFGTld9X2cl4+eWXMX36dJduDFKb8euv\nv2LixIkYOnQoLl++jPPnzyMqKgqxsbHo0KEDIiIisGnTJhQXF6NBgwbw9/dnu3lVVlairKwM9+7d\nQ1VV1QM5CnOg/ZmVTm5XVlay0lt/f380aNCAVWxVVFSgtLQUFRUVD5wsHIFcdh5UBtugQQMEBATA\n19eX9c4qLy9n7dezs7NhMBiQlZWF0aNH44033nAZUvjxxx8xZ84cLF68GGfPnkWXLl0wYsQIFBQU\nyDLezZs3sW7dOiQkJLCP0aZHdQ218sTw7rvvYunSpWZ/zzAMLl68iAMHDiA6OhqHDx+GRqPBtWvX\n0LZtW/XEICG0Wi1WrFiBOXPmmMTaxXSns6enhJhWnFLDmuLJWsLX3tCZMzyeaPiIYRhotVp06tQJ\nPj4+aNCgAZ588kls2LDBZbqY9enTB71798ZXX30FoOZzCAkJwZtvvol58+ZJPt7333+P1NRUbN++\nHc888wy6dOkiyzgyo+6Gku7evYu7d+9afE6bNm0wbtw4xMfHmzxuMBjg7u6O8ePHY9OmTXJOU8Vf\nEOpO17VrV0RERCAyMhIhISGiekq4IikIQch51NbQmTNIgf59NRoNGjRoAKPRiAMHDmDVqlU4f/48\nCgsLERAQgPDwcGzevFmRk5o5VFdXw9fXFzExMSb1CJMmTUJJSQni4uJkG/vixYvYunUr4uPj4e/v\nj7i4ODRp0kS28SRG3SUGsbh58yZKS0vZn3NycjBixAjExMQgNDQUwcHBNr9nZGQk0tLScOfOHQQF\nBWHYsGFYunQpmjdvLuXU6ywIISgrK0N8fDxiYmJw8OBBPP744ybd6YAHe0poNBoYjUZ20VKKFByV\nwdqjCnImKXBJNzc3FyNHjsSYMWPw6aef4sKFC4iLi8O1a9ecLvHMzc1FixYtkJqaatIVbv78+UhJ\nSUFqaqqk49FcAv0OVlZW4uLFi3jttddw79497Nu3D23btmV/78JQiYGP7Oxsh5PPX331Ffr27Yvm\nzZvj1q1bbLz1l19+kXi2dR904d23bx9iYmKwf//+B7rTAcD58+fRpEkTtqeBLUVojs5NShmsOXM8\nLklIYcJnK4RIIS8vD6NGjcKoUaPw+eefu9xipxQxWEsu5+fnY8yYMSguLkZ6ejrc3NxcPSGtEgMf\n2dnZaNu2raR1DHv27MGYMWNQVVXl1KN1XQC3O118fDxatWqFvn37YsuWLXjhhRfwzTffmFhzAPLY\nhctBCkJj8E9F3P4YSvVTECKF/Px8hIWF4amnnsLXX3/tcqQAyB9KqqqqYr9b1pCVlYWxY8eiW7du\nTj9JiYCqSuKjdevWMBgMkpFCYWEhtm3bxjqTqnAMvr6+GDNmDL7//nvcvn0bzzzzDDZs2ICQkBCc\nPHkSH330ES5dugRvb29BJU15eTm0Wi0MBoPdqiCj0Yjy8nIYjUb4+fnJVrnNVwXR9phAzaInxbVY\ngxAp3L17F+Hh4ejfv7/LkgJQY/fRo0cPHDp0iH2MEIJDhw6hX79+Dr13RUUFZs+ejT/++EPU81u1\naoVZs2bh5s2buHLlCjuX2gzX/NRdHAsWLICfnx8eeugh3LhxAzt37nT2lOocYmNjsWzZMrzwwgs4\nc+YMli9fjoKCAoSHh6Nr165YtGgRzp8//wBJ6HQ6lJeXo6ysDJWVlWyPCTFwRm0EUGPXUl1dDS8v\nL/j7+0tyLdZAr5VLCkVFRYiIiEDPnj2xevVqlyUFitmzZ2P9+vXYsmULLl26hKlTp6KiogKTJk1y\n6H1zc3PRuHFj6HQ69jFLf3c3NzdERkYiJycHW7duBQBXDiWJQr0KJZmDWPkrtXooLCxEYWEhsrOz\nsXjxYgQEBDygflLhGNLT07F582YsW7bMZIHmd6fz8vJCREQE252OYRi7eko4ixQs5RTMtTF1tIkS\nPRVxSaGkpAQRERHo0KEDvvvuu1pzAl69ejU+++wz5OXloWvXrvjmm2/Qs2dPu96ruroaBQUFrJDk\n4MGDeOSRR/DYY48BgNnEMn08NjYWsbGxWLt2Lfz8/Oy/KHmh5hjEQoz8tW3btoJhhVu3biEkJOSB\nJJgK+cHvTgeAlcD27duXlbhaW1idRQq2NPaxp9e1ELjX6ufnx/ZqjoqKwiOPPIKtW7cq1tvBlaDX\n6/HSSy8hNzcXPXv2REFBARo1aoSbN2+iTZs2FjeOFJcuXcKhQ4cwefJkxUwV7YBKDErg+vXreOSR\nR3D48GEMHDjQ5tdnZ2fjww8/ZFsgtmjRAuPHj8d7772nSGObugKj0Yjjx48jOjoacXFx0Gq1orrT\nubu7s4lsf39/xcInXFKwtWDMWq9rcyQhRArl5eUYO3Ysmjdvju3bt9fb79yVK1eQmJgIjUaDyMhI\nXL58GeXl5cjKykJiYiLmzp1rNnfBVSGVlJQgMDBQyanbCpUYpMaJEydw8uRJPPnkkwgKCkJmZiYW\nLVqE/Px8ZGRk2HVTHTx4EDt27MC//vUvtGvXDhkZGZg8eTImTJiAzz77TIarqPswGo1sd7rY2FiU\nlJRg9OjRiIyMxKBBg+Dh4QGDwYCCggJ4eXmxZCBl/wJLcIQU+BBbdS1EChUVFXj22WcRFBSEHTt2\nKOYSaws++eQT7N27F2lpafDy8kJhYaGs4506dQq3bt1CWFiY2ZMT7QJYVlaGRo0asT1IakleQSUG\nqZGRkYFZs2bh3LlzuHfvHpo3b45Ro0bhvffek7TAbfny5Vi7di0yMzMle8/6CqPRiPT0dNYunHan\n69evHxYvXowRI0bgq6++Yk8TcjS54UJKUhCCUNW1m5sbjEYjSwpubm6orKzEuHHj4O3tjdjYWJcN\nfSxevBgNGzbEjRs3sHHjRtmJAaipm7l48SKaNm2K0NBQtn4GqAkdnzp1Ci1btoRWq0VJSQmGDh3q\nsn8/AajEUFvx73//GwkJCThx4oSzp1KnQLvTrVmzBhs2bMDDDz+MwYMHIzw8HMOGDYOPj49JuIlL\nEnQH7ghJyE0KfBiNRpPe0GVlZXj99dcxcuRIJCcnAwB27txpsvC5KjZv3oy3335bcmI4dOgQzp8/\nj1atWmHs2LHs4wsXLkTjxo0xZ84c9rHCwkL88ssv6NWrF7sRzMrKwoULFzB06FCX8ZCyArWOoTYi\nMzMTK1euxNSpU509lToHGlL56aef0LFjR/a/n332Gdq0aYMJEyZg165dMBgM8PPzg5+fH7y8vGA0\nGlFZWYnS0lLcu3cPOp3OZtmo0qRAUV1dDYZh4Ovry/ogvf/++0hISEBxcTFWrVqFq1evKjYfV8LK\nlSvx0ksvYcOGDfjHP/6BmJgY9ndTpkwxIQWgpv9G586d0bx5c1y/fh179uxBmzZtEBwcjGvXrik8\ne3mhEoNMePfdd6HRaMz+c3Nze6CA5tatWxg1ahSef/55vPLKK06aed1GVlYW2rVrh6SkJPTr1w/v\nvfceTp06hbNnz6JXr15YuXIl2rRpg/HjxyM6Oho6nU7QLpxLEtZsl51BCvy+2x4eHmjbti38/f3R\nv39/fPvttwgODsb777+PiRMnKjInV8KWLVuwcuVKHDhwACdOnMD69etx7tw59vetW7cGYFq/kJub\ni1u3bgGoIZXffvsNQE0L2wYNGig4e/mhhpJkgq0S2JycHAwePBj9+vVTXV9lhiWjM253utjYWJw9\nexaDBw9mGw8FBgba1HjImaRgNBpZ+W11dTVeffVV3L59G/v374e/vz8AoLy8HLdv30b79u0VmRtg\ne90QIG0oaceOHVi4cCFSU1Px8MMPAwDOnDmDhIQELFiwwOS5BoOBlS+XlZUhOzsbnTp1MnlOamoq\nWrVqhRYtWjg8NwWg5hhqC27duoUhQ4agV69e2Lp1q2TJTqUVHXUNhBDk5OQgNjYWMTExOHHiBAYM\nGICoqCiL3emoMR6N8TuDFGg4zM3NDXq9HlOmTEFWVhYOHjzodDmlPXVDUhFDSkoKZs2ahZycHKSm\npqJt27YAgOTkZGRnZ5tUTWdlZaGwsBA9evQAUBOWO3z4MJo1a4amTZvC29sbFy5cAFDTG6KWQCWG\n2oCcnBwMGjQIbdq0eaDitGnTpg69tzMUHXUVhBDk5eVh586diImJwbFjx9C3b19ERUVh9OjRaNKk\nCdtTQqfTsSTBMAy8vLzMWmxLPUe++Z/BYMD06dNx6dIlJCQkICgoSNY5yAVHiYGeEj///HMMHDgQ\ncXFxuHLlCpYsWYI2bdpg48aNaNq0KUaMGMES0o8//ojAwECMHDmSfZ/q6mpcunQJ1dXVrFiBf4Jw\ncYgihvpX4uhi+Pnnn3H16lVcvXoVISEhAO4XzNDFxV588MEHAGpuKhWOgWEYNGvWDFOnTsWUKVPY\n7nQxMTGYP38+250uPDyclRqvX7+e7YSm1WrZk4S7u7vk1dVCpGA0GjFr1ixkZGQgMTGxVpLCjRs3\nWPsZg8GA9PR0AED79u1tiuvTfh5ZWVmYNWsWKisrsXr1aqSkpKCgoACTJ08GAIwePRp9+vTBs88+\ni0uXLmHUqFEm7+Ph4YHOnTtLd4EuCjX57GRMnDgRBoPB5J/RaHSYFFTIB4Zh8PDDD2Py5MnYt28f\nbt68iVdffRXJycno2rUrvvjiCzRu3BiFhYXw9fVFQEAAa6Ot1WpZYzzqnuoozJHC7NmzcerUKSQk\nJKBx48YSXLnyWLRoEbp3747FixejvLwc3bt3R/fu3XH69GlRrz948KDJzzSMNnDgQPTq1Qtffvkl\ngoODceHCBWzatAlubm747LPP0KVLF/z6668IDQ2V/JpqA9RQUj2AXBpwFfdBCMHChQuxZMkSjB8/\nHpWVlWx3usjISERGRqJNmzYAwBagSdFTgtt6lEsK8+fPR3JyMpKSktCsWTNZrtmVQQjBH3/8gdDQ\nUBw7dgydOnWC0WjEtm3b8Oyzz8LX1xcAEBUVBR8fH2zfvh0Mw6CiogLFxcU4c+YMRo8eDcCyWKEW\nQq1jqIuwRwarQn6cO3cOy5YtwxdffIHvv/8e0dHRuH37NubOnYtz586hX79+ePLJJ7F8+XJkZWXB\nx8fH4Z4S5kjh3//+NxITE5GYmFgvSYHisccew+jRo/H7778DqCHgl156Cb6+vmxzpP/+97/w8PDA\n2bNnAdTUKgQHB7OkYDAY6hIpiIZ6YqhlcKaiQ4VlXL58mbVo5oPfnS4kJASRkZGIiopCx44dAcCk\n7ac1i20hUiCEYPHixYiLi0NycjJatmypyHWLhTMMI99//31s3bqVbfDElZ8CNb5Hr7zyCpo1a4YV\nK1bIMgcXg7jjKCFE7D8VtRTfffcdCQoKsuu1K1euJI888gjx9vYmvXv3JidOnJB4dvUPlZWVZPfu\n3WTixIkkKCiIdOjQgcybN4+kpqaSsrIyUl5eToqLi0lBQQHJzc0lOTk5JDc3lxQUFJCSkhJSVlZG\n7ty5Q3JyckhxcTG5d+8eKS8vJwsXLiRt2rQh165dc/YlCuLAgQPklVdeIYmJiSQrK4vs2bOHNG3a\nlLzzzjuSj2U0GgkhhGi1WjJgwADyyiuvkIqKCkIIIQaDweQ5mZmZZPLkyaSwsFDyebggRK33KjHU\nYVy/fp2kpaWRxYsXk4CAAJKWlkbS0tJIeXm5qNf/8MMPxMvLi2zevJlcvHiRvP766yQoKIjk5+fL\nPPP6A61WS/bv309effVV8tBDD5F27dqR2bNnk6NHj7IkUVJSYkIS9F9WVhYpLi4m5eXlZNGiRaRV\nq1bkypUrzr4km7Bs2TLSrl072d7fYDCQ1atXk/79+5NPPvmEVFZWso/T/+p0OnLy5EnZ5uBiUImh\nvmPSpElEo9E88O/IkSOiXt+7d2/y5ptvsj8bjUbSokULsnTpUrmmXK+h0+lIYmIimTp1KmnWrBlp\n3bo1mTlzJklKSiJlZWWkqKiIfP755+Tq1askNzeXvPbaa6Rx48ZkxIgRpHHjxuTChQvOvgSb8d57\n75FevXrJOkZFRQWZOXMm6dOnD/nPf/5DSkpKCCGE6PV6Wcd1UajEoMJ+6HQ64u7uTnbt2mXy+MSJ\nE0lUVJSTZlV/oNfryeHDh8mMGTNIy5YtSYsWLUi/fv2Im5sbiY6OJuXl5eTo0aNk3LhxJDAwkAAg\nQUFBZNKkSSQtLc3Z0xeFP//8kwQGBpL/+7//k/y9uScCQmrCd3PnziUDBgwgw4YNI5mZmZKPWUsg\nar2vf+l2FaJQUFAAg8HwQPV106ZNcfv2bSfNqv7Azc0NgwYNwjfffIOrV6+ic+fOSE1NRa9evTBj\nxgy8/fbb2LZtG44cOYJffvkFZ8+exfTp05Gamor8/HxF5+pKhpHp6ekoLS1llUS0sM3b2xuffvop\n5s+fj4YNG6Jfv3544403sH79esnGrktQVUkqBJGbm4sWLVo80Mt6/vz5SElJQWpqqhNnV78wc+ZM\nrF69Gt9//z2ef/55nD59Gtu3b8e6deuQmJho0nKS3s9KdhNzFcPIa9euYezYsfjwww8RFhZmokDi\n1yLs2bMHV65cQVpaGt588010795dsnm4OFSvJBX2o7q6Gr6+voiJiUFERAT7+KRJk1BSUoK4uDgn\nzq5+ISUlBTdu3MD48eNNHq+NhVdyGUYCNTUH/fr1Q8uWLU16K1gCqT0tOaWCWuCmwn54eHigR48e\nOHToEPsYIQSHDh0y2xRdLI4ePYqIiAi0aNECGo0Gu3fvdnS6dRoDBw58gBQA1DpSyMnJwVNPPYXW\nrVvjs88+w507d5CXl4e8vDy735P2wqiuroabmxtWrFiBjIyMB6wwhMAlBRs2yPUCteubpUJRzJ49\nG+vXr8eWLVtw6dIlTJ06FRUVFSbWxPbg3r176Nq1K1avXl3fdmv1GtQw8tChQwgJCUFwcDCaN2+O\n4OBgu98zNzcXANgCuZCQEDz88MNISUmx+lrud0/9HvIgNkutVMpchWth1apVpHXr1sTb25v06dNH\ncr03wzAPKJ9UqBCDGzdukObNm5Phw4eTLVu2sAVqO3bsIH5+fuT48eNOnqFLQlUlqXAc06dPx7Vr\n11BZWYnU1FT07NnT2VNSoQJATUvN6Oho+Pn54dtvv0Xnzp2xadMm+Pv744UXXkBSUhIAqE7FdkBN\nPqtwKjQaDXbu3GmS4Fahwhbo9Xrk5ORg3bp1SElJQWlpKc6fP4+OHTvi1KlT8PHxcfYUXQlq8lmF\nChXyIjIyEq1bt4aPjw+Cg4MxYcIENu6vBAghcHd3R6tWrfDxxx9j48aN+PLLLzFixAjk5eVhyZIl\namLZDqjEUMugfslVuBKGDBmCn376CX/88QdiY2Nx5coVPPfcc4qNz1cVPfroo/aSs38AAAoiSURB\nVBg8eDCio6Px0ksv4fjx42pi2Q6ooSQVToUaSqpb2LNnD8aMGYOqqirJ25eKBa3vuHTpEnr37o3E\nxET06tXLKXNxQaihpLqGjIwMbNq0idVuUxiNxlp1krh37x7S09ORlpYGALh69SrS09Nx48YNu9/z\n008/RWhoKAICAtC0aVOMGTNGbVikMAoLC7Ft2zb079/faaQA3K/vuHHjBjw9PdUcgx1QiaEWIT8/\nHwsWLMCRI0fYx/R6vcV2kK5IGKdOnUK3bt3Qo0cPMAyDOXPmoHv37vjggw/sfs+jR49i5syZ+O23\n35CYmIjq6moMHz4clZWVEs5chRAWLFgAPz8/PPTQQ7hx4wZ27tzp7CkBAOLi4vDWW2+hU6dOzp5K\n7YNYXatCGlsVVtC5c2fyySefEEIIOXnyJBk4cCCZNm0a0Wq1gs/X6/XkyJEjpHXr1uT27dtKTtWp\nyM/PJwzDkKNHjzp7KrUOCxYsIAzDmP2n0WjI5cuX2effvXuX/PnnnyQxMZEMGDCAhIWFOXH2wqBN\neVSIW+/drVOHClfCxIkTsXXrVjRr1gzvvPMOhg8fjldeeQVeXl4mz6Nx1i1btiA6OhozZsx4wCmV\n+1yGYepUkq64uBgMw6BRo0bOnkqtw9y5c/Hyyy9bfE7btm3Z/2/UqBEaNWqE9u3bo0OHDggJCcFv\nv/1mYr6oNAjPA6kufbeVgJp8riWgC31iYiKGDx+O0NBQDB06FB9//LHZ12RmZmLQoEFYsGABpk2b\nZtIHOjs7G6WlpejcufMD49R2kiCEIDw8HGVlZSZhNxXy4/r163jkkUdw+PBhDBw40NnTUfEgRN3Y\n6omhlkCj0WDv3r1Yvnw5AGDUqFFsTF7IZbOoqAgrVqxAcHAwZs6cyT5+8eJFfPbZZxgxYgSSk5OR\nnJyMQYMGYdy4cXj66adrnTGbEKZPn44LFy7g2LFjzp5KncaJEydw8uRJPPnkkwgKCkJmZiYWLVqE\nRx99FH379nX29FQ4gNq/CtQDlJeXY82aNfjnP/+Jzp07Y8qUKbh06RKqq6sBmLpsUsVSUlIS0tLS\nMHnyZPZ3hYWFOHToEKZNm4YXXngBzzzzDLRaLTp27IglS5YgODgY06dPN3G7rKyshFarVehKHceM\nGTOwb98+HD58GM2bN3f2dOo0fH19ERsbi2HDhqFDhw547bXX0LVrVxw+fJg1tVNRSyE2GaFkdkTF\nfVRWVpLnn3+eBAcHk2+++YYQQkhCQgIJCgoiZWVlbOtCPl577TUSFhbGtjDkPy89PZ1MmDCBzJs3\nj33szJkzpFOnTuTDDz9kH7t8+TIZNWoU+eSTT8yO5Sp44403SMuWLcmVK1ckfd81a9aQJ554ggQE\nBJCAgADSt29fsn//fknHUKFCIagmenUB3t7e2LBhA1JSUjBjxgwAQLdu3fDwww9j+/btbOtCLoqK\nipCTk4N27dqhXbt2AGpOFYQQ9rn/+9//oNFoMG7cOAA1J41u3bqhSZMmSEhIYN+rbdu2GDZsGI4f\nPw6j0YisrCzMmzcPx48fV+LyRWP69OnYtm0btm/fjgYNGrA+/1KcdkJCQrB06VKcOXMGp0+fxpAh\nQxAZGYmLFy9KMHMVKlwPKjHUAvj5+bELPAA89NBDGDRoENLT06HVagXzC9XV1azPPSUDhmGg0Whw\n584dbNmyBc899xx69OgBoIY4aAhg1qxZ7Hv98ssvePrpp9G5c2ckJyfD09MTjRs3drnk9Nq1a1Fa\nWoqnnnoKwcHB7L8dO3Y4/N5hYWEYOXIk2rVrh/bt2+Ojjz6Cn5+fy5GjChVSQU0+11J8++23Zls7\nBgcHIyMjA2+++Sb7GPlLvnfnzh0sXboUvXv3RqtWrfD000/jueeew927d1FVVYWPPvrIxFq7qKgI\nPXv2RIcOHfD777/D29sb//jHP0yIivv+d+/eBSEEDz30kHwXLwD+qUnOcXbs2IGKioo6nWDV6XQI\nDQ3FuXPnkJaWhieeeMLZU1KhIFRiqIVg44BmFETV1dVwd3dnteY0jAQAa9asQUlJCd599108/vjj\nOHLkCIKDg7F582aUlJQgMDCQfY9jx46hoKAAfn5+iIiIQFhYGBo3bow+ffo8MCY9QZw8eRJr167F\nF198YaJ1r+3IyMhA3759odVq4e/vj7i4OHTo0MHZ05IN8+bNQ8uWLXH+/HlnT0WFE6CGkmohaEjI\nEvr27WvSr5lhGJw7dw7ffPMNZs+ejZ49e0Kj0aBHjx7w9fWFXq9HYGAgu/M2GAy4fPkyMjMzUVZW\nhoCAAHTq1AnJyckWFScjR47EqVOnUFxcLM3Fugg6dOiA9PR0nDhxAtOmTcOECRNw6dIlZ09LFuzf\nvx8///wzli9f7pKWKirkhy0FbipqERiG+RTA74SQ7//6uTGAHgC8CCF7GIbRAHADsBZAN0JId4Zh\nNISQB2Iy9HGGYd4CUEgI2cIwjBsh5IHWWAzDNANwAMAbhJA6W0jAMMzPADIJIdOcPRcpwTBMUwCn\nAEQAKASQBaArIeScUyemQlGooaS6i3UA9PQHQshdAAmc3zOEkGqGYW4AGPEXUQT8RSA3CSFVlBD+\nIgUNIeTLv54HIVL4CwEA7gB4HMAxhmEYUjd3HxoAXlafVfuwCcBqQshZhmFaO3syKpwDNZRUR0EI\nuUYIuWnh93RhPwygGsAuAP8CMASAx1/PMXKeT//f2iJfDaAbgGy7Ju6CYBjmE4ZhBjAM05phmE5/\nncYGAfhe4nEWMAxjZBjmC4nf99O/3tfcPwPDMH9jGOZNAH4AltKXSjkPFbUH6omhnoMQchhAG4Zh\nOgC4Tgip4D+HYZjmAAyEkDsidv9tATQE8Ntf718XTgtNAGwG0BxACYBzAIYTQpKkGoBhmF4AXgeQ\nLtV7crAcNScBS8gCMBhAXwBVPDnyKYZhthFCLDvrqagzUHMM9RzmcgV//a4BgOEAMgG0ANAAQAoh\nJN/C+60E0JsQorbMEgmGYfwAnAYwDcD7AM4SQmY7YR4tURMKpAgGcBDAswBOEEJylJ6TCudAPTHU\nc1gghUcBNEINEdwFcJ5hmEYAWjEMU0QI0Qu8ph2A5wB8KOec6yBWAdhDCEliGOZ9Z02CH3pkGOYe\nasJJV1VSqF9QcwwqzCEEwC1CyF2GYUYwDNOUEFIIwBtAS/ok5q+Yw1+73okAygGsccaEayMYhnkB\nQFcA7zp7LmaghhTqIVRiUGEOdwHQYoQXUbPoA4APAB1wnxT+wguoSch+RggxUPWSCvP4K3TzJYDx\nhJBqZ8+HD0JINiHETZWq1j+oOQYVgmAYpgmA7oSQA5zHGABhhJB43nOfArAewOcA1luQsqrggGGY\nSACxAAy4rwByQ80u3YCamhP1BlWhOFRiUPEAaO0BwzCdUSNdzUJNPupRADpCyCnOc/0BTAFQRghZ\n55QJ11L8ldzn1wp8B+AigCWEENW+VYVT8P98Sx1KS3Ik4AAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f2a8206ec90>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig = plt.figure()\n",
"ax = fig.add_subplot(111, projection='3d')\n",
"\n",
"axs, artists = b['orb@model'].plot(xlim=(-4,4), ylim=(-4,4), zlim=(-4,4))"
]
}
],
"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 |
tomkraljevic/gtc2017-labs | Kaggle-NDSB-2-MXNet-R.ipynb | 1 | 72476 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Transforming How We Diagnose Heart Disease\n",
"\n",
"\n",
"Cardiovascular disease, also commonly referred to as heart disease, takes on many forms - heart attacks, stroke, heart failure and arrhythmia - to name a few. According to a report published on behalf of the American Heart Association, 610,000 Americans die each year from heart disease.\n",
"\n",
"Booz | Allen | Hamilton and Kaggle offered the [2015 Data Science Bowl](https://www.kaggle.com/c/second-annual-data-science-bowl) as an opportunity for data scientist to help automate the diagnosis of heart disease through a competition. Part of the initial overview of the challenge included the following image identifying some of the metrics used for determining heart disease.\n",
"\n",
"![alt text](img/fig2.jpg \"Only One Unit of Time That Matters\")\n",
"\n",
"End-systolic and end-diastolic volumes yield the ejection fraction (EF) noted above in the image and represented below in the formula where $V_S$ denotes the volumes at systole and $V_D$ denotes the volumes at diastole. Volumes and EF can be used as an indication of the presence of heart disease.\n",
"\n",
"$$100\\cdot\\frac{V_D-V_S}{V_D}$$ \n",
"\n",
"In addition, Magnetic Resonance Imaging (MRI) correctly assess the heart's squeezing ability. However, evaluation of MRIs is a manual, time consuming process - deep neural networks can automate this process. Variations in anatomy, function, image quality, and acquisition make automated quantification of left ventricle size a challenging problem. You will encounter this variation in the competition dataset, which aims to provide a diverse representation of cases. \n",
"\n",
"<img src=\"img/fig8.png\" alt=\"Manual determination of ejection fraction\" style=\"width: 700px;\"/>\n",
"<p style=\"text-align: center;\">*C.M.S Nambakhsh et al., Medical Image Analysis 17(2013) 1010-1024*</p>\n",
"\n",
"\n",
"## The Dataset\n",
"The National Heart, Lung, and Blood Institute (NHLBI) provided the MRI images for the Data Science Bowl. The [dataset](https://www.kaggle.com/c/second-annual-data-science-bowl/data) consists of hundreds of cardiac MRI images in [DICOM](https://en.wikipedia.org/wiki/DICOM) format. These 2D cine images contain approximately 30 images across the cardiac cycle. Each slice is acquired on a separate breath hold. This is important since the registration from slice to slice is expected to be imperfect.\n",
"\n",
"\n",
"<img src=\"img/fig3.gif\" alt=\"MRI imaging of heartbeat\" style=\"width: 500px;\"/>\n",
"\n",
"The dataset contains patients from young to old, images from numerous hospitals, and hearts from normal to abnormal cardiac function. A computational method which is robust to these variations could both validate and automate the cardiologists' manual measurement of ejection fraction. Each case has an associated directory of DICOM files. The exact number of images will differ from case to case, either varying in the number of slices, the views which are captured, or the number of frames in the time sequences.\n",
"\n",
"The main view for assessing ventricle size is the short axis stack (PSAX), which contains images taken in a plane perpendicular to the long axis (PLAX) of the left ventricle: \n",
"\n",
"<img src=\"img/fig6.png\" alt=\"PSAX diagram\" style=\"width: 250px;\"/>\n",
"<p style=\"text-align: center;\">*image credit: fpnotebook.com*</p>\n",
"\n",
"These have the prefix \"sax_\" in the dataset. Most cases also have alternative views."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Evaluation\n",
"Performance is evaluated using the Continuous Ranked Probability Score (CRPS). For each MRI, a cumulative probability distribution is predicted for both the systolic and diastolic volumes (two separate distributions per case). The CRPS is computed as follows:\n",
"\n",
"$$CRPS = \\frac{1}{600\\cdot N}\\sum_{m=1}^{N}\\sum_{n=0}^{599} (P(y\\le n)-H(n-V_{m}))^2$$\n",
"\n",
"where $P$ is the predicted distribution, $N$ is the number of rows in the test set (equal to twice the number of cases), $V$ is the actual volume (in mL) and $H(x)$ is the Heaviside step function ($H(x\\lt0)=0$ and $H(x\\ge 0)=1$). While it is not simple to visualize the CRPS, the shaded area on the figure below may be a helpful guide for understanding the error term between the predicted distribution and actual volume:\n",
"<img src=\"img/fig4.png\" alt=\"CRPS error between predicted and actual volume\" style=\"width: 500px;\"/>\n",
"Note that the entry will not score if any of the predicted values has $P(y \\le k) > P(y \\le k+1)$ for any $k$. That is the CDF is non-decreasing."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# End-to-End Deep Learning for NDSB-II\n",
"\n",
"In this example, we will show how to use GPU accelerated MXNet library to build an end-to-end deep learning system to help diagnose heart disease. Keep in mind, this is a very simple model without any network structure optimizations or hyper parameter tuning. However, it is possible to build fantastic networks based on the example solution.\n",
"\n",
"### About MXNet\n",
"[MXNet](https://github.com/dmlc/mxnet) is a deep learning framework designed for both efficiency and flexibility by DMLC group. MXNet will fully utilize all the resources to solve the problem under limited resource constraint, with a flexible programming interface. You can use it for all purposes of data science and deep learning tasks with R, Julia, Python and more. To run on multiple GPU with huge network, or questions about saving network parameters etc, please refer [MXNet docs](https://mxnet.readthedocs.org/en/latest/)\n",
"\n",
"## General Overview of Model\n",
"### Input Data\n",
"The dataset itself contains 500 training studies with on average 10 unique SAX observation at various points along the the PLAX of the left ventricle. That’s roughly 5000 observations in total. Each of these SAX observation usually contain 30 DICOM images (“frames”) in a time sequence which captures an entire heartbeat (expansion and contraction). The idea here is to pack each of the 30 image frames of an observation into a 64x64x30 tensor. While this might sound complicated, just think of stacking playing cards to form a deck. Each card has the same rectangular dimensions (say, 120x90) and then cards are stacked one on top of the other to form a deck. It’s the same thing here. We’re going to take each of the 30 square image frames of 64x64 pixels and stack them one-by-one to form an input “deck” to the deep neural network. The only difference with the card deck analogy is that the image frames are organized in time so that the video clip of the heartbeat is not all garbled. This tutorial is based on this simple idea: we first accumulate all suitable observations having 30 frames, then feed to the deep neural network to learn the target directly.\n",
"\n",
"The label data set contains only 500 labels. That is, each of the roughly 10 SAX observation for a particular study get the same diastole and systole volume. Again, each SAX observation of a study is just a different view of the same heart (i.e. a single heartbeat as measured from different cross sections perpendicular to the long axis of the heart). Therefore, in the data preprocessing step, each label must be duplicated for each unique observation.\n",
"\n",
"Additionally, the same 64x64x30 deck of images is used to predict both systole and diastole volumes. Therefore, we’re going to build a network that ingests each observation tensor to predict systole volume. Then a separate network that ingests that same image deck to predict diastole volume.\n",
"\n",
"Another idea used in this tutorial is taking a frame-by-frame difference to measure change per frame (in time). By using MXNet symbolic interface, we can dynamically difference the input inside of the network. It helps a little in the final result.\n",
"\n",
"### Network Objective\n",
"For the network, we use the well documented LeNet style convolution network with batch normalization and dropout. This is a basic network with a generic configuration. In this challenge, we are asked to predict a CDF value of 600 data-point. Therefore the problem is formulated as a regression problem. We ask the neural-net that given a stack of images 64x64x30 to output 600x1 vector - one predicted value for each of the 600 points in the CDF. Note here that the label is just a single floating point value like 83.3 (mL). This label is transformed into a step function having 600 discrete values where all the y-values of the CDF with x less than 83 are 0 and all y-values with x greater than 83 are 1.\n",
"\n",
"\n",
"### Preprocessing\n",
"We first run a preprocessing step, to pack the data into a csv file (```train-64x64-data.csv```). Each line of this csv file corresponds to a 64 x 64 x 30 tensor, which gives 30 frames of images. We can also use other inputs besides csv. The CSV is used here since this format is common for all languages and is easy to parse.\n",
"\n",
"The input dataset is quite big (5293 observations of size 64x64x30). While this data set can likely fit into memory of a big machine, we want to be safe, so we will use the ```CSVIter``` from ```mxnet``` to load data from disk on-the-fly during training, without loading all the data into memory at once.\n",
"\n",
"The labels for the training data are stored in ```train-systole.csv``` and ```train-diastole.csv``` where each line is a step function of 0s and 1s as described above. Line ```i``` in the training CSV data file is label by the associated line ```i``` in the label CSV files. Again, when training the systole network we will use labels from ```train-systole.csv``` and likewise when training the diastole network we will use labels from ```train-diastole.csv```.\n",
"\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Code"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"First we load the necessary libraries"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Loading required package: mxnet\n",
"Loading required package: data.table\n",
"Loading required package: ggplot2\n"
]
}
],
"source": [
"# import libraries\n",
"require(mxnet)\n",
"require(data.table)\n",
"require(ggplot2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Next we define a function that will architect (not trained yet) neural networks. This network is a classic network that has it's origins from Yann LeCun's paper many years ago and hence has the nastalgic name \"LeNet\". \n",
"\n",
"Notice here that we create a variable using ```mx.symbol.Variable``` which *represents* the input data. Just like regular variables we can manipulate the ```source``` variable. For example, the first thing we do to the image data input is to normalize the pixel values. Next we utilize ```mx.symbol.SliceChannel``` so that we can actually access each individual frame of the input. Recall that the input is 64x64x30 so that each indexed frame is of size 64x64. The purpose of using the frames is so that differences between successive frames can be calculated. That is, instead of training the network parameters using the images directly, the network is trained on the differences between each successive frame. This is pretty cool since we can actually manipulate the data within the network definition rather than having to preprocess the whole dataset when we want to experiment with a new idea. Once each frame delta has been computed the individual frames are put back into a 64x64x29 tensor input using ```mxnet:::mx.varg.symbol.Concat```. Since we've taken successive deltas we actually only have 29 total deltas now! \n",
"\n",
"From here we just chain various layers together, such as ```mx.symbol.Convolution```, to form the network described in LeCun's paper. There is no magic here. Feel free to add layers in between or remove layers etc. That's part of the fun of working with DL; various recipies have been described in papers over the years but you're free to construct anything you'd like. Play with the network structure to get a feel for how training performance changes. \n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Create LeNet style network\n",
"get.lenet <- function() {\n",
" \n",
" # create data variable (i.e. symbol)\n",
" source <- mx.symbol.Variable(\"data\")\n",
" \n",
" # normalize values of input data \n",
" source <- (source-128) / 128\n",
" \n",
" # SliceChannel is a symbol that can be indexed just like an array\n",
" frames <- mx.symbol.SliceChannel(source, num.outputs = 30);\n",
" \n",
" # init list of differences\n",
" diffs <- list()\n",
" \n",
" # compute differences for each \"frame\"\n",
" for (i in 1:29) {\n",
" diffs <- c(diffs, frames[[i + 1]] - frames[[i]])\n",
" }\n",
" \n",
" # set the property for number of arugments in diffs variable\n",
" diffs$num.args = 29\n",
" \n",
" # concatonate frame variables into single variable\n",
" source <- mxnet:::mx.varg.symbol.Concat(diffs)\n",
" \n",
" # convolution layer with 5x5 kernel dimention\n",
" net <- mx.symbol.Convolution(source, kernel = c(5, 5), num.filter = 40)\n",
" \n",
" # normalization layer: simply subtract mean divide by std\n",
" net <- mx.symbol.BatchNorm(net, fix.gamma = TRUE)\n",
" \n",
" # activation layer using Rectified Linear Unit (relu) activation function\n",
" net <- mx.symbol.Activation(net, act.type = \"relu\")\n",
" \n",
" # Max pooling layer with a 2x2 kernel and no overlap since stride is 2x2\n",
" net <- mx.symbol.Pooling(net, pool.type = \"max\", kernel = c(2, 2), stride = c(2, 2))\n",
" \n",
" # continue building the network ...\n",
" net <- mx.symbol.Convolution(net, kernel = c(3, 3), num.filter = 40)\n",
" net <- mx.symbol.BatchNorm(net, fix.gamma = TRUE)\n",
" net <- mx.symbol.Activation(net, act.type = \"relu\")\n",
" net <- mx.symbol.Pooling(net, pool.type = \"max\", kernel = c(2, 2), stride = c(2, 2))\n",
" \n",
" # flatten the features to single variable\n",
" flatten <- mx.symbol.Flatten(net)\n",
" \n",
" # add a drop out layer where 50% of data gets dropped out at training time\n",
" flatten <- mx.symbol.Dropout(flatten)\n",
" \n",
" # add the final fully connected layer for output of dimension 600\n",
" fc1 <- mx.symbol.FullyConnected(data = flatten, num.hidden = 600)\n",
" \n",
" # Name the final layer as softmax so it auto matches the naming of data iterator\n",
" # Otherwise we can also change the provide_data in the data iter\n",
" return(mx.symbol.LogisticRegressionOutput(data = fc1, name = 'softmax'))\n",
"}"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If you want to know more about a particular layer, pull up the help listing for that layer. For example,?mx.symbol.Activation provides the help information for the activation layer where additional functions are listed for use such as 'sigmoid', 'tanh' and so on."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"?mx.symbol.Activation"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Next the training batch size is specified. The size can be smaller or larger arbitrarily but since we're using GPUs for the training we have to be careful not to use a really large batch size since the GPU has limited memory. The batch size is also a factor of how large the input images are. Since each input is a 64x64x30 tensor, 16 is a good size. It is not the end of the world if batch size is too large. If the batch size is too large, the training command will just fail and complain about something like \"device out of memory\" which is likely your queue to reduce the batch size."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"batch_size <- 16"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Next we load the training data using an iterator so that we use host memory efficiently. This is nice since it does not consume huge amounts of data but the trade off is that it does increase the training time since we have to keep loading data over and over. "
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# CSVIter is used here, since the data can't fit into memory\n",
"data_train <- mx.io.CSVIter(\n",
" data.csv = \"./train-64x64-data.csv\", data.shape = c(64, 64, 30),\n",
" label.csv = \"./train-systole.csv\" , label.shape = 600,\n",
" batch.size = batch_size\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Lets now initialize a LeNet style network using our network function. This network has structure but none of the parameters are determined yet. Think of it as raw materials that will be shaped into meaningful values/parameters during the training phase. Notice that when this function is called all of the parameters are initialized to random values which does actually consume memory on the system. If the network has 10 million parameters in it then we have to initialize 10 million values in memory. Also, all the network parameters must actually fit into the GPU memory also (with the input data too!) so if the network structure is too large then it is possible to run out of memory on the GPU. So start conservatively and grow the network as you work through the performance requirements. "
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"network <- get.lenet()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Before we can train the network parameters we must specify a *loss function* also called a *cost function*. This function tells the training algoritm how closely the current set of parameters reproduce the target output. In general, for a given cost function the training algorithm will try to choose parameters for which the cost function is minimized. That is, a lower cost is better than higher cost. "
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Custom evaluation metric using CRPS.\n",
"costfun <- function(label, pred) {\n",
" pred <- as.array(pred )\n",
" label <- as.array(label)\n",
" return(sum((label - pred) ^ 2) / length(label))\n",
"}\n",
"\n",
"# create custom mxnet metric for training\n",
"mx.metric.CRPS <- mx.metric.custom(\"CRPS\", costfun)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now you are ready to train your architected network with training data to build a model. Below we offer two options - use our pretrained model provided in the first code block below that was created with 65 training rounds (epochs) or train your own model in the second code block below using a smaller number of training rounds (five training rounds will likely take 10+ minutes to execute). Once you feel comfortable, feel free to play around with the learning rate, momentum etc. and see how the training performance changes.\n",
"\n",
"NOTE: No changes are needed in the first of two code blocks that follow this explanation and you only need to execute one of the two code blocks.\n",
"\n",
"Notice that one of the arguments provided is ```ctx = mx.gpu(0)``` which effectively tells MXNet to use GPU with device id equal to 0. If you want to perform the training with the CPU instead then set the context to ```ctx=mx.cpu()```. Furthermore, an array of GPU devices can be passed into the context for multi-gpu training if there are more than one GPU devices present on the machine. Keep in mind here that using multiple GPU devices for training incurs some overhead - especially with large datasets. It's not unusual that training time actually increases when using multiple GPU due to the additional data transfer required. So definitely start small and grow the training strategy and always profile execution times etc. to keep an eye on learning performance. \n",
"\n",
"If you want to save some time, simply load the pretrained model using the cell below and skip the cell that follows."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# load the pretrained result\n",
"systole_model <- mx.model.load(\"stytole_model_BN\",65)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Execute the cell above if you would like to use the pretrained results OR execute the code below if you would like to train the model yourself."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Training the systole model (this takes a long time).\n",
"\n",
"# set the random seed so the results are reproducible\n",
"mx.set.seed(0)\n",
"\n",
"# start the clock\n",
"start_time <- proc.time()\n",
"\n",
"# train the model parameters with the data\n",
"systole_model <- mx.model.FeedForward.create(\n",
" X = data_train,\n",
" ctx = mx.gpu(0) ,\n",
" symbol = network ,\n",
" num.round = 5 ,\n",
" learning.rate = 0.01 ,\n",
" wd = 0.0001 ,\n",
" momentum = 0.9 ,\n",
" eval.metric = mx.metric.CRPS\n",
")\n",
"\n",
"# get the time delta\n",
"proc.time() - start_time"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If you've trained a network and want to save the results then export the trained model so it can be reloaded without having to perform training phase next time"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Save the trained neural network model to disk.\n",
"# This creates two files: \n",
"# 1. systole_model-0065.params\n",
"# 2. systol_model-symbol.json\n",
"#\n",
"# WARNING: the 65 here must match num.rounds parameter provided to mx.model.FeedForward.create\n",
"# So if the model was saved at 65, then it must be loaded with 65.\n",
"#\n",
"mx.model.save(systole_model,\"systole_model_BN\",5)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Just to get a better feel for the data, lets extract all the data from the CSV iterator. Doing this reads in the entire dataset in to host memory. This can be prohibative with a large dataset. "
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# this reads in all the data at once (not a great idea, but we can do it)\n",
"labels <- mx.io.extract(data_train,\"label\")\n",
"data <- mx.io.extract(data_train,\"data\" )"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Once the data has been loaded we can check the dimensions. As advertized we have ~ 5000 training samples each of size 64x64x30."
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<ol class=list-inline>\n",
"\t<li>600</li>\n",
"\t<li>5293</li>\n",
"</ol>\n"
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"\\item 600\n",
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"2. 5293\n",
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"source": [
"# \n",
"dim(labels)\n",
"dim(data)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Using the trained model we can pass input samples into the model to predict their systolic volume. To select various samples we index into the data variable ```data[,,,1:20]``` to extract some samples. Notice here we're selecting input data along the 4th dimension. That is we're selecting ```N``` input samples with dimension 64x64x30 and by leaving the first three index empty we are effectively saying \"give me everything\" along that dimension."
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
" Min. 1st Qu. Median Mean 3rd Qu. Max. \n",
" 0.00 9.00 24.00 37.86 51.00 255.00 "
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"summary(data)"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# create index for selecting samples\n",
"sample_index = c(1:20);\n",
"\n",
"# apply the model to just a few data to generate some predictions\n",
"model_predictions <- predict(systole_model, data[,,,sample_index])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now lets have a look at what was predicted by the model for these input. Try selecting a different index for the plot to view other predictions and their label. Don't hesitate to predict more input in the cell above to investigate even further."
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {},
"metadata": {},
"output_type": "display_data"
},
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dca3QQAAAgX7LEzszvu0PHjRjcBAADCBcHOzKZONboDAAAQRjgUCwAAECEIdgAA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\nPXy6AAAAAElFTkSuQmCC",
"text/plain": [
"plot without title"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# have a look at what the label and associated prediction looks like\n",
"ix = 14;\n",
"x <- seq(1, 600, 1)\n",
"y1 <- model_predictions[,ix]\n",
"y2 <- labels[,ix]\n",
"df <- data.frame(volume=x,prediction=y1,label=y2)\n",
"\n",
"# basic graphical object\n",
"ggplot(df, aes(volume)) +\n",
" \n",
" # the predicted CDF using the trained model\n",
" geom_point(aes(y=prediction), color=\"blue\") + \n",
"\n",
" # the label for this set of images (i.e. systole volume)\n",
" geom_line(aes(y=label), color=\"red\", linetype=\"dotted\") + \n",
"\n",
" # make the plot theme simple \n",
" theme_bw()\n",
"\n",
"# evaluate the cost function for this prediction\n",
"costfun(labels[,ix],model_predictions[,ix])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Notice how the prediction(blue curve) is a logistic like curve (CDF) while the label is a step function. Certainly we can use our cost function to get a sense for how \"close\" the set of predictions are to their labels. However, notice with this formulation of the problem it's not very easy to look at the prediction and call out what the actual volume predicted is. "
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
"0.0155942614742691"
],
"text/latex": [
"0.0155942614742691"
],
"text/markdown": [
"0.0155942614742691"
],
"text/plain": [
"[1] 0.01559426"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"costfun(labels[,sample_index],model_predictions[,sample_index])"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"## OK Now it is your Turn!\n",
"\n",
"Lets use what we've learned from creating the ```systole_network``` and create a network to predict diastole volume.\n",
"\n",
"Step 1. Create a training data set using the diastole labels \n",
"\n",
"Step 2. Create network \n",
"\n",
"Step 3. Train the network\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# step 1\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# step 2\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# step 3\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Discovery Requires Experimentation ...\n",
"\n",
"There are many ways to explore and even improve this model:\n",
"\n",
"1. Maybe try removing batch normalization layers ([ref](https://arxiv.org/abs/1502.03167)) [hint: modify get.lenet()]\n",
"2. Try increasing or decreasing the number of features in the convolution layer [hint: modify ```num.filter``` in get.lenet()]\n",
"3. How does batch size effect training?\n",
"4. Have a go at modifying the learning rate and momentum of the training phase [hint: learning.rate, wd, and momentum]\n",
"5. Notice that the CRPS function uses the residual squared. What else might we try (hint: try abs in costfun)\n",
"6. Try using different activation function (i.e. other than relu). How does this effect performance?\n",
"7. Maybe try using different pooling functions (i.e. other than max)[hint: use help to see other functions]\n",
"8. Maybe try removing dropout layer or modifying the percentage dropout (i.e. default is 50%)\n",
"9. Maybe try double differencing the data or not differencing the data at all\n",
"10. What are other ways we could formulate the network output (i.e. other than discrete CDF)?"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# NDSB Competition Results\n",
"\n",
"There were nearly [200 participants](https://www.kaggle.com/c/second-annual-data-science-bowl/leaderboard) in the NDSB-II with a variety of different approaches to solving this challange. The CRPS scores for the leaderboard top ten ranged from 0.009485 in first place to 0.012611 in tenth place. To get a better feel for how your results stackup against the competion, we can create an empirical CDF from the leaderboard CRPS scores\n",
"<img src=\"img/fig10.png\" alt=\"CRPS ECDF\" style=\"width: 500px;\"/>\n",
"Therefore, achieving a CRPS score of 0.03 is at about the 80th percentile w.r.t. the overall competition results. \n",
"\n",
"So how much better is a CRPS of 0.009485 than 0.012611? To get a loose sense for how CRPS effects ejection fraction calculations, the competition provided this infographic to performers\n",
"\n",
"<img src=\"img/fig11.png\" alt=\"CRPS ECDF\" style=\"width: 700px;\"/>\n",
"\n",
"Analysis of the competion results seem more favorable than this graphic might imply and noted that top performer models agreed well with previous studies of human performance. For a medical perspective on the competion results check out this [kaggle blog](https://www.kaggle.com/c/second-annual-data-science-bowl/forums/t/19839/a-medical-perspective-on-the-quality-of-the-left-ventricular-volume-and) by Dr. Andrew Arai of the NIH. Additional analysis of the clinical applicability provided by Jonathan Mulholland of Booze Allen Hamilton [here](http://www.datasciencebowl.com/leading-and-winning-team-submissions-analysis/). These analysis conclude, based on the top four models submitted to the competion, that \"*The models keep the diagnosis categories pretty tightly grouped together. While the models are not right 100% the time, there is a very low probability of a severely abnormal EF being incorrectly categorized in the mild to hyperdynamic range. The normal to mild diagnoses are very likely to stay within their domain ... This is a pretty good sign pointing towards suitability for clinical applications*\"."
]
}
],
"metadata": {
"kernelspec": {
"display_name": "R",
"language": "R",
"name": "ir"
},
"language_info": {
"codemirror_mode": "r",
"file_extension": ".r",
"mimetype": "text/x-r-source",
"name": "R",
"pygments_lexer": "r",
"version": "3.2.2"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
| apache-2.0 |
xR86/ml-stuff | kaggle/machine-learning-with-a-heart/Lab4.ipynb | 1 | 250187 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Tema 4.1 <a class=\"tocSkip\">"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Imports"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import math\n",
"\n",
"import numpy as np\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import graphviz\n",
"\n",
"import sklearn.tree\n",
"import sklearn.neighbors\n",
"import sklearn.naive_bayes\n",
"import sklearn.svm\n",
"import sklearn.metrics\n",
"import sklearn.preprocessing\n",
"import sklearn.model_selection"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Data\n",
"https://www.drivendata.org/competitions/54/machine-learning-with-a-heart/page/109/\n",
"- Numeric\n",
" - slope\\_of\\_peak\\_exercise\\_st\\_segment (int, semi-categorical, 1-3)\n",
" - resting\\_blood\\_pressure (int)\n",
" - chest\\_pain\\_type (int, semi-categorical, 1-4)\n",
" - num\\_major\\_vessels (int, semi-categorical, 0-3)\n",
" - resting\\_ekg\\_results (int, semi-categorical, 0-2)\n",
" - serum\\_cholesterol\\_mg\\_per\\_dl (int)\n",
" - oldpeak\\_eq\\_st\\_depression (float)\n",
" - age (int)\n",
" - max\\_heart\\_rate\\_achieved (int)\n",
"- Categorical\n",
" - thal\n",
" - normal\n",
" - fixed\\_defect\n",
" - reversible\\_defect\n",
" - fasting\\_blood\\_sugar\\_gt\\_120\\_mg\\_per\\_dl (blood sugar > 120)\n",
" - 0\n",
" - 1\n",
" - sex\n",
" - 0 (f)\n",
" - 1 (m)\n",
" - exercise\\_induced\\_angina \n",
" - 0\n",
" - 1\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"features = pd.read_csv('train_values.csv')\n",
"labels = pd.read_csv('train_labels.csv')"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>patient_id</th>\n",
" <th>slope_of_peak_exercise_st_segment</th>\n",
" <th>thal</th>\n",
" <th>resting_blood_pressure</th>\n",
" <th>chest_pain_type</th>\n",
" <th>num_major_vessels</th>\n",
" <th>fasting_blood_sugar_gt_120_mg_per_dl</th>\n",
" <th>resting_ekg_results</th>\n",
" <th>serum_cholesterol_mg_per_dl</th>\n",
" <th>oldpeak_eq_st_depression</th>\n",
" <th>sex</th>\n",
" <th>age</th>\n",
" <th>max_heart_rate_achieved</th>\n",
" <th>exercise_induced_angina</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0z64un</td>\n",
" <td>1</td>\n",
" <td>normal</td>\n",
" <td>128</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>308</td>\n",
" <td>0.0</td>\n",
" <td>1</td>\n",
" <td>45</td>\n",
" <td>170</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>ryoo3j</td>\n",
" <td>2</td>\n",
" <td>normal</td>\n",
" <td>110</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>214</td>\n",
" <td>1.6</td>\n",
" <td>0</td>\n",
" <td>54</td>\n",
" <td>158</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>yt1s1x</td>\n",
" <td>1</td>\n",
" <td>normal</td>\n",
" <td>125</td>\n",
" <td>4</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>304</td>\n",
" <td>0.0</td>\n",
" <td>1</td>\n",
" <td>77</td>\n",
" <td>162</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>l2xjde</td>\n",
" <td>1</td>\n",
" <td>reversible_defect</td>\n",
" <td>152</td>\n",
" <td>4</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>223</td>\n",
" <td>0.0</td>\n",
" <td>1</td>\n",
" <td>40</td>\n",
" <td>181</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>oyt4ek</td>\n",
" <td>3</td>\n",
" <td>reversible_defect</td>\n",
" <td>178</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>270</td>\n",
" <td>4.2</td>\n",
" <td>1</td>\n",
" <td>59</td>\n",
" <td>145</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" patient_id slope_of_peak_exercise_st_segment thal \\\n",
"0 0z64un 1 normal \n",
"1 ryoo3j 2 normal \n",
"2 yt1s1x 1 normal \n",
"3 l2xjde 1 reversible_defect \n",
"4 oyt4ek 3 reversible_defect \n",
"\n",
" resting_blood_pressure chest_pain_type num_major_vessels \\\n",
"0 128 2 0 \n",
"1 110 3 0 \n",
"2 125 4 3 \n",
"3 152 4 0 \n",
"4 178 1 0 \n",
"\n",
" fasting_blood_sugar_gt_120_mg_per_dl resting_ekg_results \\\n",
"0 0 2 \n",
"1 0 0 \n",
"2 0 2 \n",
"3 0 0 \n",
"4 0 2 \n",
"\n",
" serum_cholesterol_mg_per_dl oldpeak_eq_st_depression sex age \\\n",
"0 308 0.0 1 45 \n",
"1 214 1.6 0 54 \n",
"2 304 0.0 1 77 \n",
"3 223 0.0 1 40 \n",
"4 270 4.2 1 59 \n",
"\n",
" max_heart_rate_achieved exercise_induced_angina \n",
"0 170 0 \n",
"1 158 0 \n",
"2 162 1 \n",
"3 181 0 \n",
"4 145 0 "
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"features.head()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"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>patient_id</th>\n",
" <th>heart_disease_present</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0z64un</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>ryoo3j</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>yt1s1x</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>l2xjde</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>oyt4ek</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" patient_id heart_disease_present\n",
"0 0z64un 0\n",
"1 ryoo3j 0\n",
"2 yt1s1x 1\n",
"3 l2xjde 1\n",
"4 oyt4ek 0"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"labels.head()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"FEATURES = ['slope_of_peak_exercise_st_segment', \n",
" 'thal',\n",
" 'resting_blood_pressure', \n",
" 'chest_pain_type', \n",
" 'num_major_vessels', \n",
" 'fasting_blood_sugar_gt_120_mg_per_dl',\n",
" 'resting_ekg_results', \n",
" 'serum_cholesterol_mg_per_dl', \n",
" 'oldpeak_eq_st_depression', \n",
" 'sex',\n",
" 'age', \n",
" 'max_heart_rate_achieved', \n",
" 'exercise_induced_angina']\n",
"\n",
"LABEL = 'heart_disease_present'\n",
"\n",
"EXPLANATIONS = {'slope_of_peak_exercise_st_segment' : 'Quality of Blood Flow to the Heart',\n",
" 'thal' : 'Thallium Stress Test Measuring Blood Flow to the Heart',\n",
" 'resting_blood_pressure' : 'Resting Blood Pressure', \n",
" 'chest_pain_type' : 'Chest Pain Type (1-4)',\n",
" 'num_major_vessels' : 'Major Vessels (0-3) Colored by Flourosopy',\n",
" 'fasting_blood_sugar_gt_120_mg_per_dl' : 'Fasting Blood Sugar > 120 mg/dl',\n",
" 'resting_ekg_results' : 'Resting Electrocardiographic Results (0-2)',\n",
" 'serum_cholesterol_mg_per_dl' : 'Serum Cholesterol in mg/dl',\n",
" 'oldpeak_eq_st_depression' : 'Exercise vs. Rest\\nA Measure of Abnormality in Electrocardiograms',\n",
" 'age' : 'Age (years)',\n",
" 'sex' : 'Sex (m/f)',\n",
" 'max_heart_rate_achieved' : 'Maximum Heart Rate Achieved (bpm)',\n",
" 'exercise_induced_angina' : 'Exercise-Induced Chest Pain (yes/no)'}\n",
"\n",
"NUMERICAL_FEATURES = ['slope_of_peak_exercise_st_segment', \n",
" 'resting_blood_pressure', \n",
" 'chest_pain_type', \n",
" 'num_major_vessels', \n",
" 'resting_ekg_results', \n",
" 'serum_cholesterol_mg_per_dl', \n",
" 'oldpeak_eq_st_depression', \n",
" 'age', \n",
" 'max_heart_rate_achieved']\n",
"\n",
"CATEGORICAL_FEATURES = ['thal', \n",
" 'fasting_blood_sugar_gt_120_mg_per_dl', \n",
" 'sex', \n",
" 'exercise_induced_angina']\n",
"\n",
"CATEGORICAL_FEATURE_VALUES = {'thal' : [[0, 1, 2], ['Normal', \n",
" 'Fixed Defect', \n",
" 'Reversible Defect']], \n",
" 'fasting_blood_sugar_gt_120_mg_per_dl' : [[0, 1], ['No', 'Yes']],\n",
" 'sex' : [[0, 1], ['F', 'M']], \n",
" 'exercise_induced_angina' : [[0, 1], ['No', 'Yes']]}\n",
"\n",
"SEMI_CATEGORICAL_FEATURES = ['slope_of_peak_exercise_st_segment',\n",
" 'chest_pain_type',\n",
" 'num_major_vessels',\n",
" 'resting_ekg_results']\n",
"\n",
"SEMI_CATEGORICAL_FEATURE_LIMITS = {'slope_of_peak_exercise_st_segment' : [1, 3],\n",
" 'chest_pain_type' : [1, 4],\n",
" 'num_major_vessels' : [0, 3],\n",
" 'resting_ekg_results' : [0, 2]}\n",
"\n",
"LABEL_VALUES = [[0, 1], ['No', 'Yes']]\n"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"for feature in CATEGORICAL_FEATURES:\n",
" if len(CATEGORICAL_FEATURE_VALUES[feature][0]) > 2:\n",
" \n",
" onehot_feature = pd.get_dummies(features[feature])\n",
" \n",
" feature_index = features.columns.get_loc(feature)\n",
" features.drop(feature, axis=1, inplace=True)\n",
" \n",
" onehot_feature.columns = [f'{feature}={feature_value}' for feature_value in onehot_feature.columns]\n",
" for colname in onehot_feature.columns[::-1]:\n",
" features.insert(feature_index, colname, onehot_feature[colname])"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>patient_id</th>\n",
" <th>slope_of_peak_exercise_st_segment</th>\n",
" <th>thal=fixed_defect</th>\n",
" <th>thal=normal</th>\n",
" <th>thal=reversible_defect</th>\n",
" <th>resting_blood_pressure</th>\n",
" <th>chest_pain_type</th>\n",
" <th>num_major_vessels</th>\n",
" <th>fasting_blood_sugar_gt_120_mg_per_dl</th>\n",
" <th>resting_ekg_results</th>\n",
" <th>serum_cholesterol_mg_per_dl</th>\n",
" <th>oldpeak_eq_st_depression</th>\n",
" <th>sex</th>\n",
" <th>age</th>\n",
" <th>max_heart_rate_achieved</th>\n",
" <th>exercise_induced_angina</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0z64un</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>128</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>308</td>\n",
" <td>0.0</td>\n",
" <td>1</td>\n",
" <td>45</td>\n",
" <td>170</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>ryoo3j</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>110</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>214</td>\n",
" <td>1.6</td>\n",
" <td>0</td>\n",
" <td>54</td>\n",
" <td>158</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>yt1s1x</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>125</td>\n",
" <td>4</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>304</td>\n",
" <td>0.0</td>\n",
" <td>1</td>\n",
" <td>77</td>\n",
" <td>162</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>l2xjde</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>152</td>\n",
" <td>4</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>223</td>\n",
" <td>0.0</td>\n",
" <td>1</td>\n",
" <td>40</td>\n",
" <td>181</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>oyt4ek</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>178</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>270</td>\n",
" <td>4.2</td>\n",
" <td>1</td>\n",
" <td>59</td>\n",
" <td>145</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" patient_id slope_of_peak_exercise_st_segment thal=fixed_defect \\\n",
"0 0z64un 1 0 \n",
"1 ryoo3j 2 0 \n",
"2 yt1s1x 1 0 \n",
"3 l2xjde 1 0 \n",
"4 oyt4ek 3 0 \n",
"\n",
" thal=normal thal=reversible_defect resting_blood_pressure \\\n",
"0 1 0 128 \n",
"1 1 0 110 \n",
"2 1 0 125 \n",
"3 0 1 152 \n",
"4 0 1 178 \n",
"\n",
" chest_pain_type num_major_vessels fasting_blood_sugar_gt_120_mg_per_dl \\\n",
"0 2 0 0 \n",
"1 3 0 0 \n",
"2 4 3 0 \n",
"3 4 0 0 \n",
"4 1 0 0 \n",
"\n",
" resting_ekg_results serum_cholesterol_mg_per_dl oldpeak_eq_st_depression \\\n",
"0 2 308 0.0 \n",
"1 0 214 1.6 \n",
"2 2 304 0.0 \n",
"3 0 223 0.0 \n",
"4 2 270 4.2 \n",
"\n",
" sex age max_heart_rate_achieved exercise_induced_angina \n",
"0 1 45 170 0 \n",
"1 0 54 158 0 \n",
"2 1 77 162 1 \n",
"3 1 40 181 0 \n",
"4 1 59 145 0 "
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"features.head()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"x =\n",
" [[ 1 0 1 ... 45 170 0]\n",
" [ 2 0 1 ... 54 158 0]\n",
" [ 1 0 1 ... 77 162 1]\n",
" ...\n",
" [ 2 0 0 ... 64 131 1]\n",
" [ 1 0 1 ... 48 175 0]\n",
" [ 1 0 1 ... 54 163 0]]\n",
"y =\n",
" [0 0 1 1 0 0 1 1 1 0 0 0 1 0 1 1 0 1 0 1 0 0 0 0 0 0 0 0 1 0 0 1 1 0 1 0 0\n",
" 0 0 1 0 0 0 0 0 1 0 0 1 0 1 0 1 1 1 0 1 1 0 1 0 1 1 0 0 1 1 0 0 1 0 0 0 0\n",
" 1 1 0 1 1 1 0 1 1 1 0 1 0 1 0 0 0 1 0 1 0 0 1 0 0 0 0 0 1 0 0 0 0 1 0 1 0\n",
" 0 1 1 0 0 0 1 1 1 1 0 0 0 1 0 0 0 1 1 0 0 0 1 1 0 1 1 0 0 1 0 0 1 1 0 0 1\n",
" 1 1 0 0 1 1 1 1 0 0 0 1 0 1 1 1 0 0 0 1 0 0 1 1 1 1 0 1 1 1 0 0]\n"
]
}
],
"source": [
"x = features.values[:,1:].astype(int)\n",
"y = labels.values[:,-1].astype(int)\n",
"\n",
"print('x =\\n', x)\n",
"print('y =\\n', y)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"StratifiedKFold(n_splits=5, random_state=None, shuffle=True)"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"stratified_kflod_validator = sklearn.model_selection.StratifiedKFold(n_splits=5, shuffle=True)\n",
"\n",
"stratified_kflod_validator"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Decision Trees"
]
},
{
"cell_type": "code",
"execution_count": 10,
"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>Fold</th>\n",
" <th>Accuracy</th>\n",
" <th>Precision</th>\n",
" <th>Recall</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>66.67 %</td>\n",
" <td>61.11 %</td>\n",
" <td>68.75 %</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2</td>\n",
" <td>77.78 %</td>\n",
" <td>72.22 %</td>\n",
" <td>81.25 %</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>3</td>\n",
" <td>55.56 %</td>\n",
" <td>50.00 %</td>\n",
" <td>62.50 %</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>4</td>\n",
" <td>77.78 %</td>\n",
" <td>72.22 %</td>\n",
" <td>81.25 %</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>5</td>\n",
" <td>91.67 %</td>\n",
" <td>93.33 %</td>\n",
" <td>87.50 %</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>Avg</td>\n",
" <td>73.89 %</td>\n",
" <td>69.78 %</td>\n",
" <td>76.25 %</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Fold Accuracy Precision Recall\n",
"1 1 66.67 % 61.11 % 68.75 %\n",
"2 2 77.78 % 72.22 % 81.25 %\n",
"3 3 55.56 % 50.00 % 62.50 %\n",
"4 4 77.78 % 72.22 % 81.25 %\n",
"5 5 91.67 % 93.33 % 87.50 %\n",
"6 Avg 73.89 % 69.78 % 76.25 %"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"tree_mean_acc = 0\n",
"tree_score_df = pd.DataFrame(columns = ['Fold', 'Accuracy', 'Precision', 'Recall'])\n",
"\n",
"for fold_ind, (train_indices, test_indices) in enumerate(stratified_kflod_validator.split(x, y), 1):\n",
" \n",
" x_train, x_test = x[train_indices], x[test_indices]\n",
" y_train, y_test = y[train_indices], y[test_indices]\n",
" \n",
" dec_tree = sklearn.tree.DecisionTreeClassifier(min_samples_split = 5)\n",
" dec_tree.fit(x_train, y_train)\n",
" \n",
" acc = dec_tree.score(x_test, y_test)\n",
" tree_mean_acc += acc\n",
" \n",
" y_pred = dec_tree.predict(x_test)\n",
" precision = sklearn.metrics.precision_score(y_test, y_pred)\n",
" recall = sklearn.metrics.recall_score(y_test, y_pred)\n",
" \n",
" tree_score_df.loc[fold_ind] = [f'{fold_ind}', \n",
" f'{acc*100:.2f} %', \n",
" f'{precision*100:.2f} %', \n",
" f'{recall*100:.2f} %']\n",
" \n",
" tree_plot_data = sklearn.tree.export_graphviz(dec_tree, out_file = None,\n",
" feature_names = features.columns[1:], \n",
" class_names = [f'{labels.columns[1]}={label_value}' \n",
" for label_value \n",
" in LABEL_VALUES[1]],\n",
" filled = True, \n",
" rounded = True, \n",
" special_characters = True) \n",
" graph = graphviz.Source(tree_plot_data) \n",
" graph.render(f'Fold {fold_ind}')\n",
" \n",
"next_ind = len(tree_score_df) + 1\n",
"\n",
"mean_acc = tree_score_df['Accuracy'].apply(lambda n: float(n[:-2])).mean()\n",
"mean_prec = tree_score_df['Precision'].apply(lambda n: float(n[:-2])).mean()\n",
"mean_rec = tree_score_df['Recall'].apply(lambda n: float(n[:-2])).mean()\n",
"\n",
"tree_score_df.loc[next_ind] = ['Avg', f'{mean_acc:.2f} %', f'{mean_prec:.2f} %', f'{mean_rec:.2f} %']\n",
"tree_score_df"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# KNN"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Best avg. accuracy is 62.78 % for k = 90 .\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>k</th>\n",
" <th>Avg. Accuracy</th>\n",
" <th>Avg. Precision</th>\n",
" <th>Avg. Recall</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>4</td>\n",
" <td>53.89 %</td>\n",
" <td>44.75 %</td>\n",
" <td>28.75 %</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>5</td>\n",
" <td>55.00 %</td>\n",
" <td>49.40 %</td>\n",
" <td>47.50 %</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>7</td>\n",
" <td>55.56 %</td>\n",
" <td>50.45 %</td>\n",
" <td>45.00 %</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>56.67 %</td>\n",
" <td>50.45 %</td>\n",
" <td>55.00 %</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>6</td>\n",
" <td>56.67 %</td>\n",
" <td>50.72 %</td>\n",
" <td>32.50 %</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>9</td>\n",
" <td>56.67 %</td>\n",
" <td>51.97 %</td>\n",
" <td>47.50 %</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>3</td>\n",
" <td>57.78 %</td>\n",
" <td>52.35 %</td>\n",
" <td>47.50 %</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2</td>\n",
" <td>58.89 %</td>\n",
" <td>56.95 %</td>\n",
" <td>30.00 %</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>8</td>\n",
" <td>58.89 %</td>\n",
" <td>56.52 %</td>\n",
" <td>37.50 %</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>18</td>\n",
" <td>58.89 %</td>\n",
" <td>58.50 %</td>\n",
" <td>41.25 %</td>\n",
" </tr>\n",
" <tr>\n",
" <th>36</th>\n",
" <td>36</td>\n",
" <td>60.00 %</td>\n",
" <td>56.88 %</td>\n",
" <td>42.50 %</td>\n",
" </tr>\n",
" <tr>\n",
" <th>54</th>\n",
" <td>54</td>\n",
" <td>61.11 %</td>\n",
" <td>60.67 %</td>\n",
" <td>38.75 %</td>\n",
" </tr>\n",
" <tr>\n",
" <th>72</th>\n",
" <td>72</td>\n",
" <td>62.78 %</td>\n",
" <td>64.17 %</td>\n",
" <td>37.50 %</td>\n",
" </tr>\n",
" <tr>\n",
" <th>90</th>\n",
" <td>90</td>\n",
" <td>62.78 %</td>\n",
" <td>65.40 %</td>\n",
" <td>35.00 %</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" k Avg. Accuracy Avg. Precision Avg. Recall\n",
"4 4 53.89 % 44.75 % 28.75 %\n",
"5 5 55.00 % 49.40 % 47.50 %\n",
"7 7 55.56 % 50.45 % 45.00 %\n",
"1 1 56.67 % 50.45 % 55.00 %\n",
"6 6 56.67 % 50.72 % 32.50 %\n",
"9 9 56.67 % 51.97 % 47.50 %\n",
"3 3 57.78 % 52.35 % 47.50 %\n",
"2 2 58.89 % 56.95 % 30.00 %\n",
"8 8 58.89 % 56.52 % 37.50 %\n",
"18 18 58.89 % 58.50 % 41.25 %\n",
"36 36 60.00 % 56.88 % 42.50 %\n",
"54 54 61.11 % 60.67 % 38.75 %\n",
"72 72 62.78 % 64.17 % 37.50 %\n",
"90 90 62.78 % 65.40 % 35.00 %"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# TODO Normalize\n",
"\n",
"knn_mean_score_df = pd.DataFrame(columns = ['k', 'Avg. Accuracy', 'Avg. Precision', 'Avg. Recall'])\n",
"\n",
"normalized_x = sklearn.preprocessing.normalize(x) # No improvement over un-normalized data.\n",
"\n",
"mean_accs = []\n",
"for k in list(range(1, 10)) + [math.ceil(len(features) * step) for step in [0.1, 0.2, 0.3, 0.4, 0.5]]:\n",
" \n",
" knn_score_df = pd.DataFrame(columns = ['Fold', 'Accuracy', 'Precision', 'Recall'])\n",
"\n",
" mean_acc = 0\n",
" for fold_ind, (train_indices, test_indices) in enumerate(stratified_kflod_validator.split(x, y), 1):\n",
"\n",
" x_train, x_test = normalized_x[train_indices], normalized_x[test_indices]\n",
" y_train, y_test = y[train_indices], y[test_indices]\n",
"\n",
" knn = sklearn.neighbors.KNeighborsClassifier(n_neighbors = k)\n",
" knn.fit(x_train, y_train)\n",
"\n",
" acc = knn.score(x_test, y_test)\n",
" mean_acc += acc\n",
" \n",
" y_pred = knn.predict(x_test)\n",
" precision = sklearn.metrics.precision_score(y_test, y_pred)\n",
" recall = sklearn.metrics.recall_score(y_test, y_pred)\n",
"\n",
" knn_score_df.loc[fold_ind] = [f'{fold_ind}', \n",
" f'{acc*100:.2f} %',\n",
" f'{precision*100:.2f} %', \n",
" f'{recall*100:.2f} %']\n",
"\n",
" next_ind = len(knn_score_df) + 1\n",
" \n",
" mean_acc = knn_score_df['Accuracy'].apply(lambda n: float(n[:-2])).mean()\n",
" mean_prec = knn_score_df['Precision'].apply(lambda n: float(n[:-2])).mean()\n",
" mean_rec = knn_score_df['Recall'].apply(lambda n: float(n[:-2])).mean()\n",
" \n",
" knn_score_df.loc[next_ind] = ['Avg', \n",
" f'{acc*100:.2f} %',\n",
" f'{precision*100:.2f} %', \n",
" f'{recall*100:.2f} %']\n",
" \n",
" knn_mean_score_df.loc[k] = [k, \n",
" f'{mean_acc:.2f} %', \n",
" f'{mean_prec:.2f} %', \n",
" f'{mean_rec:.2f} %']\n",
"\n",
"# print(f'k = {k}')\n",
"# print(knn_score_df)\n",
"# print()\n",
" \n",
"best_accuracy = knn_mean_score_df.sort_values(by = ['Avg. Accuracy']).iloc[-1]\n",
"print('Best avg. accuracy is', best_accuracy['Avg. Accuracy'], 'for k =', best_accuracy['k'], '.')\n",
"knn_mean_score_df.sort_values(by = ['Avg. Accuracy'])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Naive Bayes"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"GaussianNB\n",
"\n",
" Fold Accuracy Precision Recall\n",
"1 1 86.11 % 82.35 % 87.50 %\n",
"2 2 77.78 % 72.22 % 81.25 %\n",
"3 3 88.89 % 87.50 % 87.50 %\n",
"4 4 77.78 % 72.22 % 81.25 %\n",
"5 5 83.33 % 81.25 % 81.25 %\n",
"6 Avg 82.78 % 79.11 % 83.75 %\n",
"\n",
"MultinomialNB\n",
"\n",
" Fold Accuracy Precision Recall\n",
"1 1 83.33 % 91.67 % 68.75 %\n",
"2 2 80.56 % 73.68 % 87.50 %\n",
"3 3 77.78 % 78.57 % 68.75 %\n",
"4 4 72.22 % 68.75 % 68.75 %\n",
"5 5 75.00 % 68.42 % 81.25 %\n",
"6 Avg 77.78 % 76.22 % 75.00 %\n",
"\n",
"ComplementNB\n",
"\n",
" Fold Accuracy Precision Recall\n",
"1 1 91.67 % 88.24 % 93.75 %\n",
"2 2 75.00 % 68.42 % 81.25 %\n",
"3 3 69.44 % 69.23 % 56.25 %\n",
"4 4 77.78 % 70.00 % 87.50 %\n",
"5 5 80.56 % 76.47 % 81.25 %\n",
"6 Avg 78.89 % 74.47 % 80.00 %\n",
"\n",
"BernoulliNB\n",
"\n",
" Fold Accuracy Precision Recall\n",
"1 1 69.44 % 69.23 % 56.25 %\n",
"2 2 86.11 % 86.67 % 81.25 %\n",
"3 3 80.56 % 80.00 % 75.00 %\n",
"4 4 80.56 % 71.43 % 93.75 %\n",
"5 5 77.78 % 72.22 % 81.25 %\n",
"6 Avg 78.89 % 75.91 % 77.50 %\n",
"\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Type</th>\n",
" <th>Avg. Accuracy</th>\n",
" <th>Avg. Precision</th>\n",
" <th>Avg. Recall</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>MultinomialNB</td>\n",
" <td>77.78 %</td>\n",
" <td>76.22 %</td>\n",
" <td>75.00 %</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>ComplementNB</td>\n",
" <td>78.89 %</td>\n",
" <td>74.47 %</td>\n",
" <td>80.00 %</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>BernoulliNB</td>\n",
" <td>78.89 %</td>\n",
" <td>75.91 %</td>\n",
" <td>77.50 %</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>GaussianNB</td>\n",
" <td>82.78 %</td>\n",
" <td>79.11 %</td>\n",
" <td>83.75 %</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Type Avg. Accuracy Avg. Precision Avg. Recall\n",
"2 MultinomialNB 77.78 % 76.22 % 75.00 %\n",
"3 ComplementNB 78.89 % 74.47 % 80.00 %\n",
"4 BernoulliNB 78.89 % 75.91 % 77.50 %\n",
"1 GaussianNB 82.78 % 79.11 % 83.75 %"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"nb_classifier_types = [sklearn.naive_bayes.GaussianNB,\n",
" sklearn.naive_bayes.MultinomialNB,\n",
" sklearn.naive_bayes.ComplementNB,\n",
" sklearn.naive_bayes.BernoulliNB]\n",
"\n",
"nb_mean_score_df = pd.DataFrame(columns = ['Type', 'Avg. Accuracy', 'Avg. Precision', 'Avg. Recall'])\n",
"\n",
"for nb_classifier_type in nb_classifier_types:\n",
" \n",
" nb_score_df = pd.DataFrame(columns = ['Fold', 'Accuracy', 'Precision', 'Recall'])\n",
"\n",
" mean_acc = 0\n",
" for fold_ind, (train_indices, test_indices) in enumerate(stratified_kflod_validator.split(x, y), 1):\n",
"\n",
" x_train, x_test = x[train_indices], x[test_indices]\n",
" y_train, y_test = y[train_indices], y[test_indices]\n",
"\n",
" nb = nb_classifier_type()\n",
" nb.fit(x_train, y_train)\n",
"\n",
" acc = nb.score(x_test, y_test)\n",
" mean_acc += acc\n",
" \n",
" y_pred = nb.predict(x_test)\n",
" precision = sklearn.metrics.precision_score(y_test, y_pred)\n",
" recall = sklearn.metrics.recall_score(y_test, y_pred)\n",
"\n",
" nb_score_df.loc[fold_ind] = [f'{fold_ind}', \n",
" f'{acc*100:.2f} %', \n",
" f'{precision*100:.2f} %', \n",
" f'{recall*100:.2f} %']\n",
"\n",
" next_ind = len(nb_score_df) + 1\n",
" \n",
" mean_acc = nb_score_df['Accuracy'].apply(lambda n: float(n[:-2])).mean()\n",
" mean_prec = nb_score_df['Precision'].apply(lambda n: float(n[:-2])).mean()\n",
" mean_rec = nb_score_df['Recall'].apply(lambda n: float(n[:-2])).mean()\n",
" \n",
" nb_score_df.loc[next_ind] = ['Avg', \n",
" f'{mean_acc:.2f} %', \n",
" f'{mean_prec:.2f} %', \n",
" f'{mean_rec:.2f} %']\n",
" \n",
" nb_mean_score_df.loc[len(nb_mean_score_df) + 1] = [nb_classifier_type.__name__, \n",
" f'{mean_acc:.2f} %', \n",
" f'{mean_prec:.2f} %', \n",
" f'{mean_rec:.2f} %']\n",
"\n",
" print(nb_classifier_type.__name__)\n",
" print()\n",
" print(nb_score_df)\n",
" print()\n",
" \n",
"nb_mean_score_df.sort_values(by = ['Avg. Accuracy'])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# SVM"
]
},
{
"cell_type": "code",
"execution_count": 158,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" Type Accuracy Precision Recall\n",
"1 1 75.00 % 81.82 % 56.25 %\n",
"2 2 91.67 % 88.24 % 93.75 %\n",
"3 3 86.11 % 82.35 % 87.50 %\n",
"4 4 77.78 % 75.00 % 75.00 %\n",
"5 5 80.56 % 80.00 % 75.00 %\n",
"6 Avg 82.22 % 81.48 % 77.50 %\n"
]
}
],
"source": [
"svm_classifier_type = sklearn.svm.SVC\n",
"\n",
"# Avg.\n",
"# Args -> acc / prec / rec\n",
"#\n",
"# kernel: linear -> 78.89 % 78.31 % 73.75 %\n",
"# kernel: linear, C: 0.1 -> 84.44 % 88.54 % 75.00 %\n",
"#\n",
"# * No improvement for larger C.\n",
"#\n",
"# kernel: poly, max_iter: 1 -> 46.67 % 34.67 % 21.25 %\n",
"# kernel: poly, max_iter: 10 -> 57.22 % 51.27 % 66.25 %\n",
"# kernel: poly, max_iter: 100 -> 61.67 % 60.18 % 40.00 %\n",
"# kernel: poly, max_iter: 100, coef0: 1 -> 62.22 % 62.19 % 41.25 %\n",
"#\n",
"# * No improvement for more iters.\n",
"# * No improvement for larger C.\n",
"# * No improvement for higher degree.\n",
"# * No improvement for different coef0.\n",
"#\n",
"# kernel: rbf, max_iter: 10 -> 48.89 % 46.07 % 72.50 %\n",
"# kernel: rbf, max_iter: 100 -> 60.00 % 74.00 % 17.50 %\n",
"# kernel: rbf, max_iter: 1000 -> 60.56 % 78.33 % 15.00 %\n",
"\n",
"\n",
"args = {'kernel': 'linear', 'C': 0.1}\n",
"\n",
"svm_score_df = pd.DataFrame(columns = ['Type', 'Accuracy', 'Precision', 'Recall'])\n",
"\n",
"# normalized_x = sklearn.preprocessing.normalize(x)\n",
"\n",
"mean_acc = 0\n",
"for fold_ind, (train_indices, test_indices) in enumerate(stratified_kflod_validator.split(x, y), 1):\n",
"\n",
" x_train, x_test = x[train_indices], x[test_indices]\n",
" y_train, y_test = y[train_indices], y[test_indices]\n",
"\n",
" svm = svm_classifier_type(**args, gamma = 'scale', cache_size = 256)\n",
" svm.fit(x_train, y_train)\n",
"\n",
" acc = svm.score(x_test, y_test)\n",
" mean_acc += acc\n",
"\n",
" y_pred = svm.predict(x_test)\n",
" precision = sklearn.metrics.precision_score(y_test, y_pred)\n",
" recall = sklearn.metrics.recall_score(y_test, y_pred)\n",
"\n",
" svm_score_df.loc[fold_ind] = [f'{fold_ind}', \n",
" f'{acc*100:.2f} %', \n",
" f'{precision*100:.2f} %', \n",
" f'{recall*100:.2f} %']\n",
"\n",
"next_ind = len(svm_score_df) + 1\n",
"\n",
"mean_acc = svm_score_df['Accuracy'].apply(lambda n: float(n[:-2])).mean()\n",
"mean_prec = svm_score_df['Precision'].apply(lambda n: float(n[:-2])).mean()\n",
"mean_rec = svm_score_df['Recall'].apply(lambda n: float(n[:-2])).mean()\n",
"\n",
"svm_score_df.loc[next_ind] = ['Avg', \n",
" f'{mean_acc:.2f} %', \n",
" f'{mean_prec:.2f} %', \n",
" f'{mean_rec:.2f} %']\n",
"\n",
"print(svm_score_df)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Shallow Neural Nets"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Import deps"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Using TensorFlow backend.\n"
]
}
],
"source": [
"import pandas as pd\n",
"\n",
"from sklearn.model_selection import train_test_split\n",
"\n",
"import keras\n",
"from keras.models import Sequential\n",
"from keras.layers import Dense, Dropout, Activation, Flatten\n",
"from keras.layers import Conv2D, MaxPooling2D\n",
"\n",
"\n",
"from keras.layers import Input, Dense, Conv2D, MaxPooling2D, Dropout, Flatten, BatchNormalization, LeakyReLU"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Import data"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" patient_id heart_disease_present\n",
"0 0z64un 0\n",
"1 ryoo3j 0\n",
"2 yt1s1x 1\n",
"3 l2xjde 1\n",
"4 oyt4ek 0\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>patient_id</th>\n",
" <th>slope_of_peak_exercise_st_segment</th>\n",
" <th>thal</th>\n",
" <th>resting_blood_pressure</th>\n",
" <th>chest_pain_type</th>\n",
" <th>num_major_vessels</th>\n",
" <th>fasting_blood_sugar_gt_120_mg_per_dl</th>\n",
" <th>resting_ekg_results</th>\n",
" <th>serum_cholesterol_mg_per_dl</th>\n",
" <th>oldpeak_eq_st_depression</th>\n",
" <th>sex</th>\n",
" <th>age</th>\n",
" <th>max_heart_rate_achieved</th>\n",
" <th>exercise_induced_angina</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0z64un</td>\n",
" <td>1</td>\n",
" <td>normal</td>\n",
" <td>128</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>308</td>\n",
" <td>0.0</td>\n",
" <td>1</td>\n",
" <td>45</td>\n",
" <td>170</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>ryoo3j</td>\n",
" <td>2</td>\n",
" <td>normal</td>\n",
" <td>110</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>214</td>\n",
" <td>1.6</td>\n",
" <td>0</td>\n",
" <td>54</td>\n",
" <td>158</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>yt1s1x</td>\n",
" <td>1</td>\n",
" <td>normal</td>\n",
" <td>125</td>\n",
" <td>4</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>304</td>\n",
" <td>0.0</td>\n",
" <td>1</td>\n",
" <td>77</td>\n",
" <td>162</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>l2xjde</td>\n",
" <td>1</td>\n",
" <td>reversible_defect</td>\n",
" <td>152</td>\n",
" <td>4</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>223</td>\n",
" <td>0.0</td>\n",
" <td>1</td>\n",
" <td>40</td>\n",
" <td>181</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>oyt4ek</td>\n",
" <td>3</td>\n",
" <td>reversible_defect</td>\n",
" <td>178</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>270</td>\n",
" <td>4.2</td>\n",
" <td>1</td>\n",
" <td>59</td>\n",
" <td>145</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" patient_id slope_of_peak_exercise_st_segment thal \\\n",
"0 0z64un 1 normal \n",
"1 ryoo3j 2 normal \n",
"2 yt1s1x 1 normal \n",
"3 l2xjde 1 reversible_defect \n",
"4 oyt4ek 3 reversible_defect \n",
"\n",
" resting_blood_pressure chest_pain_type num_major_vessels \\\n",
"0 128 2 0 \n",
"1 110 3 0 \n",
"2 125 4 3 \n",
"3 152 4 0 \n",
"4 178 1 0 \n",
"\n",
" fasting_blood_sugar_gt_120_mg_per_dl resting_ekg_results \\\n",
"0 0 2 \n",
"1 0 0 \n",
"2 0 2 \n",
"3 0 0 \n",
"4 0 2 \n",
"\n",
" serum_cholesterol_mg_per_dl oldpeak_eq_st_depression sex age \\\n",
"0 308 0.0 1 45 \n",
"1 214 1.6 0 54 \n",
"2 304 0.0 1 77 \n",
"3 223 0.0 1 40 \n",
"4 270 4.2 1 59 \n",
"\n",
" max_heart_rate_achieved exercise_induced_angina \n",
"0 170 0 \n",
"1 158 0 \n",
"2 162 1 \n",
"3 181 0 \n",
"4 145 0 "
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"features = pd.read_csv('train_values.csv')\n",
"labels = pd.read_csv('train_labels.csv')\n",
"\n",
"print(labels.head())\n",
"features.head()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"FEATURES = ['slope_of_peak_exercise_st_segment', \n",
" 'thal',\n",
" 'resting_blood_pressure', \n",
" 'chest_pain_type', \n",
" 'num_major_vessels', \n",
" 'fasting_blood_sugar_gt_120_mg_per_dl',\n",
" 'resting_ekg_results', \n",
" 'serum_cholesterol_mg_per_dl', \n",
" 'oldpeak_eq_st_depression', \n",
" 'sex',\n",
" 'age', \n",
" 'max_heart_rate_achieved', \n",
" 'exercise_induced_angina']\n",
"\n",
"LABEL = 'heart_disease_present'\n",
"\n",
"EXPLANATIONS = {'slope_of_peak_exercise_st_segment' : 'Quality of Blood Flow to the Heart',\n",
" 'thal' : 'Thallium Stress Test Measuring Blood Flow to the Heart',\n",
" 'resting_blood_pressure' : 'Resting Blood Pressure', \n",
" 'chest_pain_type' : 'Chest Pain Type (1-4)',\n",
" 'num_major_vessels' : 'Major Vessels (0-3) Colored by Flourosopy',\n",
" 'fasting_blood_sugar_gt_120_mg_per_dl' : 'Fasting Blood Sugar > 120 mg/dl',\n",
" 'resting_ekg_results' : 'Resting Electrocardiographic Results (0-2)',\n",
" 'serum_cholesterol_mg_per_dl' : 'Serum Cholesterol in mg/dl',\n",
" 'oldpeak_eq_st_depression' : 'Exercise vs. Rest\\nA Measure of Abnormality in Electrocardiograms',\n",
" 'age' : 'Age (years)',\n",
" 'sex' : 'Sex (m/f)',\n",
" 'max_heart_rate_achieved' : 'Maximum Heart Rate Achieved (bpm)',\n",
" 'exercise_induced_angina' : 'Exercise-Induced Chest Pain (yes/no)'}\n",
"\n",
"NUMERICAL_FEATURES = ['slope_of_peak_exercise_st_segment', \n",
" 'resting_blood_pressure', \n",
" 'chest_pain_type', \n",
" 'num_major_vessels', \n",
" 'resting_ekg_results', \n",
" 'serum_cholesterol_mg_per_dl', \n",
" 'oldpeak_eq_st_depression', \n",
" 'age', \n",
" 'max_heart_rate_achieved']\n",
"\n",
"CATEGORICAL_FEATURES = ['thal', \n",
" 'fasting_blood_sugar_gt_120_mg_per_dl', \n",
" 'sex', \n",
" 'exercise_induced_angina']\n",
"\n",
"CATEGORICAL_FEATURE_VALUES = {'thal' : [[0, 1, 2], ['Normal', \n",
" 'Fixed Defect', \n",
" 'Reversible Defect']], \n",
" 'fasting_blood_sugar_gt_120_mg_per_dl' : [[0, 1], ['No', 'Yes']],\n",
" 'sex' : [[0, 1], ['F', 'M']], \n",
" 'exercise_induced_angina' : [[0, 1], ['No', 'Yes']]}\n",
"\n",
"SEMI_CATEGORICAL_FEATURES = ['slope_of_peak_exercise_st_segment',\n",
" 'chest_pain_type',\n",
" 'num_major_vessels',\n",
" 'resting_ekg_results']\n",
"\n",
"SEMI_CATEGORICAL_FEATURE_LIMITS = {'slope_of_peak_exercise_st_segment' : [1, 3],\n",
" 'chest_pain_type' : [1, 4],\n",
" 'num_major_vessels' : [0, 3],\n",
" 'resting_ekg_results' : [0, 2]}\n",
"\n",
"LABEL_VALUES = [[0, 1], ['No', 'Yes']]\n"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"for feature in CATEGORICAL_FEATURES:\n",
" if len(CATEGORICAL_FEATURE_VALUES[feature][0]) > 2:\n",
" \n",
" onehot_feature = pd.get_dummies(features[feature])\n",
" \n",
" feature_index = features.columns.get_loc(feature)\n",
" features.drop(feature, axis=1, inplace=True)\n",
" \n",
" onehot_feature.columns = ['%s=%s' % (feature, feature_value) for feature_value in onehot_feature.columns]\n",
" for colname in onehot_feature.columns[::-1]:\n",
" features.insert(feature_index, colname, onehot_feature[colname])"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"x =\n",
" [[ 1 0 1 ... 45 170 0]\n",
" [ 2 0 1 ... 54 158 0]\n",
" [ 1 0 1 ... 77 162 1]\n",
" ...\n",
" [ 2 0 0 ... 64 131 1]\n",
" [ 1 0 1 ... 48 175 0]\n",
" [ 1 0 1 ... 54 163 0]]\n",
"y =\n",
" [0 0 1 1 0 0 1 1 1 0 0 0 1 0 1 1 0 1 0 1 0 0 0 0 0 0 0 0 1 0 0 1 1 0 1 0 0\n",
" 0 0 1 0 0 0 0 0 1 0 0 1 0 1 0 1 1 1 0 1 1 0 1 0 1 1 0 0 1 1 0 0 1 0 0 0 0\n",
" 1 1 0 1 1 1 0 1 1 1 0 1 0 1 0 0 0 1 0 1 0 0 1 0 0 0 0 0 1 0 0 0 0 1 0 1 0\n",
" 0 1 1 0 0 0 1 1 1 1 0 0 0 1 0 0 0 1 1 0 0 0 1 1 0 1 1 0 0 1 0 0 1 1 0 0 1\n",
" 1 1 0 0 1 1 1 1 0 0 0 1 0 1 1 1 0 0 0 1 0 0 1 1 1 1 0 1 1 1 0 0]\n"
]
}
],
"source": [
"x = features.values[:,1:].astype(int)\n",
"y = labels.values[:,-1].astype(int)\n",
"\n",
"print('x =\\n', x)\n",
"print('y =\\n', y)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"# for fold_ind, (train_indices, test_indices) in enumerate(stratified_kflod_validator.split(x, y), 1):\n",
"\n",
"# x_train, x_test = x[train_indices], x[test_indices]\n",
"# y_train, y_test = y[train_indices], y[test_indices]"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(144, 15) (36, 15)\n",
"(144,) (36,)\n"
]
}
],
"source": [
"x_train, x_test, y_train, y_test = \\\n",
" train_test_split(x, y, test_size=0.2, random_state=42)\n",
"\n",
"print(x_train.shape, x_test.shape)\n",
"print(y_train.shape, y_test.shape)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Define model"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(180, 15)\n",
"(180,)\n",
"[[ 1 0 1 0 128 2 0 0 2 308 0 1 45 170 0]]\n",
"[0]\n"
]
}
],
"source": [
"input_shape = (1,15)\n",
"num_classes = 2\n",
"\n",
"print(x.shape)\n",
"print(y.shape)\n",
"\n",
"print(x[:1])\n",
"print(y[:1])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Architecture 0 - Inflating Dense 120-225, 0.5 Dropout, Batch Norm, Sigmoid Classification"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"_________________________________________________________________\n",
"Layer (type) Output Shape Param # \n",
"=================================================================\n",
"dense_19 (Dense) (None, 120) 1920 \n",
"_________________________________________________________________\n",
"dropout_7 (Dropout) (None, 120) 0 \n",
"_________________________________________________________________\n",
"dense_20 (Dense) (None, 225) 27225 \n",
"_________________________________________________________________\n",
"batch_normalization_7 (Batch (None, 225) 900 \n",
"_________________________________________________________________\n",
"dense_21 (Dense) (None, 1) 226 \n",
"=================================================================\n",
"Total params: 30,271\n",
"Trainable params: 29,821\n",
"Non-trainable params: 450\n",
"_________________________________________________________________\n"
]
}
],
"source": [
"arch_cnt = 'arch-0-3'\n",
"\n",
"model = Sequential()\n",
"model.add(\n",
" Dense(120, input_dim=15, kernel_initializer='normal',\n",
" # kernel_regularizer=keras.regularizers.l2(0.001), # pierd 0.2 acc\n",
" activation='relu'))\n",
"model.add(Dropout(0.5))\n",
"model.add(Dense(225, input_dim=15, kernel_initializer='normal', activation='relu'))\n",
"# model.add(LeakyReLU(alpha=0.1))\n",
"model.add(BatchNormalization(axis = 1))\n",
"model.add(Dense(1, kernel_initializer='normal', activation='sigmoid'))\n",
"\n",
"\n",
"model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
"\n",
"model.summary()"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Train on 144 samples, validate on 36 samples\n",
"Epoch 1/50\n",
"144/144 [==============================] - 0s 2ms/step - loss: 0.6969 - acc: 0.5417 - val_loss: 0.6813 - val_acc: 0.5556\n",
"Epoch 2/50\n",
"144/144 [==============================] - 0s 71us/step - loss: 0.7291 - acc: 0.5417 - val_loss: 0.6876 - val_acc: 0.5556\n",
"Epoch 3/50\n",
"144/144 [==============================] - 0s 81us/step - loss: 0.7537 - acc: 0.5000 - val_loss: 0.7145 - val_acc: 0.4444\n",
"Epoch 4/50\n",
"144/144 [==============================] - 0s 138us/step - loss: 0.6780 - acc: 0.5556 - val_loss: 0.7250 - val_acc: 0.4444\n",
"Epoch 5/50\n",
"144/144 [==============================] - 0s 80us/step - loss: 0.7757 - acc: 0.4444 - val_loss: 0.7068 - val_acc: 0.4444\n",
"Epoch 6/50\n",
"144/144 [==============================] - 0s 106us/step - loss: 0.6985 - acc: 0.5556 - val_loss: 0.6883 - val_acc: 0.4167\n",
"\n",
"Epoch 00006: ReduceLROnPlateau reducing learning rate to 0.001.\n",
"Epoch 7/50\n",
"144/144 [==============================] - 0s 84us/step - loss: 0.7108 - acc: 0.5556 - val_loss: 0.6835 - val_acc: 0.5278\n",
"Epoch 8/50\n",
"144/144 [==============================] - 0s 115us/step - loss: 0.6764 - acc: 0.5903 - val_loss: 0.6797 - val_acc: 0.5556\n",
"Epoch 00008: early stopping\n",
"CPU times: user 2.04 s, sys: 47.7 ms, total: 2.09 s\n",
"Wall time: 2.04 s\n"
]
}
],
"source": [
"%%time\n",
"\n",
"# earlystop_cb = keras.callbacks.EarlyStopping(\n",
"# monitor='val_loss',\n",
"# patience=5, restore_best_weights=True,\n",
"# verbose=1)\n",
"reduce_lr_cb = keras.callbacks.ReduceLROnPlateau(\n",
" monitor='val_loss', factor=0.05,\n",
" patience=5, min_lr=0.001,\n",
" verbose=1)\n",
"\n",
"# es_cb = keras.callbacks.EarlyStopping(\n",
"# monitor='val_loss',\n",
"# min_delta=0.1,\n",
"# patience=7,\n",
"# verbose=1,\n",
"# mode='auto'\n",
"# )\n",
"# 'restore_best_weights' in dir(keras.callbacks.EarlyStopping()) # FALSE = library is not up-to-date\n",
"\n",
"tb_cb = keras.callbacks.TensorBoard(log_dir='./tensorboard/%s' % arch_cnt, histogram_freq=0, \n",
" write_graph=True, write_images=True)\n",
"\n",
"\n",
"epochs = 50\n",
"batch_size = 32\n",
"\n",
"model.fit(\n",
" x_train, y_train,\n",
" batch_size=batch_size,\n",
" epochs=epochs,\n",
" verbose=1,\n",
" \n",
" shuffle=False,\n",
" validation_data=(x_test, y_test),\n",
" callbacks=[reduce_lr_cb, es_cb, tb_cb]\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Test loss: 0.6797179447280036\n",
"Test accuracy: 0.5555555555555556\n"
]
}
],
"source": [
"score = model.evaluate(x_test, y_test, verbose=0)\n",
"print('Test loss:', score[0])\n",
"print('Test accuracy:', score[1])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Architecture 1 - **`Deflating Dense 225-112`**, 0.5 Dropout, Batch Norm, Sigmoid Classification"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"_________________________________________________________________\n",
"Layer (type) Output Shape Param # \n",
"=================================================================\n",
"dense_4 (Dense) (None, 225) 3600 \n",
"_________________________________________________________________\n",
"dropout_2 (Dropout) (None, 225) 0 \n",
"_________________________________________________________________\n",
"dense_5 (Dense) (None, 112) 25312 \n",
"_________________________________________________________________\n",
"batch_normalization_2 (Batch (None, 112) 448 \n",
"_________________________________________________________________\n",
"dense_6 (Dense) (None, 1) 113 \n",
"=================================================================\n",
"Total params: 29,473\n",
"Trainable params: 29,249\n",
"Non-trainable params: 224\n",
"_________________________________________________________________\n"
]
}
],
"source": [
"arch_cnt = 'arch-1'\n",
"\n",
"model = Sequential()\n",
"model.add(\n",
" Dense(225, input_dim=15, kernel_initializer='normal',\n",
" # kernel_regularizer=keras.regularizers.l2(0.001), # pierd 0.2 acc\n",
" activation='relu'))\n",
"model.add(Dropout(0.5))\n",
"model.add(Dense(112, input_dim=15, kernel_initializer='normal', activation='relu'))\n",
"# model.add(LeakyReLU(alpha=0.1))\n",
"model.add(BatchNormalization(axis = 1))\n",
"model.add(Dense(1, kernel_initializer='normal', activation='sigmoid'))\n",
"\n",
"\n",
"model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
"\n",
"model.summary()"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Train on 144 samples, validate on 36 samples\n",
"Epoch 1/50\n",
"144/144 [==============================] - 0s 2ms/step - loss: 0.7093 - acc: 0.5069 - val_loss: 0.6825 - val_acc: 0.5833\n",
"Epoch 2/50\n",
"144/144 [==============================] - 0s 73us/step - loss: 0.6929 - acc: 0.5694 - val_loss: 0.6799 - val_acc: 0.7500\n",
"Epoch 3/50\n",
"144/144 [==============================] - 0s 80us/step - loss: 0.6967 - acc: 0.5069 - val_loss: 0.6785 - val_acc: 0.6111\n",
"Epoch 4/50\n",
"144/144 [==============================] - 0s 81us/step - loss: 0.6565 - acc: 0.6042 - val_loss: 0.6805 - val_acc: 0.5278\n",
"Epoch 5/50\n",
"144/144 [==============================] - 0s 75us/step - loss: 0.7139 - acc: 0.4861 - val_loss: 0.6793 - val_acc: 0.5000\n",
"Epoch 6/50\n",
"144/144 [==============================] - 0s 71us/step - loss: 0.6951 - acc: 0.5833 - val_loss: 0.6761 - val_acc: 0.5278\n",
"Epoch 7/50\n",
"144/144 [==============================] - 0s 88us/step - loss: 0.6469 - acc: 0.6181 - val_loss: 0.6785 - val_acc: 0.4722\n",
"Epoch 8/50\n",
"144/144 [==============================] - 0s 99us/step - loss: 0.6730 - acc: 0.6042 - val_loss: 0.6817 - val_acc: 0.4444\n",
"Epoch 9/50\n",
"144/144 [==============================] - 0s 97us/step - loss: 0.6321 - acc: 0.6389 - val_loss: 0.6897 - val_acc: 0.5000\n",
"Epoch 10/50\n",
"144/144 [==============================] - 0s 77us/step - loss: 0.6615 - acc: 0.5694 - val_loss: 0.6838 - val_acc: 0.4722\n",
"Epoch 11/50\n",
"144/144 [==============================] - 0s 115us/step - loss: 0.6357 - acc: 0.6458 - val_loss: 0.6758 - val_acc: 0.4444\n",
"Epoch 12/50\n",
"144/144 [==============================] - 0s 70us/step - loss: 0.6651 - acc: 0.5556 - val_loss: 0.6755 - val_acc: 0.4722\n",
"Epoch 13/50\n",
"144/144 [==============================] - 0s 83us/step - loss: 0.6498 - acc: 0.6111 - val_loss: 0.6762 - val_acc: 0.4167\n",
"Epoch 14/50\n",
"144/144 [==============================] - 0s 77us/step - loss: 0.6679 - acc: 0.5903 - val_loss: 0.6807 - val_acc: 0.5000\n",
"Epoch 15/50\n",
"144/144 [==============================] - 0s 75us/step - loss: 0.6756 - acc: 0.5972 - val_loss: 0.6921 - val_acc: 0.4722\n",
"Epoch 16/50\n",
"144/144 [==============================] - 0s 64us/step - loss: 0.6558 - acc: 0.6458 - val_loss: 0.7126 - val_acc: 0.5000\n",
"Epoch 17/50\n",
"144/144 [==============================] - 0s 85us/step - loss: 0.6583 - acc: 0.5764 - val_loss: 0.7052 - val_acc: 0.5000\n",
"Epoch 18/50\n",
"144/144 [==============================] - 0s 97us/step - loss: 0.6644 - acc: 0.5972 - val_loss: 0.7022 - val_acc: 0.5000\n",
"Epoch 19/50\n",
"144/144 [==============================] - 0s 67us/step - loss: 0.6542 - acc: 0.6181 - val_loss: 0.6827 - val_acc: 0.5000\n",
"\n",
"Epoch 00019: ReduceLROnPlateau reducing learning rate to 0.001.\n",
"Epoch 20/50\n",
"144/144 [==============================] - 0s 60us/step - loss: 0.6295 - acc: 0.6528 - val_loss: 0.6693 - val_acc: 0.4722\n",
"Epoch 21/50\n",
"144/144 [==============================] - 0s 88us/step - loss: 0.6372 - acc: 0.6458 - val_loss: 0.6694 - val_acc: 0.5278\n",
"Epoch 22/50\n",
"144/144 [==============================] - 0s 86us/step - loss: 0.6372 - acc: 0.6528 - val_loss: 0.6713 - val_acc: 0.5278\n",
"Epoch 23/50\n",
"144/144 [==============================] - 0s 126us/step - loss: 0.6302 - acc: 0.6667 - val_loss: 0.6722 - val_acc: 0.5278\n",
"Epoch 24/50\n",
"144/144 [==============================] - 0s 95us/step - loss: 0.6399 - acc: 0.6389 - val_loss: 0.6669 - val_acc: 0.5278\n",
"Epoch 25/50\n",
"144/144 [==============================] - 0s 72us/step - loss: 0.6367 - acc: 0.6875 - val_loss: 0.6621 - val_acc: 0.5833\n",
"Epoch 26/50\n",
"144/144 [==============================] - 0s 104us/step - loss: 0.6440 - acc: 0.6111 - val_loss: 0.6633 - val_acc: 0.5833\n",
"Epoch 27/50\n",
"144/144 [==============================] - 0s 81us/step - loss: 0.6119 - acc: 0.6875 - val_loss: 0.6646 - val_acc: 0.6111\n",
"Epoch 28/50\n",
"144/144 [==============================] - 0s 103us/step - loss: 0.6463 - acc: 0.6458 - val_loss: 0.6685 - val_acc: 0.6111\n",
"Epoch 29/50\n",
"144/144 [==============================] - 0s 80us/step - loss: 0.6254 - acc: 0.6528 - val_loss: 0.6906 - val_acc: 0.5278\n",
"Epoch 30/50\n",
"144/144 [==============================] - 0s 89us/step - loss: 0.6357 - acc: 0.6667 - val_loss: 0.6953 - val_acc: 0.5000\n",
"Epoch 31/50\n",
"144/144 [==============================] - 0s 130us/step - loss: 0.6132 - acc: 0.7014 - val_loss: 0.6744 - val_acc: 0.5556\n",
"Epoch 32/50\n",
"144/144 [==============================] - 0s 98us/step - loss: 0.5881 - acc: 0.6806 - val_loss: 0.6497 - val_acc: 0.6389\n",
"Epoch 33/50\n",
"144/144 [==============================] - 0s 75us/step - loss: 0.6306 - acc: 0.6597 - val_loss: 0.6354 - val_acc: 0.6111\n",
"Epoch 34/50\n",
"144/144 [==============================] - 0s 76us/step - loss: 0.5916 - acc: 0.6458 - val_loss: 0.6416 - val_acc: 0.6389\n",
"Epoch 35/50\n",
"144/144 [==============================] - 0s 82us/step - loss: 0.6336 - acc: 0.6389 - val_loss: 0.6559 - val_acc: 0.5833\n",
"Epoch 36/50\n",
"144/144 [==============================] - 0s 79us/step - loss: 0.6226 - acc: 0.6667 - val_loss: 0.6697 - val_acc: 0.6111\n",
"Epoch 37/50\n",
"144/144 [==============================] - 0s 83us/step - loss: 0.6108 - acc: 0.6875 - val_loss: 0.6779 - val_acc: 0.5278\n",
"Epoch 38/50\n",
"144/144 [==============================] - 0s 100us/step - loss: 0.6044 - acc: 0.6667 - val_loss: 0.6830 - val_acc: 0.5278\n",
"Epoch 39/50\n",
"144/144 [==============================] - 0s 92us/step - loss: 0.5959 - acc: 0.6806 - val_loss: 0.6889 - val_acc: 0.5556\n",
"Epoch 40/50\n",
"144/144 [==============================] - 0s 80us/step - loss: 0.6000 - acc: 0.7153 - val_loss: 0.7006 - val_acc: 0.5278\n",
"\n",
"Epoch 00040: ReduceLROnPlateau reducing learning rate to 0.001.\n",
"Epoch 41/50\n",
"144/144 [==============================] - 0s 96us/step - loss: 0.5673 - acc: 0.7014 - val_loss: 0.7056 - val_acc: 0.5278\n",
"Epoch 42/50\n",
"144/144 [==============================] - 0s 73us/step - loss: 0.6208 - acc: 0.6111 - val_loss: 0.6841 - val_acc: 0.5556\n",
"Epoch 43/50\n",
"144/144 [==============================] - 0s 90us/step - loss: 0.5972 - acc: 0.7083 - val_loss: 0.6565 - val_acc: 0.6944\n",
"Epoch 44/50\n",
"144/144 [==============================] - 0s 89us/step - loss: 0.6086 - acc: 0.7014 - val_loss: 0.6394 - val_acc: 0.6667\n",
"Epoch 45/50\n",
"144/144 [==============================] - 0s 88us/step - loss: 0.5894 - acc: 0.6806 - val_loss: 0.6096 - val_acc: 0.6389\n",
"Epoch 46/50\n",
"144/144 [==============================] - 0s 87us/step - loss: 0.5571 - acc: 0.7569 - val_loss: 0.6087 - val_acc: 0.6389\n",
"Epoch 47/50\n",
"144/144 [==============================] - 0s 144us/step - loss: 0.5807 - acc: 0.6875 - val_loss: 0.6171 - val_acc: 0.6389\n",
"Epoch 48/50\n",
"144/144 [==============================] - 0s 110us/step - loss: 0.5782 - acc: 0.6736 - val_loss: 0.6319 - val_acc: 0.6944\n",
"Epoch 49/50\n",
"144/144 [==============================] - 0s 99us/step - loss: 0.6167 - acc: 0.6458 - val_loss: 0.6380 - val_acc: 0.6944\n",
"Epoch 50/50\n",
"144/144 [==============================] - 0s 120us/step - loss: 0.5846 - acc: 0.6806 - val_loss: 0.6483 - val_acc: 0.6944\n",
"CPU times: user 2.14 s, sys: 50.7 ms, total: 2.19 s\n",
"Wall time: 2.05 s\n"
]
}
],
"source": [
"%%time\n",
"\n",
"# earlystop_cb = keras.callbacks.EarlyStopping(\n",
"# monitor='val_loss',\n",
"# patience=5, restore_best_weights=True,\n",
"# verbose=1)\n",
"reduce_lr_cb = keras.callbacks.ReduceLROnPlateau(\n",
" monitor='val_loss', factor=0.05,\n",
" patience=7, min_lr=0.001,\n",
" verbose=1)\n",
"\n",
"tb_cb = keras.callbacks.TensorBoard(log_dir='./tensorboard/%s' % arch_cnt, histogram_freq=0, \n",
" write_graph=True, write_images=True)\n",
"\n",
"\n",
"epochs = 50\n",
"batch_size = 32\n",
"\n",
"model.fit(\n",
" x_train, y_train,\n",
" batch_size=batch_size,\n",
" epochs=epochs,\n",
" verbose=1,\n",
" \n",
" shuffle=False,\n",
" validation_data=(x_test, y_test),\n",
" callbacks=[reduce_lr_cb, tb_cb]\n",
" # callbacks=[earlystop_cb, reduce_lr_cb]\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Test loss: 0.6483488314681582\n",
"Test accuracy: 0.6944444444444444\n"
]
}
],
"source": [
"score = model.evaluate(x_test, y_test, verbose=0)\n",
"print('Test loss:', score[0])\n",
"print('Test accuracy:', score[1])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Architecture 2 - Deflating Dense 225-112, 0.5 Dropout, Batch Norm, Sigmoid Classification, **`HE Initialization`**"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"_________________________________________________________________\n",
"Layer (type) Output Shape Param # \n",
"=================================================================\n",
"dense_7 (Dense) (None, 225) 3600 \n",
"_________________________________________________________________\n",
"dropout_3 (Dropout) (None, 225) 0 \n",
"_________________________________________________________________\n",
"dense_8 (Dense) (None, 112) 25312 \n",
"_________________________________________________________________\n",
"batch_normalization_3 (Batch (None, 112) 448 \n",
"_________________________________________________________________\n",
"dense_9 (Dense) (None, 1) 113 \n",
"=================================================================\n",
"Total params: 29,473\n",
"Trainable params: 29,249\n",
"Non-trainable params: 224\n",
"_________________________________________________________________\n"
]
}
],
"source": [
"arch_cnt = 'arch-2'\n",
"\n",
"model = Sequential()\n",
"model.add(\n",
" Dense(225, input_dim=15, kernel_initializer='he_uniform',\n",
" kernel_regularizer=keras.regularizers.l2(0.001), # pierd 0.2 acc\n",
" activation='relu'))\n",
"model.add(Dropout(0.5))\n",
"model.add(Dense(112, input_dim=15, kernel_initializer='he_uniform', activation='relu'))\n",
"# model.add(LeakyReLU(alpha=0.1))\n",
"model.add(BatchNormalization(axis = 1))\n",
"model.add(Dense(1, kernel_initializer='normal', activation='sigmoid'))\n",
"\n",
"\n",
"model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
"\n",
"model.summary()"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Train on 144 samples, validate on 36 samples\n",
"Epoch 1/50\n",
"144/144 [==============================] - 0s 2ms/step - loss: 1.1839 - acc: 0.4792 - val_loss: 1.1616 - val_acc: 0.4167\n",
"Epoch 2/50\n",
"144/144 [==============================] - 0s 105us/step - loss: 1.1813 - acc: 0.4583 - val_loss: 1.1539 - val_acc: 0.4167\n",
"Epoch 3/50\n",
"144/144 [==============================] - 0s 79us/step - loss: 1.1809 - acc: 0.3958 - val_loss: 1.1394 - val_acc: 0.4444\n",
"Epoch 4/50\n",
"144/144 [==============================] - 0s 104us/step - loss: 1.1154 - acc: 0.5347 - val_loss: 1.1291 - val_acc: 0.4444\n",
"Epoch 5/50\n",
"144/144 [==============================] - 0s 80us/step - loss: 1.1076 - acc: 0.5417 - val_loss: 1.1173 - val_acc: 0.4444\n",
"Epoch 6/50\n",
"144/144 [==============================] - 0s 112us/step - loss: 1.0832 - acc: 0.5764 - val_loss: 1.1076 - val_acc: 0.4444\n",
"Epoch 7/50\n",
"144/144 [==============================] - 0s 81us/step - loss: 1.0518 - acc: 0.6458 - val_loss: 1.0964 - val_acc: 0.4444\n",
"Epoch 8/50\n",
"144/144 [==============================] - 0s 80us/step - loss: 1.0829 - acc: 0.5417 - val_loss: 1.0868 - val_acc: 0.4444\n",
"Epoch 9/50\n",
"144/144 [==============================] - 0s 91us/step - loss: 1.0699 - acc: 0.5278 - val_loss: 1.0821 - val_acc: 0.4444\n",
"Epoch 10/50\n",
"144/144 [==============================] - 0s 116us/step - loss: 1.0633 - acc: 0.5833 - val_loss: 1.0795 - val_acc: 0.4444\n",
"Epoch 11/50\n",
"144/144 [==============================] - 0s 136us/step - loss: 1.0887 - acc: 0.5139 - val_loss: 1.0843 - val_acc: 0.4722\n",
"Epoch 12/50\n",
"144/144 [==============================] - 0s 96us/step - loss: 1.0217 - acc: 0.6111 - val_loss: 1.0854 - val_acc: 0.4444\n",
"Epoch 13/50\n",
"144/144 [==============================] - 0s 113us/step - loss: 1.0282 - acc: 0.6042 - val_loss: 1.0917 - val_acc: 0.4167\n",
"Epoch 14/50\n",
"144/144 [==============================] - 0s 92us/step - loss: 1.0108 - acc: 0.5694 - val_loss: 1.0987 - val_acc: 0.4167\n",
"Epoch 15/50\n",
"144/144 [==============================] - 0s 113us/step - loss: 0.9797 - acc: 0.6597 - val_loss: 1.1029 - val_acc: 0.4444\n",
"Epoch 16/50\n",
"144/144 [==============================] - 0s 82us/step - loss: 1.0015 - acc: 0.5764 - val_loss: 1.1065 - val_acc: 0.4444\n",
"Epoch 17/50\n",
"144/144 [==============================] - 0s 97us/step - loss: 0.9799 - acc: 0.6181 - val_loss: 1.1098 - val_acc: 0.4444\n",
"\n",
"Epoch 00017: ReduceLROnPlateau reducing learning rate to 0.001.\n",
"Epoch 18/50\n",
"144/144 [==============================] - 0s 83us/step - loss: 0.9983 - acc: 0.5764 - val_loss: 1.1117 - val_acc: 0.4444\n",
"Epoch 19/50\n",
"144/144 [==============================] - 0s 85us/step - loss: 0.9640 - acc: 0.6458 - val_loss: 1.1135 - val_acc: 0.4444\n",
"Epoch 20/50\n",
"144/144 [==============================] - 0s 124us/step - loss: 0.9538 - acc: 0.6181 - val_loss: 1.1163 - val_acc: 0.4444\n",
"Epoch 21/50\n",
"144/144 [==============================] - 0s 117us/step - loss: 0.9511 - acc: 0.6528 - val_loss: 1.1067 - val_acc: 0.4444\n",
"Epoch 22/50\n",
"144/144 [==============================] - 0s 70us/step - loss: 0.9432 - acc: 0.6319 - val_loss: 1.1034 - val_acc: 0.4444\n",
"Epoch 23/50\n",
"144/144 [==============================] - 0s 77us/step - loss: 0.9707 - acc: 0.5556 - val_loss: 1.0927 - val_acc: 0.4444\n",
"Epoch 24/50\n",
"144/144 [==============================] - 0s 108us/step - loss: 0.9418 - acc: 0.5903 - val_loss: 1.0725 - val_acc: 0.4722\n",
"Epoch 25/50\n",
"144/144 [==============================] - 0s 93us/step - loss: 0.9269 - acc: 0.6528 - val_loss: 1.0518 - val_acc: 0.4444\n",
"Epoch 26/50\n",
"144/144 [==============================] - 0s 97us/step - loss: 0.9566 - acc: 0.5903 - val_loss: 1.0330 - val_acc: 0.4444\n",
"Epoch 27/50\n",
"144/144 [==============================] - 0s 83us/step - loss: 0.9245 - acc: 0.6250 - val_loss: 1.0146 - val_acc: 0.4722\n",
"Epoch 28/50\n",
"144/144 [==============================] - 0s 66us/step - loss: 0.8902 - acc: 0.6597 - val_loss: 1.0130 - val_acc: 0.4722\n",
"Epoch 29/50\n",
"144/144 [==============================] - 0s 84us/step - loss: 0.9109 - acc: 0.6181 - val_loss: 1.0215 - val_acc: 0.4444\n",
"Epoch 30/50\n",
"144/144 [==============================] - 0s 89us/step - loss: 0.8991 - acc: 0.6389 - val_loss: 1.0251 - val_acc: 0.4444\n",
"Epoch 31/50\n",
"144/144 [==============================] - 0s 94us/step - loss: 0.8714 - acc: 0.6528 - val_loss: 1.0364 - val_acc: 0.4722\n",
"Epoch 32/50\n",
"144/144 [==============================] - 0s 87us/step - loss: 0.8695 - acc: 0.6736 - val_loss: 1.0542 - val_acc: 0.4444\n",
"Epoch 33/50\n",
"144/144 [==============================] - 0s 118us/step - loss: 0.8849 - acc: 0.6667 - val_loss: 1.0607 - val_acc: 0.4444\n",
"Epoch 34/50\n",
"144/144 [==============================] - 0s 77us/step - loss: 0.8582 - acc: 0.6944 - val_loss: 1.0637 - val_acc: 0.4444\n",
"Epoch 35/50\n",
"144/144 [==============================] - 0s 69us/step - loss: 0.8696 - acc: 0.6736 - val_loss: 1.0548 - val_acc: 0.4444\n",
"\n",
"Epoch 00035: ReduceLROnPlateau reducing learning rate to 0.001.\n",
"Epoch 36/50\n",
"144/144 [==============================] - 0s 74us/step - loss: 0.8510 - acc: 0.6389 - val_loss: 1.0397 - val_acc: 0.4444\n",
"Epoch 37/50\n",
"144/144 [==============================] - 0s 91us/step - loss: 0.8735 - acc: 0.6319 - val_loss: 1.0156 - val_acc: 0.4722\n",
"Epoch 38/50\n",
"144/144 [==============================] - 0s 78us/step - loss: 0.8834 - acc: 0.6250 - val_loss: 0.9912 - val_acc: 0.4722\n",
"Epoch 39/50\n",
"144/144 [==============================] - 0s 81us/step - loss: 0.8446 - acc: 0.6736 - val_loss: 0.9728 - val_acc: 0.4444\n",
"Epoch 40/50\n",
"144/144 [==============================] - 0s 138us/step - loss: 0.8724 - acc: 0.6111 - val_loss: 0.9568 - val_acc: 0.4722\n",
"Epoch 41/50\n",
"144/144 [==============================] - 0s 66us/step - loss: 0.8606 - acc: 0.6389 - val_loss: 0.9440 - val_acc: 0.5000\n",
"Epoch 42/50\n",
"144/144 [==============================] - 0s 74us/step - loss: 0.8378 - acc: 0.6181 - val_loss: 0.9356 - val_acc: 0.5000\n",
"Epoch 43/50\n",
"144/144 [==============================] - 0s 68us/step - loss: 0.8190 - acc: 0.6597 - val_loss: 0.9292 - val_acc: 0.5000\n",
"Epoch 44/50\n",
"144/144 [==============================] - 0s 82us/step - loss: 0.8186 - acc: 0.6944 - val_loss: 0.9218 - val_acc: 0.5000\n",
"Epoch 45/50\n",
"144/144 [==============================] - 0s 77us/step - loss: 0.8526 - acc: 0.6389 - val_loss: 0.9180 - val_acc: 0.4722\n",
"Epoch 46/50\n",
"144/144 [==============================] - 0s 93us/step - loss: 0.8269 - acc: 0.6181 - val_loss: 0.9148 - val_acc: 0.4722\n",
"Epoch 47/50\n",
"144/144 [==============================] - 0s 81us/step - loss: 0.8286 - acc: 0.6111 - val_loss: 0.9087 - val_acc: 0.4444\n",
"Epoch 48/50\n",
"144/144 [==============================] - 0s 67us/step - loss: 0.8373 - acc: 0.6458 - val_loss: 0.9028 - val_acc: 0.4444\n",
"Epoch 49/50\n",
"144/144 [==============================] - 0s 72us/step - loss: 0.8445 - acc: 0.5833 - val_loss: 0.8974 - val_acc: 0.4444\n",
"Epoch 50/50\n",
"144/144 [==============================] - 0s 73us/step - loss: 0.8133 - acc: 0.6319 - val_loss: 0.8961 - val_acc: 0.4444\n",
"CPU times: user 2.22 s, sys: 91.4 ms, total: 2.31 s\n",
"Wall time: 2.16 s\n"
]
}
],
"source": [
"%%time\n",
"\n",
"# earlystop_cb = keras.callbacks.EarlyStopping(\n",
"# monitor='val_loss',\n",
"# patience=5, restore_best_weights=True,\n",
"# verbose=1)\n",
"reduce_lr_cb = keras.callbacks.ReduceLROnPlateau(\n",
" monitor='val_loss', factor=0.05,\n",
" patience=7, min_lr=0.001,\n",
" verbose=1)\n",
"\n",
"tb_cb = keras.callbacks.TensorBoard(log_dir='./tensorboard/%s' % arch_cnt, histogram_freq=0, \n",
" write_graph=True, write_images=True)\n",
"\n",
"\n",
"epochs = 50\n",
"batch_size = 32\n",
"\n",
"model.fit(\n",
" x_train, y_train,\n",
" batch_size=batch_size,\n",
" epochs=epochs,\n",
" verbose=1,\n",
" \n",
" shuffle=False,\n",
" validation_data=(x_test, y_test),\n",
" callbacks=[reduce_lr_cb, tb_cb]\n",
" # callbacks=[earlystop_cb, reduce_lr_cb]\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Test loss: 0.8960549301571317\n",
"Test accuracy: 0.4444444444444444\n"
]
}
],
"source": [
"score = model.evaluate(x_test, y_test, verbose=0)\n",
"print('Test loss:', score[0])\n",
"print('Test accuracy:', score[1])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Architecture 3 - Deflating Dense 225-112, 0.5 Dropout, Batch Norm, Sigmoid Classification, **`L2 = 1e^-4`**"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"_________________________________________________________________\n",
"Layer (type) Output Shape Param # \n",
"=================================================================\n",
"dense_31 (Dense) (None, 225) 3600 \n",
"_________________________________________________________________\n",
"dropout_11 (Dropout) (None, 225) 0 \n",
"_________________________________________________________________\n",
"dense_32 (Dense) (None, 112) 25312 \n",
"_________________________________________________________________\n",
"batch_normalization_11 (Batc (None, 112) 448 \n",
"_________________________________________________________________\n",
"dense_33 (Dense) (None, 1) 113 \n",
"=================================================================\n",
"Total params: 29,473\n",
"Trainable params: 29,249\n",
"Non-trainable params: 224\n",
"_________________________________________________________________\n"
]
}
],
"source": [
"arch_cnt = 'arch-3-4'\n",
"\n",
"model = Sequential()\n",
"model.add(\n",
" Dense(225, input_dim=15, kernel_initializer='normal',\n",
" kernel_regularizer=keras.regularizers.l2(0.0001), # pierd 0.2 acc\n",
" activation='relu'))\n",
"model.add(Dropout(0.5))\n",
"model.add(\n",
" Dense(112, input_dim=15, kernel_initializer='normal',\n",
" kernel_regularizer=keras.regularizers.l2(0.0001), # pierd 0.2 acc\n",
" activation='relu'))\n",
"# model.add(LeakyReLU(alpha=0.1))\n",
"model.add(BatchNormalization(axis = 1))\n",
"model.add(Dense(1, kernel_initializer='normal', activation='sigmoid'))\n",
"\n",
"\n",
"model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
"\n",
"model.summary()"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Train on 144 samples, validate on 36 samples\n",
"Epoch 1/50\n",
"144/144 [==============================] - 0s 2ms/step - loss: 0.6725 - acc: 0.6528 - val_loss: 0.7009 - val_acc: 0.5000\n",
"Epoch 2/50\n",
"144/144 [==============================] - 0s 90us/step - loss: 0.7097 - acc: 0.5417 - val_loss: 0.6948 - val_acc: 0.6111\n",
"Epoch 3/50\n",
"144/144 [==============================] - 0s 105us/step - loss: 0.7187 - acc: 0.5069 - val_loss: 0.6937 - val_acc: 0.6111\n",
"Epoch 4/50\n",
"144/144 [==============================] - 0s 88us/step - loss: 0.6837 - acc: 0.6042 - val_loss: 0.6939 - val_acc: 0.6111\n",
"Epoch 5/50\n",
"144/144 [==============================] - 0s 108us/step - loss: 0.6921 - acc: 0.5694 - val_loss: 0.7035 - val_acc: 0.4722\n",
"Epoch 6/50\n",
"144/144 [==============================] - 0s 115us/step - loss: 0.6707 - acc: 0.5972 - val_loss: 0.7112 - val_acc: 0.4444\n",
"Epoch 7/50\n",
"144/144 [==============================] - 0s 119us/step - loss: 0.6915 - acc: 0.5764 - val_loss: 0.7157 - val_acc: 0.4444\n",
"Epoch 8/50\n",
"144/144 [==============================] - 0s 133us/step - loss: 0.6749 - acc: 0.5903 - val_loss: 0.7153 - val_acc: 0.4444\n",
"Epoch 9/50\n",
"144/144 [==============================] - 0s 167us/step - loss: 0.6569 - acc: 0.6389 - val_loss: 0.7225 - val_acc: 0.4444\n",
"Epoch 10/50\n",
"144/144 [==============================] - 0s 110us/step - loss: 0.6670 - acc: 0.6319 - val_loss: 0.7271 - val_acc: 0.4444\n",
"\n",
"Epoch 00010: ReduceLROnPlateau reducing learning rate to 0.001.\n",
"Epoch 11/50\n",
"144/144 [==============================] - 0s 88us/step - loss: 0.6593 - acc: 0.6042 - val_loss: 0.7341 - val_acc: 0.4444\n",
"Epoch 12/50\n",
"144/144 [==============================] - 0s 97us/step - loss: 0.6468 - acc: 0.6181 - val_loss: 0.7384 - val_acc: 0.4444\n",
"Epoch 13/50\n",
"144/144 [==============================] - 0s 105us/step - loss: 0.6363 - acc: 0.6944 - val_loss: 0.7379 - val_acc: 0.4444\n",
"Epoch 14/50\n",
"144/144 [==============================] - 0s 96us/step - loss: 0.6457 - acc: 0.6250 - val_loss: 0.7422 - val_acc: 0.4444\n",
"Epoch 15/50\n",
"144/144 [==============================] - 0s 91us/step - loss: 0.6465 - acc: 0.6458 - val_loss: 0.7480 - val_acc: 0.4722\n",
"Epoch 16/50\n",
"144/144 [==============================] - 0s 98us/step - loss: 0.6247 - acc: 0.6458 - val_loss: 0.7744 - val_acc: 0.4722\n",
"Epoch 17/50\n",
"144/144 [==============================] - 0s 96us/step - loss: 0.6359 - acc: 0.6597 - val_loss: 0.8232 - val_acc: 0.4444\n",
"\n",
"Epoch 00017: ReduceLROnPlateau reducing learning rate to 0.001.\n",
"Epoch 18/50\n",
"144/144 [==============================] - 0s 81us/step - loss: 0.6326 - acc: 0.6806 - val_loss: 0.8864 - val_acc: 0.4722\n",
"Epoch 19/50\n",
"144/144 [==============================] - 0s 108us/step - loss: 0.6292 - acc: 0.6250 - val_loss: 0.9086 - val_acc: 0.4444\n",
"Epoch 20/50\n",
"144/144 [==============================] - 0s 117us/step - loss: 0.6426 - acc: 0.6458 - val_loss: 0.9152 - val_acc: 0.4722\n",
"Epoch 21/50\n",
"144/144 [==============================] - 0s 122us/step - loss: 0.6155 - acc: 0.6389 - val_loss: 0.9242 - val_acc: 0.4722\n",
"Epoch 22/50\n",
"144/144 [==============================] - 0s 116us/step - loss: 0.6173 - acc: 0.6667 - val_loss: 0.9376 - val_acc: 0.4444\n",
"Epoch 23/50\n",
"144/144 [==============================] - 0s 95us/step - loss: 0.6576 - acc: 0.6042 - val_loss: 0.9239 - val_acc: 0.4722\n",
"Epoch 24/50\n",
"144/144 [==============================] - 0s 95us/step - loss: 0.6005 - acc: 0.6944 - val_loss: 0.8889 - val_acc: 0.4722\n",
"\n",
"Epoch 00024: ReduceLROnPlateau reducing learning rate to 0.001.\n",
"Epoch 25/50\n",
"144/144 [==============================] - 0s 105us/step - loss: 0.6153 - acc: 0.6944 - val_loss: 0.8436 - val_acc: 0.4722\n",
"Epoch 26/50\n",
"144/144 [==============================] - 0s 90us/step - loss: 0.5863 - acc: 0.7153 - val_loss: 0.8369 - val_acc: 0.4722\n",
"Epoch 27/50\n",
"144/144 [==============================] - 0s 91us/step - loss: 0.6248 - acc: 0.6667 - val_loss: 0.8485 - val_acc: 0.4722\n",
"Epoch 28/50\n",
"144/144 [==============================] - 0s 115us/step - loss: 0.6113 - acc: 0.6667 - val_loss: 0.8225 - val_acc: 0.4722\n",
"Epoch 29/50\n",
"144/144 [==============================] - 0s 104us/step - loss: 0.5913 - acc: 0.7083 - val_loss: 0.8296 - val_acc: 0.4722\n",
"Epoch 30/50\n",
"144/144 [==============================] - 0s 98us/step - loss: 0.6350 - acc: 0.6528 - val_loss: 0.8113 - val_acc: 0.5000\n",
"Epoch 31/50\n",
"144/144 [==============================] - 0s 91us/step - loss: 0.6476 - acc: 0.6736 - val_loss: 0.7497 - val_acc: 0.5000\n",
"\n",
"Epoch 00031: ReduceLROnPlateau reducing learning rate to 0.001.\n",
"Epoch 32/50\n",
"144/144 [==============================] - 0s 144us/step - loss: 0.5939 - acc: 0.7083 - val_loss: 0.7081 - val_acc: 0.5000\n",
"Epoch 33/50\n",
"144/144 [==============================] - 0s 84us/step - loss: 0.6042 - acc: 0.6667 - val_loss: 0.6770 - val_acc: 0.5833\n",
"Epoch 34/50\n",
"144/144 [==============================] - 0s 135us/step - loss: 0.6072 - acc: 0.6667 - val_loss: 0.6625 - val_acc: 0.6111\n",
"Epoch 35/50\n",
"144/144 [==============================] - 0s 92us/step - loss: 0.5964 - acc: 0.7153 - val_loss: 0.6697 - val_acc: 0.6111\n",
"Epoch 36/50\n",
"144/144 [==============================] - 0s 105us/step - loss: 0.6051 - acc: 0.6806 - val_loss: 0.6875 - val_acc: 0.5833\n",
"Epoch 37/50\n",
"144/144 [==============================] - 0s 95us/step - loss: 0.5794 - acc: 0.7014 - val_loss: 0.7249 - val_acc: 0.5278\n",
"Epoch 38/50\n",
"144/144 [==============================] - 0s 101us/step - loss: 0.5880 - acc: 0.6944 - val_loss: 0.7636 - val_acc: 0.5000\n",
"Epoch 39/50\n",
"144/144 [==============================] - 0s 99us/step - loss: 0.6396 - acc: 0.6736 - val_loss: 0.7869 - val_acc: 0.5000\n",
"Epoch 40/50\n",
"144/144 [==============================] - 0s 114us/step - loss: 0.5761 - acc: 0.7083 - val_loss: 0.7492 - val_acc: 0.5278\n",
"Epoch 41/50\n",
"144/144 [==============================] - 0s 127us/step - loss: 0.5895 - acc: 0.6458 - val_loss: 0.7077 - val_acc: 0.6111\n",
"\n",
"Epoch 00041: ReduceLROnPlateau reducing learning rate to 0.001.\n",
"Epoch 42/50\n",
"144/144 [==============================] - 0s 109us/step - loss: 0.6104 - acc: 0.6667 - val_loss: 0.6913 - val_acc: 0.6111\n",
"Epoch 43/50\n",
"144/144 [==============================] - 0s 103us/step - loss: 0.5877 - acc: 0.6736 - val_loss: 0.6822 - val_acc: 0.6389\n",
"Epoch 44/50\n",
"144/144 [==============================] - 0s 102us/step - loss: 0.5405 - acc: 0.7361 - val_loss: 0.6942 - val_acc: 0.6111\n",
"Epoch 45/50\n",
"144/144 [==============================] - 0s 91us/step - loss: 0.6039 - acc: 0.7014 - val_loss: 0.6933 - val_acc: 0.6389\n",
"Epoch 46/50\n",
"144/144 [==============================] - 0s 129us/step - loss: 0.5611 - acc: 0.7153 - val_loss: 0.6480 - val_acc: 0.6667\n",
"Epoch 47/50\n",
"144/144 [==============================] - 0s 92us/step - loss: 0.5768 - acc: 0.7222 - val_loss: 0.6161 - val_acc: 0.6667\n",
"Epoch 48/50\n",
"144/144 [==============================] - 0s 122us/step - loss: 0.5598 - acc: 0.7361 - val_loss: 0.6042 - val_acc: 0.6667\n",
"Epoch 49/50\n",
"144/144 [==============================] - 0s 114us/step - loss: 0.5568 - acc: 0.7708 - val_loss: 0.5950 - val_acc: 0.6667\n",
"Epoch 50/50\n",
"144/144 [==============================] - 0s 98us/step - loss: 0.6006 - acc: 0.6944 - val_loss: 0.5939 - val_acc: 0.6667\n",
"CPU times: user 3.43 s, sys: 101 ms, total: 3.54 s\n",
"Wall time: 3.38 s\n"
]
}
],
"source": [
"%%time\n",
"\n",
"# earlystop_cb = keras.callbacks.EarlyStopping(\n",
"# monitor='val_loss',\n",
"# patience=5, restore_best_weights=True,\n",
"# verbose=1)\n",
"reduce_lr_cb = keras.callbacks.ReduceLROnPlateau(\n",
" monitor='val_loss', factor=0.05,\n",
" patience=7, min_lr=0.001,\n",
" verbose=1)\n",
"\n",
"tb_cb = keras.callbacks.TensorBoard(log_dir='./tensorboard/%s' % arch_cnt, histogram_freq=0, \n",
" write_graph=True, write_images=True)\n",
"\n",
"\n",
"epochs = 50\n",
"batch_size = 32\n",
"\n",
"model.fit(\n",
" x_train, y_train,\n",
" batch_size=batch_size,\n",
" epochs=epochs,\n",
" verbose=1,\n",
" \n",
" shuffle=False,\n",
" validation_data=(x_test, y_test),\n",
" callbacks=[reduce_lr_cb, tb_cb]\n",
" # callbacks=[earlystop_cb, reduce_lr_cb]\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Test loss: 0.5939429865943061\n",
"Test accuracy: 0.6666666666666666\n"
]
}
],
"source": [
"score = model.evaluate(x_test, y_test, verbose=0)\n",
"print('Test loss:', score[0])\n",
"print('Test accuracy:', score[1])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Architecture 3 - Deflating Dense 225-112, 0.5 Dropout, Batch Norm, Sigmoid Classification, **`L2 = 1e^-3`**"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"_________________________________________________________________\n",
"Layer (type) Output Shape Param # \n",
"=================================================================\n",
"dense_28 (Dense) (None, 225) 3600 \n",
"_________________________________________________________________\n",
"dropout_10 (Dropout) (None, 225) 0 \n",
"_________________________________________________________________\n",
"dense_29 (Dense) (None, 112) 25312 \n",
"_________________________________________________________________\n",
"batch_normalization_10 (Batc (None, 112) 448 \n",
"_________________________________________________________________\n",
"dense_30 (Dense) (None, 1) 113 \n",
"=================================================================\n",
"Total params: 29,473\n",
"Trainable params: 29,249\n",
"Non-trainable params: 224\n",
"_________________________________________________________________\n"
]
}
],
"source": [
"arch_cnt = 'arch-3-3'\n",
"\n",
"model = Sequential()\n",
"model.add(\n",
" Dense(225, input_dim=15, kernel_initializer='normal',\n",
" kernel_regularizer=keras.regularizers.l2(0.001), # pierd 0.2 acc\n",
" activation='relu'))\n",
"model.add(Dropout(0.5))\n",
"model.add(\n",
" Dense(112, input_dim=15, kernel_initializer='normal',\n",
" kernel_regularizer=keras.regularizers.l2(0.001), # pierd 0.2 acc\n",
" activation='relu'))\n",
"# model.add(LeakyReLU(alpha=0.1))\n",
"model.add(BatchNormalization(axis = 1))\n",
"model.add(Dense(1, kernel_initializer='normal', activation='sigmoid'))\n",
"\n",
"\n",
"model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
"\n",
"model.summary()"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Train on 144 samples, validate on 36 samples\n",
"Epoch 1/50\n",
"144/144 [==============================] - 0s 3ms/step - loss: 0.7692 - acc: 0.5417 - val_loss: 0.7977 - val_acc: 0.4167\n",
"Epoch 2/50\n",
"144/144 [==============================] - 0s 105us/step - loss: 0.7819 - acc: 0.4792 - val_loss: 0.7888 - val_acc: 0.4167\n",
"Epoch 3/50\n",
"144/144 [==============================] - 0s 98us/step - loss: 0.7486 - acc: 0.5972 - val_loss: 0.7901 - val_acc: 0.4167\n",
"Epoch 4/50\n",
"144/144 [==============================] - 0s 89us/step - loss: 0.8262 - acc: 0.4514 - val_loss: 0.8170 - val_acc: 0.4167\n",
"Epoch 5/50\n",
"144/144 [==============================] - 0s 89us/step - loss: 0.7510 - acc: 0.5139 - val_loss: 0.8423 - val_acc: 0.4167\n",
"Epoch 6/50\n",
"144/144 [==============================] - 0s 111us/step - loss: 0.7443 - acc: 0.5278 - val_loss: 0.8328 - val_acc: 0.4167\n",
"Epoch 7/50\n",
"144/144 [==============================] - 0s 112us/step - loss: 0.7216 - acc: 0.5833 - val_loss: 0.8259 - val_acc: 0.4167\n",
"Epoch 8/50\n",
"144/144 [==============================] - 0s 129us/step - loss: 0.6948 - acc: 0.6250 - val_loss: 0.8210 - val_acc: 0.4167\n",
"Epoch 9/50\n",
"144/144 [==============================] - 0s 93us/step - loss: 0.7271 - acc: 0.5694 - val_loss: 0.8228 - val_acc: 0.4167\n",
"\n",
"Epoch 00009: ReduceLROnPlateau reducing learning rate to 0.001.\n",
"Epoch 10/50\n",
"144/144 [==============================] - 0s 81us/step - loss: 0.6597 - acc: 0.6875 - val_loss: 0.8274 - val_acc: 0.4444\n",
"Epoch 11/50\n",
"144/144 [==============================] - 0s 93us/step - loss: 0.7129 - acc: 0.6042 - val_loss: 0.8302 - val_acc: 0.4444\n",
"Epoch 12/50\n",
"144/144 [==============================] - 0s 115us/step - loss: 0.7131 - acc: 0.5903 - val_loss: 0.8399 - val_acc: 0.4444\n",
"Epoch 13/50\n",
"144/144 [==============================] - 0s 119us/step - loss: 0.6937 - acc: 0.6389 - val_loss: 0.8475 - val_acc: 0.4444\n",
"Epoch 14/50\n",
"144/144 [==============================] - 0s 110us/step - loss: 0.7281 - acc: 0.6111 - val_loss: 0.8439 - val_acc: 0.4444\n",
"Epoch 15/50\n",
"144/144 [==============================] - 0s 89us/step - loss: 0.7205 - acc: 0.5625 - val_loss: 0.8283 - val_acc: 0.4444\n",
"Epoch 16/50\n",
"144/144 [==============================] - 0s 85us/step - loss: 0.6667 - acc: 0.6875 - val_loss: 0.8356 - val_acc: 0.4444\n",
"\n",
"Epoch 00016: ReduceLROnPlateau reducing learning rate to 0.001.\n",
"Epoch 17/50\n",
"144/144 [==============================] - 0s 83us/step - loss: 0.7025 - acc: 0.6389 - val_loss: 0.8482 - val_acc: 0.4444\n",
"Epoch 18/50\n",
"144/144 [==============================] - 0s 130us/step - loss: 0.6816 - acc: 0.6250 - val_loss: 0.8651 - val_acc: 0.4444\n",
"Epoch 19/50\n",
"144/144 [==============================] - 0s 114us/step - loss: 0.6924 - acc: 0.6181 - val_loss: 0.8572 - val_acc: 0.4444\n",
"Epoch 20/50\n",
"144/144 [==============================] - 0s 117us/step - loss: 0.7176 - acc: 0.5347 - val_loss: 0.8635 - val_acc: 0.4722\n",
"Epoch 21/50\n",
"144/144 [==============================] - 0s 92us/step - loss: 0.6687 - acc: 0.6458 - val_loss: 0.8664 - val_acc: 0.4722\n",
"Epoch 22/50\n",
"144/144 [==============================] - 0s 102us/step - loss: 0.6501 - acc: 0.6458 - val_loss: 0.8912 - val_acc: 0.4444\n",
"Epoch 23/50\n",
"144/144 [==============================] - 0s 126us/step - loss: 0.6612 - acc: 0.6597 - val_loss: 0.9411 - val_acc: 0.4444\n",
"\n",
"Epoch 00023: ReduceLROnPlateau reducing learning rate to 0.001.\n",
"Epoch 24/50\n",
"144/144 [==============================] - 0s 104us/step - loss: 0.6831 - acc: 0.6111 - val_loss: 0.9712 - val_acc: 0.4444\n",
"Epoch 25/50\n",
"144/144 [==============================] - 0s 106us/step - loss: 0.6770 - acc: 0.6597 - val_loss: 0.9774 - val_acc: 0.4444\n",
"Epoch 26/50\n",
"144/144 [==============================] - 0s 84us/step - loss: 0.6666 - acc: 0.6667 - val_loss: 0.9166 - val_acc: 0.4444\n",
"Epoch 27/50\n",
"144/144 [==============================] - 0s 117us/step - loss: 0.6062 - acc: 0.7431 - val_loss: 0.8773 - val_acc: 0.4722\n",
"Epoch 28/50\n",
"144/144 [==============================] - 0s 136us/step - loss: 0.6729 - acc: 0.6181 - val_loss: 0.8392 - val_acc: 0.4722\n",
"Epoch 29/50\n",
"144/144 [==============================] - 0s 83us/step - loss: 0.6451 - acc: 0.6458 - val_loss: 0.8356 - val_acc: 0.4722\n",
"Epoch 30/50\n",
"144/144 [==============================] - 0s 101us/step - loss: 0.6248 - acc: 0.6875 - val_loss: 0.8405 - val_acc: 0.4722\n",
"\n",
"Epoch 00030: ReduceLROnPlateau reducing learning rate to 0.001.\n",
"Epoch 31/50\n",
"144/144 [==============================] - 0s 117us/step - loss: 0.6316 - acc: 0.7014 - val_loss: 0.8406 - val_acc: 0.4722\n",
"Epoch 32/50\n",
"144/144 [==============================] - 0s 101us/step - loss: 0.6501 - acc: 0.6597 - val_loss: 0.8330 - val_acc: 0.4722\n",
"Epoch 33/50\n",
"144/144 [==============================] - 0s 100us/step - loss: 0.6490 - acc: 0.6528 - val_loss: 0.7997 - val_acc: 0.5000\n",
"Epoch 34/50\n",
"144/144 [==============================] - 0s 89us/step - loss: 0.6211 - acc: 0.7431 - val_loss: 0.7778 - val_acc: 0.5278\n",
"Epoch 35/50\n",
"144/144 [==============================] - 0s 81us/step - loss: 0.6794 - acc: 0.6181 - val_loss: 0.7906 - val_acc: 0.5278\n",
"Epoch 36/50\n",
"144/144 [==============================] - 0s 103us/step - loss: 0.6693 - acc: 0.6806 - val_loss: 0.7985 - val_acc: 0.5278\n",
"Epoch 37/50\n",
"144/144 [==============================] - 0s 89us/step - loss: 0.6580 - acc: 0.6528 - val_loss: 0.8106 - val_acc: 0.5000\n",
"Epoch 38/50\n",
"144/144 [==============================] - 0s 124us/step - loss: 0.6284 - acc: 0.6667 - val_loss: 0.8280 - val_acc: 0.5000\n",
"Epoch 39/50\n",
"144/144 [==============================] - 0s 80us/step - loss: 0.6384 - acc: 0.6597 - val_loss: 0.8437 - val_acc: 0.5000\n",
"Epoch 40/50\n",
"144/144 [==============================] - 0s 72us/step - loss: 0.6360 - acc: 0.6111 - val_loss: 0.8712 - val_acc: 0.4722\n",
"Epoch 41/50\n",
"144/144 [==============================] - 0s 127us/step - loss: 0.6752 - acc: 0.6111 - val_loss: 0.9283 - val_acc: 0.4722\n",
"\n",
"Epoch 00041: ReduceLROnPlateau reducing learning rate to 0.001.\n",
"Epoch 42/50\n",
"144/144 [==============================] - 0s 159us/step - loss: 0.6466 - acc: 0.6458 - val_loss: 0.9391 - val_acc: 0.4722\n",
"Epoch 43/50\n",
"144/144 [==============================] - 0s 115us/step - loss: 0.6189 - acc: 0.6667 - val_loss: 0.9385 - val_acc: 0.4722\n",
"Epoch 44/50\n",
"144/144 [==============================] - 0s 110us/step - loss: 0.6068 - acc: 0.6944 - val_loss: 0.9006 - val_acc: 0.4722\n",
"Epoch 45/50\n",
"144/144 [==============================] - 0s 79us/step - loss: 0.6409 - acc: 0.6875 - val_loss: 0.9075 - val_acc: 0.5000\n",
"Epoch 46/50\n",
"144/144 [==============================] - 0s 159us/step - loss: 0.6519 - acc: 0.6528 - val_loss: 0.8841 - val_acc: 0.5000\n",
"Epoch 47/50\n",
"144/144 [==============================] - 0s 118us/step - loss: 0.6561 - acc: 0.6806 - val_loss: 0.8750 - val_acc: 0.5000\n",
"Epoch 48/50\n",
"144/144 [==============================] - 0s 88us/step - loss: 0.6223 - acc: 0.7014 - val_loss: 0.8971 - val_acc: 0.5000\n",
"\n",
"Epoch 00048: ReduceLROnPlateau reducing learning rate to 0.001.\n",
"Epoch 49/50\n",
"144/144 [==============================] - 0s 93us/step - loss: 0.6592 - acc: 0.6319 - val_loss: 0.8518 - val_acc: 0.5000\n",
"Epoch 50/50\n",
"144/144 [==============================] - 0s 107us/step - loss: 0.5995 - acc: 0.6944 - val_loss: 0.8409 - val_acc: 0.5000\n",
"CPU times: user 3.56 s, sys: 77.9 ms, total: 3.64 s\n",
"Wall time: 3.49 s\n"
]
}
],
"source": [
"%%time\n",
"\n",
"# earlystop_cb = keras.callbacks.EarlyStopping(\n",
"# monitor='val_loss',\n",
"# patience=5, restore_best_weights=True,\n",
"# verbose=1)\n",
"reduce_lr_cb = keras.callbacks.ReduceLROnPlateau(\n",
" monitor='val_loss', factor=0.05,\n",
" patience=7, min_lr=0.001,\n",
" verbose=1)\n",
"\n",
"tb_cb = keras.callbacks.TensorBoard(log_dir='./tensorboard/%s' % arch_cnt, histogram_freq=0, \n",
" write_graph=True, write_images=True)\n",
"\n",
"\n",
"epochs = 50\n",
"batch_size = 32\n",
"\n",
"model.fit(\n",
" x_train, y_train,\n",
" batch_size=batch_size,\n",
" epochs=epochs,\n",
" verbose=1,\n",
" \n",
" shuffle=False,\n",
" validation_data=(x_test, y_test),\n",
" callbacks=[reduce_lr_cb, tb_cb]\n",
" # callbacks=[earlystop_cb, reduce_lr_cb]\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Test loss: 0.8408659166759915\n",
"Test accuracy: 0.5\n"
]
}
],
"source": [
"score = model.evaluate(x_test, y_test, verbose=0)\n",
"print('Test loss:', score[0])\n",
"print('Test accuracy:', score[1])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Architecture 3 - Deflating Dense 225-112, 0.5 Dropout, Batch Norm, Sigmoid Classification, **`L2 = 1e^-2`**"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"_________________________________________________________________\n",
"Layer (type) Output Shape Param # \n",
"=================================================================\n",
"dense_25 (Dense) (None, 225) 3600 \n",
"_________________________________________________________________\n",
"dropout_9 (Dropout) (None, 225) 0 \n",
"_________________________________________________________________\n",
"dense_26 (Dense) (None, 112) 25312 \n",
"_________________________________________________________________\n",
"batch_normalization_9 (Batch (None, 112) 448 \n",
"_________________________________________________________________\n",
"dense_27 (Dense) (None, 1) 113 \n",
"=================================================================\n",
"Total params: 29,473\n",
"Trainable params: 29,249\n",
"Non-trainable params: 224\n",
"_________________________________________________________________\n"
]
}
],
"source": [
"arch_cnt = 'arch-3-2'\n",
"\n",
"model = Sequential()\n",
"model.add(\n",
" Dense(225, input_dim=15, kernel_initializer='normal',\n",
" kernel_regularizer=keras.regularizers.l2(0.01), # pierd 0.2 acc\n",
" activation='relu'))\n",
"model.add(Dropout(0.5))\n",
"model.add(\n",
" Dense(112, input_dim=15, kernel_initializer='normal',\n",
" kernel_regularizer=keras.regularizers.l2(0.01), # pierd 0.2 acc\n",
" activation='relu'))\n",
"# model.add(LeakyReLU(alpha=0.1))\n",
"model.add(BatchNormalization(axis = 1))\n",
"model.add(Dense(1, kernel_initializer='normal', activation='sigmoid'))\n",
"\n",
"\n",
"model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
"\n",
"model.summary()"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Train on 144 samples, validate on 36 samples\n",
"Epoch 1/50\n",
"144/144 [==============================] - 0s 2ms/step - loss: 1.3970 - acc: 0.6111 - val_loss: 1.3253 - val_acc: 0.5833\n",
"Epoch 2/50\n",
"144/144 [==============================] - 0s 78us/step - loss: 1.3548 - acc: 0.5069 - val_loss: 1.2600 - val_acc: 0.6389\n",
"Epoch 3/50\n",
"144/144 [==============================] - 0s 134us/step - loss: 1.2752 - acc: 0.5417 - val_loss: 1.2089 - val_acc: 0.5278\n",
"Epoch 4/50\n",
"144/144 [==============================] - 0s 95us/step - loss: 1.1935 - acc: 0.5833 - val_loss: 1.1714 - val_acc: 0.5000\n",
"Epoch 5/50\n",
"144/144 [==============================] - 0s 103us/step - loss: 1.1574 - acc: 0.5347 - val_loss: 1.1544 - val_acc: 0.4444\n",
"Epoch 6/50\n",
"144/144 [==============================] - 0s 85us/step - loss: 1.1429 - acc: 0.5000 - val_loss: 1.1270 - val_acc: 0.4722\n",
"Epoch 7/50\n",
"144/144 [==============================] - 0s 93us/step - loss: 1.0410 - acc: 0.6458 - val_loss: 1.0989 - val_acc: 0.4444\n",
"Epoch 8/50\n",
"144/144 [==============================] - 0s 104us/step - loss: 1.0218 - acc: 0.6181 - val_loss: 1.0568 - val_acc: 0.5000\n",
"Epoch 9/50\n",
"144/144 [==============================] - 0s 96us/step - loss: 0.9996 - acc: 0.6458 - val_loss: 1.0285 - val_acc: 0.5000\n",
"Epoch 10/50\n",
"144/144 [==============================] - 0s 94us/step - loss: 0.9516 - acc: 0.6736 - val_loss: 1.0158 - val_acc: 0.5000\n",
"Epoch 11/50\n",
"144/144 [==============================] - 0s 85us/step - loss: 0.9309 - acc: 0.6528 - val_loss: 1.0130 - val_acc: 0.5000\n",
"Epoch 12/50\n",
"144/144 [==============================] - 0s 113us/step - loss: 0.9125 - acc: 0.6528 - val_loss: 1.0007 - val_acc: 0.5000\n",
"Epoch 13/50\n",
"144/144 [==============================] - 0s 83us/step - loss: 0.9088 - acc: 0.6111 - val_loss: 0.9850 - val_acc: 0.5000\n",
"Epoch 14/50\n",
"144/144 [==============================] - 0s 93us/step - loss: 0.9277 - acc: 0.6181 - val_loss: 0.9572 - val_acc: 0.4722\n",
"Epoch 15/50\n",
"144/144 [==============================] - 0s 93us/step - loss: 0.8736 - acc: 0.6458 - val_loss: 0.9331 - val_acc: 0.4444\n",
"Epoch 16/50\n",
"144/144 [==============================] - 0s 104us/step - loss: 0.9136 - acc: 0.5694 - val_loss: 0.9267 - val_acc: 0.4444\n",
"Epoch 17/50\n",
"144/144 [==============================] - 0s 124us/step - loss: 0.8606 - acc: 0.6042 - val_loss: 0.9140 - val_acc: 0.4444\n",
"Epoch 18/50\n",
"144/144 [==============================] - 0s 157us/step - loss: 0.8596 - acc: 0.6875 - val_loss: 0.9056 - val_acc: 0.4444\n",
"Epoch 19/50\n",
"144/144 [==============================] - 0s 111us/step - loss: 0.8568 - acc: 0.6389 - val_loss: 0.8979 - val_acc: 0.4444\n",
"Epoch 20/50\n",
"144/144 [==============================] - 0s 114us/step - loss: 0.8296 - acc: 0.6736 - val_loss: 0.8832 - val_acc: 0.4722\n",
"Epoch 21/50\n",
"144/144 [==============================] - 0s 99us/step - loss: 0.8324 - acc: 0.6458 - val_loss: 0.8805 - val_acc: 0.4444\n",
"Epoch 22/50\n",
"144/144 [==============================] - 0s 100us/step - loss: 0.8512 - acc: 0.6111 - val_loss: 0.8644 - val_acc: 0.5000\n",
"Epoch 23/50\n",
"144/144 [==============================] - 0s 87us/step - loss: 0.8416 - acc: 0.6319 - val_loss: 0.8458 - val_acc: 0.5000\n",
"Epoch 24/50\n",
"144/144 [==============================] - 0s 90us/step - loss: 0.8018 - acc: 0.6389 - val_loss: 0.8344 - val_acc: 0.5556\n",
"Epoch 25/50\n",
"144/144 [==============================] - 0s 88us/step - loss: 0.8188 - acc: 0.6736 - val_loss: 0.8333 - val_acc: 0.5278\n",
"Epoch 26/50\n",
"144/144 [==============================] - 0s 81us/step - loss: 0.7958 - acc: 0.6597 - val_loss: 0.8315 - val_acc: 0.5278\n",
"Epoch 27/50\n",
"144/144 [==============================] - 0s 86us/step - loss: 0.8079 - acc: 0.6597 - val_loss: 0.8293 - val_acc: 0.5000\n",
"Epoch 28/50\n",
"144/144 [==============================] - 0s 111us/step - loss: 0.8112 - acc: 0.6250 - val_loss: 0.8219 - val_acc: 0.5278\n",
"Epoch 29/50\n",
"144/144 [==============================] - 0s 86us/step - loss: 0.7719 - acc: 0.6667 - val_loss: 0.8144 - val_acc: 0.5556\n",
"Epoch 30/50\n",
"144/144 [==============================] - 0s 98us/step - loss: 0.7737 - acc: 0.6319 - val_loss: 0.8104 - val_acc: 0.5556\n",
"Epoch 31/50\n",
"144/144 [==============================] - 0s 85us/step - loss: 0.7806 - acc: 0.6458 - val_loss: 0.8084 - val_acc: 0.5556\n",
"Epoch 32/50\n",
"144/144 [==============================] - 0s 85us/step - loss: 0.7911 - acc: 0.6319 - val_loss: 0.8180 - val_acc: 0.5000\n",
"Epoch 33/50\n",
"144/144 [==============================] - 0s 90us/step - loss: 0.7738 - acc: 0.6458 - val_loss: 0.8365 - val_acc: 0.4444\n",
"Epoch 34/50\n",
"144/144 [==============================] - 0s 119us/step - loss: 0.7785 - acc: 0.6250 - val_loss: 0.8442 - val_acc: 0.4444\n",
"Epoch 35/50\n",
"144/144 [==============================] - 0s 107us/step - loss: 0.7921 - acc: 0.6250 - val_loss: 0.8281 - val_acc: 0.4444\n",
"Epoch 36/50\n",
"144/144 [==============================] - 0s 109us/step - loss: 0.7810 - acc: 0.6250 - val_loss: 0.8109 - val_acc: 0.5000\n",
"Epoch 37/50\n",
"144/144 [==============================] - 0s 98us/step - loss: 0.7913 - acc: 0.5903 - val_loss: 0.7972 - val_acc: 0.5278\n",
"Epoch 38/50\n",
"144/144 [==============================] - 0s 125us/step - loss: 0.7589 - acc: 0.6806 - val_loss: 0.7809 - val_acc: 0.6111\n",
"Epoch 39/50\n",
"144/144 [==============================] - 0s 112us/step - loss: 0.7842 - acc: 0.6458 - val_loss: 0.7754 - val_acc: 0.5833\n",
"Epoch 40/50\n",
"144/144 [==============================] - 0s 77us/step - loss: 0.7520 - acc: 0.6806 - val_loss: 0.7724 - val_acc: 0.5833\n",
"Epoch 41/50\n",
"144/144 [==============================] - 0s 97us/step - loss: 0.7662 - acc: 0.6597 - val_loss: 0.7766 - val_acc: 0.6111\n",
"Epoch 42/50\n",
"144/144 [==============================] - 0s 73us/step - loss: 0.7877 - acc: 0.6944 - val_loss: 0.7872 - val_acc: 0.5556\n",
"Epoch 43/50\n",
"144/144 [==============================] - 0s 100us/step - loss: 0.7609 - acc: 0.6597 - val_loss: 0.8043 - val_acc: 0.4722\n",
"Epoch 44/50\n",
"144/144 [==============================] - 0s 79us/step - loss: 0.7633 - acc: 0.6458 - val_loss: 0.8180 - val_acc: 0.4722\n",
"Epoch 45/50\n",
"144/144 [==============================] - 0s 75us/step - loss: 0.7369 - acc: 0.6875 - val_loss: 0.8321 - val_acc: 0.5000\n",
"Epoch 46/50\n",
"144/144 [==============================] - 0s 111us/step - loss: 0.7548 - acc: 0.6458 - val_loss: 0.8193 - val_acc: 0.4444\n",
"Epoch 47/50\n",
"144/144 [==============================] - 0s 136us/step - loss: 0.7429 - acc: 0.6319 - val_loss: 0.8042 - val_acc: 0.5278\n",
"\n",
"Epoch 00047: ReduceLROnPlateau reducing learning rate to 0.001.\n",
"Epoch 48/50\n",
"144/144 [==============================] - 0s 79us/step - loss: 0.7433 - acc: 0.6181 - val_loss: 0.7955 - val_acc: 0.5278\n",
"Epoch 49/50\n",
"144/144 [==============================] - 0s 84us/step - loss: 0.7543 - acc: 0.6389 - val_loss: 0.7788 - val_acc: 0.5556\n",
"Epoch 50/50\n",
"144/144 [==============================] - 0s 91us/step - loss: 0.7357 - acc: 0.6250 - val_loss: 0.7693 - val_acc: 0.5833\n",
"CPU times: user 3.09 s, sys: 102 ms, total: 3.19 s\n",
"Wall time: 3.01 s\n"
]
}
],
"source": [
"%%time\n",
"\n",
"# earlystop_cb = keras.callbacks.EarlyStopping(\n",
"# monitor='val_loss',\n",
"# patience=5, restore_best_weights=True,\n",
"# verbose=1)\n",
"reduce_lr_cb = keras.callbacks.ReduceLROnPlateau(\n",
" monitor='val_loss', factor=0.05,\n",
" patience=7, min_lr=0.001,\n",
" verbose=1)\n",
"\n",
"tb_cb = keras.callbacks.TensorBoard(log_dir='./tensorboard/%s' % arch_cnt, histogram_freq=0, \n",
" write_graph=True, write_images=True)\n",
"\n",
"\n",
"epochs = 50\n",
"batch_size = 32\n",
"\n",
"model.fit(\n",
" x_train, y_train,\n",
" batch_size=batch_size,\n",
" epochs=epochs,\n",
" verbose=1,\n",
" \n",
" shuffle=False,\n",
" validation_data=(x_test, y_test),\n",
" callbacks=[reduce_lr_cb, tb_cb]\n",
" # callbacks=[earlystop_cb, reduce_lr_cb]\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Test loss: 0.7693172958162096\n",
"Test accuracy: 0.5833333333333334\n"
]
}
],
"source": [
"score = model.evaluate(x_test, y_test, verbose=0)\n",
"print('Test loss:', score[0])\n",
"print('Test accuracy:', score[1])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Architecture 3 - Deflating Dense 225-112, 0.5 Dropout, Batch Norm, Sigmoid Classification, **`L2 = 1e^-1`**"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"_________________________________________________________________\n",
"Layer (type) Output Shape Param # \n",
"=================================================================\n",
"dense_22 (Dense) (None, 225) 3600 \n",
"_________________________________________________________________\n",
"dropout_8 (Dropout) (None, 225) 0 \n",
"_________________________________________________________________\n",
"dense_23 (Dense) (None, 112) 25312 \n",
"_________________________________________________________________\n",
"batch_normalization_8 (Batch (None, 112) 448 \n",
"_________________________________________________________________\n",
"dense_24 (Dense) (None, 1) 113 \n",
"=================================================================\n",
"Total params: 29,473\n",
"Trainable params: 29,249\n",
"Non-trainable params: 224\n",
"_________________________________________________________________\n"
]
}
],
"source": [
"arch_cnt = 'arch-3-1'\n",
"\n",
"model = Sequential()\n",
"model.add(\n",
" Dense(225, input_dim=15, kernel_initializer='normal',\n",
" kernel_regularizer=keras.regularizers.l2(0.1), # pierd 0.2 acc\n",
" activation='relu'))\n",
"model.add(Dropout(0.5))\n",
"model.add(\n",
" Dense(112, input_dim=15, kernel_initializer='normal',\n",
" kernel_regularizer=keras.regularizers.l2(0.1), # pierd 0.2 acc\n",
" activation='relu'))\n",
"# model.add(LeakyReLU(alpha=0.1))\n",
"model.add(BatchNormalization(axis = 1))\n",
"model.add(Dense(1, kernel_initializer='normal', activation='sigmoid'))\n",
"\n",
"\n",
"model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
"\n",
"model.summary()"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Train on 144 samples, validate on 36 samples\n",
"Epoch 1/50\n",
"144/144 [==============================] - 0s 2ms/step - loss: 7.4829 - acc: 0.5486 - val_loss: 6.8780 - val_acc: 0.4444\n",
"Epoch 2/50\n",
"144/144 [==============================] - 0s 94us/step - loss: 6.5561 - acc: 0.5764 - val_loss: 6.0230 - val_acc: 0.4444\n",
"Epoch 3/50\n",
"144/144 [==============================] - 0s 90us/step - loss: 5.7389 - acc: 0.5208 - val_loss: 5.2785 - val_acc: 0.4444\n",
"Epoch 4/50\n",
"144/144 [==============================] - 0s 109us/step - loss: 5.0140 - acc: 0.5903 - val_loss: 4.6354 - val_acc: 0.4722\n",
"Epoch 5/50\n",
"144/144 [==============================] - 0s 107us/step - loss: 4.4265 - acc: 0.5556 - val_loss: 4.0890 - val_acc: 0.4444\n",
"Epoch 6/50\n",
"144/144 [==============================] - 0s 104us/step - loss: 3.9032 - acc: 0.5833 - val_loss: 3.6264 - val_acc: 0.4444\n",
"Epoch 7/50\n",
"144/144 [==============================] - 0s 98us/step - loss: 3.4811 - acc: 0.5208 - val_loss: 3.2298 - val_acc: 0.4444\n",
"Epoch 8/50\n",
"144/144 [==============================] - 0s 79us/step - loss: 3.0772 - acc: 0.6111 - val_loss: 2.8905 - val_acc: 0.4722\n",
"Epoch 9/50\n",
"144/144 [==============================] - 0s 123us/step - loss: 2.7821 - acc: 0.5486 - val_loss: 2.6020 - val_acc: 0.5278\n",
"Epoch 10/50\n",
"144/144 [==============================] - 0s 100us/step - loss: 2.5395 - acc: 0.5417 - val_loss: 2.3626 - val_acc: 0.4722\n",
"Epoch 11/50\n",
"144/144 [==============================] - 0s 100us/step - loss: 2.2919 - acc: 0.5625 - val_loss: 2.1634 - val_acc: 0.4444\n",
"Epoch 12/50\n",
"144/144 [==============================] - 0s 128us/step - loss: 2.0781 - acc: 0.5764 - val_loss: 1.9884 - val_acc: 0.4444\n",
"Epoch 13/50\n",
"144/144 [==============================] - 0s 88us/step - loss: 1.9107 - acc: 0.5903 - val_loss: 1.8417 - val_acc: 0.4444\n",
"Epoch 14/50\n",
"144/144 [==============================] - 0s 84us/step - loss: 1.7622 - acc: 0.5417 - val_loss: 1.7146 - val_acc: 0.4444\n",
"Epoch 15/50\n",
"144/144 [==============================] - 0s 95us/step - loss: 1.6451 - acc: 0.5972 - val_loss: 1.6169 - val_acc: 0.4444\n",
"Epoch 16/50\n",
"144/144 [==============================] - 0s 120us/step - loss: 1.5554 - acc: 0.5694 - val_loss: 1.5401 - val_acc: 0.4167\n",
"Epoch 17/50\n",
"144/144 [==============================] - 0s 116us/step - loss: 1.4397 - acc: 0.6111 - val_loss: 1.4735 - val_acc: 0.4444\n",
"Epoch 18/50\n",
"144/144 [==============================] - 0s 104us/step - loss: 1.4129 - acc: 0.5139 - val_loss: 1.4041 - val_acc: 0.4444\n",
"Epoch 19/50\n",
"144/144 [==============================] - 0s 90us/step - loss: 1.3058 - acc: 0.5903 - val_loss: 1.3251 - val_acc: 0.4444\n",
"Epoch 20/50\n",
"144/144 [==============================] - 0s 81us/step - loss: 1.2432 - acc: 0.6250 - val_loss: 1.2571 - val_acc: 0.4444\n",
"Epoch 21/50\n",
"144/144 [==============================] - 0s 97us/step - loss: 1.1539 - acc: 0.6597 - val_loss: 1.2116 - val_acc: 0.4722\n",
"Epoch 22/50\n",
"144/144 [==============================] - 0s 107us/step - loss: 1.1573 - acc: 0.6042 - val_loss: 1.1877 - val_acc: 0.4444\n",
"Epoch 23/50\n",
"144/144 [==============================] - 0s 93us/step - loss: 1.1166 - acc: 0.5833 - val_loss: 1.1431 - val_acc: 0.4722\n",
"Epoch 24/50\n",
"144/144 [==============================] - 0s 84us/step - loss: 1.0777 - acc: 0.6042 - val_loss: 1.1021 - val_acc: 0.4444\n",
"Epoch 25/50\n",
"144/144 [==============================] - 0s 90us/step - loss: 0.9982 - acc: 0.6250 - val_loss: 1.0659 - val_acc: 0.4722\n",
"Epoch 26/50\n",
"144/144 [==============================] - 0s 120us/step - loss: 1.0227 - acc: 0.6319 - val_loss: 1.0479 - val_acc: 0.4444\n",
"Epoch 27/50\n",
"144/144 [==============================] - 0s 103us/step - loss: 0.9950 - acc: 0.5833 - val_loss: 1.0425 - val_acc: 0.4722\n",
"Epoch 28/50\n",
"144/144 [==============================] - 0s 101us/step - loss: 0.9684 - acc: 0.6458 - val_loss: 1.0459 - val_acc: 0.4444\n",
"Epoch 29/50\n",
"144/144 [==============================] - 0s 94us/step - loss: 0.9391 - acc: 0.6042 - val_loss: 1.0335 - val_acc: 0.4444\n",
"Epoch 30/50\n",
"144/144 [==============================] - 0s 90us/step - loss: 0.9301 - acc: 0.6667 - val_loss: 0.9903 - val_acc: 0.4722\n",
"Epoch 31/50\n",
"144/144 [==============================] - 0s 109us/step - loss: 0.8917 - acc: 0.6319 - val_loss: 0.9629 - val_acc: 0.4444\n",
"Epoch 32/50\n",
"144/144 [==============================] - 0s 85us/step - loss: 0.8565 - acc: 0.6319 - val_loss: 0.9693 - val_acc: 0.4444\n",
"Epoch 33/50\n",
"144/144 [==============================] - 0s 122us/step - loss: 0.8659 - acc: 0.6736 - val_loss: 0.9631 - val_acc: 0.4444\n",
"Epoch 34/50\n",
"144/144 [==============================] - 0s 98us/step - loss: 0.8961 - acc: 0.5833 - val_loss: 0.9642 - val_acc: 0.4444\n",
"Epoch 35/50\n",
"144/144 [==============================] - 0s 114us/step - loss: 0.8323 - acc: 0.6528 - val_loss: 0.9652 - val_acc: 0.4444\n",
"Epoch 36/50\n",
"144/144 [==============================] - 0s 108us/step - loss: 0.8590 - acc: 0.5972 - val_loss: 0.9168 - val_acc: 0.4722\n",
"Epoch 37/50\n",
"144/144 [==============================] - 0s 87us/step - loss: 0.8576 - acc: 0.6458 - val_loss: 0.8841 - val_acc: 0.4722\n",
"Epoch 38/50\n",
"144/144 [==============================] - 0s 84us/step - loss: 0.8472 - acc: 0.5972 - val_loss: 0.8537 - val_acc: 0.4444\n",
"Epoch 39/50\n",
"144/144 [==============================] - 0s 111us/step - loss: 0.8216 - acc: 0.6458 - val_loss: 0.8376 - val_acc: 0.5556\n",
"Epoch 40/50\n",
"144/144 [==============================] - 0s 127us/step - loss: 0.8348 - acc: 0.5903 - val_loss: 0.8320 - val_acc: 0.6667\n",
"Epoch 41/50\n",
"144/144 [==============================] - 0s 97us/step - loss: 0.7779 - acc: 0.6528 - val_loss: 0.8274 - val_acc: 0.6111\n",
"Epoch 42/50\n",
"144/144 [==============================] - 0s 95us/step - loss: 0.8031 - acc: 0.6181 - val_loss: 0.8249 - val_acc: 0.5556\n",
"Epoch 43/50\n",
"144/144 [==============================] - 0s 93us/step - loss: 0.8192 - acc: 0.6042 - val_loss: 0.8221 - val_acc: 0.5278\n",
"Epoch 44/50\n",
"144/144 [==============================] - 0s 73us/step - loss: 0.8374 - acc: 0.5903 - val_loss: 0.8187 - val_acc: 0.5556\n",
"Epoch 45/50\n",
"144/144 [==============================] - 0s 77us/step - loss: 0.7896 - acc: 0.5903 - val_loss: 0.8259 - val_acc: 0.4722\n",
"Epoch 46/50\n",
"144/144 [==============================] - 0s 83us/step - loss: 0.8045 - acc: 0.5833 - val_loss: 0.8416 - val_acc: 0.4444\n",
"Epoch 47/50\n",
"144/144 [==============================] - 0s 97us/step - loss: 0.7633 - acc: 0.6806 - val_loss: 0.8655 - val_acc: 0.4444\n",
"Epoch 48/50\n",
"144/144 [==============================] - 0s 87us/step - loss: 0.8088 - acc: 0.6250 - val_loss: 0.8355 - val_acc: 0.4444\n",
"Epoch 49/50\n",
"144/144 [==============================] - 0s 119us/step - loss: 0.7518 - acc: 0.6597 - val_loss: 0.8255 - val_acc: 0.4722\n",
"Epoch 50/50\n",
"144/144 [==============================] - 0s 110us/step - loss: 0.7830 - acc: 0.6389 - val_loss: 0.8165 - val_acc: 0.4722\n",
"CPU times: user 3.01 s, sys: 70.6 ms, total: 3.08 s\n",
"Wall time: 2.89 s\n"
]
}
],
"source": [
"%%time\n",
"\n",
"# earlystop_cb = keras.callbacks.EarlyStopping(\n",
"# monitor='val_loss',\n",
"# patience=5, restore_best_weights=True,\n",
"# verbose=1)\n",
"reduce_lr_cb = keras.callbacks.ReduceLROnPlateau(\n",
" monitor='val_loss', factor=0.05,\n",
" patience=7, min_lr=0.001,\n",
" verbose=1)\n",
"\n",
"tb_cb = keras.callbacks.TensorBoard(log_dir='./tensorboard/%s' % arch_cnt, histogram_freq=0, \n",
" write_graph=True, write_images=True)\n",
"\n",
"\n",
"epochs = 50\n",
"batch_size = 32\n",
"\n",
"model.fit(\n",
" x_train, y_train,\n",
" batch_size=batch_size,\n",
" epochs=epochs,\n",
" verbose=1,\n",
" \n",
" shuffle=False,\n",
" validation_data=(x_test, y_test),\n",
" callbacks=[reduce_lr_cb, tb_cb]\n",
" # callbacks=[earlystop_cb, reduce_lr_cb]\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Test loss: 0.8165454334682889\n",
"Test accuracy: 0.4722222222222222\n"
]
}
],
"source": [
"score = model.evaluate(x_test, y_test, verbose=0)\n",
"print('Test loss:', score[0])\n",
"print('Test accuracy:', score[1])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Ensemble Methods"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Bagging Strategies"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Random Forests"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.ensemble import RandomForestClassifier"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [],
"source": [
"# x_train, x_test, y_train, y_test"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [],
"source": [
"\n",
"clf = RandomForestClassifier(n_estimators=100, max_depth=2, random_state=0)"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
" max_depth=2, max_features='auto', max_leaf_nodes=None,\n",
" min_impurity_decrease=0.0, min_impurity_split=None,\n",
" min_samples_leaf=1, min_samples_split=2,\n",
" min_weight_fraction_leaf=0.0, n_estimators=100, n_jobs=1,\n",
" oob_score=False, random_state=0, verbose=0, warm_start=False)"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"clf.fit(x_train, y_train)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[0.04117183 0.00496237 0.2124435 0.11033686 0.03552996 0.12413992\n",
" 0.09559455 0. 0.00413384 0.03759457 0.05501764 0.02024867\n",
" 0.03323923 0.07819483 0.14739222]\n"
]
}
],
"source": [
"print(clf.feature_importances_)"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[0 0 0 1 0 1 0 0 1 1 1 1 1 0 0 0 0 0 1 1 0 0 1 0 0 1 0 0 0 1 1 0 1 0 0 0]\n"
]
}
],
"source": [
"print(clf.predict(x_test))"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [],
"source": [
"# make predictions for test data\n",
"y_pred = clf.predict(x_test)\n",
"predictions = [round(value) for value in y_pred]"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Accuracy: 83.33%\n"
]
}
],
"source": [
"# evaluate predictions\n",
"accuracy = accuracy_score(y_test, predictions)\n",
"print(\"Accuracy: %.2f%%\" % (accuracy * 100.0))"
]
},
{
"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": "markdown",
"metadata": {},
"source": [
"### ExtraTrees"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.ensemble import ExtraTreesClassifier"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [],
"source": [
"# x_train, x_test, y_train, y_test"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [],
"source": [
"\n",
"clf = ExtraTreesClassifier(n_estimators=100, max_depth=2, random_state=0)"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"ExtraTreesClassifier(bootstrap=False, class_weight=None, criterion='gini',\n",
" max_depth=2, max_features='auto', max_leaf_nodes=None,\n",
" min_impurity_decrease=0.0, min_impurity_split=None,\n",
" min_samples_leaf=1, min_samples_split=2,\n",
" min_weight_fraction_leaf=0.0, n_estimators=100, n_jobs=1,\n",
" oob_score=False, random_state=0, verbose=0, warm_start=False)"
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"clf.fit(x_train, y_train)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[0.03595362 0.00066439 0.19711 0.21279172 0.00303455 0.06847946\n",
" 0.10905054 0.00071238 0.00961193 0.00356046 0.03753535 0.04054296\n",
" 0.01259753 0.06053845 0.20781666]\n"
]
}
],
"source": [
"print(clf.feature_importances_)"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[0 0 0 1 0 1 0 0 1 1 1 1 1 0 0 0 0 0 1 1 0 0 1 0 0 1 1 0 0 1 1 0 1 0 0 0]\n"
]
}
],
"source": [
"print(clf.predict(x_test))"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [],
"source": [
"# make predictions for test data\n",
"y_pred = clf.predict(x_test)\n",
"predictions = [round(value) for value in y_pred]"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Accuracy: 80.56%\n"
]
}
],
"source": [
"# evaluate predictions\n",
"accuracy = accuracy_score(y_test, predictions)\n",
"print(\"Accuracy: %.2f%%\" % (accuracy * 100.0))"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f4239ad4a20>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig = plt.figure(figsize=(10,5))\n",
"\n",
"plot_learning_curves(x_train, y_train, x_test, y_test, clf)\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Stacking Strategies"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### SuperLearner"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Boosting Strategies"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### xgboost"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": []
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"# import xgboost as xgb\n",
"from xgboost import XGBClassifier\n",
"from sklearn.metrics import accuracy_score"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"# x_train, x_test, y_train, y_test"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
" colsample_bytree=1, gamma=0, learning_rate=0.1, max_delta_step=0,\n",
" max_depth=3, min_child_weight=1, missing=None, n_estimators=100,\n",
" n_jobs=1, nthread=None, objective='binary:logistic', random_state=0,\n",
" reg_alpha=0, reg_lambda=1, scale_pos_weight=1, seed=None,\n",
" silent=True, subsample=1)"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model = XGBClassifier()\n",
"model.fit(x_train, y_train)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
" colsample_bytree=1, gamma=0, learning_rate=0.1, max_delta_step=0,\n",
" max_depth=3, min_child_weight=1, missing=None, n_estimators=100,\n",
" n_jobs=1, nthread=None, objective='binary:logistic', random_state=0,\n",
" reg_alpha=0, reg_lambda=1, scale_pos_weight=1, seed=None,\n",
" silent=True, subsample=1)\n"
]
}
],
"source": [
"print(model)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/usr/local/lib/python3.5/dist-packages/sklearn/preprocessing/label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
" if diff:\n"
]
}
],
"source": [
"# make predictions for test data\n",
"y_pred = model.predict(x_test)\n",
"predictions = [round(value) for value in y_pred]"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Accuracy: 86.11%\n"
]
}
],
"source": [
"# evaluate predictions\n",
"accuracy = accuracy_score(y_test, predictions)\n",
"print(\"Accuracy: %.2f%%\" % (accuracy * 100.0))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Bibliography"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"+ https://medium.com/@datalesdatales/why-you-should-be-plotting-learning-curves-in-your-next-machine-learning-project-221bae60c53\n",
"\n",
"+ https://slideplayer.com/slide/4684120/15/images/6/Outline+Bias%2FVariance+Tradeoff+Ensemble+methods+that+minimize+variance.jpg\n",
" + https://slideplayer.com/slide/4684120/\n",
"\n",
"+ plot confusion matrix\n",
"+ http://rasbt.github.io/mlxtend/user_guide/plotting/plot_learning_curves/\n",
"+ https://machinelearningmastery.com/roc-curves-and-precision-recall-curves-for-classification-in-python/\n",
"+ \n",
"\n",
"\n",
"+ http://docs.h2o.ai/h2o-tutorials/latest-stable/tutorials/ensembles-stacking/index.html\n"
]
},
{
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"metadata": {},
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"metadata": {},
"source": [
"---"
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| mit |
dataDogma/Computer-Science | .ipynb_checkpoints/DAT208x - Week 1 - Python Basics-checkpoint.ipynb | 1 | 5192 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Lecture : Hello Python!\n",
"-- -- \n",
"\n",
"+ **[RQ-1]** : Which of the following statements is correct?\n",
"\n",
" **Ans:** _The Ipython Shell is typically used to work with Python interactively_.\n",
"\n",
"\n",
"+ **[RQ-2]** : Which file extension is used for Python script files?**\n",
"\n",
" **Ans:** .py\n",
"\n",
"\n",
"+ **[RQ-3]** : You need to print the result of adding 3 and 4 inside a script. Which line of code should you write in the script?\n",
" \n",
" **Ans:** print(int x + int y)\n",
"-- --"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Lab : Hello Python!\n",
"-- --\n",
"\n",
"Objective :\n",
"\n",
"+ How to work with Ipython shell.\n",
"+ Writing python scripts.\n",
"\n",
"-- --"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**The Python Interface -- 100xp, Status : Earned**"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.625\n",
"17\n"
]
}
],
"source": [
"# working with print function\n",
"print(5 / 8)\n",
"\n",
"# Add another print function on new line\n",
"print(7 + 10)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**When to use python? -- 50xp, Status : Earned** \n",
"-- --\n",
"\n",
"Python is a pretty versatile language. For what applications can you use Python?\n",
"\n",
"**Ans:** _All of the above_\n",
"\n",
"-- --"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Any comments? -- 100xp, Satatus : Earned**\n",
"\n",
"-- --\n",
"\n",
"+ We can add comments to python scripts.\n",
"\n",
"+ Comments are short snippets of plain english, to help you and others understand what the code is about.\n",
"\n",
"+ To add a comment, use '#'tag, insert it at the front of the text.\n",
"\n",
"+ Comments have idle state, i.e. they don't affect the code results.\n",
"\n",
"+ Comments are ignored by the python interpretor.\n",
"\n",
"-- --"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.625\n",
"17\n"
]
}
],
"source": [
"# Just testing division\n",
"print(5 / 8)\n",
"\n",
"# Additon works too ( added comment here )\n",
"print(7 + 10)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Python as a calculator -- 100xp, Status : Earned**\n",
"-- --\n",
"Python is perfectly suited to do basic calculations. Apart from addition, subtraction, multiplication and division, there is also support for more advanced operations such as:\n",
"\n",
"+ Exponentiation:**. This operator raises the number to its left to the power of the number to its right: for example 4**2 will give 16.\n",
"\n",
"\n",
"+ Modulo: %. It returns the remainder of the division of the number to the left by the number on its right, for example 18 % 7 equals 4.\n",
"-- --"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"10\n",
"0\n",
"15\n",
"5.0\n",
"16\n",
"4\n",
"194.87171000000012\n"
]
}
],
"source": [
"\"\"\"Suppose you have $100, which you can invest with a 10% return each year. After one year, it's \n",
"100 x 1.1 = 110 dollars, and after two years it's 100 x 1.1 x 1.1 = 121.\n",
"\n",
"Add code to calculate how much money you end up with after 7 years\"\"\"\n",
"\n",
"print(5 + 5)\n",
"print(5 - 5)\n",
"\n",
"# Multiplication and division\n",
"print(3 * 5)\n",
"print(10 / 2)\n",
"\n",
"# Exponentiation\n",
"print(4 ** 2)\n",
"\n",
"# Modulo\n",
"print(18 % 7)\n",
"\n",
"# How much is your $100 worth after 7 years?\n",
"# first try was unsuccesful, so used the only two things * and ** operators.\n",
"print ( 100 * ( 1.1 ** 7 ) )\n"
]
}
],
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| gpl-3.0 |
zhangfang615/Tuberculosis | Experiments on SNPs' depth lower than 10.ipynb | 1 | 72903 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Experiments on SNPs' depth lower than 10\n",
"=========\n",
"- Author: Fang Zhang\n",
"- Date: 2016.7.10\n",
"- E-mail: [email protected]\n",
"\n",
"Experiment SNPs' depth\n",
"---------\n",
"\n",
"1. After depth filter, 6925 SNPs in total including 4003 SNPs' frequency greater than 1. It is not relaible.\n",
"\n",
"2. The number and propotion of SNPs that have depth lower than10.\n",
"\n",
"| sample | number_SNPs | unresi | propotion |\n",
"|-----------|-------------|--------|-------------|\n",
"| ERR550643 | 92 | 1248 | 0.073717949 |\n",
"| ERR550644 | 83 | 1263 | 0.065716548 |\n",
"| ERR550658 | 103 | 1378 | 0.074746009 |\n",
"| ERR550659 | 121 | 1446 | 0.083679115 |\n",
"| ERR550670 | 51 | 1331 | 0.038317055 |\n",
"| ERR550724 | 53 | 1491 | 0.035546613 |\n",
"| ERR550738 | 66 | 1347 | 0.048997773 |\n",
"| ERR550739 | 42 | 1415 | 0.029681979 |\n",
"| ERR550777 | 172 | 1319 | 0.13040182 |\n",
"| ERR550778 | 91 | 1347 | 0.067557535 |\n",
"| ERR550779 | 727 | 1300 | 0.559230769 |\n",
"| ERR550782 | 56 | 1404 | 0.03988604 |\n",
"| ERR550783 | 90 | 1404 | 0.064102564 |\n",
"| ERR550887 | 71 | 1383 | 0.051337672 |\n",
"| ERR550910 | 24 | 1497 | 0.016032064 |\n",
"| ERR550927 | 37 | 1345 | 0.027509294 |\n",
"| ERR550940 | 63 | 1383 | 0.045553145 |\n",
"| ERR550941 | 60 | 1331 | 0.045078888 |\n",
"| ERR550946 | 42 | 1324 | 0.031722054 |\n",
"| ERR550947 | 51 | 1309 | 0.038961039 |\n",
"| ERR550957 | 54 | 1457 | 0.037062457 |\n",
"| ERR550984 | 45 | 1397 | 0.032211883 |\n",
"| ERR551007 | 24 | 1433 | 0.016748081 |\n",
"| ERR551071 | 29 | 1397 | 0.020758769 |\n",
"| ERR551079 | 57 | 1392 | 0.040948276 |\n",
"| ERR551086 | 33 | 1360 | 0.024264706 |\n",
"| ERR551090 | 55 | 1395 | 0.039426523 |\n",
"| ERR551155 | 58 | 1355 | 0.042804428 |\n",
"| ERR551156 | 69 | 1324 | 0.052114804 |\n",
"| ERR551159 | 49 | 1433 | 0.034193999 |\n",
"| ERR551167 | 62 | 1341 | 0.046234154 |\n",
"| ERR551168 | 50 | 1368 | 0.036549708 |\n",
"| ERR551184 | 56 | 1320 | 0.042424242 |\n",
"| ERR551185 | 363 | 1244 | 0.291800643 |\n",
"| ERR551201 | 34 | 1377 | 0.024691358 |\n",
"| ERR551212 | 50 | 1399 | 0.035739814 |\n",
"| ERR551214 | 79 | 1229 | 0.064279902 |\n",
"| ERR551225 | 27 | 1384 | 0.019508671 |\n",
"| ERR551293 | 36 | 1443 | 0.024948025 |\n",
"| ERR551305 | 98 | 1398 | 0.070100143 |\n",
"| ERR551311 | 48 | 1411 | 0.034018427 |\n",
"| ERR551360 | 85 | 1399 | 0.060757684 |\n",
"| ERR551361 | 141 | 1355 | 0.104059041 |\n",
"| ERR551362 | 229 | 1339 | 0.171023152 |\n",
"| ERR551369 | 65 | 1350 | 0.048148148 |\n",
"| ERR551370 | 98 | 1336 | 0.073353293 |\n",
"| ERR551412 | 73 | 1287 | 0.056721057 |\n",
"| ERR551494 | 65 | 1422 | 0.045710267 |\n",
"| ERR551520 | 23 | 1477 | 0.015572106 |\n",
"| ERR551549 | 114 | 1498 | 0.076101469 |\n",
"| ERR551554 | 43 | 1389 | 0.030957523 |\n",
"| ERR551556 | 63 | 1376 | 0.045784884 |\n",
"| ERR551557 | 500 | 1294 | 0.386398764 |\n",
"| ERR551558 | 396 | 1308 | 0.302752294 |\n",
"| ERR551572 | 45 | 1347 | 0.033407572 |\n",
"| ERR551575 | 126 | 1197 | 0.105263158 |\n",
"| ERR551636 | 51 | 1353 | 0.037694013 |\n",
"| ERR551638 | 77 | 1379 | 0.055837563 |\n",
"| ERR551680 | 47 | 1369 | 0.034331629 |\n",
"| ERR551681 | 59 | 1337 | 0.044128646 |\n",
"| ERR551688 | 71 | 1353 | 0.052475979 |\n",
"| ERR551693 | 69 | 1344 | 0.051339286 |\n",
"| ERR551694 | 67 | 1327 | 0.050489827 |\n",
"| ERR551772 | 40 | 1473 | 0.027155465 |\n",
"| ERR551804 | 93 | 1377 | 0.067538126 |\n",
"| ERR551805 | 275 | 1328 | 0.207078313 |\n",
"| ERR551806 | 347 | 1333 | 0.260315079 |\n",
"| ERR551821 | 82 | 1405 | 0.058362989 |\n",
"| ERR551822 | 88 | 1417 | 0.062103035 |\n",
"| ERR551854 | 86 | 1298 | 0.066255778 |\n",
"| ERR551855 | 70 | 1346 | 0.052005944 |\n",
"| ERR551879 | 42 | 1398 | 0.030042918 |\n",
"| ERR551927 | 91 | 1317 | 0.069096431 |\n",
"| ERR551928 | 76 | 1341 | 0.056674124 |\n",
"| ERR551930 | 55 | 1415 | 0.038869258 |\n",
"| ERR551934 | 23 | 1481 | 0.015530047 |\n",
"| ERR551943 | 829 | 1218 | 0.680623974 |\n",
"| ERR551944 | 66 | 1354 | 0.048744461 |\n",
"| ERR551945 | 48 | 1381 | 0.034757422 |\n",
"| ERR551956 | 48 | 1367 | 0.035113387 |\n",
"| ERR551957 | 47 | 1334 | 0.035232384 |\n",
"| ERR551977 | 114 | 1314 | 0.086757991 |\n",
"| ERR551978 | 58 | 1340 | 0.043283582 |\n",
"| ERR551979 | 971 | 1103 | 0.880326383 |\n",
"| ERR551980 | 585 | 1254 | 0.466507177 |\n",
"| ERR551981 | 984 | 1187 | 0.828980623 |\n",
"| ERR551982 | 183 | 1294 | 0.141421947 |\n",
"| ERR551983 | 905 | 1026 | 0.882066277 |\n",
"| ERR551990 | 30 | 1466 | 0.020463847 |\n",
"| ERR552090 | 38 | 1469 | 0.025867937 |\n",
"| ERR552130 | 35 | 1411 | 0.024805103 |\n",
"| ERR552136 | 43 | 1405 | 0.030604982 |\n",
"| ERR552141 | 48 | 1376 | 0.034883721 |\n",
"| ERR552177 | 32 | 1383 | 0.023138106 |\n",
"| ERR552190 | 26 | 1444 | 0.01800554 |\n",
"| ERR552194 | 79 | 1429 | 0.055283415 |\n",
"| ERR552219 | 60 | 1343 | 0.044676098 |\n",
"| ERR552331 | 55 | 1388 | 0.03962536 |\n",
"| ERR552358 | 37 | 1416 | 0.026129944 |\n",
"| ERR552429 | 50 | 1406 | 0.035561878 |\n",
"| ERR552444 | 54 | 1344 | 0.040178571 |\n",
"| ERR552445 | 156 | 1286 | 0.121306376 |\n",
"| ERR552479 | 60 | 1611 | 0.037243948 |\n",
"| ERR552482 | 36 | 1436 | 0.025069638 |\n",
"| ERR552493 | 69 | 1420 | 0.048591549 |\n",
"| ERR552494 | 113 | 1415 | 0.079858657 |\n",
"| ERR552549 | 55 | 1335 | 0.041198502 |\n",
"| ERR552550 | 54 | 1318 | 0.040971168 |\n",
"| ERR552555 | 41 | 1398 | 0.029327611 |\n",
"| ERR552580 | 42 | 1401 | 0.029978587 |\n",
"| ERR552668 | 116 | 1268 | 0.09148265 |\n",
"| ERR552669 | 969 | 1227 | 0.789731051 |\n",
"| ERR552670 | 432 | 1370 | 0.315328467 |\n",
"| ERR552671 | 220 | 1372 | 0.160349854 |\n",
"| ERR552689 | 67 | 1355 | 0.049446494 |\n",
"| ERR552690 | 971 | 1158 | 0.83851468 |\n",
"| ERR552743 | 43 | 1356 | 0.031710914 |\n",
"| ERR552755 | 41 | 1337 | 0.030665669 |\n",
"| ERR552760 | 48 | 1350 | 0.035555556 |\n",
"| ERR552787 | 53 | 1391 | 0.038102085 |\n",
"| ERR552830 | 64 | 1360 | 0.047058824 |\n",
"| ERR552838 | 32 | 1376 | 0.023255814 |\n",
"| ERR552894 | 62 | 1345 | 0.046096654 |\n",
"| ERR552895 | 123 | 1288 | 0.095496894 |\n",
"| ERR552907 | 66 | 1401 | 0.047109208 |\n",
"| ERR552910 | 43 | 1384 | 0.031069364 |\n",
"| ERR552911 | 68 | 1362 | 0.049926579 |\n",
"| ERR552912 | 24 | 1419 | 0.016913319 |\n",
"| ERR552939 | 98 | 1434 | 0.068340307 |\n",
"| ERR552940 | 253 | 1487 | 0.170141224 |\n",
"| ERR552949 | 82 | 1349 | 0.060785767 |\n",
"| ERR552954 | 48 | 1383 | 0.034707158 |\n",
"| ERR553068 | 48 | 1366 | 0.035139092 |\n",
"| ERR553081 | 86 | 1338 | 0.064275037 |\n",
"| ERR553082 | 53 | 1368 | 0.03874269 |\n",
"| ERR553086 | 22 | 1463 | 0.015037594 |\n",
"| ERR553098 | 55 | 1402 | 0.039229672 |\n",
"| ERR553107 | 38 | 1372 | 0.027696793 |\n",
"| ERR553116 | 53 | 1366 | 0.038799414 |\n",
"| ERR553139 | 42 | 1406 | 0.029871977 |\n",
"| ERR553156 | 50 | 1357 | 0.036845984 |\n",
"| ERR553157 | 505 | 1306 | 0.386676876 |\n",
"| ERR553171 | 36 | 1420 | 0.025352113 |\n",
"| ERR553237 | 49 | 1392 | 0.035201149 |\n",
"| ERR553251 | 38 | 1471 | 0.025832767 |\n",
"| ERR553274 | 57 | 1312 | 0.043445122 |\n",
"| ERR553275 | 113 | 1271 | 0.088906373 |\n",
"| ERR553277 | 44 | 1382 | 0.031837916 |\n",
"| ERR553291 | 46 | 1379 | 0.033357505 |\n",
"| ERR553303 | 836 | 1272 | 0.657232704 |\n",
"| ERR553304 | 95 | 1320 | 0.071969697 |\n",
"| ERR553313 | 64 | 1390 | 0.046043165 |\n",
"| ERR553324 | 36 | 1396 | 0.025787966 |\n",
"| ERR619080 | 59 | 1398 | 0.042203147 |\n",
"\n",
"\n",
"3. Many samples have a lot of SNPs that have depth lower than 10. We should check the fastq files first. mrFAST cannot work on fastq files that contain reads of different lengths. If reads are cut at 100, a lot information would be lost. Calculate top 2 long reads and the coverage. Almost at leat 1 sequence of one sample have low coverage. ERR551979-ERR551983 all have very low coverage. Thus we should delete these sequences. For those samples that have several high coverage sequences, we choose the higher one. \n",
"\n",
"\n",
"\n",
"| sample | total_top2_1 | total_1 | second_top_length_1 | propotion_1 | average_depth_1 | deleted | total_top2_2 | total_2 | second_top_length_2 | propotion_2 | average_depth_2 |\n",
"|-----------|--------------|----------|---------------------|-------------|-----------------|---------|--------------|----------|---------------------|-------------|-----------------|\n",
"| ERR550643 | 352629 | 503810 | 250 | 0.69 | 20.04 | 1 | 365368 | 503810 | 250 | 0.72 | 20.76 |\n",
"| ERR550659 | 457109 | 888025 | 150 | 0.51 | 15.58 | 1 | 459162 | 888025 | 150 | 0.51 | 15.65 |\n",
"| ERR550777 | 366537 | 366537 | 151 | 1 | 12.58 | 1 | 366537 | 366537 | 151 | 1 | 12.58 |\n",
"| ERR550779 | 175080 | 213152 | 150 | 0.82 | 5.97 | 1 | 175478 | 213152 | 150 | 0.82 | 5.98 |\n",
"| ERR550783 | 301101 | 709644 | 250 | 0.42 | 17.11 | 1 | 329686 | 709644 | 250 | 0.46 | 18.73 |\n",
"| ERR550940 | 816797 | 986163 | 150 | 0.82 | 27.85 | 1 | 816667 | 986163 | 150 | 0.82 | 27.84 |\n",
"| ERR550946 | 847041 | 1062557 | 150 | 0.79 | 28.88 | 1 | 850563 | 1062557 | 150 | 0.8 | 29.00 |\n",
"| ERR551155 | 657758 | 773602 | 250 | 0.85 | 37.37 | 1 | 681991 | 773602 | 250 | 0.88 | 38.75 |\n",
"| ERR551168 | 730841 | 884768 | 150 | 0.82 | 24.92 | 1 | 732908 | 884768 | 150 | 0.82 | 24.99 |\n",
"| ERR551185 | 201119 | 310721 | 250 | 0.64 | 11.43 | 1 | 205086 | 310721 | 250 | 0.66 | 11.65 |\n",
"| ERR551361 | 301576 | 388916 | 250 | 0.77 | 17.14 | 1 | 311362 | 388916 | 250 | 0.8 | 17.69 |\n",
"| ERR551362 | 272194 | 343012 | 150 | 0.79 | 9.28 | 1 | 272760 | 343012 | 150 | 0.79 | 9.30 |\n",
"| ERR551370 | 560956 | 668918 | 250 | 0.83 | 31.87 | 1 | 525685 | 668918 | 250 | 0.78 | 29.87 |\n",
"| ERR551557 | 193635 | 245514 | 250 | 0.78 | 11.00 | 1 | 199966 | 245514 | 250 | 0.81 | 11.36 |\n",
"| ERR551558 | 228464 | 283281 | 150 | 0.8 | 7.79 | 1 | 228863 | 283281 | 150 | 0.8 | 7.80 |\n",
"| ERR551681 | 450011 | 561151 | 250 | 0.8 | 25.57 | 1 | 466319 | 561151 | 250 | 0.83 | 26.50 |\n",
"| ERR551694 | 555662 | 634595 | 250 | 0.87 | 31.57 | 1 | 523829 | 634595 | 250 | 0.82 | 29.76 |\n",
"| ERR551805 | 242169 | 313777 | 250 | 0.77 | 13.76 | 1 | 250503 | 313777 | 250 | 0.79 | 14.23 |\n",
"| ERR551806 | 238910 | 295816 | 150 | 0.8 | 8.14 | 1 | 238726 | 295816 | 150 | 0.8 | 8.14 |\n",
"| ERR551822 | 498401 | 827650 | 150 | 0.6 | 16.99 | 1 | 500159 | 827650 | 150 | 0.6 | 17.05 |\n",
"| ERR551854 | 367993 | 536581 | 250 | 0.68 | 20.91 | 1 | 376291 | 536581 | 250 | 0.7 | 21.38 |\n",
"| ERR551927 | 371298 | 519638 | 250 | 0.71 | 21.10 | 1 | 377836 | 519638 | 250 | 0.72 | 21.47 |\n",
"| ERR551943 | 166621 | 200229 | 150 | 0.83 | 5.68 | 1 | 166831 | 200229 | 150 | 0.83 | 5.69 |\n",
"| ERR551945 | 764186 | 764186 | 151 | 1 | 26.23 | 1 | 764186 | 764186 | 151 | 1 | 26.23 |\n",
"| ERR551957 | 520790 | 679521 | 250 | 0.76 | 29.59 | 1 | 544932 | 679521 | 250 | 0.8 | 30.96 |\n",
"| ERR551977 | 345033 | 505726 | 250 | 0.68 | 19.60 | 1 | 354319 | 505726 | 250 | 0.7 | 20.13 |\n",
"| ERR551979 | 73517 | 105647 | 250 | 0.69 | 4.18 | 1 | 73419 | 105647 | 250 | 0.69 | 4.17 |\n",
"| ERR551980 | 190883 | 251974 | 150 | 0.75 | 6.51 | 1 | 190285 | 251974 | 150 | 0.75 | 6.49 |\n",
"| ERR551981 | 78999 | 125109 | 300 | 0.63 | 5.39 | 1 | 78150 | 125109 | 300 | 0.62 | 5.33 |\n",
"| ERR551982 | 389417 | 517259 | 150 | 0.75 | 13.28 | 1 | 385982 | 517259 | 150 | 0.74 | 13.16 |\n",
"| ERR551983 | 64960 | 91448 | 250 | 0.71 | 3.69 | 1 | 65482 | 91448 | 250 | 0.71 | 3.72 |\n",
"| ERR552445 | 293200 | 446898 | 250 | 0.65 | 16.66 | 1 | 301814 | 446898 | 250 | 0.67 | 17.15 |\n",
"| ERR552494 | 263183 | 622615 | 250 | 0.42 | 14.95 | 1 | 283860 | 622615 | 250 | 0.45 | 16.13 |\n",
"| ERR552549 | 623022 | 762902 | 250 | 0.81 | 35.40 | 1 | 647569 | 762902 | 250 | 0.84 | 36.79 |\n",
"| ERR552669 | 55934 | 155915 | 250 | 0.35 | 3.18 | 1 | 69726 | 155915 | 250 | 0.44 | 3.96 |\n",
"| ERR552670 | 184521 | 345974 | 150 | 0.53 | 6.29 | 1 | 183221 | 345974 | 150 | 0.52 | 6.25 |\n",
"| ERR552671 | 489347 | 489347 | 151 | 1 | 16.79 | 1 | 489347 | 489347 | 151 | 1 | 16.79 |\n",
"| ERR552690 | 89514 | 125815 | 250 | 0.71 | 5.09 | 1 | 91766 | 125815 | 250 | 0.72 | 5.21 |\n",
"| ERR552895 | 471918 | 517150 | 250 | 0.91 | 26.81 | 1 | 484124 | 517150 | 250 | 0.93 | 27.51 |\n",
"| ERR552911 | 478195 | 617093 | 250 | 0.77 | 27.17 | 1 | 502770 | 617093 | 250 | 0.81 | 28.57 |\n",
"| ERR552940 | 261884 | 567079 | 150 | 0.46 | 8.93 | 1 | 263299 | 567079 | 150 | 0.46 | 8.98 |\n",
"| ERR553081 | 466048 | 660126 | 250 | 0.7 | 26.48 | 1 | 501668 | 660126 | 250 | 0.75 | 28.50 |\n",
"| ERR553157 | 185233 | 273412 | 250 | 0.67 | 10.52 | 1 | 190554 | 273412 | 250 | 0.69 | 10.83 |\n",
"| ERR553275 | 397740 | 523197 | 250 | 0.76 | 22.60 | 1 | 418654 | 523197 | 250 | 0.8 | 23.79 |\n",
"| ERR553303 | 159293 | 186850 | 250 | 0.85 | 9.05 | 1 | 170553 | 186850 | 250 | 0.91 | 9.69 |\n",
"| ERR550644 | 368387 | 500989 | 250 | 0.73 | 20.93 | | 390100 | 500989 | 250 | 0.77 | 22.16 |\n",
"| ERR550658 | 694536 | 694536 | 151 | 1 | 23.84 | | 694536 | 694536 | 151 | 1 | 23.84 |\n",
"| ERR550670 | 942945 | 1067515 | 250 | 0.88 | 53.58 | | 975660 | 1067515 | 250 | 0.91 | 55.44 |\n",
"| ERR550724 | 4406794 | 4406794 | 51 | 1 | 51.08 | | 4406794 | 4406794 | 51 | 1 | 51.08 |\n",
"| ERR550738 | 664927 | 802441 | 250 | 0.82 | 37.78 | | 618779 | 802441 | 250 | 0.77 | 35.16 |\n",
"| ERR550739 | 1202191 | 1603567 | 150 | 0.74 | 40.98 | | 1209489 | 1603567 | 150 | 0.75 | 41.23 |\n",
"| ERR550778 | 403711 | 492341 | 250 | 0.81 | 22.94 | | 417005 | 492341 | 250 | 0.84 | 23.69 |\n",
"| ERR550782 | 1142598 | 1142598 | 151 | 1 | 39.21 | | 1142598 | 1142598 | 151 | 1 | 39.21 |\n",
"| ERR550887 | 681613 | 681613 | 150 | 1 | 23.24 | | 681613 | 681613 | 150 | 1 | 23.24 |\n",
"| ERR550910 | 6148758 | 6148758 | 101 | 1 | 141.14 | | 6148758 | 6148758 | 101 | 1 | 141.14 |\n",
"| ERR550927 | 895354 | 1126439 | 150 | 0.79 | 30.52 | | 897513 | 1126439 | 150 | 0.79 | 30.60 |\n",
"| ERR550941 | 528566 | 694478 | 250 | 0.76 | 30.03 | | 553340 | 694478 | 250 | 0.79 | 31.44 |\n",
"| ERR550947 | 560003 | 817328 | 250 | 0.68 | 31.82 | | 593168 | 817328 | 250 | 0.72 | 33.70 |\n",
"| ERR550957 | 2847379 | 2847379 | 101 | 1 | 65.36 | | 2847379 | 2847379 | 101 | 1 | 65.36 |\n",
"| ERR550984 | 1232580 | 1232580 | 151 | 1 | 42.30 | | 1232580 | 1232580 | 151 | 1 | 42.30 |\n",
"| ERR551007 | 3221955 | 3221955 | 101 | 1 | 73.96 | | 3221955 | 3221955 | 101 | 1 | 73.96 |\n",
"| ERR551071 | 1538003 | 2275846 | 150 | 0.67 | 52.43 | | 1527328 | 2275846 | 150 | 0.67 | 52.07 |\n",
"| ERR551079 | 831236 | 1153100 | 150 | 0.72 | 28.34 | | 830923 | 1153100 | 150 | 0.72 | 28.33 |\n",
"| ERR551086 | 1381621 | 1709345 | 150 | 0.8 | 47.10 | | 1385599 | 1709345 | 150 | 0.81 | 47.24 |\n",
"| ERR551090 | 912137 | 1125813 | 150 | 0.81 | 31.10 | | 912726 | 1125813 | 150 | 0.81 | 31.12 |\n",
"| ERR551156 | 667053 | 734670 | 250 | 0.9 | 37.90 | | 686748 | 734670 | 250 | 0.93 | 39.02 |\n",
"| ERR551159 | 1178805 | 1178805 | 151 | 1 | 40.45 | | 1178805 | 1178805 | 151 | 1 | 40.45 |\n",
"| ERR551167 | 746944 | 746944 | 151 | 1 | 25.63 | | 746944 | 746944 | 151 | 1 | 25.63 |\n",
"| ERR551184 | 639791 | 840348 | 150 | 0.76 | 21.81 | | 639780 | 840348 | 150 | 0.76 | 21.81 |\n",
"| ERR551201 | 1836130 | 1836130 | 151 | 1 | 63.01 | | 1836130 | 1836130 | 151 | 1 | 63.01 |\n",
"| ERR551212 | 1112021 | 1392359 | 150 | 0.79 | 37.91 | | 1110539 | 1392359 | 150 | 0.79 | 37.86 |\n",
"| ERR551214 | 2950693 | 2950693 | 72 | 1 | 48.28 | | 2950693 | 2950693 | 72 | 1 | 48.28 |\n",
"| ERR551225 | 2359668 | 2359668 | 101 | 1 | 54.17 | | 2359668 | 2359668 | 101 | 1 | 54.17 |\n",
"| ERR551293 | 3593886 | 3593886 | 100 | 1 | 81.68 | | 3593886 | 3593886 | 100 | 1 | 81.68 |\n",
"| ERR551305 | 649772 | 649772 | 150 | 1 | 22.15 | | 649772 | 649772 | 150 | 1 | 22.15 |\n",
"| ERR551311 | 920836 | 1595233 | 250 | 0.57 | 52.32 | | 934011 | 1595233 | 250 | 0.58 | 53.07 |\n",
"| ERR551360 | 531862 | 531862 | 151 | 1 | 18.25 | | 531862 | 531862 | 151 | 1 | 18.25 |\n",
"| ERR551369 | 634497 | 789026 | 250 | 0.8 | 36.05 | | 664714 | 789026 | 250 | 0.84 | 37.77 |\n",
"| ERR551412 | 501209 | 580388 | 250 | 0.86 | 28.48 | | 510344 | 580388 | 250 | 0.87 | 29.00 |\n",
"| ERR551494 | 997921 | 1252809 | 150 | 0.79 | 34.02 | | 999254 | 1252809 | 150 | 0.79 | 34.07 |\n",
"| ERR551520 | 7542333 | 7542333 | 101 | 1 | 173.13 | | 7542333 | 7542333 | 101 | 1 | 173.13 |\n",
"| ERR551549 | 3069058 | 3069058 | 101 | 1 | 70.45 | | 3069058 | 3069058 | 101 | 1 | 70.45 |\n",
"| ERR551554 | 1146272 | 1146272 | 151 | 1 | 39.34 | | 1146272 | 1146272 | 151 | 1 | 39.34 |\n",
"| ERR551556 | 557238 | 557238 | 151 | 1 | 19.12 | | 557238 | 557238 | 151 | 1 | 19.12 |\n",
"| ERR551572 | 966186 | 1156589 | 150 | 0.83 | 32.94 | | 964154 | 1156589 | 150 | 0.83 | 32.87 |\n",
"| ERR551575 | 2437684 | 2437684 | 72 | 1 | 39.89 | | 2437684 | 2437684 | 72 | 1 | 39.89 |\n",
"| ERR551636 | 905255 | 1116387 | 150 | 0.81 | 30.86 | | 904961 | 1116387 | 150 | 0.81 | 30.85 |\n",
"| ERR551638 | 696445 | 696445 | 150 | 1 | 23.74 | | 696445 | 696445 | 150 | 1 | 23.74 |\n",
"| ERR551680 | 856628 | 856628 | 151 | 1 | 29.40 | | 856628 | 856628 | 151 | 1 | 29.40 |\n",
"| ERR551688 | 714189 | 807272 | 250 | 0.88 | 40.58 | | 741391 | 807272 | 250 | 0.91 | 42.12 |\n",
"| ERR551693 | 617307 | 702723 | 250 | 0.87 | 35.07 | | 636967 | 702723 | 250 | 0.9 | 36.19 |\n",
"| ERR551772 | 4858274 | 4858274 | 51 | 1 | 56.31 | | 4858274 | 4858274 | 51 | 1 | 56.31 |\n",
"| ERR551804 | 528297 | 528297 | 151 | 1 | 18.13 | | 528297 | 528297 | 151 | 1 | 18.13 |\n",
"| ERR551821 | 751179 | 751179 | 151 | 1 | 25.78 | | 751179 | 751179 | 151 | 1 | 25.78 |\n",
"| ERR551855 | 549848 | 709020 | 250 | 0.77 | 31.24 | | 574161 | 709020 | 250 | 0.8 | 32.62 |\n",
"| ERR551879 | 1448715 | 1763371 | 150 | 0.82 | 49.39 | | 1436155 | 1763371 | 150 | 0.81 | 48.96 |\n",
"| ERR551928 | 485859 | 626266 | 250 | 0.77 | 27.61 | | 507987 | 626266 | 250 | 0.81 | 28.86 |\n",
"| ERR551930 | 1039805 | 1284539 | 150 | 0.8 | 35.45 | | 1042954 | 1284539 | 150 | 0.81 | 35.56 |\n",
"| ERR551934 | 4930930 | 4930930 | 101 | 1 | 113.19 | | 4930930 | 4930930 | 101 | 1 | 113.19 |\n",
"| ERR551944 | 626733 | 749579 | 250 | 0.83 | 35.61 | | 646916 | 749579 | 250 | 0.86 | 36.76 |\n",
"| ERR551956 | 1036490 | 1036490 | 151 | 1 | 35.57 | | 1036490 | 1036490 | 151 | 1 | 35.57 |\n",
"| ERR551978 | 579051 | 746633 | 250 | 0.77 | 32.90 | | 610553 | 746633 | 250 | 0.81 | 34.69 |\n",
"| ERR551990 | 2501148 | 2501148 | 101 | 1 | 57.41 | | 2501148 | 2501148 | 101 | 1 | 57.41 |\n",
"| ERR552090 | 3325371 | 3325371 | 100 | 1 | 75.58 | | 3325371 | 3325371 | 100 | 1 | 75.58 |\n",
"| ERR552130 | 1416680 | 1416680 | 151 | 1 | 48.62 | | 1416680 | 1416680 | 151 | 1 | 48.62 |\n",
"| ERR552136 | 1331643 | 1702445 | 150 | 0.78 | 45.40 | | 1332191 | 1702445 | 150 | 0.78 | 45.42 |\n",
"| ERR552141 | 775490 | 1344531 | 250 | 0.57 | 44.06 | | 778789 | 1344531 | 250 | 0.57 | 44.25 |\n",
"| ERR552177 | 2126018 | 2126018 | 101 | 1 | 48.80 | | 2126018 | 2126018 | 101 | 1 | 48.80 |\n",
"| ERR552190 | 2907157 | 2907157 | 101 | 1 | 66.73 | | 2907157 | 2907157 | 101 | 1 | 66.73 |\n",
"| ERR552194 | 833791 | 1147014 | 150 | 0.72 | 28.42 | | 836118 | 1147014 | 150 | 0.72 | 28.50 |\n",
"| ERR552219 | 772464 | 930462 | 250 | 0.83 | 43.89 | | 723511 | 930462 | 250 | 0.77 | 41.11 |\n",
"| ERR552331 | 1107102 | 1358539 | 150 | 0.81 | 37.74 | | 1107211 | 1358539 | 150 | 0.81 | 37.75 |\n",
"| ERR552358 | 1209857 | 1525080 | 150 | 0.79 | 41.25 | | 1209470 | 1525080 | 150 | 0.79 | 41.23 |\n",
"| ERR552429 | 1141376 | 1141376 | 151 | 1 | 39.17 | | 1141376 | 1141376 | 151 | 1 | 39.17 |\n",
"| ERR552444 | 688009 | 947208 | 150 | 0.72 | 23.45 | | 686250 | 947208 | 150 | 0.72 | 23.39 |\n",
"| ERR552479 | 19714536 | 19714536 | 51 | 1 | 228.51 | | 19714536 | 19714536 | 51 | 1 | 228.51 |\n",
"| ERR552482 | 1616551 | 2143831 | 150 | 0.75 | 55.11 | | 1620080 | 2143831 | 150 | 0.75 | 55.23 |\n",
"| ERR552493 | 1006307 | 1006307 | 151 | 1 | 34.53 | | 1006307 | 1006307 | 151 | 1 | 34.53 |\n",
"| ERR552550 | 635108 | 713215 | 250 | 0.89 | 36.09 | | 657053 | 713215 | 250 | 0.92 | 37.33 |\n",
"| ERR552555 | 1137494 | 1412348 | 150 | 0.8 | 38.78 | | 1139900 | 1412348 | 150 | 0.8 | 38.86 |\n",
"| ERR552580 | 1213962 | 1213962 | 151 | 1 | 41.66 | | 1213962 | 1213962 | 151 | 1 | 41.66 |\n",
"| ERR552668 | 480196 | 694259 | 250 | 0.69 | 27.28 | | 555166 | 694259 | 250 | 0.79 | 31.54 |\n",
"| ERR552689 | 771015 | 1014227 | 150 | 0.76 | 26.28 | | 763536 | 1014227 | 150 | 0.75 | 26.03 |\n",
"| ERR552743 | 874653 | 1227467 | 150 | 0.71 | 29.82 | | 869799 | 1227467 | 150 | 0.7 | 29.65 |\n",
"| ERR552755 | 932272 | 1145975 | 150 | 0.81 | 31.78 | | 933642 | 1145975 | 150 | 0.81 | 31.83 |\n",
"| ERR552760 | 916048 | 1122827 | 150 | 0.81 | 31.23 | | 916825 | 1122827 | 150 | 0.81 | 31.26 |\n",
"| ERR552787 | 1087121 | 1386109 | 150 | 0.78 | 37.06 | | 1089473 | 1386109 | 150 | 0.78 | 37.14 |\n",
"| ERR552830 | 703060 | 824463 | 250 | 0.85 | 39.95 | | 661031 | 824463 | 250 | 0.8 | 37.56 |\n",
"| ERR552838 | 1355502 | 1872386 | 150 | 0.72 | 46.21 | | 1351777 | 1872386 | 150 | 0.72 | 46.08 |\n",
"| ERR552894 | 573476 | 667626 | 250 | 0.85 | 32.58 | | 592430 | 667626 | 250 | 0.88 | 33.66 |\n",
"| ERR552907 | 849602 | 849602 | 150 | 1 | 28.96 | | 849602 | 849602 | 150 | 1 | 28.96 |\n",
"| ERR552910 | 909326 | 909326 | 151 | 1 | 31.21 | | 909326 | 909326 | 151 | 1 | 31.21 |\n",
"| ERR552912 | 3735481 | 3735481 | 101 | 1 | 85.75 | | 3735481 | 3735481 | 101 | 1 | 85.75 |\n",
"| ERR552939 | 942214 | 942214 | 151 | 1 | 32.34 | | 942214 | 942214 | 151 | 1 | 32.34 |\n",
"| ERR552949 | 565475 | 672440 | 250 | 0.84 | 32.13 | | 531302 | 672440 | 250 | 0.79 | 30.19 |\n",
"| ERR552954 | 1263899 | 1545167 | 150 | 0.81 | 43.09 | | 1265777 | 1545167 | 150 | 0.81 | 43.15 |\n",
"| ERR553068 | 952882 | 1037422 | 150 | 0.91 | 32.48 | | 953885 | 1037422 | 150 | 0.91 | 32.52 |\n",
"| ERR553082 | 843849 | 1039372 | 150 | 0.81 | 28.77 | | 846859 | 1039372 | 150 | 0.81 | 28.87 |\n",
"| ERR553086 | 4316346 | 4316346 | 101 | 1 | 99.08 | | 4316346 | 4316346 | 101 | 1 | 99.08 |\n",
"| ERR553098 | 1371902 | 1371902 | 150 | 1 | 46.77 | | 1371902 | 1371902 | 150 | 1 | 46.77 |\n",
"| ERR553107 | 1157028 | 1157028 | 151 | 1 | 39.71 | | 1157028 | 1157028 | 151 | 1 | 39.71 |\n",
"| ERR553116 | 639742 | 703989 | 150 | 0.9 | 21.81 | | 639742 | 703989 | 150 | 0.9 | 21.81 |\n",
"| ERR553139 | 1121756 | 1396599 | 150 | 0.8 | 38.24 | | 1124357 | 1396599 | 150 | 0.8 | 38.33 |\n",
"| ERR553156 | 604012 | 818966 | 150 | 0.73 | 20.59 | | 600320 | 818966 | 150 | 0.73 | 20.47 |\n",
"| ERR553171 | 1746795 | 1746795 | 101 | 1 | 40.10 | | 1746795 | 1746795 | 101 | 1 | 40.10 |\n",
"| ERR553237 | 921485 | 1137866 | 150 | 0.8 | 31.41 | | 921637 | 1137866 | 150 | 0.8 | 31.42 |\n",
"| ERR553251 | 4809482 | 4809482 | 51 | 1 | 55.75 | | 4809482 | 4809482 | 51 | 1 | 55.75 |\n",
"| ERR553274 | 726240 | 892990 | 150 | 0.81 | 24.76 | | 727986 | 892990 | 150 | 0.81 | 24.82 |\n",
"| ERR553277 | 907668 | 1203167 | 150 | 0.75 | 30.94 | | 905914 | 1203167 | 150 | 0.75 | 30.88 |\n",
"| ERR553291 | 892724 | 1083213 | 150 | 0.82 | 30.43 | | 894784 | 1083213 | 150 | 0.82 | 30.50 |\n",
"| ERR553304 | 513906 | 561524 | 250 | 0.91 | 29.20 | | 527555 | 561524 | 250 | 0.93 | 29.97 |\n",
"| ERR553313 | 1048464 | 1048464 | 151 | 1 | 35.98 | | 1048464 | 1048464 | 151 | 1 | 35.98 |\n",
"| ERR553324 | 1235342 | 1235342 | 151 | 1 | 42.39 | | 1235342 | 1235342 | 151 | 1 | 42.39 |\n",
"| ERR619080 | 669268 | 983056 | 300 | 0.68 | 45.63 | | 718129 | 983056 | 300 | 0.73 | 48.96 |\n",
"\n",
"4. Samples should be deleted (45):\n",
"\n",
"| sample | total_top2_1 | total_1 | second_top_length_1 | propotion_1 | average_depth_1 | deleted | total_top2_2 | total_2 | second_top_length_2 | propotion_2 | average_depth_2 |\n",
"|-----------|--------------|---------|---------------------|-------------|-----------------|---------|--------------|---------|---------------------|-------------|-----------------|\n",
"| ERR550643 | 352629 | 503810 | 250 | 0.69 | 20.04 | 1 | 365368 | 503810 | 250 | 0.72 | 20.76 |\n",
"| ERR550659 | 457109 | 888025 | 150 | 0.51 | 15.58 | 1 | 459162 | 888025 | 150 | 0.51 | 15.65 |\n",
"| ERR550777 | 366537 | 366537 | 151 | 1 | 12.58 | 1 | 366537 | 366537 | 151 | 1 | 12.58 |\n",
"| ERR550779 | 175080 | 213152 | 150 | 0.82 | 5.97 | 1 | 175478 | 213152 | 150 | 0.82 | 5.98 |\n",
"| ERR550783 | 301101 | 709644 | 250 | 0.42 | 17.11 | 1 | 329686 | 709644 | 250 | 0.46 | 18.73 |\n",
"| ERR550940 | 816797 | 986163 | 150 | 0.82 | 27.85 | 1 | 816667 | 986163 | 150 | 0.82 | 27.84 |\n",
"| ERR550946 | 847041 | 1062557 | 150 | 0.79 | 28.88 | 1 | 850563 | 1062557 | 150 | 0.8 | 29.00 |\n",
"| ERR551155 | 657758 | 773602 | 250 | 0.85 | 37.37 | 1 | 681991 | 773602 | 250 | 0.88 | 38.75 |\n",
"| ERR551168 | 730841 | 884768 | 150 | 0.82 | 24.92 | 1 | 732908 | 884768 | 150 | 0.82 | 24.99 |\n",
"| ERR551185 | 201119 | 310721 | 250 | 0.64 | 11.43 | 1 | 205086 | 310721 | 250 | 0.66 | 11.65 |\n",
"| ERR551361 | 301576 | 388916 | 250 | 0.77 | 17.14 | 1 | 311362 | 388916 | 250 | 0.8 | 17.69 |\n",
"| ERR551362 | 272194 | 343012 | 150 | 0.79 | 9.28 | 1 | 272760 | 343012 | 150 | 0.79 | 9.30 |\n",
"| ERR551370 | 560956 | 668918 | 250 | 0.83 | 31.87 | 1 | 525685 | 668918 | 250 | 0.78 | 29.87 |\n",
"| ERR551557 | 193635 | 245514 | 250 | 0.78 | 11.00 | 1 | 199966 | 245514 | 250 | 0.81 | 11.36 |\n",
"| ERR551558 | 228464 | 283281 | 150 | 0.8 | 7.79 | 1 | 228863 | 283281 | 150 | 0.8 | 7.80 |\n",
"| ERR551681 | 450011 | 561151 | 250 | 0.8 | 25.57 | 1 | 466319 | 561151 | 250 | 0.83 | 26.50 |\n",
"| ERR551694 | 555662 | 634595 | 250 | 0.87 | 31.57 | 1 | 523829 | 634595 | 250 | 0.82 | 29.76 |\n",
"| ERR551805 | 242169 | 313777 | 250 | 0.77 | 13.76 | 1 | 250503 | 313777 | 250 | 0.79 | 14.23 |\n",
"| ERR551806 | 238910 | 295816 | 150 | 0.8 | 8.14 | 1 | 238726 | 295816 | 150 | 0.8 | 8.14 |\n",
"| ERR551822 | 498401 | 827650 | 150 | 0.6 | 16.99 | 1 | 500159 | 827650 | 150 | 0.6 | 17.05 |\n",
"| ERR551854 | 367993 | 536581 | 250 | 0.68 | 20.91 | 1 | 376291 | 536581 | 250 | 0.7 | 21.38 |\n",
"| ERR551927 | 371298 | 519638 | 250 | 0.71 | 21.10 | 1 | 377836 | 519638 | 250 | 0.72 | 21.47 |\n",
"| ERR551943 | 166621 | 200229 | 150 | 0.83 | 5.68 | 1 | 166831 | 200229 | 150 | 0.83 | 5.69 |\n",
"| ERR551945 | 764186 | 764186 | 151 | 1 | 26.23 | 1 | 764186 | 764186 | 151 | 1 | 26.23 |\n",
"| ERR551957 | 520790 | 679521 | 250 | 0.76 | 29.59 | 1 | 544932 | 679521 | 250 | 0.8 | 30.96 |\n",
"| ERR551977 | 345033 | 505726 | 250 | 0.68 | 19.60 | 1 | 354319 | 505726 | 250 | 0.7 | 20.13 |\n",
"| ERR551979 | 73517 | 105647 | 250 | 0.69 | 4.18 | 1 | 73419 | 105647 | 250 | 0.69 | 4.17 |\n",
"| ERR551980 | 190883 | 251974 | 150 | 0.75 | 6.51 | 1 | 190285 | 251974 | 150 | 0.75 | 6.49 |\n",
"| ERR551981 | 78999 | 125109 | 300 | 0.63 | 5.39 | 1 | 78150 | 125109 | 300 | 0.62 | 5.33 |\n",
"| ERR551982 | 389417 | 517259 | 150 | 0.75 | 13.28 | 1 | 385982 | 517259 | 150 | 0.74 | 13.16 |\n",
"| ERR551983 | 64960 | 91448 | 250 | 0.71 | 3.69 | 1 | 65482 | 91448 | 250 | 0.71 | 3.72 |\n",
"| ERR552445 | 293200 | 446898 | 250 | 0.65 | 16.66 | 1 | 301814 | 446898 | 250 | 0.67 | 17.15 |\n",
"| ERR552494 | 263183 | 622615 | 250 | 0.42 | 14.95 | 1 | 283860 | 622615 | 250 | 0.45 | 16.13 |\n",
"| ERR552549 | 623022 | 762902 | 250 | 0.81 | 35.40 | 1 | 647569 | 762902 | 250 | 0.84 | 36.79 |\n",
"| ERR552669 | 55934 | 155915 | 250 | 0.35 | 3.18 | 1 | 69726 | 155915 | 250 | 0.44 | 3.96 |\n",
"| ERR552670 | 184521 | 345974 | 150 | 0.53 | 6.29 | 1 | 183221 | 345974 | 150 | 0.52 | 6.25 |\n",
"| ERR552671 | 489347 | 489347 | 151 | 1 | 16.79 | 1 | 489347 | 489347 | 151 | 1 | 16.79 |\n",
"| ERR552690 | 89514 | 125815 | 250 | 0.71 | 5.09 | 1 | 91766 | 125815 | 250 | 0.72 | 5.21 |\n",
"| ERR552895 | 471918 | 517150 | 250 | 0.91 | 26.81 | 1 | 484124 | 517150 | 250 | 0.93 | 27.51 |\n",
"| ERR552911 | 478195 | 617093 | 250 | 0.77 | 27.17 | 1 | 502770 | 617093 | 250 | 0.81 | 28.57 |\n",
"| ERR552940 | 261884 | 567079 | 150 | 0.46 | 8.93 | 1 | 263299 | 567079 | 150 | 0.46 | 8.98 |\n",
"| ERR553081 | 466048 | 660126 | 250 | 0.7 | 26.48 | 1 | 501668 | 660126 | 250 | 0.75 | 28.50 |\n",
"| ERR553157 | 185233 | 273412 | 250 | 0.67 | 10.52 | 1 | 190554 | 273412 | 250 | 0.69 | 10.83 |\n",
"| ERR553275 | 397740 | 523197 | 250 | 0.76 | 22.60 | 1 | 418654 | 523197 | 250 | 0.8 | 23.79 |\n",
"| ERR553303 | 159293 | 186850 | 250 | 0.85 | 9.05 | 1 | 170553 | 186850 | 250 | 0.91 | 9.69 |\n",
"\n",
"5. samples' reads length at 150 (36):\n",
"\n",
"| sample | total_top2_1 | total_1 | second_top_length_1 | propotion_1 | average_depth_1 | deleted | total_top2_2 | total_2 | second_top_length_2 | propotion_2 | average_depth_2 |\n",
"|-----------|--------------|---------|---------------------|-------------|-----------------|---------|--------------|---------|---------------------|-------------|-----------------|\n",
"| ERR553068 | 952882 | 1037422 | 150 | 0.91 | 32.48 | | 953885 | 1037422 | 150 | 0.91 | 32.52 |\n",
"| ERR553116 | 639742 | 703989 | 150 | 0.9 | 21.81 | | 639742 | 703989 | 150 | 0.9 | 21.81 |\n",
"| ERR551572 | 966186 | 1156589 | 150 | 0.83 | 32.94 | | 964154 | 1156589 | 150 | 0.83 | 32.87 |\n",
"| ERR551879 | 1448715 | 1763371 | 150 | 0.82 | 49.39 | | 1436155 | 1763371 | 150 | 0.81 | 48.96 |\n",
"| ERR553291 | 892724 | 1083213 | 150 | 0.82 | 30.43 | | 894784 | 1083213 | 150 | 0.82 | 30.50 |\n",
"| ERR551090 | 912137 | 1125813 | 150 | 0.81 | 31.10 | | 912726 | 1125813 | 150 | 0.81 | 31.12 |\n",
"| ERR551636 | 905255 | 1116387 | 150 | 0.81 | 30.86 | | 904961 | 1116387 | 150 | 0.81 | 30.85 |\n",
"| ERR552331 | 1107102 | 1358539 | 150 | 0.81 | 37.74 | | 1107211 | 1358539 | 150 | 0.81 | 37.75 |\n",
"| ERR552755 | 932272 | 1145975 | 150 | 0.81 | 31.78 | | 933642 | 1145975 | 150 | 0.81 | 31.83 |\n",
"| ERR552760 | 916048 | 1122827 | 150 | 0.81 | 31.23 | | 916825 | 1122827 | 150 | 0.81 | 31.26 |\n",
"| ERR552954 | 1263899 | 1545167 | 150 | 0.81 | 43.09 | | 1265777 | 1545167 | 150 | 0.81 | 43.15 |\n",
"| ERR553082 | 843849 | 1039372 | 150 | 0.81 | 28.77 | | 846859 | 1039372 | 150 | 0.81 | 28.87 |\n",
"| ERR553274 | 726240 | 892990 | 150 | 0.81 | 24.76 | | 727986 | 892990 | 150 | 0.81 | 24.82 |\n",
"| ERR551086 | 1381621 | 1709345 | 150 | 0.8 | 47.10 | | 1385599 | 1709345 | 150 | 0.81 | 47.24 |\n",
"| ERR551930 | 1039805 | 1284539 | 150 | 0.8 | 35.45 | | 1042954 | 1284539 | 150 | 0.81 | 35.56 |\n",
"| ERR552555 | 1137494 | 1412348 | 150 | 0.8 | 38.78 | | 1139900 | 1412348 | 150 | 0.8 | 38.86 |\n",
"| ERR553139 | 1121756 | 1396599 | 150 | 0.8 | 38.24 | | 1124357 | 1396599 | 150 | 0.8 | 38.33 |\n",
"| ERR553237 | 921485 | 1137866 | 150 | 0.8 | 31.41 | | 921637 | 1137866 | 150 | 0.8 | 31.42 |\n",
"| ERR550927 | 895354 | 1126439 | 150 | 0.79 | 30.52 | | 897513 | 1126439 | 150 | 0.79 | 30.60 |\n",
"| ERR551212 | 1112021 | 1392359 | 150 | 0.79 | 37.91 | | 1110539 | 1392359 | 150 | 0.79 | 37.86 |\n",
"| ERR551494 | 997921 | 1252809 | 150 | 0.79 | 34.02 | | 999254 | 1252809 | 150 | 0.79 | 34.07 |\n",
"| ERR552358 | 1209857 | 1525080 | 150 | 0.79 | 41.25 | | 1209470 | 1525080 | 150 | 0.79 | 41.23 |\n",
"| ERR552136 | 1331643 | 1702445 | 150 | 0.78 | 45.40 | | 1332191 | 1702445 | 150 | 0.78 | 45.42 |\n",
"| ERR552787 | 1087121 | 1386109 | 150 | 0.78 | 37.06 | | 1089473 | 1386109 | 150 | 0.78 | 37.14 |\n",
"| ERR551184 | 639791 | 840348 | 150 | 0.76 | 21.81 | | 639780 | 840348 | 150 | 0.76 | 21.81 |\n",
"| ERR552689 | 771015 | 1014227 | 150 | 0.76 | 26.28 | | 763536 | 1014227 | 150 | 0.75 | 26.03 |\n",
"| ERR552482 | 1616551 | 2143831 | 150 | 0.75 | 55.11 | | 1620080 | 2143831 | 150 | 0.75 | 55.23 |\n",
"| ERR553277 | 907668 | 1203167 | 150 | 0.75 | 30.94 | | 905914 | 1203167 | 150 | 0.75 | 30.88 |\n",
"| ERR550739 | 1202191 | 1603567 | 150 | 0.74 | 40.98 | | 1209489 | 1603567 | 150 | 0.75 | 41.23 |\n",
"| ERR553156 | 604012 | 818966 | 150 | 0.73 | 20.59 | | 600320 | 818966 | 150 | 0.73 | 20.47 |\n",
"| ERR551079 | 831236 | 1153100 | 150 | 0.72 | 28.34 | | 830923 | 1153100 | 150 | 0.72 | 28.33 |\n",
"| ERR552194 | 833791 | 1147014 | 150 | 0.72 | 28.42 | | 836118 | 1147014 | 150 | 0.72 | 28.50 |\n",
"| ERR552444 | 688009 | 947208 | 150 | 0.72 | 23.45 | | 686250 | 947208 | 150 | 0.72 | 23.39 |\n",
"| ERR552838 | 1355502 | 1872386 | 150 | 0.72 | 46.21 | | 1351777 | 1872386 | 150 | 0.72 | 46.08 |\n",
"| ERR552743 | 874653 | 1227467 | 150 | 0.71 | 29.82 | | 869799 | 1227467 | 150 | 0.7 | 29.65 |\n",
"| ERR551071 | 1538003 | 2275846 | 150 | 0.67 | 52.43 | | 1527328 | 2275846 | 150 | 0.67 | 52.07 |\n",
"\n",
"6. samples' reads length at 250 (24):\n",
"\n",
"| sample | total_top2_1 | total_1 | second_top_length_1 | propotion_1 | average_depth_1 | deleted | total_top2_2 | total_2 | second_top_length_2 | propotion_2 | average_depth_2 |\n",
"|-----------|--------------|---------|---------------------|-------------|-----------------|---------|--------------|---------|---------------------|-------------|-----------------|\n",
"| ERR553304 | 513906 | 561524 | 250 | 0.91 | 29.20 | | 527555 | 561524 | 250 | 0.93 | 29.97 |\n",
"| ERR551156 | 667053 | 734670 | 250 | 0.9 | 37.90 | | 686748 | 734670 | 250 | 0.93 | 39.02 |\n",
"| ERR552550 | 635108 | 713215 | 250 | 0.89 | 36.09 | | 657053 | 713215 | 250 | 0.92 | 37.33 |\n",
"| ERR550670 | 942945 | 1067515 | 250 | 0.88 | 53.58 | | 975660 | 1067515 | 250 | 0.91 | 55.44 |\n",
"| ERR551688 | 714189 | 807272 | 250 | 0.88 | 40.58 | | 741391 | 807272 | 250 | 0.91 | 42.12 |\n",
"| ERR551693 | 617307 | 702723 | 250 | 0.87 | 35.07 | | 636967 | 702723 | 250 | 0.9 | 36.19 |\n",
"| ERR551412 | 501209 | 580388 | 250 | 0.86 | 28.48 | | 510344 | 580388 | 250 | 0.87 | 29.00 |\n",
"| ERR552830 | 703060 | 824463 | 250 | 0.85 | 39.95 | | 661031 | 824463 | 250 | 0.8 | 37.56 |\n",
"| ERR552894 | 573476 | 667626 | 250 | 0.85 | 32.58 | | 592430 | 667626 | 250 | 0.88 | 33.66 |\n",
"| ERR552949 | 565475 | 672440 | 250 | 0.84 | 32.13 | | 531302 | 672440 | 250 | 0.79 | 30.19 |\n",
"| ERR551944 | 626733 | 749579 | 250 | 0.83 | 35.61 | | 646916 | 749579 | 250 | 0.86 | 36.76 |\n",
"| ERR552219 | 772464 | 930462 | 250 | 0.83 | 43.89 | | 723511 | 930462 | 250 | 0.77 | 41.11 |\n",
"| ERR550738 | 664927 | 802441 | 250 | 0.82 | 37.78 | | 618779 | 802441 | 250 | 0.77 | 35.16 |\n",
"| ERR550778 | 403711 | 492341 | 250 | 0.81 | 22.94 | | 417005 | 492341 | 250 | 0.84 | 23.69 |\n",
"| ERR551369 | 634497 | 789026 | 250 | 0.8 | 36.05 | | 664714 | 789026 | 250 | 0.84 | 37.77 |\n",
"| ERR551855 | 549848 | 709020 | 250 | 0.77 | 31.24 | | 574161 | 709020 | 250 | 0.8 | 32.62 |\n",
"| ERR551928 | 485859 | 626266 | 250 | 0.77 | 27.61 | | 507987 | 626266 | 250 | 0.81 | 28.86 |\n",
"| ERR551978 | 579051 | 746633 | 250 | 0.77 | 32.90 | | 610553 | 746633 | 250 | 0.81 | 34.69 |\n",
"| ERR550941 | 528566 | 694478 | 250 | 0.76 | 30.03 | | 553340 | 694478 | 250 | 0.79 | 31.44 |\n",
"| ERR550644 | 368387 | 500989 | 250 | 0.73 | 20.93 | | 390100 | 500989 | 250 | 0.77 | 22.16 |\n",
"| ERR552668 | 480196 | 694259 | 250 | 0.69 | 27.28 | | 555166 | 694259 | 250 | 0.79 | 31.54 |\n",
"| ERR550947 | 560003 | 817328 | 250 | 0.68 | 31.82 | | 593168 | 817328 | 250 | 0.72 | 33.70 |\n",
"| ERR551311 | 920836 | 1595233 | 250 | 0.57 | 52.32 | | 934011 | 1595233 | 250 | 0.58 | 53.07 |\n",
"| ERR552141 | 775490 | 1344531 | 250 | 0.57 | 44.06 | | 778789 | 1344531 | 250 | 0.57 | 44.25 |\n",
"\n",
"6. sample's reads length at 300 (1):\n",
"\n",
"| sample | total_top2_1 | total_1 | second_top_length_1 | propotion_1 | average_depth_1 | deleted | total_top2_2 | total_2 | second_top_length_2 | propotion_2 | average_depth_2 |\n",
"|-----------|--------------|---------|---------------------|-------------|-----------------|---------|--------------|---------|---------------------|-------------|-----------------|\n",
"| ERR619080 | 669268 | 983056 | 300 | 0.68 | 45.63 | | 718129 | 983056 | 300 | 0.73 | 48.96 |\n",
"\n",
"7. samples have uniform reads length (48):\n",
"\n",
"| sample | total_top2_1 | total_1 | second_top_length_1 | propotion_1 | average_depth_1 | deleted | total_top2_2 | total_2 | second_top_length_2 | propotion_2 | average_depth_2 |\n",
"|-----------|--------------|----------|---------------------|-------------|-----------------|---------|--------------|----------|---------------------|-------------|-----------------|\n",
"| ERR550658 | 694536 | 694536 | 151 | 1 | 23.84 | | 694536 | 694536 | 151 | 1 | 23.84 |\n",
"| ERR550724 | 4406794 | 4406794 | 51 | 1 | 51.08 | | 4406794 | 4406794 | 51 | 1 | 51.08 |\n",
"| ERR550782 | 1142598 | 1142598 | 151 | 1 | 39.21 | | 1142598 | 1142598 | 151 | 1 | 39.21 |\n",
"| ERR550887 | 681613 | 681613 | 150 | 1 | 23.24 | | 681613 | 681613 | 150 | 1 | 23.24 |\n",
"| ERR550910 | 6148758 | 6148758 | 101 | 1 | 141.14 | | 6148758 | 6148758 | 101 | 1 | 141.14 |\n",
"| ERR550957 | 2847379 | 2847379 | 101 | 1 | 65.36 | | 2847379 | 2847379 | 101 | 1 | 65.36 |\n",
"| ERR550984 | 1232580 | 1232580 | 151 | 1 | 42.30 | | 1232580 | 1232580 | 151 | 1 | 42.30 |\n",
"| ERR551007 | 3221955 | 3221955 | 101 | 1 | 73.96 | | 3221955 | 3221955 | 101 | 1 | 73.96 |\n",
"| ERR551159 | 1178805 | 1178805 | 151 | 1 | 40.45 | | 1178805 | 1178805 | 151 | 1 | 40.45 |\n",
"| ERR551167 | 746944 | 746944 | 151 | 1 | 25.63 | | 746944 | 746944 | 151 | 1 | 25.63 |\n",
"| ERR551201 | 1836130 | 1836130 | 151 | 1 | 63.01 | | 1836130 | 1836130 | 151 | 1 | 63.01 |\n",
"| ERR551214 | 2950693 | 2950693 | 72 | 1 | 48.28 | | 2950693 | 2950693 | 72 | 1 | 48.28 |\n",
"| ERR551225 | 2359668 | 2359668 | 101 | 1 | 54.17 | | 2359668 | 2359668 | 101 | 1 | 54.17 |\n",
"| ERR551293 | 3593886 | 3593886 | 100 | 1 | 81.68 | | 3593886 | 3593886 | 100 | 1 | 81.68 |\n",
"| ERR551305 | 649772 | 649772 | 150 | 1 | 22.15 | | 649772 | 649772 | 150 | 1 | 22.15 |\n",
"| ERR551360 | 531862 | 531862 | 151 | 1 | 18.25 | | 531862 | 531862 | 151 | 1 | 18.25 |\n",
"| ERR551520 | 7542333 | 7542333 | 101 | 1 | 173.13 | | 7542333 | 7542333 | 101 | 1 | 173.13 |\n",
"| ERR551549 | 3069058 | 3069058 | 101 | 1 | 70.45 | | 3069058 | 3069058 | 101 | 1 | 70.45 |\n",
"| ERR551554 | 1146272 | 1146272 | 151 | 1 | 39.34 | | 1146272 | 1146272 | 151 | 1 | 39.34 |\n",
"| ERR551556 | 557238 | 557238 | 151 | 1 | 19.12 | | 557238 | 557238 | 151 | 1 | 19.12 |\n",
"| ERR551575 | 2437684 | 2437684 | 72 | 1 | 39.89 | | 2437684 | 2437684 | 72 | 1 | 39.89 |\n",
"| ERR551638 | 696445 | 696445 | 150 | 1 | 23.74 | | 696445 | 696445 | 150 | 1 | 23.74 |\n",
"| ERR551680 | 856628 | 856628 | 151 | 1 | 29.40 | | 856628 | 856628 | 151 | 1 | 29.40 |\n",
"| ERR551772 | 4858274 | 4858274 | 51 | 1 | 56.31 | | 4858274 | 4858274 | 51 | 1 | 56.31 |\n",
"| ERR551804 | 528297 | 528297 | 151 | 1 | 18.13 | | 528297 | 528297 | 151 | 1 | 18.13 |\n",
"| ERR551821 | 751179 | 751179 | 151 | 1 | 25.78 | | 751179 | 751179 | 151 | 1 | 25.78 |\n",
"| ERR551934 | 4930930 | 4930930 | 101 | 1 | 113.19 | | 4930930 | 4930930 | 101 | 1 | 113.19 |\n",
"| ERR551956 | 1036490 | 1036490 | 151 | 1 | 35.57 | | 1036490 | 1036490 | 151 | 1 | 35.57 |\n",
"| ERR551990 | 2501148 | 2501148 | 101 | 1 | 57.41 | | 2501148 | 2501148 | 101 | 1 | 57.41 |\n",
"| ERR552090 | 3325371 | 3325371 | 100 | 1 | 75.58 | | 3325371 | 3325371 | 100 | 1 | 75.58 |\n",
"| ERR552130 | 1416680 | 1416680 | 151 | 1 | 48.62 | | 1416680 | 1416680 | 151 | 1 | 48.62 |\n",
"| ERR552177 | 2126018 | 2126018 | 101 | 1 | 48.80 | | 2126018 | 2126018 | 101 | 1 | 48.80 |\n",
"| ERR552190 | 2907157 | 2907157 | 101 | 1 | 66.73 | | 2907157 | 2907157 | 101 | 1 | 66.73 |\n",
"| ERR552429 | 1141376 | 1141376 | 151 | 1 | 39.17 | | 1141376 | 1141376 | 151 | 1 | 39.17 |\n",
"| ERR552479 | 19714536 | 19714536 | 51 | 1 | 228.51 | | 19714536 | 19714536 | 51 | 1 | 228.51 |\n",
"| ERR552493 | 1006307 | 1006307 | 151 | 1 | 34.53 | | 1006307 | 1006307 | 151 | 1 | 34.53 |\n",
"| ERR552580 | 1213962 | 1213962 | 151 | 1 | 41.66 | | 1213962 | 1213962 | 151 | 1 | 41.66 |\n",
"| ERR552907 | 849602 | 849602 | 150 | 1 | 28.96 | | 849602 | 849602 | 150 | 1 | 28.96 |\n",
"| ERR552910 | 909326 | 909326 | 151 | 1 | 31.21 | | 909326 | 909326 | 151 | 1 | 31.21 |\n",
"| ERR552912 | 3735481 | 3735481 | 101 | 1 | 85.75 | | 3735481 | 3735481 | 101 | 1 | 85.75 |\n",
"| ERR552939 | 942214 | 942214 | 151 | 1 | 32.34 | | 942214 | 942214 | 151 | 1 | 32.34 |\n",
"| ERR553086 | 4316346 | 4316346 | 101 | 1 | 99.08 | | 4316346 | 4316346 | 101 | 1 | 99.08 |\n",
"| ERR553098 | 1371902 | 1371902 | 150 | 1 | 46.77 | | 1371902 | 1371902 | 150 | 1 | 46.77 |\n",
"| ERR553107 | 1157028 | 1157028 | 151 | 1 | 39.71 | | 1157028 | 1157028 | 151 | 1 | 39.71 |\n",
"| ERR553171 | 1746795 | 1746795 | 101 | 1 | 40.10 | | 1746795 | 1746795 | 101 | 1 | 40.10 |\n",
"| ERR553251 | 4809482 | 4809482 | 51 | 1 | 55.75 | | 4809482 | 4809482 | 51 | 1 | 55.75 |\n",
"| ERR553313 | 1048464 | 1048464 | 151 | 1 | 35.98 | | 1048464 | 1048464 | 151 | 1 | 35.98 |\n",
"| ERR553324 | 1235342 | 1235342 | 151 | 1 | 42.39 | | 1235342 | 1235342 | 151 | 1 | 42.39 |\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
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"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
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"file_extension": ".py",
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| apache-2.0 |
phockett/ePSproc | notebooks/methodDev/geometric_method_dev_pt3_AFBLM_090620.ipynb | 1 | 1834948 | null | gpl-3.0 |
JackWalpole/splitwavepy | devel/Parseval.ipynb | 1 | 40942 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Check whether the Silver and Chan (1991) coefficients for their implementation of Parseval's theorem is correct for digitised data."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import math\n",
"from scipy import signal\n",
"from scipy import stats"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Parsevals theorem when applied to discrete Fourier Transform looks like this.\n",
"\n",
"$\\sum _{n=0}^{N-1}|x[n]|^{2}={\\frac {1}{N}}\\sum _{k=0}^{N-1}|X[k]|^{2}$\n",
"\n",
"Source: https://en.wikipedia.org/wiki/Parseval%27s_theorem"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"time domain 2778.93808225\n",
"fourier domain 2778.93808225\n",
"difference 9.09494701773e-13\n",
"percent 3.27281384059e-14\n"
]
}
],
"source": [
"# check Parseval's theorem holds numerically \n",
"nsamps=1000\n",
"\n",
"# window\n",
"w = signal.tukey(nsamps,0.1)\n",
"\n",
"a = np.random.normal(0,1,nsamps) * w\n",
"A = np.fft.fft(a)\n",
"b = (1/np.sqrt(2*np.pi))*(signal.gaussian(nsamps,10))\n",
"B = np.fft.fft(b)\n",
"c = np.convolve(a,b,'same')\n",
"C = np.fft.fft(c)\n",
"\n",
"# signal c is convolution of Gaussian noise (a) with a Gaussian wavelet (b)\n",
"# C is the fourier transform of c.\n",
"\n",
"sumt = np.sum(c**2)\n",
"sumf = np.sum(np.abs(C)**2)/nsamps\n",
"\n",
"print('time domain',sumt)\n",
"print('fourier domain',sumf)\n",
"print('difference',np.abs(sumt-sumf))\n",
"print('percent', (np.abs(sumt-sumf)/sumt)*100)\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Furthermore by the convolution theorem: C = A \\* B.\n",
"And therefore sum(C^2) = sum(A^2 * B^2)"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/glyjw/anaconda/envs/py35/lib/python3.5/site-packages/numpy/core/numeric.py:531: ComplexWarning: Casting complex values to real discards the imaginary part\n",
" return array(a, dtype, copy=False, order=order)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"sum A*B 2779.02384468\n",
"difference 0.0857624340774\n",
"percent 0.00308615850872\n"
]
},
{
"data": {
"image/png": 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ewe+2djEYvJz3nJcuBhCaBRpqr/bPvamthTDjm1Ze+dFzJ9hRcxcM9/D08HOb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\nU27SNmfstkgAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x113e41128>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"AB = A * B\n",
"ab = np.fft.ifft(AB)\n",
"plt.plot(np.roll(ab,500))\n",
"plt.plot(c)\n",
"\n",
"sumAB = np.sum(np.abs(A**2*B**2))/nsamps\n",
"print('sum A*B',sumAB)\n",
"print('difference',np.abs(sumt-sumAB))\n",
"print('percent',(np.abs(sumt-sumAB)/sumt)*100)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Parsevals theorem as applied in Silver and Chan (and Walsh).\n",
"\n",
"$\\sum _{n=0}^{N-1}|x[n]|^{2}={\\frac {1}{N}}\\sum _{k=1}^{N-2}|X[k]|^{2}+\\frac{1}_{2}\\sum|X[0,N-1]|$\n",
"\n",
"Source: https://en.wikipedia.org/wiki/Parseval%27s_theorem"
]
},
{
"cell_type": "code",
"execution_count": 68,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def ndf(y,taper=True,detrend=True):\n",
" \"\"\"\n",
" Uses the improvement found by Walsh et al (2013).\n",
" By default will detrend data to ensure zero mean\n",
" and will taper edges using a Tukey filter affecting amplitudes of 5% of data at edges\n",
" \"\"\"\n",
"\n",
" if taper is True:\n",
" y = y * signal.tukey(y.size,0.05)\n",
" \n",
" if detrend is True:\n",
" # ensure no trend on the noise trace\n",
" y = signal.detrend(y)\n",
"\n",
" \n",
" Y = np.fft.fft(y)\n",
" amp = np.absolute(Y)\n",
" \n",
" # estimate E2 and E4 following Walsh et al (2013)\n",
" a = np.ones(Y.size)\n",
" a[0] = a[-1] = 0.5\n",
" E2 = np.sum( a * amp**2)\n",
" E4 = (np.sum( (4 * a**2 / 3) * amp**4))\n",
" \n",
" ndf = 2 * ( 2 * E2**2 / E4 - 1 )\n",
" \n",
" return ndf\n",
" \n",
"def ndf2(y,taper=True,detrend=True):\n",
" \"\"\"\n",
" \n",
" \"\"\"\n",
"\n",
" if taper is True:\n",
" y = y * signal.tukey(y.size,0.05)\n",
" \n",
" if detrend is True:\n",
" # ensure no trend on the noise trace\n",
" y = signal.detrend(y)\n",
"\n",
" \n",
" Y = np.fft.fft(y)\n",
" amp = np.absolute(Y)**2\n",
" \n",
" E2 = np.sum(amp**2)\n",
" E4 = (np.sum( (4/3) * amp**4))\n",
" \n",
" ndf = 2 * ( 2 * E2**2 / E4 - 1 )\n",
" \n",
" return ndf"
]
},
{
"cell_type": "code",
"execution_count": 69,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"48.3596763722\n"
]
}
],
"source": [
"print(ndf(c))"
]
},
{
"cell_type": "code",
"execution_count": 70,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"48.444600887\n"
]
}
],
"source": [
"print(ndf2(c))"
]
},
{
"cell_type": "code",
"execution_count": 72,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"18.130707772254976"
]
},
"execution_count": 72,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"stats.moment(c,moment=4)"
]
},
{
"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": "3.5.3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| mit |
davidgutierrez/HeartRatePatterns | Jupyter/LoadDataMimic-II.ipynb | 1 | 8176 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Cargue de datos s SciDB\n",
"## 1) Verificar Prerequisitos\n",
"### Python\n",
"SciDB-Py requires Python 2.6-2.7 or 3.3"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"sys.version_info(major=3, minor=4, micro=3, releaselevel='final', serial=0)"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import sys\n",
"sys.version_info"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### NumPy\n",
"tested with version 1.9 (1.13.1)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'1.13.1'"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import numpy as np\n",
"np.__version__"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Requests\n",
"tested with version 2.7 (2.18.1) Required for using the Shim interface to SciDB."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'2.18.4'"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import requests\n",
"requests.__version__"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Pandas (optional)\n",
"tested with version 0.15. (0.20.3) Required only for importing/exporting SciDB arrays as Pandas Dataframe objects."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'0.20.3'"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import pandas as pd\n",
"pd.__version__"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### SciPy (optional)\n",
"tested with versions 0.10-0.12. (0.19.0) Required only for importing/exporting SciDB arrays as SciPy sparse matrices."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'0.19.0'"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import scipy\n",
"scipy.__version__"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 2) Importar scidbpy\n",
"pip install git+http://github.com/paradigm4/scidb-py.git@devel"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'16.9'"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import scidbpy\n",
"scidbpy.__version__"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from scidbpy import connect"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"conectarse al servidor de Base de datos"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": true,
"scrolled": true
},
"outputs": [],
"source": [
"sdb = connect('http://localhost:8080')"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"## 3) Leer archivo con cada una de las ondas"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import urllib.request # urllib2 in python2 the lib that handles the url stuff\n",
"target_url = \"https://www.physionet.org/physiobank/database/mimic2wdb/matched/RECORDS-waveforms\"\n",
"data = urllib.request.urlopen(target_url) # it's a file like object and works just like a file"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"lines = data.readlines();\n",
"line = str(lines[100])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Quitarle caracteres especiales"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'s00652-2965-06-28-16-10'"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"carpeta,onda = line.replace('b\\'','').replace('\\'','').replace('\\\\n','').split(\"/\")\n",
"onda"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 4) Importar WFDB para conectarse a physionet"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import wfdb"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"sig, fields = wfdb.srdsamp(onda,pbdir='mimic2wdb/matched/'+carpeta) #, sampfrom=11000"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"print(sig)\n",
"print(\"signame: \" + str(fields['signame']))\n",
"print(\"units: \" + str(fields['units']))\n",
"print(\"fs: \" + str(fields['fs']))\n",
"print(\"comments: \" + str(fields['comments']))\n",
"print(\"fields: \" + str(fields))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Busca la ubicacion de la señal tipo II "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"signalII = None\n",
"try:\n",
" signalII = fields['signame'].index(\"II\")\n",
"except ValueError:\n",
" print(\"List does not contain value\")\n",
"if(signalII!=None):\n",
" print(\"List contain value\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Normaliza la señal y le quita los valores en null"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"array = wfdb.processing.normalize(x=sig[:, signalII], lb=-2, ub=2)\n",
"arrayNun = array[~np.isnan(array)]\n",
"arrayNun = np.trim_zeros(arrayNun)\n",
"arrayNun"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Cambiar los guiones \"-\" por raya al piso \"_\" porque por algun motivo SciDB tiene problemas con estos caracteres\n",
"Si el arreglo sin valores nulos no queda vacio lo sube al SciDB"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"scrolled": true
},
"outputs": [],
"source": [
"ondaName = onda.replace(\"-\", \"_\")\n",
"if arrayNun.size>0 :\n",
" sdb.input(upload_data=array).store(ondaName,gc=False)\n",
"# sdb.iquery(\"store(input(<x:int64>[i], '{fn}', 0, '{fmt}'), \"+ondaName+\")\", upload_data=array)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.4.3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| gpl-3.0 |
ds-hwang/deeplearning_udacity | udacity_notebook/3_regularization.ipynb | 1 | 8431 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "kR-4eNdK6lYS"
},
"source": [
"Deep Learning\n",
"=============\n",
"\n",
"Assignment 3\n",
"------------\n",
"\n",
"Previously in `2_fullyconnected.ipynb`, you trained a logistic regression and a neural network model.\n",
"\n",
"The goal of this assignment is to explore regularization techniques."
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"cellView": "both",
"colab": {
"autoexec": {
"startup": false,
"wait_interval": 0
}
},
"colab_type": "code",
"collapsed": true,
"id": "JLpLa8Jt7Vu4"
},
"outputs": [],
"source": [
"# These are all the modules we'll be using later. Make sure you can import them\n",
"# before proceeding further.\n",
"from __future__ import print_function\n",
"import numpy as np\n",
"import tensorflow as tf\n",
"from six.moves import cPickle as pickle"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "1HrCK6e17WzV"
},
"source": [
"First reload the data we generated in _notmist.ipynb_."
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"cellView": "both",
"colab": {
"autoexec": {
"startup": false,
"wait_interval": 0
},
"output_extras": [
{
"item_id": 1
}
]
},
"colab_type": "code",
"collapsed": false,
"executionInfo": {
"elapsed": 11777,
"status": "ok",
"timestamp": 1449849322348,
"user": {
"color": "",
"displayName": "",
"isAnonymous": false,
"isMe": true,
"permissionId": "",
"photoUrl": "",
"sessionId": "0",
"userId": ""
},
"user_tz": 480
},
"id": "y3-cj1bpmuxc",
"outputId": "e03576f1-ebbe-4838-c388-f1777bcc9873"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Training set (200000, 28, 28) (200000,)\n",
"Validation set (10000, 28, 28) (10000,)\n",
"Test set (18724, 28, 28) (18724,)\n"
]
}
],
"source": [
"pickle_file = 'notMNIST.pickle'\n",
"\n",
"with open(pickle_file, 'rb') as f:\n",
" save = pickle.load(f)\n",
" train_dataset = save['train_dataset']\n",
" train_labels = save['train_labels']\n",
" valid_dataset = save['valid_dataset']\n",
" valid_labels = save['valid_labels']\n",
" test_dataset = save['test_dataset']\n",
" test_labels = save['test_labels']\n",
" del save # hint to help gc free up memory\n",
" print('Training set', train_dataset.shape, train_labels.shape)\n",
" print('Validation set', valid_dataset.shape, valid_labels.shape)\n",
" print('Test set', test_dataset.shape, test_labels.shape)"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "L7aHrm6nGDMB"
},
"source": [
"Reformat into a shape that's more adapted to the models we're going to train:\n",
"- data as a flat matrix,\n",
"- labels as float 1-hot encodings."
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"cellView": "both",
"colab": {
"autoexec": {
"startup": false,
"wait_interval": 0
},
"output_extras": [
{
"item_id": 1
}
]
},
"colab_type": "code",
"collapsed": false,
"executionInfo": {
"elapsed": 11728,
"status": "ok",
"timestamp": 1449849322356,
"user": {
"color": "",
"displayName": "",
"isAnonymous": false,
"isMe": true,
"permissionId": "",
"photoUrl": "",
"sessionId": "0",
"userId": ""
},
"user_tz": 480
},
"id": "IRSyYiIIGIzS",
"outputId": "3f8996ee-3574-4f44-c953-5c8a04636582"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Training set (200000, 784) (200000, 10)\n",
"Validation set (10000, 784) (10000, 10)\n",
"Test set (18724, 784) (18724, 10)\n"
]
}
],
"source": [
"image_size = 28\n",
"num_labels = 10\n",
"\n",
"def reformat(dataset, labels):\n",
" dataset = dataset.reshape((-1, image_size * image_size)).astype(np.float32)\n",
" # Map 2 to [0.0, 1.0, 0.0 ...], 3 to [0.0, 0.0, 1.0 ...]\n",
" labels = (np.arange(num_labels) == labels[:,None]).astype(np.float32)\n",
" return dataset, labels\n",
"train_dataset, train_labels = reformat(train_dataset, train_labels)\n",
"valid_dataset, valid_labels = reformat(valid_dataset, valid_labels)\n",
"test_dataset, test_labels = reformat(test_dataset, test_labels)\n",
"print('Training set', train_dataset.shape, train_labels.shape)\n",
"print('Validation set', valid_dataset.shape, valid_labels.shape)\n",
"print('Test set', test_dataset.shape, test_labels.shape)"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"cellView": "both",
"colab": {
"autoexec": {
"startup": false,
"wait_interval": 0
}
},
"colab_type": "code",
"collapsed": true,
"id": "RajPLaL_ZW6w"
},
"outputs": [],
"source": [
"def accuracy(predictions, labels):\n",
" return (100.0 * np.sum(np.argmax(predictions, 1) == np.argmax(labels, 1))\n",
" / predictions.shape[0])"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "sgLbUAQ1CW-1"
},
"source": [
"---\n",
"Problem 1\n",
"---------\n",
"\n",
"Introduce and tune L2 regularization for both logistic and neural network models. Remember that L2 amounts to adding a penalty on the norm of the weights to the loss. In TensorFlow, you can compute the L2 loss for a tensor `t` using `nn.l2_loss(t)`. The right amount of regularization should improve your validation / test accuracy.\n",
"\n",
"---"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "na8xX2yHZzNF"
},
"source": [
"---\n",
"Problem 2\n",
"---------\n",
"Let's demonstrate an extreme case of overfitting. Restrict your training data to just a few batches. What happens?\n",
"\n",
"---"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "ww3SCBUdlkRc"
},
"source": [
"---\n",
"Problem 3\n",
"---------\n",
"Introduce Dropout on the hidden layer of the neural network. Remember: Dropout should only be introduced during training, not evaluation, otherwise your evaluation results would be stochastic as well. TensorFlow provides `nn.dropout()` for that, but you have to make sure it's only inserted during training.\n",
"\n",
"What happens to our extreme overfitting case?\n",
"\n",
"---"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "-b1hTz3VWZjw"
},
"source": [
"---\n",
"Problem 4\n",
"---------\n",
"\n",
"Try to get the best performance you can using a multi-layer model! The best reported test accuracy using a deep network is [97.1%](http://yaroslavvb.blogspot.com/2011/09/notmnist-dataset.html?showComment=1391023266211#c8758720086795711595).\n",
"\n",
"One avenue you can explore is to add multiple layers.\n",
"\n",
"Another one is to use learning rate decay:\n",
"\n",
" global_step = tf.Variable(0) # count the number of steps taken.\n",
" learning_rate = tf.train.exponential_decay(0.5, global_step, ...)\n",
" optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(loss, global_step=global_step)\n",
" \n",
" ---\n"
]
}
],
"metadata": {
"colab": {
"default_view": {},
"name": "3_regularization.ipynb",
"provenance": [],
"version": "0.3.2",
"views": {}
},
"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
}
| mit |
RealPolitiX/mpes | examples/Tutorial_01_HDF5 File Management.ipynb | 1 | 9783 | {
"cells": [
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"from mpes import fprocessing as fp\n",
"# from imp import reload\n",
"# reload(fp)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"fpath = r'../data/data_20180605_131.h5'"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 1.1 Loading HDF5 files\n",
"HDF5 files can be read using a few different classes operating on different levels. The hierarchy meaningful to the end user is in the following (from low to high),\n",
"* **mpes.fprocessing.File()** -- local import of h5py.File(), a low-level Python HDF5 parser (wrapped over even lower C code).\n",
"* **mpes.fprocessing.hdf5Reader()** -- built on the File() class, with the inclusion of several file structure parsing, file component readout and format conversion functions.\n",
"* **mpes.fprocessing.hdf5Splitter()** -- built on the hdf5Reader() class, used for splitting large hdf5 files.\n",
"* **mpes.fprocessing.hdf5Processor()** -- built on the hdf5Reader() class, with the inclusion of binning operations and io.\n",
"\n",
"The hierarchy goes **File $\\in$ hdf5Reader $\\in$ (hdf5Splitter, hdf5Processor)**"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<HDF5 file \"data_20180605_131.h5\" (mode r+)>"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"hdff = fp.File(fpath)\n",
"hdff"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<HDF5 file \"data_20180605_131.h5\" (mode r+)>"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"hdfr = fp.hdf5Reader(fpath)\n",
"hdfr"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**New attributes and methods in the hdf5Reader() class**"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['groupAliases', 'readGroup', 'nameLookupDict', 'CHUNK_SIZE', 'attributeNames', 'convert', '_assembleGroups', 'summarize', 'name2alias', 'faddress', 'readAttribute', 'getAttributeNames', 'getGroupNames', 'ncores', 'nEvents', 'groupNames']\n"
]
}
],
"source": [
"print( list(set(dir(hdfr)) - set(dir(hdff))) )"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"<HDF5 file \"data_20180605_131.h5\" (mode r+)>"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"hdfp = fp.hdf5Processor(fpath)\n",
"hdfp"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**New attributes and methods in the hdf5Processer() class**"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['saveHistogram', 'toSplitter', 'toBandStructure', 'histdict', 'localBinning', '_addBinners', 'viewEventHistogram', 'loadMapping', 'hdfdict', 'distributedBinning', 'saveParameters', '_delayedBinning', 'ua', 'axesdict', 'updateHistogram', 'distributedProcessBinning']\n"
]
}
],
"source": [
"print( list(set(dir(hdfp)) - set(dir(hdfr))) )"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 1.2 Retrieving components from HDF5 files\n",
"Reading components can also be done at different levels, the level of hdf5Reader() or above is recommended."
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"*** HDF5 file info ***\n",
" File address = /scratch/metis_storage/data_20180605_131.h5\n",
"\n",
"\n",
">>> Attributes <<<\n",
"\n",
"CAClientMajorVersion = 0\n",
"CAClientMinorVersion = 1\n",
"CompileTimeStamp = Wed Jun 13 15:31:04 2018\n",
"KTOF:Lens:A:VSet = 514.63\n",
"KTOF:Lens:B:VSet = 2199.8\n",
"KTOF:Lens:C:VSet = 76.402\n",
"KTOF:Lens:D:VSet = 261.24\n",
"KTOF:Lens:E:VSet = 558.98\n",
"KTOF:Lens:Extr:VSet = 6000.0\n",
"KTOF:Lens:F:VSet = 48.904\n",
"KTOF:Lens:Foc:VSet = 167.0\n",
"KTOF:Lens:G:VSet = 20.1\n",
"KTOF:Lens:H:VSet = 35.0\n",
"KTOF:Lens:I:VSet = 42.25\n",
"KTOF:Lens:MCPback:VSet = 1825.0\n",
"KTOF:Lens:MCPfront:VSet = 20.0\n",
"KTOF:Lens:TOF:VSet = 20.0\n",
"KTOF:Lens:UCA:VSet = 1200.0\n",
"KTOF:Lens:UFA:VSet = 600.0\n",
"KTOF:Lens:Z1:VSet = 2452.9\n",
"KTOF:Lens:Z2:VSet = 1489.9\n",
"\n",
">>> Groups <<<\n",
"\n",
"EventFormat, Shape = (64,), Alias = None\n",
"Stream_0, Shape = (27296214,), Alias = X\n",
"Stream_1, Shape = (27296214,), Alias = Y\n",
"Stream_2, Shape = (27296214,), Alias = t\n",
"Stream_3, Shape = (27296214,), Alias = MasterRstCtr\n",
"Stream_4, Shape = (27296214,), Alias = ADC\n",
"Stream_5, Shape = (27296214,), Alias = State Input\n"
]
}
],
"source": [
"hdfp.summarize()"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 64, 64, 0, 64, 0, 64, 0, 64, 0, 53, 11, 42, 11, 16, 26, 15, 1, 15, 0, 0, 15, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n"
]
}
],
"source": [
"print(list(hdfr.readGroup(hdfr, 'EventFormat')))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 1.3 Converting HDF5 files\n",
"Conversion of hdf5 to Matlab (mat) format (no data processing)."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"hdfr.convert('mat', save_addr='../data/data_131')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Conversion to parquet format"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"hdfr.convert('parquet', save_addr='../data/data_131_parquet', pq_append=False, chunksz=1e7, \\\n",
" compression='gzip')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 1.4 Splitting HDF5 files"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"hdfs = fp.hdf5Splitter(fpath)\n",
"hdfs.split(nsplit=50, save_addr=r'../data/data_114_parts/data_114_', pbar=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 1.5 Retrieve binned data from stored HDF5 file\n",
"Read binned data over 3 axes"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"fpath_binned = r'../data/binres_114.h5'"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"bindict = fp.readBinnedhdf5(fpath_binned, combined=True)\n",
"bindict.keys()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Read binned data over 4 axes"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"fpath_binned = r'../data/data_114_4axis_binned.h5'"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"dict_keys(['ADC', 'X', 'Y', 't', 'V'])"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"bindict = fp.readBinnedhdf5(fpath_binned, combined=True)\n",
"bindict.keys()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"dict_keys(['ADC', 'X', 'Y', 't', 'V0', 'V1', 'V10', 'V11', 'V12', 'V13', 'V14', 'V15', 'V16', 'V17', 'V18', 'V19', 'V2', 'V20', 'V21', 'V22', 'V23', 'V24', 'V25', 'V26', 'V27', 'V28', 'V29', 'V3', 'V30', 'V31', 'V32', 'V33', 'V34', 'V35', 'V36', 'V37', 'V38', 'V39', 'V4', 'V40', 'V41', 'V42', 'V43', 'V44', 'V45', 'V46', 'V47', 'V48', 'V49', 'V5', 'V6', 'V7', 'V8', 'V9'])"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"bindict = fp.readBinnedhdf5(fpath_binned, combined=False)\n",
"bindict.keys()"
]
},
{
"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": "3.6.4"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| mit |
AllenDowney/ThinkStats2 | code/chap12ex.ipynb | 1 | 36103 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Chapter 12\n",
"\n",
"Examples and Exercises from Think Stats, 2nd Edition\n",
"\n",
"http://thinkstats2.com\n",
"\n",
"Copyright 2016 Allen B. Downey\n",
"\n",
"MIT License: https://opensource.org/licenses/MIT\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"from os.path import basename, exists\n",
"\n",
"\n",
"def download(url):\n",
" filename = basename(url)\n",
" if not exists(filename):\n",
" from urllib.request import urlretrieve\n",
"\n",
" local, _ = urlretrieve(url, filename)\n",
" print(\"Downloaded \" + local)\n",
"\n",
"\n",
"download(\"https://github.com/AllenDowney/ThinkStats2/raw/master/code/thinkstats2.py\")\n",
"download(\"https://github.com/AllenDowney/ThinkStats2/raw/master/code/thinkplot.py\")"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"\n",
"import random\n",
"\n",
"import thinkstats2\n",
"import thinkplot"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Time series analysis\n",
"\n",
"NOTE: Some of the example in this chapter have been updated to work with more recent versions of the libraries.\n",
"\n",
"Load the data from \"Price of Weed\"."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"download(\"https://github.com/AllenDowney/ThinkStats2/raw/master/code/mj-clean.csv\")"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"transactions = pd.read_csv(\"mj-clean.csv\", parse_dates=[5])\n",
"transactions.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The following function takes a DataFrame of transactions and compute daily averages."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"def GroupByDay(transactions, func=np.mean):\n",
" \"\"\"Groups transactions by day and compute the daily mean ppg.\n",
"\n",
" transactions: DataFrame of transactions\n",
"\n",
" returns: DataFrame of daily prices\n",
" \"\"\"\n",
" grouped = transactions[[\"date\", \"ppg\"]].groupby(\"date\")\n",
" daily = grouped.aggregate(func)\n",
"\n",
" daily[\"date\"] = daily.index\n",
" start = daily.date[0]\n",
" one_year = np.timedelta64(1, \"Y\")\n",
" daily[\"years\"] = (daily.date - start) / one_year\n",
"\n",
" return daily"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The following function returns a map from quality name to a DataFrame of daily averages."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"def GroupByQualityAndDay(transactions):\n",
" \"\"\"Divides transactions by quality and computes mean daily price.\n",
"\n",
" transaction: DataFrame of transactions\n",
"\n",
" returns: map from quality to time series of ppg\n",
" \"\"\"\n",
" groups = transactions.groupby(\"quality\")\n",
" dailies = {}\n",
" for name, group in groups:\n",
" dailies[name] = GroupByDay(group)\n",
"\n",
" return dailies"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"`dailies` is the map from quality name to DataFrame."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"dailies = GroupByQualityAndDay(transactions)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The following plots the daily average price for each quality."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"\n",
"thinkplot.PrePlot(rows=3)\n",
"for i, (name, daily) in enumerate(dailies.items()):\n",
" thinkplot.SubPlot(i + 1)\n",
" title = \"Price per gram ($)\" if i == 0 else \"\"\n",
" thinkplot.Config(ylim=[0, 20], title=title)\n",
" thinkplot.Scatter(daily.ppg, s=10, label=name)\n",
" if i == 2:\n",
" plt.xticks(rotation=30)\n",
" thinkplot.Config()\n",
" else:\n",
" thinkplot.Config(xticks=[])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can use `statsmodels` to run a linear model of price as a function of time."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"import statsmodels.formula.api as smf\n",
"\n",
"\n",
"def RunLinearModel(daily):\n",
" model = smf.ols(\"ppg ~ years\", data=daily)\n",
" results = model.fit()\n",
" return model, results"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Here's what the results look like."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"from IPython.display import display\n",
"\n",
"for name, daily in dailies.items():\n",
" model, results = RunLinearModel(daily)\n",
" print(name)\n",
" display(results.summary())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now let's plot the fitted model with the data."
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"def PlotFittedValues(model, results, label=\"\"):\n",
" \"\"\"Plots original data and fitted values.\n",
"\n",
" model: StatsModel model object\n",
" results: StatsModel results object\n",
" \"\"\"\n",
" years = model.exog[:, 1]\n",
" values = model.endog\n",
" thinkplot.Scatter(years, values, s=15, label=label)\n",
" thinkplot.Plot(years, results.fittedvalues, label=\"model\", color=\"#ff7f00\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The following function plots the original data and the fitted curve."
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"def PlotLinearModel(daily, name):\n",
" \"\"\"Plots a linear fit to a sequence of prices, and the residuals.\n",
"\n",
" daily: DataFrame of daily prices\n",
" name: string\n",
" \"\"\"\n",
" model, results = RunLinearModel(daily)\n",
" PlotFittedValues(model, results, label=name)\n",
" thinkplot.Config(\n",
" title=\"Fitted values\",\n",
" xlabel=\"Years\",\n",
" xlim=[-0.1, 3.8],\n",
" ylabel=\"Price per gram ($)\",\n",
" )"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Here are results for the high quality category:"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"name = \"high\"\n",
"daily = dailies[name]\n",
"\n",
"PlotLinearModel(daily, name)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Moving averages\n",
"\n",
"As a simple example, I'll show the rolling average of the numbers from 1 to 10."
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"array = np.arange(10)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"With a \"window\" of size 3, we get the average of the previous 3 elements, or nan when there are fewer than 3."
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
"series = pd.Series(array)\n",
"series.rolling(3).mean()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The following function plots the rolling mean."
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
"def PlotRollingMean(daily, name):\n",
" \"\"\"Plots rolling mean.\n",
"\n",
" daily: DataFrame of daily prices\n",
" \"\"\"\n",
" dates = pd.date_range(daily.index.min(), daily.index.max())\n",
" reindexed = daily.reindex(dates)\n",
"\n",
" thinkplot.Scatter(reindexed.ppg, s=15, alpha=0.2, label=name)\n",
" roll_mean = pd.Series(reindexed.ppg).rolling(30).mean()\n",
" thinkplot.Plot(roll_mean, label=\"rolling mean\", color=\"#ff7f00\")\n",
" plt.xticks(rotation=30)\n",
" thinkplot.Config(ylabel=\"price per gram ($)\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Here's what it looks like for the high quality category."
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
"PlotRollingMean(daily, name)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The exponentially-weighted moving average gives more weight to more recent points."
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [],
"source": [
"def PlotEWMA(daily, name):\n",
" \"\"\"Plots rolling mean.\n",
"\n",
" daily: DataFrame of daily prices\n",
" \"\"\"\n",
" dates = pd.date_range(daily.index.min(), daily.index.max())\n",
" reindexed = daily.reindex(dates)\n",
"\n",
" thinkplot.Scatter(reindexed.ppg, s=15, alpha=0.2, label=name)\n",
" roll_mean = reindexed.ppg.ewm(30).mean()\n",
" thinkplot.Plot(roll_mean, label=\"EWMA\", color=\"#ff7f00\")\n",
" plt.xticks(rotation=30)\n",
" thinkplot.Config(ylabel=\"price per gram ($)\")"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [],
"source": [
"PlotEWMA(daily, name)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can use resampling to generate missing values with the right amount of noise."
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [],
"source": [
"def FillMissing(daily, span=30):\n",
" \"\"\"Fills missing values with an exponentially weighted moving average.\n",
"\n",
" Resulting DataFrame has new columns 'ewma' and 'resid'.\n",
"\n",
" daily: DataFrame of daily prices\n",
" span: window size (sort of) passed to ewma\n",
"\n",
" returns: new DataFrame of daily prices\n",
" \"\"\"\n",
" dates = pd.date_range(daily.index.min(), daily.index.max())\n",
" reindexed = daily.reindex(dates)\n",
"\n",
" ewma = pd.Series(reindexed.ppg).ewm(span=span).mean()\n",
"\n",
" resid = (reindexed.ppg - ewma).dropna()\n",
" fake_data = ewma + thinkstats2.Resample(resid, len(reindexed))\n",
" reindexed.ppg.fillna(fake_data, inplace=True)\n",
"\n",
" reindexed[\"ewma\"] = ewma\n",
" reindexed[\"resid\"] = reindexed.ppg - ewma\n",
" return reindexed"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [],
"source": [
"def PlotFilled(daily, name):\n",
" \"\"\"Plots the EWMA and filled data.\n",
"\n",
" daily: DataFrame of daily prices\n",
" \"\"\"\n",
" filled = FillMissing(daily, span=30)\n",
" thinkplot.Scatter(filled.ppg, s=15, alpha=0.2, label=name)\n",
" thinkplot.Plot(filled.ewma, label=\"EWMA\", color=\"#ff7f00\")\n",
" plt.xticks(rotation=30)\n",
" thinkplot.Config(ylabel=\"Price per gram ($)\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Here's what the EWMA model looks like with missing values filled."
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [],
"source": [
"PlotFilled(daily, name)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Serial correlation\n",
"\n",
"The following function computes serial correlation with the given lag."
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [],
"source": [
"def SerialCorr(series, lag=1):\n",
" xs = series[lag:]\n",
" ys = series.shift(lag)[lag:]\n",
" corr = thinkstats2.Corr(xs, ys)\n",
" return corr"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Before computing correlations, we'll fill missing values."
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [],
"source": [
"filled_dailies = {}\n",
"for name, daily in dailies.items():\n",
" filled_dailies[name] = FillMissing(daily, span=30)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Here are the serial correlations for raw price data."
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [],
"source": [
"for name, filled in filled_dailies.items():\n",
" corr = thinkstats2.SerialCorr(filled.ppg, lag=1)\n",
" print(name, corr)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"It's not surprising that there are correlations between consecutive days, because there are obvious trends in the data.\n",
"\n",
"It is more interested to see whether there are still correlations after we subtract away the trends."
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [],
"source": [
"for name, filled in filled_dailies.items():\n",
" corr = thinkstats2.SerialCorr(filled.resid, lag=1)\n",
" print(name, corr)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Even if the correlations between consecutive days are weak, there might be correlations across intervals of one week, one month, or one year."
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [],
"source": [
"rows = []\n",
"for lag in [1, 7, 30, 365]:\n",
" print(lag, end=\"\\t\")\n",
" for name, filled in filled_dailies.items():\n",
" corr = SerialCorr(filled.resid, lag)\n",
" print(\"%.2g\" % corr, end=\"\\t\")\n",
" print()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The strongest correlation is a weekly cycle in the medium quality category.\n",
"\n",
"## Autocorrelation\n",
"\n",
"The autocorrelation function is the serial correlation computed for all lags.\n",
"\n",
"We can use it to replicate the results from the previous section."
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [],
"source": [
"# NOTE: acf throws a FutureWarning because we need to replace `unbiased` with `adjusted`,\n",
"# just as soon as Colab gets updated :)\n",
"\n",
"import warnings\n",
"\n",
"warnings.simplefilter(action=\"ignore\", category=FutureWarning)"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [],
"source": [
"import statsmodels.tsa.stattools as smtsa\n",
"\n",
"filled = filled_dailies[\"high\"]\n",
"acf = smtsa.acf(filled.resid, nlags=365, unbiased=True, fft=False)\n",
"print(\"%0.2g, %.2g, %0.2g, %0.2g, %0.2g\" % (acf[0], acf[1], acf[7], acf[30], acf[365]))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To get a sense of how much autocorrelation we should expect by chance, we can resample the data (which eliminates any actual autocorrelation) and compute the ACF."
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [],
"source": [
"def SimulateAutocorrelation(daily, iters=1001, nlags=40):\n",
" \"\"\"Resample residuals, compute autocorrelation, and plot percentiles.\n",
"\n",
" daily: DataFrame\n",
" iters: number of simulations to run\n",
" nlags: maximum lags to compute autocorrelation\n",
" \"\"\"\n",
" # run simulations\n",
" t = []\n",
" for _ in range(iters):\n",
" filled = FillMissing(daily, span=30)\n",
" resid = thinkstats2.Resample(filled.resid)\n",
" acf = smtsa.acf(resid, nlags=nlags, unbiased=True, fft=False)[1:]\n",
" t.append(np.abs(acf))\n",
"\n",
" high = thinkstats2.PercentileRows(t, [97.5])[0]\n",
" low = -high\n",
" lags = range(1, nlags + 1)\n",
" thinkplot.FillBetween(lags, low, high, alpha=0.2, color=\"gray\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The following function plots the actual autocorrelation for lags up to 40 days.\n",
"\n",
"The flag `add_weekly` indicates whether we should add a simulated weekly cycle."
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [],
"source": [
"def PlotAutoCorrelation(dailies, nlags=40, add_weekly=False):\n",
" \"\"\"Plots autocorrelation functions.\n",
"\n",
" dailies: map from category name to DataFrame of daily prices\n",
" nlags: number of lags to compute\n",
" add_weekly: boolean, whether to add a simulated weekly pattern\n",
" \"\"\"\n",
" thinkplot.PrePlot(3)\n",
" daily = dailies[\"high\"]\n",
" SimulateAutocorrelation(daily)\n",
"\n",
" for name, daily in dailies.items():\n",
"\n",
" if add_weekly:\n",
" daily.ppg = AddWeeklySeasonality(daily)\n",
"\n",
" filled = FillMissing(daily, span=30)\n",
"\n",
" acf = smtsa.acf(filled.resid, nlags=nlags, unbiased=True, fft=False)\n",
" lags = np.arange(len(acf))\n",
" thinkplot.Plot(lags[1:], acf[1:], label=name)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To show what a strong weekly cycle would look like, we have the option of adding a price increase of 1-2 dollars on Friday and Saturdays."
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [],
"source": [
"def AddWeeklySeasonality(daily):\n",
" \"\"\"Adds a weekly pattern.\n",
"\n",
" daily: DataFrame of daily prices\n",
"\n",
" returns: new DataFrame of daily prices\n",
" \"\"\"\n",
" fri_or_sat = (daily.index.dayofweek == 4) | (daily.index.dayofweek == 5)\n",
" fake = daily.ppg.copy()\n",
" fake[fri_or_sat] += np.random.uniform(0, 2, fri_or_sat.sum())\n",
" return fake"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Here's what the real ACFs look like. The gray regions indicate the levels we expect by chance."
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [],
"source": [
"axis = [0, 41, -0.2, 0.2]\n",
"\n",
"PlotAutoCorrelation(dailies, add_weekly=False)\n",
"thinkplot.Config(axis=axis, loc=\"lower right\", ylabel=\"correlation\", xlabel=\"lag (day)\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Here's what it would look like if there were a weekly cycle."
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [],
"source": [
"PlotAutoCorrelation(dailies, add_weekly=True)\n",
"thinkplot.Config(axis=axis, loc=\"lower right\", xlabel=\"lag (days)\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Prediction\n",
"\n",
"The simplest way to generate predictions is to use `statsmodels` to fit a model to the data, then use the `predict` method from the results."
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [],
"source": [
"def GenerateSimplePrediction(results, years):\n",
" \"\"\"Generates a simple prediction.\n",
"\n",
" results: results object\n",
" years: sequence of times (in years) to make predictions for\n",
"\n",
" returns: sequence of predicted values\n",
" \"\"\"\n",
" n = len(years)\n",
" inter = np.ones(n)\n",
" d = dict(Intercept=inter, years=years, years2=years**2)\n",
" predict_df = pd.DataFrame(d)\n",
" predict = results.predict(predict_df)\n",
" return predict"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [],
"source": [
"def PlotSimplePrediction(results, years):\n",
" predict = GenerateSimplePrediction(results, years)\n",
"\n",
" thinkplot.Scatter(daily.years, daily.ppg, alpha=0.2, label=name)\n",
" thinkplot.plot(years, predict, color=\"#ff7f00\")\n",
" xlim = years[0] - 0.1, years[-1] + 0.1\n",
" thinkplot.Config(\n",
" title=\"Predictions\",\n",
" xlabel=\"Years\",\n",
" xlim=xlim,\n",
" ylabel=\"Price per gram ($)\",\n",
" loc=\"upper right\",\n",
" )"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Here's what the prediction looks like for the high quality category, using the linear model."
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [],
"source": [
"name = \"high\"\n",
"daily = dailies[name]\n",
"\n",
"_, results = RunLinearModel(daily)\n",
"years = np.linspace(0, 5, 101)\n",
"PlotSimplePrediction(results, years)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"When we generate predictions, we want to quatify the uncertainty in the prediction. We can do that by resampling. The following function fits a model to the data, computes residuals, then resamples from the residuals to general fake datasets. It fits the same model to each fake dataset and returns a list of results."
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [],
"source": [
"def SimulateResults(daily, iters=101, func=RunLinearModel):\n",
" \"\"\"Run simulations based on resampling residuals.\n",
"\n",
" daily: DataFrame of daily prices\n",
" iters: number of simulations\n",
" func: function that fits a model to the data\n",
"\n",
" returns: list of result objects\n",
" \"\"\"\n",
" _, results = func(daily)\n",
" fake = daily.copy()\n",
"\n",
" result_seq = []\n",
" for _ in range(iters):\n",
" fake.ppg = results.fittedvalues + thinkstats2.Resample(results.resid)\n",
" _, fake_results = func(fake)\n",
" result_seq.append(fake_results)\n",
"\n",
" return result_seq"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To generate predictions, we take the list of results fitted to resampled data. For each model, we use the `predict` method to generate predictions, and return a sequence of predictions.\n",
"\n",
"If `add_resid` is true, we add resampled residuals to the predicted values, which generates predictions that include predictive uncertainty (due to random noise) as well as modeling uncertainty (due to random sampling)."
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [],
"source": [
"def GeneratePredictions(result_seq, years, add_resid=False):\n",
" \"\"\"Generates an array of predicted values from a list of model results.\n",
"\n",
" When add_resid is False, predictions represent sampling error only.\n",
"\n",
" When add_resid is True, they also include residual error (which is\n",
" more relevant to prediction).\n",
"\n",
" result_seq: list of model results\n",
" years: sequence of times (in years) to make predictions for\n",
" add_resid: boolean, whether to add in resampled residuals\n",
"\n",
" returns: sequence of predictions\n",
" \"\"\"\n",
" n = len(years)\n",
" d = dict(Intercept=np.ones(n), years=years, years2=years**2)\n",
" predict_df = pd.DataFrame(d)\n",
"\n",
" predict_seq = []\n",
" for fake_results in result_seq:\n",
" predict = fake_results.predict(predict_df)\n",
" if add_resid:\n",
" predict += thinkstats2.Resample(fake_results.resid, n)\n",
" predict_seq.append(predict)\n",
"\n",
" return predict_seq"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To visualize predictions, I show a darker region that quantifies modeling uncertainty and a lighter region that quantifies predictive uncertainty."
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [],
"source": [
"def PlotPredictions(daily, years, iters=101, percent=90, func=RunLinearModel):\n",
" \"\"\"Plots predictions.\n",
"\n",
" daily: DataFrame of daily prices\n",
" years: sequence of times (in years) to make predictions for\n",
" iters: number of simulations\n",
" percent: what percentile range to show\n",
" func: function that fits a model to the data\n",
" \"\"\"\n",
" result_seq = SimulateResults(daily, iters=iters, func=func)\n",
" p = (100 - percent) / 2\n",
" percents = p, 100 - p\n",
"\n",
" predict_seq = GeneratePredictions(result_seq, years, add_resid=True)\n",
" low, high = thinkstats2.PercentileRows(predict_seq, percents)\n",
" thinkplot.FillBetween(years, low, high, alpha=0.3, color=\"gray\")\n",
"\n",
" predict_seq = GeneratePredictions(result_seq, years, add_resid=False)\n",
" low, high = thinkstats2.PercentileRows(predict_seq, percents)\n",
" thinkplot.FillBetween(years, low, high, alpha=0.5, color=\"gray\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Here are the results for the high quality category."
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [],
"source": [
"years = np.linspace(0, 5, 101)\n",
"thinkplot.Scatter(daily.years, daily.ppg, alpha=0.1, label=name)\n",
"PlotPredictions(daily, years)\n",
"xlim = years[0] - 0.1, years[-1] + 0.1\n",
"thinkplot.Config(\n",
" title=\"Predictions\", xlabel=\"Years\", xlim=xlim, ylabel=\"Price per gram ($)\"\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"But there is one more source of uncertainty: how much past data should we use to build the model?\n",
"\n",
"The following function generates a sequence of models based on different amounts of past data."
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [],
"source": [
"def SimulateIntervals(daily, iters=101, func=RunLinearModel):\n",
" \"\"\"Run simulations based on different subsets of the data.\n",
"\n",
" daily: DataFrame of daily prices\n",
" iters: number of simulations\n",
" func: function that fits a model to the data\n",
"\n",
" returns: list of result objects\n",
" \"\"\"\n",
" result_seq = []\n",
" starts = np.linspace(0, len(daily), iters).astype(int)\n",
"\n",
" for start in starts[:-2]:\n",
" subset = daily[start:]\n",
" _, results = func(subset)\n",
" fake = subset.copy()\n",
"\n",
" for _ in range(iters):\n",
" fake.ppg = results.fittedvalues + thinkstats2.Resample(results.resid)\n",
" _, fake_results = func(fake)\n",
" result_seq.append(fake_results)\n",
"\n",
" return result_seq"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"And this function plots the results."
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {},
"outputs": [],
"source": [
"def PlotIntervals(daily, years, iters=101, percent=90, func=RunLinearModel):\n",
" \"\"\"Plots predictions based on different intervals.\n",
"\n",
" daily: DataFrame of daily prices\n",
" years: sequence of times (in years) to make predictions for\n",
" iters: number of simulations\n",
" percent: what percentile range to show\n",
" func: function that fits a model to the data\n",
" \"\"\"\n",
" result_seq = SimulateIntervals(daily, iters=iters, func=func)\n",
" p = (100 - percent) / 2\n",
" percents = p, 100 - p\n",
"\n",
" predict_seq = GeneratePredictions(result_seq, years, add_resid=True)\n",
" low, high = thinkstats2.PercentileRows(predict_seq, percents)\n",
" thinkplot.FillBetween(years, low, high, alpha=0.2, color=\"gray\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Here's what the high quality category looks like if we take into account uncertainty about how much past data to use."
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {},
"outputs": [],
"source": [
"name = \"high\"\n",
"daily = dailies[name]\n",
"\n",
"thinkplot.Scatter(daily.years, daily.ppg, alpha=0.1, label=name)\n",
"PlotIntervals(daily, years)\n",
"PlotPredictions(daily, years)\n",
"xlim = years[0] - 0.1, years[-1] + 0.1\n",
"thinkplot.Config(\n",
" title=\"Predictions\", xlabel=\"Years\", xlim=xlim, ylabel=\"Price per gram ($)\"\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"## Exercises"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"**Exercise:** The linear model I used in this chapter has the obvious drawback that it is linear, and there is no reason to expect prices to change linearly over time. We can add flexibility to the model by adding a quadratic term, as we did in Section 11.3.\n",
"\n",
"Use a quadratic model to fit the time series of daily prices, and use the model to generate predictions. You will have to write a version of `RunLinearModel` that runs that quadratic model, but after that you should be able to reuse code from the chapter to generate predictions."
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Exercise:** Write a definition for a class named `SerialCorrelationTest` that extends `HypothesisTest` from Section 9.2. It should take a series and a lag as data, compute the serial correlation of the series with the given lag, and then compute the p-value of the observed correlation.\n",
"\n",
"Use this class to test whether the serial correlation in raw price data is statistically significant. Also test the residuals of the linear model and (if you did the previous exercise), the quadratic model."
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Bonus Example:** There are several ways to extend the EWMA model to generate predictions. One of the simplest is something like this:\n",
"\n",
"1. Compute the EWMA of the time series and use the last point as an intercept, `inter`.\n",
"\n",
"2. Compute the EWMA of differences between successive elements in the time series and use the last point as a slope, `slope`.\n",
"\n",
"3. To predict values at future times, compute `inter + slope * dt`, where `dt` is the difference between the time of the prediction and the time of the last observation.\n"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {},
"outputs": [],
"source": [
"name = \"high\"\n",
"daily = dailies[name]\n",
"\n",
"filled = FillMissing(daily)\n",
"diffs = filled.ppg.diff()\n",
"\n",
"thinkplot.plot(diffs)\n",
"plt.xticks(rotation=30)\n",
"thinkplot.Config(ylabel=\"Daily change in price per gram ($)\")"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {},
"outputs": [],
"source": [
"filled[\"slope\"] = diffs.ewm(span=365).mean()\n",
"thinkplot.plot(filled.slope[-365:])\n",
"plt.xticks(rotation=30)\n",
"thinkplot.Config(ylabel=\"EWMA of diff ($)\")"
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {},
"outputs": [],
"source": [
"# extract the last inter and the mean of the last 30 slopes\n",
"start = filled.index[-1]\n",
"inter = filled.ewma[-1]\n",
"slope = filled.slope[-30:].mean()\n",
"\n",
"start, inter, slope"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {},
"outputs": [],
"source": [
"# reindex the DataFrame, adding a year to the end\n",
"dates = pd.date_range(filled.index.min(), filled.index.max() + np.timedelta64(365, \"D\"))\n",
"predicted = filled.reindex(dates)"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {},
"outputs": [],
"source": [
"# generate predicted values and add them to the end\n",
"predicted[\"date\"] = predicted.index\n",
"one_day = np.timedelta64(1, \"D\")\n",
"predicted[\"days\"] = (predicted.date - start) / one_day\n",
"predict = inter + slope * predicted.days\n",
"predicted.ewma.fillna(predict, inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {},
"outputs": [],
"source": [
"# plot the actual values and predictions\n",
"thinkplot.Scatter(daily.ppg, alpha=0.1, label=name)\n",
"thinkplot.Plot(predicted.ewma, color=\"#ff7f00\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As an exercise, run this analysis again for the other quality categories."
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.11"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
| gpl-3.0 |
Joshuaalbert/IonoTomo | src/ionotomo/notebooks/Covariance.ipynb | 1 | 5824 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"'''Handle covariance operations'''\n",
"import numpy as np\n",
"from scipy.special import gamma\n",
"\n",
"class CovarianceClass(object):\n",
" '''Use for repeated use of covariance'''\n",
" def __init__(self,TCI,sigma,corr,nu):\n",
" self.sigma = sigma\n",
" self.corr = corr\n",
" self.nu = nu\n",
" self.nx = TCI.nx\n",
" self.ny = TCI.ny\n",
" self.nz = TCI.nz\n",
" self.dx = TCI.xvec[1] - TCI.xvec[0]\n",
" lvec = np.fft.fftfreq(TCI.nx,d=self.dx)\n",
" self.dy = TCI.yvec[1] - TCI.yvec[0]\n",
" mvec = np.fft.fftfreq(TCI.ny,d=self.dy)\n",
" self.dz = TCI.zvec[1] - TCI.zvec[0]\n",
" self.dV = self.dx*self.dy*self.dz\n",
" nvec = np.fft.fftfreq(TCI.nz,d=self.dz)\n",
" L,M,N = np.meshgrid(lvec,mvec,nvec,indexing='ij')\n",
" self.r = L**2\n",
" self.r += M**2\n",
" self.r += N**2\n",
" np.sqrt(self.r,out=self.r)\n",
" n = 3.\n",
" self.f = sigma**2*2**(n) * np.pi**(n/2.) * gamma(nu+n/2.) * (2*nu)**(nu) / gamma(nu) / corr**(2*nu) * (2*nu/corr**2 + 4*np.pi**2*self.r**2)**(-nu - n/2.)\n",
" def realization(self):\n",
" B = np.random.normal(size=[self.nx,self.ny, self.nz])\n",
" A = np.fft.fftn(B)\n",
" A *= np.sqrt(self.f/self.dV)/4.\n",
" B = (np.fft.ifftn(A)).real\n",
" return B\n",
" \n",
" def contract(self,phi):\n",
" '''Do Cm^{-1}.phi using ffts'''\n",
" Phi = np.fft.fftn(phi)\n",
" Phi /= self.f\n",
" #factor which lines up with theory. Not sure where it comes from\n",
" #Phi /= 2.\n",
" #Phi /= self.dV*4.\n",
" phihat = (np.fft.ifftn(Phi)).real\n",
" return phihat\n",
" \n",
"def test_CovarianceClass():\n",
" from TricubicInterpolation import TriCubic\n",
" import pylab as plt\n",
" neTCI = TriCubic(filename='output/test/simulate/simulate_0/neModel.hdf5')\n",
" covC = CovarianceClass(neTCI,np.log(5),25.,7./2.)\n",
" B = covC.realization()\n",
" print(\"Fluctuations:\",(np.max(B) + np.max(-B))/2.)\n",
" #xy slice\n",
" x = neTCI.xvec\n",
" y = neTCI.yvec\n",
" z = neTCI.zvec\n",
" plt.imshow(B[0,:,:],extent=(z[0],z[-1],y[0],y[-1]))\n",
" plt.colorbar()\n",
" plt.show()\n",
" plt.imshow(B[:,0,:],extent=(z[0],z[-1],x[0],x[-1]))\n",
" plt.colorbar()\n",
" plt.show()\n",
" plt.imshow(B[:,:,0],extent=(y[0],y[-1],x[0],x[-1]))\n",
" plt.colorbar()\n",
" plt.show()\n",
" \n",
" phi = np.zeros_like(neTCI.getShapedArray())\n",
" phi[30:40,:,:] = 1.\n",
" phihat = covC.contract(phi)\n",
" #Analytic for exp covariance is 1/(8*np.pi*sigma**2) * (1/L**3 * phi - 2/L * Lap phi + L * Lap Lap phi)\n",
" phih = phi.copy()/covC.corr**3\n",
" from scipy import ndimage\n",
" stencil = np.zeros([3,3,3])\n",
" for i in range(-1,2):\n",
" for j in range(-1,2):\n",
" for k in range(-1,2):\n",
" s = 0\n",
" if i == 0:\n",
" s += 1\n",
" if j == 0:\n",
" s += 1\n",
" if k == 0:\n",
" s += 1\n",
" if s == 3:\n",
" stencil[i,j,k] = -2*3.\n",
" if s == 3 - 1:\n",
" stencil[i,j,k] = 1.\n",
" stencil /= covC.dV**(2./3.)\n",
" \n",
"\n",
" lap = ndimage.convolve(phi,stencil,mode='wrap')\n",
" phih -= 2/covC.corr*lap\n",
" \n",
" laplap = ndimage.convolve(lap,stencil,mode='wrap')\n",
" phih += covC.corr*laplap\n",
" \n",
" phih /= 8*np.pi*covC.sigma**2\n",
" \n",
" \n",
" plt.imshow(phi[0,:,:],extent=(z[0],z[-1],y[0],y[-1]))\n",
" plt.colorbar()\n",
" plt.show()\n",
" plt.imshow(phihat[0,:,:],extent=(z[0],z[-1],y[0],y[-1]))\n",
" plt.colorbar()\n",
" plt.show()\n",
" plt.imshow(phih[0,:,:],extent=(z[0],z[-1],y[0],y[-1]))\n",
" plt.colorbar()\n",
" plt.show()\n",
" \n",
" plt.imshow(phi[:,0,:],extent=(z[0],z[-1],x[0],x[-1]))\n",
" plt.colorbar()\n",
" plt.show()\n",
" plt.imshow(phihat[:,0,:],extent=(z[0],z[-1],x[0],x[-1]))\n",
" plt.colorbar()\n",
" plt.show()\n",
" plt.imshow(phih[:,0,:],extent=(z[0],z[-1],x[0],x[-1]))\n",
" plt.colorbar()\n",
" plt.show()\n",
" \n",
" plt.imshow(phi[:,:,0],extent=(y[0],y[-1],x[0],x[-1]))\n",
" plt.colorbar()\n",
" plt.show()\n",
" plt.imshow(phihat[:,:,0],extent=(y[0],y[-1],x[0],x[-1]))\n",
" plt.colorbar()\n",
" plt.show()\n",
" plt.imshow(phih[:,:,0],extent=(y[0],y[-1],x[0],x[-1]))\n",
" plt.colorbar()\n",
" plt.show() \n",
" \n",
"if __name__ == '__main__':\n",
" test_CovarianceClass()"
]
}
],
"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 |
seavoyage/BigDataMiner | RDF HDFS.ipynb | 1 | 2774 | {
"metadata": {
"name": "",
"signature": "sha256:cfddd227bfedd639799e6f0ac16ce96927a3d76465327ed424c269b640077aef"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": [
"Hadoop Distributed RDF Store"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from IPython.core.display import HTML\n",
"# https://code.google.com/p/hdrs/\n",
"HTML(\"<iframe src=https://code.google.com/p/hdrs/ </iframe>\")"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Processing RDF Using Hadoop"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from IPython.core.display import HTML\n",
"# http://link.springer.com/chapter/10.1007%2F978-3-642-31552-7_40#page-1\n",
"HTML(\"<iframe src=ttp://link.springer.com/chapter/10.1007%2F978-3-642-31552-7_40#page-1 </iframe>\")"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Spark RDF API (Java)"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from IPython.core.display import HTML\n",
"# http://grepcode.com/file/repo1.maven.org/maven2/com.revelytix/spark-api/0.1.6/spark/api/rdf/RDFNode.java\n",
"HTML(\"<iframe src=http://grepcode.com/file/repo1.maven.org/maven2/com.revelytix/spark-api/0.1.6/spark/api/rdf/RDFNode.java </iframe>\")"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Spark Python APIs"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from IPython.core.display import HTML\n",
"# https://spark.apache.org/docs/latest/api/python/\n",
"HTML(\"<iframe src=https://spark.apache.org/docs/latest/api/python/ </iframe>\")"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Spark RDF"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from IPython.core.display import HTML\n",
"# http://ceur-ws.org/Vol-1272/paper_43.pdf\n",
"HTML(\"<iframe src=http://ceur-ws.org/Vol-1272/paper_43.pdf </iframe>\")"
],
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
} | apache-2.0 |
aaai2018-paperid-62/aaai2018-paperid-62 | parameter_figures_by_year.ipynb | 1 | 1066853 | null | mit |
dwhswenson/OPSPiggybacker | examples/example_one_way_shooting.ipynb | 1 | 32580 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import matplotlib.pyplot as plt\n",
"import openpathsampling as paths\n",
"import ops_piggybacker as oink"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Create fake trajectories\n",
"\n",
"The input trajectories for the one-way shooting version must\n",
"\n",
"* not include the shooting point (which is shared between the two trajectories)\n",
"* be in forward-time order (so reversed paths, which are created as time goes backward, need to be reversed)\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from openpathsampling.tests.test_helpers import make_1d_traj\n",
"\n",
"traj1 = make_1d_traj([-0.9, 0.1, 1.1, 2.1, 3.1, 4.1, 5.1, 6.1, 7.1, 8.1, 9.1, 10.1])\n",
"traj2 = make_1d_traj([-0.8, 1.2])\n",
"traj3 = make_1d_traj([5.3, 8.3, 11.3])\n",
"traj4 = make_1d_traj([-0.6, 1.4, 3.4, 5.4, 7.4])\n",
"traj5 = make_1d_traj([-0.5, 0.5, 1.5, 2.5, 3.5, 4.5, 5.5])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Make list of move data\n",
"\n",
"The input to the pseudo-simulator is a list of data related to the move. For one-way shooting, you need the following information with each move:\n",
"\n",
"* the replica this move applies to (for TPS, just use `0`)\n",
"* the single-direction trajectory (as described in the previous section)\n",
"* the index of the shooting point from the previous *full* trajectory\n",
"* whether the trajectory was accepted\n",
"* the direction of the one-way shooting move (forward is `+1`, backward is `-1`)\n",
"\n",
"The `moves` object below is a list of tuples of that information, in the order listed above. This is what you need to create from your previous simulation."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"moves = [\n",
" (0, traj2, 3, True, -1),\n",
" (0, traj3, 4, True, +1),\n",
" (0, traj4, 6, False, -1),\n",
" (0, traj5, 6, True, -1)\n",
"]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"From here, you've already done everything that needs to be done to reshape your already-run simulation. Now you just need to create the fake OPS simulations."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Create OPS objects"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# volumes\n",
"cv = paths.FunctionCV(\"x\", lambda snap: snap.xyz[0][0])\n",
"left_state = paths.CVDefinedVolume(cv, float(\"-inf\"), 0.0)\n",
"right_state = paths.CVDefinedVolume(cv, 10.0, float(\"inf\"))"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# network\n",
"network = paths.TPSNetwork(left_state, right_state)\n",
"ensemble = network.sampling_ensembles[0] # the only one"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Create initial conditions"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"initial_conditions = paths.SampleSet([\n",
" paths.Sample(replica=0,\n",
" trajectory=traj1,\n",
" ensemble=ensemble)\n",
"])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Create OPSPiggybacker objects\n",
"\n",
"Note that the big difference here is that you use `pre_joined=False`. This is essential for the automated one-way shooting treatment."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"shoot = oink.ShootingStub(ensemble, pre_joined=False)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"sim = oink.ShootingPseudoSimulator(storage=paths.Storage('one_way.nc', 'w'),\n",
" initial_conditions=initial_conditions,\n",
" mover=shoot,\n",
" network=network)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Run the pseudo-simulator"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"sim.run(moves)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"sim.storage.close()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Analyze with OPS"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"analysis_file = paths.AnalysisStorage(\"one_way.nc\")"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"shooting ran 100.000% (expected 100.00%) of the cycles with acceptance 3/4 (75.00%)\n"
]
}
],
"source": [
"scheme = analysis_file.schemes[0]\n",
"scheme.move_summary(analysis_file.steps)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
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cy=\"30\" r=\"0.25\"/><text x=\"30.0\" y=\"30\"/></g><g class=\"shooting fw block\"><desc>{}</desc><rect height=\"9.0\" width=\"4.5\" x=\"32.75\" y=\"25.5\"/><circle cx=\"32.5\" cy=\"30\" r=\"0.25\"/><circle cx=\"37.5\" cy=\"30\" r=\"0.25\"/><text x=\"35.0\" y=\"30\"/></g><g class=\"shooting fw block\"><desc>{}</desc><rect height=\"9.0\" width=\"4.5\" x=\"37.75\" y=\"25.5\"/><circle cx=\"37.5\" cy=\"30\" r=\"0.25\"/><text x=\"40.0\" y=\"30\"/></g><g class=\"shooting bw block\"><desc>{}</desc><rect height=\"9.0\" width=\"4.5\" x=\"-2.25\" y=\"40.5\"/><circle cx=\"2.5\" cy=\"45\" r=\"0.25\"/><text x=\"0.0\" y=\"45\"/></g><g class=\"shooting bw block\"><desc>{}</desc><rect height=\"9.0\" width=\"4.5\" x=\"2.75\" y=\"40.5\"/><circle cx=\"2.5\" cy=\"45\" r=\"0.25\"/><circle cx=\"7.5\" cy=\"45\" r=\"0.25\"/><text x=\"5.0\" y=\"45\"/></g><g class=\"shooting bw block\"><desc>{}</desc><rect height=\"9.0\" width=\"4.5\" x=\"7.75\" y=\"40.5\"/><circle cx=\"7.5\" cy=\"45\" r=\"0.25\"/><circle cx=\"12.5\" cy=\"45\" r=\"0.25\"/><text x=\"10.0\" y=\"45\"/></g><g class=\"shooting bw block\"><desc>{}</desc><rect height=\"9.0\" width=\"4.5\" x=\"12.75\" y=\"40.5\"/><circle cx=\"12.5\" cy=\"45\" r=\"0.25\"/><circle cx=\"17.5\" cy=\"45\" r=\"0.25\"/><text x=\"15.0\" y=\"45\"/></g><g class=\"shooting bw block\"><desc>{}</desc><rect height=\"9.0\" width=\"4.5\" x=\"17.75\" y=\"40.5\"/><circle cx=\"17.5\" cy=\"45\" r=\"0.25\"/><circle cx=\"22.5\" cy=\"45\" r=\"0.25\"/><text x=\"20.0\" y=\"45\"/></g><g class=\"shooting bw block\"><desc>{}</desc><rect height=\"9.0\" width=\"4.5\" x=\"22.75\" y=\"40.5\"/><circle cx=\"22.5\" cy=\"45\" r=\"0.25\"/><circle cx=\"27.5\" cy=\"45\" r=\"0.25\"/><text x=\"25.0\" y=\"45\"/></g><g class=\"shooting bw block\"><desc>{}</desc><rect height=\"9.0\" width=\"4.5\" x=\"27.75\" y=\"40.5\"/><circle cx=\"27.5\" cy=\"45\" r=\"0.25\"/><circle cx=\"32.5\" cy=\"45\" r=\"0.25\"/><text x=\"30.0\" y=\"45\"/></g></g></g><g class=\"legend\"><g class=\"legend\" transform=\"translate(0)\"><g class=\"label\" transform=\"translate(0,0)\"><g class=\"shift\"><text x=\"0\" y=\"0\">cor</text></g></g><g class=\"correlation v-region\"><line x1=\"0\" x2=\"0\" y1=\"7.5\" y2=\"52.5\"/><circle cx=\"0\" cy=\"7.5\" r=\"1.6\"/><line x1=\"-6.4\" x2=\"6.4\" y1=\"7.5\" y2=\"7.5\"/><circle cx=\"0\" cy=\"52.5\" r=\"1.6\"/><line x1=\"-6.4\" x2=\"6.4\" y1=\"52.5\" y2=\"52.5\"/><text x=\"-6.4\" y=\"30.0\"/></g><g class=\"correlation v-region\"><line x1=\"0\" x2=\"0\" y1=\"52.5\" y2=\"67.5\"/><circle cx=\"0\" cy=\"52.5\" r=\"1.6\"/><line x1=\"-6.4\" x2=\"6.4\" y1=\"52.5\" y2=\"52.5\"/><text x=\"-6.4\" y=\"60.0\"/></g></g><g class=\"legend\" transform=\"translate(-32)\"><g class=\"label\" transform=\"translate(0,0)\"><g class=\"shift\"><text x=\"0\" y=\"0\">step</text></g></g><g class=\"label\" transform=\"translate(0,15)\"><g class=\"shift\"><text x=\"0\" y=\"0\">*</text></g></g><g class=\"label\" transform=\"translate(0,30)\"><g class=\"shift\"><text x=\"0\" y=\"0\">1</text></g></g><g class=\"label\" transform=\"translate(0,45)\"><g class=\"shift\"><text x=\"0\" y=\"0\">2</text></g></g><g class=\"label\" transform=\"translate(0,60)\"><g class=\"shift\"><text x=\"0\" y=\"0\">4</text></g></g></g></g><g><rect class=\"tableline\" height=\"13.5\" width=\"209.0\" x=\"-80.0\" y=\"8.25\"/><rect class=\"tableline\" height=\"13.5\" width=\"209.0\" x=\"-80.0\" y=\"23.25\"/><rect class=\"tableline\" height=\"13.5\" width=\"209.0\" x=\"-80.0\" y=\"38.25\"/><rect class=\"tableline\" height=\"13.5\" width=\"209.0\" x=\"-80.0\" y=\"53.25\"/></g></g></svg>"
],
"text/plain": [
"<IPython.core.display.SVG object>"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import openpathsampling.visualize as ops_vis\n",
"from IPython.display import SVG\n",
"history = ops_vis.PathTree(\n",
" analysis_file.steps,\n",
" ops_vis.ReplicaEvolution(replica=0)\n",
")\n",
"# switch to the \"boxcar\" look for the trajectories\n",
"history.options.movers['default']['new'] = 'single'\n",
"history.options.css['horizontal_gap'] = True\n",
"SVG(history.svg())"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x117368310>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"path_lengths = [len(step.active[0].trajectory) for step in analysis_file.steps]\n",
"plt.hist(path_lengths, alpha=0.5);"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x117ce2e90>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"cv_x = analysis_file.cvs['x']\n",
"# load the active trajectory as storage.steps[step_num].active[replica_id]\n",
"plt.plot(cv_x(analysis_file.steps[2].active[0]), 'o-');"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.12"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| lgpl-2.1 |
MTG/SymbTr-extras | wrappers/convert_mu2_to_musicxml.ipynb | 1 | 532209 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import os\n",
"from fileoperations.fileoperations import get_filenames_in_dir\n",
"from tomato.symbolic.scoreconverter import ScoreConverter"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"__Warning:__ __tomato__ is not listed in the requirements of SymbTrExtras, because ```mu2_to_musicxml``` method is experimental. You can install __tomato__ by following the instructions [here](https://github.com/sertansenturk/tomato/blob/master/README.md#installation)."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"mu2folder = os.path.join('..', '..', 'mu2')\n",
"xmlfolder = os.path.join('..', '..', 'MusicXML')\n",
"\n",
"mu2filepaths, dummyfolders, mu2files = get_filenames_in_dir(mu2folder, '*.mu2')\n",
"symbtrnames = [os.path.splitext(sf)[0] for sf in mu2files]\n",
"xmlfilepaths = [os.path.join(xmlfolder, sn + '.xml') for sn in symbtrnames]\n"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/suzinak_zirgule--sarki--aksak--gozlerin_bir--ferit_sidal.mu2 \n",
"1 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz--sarki--duyek--artik_bu--alaeddin_yavasca.mu2 \n",
"2 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/ussak--turku--nimsofyan--sabahtan_kalktim--.mu2 \n",
"3 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/ussak--sarki--yuruksemai--askin_ile--enderuni_ali_bey.mu2 \n",
"4 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nihavent--sarki--curcuna--ayrilmak_ne--ekrem_guyer.mu2 \n",
"5 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/segah--sarki--agir_aksaksemai--doldur_getir--kazasker_mustafa_izzet_efendi.mu2 \n",
"6 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nihavent--sarki--duyek--ayrilik_bukuverdi--arif_sami_toker.mu2 \n",
"7 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/sevkefza--seyir--sofyan--1--sefik_gurmeric.mu2 \n",
"8 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz--sarki--senginsemai--nesem_emelim--lemi_atli.mu2 \n",
"9 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/saba--sazsemaisi--aksaksemai----zaharya.mu2 \n",
"10 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/mahur--sarki--duyek--gul_mevsimi--tanburi_mustafa_cavus.mu2 \n",
"11 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/kurdilihicazkar--sarki--semai--uzun_yillar--zeki_muren.mu2 \n",
"12 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/suzinak--sarki--curcuna--gozumden_gitmiyor--haci_arif_bey.mu2 \n",
"13 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/muhayyerkurdi--sarki--aksak--askinla_yana_yana--ismet_nedim.mu2 \n",
"14 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/ussak--aranagme--duyek--1--.mu2 \n",
"15 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/ussak--sazsemaisi--aksaksemai----corci.mu2 \n",
"16 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz--turku--sofyan--cadir_alti--gaziantep.mu2 \n",
"17 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/saba--sarki--sofyan--asikina_eylemez--numan_aga.mu2 \n",
"18 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/saba--ornek_oz--sofyan--2--ruhi_ayangil.mu2 \n",
"19 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz_humayun--pesrev--cenber----veli_dede.mu2 \n",
"20 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/ussak--sarki--aksak--aman_felek--dede_efendi.mu2 \n",
"21 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicazkar--sarki--senginsemai--layik_mi--sekerci_cemil_bey.mu2 \n",
"22 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/suzinak_zirgule--sarki--yuruksemai--husn_alemini--haci_arif_bey.mu2 \n",
"23 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huzzam--sarki--senginsemai--mahzun_durusun--ekrem_guyer.mu2 \n",
"24 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/acemasiran--fantezi--yuruksemai_ii--ey_benim--resat_erer.mu2 \n",
"25 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/karcigar--sarki--yuruksemai--bir_suh--selahattin_icli.mu2 \n",
"26 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/rast--sarki--agiraksak--muy-i_julidem--sakir_aga.mu2 \n",
"27 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/acemasiran--yuruksemai--yuruksemai_ii----huseyin_fahreddin_dede.mu2 \n",
"28 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huzzam--yuruksemai--yuruksemai--reh-i_askinda--dede_efendi.mu2 \n",
"29 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nisaburek--pesrev--duyek----fahri_kopuz.mu2 \n",
"30 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/muhayyer--sarki--duyek--cile_bulbulum--sadettin_kaynak.mu2 \n",
"31 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/tahir--pesrev--fahte----dede_salih_efendi.mu2 \n",
"32 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/ussak--turku--sofyan--eledim_eledim--erzurum.mu2 \n",
"33 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/saba--sarki--kapali_curcuna--hicranimi_hic--yesari_asim_arsoy.mu2 \n",
"34 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huzzam--turku--sofyan--aliverin_baglamami--.mu2 \n",
"35 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/sevkefza--seyir--sofyan--2--sefik_gurmeric.mu2 \n",
"36 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/ussak--ilahi--sofyan--ey_enbiyalar--.mu2 \n",
"37 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz--sarki--nimsofyan--sari_kurdelem--fahri_kayahan.mu2 \n",
"38 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/dilnisin--kupe--devrirevanihindi--bir--ahmet_avni_konuk.mu2 \n",
"39 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/ussak--sarki--devrihindi--gozlerin_hayran--serif_icli.mu2 \n",
"40 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/buselik--sarki--aksak--bir_misli--sakir_aga.mu2 \n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"41 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/muhayyer--seyir--semai--1--rauf_yekta.mu2 \n",
"42 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/isfahan--pesrev--devrikebir----tanburi_cemil_bey.mu2 \n",
"43 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nihavent--turku--nimsofyan--uskudara_gider--.mu2 \n",
"44 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/neveser--seyir--senginsemai--1--erol_bingol.mu2 \n",
"45 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nihavent--sarki--aksak--hatirlar_misin_beni--vecdi_seyhun.mu2 \n",
"46 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/saba--nakis--yuruksemai--cefaya_ey--kara_ismail_aga.mu2 \n",
"47 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nihavent--sarki--turkaksagi--ben_buy-i--haci_arif_bey.mu2 \n",
"48 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/evic--sarki--turkaksagi--kalbimde_acilmis--isak_varon.mu2 \n",
"49 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/sehnaz--sarki--agirevfer--merami_andelibin--kemani_riza_efendi.mu2 \n",
"50 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/segah--turku--aksak--bir_gemim--ankara.mu2 \n",
"51 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/suzinak_zirgule--sarki--senginsemai--hal-i_dilimi--sekerci_cemil_bey.mu2 \n",
"52 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/saba--ornek_oz--sofyan--1--ruhi_ayangil.mu2 \n",
"53 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicazkar--sarki--sofyan--ben_sozune--numan_aga.mu2 \n",
"54 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/mahur--sarki--evfer--kalbimdeki_amal-i--muallim_ismail_hakki_bey.mu2 \n",
"55 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/yegah--pesrev--devrikebir----rauf_yekta.mu2 \n",
"56 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/sultaniyegah--kupe--yuruksemai_ii--eyledin_bezm-i--ahmet_avni_konuk.mu2 \n",
"57 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicazkar--pesrev--agirduyek----dede_salih_efendi.mu2 \n",
"58 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huseyni--sarki--agiraksak--firkat-i_canan--civan_aga.mu2 \n",
"59 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/muhayyer--turku--duyek--iste_seni--sadettin_kaynak.mu2 \n",
"60 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz_uzzal--sarki--agiraksak--doktor_ne--sevki_bey.mu2 \n",
"61 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/buselik--sarki--duyek--denizlerin_otesinden--ismail_baha_surelsan.mu2 \n",
"62 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/segah_maye--ilahi--devrirevan--sem-i_ruhuna--sultan_veled.mu2 \n",
"63 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/evcara--yuruksemai--senginsemai--saki_cekemem--kucuk_mehmet_aga.mu2 \n",
"64 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huzzam--sarki--aksak--indirip_yerlere--cinucen_tanrikorur.mu2 \n",
"65 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/acemkurdi--sarki--aksak--altin_tasta--nimet_hanim.mu2 \n",
"66 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/buselik--seyir--turkaksagi--1--erol_bingol.mu2 \n",
"67 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/rast--sarki--aksak--bin_can_ile--tanburi_buyuk_osman_bey.mu2 \n",
"68 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nihavent--sarki--kapali_curcuna--kimseye_etmem--kemani_sarkis_efendi.mu2 \n",
"69 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huzzam--turku--musemmen_ii--su_karsiki--hatay.mu2 \n",
"70 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nikriz--kupe--devrirevanihindi--halka-i_mecliste--ahmet_avni_konuk.mu2 \n",
"71 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huzzam--sarki--duyek--icimde_bin--avni_anil.mu2 \n",
"72 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/acemasiran--pesrev--devrikebir----dede_salih_efendi.mu2 \n",
"73 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz--ilahi--sofyan--daglar_ile--kutbi_dede.mu2 \n",
"74 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/tarzinevin--kupe--devrirevanihindi--dilrubamiz_nazina--ahmet_avni_konuk.mu2 \n",
"75 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/muhayyerkurdi--sarki--nimsofyan--kadehinde_zehir--erol_sayan.mu2 \n",
"76 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/ferahnak--sarki--aksak--ben_muptela--dede_efendi.mu2 \n",
"77 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/yegah--beste--zencir--gonul_ki--dellalzade_haci_ismail_efendi.mu2 \n",
"78 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/segah--turku--curcuna--cayda_cira--elazig.mu2 \n",
"79 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huseyni--turku--devrituran--yagmur_yagar--.mu2 \n",
"80 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz--sarki--aksak--ben_gamli_hazan--melahat_pars.mu2 \n",
"81 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/kurdilihicazkar--sarki--musemmen--gozedir_yumnile--muallim_ismail_hakki_bey.mu2 \n",
"82 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/tahir--turku--musemmen_ii--alli_durnam--kirikkale-keskin.mu2 \n",
"83 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/ferahfeza--beste--firengifer--ey_kasi--dede_efendi.mu2 \n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"84 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/sedaraban--seyir--sofyan--1--sefik_gurmeric.mu2 \n",
"85 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz--popsarkisi--sofyan--basin_one--kerem_guney.mu2 \n",
"86 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/acemkurdi--sarki--senginsemai--ruzgar_uyumus--refik_fersan.mu2 \n",
"87 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/karcigar--sarki--agiraksak--ah_efendim--hafiz_yusuf_efendi.mu2 \n",
"88 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/rast--sarki--kapali_curcuna--nedendir_bu--sevki_bey.mu2 \n",
"89 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/segah--turku--turkaksagi--terazi--macka.mu2 \n",
"90 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nihavent--fantezi--nimsofyan--ayaginda_halhali--bilge_ozgen.mu2 \n",
"91 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/buselik--aranagme--agiraksak--1--.mu2 \n",
"92 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/mahur--sarki--nimsofyan--hadi_canim--selahattin_icli.mu2 \n",
"93 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/karcigar--pesrev--cifteduyek----tatyos_efendi.mu2 \n",
"94 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nuhuft--seyir--sofyan--1--sefik_gurmeric.mu2 \n",
"95 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/mahur--pesrev--duyek----gazi_giray_han.mu2 \n",
"96 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/neva--seyir--sofyan--1--erol_bingol.mu2 \n",
"97 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/ussak--yuruksemai--yuruksemai--omrun_su--suleyman_erguner.mu2 \n",
"98 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/arazbar--seyir--duyek--1--sefik_gurmeric.mu2 \n",
"99 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/rast--turku--semai--gul_agaci--necip_mirkelamoglu.mu2 \n",
"100 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz--sarki--agiraksak--firkatin_aldi--bimen_sen.mu2 \n",
"101 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/buselik--sarki--duyek--keremkani_efendim--tanburi_mustafa_cavus.mu2 \n",
"102 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/mahur--sarki--aksak--alamam_dogrusu--serif_icli.mu2 \n",
"103 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/muhayyer--seyir--sofyan--1--erol_bingol.mu2 \n",
"104 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huzzam--sarki--iraksak--gozum_hasretle--tatyos_efendi.mu2 \n",
"105 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz--sarki--devrisureyya--kuru_dallar--ismet_burkay.mu2 \n",
"106 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/ferahfeza--seyir--sofyan--1--sefik_gurmeric.mu2 \n",
"107 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/mahur--sarki--duyek--ben_guzele--sadettin_kaynak.mu2 \n",
"108 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nihavent--sarki--duyek--sevil_neselen--sadettin_oktenay.mu2 \n",
"109 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/yegah--sarki--kapali_curcuna--mutad_edeli--sevki_bey.mu2 \n",
"110 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/segah--seyir--sofyan--1--erol_bingol.mu2 \n",
"111 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz_humayun--sarki--agirduyek--var_mi--haci_arif_bey.mu2 \n",
"112 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz--turku--aksak--ihtiyatlar_silah--.mu2 \n",
"113 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nihavent--fantezi--nimsofyan--benim_gonlum--kaptanzade_ali_riza_bey.mu2 \n",
"114 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/karcigar--sarki--ciftesofyan--kara_bulutlari--sadettin_kaynak.mu2 \n",
"115 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huzi--ilahi--duyek--tasti_rahmet--.mu2 \n",
"116 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/pencgahizaid--seyir--sofyan--1--sefik_gurmeric.mu2 \n",
"117 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicazkar--pesrev--devrikebir----tanburi_buyuk_osman_bey.mu2 \n",
"118 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/reyya--sarki--sofyan--suzme_cesmin--erol_basara.mu2 \n",
"119 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz_humayun--beste--hafif--olmada_diller--abdulhalim_aga.mu2 \n",
"120 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/mahur--sarki--duyek--dudagimda_acan--erol_sayan.mu2 \n",
"121 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/saba--sarki--agiraksak--degilsem_de--numan_aga.mu2 \n",
"122 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nihavent--sarki--duyek--bir_demet--zeki_muren.mu2 \n",
"123 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/yeni_cargah--sazeseri--sofyan--sinsin--bunyan.mu2 \n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"124 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz_humayun--turku--sofyan--bulbulun_gogsu--urfa.mu2 \n",
"125 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/muhayyerbuselik--kupe--aksak--bir_muhayyer--ahmet_avni_konuk.mu2 \n",
"126 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicazkar--sarki--agiraksak--kirsa_bin_tel--bimen_sen.mu2 \n",
"127 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/pesendide--seyir--sofyan--1--sefik_gurmeric.mu2 \n",
"128 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/muhayyer--turku--sofyan--binip_de--denizli.mu2 \n",
"129 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz_humayun--sarki--aksak--kurbanin_olam--haci_arif_bey.mu2 \n",
"130 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nihavent--beste--fahte--aciyaydi_bana--muallim_ismail_hakki_bey.mu2 \n",
"131 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nihavent--sarki--duyek--ellerim_boyle--sekip_ayhan_ozisik.mu2 \n",
"132 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/tahir--turku--sofyan--karanfil_ocak--nevsehir.mu2 \n",
"133 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hisarbuselik--aranagme--yuruksemai_ii--1--.mu2 \n",
"134 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicazkar--pesrev--berefsan----gazi_giray_han.mu2 \n",
"135 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/saba--sarki--duyek--gordum_seni--nikogos_aga.mu2 \n",
"136 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nihavent--sarki--semai--bir_alev--avni_anil.mu2 \n",
"137 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/mahur--sarki--senginsemai--hala_kanayan--yorgo_bacanos.mu2 \n",
"138 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huseyni--pesrev--devrikebir----kul_mehmet.mu2 \n",
"139 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/ussak--sarki--curcuna--esiri_zulfunum--sevki_bey.mu2 \n",
"140 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/kurdilihicazkar--fantezi--aksak--bugun_biz--neveser_kokdes.mu2 \n",
"141 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hisarbuselik--turku--azeriyuruksemai--diliyrem_ki--sinan_sipahi.mu2 \n",
"142 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/evicbuselik--kupe--aksak--o_mah-i--ahmet_avni_konuk.mu2 \n",
"143 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/beyati--sarki--duyek--askn_fecri--sadettin_kaynak.mu2 \n",
"144 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/karcigar--sazeseri--sofyan----mutlu_torun.mu2 \n",
"145 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/isfahan--seyir--sofyan--1--sefik_gurmeric.mu2 \n",
"146 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/karcigar--sarki--duyek--oh_guzel_kiz--sadettin_kaynak.mu2 \n",
"147 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huseyni--sarki--devrihindi--dil-rubasin_sevdigim--hafiz_mehmet_esref_efendi.mu2 \n",
"148 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/gerdaniye--seyir--sofyan--1--sefik_gurmeric.mu2 \n",
"149 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/ussak--sarki--duyek--menekse_gozler--yesari_asim_arsoy.mu2 \n",
"150 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nihavent--fantezi--sofyan--durusun_andirir--unal_narcin.mu2 \n",
"151 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/acemasiran--sarki--aksak--lutfeyle_meded--dede_efendi.mu2 \n",
"152 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz--sarki--agiraksak--seni_sevda--leyla_saz.mu2 \n",
"153 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicazkar--sarki--devrihindi--gonlumu_ducar--sevki_bey.mu2 \n",
"154 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicazkar--sarki--kapali_curcuna--mani_oluyor--tatyos_efendi.mu2 \n",
"155 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/segah--turku--azeriyuruksemai--ayli_gece--azeri.mu2 \n",
"156 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz_humayun--sarki--nimsofyan--kapildim_gidiyorum--kaptanzade_ali_riza_bey.mu2 \n",
"157 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/evic--karsilama--yuruksemai_ii--on_kerre--.mu2 \n",
"158 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/isfahan--yuruksemai--yuruksemai--o_guzler--dellalzade_haci_ismail_efendi.mu2 \n",
"159 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/acemasiran--sarki--raksaksagi--bana_tavr-i--sakir_aga.mu2 \n",
"160 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/karcigar--sazsemaisi--aksaksemai----ahmet_mukerrem_akinci.mu2 \n",
"161 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/buselik--pesrev--devrikebir----bolahenk_nuri_bey.mu2 \n",
"162 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/rehavi--kupe--devrirevanihindi--vad-i_vasl--ahmet_avni_konuk.mu2 \n",
"163 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/yegah--sarki--duyek--meclisi_vaslinda--ali_rifat_cagatay.mu2 \n",
"164 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/ferahnak--sarki--semai--ruhumda_bahar--artaki_candan.mu2 \n",
"165 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huzzam--sarki--agiraksak--sabrimi_gamzelerin--bimen_sen.mu2 \n",
"166 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/kurdilihicazkar--seyir--sofyan--1--sefik_gurmeric.mu2 \n",
"167 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/sedaraban--etud--sofyan----ozer_ozel.mu2 \n",
"168 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nihavent--sarki--senginsemai--bir_gun--musa_sureyya_bey.mu2 \n",
"169 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n"
]
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"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nihavent--sarki--semai--bekledim_de--yesari_asim_arsoy.mu2 \n",
"170 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nihavent--sarki--sofyan--ne_dert--sadettin_kaynak.mu2 \n",
"171 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/beyati--sarki--aksak--sebep_ne--markar_aga.mu2 \n",
"172 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/acemkurdi--sarki--agiraksak--sevdi_gonlum--nikogos_aga.mu2 \n",
"173 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huzzam--sarki--devrihindi--ey_cefacu--sakir_aga.mu2 \n",
"174 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz_uzzal--sarki--turkaksagi--kendine_nicin--medeni_aziz_efendi.mu2 \n",
"175 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/beyati--sarki--agiraksak--ne_bahar--rakim_elkutlu.mu2 \n",
"176 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/sedaraban--agirsemai--aksaksemai--nedir_murad-i--sadullah_aga.mu2 \n",
"177 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/selmek--sazsemaisi--aksaksemai----mehmet_aga.mu2 \n",
"178 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/rast--sarki--sofyan--gelmez_oldu--dramali_hasan_hasguler.mu2 \n",
"179 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/isfahanek--kupe--duyek--tutiya-yi--ahmet_avni_konuk.mu2 \n",
"180 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/yegah--kupe--yuruksemai_ii--cevr-i_peyderpeyle--ahmet_avni_konuk.mu2 \n",
"181 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/suzinak--sarki--aksak--beni_bigane-i--mehmet_cemil_bey.mu2 \n",
"182 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/gerdaniye--turku--kapali_curcuna--daglar_basi--kerkuk.mu2 \n",
"183 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/ussak--turku--aksak--findikli_bizim--.mu2 \n",
"184 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/ussak--turku--yuruksemai_ii--cilveloy--artvin.mu2 \n",
"185 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huseyni_gulizar--ilahi--sofyan--cun_sana--hafiz_post.mu2 \n",
"186 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/mahur--sarki--curcuna--gucendim_ben--muhlis_sabahaddin_ezgi.mu2 \n",
"187 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huseyni--pesrev--devrikebir----iii_selim.mu2 \n",
"188 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/mahur--sarki--aksak--gonul_adli--dellalzade_haci_ismail_efendi.mu2 \n",
"189 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz--sarki--aksak--gulsen-i_husnune--rifat_bey.mu2 \n",
"190 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/yeni_cargah--turku--sofyan--halkali_seker--eskisehir.mu2 \n",
"191 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huzzam--sarki--duyek--pisman_olur_da--irfan_ozbakir.mu2 \n",
"192 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/mahur--sarki--duyek--bu_son_sarkimda--muzaffer_ilkar.mu2 \n",
"193 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/rast--sarki--turkaksagi--esti_nesim-i_nevbahar--haci_arif_bey.mu2 \n",
"194 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/suzinak_zirgule--sarki--aksak--beni_bizar--haci_arif_bey.mu2 \n",
"195 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/sevkutarab--seyir--sofyan--1--sefik_gurmeric.mu2 \n",
"196 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/tahir--seyir--turkaksagi--1--erol_bingol.mu2 \n",
"197 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/ferahnak--yuruksemai--yuruksemai--bir_dilbere--sakir_aga.mu2 \n",
"198 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/neveser--turku--aksak--ferayi--.mu2 \n",
"199 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/segah--ilahi--duyek--ey_acep--seyh_huseyin_halis_efendi.mu2 \n",
"200 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz--fantezi--nimsofyan--ayni_cati--turhan_tasan.mu2 \n",
"201 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/segah--ornek_oz--aksak--1--ruhi_ayangil.mu2 \n",
"202 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huseyni--yuruksemai--senginsemai--ben_gibi--tabi_mustafa_efendi.mu2 \n",
"203 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/rast--sarki--aksak--calima_bak--lavtaci_hristo.mu2 \n",
"204 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/sultaniyegah--sazsemaisi--aksaksemai----kanuni_haci_arif_bey.mu2 \n",
"205 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/tahir--turku--nimsofyan--basinda_altin--diyarbakir.mu2 \n",
"206 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nihavent--sarki--yuruksemai_ii--ates_alevde--ozgen_gurbuz.mu2 \n",
"207 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/sevkaver--pesrev--hafif----i_mahmut.mu2 \n",
"208 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/arazbar--seyir--sofyan--1--erol_bingol.mu2 \n",
"209 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/pencgah--tevsihilahi--sofyan--ya_resulallah--.mu2 \n",
"210 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/rast--sugul--duyek--ya_men--tanburi_ali_efendi.mu2 \n",
"211 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/segah--ilahi--frenkcin--bu_ask--yenikoylu_hadi_bey.mu2 \n",
"212 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huseyni--turku--sofyan--kopruden_gecti--kirsehir.mu2 \n",
"213 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nihavent--sazeseri--nimsofyan--seytanin_ruyasi--muhammet_yildirir.mu2 \n",
"214 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huseyni--turku--sofyan--keklik_daglarda--.mu2 \n",
"215 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/mahur--mars--nimhafif--vur_pence-i--munir_nurettin_selcuk.mu2 \n",
"216 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/ussak--ornek_oz--semai--1--ruhi_ayangil.mu2 \n",
"217 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/segah--sarki--duyek--issiz_gecelerin--omer_sami_gupgup.mu2 \n",
"218 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/rahatulervah--seyir--sofyan--1--sefik_gurmeric.mu2 \n",
"219 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/suzinak--sarki--curcuna--pek_revadir--ahmet_rasim_bey.mu2 \n",
"220 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/tahir--kupe--duyek--al_al--ahmet_avni_konuk.mu2 \n",
"221 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/suzidil--agirsemai--aksaksemai--kani_yad-i--tanburi_ali_efendi.mu2 \n",
"222 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huseyni--rumeliturkusu--aksak--akzadeler_giyer--rumeli.mu2 \n",
"223 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/yegah--sarki--devrihindi--vadin_unutma--latif_aga.mu2 \n",
"224 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/evic--sarki--agiraksak--bir_goren--ali_icinger.mu2 \n",
"225 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz--sarki--raksaksagi--benim_yarim--sadettin_kaynak.mu2 \n",
"226 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hisarbuselik--sarki--duyek--du_cesmimden--tanburi_mustafa_cavus.mu2 \n",
"227 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/mahur--beste--devrikebir--ta-be-key_sinemde--dilhayat_kalfa.mu2 \n",
"228 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nisabur--seyir--sofyan--1--sefik_gurmeric.mu2 \n",
"229 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/sehnazbuselik--sarki--aksak--kucuksuda_gordum--tanburi_mustafa_cavus.mu2 \n",
"230 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/rast--agirsemai--aksaksemai--cekmis_yuzune--tascizade_recep_celebi.mu2 \n",
"231 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/beyati--sarki--agiraksak--muptelayim--dede_efendi.mu2 \n",
"232 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/saba--kupe--duyek--gayri--ahmet_avni_konuk.mu2 \n",
"233 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/pencgahizaid--kupe--devrirevanihindi--pencugah-i_zaide--ahmet_avni_konuk.mu2 \n",
"234 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/buselikasiran--seyir--sofyan--1--sefik_gurmeric.mu2 \n",
"235 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/ferahnuma--turku--sofyan--fidayda--ankara.mu2 \n",
"236 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/muhayyer--turku--sofyan--cayir_ince--.mu2 \n",
"237 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huzzam--sarki--curcuna--askinla_surunsem--selahaddin_pinar.mu2 \n",
"238 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huzzam--sarki--curcuna--aksam_donusu--udi_marko_colakoglu.mu2 \n",
"239 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz--turku--aksak--el_zanneder--urfa.mu2 \n",
"240 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/beyati--aranagme--curcuna--1--.mu2 \n",
"241 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nihavent--sarki--duyek--bulbul_asikmis--zeki_muren.mu2 \n",
"242 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz--seyir--duyek--1--sefik_gurmeric.mu2 \n",
"243 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/suzidil--sarki--agiraksak--ulfetin_gecti--ismet_aga.mu2 \n",
"244 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huseyni--turku--nimsofyan--bir_of_ceksem--kayseri.mu2 \n",
"245 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz--sarki--sofyan--egilmez_basin--kaptanzade_ali_riza_bey.mu2 \n",
"246 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/muhayyer--pesrev--devrikebir----tanburi_cemil_bey.mu2 \n",
"247 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/gevest--sazsemaisi--aksaksemai----.mu2 \n",
"248 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n"
]
},
{
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"text": [
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/zavil--pesrev--hafif----zeki_mehmet_aga.mu2 \n",
"249 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/muhayyer--sarki--aksak--gunes_gibi--sadettin_kaynak.mu2 \n",
"250 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/suzidilara--sazsemaisi--aksaksemai----tanburi_cemil_bey.mu2 \n",
"251 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/segah--turku--iki_bir--ay_giz--kars.mu2 \n",
"252 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/karcigar--sarki--duyek--kemend-i_zulfu--afet_misirliyan.mu2 \n",
"253 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/beyati--beste--hafif--bir_gonca_femin--dede_efendi.mu2 \n",
"254 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/ferahnak--kupe--agirevfer--ol_guzel--ahmet_avni_konuk.mu2 \n",
"255 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/beyati--aranagme--agiraksak--1--.mu2 \n",
"256 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/mahur--sarki--duyek--petek_petek--erol_sayan.mu2 \n",
"257 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/ussak--beste--zencir--bahar_geldi--hafiz_post.mu2 \n",
"258 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nevabuselik--sarki--duyek--elbet_gonullerde--sadettin_kaynak.mu2 \n",
"259 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/ussak--ilahi--dolap--benim_adim--.mu2 \n",
"260 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/kurdilihicazkar--sarki--nimsofyan--cicek_nedir--selahattin_icli.mu2 \n",
"261 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicazkar--sarki--aksak--severim_her--nefise_ozses.mu2 \n",
"262 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/tahir--turku--raksan--top_yatagin--aydin.mu2 \n",
"263 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/karcigar--sarki--curcuna--gul_acar--cevdet_cagla.mu2 \n",
"264 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/zevkidil--kupe--devrirevanihindi--gel_terennum--ahmet_avni_konuk.mu2 \n",
"265 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/saba--pesrev--fahte----hasan_esen.mu2 \n",
"266 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nihavent--aranagme--agiraksak--1--.mu2 \n",
"267 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/segah--turku--aksak--ey_su--usak.mu2 \n",
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"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nihavent--sarki--turkaksagi--korfezdeki_dalgin--osman_nihat_akin.mu2 \n",
"269 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huseyni--sarki--aksak--bir_damlacik--omer_dilek.mu2 \n",
"270 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/rast--zeybek--agiraksak--kordon_zeybegi--.mu2 \n",
"271 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nihavent--fantezi--sofyan--sarahaten_acaba--ali_rifat_cagatay.mu2 \n",
"272 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/saba--sarki--aksak--sen_sinemde--necip_mirkelamoglu.mu2 \n",
"273 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/suzidilara--seyir--sofyan--1--erol_bingol.mu2 \n",
"274 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/ussak--sarki--curcuna--derdimi_anlatirdim--tahsin_karakus.mu2 \n",
"275 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/tahir--turku--kapali_curcuna--don_beri--urfa.mu2 \n",
"276 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/suzinak--mersiye--devrirevan--ehl-i_aska--.mu2 \n",
"277 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz--sarki--semai--degdi_saclarima--bekirof.mu2 \n",
"278 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicazkar--seyir--devrihindi--1--erol_bingol.mu2 \n",
"279 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huseyni--turku--devrihindi--semsiyemin_ucu--trakya.mu2 \n",
"280 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/ussak--sarki--aksak--bir_melek--haci_arif_bey.mu2 \n",
"281 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huzzam--aranagme--duyek--1--.mu2 \n",
"282 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/mahur--sarki--nimsofyan--cek_kuregi_guzelim--arif_sami_toker.mu2 \n",
"283 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/buselik--karce--muhammes--sunbuli_sunbuli--sestari_murat_aga.mu2 \n",
"284 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/sultaniyegah--seyir--semai--1--rauf_yekta.mu2 \n",
"285 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nisaburek--sarki--senginsemai--gormek_ister--garbis_efendi.mu2 \n",
"286 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicazkar--sarki--duyek--serpil_yagmur--cevdet_cagla.mu2 \n",
"287 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/suzidil--pesrev--devrikebir----tanburi_ali_efendi.mu2 \n",
"288 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/suzinak_zirgule--sarki--semai--simdi_uzaklardasin--zeki_muren.mu2 \n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"289 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nihavent--fantezi--nimsofyan--yemeni_baglamis--ali_ulvi_baradan.mu2 \n",
"290 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huseyni--sazsemaisi--aksaksemai----lavtaci_andon.mu2 \n",
"291 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huseyni--turku--evfer--dere_geliyor--.mu2 \n",
"292 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/mahur--turku--aksak--setiremin_dugmeleri--istanbul.mu2 \n",
"293 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huzzam--sarki--curcuna--gokyuzune_ciksan--omer_sami_gupgup.mu2 \n",
"294 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/neveser--sarki--senginsemai--sesimde_sarkisi--suphi_ziya_ozbekkan.mu2 \n",
"295 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
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"296 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicazkar--sarki--aksak--nolur_bir--mildan_niyazi_bey.mu2 \n",
"297 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/neva--kar--nimsakil--ey_gulbun-i--itri.mu2 \n",
"298 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
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"299 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
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"301 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huseyni--sarki--yuruksemai--senden_bilirim--tanburi_ali_efendi.mu2 \n",
"302 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nihavendikebir--kupe--devrirevanihindi--bu_nihavend_i--ahmet_avni_konuk.mu2 \n",
"303 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/rast--pesrev--cifteduyek--du-sems--farabi.mu2 \n",
"304 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/tahir--sarki--aksak--ben_sana--dellalzade_haci_ismail_efendi.mu2 \n",
"305 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz--sarki--turkaksagi--saydeyledi_bu--haci_arif_bey.mu2 \n",
"306 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
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"307 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
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"308 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz--sarki--duyek--sensizligi_ben--zekai_tunca.mu2 \n",
"309 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
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"310 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
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"311 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
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"313 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/beyati--mehter--yuruksemai_ii----zurnazen_dagi_ahmet_celebi.mu2 \n",
"314 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/isfahan--seyir--sofyan--1--erol_bingol.mu2 \n",
"315 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/tahirbuselik--beste--berefsan--bin_cefa--ahmet_irsoy.mu2 \n",
"316 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nikriz--zeybek--aksak--sobalarinda_guru--denizli.mu2 \n",
"317 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/beyati--sarki--senginsemai--bir_boyle--fehmi_tokay.mu2 \n",
"318 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/ussak--sazsemaisi--aksaksemai----dede_salih_efendi.mu2 \n",
"319 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/rast--kar_i_natik--cenber--rast_geldim--hatibzade_osman_efendi.mu2 \n",
"320 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huseyni--turku--sofyan--koskum_var--.mu2 \n",
"321 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/gerdaniye--turku--sofyan--kaleden_kaleye--adiyaman.mu2 \n",
"322 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/segah--sarki--yuruksemai_ii--kapat_gozlerini--mustafa_seyran.mu2 \n",
"323 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/segah--turku--aksak--cemberim_de--biga.mu2 \n",
"324 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/arazbarbuselik--sazsemaisi--aksaksemai----refik_fersan.mu2 \n",
"325 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/suzidil--sarki--agiraksak--bir_nigah_ile--nikogos_aga.mu2 \n",
"326 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/segah--turku--iki_bir--pencereden_tas--kars.mu2 \n",
"327 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/evic--sarki--aydin--kakulleri_lule--tanburi_mustafa_cavus.mu2 \n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"328 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/isfahan--sarki--murekkepsofyan--canda_hasiyyet--haci_arif_bey.mu2 \n",
"329 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/ussak--pesrev--devrikebir----iii_selim.mu2 \n",
"330 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/beyati--sarki--aksak--delisin_deli--selahattin_pinar.mu2 \n",
"331 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/buselik--sarki--senginsemai--bir_tek--ali_ulvi_baradan.mu2 \n",
"332 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/rast--sarki--duyek--sensiz_kalan--irfan_ozbakir.mu2 \n",
"333 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/muhayyer--turku--duyek--sari_kizin--kirsehir.mu2 \n",
"334 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/beyati--sarki--ciftesofyan--cikalim_sayd_u--tanburi_mustafa_cavus.mu2 \n",
"335 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/evic--seyir--semai--1--rauf_yekta.mu2 \n",
"336 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huzzam--pesrev--devrikebir----tanburi_buyuk_osman_bey.mu2 \n",
"337 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicazkar--aranagme--sofyan--1--.mu2 \n",
"338 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nihavent--mars--sofyan--istiklal_marsi--zeki_ungor.mu2 \n",
"339 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huseyni--mars--sofyan--ceddin_deden--kaptanzade_ali_riza_bey.mu2 \n",
"340 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nihavent--sarki--duyek--sen_benim--emin_ongan.mu2 \n",
"341 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/sevkefza--sarki--ciftesofyan--gecip_de_karsima--ahmed_aga.mu2 \n",
"342 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/muhayyerkurdi--sarki--duyek--var_mi_hacet--nikogos_aga.mu2 \n",
"343 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/rast--ornek_oz--sofyan--1--huseyin_sadettin_arel.mu2 \n",
"344 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/acemasiran--aranagme--senginsemai--1--.mu2 \n",
"345 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/ussak--tevsihilahi--evsat--bir_ismi_mustafa--dede_efendi.mu2 \n",
"346 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/acemasiran--pesrev--frenkcin----nuri_halil_poyraz.mu2 \n",
"347 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huzzam--sarki--turkaksagi--meftun_olali--haci_arif_bey.mu2 \n",
"348 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/acemasiran--sarki--aksak--oldu_gonul--dede_efendi.mu2 \n",
"349 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/muhayyer--sarki--sofyan--ne_zaman--sadettin_kaynak.mu2 \n",
"350 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/muberka--sazsemaisi--aksaksemai----.mu2 \n",
"351 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/ussak--kurthavasi--sofyan--karsida_kurt--.mu2 \n",
"352 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/sedaraban--sazsemaisi--aksaksemai----tanburi_cemil_bey.mu2 \n",
"353 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/muhayyer--ilahi--sofyan--duseli_bu_askin--dede_efendi.mu2 \n",
"354 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/beyati_araban--sarki--aksak--baga_girdim--sadi_hosses.mu2 \n",
"355 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/mahur--sarki--aksak--su_guzele--semseddin_ziya_bey.mu2 \n",
"356 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz--sarki--agiraksak--bir_nigah--sekerci_cemil_bey.mu2 \n",
"357 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nihavent--sarki--aksak--suzis-i_askinla--musa_sureyya_bey.mu2 \n",
"358 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/acemkurdi--sarki--kapali_curcuna--yukselen_nagmenle--muzaffer_ilkar.mu2 \n",
"359 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/suzinak_zirgule--turku--nimsofyan--yarim_gitti--istanbul.mu2 \n",
"360 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/rast--turku--sofyan--su_dereler--orta_anadolu.mu2 \n",
"361 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/kurdi--turku--nimsofyan--hastane_onunde--akdagmadeni.mu2 \n",
"362 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/mustear--seyir--duyek--1--erol_bingol.mu2 \n",
"363 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/rast--sarki--curcuna--aylar_geciyor--selahaddin_pinar.mu2 \n",
"364 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/kurdilihicazkar--sarki--evfer--karsiyakada--lavtaci_hristo.mu2 \n",
"365 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/mahur--aranagme--musemmen--1--.mu2 \n",
"366 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/askefza--sarki--sofyan--kimbilir_belki--murat_demirhan.mu2 \n",
"367 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/beyati--sarki--agiraksak--bulbul-u_seydaya--serif_icli.mu2 \n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"368 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/ussak--sarki--musemmen--gelmedin_bir--fehmi_tokay.mu2 \n",
"369 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nihavent--pesrev--devrikebir----kanuni_mehmed_bey.mu2 \n",
"370 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/suzinak--kupe--devrirevanihindi--nagmei_hungarey--ahmet_avni_konuk.mu2 \n",
"371 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/zavil--kupe--devrirevanihindi--sanma_ki--ahmet_avni_konuk.mu2 \n",
"372 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/ussak--sarki--nimsofyan--gordum_seni--sadettin_kaynak.mu2 \n",
"373 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huseyni--turku--yuruksemai--bir_hisminan--kars.mu2 \n",
"374 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/ussak--sarki--muasser--dilber_sana--erol_basara.mu2 \n",
"375 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/buselik--agirsemai--aksaksemai--niyaz-i_nagme-i--comlekcizade_recep_celebi.mu2 \n",
"376 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huzzam--sarki--turkaksagi--baglandi_gonul--selanikli_ahmed_bey.mu2 \n",
"377 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/gerdaniye--kupe--duyek--perde-i--ahmet_avni_konuk.mu2 \n",
"378 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/acem--selam--devrikebir--asik-i_ger--huseyin_fahreddin_dede.mu2 \n",
"379 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/evcara--seyir--sofyan--1--erol_bingol.mu2 \n",
"380 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nihavent--sarki--semai--suzul_guzel--necdet_varol.mu2 \n",
"381 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz--sarki--kapali_curcuna--anladim_sevmeyeceksin--selahattin_pinar.mu2 \n",
"382 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/rehavi--sarki--sofyan--mevsim-i_gul--muallim_ismail_hakki_bey.mu2 \n",
"383 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/segah--turku--sofyan--kalaliyam--urfa.mu2 \n",
"384 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nikriz--turku--turkaksagi--sen_bu--trabzon.mu2 \n",
"385 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/dugah--turku--evfer--aksarayda_cevirdiler--.mu2 \n",
"386 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/neva--turku--sofyan--canakkale_icinde--.mu2 \n",
"387 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/tahir--turku--sofyan--gine_gordum--yozgat.mu2 \n",
"388 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz--sarki--agir_aksaksemai--hasret_odu--tanburi_ali_efendi.mu2 \n",
"389 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huseyni--sarki--curcuna--bak_su--danbeni_riza_bey.mu2 \n",
"390 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/beyati--sarki--duyek--dilbera_sazin--tanburi_isak.mu2 \n",
"391 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/acemkurdi--sarki--kapali_curcuna--canan_bilirim--ekrem_guyer.mu2 \n",
"392 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/mahur--turku--sofyan--beyoglunda_gezersin--pirincci_riza_bey.mu2 \n",
"393 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/saba--sarki--duyek--donmem_kucagina--avni_anil.mu2 \n",
"394 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/rast--sarki--yuruksemai_ii--yuzundur_cihani--dede_efendi.mu2 \n",
"395 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/suzinak_zirgule--sarki--sofyan--gecti_alam-i--klarnet_ibrahim_efendi.mu2 \n",
"396 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/irak--seyir--sofyan--1--sefik_gurmeric.mu2 \n",
"397 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz--turku--sofyan--su_yaltadan--kirim.mu2 \n",
"398 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nihavent--sarki--nimsofyan--ben_bir--sivelioglu_yorgaki.mu2 \n",
"399 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz_humayun--seyir--sofyan--1--sefik_gurmeric.mu2 \n",
"400 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huseyni--turku--sofyan--yaylanin_cimenine--trabzon.mu2 \n",
"401 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/segah--sarki--aksak--suyi_kagithanede--lavtaci_hristo.mu2 \n",
"402 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/acemasiran--sarki--agiraksak--bir_haber--bimen_sen.mu2 \n",
"403 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/suzinak_zirgule--sarki--duyek--aylardir_gul--erol_sayan.mu2 \n",
"404 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nikriz--sarki--aksak--sevdim_seni--sinan_sipahi.mu2 \n",
"405 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nihavent--beste--lenkfahte--zulfun_gorenlerin--ali_rifat_cagatay.mu2 \n",
"406 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/beyati--sarki--agiraksak--gecti_sevdalarla--nuri_halil_poyraz.mu2 \n",
"407 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/acemasiran--sarki--duyek--gonul_seni--sadettin_kaynak.mu2 \n",
"408 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huseyni--turku--kapali_curcuna--havada_bulut--.mu2 \n",
"409 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/beyati--agirsemai--agir_aksaksemai--soylen_ol--bolahenk_nuri_bey.mu2 \n",
"410 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/mahur--turku--sofyan--yemenimde_hare_var--.mu2 \n",
"411 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huzzam--sarki--duyek--omrunde_sarmadin--omer_sami_gupgup.mu2 \n",
"412 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/suzinak_zirgule--nakis--yuruksemai_ii--cana_firak-i--dede_efendi.mu2 \n",
"413 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hisar--seyir--duyek--1--sefik_gurmeric.mu2 \n",
"414 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/evcara--sazsemaisi--aksaksemai----dilhayat_kalfa.mu2 \n",
"415 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/ussak--sarki--aksak--muntazirdir_sana--dellalzade_haci_ismail_efendi.mu2 \n",
"416 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/ruyiirak--kupe--agirevfer--ey_terennumsaz--ahmet_avni_konuk.mu2 \n",
"417 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz--sarki--semai--sana_dun--munir_nurettin_selcuk.mu2 \n",
"418 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/rast--ornek_oz--devrihindi--1--ruhi_ayangil.mu2 \n",
"419 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz--turku--sofyan--mektebin_bacalari--mus.mu2 \n",
"420 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/bestenigar--sarki--kapali_curcuna--ben_seni--dede_efendi.mu2 \n",
"421 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/kurdi--seyir--sofyan--1--erol_bingol.mu2 \n",
"422 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nihavent--sarki--sofyan--korfezde_meltem--ismail_otenkaya.mu2 \n",
"423 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huseyni--turku--curcuna--coban_kizi--.mu2 \n",
"424 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/karcigar--aranagme--curcuna--1--.mu2 \n",
"425 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/saba--miraciye--serbest--pes_heman--nayi_osman_dede.mu2 \n",
"426 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/muhayyerkurdi--pesrev--devrikebir----asdik_aga.mu2 \n",
"427 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/kurdilihicazkar--sarki--turkaksagi--ay_dalgalanirken--artaki_candan.mu2 \n",
"428 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/suzidilara--pesrev--agirduyek----iii_selim.mu2 \n",
"429 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/arazbar--kupe--duyek--ol_kadar--ahmet_avni_konuk.mu2 \n",
"430 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz--turku--nimsofyan--seni_sevdim--istanbul.mu2 \n",
"431 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/buselik--sarki--agiraksak--bir_peri-ruyin--hamparsum.mu2 \n",
"432 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/ussak--turku--nimsofyan--kadifeden_kesesi--.mu2 \n",
"433 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/ussak--seyir--sofyan--1--sefik_gurmeric.mu2 \n",
"434 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz_humayun--beste--agircenber--dusmesin_miskin--zaharya.mu2 \n",
"435 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huzzam--sarki--senginsemai--hal-i_dil-i--haci_arif_bey.mu2 \n",
"436 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/rast--sarki--musemmen--hab-gahi--giriftzen_asim_bey.mu2 \n",
"437 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nihavent--sarki--duyek--yillari_durduracak--necdet_tokatlioglu.mu2 \n",
"438 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/segah--sarki--agiraksak--sun_da_icsin--bimen_sen.mu2 \n",
"439 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/acemasiran--sarki--duyek--bu_hal--sadettin_kaynak.mu2 \n",
"440 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/yegah--sarki--nimsofyan--edersen_de--sevki_bey.mu2 \n",
"441 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz_humayun--sarki--aksak--beni_canimdan--muzaffer_ilkar.mu2 \n",
"442 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nihavent--fantezi--duyek--bahar_meltemidir--emin_ongan.mu2 \n",
"443 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/mahur--sarki--duyek--kadehin_dudagimda--rustu_eric.mu2 \n",
"444 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz--sarki--sofyan--gul_acarken--nihal_erkutun.mu2 \n",
"445 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/muhayyer--turku--sofyan--keklik_gibi--erzincan.mu2 \n",
"446 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/sultaniyegah--aranagme--turkaksagi--1--.mu2 \n",
"447 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/mahur--seyir--sofyan--1--erol_bingol.mu2 \n",
"448 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/tahir--turku--devrituran--asagidan_gelir--sivas.mu2 \n",
"449 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/muhayyerkurdi--sarki--duyek--bir_kizil--amir_ates.mu2 \n",
"450 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/sultaniyegah--sarki--aksak--askin_karanlik--mustafa_sunar.mu2 \n",
"451 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/ussak--agirsemai--agir_aksaksemai--padisah-i_isvesin--tahir_aga.mu2 \n",
"452 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/ussak--turku--musemmen--su_kislanin--adana.mu2 \n",
"453 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/muhayyer--sarki--sofyan--nicin_mahzun--haci_arif_bey.mu2 \n",
"454 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/kurdi--kupe--musemmen--seyr-i--ahmet_avni_konuk.mu2 \n",
"455 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/beyati--sarki--duyek--bende_oldum--rifat_bey.mu2 \n",
"456 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nevabuselik--kupe--aksak--buse_ummidiyle--ahmet_avni_konuk.mu2 \n",
"457 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/mahur--sarki--evsat--sabah_olsun--ibrahim_aga.mu2 \n",
"458 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/sevkefza--sazsemaisi--aksaksemai----said_dede.mu2 \n",
"459 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/yegah--sarki--agiraksak--bais_oldu--bolahenk_nuri_bey.mu2 \n",
"460 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/saba--sarki--aksak--can_verme--emin_ongan.mu2 \n",
"461 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz--sarki--yuruksemai_ii--ey_but-i--dede_efendi.mu2 \n",
"462 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz--kocekce--aksak--baharin_zamani--dede_efendi.mu2 \n",
"463 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nihavent--karsilama--sofyan----merzifon.mu2 \n",
"464 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/gulizar--turku--sofyan--dertli--erol_sayan.mu2 \n",
"465 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huseyni--sarki--agiraksak--ben_ezelden--misirli_udi_ibrahim_efendi.mu2 \n",
"466 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nihavent--sarki--semai--kacsam_birakip--mehves_hanim.mu2 \n",
"467 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huseyniasiran--sarki--curcuna--cemalin_semine--zekai_dede.mu2 \n",
"468 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/pencgah--seyir--semai--1--rauf_yekta.mu2 \n",
"469 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/ussak--sarki--sofyan--gamzedeyim_deva--tatyos_efendi.mu2 \n",
"470 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/ussak--ilahi--duyek--dostun_senden--ali_rifat_cagatay.mu2 \n",
"471 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz--turku--evfer--suruverin_cezveler--istanbul.mu2 \n",
"472 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/ussak--sarki--kapali_curcuna--cana_rakibi--giriftzen_asim_bey.mu2 \n",
"473 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/rehavi--mars--ikizaksak--hos_gelisler--.mu2 \n",
"474 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huzzam--sarki--aksak--guzellerde_ne--tanburi_mustafa_cavus.mu2 \n",
"475 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/muhayyer--turku--raksaksagi_ii--su_gelir--burdur.mu2 \n",
"476 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/muhayyer--turku--aksak--serenler--burdur.mu2 \n",
"477 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/rast--pesrev--devrikebir----giriftzen_asim_bey.mu2 \n",
"478 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nihavent--sazeseri--nimsofyan--kanatlarim_olsaydi--serif_muhittin_targan.mu2 \n",
"479 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/ussak--turku--aksak--su_dalmanin--aydin.mu2 \n",
"480 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/ferahnak--turku--duyek--mapusane_icinde--bartin.mu2 \n",
"481 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/yegah--agirsemai--aksaksemai--piyale_elde--dellalzade_haci_ismail_efendi.mu2 \n",
"482 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/muhayyer--turku--duyek--zehram_bana--yesari_asim_arsoy.mu2 \n",
"483 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huseyni--turku--curcuna--sari_gelin--erzurum.mu2 \n",
"484 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nihavent--kasaphavasi--duyek----.mu2 \n",
"485 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/acemasiran--sarki--devrihindi--neseyab_ol--ahmet_mukerrem_akinci.mu2 \n",
"486 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huseyni--ilahi--turkdarbi--ey_dil--ali_ufki.mu2 \n",
"487 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/acemkurdi--aranagme--senginsemai--1--.mu2 \n",
"488 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/dilkeshaveran--seyir--duyek--1--sefik_gurmeric.mu2 \n",
"489 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicazkar--seyir--semai--1--rauf_yekta.mu2 \n",
"490 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/segah--sazsemaisi--aksaksemai----nayi_osman_dede.mu2 \n",
"491 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz_humayun--sarki--agiraksak--pek_cefacusun--kemani_riza_efendi.mu2 \n",
"492 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/rast--sarki--senginsemai--dil_bir--dede_efendi.mu2 \n",
"493 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz--sarki--senginsemai--hep_saye-i--tanburi_cemil_bey.mu2 \n",
"494 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huseyni--beste--cenber--nigaha_ruhsat--zaharya.mu2 \n",
"495 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/karcigar--zeybek--oynak----.mu2 \n",
"496 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/buselik--sazsemaisi--aksaksemai----nefiri_behram_aga.mu2 \n",
"497 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nihavent--sarki--duyek--istanbul_biricik--sadettin_kaynak.mu2 \n",
"498 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/ussak--oyunhavasi--ikizaksak--ata_bari--artvin.mu2 \n",
"499 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/acemasiran--sarki--semai--bitmesin_icimdeki--omer_dilek.mu2 \n",
"500 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/saba--sarki--senginsemai--neydin_guzelim--serif_icli.mu2 \n",
"501 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huzzam--sarki--aksak--gozunun_rengini--selahattin_pinar.mu2 \n",
"502 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/mahur--sazsemaisi--aksaksemai----nikolaki.mu2 \n",
"503 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/beyati--agirsemai--aksaksemai--didi_ci--yahya_nazim_celebi.mu2 \n",
"504 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/sehnaz--aranagme--devrihindi--1--.mu2 \n",
"505 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/vechisehnaz--kupe--duyek--vech-i--ahmet_avni_konuk.mu2 \n",
"506 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/karcigar--turku--aksak--cayira_serdim--.mu2 \n",
"507 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/muhayyer--turku--senginsemai--bulandi_gozler--urfa.mu2 \n",
"508 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/ussak--turku--aksak--yangin_olur--.mu2 \n",
"509 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/segah--sarki--devrihindi--dil_harab-i--tanburi_ali_efendi.mu2 \n",
"510 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/gulizar--sarki--senginsemai--sormadi_hal-i--nikogos_aga.mu2 \n",
"511 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/mahur--sazsemaisi--aksaksemai----gazi_giray_han.mu2 \n",
"512 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huseyni--sarki--sofyan--zeytin_gozlum--selahattin_icli.mu2 \n",
"513 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/ussak--seyir--semai--1--rauf_yekta.mu2 \n",
"514 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz--sarki--semai--nasil_gecti--teoman_alpay.mu2 \n",
"515 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/kurdilihicazkar--sarki--kapali_curcuna--sana_ey--rahmi_bey.mu2 \n",
"516 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/muhayyer--sarki--aksaksemai--gun_kavustu--udi_nevres_bey.mu2 \n",
"517 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/beyati--yuruksemai--yuruksemai--gul_yuzlulerin--tabi_mustafa_efendi.mu2 \n",
"518 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huseyni--ilahi--nimevsat--idelim_cevlan--.mu2 \n",
"519 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/rast--mars--sofyan--baska_bir--musa_sureyya_bey.mu2 \n",
"520 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nihavent--fantezi--semai--ne_bildim--alaeddin_yavasca.mu2 \n",
"521 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nihavent--sarki--agiraksak--nar-i_askinla--rifat_bey.mu2 \n",
"522 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/ferahfeza--sarki--semai--bulbul-i_hos_neva--dede_efendi.mu2 \n",
"523 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/goncairana--kupe--musemmen--bulbul-i--ahmet_avni_konuk.mu2 \n",
"524 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/kurdi--beste--muhammes--kametin_servi--hafiz_rifat_molla.mu2 \n",
"525 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicazkar--sarki--aksak--bekledim_fecre--rakim_elkutlu.mu2 \n",
"526 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/segah--sarki--ciftesofyan--sema-i_maksudu--dellalzade_haci_ismail_efendi.mu2 \n",
"527 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/rast--pesrev--sakil----benli_hasan_aga.mu2 \n",
"528 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/mahur--sarki--musemmen--aman_saki--lemi_atli.mu2 \n",
"529 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/suzinak--aranagme--aksak--1--.mu2 \n",
"530 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huseyni--turku--curcuna--sinemde_bir--.mu2 \n",
"531 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/acemasiran--sarki--semai--ey_cesm-i--nikogos_aga.mu2 \n",
"532 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/sazkar--seyir--sofyan--1--sefik_gurmeric.mu2 \n",
"533 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nihavent--fantezi--duyek--ufacik_tefeciktin--sekip_ayhan_ozisik.mu2 \n",
"534 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huzzam--ornek_oz--sofyan--1--ruhi_ayangil.mu2 \n",
"535 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/isfahan--pesrev--fahte----kanuni_sait_bey.mu2 \n",
"536 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/rast--sarki--senginsemai--sevda_ile_dillendi--munir_nurettin_selcuk.mu2 \n",
"537 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/kurdilihicazkar--sarki--aksak--nazarin_fikrime--leyla_saz.mu2 \n",
"538 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nihavent--sarki--sofyan--gelmedin--ismail_gokce.mu2 \n",
"539 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/neva--sazsemaisi--aksaksemai----yusuf_ziya_pasa.mu2 \n",
"540 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/evic--seyir--sofyan--1--sefik_gurmeric.mu2 \n",
"541 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/kurdilihicazkar--sarki--agiraksak--kalb-i_sevdazedeler--selanikli_ahmed_bey.mu2 \n",
"542 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/evcara--seyir--semai--1--rauf_yekta.mu2 \n",
"543 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/ussak--sarki--agiraksak--na-umid-i--sekerci_cemil_bey.mu2 \n",
"544 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/ussak--ornek_oz--senginsemai--1--ruhi_ayangil.mu2 \n",
"545 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/dugah--seyir--sofyan--1--erol_bingol.mu2 \n",
"546 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/rast--sarki--aksak--reng-i_ruhsarina--erol_sayan.mu2 \n",
"547 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/rahatfeza--kupe--agirevfer--etse_agaze--ahmet_avni_konuk.mu2 \n",
"548 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/neva--pesrev--devrikebir----zeki_mehmet_aga.mu2 \n",
"549 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz--rumeliturkusu--sofyan--gemi_kalkar--rumeli.mu2 \n",
"550 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/bestenigar--sazsemaisi--aksaksemai----tanburi_cemil_bey.mu2 \n",
"551 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/ussak--oyunhavasi--aksak--bandirma_dortlemesi--.mu2 \n",
"552 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/beyati--sarki--agiraksak--gul_hazin--rahmi_bey.mu2 \n",
"553 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/saba--ornek_oz--yuruksemai_ii--1--ruhi_ayangil.mu2 \n",
"554 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huseyni--agirsemai--aksaksemai--talatin_devri--zaharya.mu2 \n",
"555 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz_humayun--turku--turkaksagi--beni_seni--trabzon.mu2 \n",
"556 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/buselik--ornek_oz--sofyan--1--huseyin_sadettin_arel.mu2 \n",
"557 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huzzam--turku--aksak--arabaya_tas--istanbul.mu2 \n",
"558 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/beyati--sarki--aksak--meclise_gel--tanburi_mustafa_cavus.mu2 \n",
"559 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/mahur--sarki--curcuna--askinla_harab--emin_ongan.mu2 \n",
"560 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huseyni--sarki--aksak--bir_dilberdir--tanburi_mustafa_cavus.mu2 \n",
"561 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/isfahan--sarki--devrihindi--dil-i_bicare--mahmud_celaleddin_pasa.mu2 \n",
"562 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/rast--sarki--duyek--hicran_olacaksa--ferit_sidal.mu2 \n",
"563 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huseyni--aranagme--raksaksagi--1--.mu2 \n",
"564 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/muhayyer--sarki--yuruksemai--ben_sana_asik--dede_efendi.mu2 \n",
"565 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/segah--sazsemaisi--aksaksemai----hizir_aga.mu2 \n",
"566 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/muhayyer--pesrev--darbifetih----tanburi_isak.mu2 \n",
"567 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz--turku--devrihindi--cayelinden_oteye--rize.mu2 \n",
"568 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicazkar--sarki--curcuna--senelerce--sabri_suha_ansen.mu2 \n",
"569 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huzzam--sarki--aksak--hicran_yine_hicran--serif_icli.mu2 \n",
"570 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/dilkeside--kupe--yuruksemai_ii--dilkeside--ahmet_avni_konuk.mu2 \n",
"571 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/mahur--sarki--duyek--ipek_saclarin--sadettin_kaynak.mu2 \n",
"572 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/segah--sarki--duyek--ayrilik_yaman--sadettin_kaynak.mu2 \n",
"573 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/muhayyerkurdi--aranagme--nimsofyan--1--.mu2 \n",
"574 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huseyni--sarki--sofyan--tez_gecse_de--selahattin_inal.mu2 \n",
"575 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/isfahan--turku--aksak--feslegen_ektim--tanburi_mustafa_cavus.mu2 \n",
"576 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/rast--fantezi--duyek--haydi_biraz--omer_sami_gupgup.mu2 \n",
"577 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/karcigar--aranagme--devrihindi--1--.mu2 \n",
"578 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/sehnaz--agirsemai--senginsemai--didem_yuzune--yahya_nazim_celebi.mu2 \n",
"579 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/muhayyerkurdi--turku--aksak--bulbulum_altin--.mu2 \n",
"580 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/segah--sarki--duyek--donulmez_aksamin--munir_nurettin_selcuk.mu2 \n",
"581 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/segah--sarki--aksak--bulbul_yetisir--haci_arif_bey.mu2 \n",
"582 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/mahur--sarki--curcuna--sakiz_hanim--bahadir_akkuzu.mu2 \n",
"583 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huzzam--turku--ikizaksak--cirpinirdi_karadeniz--uzeyir_hacibeyov.mu2 \n",
"584 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/buselik--sarki--aksak--inanmaz_vad_i--latif_aga.mu2 \n",
"585 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicazkar--kocekce--aydin--yabandan_geldim--muallim_ismail_hakki_bey.mu2 \n",
"586 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/besteisfahan--kupe--agirevfer--isfahanli_bir--ahmet_avni_konuk.mu2 \n",
"587 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huseyni--turku--aksak--yuce_dag--sadettin_kaynak.mu2 \n",
"588 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/acemkurdi--sarki--senginsemai--askin_ile--sadi_hosses.mu2 \n",
"589 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz--aranagme--curcuna--1--.mu2 \n",
"590 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz--beste--agirduyek--ey_cesm-i--dede_efendi.mu2 \n",
"591 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huseyni--turku--sofyan--ciktim_belen--mugla.mu2 \n",
"592 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/saba--sarki--agiraksak--sevdi_canim--komurcuzade_hafiz_mehmet_efendi.mu2 \n",
"593 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/gulsen_ivefa--sazsemaisi--aksaksemai----feraizcizade_ibrahim_efendi.mu2 \n",
"594 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicazkar--aranagme--aksak--1--.mu2 \n",
"595 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huzzam--seyir--duyek--1--erol_bingol.mu2 \n",
"596 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/segah--turku--aksak--yarim_istanbulu--kayseri.mu2 \n",
"597 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/pesendide--kupe--devrirevanihindi--bir_pesendide--ahmet_avni_konuk.mu2 \n",
"598 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/acemtarab--pesrev--havi----kantemiroglu.mu2 \n",
"599 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/rast--sarki--kapali_curcuna--hatirimdan_cikmaz--haci_arif_bey.mu2 \n",
"600 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/rast--sarki--sofyan--ask_denilen--erol_sayan.mu2 \n",
"601 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/sehnaz--seyir--devrihindi--1--erol_bingol.mu2 \n",
"602 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/rehavi--nakis--yuruksemai--biz_alude-i--hafiz_post.mu2 \n",
"603 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/zirefkend--kupe--aksaksemai--pertev-i--ahmet_avni_konuk.mu2 \n",
"604 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/peykinesat--pesrev--yuruksemai_ii----nefiri_behram_aga.mu2 \n",
"605 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/ferahfeza--yuruksemai--yuruksemai_ii--son_yuruksemai--zeki_mehmet_aga.mu2 \n",
"606 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/saba--ilahi--duyek--git_ey--munir_nurettin_selcuk.mu2 \n",
"607 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/segah--sarki--senginsemai--kactim_birakip--kirkor_cigercioglu.mu2 \n",
"608 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz--sarki--duyek--bir_atesim--avni_anil.mu2 \n",
"609 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/beyatibuselik--kupe--agiraksak--bir_beyatibuselik--ahmet_avni_konuk.mu2 \n",
"610 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/segah--turku--semai--baharin_gulsen--kars.mu2 \n",
"611 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/segah--turku--oynak--entarine_pes--afyon-sandikli.mu2 \n",
"612 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nihavent--sarki--senginsemai--bin_gul--lemi_atli.mu2 \n",
"613 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/isfahan--sarki--curcuna--etme_beyhude--rahmi_bey.mu2 \n",
"614 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz_humayun--sazsemaisi--aksaksemai----tanburi_isak.mu2 \n",
"615 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz--sarki--aksak--sazlar_calinir--yesari_asim_arsoy.mu2 \n",
"616 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/rast--sarki--aksak--ay_gulsun--ismail_baha_surelsan.mu2 \n",
"617 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/muhayyer--sarki--yuruksemai_ii--sevdicegim_asikini--dede_efendi.mu2 \n",
"618 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/mustear--kupe--darb--kimden--ahmet_avni_konuk.mu2 \n",
"619 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz_uzzal--sarki--yuruksemai--ulfet_etsem--sevki_bey.mu2 \n",
"620 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n"
]
},
{
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"text": [
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/acemkurdi--sazsemaisi--aksaksemai----muallim_ismail_hakki_bey.mu2 \n",
"621 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/gerdaniye--turku--musemmen_ii--dost_eline--malatya.mu2 \n",
"622 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/suzinak--sarki--curcuna--sen_bezmimize--hasan_ali_yucel.mu2 \n",
"623 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/sevkefza--pesrev--muhammes----numan_aga.mu2 \n",
"624 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/karcigar--sarki--duyek--bir_kiz_ile--sadettin_kaynak.mu2 \n",
"625 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/acemasiran--aranagme--sofyan--1--.mu2 \n",
"626 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/suzinak--sarki--senginsemai--sensiz_geceler--musa_sureyya_bey.mu2 \n",
"627 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/suzidil--agirsemai--aksaksemai--beni_ey--sadullah_aga.mu2 \n",
"628 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/saba--sarki--duyek--bu_aksam--safiye_ayla.mu2 \n",
"629 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz--sarki--turkaksagi--gonlum_yarali--kadri_sencalar.mu2 \n",
"630 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/sehnazbuselik--sazsemaisi--aksaksemai----seref_cakar.mu2 \n",
"631 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/saba--sarki--agirsenginsemai--sahilde_saba--mustafa_nafiz_irmak.mu2 \n",
"632 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicazkar--sarki--agirduyek--can_ile_ben--sakir_aga.mu2 \n",
"633 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/rast--turku--oynak--aman_aysem--.mu2 \n",
"634 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/kurdilihicazkar--aranagme--turkaksagi--1--.mu2 \n",
"635 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/suzinak--ilahi--cifteduyek--yandi_bu--ali_riza_sengel.mu2 \n",
"636 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/ussak--sarki--semai--imza_attim--gulen_ertek.mu2 \n",
"637 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/segah--sarki--turkaksagi--sevda_yaratan--yesari_asim_arsoy.mu2 \n",
"638 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/ussak--sarki--duyek--mehtapli_gecelerde--sevim_sengul.mu2 \n",
"639 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz_uzzal--sarki--aksak--kurdu_meclis--haci_arif_bey.mu2 \n",
"640 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nikriz--turku--sofyan--kahve_yemenden--.mu2 \n",
"641 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/saba--seyir--semai--1--rauf_yekta.mu2 \n",
"642 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/acemkurdi--sarki--devrihindi--bir_vefasiz--selanikli_ahmed_bey.mu2 \n",
"643 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huzzam--sarki--agiraksak--ben_esir-i--udi_arsak_comlekciyan.mu2 \n",
"644 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nikriz--fantezi--yuruksemai_ii--uzaga_gitme--bilge_ozgen.mu2 \n",
"645 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/muhayyer--turku--musemmen_ii--elma_attim--tunceli.mu2 \n",
"646 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huseyni--sarki--agiraksak--farig_olmam--yesari_asim_arsoy.mu2 \n",
"647 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/buselik--sarki--agiraksak--dun_gece--serif_icli.mu2 \n",
"648 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/neveser--sirto--sofyan----neveser_kokdes.mu2 \n",
"649 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/rast--sarki--curcuna--anlatayim_halimi--tanburi_ali_efendi.mu2 \n",
"650 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/acemasiran--sarki--agiraksak--mahveder_her--neyzen_riza_bey.mu2 \n",
"651 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nisaburek--sarki--agiraksak--ey_nihal-i--akin_ozkan.mu2 \n",
"652 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nihavent--sarki--curcuna--saclarina_baglanali--rahmi_bey.mu2 \n",
"653 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz--turku--turkaksagi--mayadagdan_kalkan--.mu2 \n",
"654 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/kurdilihicazkar--etud--sofyan----ozer_ozel.mu2 \n",
"655 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nihavent--sarki--yuruksemai_ii--vucud_ikliminin--haci_arif_bey.mu2 \n",
"656 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/evic--sarki--agiraksak--bir_sebeple--kazasker_mustafa_izzet_efendi.mu2 \n",
"657 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicazbuselik--kupe--aksak--pek_hararet--ahmet_avni_konuk.mu2 \n",
"658 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hisarbuselik--sarki--aksak--aman_ey--ii_mahmud.mu2 \n",
"659 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nihavent--turku--agir_aksaksemai--bir_ates_ver--.mu2 \n",
"660 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz_humayun--popsarkisi--sofyan--bogazinda_dugumlenen--erol_tanir.mu2 \n",
"661 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/yegah--seyir--semai--1--rauf_yekta.mu2 \n",
"662 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/buselik--sarki--turkaksagi--dun_gece_sende--sakir_aga.mu2 \n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"663 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nihavent--sarki--aksak--gel_guzelim--faiz_kapanci.mu2 \n",
"664 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/tahir--sarki--duyek--gonul_vermisken--fehmi_tokay.mu2 \n",
"665 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/saba--sarki--aksak--bir_esmere--tanburi_mustafa_cavus.mu2 \n",
"666 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/acemasiran--ilahi--muhammes--ta_dil--calakzade_mustafa_efendi.mu2 \n",
"667 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/rast--sarki--azeriyuruksemai--ben_yarali--zeki_duygulu.mu2 \n",
"668 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/saba--seyir--sofyan--1--sefik_gurmeric.mu2 \n",
"669 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/muhayyerkurdi--sarki--nimsofyan--gonlumu_gonlune--omer_sami_gupgup.mu2 \n",
"670 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/buselik--sarki--aksak--dil_sana--tanburi_isak.mu2 \n",
"671 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/ussak--sazsemaisi--aksaksemai----kantemiroglu.mu2 \n",
"672 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/segah--salatiummiye--aksaksemaievferi--allahumme_salli--itri.mu2 \n",
"673 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/sedaraban--seyir--semai--1--rauf_yekta.mu2 \n",
"674 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huzzam--sarki--aksak--gonul_verdim--iii_selim.mu2 \n",
"675 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/suzinak--seyir--semai--1--rauf_yekta.mu2 \n",
"676 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huzzam--sazsemaisi--aksaksemai----udi_nevres_bey.mu2 \n",
"677 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nihavent--sarki--agiraksak--ahter-i_duskun--haci_arif_bey.mu2 \n",
"678 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/sehnaz--sarki--aydin--ey_dilber-i--rahmi_bey.mu2 \n",
"679 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/ussak--sarki--duyek--gun_gelir_de--sekip_ayhan_ozisik.mu2 \n",
"680 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/suzinak--turku--oynak--a_benim--osman_pehlivan.mu2 \n",
"681 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/rast--sarki--curcuna--aglasam_her--gulbenkyan.mu2 \n",
"682 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/kurdi--turku--sofyan--dalda_cikmis--.mu2 \n",
"683 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/rast--sarki--duyek--senin_askinla_cakoldum--basmaci_abdi_efendi.mu2 \n",
"684 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/ussak--sarki--semai--gonlume_gir--yilmaz_yuksel.mu2 \n",
"685 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz_humayun--agirsemai--aksaksemai--kul_oldum--ibrahim_aga.mu2 \n",
"686 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/kurdi--aranagme--nimsofyan--1--.mu2 \n",
"687 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/rehavi--beste--remel--zannetme_ben--numan_aga.mu2 \n",
"688 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/ferahfeza--seyir--semai--1--rauf_yekta.mu2 \n",
"689 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicazkar--sarki--semai--sari_mimozamsin--alaeddin_yavasca.mu2 \n",
"690 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/ussak--kupe--duyek--sanma_ussakin--ahmet_avni_konuk.mu2 \n",
"691 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/karcigar--sarki--curcuna--gorunce_ben--selanikli_ahmed_bey.mu2 \n",
"692 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/ussak--sarki--nimsofyan--yillar_sonra--cahit_deniz.mu2 \n",
"693 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/segah--turku--kapali_curcuna--incecikten_bir--sadettin_kaynak.mu2 \n",
"694 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/acemasiran--sarki--agiraksak--ne_kadar--artaki_candan.mu2 \n",
"695 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/tahir--aranagme--duyek--1--.mu2 \n",
"696 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/kurdilihicazkar--sarki--aksak--safvet-i_askim--sevki_bey.mu2 \n",
"697 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz--fantezi--yuruksemai_ii--gamze_gamze--sezen_aksu.mu2 \n",
"698 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz--sarki--duyek--hicbir_seyde--fethi_karamahmutoglu.mu2 \n",
"699 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/yegah--sarki--agirduyek--yok_mu--hatipzade_ibrahim_efendi.mu2 \n",
"700 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/ussak--turku--sofyan--mevlam_bircok--malatya.mu2 \n",
"701 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n"
]
},
{
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"text": [
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/buselik--murabba--zencir--gonul_o_turra--yahya_nazim_celebi.mu2 \n",
"702 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/segah--turku--sofyan--ana_beni--.mu2 \n",
"703 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/evic--turku--aksak--havada_turna--istanbul.mu2 \n",
"704 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/beyati--ilahi--durakevferi--bulbul-i_surideyim--nalizade_ibrahim_efendi.mu2 \n",
"705 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nikriz--sarki--turkaksagi--bir_destan--selahattin_icli.mu2 \n",
"706 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huseyni--ornek_oz--sofyan--3--ruhi_ayangil.mu2 \n",
"707 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicazkar--sarki--devrihindi--lezzet_almis--ahmet_mithat_gupgupoglu.mu2 \n",
"708 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/tahirbuselik--kupe--aksak--bak_ne--ahmet_avni_konuk.mu2 \n",
"709 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz--zeybek--agiraksak--harmandali--ege.mu2 \n",
"710 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/mahur--yuruksemai--yuruksemai--beni_cun--ebubekir_aga.mu2 \n",
"711 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/mahur--sarki--duyek--olsa_da_hos--sadettin_kaynak.mu2 \n",
"712 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz_uzzal--kupe--duyek--nagme-i_uzzali--ahmet_avni_konuk.mu2 \n",
"713 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/rast--sarki--semai--perisan_saclarin--yesari_asim_arsoy.mu2 \n",
"714 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz_humayun--kar--hafif--ey_ki--makbul_ibrahim_pasa.mu2 \n",
"715 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/muhayyerkurdi--turku--ciftesofyan--sendeki_kaslar--.mu2 \n",
"716 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nikriz--turku--nimsofyan--sokak_basi--giresun.mu2 \n",
"717 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/saba--turku--duyek--hangi_bagin--.mu2 \n",
"718 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/acemkurdi--sarki--semai--yillar_sonra--yildirim_gurses.mu2 \n",
"719 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/segah--zeybek--agiraksak--antalya_zeybegi--antalya.mu2 \n",
"720 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/buselik--beste--hafif--her_gordugu--itri.mu2 \n",
"721 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/bestenigar--sarki--kapali_curcuna--cok_surmedi--enderunlu_hafiz_husnu_efendi.mu2 \n",
"722 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz--yuruksemai--yuruksemai--yine_nese-i--dede_efendi.mu2 \n",
"723 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/segah--yuruksemai--yuruksemai--tuti_i_mucize--itri.mu2 \n",
"724 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/irak--seyir--sofyan--1--erol_bingol.mu2 \n",
"725 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/beyati--seyir--sofyan--1--erol_bingol.mu2 \n",
"726 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/muhayyer--ilahi--duyek--toprakta_yatacak--dede_efendi.mu2 \n",
"727 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/suzinak_zirgule--sarki--duyek--sen_ayrilik--omer_dilek.mu2 \n",
"728 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/beyati--seyir--sofyan--1--sefik_gurmeric.mu2 \n",
"729 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/arazbar--seyir--semai--1--rauf_yekta.mu2 \n",
"730 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/dugah--ilahi--agircenber--gel_ey_salik--dede_efendi.mu2 \n",
"731 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz--sarki--aksak--bir_bahar--selahattin_pinar.mu2 \n",
"732 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicazkar--sazsemaisi--aksaksemai----gazi_giray_han.mu2 \n",
"733 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz_humayun--sazsemaisi--aksaksemai----neyzen_yusuf_pasa.mu2 \n",
"734 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicazkar--sarki--duyek--ciceklerin_guluyor--sadettin_kaynak.mu2 \n",
"735 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nihavent--pesrev--hafif--mini_mini--huseyin_sadettin_arel.mu2 \n",
"736 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nihavent--turku--aksak--evlerinin_onu--muzaffer_sarisozen.mu2 \n",
"737 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nihavent--sarki--curcuna--guzel_bir--osman_nihat_akin.mu2 \n",
"738 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz--mandra--devrituran_ii----lavtaci_andon.mu2 \n",
"739 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/buselikasiran--kupe--aksaksemai--buselikten--ahmet_avni_konuk.mu2 \n",
"740 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huzzam--sarki--duyek--a_canim--tanburi_mustafa_cavus.mu2 \n",
"741 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huseyni--pesrev--cifteduyek----tanburi_isak.mu2 \n",
"742 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/ruyidilara--sazsemaisi--aksaksemai----erzurumlu_hasib_dede.mu2 \n",
"743 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/ussak--sarki--aksak--gitti_de--dede_efendi.mu2 \n",
"744 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/ussak--turku--sofyan--sigaramin_dumani--.mu2 \n",
"745 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/mahur--sarki--duyek--saba_tarf-i--enderunlu_hafiz_husnu_efendi.mu2 \n",
"746 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/rast--mersiye--sofyan--ey_padiseh-i--rifat_bey.mu2 \n",
"747 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz_humayun--sarki--evsat--nicin_a--nikogos_aga.mu2 \n",
"748 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/karcigar--ornek_oz--devrihindi--1--ruhi_ayangil.mu2 \n",
"749 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nuhuft--beste--darbifetih--ta_kim--seyyid_nuh.mu2 \n",
"750 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz--sarki--turkaksagi--solsan_da_sararsan--misirli_udi_ibrahim_efendi.mu2 \n",
"751 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/muhayyer--turku--nimsofyan--ben_de--adana.mu2 \n",
"752 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz--turku--sofyan--gokteki_yildizin--.mu2 \n",
"753 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/kurdilihicazkar--sarki--aksak--muntazir_tesrifine--haci_arif_bey.mu2 \n",
"754 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/rast--sarki--sofyan--yarali_gonlumde--omer_sami_gupgup.mu2 \n",
"755 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/mahur--aranagme--senginsemai--1--.mu2 \n",
"756 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nikriz--longa--nimsofyan----haydar_tatliyay.mu2 \n",
"757 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz--turku--yuruksemai_ii--bitliste_bes--.mu2 \n",
"758 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/kurdilihicazkar--sazsemaisi--aksaksemai----tatyos_efendi.mu2 \n",
"759 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicazkar--sarki--duyek--sen_gozlerinle--alaeddin_yavasca.mu2 \n",
"760 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nisaburek--sarki--aydin--meclise_gel--tanburi_mustafa_cavus.mu2 \n",
"761 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/evcara--kupe--agirevfer--kaddi_bala--ahmet_avni_konuk.mu2 \n",
"762 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/suzinak--seyir--sofyan--1--erol_bingol.mu2 \n",
"763 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/segah--turku--sofyan--pancar_pezik--hacibektas.mu2 \n",
"764 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nihavent--sarki--duyek--doymadim_sana--nevzat_akay.mu2 \n",
"765 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaziussak--nefes--bektasiraksi--onume_bir--pir_sultan_abdal.mu2 \n",
"766 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/evic--turku--aksak--ey_benim--.mu2 \n",
"767 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/buselik--sarki--sofyan--ol_meh--medeni_aziz_efendi.mu2 \n",
"768 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huseyniasiran--seyir--duyek--1--erol_bingol.mu2 \n",
"769 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/acemasiran--seyir--semai--1--rauf_yekta.mu2 \n",
"770 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huseyni--sarki--kapali_curcuna--nedir_bu--sevki_bey.mu2 \n",
"771 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz_zirgule--turku--evfer--kirda_erik--ankara.mu2 \n",
"772 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/kurdilihicazkar--sarki--semai--canandan_uzak--neveser_kokdes.mu2 \n",
"773 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz--turku--musemmen_ii--zuluf_dokulmus_yuze--kirsehir.mu2 \n",
"774 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/karcigar--seyir--devrihindi--1--erol_bingol.mu2 \n",
"775 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nihavent--etud--sofyan----ozer_ozel.mu2 \n",
"776 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/ussak--sarki--aksak--bu_aksam--tatyos_efendi.mu2 \n",
"777 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/askefza--seyir--devrihindi--1--erol_bingol.mu2 \n",
"778 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/besteisfahan--yuruksemai--yuruksemai--bir_elif--ebubekir_aga.mu2 \n",
"779 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/rahatulervah--kupe--agirevfer--hic_soz--ahmet_avni_konuk.mu2 \n",
"780 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/kurdi--turku--agiraksak--elif_dedim--kutahya.mu2 \n",
"781 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/mahur--turku--sofyan--su_dere--.mu2 \n",
"782 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/sehnazbuselik--kupe--aksak--ehl-i_meclis--ahmet_avni_konuk.mu2 \n",
"783 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/asiranmaye--kupe--aksaksemai--bir--ahmet_avni_konuk.mu2 \n",
"784 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/evic--sazsemaisi--aksaksemai----muallim_ismail_hakki_bey.mu2 \n",
"785 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/muhayyerkurdi--fantezi--duyek--gunlerce_uykusuz--sinan_sipahi.mu2 \n",
"786 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/saba--sarki--duyek--gule_sorma--erol_sayan.mu2 \n",
"787 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nikriz--longa--nimsofyan----tanburi_cemil_bey.mu2 \n"
]
},
{
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"output_type": "stream",
"text": [
"788 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/suzidil--sarki--ciftesofyan--gul_olsam--bimen_sen.mu2 \n",
"789 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huzzam--sarki--duyek--beklerim_her_gun--ismail_hakki_nebiloglu.mu2 \n",
"790 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huseyni--turku--sofyan--cikayim_gideyim--.mu2 \n",
"791 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz--turku--aksak--karanfil_oylum_oylum--.mu2 \n",
"792 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/gerdaniye--turku--sofyan--bin_derdim--maras.mu2 \n",
"793 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz--sarki--semai--bulbulun_cilesi--yusuf_nalkesen.mu2 \n",
"794 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/pesendide--sazsemaisi--aksaksemai----iii_selim.mu2 \n",
"795 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/evic--pesrev--devrikebir----tanburi_kucuk_osman_bey.mu2 \n",
"796 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicazkar--kocekce--devrihindi--ay_mi--muallim_ismail_hakki_bey.mu2 \n",
"797 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/kurdilihicazkar--sarki--aksak--nicin_terkeyleyip--haci_arif_bey.mu2 \n",
"798 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/kurdilihicazkar--sarki--turkaksagi--soylendi_bugun--mediha_sen_sancakoglu.mu2 \n",
"799 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/acemkurdi--sarki--aksak--kir_atima--nasibin_mehmet_yuru.mu2 \n",
"800 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/segah--sarki--agiraksak--bensiz_ey--bimen_sen.mu2 \n",
"801 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huzzam--beste--remel--aldim_hayal-i--komurcuzade_hafiz_mehmet_efendi.mu2 \n",
"802 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/muhayyerkurdi--kupe--musemmen--bir--ahmet_avni_konuk.mu2 \n",
"803 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/sevkefza--seyir--turkaksagi--1--erol_bingol.mu2 \n",
"804 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/acemkurdi--sarki--aksak--ruhum_su--alaeddin_yavasca.mu2 \n",
"805 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/karcigar--beste--zencir--yikildi_ask_ile--dellalzade_haci_ismail_efendi.mu2 \n",
"806 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/karcigar--nakis--yuruksemai--nihani_ol--dellalzade_haci_ismail_efendi.mu2 \n",
"807 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/segah--sarki--aksak--gel_ey--tanburi_cemil_bey.mu2 \n",
"808 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nihavent--yuruksemai--yuruksemai--ayrildi_gonul--zeki_duygulu.mu2 \n",
"809 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/sultaniyegah--aranagme--nimsofyan--1--.mu2 \n",
"810 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nihavent--sarki--sofyan--anla_artik--yildirim_gurses.mu2 \n",
"811 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/rast--rumeliturkusu--devrihindi_ii--kircaliyle_arda--rumeli.mu2 \n",
"812 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huzzam--ornek_oz--oynak--1--ruhi_ayangil.mu2 \n",
"813 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/acemasiran--zeybek--agiraksak--su_izmirin--tahsin_karakus.mu2 \n",
"814 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/sehnaz--sarki--aksak--denizin_dalgasini--semseddin_ziya_bey.mu2 \n",
"815 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/sultaniyegah--seyir--sofyan--1--erol_bingol.mu2 \n",
"816 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/segah--sarki--duyek--o_gece--omer_sami_gupgup.mu2 \n",
"817 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz--sarki--semai--ey_mehlika--muallim_ismail_hakki_bey.mu2 \n",
"818 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/rast--sarki--evfer--kordonboyu--erol_sayan.mu2 \n",
"819 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/acemkurdi--pesrev--fahte----muallim_ismail_hakki_bey.mu2 \n",
"820 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/rast--etud--sofyan----ozer_ozel.mu2 \n",
"821 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huseyni--pesrev--havi----dervis_mustafa.mu2 \n",
"822 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nihavent--sazsemaisi--aksaksemai----vecdi_seyhun.mu2 \n",
"823 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz--sarki--devrihindi--baglanip_zulf--sevki_bey.mu2 \n",
"824 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/evchuzi--turku--aksak--ben_havada--.mu2 \n",
"825 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n"
]
},
{
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"text": [
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huseyni--turku--aksak--yanik_omer--sadettin_kaynak.mu2 \n",
"826 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huzzam--sarki--aksak--yine_bir--serif_icli.mu2 \n",
"827 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/muhayyer--sarki--sofyan--bir_gun--ziya_taskent.mu2 \n",
"828 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/suzinak--seyir--sofyan--1--sefik_gurmeric.mu2 \n",
"829 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/kurdilihicazkar--sarki--duyek--gecmesin_gunumuz--alaeddin_yavasca.mu2 \n",
"830 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/mahur--sarki--agiraksak--gulsen-i_ezhar--basmaci_abdi_efendi.mu2 \n",
"831 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/rast--turku--evfer--evrese_yollari--.mu2 \n",
"832 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/dilkeshaveran--seyir--duyek--1--erol_bingol.mu2 \n",
"833 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/kurdilihicazkar--sarki--musemmen--iftirakindir_sebep--haci_arif_bey.mu2 \n",
"834 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/beyati--sarki--duyek--emel-i_meyl-i--sevki_bey.mu2 \n",
"835 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/mahurbuselik--kupe--aksak--ey_gozu--ahmet_avni_konuk.mu2 \n",
"836 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/saba--sarki--nimsofyan--gel_kulbe-i--.mu2 \n",
"837 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huseyni--sarki--musemmen--gecti_sevdalarla--sukru_tunar.mu2 \n",
"838 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huseyni--beste--agircenber--cemalin_ates-i--zaharya.mu2 \n",
"839 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/muhayyer--turku--kapali_curcuna--evlerinin_onu--elazig.mu2 \n",
"840 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nisaburek--sarki--kapali_curcuna--titrer_yuregim--emin_ongan.mu2 \n",
"841 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nihavent--sarki--devrihindi--yar_senden--munir_nurettin_selcuk.mu2 \n",
"842 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/kurdilihicazkar--sarki--aksak--nereden_sevdim--selahaddin_pinar.mu2 \n",
"843 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicazasiran--seyir--sofyan--1--sefik_gurmeric.mu2 \n",
"844 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nihavent--sarki--curcuna--goze_mi--osman_nihat_akin.mu2 \n",
"845 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huzzam--sarki--aksak--gonul_dustu--kemenceci_usta_yani.mu2 \n",
"846 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/ussak--turku--sofyan--kalenin_burcu_muyam--.mu2 \n",
"847 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/kurdilihicazkar--sarki--aksak--gidelim_goksuya--lavtaci_hristo.mu2 \n",
"848 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz_zirgule--kupe--duyek--basladi_mecliste--ahmet_avni_konuk.mu2 \n",
"849 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/kurdilihicazkar--sarki--musemmen--mahmur_bakisi--sekerci_cemil_bey.mu2 \n",
"850 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huseyniasiran--seyir--semai--1--rauf_yekta.mu2 \n",
"851 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/kurdilihicazkar--sarki--curcuna--ey_benim--muhlis_sabahaddin_ezgi.mu2 \n",
"852 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/segah--seyir--semai--1--rauf_yekta.mu2 \n",
"853 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/pencgahiasl--kupe--devrirevanihindi--pencugah-i_asl--ahmet_avni_konuk.mu2 \n",
"854 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/kurdilihicazkar--sarki--yuruksemai_ii--ey_mutrib-i--rahmi_bey.mu2 \n",
"855 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/rast--ornek_oz--musemmen--1--ruhi_ayangil.mu2 \n",
"856 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/sehnazbuselik--seyir--sofyan--1--erol_bingol.mu2 \n",
"857 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/kurdilihicazkar--sarki--aydin--gozlerin_mavi--refik_fersan.mu2 \n",
"858 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/ussak--fantezi--sofyan--soyle_sevgili--munir_nurettin_selcuk.mu2 \n",
"859 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/rast--sarki--yuruksemai--unutulmus_birer--gultekin_ceki.mu2 \n",
"860 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nikriz--zeybek--aksak--2--tanburi_cemil_bey.mu2 \n",
"861 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nihavent--turku--azeriyuruksemai--aman_avci--.mu2 \n",
"862 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/karcigar--kocekce--oynak--bulbul_olsam--.mu2 \n",
"863 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huzzam--sarki--agiraksak--neseyab-i_lutfun--musullu_hafiz_osman_efendi.mu2 \n",
"864 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/mahur--fantezi--semai--gelin_gibi--necdet_tokatlioglu.mu2 \n",
"865 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n"
]
},
{
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"output_type": "stream",
"text": [
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huzzam--sarki--aksak--ey_husn--ahmet_irsoy.mu2 \n",
"866 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nisaburek--aranagme--duyek--1--.mu2 \n",
"867 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/segah--turku--curcuna--bahcada_yesil--diyarbakir.mu2 \n",
"868 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/bestenigar--pesrev--fahte----tatyos_efendi.mu2 \n",
"869 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/neva--seyir--sofyan--1--sefik_gurmeric.mu2 \n",
"870 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/acemkurdi--sarki--aksak--beyhude_yere--ercument_berker.mu2 \n",
"871 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/evic--aranagme--senginsemai--1--.mu2 \n",
"872 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz--sarki--nimsofyan--cok_yasa--muhlis_sabahaddin_ezgi.mu2 \n",
"873 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huseyni--sarki--curcuna--cektim_elimi--tatyos_efendi.mu2 \n",
"874 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/muhayyer--aranagme--curcuna--1--.mu2 \n",
"875 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/karcigar--turku--turkaksagi--tanburamin_ince--sadettin_kaynak.mu2 \n",
"876 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz--sarki--aksak--bir_an--omer_sami_gupgup.mu2 \n",
"877 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/beyati--sazsemaisi--aksaksemai----nayi_osman_dede.mu2 \n",
"878 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz--turku--nimsofyan--pencereden_kus--.mu2 \n",
"879 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/buselik--ornek_oz--semai--1--ruhi_ayangil.mu2 \n",
"880 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/suzinak_zirgule--kupe--devrirevanihindi--perdebazim_gerci--ahmet_avni_konuk.mu2 \n",
"881 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/kurdilihicazkar--sarki--agiraksak--soyle_ey--rahmi_bey.mu2 \n",
"882 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/ussak--sarki--agiraksak--reng-i_ruhsarina--sevki_bey.mu2 \n",
"883 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huseyni--turku--14_4--tutam_yar--erzurum.mu2 \n",
"884 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huzzam--sarki--aksak--vefa_yoktur--tanburi_mustafa_cavus.mu2 \n",
"885 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/buselik--aranagme--musemmen--1--.mu2 \n",
"886 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/rast--sarki--semai--yine_bir_gulnihal--dede_efendi.mu2 \n",
"887 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/acemasiran--sazsemaisi--aksaksemai----misirli_udi_ibrahim_efendi.mu2 \n",
"888 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/isfahan--sarki--agiraksak--sen_de_mi--udi_hasan.mu2 \n",
"889 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/neva--murabba--muhammes--zeyn_eden--dede_efendi.mu2 \n",
"890 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/karcigar--seyir--sofyan--1--sefik_gurmeric.mu2 \n",
"891 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nisaburek--seyir--senginsemai--1--erol_bingol.mu2 \n",
"892 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huzzam--sarki--duyek--zannetme_ki--kenan_savkli.mu2 \n",
"893 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/acemasiran--sarki--duyek--sim-ten_gonca--sakir_aga.mu2 \n",
"894 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/acemkurdi--fantezi--nimsofyan--askin_kanununu--sadettin_oktenay.mu2 \n",
"895 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/acemkurdi--sarki--kapali_curcuna--zehretme_hayati--zeki_muren.mu2 \n",
"896 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/beyati--agirsemai--aksaksemai--cikmaz_derun_u--tabi_mustafa_efendi.mu2 \n",
"897 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huseyni--pesrev--muhammes----lavtaci_andon.mu2 \n",
"898 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/ussak--sarki--senginsemai--ey_suh-i_cefa--haci_arif_bey.mu2 \n",
"899 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/segah--ilahi--yuruksemai_ii--sol_cennetin--.mu2 \n",
"900 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/neva--sarki--aksak--muntazirim_tesrifine--tanburi_mustafa_cavus.mu2 \n",
"901 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/ussak--sarki--musemmen--gonlumun_bir--emin_ongan.mu2 \n",
"902 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/gerdaniye--turku--aksak--asagidan_gelen--bilecik.mu2 \n",
"903 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/maveraunnehr--kupe--devrirevanihindi--alem-i_elhan--ahmet_avni_konuk.mu2 \n",
"904 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/sevkaver--kupe--aksaksemai--halka-i--ahmet_avni_konuk.mu2 \n",
"905 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/ferahfeza--kupe--yuruksemai_ii--bir_nigah-i--ahmet_avni_konuk.mu2 \n",
"906 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/segah_maye--seyir--sofyan--1--sefik_gurmeric.mu2 \n",
"907 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/rast--seyir--duyek--1--sefik_gurmeric.mu2 \n",
"908 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/ussak--ilahi--sofyan--hak_taala--ali_ufki.mu2 \n",
"909 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/bestenigar--kupe--agirevfer--kim_olursa--ahmet_avni_konuk.mu2 \n",
"910 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicazkar--sarki--curcuna--ben_sana--corlulu_asik.mu2 \n",
"911 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/buselik--turku--yuruksemai_ii--igdirin_al--igdir.mu2 \n",
"912 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz--aranagme--evfer--1--.mu2 \n",
"913 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/rast--sarki--aksak--aski_huznumle--mildan_niyazi_bey.mu2 \n",
"914 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/suzidilara--yuruksemai--yuruksemai--ab_u_tab--iii_selim.mu2 \n",
"915 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huseyni--sarki--turkaksagi--hicran_oku--sevki_bey.mu2 \n",
"916 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hisarbuselik--sarki--turkaksagi--bir_nevcivansin--rahmi_bey.mu2 \n",
"917 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/rast--yuruksemai--yuruksemai_ii--gelse_o--hafiz_post.mu2 \n",
"918 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicazkar--sarki--turkaksagi--gonlumu_bir--tahir_aga.mu2 \n",
"919 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nevruz--sarki--yuruksemai_ii--esmerligi_yildiz--muallim_ismail_hakki_bey.mu2 \n",
"920 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huzzam--turku--curcuna--hayatlari_degirmi--.mu2 \n",
"921 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/muhayyer--seyir--sofyan--1--sefik_gurmeric.mu2 \n",
"922 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huzzam--sarki--duyek--nicin_nalendesin--nikogos_aga.mu2 \n",
"923 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/tahir--turku--yuruksemai--cay_icinde--urfa.mu2 \n",
"924 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/rast--cocuksarkisi--sofyan--yavru_kuslar--.mu2 \n",
"925 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/muhayyer--sazsemaisi--aksaksemai----tanburi_cemil_bey.mu2 \n",
"926 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/sehnaz--sarki--curcuna--beni_ateslere--zeki_arif_ataergin.mu2 \n",
"927 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/neva--nakis--yuruksemai--bir_zaman--hatibzade_osman_efendi.mu2 \n",
"928 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/muhayyerkurdi--sarki--yuruksemai_ii--hani_o--yusuf_nalkesen.mu2 \n",
"929 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/buselik--yuruksemai--yuruksemai--dehr_olmada--dede_efendi.mu2 \n",
"930 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huseyni--nefes--devrihindi--iptidadan_yol--.mu2 \n",
"931 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/segah--sarki--aksak--yine_hicran_ile--melahat_pars.mu2 \n",
"932 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/acemasiran--sarki--curcuna--ates-i_askinla--emin_ongan.mu2 \n",
"933 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/saba--sarki--senginsemai--yollarda_kalan--bimen_sen.mu2 \n",
"934 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/suzidil--aranagme--curcuna--1--.mu2 \n",
"935 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz--sarki--aksaksemai--sezdim_dargin--rifat_ayaydin.mu2 \n",
"936 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/ferahnak--sarki--aksak--gozlerinden_dokulen--gulen_ertek.mu2 \n",
"937 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/acemasiran--sarki--devrihindi--ey_gul-i_rana--neveser_kokdes.mu2 \n",
"938 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/karcigar--sarki--duyek--nedir_bu--selahattin_icli.mu2 \n",
"939 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/gerdaniye--rumeliturkusu--dolap--kirimdan_gelirim--rumeli.mu2 \n",
"940 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huseyni--sazsemaisi--aksaksemai----neyzen_rasid_efendi.mu2 \n",
"941 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz--sarki--agiraksak--derdimi_ummana--serif_icli.mu2 \n",
"942 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz_humayun--seyir--sofyan--1--erol_bingol.mu2 \n",
"943 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicazkar--kocekce--ciftesofyan--nicin_sevdim--muallim_ismail_hakki_bey.mu2 \n",
"944 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/karcigar--sarki--semai--vah_meyus-i--mahmud_celaleddin_pasa.mu2 \n",
"945 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/kurdilihicazkar--pesrev--muhammes----tanburi_cemil_bey.mu2 \n",
"946 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/rast--sarki--senginsemai--bilmem_ki--selanikli_ahmed_bey.mu2 \n",
"947 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n"
]
},
{
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"output_type": "stream",
"text": [
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz_humayun--kupe--duyek--yalniz_bir--ahmet_avni_konuk.mu2 \n",
"948 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz--sarki--kapali_curcuna--anlatilmaz_bin--erdogan_yildizel.mu2 \n",
"949 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huzzam--sarki--aksak--manada_guzel--sadi_isilay.mu2 \n",
"950 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/rast--sarki--senginsemai--bu_zevk_u_safa--lemi_atli.mu2 \n",
"951 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/segah--yuruksemai--yuruksemai--etti_o--ebubekir_aga.mu2 \n",
"952 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nihavent--sarki--aksak--gece_sahilden--serif_icli.mu2 \n",
"953 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/acemasiran--aranagme--semai--1--.mu2 \n",
"954 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/muhayyerkurdi--turku--sofyan--eskiya_dunyaya--ordu.mu2 \n",
"955 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/mahur--sarki--curcuna--zahiri_hale--haci_arif_bey.mu2 \n",
"956 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/muhayyer--sarki--aksak--yadimda_o--sukru_tunar.mu2 \n",
"957 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/muhayyerkurdi--turku--oynak--evlerinin_onu--.mu2 \n",
"958 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/segah--sarki--senginsemai--ruhunda_olen--sadi_isilay.mu2 \n",
"959 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/bendihisar--kupe--yuruksemai_ii--her_makamin--ahmet_avni_konuk.mu2 \n",
"960 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huzzam--turku--nimsofyan--asker_oldum--.mu2 \n",
"961 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/evic--agirsemai--aksaksemai--ben_aglar_idim--tanburi_ali_efendi.mu2 \n",
"962 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nisaburek--sarki--yuruksemai_ii--meyledip_bir--ali_rifat_cagatay.mu2 \n",
"963 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huseyni--sarki--agiraksak--bendeni_gordukce--ismet_aga.mu2 \n",
"964 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz--sarki--kapali_curcuna--hancer-i_askinla--ekrem_guyer.mu2 \n",
"965 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huzzam--sarki--aksak--sevdigin_dunyalar--munir_nurettin_selcuk.mu2 \n",
"966 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/segah_maye--turku--musemmen_ii--dun_gece--erzurum.mu2 \n",
"967 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nikriz--turku--devrituran--ben_giderim--.mu2 \n",
"968 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicazasiran--kupe--aksaksemai--mutriba--ahmet_avni_konuk.mu2 \n",
"969 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/ferahnak--sarki--duyek--sinede_yarem--hamparsum.mu2 \n",
"970 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/acemkurdi--sarki--aksak--hatay--cevdet_cagla.mu2 \n",
"971 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/mahur--sazsemaisi--aksaksemai--bahar_1--goksel_baktagir.mu2 \n",
"972 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/buselik--seyir--sofyan--1--erol_bingol.mu2 \n",
"973 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/ussak--sarki--aksak--ducar-i_hicr-i--sevki_bey.mu2 \n",
"974 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz--sarki--agiraksak--soylemez_miydim--ii_mahmud.mu2 \n",
"975 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nihavent--sarki--turkaksagi--nerelerde_kaldin--sermuezzin_hakki_bey.mu2 \n",
"976 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huzzam--sarki--curcuna--mahzun_gonul--kemani_salih_efendi.mu2 \n",
"977 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/ferahnak--seyir--sofyan--1--erol_bingol.mu2 \n",
"978 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/acemasiran--sarki--agiraksak--goncasindan_gulsenin--emin_ongan.mu2 \n",
"979 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/segah--sarki--sofyan--dayanilmaz_hasretin--omer_sami_gupgup.mu2 \n",
"980 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/evic--karce--nimhafif--geldi_cevher--ebubekir_aga.mu2 \n",
"981 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/ussak--sarki--aksak--bag-i_husnun--kemal_emin_bara.mu2 \n",
"982 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/tahir--turku--musemmen_ii--hem_okudum--corum.mu2 \n",
"983 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/rasticedid--kupe--devrirevanihindi--etse_hanende--ahmet_avni_konuk.mu2 \n",
"984 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huzzam--sarki--aksak--sonbaharin_bizi--bekir_sitki_sezgin.mu2 \n",
"985 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/segah--sarki--murekkepsofyan--dustu_enginlere--refik_fersan.mu2 \n",
"986 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nisaburek--sarki--musemmen--gul_yuzun--vecihe_daryal.mu2 \n",
"987 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz--kanto--sofyan--ada_sahillerinde--.mu2 \n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"988 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/sabaasiran--seyir--sofyan--1--sefik_gurmeric.mu2 \n",
"989 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicazkar--sarki--devrihindi--seb_ta_seher--hasim_bey.mu2 \n",
"990 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz--seyir--semai--1--rauf_yekta.mu2 \n",
"991 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz--turku--turkaksagi--divane_asik--macka.mu2 \n",
"992 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huzzam--sarki--curcuna--ayrilik_umitlerin--avni_anil.mu2 \n",
"993 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicazkar--sarki--kapali_curcuna--mestim_bu--lavtaci_ovrik.mu2 \n",
"994 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/muhayyer--sarki--nimsofyan--yenilendi_derdim--arif_sami_toker.mu2 \n",
"995 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz--rumeliturkusu--aksak--yine_de--rumeli.mu2 \n",
"996 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz--sarki--curcuna--bahar_olsa--fahri_kopuz.mu2 \n",
"997 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/irak--ilahi--yuruksemai--uyan_ey--iii_murat.mu2 \n",
"998 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huzzam--sarki--devrihindi--umidim_kalmadi--medeni_aziz_efendi.mu2 \n",
"999 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/buselik--sarki--duyek--askin_once--amir_ates.mu2 \n",
"1000 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/acemasiran--aranagme--curcuna--1--.mu2 \n",
"1001 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/rast--sarki--duyek--omrumce_hep--irfan_ozbakir.mu2 \n",
"1002 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/ussak--sarki--yuruksemai--affeyle_guhanim--enderuni_ali_bey.mu2 \n",
"1003 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/rast--sarki--yuruksemai_ii--sevdim_yine--basmaci_abdi_efendi.mu2 \n",
"1004 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/mahur--sarki--aksak--muptelayim_hayli--kemenceci_usta_yani.mu2 \n",
"1005 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huseyni--turku--aksak--bursanin_ufak--bursa.mu2 \n",
"1006 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/muhayyersunbule--sarki--sarkidevrirevani--ey_nihal-i--ahmed_aga.mu2 \n",
"1007 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/saba--sugul--duyek--in_nilte--zekai_dede.mu2 \n",
"1008 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/rast--turku--nimsofyan--ibrisim_ormuyorlar--.mu2 \n",
"1009 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nisaburek--seyir--sofyan--1--sefik_gurmeric.mu2 \n",
"1010 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz--sarki--aksak--pencerenin_perdesini--muhlis_sabahaddin_ezgi.mu2 \n",
"1011 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/kurdilihicazkar--sarki--sofyan--beni_kor_kuyularda--munir_nurettin_selcuk.mu2 \n",
"1012 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicazkar--sarki--turkaksagi--vazgecip_naz--medeni_aziz_efendi.mu2 \n",
"1013 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicazkar--sarki--curcuna--usandirdi_felek--ahmet_mukerrem_akinci.mu2 \n",
"1014 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/serefnuma--sazsemaisi--aksaksemai----suphi_ezgi.mu2 \n",
"1015 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/rast--sarki--devrihindi--gonlumun_ezhar--fehmi_tokay.mu2 \n",
"1016 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/ussak--turku--nimsofyan--yine_gam--.mu2 \n",
"1017 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/murekkep_isfahan--sarki--devrihindi--kalmadi_sabra--udi_nevres_bey.mu2 \n",
"1018 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/sultaniyegah--sarki--aksak--biz_heybelide--yesari_asim_arsoy.mu2 \n",
"1019 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/acemkurdi--kupe--musemmen--bezmimizde--ahmet_avni_konuk.mu2 \n",
"1020 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huzzam--sarki--agirduyek--bag-i_husnunde--dede_salih_efendi.mu2 \n",
"1021 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/buselik--kupe--agiraksak--buselikle_ol--ahmet_avni_konuk.mu2 \n",
"1022 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/pesendide--nakis--yuruksemai--ey_afet-i_can--dede_efendi.mu2 \n",
"1023 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/rast--sarki--sofyan--karli_dagi--rifat_bey.mu2 \n",
"1024 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/suzinak_zirgule--sarki--kapali_curcuna--ayri_dustum--yesari_asim_arsoy.mu2 \n",
"1025 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/tahir--seyir--sofyan--1--sefik_gurmeric.mu2 \n",
"1026 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/ussak--popsarkisi--sofyan--ellerimde_buyuttugum--baris_manco.mu2 \n",
"1027 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/isfahan--sarki--curcuna--sevdigim_vaslini--.mu2 \n",
"1028 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nisaburek--turku--aksak--fincani_tastan--.mu2 \n",
"1029 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/karcigar--sarki--duyek--boyun_ince--arif_sami_toker.mu2 \n",
"1030 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huseyni--turku--aksak--leylak_takivermis--bahri_altintas.mu2 \n",
"1031 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nisaburek--sazsemaisi--aksaksemai----munir_mazhar_kamsoy.mu2 \n",
"1032 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz_uzzal--sarki--turkaksagi--gel_nazli--udi_hrant.mu2 \n",
"1033 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/sevkefza--sarki--aksak--ey_serv-i--iii_selim.mu2 \n",
"1034 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/acem--turku--duyek--ordunun_dereleri--ordu.mu2 \n",
"1035 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/kurdilihicazkar--pesrev--agirduyek----vasilaki.mu2 \n",
"1036 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/suzinak_zirgule--longa--nimsofyan----turhan_yalcin.mu2 \n",
"1037 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz_humayun--aranagme--curcuna--1--.mu2 \n",
"1038 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/suzinak_zirgule--turku--semai--gokyuzunde_tuten--necmi_piskin.mu2 \n",
"1039 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nikriz--aranagme--evfer--1--.mu2 \n",
"1040 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/irak--kupe--agirevfer--pur--ahmet_avni_konuk.mu2 \n",
"1041 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/muhalif--pesrev--cenber----ahmet_coban_giray.mu2 \n",
"1042 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/acemasiran--sarki--sofyan--gonul_sana--sadettin_kaynak.mu2 \n",
"1043 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicazkar--agirsemai--aksaksemai--benim_servi--bolahenk_nuri_bey.mu2 \n",
"1044 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nihavent--sarki--nimsofyan--biz_camlicanin--yesari_asim_arsoy.mu2 \n",
"1045 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/sazkar--kupe--devrirevanihindi--her_ne--ahmet_avni_konuk.mu2 \n",
"1046 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/saba--sarki--agiraksak--mey_icerken--sevki_bey.mu2 \n",
"1047 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/isfahan--sarki--musemmen--dusme_ey_asik--nikogos_aga.mu2 \n",
"1048 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/rast--sarki--turkaksagi--asude_fikrim--hafiz_yusuf_efendi.mu2 \n",
"1049 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz--ornek_oz--yuruksemai--1--ruhi_ayangil.mu2 \n",
"1050 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/saba--sarki--devrihindi--nigah-i_mestine--haci_arif_bey.mu2 \n",
"1051 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/isfahan--sarki--agiraksak--asiyan-i_murg-i--udi_nevres_bey.mu2 \n",
"1052 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/segah--turku--aksak--boyda_bosun--istanbul.mu2 \n",
"1053 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/rast--sarki--senginsemai--hafizin_kabri--munir_nurettin_selcuk.mu2 \n",
"1054 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/acemasiran--sarki--devrihindi--ruy-i_alemdir--dellalzade_haci_ismail_efendi.mu2 \n",
"1055 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/kurdi--pesrev--devrikebir----sehzade_korkut.mu2 \n",
"1056 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicazkar--sarki--devrihindi--gizli_sevdim--tahir_aga.mu2 \n",
"1057 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/tahir--turku--sofyan--hele_yar--diyarbakir.mu2 \n",
"1058 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/gerdaniye--turku--aksak--cemilemin_gezdigi--.mu2 \n",
"1059 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/buselik--sarki--duyek--bir_sitemin--durri_turan.mu2 \n",
"1060 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/sehnaz--kupe--duyek--lutfedip--ahmet_avni_konuk.mu2 \n",
"1061 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huzzam--sarki--curcuna--omrum_seni--yesari_asim_arsoy.mu2 \n",
"1062 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/ussak--sarki--duyek--sevgi_dolu--bilge_ozgen.mu2 \n",
"1063 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/kurdi--ornek_oz--devrihindi--1--ruhi_ayangil.mu2 \n",
"1064 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/segah--sarki--agir_aksaksemai--cok_kildi--dellalzade_haci_ismail_efendi.mu2 \n",
"1065 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huseyni--sarki--aksak--varalim_kuy-i--munir_nurettin_selcuk.mu2 \n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"1066 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz--sarki--aksak--hastayim_yasiyorum--udi_hrant.mu2 \n",
"1067 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/gerdaniye--turku--raksaksagi--penceresi_yesil--konya.mu2 \n",
"1068 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huzzam--seyir--duyek--1--sefik_gurmeric.mu2 \n",
"1069 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nihavent--sarki--duyek--ak_duvaklar--omer_dilek.mu2 \n",
"1070 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/neva--sarki--yuruksemai_ii--yine_baglandi--zekai_dede.mu2 \n",
"1071 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz--sarki--curcuna--severim_her--lemi_atli.mu2 \n",
"1072 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/muhayyer--turku--sofyan--el_vurup--tokat-zile.mu2 \n",
"1073 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz_humayun--sarki--curcuna--ates-i_suzan-i--haci_faik_bey.mu2 \n",
"1074 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/suzinak_zirgule--sarki--devrihindi--dogdugum_gunden--muallim_ismail_hakki_bey.mu2 \n",
"1075 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/acemasiran--sarki--duyek--bulutlar_kokunu--sadettin_kaynak.mu2 \n",
"1076 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/ferahnak--seyir--semai--1--rauf_yekta.mu2 \n",
"1077 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nihavent--seyir--sofyan--1--erol_bingol.mu2 \n",
"1078 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/sehnaz--sarki--sofyan--gel_meclise--muallim_ismail_hakki_bey.mu2 \n",
"1079 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/kurdilihicazkar--sarki--duyek--arifem_ahkami--haci_arif_bey.mu2 \n",
"1080 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hisarbuselik--sarki--curcuna--visalinle_safayab--muallim_ismail_hakki_bey.mu2 \n",
"1081 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicazkar--kocekce--devrituran--dag_basinda--muallim_ismail_hakki_bey.mu2 \n",
"1082 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/mahur--sarki--aksak--iki_gozum--kemal_emin_bara.mu2 \n",
"1083 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/kurdilihicazkar--sarki--aksak--bunca_cevrinle--emin_ongan.mu2 \n",
"1084 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/ussak--turku--bulgardarbi--oklavayim_paziyim--.mu2 \n",
"1085 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/sehnaz--sarki--aksak--meclis_ara--tanburi_mustafa_cavus.mu2 \n",
"1086 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/buselik--ornek_oz--duyek--1--ruhi_ayangil.mu2 \n",
"1087 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nihavent--sarki--aksak--vuslat_yaya--omer_dilek.mu2 \n",
"1088 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/sevkefza--seyir--semai--1--rauf_yekta.mu2 \n",
"1089 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/buselik--sarki--evfer--bir_pur--iii_selim.mu2 \n",
"1090 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nihavent--sazsemaisi--senginsemai----erzurumlu_hasib_dede.mu2 \n",
"1091 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/bestenigar--seyir--duyek--1--sefik_gurmeric.mu2 \n",
"1092 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/ussak--sarki--aksak--aglatirlar_guldururler--dede_efendi.mu2 \n",
"1093 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/beyati--turku--3334--kirmizi_gul--erzurum.mu2 \n",
"1094 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huseyni--beste--duyek--muheyya_oldu--rakim_elkutlu.mu2 \n",
"1095 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/neva--seyir--semai--1--rauf_yekta.mu2 \n",
"1096 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/sedaraban--aranagme--devrihindi--1--.mu2 \n",
"1097 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/bahrinazik--kupe--duyek--mahi-yi--ahmet_avni_konuk.mu2 \n",
"1098 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/segah--sarki--semai--gurbet_icimde--teoman_alpay.mu2 \n",
"1099 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz--kupe--duyek--gerci-2--ahmet_avni_konuk.mu2 \n",
"1100 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huzzam--sarki--agiraksak--bekledim_yillarca--rakim_elkutlu.mu2 \n",
"1101 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nihavent--sarki--aksak--mumkun_mu--rakim_elkutlu.mu2 \n",
"1102 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/isfahan--beste--zencir--gel_ey--itri.mu2 \n",
"1103 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/acem--seyir--sofyan--1--erol_bingol.mu2 \n",
"1104 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/eski_sipihr--pesrev--muhammes----dilhayat_kalfa.mu2 \n",
"1105 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huseyni--yuruksemai--yuruksemai--dok_dideden--tabi_mustafa_efendi.mu2 \n",
"1106 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/suzinak_zirgule--sarki--curcuna--benim_yarem--latif_aga.mu2 \n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"1107 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/kurdilihicazkar--sarki--curcuna--bu_aksam--avni_anil.mu2 \n",
"1108 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/gerdaniye--turku--sofyan--cabur_dagdan--urfa.mu2 \n",
"1109 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/ussak--yuruksemai--yuruksemai--dila_cunem--diyarbakirli_mahmut_celebi.mu2 \n",
"1110 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/evic--rumeliturkusu--aksak--sabah_oldu--rumeli.mu2 \n",
"1111 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/bestenigar--sarki--aksak--sana_gul--omer_sami_gupgup.mu2 \n",
"1112 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/evic--turku--aksak--ayagina_giymis--.mu2 \n",
"1113 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/acemasiran--seyir--sofyan--1--erol_bingol.mu2 \n",
"1114 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/ussak--turku--nimsofyan--sarardim_ben--.mu2 \n",
"1115 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huzzam--turku--nimsofyan--kopruler_yaptirdim--.mu2 \n",
"1116 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz--ornek_oz--senginsemai--1--ruhi_ayangil.mu2 \n",
"1117 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/karcigar--seyir--semai--1--rauf_yekta.mu2 \n",
"1118 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/segah--sarki--aksak--seni_her--misirli_udi_ibrahim_efendi.mu2 \n",
"1119 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nihavent--sarki--semai--erisdi_nevbahar--arif_sami_toker.mu2 \n",
"1120 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huseyni--sarki--musemmen--hem_cemalin--zeki_duygulu.mu2 \n",
"1121 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/acemasiran--beste--zencir--hayatin_cumleye--dellalzade_haci_ismail_efendi.mu2 \n",
"1122 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/karcigar--sarki--aksak--bu_kis--ismet_aga.mu2 \n",
"1123 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huzzam--sarki--turkaksagi--bahar_oldu--seyh_ethem_efendi.mu2 \n",
"1124 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/ussak--kalenderi--yuruksemai--ey_nur-i--numan_aga.mu2 \n",
"1125 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz_uzzal--seyir--duyek--1--sefik_gurmeric.mu2 \n",
"1126 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nihavent--sarki--duyek--su_guzeller--necip_mirkelamoglu.mu2 \n",
"1127 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nihavent--sarki--yuruksemai_ii--suzup_suzup--rahmi_bey.mu2 \n",
"1128 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/kurdi--ornek_oz--sofyan--1--ruhi_ayangil.mu2 \n",
"1129 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/buselik--sarki--aksak--cehrendeki_tilsimda--yilmaz_karakoyunlu.mu2 \n",
"1130 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/acemkurdi--sarki--duyek--baharla_hazan--avni_anil.mu2 \n",
"1131 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nihavent--sarki--duyek--bugun_yine--emin_ongan.mu2 \n",
"1132 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/yegah--sarki--kapali_curcuna--ruhlerin_ey--tanburi_ali_efendi.mu2 \n",
"1133 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/rast--sarki--duyek--bana_nasil--alaeddin_yavasca.mu2 \n",
"1134 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz--sarki--curcuna--gonul_yarasindan--selahattin_pinar.mu2 \n",
"1135 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicazkar--sarki--duyek--kulagimdan_gitmiyor--ozcan_korkut.mu2 \n",
"1136 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/acemasiran--sarki--sofyan--bakma_sakin--sakir_aga.mu2 \n",
"1137 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicazkar--beste--yuruksemai--bulbul_gibi--zekai_dede.mu2 \n",
"1138 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/sehnaz--sarki--devrihindi--etmedin_bir_lahza--dellalzade_haci_ismail_efendi.mu2 \n",
"1139 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/beyati--sarki--aksak--karsidan_yar--dede_efendi.mu2 \n",
"1140 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicazkar--sarki--oynak--cicekler_derleyeyim--muzaffer_ilkar.mu2 \n",
"1141 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/saba--pesrev--nimsofyan--mevlana--kemani_hamza.mu2 \n",
"1142 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/sehnaz--divan--sofyan--vardim_ki--nevres_pasa.mu2 \n",
"1143 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz--sarki--aksak--kis_geldi--sevki_bey.mu2 \n",
"1144 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/mustear--sarki--aksak--yaprak_nasil--gonul_pacaci.mu2 \n",
"1145 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/yegah--seyir--sofyan--1--sefik_gurmeric.mu2 \n",
"1146 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/rast--sarki--yuruksemai_ii--erdi_bahar--munir_nurettin_selcuk.mu2 \n"
]
},
{
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"text": [
"1147 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nisaburek--yuruksemai--yuruksemai--ey_gul--yusuf_ziya_pasa.mu2 \n",
"1148 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nihavent--sarki--semai--gelin_kizlar--haci_faik_bey.mu2 \n",
"1149 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/acemasiran--selam--mevlevievferi--bi_dil--huseyin_fahreddin_dede.mu2 \n",
"1150 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nihavent--sarki--curcuna--vucud_ikliminin--haci_arif_bey.mu2 \n",
"1151 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/zirefkend--pesrev--darbifetih----gazi_giray_han.mu2 \n",
"1152 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/sultaniyegah--sarki--sofyan--andikca_gecen--lemi_atli.mu2 \n",
"1153 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/ussak--sarki--agir_aksaksemai--pur-atesim--dede_efendi.mu2 \n",
"1154 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/mahur--sarki--agir_aksaksemai--sen_seh-i--iii_selim.mu2 \n",
"1155 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/segah--sarki--curcuna--bir_emele--neveser_kokdes.mu2 \n",
"1156 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/ussak--seyir--sofyan--1--erol_bingol.mu2 \n",
"1157 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/segah--sarki--agiraksak--hatir-i_nasadi--tanburi_cemil_bey.mu2 \n",
"1158 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/suzidil--sarki--evfer--yandikca_oldu--tanburi_ali_efendi.mu2 \n",
"1159 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz--sarki--curcuna--unutulmus_degilsin--irfan_ozbakir.mu2 \n",
"1160 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/suzidilara--turku--sofyan--hekimoglu--.mu2 \n",
"1161 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicazkar--kocekce--aksak--bekliyorum_salinarak--muallim_ismail_hakki_bey.mu2 \n",
"1162 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/isfahan--aranagme--aksak--1--.mu2 \n",
"1163 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/mahur--sarki--duyek--seb-i_vuslat--nasibin_mehmet_yuru.mu2 \n",
"1164 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/irak--sarki--musemmen--derd-i_hicrinle--bestenigar_ziya_bey.mu2 \n",
"1165 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huseyni--beste--berefsan--sebnem_gibi--zaharya.mu2 \n",
"1166 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/buselik--aranagme--semai--1--.mu2 \n",
"1167 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nihavent--sarki--nimsofyan--geldi_yine--arif_sami_toker.mu2 \n",
"1168 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/muhayyerkurdi--turku--sofyan--cokertmeden_ciktim_da--.mu2 \n",
"1169 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/kurdi--ornek_oz--sofyan--1--huseyin_sadettin_arel.mu2 \n",
"1170 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz--turku--evfer--horozumun_tuyleri--istanbul.mu2 \n",
"1171 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/beyati--ilahi--duyek--kapisi_yok--.mu2 \n",
"1172 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/ussak--aranagme--aksak--1--.mu2 \n",
"1173 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hisarbuselik--yuruksemai--yuruksemai--gonlum_hevesi--zekai_dede.mu2 \n",
"1174 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/ussak--sarki--curcuna--bu_yaz--yesari_asim_arsoy.mu2 \n",
"1175 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/ferahfeza--pesrev--muhammes----tanburi_cemil_bey.mu2 \n",
"1176 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/kurdilihicazkar--sarki--agiraksak--ehl-i_askin--tatyos_efendi.mu2 \n",
"1177 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicazkar--sarki--sofyan--bir_halet--haci_arif_bey.mu2 \n",
"1178 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/suzidil--sarki--turkaksagi--vadeyle_vaslin--eyyubi_mehmet_bey.mu2 \n",
"1179 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz_humayun--sazsemaisi--aksaksemai----veli_dede.mu2 \n",
"1180 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz_humayun--ilahi--turkmen--hak_hak--.mu2 \n",
"1181 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicazasiran--beste--cenber--gonlumu_viran--zekai_dede.mu2 \n",
"1182 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/mahur--sarki--aksak--olmusum_divane--komurcuzade_hafiz_mehmet_efendi.mu2 \n",
"1183 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/rast--sarki--sofyan--bir_acaib--civan_aga.mu2 \n",
"1184 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/rast--sarki--duyek--ben_kuskunum--baki_duyarlar.mu2 \n",
"1185 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/rast--aranagme--curcuna--1--.mu2 \n",
"1186 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nihavent--seyir--sofyan--1--sefik_gurmeric.mu2 \n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"1187 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/beyati--turku--kapali_curcuna--aksam_olur--urfa.mu2 \n",
"1188 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/suzinak_zirgule--sarki--yuruksemai_ii--nihal-i_gulsen-i--emin_ongan.mu2 \n",
"1189 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/buselik--sarki--duyek--yalandir_dogustan--munir_nurettin_selcuk.mu2 \n",
"1190 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/beyati_araban--sazsemaisi--aksaksemai----rauf_yekta.mu2 \n",
"1191 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz--beste--lenkfahte--baktikca_husn_u--suphi_ezgi.mu2 \n",
"1192 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz_uzzal--sarki--musemmen--kamer_cehre--haci_arif_bey.mu2 \n",
"1193 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/gerdaniye--turku--turkaksagi--uca_daglarin--erzurum.mu2 \n",
"1194 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz--sarki--evfer--mah_yuzune--dede_efendi.mu2 \n",
"1195 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/mahur--tavsanca--aksak--aldi_aklim--ii_mahmud.mu2 \n",
"1196 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/kurdilihicazkar--sarki--nimsofyan--huzun--selahattin_icli.mu2 \n",
"1197 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/gerdaniye--seyir--senginsemai--1--erol_bingol.mu2 \n",
"1198 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/sultaniyegah--sarki--aksak--habide_olan--udi_sami_bey.mu2 \n",
"1199 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huzzam--sarki--curcuna--kusade_taliim--sevki_bey.mu2 \n",
"1200 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
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"1201 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/muhayyer--turku--turkaksagi--vardim_hind--erzincan.mu2 \n",
"1202 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/sehnaz--sarki--aksaksemai--ey_verd-i_rana--dede_efendi.mu2 \n",
"1203 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/acemasiran--aranagme--agiraksak--1--.mu2 \n",
"1204 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/muhayyer--sarki--aksak--humari_yok--haci_arif_bey.mu2 \n",
"1205 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/sehnaz--sarki--aksak--gonul_durmaz--dede_efendi.mu2 \n",
"1206 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/ussak--turku--aksak--sari_gulum--.mu2 \n",
"1207 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/ussak--sarki--agiraksak--kaldi_atesler--haci_karabet_efendi.mu2 \n",
"1208 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/muhayyerkurdi--sarki--sofyan--mevsimler_yas--yildirim_gurses.mu2 \n",
"1209 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/mahur--zeybek--aydin--kirpigine_surme--refik_fersan.mu2 \n",
"1210 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/ussak--beste--agircenber--cam-i_lalin--zaharya.mu2 \n",
"1211 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/tahir--turku--aksak--havuzun_basinda--cankiri.mu2 \n",
"1212 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/sehnaz--pesrev--zencir----kemani_ali_aga.mu2 \n",
"1213 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/beyati--turku--nimsofyan--gesi_baglarinda--kayseri.mu2 \n",
"1214 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/segah--fantezi--aksak--gul_ne--omer_dilek.mu2 \n",
"1215 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/evcara--sarki--aksak--sen_hangi--ozgen_gurbuz.mu2 \n",
"1216 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nikriz--turku--oynak--yoruk_de--.mu2 \n",
"1217 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nihavent--sarki--aksak--camlica_yolunda--nuri_halil_poyraz.mu2 \n",
"1218 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huzzam--sarki--turkaksagi--sevdasi_henuz--yorgo_bacanos.mu2 \n",
"1219 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicazkar--sarki--duyek--yollari_gurbete--sadi_isilay.mu2 \n",
"1220 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/ussak--sazsemaisi--aksaksemai----neyzen_aziz_dede.mu2 \n",
"1221 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/beyati--sarki--turkaksagi--askinla_harab--serif_icli.mu2 \n",
"1222 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/neva--turku--nimsofyan--ankaranin_tasina--ankara.mu2 \n",
"1223 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/gerdaniye--turku--turkaksagi--benden_selam--kastamonu.mu2 \n",
"1224 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/evcara--seyir--sofyan--1--sefik_gurmeric.mu2 \n",
"1225 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/dugah--seyir--sofyan--1--sefik_gurmeric.mu2 \n",
"1226 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/beyati_arabanbuselik--kupe--aksak--gel_beyati--ahmet_avni_konuk.mu2 \n",
"1227 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicazkar--seyir--devrihindi--2--erol_bingol.mu2 \n",
"1228 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz_humayun--aranagme--duyek--1--.mu2 \n",
"1229 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz--sarki--duyek--kederden_mi--nasibin_mehmet_yuru.mu2 \n",
"1230 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n"
]
},
{
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"output_type": "stream",
"text": [
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/muhayyer--kupe--duyek--nideyim--ahmet_avni_konuk.mu2 \n",
"1231 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/segah--kar--hafif--ses-agaz_kar--abdulkadir_meragi.mu2 \n",
"1232 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/kurdilihicazkar--sarki--aksak--mahrum-i_sevkim--rahmi_bey.mu2 \n",
"1233 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nihavent--sarki--duyek--bir_ihtimal--osman_nihat_akin.mu2 \n",
"1234 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/mahur--sarki--duyek--ay_dogar--sadettin_kaynak.mu2 \n",
"1235 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/saba--sarki--devrihindi--oyle_bir--aleko_bacanos.mu2 \n",
"1236 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huzzam--sarki--semai--dil-hun_olurum--bimen_sen.mu2 \n",
"1237 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/rast--sarki--duyek--sevdim_seni--adnan_aydemir.mu2 \n",
"1238 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/buselik--sarki--devrihindi--bahara_indi--selahattin_icli.mu2 \n",
"1239 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/kurdilihicazkar--seyir--sofyan--2--sefik_gurmeric.mu2 \n",
"1240 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/yeni_cargah--pesrev--devrikebir----.mu2 \n",
"1241 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/bestenigar--sarki--aksak--perisan_halin--emin_ongan.mu2 \n",
"1242 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/ussak--sarki--yuruksemai_ii--tifl-i_nazim--tanburi_ali_efendi.mu2 \n",
"1243 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/acemasiran--selam--agirduyek--her_ruz--huseyin_fahreddin_dede.mu2 \n",
"1244 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/beyati--sarki--sofyan--acilan_bir--ertugrul_yalcinkaya.mu2 \n",
"1245 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/buselik--turku--azeriyuruksemai--daglara_cen--.mu2 \n",
"1246 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/suzinak--sarki--curcuna--ne_muskulmus--osman_nihat_akin.mu2 \n",
"1247 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicazkar--sarki--duyek--yaziktir_etme--erol_sayan.mu2 \n",
"1248 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/pencgah--seyir--sofyan--1--sefik_gurmeric.mu2 \n",
"1249 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huseyni--sarki--duyek--bahcede_gordum--medeni_aziz_efendi.mu2 \n",
"1250 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/mahur--sarki--sofyan--gordum_bugun--dede_efendi.mu2 \n",
"1251 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/kurdilihicazkar--aranagme--semai--1--.mu2 \n",
"1252 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/dilkeshaveran--kupe--agirevfer--alemin_aklin--ahmet_avni_konuk.mu2 \n",
"1253 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/ussak--turku--sofyan--uzun_ince--asik_veysel.mu2 \n",
"1254 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/saba--tavsanca--semai--gelmis_degil--sakir_aga.mu2 \n",
"1255 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/mahur--sarki--nimsofyan--otomobil_ucar--munir_nurettin_selcuk.mu2 \n",
"1256 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/beyati_araban--ilahi--duyek--gonul_mazhardir--enderunlu_hafiz_husnu_efendi.mu2 \n",
"1257 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz--seyir--duyek--1--erol_bingol.mu2 \n",
"1258 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huzzam--sarki--semai--her_gece--kasim_inaltekin.mu2 \n",
"1259 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/muhayyerkurdi--sarki--duyek--unutulmus_ne--avni_anil.mu2 \n",
"1260 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/gulizar--sazsemaisi--aksaksemai----tanburi_isak.mu2 \n",
"1261 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huseyni--turku--raksaksagi--menekse_kokulu--.mu2 \n",
"1262 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/ferahfeza--seyir--sofyan--2--sefik_gurmeric.mu2 \n",
"1263 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/tahir--turku--nimsofyan--bagisla_sevdigim--kirsehir.mu2 \n",
"1264 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/rast--sarki--cengiharbi--bulan_ozunu--refik_fersan.mu2 \n",
"1265 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nigar--sazsemaisi--aksaksemai----tanburi_reftar_kalfa.mu2 \n",
"1266 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicazkar--sarki--sofyan--sarsam_seni--sadettin_kaynak.mu2 \n",
"1267 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/sehnaz--sarki--senginsemai--feryad_ki--tanburi_cemil_bey.mu2 \n",
"1268 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz_humayun--sarki--agiraksak--nitti_bana--sivelioglu_yorgaki.mu2 \n",
"1269 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz--sarki--nimsofyan--yar_saclari--yesari_asim_arsoy.mu2 \n",
"1270 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/rast--sarki--duyek--belki_bir_sabah--sekip_ayhan_ozisik.mu2 \n",
"1271 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huzzam--sarki--semai--omrumuzun_son--avni_anil.mu2 \n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"1272 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/tahir--turku--bektasidevrirevani--asamadim_su--eskisehir.mu2 \n",
"1273 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/muhayyerkurdi--sarki--nimsofyan--bildir_guzel--kipti_husnu_efendi.mu2 \n",
"1274 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/ussak--sarki--duyek--aksam_oldu_huzunlendim--semahat_ozdenses.mu2 \n",
"1275 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicazkar--aranagme--aksak--1--muallim_ismail_hakki_bey.mu2 \n",
"1276 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/segah--turku--ciftesofyan--bir_findigin--.mu2 \n",
"1277 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nihavent--sarki--aksak--seni_tenhada--ismet_aga.mu2 \n",
"1278 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz--kosma--nimsofyan--yuru_dilber--ankara.mu2 \n",
"1279 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/zavil--seyir--duyek--1--erol_bingol.mu2 \n",
"1280 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/buselik--ilahi--sofyan--bizi_insan--cinucen_tanrikorur.mu2 \n",
"1281 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/rast--kanto--nimsofyan--darildin_mi--.mu2 \n",
"1282 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/beyati--sarki--curcuna--kalbim_yine--selahattin_pinar.mu2 \n",
"1283 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/sipihr--kupe--duyek--her--ahmet_avni_konuk.mu2 \n",
"1284 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/segah_maye--kupe--darb--maye-i_ask-i--ahmet_avni_konuk.mu2 \n",
"1285 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/revnaknuma--kupe--agirevfer--ol_guzel--ahmet_avni_konuk.mu2 \n",
"1286 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huseyni--agirsemai--aksaksemai--acildi_gonce-i--komurcuzade_hafiz_mehmet_efendi.mu2 \n",
"1287 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/ussak--ilahi--sofyan--ey_sehinsah-i--tiznam_yusuf_celebi.mu2 \n",
"1288 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/acem--seyir--sofyan--1--sefik_gurmeric.mu2 \n",
"1289 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/sababuselik--kupe--aksak--bak_sabada--ahmet_avni_konuk.mu2 \n",
"1290 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/sabazemzeme--sarki--kapali_curcuna--hayal-i_yare--rifat_bey.mu2 \n",
"1291 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/ussak--turku--nimsofyan--ayva_cicek--.mu2 \n",
"1292 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/rast--sarki--semai--sevmek_seni--neveser_kokdes.mu2 \n",
"1293 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicazkar--sarki--duyek--cozmek_elinde--sadettin_kaynak.mu2 \n",
"1294 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/rast--sazsemaisi--aksaksemai----benli_hasan_aga.mu2 \n",
"1295 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nikriz--turku--ciftesofyan--penceresi_yola--.mu2 \n",
"1296 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/saba--sarki--agiraksak--ince_kirpiklerinin--rakim_elkutlu.mu2 \n",
"1297 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/kurdilihicazkar--kupe--devrirevanihindi--tarz-i_kurdili--ahmet_avni_konuk.mu2 \n",
"1298 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/karcigar--sarki--agiraksak--husnune_edvar-i--lemi_atli.mu2 \n",
"1299 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nisaburek--seyir--devrihindi--1--erol_bingol.mu2 \n",
"1300 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/ussak--sarki--agiraksak--kimseler_gelmez--sevki_bey.mu2 \n",
"1301 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/zavil--sarki--semai--hub_sada--muallim_ismail_hakki_bey.mu2 \n",
"1302 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/kurdilihicazkar--seyir--senginsemai--1--erol_bingol.mu2 \n",
"1303 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/beyati--sazsemaisi--aksaksemai----kanuni_omer_efendi.mu2 \n",
"1304 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicazkar--sarki--katikofti--dus_olma--ahmet_mithat_gupgupoglu.mu2 \n",
"1305 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huzzam--pesrev--fahte----gazi_giray_han.mu2 \n",
"1306 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/neva--kupe--duyek--bezme--ahmet_avni_konuk.mu2 \n",
"1307 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/karcigar--sarki--nimsofyan--kocyigitler--sadettin_kaynak.mu2 \n",
"1308 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/mahur--sarki--aksak--gor_nitti_cana--dede_efendi.mu2 \n",
"1309 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicazkar--kocekce--aksak--gece_gunduz--muallim_ismail_hakki_bey.mu2 \n",
"1310 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/karcigar--agirsemai--aksaksemai--ne_dane--dellalzade_haci_ismail_efendi.mu2 \n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"1311 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nisaburek--sarki--aksak--artik_demir--ferit_sidal.mu2 \n",
"1312 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/saba--turku--aksak--daracik_sokaklari--istanbul.mu2 \n",
"1313 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/sedaraban--kupe--yuruksemai_ii--dinlese_sedd-i--ahmet_avni_konuk.mu2 \n",
"1314 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nisaburek--sarki--turkaksagi--varsin_gonul--lemi_atli.mu2 \n",
"1315 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/segah--sarki--duyek--leyla_bir--sadettin_kaynak.mu2 \n",
"1316 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz--fantezi--duyek--sevemez_kimse--suat_sayin.mu2 \n",
"1317 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/ussak--turku--devrisureyyasofyani--su_dereden--.mu2 \n",
"1318 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/acemkurdi--aranagme--curcuna--1--.mu2 \n",
"1319 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/kurdilihicazkar--sarki--aksak--her_gece--bimen_sen.mu2 \n",
"1320 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/rast--sarki--aksak--sen_nazla_gezerken--kazim_nami_erdolen.mu2 \n",
"1321 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/suzidil--beste--fer--hiraman_ol--abdulhalim_aga.mu2 \n",
"1322 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nihavent--sarki--agiraksak--ey_nihal-i--udi_arsak_comlekciyan.mu2 \n",
"1323 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huzzam--sarki--turkaksagi--ummidini_kirpiklerine--selahaddin_pinar.mu2 \n",
"1324 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/mahur--kar--hafif--gul_bi-ruh-i--abdulkadir_meragi.mu2 \n",
"1325 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/mahur--sarki--aksak--servinazi_seyret--rahmi_bey.mu2 \n",
"1326 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/buselik--turku--semai--fikrimden_geceler--.mu2 \n",
"1327 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/segah--sarki--aksak--gece_sessiz--gultekin_ceki.mu2 \n",
"1328 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/rast--sarki--duyek--her_telden--sakir_aga.mu2 \n",
"1329 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/muhayyer--turku--devrituran--mihrican_mi--yozgat.mu2 \n",
"1330 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/acemkurdi--turku--duyek--ham_meyveyi--.mu2 \n",
"1331 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/rast--sarki--curcuna--nihansin_dideden--haci_faik_bey.mu2 \n",
"1332 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz--sarki--yuruksemai_ii--gittin_de--munir_nurettin_selcuk.mu2 \n",
"1333 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/sevkefza--kupe--aksaksemai--sevkefza--ahmet_avni_konuk.mu2 \n",
"1334 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/mahur--sarki--kapali_curcuna--esir_ettin--rahmi_bey.mu2 \n",
"1335 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nihavendirumi--kupe--devrirevanihindi--nesesi_rumi--ahmet_avni_konuk.mu2 \n",
"1336 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/acemasiran--sarki--aksak--dustum_bir--ali_riza_avni_tinaz.mu2 \n",
"1337 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/segah--sarki--nimsofyan--gel_gitme--kaptanzade_ali_riza_bey.mu2 \n",
"1338 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/mahur--sarki--aksak--acirim_asik--giriftzen_asim_bey.mu2 \n",
"1339 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/buselik--seyir--semai--1--rauf_yekta.mu2 \n",
"1340 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huzzam_icedid--ilahi--duyek--goster_bize--huseyin_sadettin_arel.mu2 \n",
"1341 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/ruhnuvaz--seyir--sofyan--1--erol_bingol.mu2 \n",
"1342 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nihavent--yuruksemai--yuruksemai--ruhsarina_aybetme--munir_nurettin_selcuk.mu2 \n",
"1343 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/acemkurdi--fantezi--sofyan--sana_oyle--alaaddin_pakyuz.mu2 \n",
"1344 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nihavent--sarki--yuruksemai_ii--acildi_bahcede--rifat_bey.mu2 \n",
"1345 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz--kupe--duyek--gerci--ahmet_avni_konuk.mu2 \n",
"1346 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/beyati_araban--seyir--duyek--1--sefik_gurmeric.mu2 \n",
"1347 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicazkar--etud--sofyan----ozer_ozel.mu2 \n",
"1348 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/rast--nakis--yuruksemai--gulsende_yine--muallim_ismail_hakki_bey.mu2 \n",
"1349 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz--sarki--curcuna--ruya_gibi--avni_anil.mu2 \n",
"1350 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/beyati--sarki--aksak--benzemez_kimse--fehmi_tokay.mu2 \n",
"1351 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz--turku--nimsofyan--erkilet_guzeli--.mu2 \n",
"1352 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/beyati--kanto--sofyan--karga_da_seni--.mu2 \n",
"1353 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huzzam--sarki--curcuna--guzel_gun--haci_arif_bey.mu2 \n",
"1354 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/gerdaniye--turku--nimsofyan--cezayirin_harmanlari--bursa.mu2 \n",
"1355 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nihavent--sarki--aksak--gonlum_yine--seyh_ethem_efendi.mu2 \n"
]
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"text": [
"1356 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz--rumeliturkusu--sofyan--kirmizi_gulun--rumeli.mu2 \n",
"1357 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz--sarki--duyek--bogazici_sen--alaeddin_yavasca.mu2 \n",
"1358 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/segah--sarki--duyek--askin_o_sihirli--erol_sayan.mu2 \n",
"1359 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/rast--sarki--curcuna--gamdan_azade--rifat_bey.mu2 \n",
"1360 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/segah--kupe--darb--verse--ahmet_avni_konuk.mu2 \n",
"1361 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/saba--sarki--aksak--gitti_de--denizoglu_ali_bey.mu2 \n",
"1362 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nikriz--zeybek--agiraksak----izmir.mu2 \n",
"1363 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/mahur--agirsemai--aksaksemai--cihani_lal-i--itri.mu2 \n",
"1364 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/neva--murabba--zencir--piyaleler_ki--itri.mu2 \n",
"1365 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/neva--agirsemai--aksaksemai--sevdi_bu_gonul--ama_kadri_efendi.mu2 \n",
"1366 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicazkar--sarki--duyek--aglatip_kustureceksen--alaattin_sensoy.mu2 \n",
"1367 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/acemasiran--sarki--aksak--o_tebessum--ismail_baha_surelsan.mu2 \n",
"1368 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/muhayyer--bozlak--serbest--havayi_da--asaf_guven.mu2 \n",
"1369 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/sedaraban--sarki--agiraksak--gam_seni--muallim_kazim_uz.mu2 \n",
"1370 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/beyati--sarki--evfer--bulunmaz_derdime--ama_hasadur.mu2 \n",
"1371 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/saba--beste--muhammes--bir_nim-nigah--efrasiyaboglu_ali_pasa.mu2 \n",
"1372 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/acemasiran--sarki--agiraksak--bir_teselli--serif_icli.mu2 \n",
"1373 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/muhayyerkurdi--turku--aksak--cubugum_yok--.mu2 \n",
"1374 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz--sarki--sofyan--enginde_yavas--sadettin_kaynak.mu2 \n",
"1375 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicazkar--sarki--aksak--nerdesin--leyla_saz.mu2 \n",
"1376 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/ussak--rumeliturkusu--nimsofyan--alisimin_kaslari--.mu2 \n",
"1377 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/isfahan--sarki--aksak--asik_olali--dede_efendi.mu2 \n",
"1378 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/gerdaniye--turku--aksak--antalyanin_mor--antalya.mu2 \n",
"1379 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz--aranagme--aydin--1--.mu2 \n",
"1380 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/mahur--sarki--duyek--ayrilik_ruzgari--kamuran_yarkin.mu2 \n",
"1381 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huseyni--turku--sofyan--sabahin_seher--.mu2 \n",
"1382 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/sedaraban--sarki--agiraksak--ates-i_askinla--hasim_bey.mu2 \n",
"1383 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nihavent--sarki--duyek--sarkilar_seni--muzaffer_ilkar.mu2 \n",
"1384 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huzzam--sarki--aksak--ettigin_cevri--cinucen_tanrikorur.mu2 \n",
"1385 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/beyati_araban--kupe--duyek--ehl-i_zevki--ahmet_avni_konuk.mu2 \n",
"1386 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/kurdilihicazkar--seyir--devrihindi--1--erol_bingol.mu2 \n",
"1387 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nisaburek--sarki--yuruksemai_ii--mest-i_nazim--yusuf_ziya_pasa.mu2 \n",
"1388 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/saba--ornek_oz--sofyan--3--ruhi_ayangil.mu2 \n",
"1389 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/gerdaniye--turku--kapali_curcuna--celik_pazari--erzurum.mu2 \n",
"1390 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/yeni_cargah--seyir--sofyan--1--erol_bingol.mu2 \n",
"1391 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/acemasiran--pesrev--muhammes----gazi_giray_han.mu2 \n",
"1392 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/karcigar--sarki--aksak--bir_rum_dilbere--tanburi_mustafa_cavus.mu2 \n",
"1393 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nihavent--sarki--duyek--gizli_ask--zeynettin_maras.mu2 \n",
"1394 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/saba--nakis--yuruksemai--ey_bad-i--suphi_ziya_ozbekkan.mu2 \n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"1395 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/acemasiran--pesrev--duyek--son_pesrev--huseyin_fahreddin_dede.mu2 \n",
"1396 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nihavent--sarki--nimsofyan--gozleri_aska--gundogdu_duran.mu2 \n",
"1397 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/mahur--sarki--duyek--senle_durmak--kanuni_haci_arif_bey.mu2 \n",
"1398 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/tarzinevin--seyir--sofyan--1--sefik_gurmeric.mu2 \n",
"1399 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/saba--sazsemaisi--aksaksemai----neyzen_aziz_dede.mu2 \n",
"1400 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/neva_kurdi--kupe--musemmen--bir_usul--ahmet_avni_konuk.mu2 \n",
"1401 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/saba--sarki--aksak--goz_actim--omer_dilek.mu2 \n",
"1402 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/tarzicedid--kupe--aksaksemai--kuse-yi--ahmet_avni_konuk.mu2 \n",
"1403 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huzzam--turku--sofyan--indim_havuz--istanbul.mu2 \n",
"1404 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/kurdilihicazkar--longa--nimsofyan----kemani_sebuh.mu2 \n",
"1405 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz--sarki--devrihindi--asik_oldum--enderuni_ali_bey.mu2 \n",
"1406 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/kurdilihicazkar--sarki--turkaksagi--endami_guzeldir--medeni_aziz_efendi.mu2 \n",
"1407 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/mahur--pesrev--hafif----rauf_yekta.mu2 \n",
"1408 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/suzinak_zirgule--seyir--aksak--1--erol_bingol.mu2 \n",
"1409 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/acemkurdi--sarki--sofyan--yar_pesinde--zeki_duygulu.mu2 \n",
"1410 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicazkar--sarki--agirduyek--sen_gibi--numan_aga.mu2 \n",
"1411 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/isfahan--sarki--devrihindi--ey_gaziler--iii_selim.mu2 \n",
"1412 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huzzam--sarki--aksak--adanin_yesil_camlari--sukru_tunar.mu2 \n",
"1413 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/acemkurdi--sarki--semai--bak_yine--izzet_altinbas.mu2 \n",
"1414 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/gulzar--kupe--yuruksemai_ii--bak_araplar--ahmet_avni_konuk.mu2 \n",
"1415 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/sevkutarab--seyir--sofyan--1--erol_bingol.mu2 \n",
"1416 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/suzinak_zirgule--sarki--duyek--dokunma_kalbime--gavsi_baykara.mu2 \n",
"1417 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huseyni--seyir--sofyan--1--sefik_gurmeric.mu2 \n",
"1418 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/segah--turku--aksak--binnaz--usak.mu2 \n",
"1419 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huseyni--sarki--senginsemai--ah_ile--lavtaci_hristo.mu2 \n",
"1420 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/ussak--aranagme--curcuna--1--.mu2 \n",
"1421 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nikriz--sirto--nimsofyan----refik_fersan.mu2 \n",
"1422 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/acemasiran--sarki--curcuna--neden_kalbim--nasibin_mehmet_yuru.mu2 \n",
"1423 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/neveser--etud--sofyan----ozer_ozel.mu2 \n",
"1424 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/bestenigar--seyir--semai--1--rauf_yekta.mu2 \n",
"1425 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nihavent--sarki--yuruksemai_ii--sevdim_seni--tanburi_cemil_bey.mu2 \n",
"1426 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/mahur--sarki--sarkidevrirevani--ben_neler--zekai_dede.mu2 \n",
"1427 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/neva--pesrev--devrikebir----tanburi_cemil_bey.mu2 \n",
"1428 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huzzam--kanto--aksak--bana_bir--lavtaci_hristo.mu2 \n",
"1429 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/kurdilihicazkar--sarki--agiraksak--bu_gonul--sari_onnik.mu2 \n",
"1430 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/segah--turku--curcuna--havuz_basinin--erzincan.mu2 \n",
"1431 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huseyni--sarki--duyek--her_seherde--nihat_adlim.mu2 \n",
"1432 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/sultaniyegah--sarki--curcuna--bu_gulzarin--santuri_ethem_efendi.mu2 \n",
"1433 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/karcigar--kocekce--devrihindi--gul_acil--dede_efendi.mu2 \n",
"1434 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/muhayyer--sarki--curcuna--yetmez_mi_sana--rahmi_bey.mu2 \n"
]
},
{
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"output_type": "stream",
"text": [
"1435 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huseyni--zeybek--agiraksak--sarizeybek--osman_pehlivan.mu2 \n",
"1436 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz--sarki--curcuna--aksam_erdi--rahmi_bey.mu2 \n",
"1437 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huzzam--sarki--aksak--gonlum_seher--sadettin_kaynak.mu2 \n",
"1438 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/neveser--seyir--sofyan--1--sefik_gurmeric.mu2 \n",
"1439 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huzzam--sarki--duyek--yalniz_aksamlarimda--omer_sami_gupgup.mu2 \n",
"1440 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/segah--sarki--musemmen--gozlerinden_icti--sukru_senozan.mu2 \n",
"1441 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/gerdaniye--turku--sofyan--evlerinin_onu--urfa.mu2 \n",
"1442 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/gerdaniyebuselik--kupe--aksak--oturup_bir--ahmet_avni_konuk.mu2 \n",
"1443 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nihavent--sarki--duyek--unutturamaz_seni--ekrem_guyer.mu2 \n",
"1444 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nihavent--sarki--curcuna--yine_bu--osman_nihat_akin.mu2 \n",
"1445 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/ussak--pesrev--cenber----zurnazen_ibrahim_aga.mu2 \n",
"1446 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/acembuselik--sarki--aksak--coktan_idim--kemenceci_usta_yani.mu2 \n",
"1447 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/beyati--sarki--curcuna--gel_nazli_gulum--alaeddin_yavasca.mu2 \n",
"1448 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/dugah--kar--hafif--pek_sevdim--haci_faik_bey.mu2 \n",
"1449 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/beyati--sarki--duyek--zaman_icinde--semahat_ozdenses.mu2 \n",
"1450 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/mahur--turku--sofyan--niksarin_fidanlari--tokat.mu2 \n",
"1451 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/rast--sarki--duyek--bir_gonul--ferit_sidal.mu2 \n",
"1452 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/evic--seyir--devrihindi--1--erol_bingol.mu2 \n",
"1453 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huzzam--sarki--agiraksak--ey_sabah-i--leyla_saz.mu2 \n",
"1454 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz--sarki--aksak--beni_sev--misirli_udi_ibrahim_efendi.mu2 \n",
"1455 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/mustear--seyir--sofyan--1--sefik_gurmeric.mu2 \n",
"1456 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/mahur--turku--kapali_curcuna--meclisinde_mail--adiyaman.mu2 \n",
"1457 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz--sarki--agiraksak--bak_ne--haci_arif_bey.mu2 \n",
"1458 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/sedaraban--sarki--yuruksemai_ii--nevbahar-i_nusnune--rahmi_bey.mu2 \n",
"1459 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/karcigar--sarki--sofyan--yine_ey--santuri_ethem_efendi.mu2 \n",
"1460 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz--turku--aksak--gemilerde_talim--.mu2 \n",
"1461 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/segah--turku--nimsofyan--acilir_gonca--erol_sayan.mu2 \n",
"1462 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicazkar--sarki--duyek--gul_dalinda--neveser_kokdes.mu2 \n",
"1463 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huzzam--sarki--duyek--birak_duygulari--omer_sami_gupgup.mu2 \n",
"1464 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/ussak--sarki--turkaksagi--hala_aciyor--serif_icli.mu2 \n",
"1465 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz--sarki--turkaksagi--yadeller_aldi--sadettin_kaynak.mu2 \n",
"1466 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/sehnazbuselik--aranagme--semai--1--.mu2 \n",
"1467 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/acemasiran--sarki--aksak--dinle_sozum--iii_selim.mu2 \n",
"1468 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/sultaniyegah--sarki--agiraksak--yaradan_oyle--selanikli_ahmed_bey.mu2 \n",
"1469 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/beyati--sarki--duyek--hayli_demdir--suphi_ziya_ozbekkan.mu2 \n",
"1470 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/sazkar--agirsemai--aksaksemai--nice_bir--ilya_efendi.mu2 \n",
"1471 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/karcigar--kocekce--aksak--benliyi_aldim--dede_efendi.mu2 \n",
"1472 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicazkar--aranagme--curcuna--1--.mu2 \n",
"1473 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nihavent--sarki--sofyan--gonlumle_oturdum--sekip_memduh_bey.mu2 \n",
"1474 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/neva--sarki--duyek--ben_yururum--selahaddin_pinar.mu2 \n",
"1475 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz_humayun--rumeliturkusu--devrituran--gidem_dedim--rumeli.mu2 \n",
"1476 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/evic--turku--sofyan--guvercin_ucuverdi--.mu2 \n",
"1477 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huzzam--sarki--ciftesofyan--ey_gul-i--dede_efendi.mu2 \n",
"1478 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/beyati--sarki--aksak--ne_semtden--kemani_riza_efendi.mu2 \n",
"1479 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz_humayun--sarki--duyek--yarin_bahcesinde--atif_bastug.mu2 \n",
"1480 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n"
]
},
{
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"output_type": "stream",
"text": [
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hisar--nakis--yuruksemai--hava_guzel--dede_efendi.mu2 \n",
"1481 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/suzidil--sarki--duyek--sevgi_cicegimsin--omer_sami_gupgup.mu2 \n",
"1482 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz--turku--nimsofyan--dane_dane--kirsehir.mu2 \n",
"1483 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hisarbuselik--sarki--aksak--yandim_deminden--denizoglu_ali_bey.mu2 \n",
"1484 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/acemasiran--yuruksemai--yuruksemai--bileydi_dil--itri.mu2 \n",
"1485 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/saba--turku--nimsofyan--pinar_basi--.mu2 \n",
"1486 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/saba--sarki--aksak--bir_nigah--zeki_arif_ataergin.mu2 \n",
"1487 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/rast--turku--sofyan--adananin_yollari--.mu2 \n",
"1488 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huseyni--sarki--aksak--arzetmedigim--emin_ongan.mu2 \n",
"1489 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz--sarki--aksak--bilmiyorum--sevki_bey.mu2 \n",
"1490 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/kurdilihicazkar--sarki--kapali_curcuna--hancer-i_ebrusu--asdik_aga.mu2 \n",
"1491 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/ussak--sarki--aksak--gulzara_nazar--sevki_bey.mu2 \n",
"1492 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/ferahfeza--sarki--yuruksemai_ii--dinlendi_basim--lemi_atli.mu2 \n",
"1493 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicazkar--sazsemaisi--aksaksemai----tanburi_cemil_bey.mu2 \n",
"1494 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/gulizar--seyir--duyek--1--erol_bingol.mu2 \n",
"1495 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/mahur--sarki--aksak--al_yanina--dellalzade_haci_ismail_efendi.mu2 \n",
"1496 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nihavendikebir--seyir--sofyan--1--sefik_gurmeric.mu2 \n",
"1497 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/buselik--sarki--yuruksemai_ii--gule_sor--ismail_baha_surelsan.mu2 \n",
"1498 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/rast--nefes--bektasidevrirevani--al_dokeyim--.mu2 \n",
"1499 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/acemkurdi--sarki--kapali_curcuna--sana_eller--faruk_kayacikli.mu2 \n",
"1500 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/yegah--aranagme--aksak--1--.mu2 \n",
"1501 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/saba--turku--curcuna--yesil_yaprak--.mu2 \n",
"1502 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/sultaniyegah--sarki--aksak--sacinin_telleri--munir_nurettin_selcuk.mu2 \n",
"1503 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/saba--seyir--sofyan--1--erol_bingol.mu2 \n",
"1504 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/karcigar--sarki--aksak--ari_olsam--necmi_piskin.mu2 \n",
"1505 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/acemasiran--aranagme--aksak--1--.mu2 \n",
"1506 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/acemkurdi--sarki--semai--asikim_ben--necdet_tokatlioglu.mu2 \n",
"1507 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/zavil--turku--aksak--bahceye_bar--.mu2 \n",
"1508 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huzzam--sarki--senginsemai--acmam_acamam--nasibin_mehmet_yuru.mu2 \n",
"1509 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/acembuselik--kupe--aksak--istedik_suh-i--ahmet_avni_konuk.mu2 \n",
"1510 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/buselik--sarki--agiraksak--kalbimin_derdiyle--feridun_darbaz.mu2 \n",
"1511 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/besteisfahan--seyir--sofyan--1--sefik_gurmeric.mu2 \n",
"1512 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/segah--turku--aksak--izmirin_kavaklari--.mu2 \n",
"1513 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/tahir--turku--senginsemai--gele_gele--urfa.mu2 \n",
"1514 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/suzinak--sarki--senginsemai--gunden_gune--hafiz_mehmet_esref_efendi.mu2 \n",
"1515 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/rast--sarki--sofyan--acilan_bir--dramali_hasan_hasguler.mu2 \n",
"1516 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huzzam--sarki--nimsofyan--gezdigim_dikenli--kadri_sencalar.mu2 \n",
"1517 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/segah--sarki--curcuna--olmaz_ilac--haci_arif_bey.mu2 \n",
"1518 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n"
]
},
{
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"text": [
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huseyni--sarki--curcuna--edali_bir--ali_rifat_cagatay.mu2 \n",
"1519 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/acemkurdi--agirsemai--agir_aksaksemai--dur_etme--osep_aga.mu2 \n",
"1520 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nihavent--sarki--aksak--koklasam_saclarini--artaki_candan.mu2 \n",
"1521 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz--sarki--curcuna--sislendi_hava--rifat_bey.mu2 \n",
"1522 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/isfahan--sarki--duyek--gelirken_meclise--haham_nesim.mu2 \n",
"1523 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/kurdilihicazkar--sarki--senginsemai--derdin_ne--nasibin_mehmet_yuru.mu2 \n",
"1524 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huzzam--aranagme--agiraksak--1--.mu2 \n",
"1525 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/muhayyerkurdi--sarki--curcuna--bakisi_cagirir--selahaddin_pinar.mu2 \n",
"1526 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/segah--turku--nimsofyan--silifkenin_yogurdu--.mu2 \n",
"1527 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/yeni_cargah--destan--turkaksagi--bir_gemi--.mu2 \n",
"1528 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/muhayyerkurdi--sarki--duyek--ruzgar_soyluyor--sekip_ayhan_ozisik.mu2 \n",
"1529 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nihavent--sarki--aksak--kiz_sen--faize_ergin.mu2 \n",
"1530 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/buselik--mehter--yuruksemai_ii----zurnazen_ibrahim_aga.mu2 \n",
"1531 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/mahur--sarki--semai--asika_bagdat--munir_nurettin_selcuk.mu2 \n",
"1532 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/evic--sarki--devrihindi_ii--otme_ey--rifat_bey.mu2 \n",
"1533 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nihavent--seyir--turkaksagi--1--erol_bingol.mu2 \n",
"1534 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/suzidil--sarki--aksak--her_bir--tanburi_ali_efendi.mu2 \n",
"1535 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/ferahfeza--sazsemaisi--aksaksemai----tanburi_cemil_bey.mu2 \n",
"1536 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/mahur--sarki--aksak--bir_gonulde--mazhar_bey.mu2 \n",
"1537 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/tahir--turku--nimsofyan--gidin_bulutlar--kutahya.mu2 \n",
"1538 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/gerdaniye--turku--sofyan--su_daglari--fahri_kayahan.mu2 \n",
"1539 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/mahur--sarki--musemmen--gosterip_agyare--haci_arif_bey.mu2 \n",
"1540 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hisar--kupe--duyek--mahitab--ahmet_avni_konuk.mu2 \n",
"1541 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huzzam--sarki--duyek--icimde_kim--yusuf_nalkesen.mu2 \n",
"1542 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/segah--sazeseri--sofyan--uc_turkoglu--aksehir.mu2 \n",
"1543 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/beyati--yuruksemai--senginsemai--gelse_nesim-i--iv_murad.mu2 \n",
"1544 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/sabazemzeme--kupe--musemmen--ehl-i_askin--ahmet_avni_konuk.mu2 \n",
"1545 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz_zirgule--sarki--duyek--ne_bos--sadettin_kaynak.mu2 \n",
"1546 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/kurdilihicazkar--sarki--aksak--gun_dogmayacak--musa_sureyya_bey.mu2 \n",
"1547 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/sedaraban--pesrev--fahte----tanburi_cemil_bey.mu2 \n",
"1548 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nisaburek--kupe--duyek--fasl-i--ahmet_avni_konuk.mu2 \n",
"1549 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/muhayyerkurdi--sarki--semai--yalniz_kalan--teoman_alpay.mu2 \n",
"1550 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/acemasiran--aranagme--yuruksemai--1--.mu2 \n",
"1551 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huseyni--karsilama--evfer--trakya_karsilamasi--.mu2 \n",
"1552 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz--sarki--agiraksak--hali_nezimde--sevki_bey.mu2 \n",
"1553 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz--agirsemai--agirsenginsemai--guller_kizarir--ibrahim_aga.mu2 \n",
"1554 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hisarbuselik--kupe--aksak--sevdigimden_dun--ahmet_avni_konuk.mu2 \n",
"1555 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/beyati--sarki--devrihindi--nar-i_firkat--mahmud_celaleddin_pasa.mu2 \n",
"1556 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/acemasiran--mars--nimsofyan--korkma_sonmez--ali_rifat_cagatay.mu2 \n",
"1557 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz_zirgule--sarki--senginsemai--nolsun_bu--enderuni_ali_bey.mu2 \n",
"1558 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n"
]
},
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"name": "stdout",
"output_type": "stream",
"text": [
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/rast--pesrev--muhammes----tatyos_efendi.mu2 \n",
"1559 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/mahur--tavsanca--duyek--ne_ararsam--iii_selim.mu2 \n",
"1560 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huzi--turku--aksak--ayva_dibi--.mu2 \n",
"1561 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nuhuft--kupe--aksaksemai--etme--ahmet_avni_konuk.mu2 \n",
"1562 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/rast--sarki--curcuna--icime_hep--yildirim_gurses.mu2 \n",
"1563 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/kurdilihicazkar--sarki--devrihindi--sen_beni--mahmud_celaleddin_pasa.mu2 \n",
"1564 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/kurdilihicazkar--longa--nimsofyan----haydar_tatliyay.mu2 \n",
"1565 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nikriz--nakis--yuruksemai--meclis-i_meyde--kassamzade_mehmet_efendi.mu2 \n",
"1566 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/evcara--sarki--aksak--gel_ey--dede_efendi.mu2 \n",
"1567 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/ussak--sarki--aksak--daglar_dayanmaz--sevki_bey.mu2 \n",
"1568 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nihavent--seyir--semai--1--rauf_yekta.mu2 \n",
"1569 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huzzam--sarki--duyek--seni_sesini--sadi_hosses.mu2 \n",
"1570 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/kurdilihicazkar--sazsemaisi--aksaksemai----haydar_tatliyay.mu2 \n",
"1571 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/ussak--ilahi--sofyan--askinla_cak--haci_nafiz_bey.mu2 \n",
"1572 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/rast--ilahi--evsat--ya_rabbi--ali_ufki.mu2 \n",
"1573 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/ussak--sarki--duyek--her_mevsim--semahat_ozdenses.mu2 \n",
"1574 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hisarbuselik--beste--cenber--zahm-i_sinem--zekai_dede.mu2 \n",
"1575 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/saba--pesrev--hafif----zurnazen_dagi_ahmet_celebi.mu2 \n",
"1576 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/sedaraban--sarki--devrihindi--bade-i_vuslat--faize_ergin.mu2 \n",
"1577 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/suzinak_zirgule--sarki--agiraksak--sonbahar_goncasi_mi--mustafa_nafiz_irmak.mu2 \n",
"1578 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/ussak--ilahi--sofyan--seyyah_olup--.mu2 \n",
"1579 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/tahir--tesbih--sofyan--allahumme--ali_ufki.mu2 \n",
"1580 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/ussak--ilahi--duyek--allah_emrin--zekai_dede.mu2 \n",
"1581 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicazkarkurdi--seyir--semai--1--rauf_yekta.mu2 \n",
"1582 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nihavent--aranagme--turkaksagi--1--.mu2 \n",
"1583 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz--turku--sofyan--suda_balik--kirsehir.mu2 \n",
"1584 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/neva--pesrev--nimberefsan----serif_celebi.mu2 \n",
"1585 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/mahur--turku--kapali_curcuna--indim_yarin--.mu2 \n",
"1586 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/rast--turku--nimsofyan--sira_sira--.mu2 \n",
"1587 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz--turku--turkaksagi--carsambayi_sel--.mu2 \n",
"1588 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz--turku--raksaksagi--arabaci--istanbul.mu2 \n",
"1589 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz--sazsemaisi--aksaksemai----refik_talat_alpman.mu2 \n",
"1590 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/suzinak--aranagme--curcuna--1--.mu2 \n",
"1591 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hisar--seyir--devrihindi--1--erol_bingol.mu2 \n",
"1592 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz--sarki--duyek--aski_seninle--fehmi_tokay.mu2 \n",
"1593 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/acemasiran--nakis--yuruksemai--ne_hevayi--dede_efendi.mu2 \n",
"1594 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/beyati--sarki--duyek--ruhuma_sundugun--sadettin_kaynak.mu2 \n",
"1595 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/araban--kupe--musemmen--etse--ahmet_avni_konuk.mu2 \n",
"1596 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/muhayyer--zeybek--agiraksak--eskisehir--.mu2 \n",
"1597 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nihavent--sarki--duyek--askim_bahardi--yildirim_gurses.mu2 \n",
"1598 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/acem--ilahi--nimevsat--calabim_bir--haci_bayram_veli.mu2 \n",
"1599 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicazkar--sarki--duyek--harap_oldu--arif_sami_toker.mu2 \n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"1600 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz--ornek_oz--sofyan--1--ruhi_ayangil.mu2 \n",
"1601 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/buselik--sarki--duyek--sunbulistan_etmis--sakir_aga.mu2 \n",
"1602 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huseyni--turku--aksak--yuksek_yuksek--.mu2 \n",
"1603 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huseyni--seyir--duyek--1--erol_bingol.mu2 \n",
"1604 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz--sarki--aksak--sen_bu_yerden--sevki_bey.mu2 \n",
"1605 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/muhayyer--zeybek--aksak--serenler--burdur.mu2 \n",
"1606 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nisaburek--sarki--sofyan--kirdi_gecirdi--medeni_aziz_efendi.mu2 \n",
"1607 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/acemasiran--seyir--sofyan--1--sefik_gurmeric.mu2 \n",
"1608 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nisaburek--sarki--turkaksagi--bir_soz_dedi--munir_nurettin_selcuk.mu2 \n",
"1609 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/karcigar--sarki--duyek--kirpigi_oyali--sadettin_kaynak.mu2 \n",
"1610 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hisarbuselik--sarki--aksak--yorulmus_ol--latif_aga.mu2 \n",
"1611 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/karcigar--sarki--aksak--daglari_hep--sadettin_kaynak.mu2 \n",
"1612 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/muhayyerkurdi--sarki--aksak--renkler_opuyorken--omer_dilek.mu2 \n",
"1613 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/gerdaniye--turku--kapali_curcuna--sirali_daglarin--salih_turhan.mu2 \n",
"1614 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz_zirgule--seyir--sofyan--1--sefik_gurmeric.mu2 \n",
"1615 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nihavent--sarki--duyek--askimin_ilk--arif_sami_toker.mu2 \n",
"1616 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nihavent--sarki--semai--benim_gonlum--nazmi_atlig.mu2 \n",
"1617 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz_humayun--nakis--agirsenginsemai--seb_cunun--seyyid_nuh.mu2 \n",
"1618 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/tahir--turku--aksak--gimildan--manisa.mu2 \n",
"1619 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz--turku--sofyan--esmerim_bicim--diyarbakir.mu2 \n",
"1620 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/acemasiran--beste--devrikebir--ber_kusa-yi--basmaci_abdi_efendi.mu2 \n",
"1621 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huseyni--ornek_oz--sofyan--1--ruhi_ayangil.mu2 \n",
"1622 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/acemkurdi--fantezi--semai--son_verelim--sekip_ayhan_ozisik.mu2 \n",
"1623 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hisarbuselik--seyir--sofyan--1--sefik_gurmeric.mu2 \n",
"1624 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nikriz--seyir--duyek--1--sefik_gurmeric.mu2 \n",
"1625 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/yegah--seyir--sofyan--1--erol_bingol.mu2 \n",
"1626 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/suzidil--seyir--sofyan--1--sefik_gurmeric.mu2 \n",
"1627 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/rast--turku--sofyan--kalenin_dibinde--kerkuk.mu2 \n",
"1628 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huseyniasiran--seyir--sofyan--1--sefik_gurmeric.mu2 \n",
"1629 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/mahur--sazsemaisi--aksaksemai----refik_talat_alpman.mu2 \n",
"1630 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/gerdaniye--turku--sofyan--odasina_girdim--.mu2 \n",
"1631 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huzzam--agirsemai--aksaksemai--kurban_olayim--haci_faik_bey.mu2 \n",
"1632 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/segah--turku--sofyan--keten_koynek--kirsehir-mucur.mu2 \n",
"1633 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz--turku--sofyan--bahce_duvarini--neset_ertas.mu2 \n",
"1634 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/sehnaz--turku--sofyan--elmayi_top--hendek.mu2 \n",
"1635 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/suzinak_zirgule--sarki--duyek--kuzucagim_ne--haci_faik_bey.mu2 \n",
"1636 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/suzidilara--kupe--devrirevanihindi--mitriba_meclisde--ahmet_avni_konuk.mu2 \n",
"1637 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/zavil--turku--sofyan--ela_gozlum_yiktin--sadettin_kaynak.mu2 \n",
"1638 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huseyni--kupe--duyek--pesden--ahmet_avni_konuk.mu2 \n",
"1639 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/tahir--sarki--aksak--efem_simdi--lavtaci_hristo.mu2 \n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"1640 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/sevkefza--sarki--curcuna--sebep_sensin--mustafa_nafiz_irmak.mu2 \n",
"1641 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/tahir--turku--nimsofyan--bagda_gulu--ankara.mu2 \n",
"1642 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huzzam--rumeliturkusu--sofyan--aksam_oldu--rumeli.mu2 \n",
"1643 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/yegah--sarki--aksak--a_benim--dellalzade_haci_ismail_efendi.mu2 \n",
"1644 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/evic--sarki--aksak--sen_boyle--omer_sami_gupgup.mu2 \n",
"1645 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/sivenuma--beste--devrirevan--bir_goruste--abdulhalim_aga.mu2 \n",
"1646 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/ferahnakasiran--pesrev--devrikebir----cuneyd_kosal.mu2 \n",
"1647 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/segah--seyir--sofyan--1--sefik_gurmeric.mu2 \n",
"1648 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/beyati_araban--seyir--turkaksagi--1--erol_bingol.mu2 \n",
"1649 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/saba--sarki--senginsemai--hasretle_anarken--yesari_asim_arsoy.mu2 \n",
"1650 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nihavent--sarki--curcuna--yagmur_verdim--omer_dilek.mu2 \n",
"1651 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/muhayyer--turku--nimsofyan--uc_kardestik--diyarbakir.mu2 \n",
"1652 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/beyati--sazsemaisi--aksaksemai--arap--misirli_udi_ibrahim_efendi.mu2 \n",
"1653 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nihavent--fantezi--nimsofyan--gel_gonlumu--erdinc_celikkol.mu2 \n",
"1654 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/yeni_cargah--turku--sofyan--bilmem_su--.mu2 \n",
"1655 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz--sarki--semai--tadi_yok--muzaffer_ilkar.mu2 \n",
"1656 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/arazbarbuselik--kupe--aksak--buse_fikriyle--ahmet_avni_konuk.mu2 \n",
"1657 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nihavent--sarki--duyek--ahimi_hicranimi--selahattin_inal.mu2 \n",
"1658 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/saba--sarki--agiraksak--fasl-i_guldur--sivelioglu_yorgaki.mu2 \n",
"1659 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/ussak--turku--nimsofyan--urfaliyam_ezelden--.mu2 \n",
"1660 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/muhayyerkurdi--seyir--turkaksagi--1--erol_bingol.mu2 \n",
"1661 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huzzam--turku--aksak--duriyemin_gugumleri--.mu2 \n",
"1662 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huzzam--beste--agircenber--asika_tan--hekimbasi_abdulaziz_efendi.mu2 \n",
"1663 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/dilkeshaveran--agirsemai--agir_aksaksemai--hal-i_ruhsarina--kucuk_mehmet_aga.mu2 \n",
"1664 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/muhayyer--sarki--aksak--bir_gonulde--ahmet_rasim_bey.mu2 \n",
"1665 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/suzidil--sazsemaisi--aksaksemai----abdulhalim_aga.mu2 \n",
"1666 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/acemasiran--kupe--aksaksemai--bir--ahmet_avni_konuk.mu2 \n",
"1667 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/muhayyer--turku--sofyan--yandim_allah--sivas.mu2 \n",
"1668 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/acemasiran--sarki--senginsemai--canan_okuyor--ali_riza_avni_tinaz.mu2 \n",
"1669 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/ussak--ornek_oz--sofyan--1--huseyin_sadettin_arel.mu2 \n",
"1670 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/karcigar--guvende--nimdevir--edremit--.mu2 \n",
"1671 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/saba--sarki--aksak--bakisin_ahuya--omer_dilek.mu2 \n",
"1672 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/suzidil--sarki--aksak--sana_ben_arz-i--garbis_efendi.mu2 \n",
"1673 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/rast--sarki--duyek--gorsem_seni--dede_efendi.mu2 \n",
"1674 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/sultaniyegah--sarki--senginsemai--al_sazini--bimen_sen.mu2 \n",
"1675 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicazkar--sarki--senginsemai--son_askimi--lemi_atli.mu2 \n",
"1676 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/sehnaz--aranagme--sofyan--1--.mu2 \n",
"1677 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/mahur--sarki--aksak--cektikce_sineye--selahattin_icli.mu2 \n",
"1678 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz_zirgule--sarki--duyek--gonlumde_acmadan--sekip_ayhan_ozisik.mu2 \n",
"1679 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/ussak--sarki--turkaksagi--yalniz_birakip--misirli_udi_ibrahim_efendi.mu2 \n",
"1680 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/buselik--nakis--yuruksemai--cefasi_asika--dellalzade_haci_ismail_efendi.mu2 \n"
]
},
{
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"output_type": "stream",
"text": [
"1681 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huzzam--sarki--aksaksemaievferi--kerem_eyle--medeni_aziz_efendi.mu2 \n",
"1682 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/evic--rumeliturkusu--aksak--elveda_dost--rumeli.mu2 \n",
"1683 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nikriz--zeybek--aksak--1--tanburi_cemil_bey.mu2 \n",
"1684 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/suzinak_zirgule--sarki--evfer--dil-i_mahzununu--tanburi_ali_efendi.mu2 \n",
"1685 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicazkar--sirto--nimsofyan----kemani_sebuh.mu2 \n",
"1686 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz--ornek_oz--devrihindi--1--ruhi_ayangil.mu2 \n",
"1687 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/zavil--agirsemai--aksaksemai--bulunmaz_nev-civansin--kucuk_mehmet_aga.mu2 \n",
"1688 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/rengidil--sarki--turkaksagi--masum_bakislim--halis_bey.mu2 \n",
"1689 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/saba--mehter--hafif----zurnazen_dagi_ahmet_celebi.mu2 \n",
"1690 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/muhayyerkurdi--sarki--semai--duydum_ki--selahattin_altinbas.mu2 \n",
"1691 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/sultaniyegah--yuruksemai--yuruksemai--sadeyledi_can--dede_efendi.mu2 \n",
"1692 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/bestenigar--sarki--agiraksak--kacma_mecburundan--hasim_bey.mu2 \n",
"1693 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/tahirbuselik--sazsemaisi--aksaksemai----kemani_riza_efendi.mu2 \n",
"1694 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/yegah--sarki--aksak--du_cesmim--sevki_bey.mu2 \n",
"1695 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/mustear--sarki--aksak--evvel_benim--sakir_aga.mu2 \n",
"1696 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/sehnazbuselik--agirsemai--aksaksemai--kimseyi_dil--kucuk_muezzin_mehmet_celebi.mu2 \n",
"1697 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/tahir--turku--sofyan--kayada_yatan--ankara.mu2 \n",
"1698 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huseyni--ilahi--agirduyek--allah_allah--ali_ufki.mu2 \n",
"1699 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/sultaniyegah--sarki--duyek--kacinci_fasl-i--cevdet_cagla.mu2 \n",
"1700 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/mahur--sarki--semai--sarkimi_senin--irfan_ozbakir.mu2 \n",
"1701 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nihavent--sarki--duyek--seninle_bu--dursun_karaca.mu2 \n",
"1702 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/acemasiran--sarki--aksak--gordum_seni--irfan_dogrusoz.mu2 \n",
"1703 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/acemasiran--sarki--duyek--ruhumda_derin--kemal_gurses.mu2 \n",
"1704 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/acemasiran--sarki--yuruksemai_ii--gel_ey--aleko_bacanos.mu2 \n",
"1705 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicazkar--sarki--agiraksak--gel_seninle--sadullah_efendi.mu2 \n",
"1706 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/saba--sarki--duyek--seni_herkesten--yesari_asim_arsoy.mu2 \n",
"1707 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz--sarki--aksak--gormedim_omrumun--kadri_sencalar.mu2 \n",
"1708 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nisaburek--agirsemai--agir_aksaksemai--bilmem_ki--necmi_piskin.mu2 \n",
"1709 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz_uzzal--yuruksemai--yuruksemai--terk_eyledi--zaharya.mu2 \n",
"1710 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/kurdilihicazkar--sarki--kapali_curcuna--bitmez_tukenmez--selahattin_icli.mu2 \n",
"1711 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/segah--sarki--kapali_curcuna--hasta_kalbimde--kaptanzade_ali_riza_bey.mu2 \n",
"1712 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/cargah--ilahi--devrikebir--kudumun_rahmeti--aziz_mahmud_hudai.mu2 \n",
"1713 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz_humayun--ilahi--sofyan--gelin_gidelim--dede_efendi.mu2 \n",
"1714 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/saba--sarki--aksak--semt-i_dildare--suphi_ziya_ozbekkan.mu2 \n",
"1715 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz--durak--durakevferi--allah_emrin--.mu2 \n",
"1716 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huzzam--sarki--duyek--omrumuzun_son--selahattin_altinbas.mu2 \n",
"1717 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/beyati--sarki--yuruksemai_ii--uzulme_bir--omer_sami_gupgup.mu2 \n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"1718 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/mahur--sarki--aksak--yeniden_eski--ismail_baha_surelsan.mu2 \n",
"1719 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/kurdi_gerdaniye--kupe--musemmen--gordum_agaz--ahmet_avni_konuk.mu2 \n",
"1720 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/dilkusa--sazsemaisi--aksaksemai----arif_mehmet_aga.mu2 \n",
"1721 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/rast--sarki--senginsemai--bir_gonlume--tatyos_efendi.mu2 \n",
"1722 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicazkar--sarki--evfer--hic_esin--zeki_mehmet_aga.mu2 \n",
"1723 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/ussak--turku--duyek--bahceye_indim_ki--.mu2 \n",
"1724 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/acemasiran--sazsemaisi--aksaksemai----gazi_giray_han.mu2 \n",
"1725 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz--sarki--senginsemai--yillar_ne_cabuk--bimen_sen.mu2 \n",
"1726 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/ussak--sarki--curcuna--yad_ile--sevki_bey.mu2 \n",
"1727 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/sedaraban--beste--hafif--ey_safay-i--tanburi_isak.mu2 \n",
"1728 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huzzam--sarki--senginsemai--sen_sanki_baharin--musa_sureyya_bey.mu2 \n",
"1729 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicazkar--beste--devrikebir--ey_sehensah-i--sadullah_aga.mu2 \n",
"1730 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/segah--turku--aksak--beyaz_giyme--bolu.mu2 \n",
"1731 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/kurdilihicazkar--sazsemaisi--aksaksemai----yalcin_tura.mu2 \n",
"1732 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz--sarki--duyek--sarkilardan_fal--erdogan_berker.mu2 \n",
"1733 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/sevkefza--sarki--semai--geldik_gidiyoruz--omer_dilek.mu2 \n",
"1734 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/beyati--sarki--agiraksak--bir_katre--sevki_bey.mu2 \n",
"1735 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz--turku--sofyan--su_gelen--.mu2 \n",
"1736 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/ussak--turku--turkaksagi--drama_koprusu--.mu2 \n",
"1737 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz--sarki--duyek--yalancinin_birine--necdet_tokatlioglu.mu2 \n",
"1738 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/acemasiran--selam--mevlevievferi--sultan-i_meni--huseyin_fahreddin_dede.mu2 \n",
"1739 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huzzam--turku--aksak--yaniyor_mu--.mu2 \n",
"1740 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/ussak--sarki--duyek--hem_bakarsin--ugur_uzunhekim.mu2 \n",
"1741 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/kurdilihicazkar--sarki--aksak--def-i_nalis--tanburi_cemil_bey.mu2 \n",
"1742 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/segah--ilahi--sofyan--sordum_sari--.mu2 \n",
"1743 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huseyni--turku--sofyan--hey_onbesli--tokat.mu2 \n",
"1744 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nihavent--longa--nimsofyan----kevser_hanim.mu2 \n",
"1745 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/sevkidil--kupe--devrirevanihindi--dinleyenler_gussa-i--ahmet_avni_konuk.mu2 \n",
"1746 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/rast--pesrev--cifteduyek----sirri_abdulbaki_dede.mu2 \n",
"1747 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nihavent--mars--nimsofyan--ey_muhterem--mildan_niyazi_bey.mu2 \n",
"1748 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/sehnaz--turku--sofyan--arpa_bugday--orta_anadolu.mu2 \n",
"1749 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/rast--sarki--semai--askim_guzel--neveser_kokdes.mu2 \n",
"1750 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/saba--turku--azeriyuruksemai--kar_etmez--.mu2 \n",
"1751 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/beyati--sarki--duyek--seni_ne_cok--erol_sayan.mu2 \n",
"1752 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/bestenigar--sarki--duyek--yakti_cihani--ismet_aga.mu2 \n",
"1753 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz--sarki--turkaksagi--ey_cerhi_sitemger--medeni_aziz_efendi.mu2 \n",
"1754 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/segah--ornek_oz--duyek--1--ruhi_ayangil.mu2 \n",
"1755 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/acemasiran--sarki--duyek--pence-i_gamdan--misirli_udi_ibrahim_efendi.mu2 \n",
"1756 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/gulzar--sazsemaisi--aksaksemai----ismet_burkay.mu2 \n",
"1757 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/zevkitarab--kupe--musemmen--meclis-i_zevk-i--ahmet_avni_konuk.mu2 \n",
"1758 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/mahur--yuruksemai--yuruksemai--yine_zevrak-i--dede_efendi.mu2 \n",
"1759 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/buselik--fantezi--devrituran--bir_ak_duvak--fethi_karamahmutoglu.mu2 \n",
"1760 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hisar--sazsemaisi--aksaksemai----kemani_ali_aga.mu2 \n",
"1761 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicazkar--fantezi--duyek--aldi_beni--corlulu.mu2 \n",
"1762 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/saba--sarki--aksak--beni_aska--tanburi_ali_efendi.mu2 \n",
"1763 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/acemkurdi--sarki--duyek--artik_gelecek--arif_sami_toker.mu2 \n",
"1764 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huseyni--sarki--aksak--sensizlige_soylendi--ilgun_soysev.mu2 \n",
"1765 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nihavent--fantezi--sofyan--mevsimlere_aldanirim--omer_sami_gupgup.mu2 \n",
"1766 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/neva--sazsemaisi--aksaksemai----kantemiroglu.mu2 \n",
"1767 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicazkar--sarki--devrihindi--ey_verd-i--tahir_aga.mu2 \n",
"1768 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/sehnazbuselik--ornek_oz--sofyan--1--huseyin_sadettin_arel.mu2 \n",
"1769 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicazkar--sarki--aksak--acil_ey_gonce--haci_arif_bey.mu2 \n",
"1770 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/beyati_araban--sarki--aksak--bana_noldu--rahmi_bey.mu2 \n",
"1771 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz_zemzeme--sarki--aksak--gideyim_de--muallim_ismail_hakki_bey.mu2 \n",
"1772 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/kurdilihicazkar--sarki--yuruksemai--sen_gozlerine--misirli_udi_ibrahim_efendi.mu2 \n",
"1773 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/buselik--sarki--duyek--sevgim_bitti_mi--arif_sami_toker.mu2 \n",
"1774 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/suzidil--kupe--aksaksemai--yandi--ahmet_avni_konuk.mu2 \n",
"1775 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/karcigar--turku--semai--ben_bir--urfa.mu2 \n",
"1776 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/sehnaz--sarki--aksak--bir_nevcivane--iii_selim.mu2 \n",
"1777 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/kucek--kupe--duyek--bezme--ahmet_avni_konuk.mu2 \n",
"1778 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/acemkurdi--sarki--yuruksemai_ii--fikrimin_ince--muallim_ismail_hakki_bey.mu2 \n",
"1779 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hisarbuselik--sarki--kapali_curcuna--bir_hadise--servet_yesari.mu2 \n",
"1780 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/muhayyer--divan--nimsofyan--ok_gibi--.mu2 \n",
"1781 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/rast--seyir--semai--1--rauf_yekta.mu2 \n",
"1782 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/rast--sarki--curcuna--mey-i_lalinle--tatyos_efendi.mu2 \n",
"1783 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/sultaniirak--kupe--duyek--sah-i--ahmet_avni_konuk.mu2 \n",
"1784 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/acemkurdi--aranagme--duyek--1--.mu2 \n",
"1785 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/acemkurdi--turku--duyek--ankaranin_yollari--.mu2 \n",
"1786 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/acemasiran--sazsemaisi--aksaksemai----serafettin_seref_cakar.mu2 \n",
"1787 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/rast--sazsemaisi--aksaksemai----kantemiroglu.mu2 \n",
"1788 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/mahur--tavsanca--aksak--nazar_etti--tanburi_mustafa_cavus.mu2 \n",
"1789 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/acemasiran--sarki--agirduyek--kime_halim--faize_ergin.mu2 \n",
"1790 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/sehnaz--yuruksemai--yuruksemai--dem-i_visali--kara_ismail_aga.mu2 \n",
"1791 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/kurdilihicazkar--sarki--duyek--baga_girdim--manok_aga.mu2 \n",
"1792 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/rast--sarki--duyek--uftadenim_ey--dede_efendi.mu2 \n",
"1793 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/ferahnuma--seyir--sofyan--1--erol_bingol.mu2 \n",
"1794 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/muhayyer--sarki--senginsemai--pur-lerze--tanburi_cemil_bey.mu2 \n",
"1795 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicazkar--sarki--duyek--birer_birer--omer_sami_gupgup.mu2 \n",
"1796 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/bestenigar--sarki--agiraksak--gayridan_bulmaz--kazasker_mustafa_izzet_efendi.mu2 \n"
]
},
{
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"text": [
"1797 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/suzinak--pesrev--devrikebir----nayi_emin_dede.mu2 \n",
"1798 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/ruhnuvaz--sazsemaisi--aksaksemai----suphi_ezgi.mu2 \n",
"1799 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/suzinak_zirgule--sarki--aksak--bir_sihr-i--rahmi_bey.mu2 \n",
"1800 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/kurdilihicazkar--sarki--kapali_curcuna--gurub_etti--haci_arif_bey.mu2 \n",
"1801 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/sevkidil--pesrev--muhammes----seyyid_ahmed_aga.mu2 \n",
"1802 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/rast--sarki--musemmen--vuslatindan_gayri--haci_arif_bey.mu2 \n",
"1803 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/evic--kupe--agirevfer--ol_humapervaze--ahmet_avni_konuk.mu2 \n",
"1804 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/mahur--sarki--agiraksak--sen_gul--tanburi_cemil_bey.mu2 \n",
"1805 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/acemasiran--sarki--aksak--gonlum_dusuyor--ismail_baha_surelsan.mu2 \n",
"1806 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/ussak--kanto--sofyan--telgirafin_tellerine--.mu2 \n",
"1807 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/muhayyer--sarki--aksak--her_halinle--erol_sayan.mu2 \n",
"1808 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/evic--rumeliturkusu--curcuna--calin_davullari--rumeli.mu2 \n",
"1809 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/karcigar--ornek_oz--nimsofyan--1--ruhi_ayangil.mu2 \n",
"1810 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz--sarki--duyek--anilsin_yar--semseddin_ziya_bey.mu2 \n",
"1811 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/segah--rumeliturkusu--raksaksagi--sana_da--rumeli.mu2 \n",
"1812 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz_humayun--turku--sofyan--benim_gulum--kirsehir.mu2 \n",
"1813 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/segah--sarki--raksaksagi--herkes_gitti--refik_fersan.mu2 \n",
"1814 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/karcigar--aranagme--agiraksak--1--.mu2 \n",
"1815 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/sultanisegah--pesrev--cifteduyek----santuri_ethem_efendi.mu2 \n",
"1816 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/karcigar--sarki--aksak--aldin_dil-i--enderuni_ali_bey.mu2 \n",
"1817 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nihavent--sarki--senginsemai--bir_asik-i--afet_misirliyan.mu2 \n",
"1818 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/yegah--sarki--devrihindi--sad_ol--rifat_bey.mu2 \n",
"1819 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz_zirgule--seyir--sofyan--1--erol_bingol.mu2 \n",
"1820 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/mahur--seyir--sofyan--1--sefik_gurmeric.mu2 \n",
"1821 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/muhayyerkurdi--sarki--musemmen--ayrilik_var--selahattin_icli.mu2 \n",
"1822 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/karcigar--sarki--kapali_curcuna--mest_oldu--serif_icli.mu2 \n",
"1823 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nikriz--agirsemai--aksaksemai--benim_curmum--akin_ozkan.mu2 \n",
"1824 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/mahur--sarki--aksak--sana_layik_mi--dede_efendi.mu2 \n",
"1825 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/evic--aranagme--turkaksagi--1--.mu2 \n",
"1826 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/buselik--sarki--aksak--eda_ile--dede_efendi.mu2 \n",
"1827 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nihavent--sarki--duyek--gokyuzunde--teoman_alpay.mu2 \n",
"1828 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/saba--sarki--turkaksagi--dus_ben_gibi--serif_icli.mu2 \n",
"1829 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/muhayyer--turku--kapali_curcuna--su_icemem--diyarbakir.mu2 \n",
"1830 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/ussak--ornek_oz--turkaksagi--1--ruhi_ayangil.mu2 \n",
"1831 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/ussak--sarki--evfer--yavrucagim_guzellendi--tanburi_mustafa_cavus.mu2 \n",
"1832 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/muhayyer--sarki--aksak--hala_gonlum--tanburi_mustafa_cavus.mu2 \n",
"1833 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/buselik--sarki--yuruksemai--suzis-i_sinem--haci_arif_bey.mu2 \n",
"1834 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huseyniasiran--kupe--aksaksemai--gel--ahmet_avni_konuk.mu2 \n",
"1835 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/yegah--sarki--aksaksemai--benim_afet-i--dellalzade_haci_ismail_efendi.mu2 \n",
"1836 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nikriz--seyir--duyek--1--erol_bingol.mu2 \n",
"1837 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicazkar--kupe--devrirevanihindi--dun_gece--ahmet_avni_konuk.mu2 \n",
"1838 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/segah--turku--sofyan--oy_kemence--ordu.mu2 \n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"1839 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/beyati--sarki--kapali_curcuna--bilmezsin_dusundugum--rakim_elkutlu.mu2 \n",
"1840 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nihavent--sarki--yuruksemai--tez_gecse_de--sadettin_kaynak.mu2 \n",
"1841 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/buselik--seyir--sofyan--1--sefik_gurmeric.mu2 \n",
"1842 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nihavent--sarki--semai--bahcemde_acilmaz--munir_nurettin_selcuk.mu2 \n",
"1843 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/neveser--pesrev--hafif--cankurtaran--neyzen_yusuf_pasa.mu2 \n",
"1844 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/guldeste--sazsemaisi--aksaksemai----dede_salih_efendi.mu2 \n",
"1845 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huseyni--aranagme--duyek--1--.mu2 \n",
"1846 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huzzam--sarki--curcuna--bir_gun--omer_sami_gupgup.mu2 \n",
"1847 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/kurdi--seyir--sofyan--1--sefik_gurmeric.mu2 \n",
"1848 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz--sarki--nimsofyan--gonul_penceresinden--muzaffer_ilkar.mu2 \n",
"1849 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/evic--turku--agiraksak--karanfilin_moruna--canakkale.mu2 \n",
"1850 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/ferahfeza--agirsemai--senginsemai--bir_dilber-i--dede_efendi.mu2 \n",
"1851 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/dugah--kupe--duyek--itdiguycun--ahmet_avni_konuk.mu2 \n",
"1852 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/ferahfeza--yuruksemai--yuruksemai_ii--bu_gece--dede_efendi.mu2 \n",
"1853 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/rast--kar_i_natik--yuruksemai--rast_getirub--dede_efendi.mu2 \n",
"1854 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/buselik--sarki--curcuna--aman_cana--fehmi_tokay.mu2 \n",
"1855 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/muhayyer--murabba--agircenber--vakt-i_subh--hafiz_post.mu2 \n",
"1856 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/saba--sarki--duyek--kinali_keklik--zeki_muren.mu2 \n",
"1857 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nihavent--sarki--semai--icimde_nice--avni_anil.mu2 \n",
"1858 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/karcigar--sazsemaisi--aksaksemai----resat_aysu.mu2 \n",
"1859 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/buselik--sarki--semai--dil_bestenim--vecdi_seyhun.mu2 \n",
"1860 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/suzinak_zirgule--turku--oynak--uzun_da_kavak--izmir.mu2 \n",
"1861 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/ussak--turku--aksak--kizilciklar_oldu--edirne-kesan.mu2 \n",
"1862 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nisabur--turku--nimsofyan--sultan_murat--.mu2 \n",
"1863 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/rehavi--sarki--duyek--ey_bulend--dede_efendi.mu2 \n",
"1864 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/muhayyer--beste--darbifetih--hengam-i_safadir--zekai_dede.mu2 \n",
"1865 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/muhayyer--pesrev--ferimuhammes----kantemiroglu.mu2 \n",
"1866 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/segah--pesrev--devrikebir----nayi_osman_dede.mu2 \n",
"1867 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/ussak--yuruksemai--yuruksemai--ol_sim-beden--tabi_mustafa_efendi.mu2 \n",
"1868 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/sultaniyegah--sarki--nimsofyan--sen_sarki--munir_nurettin_selcuk.mu2 \n",
"1869 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/bestenigar--seyir--sofyan--1--erol_bingol.mu2 \n",
"1870 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/ferahnak--sazsemaisi--aksaksemai----leon_hanciyan.mu2 \n",
"1871 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz--sazsemaisi--aksaksemai----muhittin_erev.mu2 \n",
"1872 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/rast--nakis--duyek--amed_nesim-i--abdulkadir_meragi.mu2 \n",
"1873 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/karcigar--sarki--turkaksagi--bilmem_ki_safa--leon_hanciyan.mu2 \n",
"1874 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huzzam--kupe--darb--sazin--ahmet_avni_konuk.mu2 \n",
"1875 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/sehnazbuselik--sarki--semai--yolun_bulamam--denizoglu_ali_bey.mu2 \n",
"1876 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/ussak--sarki--devrihindi--bezm-i_gamda--mustafa_coskun.mu2 \n",
"1877 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz--kalenderi--aksak--ebrulerinin_zahmi--ii_mahmud.mu2 \n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"1878 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicazkar--sarki--kapali_curcuna--bahar_geldi--muhlis_sabahaddin_ezgi.mu2 \n",
"1879 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/asiranzemzeme--pesrev--sofyan----.mu2 \n",
"1880 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/segah--turku--nimsofyan--ermenegin_keklikleri--konya.mu2 \n",
"1881 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huseyni--sarki--agiraksak--semi_husnun--sivelioglu_yorgaki.mu2 \n",
"1882 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/gerdaniye--oyunhavasi--devrituran--bulut_gelir--.mu2 \n",
"1883 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/muhayyerkurdi--mars--semai--tuna_nehri--rumeli.mu2 \n",
"1884 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicazkar--sarki--duyek--beyhude_askina--nikogos_aga.mu2 \n",
"1885 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/kurdilihicazkar--sarki--nimsofyan--artik_sevgili--gulen_ertek.mu2 \n",
"1886 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz--sarki--duyek--ayrilik_canlari--omer_sami_gupgup.mu2 \n",
"1887 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/ferahfeza--sarki--aydin--mehtapta_guzel--muallim_ismail_hakki_bey.mu2 \n",
"1888 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz--ninni--yuruksofyan--dere_dibi--.mu2 \n",
"1889 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/ussak--sarki--aksak--siyah_ebrulerin--lemi_atli.mu2 \n",
"1890 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/mahur--pesrev--muhammes----tanburi_cemil_bey.mu2 \n",
"1891 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/sultaniyegah--sarki--curcuna--gozumde_hep--.mu2 \n",
"1892 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nisaburek--sarki--musemmen--iftirakinla_efendim--cevdet_cagla.mu2 \n",
"1893 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/rast--medhal--hafif--musahabet_i_musikiye--refik_fersan.mu2 \n",
"1894 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nihavent--pesrev--darbeyn----katip_celebi.mu2 \n",
"1895 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/saba--sarki--kapali_curcuna--pervane_gibi--yesari_asim_arsoy.mu2 \n",
"1896 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/askefza--pesrev--ayindevrirevani----huseyin_sadettin_arel.mu2 \n",
"1897 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz_uzzal--sarki--curcuna--ne_kustun--sekerci_cemil_bey.mu2 \n",
"1898 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huseyni--divan--sofyan----.mu2 \n",
"1899 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/segah--sarki--duyek--gonul_bahcemizde--omer_sami_gupgup.mu2 \n",
"1900 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/ussak--sarki--raksaksagi--gemim_gidiyor--sadettin_kaynak.mu2 \n",
"1901 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nisabur--kupe--musemmen--bezm-i--ahmet_avni_konuk.mu2 \n",
"1902 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huzzam--sarki--aksak--sevdim_yine--tanburi_mustafa_cavus.mu2 \n",
"1903 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/saba--beste--devrikebir--gulistan-i_naks-i--zaharya.mu2 \n",
"1904 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nihavent--sarki--aksak--ruya_mi--omer_dilek.mu2 \n",
"1905 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/ferahfeza--pesrev--devrikebir----dede_efendi.mu2 \n",
"1906 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicazkar--sarki--semai--yoktur_zaman--latif_aga.mu2 \n",
"1907 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hisarbuselik--sazsemaisi--aksaksemai----tanburi_buyuk_osman_bey.mu2 \n",
"1908 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/muhayyerkurdi--sarki--duyek--senden_bana--teoman_alpay.mu2 \n",
"1909 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nihavent--sarki--sofyan--karsida_kandilli--amir_ates.mu2 \n",
"1910 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/beyati--beste--nimdevir--degil_cam-i_mey--yahya_nazim_celebi.mu2 \n",
"1911 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/segah--tekbir--durakevferi--allah_u_ekber--itri.mu2 \n",
"1912 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/suzidil--sarki--senginsemai--ey_gonca--semseddin_ziya_bey.mu2 \n",
"1913 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huseyni--ornek_oz--duyek--1--ruhi_ayangil.mu2 \n",
"1914 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/gerdaniye--turku--sofyan--benusene_gideyim--.mu2 \n",
"1915 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz_humayun--murabba--agircenber--hasret-i_ruyinle--gevrekzade_mustafa_aga.mu2 \n",
"1916 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/isfahan--sazsemaisi--aksaksemai----tanburi_cemil_bey.mu2 \n",
"1917 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/canfeza--seyir--sofyan--1--sefik_gurmeric.mu2 \n",
"1918 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/muhayyer--sarki--aksak--iltimas_etmeye--haci_arif_bey.mu2 \n",
"1919 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/acemkurdi--seyir--sofyan--1--sefik_gurmeric.mu2 \n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"1920 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/sehnaz--seyir--sofyan--1--sefik_gurmeric.mu2 \n",
"1921 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicazkar--sarki--agiraksak--seni_candan--nasibin_mehmet_yuru.mu2 \n",
"1922 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/ussak--turku--devrihindi--menteseli--konya.mu2 \n",
"1923 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/mahur--sarki--agiraksak--badeler_dondukce--selanikli_ahmed_bey.mu2 \n",
"1924 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/suzidilara--seyir--sofyan--1--sefik_gurmeric.mu2 \n",
"1925 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huzzam--sarki--semai--ozledim_gel--omer_sami_gupgup.mu2 \n",
"1926 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nihavent--sarki--sofyan--semalardan_gunes--misirli_udi_ibrahim_efendi.mu2 \n",
"1927 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huseyni--sazsemaisi--aksaksemai----tatyos_efendi.mu2 \n",
"1928 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/mahur--sarki--aksak--ver_saki--refik_fersan.mu2 \n",
"1929 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/rast--sarki--sofyan--sazin_gibi--lemi_atli.mu2 \n",
"1930 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/suzinak_zirgule--sarki--aksak--hasretle_yanan--emin_ongan.mu2 \n",
"1931 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/mahur--aranagme--nimsofyan--1--.mu2 \n",
"1932 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/kurdilihicazkar--seyir--sofyan--1--erol_bingol.mu2 \n",
"1933 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/mahur--sarki--duyek--hosgeldin_elimize--sadettin_kaynak.mu2 \n",
"1934 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz--sarki--duyek--yalan_degil--yusuf_nalkesen.mu2 \n",
"1935 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/karcigar--kocekce--aksak--girdi_gonul--dede_efendi.mu2 \n",
"1936 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz--turku--sofyan--sandalim_geliyor--.mu2 \n",
"1937 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/karcigar--sazsemaisi--aksaksemai----kanuni_omer_efendi.mu2 \n",
"1938 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/muhayyer--sarki--aksak--serapa_husn--rahmi_bey.mu2 \n",
"1939 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/yeni_cargah--turku--kapali_curcuna--giderim_yolum--.mu2 \n",
"1940 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/buselik--sarki--curcuna--benim_gonlum--osman_nuri_ozpekel.mu2 \n",
"1941 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz--turku--aksak--necibemin_ikidir--istanbul.mu2 \n",
"1942 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz--ninni--sofyan--dagda_tavsanlar--tanburi_cemil_bey.mu2 \n",
"1943 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/buselik--agirsemai--agir_aksaksemai--soyle_ey_nalem--ahmed_irsoy.mu2 \n",
"1944 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/evic--aranagme--sofyan--1--.mu2 \n",
"1945 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huseyni--oyunhavasi--nimsofyan--cecen_kizi--tanburi_cemil_bey.mu2 \n",
"1946 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/buselik--pesrev--muhammes----nikolaki.mu2 \n",
"1947 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/buzurk--kupe--devrirevanihindi--sur_sal--ahmet_avni_konuk.mu2 \n",
"1948 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/sultaniyegah--sarki--curcuna--sevildim_sanma--arif_sami_toker.mu2 \n",
"1949 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huseyni--aranagme--aksak--1--.mu2 \n",
"1950 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huzzam--turku--curcuna--al_yesil--urfa.mu2 \n",
"1951 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/saba--pesrev--duyek----tabi_mustafa_efendi.mu2 \n",
"1952 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/buselik--aranagme--yuruksemai_ii--1--.mu2 \n",
"1953 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/beyati--sarki--duyek--gonul_telim--erdinc_celikkol.mu2 \n",
"1954 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nihavent--sarki--sofyan--sahilde_o--yesari_asim_arsoy.mu2 \n",
"1955 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/suzidil--seyir--aksak--1--erol_bingol.mu2 \n",
"1956 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/karcigar--ilahi--duyek--ey_derde_derman--rakim_elkutlu.mu2 \n",
"1957 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/mahur--beste--lenkfahte--seyret_izar-i--tascizade_recep_celebi.mu2 \n",
"1958 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/acemkurdi--aranagme--aksak--1--.mu2 \n",
"1959 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicazkar--pesrev--muhammes----tanburi_cemil_bey.mu2 \n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"1960 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/rast--seyir--senginsemai--1--erol_bingol.mu2 \n",
"1961 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz--sarki--duyek--istanbulda_bogazicinde--sekip_ayhan_ozisik.mu2 \n",
"1962 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/evic--sarki--ciftesofyan--ey_suh-i--suyolcuzade_salih_efendi.mu2 \n",
"1963 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nisabureyn--pesrev--devriaryan----ozan_yarman.mu2 \n",
"1964 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nisaburek--sarki--duyek--ayrilik_yari--selahaddin_pinar.mu2 \n",
"1965 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/kurdilihicazkar--sarki--duyek--gittin_biraktin--emin_ongan.mu2 \n",
"1966 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/acem--ilahi--duyek--aldanma_dunya--zekai_dede.mu2 \n",
"1967 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/evic--sarki--aksak--sevdim_bir_gonca-i_rana--dede_efendi.mu2 \n",
"1968 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/kurdilihicazkar--sazsemaisi--aksaksemai--sonbahar--coskun_acikgoz.mu2 \n",
"1969 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/sultaniyegah--pesrev--muhammes----kanuni_haci_arif_bey.mu2 \n",
"1970 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nihavent--sarki--aydin--sozbirligi--muallim_ismail_hakki_bey.mu2 \n",
"1971 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/gulizar--seyir--sofyan--1--sefik_gurmeric.mu2 \n",
"1972 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/muhayyer--turku--nimsofyan--bir_nazar--kirsehir.mu2 \n",
"1973 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/muhayyer--sarki--aksak--dilde_rast--serif_icli.mu2 \n",
"1974 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/rast--turku--devrituran--oynayin_kiz--trabzon.mu2 \n",
"1975 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huseyni--sarki--aksak--sevdigim_cemalin--mahmud_celaleddin_pasa.mu2 \n",
"1976 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/irak--sazsemaisi--aksaksemai----serif_muhittin_targan.mu2 \n",
"1977 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nikriz--sirto--nimsofyan----tanburi_cemil_bey.mu2 \n",
"1978 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huzzam--ilahi--muhammes--duseli_askin--tiznam_yusuf_celebi.mu2 \n",
"1979 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/ussak--sarki--duyek--dalinda_solarken--arif_sami_toker.mu2 \n",
"1980 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/kurdilihicazkar--sarki--agiraksak--koparan_sinemi--bimen_sen.mu2 \n",
"1981 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nihavent--sarki--semai--dil_seni--civan_aga.mu2 \n",
"1982 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/isfahan--kupe--duyek--ittifakan--ahmet_avni_konuk.mu2 \n",
"1983 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/ferahfeza--pesrev--hafif--son_pesrev--zeki_mehmet_aga.mu2 \n",
"1984 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/sultaniyegah--sarki--agiraksak--guller_acmis--santuri_ethem_efendi.mu2 \n",
"1985 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/sehnaz--sarki--aksak--firsat_bulsam--tanburi_mustafa_cavus.mu2 \n",
"1986 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz--sarki--curcuna--acaba_sen_misin--bimen_sen.mu2 \n",
"1987 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/mahur--sarki--agiraksak--var_iken--tanburi_cemil_bey.mu2 \n",
"1988 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/sabazemzeme--sarki--aksak--firakin_sinemi--mahmud_celaleddin_pasa.mu2 \n",
"1989 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/gerdaniye--turku--nimsofyan--al_almanin--malatya.mu2 \n",
"1990 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz_humayun--turku--aksak--estergon_kalasi--.mu2 \n",
"1991 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/yegah--sarki--duyek--ay_buyurken--alaeddin_yavasca.mu2 \n",
"1992 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/muhayyer--sarki--kapali_curcuna--ol_gonca--sevki_bey.mu2 \n",
"1993 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nihavent--sarki--turkaksagi--bir_tatli_urperti--fethi_karamahmutoglu.mu2 \n",
"1994 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/karcigar--kupe--duyek--bir_sada-yi--ahmet_avni_konuk.mu2 \n",
"1995 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz--sarki--agiraksak--cevr-i_hicrin--bulbuli_salih_efendi.mu2 \n",
"1996 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/segah--turku--nimsofyan--zeytinyagli_yiyemem--.mu2 \n",
"1997 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/beyati--sarki--aksaksemai--aglatma_beni--dede_efendi.mu2 \n",
"1998 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/mahur--sarki--sarkidevrirevani--zevki_coktur--sakir_aga.mu2 \n",
"1999 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz--sarki--agiraksak--omrum_artar--bimen_sen.mu2 \n",
"2000 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/kurdi--kupe--musemmen--asik-i--ahmet_avni_konuk.mu2 \n",
"2001 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/acemkurdi--sarki--yuruksemai--bari_felek--nikogos_aga.mu2 \n",
"2002 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/muhayyerkurdi--sarki--turkaksagi--bin_gulle--emin_ongan.mu2 \n",
"2003 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/saba--sarki--curcuna--suzdukce_guzel--cevdet_cagla.mu2 \n",
"2004 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/dugah_maye--kupe--duyek--bir_dugah--ahmet_avni_konuk.mu2 \n",
"2005 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/zavil--seyir--sofyan--1--sefik_gurmeric.mu2 \n",
"2006 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/evic--rumeliturkusu--sofyan--sahane_gozler--.mu2 \n",
"2007 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz--sarki--kapali_curcuna--nicin_bulbul--rifat_bey.mu2 \n",
"2008 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz--sarki--aksak--su_karsiki--dede_efendi.mu2 \n",
"2009 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/ussak--sarki--sofyan--bir_perikizi--.mu2 \n",
"2010 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nisaburek--sarki--semai--aski_fisildar--neveser_kokdes.mu2 \n",
"2011 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/ussak--sarki--turkaksagi--hasret_dolu--serif_icli.mu2 \n",
"2012 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/rast--sarki--curcuna--asik_oldur--haci_arif_bey.mu2 \n",
"2013 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/gerdaniye--turku--aksak--salina_salina--denizli.mu2 \n",
"2014 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nihavent--sarki--semai--kanatlari_gumus--mesut_cemil.mu2 \n",
"2015 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicazkar--kosma--sofyan--melin_mahzun--muallim_ismail_hakki_bey.mu2 \n",
"2016 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huzzam--sarki--duyek--sacin_yuzume--teoman_alpay.mu2 \n",
"2017 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz_uzzal--sarki--yuruksemai--affeyle_sucum--sevki_bey.mu2 \n",
"2018 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hisarbuselik--sarki--agiraksak--vuslata_nail--zekai_dede.mu2 \n",
"2019 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/tahir--turku--musemmen_ii--gonul_gurbet--gaziantep.mu2 \n",
"2020 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nihavent--sarki--agiraksak--akibet_viran--numan_aga.mu2 \n",
"2021 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/rast--aranagme--musemmen--1--.mu2 \n",
"2022 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huseyni--ilahi--yuruksemai--nur-i_fahr-i--dede_efendi.mu2 \n",
"2023 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/evic--sazsemaisi--aksaksemai----sedat_oztoprak.mu2 \n",
"2024 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz--sarki--agiraksak--severim_can_u--sevki_bey.mu2 \n",
"2025 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/muhayyer--turku--raksan--sogudun_yapragi--.mu2 \n",
"2026 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/ussak--sarki--curcuna--omrumun_gulusun--haydar_tatliyay.mu2 \n",
"2027 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/rast--kar_i_nev--agirduyek--gozumde_daim--dede_efendi.mu2 \n",
"2028 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/acemasiran--sarki--aksak--acildi_nevbahar--sadullah_aga.mu2 \n",
"2029 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/saba--pesrev--devrikebir----tanburi_buyuk_osman_bey.mu2 \n",
"2030 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/saba--aranagme--senginsemai--1--.mu2 \n",
"2031 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/beyati--pesrev--darbifetih----tanburi_isak.mu2 \n",
"2032 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/suzinak--pesrev--cenber----tatyos_efendi.mu2 \n",
"2033 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huseyni--sarki--aksak--ezelden_asinanim--serif_icli.mu2 \n",
"2034 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/neveser--kupe--devrirevanihindi--dinle_canim--ahmet_avni_konuk.mu2 \n",
"2035 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/ussak--sarki--curcuna--hastasin_zannim--sevki_bey.mu2 \n",
"2036 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/ussak--sarki--aksak--zeybeklerle_gezer--sevki_bey.mu2 \n"
]
},
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"2037 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huseyni--ornek_oz--sofyan--2--ruhi_ayangil.mu2 \n",
"2038 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/muhayyerkurdi--sazsemaisi--aksaksemai----sadi_isilay.mu2 \n",
"2039 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz--sarki--aksak--matemzedeyim--tanburi_cemil_bey.mu2 \n",
"2040 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/suzinak--agirsemai--aksaksemai--kapilir_her--kucuk_mehmet_aga.mu2 \n",
"2041 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/vecdidil--pesrev--muhammes----ruhi_ayangil.mu2 \n",
"2042 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz--pesrev--fahte----neyzen_salim_bey.mu2 \n",
"2043 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/rast--karsilama--ciftesofyan--tekirdag_karsilamasi--.mu2 \n",
"2044 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/muhayyer--turku--curcuna--kerpic_kerpic--diyarbakir.mu2 \n",
"2045 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huseyni--turku--curcuna--dersim_dort--tunceli.mu2 \n",
"2046 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nisabur--beste--muhammes--renc-i_hatir--tanburi_ali_efendi.mu2 \n",
"2047 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/sevk_icedid--kupe--musemmen--kesme_mutrib--ahmet_avni_konuk.mu2 \n",
"2048 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/ruyiirak--sazsemaisi--aksaksemai----kani_karaca.mu2 \n",
"2049 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/ferahnuma--turku--aksak--kiz_bahcende--kastamonu.mu2 \n",
"2050 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/acemkurdi--seyir--senginsemai--1--erol_bingol.mu2 \n",
"2051 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huzzam--seyir--semai--1--rauf_yekta.mu2 \n",
"2052 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/segah--turku--sofyan--acem_ulkesinde--erzurum.mu2 \n",
"2053 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/sultaniyegah--seyir--sofyan--1--sefik_gurmeric.mu2 \n",
"2054 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nihavent--agirsemai--aksaksemai--seni_hukm-u--muallim_ismail_hakki_bey.mu2 \n",
"2055 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/kurdilihicazkar--sarki--aksak--safalar_getirdiniz--avni_anil.mu2 \n",
"2056 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/ferahfeza--sazsemaisi--aksaksemai----serif_muhittin_targan.mu2 \n",
"2057 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/saba--turku--sofyan--mendilimin_yesili--.mu2 \n",
"2058 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/rast--sarki--yuruksemai--fitneler_gizlemis--mahmud_celaleddin_pasa.mu2 \n",
"2059 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hisarbuselik--yuruksemai--yuruksemai_ii--yine_bezm-i--dede_efendi.mu2 \n",
"2060 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz--aranagme--agiraksak--1--.mu2 \n",
"2061 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/ferahnuma--sarki--curcuna--guzel_kiz--huseyin_sadettin_arel.mu2 \n",
"2062 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/acemkurdi--nefes--bektasiraksani--sah_merdanin--.mu2 \n",
"2063 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nihavent--sarki--nimsofyan--istanbulda_yasanan--baki_callioglu.mu2 \n",
"2064 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/evic--rumeliturkusu--aksak--atladim_bahcene--rumeli.mu2 \n",
"2065 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hisarbuselik--sarki--aksak--eyle_kerem--numan_aga.mu2 \n",
"2066 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/segah--sarki--aksak--kirdin_ummidimi--fehmi_tokay.mu2 \n",
"2067 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz--sarki--senginsemai--agyar_ile--bimen_sen.mu2 \n",
"2068 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/muhayyer--yuruksemai--yuruksemai--bir_elif--sadullah_aga.mu2 \n",
"2069 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nihavent--fantezi--nimsofyan--aksami_suzme--kaptanzade_ali_riza_bey.mu2 \n",
"2070 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/rast--kupe--devrirevanihindi--kavli_de--ahmet_avni_konuk.mu2 \n",
"2071 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/muhayyer--sarki--curcuna--batan_gun--sadettin_kaynak.mu2 \n",
"2072 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/dugah--yuruksemai--yuruksemai--der_yemeni--seyhulislam_esad_efendi.mu2 \n",
"2073 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicazkar--seyir--sofyan--1--sefik_gurmeric.mu2 \n",
"2074 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/ferahfeza--seyir--sofyan--1--erol_bingol.mu2 \n",
"2075 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/kurdilihicazkar--sarki--duyek--her_kimde--civan_aga.mu2 \n",
"2076 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz--turku--aksak--pencere_acildi--.mu2 \n",
"2077 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huseyni--turku--aksak--a_fadimem--.mu2 \n"
]
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"text": [
"2078 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/segah--turku--223--gemiciler_kalkalim--trabzon.mu2 \n",
"2079 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/sedaraban--seyir--sofyan--1--erol_bingol.mu2 \n",
"2080 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nihavent--sarki--semai--hatirla_ey_peri--muhlis_sabahaddin_ezgi.mu2 \n",
"2081 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hisarbuselik--turku--curcuna--araz_uste--.mu2 \n",
"2082 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/sehnaz--ilahi--evsat--yuruk_degirmenler--dede_efendi.mu2 \n",
"2083 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicazkar--sarki--duyek--visali_yar--rakim_elkutlu.mu2 \n",
"2084 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/evcara--sarki--aksaksemai--husnune_mail--dede_efendi.mu2 \n",
"2085 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/tahir--turku--devrihindi--yoru_bire--sarkisla.mu2 \n",
"2086 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/rast--sarki--semai--gecsin_gunler--erol_sayan.mu2 \n",
"2087 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz--zeybek--aksak----izmir.mu2 \n",
"2088 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/mahur--sarki--sofyan--ne_dogan--munir_nurettin_selcuk.mu2 \n",
"2089 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz--sarki--semai--sirma_sacli--misirli_udi_ibrahim_efendi.mu2 \n",
"2090 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nisaburek--pesrev--muhammes----tanburi_buyuk_osman_bey.mu2 \n",
"2091 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/kurdilihicazkar--sarki--musemmen--gecti_omrun--sadi_hosses.mu2 \n",
"2092 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/gulizar--kupe--duyek--ah_eder--ahmet_avni_konuk.mu2 \n",
"2093 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz_humayun--yuruksemai--yuruksemai--nideyim_sahn_i--sadullah_aga.mu2 \n",
"2094 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/tahir--turku--aksak--islamoglu--afyon.mu2 \n",
"2095 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz--sarki--evfer--bir_seni--selahattin_icli.mu2 \n",
"2096 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/rast--sarki--duyek--ne_sarkilarda--omer_dilek.mu2 \n",
"2097 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/beyati--seyir--semai--1--rauf_yekta.mu2 \n",
"2098 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/karcigar--sarki--agirevfer--hezar_zar--suyolcuzade_salih_efendi.mu2 \n",
"2099 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/evic--sarki--aksaksemai--nazirin_yok--tanburi_cemil_bey.mu2 \n",
"2100 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/buselik--sarki--duyek--zaman_olur--selahattin_icli.mu2 \n",
"2101 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/beyati--pesrev--hafif----seyfettin_osmanoglu.mu2 \n",
"2102 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/evic--sarki--agiraksak--bulbul_asa--dellalzade_haci_ismail_efendi.mu2 \n",
"2103 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/sehnaz--longa--sofyan----santuri_ethem_efendi.mu2 \n",
"2104 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz--sarki--senginsemai--derdimi_arz--enderuni_ali_bey.mu2 \n",
"2105 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/kurdi--sarki--duyek--ayni_bedende--mahmut_ogul.mu2 \n",
"2106 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nisaburek--sarki--musemmen--bin_zeban--yusuf_ziya_pasa.mu2 \n",
"2107 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/ussak--nefes--duyek--ben_melamet--.mu2 \n",
"2108 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz_humayun--fantezi--nimsofyan--ufuklara_yaslanmis--kaptanzade_ali_riza_bey.mu2 \n",
"2109 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz--sarki--aksak--dil_yaresini--sevki_bey.mu2 \n",
"2110 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicazkar--sarki--aksak--bir_yar--k_hafiz_yasar.mu2 \n",
"2111 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/muhayyer--sarki--senginsemai--gezdim_yurudum--lemi_atli.mu2 \n",
"2112 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz_humayun--turku--curcuna--hishisi_hancer--gaziantep.mu2 \n",
"2113 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/segah--sarki--aksak--benim_sen--ahmet_rasim_bey.mu2 \n",
"2114 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huzzam--sazsemaisi--aksaksemai----gazi_giray_han.mu2 \n",
"2115 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/ferahnak--seyir--duyek--1--sefik_gurmeric.mu2 \n",
"2116 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/beyati--sazsemaisi--aksaksemai--beyatiye_bir_damla--necmi_kiran.mu2 \n",
"2117 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/segah--pesrev--devrikebir----neyzen_yusuf_pasa.mu2 \n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"2118 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nikriz--pesrev--cenber----mumin_aga.mu2 \n",
"2119 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nihavent--pesrev--devrikebir----tanburi_buyuk_osman_bey.mu2 \n",
"2120 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz_humayun--sarki--agiraksak--var_mi--sivelioglu_yorgaki.mu2 \n",
"2121 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/muhayyer--turku--nimsofyan--gidecegim_gurbet--.mu2 \n",
"2122 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/kurdilihicazkar--sarki--senginsemai--bir_kendi--lemi_atli.mu2 \n",
"2123 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/muhayyerkurdi--sarki--curcuna--aksam_olunca--nazif_girgin.mu2 \n",
"2124 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz--sarki--yuruksemai_ii--beyaz_ten--.mu2 \n",
"2125 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/buselik--sazsemaisi--aksaksemai----mutlu_torun.mu2 \n",
"2126 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/gulizar--kocekce--aksak--nazli_nazli--dede_efendi.mu2 \n",
"2127 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz--turku--nimsofyan--daglar_daglar--.mu2 \n",
"2128 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/segah--aranagme--yuruksemai_ii--1--.mu2 \n",
"2129 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/yegah--sarki--curcuna--ne_yapsam--haci_faik_bey.mu2 \n",
"2130 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/muhayyersunbule--kupe--duyek--dilberanin--ahmet_avni_konuk.mu2 \n",
"2131 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/saba--sarki--agirduyek--gus_eyle--dede_efendi.mu2 \n",
"2132 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huseyni--seyir--semai--1--rauf_yekta.mu2 \n",
"2133 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz_uzzal--seyir--senginsemai--1--erol_bingol.mu2 \n",
"2134 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/canfeza--kupe--aksaksemai--saki-yi--ahmet_avni_konuk.mu2 \n",
"2135 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/rastmaye--kupe--devrirevanihindi--nagmemiz_bu--ahmet_avni_konuk.mu2 \n",
"2136 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/muhayyer--beste--muhammes--derd-i_hicrana--tosunzade_abdullah_efendi.mu2 \n",
"2137 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz--sarki--aksak--ne_gunah--alaeddin_yavasca.mu2 \n",
"2138 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nihavent--sarki--aksak--yok_baska--munir_nurettin_selcuk.mu2 \n",
"2139 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/segah--sarki--murekkepnimsofyan--olursem_yaziktir--hayri_yenigun.mu2 \n",
"2140 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/karcigar--kocekce--oynak--iki_de_durnam--dede_efendi.mu2 \n",
"2141 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/muhayyer--turku--curcuna--yar_yuregim--urfa.mu2 \n",
"2142 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huzzam--sarki--sarkidevrirevani--isittim_ey--sakir_aga.mu2 \n",
"2143 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/neva--yuruksemai--yuruksemai--ey_gonca--dede_efendi.mu2 \n",
"2144 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/segah--sarki--semai--kus_olup--neveser_kokdes.mu2 \n",
"2145 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/karcigar--mandra--devrituran--karadeniz_oyunhavasi--.mu2 \n",
"2146 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/buselik--aranagme--duyek--1--.mu2 \n",
"2147 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/rast--sarki--aksak--bir_dame--haci_faik_bey.mu2 \n",
"2148 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/kurdilihicazkar--sarki--senginsemai--her_tel--kaptanzade_ali_riza_bey.mu2 \n",
"2149 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/beyati--kupe--duyek--mutriba--ahmet_avni_konuk.mu2 \n",
"2150 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nihavent--sazsemaisi--aksaksemai----mesut_cemil.mu2 \n",
"2151 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/mustear--sarki--aksak--gel_ey--rahmi_bey.mu2 \n",
"2152 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/saba--turku--sofyan--bir_dalda--istanbul.mu2 \n",
"2153 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/cargah--kupe--musemmen--bezl--ahmet_avni_konuk.mu2 \n",
"2154 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/zavil--turku--sofyan--uc_gun--.mu2 \n",
"2155 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/vechiarazbar--kupe--darb--vechi--ahmet_avni_konuk.mu2 \n",
"2156 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/buzurk--seyir--sofyan--1--sefik_gurmeric.mu2 \n",
"2157 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/neva--nakis--agir_aksaksemai--ey_gonca-i--dede_efendi.mu2 \n",
"2158 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/mahur--salatuselam--sofyan--allahumme--.mu2 \n",
"2159 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/segah--pesrev--sakil--karabatak--hizir_aga.mu2 \n",
"2160 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/beyati--sarki--agirduyek--her_dem--dede_efendi.mu2 \n",
"2161 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hisarbuselik--tavsanca--raksaksagi--dok_zulfunu--tanburi_mustafa_cavus.mu2 \n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"2162 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/sedaraban--sarki--duyek--gozumden_gonlumden--dede_efendi.mu2 \n",
"2163 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/muhayyer--etud--sofyan----hasan_esen.mu2 \n",
"2164 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/muhayyerkurdi--sarki--aksak--sevgi_deli--alaeddin_yavasca.mu2 \n",
"2165 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz--ilahi--duyek--icimde_bir--amir_ates.mu2 \n",
"2166 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huzzam--sarki--aksak--zamani_var_ki--klarnet_ibrahim_efendi.mu2 \n",
"2167 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/buselik--sarki--sofyan--aldin_aklim--rifat_bey.mu2 \n",
"2168 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nikriz--sarki--aksak--elmas_senin--tanburi_mustafa_cavus.mu2 \n",
"2169 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/rast--sarki--duyek--saclarin_tarumar--sekip_ayhan_ozisik.mu2 \n",
"2170 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/kurdilihicazkar--sarki--senginsemai--artik_ne--artaki_candan.mu2 \n",
"2171 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nihavent--sarki--aksak--bakmiyor_cesm-i--haci_arif_bey.mu2 \n",
"2172 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/beyati--turku--devrihindi--bir_cift--yozgat.mu2 \n",
"2173 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hisarbuselik--aranagme--turkaksagi--1--.mu2 \n",
"2174 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/ussak--pesrev--hafif----kanposoglu_mehmet_celebi.mu2 \n",
"2175 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/mahur--kupe--devrirevanihindi--her_ne--ahmet_avni_konuk.mu2 \n",
"2176 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicazkar--sarki--agiraksak--bana_hem-dem--nikogos_aga.mu2 \n",
"2177 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/acemasiran--sarki--kapali_curcuna--gam_cekme--arif_sami_toker.mu2 \n",
"2178 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicazkar--sarki--duyek--yuzun_gullerden--sadettin_kaynak.mu2 \n",
"2179 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/muhayyer--turku--raksaksagi--istanbuldan_uskudara--.mu2 \n",
"2180 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz--turku--sofyan--ela_gozlerine--sadettin_kaynak.mu2 \n",
"2181 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz_humayun--sarki--devrihindi--benim_yarim--ahmet_uzel.mu2 \n",
"2182 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huzzam--sarki--sofyan--solgun_durma--munir_nurettin_selcuk.mu2 \n",
"2183 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/tahir--turku--ciftesofyan--yayla_yollarinda--.mu2 \n",
"2184 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/kurdilihicazkar--sarki--duyek--avuclarimda_hala--yusuf_nalkesen.mu2 \n",
"2185 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz_humayun--pesrev--devrikebir----musahib_ahmed_aga.mu2 \n",
"2186 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/rast--sarki--agiraksak--cevr_olur--serif_icli.mu2 \n",
"2187 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huseyni--sarki--devrihindi--gormek_ister--tanburi_cemil_bey.mu2 \n",
"2188 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/huseyni--etud--sofyan----hasan_esen.mu2 \n",
"2189 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz--turku--raksaksagi--entarisi_ala--istanbul.mu2 \n",
"2190 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/gerdaniye--turku--kapali_curcuna--kirklar_daginin--diyarbakir.mu2 \n",
"2191 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/suzinak_zirgule--seyir--sofyan--1--sefik_gurmeric.mu2 \n",
"2192 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nihavent--sarki--semai--bir_nese--nuri_halil_poyraz.mu2 \n",
"2193 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/muhayyer--sarki--aksak--aglamaktan_dideden--rifat_bey.mu2 \n",
"2194 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/ferahnak--sarki--aksak--bir_nigahin--tanburi_cemil_bey.mu2 \n",
"2195 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/mahur--mars--sofyan--gafil_ne--muallim_ismail_hakki_bey.mu2 \n",
"2196 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicazkar--turku--tekvurus--gul_kuruttum--hatay.mu2 \n",
"2197 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/hicaz--turku--nimsofyan--acil_ey--.mu2 \n",
"2198 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/ussak--sarki--duyek--bahari_beklerken--selcuk_tekay.mu2 \n",
"2199 nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk\n",
"/home/sertansenturk/Documents/notaIcra/code/tomato/tomato/bin/MusikiToMusicXml ../../mu2/nihavent--sarki--sofyan--kandilli_yuzerken--munir_nurettin_selcuk.mu2 \n"
]
}
],
"source": [
"for ii, (mf, xf) in enumerate(zip(mu2filepaths, xmlfilepaths)):\n",
" print str(ii) + ' ' + sn\n",
" ScoreConverter.mu2_to_musicxml(mf, xml_out=xf)\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": "2.7.12"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
| gpl-3.0 |
enoordeh/StatisticalMethods | examples/XrayImage/Modeling.ipynb | 1 | 666176 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "-"
}
},
"source": [
"# Forward Modeling the X-ray Image data"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "-"
}
},
"source": [
"In this notebook, we'll take a closer look at the X-ray image data products, and build a simple, generative, *forward model* for the observed data."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false,
"slideshow": {
"slide_type": "-"
}
},
"outputs": [],
"source": [
"from __future__ import print_function\n",
"import astropy.io.fits as pyfits\n",
"import astropy.visualization as viz\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"%matplotlib inline\n",
"plt.rcParams['figure.figsize'] = (10.0, 10.0)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## The XMM Image Data"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "-"
}
},
"source": [
"* Recall that we downloaded some XMM data in the [\"First Look\"](./FirstLook.ipynb) notebook. \n",
"\n",
"\n",
"* We downloaded three files, and just looked at one - the \"science\" image."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"imfits = pyfits.open('a1835_xmm/P0098010101M2U009IMAGE_3000.FTZ')\n",
"im = imfits[0].data"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "-"
}
},
"source": [
"* `im` is the image, our observed data, presented after some \"standard processing.\" The numbers in the pixels are *counts* (i.e. numbers of photoelectrons recorded by the CCD during the exposure). \n",
"\n",
"\n",
"* We display the image on a log scale, which allows us to simultaneously see both the cluster of galaxies in the center, and the much fainter background and other sources in the field."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false,
"scrolled": false,
"slideshow": {
"slide_type": "-"
}
},
"outputs": [
{
"data": {
"image/png": 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Gh4dt7zM+Pg5bW1vwySefVHz+iYkJy9t2dXXJRYWN+OCDD+Ctt94CgO3CwEaFjksLDXd2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\nIhv+P4kqEY/JJetbAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10550c190>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.imshow(viz.scale_image(im, scale='log', max_cut=40), cmap='gray', origin='lower');"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## A Model for the Cluster of Galaxies\n",
"\n",
"\n",
"* We will use a common *parametric model* for the surface brightness of galaxy clusters: the azimuthally symmetric beta model:\n",
"\n",
"$S(r) = S_0 \\left[1.0 + \\left(\\frac{r}{r_c}\\right)^2\\right]^{-3\\beta + 1/2}$,\n",
"\n",
" where $r$ is projected distance from the cluster center. \n",
"\n",
"\n",
"* The parameters of this model are:\n",
"\n",
" 0. $x_0$, the $x$ coordinate of the cluster center\n",
" 1. $y_0$, the $y$ coordinate of the cluster center\n",
" 2. $S_0$, the normalization, in surface brightness units\n",
" 3. $r_c$, a radial scale (called the \"core radius\")\n",
" 4. $\\beta$, which determines the slope of the profile\n",
"\n",
"\n",
"* Note that this model describes a 2D surface brightness distribution, since $r^2 = x^2 + y^2$\n",
"\n",
"\n",
"* **Let's draw a cartoon of this model on the whiteboard**"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"## Planning an Expected Counts Map"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* Our data are *counts*, i.e. the number of times a physical pixel in the camera was activated while pointing at the area of sky corresponding to a pixel in our image. We can think of different sky pixels as having different effective exposure times, as encoded by an *exposure map,* `ex`. \n",
"\n",
"\n",
"* We *expect* to see counts due to a number of sources:\n",
"\n",
" 1. X-rays from the galaxy cluster\n",
" 2. X-rays from other detected sources in the field\n",
" 3. X-rays from *unresolved* sources (the Cosmic X-ray Background)\n",
" 4. Diffuse X-rays from the Galactic halo and the local bubble (the local X-ray foreground)\n",
" 5. Soft protons from the solar wind, cosmic rays, and other undesirables (the particle background)\n",
" \n",
" \n",
"Let's go through these in turn."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 1. Counts from the Cluster\n",
"\n",
"* Since our data are counts in each pixel, our model needs to first predict the *expected* counts in each pixel. Physical models predict *intensity* (counts per second per pixel per unit effective area of the telescope). The spatial variation of the effective area relative to the aimpoint is one of the things accounted for in the exposure map, and we can leave the overall area to one side when fitting (although we would need it to turn our results into physically interesting conclusions about, e.g. the luminosity of the cluster).\n",
"\n",
"\n",
"* Since the X-rays from the cluster are transformed according to the exposure map, the units of $S_0$ are counts/s/pixel, and the model prediction for the *expected number* of counts from the cluster is `CL*ex`, where `CL` is an image with pixel values computed from $S(r)$."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"\n",
"### 2-4. X-ray background model\n",
"\n",
"* The X-ray background will be \"vignetted\" in the same way as X-rays from the cluster. We can lump sources 2-4 together, to extend our model so that it is composed of a galaxy cluster, plus an X-ray background.\n",
"\n",
"\n",
"* The simplest assumption we can make about the X-ray background is that it is spatially uniform, on average. The model must account for the varying effective exposure as a function of position, however. So the model prediction associated with this component is `b*ex`, where `b` is a single number with units of counts/s/pixel.\n",
"\n",
"\n",
"* We can circumvent the problem of the other detected sources in the field by *masking them out*, leaving us with the assumption that *any remaining counts are not due to the masked sources*. This could be a source of systematic error, so we'll note it down for later.\n",
"\n",
"\n",
"### 5. Particle background model\n",
"\n",
"\n",
"* The particle background represents a flux of particles that either do not traverse the telescope optics at all, or follow a different optical path than X-rays - so the exposure map (and its vignetting correction) does not apply. \n",
"\n",
"\n",
"* Instead, we're given, from a black box, a prediction for the expected counts/pixel due to particles, so the extension to our model is simply to add this image, `pb`.\n",
"\n",
"\n",
"### Full model\n",
"\n",
"* Combining these three components, the model `(CL+b)*ex + pb` gives us an *expected number of counts/pixel across the field.*"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## A Look at the Other XMM Products\n",
"\n",
"* The \"exposure map\" and the \"particle background map\" were supplied to us by the XMM reduction pipeline, along with the science image. Let's take a look at them now."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false,
"slideshow": {
"slide_type": "-"
}
},
"outputs": [],
"source": [
"pbfits = pyfits.open('a1835_xmm/P0098010101M2X000BKGMAP3000.FTZ')\n",
"pb = pbfits[0].data\n",
"exfits = pyfits.open('a1835_xmm/P0098010101M2U009EXPMAP3000.FTZ')\n",
"ex = exfits[0].data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### The \"Exposure Map\"\n",
"\n",
"* The `ex` image is in units of seconds, and represents the effective exposure time at each pixel position. \n",
"\n",
"\n",
"* This is actually the product of the exposure time that the detector was exposed for, and a relative *sensitivity* map accounting for the vignetting of the telescope, dithering, and bad pixels whose data have been excised. \n",
"\n",
"\n",
"* Displaying the exposure map on a linear scale makes the vignetting pattern and other features clear."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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Q5LQQurMA1TZSdX/wwQe4dOlS33WGYT6GA9AZ5hHBMAxEo1HkcjkUCgXMzMw42clVr4PY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YHX623ajoQCHysmto2HSLgYxsbGEI1GMTk5ia6uLnR1dZF2srqNqA08kdBTFdh1YdneHQ4HMpkM\nfD5fU5wVX5/WZMyPjdYKNPFlgbd1uVwYGxvDhQsXsGvXroYyZMRFTI7JHxdtjaYUoFyTYv38b1WL\neMhevNgx6nz+PL79RpW0rQSYa91bVHs/SD4qXnE0cWNAWws38f975PN5zMzMAJAHl+uRFP5/6i2L\nIj6ylSoUkaJWKYm2snJlaoUs2JfZzM3NYWVlBaFQCO3t7Q3bv1itVni9XtTrdRSLxaa+FotFHDt2\nDN/73vfw4osvYvv27fjOd76Dz33uc+jv728gUnxMFHuoylQMnsxQD3bWB6OxRDL1iLcNBoPI5/PS\nwH3+PmAry8SxF+u12WxkcD6wuZLwnnvuwcTEhLo8XObW48mH6OKlwOrWUs14G9HOYrGgu7sb2WwW\nmUxGtRVtqLbKFD3Rlp1P1S22tb+/H2tra+q+gfy9TpUrBvTL7LT6QNlS400RGtnm5ECzW002LhRk\n95le+7XOpe4LGQGn2iqrx2hbtfbINPHxwFSmTOhiZmYGsVgMbW1t5I9edKuJDxujttRELCtTtOPr\n0oqXkRFCCkzxYeXV63Wsrq4inU6jv7+/IdM4K5N/wHk8HmSzWXXlWalUwvT0NP7jP/4DCwsL2Ldv\nH/74j/9Y3daDnyB5giC2UVSieNVK5q5jhEJrXETVShwv3pYnKLt27cKZM2cwPj5ObnDNSB5VJ39c\nRqDE8rZt24axsTG8+eab6OrqQjgcltqy9AXicbFv1LYvlB1/jLrvWlpa4HQ6USgU4Pf7pa46Xl3S\nI/riZ4pUszLFFYdjY2N4++23cccddzTcT1S72Ao8I/FIem0TbWVlUmNj1AVo1IUI0NeLeuGgytCL\nExPLpeoXIRsX9llPEXU6nQiFQojH45p2Jj46mMqUCV2Uy2XMzs5quj1kE47Mllc+AFoJYpMNNbnL\nyhQnfRlEW+ohzP5VKhU1V5TD4VD3iWOQxR253W5UKhWsra3h0qVL+PGPf4ynn34ahUIBDz74IL7x\njW807I8GQFVleAInU91EpUemWrEyxXGh+kupS7LymJ3T6YTT6UQ6nW6ahGQEjpowterl+9zR0YGR\nkRGsrKxgdnaWzIIttlFWHk8KZcoEI5hG47yGhoawtLSEcrmsqcyI32nVL5ZhxNZisaCtrQ25XA6X\nLl0y9NJBHaOuIaUGapVpRHERX0b440aVJNm4UHVpXRe9trJjFCGlYKR+ytXObKl+dXR0kHWZ+Hhg\nkikTuqjX64jH49jY2ABAT9qA/AFhNI7AyJsjZacVM6M1gVArbfjyKpUK1tfXkUqlUK1W0d3djUAg\n0KRoUERPURRkMhksLy/jueeew49//GNYrVY8+uijePLJJ1GtVlXyQU2CVB/EyZ/qq9g2mUrHqyJi\nmbJryxM98bqy+Bzm7rPb7Zpkhq/XCOkRXX979uxBKBTC6dOn1fuSL09rPIFm0qpVt5EJlrWR9bu7\nuxvz8/Nqu3g79k9GUowcE+93nvTx8Hg8GBwcxNWrVxtczlslPlouOK12aR2T1WV0DLbSfgrUi5/s\n3pe9tGi1X89O9uKp9ZvhwfZNNXFjwCRTJgwhn89jaWmJVAH4B4L4MJLJ+DKSxD9kZTEI7LPokpLF\nY1G2WoSwWq1ibW0N6XQatVoNfr9fTRHBbFiZfP+ZilQsFjE5OYlf/vKX+MUvfqFu0vvlL38Zt912\nG/r7+9He3o6LFy82TIJaY8OIgp4dpcrI3CkU4ZLZieMnwm63IxqNIh6P68ZOiUqPCP6aUeQI2IwZ\n2bNnD5LJJM6fP69bJl+3ljuRcrVSZcpsmF00GkUymUQ+n28i/Pw/qo08CeTr0WordT773Nvbi46O\nDqyvr+uWKyN5VHs/qDolq38r6oxRJU2L5Mlezqgyrlfdk/ULkBM4rc8AGvYBNfHxwyRTJgyhVqth\nZWVFVQEAOpZHhGzykMUwiH8zW95OFktk5KHE2/KTKouVWFtbQzKZREtLCzweD4LBYAOJ0iIzirK5\n+vFnP/sZnnnmGZw4cUIdr6WlpYZcQDfffDMuXbqEbDYr7SvQPGHJxpBNgrL+8nVTkyBVP08UqLYx\n8ssITywWQyqVasqIzZfHB2/LwJM8LXI0Pj4Oq9WKmZkZxONxTRKlF1gus+WPG7Vj/0ZHR3H+/HnN\na0OpVlrkjf9slKD4fD50dXWhXC6TcUZUm7ZCkkRoERfqmJE+sON6dfFEXK9MPdXISL9EWz72UGYn\n/m6NjBXVJrfbjba2tiY7Ex8PTDJlwjAymQxWV1fJ3C8M/KRNPTwY2DHxgaqnRPEPFKod1ENKFnfE\nl5nP51W3THt7O7xeL1wuV0NZsrYqioKlpSX85Cc/wT//8z+rrqcdO3bg0KFDcLlcSKVSOHr0qEoo\nvF4v9u7dixdffJHsA3OV8eSNsuPJDPWmzcCTKFGho2xF9UamlvH/HA6Hmv+GiomilBTKjiIpVPs8\nHg8+//nP45133sH58+c1bWWuYJ7Aa9XLX3s9UsaO+f1+OJ1OrK2tkZM5RZ6MEAz+O9mkzdtaLBb0\n9fUhlUohl8uR9Ytt34q6RC060Dtf/P1T40JBNt7UyxllJ9oaIXNazw++Lr1whq0QR726AKjPERM3\nBkwyZcIwFEXB/Pw8stms+pkCRTwoe34C4W3EByM12VBlyh7AWnVXq1VcvnwZ6+vr6OvrQyAQULOi\n86SCInj1eh3r6+t4/vnn8bd/+7c4ffo0crkcQqEQvva1r+EP/uAP8PDDDyMSiUBRFLzzzjtYXl5W\ny77llltw8eJFrKysNLSLchvxY8PsqGX3fNsY+AB0cYz4iYJ3gclseTtqEm1tbUWtVkOxWGwieloT\nEos30ot5Ee327t0Lr9eLhYUFXLlyRXMcZRMZH98l6zuAhnpl9yM/PlarFaOjo7h8+bJ6XLSliJ4W\nSdvKBM0fdzgcCAaDuHjxIrlnnVi/6A7VIllUW7VUMyNB21shPrLxopQoIySPL9uIkmeUJOk9L8W4\nLS0XKLOhEg+b+HhgkikTW0I+n8fCwgKZSI9NXlqxS0DzKi+ZEsU/kGRvh3yclZaKwytmiqKgXC4j\nHo9jcXERQ0NDiMViDQ8q0dXBkzwWU3Xs2DH84Ac/wKuvvgpFURCJRPDII4/gz/7sz7B//37Y7Xa0\ntLTg05/+NKxWK9bX1/Haa6+pcWcWiwW/+7u/ixdeeAG1Wq1hAtNa3SgSFNnY8OoW3wcRrEx+UqHK\nZGOilYXd6/Wira0NiUSCnGSoMsVViVRbefIo4jvf+Q6eeeYZdWWfUXVLJI/8d/wx/j6UkRy+P7y9\nzWZDOBzG4uJiw3H+/jJCnLTIjJE+AMDw8DDeffddLC4uNhzfSjyTbEz1XIBaShRvayS4mydZMluR\neIjPH6pckczIFijIiBcFihBqkTytF46thDGY+OhhkikTW8bc3BwymYz6mYptkT0UxAe/1tsaBVmA\ntNakzSZNRVGQz+eRTqeRSqXg8/nUfe/E8ijiUa1WsbKyghMnTuCnP/0pXnjhBeRyOfT39+Pw4cN4\n6qmnVLcej927dyMcDqNSqWBychJXr15Vy96xYwccDgeuXLnStKJOy1VG9VV8YFNjuJU4K9FOL86K\n1RsMBlGtVpHP5zXdq7y6JZsoRPJIIRaL4b777sPp06cxMzOjS6D4fzLwfdZz61EEky8jFAohnU6j\nXC5L45G01CVRsdIjdHy7RJvPfe5zeO6558i8TVSZRsuVETLq5YZSooymX2Dl6rV1q/FQsuOy+mXP\nIb5+LWLE27AXIllbtcigGYB+48AkUya2jFKppLou9JQgZiN7sIoSvhF1CWh80FIqCh9PZLFYkMvl\nkEwm1X3kwuGwmitKL59VvV5HIpHAW2+9hZ///Of45S9/iaWlJfT19eHw4cN47LHHcPjwYTVtAn+u\noihIp9Pqlh7xeBwTExMolUpqH+68806cOXOmIZ4FgJq5mldQZDFl7O3WiFuNImZUuTyR0Yqz4q+d\n1WpVt5bZ2NhoIoQioZGBV8G0iDIr86tf/Sqmp6cxOzuLdDot7TdF/GVKlFb8FH+eHulwu91wu91I\nJpOkMsHbGimXartRdam3txddXV2YmJhoql9GBhm03G1iu/hyjZAUIy44vh4jx7SOU0qYrH4ZeTRS\nplZb9eyMtN9Upm4cmGTKxHVhaWkJiUQCgHYuKXFS0HLV6SlWfJni+TLbYrGI1dVVlMtlOBwO+P1+\neDyeBneLloKTzWbx9ttv4xe/+AWOHj2Kq1evoqOjAw888AAeffRRHDp0CNFotGHSYaQvk8lgaWkJ\nAHDo0CHs27cPlUoFZ86cwcLCgtrnrq4uBAIBnD9/nowVExUKWcwLpfpRD2xqHEWCouWGolYGijaR\nSATJZFJd2cerN1oTgFE7vt9MDXvggQfwyiuvYGVlheyPbGKi+iK710QiKhIbETabDS0tLfD7/cjn\n8ygUCtIJVo/k8cdk31H9EvHggw/i7bffRqFQaDhuVJ2iyIT4IqTnAqNIvFGSJCMzPEnRO1/rPuAh\nI3myZxD1EqNla7Rcyk42DiY+HphXwsR1oVarqUkaATp+oFarNa24kz08qYeGuDWG1uQqllmr1dSt\nXzweD/x+P7xebwNB4P8WY5Sq1SpOnz6NH/3oR3j55Zdx9epVOBwO3H///fj617+OgwcPIhaLkduV\nlEolzM7OolAooKOjA+3t7ejs7MSRI0fg8/mwvLyMiYkJdTJzOp0YHBzE/Pw80ul0A6GQPUwVRWlS\njWSxaiJJ0HI/iGRGRpQpgsLbuVwu+P1+xONxMnCbmnApgkLVTbndLJbNgH673Y7z589jfX1d6qLT\nIyjii4Cs79RxWZl+vx/VahW5XE6TjIjYKsGgFC6xXL/fj/HxcRw7dqypDGpRAXOR67WBAkX8ZGQQ\nuP798fQIilFbSl2ibPlx2Qp5M0ocjRBSPXXXxEcL80qYuG6kUqmGYFZxApH92JkSxZ9jJEcVO0dr\ns2IAWF9fx+LiIlpbWxEKhdS98URb6iFbr9dx+fJl/OM//iN+8YtfqMH24+Pj+Na3voXDhw+js7MT\nDodDPYd/6M3Pz2N+fh7hcBhdXV1wu91q3V1dXThw4ADq9TqOHTuGRCKhjkVPTw8cDgeuXr2q+VDd\n6rjI4n2o/cdkahQVfyYbP95lMjAwgLm5uabyxDbqKVF8mTKFyWKxIBqNYt++fTh27Bjy+XzDWMnq\n1iJO1NhQ4y72Rbwf2bm9vb1YXl5GqVQiyeNWSZZWn0TywkNRFOzatQtLS0tNLlHqesraRbkAjbrl\nmK1W2+v1esMKUqp+LVVc/KxHfMTrptVWGbSul1FCKms/D710DSY+ephkysR1o1ar4erVq6hUKuTD\nAqBX0sgeWpT7SEuZ4SekWq2GbDarTt59fX0NCe14W7FdirKZtXxmZgY/+9nP8PTTT2N5eRlOpxOj\no6P45je/iYcffpjcC6ter6NcLiORSODKlSsIBoMYHh6G0+lssnU6ndi5cyfa29uxsbGBo0ePqvFa\nbW1t6O/vx+LiYlOsEXtwigRVS83jY6f0MsPzq+koWwYqs7qWndVqxc6dO/Huu++SbRXzY4lt40m5\nVq4ohpaWFuzfvx9dXV04duwYMpmMZgC8EeKhpW6Jf2upYOwas3tNNunKJmIjJEvWH2bL3/M+nw97\n9+7F22+/3XQdKSVKa8UgpTBSdtQxGSHT+t1rxTnx9fO2eqqfqJhTdqyveuVuJc7LyDXkj/MEEzBT\nI9xIMMmUiQ+ETCaDxcVF8g2Rh/jgkNlrkR4ejFww10kqlUKxWERvby+CwWCDHQX2QCoUCpidncXL\nL7+Mf/mXf8HU1BTcbjdGRkbwhS98AU888QT6+vqaCISiKCiVSmoiU6vViu3bt8Pn8zXVAQC5XE4l\nSWxz49dffx3Ly8vqRLVt2zaUSiXMzc1purf48qmJTC9oG6C3IqHseAJHkRh+UqNW54XDYeTzeTU3\nGd9Gqj6+XiPkkdmyugcHB9HV1YVr165heXlZWq4RaJEsviwZyeLt2PiNjIzgwvJcpWsAACAASURB\nVIULmq4mo3v2abUJ0HYV2Ww2RKNRVCqVpjxnRpUkrbbKSCnVVplqI37+IMRF1n69MQT08z6Jtrzy\nrlW/3r2vZ7uVe9nEfz1MMmXiA6FUKmFxcVF1q+g9FBm0JG/xO2pSUBQFGxsbyGazKJfL8Pv9aG9v\nJ1UNWbunp6fx6quv4tlnn8WpU6dgtVoxNjaGI0eO4NFHH8Xw8DDZ/lKphLW1NWQyGdTrdYTDYQSD\nQdI2m80imUwil8up7pWbb74ZHo8HhUIB//Zv/6YuU3c6ndi9ezfOnj3b4AqSkQnRDcMrUaKtmHBT\ni6DI8llR9sxOq52jo6O4cuWKLjFjEN1qMlDl2Ww2HDp0CNVqFcePH1dV061MPHr18vXLEk5SxywW\nC5xOJzo6OjA3N7clZYK1S0ShUMDc3FzDFj6yc8XjkUgE7e3tmJ6ebtoCSCvAnDpupK3Uy5Qe+RSh\nd0yPzBhRovi2Ui8hYt3sN8dvnC5rp6gQUmVSfZId11KSTXy0MMmUiQ+MtbU1rKysNEzi4qSgp0Tx\nAc8ixDI3NjawtrYGRVHgcrkQDAbVGCaxXJGgVatVzM3N4dVXX8WLL76IkydPolAoYHR0FJ/5zGdw\n5MgRjI2NweVyqQ9JhlqthkQigWQyCbvdDp/Ph1Ao1BA/xVAqlbC8vIxcLgebzYZIJILOzk7Y7XaM\njY1hYGAAFosFp0+fxtmzZ9XzBgcHYbPZMDU11TQuspgP2duxOB5bUbeoyYzKBq2lcLFJzefzwev1\nqok8KTtWnlY+KbGNMtLR29uL7du3Y3FxEZOTk7rEjB9DrdgYUamS1c+XQ5U9NDSE6elpVKtVaV/0\nyEixWMSVK1ewtLTU5OoxquLY7XYMDw8jkUhgbW2t6fzrIUl8GTKSZCR2SO8YFTqgR3zYMSMvfEbb\npHVcpkTplcv/dmRtzWazOHfunLoFlomPH6bD1cQHRrlcxuLiIsLhMDweDwA66FWcjGSQEbJcLods\nNguPxwOXywWn00kG/MoIWTKZxMmTJ3H16lVkMhnUajUMDg5ifHwc3d3dan4k8cFWq9WQyWSQy+UQ\nCATgdDrhcrmaJjGm6MTjcdTrdfj9frjdbjidzoYHazAYxM0334xr164hm83i+eefx+joKFpbW+Fw\nOPCpT30K//RP/4R9+/apmyyLYygSBGq8xTGUvbWz2A/etl6vN6kuYpwaVRcP/tqEQiEsLi6is7NT\nU/XQui9EkiKD1WrFQw89hH/4h3/A1atXsXPnTng8nqbJSRwb/jjfL/6YbNKm8qrJXh5sNht2796N\nCxcuYM+ePdL6qTIUZXNLp1QqhUgkAo/HA7fbTS4+kPWXPxYOh+Hz+TAzM4NQKNRwzWWTPlUuNQZW\nq7VhNa9WYkojZcpsZeMsQtwFQVaXnrrE/5YYqHuar4tS1kV7I/WXSiVMTU3hwoUL2NjYQLlcbrIx\n8fHAVKZMfCiIx+NIJpOkgsLAy/rsQSELjuYn2Gq1isXFRRSLRXR0dKgEhdkCzcSLLzefz+OVV17B\nD3/4Q5w5c0ZdNr9//3489NBDGB4ehs/nIx9yxWIRV69eRb1eRywWg8/nU+sW255MJjE1NQWPx6Pa\ntrS0kAG+Bw4cQHd3NwBgZmYGJ06cUMlHOBzG8PAwjh492jQufMA4P+nL3vZFgiKbnEQyQ7kQKLWF\nvwa8nUjOmDolrv7UCiznr62RAHS+7kAggK9+9as4d+4cTp8+TfbXiBuR+p+yE8eEJ368Hauzvb0d\nxWJRdedqKRvs+42NDbz11luo1+sYHR1FOBxWF1lQxMeIOmW1biaNnZiYQLFYJPulVa4snoi3Fe8l\nWfoFvky9OCXRTmsMxfplJInvD9VXsU+ydon4ILas/mq1iitXruDnP/853njjDTV3nokbB6YyZeJD\ngaIomJ6eRiQSQWtrq+7bnXguf4yRmnq9jmQyiVqths7OzoYNiLUmQvaQLRQKuHjxIk6cOKFuf+N0\nOtHb24s777wTsVisya3E3F5MAbDb7RgcHCQ34WUP6Hw+j5WVFQQCAYyMjJBbizAixMpobW3Ffffd\nh+npaeTzebz22mu45ZZb4Pf7AWwmVvzud7+L22+/Xc2PxZM9Shlix42MDU8oqGvAlyuWqaVuiclL\n2Xk2mw1OpxPZbBa1Wq3hWop18dDrj5YNc62urKwgkUggFArpjg2DLGZGRiC1JnKxXDZW+/fvV119\nlFoCbI5hsVjExMQEWltbcdddd0nJgWzS1lOn2trasGvXLrz99tu49957G2xFdUmrPkpJMgJWjs1m\na9jmhlKn+BcyLTWQepmjytVTjEQ7/j6VjavWeIvnifcGP9aKoqhbWB0/fpxcmGLixoFJpkx8aEgm\nk1haWsLQ0FDDhCtO/OLEwyZdoPFNbH19HYFAoGmvO8rNxE8+6XQaCwsLOHv2LOLxOBRFQVtbG8Lh\nMPbv349t27aRS4prtRoqlQry+TwqlQr6+vpUO/EhVq1WUSwWUSgUYLPZMDAwoLrkRHca/7/FspnG\noVQqIRqNor+/X90G5fjx4zh8+LBKPO699168/PLL+PznP99AzmQuOMpFKSMOFImkFANRNZD1TxZj\nwyMcDiObzaouqushSKINoB0w/uSTT+Kv//qvEY1G0d7e3nCNxLL4iVrsrx6oiVgsl4ERKrvdrt5f\nPPFik2g2m8Xy8jKy2SzGx8cbXL5UfRSZoMgQn5+J2d1111347//9v2P//v0IBAINbeVfMFg9YrkU\nMebHUyQYNputIWBbi6RoETrxmB7J0oLM1mq1Nu1lSJEirTZR/ZTl1mMEOplM4ty5c7h8+XJT/03c\neLB8XEzXYrGYFPt/Qni9Xtx7771qADc1wYoTObPTk//ZObKcN5lMBvPz87h06RLm5uZQr9fhdrsR\ni8UwPDyMkZGRJmLG6s7n8yiXy6hWq/B6vQ1bzvDtBqDGW9XrdQQCAbS1tTW1R8zJxP4vlUrI5XKo\nVCrweDxIJBL4m7/5GxSLRdx888345je/ia6uLlgsm/sJ/uu//ise+P/Ye7PgOo4rbfCri7vhAvdi\nX4h9IUCCJAiQEEmQIkWJFCVLliXZ7ZH3drvb3dEP7oeefuiOfvmjPS8TEzEPHTHj0XSHw/4tt0ZW\nW5ItSzItiZJIivtOiCBBEDsBYt8ulrvXPEBZznvuyaqCrNVRXwQCQN1TJ09m1a386suTmY8+ipqa\nmjT/gjjJP6rNiLlhN1XSs5ktrR+1M7vewlasSi/IJ3cf0Lg4gmOHbAm7119/HbOzszh8+DCqqqqU\nw9AqcEOj3DCoamib6zRlIiHbJpNJjI+PY3V1FZFIxMhp4mLiyqdqC3dMlE876GvXruHatWv45je/\nCWAtF1LcZ5RMCMJHyxLfC/m4mOnG1ZWqTuI4taWzDUUd7J5P21vVVvL1kgkQd73EMdlOdV/I10vY\nc+VHIhGMjo6iv78fd+7cydjyx8FnD13X2YeOo0w5+FgRDofR19eHbdu2AVAPCckdpCyPU2VFDIkI\nO/l88ZATC24ODg7i7t27iMfj8Hq9qKmpQVNTE2praw1yRMtYXl5GJBKBx+OB1+tFQUFB2nIAcpkL\nCwtIJBLweDzIzs5GMBhk60gJBrA2hX1ubs44t6ioCG63G/n5+di1axdOnjyJnp4e3LhxwxjSDAQC\n2LZtGy5fvowNGzZkJKNTsiYSfO0QD+66cGSGuy4qksUpVtSuuLgYExMTCIfDKCwsNG0z1T1jZkf9\nAUBnZyeef/55DA8Po6ioyNjgWlYJhK1KLVAprZwdBTf8xaktYjhSXPuamhpkZWWxZMSKRMp23PAR\nN8zV2tqKCxcuYHBwEHV1dWnfP7MEc254kovBKsGaex7I7cW1oZ2VwFXqGKeuid90aE+lWHH1V90H\nZkpiMpnEyMgIent7MTQ0hMXFRWdI7wsGh0w5+NjR19eHmpoa9o2adrQy6FAT7cQExENGJGXevn0b\nU1NTxltcVVUVWltbUVVVhWAwmLZApPAXjUaxsLBgdFw5OTlpQ39i6FG8Lc7PzyMnJwe5ubnGzDAa\nD0dCEokE7t27B5fLhby8PGRnZyM7OzvN9vDhw+jq6sL8/DxOnz6NtrY2lJWVweVyoba2FiMjIxgc\nHDTWvaJEVNWGZnZcx6LqoGmnKh/n/HKETI6jrq4Ovb29KCwsZIkR55dTrFSEkNY5Pz8fbW1tuH79\nOurq6tLanyOKXKfJzUTjCIaIi/oz8zs/P4/h4WFjKFrMKqV+aT3t5v2I41TFoWTC5XLhyJEjOHbs\nGGpqatJW8bdLCFUkRRWryi9VwlT5WCqSxOVZcZDPpyqRbENt5ZmBKp92hjA1TcPY2Jix+fni4qKz\ndtQXFA6ZcvCxIxKJoLe3Fx0dHcbDQ+6szUiSrEQJyDlVAiMjI7h48SKmpqYQj8eh6zoKCwuxZ88e\n1NbWGssm0KHDRCKBxcVFJBIJFBQUGHbc8JSu6xgZGYHf70dpaSk8Ho/SjpKCVCqFyclJrKysoLKy\nEl6vN2PDX2FbXl6Offv24Y033sCNGzdw48YNY5p6UVERysrKcOfOHdTV1aUNU3JKlNzeoq1Vbajr\nutFpy3aqVa2tlCjhx4ogCZVwYWHBIFScnZk/DrTOAm63Gw0NDRgYGEBfXx8KCwsNdUrYcx2e7IfO\n4KIx0mOcLT0Wi8XQ09ODWCyG2tpag/jToSJN+2OOkQxVR67qtLlzqW1ZWRlKS0vxwQcfYPv27Wnn\nyCRFXgTWijipFBbVvcvFJZdPFSPOL1euHXVN/v5ww7ucEqZqbyuiOzc3h4sXL6K3txcrKysOifqC\nwyFTDj4RjI2Noa6uztjPjntwch0n97cgDbq+tlbUhQsXMDQ0ZNjl5uaivb0dra2taW/T9O1weXkZ\ns7OzKC0tRWlpKVuGsB0fH8fKygqampoyFgTl1B9xLJVKYWlpyVh3q7GxMYOI0Hr7/X50dHTg4sWL\nmJycxO9+9zvs3r3bIB0tLS145513MDY2hoaGhgz1S+4YVYSCEi9KbmXIPkXsXAcmly+rW2ZDu8Jm\n586dOHr0KB577DFT5UU+n4uV1oWDpmmorq5GVVUVurq6sHXrVkOdkn0LcHvTcXaC5NhVhsR9kkql\nMDIygoGBAWzatAmlpaXK2OU6cgSFU4fsDj9xZMLr9WLz5s04d+4cNm/ebNz7MkmSO33VUBsXA52t\nR9uFi4vaynWnMZi1C3d/WClRFGb3qSjLbGhOnL+8vIyuri5cunQJy8vLSnsHXyw4ZMrBJ4JIJILB\nwUEUFBTA7XZndIbiQSsnUnMQQ2XT09Po7e1Fb28votGoMWxWX1+Pbdu2IT8/PyMnStd1I6l8bm4O\nOTk5aGhoUJYTi8WwurqKcDiM4uJiVFZWsnlWVIkS56ZSKUxMTMDv96O5uTlthqKKuInflZWV2L17\nN37/+99jaGgIJ06cwJe//GW4XC6UlJSgpKQEvb29qKqqyiCMtAyOqArQWJLJZEZCPzekJ9db5ZNT\nGVQqEwDU19djZGQkbe9DmXBZgSuXgyA9W7Zswfj4OC5duoTDhw+zEx6s/FipEiIuuhtAKpUy8uaG\nhoZQUlKChx56yFChrFQU4I9kxIok2YnVTC0qLy9HaWkpbt68ie3btyvVOqu4ON/Cl11bu3lWso18\n/6vIo2yvUqEoyRO23Mw6K1ux8O/AwAAuXryI6enpDB8OvthwyJSDTwRimGtqagrl5eUA0h98XF4P\nkK4mie1bRkZG0NfXh3A4DJfLhcLCQlRVVRkLF1LFR9f/mJgej8fhcrlQVVWlXANJLJ4Yj8eRnZ2d\nZsupP7L6IgjYysoKkskkamtrMwiiSqmRyUBOTg527dqF3t5e3L59G7/97W+xb98+FBcXAwB27tyJ\nl19+GZOTk6iurk6LxeytW6U+0famihXX2VJFjhvylP1yKpgMoYCUlpYiEAiY1kf2aUWiKNkVdtXV\n1SgqKsLo6CgmJyexYcMGY7KC6hrJUCkjKuIibFdWVjAzM4NwOAxN09DR0WFMJhDnJhIJg9Sq4uAI\nAgCWIKhIrzyEyA1tAmvrTlVUVGBgYMDYcYDWi/NNlSiOeHDqlCqfimtvK/JI60/VJzvqFrWl5dgl\npeLzubk5DA8Po6ury9n+5c8YzgroDj4xLC0tYXR01FipV/VQpA+kRCKBiYkJXL58Ge+//z6uXr2K\npaUl5OfnY/v27Thw4ADuv//+jKE64WthYcHYhDgYDKKoqIhVWcSGxWKT5oKCAuTl5Vluo6Jpa1PF\nZ2dnMTs7i2g0ivz8fIOEUXXFbJV3qtQ89NBDCIVCmJqawltvvWXY5+fno6WlBSdPnjT8izqpiI9o\nZytbEYvcmXPXRYAui6DK9eBUJuq3pqYGo6OjSlvqi8vdohCdOiUmmqahs7MTy8vLxqroVgSOq9N6\njoXDYWMj4urqamzatCltVqYZuDwtFYFVkSyVT0p6aLvX1dUhmUxidHSUHYJT1Zce466nSt2S7czI\njN3hX3Fc+LOj2FJCxilh3HeJq+vCwgKuXr2Kt956C2+//bZDpP7M4ShTDj4xpFIpjI2NoaqqCmVl\nZcZxlWqTSqWwsLCAO3fuYGxsDPPz80ilUsjOzsbGjRvR0NCA4uLitIUX5QeY2KsqGAzC6/XC5/Ox\nnYxIQgeAQCAAv9+ftlmxSlURv+fn57G0tIScnBwEg0FkZ2enKVmcsiArYSrFStM0tLe349y5c7h6\n9SpOnz6Np556CoFAAACwY8cOI1+svr4+rZ1pjFRJk2Oxqp/sV6UgcnVUlamyc7lcKCoqwuDgIBYX\nF5GXl2fa0XE+ZIhyzYaOS0pKsHXrVvT29qKnpwdbtmxJ80vrxykdZsoIPebxeFBaWpq2ECaFIKdU\nMVL5VQ1LceCIsdnMPGHr9/uxadMm3LhxA9XV1cY9KMiEHYWL8yvsqK3wa0fdUpF4zienIJmpWxS0\nfDMlSviMxWLo6upCd3e3sWaYgz9/OMqUg08Uy8vLGBkZSdtHiuu0o9Eorl27hnfeeQc3b97E7Ows\ndF1Hc3MzvvSlL+G+++7Dhg0b4PV608iXrq8llovlBwoKCpCTk2MsGiqXoes6FhcXMTExAY/Hg7y8\nPIN4CcgPfaouLS0tYXBwEPF4HCUlJcjPz0cgEEgjHMKWght64uxyc3Nx8OBBeL1eTExM4NSpU4bv\nQCCAxx9/HM8991zGeXKCuZlyI+rncrkslSjaDsInlzOiGoJTdT7Czu/3Iy8vDxMTExk21J+ZT9lG\nNTtT2B04cADDw8MYHBw0XVlapVrZUWHEsUAgkEGkaGxmdeN8yraUeHC2dhQrzra+vt7YKomLiZZv\npu5Z1Us1bGb1MiDbcXmDKnA+ubgoeZS/ExSpVArd3d147rnncOzYMQwMDCAWi6U9sxz8+cJRphx8\n4hgcHMTGjRvh9XqRTCYzOua+vr6MjVarqqqwZ8+etNmAFMlkEhMTE/B6vSgrK0tbbJMiGo3i3r17\nyM/PR2VlpeGTW9uKPpjj8TgmJibgcrlQWVlpKB9mM+eo4qNSf5LJZNqWIpqmYdOmTfD5fFhcXMTb\nb7+Nffv2IRgMQtM0NDc3Izc3F9euXUNbW1uaL67z5xLMuXi4TkpVP1lpk/0JkkZtzeLTNA35+flY\nXl7G3NwcCgoK0tpWBTqMZ9VZyTH6/X5873vfw+uvv47S0lLcd999GcqTWR1E26iGTK2g8m93BppK\n2eHAKWmCcMp5VlzcbrcbjzzyCH75y1/iRz/6UcaLhtm6UWZKlPw/9cEtX5CVlWUsf2JVX/qSYtVW\ndhRGlZ2AmJ35zjvvYGhoyCi/rKwMDz74IMbHx3HmzBlnY+I/czhkysEnjkQigZs3b6KzsxOaphl7\n4N27dw/d3d2YnZ0FsDYtu7CwEFu3bk1L5BYQD+hUKoVwOIxoNIqSkpK0ae4CoqMQuU2apqGhoSGj\nk5aJj5wLIbbbWF5eRjgcRlFRkbEOkExOVG/KqVQqY7iJEklxPpC+RU5+fj6+8pWv4L//+79x9+5d\nnDhxAo8//rhx7jPPPIPnn38emzZtSluAUiZmHKjSxrWFaAe5LVRrVHGKkVVOCtfugUAAHo/HWBWd\nU9a4IVFODVLZyf8DwNatW42E/pWVFQQCAeXwmR2CZGavUodUw1eq4S96vp0ZaGaKF6csUb9FRUUo\nKSnBBx98kDYkqopL5Veul/zbrHzZjmtDur+fCnKemJlPmZjLMYiXBJqgLnI7z58/jxs3bhgqVCgU\nwq5du7Bt2zZ4vV5jJu7Y2JhpnA6+2HDIlINPBSMjI2hubkYgEMD09DQGBwcxPj6OZDIJr9eL4uJi\n1NfXo7a2liVHqVQK8Xgc8Xgc0WgUoVDImOlGJfpUam2j0EQigWQyaWzPoursdV1Py3mKRCLGLL3c\n3FzU1tYathxRWI8SxeUhcZ3BY489hrGxMbz77rs4e/Ysdu7ciYqKCgBrW7I0Njbi4sWLeOCBBzJI\nimoTZC5muV5mBEVuN6v60baw6uw0TUNFRQX6+vqwtLSUsXI+LdtKtZLjMlOsvv3tb+Oll15CZWWl\nsQSAivh8VJKlIjMqH1yODlcHFfEwU2womVCRIdmfrut44okn8JOf/AR1dXVG7pSIla47xdWLEm65\nXmZtYKZYyX7NVDDZVjVjUKU6qdp0dXUVExMT6O7uxpUrV7C8vAyfz4eqqips3rwZ7e3tyM7ONuLN\ny8tDa2srJiYmnA2L/4zhkCkHnwpSqRSuXLmCQCCA8fFxxGIxeDwelJWVobq62lgBWkB+QMZiMUQi\nEei6Dq/Xi9LS0owZPeKBJzYh1nXd2D+PKkEcaUilUojFYlheXjYISUVFRdoK4VZDAJxKQztDmnMh\nD3fIJCgrKwuPPfYYLly4gN7eXly+fBklJSXwer3weDzYsWMHTp48iW3bthlDoXK5lMyolChZ/bNT\nP9ruqvpxCpyKxGqahuzsbIRCIUxPTyM3N5ddA+pPUaw4VFZWoqKiAv39/cayCao2sFKc7BBGaqvy\nSdUlYH1703FkhiozcllWqlUwGMTu3bvR1dWFPXv2ZJxP678e1UzVLhxUSfpmhHS914W2kYxEIoGR\nkRHcunULN27cwOzsLLKysow9QDdt2mTMHKbK15YtW3D9+nXcu3fPNB4HX1w4CegOPjVMT09jeHgY\niUQCZWVl2LlzJ3bv3o1NmzalvfEKxONxzMzMYHV11ZDP6b54wNpDcHl5GdPT0wbhKigoYH0KyJ2u\nyL0Kh8PweDwoKChIy9Wiia12t31QdSjy/7Kt3JlMTEwgGo2iqakJS0tLOHPmDKanpw0fZWVlKCkp\nQVdXV0Y5MmEUUA2HUPLB1c9MhVJ1PFa2XCdcUVGB8fHxtHWIVCTKqh52IFaeHxoaMlRSLnaVX64O\nnK2KdHBlmSlRlJxytpxyp/IJgCXHHDo7O40XFWHLEXQRA12nzapesp1ZvThiqLKldmZLOtDvH/1u\njo6O4q233sKrr76K06dPY3Z2FmVlZXj44Yfx5S9/GXv37kVJSUmG2ivawO/3Y+/evWzbOvjzgKNM\nOfhUEQwGsXnzZlRVVSEnJyftASkezqlUylgWIT8/39jXTkBWceLxOObn5+H1epGbmwufz8fm+3Cd\nnK7rmJycRDQaRXFxMfx+v7GcAgCDaKmGyMw6Sbl8u+qPruuYn5/HzMwMioqKUFlZiQcffBDd3d3o\n7u7GtWvXUFZWBo/HA7/fj4aGBnR1dWFqasrYGFn2p+p8ZTtV/bgFJLnOS0WQVEoUTWqXz3e73di4\ncSOuX7+OXbt2KRPgZahIlBmBkI/X1NSgoaEBly9fxoYNGzL2CqTlWKlAFGYEhbaX+JvzK66bnZXG\n5fOpYsQluIv73Kxefr8fmzdvRn9/PxobG9NsVdvEcEoYl2Au11u2o+2iUgHXs3wBl/tkdr3n5+dx\n8uRJdHd3Y2FhAfF4HPn5+di1axe2bNmCvLw8Y4cH1UuWiKexsRH19fUYGBhg7Rx8seGQKQefCrxe\nL7Zs2YKmpiZ4PB52+rqu6wiHw0bCtyBG3Ma7uq5jdnYWsVgszVbVqVLiMzc3h/n5eZSWlhpvlNya\nS1Zv4PQNls4M5P7mSF4kEkFfXx+CwSDq6+uNtbSam5uNtadee+017Nu3z5jxJshUf3+/sY6XHLeK\nzFgpHHIbWNWPU61o/ezaAmv5YNevX0cikUjbNoer03pUKJVtTk4OKisrMTY2hvHxcXbRVtmHaliO\nIzhWBEWGHZJGh46EnWpYjevc5fNVw1oc8dd1HXl5eZicnEQ8Hk9beJTGIHxaJc5TJchsuNGMIIl2\noXsX2skJ49oFWMuLOnPmDI4fP25sQpydnY3du3dj7969CAaD7FIUKsIHrL0sHDhwIG3Gn4M/Hzhk\nysEnBk3T4PP5UFNTw24uSztHkYxeWVmpVD/Eek+Li4sGEZLt5PPowy6ZTCIWi2F6ehqBQAC1tbUZ\nCgxHQrhlAFQz3GT1Rx5C4IiJOGdiYgKRSARVVVXGQ1rYFhYWorOzE93d3RgYGMCJEyfw1FNPAYCR\nO3X+/Hm0tLSgsLDQNBb5YU/bneaGJZNJlkDKhIx2TCoVi1NgVGoAADzwwAM4ffo0Dh48yLaxnWE/\n6tPs87a2NvT39+PcuXOoqakxhpK5jpHr9FU5OnbznFSETCZEdtqNU4I44pVMJjPWTFKRKtlO7CbQ\n29uLTZs2pZFOLhndTDXjVlVX7ZlH21RWgcyUPJl8ytdS1S7xeBxLS0u4desW3n33XUxMTEDT1vL5\nGhsbcfDgQVRUVEDTtAwljmsvcUwur6SkBC0tLbhx4wbbNg6+uHDIlINPBLm5uSgpKUFjYyMKCwvZ\nZQ4ExANH1UmKh7SYYefz+VBdXa1UrKivWCxmLHOQTCZRVlYGr9fLDv9xs+EEuARubuYcXdeJqjRi\ntmE0GkU4HEZpaamx3x4laS6XCy0tLdi5cydOnTqFl156CQcPHjRyuhob2Lw/OAAAIABJREFUG3H+\n/Hncvn0bnZ2dbMxyHddTP9UmyLTNOMWI2pjZUng8HuTn5yORSMDj8dgiTrJfTgEzg9frRVNTEy5c\nuICenh50dHSk+bUDTglaD+GjHbzqb2GvIj4qO9VvAY7McDY5OTno7u5GMBhEVVWVMkYBunyBaliN\nkjGzdqckXn5xUJFH6lO2FWkFAwMDOH36NPr6+qDrurFFVGdnJxoaGtLitrssA722brcb27dvx+Dg\nIJaXl03PdfDFgkOmHHysyMnJQVlZmbGFjJyICmSqF1bDTolEAqurqwZJKSgoMFQUqvjQIaVkMonV\n1VVEo1FjmCI7OzutLAqqnsgPQ9oRqPKxVOqWpmlYXV3F8vIyEokEAoEANm7cmOFTtAuw1skUFxfj\nkUcewdDQEAYHB/Hyyy/jb/7mb4yhyYMHD+Lll19GW1tb2rYfHMRbPd2w2G795Dd+6lf4EnYqsknb\nlh4HgG3btilJl6pDXi+JktHW1oZr167h1q1baGxsVG7/wnXk8mccAbEzpKN60eDsOLXFrHw7myDL\nSpjZ8JtY+X9kZASlpaUZw31cnayInoiNtq2In8txsqtuqa4TsHZ/z83N4c6dO7h27Rpu3ryJRCKB\nUCiExsZGbNu2DZs3b4bH4zFVnGi8ZkOLmqahpKQEzc3NuHr1qm2y7uDzD2c2n4OPBdnZ2WhoaMCO\nHTvQ1taGioqKjM1LBWgnSh8our62aOb8/DzC4TBcLhcCgQBCoVBasrRMAOQHdzKZxNLSEmZnZxGP\nx5GdnY2ioiKDSKkehLJfqm6pZq7J5ZvZxmIxjI6OIhwOG8s7FBQUKFUHSkyampqwe/dueL1evPnm\nm+jv7zfOqaysRGVlJd5++202Fg7Cv5ysz8Uit5f43+7MQK58uRNVkR4zZcssVhXJsoOsrCwcPHgQ\nw8PDxhCMWQwiDhqDHXAKrLge3OrhFKrvDudTRSZUM/7M6iQ+a2lpwejoKObn59PiVL0YcfHT+87s\nPqUzA81subbhiP/y8jIuXryIV155Ba+88gq6urrgcrmwY8cOPPXUU3jiiSfQ2tpqbDVF6yVejrj2\nojHQ+1LsNWq2X6ODLx4cMuXgT4Lb7UZNTQ3uu+8+bNu2DeXl5cbsFg4qRUJA19eS0GdmZuDz+ZCb\nm4vc3Ny0/fNkP/T/WCyGe/fuIRKJIBgMIhQKpa1uTR+sdHaTFcmjb+0c5Ad+KpXC3bt3ce/ePYRC\nIRQWFqKgoMB4o5en48tJ+bTDyMrKwvbt2+Hz+bCwsIDf/OY3xrkulwuPPPII3n33XczNzZmqHFYk\nkhJJla2IT76eXCcnn0+XmODaV/iSy1e1sVwnVX3Xg/r6emzYsAH9/f24e/fuus41I1JmhAjAugmC\nHSIr++TKp7NoVcRDXAuZyIRCIdTV1aGrq4v1y/lUtYEcp6zIUjsgc/kCla0q8V6gq6sLzz33HH77\n29/i+vXrWFlZwZYtW/C9730PX/nKV7B9+3aEQiH2ZYImuavall4r+qyqrKxEbW0tWwcHX0w4w3wO\nPjKKi4uxefNmFBUVZeS2qIZ4uLdE8cCJxWKYm5tDTk4OCgsLM3xyKpA4nkqljM1yS0tLkZWVZRAW\nzocMOuwl7Ci4JQPosJlczuTkJBYXF1FVVQWfzwe3263M86K5SSIBXD62adMmbN68GRcuXMC5c+fw\n5ptv4vHHHwcAhEIhfOtb38LU1FRaIrrwRdtClUCval8z1YfGzdXRbEkEK9AOiXZMXB0+CjRtbaue\nf/7nf0ZdXR2qqqoy4rWKW7WQpqo8u9vE2F29G1CrkRzMhgXlzykx2LlzJ37yk5+gs7MTgUBASfI4\nvypSKKuLso3qmqpUMG75BZfLhb6+Pvzud7/D4OCgMfRfW1uLhx9+GPX19fD7/SwRMouXXm85Xq5t\nRVw+nw/Nzc0YHBzE/Py8st0cfHHgkCkH64LL5TLWihLbm5gpNFzSNl0PKZlMYmZmBm63OyPPyuoh\nmkwmMTc3h5WVFZSXlxvT6elbJS3fqmOmJEnuvFSEUHR6kUgEY2NjKC4uRlNTU1p95Twv2acZyZPV\nmm9+85u4fPky5ufncezYMbS1tRmJwB0dHcoYreonPld1zjRmO21hVT8xi3A9JJzeO7Re1H69yMnJ\nwaOPPorbt2+joqICDQ0NGeXJZVKSpep06Wcq8qHqzOm1oW0LpKuF1FY1hKjq9MVQrmwr24lNkH/9\n61/ju9/9btpnVutZWZUvvkeqmZD0GJ1ZJ9c1mUxicnISb731Fi5cuIB4PI6srCwUFxfjoYceQkdH\nB3w+n3LtKzvtpSK7FPKLDLC2xllVVRUWFxedpRL+DOBojA5swe12o6CgAFu3bsX+/fsNIgXwb24q\nBUI8dFKptb32FhcXMTU1heLiYkNRkv3KkB9kYuuXyclJZGVlGeqPXLbZA0p0MFZKFJC5QTCFruvG\nTEORS9LY2GhsT0JthU+rnCxumKahoQEPPvggAKC3txdnz55FPB5P888Nr3B5TrJ/q2E9u20mjnMk\n1gyU6FnZWGE9yhfFkSNHMDo6irGxMUPBMItHpVyoYrLT6XIdOWcnbM2WJJDLjcViluXLdaJlyZ81\nNzdjfn4eIyMjaedyw7kqZY/6VYEr30y1isfjGBkZwWuvvYZ///d/x+nTp6FpGsrLy/HII4/gH/7h\nH7B//352H1A5DlUbyJ+p4qbEnyOt7e3tyMnJUdbbwRcHjjLlwBQulwv5+fkoKytDRUWFsWo5fYjJ\n/8sPPe5BJW9CHAgEkJ+fzyod4gEkP5Si0aixXlRWVlbGyt8q6Z/GwXX29Jiqo5HVEzHbUOwduGHD\nBiO/S3RyVAlTETdu5hwX11e/+lVcuXIFs7OzOH36NDo6OlBXV5dhp/Iv199KPeQUGKvrzoHr+ET9\nsrKyMhQQSshUHSd3ff8UZQpYW5rh8OHD6OrqQkVFhdG2XLn0b7kO8mdmRMrMRgY3pKQqnxJjql4J\nO27dJpUSJlQgoQS5XC58/etfx5tvvolvfvObaTP7hK2VsiOO2VV2OMJPX7Lu3r2LDz74AOfPn8e9\ne/fg8XhQU1ODlpYW7N69O21tOrludvbzEzFYLYugeg7ReDds2ICmpiZcvnxZ6cvBFwMOmXLAQtM0\n5Ofno7y8HGVlZWkbBnP5NirID9JoNIpIJIKsrCz4fL6MVYTNptLH43GsrKwYD/r8/HzlJsRmw0b0\nYczZWpEZcSwcDiMajRpDn9wGvWZ5VtzsJ64sardhwwY8/PDDePHFF3Hr1i1cvnwZlZWVxlY4KuKh\nqh8XB7UF1FvPqM7njtM2NyNJHMkzI4z0fFEWRzZU54hYWlpaMD4+jrGxMZSUlCA3NzfDTgWOTJlB\nJhNmcavKX0+eFV1VXuWTayOZkInPKyoqUFpaips3b2L79u1pdiIGGXTrGSsVlJI8eo78TBoZGcGV\nK1fQ1dWFwcFBuFwuVFRUYPv27di2bRuqq6vhdrtZFZhTx+hzRf4skUiYDpFbxSsf6+joQE9Pj7Pu\n1BccDplykIGsrCzU1taisrISwWAwo1OVQRUXrlMUSxW43W74/X5jrz3OlhKKVCqFxcVFJJNJY+88\nkZhuRwnjYhV+zbYNkWOitmIFdjFb0OfzISsr6yOrP3L7qUiQ6FyAteuzb98+nD17FsPDw3jzzTex\nf/9+lJeXs21J24hbnFMmSaqOfL3qkN3zOeJmVpadY2ZxqOKSkZeXhx07duCDDz7A4uIicnJyWIIC\nmKuN1E6OSf7NxcB10FydqGJjRlBkO6uhV1kJk4eEKaFua2vDqVOn0NTUBJ/PZ1uJkuuisgP4TbLl\ntpiZmcHJkydx9epVjI2NIRaLIRQKYf/+/WhtbUVpaWnGnpviXLm9VGtEyXbih36HOIXNinAL27y8\nPHR0dODEiRNKeweffzhkykEGfD4fSkpKDDXKrAMz69gEidJ13Vgjiq4ZI59PH3aCtOTl5bGkjnuo\ncQRCpXjZVUrkzkcMG5SWlsLr9aYNbYiy6MbGKpVGjl+OlWtXWqeKigocPHgQv/zlL9Hf34/jx4/j\nG9/4But/vbHIdaHtJm8zY6d9VaqeHeJk5Zcet0PyZFuzTs/lcqGqqgqzs7Po7u5Gfn4+/H4/a0t9\nrYfAmREfYWe2Vpd8zIx00GNcIjdNGhd2ViSvuLgYGzZswLVr17Br1y5TW9r2tL6ynUrZEefHYjG8\n8847eO+99zAzM4NYLAa/349Dhw7h4MGDKCgogNfrzaiviqRx8VLiJY653W5T4sXFSyEfa21txY0b\nNzAzM5Nh5+CLAYdMOchAIBCA3+/PeJBYkScgfaXvcDhsrPOkWhIAyOw4YrEYZmdn4ff7jUR3bmPk\nZDLJdsxA5tYoKvVC1bHLcQmINbXkc+2qP5xqxSkyNHdKRVh8Ph/a29tx9uxZ9Pb24oUXXsCTTz5p\nrIAux0hjM8vjUsVC62xFvOyqW9w9ZseWxqq6ZipYkQ5g7Xrn5+djeHgYS0tLxndCRajsLHMgYEZm\nODVKLocjbypiJqtAnD/ZpzyDzswfVbdE7mJPTw+WlpaMIVFNS8/JkuPkSAs3i5DLE0smkzh//jx+\n9atfGeTD4/Fg586dePLJJ1FeXm6pVKrqJsdgZq9SAzl1i6uX8CH+9/l8uO+++/CHP/yBjdvB5x8O\nmXKQATEMJ0OlHMizwJLJpKFG+f3+tGEnszwr8ZCJx+NG3kBJSUnaOlN2FQlVZ6dakkAVC6ducQRN\npS5REsIlmIvjZnlW3PkCtbW12LVrF4aGhrCwsIBf/OIX+Lu/+7s0f6q45faS6yIfA9KXL5AVR/q/\nPDuRzmIyu29oW9LyVUOAdpQpCjM1hFOqdF1HXV0dJicncfbsWTz++OPsrEcrhYkjM9x1kW2tZqLK\nSpTcbqo6WilmMsGR7bgcJ/m6C1RXV2N4eBh9fX1obW01SJQAl/hutZ6VgMu1tglxOBzGnTt3cPTo\nUfT09CCZTCIYDBprRbW0tBh75nFtZqXcmZFYSozF914k2cs+VaqnmQqmaRqqq6tRWVmJ0dHRjPgd\nfP7hkCkHadA0DYFAIC1R1aqTSiQSiMfjxrTr4uJiWx04AGNmnvARCASQnZ2dkbQtfKynY+Y6XI7k\nqNY1oj65srg6qWy5zoxbnNOsrnJcXq8Xu3fvxqVLl9DT04NXX30Vjz76KBobG03LVB2zm0cmYhG2\nsVgM2dnZSsVKjlnu3O3UWUWyzOrF+eAgl6dSIjRtbbXq8fFxDA0Nob6+3haJUsUmEIlE0lREORZK\nPLjYxPWSwdmmUikkEomM66JabJIDXaOKGx4TM+bu3LljKNJ2iJ5MHjnFCgAWFxdx69YtnD59Gteu\nXcPq6iry8vJQW1uLPXv2oL29HX6/X1k3uY1p+RwxpqAKrqr95fJldUtlR0lxTk4ONm/ejKmpKcRi\nMfYcB59fOGTKQRpEkjig7pTkDmhpaQnAH3eUF4qWigzIn0ciEcTjcbhcLng8HoRCIcv4KNERDzgr\nkmUm+6t80vPokgH0QayylX1atQtVe2isctnAmjp13333YWhoCKurq3jxxRfxT//0TxnLM6jUL1Vb\nyMes8pwEKbAisTJUQ7Qq4qQiUh/FjsKqM62srMTIyAiuXr2asfSFDBWZ4Xzm5uamdchW6pbciXPl\nif+pYqUqX6673NmbJWHLvoS6JJ9bU1ODnp4ejI6OGgn7qljl8uk1F7aLi4u4ceMGLl26hCtXriAc\nDiMnJwc7d+7Ejh070N7ejmAwaLqxMG0bFaGzY0vbiytHVTeAV7dkO5fLherqalRUVGBwcJD16eDz\nC4dMOUiDx+MxEm25zkn8v7y8jEQiAa/XC6/XmzZbRthyJEW8KYvZfR6PB9nZ2RlkQtWBi2NWShT3\ngBZ+uaRqO9vhCL921C3ZVv6fU2pUBIKSN64st9uNBx54ACdOnMDIyAguXryYkQhsd9hQlEvbcr1J\n62aqHrW1s+WQPBVfthNQESiu46TgSAqFpmnG5r43b95EW1ubcdyqw7Wy4wgSZ2/HTvYnjosXFXo+\nnaVH6yt39KL9Ve0p3zOtra04ceIE6uvr0zYJlld2p6SM1isWi+H69es4deoUbt68iZmZGWRlZaG1\ntRV79+5Fc3MzCgsLjfXJaLyqa8kNLaqUKJVPlT1td9U1l9U4zjYUCqGhoQETExNYXV1l6+Hg8wmH\nTDlIg9/vN12RNxKJYHV1FdnZ2cjNzTXyabhOEEhXUXRdx/T0NNxuN3Jzc+HxeEzVI1UHxB1TKSLU\nr5k6ZHeNKLPyuSEBjphYLU8gjnGJ4sKHrq/t/zc3N4cf/vCH+Ld/+zfMzc3h6NGj2Lp1a9owkopE\natra+l9erzdt8UxaPxUJ5Eis2bWg7WtnKFQVv6oc1XGVCmYGYRcMBlFTU4O7d++ivr7eUFFpR2rX\np5liZNcnp0TJ58t2qnK5JHmRC2Q1i1CULw9plZaWIhgM4tatW8a6UzI4FUhul/7+frz66qvGkhTJ\nZBI1NTV44okn0NLSgtzc3AzFV5Akq5wo2dbMTo7LzqxHETv1oZqAwJUlYsjKykJDQwPu3LmD4eHh\njHMdfH7hbCfjIA1ut9vIl5IfTmIPvEgkgry8PAQCAXYjYhny/3NzcxgbG0NeXh7y8/ONtZlUDy1a\nPqB+wNGy5IeTqhO3Kkv2Sckel+BKE9zF+WbqCLVVdV5U6Ukmk1heXsbVq1cRi8WwefNm7Ny5E48+\n+iiSySQ++OADXL58OS0es/rJOWqq9pHjMKsfbUuz+qnKUpVvRmTlv81saXxmoORjx44dSKVSuH37\ndlrd7PgU11J0+vJxO2Wb2dqtj2o4y64/7mVDxCrgcrlw6NAhvPHGGxnqFgcRw+zsLH7yk5/gX//1\nX/H+++9jbm4O+fn5+MEPfoAf//jH2Ldvn7H5OQer75ocp4rMmPmS7Thbzt5uLif1mZuba6zZ5eCL\nA0eZcmDA5XKlreItHv6rq6uIx+MIhUIZBEpAfkjIHcfq6ipWVlaQm5trbBvDPZSoD/mYahadSgmz\nY8spFbL6IZMQsxW+qS3X4Yg3YpmscNu1cDP+5GNiC57BwUG43W5s27YNXq/XsN21axfefPNNjI+P\n4/3330drayvy8/ONWJLJZJrSZlY/0T5y28vn0/pxnYzZtaCEzKx95bJUq8aryJP8GSV4HGTCRaFp\nGjZu3IiRkRFMT0+jqKgIqVTmhrzClpbLdeIiRpmYmM3kEzaynZkCI9ur8qG4qfucTxErVXaoAuPz\n+XD//ffj+PHjOHDgQIatOD8ej2N6ehrvvfceXnvtNWPov7S0FPv27cORI0eQl5fHxkuHKTmCIret\nWXuJuLjtbzjIShgtj0K+trJP2Za7hzdt2oTu7m7cu3ePjcHB5w8OmXJgICsrCzk5OdB13diDKxaL\nIScnJy053IqgiJl50WgUbrcbxcXFGRsYcx0jR2ZUZdE8IBUx4IiLiMHsmCouWleuLKtyVD4F6Pmx\nWAzRaBThcBgrKyuora1FIBDIWEX+vvvuw9atW3H9+nVcunQJXV1d2LdvX0Y5lMyoSBK3dYzdRTTl\nPCeuA6G23HVTqQC04+GIlxmRUt27wpcKmqahpqYGIyMjGBkZQTAYzGgjzqeKoHDtICC2K+HsVQni\nXLkqAkftOFWU2qpUGDkfSny2d+9e/PSnP8XWrVtRUFCQ5jeZTBoJ/W+//TZGRkaMRVK3bt2KQ4cO\noba21vhOq5K2KUmRSR29P8zanWs7uW7y+ao2oM8r2ZYDJdD0fvV6vdi5cyd+//vfW5JrB58POGTK\ngQHxQFhdXYWur43fCzWJgntICBIlOsKcnBxDyeLUBw6iXBmqZGkRs1Wd7ChTcodvR91SkQhuaxYz\nkkcJh0wiUqkUwuGwkYial5eHyspKgzhQwpGVlYWnn34ad+7cweTkJE6cOIFt27aldWZcu6vaV5Vn\npaofhRkJpnZ2bVXJ6Ou5vyi5UylH3LlerxfV1dUYGhpCRUWFsQwI1+FzPrkOlyMMcucqEz07nbOV\nAiOrUGbqlkxmZBLB+aXEx+Vy4f7778f58+dx5MgRo42GhoZw6dIlnD59Gr29vdB1HVVVVWhra8Oe\nPXvQ2NiYds3psgxcm8oxyJ+r4qWqKH2JUrUDp7DR8u365Ig2rVt9fT2qqqqc3KkvCBwy5cBAIpHA\n4uIiiouLjYRkAbkTosQjlUphdXXV6Gizs7PTchvMCIrsT0Vo5IeibMt1olSxEseoyiH8csNqZoqI\nXfWEm8W3nsU5l5eXMT09DZ/Ph0AggLy8PNMZkwItLS3Yu3cv3nvvPZw5cwaHDh1CZ2dnGgFTzeIT\nkNuCI4G0fnJdVD5pzKIcruNT1U8mFaryOX+qlwEz5UBl63K5UFZWhnv37mF0dBShUMgYauVUEQra\n4XJt4XK5jG1QqAJDbbk4zWxVxIyz48pXERau7Lq6OgwMDGB0dBRZWVk4deoULly4gJ6eHsTjcZSU\nlKCzsxMdHR1p+/qpEuK5e5QDtZOVs4/aXpwdd/+qbO0Sdvn+cLlc6OzsxL179xCPx03Pc/DZwyFT\nDgykUmtbufh8PnbYhFs3KRqNYmVlBX6/31jiQBApTimgx7jcIY58yTZWttwxbt0m6lc+n+vYKTiy\noero5Jl5HPkTiEQimJiYgN/vR0lJCfx+f9qMSVp/SiyCwSC+/OUv4969e+ju7sYrr7yCjo6OtGRW\nuySQi08M03D1UOWWqZRGeowjPuu1tQO7diqSEAwGUVpaisHBQdTW1qatnWbHH5dnxXXMnBLFkRkr\nxcqM8Mh2sgKjykWSIddDfF9k/16vFzk5OfjP//xPLC8vY2hoCCsrK/B6vXj00Ufx4IMPorKyEoFA\ngP0OmcUtSBJVzVSwS2g4JYqrG8DnTtn1SWMS/wPpRLe0tBRNTU3o7u42LcPBZw+HTDlIg1j6gD7g\ngHTyk0gkjP3zaGK5WY6RgEqFEsfk4/RtzY4tB7OkZrNjKhWM2lJ1i/PLLc4ptuwYHx/H8vIyqqur\nEQqFWNVKNUQmx7Bx40bs3bsXd+7cwfnz53H58mXs3bs3I2baZnZVOVVbcMs7rGdYT6VEWRFmufM3\n2wZJ7nRVihW14+ByuVBTU4PJyUncvHkTHR0dSqIu+7PT6aZSqYwEb5UtVWs4G1XZqmEqrsNXbdHC\nKVaizKmpKTz33HM4efIkIpGI0ea7d+/Gd77zHVRUVMDtdiuJEiWcXAzyfSsf40gK91zg7LhrL77L\ntHwrkkfbyexamZHGtrY29Pf3IxKJKM938NnDIVMO0iASx+U1iuSHVjKZRDgcRiqVQklJifHw4Dph\nrgO0uxK3XLZsy6lTKluzFcTlY2YJzLKtrMjYqat8XB5qEGXp+lqifzgcxvj4OMrKylBVVaUkhKo8\nK1qW2+3Gxo0bUV5ejuHhYTz77LPo6OgwFlFUtQVHDM2IkxnxkiGSqWldOHVL1Y40VpUiQ6+lrutG\nrLItp/LR8lTQdR3Z2dnIz89Hf38/YrFY2nYmNHaOzHBKiZkaJccl5zGZ2dlRa9abZ6WyFffy1NQU\n3njjDbzyyiuIRqPQNA0+nw9NTU34+te/jtbW1oy9I7l2sCIechwUduIV/9tV7uT2ovck117UTj4u\n+1MNYcr+Q6EQWlpacOXKFWVsDj57OGTKQRpisRhWVlbS1CZd14299xKJBILBYMYmxCrFxkxRUB0z\nU7fM8mWorUyI7MQqyqbHqF87K6DLxIkjDNFoFLFYDFNTU/D7/diyZUtaeaLDtEM+Raxy3RoaGrBx\n40aMjo6iv78fR48exVe+8pU0Rczu9VEpVtw15mYGciRLpUKp8rToatdyZ8P5tYpTLlO2Ez450M65\nubkZExMTOHfuHB544AG2LqrOWb7P7JZN7TiCyKk1wt6KfHF+5Xamyow8zDU1NYXjx4/j9ddfx9jY\nGHRdR25uLhoaGnDo0CHs3bsXgUCAjUuUQ4kGjUsouNz159rBrP2pnZm6RYmTqh2praqusp3Z/SHK\n8nq9aGhoQH9/PxYWFpT1cfDZwiFTDtIQj8eN2XzA2rBfMpk0tlwRKxDbUWcEOPXD6pjKJ/fwUakX\n4n+OzHB+7ZA0M+LElU/totEoVldXjU2h6+vrTafAW61xpToWCoVw8OBBXL9+HdPT03j55ZfR2dmJ\n0tLSDP+0fqqhVHo9zGw5QmZnZp6KxMqEkXaytHy5E5OJjeq6qTpIzk6G2Ny3u7sbk5OTRttyZIZC\n2HAdPp3iT4mUHCNVc+x09lYqjNyRU7InIGJfWVnB1atX8c477+DkyZNIJpMIBALYvHkzdu3ahX37\n9qG4uDijLenfZsSDfv5R6yfalbatVRvQ7zd3/1FblV9uTS+rmYEAUFhYiPr6enR1dbFDrg4+ezhk\nykEaxANyZWUFur42LCcW8zTLCwEyyY+KDKmOW3WsZkRlvbk5dtQXmeRZKVEiH8qM+ExNTSGZTMLn\n86GgoADZ2dkZShh3PlV67CaAb9++HW1tbXj33Xdx9+5dHD16FN/97nfTZvZx5EWV+8QRJxU5tjME\nyJFArnwzssp1cma2tJPkwNlxPjVNMxSDmzdvoqioyPSFghIUK9jpyGXFyMynrMBQH9x5nK1M8iKR\nCC5duoRTp07hypUrmJiYgM/nQ2trK/bv34+dO3eivLycJTSJRMIowyxe2dbsZUn8lof2xMuRqgyr\nPCsBjrxz9xolnCJ+SgKtiJ7sU4bX60V9fT1GRkYwMzPD1snBZwuHTDnIwNLSElZWVhAMBuH3+23v\niUY7GjNbaiceJGbqkGxrN89K1YlzdmYkSYZKMeLK0fW1/QjD4TAKCgoQDAaNpSOsyhcdoB2SyBET\nv9+PJ598EqdPn8bq6iref/997N+/Hw0NDR+pLmaqolV8wtZO7pQoi3ZwqrZQDVdRWJEoGpcdW03T\n0N7ejrNnz6Kvrw8bN25k7USdrBQruWwzWzNiplJqxP9cHTh/HOkM5eNDAAAgAElEQVRKJpPo7u7G\n66+/juvXr2NsbAwulwsbN27EY489hvb2dpSXlxvXiUvaFnWUfZsRD1W81M6KJFoNu3K+uLa1E6Ns\nT9tURdxkn5wfkVM5Pz/vqFOfQzhkykEGYrEYXC6Xcm8oVWcpYHe4TpUgzh2zQ96sSJadmOTj4qFn\nRz0R5ct24XAYU1NTKCoqQnV1Ndxut3LBTXGMKmFmJI/L86IxNDY24qGHHsIbb7yBgYEBHD9+HLW1\ntYY/VW4Wp04lEgmsrKxkLALKXSOrPCtaF6789ZBIM8WK2pkpnLRO3GeUfBQWFiIQCGBiYgJ1dXVp\nS1nIdhzoMJHc8XKdrkph4vzaUaLke9wqb2h8fBzPP/88Tp48aXTooVAI3/rWt/DQQw8hGAymqaiy\nOmQnXnGOrNCokrbtbGxM28EM9KVOgFO3XC4X4vG4qWIlwN1LVkoYtRXH3G43Nm3ahMHBQSd36nMI\ny42ONU3zaZp2TtO0K5qmdWma9j8+PF6nadpZTdN6NE37/zRNc3943Ktp2guapvVqmnZG07SaT7oS\nDj5exGKxtEXi1vulp3Zy50A7D85W2MkPOO4tE1h7sMlqCRcDTdq1Kl/YcsetfCaTSUSjUQwPD2N+\nfh7V1dUoKCiAx+PJSDBXxUDBTZVXddDUNisrC3/5l3+JQCCASCSCc+fOGStP01hWV1eNTlImAslk\nEgMDAzh9+jS7BQ73lkzf8s2UFDNbCrndZAXHjPxQZUC2tRMf509eX0jT1qb8z8/P486dOxltR8kA\nV7bISzSrh6g/tTPzSf3JM0llEsXlLGmaZsw2/dnPfoa/+qu/wm9+8xvMzc0hEAjgG9/4Bn75y1/i\nmWeeQUlJSdo+kTI40qNSijlCx9XNrk9V3bh24NpeJj7y9eTqKftUETj6bLNzzwm7ZDKJoqIi1NXV\nWb4QOPj0YalM6boe1TTtIV3XVzRNywJwStO0owD+VwD/p67r/61p2v8D4G8A/L8f/p7Vdb1J07Rv\nAPg/AHzzE6yDg48ZQoEQioEqn0e8OcqgyoEKHHGRH1pWKoeKvK1XfZDLFsetfIq/5ZlryWQSsVgM\ny8vLiEajKCkpQW5uLluW7JfmWYl8KBqTarjTzhBbXl4efvCDH+CnP/0penp6cOHCBdTV1RnT+UWZ\nfr8fy8vLhl8x3DsxMWGsVi3PqpPLpOoSd0yOT9RD7vQ4pU5FgLm2lDfoVvmU45DPlUk8B9knB4/H\ng+LiYszPzxuL2FqRMuHPTLWyS/QEEZHtVN8R2VZ8LiswqdTaNkY9PT34wx/+gHPnzmF6ehpZWVnY\nsGEDOjo68NWvfhU1NTUZOY0yIVIRYlldonXjlDPZViawtG70PpGvqRlJpeVxfqkdNxlClGe2dyJH\n5GldVS8RAq2trejt7cXKygpbJwefDWwN8+m6Lq6a78NzdAAPAfjWh8f/J4D/gTUy9dSHfwPArwH8\nXx9XsA4+PaysrKQNtXDEQzWUY0ZSqC1npyIedtdA4jpGbthKFZNZrByJiEQixk9ubi7KysrYYSuO\nLKjqamd/QlG+nQTwQ4cO4datWzh27Bjeffdd7NmzB01NTWnXMisrC8FgENFoFDMzM1heXobX68Wm\nTZvShny55Q9UMwNlFYDWT+5g5HuN1kV+41eVL3/OdVLUTiYU3GeyPxGHGTweDxobG3HhwgWMjo6i\nrq5OubSG8EfjVNVF7pxVpF/4M8ulocRMhvAbDofxwQcf4Pjx4zhx4gRmZ2cBAJWVlWhra8PDDz+M\n1tZWeL1epZJG/VOCItebDmvSuqlipoqRfB5XN5VP7j6h95HqOtH1rFR2XJxmRJsSUkoyg8Eg2tra\ncObMGaUPB58+bJEpTdNcAC4BaATwfwPoAzCv67q4a+8CqPzw70oAIwCg63pS07R5TdMKdV2f/Vgj\nd/CJYnFxEYlEwljoUaUIqMjMR1WshK1dvyqSwYEqOWbEjSaCU+Ki6zoikQiWl5eRlZUFr9eLiooK\n481StcI4JQwqEsKRKavcKbM28fv9OHToEK5du4a+vj6cPn0adXV1xvVNpdZmcc7NzSGRSMDlcqG8\nvBy5ubm2N57m8pzM1DMKei2owmRmqyKm9BpbERiqIqjuEXpeKpVCIBBAaWkp7t27h7KyMuTk5GTE\nYdZB21WiODu5vThbSt4owYhEIrh8+TKOHz+Oc+fOYXR0FABQVlaGvXv3Yu/evdi+fXvaYr7c4piq\neqnqpopZ/lx1vai6paobp4TR9jErX5SlItRcWRyhs5PjJfyZkTwA2Lx5M3p6egyy6+Czh11lKgVg\nh6ZpIQCvAGjhzD78TZ86mvSZgy8IhDIlw05nJY6pOlFO3bIzM09+GJsRJ/nBY0fdslLXOJUkmUxi\nfHwcWVlZyMnJQW5ubka+CDfsxBFSVVt93OqUy+VCZWUlNm7ciLNnz+K1117D4cOHUV1djWQyiamp\nKSwvL6OgoADFxcVpQ5SqWOgQHKdE0fqJNlARL+460Gsmfyb/LWzpdV9aWkJubm6GuqFa6oPaiWOq\ne1/Yer1elJeXo6urC9PT08bSF2bEiIIO+3EdrhnZ4lQYVd6Qrq8txvvBBx/g9ddfx5UrVzAyMgJd\n1xEKhXDgwAE89NBD2LJlC0KhEDt8JYa17JAEjkCZDWtRn1zdhK1VG3DEjFOiqL3Kr9WQKyWPf4od\nLV/Xdfh8PrS3t+Pdd9+1dV85+OSxrtl8uq4vapp2HEAngHxN01wfEq0qAGMfmt0FUA1gTFvLsQrp\nuj73cQbt4JNHPB7H8vIycnNzTYfVVG/uqjcu7g1fdb6dKfvy+Va24mG0HuJCj42NjSEWi2HDhg1w\nu91pM/S48ykZ5IYb16tO2Vlokyu/qKgI7e3txrpAL7/8Mn7wgx+gv78fJSUlxqazHo+H7YxUw2+y\nHXdczgMz82lWF64tzO4FGWJIioKrk+oe5erJ3ePFxcUoLi5Gb28vNmzYYAxnqhQYuXNeb+6U2YuE\nXB9VHUZGRvD888/j1KlTmJqaMrb96ezsxDPPPIPm5mZjfTnRLjLhEDFzcajqxpEZO+1K20H2xylR\nnLqkInl2VCOubDNbQTLN7Khfer6qfIGCggK43e60yUIOPjtYkilN04oBxHVdX9A0LRvAwwD+dwDv\nAvhfAPwKwPcB/PbDU1798P9zH37+zicQt4NPAUtLSygtLVWSKSuSRTtG8dBS5dZwSofVNjFW5dtN\n2qag8c/Pz2NmZgaVlZXIy8tTqiT0uB3CIR+Tz08kEuzyC1ysXPm0HI/HgwcffBC3bt3C8ePH8fLL\nL6O1tRWHDh3K8KfyIx/ncpco4RNxcetZ0fO5a7u6ugpN09KGl2Q7M8It2lW0G0e45bhV9ZdtrRQm\noQDeu3cPQ0NDqKurU9oKv1akR8BMAaLkV9Xh67qOWCyGn//85/iv//ovLC0tGTb19fX4x3/8R+ze\nvZuNSahIdFkCruMXC25yOVFynDLxMFPk6HOBqxsFZye3A73vaFlynNzftGz5N0feZZLHlStDlY8l\nlzc6OuoQqc8R7ChTGwD8T20tb8oF4Fe6rr+hadpNAC9omva/AbgC4Kcf2v8UwHOapvUCmIEzk+8L\ni/n5+YxjZg9Gelx1TPX2b0e9MCvfrl/asapIntjyZWZmBtnZ2WhoaGBnswmf3IKbHEnkSKmV0iN/\nRkkWoF61nPrNz8/HkSNHcPv2bYyNjeH48eM4cOAAPB5P2jl/yjY68nCdTELMErJpXQTJys7OTmsH\n2rFxHbpK4eHKVd03tOOy8in7KygoQFFREW7fvo3KykqjbWU7QTLM/Mr3iplqZdU56/racN7MzAze\ne+89/PznP8fExIRBUmtra/H000/jscceM3LoOJIg7g2rrU9EvCpCJp/PrRclfNK6qey4dhD3oFn5\nwl71zLCrbnGqFb3/abxmhFAuS0W0U6kUBgYGMo47+OxgZ2mELgA7meMDAPYwx6MAnvlYonPwmWJp\naSlDlZC/9JTkqAgOZ0uPqZQDK3WK+hSgDzNVrJwqIjZ1XllZgaZp2LBhQ1qHyM3ME+fbSbznlB6V\nOqU6n6u/VVkCra2taG5uxvj4OM6cOYOrV69i165dafVTrXRuRiIp8aDqm0yShK3VzDy5bVT3Akd6\nVHlWtL2oDfVNiQxny9k1NjZC0zTcvn0bW7duTauHVedM62wGmXDJMcod/NzcHE6ePImXXnoJ3d3d\nSCQSyM7ORnNzMx544AE8+uijafvnyT7kGOT7jvu+2SUesi8ar6od6PmUeMhtwdnJNtxxFZkxa1u5\nTDvEmCuXs6PElELTNITDYUxOTpr6cvDpwlkB3YESq6urWFlZYYe1VB0QhVkHSI/ZJU4c8ZL9Ulu7\nq6InEgmsrq4aO9Pn5+fD5/OxSyqolCCr3Cu5M7Gbp8XZqnKvuPpTW6/Xi927d+Ps2bOIRqP4zW9+\ng82bNyMYDBr1U5WpIpHib/m3HVurtuA6S45w085YRbZVHbaI1yxO7jyOzABra3s1NTWhp6cH8/Pz\nCIVCSlvqU6XqiBhpuSq7eDyOS5cu4e2338axY8cwOzsLt9uN9vZ23H///Thw4ADq6+uNdqZDnRyh\n44iPKhYV8eDalRJAVd04hdesTe22l92ZgeIYl7dGbSlxtCJStI1EXBxJGxgYsOXPwacHh0w5MMXc\n3BxCoZCSuNBjgHrWlR3FxUr9kI9xxMFsKElFsnRdx9LSElZXV+H3+5GTkwO/329JfFQdO0eIOELG\ndeJcG6iOqXKv7MTa0dGBuro63Lp1Czdu3MD58+dx+PBh4/NEIgFN42fm0cU57ShZMhFSkSxuCMRK\nCRNtaFexEj7tKkyqe1zYmb1Y5ObmoqGhAQMDA2htbWVtRBkygbIiBlZ2yWQSXV1dePXVV3Hp0iUM\nDQ1B13W0tLTgS1/6Enbv3o36+vqMYWmZJJjlcVHVar0x02sj+7Q7MxDgZ/ypyIxVnhV3n8hxWRFC\n8V2RhzA/CsnkQG0TiQT6+vqU9g4+GzhkyoEp5ubmUFtbm3Fc9cbIQdfV09vtqEu0szU7365iJHyu\nrq5ienoaOTk5yMvLg9frzdiEWBA3u3vKcSRPRbz+FHVKZcuRINouoVAIzzzzDH784x9jbm4Ox48f\nR3t7O4qKitLqws3MU63VxV0fep9ww3oyiZTPMbtu1K9ZW1i9wVupG9SWduKqF4NUKoXs7Gxj376y\nsjKlPzPyQn2qYtV1HQMDA3jxxRfx/vvvY3R0FIlEAhs2bMDXvvY1HDx4EFVVVRlrx3FlCKiG4Gju\nFDesJpNTKzKjagOOeJi1gV11i/oTf6tIHke0OdDyubrK7UXJF+dPtI/A3bt3MTfnTJD/vMEhUw5M\nsbCwYKq4WA3hyQ9ZM8KlynFQ+RXl252eT2ONx+MYGxuD2+1GVVVVGhlQETcaL0fyVCRpPSTv41Cn\nuPrTWPfs2YPt27fj+vXruHr1Ki5fvowjR46Y+hd1sbM8g+goVIt2yu0tFAkrpU+8mZsNB8udGad0\n0g5fdFRmQ8dWao2wo6TA5XKhoKAA4+PjBpmiCpAA15GryAjF4uIiXnjhBbz44ouYmpoy2u073/kO\nvv3tb6OkpCRDKZRj5xa0pHGIH7NV1oW6BPB7Nsr+hK1MZuiwFqeCmbWXKHc96hYXGwV3rThbFeHi\nlCiubPG5mb+BgQHTa+Dgs4FDphyYYmVlBZFIxJhVxalGXAdkdzYYAOUK5nbVIbOlFmSfoiMWe84V\nFxcjEAis63xKuijJUZEsM2LCkSwVGVK1ix0SQhfKdLlc+P73v49/+Zd/wdzcHN5//320tbWhpKQk\nrWPi1D+zMqniwCl1VIUSPmj95OPUrx0lihtulD+joCRP2JmpRuIckWsnQ9M0hEIhhMNh3Lt3D+Xl\n5ZZKjeyD27JFkIvV1VUsLCzg/Pnz+I//+A8MDw8bWwLdf//9+Pu//3vU1NSYKm9WSxKI6xqJRDA2\nNoZAIGColzLxMSMAIt54PI7BwUGUlpYiPz+fVcFoG5gpTKrlAyiousTFKJevIrucXzk2mZRzJM/K\njvqlyp+u61hZWcHU1JTp/ejgs4FDphxYYnFxEdnZ2exnnGICmA/hWUHuKK3IGyUunLqk6zqi0ShS\nqbUtU3JyclBZWWmUZYc4CVuz3C1Ksj7uIUAr4kXL58qihLSxsREHDhzAO++8g/Pnz+PgwYM4ePCg\nsQSE2FpGRYjkYxzxUpEZ2S/XvsJObjPVNZJJD0e4VR29aualqoPk7l0rxQpYy+fKzs5GOBxGJBLJ\nuIYCtC4qhSQcDuPOnTs4evQoXnvtNSwsLMDlWtv+Z8eOHXj66aexc+dOYwYqbXu5bhxREyQpkUhg\neXkZCwsLmJ2dRVlZGQoKCtJsOX+UDK6srGBhYQFLS0uoqamBz+dLUyIp8eCUQJUSJNuagV5X2t5y\n+4i/7apWVooZdz9xccn2KuVpYmICkUjEtK4OPhs4ZMqBJcSDVEWG7BInK+JCz7cz1MW9Acp+o9Eo\notGoMTRUWlqqzPmhsaqGp2hZHOyQPAHV4pdc/VWx2l0+gh73eDx45JFHcO3aNczMzODo0aNob29H\nYWGhQabcbrcpiaSdJ7VNJBJGx07bUKVC0b85O5VqYVZvMztRL/k3B9mPVScubPPy8jA9PY2FhQUU\nFhYq62OmWkWjUVy7dg0nTpzAsWPHjK1fioqK0NnZiUOHDmHPnj0IBoNKckcJD0cQUqkUFhYWMD8/\nj1gshpycHGzdujWDNIj2o4qcIGOzs7PG7Njc3FyUlpZmEFThh/5WET1KCDmCIieCc/acT0piOTvq\nh7OT7eU6qJ6dXJ3ocQFd1zE+Po5oNMqW6eCzhUOmHFhiYWHB+JsjE6pjdkmW2Vs9BUdSODIhtsNx\nuVzIyspCQUFBho0AVYzEb5USxS3OaZYXxKlDdkgip0QJciOX91HUKXHM5XKhvr4ee/fuxeuvv47L\nly/j0qVLOHLkCFwuF7xeb5riw/lX1ZtrY9qWVNHjZgZynYzZ/SUfVw2PCltqZ/XCINuZEWu5oxcv\nBkVFRZidnUUoFMogl2bqViqVwvXr13H06FGcPXsW/f39SCQSCAaDeOCBB3D48GG0tbWhuLhYSVAE\nSbPK51lZWcHdu3eRlZUFn8+HkpISI1a6fIAqZqFkud1u+Hw+FBcXs8O6omxVu1I7uT0oSaHn0Fjl\nsihJ5+qiUqE48mZma0YIhY2oExe/jHA4jOnpaSdf6nMKh0w5sMTq6ioikQj8fr9lZ2N2TO4IuWEt\nSghUigslPeK4ODY7O2u8DQcCAbjd7ow3S5USJmM9SpiZukTLsRoulI9xuU/rzb0yU6d0XUdOTg72\n7NmDK1euYHR0FM8//zwOHjxozPqidZE7UzqsRusnl2c2REk7HhWJlTszcYy7llYKg7CjCob4m7vH\naX4RZ29GNAoKCjA6OopoNAq3261UVuQ69/f349e//jVOnTqF4eFhxONx+Hw+3H///fiLv/gLbNu2\nLWPBTVFPEYNVwriur62Qfvv2bQBAWVkZfD4fvF4vSxJUCd4rKysYHx+H2+1GYWEhvF5vxhprqiEw\nO0NlZtdUPmZGzFTqFtculLibkTw7w4gqO5WtUPjE8fHxcczOzmbE4ODzAYdMObCEyHvw+/1KGxVJ\n4kiKiswA9tQtFRkR+R1FRUXIy8szVCm7JE+V1Gwnd8nsLdZs/zi5fFVdVUsl2B2upCSNqkQulwst\nLS3YsWMH7t27h76+Prz22mv42te+luFfrqtMqDj1S9UWMsxmBnLEhyovXE4WF5P4jFM4OFsaDx2a\norBSa8S5zc3NuHbtGjo6OkyHCGdnZ/GrX/0Kr7zyCiYmJhCPx6FpGjZv3oy//du/RWdnJ3JycpTr\nqolYrFSUZDKJgYEBTE1Nobm5GV6v13j5kM8V/riE+GQyidHRUYTDYVRWViI7OzvtO8WVb0UkBUm3\nIoTCn6alr1ElvyxQe2qrgmgDq6FcASvVD1jbokq8FJjZCYhjsVgM09PTWF1dtRWLg08fDplyYIlU\nKoXFxUUUFhYa/1sNxdDjqmNWw13yMfr2L/6ORqOYn59HTk4OqqurLYfVZJJnFet66irnBtHzZZjV\nfz3qFLUV5VupY1y9AoEA9u7di6tXr+Lu3bv42c9+hsOHDyMUChnnxOPxNJXPrH5A5ixNSrLkzoMj\nWYLEygRFlK8a3pNJj9myCKqcHBoX7cS5e1n440gktRNDtGJVdFrnSCSCt956C88++yyGhoYArOW1\nlZeX44c//CGefvpp+Hw+1r9dQieUqLm5OfT396OiogI7duxQ2goSJY6JIahkMonFxUUMDg6ioqIC\nFRUVtobANG0tz0oF2v5UwaHqklx/Wq5M3oQd1z50ZiCgvkeoYqRqb/q80nXdUCQ5yARWvo91fW1R\n4ampKWWbOfjs4ZApB5YQqg+nfMhQKU4c8VCdrzpGz00kEsb2L7quo7i4OIPImJWvKstMHaLqDjcr\ni+tsVUqayCOhqoqKhNDyObJgV0njSNDWrVuxbds2jI+PY2FhAS+99BK+//3vZ9RHVabcpmZKnZV6\nRjtGLm65Q+VUCy4WlU96DWQlxIocCRKl6zoikQgCgQBrJ3fira2tOHXqFPbt24esrCzE43FMT0/j\n8uXL+MUvfoHr168DALKzs1FdXY1HHnkE3/jGN5Cfn6+MxY4SJWKMRCIYGhqCx+PB9u3bjXtD7vTl\nmOnCktFoFMvLyxgfH4ff70d7e7uybNmv3F4UgqDQITqroTLV0B9XPke2OL9yfFzuktwWsi03NKmy\nkz+nPrnj4XDYWajzcw6HTDmwhK7rWF5eTuuUaacvPyCshlfE+VxCuBWZEG/v4s2Wbv1it6z1KGnr\nyX0yU6c4kmU2jd8sJtouXFn0Ya7KbRJ2Ho8HR44cweXLlzE5OYljx47hgQceQENDg3GenKxPyYzd\n3C5uBXTaCYkfM3VL7qRUxFIQVjqkpyL9tOMDMgkVl5OjaRpLpDglxOVyoaqqCqOjo/B4PDh//jyO\nHj2KM2fOIBqNwuPxoLm5Gfv378eXvvSljK1fuJjNCCgAY02qcDiMWCyGhoaGtJw4UQcRK41Z1/+o\nZkWjUSSTSSMuqqJwsJMTpbKjdRa/zRbnlH9zRIraiN9mQ3XUpxVxtXNdrMoG1p4pMzMzzpIIn3M4\nZMqBLUSjUSwtLaW9HYvOToaKDIjfVkNoHMSDRixz4HK54PF4kJOTo+yYrY6J8u0sKcDFKj9gVbay\nT7vqEEfyRP3tDIuKsuysccUda2pqwt69e/Hb3/4WExMTePPNN/HXf/3XaVuQyCTFrC6cnSpuLndK\n1b7Clpav2pOQdtp0CEjYcpCvhZkKIhCPxwFAOXtNoKamBi+88ALOnTuHS5cuGYnFTU1NePjhh7F/\n/35s2rSJHdITcZkN5wmbZDKJyclJhMNhZGVloaioCLm5uRl1lgkC1xZ3795FMpmE1+tFfn4+srOz\nLXOEVMSMkhnZjoOsDtm5BnZJJr2m9J6gdaLfTZXtelZgV33f5c8jkQgGBgaU9XXw+YBDphzYwsrK\nCiYnJxEMBjNmalmRCfHQskO8xHH5wRKPx7G0tAS/34/s7GxjltB6SNJ6OmsVIZPrI46ZDeFxtnZI\n1npm5skkS3RGInarWXZcWVlZWXjqqafw3nvvYWFhARcuXMD999+ftlEvNwRGc7tEG6q2fpH/ln/M\n8tisVAuVEmW2OCfnk7sfzIaxZLt4PA5d1zNUHxnDw8N48cUX8Yc//AF3794FAFRWVuKpp54ylEDV\nIrlmJIEOa01NTWF8fByhUAhFRUUIBAJp15+2AfWn62vrGs3Pz6OwsBAejweBQEBJUET5oq3k+5GD\npv1xjSozciRAVwRXDcFZkTdOPeLsxN+q9qG5U2btsh7VipKv6elpTE9PK9vFwecDDplyYAup1Noi\nfOXl5WmJsyp1R8CO6kQ7MPkhMzExAY/Hg7y8PHg8Hnb5Aa4DVNVBpZ6o4qcxqTpaM+JiVb7Kr91h\nPbP2s1M+PV5aWoqvfvWr+PnPf47BwUGcOXMGGzduTJvNqSKsdEjEaoiTqiMUKsWC+lV1UCpizK17\nZbdsDnI7Li4uAkAGoZqZmcFLL72El156CRMTE4hGowiFQnjiiSfwta99DTU1NWlD1lwZdkjH4uIi\nenp6kJubi/r6eng8HlYhBNTb1czPz2N4eBhFRUWoqamBx+OxVFxk0mmWIyTbqUihOG6mWskkhZIi\nKyXIrB05xcqOrapd5GO0XaitTEhFvIODg7auu4PPFg6ZcmAbS0tLWF5eRjAYzOjE7JIB1cw8+uYm\ncjPKy8sN5YTrZLg3S4BP0DZL2lYticANq8l1kx+4ZkNwcudglkck6mI2M49rO66u4rjcrlwbyHlI\nwu7JJ5/E0aNHMT4+jlOnTqGzsxPbtm1Le9hT/3L+kuxLVT9uiJa2j1wOpxTKZZspUbQMESd338p2\n8r2lUiplkuFyudK2XNH1tfWXjh07hmeffdYYrnG73XjwwQfxox/9CJs2bYIKtG1lUOK6urqKvr4+\nrK6uoqWlJWOIUL5XVUn7iUQCPT090DQNTU1NaethqUgCJVAqkiDalNtzjquT3K4q1ciOYkmvKdeG\nnG9Kzmh70xhln9xLBQc6i5B7GRgeHmbPdfD5gkOmHNhGPB7H+Pg4CgoKMlQKroNWJc1yxEVI/tFo\nFKurq8jNzU1b0VmcyxEnVVkcVLGq1B27xEuVoK6qK0eSuFhV6g9XPiV5lCStJ363242vf/3rePbZ\nZzE4OIizZ8+isbExLcmae3O3qp/cqZipmlw+D5DeqcpKGLXlri/t9Dglg+tYuXMo2aI2q6urWFpa\nwo0bN/Czn/0MFy9ehKZpKCwsREtLC77//e9j7969pmXQduDsdH1tr775+XlMTk6isrISRUVFGW0i\n+6M+k8nk/8/emwXHdV1no1+j0Y3uRmOeB2IiRRIESXGmJGmvKeoAACAASURBVNuUaFGSJVcsyyqn\n7DiV2Ck7lUpSdR/ycF0373nw2++6SVXi0nXFsRwncWhrKCtSRFMURYmkRIozhImYCaAx9IDuRs/n\nPkD7aPfqtfc5ICmblM5XhQJwsM7aw2n0/vpba6+NdDqNxcVFRCIR9PT0IBAI2FKixP06dUmeL0ri\nVMRDp8LZsZNDcIaxHn7l/se4DzscVHYcyRPX7eZ3qVQrw1gPsyaTSeX9Du4dOGTKwYawvLyMZDKJ\niooKW2ESwHq3XCaTQaGwfqq82+1GfX29uUvI6jgX2SfXlnxto3lWGyU+dkgaZ6e7n7tG+68jbroQ\nm7w4UsXK5XJh37592L59O65du4YTJ07gi1/8IrZv317y5m/1fIXioQp7yv0Q41MphfKz5nYGivZo\nf0Q/VeoSJUcqdYsjUbLPZDKJsbExnD59GteuXcPp06dNAlFVVYUf/OAHeP7555U5UfJ4VRBjikaj\nSCQSWF5eRlVVFXbt2lX0/ORQERcuy+fzSCQSWF1dxerqKurq6tDZ2clWeqd+OSLN2VvZyT7thMvE\n3OgUK3mexDjC4TD8fj8qKytZwqxrm6pGVs+G+0792vU5NDSkbMvBvQWHTDnYELLZLJaXl1FTU1OS\niA6ULqwc4RILYzabRSqVMhfPqqqqomRoFfGxS4aEnR3Vye41wH7dJp2SxpEsrp8yiRAQ5/NxYT0d\ncaJt0f7T+2tra/HYY4/h5s2bmJ+fx+uvv46tW7eWJJmrxsfNo27Hn0ySaF+EH3EPvV8VApR9AsV5\nTHRRo23RMXF24lo+n8fQ0BBOnjyJt956C8PDw2b5Dr/fj5aWFnMTBVefTO6PlZKxurqK5eVlk6Bu\n375daasiCfF4HCsrKzAMAz6fD93d3UUJ3SoVivNJ7eVxUJIg58lRO46gcqTDan44u6qqKiwuLqKy\nsrKkbR0hpMSRsxV9taNE0X5y8yIQjUadEN99BIdMOdgwZmZm0N7ebr4xAfqwHlCaz5RMJpHP5+Hx\neOD1es0kWQpdKIheUxE6le3tqlMySbFzv53dfqp+WZEsGdyORXqNEhtVW4axXq15586d6O/vx4UL\nF3DixAk8/fTT2Lp1q3Z8glxYhTiFndWzEOPlSj7I46GLt9yW3KashAiodvxRRYd7jc7OzuL48eM4\nffo0bt68WXTkx549e/BHf/RHaGpqwosvvojjx4/j0UcfRVNTU8kYrBbhbDaL8fFxGMb6eYqNjY1m\nXhQlPvSQX4F0Oo2JiQl4PB5UVVUhEAjA4/FoCZJMTnS5W0Bx6E1FPDglSqVCyT5VEM+WG7Pw6/P5\n4PV6EQ6HUVNTY1sFU72m6HyrnqHOlhuHTB7Hx8ed42PuIzhkysGGkUqlEA6Hi8gUUKwE0WsCa2tr\nSCaTqKysRDAYLArTbEQxsktmVOrMRtpSLfZ2w2p3g7jpQmSyrarGlJ2dhVxfW1pacOjQIYyMjCAW\ni+GnP/0p/uEf/kEZ+pQXR5UiyP2sUvroYqbqtypvR0DO2eIWM+65i237qnlbXl7G8ePH8dJLL2F+\nfr5o4evr68Of/umf4qGHHkJDQwNcLhemp6fxwgsv4M0338S3v/1t2ySqUChgbGwMkUgEXV1dCAQC\nRWF2ShA5YpTL5TA1NYVkMonu7m64XC54PJ4SMkoh5lZHZgSoHReCE+1xuVO0XfHsuZAjfR1xc0nH\n1NDQgOHh4ZJjfNLptFluxW4I066yRccifufmWw7NFgoFR5W6z+CQKQe3hbGxMXR2dpq/c+E8AcMw\nzPBgIBBAfX09XC6XrYKbwMbDarocGt01cd1OCGwjhE53mC9gPwRot0aVqn0rkiYvcsLW7Xbj0KFD\neO+993Dp0iWcPXsWFy5cwIEDB4ru48gJJXzCv525LBQK5qGwqnmTfVrlocnqiiqEKCscOiVqbW0N\n//u//4t//ud/xtTUVNFi39jYiG9961v4xje+gerq6qL7jx07hhMnTuAnP/kJnn32WWVBTrlP4uDp\nvr4+dHV1KfsulDvuOSwuLmJ8fNz0oSOfssKkUrcoSbJzaDAlRhyZkHe2UVLGkSQrOxniddfS0oKF\nhQW0tLSYtjT0a6VEyWE9K8ivO6t+yv7C4bBZYsPB/QGHTDm4LSSTSczMzKCzs7NkEZRVilwuh2Qy\nCZfLhdbWVvbTv5z/oyNEdtWhO1F3rIgTDVfaIT7CVnVmHv3kbjcEKPpF+8CRN1WeFVWURPuyXW1t\nLQ4dOoTR0VHE43G8+OKLGBgYgM/nK7pPFY6ln+CFHTdv8mImfleRW3mRUh1TI/6mU6JkO7pAyqpP\nLBbDtWvX8MILL+DixYumrdvtRmNjIx599FF8+9vfRldXFygMY/38yKeeego//vGP8cILL+Cv/uqv\n2A8EuVwOq6urmJ6ehs/nw/79+0uOKKKkR54vYH1TRzabxejoKKqqqrBv3z7Tntpy7VuRI+FLRyjk\n52/nYGMVQaI24junWMlEi7Orra3F+Pi4+dqXlSCVykR9quaQkizdXFupW7Ozs0in08o5c3DvwSFT\nDm4bCwsL6OjoKLleKBSQy+WQzWaRy+VQWVnJVoQuFAolCzzA51lRMiO/GdlRtzZqS9tXLS52CZkq\nLGdH3ZJJk9wvShJlUqAKsdHFSbXLTsyv2O3V09ODTZs2YXBwEKOjo3j77bdx7NgxLQmW+2w1PtXC\nTtUl0WdOMaLhE7HYc3NBQ0XcAi58LC0t4caNG3jppZdw6tQpkxiUl5ejvb0de/fuxXPPPVdUh0tu\nSx7j448/jt/+9rf4j//4Dzz99NPo7e01/57NZhGNRhGLxZBKpdDV1YWqqqoSP+I7RxLT6TSSySRW\nVlaQyWSwY8cOZaVwOv+qECElCVRlku0oSZDVQBXxkO1UZEYmz/L/J9dXK7umpiYsLi6a6pQdhUke\nv9wW/SAkvtOx0tebzmc6ncbCwoKWgDq49+CQKQe3jeXlZSwvL6O5uRnA+ptEKpUyF3Ov14vKykql\nwiDuUS121M6urUwm6MJotyr5RnKvuDGpFJXbLb+gUtfop3BdX3U1qmj7q6urSCaTyOVyCAQC2LZt\nG772ta9hZGQEiUQCJ06cwJ49e4oSqUX/qCJxNw425j7BqxREamtVnJNb5A1jvXDshQsX8NZbb+Ht\nt982wy5lZWXo6OjA4cOHcfToUezfv1/7oUBuo7q6Gn/yJ3+Cv//7v8cvf/lL/N3f/R1cLpdZciSb\nzaKurg7d3d2gUI0PWCeYKysrWFtbQz6fR2trq6kccmRBJjOyIqf60CC3LftQkQmr0B8lcLdjxxEU\nFSmTbSsqKrC6uopEIlESaqUfDuT2OQjyRu/h7OjfVfMTCoWwtLSknT8H9x4cMuXgtpHP5zEzM4PG\nxkazzEF5eTncbjcCgYByh5QMjjgIW+D2d+ZZETKrPmyk/Y2oUzQEqCJJGw1L2tk5J/dV1VYikcDS\n0pJ5DltjY6MZYtq3bx92796NixcvYnh4GOfOncMzzzxTFCrkFh1VHhkt+UDVBHHNDgmU29Y9d6qW\ncAQ/m83inXfewRtvvIGLFy8iFAqZvmtra3Hs2DEcOXIEO3fuLDkNgCou3OvtkUcewcGDB/HOO+/g\nwIED6OzshM/nQzAYRF1dHdt/TjUS41lYWMDq6ir8fj+qq6uLtv/TuZTnT/6Sfcq2OtWIC39xpEu2\npXPD2XFjVhELQWbkNnVhNfF/UVFRgXg8fkcHScu2sh1X5kC2lftE+1ooFBAKhZBIJCzbdXBvwSFT\nDu4I0WgUc3NzqK6uhs/n054DthHFRkeGAOt6VhyZUbV1p+qUiozJ/ZIXEjshQB1JovOryt2iR8no\nbLPZLEKhEPL5POrq6uD3++Hz+YrmoKKiAt/5zndw5coVxGIxvPfee9i7dy86OjqKyBpNiqdkhi48\n1Jb7FK8qzskteqodlSp1RZ6Hy5cv4xe/+AWuXr2KpaUlMzzm8Xhw7NgxfP3rX0dvby9qamqUSe90\njPT1WVFRgb/8y7/E3/zN3+B3v/sd/vZv/xYNDQ3K43VUi3o4HMb09DRqa2vR3NyMiooK5f+eGKvw\nJ8alIwuqBHTOJz0ihiM0sk/ZlrPLZDIwjOL8QWqneqbUXrYT/fT7/WZIVBzeLNvScetIpmpeVL6o\nHfBJuC8ej2NpackJ8d2HcMiUgztCKpVCLBZDa2urbZJhJ9Sks9XVGpKvqfJlVOTJbt0ou0oalzOk\nGqtKveH6ZNcv3Zkn3y/nWYmwQldXl7kDTSyS1Gdvby++9KUv4eTJk7h06RKuXLmClpYWy+eu67MM\n1eLDKVHyM6Y+6DPP5XJacj4+Po6f/vSnOHXqFOLxeNHzPHToEH7wgx9g69atysr/ssLD/V1us1Ao\noK+vD0ePHsXVq1cxNjaGlpaWkn6pQmXJZBLj4+Nwu91FRVQFdESGLtIqkmAVVpPHzOVjUTJjlRNF\niZE4AUFlJ2ytoMqfcrvdKCsrK5oPu0qUVWhSwE4f6et0dXUV4XDY0reDew8OmXJwRygUCohGo1hb\nW2PrTskLGxcOkf3cbi0ocV1uQ7al17k+3A7xosSF25knfKraooqVaheg7JfbmccpVqJ9rq+FQgFr\na2sYHx83z4sTPlXESPj+1re+hYWFBdy4cQMnT57Erl27inZqUoKjU5dkW5lEqHbmyfOlGp/83Gn4\nRx6fUONefvll/PrXv8bKyorpw+fzoa+vD9/5znfwhS98AX6/X0nGrJQoue+CeLjdbjz33HM4c+YM\nTp8+jV27diEYDBaRGTpPmUwGCwsLCIfD6OnpQVVVlTKxXJBhca+qsrkMq3pS8pxahdXEdbozUBWC\ns1K37CpCnK0MkYzvcrlQV1eH+fl5s3Aw7SenpKpIJgDz/D/aZ2pLX5+y72g0img0qhybg3sXDply\ncMeIx+OIRCIIBAJK0sItLlZkRr5fhor4qBa7O1HCOB+6EOLtqluckqYic+I6VyNK15ZhrO8YW1tb\nQywWQ1lZGQYGBooKWnI7A4HiZ1VfX4/HH38cw8PDuHz5Mq5evYrm5uaSiuWcksERO93OPDo+6lP1\nM0cyhM90Oo3Z2Vm89957OH78OCYnJ02bYDCI7u5ufOUrX8ETTzyB2trakv7IiyX3mpHtKTmSP1h0\ndHTgiSeewLlz53DkyBEcOHCAVeQSiQTW1tawsLCAhoYG7Nq1S6kI0f7pwmrUTqdEyYu/1c5AQB0i\npGRCRR5Vtiqfom/iu0oFE/bCxuv1IpFIsLuNKSEV13Tk0Y6yJfuTkUqlcOvWLVuql4N7Dw6ZcnDH\nSKfTCIVCaGhogM/n04Y5dLCrRG1EnZKVj9vxqyNkHPHhSN7tKHF0obNbvJKqW/Kn83A4bB4q3dTU\nhMrKSmWNMHk+aV/dbje6urrQ3t6OqakpvPLKKzhw4ADq6+uLFjxdKQu6mFqFBeXFTJdHJi+iHGEO\nhUI4efIkfvvb3+LGjRvmdbFj8ZFHHsGxY8dKaqLR+VAt6NSOKjgyqqqq8KUvfQlnz57FiRMn8MAD\nD5jVuQ1jvSxFLBbD2toaysvLsWPHDlskShWmk/sq901nJytBOjuuXfqsaV/tqFscgVIRatpHSn6p\nrWEYqKmpwczMDILBYFFFeDEeHSGk/aP9ooor/Zlifn7eqXp+H8MhUw7uCiKRCBKJBHw+X9F1+c3j\nbqpTqlpK3L2qPCsKXV9VJMtOW1ZkgSNkXL+4EKRK/ZHt5ufnUSgU4PV6EQwGi8pV0HZUxI0Sx9bW\nVjz88MNYWlrC8PAwTp06hWeffbakv5yaIy+24rtK6dMpUbId9zd5zpLJJC5evIg333wTp0+fRjwe\nB7CeWL5t2zYcPXoUhw8fRnd3txlWVSljKsgLq+r1ARQTy82bN+Pw4cM4deoUjh07hr179yKbzeLW\nrVtwudaPfGlvb0d5ebllCM5O+E3Y2QnpcT45v2JuODJD7WRipiIpdP7skBk7oT8VMauvr8fi4iLa\n29sB2FO2KCHVwQ4hTKfTuHbtmlOo8z6GQ6Yc3BWkUiksLy+jpqampFozRzBU0BEXbrG93ZICqusq\n4rWR9u2embeR++Xr4k1Y5VPYhsNh82DXQCCAqqqqIqIlE1J5UQKgnAPxs8/nw6OPPorR0VFcuHAB\n//Vf/4UvfvGLqK+vL+oLJVRc3Sk6brkt3XOTFyndc7t+/TqOHz+ODz/8EKFQyEw67ujowHPPPYfD\nhw+jo6OjJLlcVihkkqAi5iriQW2FTSaTwdLSEjZt2gSv14v//u//RmVlJQqFgrk7j9u6zxFoKwIg\n/qbboUdfByqSSsdiZQfY2xko+9QRD3HNzlmAsk+VXWVlpVnnq6KiwhbJVBFmna3ObnR0FAsLC0ob\nB/c+HDLl4K7AMAxMT0+js7OzhEwBapIA3Fnu1EZ2AapqSXFjof3SqUMbyZOyCusB+qNRuE/6VIlK\nJBKYn59HIBBAa2uruWVetbBzqp/VmX0ulwtNTU14+OGHMTo6iqWlJRw/fhzf//732fHJC6RuLujC\noyJJnCJAn8/s7Cz+9V//1VSixBhqamrwzW9+E1/96ldRXV0Nr9fLqpwq1YG2Y+dsOuozFAphdnbW\nzJsaHx/H66+/jqeeegqHDx9m88goaMhPRZAKhYLtI2KscqfktuzsDKSES+VXpYJR3KkSxbVvGOtK\n69TUlHkItOq5W7VLQ6N27NLpNN5//30nV+o+h0OmHNw1pNNpxGIx+P1+28RF/O1OcpdUJRnoNRXx\nUalTcnvym7JVvwRB0bUlq0vUJyUQOqVG/luhUMD8/DzS6TQ2bdpUsiGAGz+3M1CQJzkxXUAmeS6X\nC5s2bUIwGEQ0GsUbb7yBp556Cps2bTL7lM1mS4ihrCzJ1+TrYncoXQzl+aaqlyA1KysreOmll/Cr\nX/3KDOe5XC5UVVXh6NGj+LM/+zM0Nzcrc6IoeVS95mgukepDgLhHnLk3MjKChoYG7N2717R75pln\ncO7cObzwwgs4ePCgkkzpCAolJ8D660tVukD2J8+fyla0ZxUiFLZ2Q2C6/C65D5wSRV8bcvt27cTz\nrKysRDweL9mVLO6xUwJC+KTKGv1AIewNw8CFCxeQSqW043dw78MhUw7uKsbGxtDQ0ACPx2MrLKYi\nSDSfSNiqrlFwfmlYS7azq27ZDQHqSBa14/zaSToXdplMBslkEuFwGM3Nzejo6LDcLWil/giSxbUv\nL8R9fX3Yt28f5ufnkUwm8fLLL+MHP/hBUY4Pp4rJJR8oYXK5XEVVvEW/OfIgbGKxGG7evIkTJ07g\n/fffx61bt0ybpqYm7Nq1C8899xz6+/u1z5Ajb7QtMT9WHxDEnGWzWcTjcSwvL6NQKODBBx80d48J\nu76+Pjz88MM4fvw4Tpw4gSeffJIlzXaIh7Czc7iw3TwrK+WIKkyUeKhIh07V41QrCvrMZHIrQya/\nom36rOvq6hAKhRAIBEr6yc0NJZm6OZQh929paQkjIyNKWwf3Dxwy5eCuIhqNYnl5uaSQI6AmHhxU\nBEVFRuzWqFKF+zjiw+V5bVSdoouw3b5yO/Pkfgn1JplMIpPJIBgMYvPmzUVv8Ny9QCm54RQzwzBY\nQiUnYYt+Hzt2DOfPn8fCwgIuXryIwcFBDAwMlNwj3ycWM9XzoSoRF4YzDAPRaBRDQ0M4ffo0Tp48\naZ6fBwCtra3YsWMHjh07hv379xflH1HCpFOX5HmSF2CdsiXmb3l5GZlMBul0Gs3NzSUHF8s+v/a1\nr+HUqVP49a9/jQMHDqChocE2iaJ2VrayqqYjM7JqReeNa1vlS35d2rGVfdLrun6q7FR9lG3LysoQ\nCASwurqKqqqqErKlapebF/q/xI0nk8ngxo0byGQyyjYc3D9wyJSDu47Z2Vk0NTUpQyQbUadUC5aO\nuGzUr3iDk+skiXt1SeOyHaekcUoU176sTslv0Kq+5vN5s8Ckx+NBY2NjyS5Kua9UheLa5+o9ceST\nW6hbW1vxxBNP4Oc//zkWFxdx+vRp9PT0lKhLctI48EmhU25+5PkFPjl2RvwtmUzi2rVrePfdd3H2\n7Nmi5N2Ghgbs378fDz/8MPbv319SAkJuhy6Yqg8A4h4K7jkWCgUsLi4imUzC7XajsrISHR0dJWRS\n/CzQ3t6O5557Dr/4xS9w4sQJPP/88yVEgYN4fnZJ1O2E38RccSEzmWzJ88IRVtpH+mFDRTxUfVTN\nj/xsrezksVVUVCAWi6GioqLkf58SQqs51NkahoHZ2VnMzs46uVKfEThkysFdx9LSEpaXl01CRcGR\nHNU1O/eK63b86uzom5pKHaJvjjIZstsnlSpD25dVpFwuh4WFBbhcLtTW1hbt9qLERF7gVKqOvOAJ\nW5rMTomYYawX/qR5VkePHsXbb7+NqakpXLp0CYcPHy7KCRLhHFX+EW1Dp8jFYjG89tpreO211zA7\nO2v2vaKiAg8//DCOHj2K/v5+1NbWFhE4eTGUVTquHfmZcARe/F1cF+OLRCJYXFxEMBhEdXW12Qfq\nT+Xz6aefxsmTJ3H69GnzEGQVZIJiVUiTa1tla3fHn47wcHZ2fHIkU7YDitUtla2ATomSr4kxl5eX\no6ysDKlUyqxIT+dnI0RKNT8id07k9Dm4/+GQKQd3Hfl8HjMzM6ivr2dVGzukR7zpqcJyVouysJPb\nlW11BEPlVyYeVrv1ZDVDtYuRtg2ULu4i1La8vIzV1VU0NzfD4/GwSf6qvlqpP7p54RYDjjwGg0F8\n/etfx49//GPMzc3h/fffx5YtW1BVVVXkQzVvVD3glMZ8Po/Tp0/jV7/6FSYnJ5FIJMy/7dq1C3/8\nx3+Mbdu2obq6mj1smVNlVM+cIwociRc+s9ksRkdH4fV60dbWBr/fbybe65QR2n5lZSWee+45/NM/\n/RPeffddPP/888rdjDJB2Uhoi8Ll+uQ4Fys7qliJdjjIxIz6kME9F+71qXqGnAqmCkty4TfZX1lZ\nGaqqqkrC0jryprLjIOZ6bm4Os7OztkiZg/sDDply8KlgZWUFoVAIbW1tyrAWd50jONx1DhvJneKI\nFndNvHnbUdK4oo4qkmeH0BnGel7U3NwcGhsbzW3bcltyiILbGSgTH3nRpaoXnRd5TOJQWDovVMXa\nvXs3+vv7MTg4iNOnT+ORRx5Bf39/kaIg+iwvatwhxPSsw6tXr+Jf/uVfMDQ0VGTX1dWF7373u3jk\nkUfMa1xIilPeRJ/kUKhqbmSIeRI+R0ZGEIvFsGPHDjO0KY9ZVuV0PsXOu927d6Ovrw/nz5/HwYMH\n0dPTU0TKrAiK/HpQKTzygq8K/dEw6EaOiLFSt0T7Gwk52rGjZEY1N1TNEwnqAMxK6AJU9ZNthU+5\nn3LbXPvxeByjo6NOgc7PGBwy5eBTQSaTwfT0NKqrqxEMBgGULvI6giIvhiriYme3m6o9u7lbsl+O\nTHDExc6CbRiGSRboJ1uxqC4sLKC8vBy9vb0luyPpQiUvTrp5kT9d6xLx6WKrI4SiHxUVFXjiiScw\nNjaGSCSCt956Cz09PebuKDoXMvnj6mqtrq5iYmICr7zyCt59911z8amoqEBHRwe+/OUv4/HHHzfP\nz+PmhrajUqKA4kWTI9aCwObzeaTTaYTDYYRCIXR1dWHbtm0l7dPFVfZJXwty29XV1XjyySfxk5/8\nBJcuXUJHRwc8Ho+tkgTC30Z28ln5ov1T+QOKa26p1CXxXdW2bm6oX/o/oCNcdsZD27byZ2XLEdLp\n6WnMzs4q/Tq4P+GQKQefGsLhMGZmZrB582bzVHYrJUgs8vRNl/tUryJZqi399F676pTqDVW1MHOh\nMhp+44hXLpdDJpNBKpVCNptFW1ubWQBVtbjbKQ5aKBTMyuMy5LmSVSKO2Mh+5cVDtnW73diyZQt2\n796NDz74AO+88w4ee+wx7Nixo6gNupBR/4lEAiMjI3jvvfdw+vRphMNhAOuKQW9vLw4cOIDHHnsM\nbW1t5vhUBzTTdiihUZF1juQWCgWsra0hHo8jHo/D7/djx44dRXNISRRHzmUbjlC43W709/djx44d\nOHnyJB588MGiBHYrf7rXq5gvO0qUqo90XuT2dW3bsaO2cr9UdvLYKOQx0fw31fjt5llRQsipUPTn\nZDKJwcFBJ+n8MwiHTDn41FAoFBAKhVBXV4empiZbShD3Bqla8Daa9G33fu7ejYYQ6TVut5y4VigU\nEI/HzUOIq6urzfPzuL7K8+Jy6csnyNdUShpdBFTqFrfYivaF37q6Ohw6dAgjIyOIRqP4zW9+g61b\nt5r9EX44wre8vIzJyUlcuHAB586dw/z8PACY6tz+/ftx+PDhohIQ8tjkMQlSzoVSqR2gPrZGqHRr\na2tYWlpCoVAoyouSbXVqC7WzUj0aGxtx6NAhvPjiizh37hyeffZZNg9MHofO5+3a2RmLHWJA1SAd\nQbKrvtE+cmRGfNlR1jaSeL8RO7md4eFhLC4uasfn4P6EQ6YcfKqIx+OYnJxEdXV1UbhHgFNcVMRn\nI+qU6jgWeq+KPNE+baR9LsdKLBCU+MRiMSSTSXi9Xvj9fvh8PjZUKRM6qzGo+kpJklgMVRXKuXCK\nHdVt27Zt2Lp1Kz744ANcvHgRFy9exL59+0rGJO6JRqN47733cOHCBczMzGBubs4cU0dHB44cOYK9\ne/eip6eHPaoI+CTvSkcKZTLH9UOMT14IxQ7KbDaLYDCIQCBQVGpBVnmscqJklUdlB3yiqu3atQt9\nfX14/fXXceTIETQ1NRWNzQ7p2agapFKi6AcbK3WLjtmuEnUnREa2pXNDXxtyTptV/1SKlW483BxG\nIhFcu3bN8n4H9yccMuXgU8fKygpmZ2fR19fHfrq2Q2boG6OAXcUI4MNiqvpKuvCMXSVKdSAusH70\nztLSEioqKlBbWwuv11tCdKgSpQohytfk/uiSxumCp6rALveZC6GKscrt19TUYP/+/RgdHUU4HMYv\nf/lLDAwMmGUcZGJ4+vRp/M///A8mJycRj8fN9oLBluokngAAIABJREFUIJ588kl88YtfRHt7e9Eh\nxHRuZKKqIrtUneBeM7JdPp/H8vIylpeX0dzcjIaGhhIlShcCk/3qiAe1k8dTV1eHhx56CD//+c/x\n6quv4nvf+575d5XSIpOGjdjZIWaqMXNQHWND+6DrI9e+FZlREUKdHUeyKCnXPT95HnX9+/DDD7G2\ntqbtl4P7Fw6ZcvCpo1AoYHR0FK2trea2Yxkq8sL5UVUlvxtKFr2fIyPidysljbtuGOs1mlZWVpDN\nZs0yBy4XXxxUlfTN2VKoxs+RR9VYAX0dJtEnmmdVVlaGffv24cKFC/jggw8wOTmJd955B48//rh5\n37Vr1/Bv//ZvGBsbK1pM3W43Hn30UXzzm99EfX093G53iXIm+kEXLtXrgJsf1bMB1onu9evX0dDQ\ngAceeMCsPSTshE9ViFpWMrgdntw93M67srIyPPTQQ3jrrbfw8ssv4+mnn0Z9fb3Sl+zTqsyBql0u\n/CbGwiW1c2qgKlGeU63skiM7djpSJhOfjRxMbYfocUqUjNXVVQwODmJsbMzSl4P7Fw6ZcvB7QaFQ\nwPXr17F3794ilQFQqzsA/+n9dssfqO5X+eQgtyPb6NoH1sNQa2triMViaGhoMI8VoX2gSpBVSQJx\njcsNkhd8eS5UIUCuRpVOCZMXEDp+n8+H73znO5icnEQoFMIbb7yBXbt2IRKJ4NVXX8X58+dN8lJW\nVga/34+BgQF84xvfKMmJos9CpUTROc/n80gkEiV1g6htobBeJyqbzWJychKGYWDPnj1F86FShLg5\nFz51qpWYR4540Hl85plnMDo6ihdffBF//dd/rVQ87agy8lg2qt6o8pzEeFSlBri2df9fctt2SI+d\n0KT4Tgkh/f+Qv9s9X5Dro2EYSKVSiEajKBQKmJmZsa3AObg/4bLzYv1UGna5/jANO/iDYvPmzdi2\nbVsJIRBhKXqN+1RPq2/LtvJ1ofjQRYBTOsrKythcJc6Wu8aF1YB1EpXNZhGLxeD3+1FXV2fez/WV\na5/2S/SVKgp2+yrul+dF1X+5r9SWQjwXecF5/fXX8e///u/w+Xzo7u7GwsICIpGIeU9tbS22bNmC\nI0eOYNeuXUXFLmmfKVmiCeZiwadETx6zsBPfC4X15H+Ru9bV1QWv11uyQ48jErJKKexUoTJ5Dq2I\nAg3BZbNZ/OhHP8LY2Bh++MMfFp17aJcc2Qn9cXacSmW3bXGNEg8V+diIndU8UjvZnoMcYpX7ofJL\n25U/gKTTaaRSKeRyOQQCAYyNjeGDDz5AKpVi23Zwf8EwDFZqdpQpB79XzM7Oor6+Hs3NzbelTgG3\ntwuP3q8K4dkJC6ral3/OZrNIJpMwjPVdZW1tbUWLuUol0fVLfsOm86Ja9LlkfBqismqfKjQCqnkV\ndolEAk1NTairq0M4HC4quFlVVYVt27bh0KFD2LNnT9EBwPI45MWLU5eo6iiu6UphCLuVlRVkMhlk\ns1nU1NSgvb0dZWVlyGQyRbsPdT4FcZMJhep1JL506pbcP/Hldrvx/PPP40c/+hHeeOMN9Pb2wu/3\nb0iJsqP0UCWKgxzCtPIn5kZHMmk/b2csVooZfc1SRVX1XGRbSsBpe4VCAclkEtlsFvl8Hn6/HzU1\nNVhZWcH4+LhDpD4HcMiUg98rUqkUJiYmUFVVVbK7T0WIVCEpzhaw3oWne+NVlQRQhRs5W1EXqby8\nHJWVlaioqNCeeWfVlvxGrusrl5+jIql2woXyQmPHVigZ2WwWExMTiEQiqKysxLPPPouf/exnKBQK\nZm2mgwcPor+/Hw0NDcocMLq4qo6I4eaUG7NYLCORCMLhsLmDsrm5uUiJKi8vL1Fl6DxSxYP2C7Cn\nWHHj4Z7Zli1bcOjQIVy9ehVXrlzBgQMHbBEUO6RHJhN2CY9ViJBTeuyMWYYqx0o3bjrfnN1GSB61\n5dpbW1szD7X2eDyorq4GsK5KT09PO6UQPidwyJSD3zsikQhmZmbMyt4C3CIirt+uOiWuA/Z2AXIk\nQ9U+JT5C6aiurobX6zWTy6lPuR0d+RH91uVOiWticbPasUg/hatIkuyTC6FSkie+ZmZmcPPmTTQ3\nN6Ovrw91dXXIZDK4ePEi0uk0vvzlL2Pr1q2oq6szP/nTcQjypnvmMpFS9U32WSis14oKhULweDyo\nq6tDIBCAx+MpKXMg5kB11iMlFKpwtOxTfg2qFFTdwl5WVoavfOUruHTpEt577z1s3rwZNTU1rO1G\nSZSubZkQ6sgWDWFSRYeD1ZhlO7s7A+Ux0/8jbtzcGKityi6Xy2FpaQlerxeVlZUl6QexWAzj4+PI\nZDLavjv4bMAhUw5+78hms2a4r7Gxsehvd0Odsku8VG3R66oQoPAbj8cRDodRW1uL6upqy6Nf6DWO\nJFGyomrfTtK6sKVQEUeqcKnmSiZe8Xgcp06dQl1dHfbt24fKykq43W6kUim88cYbiMVi+PM//3N0\nd3cr61rRRVhVyZ4qI6rXgViE8/k85ubmkEgk0NnZiWAwyBI4Gv6hsKvKAPoCkLI/mdjqXsuGYaCh\noQFf+MIXcObMGQwPD+PgwYNKf3YICiVbKjKRy+Vsq2p2fVoRPbmP3HzrVCtV23bVKNlW9bf5+Xnk\n83m0tLSY5F/0BVhXpaamprCwsKBtx8FnBw6ZcvAHQSKRwPT0NILBYNHuvo2qU3ZrTNklTlYkR16A\nRWK5x+NBW1sbm0AuwB3TQkmeaFdWPFR9lRcQVe6SfG+hUCj55Cyu07kvKytThjvl3w1jPaT34Ycf\nIhKJ4LHHHjMP+k2lUrhw4QJefvllJBIJc4ceF27ldrTJ6p28aGYymRI/tG8yQYlGo5iZmUF7ezs6\nOzuLFCd58VeRVapuieeiChvLbQtbOi55fPRe+VnICohhGPB4PDh48CDef/99vP/++3jggQdQW1tr\nm0TJ823HTkccaQhzIyUE7BCzu7kzELAeswz5YGrgk7Hm83nEYjHE43E0NzcXHR4uIGxXV1dx/fp1\n2206uP/hkCkHfzDMzs6ioaEBmzZtMq+pyIyK4MhveLKtnbAc94lZdb+8EInk8lwuh9raWvh8PiXx\nUi2W1NbONd3CrFLS6CdzWtmcK87JkVRKujKZDJLJJCKRCLZu3WoqjCsrKxgdHcXbb7+NxcVFtLS0\n4Jvf/Cb27duH8vLyEj9ikeSOfhG2VDXS5csVCgWkUikkk0msrKwgEAjgwQcfLLLTEQCO1HNqC22b\n9pEDVRut7DgC0NLSgsOHD+Ptt9/G4cOHsWfPHsu27ZAjOg47/qwInI443kkf5b5SnzLJk/1xdrIf\nYSu/LsQHikwmg0wmg1gshkAggPb29qK+cH28fPkyVldXleNw8NmDQ6Yc/EExMTGBmpoaM2kT+ESx\n4cgHxUZCeJytFXGRlQmx3TmbzSIQCKCuro7tkyqMQAmd3E8rdU1WaqwIobhmtTNQ1b48V3ThzOVy\n5jmCbrcbnZ2d8Pl8CIfDGBkZwYULFzA9PY329nZ89atfxcGDB4tqaqkWfpVCyJFgbhyib9Fo1MxR\n6erqKqq6Lr7E0TMqAk7JkYq4yeNRzSMlFHLfude2HHLkPkD4/X7s3r0bly5dwsmTJ9HX12eqgdyc\nCp8bIVvca1c+KNsqXEZfXyrSQfOsdGRLJrV2lC1qy32wobYCYpzpdBrpdNpUQ5uamizfjwzDwPz8\nPEZGRrR2Dj57cMiUgz8oYrEYhoaGsH///iJSo8rnAawLeeqIB6fiAOpK34axXnxvbW0NZWVl8Pl8\nqK6uVqpm8sJkVVRS7o+K5Mn3cEU0VXOlU+3kvCTh16p8QjabNXcper1e1NbWwu/3IxaL4eLFi7h6\n9SrGxsawadMmPP300+jv7zcrmMtjFe2L65xCQImequq9TKTEociib4FAwFyA6bjpnMhjpn2R7TmS\nwC3QdEzUVkUc7RBNwzDQ2dmJnTt34vTp0xgaGmLPPeQIBYeN2HHEg8uJ0pEy2Z9sK4NTVOU+qGzl\n/tlR4FQkTnxgEP8DVVVVJeqtaJv6S6fTOHv2rFOg83MIh0w5+INjcXER09PT6O7uNq9xigvAL4Ly\nm6l8jS5aQKniIq5xxC2XyyEcDqO8vBx+vx8ej4c9aJfzqWufJquKN22a58SVOqCESm6HU0VkO3G/\nFcmkZCYcDiOZTKKqqgo+nw+VlZXmDr3z589jcnISjY2NePbZZ/HAAw8UlTsQ41DNGb1GwakL8mK5\nuLiI1dVV1NfXw+v1oqqqquToF9UROnRnoGoeaV/khHGVrZw8rSJugizLZEanfAh/5eXlOHDgAC5f\nvoxXXnkFu3fvLirtcDdJlNxXOwRJ53MjSpTwx5F/VR/tqltU9ZP/D2OxGNLpNHw+n1lA1gpyH8fG\nxhAKhSzvcfDZg0OmHPzBYRgGRkdHUVtbW7TdW5dPpFKnqF9OBeAWOPn+QqGA5eVlFAoFNDc3FyVk\nq8KKGyEpgPoQZ/l31cHCHHm0UpfkcavULfopP5lMYmFhAXV1dWhtbTXPqLt58ybefPNNjI2Noaam\nBl/96lcxMDCAYDAIj8dTQlLEnKnmRl5cVblTchHNQqGA1dVVLCwsoL6+Hh0dHeYmBnkBpuRM9fqQ\nyZ5hGMqwse7QYLmvVBnhSJLwxz177tnQsXR0dGDPnj1444038O677+JLX/qSto/Un9U5dvIccq83\nCjtn/KnGQqGab86nsLPjU0X0XC4X4vE4lpeXUVNTg5qaGlMR1Y2J+kskEhgcHLQkiQ4+m3DIlIN7\nAul0GuPj49i5cyd7lIcVVMSJe2PjCI14M47H41hdXUVjYyO8Xq9yZ5sqBMlds6OyWSkYMridgZSc\nCNBt97LiRcmTsMvn8xgbG0NFRQV6e3vNRWVpaQmvvfYaPvzwQwSDQRw5cgRHjhwpyomipIyOQf6b\n6Ae1pTscZeUgn89jfHwcbrcbPT09JSRKpehxpEVVkkBWjUQ/BdGjPul4uBwvla2AVZ4V93pxu904\nevQozpw5g+PHj2Pv3r3w+XzgoPKns+MSy7nXi918LEowOb90blSEjOsjtbVSwcTv6XQaCwsLqKio\nQGdnZ8lrhLtHRcqGhoYQjUaVc+Hgsw2HTDm4J2AYBkKhEKamptDT01OkEqnIjIo4qfKZVPeLpPJE\nIoFAIGAe/ZLJZOD1ell1SaXuyKoMoA6rqZQKO8fJqHzIJEKeD5XSQtvO5XKIxWJIpVLYvHkz3G43\n0uk0VlZW8OGHH+Ly5ctwuVx4+OGHceTIEbS0tLClHXK5XNE40uk0AMDj8RSRHkFQdMTUMAzz+UQi\nESQSCXR0dJhJ15xSoMoVo4uhsFGpdJQocGRdlYxNXzNyH3TKBddHlc+amho8/vjj+M1vfoOTJ0/i\nqaeeKgkV037q2pXVKB1kW8Ca9NyuEkVtZH9WeVZWRCqVSiEejyOXy6G1tVWbEyW3pSJSKysrmJqa\nMg/vdvD5g0OmHNwzyGazmJ+fR319vSm1A2rysZFr3EKYyWSQy+WQTqfhcrlQX19fVHDT6/UC4NUh\nLqwmv8nK31VKBaCvJUVtZXAKiE6V4YiXWEASiYS5c6m+vh5tbW3I5/OYmprC0NAQhoaGUFFRgcce\newwPPPAAWlpaivpBc8Bom0Ix4ZQobszCZz6fRzKZRDKZxNraGurq6tDR0VF0L6eEcc9atE8ht0/V\nFit1SSYAKrJMiQe1lfvHkRRVf4XdI488gnfffRdnz57F3r170draytqpYJdEcWO28mm3bR3BpKrj\nRsgo97d4PI58Po9UKoXKykqlmsf5pP+z4vdsNovx8XGsrKxY+nLw2YVDphzcU4hEIpienobf7y/a\n1q4K29ghI5QM5XI58+BRl2t9t44cWhTX5d/tkDd5cde1L/rJlX9QLe528sTEQqfKnaJEIB6Pm0RS\n7IIrKyvD9PQ0bty4YYb69u7di+3bt6O+vr5knlSJ2/I45IVNl1cmL5Yi3OpyuVBRUYGOjo4S1U/O\ns9KpW7I6oTvChy7WukryqvAb9UmVEauQng4cQQkEAnj66afxy1/+EufOncMzzzxTlF9m5csuObJK\nVqe5bzrCQ33asePIKEfKVWMW5TwMY72yfmNjoyUps0Pe8vk8pqenMTY2hlwupx2Lg882HDLl4J6C\nYazXaamrq0N7e3tReQA7iosqZ0XYJxIJZLNZeL1eVFRUFFVfF/fTRVvXPn2jVSWo2yFZKp/yNS7P\ny+68iLbW1tYQjUbNg5gDgQDKysqwtLSES5cuYWRkBOXl5di3bx82b95cUuKA9kk3N3Q8KluxsGYy\nGczNzcHj8ZiHYQuFEPhEXaI+KTjFSraVF2TVAiz3VV6sreZbJhQ6lVFuW/f65kiKbDswMIDu7m5c\nvXoVO3fuRFdXFzse2q6d0J8dcqQijqKflNiq2qVk3y7ZUpEew1gvVRCJRODxeFBeXm6WzNC1z20i\n4GwLhQJu3bqFCxcuIBKJaPvp4LMPh0w5uOeQyWQwNjaGxsZGU4bX5UlxISy6Cy6ZTCIejyMQCJh1\nY1ThPx3JsWO7kbAghezTiuTZmRdZxRGlHvL5PGpqalBRUQGPx4N0Oo0PPvgAly5dQllZGQ4cOIDt\n27ebBzbLkIs3yn2QCay8uOpywMTvwj4UCiGVSqG1tRUejwder5ctX8DNo5w0LhZsbmeg/Azlhd3l\n4g82Vqk3KpLE7WrjYOd4E06V4eDz+XDkyBH87Gc/w9WrV9Ha2lry3GR/dsN0dsiMHVImfNo90sXO\nmK3sCoX18/PKyspM5Vl+LajULbsk0zDWN2ScOXPGSTp3AMAhUw7uUcTjcYyNjWFgYEBrx5Ep+Xo2\nm8XKygo8Hg8aGxvZxZ0eFqzKR6ILMfBJAvPtkjxxjQtZ2MmdUl2TP+ED6+HTcDiM1tZWc/edy+XC\n5cuX8bvf/Q6ZTAaHDx/G4cOHUVlZWZKvJvtUEUj5k70AtZP7JBauSCSChYUFdHZ2oq2tzeybShlR\nbUqgbXNkTra1mnPDMNjQDfdsVCEeakvPfeNsKYHT2Qpy1NHRgW3btuHDDz/Ezp07S2q22SFRwvZu\nEr2NtG2XmFmpW6FQCNFoFB0dHeamBztty6qf7v/KMAysrKzg1VdfNavtO3DgkCkH9ywmJibMhGhg\nY7lDIqE6m82auT46JUgVFqPhIG7BpaEXlSoj/kY/FauKStI8J2Fn92BjMQ+Li4uoqqpCb28vXC4X\nkskkpqamcObMGUQiETzwwAN45JFH0NLSUtKmam44JYqDID7y33O5HPL5PPL5PGZmZlBZWYlt27YV\nVUWX1REVWaXj5dQluf/yvOjO9xP3CaJgdRYgJdacnZW6JdtRosC9ZsQ8it+rq6uxd+9e/Od//ieu\nX7+OtrY2eDwe20qLGMtG7XShVJnccocVCxv5SwfVWMTcJhIJRKNR1NTUoLe3Vzke7rVDQUOOsm00\nGsXJkycdIuWgCA6ZcnBPY2RkBNXV1SVb4VWLVjqdRqFQQDqdRmVlZdGZf8KOLgC6EBx94xW43Twr\nnapjV4nSETqhpqRSKaTTaZSVlaG7uxsulwurq6u4desWbty4gaWlJbS1teHJJ59EZ2enqXLIxEcs\nXmJuVCFKmfhwye9ymYtMJoNUKoVEIoFCoWDWsaI+6QKnInR07NzcqJQolZqoWvQpyeTmQe6rjiRw\nhIAj69SGI1uizZ6eHmzbtg3nzp3Drl270NbWplWO7JIo2raVPys7q7YpkVE9Q2B9J5047sntdqO1\ntbVEaeagG7Pq+RUKBaysrOD8+fPOzj0HJXDZkUA/lYZdrj9Mww7uO3R0dBQV8+SUgmw2ayoeItGU\ny5kBSusuCZ+U5HBKh6p9SiLk+2nIjLvf5XKV1GxS2Yq+yguOsItEIuZCVlVVBb/fj3Q6jZs3b2Ji\nYgJra2uorq5GX18fNm/eXBJ25MYr+kbVEdXccLWOAJiHEBuGgbq6OjMZWNiJMXPPTIyPKoGqOacL\nocqn3LZqXKocL86nsKekR2UrfFrlbsltq2AYBi5evIiXXnoJBw4cwLPPPqu03UhiuZVyJP/dikTJ\n7XIfWGSfnBIlfs7lclhdXTXbE8c90T5x/aR95PzT52cYBsLhMD744ANMTU05Z+99jmEYBvuP7ChT\nDu55LC4uYm5uDh0dHeZCKRasXC5nHkLs8XhMEgWo86ms1CH6RqyqfC3DKsGcvklbHYIsX9OpMsJu\nZWUFuVwOfr8fPp8PPp8P2WwWH330EQYHB1EoFNDV1YWenh7U1dWZR8PQvsnEjOsb/dmObSQSMc87\nCwaD8Pv9JWRQ/tnqYGNKeuwoUdROzKM8fhXkxdeOEmXXp0rdkl/fdhQrcV+hUMCWLVvQ09OD8+fP\n4/Dhw2bdKbndjZAouzlRdn3aVaJUtoXC+vl52WwWHo8Hfr/fLKxLfVJ/OiWKa1O2TyQSuHjxokOk\nHCjhkCkH9zwymQxmZmZQXV2Nmpoa840vFovBMAz4fD5z67MqVEZDezqSQq/ZOTrGyqeKZMnXrCqg\ni98FRL/khSUQCKBQKGB6ehoXLlxAKpXCtm3b0Nvba5Io6lu1mFvNDTcOeeGPx+NYWlpCMBhETU1N\nEdEFSiulc/MoL4RWhxXLfed8yiRFpwTJmxJkQsEpcfI94kunLqlIgsqf1WuW2vr9fuzfvx+jo6P4\n7W9/i7/4i7/YULuyImTHzoo82iVlVn00jPWzIiORCCorK81zIO36vJ22xWslk8ngwoULmJiYcIiU\nAyUcMuXgvkA4HEYoFEIwGDQrYouK5cAndV+4c93sljkQfqitTKiEnVCiZHCJ4Jx6Q9ujn8o3SvI8\nHo9ZuT0ajeLMmTOYmZnBzp07sXv3bpPEcH3jlCA6Xrmvqt2QYv7F4rO4uIjy8nKz2CaXIC/uV5Va\noIoCNzeCgFI71ZxToqAiXxyhUD0bbsGmtnaIjIBILLejRHG5Zdu3b0dPTw+uXLmCkZERbN682TY5\nsks87O4MvBMCJ14Dohp/IBBAS0sL649+KLCTeE/HQm2j0Sg8Hg8uXLiAoaEh28/PwecTDplycF/A\nMAzcvHkTqVQK3d3daGxsZPOUVGqJ3bCaLgRIbXWLO3e/rLbIx7moEqGtyIHwK45duXLlCgYHB7Fp\n0yY8//zzaGhoYO2p2sL1gQtBqeZajCebzSIajSKRSKC1tdUssaAL29B5FH8XpQZUxM2KgFKyKi/U\nuteMvLhyrxkZ9KBdlZ1MFFSqmfiuS7yXyaWO9JSVleHYsWMYHBzEm2++ic7OTlbF2YhaJv6msqPh\n8TvxJ/zk83mEQiFkMhmzECn3WuH8cR9WKLgSEIXC+uYVUdT28uXLuHHjhnIcDhwIOGTKwX2DXC6H\n2dlZ1NbWmnk3KsVG96ke+GQBUS1uHLmgtqrFXbdgcn3l7CjZo4u2YRjIZrOIxWKYmJjAzZs3EQgE\n8JWvfKUkt0z2xykmNCxlGAYbgqPhLllRyGazWFhYQHV1NXp6etjxyPPFzaP4Wdyjyp2S/Yk54arO\nUzthq5oDbs5VBNxOorVQ67gxqPKEdLY0+Z2D8NHe3o59+/ZhcHAQ169fx4MPPsiO5U5JlGxjh+jp\niKNoJ51Ol6jPVn3UtU2fLTfufD6PdDqNVCqFQqGAqqoqXL9+HUNDQ8p2HTiQ4ZApB/cV8vk8BgcH\nkc/nsWnTJltkCFAf86IjPXTxUalTFKrQlWrBp9dUSphMjJaWljA1NYWFhQVUVFTgwIED6OnpYcNz\nunEBnxxATO10Y6Z2Ho+HLRRplaumIxKUGOvmhiOO3Hh1dpwttwBzrzluPIbBh5hVap3qmdsJWdF2\nAeDo0aMYGRnBxYsX0dfXh+rq6g2RKN2z4eysSJSVz0QigUwmg2w2i4qKiqIDtTkIVdaOEqVqN5/P\nY21tDblcDoVCAX6/H4ZhYHh42HyfceDADhwy5eC+QzabxcjICAzDwKZNm8x8IQHVp3eVEmSHjKne\nsFUhRC7PimtfTjoXdqqdgYaxvjNuZGQECwsLCAaD2Lp1Kzo6OorKDHB9sJobQag4xYaSGY4kcPMk\njp7RhVdlgqQLxVLSoTquhxIFTt2S/cl94cbE2aleW6rxqcgjXdg5oqciMrIt10cAqK+vx4EDB/D+\n++9jaGgI+/bts0WOZJ86qPLFOJ+qPgKfHPVUVlYGt9uN2tpa28qynbZVHwCSySRSqRTcbje8Xi98\nPh8ymQxGRkZw5coVrK2taX07cCDDIVMO7ktkMhncvHkTLpcL3d3dWtVJfhNVJYlzCgrAF+ekdlbk\nS77fbgiQHj2TSCQwPDyMpaUldHR0YM+ePaivry8qM6DKJbJSucR3XaI8JTbUr2HwSckq4sHNN0c8\nVGEyzpY70kVWBOVFlVusKVm2yrMS11S5U7IdVYSs1C2rPCsxNqt+7t69G9evX8e1a9fQ3d2Nuro6\ntk25XbskysqOKlbUPpPJmAcE+3w+8yxGK7Jn1Ueh5sl9lO3T6TTC4TAqKioQCATMfLJcLofx8XFc\nunQJiURCOzYHDigsyZTL5eoE8DMArQDyAH5iGMaPXS5XHYD/ANANYALAHxuGEf34nh8DeBpAAsB3\nDcO49Ol038HnGalUCmNjYygvLzdDfgJWn/xlqJLOOYKmIhIcmQFK1Q66uItrtHim+Hsul8PNmzcx\nMjKC9vZ2PPTQQ+YCwO2QoyRFp5zZCZdxihVnJ77oOOT5osqWzq+8GHIFVeX51ilRlATKBEXls1Ao\nPktOdb6gagcaxUZ2lnE+KVHTEQr6Gq+trcWePXvw1ltvYXJyEjU1Ney47fbPjhIlbFUhMsNYL4CZ\nSCRQW1sLr9db1CdVqE6348+OXT6fx61bt1BeXo76+voS5Xh+fh5nz55FKpWyHJ8DBxSWFdBdLlcr\ngFbDMC65XK4ggAsAngXwPQDLhmH8yOVy/d8A6gzD+KHL5XoawN8ahvFVl8t1GMD/MQzjIcavs8/U\nwV2B3+/HwMCAebacgFis6QJHw2qAvoI5BWcel+ixAAAgAElEQVSrqmquqoBOIRMGsRAtLi7i8uXL\nZu2gmpqaoja4Y1i48XLVy7mwGp0bna08Drl9SpLkMdMFjuuXUCbomOgz41RHVb9UJNruzkCO9Mmk\njIKqmSrVitrZ8cmRFJXKJhSzubk5vPzyy6iursZzzz1XdMSS3QR0HYmiHyB0O/SSySTm5uZQX19f\nVDNO5VdFMDk7lRJlGAaWl5eRTCbR1tbGqqKLi4t47bXXHCLlwBLG7VZANwxjHsD8xz/HXS7XIIBO\nrBOqRz82+1cAJwH88OPrP/vY/pzL5apxuVwthmEs3PEoHDhgsLa2hhs3bqCsrAyNjY2WFdBVn+gB\nPgHZTphMpQBxn5plZUa2FTuZwuEwxsfHkUwm8eCDD6KtrY0lcFaJ6rKdVeFReRx0HijkhYsSDRoK\nlVUPq/BmPp9XziPto9y2as5lAqDb+ckRBa59uW1hoxoX9al6HdpReuzYUUIo2zY1NaG/vx/vvPMO\nJicnsXPnTnMsdvzZVaJUJCqXy5nlBtxuN/r6+th5pG1vJPHeaiy1tbWoq6sr+Z8sFAqYn5/H7373\nO4dIObgjbChnyuVy9QDYA+AsAJMgGYYx73K5mj826wAwLd02+/E1h0w5+NSQTCZx/fp1DAwMoLm5\n2byuIkn0GhfWU123EyKTbe0QmXQ6jaWlJSwtLWFtbQ2dnZ3o6upi1R85ad2qgrj4XRXKVBUZ5cbG\n9V9HVuX7VPPIFdzk/IlwI+0H91y5HB1O0VKpIpQs65QTjlSrbEX7VEmxyrNS2cn2OlJRXl6OrVu3\nYmhoCGfOnEFvby/8fr+2n7r5sdtuPp9HPB5HNptFPp9HQ0MD3G63LYXLKi+LU6NkcOoqZxcKhfDu\nu+86OVIO7hi2ydTHIb5fAfi/PlaoVP9l3H+8E9Jz8KkjkUjgxo0bKBQKaG5uZgtS6sCpIoB9tYfm\nB9m5v1Ao4NatW1hYWIDL5UJ9fT127NgBn89XQvbkWkwyCVGpKFYqm1i0bpeMcYuVjiBwJEW1w1Hk\nXlE7nbpF+6OaF7oIq/xSOy6EqlvU7dpy823HDrCfDN7U1IStW7finXfewbVr13Dw4MESGyuCwtmp\nNgisrKyYz8zn86GiokLbP+oXUIdyZbJl5cvKbnV1Fclk0rJvDhxYwRaZcrlc5VgnUv9mGMZLH19e\nEOG7j/OqQh9fnwGwSbq9E8Ctu9VhBw50iMfj+Oijj+ByucyjJ+ibM6BWrFRhJvrGLi/EMmSiI1/j\nVJlQKISRkRH4/X60tLSgoaGhpMSBaEvur5zkLZMUeTGye+YdUJoDpvIp2udInmwj7tVVkrdDEuTv\nQOkOR9mnasGk49XZyj5VhHAjuVO6ObRq2+pDgB2iIOyEzc6dO3H9+nW89dZb6O/vRzAYLLLZCIlS\n2cZiMcTjcQQCAXi9XrNuk5VP1Vi4nDarfso5VlZtt7W1oaamximD4OCOoT65sxj/H4AbhmH8H+na\nywC++/HP3wXwknT9zwDA5XI9BCBiOPlSDn6PiMfjGBoawuLiIoCNnYvGQfVmz/nkjqigRCMSieD8\n+fMYGhpCX18fdu7cic7OTvP4FfkeuV1dmQDaP27BEXZyLopqYeLyVVRzwI2Zzo/cttU8FgoFs4ii\nyqdoN5/PsyURaL/FDj3OpzwXuVzOtFONSbQt+qhb3Om4dfMt++PmRdiJcdshUrK/mpoa7NmzB9Fo\nFGfPnjX/LvxZ5R3RcchIpVKYnp5GNptFbW0tKisrlaFE2Z/dsdDXji7p3ipXTW7f5/Ohv79fe4i1\nAwd2YGc33xcAvA3gKtbDdQaA/wfAeQD/iXUVagrANw3DiHx8z/8L4CtYL43wPcMwLjJ+ndCfg08V\n1dXV2LlzJ2pra83dclSZ0O1o24gttePenPP5PG7cuIHBwUEEAgF8+ctfRlVVFVwuF7uzTbVDkNZN\nUuVOiX7QxY/rW3l5eUmeiW6HIlBM7lQ7HOV+il1oXq9XaUsXQtXOR7rwc2oiN3bZlipV1I62ryND\nVnlm1K9sZ2fxB1C0i0+Xqwaoc7dyuRz+8R//Efl8Ht///vdRU1Nj2T/aR/q32dlZFAoFtLS0KIuk\nUmKtI1AbUaJkBdOOAkZtxffjx48jHA4r73fgQMBQ7OazJFOfFhwy5eD3gfr6evT395uESkcO5GvC\nTrYXdjLp4XaUAZ+Qi3w+j1QqhdnZWdy4cQOrq6umzYEDBzAwMFBE9IRf0Q63OHElEcQ1uR9cvzgb\nMTbOluuXndIOXFvUJxdysyKrIqynKllBF0wVyRQQiofqOdJyFSqfct/lMJPOlm755/K8hJ0qBCbs\n5PHoYBgGbty4gZdeegkHDx7E448/riVAnBokVLRYLIZYLIbm5mb4fD5b5KhQWD/HUSb6nC2ntHJ+\nVaqxqm35ei6XQzKZRCKRwNraGk6dOuUcH+PAEioy5VRAd/CZxsrKCgYHBzEwMIDq6mpl+QKqUOi2\n0gsb8Z1bLPP5PBKJBBYWFjA2NoalpaUSm48++gi9vb2orKzUJnjTRZNLGlf1jRJA2a+8mFkl38uf\n7MXRLbR9Lh+LIz6yT3mBUxXHpD7pvMh9oCqGKl+N8ykgj0MsrnI/VXl1nILDPT+d0sIpOHKfOHuq\n3qhUK/H1wAMPoL29HSMjI9ixYwc6Ozu1/uRxi8OAU6kU/H4/urq6LMejU7c4W7t2OltO3RLIZDJI\np9PIZDLweDzmGYDj4+MYHx/Xtu3AgQqOMuXgc4GmpiZs376drQCtUnC4xGyVuiUrLYlEArdu3cKt\nW7ewuLjI5vQI7Nixw9xZxYUHZTJClShODaN2qhCglWIk/KjmRpWkr5pH2U7cT0HnUeeXzotMEFV9\ntUM6OJ+crWifJsrrfFIFRadY6ZQoARXpUZE32W5sbAyvvPIKHnzwQTz66KPwer1Kf8D6ESxCVXW7\n3aisrLTMM7LKE9uInUz67eRAcsQ6m80ikUjAMAy43W74/f6iIrGLi4t48803nXpTDrRwlCkHn2sI\nUtPV1YW2tjZ4vV4AxSoNp3TQs8J0u/0ymQwmJycxPT2NcDiMTCZj2a/R0VFs3rwZ9fX1rBKlWsyp\nMiPCFnaVNy6sJas48id/SrLoPMgKga64qDwe0RY3j1YkQdhyO/5UihVtn5tT2ZfVYi1UI2rHPRc7\noSMrwkHtdLYq5U9GR0cHenp6MDIygq1bt2LTpk2sz1wuh+XlZQDrJw14vV726CMZgsjYUZjslnaw\na8spa/l8Hqurq8jn86ioqDDHQPtYW1uLvr4+3LhxQ9sXBw44OGTKwecG4jywubk59PX1oaGhoUg1\n0J3rpkMul8OtW7cwOjqKWCxmi0QJZDIZXL16FUeOHDHb5MJ6VmE1uc86BUMmVKoipRScT7kPVu1z\nYUnOVhAPO8RRRTIp2ZXLHOj8WqlbHNFTKXHyPbJipWrfKneK+tORTNmOPkvZrqKiArt27cKrr76K\noaEhNDY2wufzmX8vFApYWVlBKpVCXV0dysrKzAOBrca7EWIkfleBG4fKJ/1/NYx1lTiRSCAYDKKy\nspIlUQLl5eXo6enB9PR0UW6jAwd24JApB58rZDIZhEIhLC8vo62tDVu3bkVlZSUANRHg6iWJSs5L\nS0u4fv06lpeXbb3pc5ifn8fc3Bza29vZN3qqWAmISui0b0BxGE0oKCrljWtHR17khVCVjyTvpBM+\ndXWnZJ9cuFNeeGlIzS7x4EgHHZ/4XeeT3q8iM1xbHHmzgh2VR7az67O5uRl9fX24du0aduzYgdbW\nVgDrxW9DoRDq6+vNfCIr0riRPnJnC3IKn4o40mfFzWMmk8HCwgIqKyvNExGsVEKXy4Xq6mq0tLQ4\nZMrBhuGQKQefS+TzeczMzGBhYQG9vb3o7OyE3+8vCWHIC7z4PZfLIRqNYmhoCLdu3brjHUDpdBrD\nw8NoampCeXk5mzROSZ28eKgS12UbXdI09Ufblv/O7bJSkQlu0eNCddxCKitBol1VnyhEjpqdcKeK\nPHL94eaF2uuUMNmXrhwC58/K1orMcHZ+vx9btmzBxMQErly5gurqaoRCIVRUVKCrq0vpayPtyrY6\noidIuh2fulCwQDabNRPrrUiUYRjIZDJYWlrClStXMD8/rx2PAwccHDLl4HONbDaL4eFhzM3Noaur\nCy0tLSXJteJNO5vNIhaLYWZmBlNTU8hms3elD4ZhYGlpCbOzs+jq6lKqYYC+0rbcV86WIzN286zk\n8gHUp2hLXmB1YS26uOqUMKpEqXYGWo1d7qsVmaG2t6Nu6fqms+P6x9lulMyo7Nra2tDe3o6rV6+i\nubkZO3futFSh7LYr2+nUsjshhAJUsQoEApZtG4aBtbU1LC8vY3R0FJOTk7ZUPQcOODhkyoEDrJ/R\n9dFHHyEUCqG9vR3Nzc3w+/1wuVzIZrOIRqNYWFjA7Ozsp3KWVzKZxPT0NJqamsyFgFs0KfHhksbF\ndSslSiYeOpJAr6kUM24hUpEESrwoVPlYHPFQ5VmJ9lUkRmXLtSPbcsqIFXEUY1eFJlXk0SqEafUM\nVQqOYRhIJpNIp9Po6enBzMwMbt68iYGBASWZ0vlT2dkhRxtJLLdLHMXPqhBiOp3GwsICpqamMD09\njXQ6rfXrwIEVHDLlwMHHyOfzWFxcRDQaxfz8PNrb2xEMBnHr1i2EQiHE4/FP7ZOrYRiYm5vD4uKi\nGWKhC4xqkbcTApMXLgqqOqmIm6rfnGpFVRn5uyofipIUqx2W8vzo+sqpS1xYjyMKdsOdOnXLareh\njvDIvjkyyvm1Ih7JZBLxeBwejwderxdbtmzB9PQ0hoeHMTk5id7eXmX/7BIeO0oUN4fcuDZCorg+\nUlI7PT2NiYkJzM/PI5FIaP06cGAXDply4IBAJK9GIhGUl5djbW3ttpPLN4K1tTVMTU2hqanJrCit\nIh7iZ/E7V5KAU5XEomtHsVLtcOQUI0BPPDgfXN+oX46kcPkvXDK9ahehqo86dcvKJ0dmVP2nfbSj\nRNkhFHKpCO6ZpNNpLC8vo6KiAlVVVWZ+nmEY2LNnD0ZHR3HmzBl0dnaaO/fs7KazS6LkMdv5UHKn\nJEpAvF7n5+cxODiIxcVFJJPJT+2DkYPPJxwy5cCBAul0+vcu/09NTaGnpwft7e3s31ULjCoEqFqg\nKbidgbJCJV9T2dL+APoQoqxEcba0fXnsXFkHmZRZ5VnJ/bRStzjSo9ttaEUIhY2djQt2Nzfo1K18\nPo/l5WXkcjk0NjayRVSbm5uxfft2XL9+HUNDQxgYGGBLNnDt2jmoGNhYmYO7mY8ViURw9epVTE1N\nOeE8B58aHDLlwME9hHw+b+7s83q9JpmQFwxBZlTqjnxNLPycLVWYVOE6jihwJEncT8NaKnVLFeah\n4UHVjj8ud4pCVoioOqcKq8ngykoUCgXzSB1aAgJQhzHl+zlwdrr5VpFH+VoikUA4HEZDQ4N5bBEH\nl8uFhx56CIODg7h06RK6u7uL6k5x/eNIlCBM8/Pz8Pv9ZjHa200sl/vHvRZVRKpQKCCZTGJoaAgf\nffSRU9XcwacOh0w5cHCPQeRodXZ2KhcMTvHgiJcdwqDyabVYcYcQWxW8pH6t8qxUChdty44SZof0\niHuttvFz1d9146DjpmOXYbUDTf6ZazubzSKbzWJlZQWBQMCsbm5FaAKBAPbv349Lly5haGgIu3bt\nYp8nlzBeKBTM41pSqRSam5tRXl5uSzmifTOMT3aZ0iOSdK9JYL0sRjweNw8Wj8ViSlsHDu4mHDLl\nwME9BsMwzK3qomKz3fIFXJ4TDZWpFCvxd/qzSgnjFjhVWQdZwRH32M0HA/SH99Jr3Jg4IsGRR9Xi\nTwmSHVJkpZ5wPrn+i79TO3p/NptFKpUyS3aIIrBCsVMRXfmrv78fQ0NDGB0dRXd3N2pra1k7Gclk\nEplMBrlcDn6/H7W1tbbIG/Un+ppMJpHP5xEIBFjlT4Z4/efzeUSjUdy6dQs3b97E4uKism0HDj4N\nOGTKgYN7EMvLyxgfH8eWLVuUNvIiJF9TkQR6r07d4triwmqqgqF2SIIqaVzVPve7SoniyheolChK\nBnV5ZhsheRu1pf2y41P8HA6HYRjrIdVgMGiqQtx4df78fj927tyJDz74AJOTk6iqqiqZI4G1tTUk\nEgm43W6Ul5cjGAyWhHk5cMqWYRgIh8MA1o91CQQCZpjbStlaXV3FxMQEpqamsLS0pD1Y3IGDTwsO\nmXLg4B7F0NAQ2tvbEQgE2LCaWBxVZAIoDn+pjnOxSzxk8iEvmCqSIi+EYqGn4OpJcYSKIz4yONXM\nSrWSVRudneyTA0fe7uaOPzHfnN3KygrS6bS5O0/swtMREKFgyoRUoKysDF1dXRgdHcXIyAg6OztR\nU1NTdH8mk0E0GoXb7TYPP6bKo2oslBwZhoFYLIZUKmWSQK/Xy9pSZDIZDA8P4+bNm4hEIhs6E9OB\ng7sNh0w5cHCPIh6PY2xsDLt27SoJ88jgFB8KleqjKo7JQeVXRRK4+2n7XAhR1a6OZNG2rUimTAit\n8rzsEEe6+Kv6IMZB51JF3lQ1qlZXVxGJRFBTU2OqR3bKQHBJ47Jfl8uFmpoabNmyBWfPnsXMzAyC\nwSDcbrcZSkun06ipqYHX69USXN1YgHVlKxwOIxgMor6+3gxpW+1gzOVyGB8fx5UrV7C6uuooUQ7u\nCThkyoGDexSFQgEzMzPo7u5GdXW1WenbKndKFQLjFj2xK01VkkClWNEEazlMZhif5HjpksHpNa6s\nA2erUqGo2iKTNE4tk9vizj20UrfkMatA+65Tt+Sf6e48cV2cIefxeNDW1layA1Ll1w7hke06OjrQ\n1NSEK1euoLu7Gy6XC4uLi6irq0NraysbqlP5o4SwUCggFArB7XajubnZVCytzs/L5/NYWFjAxYsX\nEQqFtONx4OD3Ddcf6gXpcrmc/wQHDixQXl6Obdu2YWBgwNyKTxdOOVQnhwO5pHHZVkBFfORK47Jt\nNpuF1+vV+hT3U+KhUlC4EKawp2OVdyyqQmV0x508Dq4PKoLK9ZVTt3S2nCrDtc+FtsrKypDP55HL\n5bC6uopsNov6+np4PB6tQin/zS7Zo6TnypUrOH/+PHbu3In+/n40NjbaymES32V/hULB3GmXTqfR\n0NBgjoEj2LK/dDqNcDiMjz76CBMTE3d8sLgDB3cCwzDYTy6OMuXAwT2MXC6Hubk5dHR0oL6+vihp\nW1ZGBHHgds7RHX904ed8ivu5sKDIaZHtdCqUfC/wScFNbvHnVBmVEmW1qMshTNlW10+dYmZXsVLZ\nqPqpIhOFQgGpVAqZTAbpdBqVlZVm3aaNKEJWyfRcYU6Xy4Wenh6Mj49jfHwc+/bts1SOaLtiDKL/\n2WwWgUAAdXV1SoIpjyuVSmF5eRkTExMYGxtzcqIc3NNwyJQDB/c4xJbv6upqeDweliSI7+Xln/xL\nC5KkWjBlcAnOKtXHTpkB6pMqIPRwZJUqwfnkruvIjBXJk4mXjnhspIim3L6dkgQC4rmmUimsra3B\n5XLB4/GgsbHR8jlyypZqLDoiJVBVVYXNmzfjvffew6VLl/DQQw9Z+pPbTqfT5qHgXq/XzO2y2u2X\nzWbNQ4inpqYQj8e19g4c3AtwyJQDB/c48vk8pqen0dHRYX6q50gGV2lc5FnR60Cx4iJUKKukbZkg\n2NmFZpd06Nrn7rFjK6Aak4p4UNWO+pd90PFy7asKjHJt5/N5MyfK5/PB4/GYhFgFOo+q8coESkde\nZV+9vb0YHh7GjRs3sHXrVtTX15eMjZKobDZrnmtZUVEBr9fLknqu/VAohOHhYczNzWF1ddWSeDlw\ncK/AyZly4OA+gMvlQn9/P/r7+1FeXs6SlLKyMnaLOkc8aD6RaEOVkwWgxCenuHC5U5yduEZ3ynFJ\n8yrSRhPMVUoUF9bk7GSfMvHQ5UPJpExFHoVfTt2S+ySSqhsbG81nQdujUIXLVHYbDT0CwMTEBF5/\n/XUMDAzgscceM8dM283n84hEIsjlcqitrYXb7Ybb7bZFiKLRKK5cuYLp6enf28HiDhzcDgwnZ8qB\ng/sXhmFgbGwMvb29CAaD2vCbrEgJRUBlq0paFr+rCAIlGWKxtlK3aP+oTzl3SyYzukOIaX6QTpmx\nE+6kc6ELF3J1kzi/qpIEABCJRBCNRtHa2mom9ltBR4w4hel2SJRAd3c32traMDU1hbm5OTQ3N5fc\nF4/HEYlE0NDQAL/fb/bx/2/vbmMju+o7jv/+tmfGzxvHu3b2Ibtkk82GVlEIqSBtQCEFldBKpG8i\ngYQgiEq8oCpSq6rAG15VopWqAqISqkqjgAq0gAoRqgRFqRohlZAqScGbTbLe7IPttT121rvr9Y49\nDz59MXOH6+tz74z32Ou19/uRVrGvj++9c5PM/PZ//vfcVqGoVCrpxIkTGhsba67cDuxEVKaAHeTY\nsWN68MEHm6EhWQmKb4/z3fqfVoXq6OhY9yGYVt2K7y9tbDQtlxybdlddsq8mXqWJv15fxSx5/PjU\nW1rFKDkuvu/4OcTFe7eyxqYFvWq1qnK5rEuXLqmvr08DAwNrztN3rpVKZd2NAr5zS55jGl9gTFMs\nFvXjH/9Y99xzjx555JHmulPRtGRPT4+Ghoaa+8ra3+pq/QHMExMTGhsb0+XLlzOPDdxMqEwBu8CZ\nM2d09OjRdatSR3xhIF7hifM9ziWr2hHtN/rat+5VNDY+Ll6JSpvCi8b6ptXiVa9kJarV8wXbWRzT\n99p91yoKme0GFF9Vplwuq1KpNBuzR0ZGUh9OnbyGyXG+Y6f1bbUam9V4L0lDQ0M6cuSIpqenNTEx\nodHRUZVKJdVqtTXrXbUKUYuLi5qZmdHrr7+u2dnZzHMEdhLCFLCDVKtVjY2N6eGHH17TY5QVKKIP\nuY082NjXzO77OuJrxm4VUOL7ajVVFg9QWVNwWb1Tyf1m9U4lA2FW03a0D9+UY6RarTbDh3NOg4OD\nzRW/0+6kjB83Xk30ncNGrner0JMMmNH3x48f18zMjE6fPq2enh4NDw97Vy33vZYrV65oampK586d\n09TUVFvTjsBOQpgCdpiZmRnNzs5q//793hXM05qmfSHF97Bi39joQ7Wd6lJ8bHKf0m8+bNvpc4rv\nT8p+nEt8n9HYrEpUFFSS1y8+LvmIGN+1io/zVZUWFxdVLpeVy+XU09PT8vl5WQEuLRC2W4nKOqYv\nEEbMTENDQzp8+LAmJiZ0/PjxlpU65+pLPJw9e1Znz55VsVhkrSjsWoQpYIep1WoaHx/X3r17m+tK\nJT8MI1nTZVnPnIuvUdVudSm5jEDaCuy+D+Cs0JPcntZMn3ZeyWbspGTjfHxs2nRf/Pu04FgqlZo9\nUdHz7aJzadUk36q61mpc/PW3W7Vq1TCez+d15MgRXbhwQadPn9btt9+uQqHgPa5zTmfOnNHJkyeb\nD2MGdjPCFLADLSwsaHp6WocPH06dKpP8H8bJlazTKlmt7riL9pcMGGmcc6pWq97q1kYrUfHzyZrC\njMJbq8Uu4/trdcdffDo0vi0613K5rLm5OeXzee3fv39dQPGFqWh/7Ux/tXpYcXxbO0sMtDtOkoaH\nh7V//36Nj4/r3nvv1b59+9Ydd2ZmRi+++KLm5+d59AtuGdzNB+xQ+/fv10MPPaRCoeDtnfKFFt92\nyX8Hnm97/PeT01rtrieVda7JCkpadSt+jr6x7byv+camvdb4uLRlJWq1WnNKb3h42PvYHN9xWlWY\n0qYxs5rGk/9u0sTDY1bvXfy409PTeuGFFzQ0NKTHHntMZqZqtapLly5pbGyM5+dhV3PczQfsLgsL\nC5qZmdGdd97pDT1ZFSNfhSetd0pa3+Dt+9D1BR1fdcu3/3gTti/Uxb/P6glLno/kb9qOj09OB6Yd\n3xd6or6garWqa9euaWBgQENDQ+umMtOmJjejwpT2utPGJqf/fMf2hajIvn37dMcdd2h8fFzT09Pq\n6urSmTNndPr0aZVKpdRjA7sZlSlgBzt48KDuv/9+9fX1NatD8Q/LtLv94pWg5DZf1SoSD02+Fbp9\n1Z6silWrSlS03+Q+fecZbW+nuhUf285r8L1PRg/vjZrLo7WifOfUTj9W2mtot8LUzrhWwSx+nllV\nsJmZGT3//PPq7u5WtVrVlStXUvcJ7CZUpoBdqFgsNhdN9D1KJqt3KmmjVRFfdcsXcpJVr2hKLKsf\nyTeNmBaIomNE0vq84kEmrc8qvu9kc3n0z3K5rKWlJXV2dqqjo2PNYpW+419PI3j893zSqktpY9sZ\nFx07a9zq6qoWFhY0OTm5Zr0s4FZHmAJ2sEqlovPnz2tkZESFQmFNI3Qka/qr1VSZtP5ut+hnvobx\ntJCSrLJE0prG03pufI3rvg9/X6DKmtaKj0ueZzTOOaeLFy/KOafe3l7lcrnm3ZS+1xA/Rx9fGN1I\ndSkrvEXn225jeTvTg0tLSzp16pQmJiZ0+fJl+qKAGMIUsMPNzc2pWCzq0KFDktJDQnJaK3lnXvS7\nvq99fItN+o6f1Wfl2+arWvkCSnwaM0v0oe8LmcngkbbPhYUFLS0tae/evcrlcmsqYmnnkDVV5lz9\nzsboWXztLhfRzt2G8dfcSjsVq2q1qlOnTunVV19tLjwKYC16poBdYHBwUA899JAGBwfV0dHRrA61\n6jPKmj7z9U7F95nVk+Xricrqh9rI2Kw+p/i2ZJhJ26cvUETbS6WSLl++rIGBAQ0MDKRWwZLXOi1A\nJl9Dq3GRrOm3ZJ9cq+pWvOLoO17082q1qsnJSb388staXFz07gu41aT1TBGmgF2is7NTd999t44c\nOaK+vr51i27GHz8T53vmW7xqEnHONfeZ/P1kSEj7IE8b6wszaQ9SbtVMnxVQkmPjz/iL1Go1VSoV\nlUolmZkGBwebv5PWZxWvVMWrVVmBJmtJgvi4Vu/Rvr6x5M/SpnqTY6MAOTc313x+XrtThcCtgDAF\n3ALMrPlQ2tHRUXV3d6/5oI4/OiX+/3xPtwgAAA/sSURBVH6rO+4kaXl5WT09PZnVrXaqS2nVrazj\nx4OX71yzKkxp3yerdqurq81lDmq1mvr7+5XL5daNSzbTR5Khwxccs3rH2umzSkpex6xxrapWpVJJ\nxWKx+fy8SqXS1jkAtxLCFHALyefzGhkZ0eHDhzUyMtL88Exb2HIjSxJkhZnktmRIiO/XNzaSFQ58\n041Z+43v07c9ClGVSkUdHR3K5/PNZv52Ql6rc43GZvU6ZQWptH6sjYxLGxvdnXfx4kXNzMxoenpa\ny8vL3tcCgDAF3JL6+/t14MAB3XPPPc3nqKX1GKX1OSX5nmPXqrokrZ1Sy6pu+VZL941NhoSsR9r4\nesecc1pZWdHVq1dVKBSUz+fXTW1G41vtcyPjfGOz3oeT17qd8JgMbz5LS0s6ffq0JicndfXqVZ6f\nB7SBMAXcojo6OjQwMKBjx47p4MGDzeDUzhSYrxLla7iOjpPUTsWqnfP3nZfvWL6G9rSKzdzcnHK5\nXLMnqlU/VKt9+sZlnWs0rlV4jCSXQ2hnnO/41WpV4+PjeuONN7S0tKRqterdD4D1CFPALa6jo0MH\nDhzQfffdp4GBgdQP4/h2X9N2fIxvbLLZ2ffBn7aqelolKpJs2k4bm3Wn2vz8vKrVqg4cONDclnwN\nyfNPjkvuu91xyZ+nBc12G8aT051Z7+erq6uamJjQSy+9pKWlpdRxANIRpgBIqk/9HTt2TLfffrvy\n+bzMTF1dXWuWVIjLqkQlQ0LaHXi+NZ58dxFG43zHS9rIg41rtZpKpZJKpZL27NnTnM5r57W2qjD5\nmsp9+/WFno1WrTY6LprKnJ+f12uvvaZiscjdeUAAwhSANXK5nPr7+9Xf3689e/aor69P3d3d6unp\nUVdXV7MJWwpfo8pXRWm1z2TouJ6xKysrqlarzefn9fX1rdlHu3cGZk2rtTu2nabxtLv9kmOzxkVj\nl5eXNTc3x915wCYiTAHI1NXVpZ6eHvX396tQKGhwcFDd3d3q7e1VX1+fCoXCuqDgqwxl9R5db59V\ntL2dPiszU6VS0fLycvNROL29vetWe0/ucyN35rVTXTKzdY+nybouyaCZNS6t8d85p3K5rGKxqMnJ\nSV24cIG784BNRJgCsCGdnZ3K5XLq7u5WoVBQT0+P9uzZ06xkFQqFzGlBX0hpt7qUFrKSgSJ5rGq1\n2nz4blRdi/d3hVSi4v1YyXCU1Qyetc+0ClNWZcsXSJ1zunr1qs6dO6f5+XktLCzwEGJgCxCmAASJ\neqviIeu2227T0NCQhoeH1d3d3az+RKElGSY2esefT1p16+rVq1peXlZvb28z6CVtpM/K1yTvkwyE\n8Sb5tLHR/rKe7ZfWE5W0urqq8fFxvfnmm1pcXOTuPGALEaYAbIkoDBQKBe3du1f79u3Tvn37vFNr\nyUqW73Eu8bHxcfEGd9/YlZUV5XK5NeflO9eokpN8bp9vbPzYaeOisb7pv7TqVvxhwVnBsVWz+MTE\nhF555RUtLS21DIcAwhGmANxQuVxOAwMD6u/v1969ezUwMKDe3t7mHYSSmncQ+u4MTPYdRbIW50zy\n7TNtnK9i5Xt/9K171arHq1WfVbJBP1nRq9Vqcq7+bMRKpaL5+XmdPHlSc3NzhCjgBiJMAdh2+Xxe\n/f39zUb3wcFBFQoFdXd3K5/PK5fLNatLaf1IvjsDpfaa2eOBKqsnyrfd9330YONaraZardZ8rl8+\nn1dPT493VXnfPqNtWQ8rXl5e1sWLF3X27FlduHCBu/OAbUCYAnBT6urqUm9vb/NPT09PcxmDaKmG\nXC63ph8rtM/Kdxdc1l2EUUhaWVlRpVJRuVxWuVxuPhh5eXlZ5XJZKysrKpfLGh4e1gMPPOC9gzDa\nZ9q5JZXLZc3NzWlqaoq784BtRpgCsGN0dnY2q1Xd3d3q6upqLiyay+Wa33d1dTWDVnxcNLazs7Pt\nOwPjDzwulUpaWVlp/imVSqrVaiqXy6pUKs0/5XLZW03K5XJ6z3ves+Yh08njZ93J55xTqVTS/Py8\npqenVSwWWbUcuAkQpgDsePGlEOK9VlFwir6OxuRyORUKheYdiIVCoblkQmdnpxYXF1UqlXTt2rXm\nAp+rq6vNf0bTd9ezavjo6KgeffTRzCURfCqVis6ePavz589raWlJy8vL9EUBN4m0MNV1o08EAK5X\nvPcofkdcK8k+qbTG8M1ULBZ14cIFHTx4sLktq3fKOafJyUmdOHFCi4uLPPYF2EEIUwB2vVaPX9mq\nY46NjWl0dDS1dypqYJ+fn9eJEyc0Pz9/w84PwOYhTAHAFllcXNSZM2d09OjRNVWx6LEvCwsLzWfn\nsdgmsHMRpgBgi9RqNZ07d06jo6Pq6+uTmalcLuutt97i7jxgFyFMAcAWunLliqampnTXXXfp4sWL\nmpqa0uzsLHfnAbsId/MBwBYbHBxUf3+/Ll++rGvXrnF3HrBDsTQCAABAgLQw1f5DrgAAALAOYQoA\nACAAYQoAACAAYQoAACAAYQoAACAAYQoAACAAYQoAACAAYQoAACAAYQoAACAAYQoAACAAYQoAACAA\nYQoAACAAYQoAACAAYQoAACAAYQoAACAAYQoAACBAyzBlZt8ws1kz+1Vs25CZ/dTMXjezn5jZntjP\nvmpmp8zsFTN7x1adOAAAwM2gncrU05I+mNj2OUk/c84dl/ScpM9Lkpl9SNLdzrljkj4t6eubeK4A\nAAA3nZZhyjn3c0kLic1PSHqm8fUzje+j7d9s/N4LkvaY2ejmnCoAAMDN53p7pkacc7OS5JybkTTS\n2H5Q0kRs3FRjGwAAwK602Q3o5tnmNvkYAAAAN43rDVOz0fSdmd0hqdjYPinpzti4Q5IuXP/pAQAA\n3NzaDVOmtVWnZyU91fj6KUk/im3/uCSZ2cOSLkXTgQAAALuROZc9C2dm35b0PknDkmYlfVHSDyV9\nT/Uq1HlJTzrnLjXGf03S45KWJH3SOfdSyn6Z/gMAADuGc87XztQ6TG0VwhQAANhJ0sIUK6ADAAAE\nIEwBAAAEIEwBAAAEIEwBAAAEIEwBAAAEIEwBAAAEIEwBAAAEIEwBAAAEIEwBAAAEIEwBAAAEIEwB\nAAAEIEwBAAAEIEwBAAAEIEwBAAAEIEwBAAAEIEwBAAAEIEwBAAAEIEwBAAAEIEwBAAAEIEwBAAAE\nIEwBAAAEIEwBAAAEIEwBAAAEIEwBAAAEIEwBAAAEIEwBAAAEIEwBAAAEIEwBAAAEIEwBAAAEIEwB\nAAAEIEwBAAAEIEwBAAAEIEwBAAAEIEwBAAAEIEwBAAAEIEwBAAAEIEwBAAAEIEwBAAAEIEwBAAAE\nIEwBAAAEIEwBAAAEIEwBAAAEIEwBAAAEIEwBAAAEIEwBAAAEIEwBAAAEIEwBAAAEIEwBAAAEIEwB\nAAAEIEwBAAAEIEwBAAAEIEwBAAAEIEwBAAAEIEwBAAAEIEwBAAAEIEwBAAAEIEwBAAAEIEwBAAAE\nIEwBAAAEIEwBAAAEIEwBAAAEIEwBAAAEIEwBAAAEIEwBAAAEIEwBAAAEIEwBAAAEIEwBAAAEIEwB\nAAAEIEwBAAAEIEwBAAAEIEwBAAAEIEwBAAAEIEwBAAAEIEwBAAAEIEwBAAAEIEwBAAAEIEwBAAAE\n2JIwZWaPm9lrZvaGmf3VVhwDAADgZmDOuc3doVmHpDckvV/SBUkvSvqIc+61xLjNPTAAAMAWcs6Z\nb/tWVKbeJemUc+6cc64i6buSntiC4wAAAGy7rQhTByVNxL6fbGwDAADYdbYiTPlKYEzpAQCAXWkr\nwtSkpMOx7w+p3jsFAACw62xFA3qnpNdVb0CflvRLSR91zp3c1AMBAADcBLo2e4fOuZqZ/amkn6pe\n+foGQQoAAOxWm16ZAgAAuJVsywroLOp5/czsG2Y2a2a/im0bMrOfmtnrZvYTM9sT+9lXzeyUmb1i\nZu/YnrO++ZnZITN7zsxeNbNfm9mfNbZzbQOYWcHMXjCzlxvX9YuN7W8zs180rut3zKyrsT1vZt9t\nXNf/MbPD2Ue4tZlZh5m9ZGbPNr7nugYys7Nm9n+N/2Z/2djG+0AgM9tjZt8zs5NmdsLM3r2brusN\nD1ONRT2/JumDkn5b0kfN7L4bfR472NOqX7u4z0n6mXPuuKTnJH1ekszsQ5Luds4dk/RpSV+/kSe6\nw1Ql/blz7rck/a6kzzT+u+TaBnDOrUh6zDn3oKR3SPqQmb1b0t9I+rvGdb0k6VONX/mUpIuN6/pl\nSX+7Dae9k3xW0qux77mu4VYlvc8596Bz7l2NbbwPhPuKpP9wzr1d0gOSXtMuuq7bUZliUc8Azrmf\nS1pIbH5C0jONr5/Rb67nE5K+2fi9FyTtMbPRG3GeO41zbsY590rj66uSTqp+JyrXNpBz7lrjy4Lq\nfZpO0mOSftDY/oykP258Hb/e31f9RhZ4mNkhSX8o6Z9im39fXNdQpvWfjbwPBDCzAUnvdc49LUnO\nuapz7rJ20XXdjjDFop6bb8Q5NyvVQ4Gkkcb25LWeEte6JTN7m+pVlF9IGuXahmlMRb0saUbSf0o6\nLemSc261MST+HtC8rs65mqRLZnb7DT7lneLvJf2lGuv4mdmwpAWuazAn6Sdm9qKZ/UljG+8DYY5K\nmjezpxvT0v9oZr3aRdd1O8IUi3reOFzrDTKzftX/5v7ZRoUq7XpxbdvknFttTPMdUr0y/XbfsMY/\nk9fVxHVdx8z+SNJso5oaXTPT+uvHdd2433PO/Y7qVb/PmNl7xftAqC5J75T0D865d0paUn2Kb9dc\n1+0IUyzquflmoxKomd0hqdjYPinpztg4rnWGRrPu9yV9yzn3o8Zmru0mcc5dkfTfkh6WdFujf1Ja\ne+2a17WxZt2gcy45rQ3pEUkfNrM3JX1H9em9L6s+HcJ1DdCokMg5Nyfph6r/BYD3gTCTkiacc//b\n+P4HqoerXXNdtyNMvSjpHjM7YmZ5SR+R9Ow2nMdOlvwb6LOSnmp8/ZSkH8W2f1ySzOxh1adWZm/M\nKe5I/yzpVefcV2LbuLYBzGxvdIeOmfVI+oDqDdP/JenJxrBPaO11/UTj6ydVb0pFgnPuC865w865\no6q/hz7nnPuYuK5BzKy3UZ2WmfVJ+gNJvxbvA0Ea12TCzO5tbHq/pBPaRdd1W9aZMrPHVe/sjxb1\n/NINP4kdysy+Lel9koYlzUr6oup/e/qe6kn+vKQnnXOXGuO/Julx1cuqn3TOvbQNp33TM7NHJD2v\n+huna/z5guor+P+buLbXxczuV72xtKPx51+dc39tZnepfvPJkKSXJX3MOVcxs4Kkb0l6UNJbkj7i\nnDu7LSe/Q5jZo5L+wjn3Ya5rmMb1+3fV///vkvQvzrkvNfrLeB8IYGYPqH6zRE7Sm5I+KalTu+S6\nsmgnAABAgG1ZtBMAAGC3IEwBAAAEIEwBAAAEIEwBAAAEIEwBAAAEIEwBAAAEIEwBAAAEIEwBAAAE\n+H981baEh4Ns4gAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10d63f250>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.imshow(ex, cmap='gray', origin='lower');\n",
"plt.savefig(\"figures/cluster_expmap.png\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### The \"Particle Background Map\"\n",
"\n",
"* `pb` is not data at all, but rather a model for the expected counts/pixel *in this specific observation* due to the \"quiescent particle background.\"\n",
"\n",
"\n",
"* This map comes out of a blackbox in the processing pipeline. Even though there are surely uncertainties in it, we have no quantitative description of them to work with. \n",
"\n",
"\n",
"* Note that the exposure map above does *not* apply to the particle backround; some particles are vignetted by the telescope optics, but not to the same degree as X-rays. The resulting spatial pattern and the total exposure time are accounted for in `pb`."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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tDIdDrK+vn/V0iAsKWaaIC025XMbi4uLYtmlZjYK4A90sV1HG8DrOrx8rv/zl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5/ve2IiIlR2ux3z8/NH0BsL3YJFpiw0gZjjRBBNDiTWE4lcHggEaBRuM+Wr1SrK5TIqlQqcTicG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\nISH4/wFMRZc2VXaZZQAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11085c350>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.imshow(pb, cmap='gray', origin='lower');\n",
"plt.savefig(\"figures/cluster_pbmap.png\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Masking out the other sources\n",
"\n",
"* There are non-cluster sources in this field. To simplify the model-building exercise, we will crudely mask them out for the moment. \n",
"\n",
"\n",
"* A convenient way to do this is by setting the exposure map to zero in these locations - as if a set of tiny little shutters in front of each of those pixels had not been opened. \"Not observed\" is different from \"observed zero counts.\"\n",
"\n",
"\n",
"* Let's read in a text file encoding a list of circular regions in the image, and set the exposure map pixels within each of those regions in to zero."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"mask = np.loadtxt('a1835_xmm/M2ptsrc.txt')\n",
"for reg in mask:\n",
" # this is inefficient but effective\n",
" for i in np.round(reg[1]+np.arange(-np.ceil(reg[2]),np.ceil(reg[2]))):\n",
" for j in np.round(reg[0]+np.arange(-np.ceil(reg[2]),np.ceil(reg[2]))):\n",
" if (i-reg[1])**2 + (j-reg[0])**2 <= reg[2]**2:\n",
" ex[np.int(i-1), np.int(j-1)] = 0.0"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* As a sanity check, let's have a look at the modified exposure map. \n",
"\n",
"\n",
"* Compare the location of the \"holes\" to the science image above."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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SnxZCdRag2EZZ3ZcvX8b58+d7rhME8TkUgE4QjwmapiEYDCKdTiObzWJqasrKTi56Hdhu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CRJgtfrhdvtxuzsrEqO/m7SxEY0ro2QpI2cz8qy94iWx8hoWxshfqJnjJYXyki4kPWEibARjxew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OZcvqIhUVFcHr9WLXrl34/e9/T7YJAG63G5s2bZK2RalqVF+p/BwZSbUSLtR1HcPD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\npjwTYBIAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x110819250>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.imshow(ex, cmap='gray', origin='lower');\n",
"plt.savefig(\"figures/cluster_expmap_masked.png\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# A Generative Model for the X-ray Image\n",
"\n",
"* All of the discussion above was in terms of predicting the *expected number of counts in each pixel,* $\\mu_k$. This is not what we observe: we observe *counts*.\n",
"\n",
"\n",
"* To be able to generate a mock dataset, we need to make an assumption about the form of the *sampling distribution* for the counts $N$ in each pixel, ${\\rm Pr}(N_k|\\mu_k)$.\n",
"\n",
"\n",
"* Let's assume that this distribution is *Poisson*, since we expect X-ray photon arrivals to be \"rare events.\"\n",
"\n",
"${\\rm Pr}(N_k|\\mu_k) = \\frac{{\\rm e}^{-\\mu_k} \\mu_k^{N_k}}{N_k !}$\n",
"\n",
"\n",
"* Here, $\\mu_k(\\theta)$ is the expected number of counts in the $k$th pixel:\n",
"\n",
"$\\mu_k(\\theta) = \\left( S(r_k;\\theta) + b \\right) \\cdot$ ex + pb\n",
"\n",
"\n",
"* Note that writing the sampling distribution like this contains the assumption that *the pixels are independent* (i.e., there is no cross-talk between the cuboids of silicon that make up the pixels in the CCD chip). (Also note that this assumption is different from the assumption that the *expected numbers of counts* are independent! They are explicitly *not* independent: we wrote down a model for a cluster surface brightness distribution that is potentially many pixels in diameter.)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
" "
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"* At this point we can draw the PGM for a *forward model* of this dataset, using the exposure and particle background maps supplied, and some choices for the model parameters.\n",
"\n",
"\n",
"* Then, we can go ahead and simulate some mock data and compare with the image we have."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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AAJAooR4AAAASJdQDAABAooR6AAAASJRQDwAAAIkS6gEAACBRQj0AAAAkSqgHAACARAn1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\nQNXBvkmoJ28GDx6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cEwcffHD06NEjxo4dG4cffnhcf/31MXTo0EKXyF7m5Zdfzr7e9jFz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\nAAAASUVORK5CYII=\n",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# import cluster_pgm\n",
"# cluster_pgm.forward()\n",
"\n",
"from IPython.display import Image\n",
"Image(filename=\"cluster_pgm_forward.png\")"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def beta_model_profile(r, S0, rc, beta):\n",
" '''\n",
" The fabled beta model, radial profile S(r)\n",
" '''\n",
" return S0 * (1.0 + (r/rc)**2)**(-3.0*beta + 0.5)\n",
"\n",
"def beta_model_image(x, y, x0, y0, S0, rc, beta):\n",
" '''\n",
" Here, x and y are arrays (\"meshgrids\" or \"ramps\") containing x and y pixel numbers, \n",
" and the other arguments are galaxy cluster beta model parameters.\n",
" Returns a surface brightness image of the same shape as x and y.\n",
" '''\n",
" r = np.sqrt((x-x0)**2 + (y-y0)**2)\n",
" return beta_model_profile(r, S0, rc, beta)\n",
"\n",
"def model_image(x, y, ex, pb, x0, y0, S0, rc, beta, b):\n",
" '''\n",
" Here, x, y, ex and pb are images, all of the same shape, and the other args are \n",
" cluster model and X-ray background parameters. ex is the (constant) exposure map\n",
" and pb is the (constant) particle background map.\n",
" '''\n",
" return (beta_model_image(x, y, x0, y0, S0, rc, beta) + b) * ex + pb"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.colorbar.Colorbar at 0x1103a23d0>"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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+Pl66BEkqrraMtLmSpMJqCw5JkvpSW0baXElSYbUFh/rhuJCk+uZCmytJKqy2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\nl9oy0uZKkgqrLTjUD8eFJNU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"text/plain": [
"<matplotlib.figure.Figure at 0x11080db50>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Set up the ramp images, to enable fast array calculations:\n",
"\n",
"nx,ny = ex.shape\n",
"x = np.outer(np.ones(ny),np.arange(nx))\n",
"y = np.outer(np.arange(ny),np.ones(nx))\n",
"\n",
"fig,ax = plt.subplots(nrows=1, ncols=2)\n",
"fig.set_size_inches(15, 6)\n",
"plt.subplots_adjust(wspace=0.2)\n",
"left = ax[0].imshow(x, cmap='gray', origin='lower')\n",
"ax[0].set_title('x')\n",
"fig.colorbar(left,ax=ax[0],shrink=0.9)\n",
"right = ax[1].imshow(y, cmap='gray', origin='lower')\n",
"ax[1].set_title('y')\n",
"fig.colorbar(right,ax=ax[1],shrink=0.9)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Now choose parameters, compute model and plot, compared to data!\n",
"\n",
"x0,y0 = 328,348 # The center of the image is 328,328\n",
"S0,b = 0.01,5e-7 # Cluster and background surface brightness, arbitrary units\n",
"beta = 2.0/3.0 # Canonical value is beta = 2/3\n",
"rc = 4 # Core radius, in pixels\n",
"\n",
"# Realize the expected counts map for the model:\n",
"mu = model_image(x,y,ex,pb,x0,y0,S0,rc,beta,b)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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8+uqrQ8+rK6ZFVWAMesQqKipM4j94i1hPnjyZ6upqE4qklDIhUHl5eZFLAjia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\nM7Mh4QGamZmZmZnZkPg/pVxxZCABlb4AAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1153a2510>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Draw a *sample image* from the Poisson sampling distribution:\n",
"mock = np.random.poisson(mu,mu.shape)\n",
"\n",
"# The difference between the mock and the real data should be symmetrical noise if the model\n",
"# is a good match...\n",
"diff = im - mock\n",
"\n",
"\n",
"# Plot three panels:\n",
"\n",
"fig,ax = plt.subplots(nrows=1, ncols=3)\n",
"fig.set_size_inches(15, 6)\n",
"plt.subplots_adjust(wspace=0.2)\n",
"\n",
"left = ax[0].imshow(viz.scale_image(mock, scale='log', max_cut=40), cmap='gray', origin='lower')\n",
"ax[0].set_title('Mock (log, rescaled)')\n",
"fig.colorbar(left,ax=ax[0],shrink=0.6)\n",
"\n",
"center = ax[1].imshow(viz.scale_image(im, scale='log', max_cut=40), cmap='gray', origin='lower')\n",
"ax[1].set_title('Data (log, rescaled)')\n",
"fig.colorbar(center,ax=ax[1],shrink=0.6)\n",
"\n",
"right = ax[2].imshow(diff, vmin=-40, vmax=40, cmap='gray', origin='lower')\n",
"ax[2].set_title('Difference (linear)')\n",
"fig.colorbar(right,ax=ax[2],shrink=0.6)\n",
"\n",
"fig.savefig(\"figures/cluster_mock-data-diff.png\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Exercise: Adjust the model parameters and generate a mock that matches the data\n",
"\n",
"If you are not following in your own notebook, you'll need to sit close to someone who is running it. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.12"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| gpl-2.0 |
bicv/SLIP | test_SLIP.ipynb | 1 | 2283154 | null | gpl-2.0 |
siddhartha-gadgil/ProvingGround | notes/2020-05-26-bot-modus-ponens.ipynb | 1 | 66065 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Bot based Modus Ponens\n",
"\n",
"We continue to explore the use of Bots. Here we prove Modus Ponens but without islands. Instead we use backward reasoning to introduce variables."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"\u001b[32mimport \u001b[39m\u001b[36m$cp.$ \n",
"\u001b[39m\n",
"\u001b[32mimport \u001b[39m\u001b[36mprovingground._ , interface._, HoTT._, learning._ \n",
"\u001b[39m"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import $cp.bin.`provingground-core-jvm-6a7a553306.fat.jar`\n",
"import provingground._ , interface._, HoTT._, learning._ \n",
"repl.pprinter() = {\n",
" val p = repl.pprinter()\n",
" p.copy(\n",
" additionalHandlers = p.additionalHandlers.orElse {\n",
" translation.FansiShow.fansiHandler\n",
" }\n",
" )\n",
"}\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"Utils.logger = {\n",
" import scribe._, writer._, Utils._\n",
" logger.withHandler(writer = FileWriter().path(file.LogPath.simple(\"modus-ponens.log\"))).replace()\n",
"}"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"First we set things up."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"\u001b[32mimport \u001b[39m\u001b[36mprovingground._ , learning._, interface._, translation._, HoTT._\n",
"\u001b[39m\n",
"\u001b[32mimport \u001b[39m\u001b[36mmonix.execution.Scheduler.Implicits.global\n",
"\u001b[39m\n",
"\u001b[36mA\u001b[39m: \u001b[32mTyp\u001b[39m[\u001b[32mTerm\u001b[39m] = \u001b[32mA\u001b[39m\n",
"\u001b[36mB\u001b[39m: \u001b[32mTyp\u001b[39m[\u001b[32mTerm\u001b[39m] = \u001b[32mB\u001b[39m\n",
"\u001b[36mMP\u001b[39m: \u001b[32mGenFuncTyp\u001b[39m[\u001b[32mTyp\u001b[39m[\u001b[32mTerm\u001b[39m], \u001b[32mFuncLike\u001b[39m[\u001b[32mTyp\u001b[39m[\u001b[32mTerm\u001b[39m], \u001b[32mFunc\u001b[39m[\u001b[32mTerm\u001b[39m, \u001b[32mFunc\u001b[39m[\u001b[32mFunc\u001b[39m[\u001b[32mTerm\u001b[39m, \u001b[32mTerm\u001b[39m], \u001b[32mTerm\u001b[39m]]]] = \u001b[32m∏(A : 𝒰 ){ ∏(B : 𝒰 ){ (A → ((A → B) → B)) } }\u001b[39m\n",
"\u001b[36mts\u001b[39m: \u001b[32mTermState\u001b[39m = \u001b[33mTermState\u001b[39m(\n",
" \u001b[33mFiniteDistribution\u001b[39m(\u001b[33mVector\u001b[39m(\u001b[33mWeighted\u001b[39m(\u001b[32m𝒰 \u001b[39m, \u001b[32m1.0\u001b[39m))),\n",
" \u001b[33mFiniteDistribution\u001b[39m(\u001b[33mVector\u001b[39m(\u001b[33mWeighted\u001b[39m(\u001b[32m𝒰 \u001b[39m, \u001b[32m1.0\u001b[39m))),\n",
" \u001b[33mVector\u001b[39m(),\n",
" \u001b[33mFiniteDistribution\u001b[39m(\u001b[33mVector\u001b[39m()),\n",
" \u001b[33mFiniteDistribution\u001b[39m(\n",
" \u001b[33mVector\u001b[39m(\u001b[33mWeighted\u001b[39m(\u001b[32m∏(A : 𝒰 ){ ∏(B : 𝒰 ){ (A → ((A → B) → B)) } }\u001b[39m, \u001b[32m1.0\u001b[39m))\n",
" ),\n",
" Empty\n",
")\n",
"\u001b[36mtg\u001b[39m: \u001b[32mTermGenParams\u001b[39m = \u001b[33mTermGenParams\u001b[39m(\n",
" \u001b[32m0.1\u001b[39m,\n",
" \u001b[32m0.1\u001b[39m,\n",
" \u001b[32m0.0\u001b[39m,\n",
" \u001b[32m0.0\u001b[39m,\n",
" \u001b[32m0.0\u001b[39m,\n",
" \u001b[32m0.0\u001b[39m,\n",
" \u001b[32m0.0\u001b[39m,\n",
" \u001b[32m0.0\u001b[39m,\n",
" \u001b[32m0.0\u001b[39m,\n",
" \u001b[32m0.0\u001b[39m,\n",
" \u001b[32m0.0\u001b[39m,\n",
" \u001b[32m0.0\u001b[39m,\n",
" \u001b[32m0.0\u001b[39m,\n",
" \u001b[32m0.3\u001b[39m,\n",
" \u001b[32m0.7\u001b[39m,\n",
" \u001b[32m0.5\u001b[39m,\n",
" \u001b[32m0.0\u001b[39m,\n",
" \u001b[32m0.0\u001b[39m,\n",
" \u001b[32m0.0\u001b[39m,\n",
" \u001b[33mOrElse\u001b[39m(\n",
" \u001b[33mOrElse\u001b[39m(\u001b[33mOrElse\u001b[39m(\u001b[33mOrElse\u001b[39m(<function1>, <function1>), <function1>), <function1>),\n",
" <function1>\n",
" )\n",
")\n",
"\u001b[36mlp\u001b[39m: \u001b[32mLocalProver\u001b[39m = \u001b[33mLocalProver\u001b[39m(\n",
" \u001b[33mTermState\u001b[39m(\n",
" \u001b[33mFiniteDistribution\u001b[39m(\u001b[33mVector\u001b[39m(\u001b[33mWeighted\u001b[39m(\u001b[32m𝒰 \u001b[39m, \u001b[32m1.0\u001b[39m))),\n",
" \u001b[33mFiniteDistribution\u001b[39m(\u001b[33mVector\u001b[39m(\u001b[33mWeighted\u001b[39m(\u001b[32m𝒰 \u001b[39m, \u001b[32m1.0\u001b[39m))),\n",
" \u001b[33mVector\u001b[39m(),\n",
" \u001b[33mFiniteDistribution\u001b[39m(\u001b[33mVector\u001b[39m()),\n",
" \u001b[33mFiniteDistribution\u001b[39m(\n",
" \u001b[33mVector\u001b[39m(\u001b[33mWeighted\u001b[39m(\u001b[32m∏(A : 𝒰 ){ ∏(B : 𝒰 ){ (A → ((A → B) → B)) } }\u001b[39m, \u001b[32m1.0\u001b[39m))\n",
" ),\n",
" Empty\n",
" ),\n",
" \u001b[33mTermGenParams\u001b[39m(\n",
" \u001b[32m0.1\u001b[39m,\n",
" \u001b[32m0.1\u001b[39m,\n",
" \u001b[32m0.0\u001b[39m,\n",
" \u001b[32m0.0\u001b[39m,\n",
" \u001b[32m0.0\u001b[39m,\n",
" \u001b[32m0.0\u001b[39m,\n",
" \u001b[32m0.0\u001b[39m,\n",
" \u001b[32m0.0\u001b[39m,\n",
" \u001b[32m0.0\u001b[39m,\n",
" \u001b[32m0.0\u001b[39m,\n",
" \u001b[32m0.0\u001b[39m,\n",
" \u001b[32m0.0\u001b[39m,\n",
" \u001b[32m0.0\u001b[39m,\n",
" \u001b[32m0.3\u001b[39m,\n",
" \u001b[32m0.7\u001b[39m,\n",
" \u001b[32m0.5\u001b[39m,\n",
" \u001b[32m0.0\u001b[39m,\n",
" \u001b[32m0.0\u001b[39m,\n",
" \u001b[32m0.0\u001b[39m,\n",
" \u001b[33mOrElse\u001b[39m(\n",
" \u001b[33mOrElse\u001b[39m(\u001b[33mOrElse\u001b[39m(\u001b[33mOrElse\u001b[39m(<function1>, <function1>), <function1>), <function1>),\n",
" <function1>\n",
" )\n",
" ),\n",
" \u001b[32m1.0E-4\u001b[39m,\n",
" \u001b[32mNone\u001b[39m,\n",
"...\n",
"\u001b[32mimport \u001b[39m\u001b[36mHoTTMessages._\n",
"\u001b[39m"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import provingground._ , learning._, interface._, translation._, HoTT._\n",
"import monix.execution.Scheduler.Implicits.global\n",
"val A = Type.sym\n",
"val B = Type.sym\n",
"val MP = A ~>: (B ~>: (A ->: (A ->: B) ->: B))\n",
"val ts = TermState(FiniteDistribution.unif(Type), FiniteDistribution.unif(Type), goals = FiniteDistribution.unif(MP))\n",
"val tg = TermGenParams.zero.copy(appW = 0.1, unAppW = 0.1)\n",
"val lp = LocalProver(ts, tg)\n",
"import HoTTMessages._\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Next we post stuff. Note that this is still manual bot mode, but a session will only have a couple of useless triggers."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div class=\"jp-RenderedText\">\n",
"<pre><code><span style=\"color: rgb(0, 187, 187)\"><span class=\"ansi-cyan-fg\">web</span></span>: <span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">HoTTPostWeb</span></span> = provingground.learning.HoTTPostWeb@2f96de42\n",
"<span style=\"color: rgb(0, 187, 187)\"><span class=\"ansi-cyan-fg\">ws</span></span>: <span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">WebState</span></span>[<span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">HoTTPostWeb</span></span>, (<span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">Int</span></span>, <span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">Int</span></span>)] = <span style=\"color: yellow\"><span class=\"ansi-yellow-fg\">WebState</span></span>(\n",
" provingground.learning.HoTTPostWeb@2f96de42,\n",
" <span style=\"color: yellow\"><span class=\"ansi-yellow-fg\">Vector</span></span>()\n",
")\n",
"<span style=\"color: rgb(0, 187, 187)\"><span class=\"ansi-cyan-fg\">ws1</span></span>: <span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">concurrent</span></span>.<span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">Future</span></span>[<span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">WebState</span></span>[<span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">HoTTPostWeb</span></span>, (<span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">Int</span></span>, <span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">Int</span></span>)]] = <span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\"><span style=\"color: yellow\"><span class=\"ansi-yellow-fg\">Success</span></span>(\n",
" <span style=\"color: yellow\"><span class=\"ansi-yellow-fg\">WebState</span></span>(\n",
" provingground.learning.HoTTPostWeb@2f96de42,\n",
" <span style=\"color: yellow\"><span class=\"ansi-yellow-fg\">Vector</span></span>(\n",
" <span style=\"color: yellow\"><span class=\"ansi-yellow-fg\">PostData</span></span>(\n",
" <span style=\"color: yellow\"><span class=\"ansi-yellow-fg\">LocalProver</span></span>(\n",
" <span style=\"color: yellow\"><span class=\"ansi-yellow-fg\">TermState</span></span>(\n",
" <span style=\"color: yellow\"><span class=\"ansi-yellow-fg\">FiniteDistribution</span></span>(<span style=\"color: yellow\"><span class=\"ansi-yellow-fg\">Vector</span></span>(<span style=\"color: yellow\"><span class=\"ansi-yellow-fg\">Weighted</span></span>(<span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">𝒰 </span></span>, <span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">1.0</span></span>))),\n",
" <span style=\"color: yellow\"><span class=\"ansi-yellow-fg\">FiniteDistribution</span></span>(<span style=\"color: yellow\"><span class=\"ansi-yellow-fg\">Vector</span></span>(<span style=\"color: yellow\"><span class=\"ansi-yellow-fg\">Weighted</span></span>(<span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">𝒰 </span></span>, <span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">1.0</span></span>))),\n",
" <span style=\"color: yellow\"><span class=\"ansi-yellow-fg\">Vector</span></span>(),\n",
" <span style=\"color: yellow\"><span class=\"ansi-yellow-fg\">FiniteDistribution</span></span>(<span style=\"color: yellow\"><span class=\"ansi-yellow-fg\">Vector</span></span>()),\n",
" <span style=\"color: yellow\"><span class=\"ansi-yellow-fg\">FiniteDistribution</span></span>(\n",
" <span style=\"color: yellow\"><span class=\"ansi-yellow-fg\">Vector</span></span>(\n",
" <span style=\"color: yellow\"><span class=\"ansi-yellow-fg\">Weighted</span></span>(<span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">∏(A : 𝒰 ){ ∏(B : 𝒰 ){ (A → ((A → B) → B)) } }</span></span>, <span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">1.0</span></span>)\n",
" )\n",
" ),\n",
" Empty\n",
" ),\n",
" <span style=\"color: yellow\"><span class=\"ansi-yellow-fg\">TermGenParams</span></span>(\n",
" <span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">0.1</span></span>,\n",
" <span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">0.1</span></span>,\n",
" <span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">0.0</span></span>,\n",
" <span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">0.0</span></span>,\n",
" <span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">0.0</span></span>,\n",
" <span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">0.0</span></span>,\n",
" <span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">0.0</span></span>,\n",
" <span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">0.0</span></span>,\n",
" <span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">0.0</span></span>,\n",
" <span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">0.0</span></span>,\n",
" <span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">0.0</span></span>,\n",
" <span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">0.0</span></span>,\n",
" <span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">0.0</span></span>,\n",
" <span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">0.3</span></span>,\n",
" <span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">0.7</span></span>,\n",
" <span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">0.5</span></span>,\n",
" <span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">0.0</span></span>,\n",
" <span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">0.0</span></span>,\n",
" <span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">0.0</span></span>,\n",
" <span style=\"color: yellow\"><span class=\"ansi-yellow-fg\">OrElse</span></span>(\n",
"...</span></span>\n",
"<span style=\"color: rgb(0, 187, 187)\"><span class=\"ansi-cyan-fg\">ws2</span></span>: <span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">concurrent</span></span>.<span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">Future</span></span>[<span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">WebState</span></span>[<span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">HoTTPostWeb</span></span>, (<span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">Int</span></span>, <span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">Int</span></span>)]] = <span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\"><span style=\"color: yellow\"><span class=\"ansi-yellow-fg\">Success</span></span>(\n",
" <span style=\"color: yellow\"><span class=\"ansi-yellow-fg\">WebState</span></span>(\n",
" provingground.learning.HoTTPostWeb@2f96de42,\n",
" <span style=\"color: yellow\"><span class=\"ansi-yellow-fg\">Vector</span></span>(\n",
" <span style=\"color: yellow\"><span class=\"ansi-yellow-fg\">PostData</span></span>(\n",
" <span style=\"color: yellow\"><span class=\"ansi-yellow-fg\">SeekGoal</span></span>(<span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">∏(A : 𝒰 ){ ∏(B : 𝒰 ){ (A → ((A → B) → B)) } }</span></span>, Empty, <span style=\"color: yellow\"><span class=\"ansi-yellow-fg\">Set</span></span>()),\n",
" (<span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">2</span></span>, <span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">260968439</span></span>)\n",
" ),\n",
" <span style=\"color: yellow\"><span class=\"ansi-yellow-fg\">PostData</span></span>(\n",
" <span style=\"color: yellow\"><span class=\"ansi-yellow-fg\">LocalProver</span></span>(\n",
" <span style=\"color: yellow\"><span class=\"ansi-yellow-fg\">TermState</span></span>(\n",
" <span style=\"color: yellow\"><span class=\"ansi-yellow-fg\">FiniteDistribution</span></span>(<span style=\"color: yellow\"><span class=\"ansi-yellow-fg\">Vector</span></span>(<span style=\"color: yellow\"><span class=\"ansi-yellow-fg\">Weighted</span></span>(<span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">𝒰 </span></span>, <span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">1.0</span></span>))),\n",
" <span style=\"color: yellow\"><span class=\"ansi-yellow-fg\">FiniteDistribution</span></span>(<span style=\"color: yellow\"><span class=\"ansi-yellow-fg\">Vector</span></span>(<span style=\"color: yellow\"><span class=\"ansi-yellow-fg\">Weighted</span></span>(<span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">𝒰 </span></span>, <span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">1.0</span></span>))),\n",
" <span style=\"color: yellow\"><span class=\"ansi-yellow-fg\">Vector</span></span>(),\n",
" <span style=\"color: yellow\"><span class=\"ansi-yellow-fg\">FiniteDistribution</span></span>(<span style=\"color: yellow\"><span class=\"ansi-yellow-fg\">Vector</span></span>()),\n",
" <span style=\"color: yellow\"><span class=\"ansi-yellow-fg\">FiniteDistribution</span></span>(\n",
" <span style=\"color: yellow\"><span class=\"ansi-yellow-fg\">Vector</span></span>(\n",
" <span style=\"color: yellow\"><span class=\"ansi-yellow-fg\">Weighted</span></span>(<span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">∏(A : 𝒰 ){ ∏(B : 𝒰 ){ (A → ((A → B) → B)) } }</span></span>, <span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">1.0</span></span>)\n",
" )\n",
" ),\n",
" Empty\n",
" ),\n",
" <span style=\"color: yellow\"><span class=\"ansi-yellow-fg\">TermGenParams</span></span>(\n",
" <span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">0.1</span></span>,\n",
" <span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">0.1</span></span>,\n",
" <span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">0.0</span></span>,\n",
" <span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">0.0</span></span>,\n",
" <span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">0.0</span></span>,\n",
" <span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">0.0</span></span>,\n",
" <span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">0.0</span></span>,\n",
" <span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">0.0</span></span>,\n",
" <span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">0.0</span></span>,\n",
" <span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">0.0</span></span>,\n",
" <span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">0.0</span></span>,\n",
" <span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">0.0</span></span>,\n",
" <span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">0.0</span></span>,\n",
" <span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">0.3</span></span>,\n",
" <span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">0.7</span></span>,\n",
" <span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">0.5</span></span>,\n",
"...</span></span>\n",
"<span style=\"color: rgb(0, 187, 187)\"><span class=\"ansi-cyan-fg\">ws3</span></span>: <span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">concurrent</span></span>.<span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">Future</span></span>[<span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">WebState</span></span>[<span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">HoTTPostWeb</span></span>, (<span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">Int</span></span>, <span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">Int</span></span>)]] = <span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\"><span style=\"color: yellow\"><span class=\"ansi-yellow-fg\">Success</span></span>(\n",
" <span style=\"color: yellow\"><span class=\"ansi-yellow-fg\">WebState</span></span>(\n",
" provingground.learning.HoTTPostWeb@2f96de42,\n",
" <span style=\"color: yellow\"><span class=\"ansi-yellow-fg\">Vector</span></span>(\n",
" <span style=\"color: yellow\"><span class=\"ansi-yellow-fg\">PostData</span></span>(\n",
" <span style=\"color: yellow\"><span class=\"ansi-yellow-fg\">SeekGoal</span></span>(\n",
" <span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">B</span></span>,\n",
" <span style=\"color: yellow\"><span class=\"ansi-yellow-fg\">AppendVariable</span></span>(\n",
" <span style=\"color: yellow\"><span class=\"ansi-yellow-fg\">AppendVariable</span></span>(<span style=\"color: yellow\"><span class=\"ansi-yellow-fg\">AppendVariable</span></span>(<span style=\"color: yellow\"><span class=\"ansi-yellow-fg\">AppendVariable</span></span>(Empty, <span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">A</span></span>), <span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">B</span></span>), <span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">$a</span></span>),\n",
" <span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">$b</span></span>\n",
" ),\n",
" <span style=\"color: yellow\"><span class=\"ansi-yellow-fg\">Set</span></span>()\n",
" ),\n",
" (<span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">3</span></span>, <span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">-424370462</span></span>)\n",
" ),\n",
" <span style=\"color: yellow\"><span class=\"ansi-yellow-fg\">PostData</span></span>(\n",
" <span style=\"color: yellow\"><span class=\"ansi-yellow-fg\">LocalProver</span></span>(\n",
" <span style=\"color: yellow\"><span class=\"ansi-yellow-fg\">TermState</span></span>(\n",
" <span style=\"color: yellow\"><span class=\"ansi-yellow-fg\">FiniteDistribution</span></span>(<span style=\"color: yellow\"><span class=\"ansi-yellow-fg\">Vector</span></span>(<span style=\"color: yellow\"><span class=\"ansi-yellow-fg\">Weighted</span></span>(<span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">𝒰 </span></span>, <span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">1.0</span></span>))),\n",
" <span style=\"color: yellow\"><span class=\"ansi-yellow-fg\">FiniteDistribution</span></span>(<span style=\"color: yellow\"><span class=\"ansi-yellow-fg\">Vector</span></span>(<span style=\"color: yellow\"><span class=\"ansi-yellow-fg\">Weighted</span></span>(<span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">𝒰 </span></span>, <span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">1.0</span></span>))),\n",
" <span style=\"color: yellow\"><span class=\"ansi-yellow-fg\">Vector</span></span>(),\n",
" <span style=\"color: yellow\"><span class=\"ansi-yellow-fg\">FiniteDistribution</span></span>(<span style=\"color: yellow\"><span class=\"ansi-yellow-fg\">Vector</span></span>()),\n",
" <span style=\"color: yellow\"><span class=\"ansi-yellow-fg\">FiniteDistribution</span></span>(\n",
" <span style=\"color: yellow\"><span class=\"ansi-yellow-fg\">Vector</span></span>(\n",
" <span style=\"color: yellow\"><span class=\"ansi-yellow-fg\">Weighted</span></span>(<span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">∏(A : 𝒰 ){ ∏(B : 𝒰 ){ (A → ((A → B) → B)) } }</span></span>, <span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">1.0</span></span>)\n",
" )\n",
" ),\n",
" Empty\n",
" ),\n",
" <span style=\"color: yellow\"><span class=\"ansi-yellow-fg\">TermGenParams</span></span>(\n",
" <span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">0.1</span></span>,\n",
" <span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">0.1</span></span>,\n",
" <span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">0.0</span></span>,\n",
" <span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">0.0</span></span>,\n",
" <span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">0.0</span></span>,\n",
" <span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">0.0</span></span>,\n",
" <span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">0.0</span></span>,\n",
" <span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">0.0</span></span>,\n",
" <span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">0.0</span></span>,\n",
"...</span></span>\n",
"<span style=\"color: rgb(0, 187, 187)\"><span class=\"ansi-cyan-fg\">ws4</span></span>: <span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">concurrent</span></span>.<span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">Future</span></span>[<span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">WebState</span></span>[<span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">HoTTPostWeb</span></span>, (<span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">Int</span></span>, <span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">Int</span></span>)]] = <span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\"><span style=\"color: yellow\"><span class=\"ansi-yellow-fg\">Success</span></span>(\n",
" <span style=\"color: yellow\"><span class=\"ansi-yellow-fg\">WebState</span></span>(\n",
" provingground.learning.HoTTPostWeb@2f96de42,\n",
" <span style=\"color: yellow\"><span class=\"ansi-yellow-fg\">Vector</span></span>(\n",
" <span style=\"color: yellow\"><span class=\"ansi-yellow-fg\">PostData</span></span>(\n",
" <span style=\"color: yellow\"><span class=\"ansi-yellow-fg\">LocalProver</span></span>(\n",
" <span style=\"color: yellow\"><span class=\"ansi-yellow-fg\">TermState</span></span>(\n",
" <span style=\"color: yellow\"><span class=\"ansi-yellow-fg\">FiniteDistribution</span></span>(\n",
" <span style=\"color: yellow\"><span class=\"ansi-yellow-fg\">Vector</span></span>(\n",
" <span style=\"color: yellow\"><span class=\"ansi-yellow-fg\">Weighted</span></span>(<span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">𝒰 </span></span>, <span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">0.24009999999999995</span></span>),\n",
" <span style=\"color: yellow\"><span class=\"ansi-yellow-fg\">Weighted</span></span>(<span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">A</span></span>, <span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">0.1029</span></span>),\n",
" <span style=\"color: yellow\"><span class=\"ansi-yellow-fg\">Weighted</span></span>(<span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">B</span></span>, <span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">0.14700000000000002</span></span>),\n",
" <span style=\"color: yellow\"><span class=\"ansi-yellow-fg\">Weighted</span></span>(<span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">$a</span></span>, <span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">0.21000000000000002</span></span>),\n",
" <span style=\"color: yellow\"><span class=\"ansi-yellow-fg\">Weighted</span></span>(<span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">$b</span></span>, <span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">0.3</span></span>)\n",
" )\n",
" ),\n",
" <span style=\"color: yellow\"><span class=\"ansi-yellow-fg\">FiniteDistribution</span></span>(<span style=\"color: yellow\"><span class=\"ansi-yellow-fg\">Vector</span></span>(<span style=\"color: yellow\"><span class=\"ansi-yellow-fg\">Weighted</span></span>(<span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">𝒰 </span></span>, <span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">1.0</span></span>))),\n",
" <span style=\"color: yellow\"><span class=\"ansi-yellow-fg\">Vector</span></span>(<span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">$b</span></span>, <span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">$a</span></span>, <span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">B</span></span>, <span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">A</span></span>),\n",
" <span style=\"color: yellow\"><span class=\"ansi-yellow-fg\">FiniteDistribution</span></span>(<span style=\"color: yellow\"><span class=\"ansi-yellow-fg\">Vector</span></span>()),\n",
" <span style=\"color: yellow\"><span class=\"ansi-yellow-fg\">FiniteDistribution</span></span>(\n",
" <span style=\"color: yellow\"><span class=\"ansi-yellow-fg\">Vector</span></span>(\n",
" <span style=\"color: yellow\"><span class=\"ansi-yellow-fg\">Weighted</span></span>(\n",
" <span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">∏(A : 𝒰 ){ ∏(B : 𝒰 ){ (A → ((A → B) → B)) } }</span></span>,\n",
" <span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">0.30000000000000004</span></span>\n",
" ),\n",
" <span style=\"color: yellow\"><span class=\"ansi-yellow-fg\">Weighted</span></span>(<span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">B</span></span>, <span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">0.7</span></span>)\n",
" )\n",
" ),\n",
" <span style=\"color: yellow\"><span class=\"ansi-yellow-fg\">AppendVariable</span></span>(\n",
" <span style=\"color: yellow\"><span class=\"ansi-yellow-fg\">AppendVariable</span></span>(<span style=\"color: yellow\"><span class=\"ansi-yellow-fg\">AppendVariable</span></span>(<span style=\"color: yellow\"><span class=\"ansi-yellow-fg\">AppendVariable</span></span>(Empty, <span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">A</span></span>), <span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">B</span></span>), <span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">$a</span></span>),\n",
" <span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">$b</span></span>\n",
" )\n",
" ),\n",
" <span style=\"color: yellow\"><span class=\"ansi-yellow-fg\">TermGenParams</span></span>(\n",
" <span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">0.1</span></span>,\n",
" <span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">0.1</span></span>,\n",
" <span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">0.0</span></span>,\n",
" <span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">0.0</span></span>,\n",
" <span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">0.0</span></span>,\n",
"...</span></span>\n",
"<span style=\"color: rgb(0, 187, 187)\"><span class=\"ansi-cyan-fg\">ws5</span></span>: <span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">concurrent</span></span>.<span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">Future</span></span>[<span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">WebState</span></span>[<span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">HoTTPostWeb</span></span>, (<span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">Int</span></span>, <span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">Int</span></span>)]] = <span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\"><span style=\"color: yellow\"><span class=\"ansi-yellow-fg\">Success</span></span>(\n",
" <span style=\"color: yellow\"><span class=\"ansi-yellow-fg\">WebState</span></span>(\n",
" provingground.learning.HoTTPostWeb@2f96de42,\n",
" <span style=\"color: yellow\"><span class=\"ansi-yellow-fg\">Vector</span></span>(\n",
" <span style=\"color: yellow\"><span class=\"ansi-yellow-fg\">PostData</span></span>(\n",
" <span style=\"color: yellow\"><span class=\"ansi-yellow-fg\">FinalState</span></span>(\n",
" <span style=\"color: yellow\"><span class=\"ansi-yellow-fg\">TermState</span></span>(\n",
" <span style=\"color: yellow\"><span class=\"ansi-yellow-fg\">FiniteDistribution</span></span>(\n",
" <span style=\"color: yellow\"><span class=\"ansi-yellow-fg\">Vector</span></span>(\n",
" <span style=\"color: yellow\"><span class=\"ansi-yellow-fg\">Weighted</span></span>(<span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">$b</span></span>, <span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">0.24000000000000005</span></span>),\n",
" <span style=\"color: yellow\"><span class=\"ansi-yellow-fg\">Weighted</span></span>(<span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">𝒰 </span></span>, <span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">0.19208000000000003</span></span>),\n",
" <span style=\"color: yellow\"><span class=\"ansi-yellow-fg\">Weighted</span></span>(<span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">B</span></span>, <span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">0.11760000000000005</span></span>),\n",
" <span style=\"color: yellow\"><span class=\"ansi-yellow-fg\">Weighted</span></span>(<span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">A</span></span>, <span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">0.08232000000000002</span></span>),\n",
" <span style=\"color: yellow\"><span class=\"ansi-yellow-fg\">Weighted</span></span>(<span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">$b($a)</span></span>, <span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">0.20000000000000007</span></span>),\n",
" <span style=\"color: yellow\"><span class=\"ansi-yellow-fg\">Weighted</span></span>(<span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">$a</span></span>, <span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">0.16800000000000007</span></span>)\n",
" )\n",
" ),\n",
" <span style=\"color: yellow\"><span class=\"ansi-yellow-fg\">FiniteDistribution</span></span>(<span style=\"color: yellow\"><span class=\"ansi-yellow-fg\">Vector</span></span>(<span style=\"color: yellow\"><span class=\"ansi-yellow-fg\">Weighted</span></span>(<span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">𝒰 </span></span>, <span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">1.0</span></span>))),\n",
" <span style=\"color: yellow\"><span class=\"ansi-yellow-fg\">Vector</span></span>(<span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">$b</span></span>, <span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">$a</span></span>, <span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">B</span></span>, <span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">A</span></span>),\n",
" <span style=\"color: yellow\"><span class=\"ansi-yellow-fg\">FiniteDistribution</span></span>(<span style=\"color: yellow\"><span class=\"ansi-yellow-fg\">Vector</span></span>()),\n",
" <span style=\"color: yellow\"><span class=\"ansi-yellow-fg\">FiniteDistribution</span></span>(\n",
" <span style=\"color: yellow\"><span class=\"ansi-yellow-fg\">Vector</span></span>(\n",
" <span style=\"color: yellow\"><span class=\"ansi-yellow-fg\">Weighted</span></span>(\n",
" <span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">∏(A : 𝒰 ){ ∏(B : 𝒰 ){ (A → ((A → B) → B)) } }</span></span>,\n",
" <span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">0.30000000000000004</span></span>\n",
" ),\n",
" <span style=\"color: yellow\"><span class=\"ansi-yellow-fg\">Weighted</span></span>(<span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">B</span></span>, <span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">0.7</span></span>)\n",
" )\n",
" ),\n",
" <span style=\"color: yellow\"><span class=\"ansi-yellow-fg\">AppendVariable</span></span>(\n",
" <span style=\"color: yellow\"><span class=\"ansi-yellow-fg\">AppendVariable</span></span>(<span style=\"color: yellow\"><span class=\"ansi-yellow-fg\">AppendVariable</span></span>(<span style=\"color: yellow\"><span class=\"ansi-yellow-fg\">AppendVariable</span></span>(Empty, <span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">A</span></span>), <span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">B</span></span>), <span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">$a</span></span>),\n",
" <span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">$b</span></span>\n",
" )\n",
" )\n",
" ),\n",
" (<span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">6</span></span>, <span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">-1306168695</span></span>)\n",
" ),\n",
" <span style=\"color: yellow\"><span class=\"ansi-yellow-fg\">PostData</span></span>(\n",
" <span style=\"color: yellow\"><span class=\"ansi-yellow-fg\">FinalState</span></span>(\n",
"...</span></span>\n",
"<span style=\"color: rgb(0, 187, 187)\"><span class=\"ansi-cyan-fg\">ws6</span></span>: <span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">concurrent</span></span>.<span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">Future</span></span>[<span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">WebState</span></span>[<span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">HoTTPostWeb</span></span>, (<span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">Int</span></span>, <span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">Int</span></span>)]] = <span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\"><style>@keyframes fadein { from { opacity: 0; } to { opacity: 1; } }</style><span style=\"animation: fadein 2s;\"><span style=\"color: yellow\"><span class=\"ansi-yellow-fg\">Success</span></span>(\n",
" <span style=\"color: yellow\"><span class=\"ansi-yellow-fg\">WebState</span></span>(\n",
" provingground.learning.HoTTPostWeb@2f96de42,\n",
" <span style=\"color: yellow\"><span class=\"ansi-yellow-fg\">Vector</span></span>(\n",
" <span style=\"color: yellow\"><span class=\"ansi-yellow-fg\">PostData</span></span>(\n",
" <span style=\"color: yellow\"><span class=\"ansi-yellow-fg\">FinalState</span></span>(\n",
" <span style=\"color: yellow\"><span class=\"ansi-yellow-fg\">TermState</span></span>(\n",
" <span style=\"color: yellow\"><span class=\"ansi-yellow-fg\">FiniteDistribution</span></span>(\n",
" <span style=\"color: yellow\"><span class=\"ansi-yellow-fg\">Vector</span></span>(\n",
" <span style=\"color: yellow\"><span class=\"ansi-yellow-fg\">Weighted</span></span>(<span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">$b</span></span>, <span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">0.24000000000000005</span></span>),\n",
" <span style=\"color: yellow\"><span class=\"ansi-yellow-fg\">Weighted</span></span>(<span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">𝒰 </span></span>, <span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">0.19208000000000003</span></span>),\n",
" <span style=\"color: yellow\"><span class=\"ansi-yellow-fg\">Weighted</span></span>(<span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">B</span></span>, <span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">0.11760000000000005</span></span>),\n",
" <span style=\"color: yellow\"><span class=\"ansi-yellow-fg\">Weighted</span></span>(<span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">A</span></span>, <span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">0.08232000000000002</span></span>),\n",
" <span style=\"color: yellow\"><span class=\"ansi-yellow-fg\">Weighted</span></span>(<span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">$b($a)</span></span>, <span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">0.20000000000000007</span></span>),\n",
" <span style=\"color: yellow\"><span class=\"ansi-yellow-fg\">Weighted</span></span>(<span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">$a</span></span>, <span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">0.16800000000000007</span></span>)\n",
" )\n",
" ),\n",
" <span style=\"color: yellow\"><span class=\"ansi-yellow-fg\">FiniteDistribution</span></span>(<span style=\"color: yellow\"><span class=\"ansi-yellow-fg\">Vector</span></span>(<span style=\"color: yellow\"><span class=\"ansi-yellow-fg\">Weighted</span></span>(<span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">𝒰 </span></span>, <span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">1.0</span></span>))),\n",
" <span style=\"color: yellow\"><span class=\"ansi-yellow-fg\">Vector</span></span>(<span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">$b</span></span>, <span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">$a</span></span>, <span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">B</span></span>, <span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">A</span></span>),\n",
" <span style=\"color: yellow\"><span class=\"ansi-yellow-fg\">FiniteDistribution</span></span>(<span style=\"color: yellow\"><span class=\"ansi-yellow-fg\">Vector</span></span>()),\n",
" <span style=\"color: yellow\"><span class=\"ansi-yellow-fg\">FiniteDistribution</span></span>(\n",
" <span style=\"color: yellow\"><span class=\"ansi-yellow-fg\">Vector</span></span>(\n",
" <span style=\"color: yellow\"><span class=\"ansi-yellow-fg\">Weighted</span></span>(\n",
" <span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">∏(A : 𝒰 ){ ∏(B : 𝒰 ){ (A → ((A → B) → B)) } }</span></span>,\n",
" <span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">0.30000000000000004</span></span>\n",
" ),\n",
" <span style=\"color: yellow\"><span class=\"ansi-yellow-fg\">Weighted</span></span>(<span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">B</span></span>, <span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">0.7</span></span>)\n",
" )\n",
" ),\n",
" <span style=\"color: yellow\"><span class=\"ansi-yellow-fg\">AppendVariable</span></span>(\n",
" <span style=\"color: yellow\"><span class=\"ansi-yellow-fg\">AppendVariable</span></span>(<span style=\"color: yellow\"><span class=\"ansi-yellow-fg\">AppendVariable</span></span>(<span style=\"color: yellow\"><span class=\"ansi-yellow-fg\">AppendVariable</span></span>(Empty, <span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">A</span></span>), <span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">B</span></span>), <span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">$a</span></span>),\n",
" <span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">$b</span></span>\n",
" )\n",
" )\n",
" ),\n",
" (<span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">6</span></span>, <span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">-1306168695</span></span>)\n",
" ),\n",
" <span style=\"color: yellow\"><span class=\"ansi-yellow-fg\">PostData</span></span>(\n",
" <span style=\"color: yellow\"><span class=\"ansi-yellow-fg\">FinalState</span></span>(\n",
"...</span></span></span></code></pre>\n",
"</div>"
],
"text/plain": [
"\u001b[36mweb\u001b[39m: \u001b[32mHoTTPostWeb\u001b[39m = provingground.learning.HoTTPostWeb@2f96de42\n",
"\u001b[36mws\u001b[39m: \u001b[32mWebState\u001b[39m[\u001b[32mHoTTPostWeb\u001b[39m, (\u001b[32mInt\u001b[39m, \u001b[32mInt\u001b[39m)] = \u001b[33mWebState\u001b[39m(\n",
" provingground.learning.HoTTPostWeb@2f96de42,\n",
" \u001b[33mVector\u001b[39m()\n",
")\n",
"\u001b[36mws1\u001b[39m: \u001b[32mconcurrent\u001b[39m.\u001b[32mFuture\u001b[39m[\u001b[32mWebState\u001b[39m[\u001b[32mHoTTPostWeb\u001b[39m, (\u001b[32mInt\u001b[39m, \u001b[32mInt\u001b[39m)]] = \u001b[32m\u001b[33mSuccess\u001b[39m(\n",
" \u001b[33mWebState\u001b[39m(\n",
" provingground.learning.HoTTPostWeb@2f96de42,\n",
" \u001b[33mVector\u001b[39m(\n",
" \u001b[33mPostData\u001b[39m(\n",
" \u001b[33mLocalProver\u001b[39m(\n",
" \u001b[33mTermState\u001b[39m(\n",
" \u001b[33mFiniteDistribution\u001b[39m(\u001b[33mVector\u001b[39m(\u001b[33mWeighted\u001b[39m(\u001b[32m𝒰 \u001b[39m, \u001b[32m1.0\u001b[39m))),\n",
" \u001b[33mFiniteDistribution\u001b[39m(\u001b[33mVector\u001b[39m(\u001b[33mWeighted\u001b[39m(\u001b[32m𝒰 \u001b[39m, \u001b[32m1.0\u001b[39m))),\n",
" \u001b[33mVector\u001b[39m(),\n",
" \u001b[33mFiniteDistribution\u001b[39m(\u001b[33mVector\u001b[39m()),\n",
" \u001b[33mFiniteDistribution\u001b[39m(\n",
" \u001b[33mVector\u001b[39m(\n",
" \u001b[33mWeighted\u001b[39m(\u001b[32m∏(A : 𝒰 ){ ∏(B : 𝒰 ){ (A → ((A → B) → B)) } }\u001b[39m, \u001b[32m1.0\u001b[39m)\n",
" )\n",
" ),\n",
" Empty\n",
" ),\n",
" \u001b[33mTermGenParams\u001b[39m(\n",
" \u001b[32m0.1\u001b[39m,\n",
" \u001b[32m0.1\u001b[39m,\n",
" \u001b[32m0.0\u001b[39m,\n",
" \u001b[32m0.0\u001b[39m,\n",
" \u001b[32m0.0\u001b[39m,\n",
" \u001b[32m0.0\u001b[39m,\n",
" \u001b[32m0.0\u001b[39m,\n",
" \u001b[32m0.0\u001b[39m,\n",
" \u001b[32m0.0\u001b[39m,\n",
" \u001b[32m0.0\u001b[39m,\n",
" \u001b[32m0.0\u001b[39m,\n",
" \u001b[32m0.0\u001b[39m,\n",
" \u001b[32m0.0\u001b[39m,\n",
" \u001b[32m0.3\u001b[39m,\n",
" \u001b[32m0.7\u001b[39m,\n",
" \u001b[32m0.5\u001b[39m,\n",
" \u001b[32m0.0\u001b[39m,\n",
" \u001b[32m0.0\u001b[39m,\n",
" \u001b[32m0.0\u001b[39m,\n",
" \u001b[33mOrElse\u001b[39m(\n",
"...\u001b[39m\n",
"\u001b[36mws2\u001b[39m: \u001b[32mconcurrent\u001b[39m.\u001b[32mFuture\u001b[39m[\u001b[32mWebState\u001b[39m[\u001b[32mHoTTPostWeb\u001b[39m, (\u001b[32mInt\u001b[39m, \u001b[32mInt\u001b[39m)]] = \u001b[32m\u001b[33mSuccess\u001b[39m(\n",
" \u001b[33mWebState\u001b[39m(\n",
" provingground.learning.HoTTPostWeb@2f96de42,\n",
" \u001b[33mVector\u001b[39m(\n",
" \u001b[33mPostData\u001b[39m(\n",
" \u001b[33mSeekGoal\u001b[39m(\u001b[32m∏(A : 𝒰 ){ ∏(B : 𝒰 ){ (A → ((A → B) → B)) } }\u001b[39m, Empty, \u001b[33mSet\u001b[39m()),\n",
" (\u001b[32m2\u001b[39m, \u001b[32m260968439\u001b[39m)\n",
" ),\n",
" \u001b[33mPostData\u001b[39m(\n",
" \u001b[33mLocalProver\u001b[39m(\n",
" \u001b[33mTermState\u001b[39m(\n",
" \u001b[33mFiniteDistribution\u001b[39m(\u001b[33mVector\u001b[39m(\u001b[33mWeighted\u001b[39m(\u001b[32m𝒰 \u001b[39m, \u001b[32m1.0\u001b[39m))),\n",
" \u001b[33mFiniteDistribution\u001b[39m(\u001b[33mVector\u001b[39m(\u001b[33mWeighted\u001b[39m(\u001b[32m𝒰 \u001b[39m, \u001b[32m1.0\u001b[39m))),\n",
" \u001b[33mVector\u001b[39m(),\n",
" \u001b[33mFiniteDistribution\u001b[39m(\u001b[33mVector\u001b[39m()),\n",
" \u001b[33mFiniteDistribution\u001b[39m(\n",
" \u001b[33mVector\u001b[39m(\n",
" \u001b[33mWeighted\u001b[39m(\u001b[32m∏(A : 𝒰 ){ ∏(B : 𝒰 ){ (A → ((A → B) → B)) } }\u001b[39m, \u001b[32m1.0\u001b[39m)\n",
" )\n",
" ),\n",
" Empty\n",
" ),\n",
" \u001b[33mTermGenParams\u001b[39m(\n",
" \u001b[32m0.1\u001b[39m,\n",
" \u001b[32m0.1\u001b[39m,\n",
" \u001b[32m0.0\u001b[39m,\n",
" \u001b[32m0.0\u001b[39m,\n",
" \u001b[32m0.0\u001b[39m,\n",
" \u001b[32m0.0\u001b[39m,\n",
" \u001b[32m0.0\u001b[39m,\n",
" \u001b[32m0.0\u001b[39m,\n",
" \u001b[32m0.0\u001b[39m,\n",
" \u001b[32m0.0\u001b[39m,\n",
" \u001b[32m0.0\u001b[39m,\n",
" \u001b[32m0.0\u001b[39m,\n",
" \u001b[32m0.0\u001b[39m,\n",
" \u001b[32m0.3\u001b[39m,\n",
" \u001b[32m0.7\u001b[39m,\n",
" \u001b[32m0.5\u001b[39m,\n",
"...\u001b[39m\n",
"\u001b[36mws3\u001b[39m: \u001b[32mconcurrent\u001b[39m.\u001b[32mFuture\u001b[39m[\u001b[32mWebState\u001b[39m[\u001b[32mHoTTPostWeb\u001b[39m, (\u001b[32mInt\u001b[39m, \u001b[32mInt\u001b[39m)]] = \u001b[32m\u001b[33mSuccess\u001b[39m(\n",
" \u001b[33mWebState\u001b[39m(\n",
" provingground.learning.HoTTPostWeb@2f96de42,\n",
" \u001b[33mVector\u001b[39m(\n",
" \u001b[33mPostData\u001b[39m(\n",
" \u001b[33mSeekGoal\u001b[39m(\n",
" \u001b[32mB\u001b[39m,\n",
" \u001b[33mAppendVariable\u001b[39m(\n",
" \u001b[33mAppendVariable\u001b[39m(\u001b[33mAppendVariable\u001b[39m(\u001b[33mAppendVariable\u001b[39m(Empty, \u001b[32mA\u001b[39m), \u001b[32mB\u001b[39m), \u001b[32m$a\u001b[39m),\n",
" \u001b[32m$b\u001b[39m\n",
" ),\n",
" \u001b[33mSet\u001b[39m()\n",
" ),\n",
" (\u001b[32m3\u001b[39m, \u001b[32m-424370462\u001b[39m)\n",
" ),\n",
" \u001b[33mPostData\u001b[39m(\n",
" \u001b[33mLocalProver\u001b[39m(\n",
" \u001b[33mTermState\u001b[39m(\n",
" \u001b[33mFiniteDistribution\u001b[39m(\u001b[33mVector\u001b[39m(\u001b[33mWeighted\u001b[39m(\u001b[32m𝒰 \u001b[39m, \u001b[32m1.0\u001b[39m))),\n",
" \u001b[33mFiniteDistribution\u001b[39m(\u001b[33mVector\u001b[39m(\u001b[33mWeighted\u001b[39m(\u001b[32m𝒰 \u001b[39m, \u001b[32m1.0\u001b[39m))),\n",
" \u001b[33mVector\u001b[39m(),\n",
" \u001b[33mFiniteDistribution\u001b[39m(\u001b[33mVector\u001b[39m()),\n",
" \u001b[33mFiniteDistribution\u001b[39m(\n",
" \u001b[33mVector\u001b[39m(\n",
" \u001b[33mWeighted\u001b[39m(\u001b[32m∏(A : 𝒰 ){ ∏(B : 𝒰 ){ (A → ((A → B) → B)) } }\u001b[39m, \u001b[32m1.0\u001b[39m)\n",
" )\n",
" ),\n",
" Empty\n",
" ),\n",
" \u001b[33mTermGenParams\u001b[39m(\n",
" \u001b[32m0.1\u001b[39m,\n",
" \u001b[32m0.1\u001b[39m,\n",
" \u001b[32m0.0\u001b[39m,\n",
" \u001b[32m0.0\u001b[39m,\n",
" \u001b[32m0.0\u001b[39m,\n",
" \u001b[32m0.0\u001b[39m,\n",
" \u001b[32m0.0\u001b[39m,\n",
" \u001b[32m0.0\u001b[39m,\n",
" \u001b[32m0.0\u001b[39m,\n",
"...\u001b[39m\n",
"\u001b[36mws4\u001b[39m: \u001b[32mconcurrent\u001b[39m.\u001b[32mFuture\u001b[39m[\u001b[32mWebState\u001b[39m[\u001b[32mHoTTPostWeb\u001b[39m, (\u001b[32mInt\u001b[39m, \u001b[32mInt\u001b[39m)]] = \u001b[32m\u001b[33mSuccess\u001b[39m(\n",
" \u001b[33mWebState\u001b[39m(\n",
" provingground.learning.HoTTPostWeb@2f96de42,\n",
" \u001b[33mVector\u001b[39m(\n",
" \u001b[33mPostData\u001b[39m(\n",
" \u001b[33mLocalProver\u001b[39m(\n",
" \u001b[33mTermState\u001b[39m(\n",
" \u001b[33mFiniteDistribution\u001b[39m(\n",
" \u001b[33mVector\u001b[39m(\n",
" \u001b[33mWeighted\u001b[39m(\u001b[32m𝒰 \u001b[39m, \u001b[32m0.24009999999999995\u001b[39m),\n",
" \u001b[33mWeighted\u001b[39m(\u001b[32mA\u001b[39m, \u001b[32m0.1029\u001b[39m),\n",
" \u001b[33mWeighted\u001b[39m(\u001b[32mB\u001b[39m, \u001b[32m0.14700000000000002\u001b[39m),\n",
" \u001b[33mWeighted\u001b[39m(\u001b[32m$a\u001b[39m, \u001b[32m0.21000000000000002\u001b[39m),\n",
" \u001b[33mWeighted\u001b[39m(\u001b[32m$b\u001b[39m, \u001b[32m0.3\u001b[39m)\n",
" )\n",
" ),\n",
" \u001b[33mFiniteDistribution\u001b[39m(\u001b[33mVector\u001b[39m(\u001b[33mWeighted\u001b[39m(\u001b[32m𝒰 \u001b[39m, \u001b[32m1.0\u001b[39m))),\n",
" \u001b[33mVector\u001b[39m(\u001b[32m$b\u001b[39m, \u001b[32m$a\u001b[39m, \u001b[32mB\u001b[39m, \u001b[32mA\u001b[39m),\n",
" \u001b[33mFiniteDistribution\u001b[39m(\u001b[33mVector\u001b[39m()),\n",
" \u001b[33mFiniteDistribution\u001b[39m(\n",
" \u001b[33mVector\u001b[39m(\n",
" \u001b[33mWeighted\u001b[39m(\n",
" \u001b[32m∏(A : 𝒰 ){ ∏(B : 𝒰 ){ (A → ((A → B) → B)) } }\u001b[39m,\n",
" \u001b[32m0.30000000000000004\u001b[39m\n",
" ),\n",
" \u001b[33mWeighted\u001b[39m(\u001b[32mB\u001b[39m, \u001b[32m0.7\u001b[39m)\n",
" )\n",
" ),\n",
" \u001b[33mAppendVariable\u001b[39m(\n",
" \u001b[33mAppendVariable\u001b[39m(\u001b[33mAppendVariable\u001b[39m(\u001b[33mAppendVariable\u001b[39m(Empty, \u001b[32mA\u001b[39m), \u001b[32mB\u001b[39m), \u001b[32m$a\u001b[39m),\n",
" \u001b[32m$b\u001b[39m\n",
" )\n",
" ),\n",
" \u001b[33mTermGenParams\u001b[39m(\n",
" \u001b[32m0.1\u001b[39m,\n",
" \u001b[32m0.1\u001b[39m,\n",
" \u001b[32m0.0\u001b[39m,\n",
" \u001b[32m0.0\u001b[39m,\n",
" \u001b[32m0.0\u001b[39m,\n",
"...\u001b[39m\n",
"\u001b[36mws5\u001b[39m: \u001b[32mconcurrent\u001b[39m.\u001b[32mFuture\u001b[39m[\u001b[32mWebState\u001b[39m[\u001b[32mHoTTPostWeb\u001b[39m, (\u001b[32mInt\u001b[39m, \u001b[32mInt\u001b[39m)]] = \u001b[32m\u001b[33mSuccess\u001b[39m(\n",
" \u001b[33mWebState\u001b[39m(\n",
" provingground.learning.HoTTPostWeb@2f96de42,\n",
" \u001b[33mVector\u001b[39m(\n",
" \u001b[33mPostData\u001b[39m(\n",
" \u001b[33mFinalState\u001b[39m(\n",
" \u001b[33mTermState\u001b[39m(\n",
" \u001b[33mFiniteDistribution\u001b[39m(\n",
" \u001b[33mVector\u001b[39m(\n",
" \u001b[33mWeighted\u001b[39m(\u001b[32m$b\u001b[39m, \u001b[32m0.24000000000000005\u001b[39m),\n",
" \u001b[33mWeighted\u001b[39m(\u001b[32m𝒰 \u001b[39m, \u001b[32m0.19208000000000003\u001b[39m),\n",
" \u001b[33mWeighted\u001b[39m(\u001b[32mB\u001b[39m, \u001b[32m0.11760000000000005\u001b[39m),\n",
" \u001b[33mWeighted\u001b[39m(\u001b[32mA\u001b[39m, \u001b[32m0.08232000000000002\u001b[39m),\n",
" \u001b[33mWeighted\u001b[39m(\u001b[32m$b($a)\u001b[39m, \u001b[32m0.20000000000000007\u001b[39m),\n",
" \u001b[33mWeighted\u001b[39m(\u001b[32m$a\u001b[39m, \u001b[32m0.16800000000000007\u001b[39m)\n",
" )\n",
" ),\n",
" \u001b[33mFiniteDistribution\u001b[39m(\u001b[33mVector\u001b[39m(\u001b[33mWeighted\u001b[39m(\u001b[32m𝒰 \u001b[39m, \u001b[32m1.0\u001b[39m))),\n",
" \u001b[33mVector\u001b[39m(\u001b[32m$b\u001b[39m, \u001b[32m$a\u001b[39m, \u001b[32mB\u001b[39m, \u001b[32mA\u001b[39m),\n",
" \u001b[33mFiniteDistribution\u001b[39m(\u001b[33mVector\u001b[39m()),\n",
" \u001b[33mFiniteDistribution\u001b[39m(\n",
" \u001b[33mVector\u001b[39m(\n",
" \u001b[33mWeighted\u001b[39m(\n",
" \u001b[32m∏(A : 𝒰 ){ ∏(B : 𝒰 ){ (A → ((A → B) → B)) } }\u001b[39m,\n",
" \u001b[32m0.30000000000000004\u001b[39m\n",
" ),\n",
" \u001b[33mWeighted\u001b[39m(\u001b[32mB\u001b[39m, \u001b[32m0.7\u001b[39m)\n",
" )\n",
" ),\n",
" \u001b[33mAppendVariable\u001b[39m(\n",
" \u001b[33mAppendVariable\u001b[39m(\u001b[33mAppendVariable\u001b[39m(\u001b[33mAppendVariable\u001b[39m(Empty, \u001b[32mA\u001b[39m), \u001b[32mB\u001b[39m), \u001b[32m$a\u001b[39m),\n",
" \u001b[32m$b\u001b[39m\n",
" )\n",
" )\n",
" ),\n",
" (\u001b[32m6\u001b[39m, \u001b[32m-1306168695\u001b[39m)\n",
" ),\n",
" \u001b[33mPostData\u001b[39m(\n",
" \u001b[33mFinalState\u001b[39m(\n",
"...\u001b[39m\n",
"\u001b[36mws6\u001b[39m: \u001b[32mconcurrent\u001b[39m.\u001b[32mFuture\u001b[39m[\u001b[32mWebState\u001b[39m[\u001b[32mHoTTPostWeb\u001b[39m, (\u001b[32mInt\u001b[39m, \u001b[32mInt\u001b[39m)]] = \u001b[32m\u001b[33mSuccess\u001b[39m(\n",
" \u001b[33mWebState\u001b[39m(\n",
" provingground.learning.HoTTPostWeb@2f96de42,\n",
" \u001b[33mVector\u001b[39m(\n",
" \u001b[33mPostData\u001b[39m(\n",
" \u001b[33mFinalState\u001b[39m(\n",
" \u001b[33mTermState\u001b[39m(\n",
" \u001b[33mFiniteDistribution\u001b[39m(\n",
" \u001b[33mVector\u001b[39m(\n",
" \u001b[33mWeighted\u001b[39m(\u001b[32m$b\u001b[39m, \u001b[32m0.24000000000000005\u001b[39m),\n",
" \u001b[33mWeighted\u001b[39m(\u001b[32m𝒰 \u001b[39m, \u001b[32m0.19208000000000003\u001b[39m),\n",
" \u001b[33mWeighted\u001b[39m(\u001b[32mB\u001b[39m, \u001b[32m0.11760000000000005\u001b[39m),\n",
" \u001b[33mWeighted\u001b[39m(\u001b[32mA\u001b[39m, \u001b[32m0.08232000000000002\u001b[39m),\n",
" \u001b[33mWeighted\u001b[39m(\u001b[32m$b($a)\u001b[39m, \u001b[32m0.20000000000000007\u001b[39m),\n",
" \u001b[33mWeighted\u001b[39m(\u001b[32m$a\u001b[39m, \u001b[32m0.16800000000000007\u001b[39m)\n",
" )\n",
" ),\n",
" \u001b[33mFiniteDistribution\u001b[39m(\u001b[33mVector\u001b[39m(\u001b[33mWeighted\u001b[39m(\u001b[32m𝒰 \u001b[39m, \u001b[32m1.0\u001b[39m))),\n",
" \u001b[33mVector\u001b[39m(\u001b[32m$b\u001b[39m, \u001b[32m$a\u001b[39m, \u001b[32mB\u001b[39m, \u001b[32mA\u001b[39m),\n",
" \u001b[33mFiniteDistribution\u001b[39m(\u001b[33mVector\u001b[39m()),\n",
" \u001b[33mFiniteDistribution\u001b[39m(\n",
" \u001b[33mVector\u001b[39m(\n",
" \u001b[33mWeighted\u001b[39m(\n",
" \u001b[32m∏(A : 𝒰 ){ ∏(B : 𝒰 ){ (A → ((A → B) → B)) } }\u001b[39m,\n",
" \u001b[32m0.30000000000000004\u001b[39m\n",
" ),\n",
" \u001b[33mWeighted\u001b[39m(\u001b[32mB\u001b[39m, \u001b[32m0.7\u001b[39m)\n",
" )\n",
" ),\n",
" \u001b[33mAppendVariable\u001b[39m(\n",
" \u001b[33mAppendVariable\u001b[39m(\u001b[33mAppendVariable\u001b[39m(\u001b[33mAppendVariable\u001b[39m(Empty, \u001b[32mA\u001b[39m), \u001b[32mB\u001b[39m), \u001b[32m$a\u001b[39m),\n",
" \u001b[32m$b\u001b[39m\n",
" )\n",
" )\n",
" ),\n",
" (\u001b[32m6\u001b[39m, \u001b[32m-1306168695\u001b[39m)\n",
" ),\n",
" \u001b[33mPostData\u001b[39m(\n",
" \u001b[33mFinalState\u001b[39m(\n",
"...\u001b[39m"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"val web = new HoTTPostWeb()\n",
"val ws = WebState[HoTTPostWeb, HoTTPostWeb.ID](web)\n",
"val ws1 = ws.post(lp, Set())\n",
"val ws2 = ws1.flatMap(w => w.postApex(SeekGoal(MP, Context.Empty)))\n",
"val ws3 = ws2.flatMap(w => w.act(HoTTBot.fullGoalInContext ))\n",
"val ws4 = ws3.flatMap(w => w.act(HoTTBot.goalToProver(0.3, 0.7)))\n",
"val ws5 = ws4.flatMap(w => w.act(HoTTBot.lpToFinalState ))\n",
"val ws6 = ws5.flatMap(w => w.act(HoTTBot.reportSuccesses ))\n"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"\u001b[36mres4\u001b[39m: \u001b[32mString\u001b[39m = \u001b[32mSuccess: Vector(((``A : 𝒰 _0 ) ~> ((``B : 𝒰 _0 ) ~> ((`$a : ``A ) ~> ((`$b : (``A) → (``B) ) ~> (``B)))),0.7,[(``A : 𝒰 _0) ↦ ((``B : 𝒰 _0) ↦ ((``$a : ``A) ↦ ((```$b : (``A) → (``B)) ↦ ((```$b) (``$a))))) : 0.20000000000000007]))\u001b[39m"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Utils.reportText"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Conclusion\n",
"\n",
"Modus Ponens was easily proved by introducing variables."
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Scala",
"language": "scala",
"name": "scala"
},
"language_info": {
"codemirror_mode": "text/x-scala",
"file_extension": ".scala",
"mimetype": "text/x-scala",
"name": "scala",
"nbconvert_exporter": "script",
"version": "2.12.9"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
| mit |
RadoslawDryzner/LeRepoDuGuerrier | Homework00/Homework00.ipynb | 1 | 2201 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Generating data"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"deaths_data_path = \"./data/interactive_data.csv\"\n",
"\n",
"deaths_file = open(deaths_data_path, \"r\")\n",
"next(deaths_file)\n",
"deaths = []\n",
"for line in deaths_file:\n",
" parts = line.split(',')\n",
" deaths.append({'intent' : parts[1], 'gender' : parts[2], 'age' : parts[3], 'race' : parts[4], 'deaths' : parts[5]})"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Getting the correct parts of deaths"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
"def get_deaths(intent='\"None selected\"', gender='\"None selected\"', age='\"None selected\"', race='\"None selected\"'):\n",
" found = None\n",
" for death in deaths:\n",
" if death['intent'] == intent and death['gender'] == gender and death['age'] == age and death['race'] == race:\n",
" found = death\n",
" if found:\n",
" return found['deaths']\n",
" else:\n",
" return 0"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'131'"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"get_deaths('\"Homicide\"', '\"Female\"', '\"15 - 34\"', '\"Hispanic\"')"
]
},
{
"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": "3.6.2"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| mit |
dikien/personnel-study | deep-learning/pylearn2-study/Multilayer-Perceptron.ipynb | 1 | 41938 | {
"cells": [
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"from pylearn2.config import yaml_parse\n",
"from matplotlib import pyplot as plt, cm"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"README mlp_tutorial_part_2.yaml mlp_tutorial_part_4.yaml \u001b[1m\u001b[34mtests\u001b[m\u001b[m\n",
"mlp_best.pkl mlp_tutorial_part_3.yaml multilayer_perceptron.ipynb\n",
"/Users/dikien/Downloads/pylearn2/pylearn2/scripts/tutorials/multilayer_perceptron\n"
]
}
],
"source": [
"!ls /Users/dikien/Downloads/pylearn2/pylearn2/scripts/tutorials/multilayer_perceptron/\n",
"%cd /Users/dikien/Downloads/pylearn2/pylearn2/scripts/tutorials/multilayer_perceptron/ "
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"!obj:pylearn2.train.Train {\n",
" dataset: &train !obj:pylearn2.datasets.mnist.MNIST {\n",
" which_set: 'train',\n",
" start: 0,\n",
" stop: 50000\n",
" },\n",
" model: !obj:pylearn2.models.mlp.MLP {\n",
" layers: [\n",
" !obj:pylearn2.models.mlp.Sigmoid {\n",
" layer_name: 'h0',\n",
" dim: 500,\n",
" sparse_init: 15,\n",
" }, !obj:pylearn2.models.mlp.Softmax {\n",
" layer_name: 'y',\n",
" n_classes: 10,\n",
" irange: 0.\n",
" }\n",
" ],\n",
" nvis: 784,\n",
" },\n",
" algorithm: !obj:pylearn2.training_algorithms.bgd.BGD {\n",
" batch_size: 10000,\n",
" line_search_mode: 'exhaustive',\n",
" conjugate: 1,\n",
" updates_per_batch: 10,\n",
" monitoring_dataset:\n",
" {\n",
" 'train' : *train,\n",
" 'valid' : !obj:pylearn2.datasets.mnist.MNIST {\n",
" which_set: 'train',\n",
" start: 50000,\n",
" stop: 60000\n",
" },\n",
" 'test' : !obj:pylearn2.datasets.mnist.MNIST {\n",
" which_set: 'test',\n",
" }\n",
" },\n",
" termination_criterion: !obj:pylearn2.termination_criteria.And {\n",
" criteria: [\n",
" !obj:pylearn2.termination_criteria.MonitorBased {\n",
" channel_name: \"valid_y_misclass\"\n",
" },\n",
" !obj:pylearn2.termination_criteria.EpochCounter {\n",
" max_epochs: 1\n",
" }\n",
" ]\n",
" }\n",
" },\n",
" extensions: [\n",
" !obj:pylearn2.train_extensions.best_params.MonitorBasedSaveBest {\n",
" channel_name: 'valid_y_misclass',\n",
" save_path: \"./mlp_best.pkl\"\n",
" },\n",
" ]\n",
"}\n",
"\n"
]
}
],
"source": [
"with open(\"mlp_tutorial_part_2.yaml\", 'r') as f:\n",
" train = f.read()\n",
"hyper_params = {'train_stop' : 50000,\n",
" 'valid_stop' : 60000,\n",
" 'dim_h0' : 500,\n",
" 'max_epochs' : 1, #10000\n",
" 'save_path' : '.'}\n",
"train = train % (hyper_params)\n",
"print train"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"compiling begin_record_entry...\n",
"compiling begin_record_entry done. Time elapsed: 0.316489 seconds\n",
"Monitored channels: \n",
"\tave_grad_mult\n",
"\tave_grad_size\n",
"\tave_step_size\n",
"\ttest_h0_col_norms_max\n",
"\ttest_h0_col_norms_mean\n",
"\ttest_h0_col_norms_min\n",
"\ttest_h0_max_x_max_u\n",
"\ttest_h0_max_x_mean_u\n",
"\ttest_h0_max_x_min_u\n",
"\ttest_h0_mean_x_max_u\n",
"\ttest_h0_mean_x_mean_u\n",
"\ttest_h0_mean_x_min_u\n",
"\ttest_h0_min_x_max_u\n",
"\ttest_h0_min_x_mean_u\n",
"\ttest_h0_min_x_min_u\n",
"\ttest_h0_range_x_max_u\n",
"\ttest_h0_range_x_mean_u\n",
"\ttest_h0_range_x_min_u\n",
"\ttest_h0_row_norms_max\n",
"\ttest_h0_row_norms_mean\n",
"\ttest_h0_row_norms_min\n",
"\ttest_objective\n",
"\ttest_y_col_norms_max\n",
"\ttest_y_col_norms_mean\n",
"\ttest_y_col_norms_min\n",
"\ttest_y_max_max_class\n",
"\ttest_y_mean_max_class\n",
"\ttest_y_min_max_class\n",
"\ttest_y_misclass\n",
"\ttest_y_nll\n",
"\ttest_y_row_norms_max\n",
"\ttest_y_row_norms_mean\n",
"\ttest_y_row_norms_min\n",
"\ttotal_seconds_last_epoch\n",
"\ttrain_h0_col_norms_max\n",
"\ttrain_h0_col_norms_mean\n",
"\ttrain_h0_col_norms_min\n",
"\ttrain_h0_max_x_max_u\n",
"\ttrain_h0_max_x_mean_u\n",
"\ttrain_h0_max_x_min_u\n",
"\ttrain_h0_mean_x_max_u\n",
"\ttrain_h0_mean_x_mean_u\n",
"\ttrain_h0_mean_x_min_u\n",
"\ttrain_h0_min_x_max_u\n",
"\ttrain_h0_min_x_mean_u\n",
"\ttrain_h0_min_x_min_u\n",
"\ttrain_h0_range_x_max_u\n",
"\ttrain_h0_range_x_mean_u\n",
"\ttrain_h0_range_x_min_u\n",
"\ttrain_h0_row_norms_max\n",
"\ttrain_h0_row_norms_mean\n",
"\ttrain_h0_row_norms_min\n",
"\ttrain_objective\n",
"\ttrain_y_col_norms_max\n",
"\ttrain_y_col_norms_mean\n",
"\ttrain_y_col_norms_min\n",
"\ttrain_y_max_max_class\n",
"\ttrain_y_mean_max_class\n",
"\ttrain_y_min_max_class\n",
"\ttrain_y_misclass\n",
"\ttrain_y_nll\n",
"\ttrain_y_row_norms_max\n",
"\ttrain_y_row_norms_mean\n",
"\ttrain_y_row_norms_min\n",
"\ttraining_seconds_this_epoch\n",
"\tvalid_h0_col_norms_max\n",
"\tvalid_h0_col_norms_mean\n",
"\tvalid_h0_col_norms_min\n",
"\tvalid_h0_max_x_max_u\n",
"\tvalid_h0_max_x_mean_u\n",
"\tvalid_h0_max_x_min_u\n",
"\tvalid_h0_mean_x_max_u\n",
"\tvalid_h0_mean_x_mean_u\n",
"\tvalid_h0_mean_x_min_u\n",
"\tvalid_h0_min_x_max_u\n",
"\tvalid_h0_min_x_mean_u\n",
"\tvalid_h0_min_x_min_u\n",
"\tvalid_h0_range_x_max_u\n",
"\tvalid_h0_range_x_mean_u\n",
"\tvalid_h0_range_x_min_u\n",
"\tvalid_h0_row_norms_max\n",
"\tvalid_h0_row_norms_mean\n",
"\tvalid_h0_row_norms_min\n",
"\tvalid_objective\n",
"\tvalid_y_col_norms_max\n",
"\tvalid_y_col_norms_mean\n",
"\tvalid_y_col_norms_min\n",
"\tvalid_y_max_max_class\n",
"\tvalid_y_mean_max_class\n",
"\tvalid_y_min_max_class\n",
"\tvalid_y_misclass\n",
"\tvalid_y_nll\n",
"\tvalid_y_row_norms_max\n",
"\tvalid_y_row_norms_mean\n",
"\tvalid_y_row_norms_min\n",
"Compiling accum...\n",
"graph size: 118\n",
"graph size: 113\n",
"graph size: 113\n",
"Compiling accum done. Time elapsed: 4.033446 seconds\n",
"Monitoring step:\n",
"\tEpochs seen: 0\n",
"\tBatches seen: 0\n",
"\tExamples seen: 0\n",
"\tave_grad_mult: 0.0\n",
"\tave_grad_size: 0.0\n",
"\tave_step_size: 0.0\n",
"\ttest_h0_col_norms_max: 6.23503405999\n",
"\ttest_h0_col_norms_mean: 3.82355643971\n",
"\ttest_h0_col_norms_min: 2.06193996111\n",
"\ttest_h0_max_x_max_u: 0.999900672858\n",
"\ttest_h0_max_x_mean_u: 0.909941495671\n",
"\ttest_h0_max_x_min_u: 0.508436836559\n",
"\ttest_h0_mean_x_max_u: 0.901069905001\n",
"\ttest_h0_mean_x_mean_u: 0.476713326581\n",
"\ttest_h0_mean_x_min_u: 0.152832768345\n",
"\ttest_h0_min_x_max_u: 0.480607664972\n",
"\ttest_h0_min_x_mean_u: 0.0718067763455\n",
"\ttest_h0_min_x_min_u: 0.000174344384626\n",
"\ttest_h0_range_x_max_u: 0.98963074706\n",
"\ttest_h0_range_x_mean_u: 0.838134719326\n",
"\ttest_h0_range_x_min_u: 0.461663761987\n",
"\ttest_h0_row_norms_max: 5.89326124667\n",
"\ttest_h0_row_norms_mean: 2.98549156744\n",
"\ttest_h0_row_norms_min: 0.0\n",
"\ttest_objective: 2.30258509299\n",
"\ttest_y_col_norms_max: 0.0\n",
"\ttest_y_col_norms_mean: 0.0\n",
"\ttest_y_col_norms_min: 0.0\n",
"\ttest_y_max_max_class: 0.1\n",
"\ttest_y_mean_max_class: 0.1\n",
"\ttest_y_min_max_class: 0.1\n",
"\ttest_y_misclass: 0.902\n",
"\ttest_y_nll: 2.30258509299\n",
"\ttest_y_row_norms_max: 0.0\n",
"\ttest_y_row_norms_mean: 0.0\n",
"\ttest_y_row_norms_min: 0.0\n",
"\ttotal_seconds_last_epoch: 0.0\n",
"\ttrain_h0_col_norms_max: 6.23503405999\n",
"\ttrain_h0_col_norms_mean: 3.82355643971\n",
"\ttrain_h0_col_norms_min: 2.06193996111\n",
"\ttrain_h0_max_x_max_u: 0.999884207079\n",
"\ttrain_h0_max_x_mean_u: 0.910601234661\n",
"\ttrain_h0_max_x_min_u: 0.542480235261\n",
"\ttrain_h0_mean_x_max_u: 0.899177645344\n",
"\ttrain_h0_mean_x_mean_u: 0.477026786605\n",
"\ttrain_h0_mean_x_min_u: 0.158626428409\n",
"\ttrain_h0_min_x_max_u: 0.458495451967\n",
"\ttrain_h0_min_x_mean_u: 0.0697232989866\n",
"\ttrain_h0_min_x_min_u: 0.000107248355934\n",
"\ttrain_h0_range_x_max_u: 0.993503615767\n",
"\ttrain_h0_range_x_mean_u: 0.840877935674\n",
"\ttrain_h0_range_x_min_u: 0.432142549731\n",
"\ttrain_h0_row_norms_max: 5.89326124667\n",
"\ttrain_h0_row_norms_mean: 2.98549156744\n",
"\ttrain_h0_row_norms_min: 0.0\n",
"\ttrain_objective: 2.30258509299\n",
"\ttrain_y_col_norms_max: 0.0\n",
"\ttrain_y_col_norms_mean: 0.0\n",
"\ttrain_y_col_norms_min: 0.0\n",
"\ttrain_y_max_max_class: 0.1\n",
"\ttrain_y_mean_max_class: 0.1\n",
"\ttrain_y_min_max_class: 0.1\n",
"\ttrain_y_misclass: 0.90136\n",
"\ttrain_y_nll: 2.30258509299\n",
"\ttrain_y_row_norms_max: 0.0\n",
"\ttrain_y_row_norms_mean: 0.0\n",
"\ttrain_y_row_norms_min: 0.0\n",
"\ttraining_seconds_this_epoch: 0.0\n",
"\tvalid_h0_col_norms_max: 6.23503405999\n",
"\tvalid_h0_col_norms_mean: 3.82355643971\n",
"\tvalid_h0_col_norms_min: 2.06193996111\n",
"\tvalid_h0_max_x_max_u: 0.999902364459\n",
"\tvalid_h0_max_x_mean_u: 0.910734674045\n",
"\tvalid_h0_max_x_min_u: 0.505713638328\n",
"\tvalid_h0_mean_x_max_u: 0.897212634566\n",
"\tvalid_h0_mean_x_mean_u: 0.477113329951\n",
"\tvalid_h0_mean_x_min_u: 0.159442692765\n",
"\tvalid_h0_min_x_max_u: 0.474104176772\n",
"\tvalid_h0_min_x_mean_u: 0.07068185398\n",
"\tvalid_h0_min_x_min_u: 0.000110276493931\n",
"\tvalid_h0_range_x_max_u: 0.994406994152\n",
"\tvalid_h0_range_x_mean_u: 0.840052820065\n",
"\tvalid_h0_range_x_min_u: 0.445501338425\n",
"\tvalid_h0_row_norms_max: 5.89326124667\n",
"\tvalid_h0_row_norms_mean: 2.98549156744\n",
"\tvalid_h0_row_norms_min: 0.0\n",
"\tvalid_objective: 2.30258509299\n",
"\tvalid_y_col_norms_max: 0.0\n",
"\tvalid_y_col_norms_mean: 0.0\n",
"\tvalid_y_col_norms_min: 0.0\n",
"\tvalid_y_max_max_class: 0.1\n",
"\tvalid_y_mean_max_class: 0.1\n",
"\tvalid_y_min_max_class: 0.1\n",
"\tvalid_y_misclass: 0.9009\n",
"\tvalid_y_nll: 2.30258509299\n",
"\tvalid_y_row_norms_max: 0.0\n",
"\tvalid_y_row_norms_mean: 0.0\n",
"\tvalid_y_row_norms_min: 0.0\n",
"Saving to ./mlp_best.pkl...\n",
"Saving to ./mlp_best.pkl done. Time elapsed: 0.621315 seconds\n",
"Time this epoch: 0:03:22.669563\n",
"Monitoring step:\n",
"\tEpochs seen: 1\n",
"\tBatches seen: 5\n",
"\tExamples seen: 50000\n",
"\tave_grad_mult: 0.568428376745\n",
"\tave_grad_size: 0.569275215102\n",
"\tave_step_size: 0.293164906817\n",
"\ttest_h0_col_norms_max: 6.24084816402\n",
"\ttest_h0_col_norms_mean: 3.8330191565\n",
"\ttest_h0_col_norms_min: 2.07277098332\n",
"\ttest_h0_max_x_max_u: 0.999797012523\n",
"\ttest_h0_max_x_mean_u: 0.930507070205\n",
"\ttest_h0_max_x_min_u: 0.600885181612\n",
"\ttest_h0_mean_x_max_u: 0.86138560511\n",
"\ttest_h0_mean_x_mean_u: 0.47684242763\n",
"\ttest_h0_mean_x_min_u: 0.171815715135\n",
"\ttest_h0_min_x_max_u: 0.410199041785\n",
"\ttest_h0_min_x_mean_u: 0.0532412819482\n",
"\ttest_h0_min_x_min_u: 0.000194870876317\n",
"\ttest_h0_range_x_max_u: 0.995852544072\n",
"\ttest_h0_range_x_mean_u: 0.877265788257\n",
"\ttest_h0_range_x_min_u: 0.548945839167\n",
"\ttest_h0_row_norms_max: 5.89793230281\n",
"\ttest_h0_row_norms_mean: 2.99315610991\n",
"\ttest_h0_row_norms_min: 0.00719804802815\n",
"\ttest_objective: 0.345809614411\n",
"\ttest_y_col_norms_max: 2.78234106666\n",
"\ttest_y_col_norms_mean: 2.59701149502\n",
"\ttest_y_col_norms_min: 2.3810874603\n",
"\ttest_y_max_max_class: 0.999821373414\n",
"\ttest_y_mean_max_class: 0.844709547442\n",
"\ttest_y_min_max_class: 0.206097493724\n",
"\ttest_y_misclass: 0.0964\n",
"\ttest_y_nll: 0.345809614411\n",
"\ttest_y_row_norms_max: 0.705822478407\n",
"\ttest_y_row_norms_mean: 0.347877919902\n",
"\ttest_y_row_norms_min: 0.0778698200526\n",
"\ttotal_seconds_last_epoch: 0.0\n",
"\ttrain_h0_col_norms_max: 6.24084816402\n",
"\ttrain_h0_col_norms_mean: 3.8330191565\n",
"\ttrain_h0_col_norms_min: 2.07277098332\n",
"\ttrain_h0_max_x_max_u: 0.999829597379\n",
"\ttrain_h0_max_x_mean_u: 0.931275661933\n",
"\ttrain_h0_max_x_min_u: 0.618205979121\n",
"\ttrain_h0_mean_x_max_u: 0.858717658701\n",
"\ttrain_h0_mean_x_mean_u: 0.477121657696\n",
"\ttrain_h0_mean_x_min_u: 0.178341485107\n",
"\ttrain_h0_min_x_max_u: 0.385012327626\n",
"\ttrain_h0_min_x_mean_u: 0.0521292482027\n",
"\ttrain_h0_min_x_min_u: 0.000150238486259\n",
"\ttrain_h0_range_x_max_u: 0.996723214144\n",
"\ttrain_h0_range_x_mean_u: 0.879146413731\n",
"\ttrain_h0_range_x_min_u: 0.547978559446\n",
"\ttrain_h0_row_norms_max: 5.89793230281\n",
"\ttrain_h0_row_norms_mean: 2.99315610991\n",
"\ttrain_h0_row_norms_min: 0.00719804802815\n",
"\ttrain_objective: 0.367122860086\n",
"\ttrain_y_col_norms_max: 2.78234106666\n",
"\ttrain_y_col_norms_mean: 2.59701149502\n",
"\ttrain_y_col_norms_min: 2.3810874603\n",
"\ttrain_y_max_max_class: 0.999852802841\n",
"\ttrain_y_mean_max_class: 0.837913061583\n",
"\ttrain_y_min_max_class: 0.200456685509\n",
"\ttrain_y_misclass: 0.10438\n",
"\ttrain_y_nll: 0.367122860086\n",
"\ttrain_y_row_norms_max: 0.705822478407\n",
"\ttrain_y_row_norms_mean: 0.347877919902\n",
"\ttrain_y_row_norms_min: 0.0778698200526\n",
"\ttraining_seconds_this_epoch: 202.669563\n",
"\tvalid_h0_col_norms_max: 6.24084816402\n",
"\tvalid_h0_col_norms_mean: 3.8330191565\n",
"\tvalid_h0_col_norms_min: 2.07277098332\n",
"\tvalid_h0_max_x_max_u: 0.999862677883\n",
"\tvalid_h0_max_x_mean_u: 0.930968702242\n",
"\tvalid_h0_max_x_min_u: 0.640350398959\n",
"\tvalid_h0_mean_x_max_u: 0.856678213378\n",
"\tvalid_h0_mean_x_mean_u: 0.477206979816\n",
"\tvalid_h0_mean_x_min_u: 0.178252463979\n",
"\tvalid_h0_min_x_max_u: 0.360575213125\n",
"\tvalid_h0_min_x_mean_u: 0.0527645351181\n",
"\tvalid_h0_min_x_min_u: 0.000214541742634\n",
"\tvalid_h0_range_x_max_u: 0.997314378604\n",
"\tvalid_h0_range_x_mean_u: 0.878204167123\n",
"\tvalid_h0_range_x_min_u: 0.521885364154\n",
"\tvalid_h0_row_norms_max: 5.89793230281\n",
"\tvalid_h0_row_norms_mean: 2.99315610991\n",
"\tvalid_h0_row_norms_min: 0.00719804802815\n",
"\tvalid_objective: 0.334305279604\n",
"\tvalid_y_col_norms_max: 2.78234106666\n",
"\tvalid_y_col_norms_mean: 2.59701149502\n",
"\tvalid_y_col_norms_min: 2.3810874603\n",
"\tvalid_y_max_max_class: 0.999953240738\n",
"\tvalid_y_mean_max_class: 0.84881891044\n",
"\tvalid_y_min_max_class: 0.193943289318\n",
"\tvalid_y_misclass: 0.0947\n",
"\tvalid_y_nll: 0.334305279604\n",
"\tvalid_y_row_norms_max: 0.705822478407\n",
"\tvalid_y_row_norms_mean: 0.347877919902\n",
"\tvalid_y_row_norms_min: 0.0778698200526\n",
"Saving to ./mlp_best.pkl...\n",
"Saving to ./mlp_best.pkl done. Time elapsed: 0.342858 seconds\n"
]
}
],
"source": [
"train = yaml_parse.load(train)\n",
"train.main_loop()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"README mlp_tutorial_part_2.yaml mlp_tutorial_part_4.yaml \u001b[1m\u001b[34mtests\u001b[m\u001b[m/\r\n",
"mlp_best.pkl mlp_tutorial_part_3.yaml multilayer_perceptron.ipynb\r\n"
]
}
],
"source": [
"ls"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"epochs seen: 1\n",
"time trained: 218.477574825\n",
"ave_grad_mult : 0.568428376745\n",
"ave_grad_size : 0.569275215102\n",
"ave_step_size : 0.293164906817\n",
"test_h0_col_norms_max : 6.24084816402\n",
"test_h0_col_norms_mean : 3.8330191565\n",
"test_h0_col_norms_min : 2.07277098332\n",
"test_h0_max_x_max_u : 0.999797012523\n",
"test_h0_max_x_mean_u : 0.930507070205\n",
"test_h0_max_x_min_u : 0.600885181612\n",
"test_h0_mean_x_max_u : 0.86138560511\n",
"test_h0_mean_x_mean_u : 0.47684242763\n",
"test_h0_mean_x_min_u : 0.171815715135\n",
"test_h0_min_x_max_u : 0.410199041785\n",
"test_h0_min_x_mean_u : 0.0532412819482\n",
"test_h0_min_x_min_u : 0.000194870876317\n",
"test_h0_range_x_max_u : 0.995852544072\n",
"test_h0_range_x_mean_u : 0.877265788257\n",
"test_h0_range_x_min_u : 0.548945839167\n",
"test_h0_row_norms_max : 5.89793230281\n",
"test_h0_row_norms_mean : 2.99315610991\n",
"test_h0_row_norms_min : 0.00719804802815\n",
"test_objective : 0.345809614411\n",
"test_y_col_norms_max : 2.78234106666\n",
"test_y_col_norms_mean : 2.59701149502\n",
"test_y_col_norms_min : 2.3810874603\n",
"test_y_max_max_class : 0.999821373414\n",
"test_y_mean_max_class : 0.844709547442\n",
"test_y_min_max_class : 0.206097493724\n",
"test_y_misclass : 0.0964\n",
"test_y_nll : 0.345809614411\n",
"test_y_row_norms_max : 0.705822478407\n",
"test_y_row_norms_mean : 0.347877919902\n",
"test_y_row_norms_min : 0.0778698200526\n",
"total_seconds_last_epoch : 0.0\n",
"train_h0_col_norms_max : 6.24084816402\n",
"train_h0_col_norms_mean : 3.8330191565\n",
"train_h0_col_norms_min : 2.07277098332\n",
"train_h0_max_x_max_u : 0.999829597379\n",
"train_h0_max_x_mean_u : 0.931275661933\n",
"train_h0_max_x_min_u : 0.618205979121\n",
"train_h0_mean_x_max_u : 0.858717658701\n",
"train_h0_mean_x_mean_u : 0.477121657696\n",
"train_h0_mean_x_min_u : 0.178341485107\n",
"train_h0_min_x_max_u : 0.385012327626\n",
"train_h0_min_x_mean_u : 0.0521292482027\n",
"train_h0_min_x_min_u : 0.000150238486259\n",
"train_h0_range_x_max_u : 0.996723214144\n",
"train_h0_range_x_mean_u : 0.879146413731\n",
"train_h0_range_x_min_u : 0.547978559446\n",
"train_h0_row_norms_max : 5.89793230281\n",
"train_h0_row_norms_mean : 2.99315610991\n",
"train_h0_row_norms_min : 0.00719804802815\n",
"train_objective : 0.367122860086\n",
"train_y_col_norms_max : 2.78234106666\n",
"train_y_col_norms_mean : 2.59701149502\n",
"train_y_col_norms_min : 2.3810874603\n",
"train_y_max_max_class : 0.999852802841\n",
"train_y_mean_max_class : 0.837913061583\n",
"train_y_min_max_class : 0.200456685509\n",
"train_y_misclass : 0.10438\n",
"train_y_nll : 0.367122860086\n",
"train_y_row_norms_max : 0.705822478407\n",
"train_y_row_norms_mean : 0.347877919902\n",
"train_y_row_norms_min : 0.0778698200526\n",
"training_seconds_this_epoch : 202.669563\n",
"valid_h0_col_norms_max : 6.24084816402\n",
"valid_h0_col_norms_mean : 3.8330191565\n",
"valid_h0_col_norms_min : 2.07277098332\n",
"valid_h0_max_x_max_u : 0.999862677883\n",
"valid_h0_max_x_mean_u : 0.930968702242\n",
"valid_h0_max_x_min_u : 0.640350398959\n",
"valid_h0_mean_x_max_u : 0.856678213378\n",
"valid_h0_mean_x_mean_u : 0.477206979816\n",
"valid_h0_mean_x_min_u : 0.178252463979\n",
"valid_h0_min_x_max_u : 0.360575213125\n",
"valid_h0_min_x_mean_u : 0.0527645351181\n",
"valid_h0_min_x_min_u : 0.000214541742634\n",
"valid_h0_range_x_max_u : 0.997314378604\n",
"valid_h0_range_x_mean_u : 0.878204167123\n",
"valid_h0_range_x_min_u : 0.521885364154\n",
"valid_h0_row_norms_max : 5.89793230281\n",
"valid_h0_row_norms_mean : 2.99315610991\n",
"valid_h0_row_norms_min : 0.00719804802815\n",
"valid_objective : 0.334305279604\n",
"valid_y_col_norms_max : 2.78234106666\n",
"valid_y_col_norms_mean : 2.59701149502\n",
"valid_y_col_norms_min : 2.3810874603\n",
"valid_y_max_max_class : 0.999953240738\n",
"valid_y_mean_max_class : 0.84881891044\n",
"valid_y_min_max_class : 0.193943289318\n",
"valid_y_misclass : 0.0947\n",
"valid_y_nll : 0.334305279604\n",
"valid_y_row_norms_max : 0.705822478407\n",
"valid_y_row_norms_mean : 0.347877919902\n",
"valid_y_row_norms_min : 0.0778698200526\n"
]
}
],
"source": [
"%%bash\n",
"pylearn2-print-monitor mlp_best.pkl"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"making weights report\n",
"loading model\n",
"loading done\n",
"loading dataset...\n",
"...done\n",
"smallest enc weight magnitude: 0.0\n",
"mean enc weight magnitude: 0.0182802173879\n",
"max enc weight magnitude: 4.66034334324\n",
"min norm: 2.07277098332\n",
"mean norm: 3.8330191565\n",
"max norm: 6.24084816402\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/bin/sh: eog: command not found\n"
]
}
],
"source": [
"%%bash\n",
"pylearn2-show-weights mlp_best.pkl"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"!obj:pylearn2.train.Train {\n",
" dataset: &train !obj:pylearn2.datasets.mnist.MNIST {\n",
" which_set: 'train',\n",
" start: 0,\n",
" stop: 50000\n",
" },\n",
" model: !obj:pylearn2.models.mlp.MLP {\n",
" layers: [\n",
" !obj:pylearn2.models.mlp.Sigmoid {\n",
" layer_name: 'h0',\n",
" dim: 500,\n",
" sparse_init: 15,\n",
" }, !obj:pylearn2.models.mlp.Softmax {\n",
" layer_name: 'y',\n",
" n_classes: 10,\n",
" irange: 0.\n",
" }\n",
" ],\n",
" nvis: 784,\n",
" },\n",
" algorithm: !obj:pylearn2.training_algorithms.bgd.BGD {\n",
" batch_size: 10000,\n",
" line_search_mode: 'exhaustive',\n",
" conjugate: 1,\n",
" updates_per_batch: 10,\n",
" monitoring_dataset:\n",
" {\n",
" 'train' : *train,\n",
" 'valid' : !obj:pylearn2.datasets.mnist.MNIST {\n",
" which_set: 'train',\n",
" start: 50000,\n",
" stop: 60000\n",
" },\n",
" 'test' : !obj:pylearn2.datasets.mnist.MNIST {\n",
" which_set: 'test',\n",
" }\n",
" },\n",
" termination_criterion: !obj:pylearn2.termination_criteria.And {\n",
" criteria: [\n",
" !obj:pylearn2.termination_criteria.MonitorBased {\n",
" channel_name: \"valid_y_misclass\"\n",
" },\n",
" !obj:pylearn2.termination_criteria.EpochCounter {\n",
" max_epochs: 1\n",
" }\n",
" ]\n",
" }\n",
" },\n",
" extensions: [\n",
" !obj:pylearn2.train_extensions.best_params.MonitorBasedSaveBest {\n",
" channel_name: 'valid_y_misclass',\n",
" save_path: \"./mlp_best.pkl\"\n",
" },\n",
" ]\n",
"}\n",
"\n"
]
}
],
"source": [
"with open(\"mlp_tutorial_part_2.yaml\", 'r') as f:\n",
" train = f.read()\n",
"hyper_params = {'train_stop' : 50000,\n",
" 'valid_stop' : 60000,\n",
" 'dim_h0' : 500,\n",
" 'max_epochs' : 1, # 10000\n",
" 'save_path' : '.'}\n",
"train = train % (hyper_params)\n",
"print train"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"compiling begin_record_entry...\n",
"compiling begin_record_entry done. Time elapsed: 0.309946 seconds\n",
"Monitored channels: \n",
"\tave_grad_mult\n",
"\tave_grad_size\n",
"\tave_step_size\n",
"\ttest_h0_col_norms_max\n",
"\ttest_h0_col_norms_mean\n",
"\ttest_h0_col_norms_min\n",
"\ttest_h0_max_x_max_u\n",
"\ttest_h0_max_x_mean_u\n",
"\ttest_h0_max_x_min_u\n",
"\ttest_h0_mean_x_max_u\n",
"\ttest_h0_mean_x_mean_u\n",
"\ttest_h0_mean_x_min_u\n",
"\ttest_h0_min_x_max_u\n",
"\ttest_h0_min_x_mean_u\n",
"\ttest_h0_min_x_min_u\n",
"\ttest_h0_range_x_max_u\n",
"\ttest_h0_range_x_mean_u\n",
"\ttest_h0_range_x_min_u\n",
"\ttest_h0_row_norms_max\n",
"\ttest_h0_row_norms_mean\n",
"\ttest_h0_row_norms_min\n",
"\ttest_objective\n",
"\ttest_y_col_norms_max\n",
"\ttest_y_col_norms_mean\n",
"\ttest_y_col_norms_min\n",
"\ttest_y_max_max_class\n",
"\ttest_y_mean_max_class\n",
"\ttest_y_min_max_class\n",
"\ttest_y_misclass\n",
"\ttest_y_nll\n",
"\ttest_y_row_norms_max\n",
"\ttest_y_row_norms_mean\n",
"\ttest_y_row_norms_min\n",
"\ttotal_seconds_last_epoch\n",
"\ttrain_h0_col_norms_max\n",
"\ttrain_h0_col_norms_mean\n",
"\ttrain_h0_col_norms_min\n",
"\ttrain_h0_max_x_max_u\n",
"\ttrain_h0_max_x_mean_u\n",
"\ttrain_h0_max_x_min_u\n",
"\ttrain_h0_mean_x_max_u\n",
"\ttrain_h0_mean_x_mean_u\n",
"\ttrain_h0_mean_x_min_u\n",
"\ttrain_h0_min_x_max_u\n",
"\ttrain_h0_min_x_mean_u\n",
"\ttrain_h0_min_x_min_u\n",
"\ttrain_h0_range_x_max_u\n",
"\ttrain_h0_range_x_mean_u\n",
"\ttrain_h0_range_x_min_u\n",
"\ttrain_h0_row_norms_max\n",
"\ttrain_h0_row_norms_mean\n",
"\ttrain_h0_row_norms_min\n",
"\ttrain_objective\n",
"\ttrain_y_col_norms_max\n",
"\ttrain_y_col_norms_mean\n",
"\ttrain_y_col_norms_min\n",
"\ttrain_y_max_max_class\n",
"\ttrain_y_mean_max_class\n",
"\ttrain_y_min_max_class\n",
"\ttrain_y_misclass\n",
"\ttrain_y_nll\n",
"\ttrain_y_row_norms_max\n",
"\ttrain_y_row_norms_mean\n",
"\ttrain_y_row_norms_min\n",
"\ttraining_seconds_this_epoch\n",
"\tvalid_h0_col_norms_max\n",
"\tvalid_h0_col_norms_mean\n",
"\tvalid_h0_col_norms_min\n",
"\tvalid_h0_max_x_max_u\n",
"\tvalid_h0_max_x_mean_u\n",
"\tvalid_h0_max_x_min_u\n",
"\tvalid_h0_mean_x_max_u\n",
"\tvalid_h0_mean_x_mean_u\n",
"\tvalid_h0_mean_x_min_u\n",
"\tvalid_h0_min_x_max_u\n",
"\tvalid_h0_min_x_mean_u\n",
"\tvalid_h0_min_x_min_u\n",
"\tvalid_h0_range_x_max_u\n",
"\tvalid_h0_range_x_mean_u\n",
"\tvalid_h0_range_x_min_u\n",
"\tvalid_h0_row_norms_max\n",
"\tvalid_h0_row_norms_mean\n",
"\tvalid_h0_row_norms_min\n",
"\tvalid_objective\n",
"\tvalid_y_col_norms_max\n",
"\tvalid_y_col_norms_mean\n",
"\tvalid_y_col_norms_min\n",
"\tvalid_y_max_max_class\n",
"\tvalid_y_mean_max_class\n",
"\tvalid_y_min_max_class\n",
"\tvalid_y_misclass\n",
"\tvalid_y_nll\n",
"\tvalid_y_row_norms_max\n",
"\tvalid_y_row_norms_mean\n",
"\tvalid_y_row_norms_min\n",
"Compiling accum...\n",
"graph size: 118\n",
"graph size: 113\n",
"graph size: 113\n",
"Compiling accum done. Time elapsed: 3.557186 seconds\n",
"Monitoring step:\n",
"\tEpochs seen: 0\n",
"\tBatches seen: 0\n",
"\tExamples seen: 0\n",
"\tave_grad_mult: 0.0\n",
"\tave_grad_size: 0.0\n",
"\tave_step_size: 0.0\n",
"\ttest_h0_col_norms_max: 6.23503405999\n",
"\ttest_h0_col_norms_mean: 3.82355643971\n",
"\ttest_h0_col_norms_min: 2.06193996111\n",
"\ttest_h0_max_x_max_u: 0.999900672858\n",
"\ttest_h0_max_x_mean_u: 0.909941495671\n",
"\ttest_h0_max_x_min_u: 0.508436836559\n",
"\ttest_h0_mean_x_max_u: 0.901069905001\n",
"\ttest_h0_mean_x_mean_u: 0.476713326581\n",
"\ttest_h0_mean_x_min_u: 0.152832768345\n",
"\ttest_h0_min_x_max_u: 0.480607664972\n",
"\ttest_h0_min_x_mean_u: 0.0718067763455\n",
"\ttest_h0_min_x_min_u: 0.000174344384626\n",
"\ttest_h0_range_x_max_u: 0.98963074706\n",
"\ttest_h0_range_x_mean_u: 0.838134719326\n",
"\ttest_h0_range_x_min_u: 0.461663761987\n",
"\ttest_h0_row_norms_max: 5.89326124667\n",
"\ttest_h0_row_norms_mean: 2.98549156744\n",
"\ttest_h0_row_norms_min: 0.0\n",
"\ttest_objective: 2.30258509299\n",
"\ttest_y_col_norms_max: 0.0\n",
"\ttest_y_col_norms_mean: 0.0\n",
"\ttest_y_col_norms_min: 0.0\n",
"\ttest_y_max_max_class: 0.1\n",
"\ttest_y_mean_max_class: 0.1\n",
"\ttest_y_min_max_class: 0.1\n",
"\ttest_y_misclass: 0.902\n",
"\ttest_y_nll: 2.30258509299\n",
"\ttest_y_row_norms_max: 0.0\n",
"\ttest_y_row_norms_mean: 0.0\n",
"\ttest_y_row_norms_min: 0.0\n",
"\ttotal_seconds_last_epoch: 0.0\n",
"\ttrain_h0_col_norms_max: 6.23503405999\n",
"\ttrain_h0_col_norms_mean: 3.82355643971\n",
"\ttrain_h0_col_norms_min: 2.06193996111\n",
"\ttrain_h0_max_x_max_u: 0.999884207079\n",
"\ttrain_h0_max_x_mean_u: 0.910601234661\n",
"\ttrain_h0_max_x_min_u: 0.542480235261\n",
"\ttrain_h0_mean_x_max_u: 0.899177645344\n",
"\ttrain_h0_mean_x_mean_u: 0.477026786605\n",
"\ttrain_h0_mean_x_min_u: 0.158626428409\n",
"\ttrain_h0_min_x_max_u: 0.458495451967\n",
"\ttrain_h0_min_x_mean_u: 0.0697232989866\n",
"\ttrain_h0_min_x_min_u: 0.000107248355934\n",
"\ttrain_h0_range_x_max_u: 0.993503615767\n",
"\ttrain_h0_range_x_mean_u: 0.840877935674\n",
"\ttrain_h0_range_x_min_u: 0.432142549731\n",
"\ttrain_h0_row_norms_max: 5.89326124667\n",
"\ttrain_h0_row_norms_mean: 2.98549156744\n",
"\ttrain_h0_row_norms_min: 0.0\n",
"\ttrain_objective: 2.30258509299\n",
"\ttrain_y_col_norms_max: 0.0\n",
"\ttrain_y_col_norms_mean: 0.0\n",
"\ttrain_y_col_norms_min: 0.0\n",
"\ttrain_y_max_max_class: 0.1\n",
"\ttrain_y_mean_max_class: 0.1\n",
"\ttrain_y_min_max_class: 0.1\n",
"\ttrain_y_misclass: 0.90136\n",
"\ttrain_y_nll: 2.30258509299\n",
"\ttrain_y_row_norms_max: 0.0\n",
"\ttrain_y_row_norms_mean: 0.0\n",
"\ttrain_y_row_norms_min: 0.0\n",
"\ttraining_seconds_this_epoch: 0.0\n",
"\tvalid_h0_col_norms_max: 6.23503405999\n",
"\tvalid_h0_col_norms_mean: 3.82355643971\n",
"\tvalid_h0_col_norms_min: 2.06193996111\n",
"\tvalid_h0_max_x_max_u: 0.999902364459\n",
"\tvalid_h0_max_x_mean_u: 0.910734674045\n",
"\tvalid_h0_max_x_min_u: 0.505713638328\n",
"\tvalid_h0_mean_x_max_u: 0.897212634566\n",
"\tvalid_h0_mean_x_mean_u: 0.477113329951\n",
"\tvalid_h0_mean_x_min_u: 0.159442692765\n",
"\tvalid_h0_min_x_max_u: 0.474104176772\n",
"\tvalid_h0_min_x_mean_u: 0.07068185398\n",
"\tvalid_h0_min_x_min_u: 0.000110276493931\n",
"\tvalid_h0_range_x_max_u: 0.994406994152\n",
"\tvalid_h0_range_x_mean_u: 0.840052820065\n",
"\tvalid_h0_range_x_min_u: 0.445501338425\n",
"\tvalid_h0_row_norms_max: 5.89326124667\n",
"\tvalid_h0_row_norms_mean: 2.98549156744\n",
"\tvalid_h0_row_norms_min: 0.0\n",
"\tvalid_objective: 2.30258509299\n",
"\tvalid_y_col_norms_max: 0.0\n",
"\tvalid_y_col_norms_mean: 0.0\n",
"\tvalid_y_col_norms_min: 0.0\n",
"\tvalid_y_max_max_class: 0.1\n",
"\tvalid_y_mean_max_class: 0.1\n",
"\tvalid_y_min_max_class: 0.1\n",
"\tvalid_y_misclass: 0.9009\n",
"\tvalid_y_nll: 2.30258509299\n",
"\tvalid_y_row_norms_max: 0.0\n",
"\tvalid_y_row_norms_mean: 0.0\n",
"\tvalid_y_row_norms_min: 0.0\n",
"Saving to ./mlp_best.pkl...\n",
"Saving to ./mlp_best.pkl done. Time elapsed: 0.650340 seconds\n",
"Time this epoch: 0:03:24.065674\n",
"Monitoring step:\n",
"\tEpochs seen: 1\n",
"\tBatches seen: 5\n",
"\tExamples seen: 50000\n",
"\tave_grad_mult: 0.568428376745\n",
"\tave_grad_size: 0.569275215102\n",
"\tave_step_size: 0.293164906817\n",
"\ttest_h0_col_norms_max: 6.24084816402\n",
"\ttest_h0_col_norms_mean: 3.8330191565\n",
"\ttest_h0_col_norms_min: 2.07277098332\n",
"\ttest_h0_max_x_max_u: 0.999797012523\n",
"\ttest_h0_max_x_mean_u: 0.930507070205\n",
"\ttest_h0_max_x_min_u: 0.600885181612\n",
"\ttest_h0_mean_x_max_u: 0.86138560511\n",
"\ttest_h0_mean_x_mean_u: 0.47684242763\n",
"\ttest_h0_mean_x_min_u: 0.171815715135\n",
"\ttest_h0_min_x_max_u: 0.410199041785\n",
"\ttest_h0_min_x_mean_u: 0.0532412819482\n",
"\ttest_h0_min_x_min_u: 0.000194870876317\n",
"\ttest_h0_range_x_max_u: 0.995852544072\n",
"\ttest_h0_range_x_mean_u: 0.877265788257\n",
"\ttest_h0_range_x_min_u: 0.548945839167\n",
"\ttest_h0_row_norms_max: 5.89793230281\n",
"\ttest_h0_row_norms_mean: 2.99315610991\n",
"\ttest_h0_row_norms_min: 0.00719804802815\n",
"\ttest_objective: 0.345809614411\n",
"\ttest_y_col_norms_max: 2.78234106666\n",
"\ttest_y_col_norms_mean: 2.59701149502\n",
"\ttest_y_col_norms_min: 2.3810874603\n",
"\ttest_y_max_max_class: 0.999821373414\n",
"\ttest_y_mean_max_class: 0.844709547442\n",
"\ttest_y_min_max_class: 0.206097493724\n",
"\ttest_y_misclass: 0.0964\n",
"\ttest_y_nll: 0.345809614411\n",
"\ttest_y_row_norms_max: 0.705822478407\n",
"\ttest_y_row_norms_mean: 0.347877919902\n",
"\ttest_y_row_norms_min: 0.0778698200526\n",
"\ttotal_seconds_last_epoch: 0.0\n",
"\ttrain_h0_col_norms_max: 6.24084816402\n",
"\ttrain_h0_col_norms_mean: 3.8330191565\n",
"\ttrain_h0_col_norms_min: 2.07277098332\n",
"\ttrain_h0_max_x_max_u: 0.999829597379\n",
"\ttrain_h0_max_x_mean_u: 0.931275661933\n",
"\ttrain_h0_max_x_min_u: 0.618205979121\n",
"\ttrain_h0_mean_x_max_u: 0.858717658701\n",
"\ttrain_h0_mean_x_mean_u: 0.477121657696\n",
"\ttrain_h0_mean_x_min_u: 0.178341485107\n",
"\ttrain_h0_min_x_max_u: 0.385012327626\n",
"\ttrain_h0_min_x_mean_u: 0.0521292482027\n",
"\ttrain_h0_min_x_min_u: 0.000150238486259\n",
"\ttrain_h0_range_x_max_u: 0.996723214144\n",
"\ttrain_h0_range_x_mean_u: 0.879146413731\n",
"\ttrain_h0_range_x_min_u: 0.547978559446\n",
"\ttrain_h0_row_norms_max: 5.89793230281\n",
"\ttrain_h0_row_norms_mean: 2.99315610991\n",
"\ttrain_h0_row_norms_min: 0.00719804802815\n",
"\ttrain_objective: 0.367122860086\n",
"\ttrain_y_col_norms_max: 2.78234106666\n",
"\ttrain_y_col_norms_mean: 2.59701149502\n",
"\ttrain_y_col_norms_min: 2.3810874603\n",
"\ttrain_y_max_max_class: 0.999852802841\n",
"\ttrain_y_mean_max_class: 0.837913061583\n",
"\ttrain_y_min_max_class: 0.200456685509\n",
"\ttrain_y_misclass: 0.10438\n",
"\ttrain_y_nll: 0.367122860086\n",
"\ttrain_y_row_norms_max: 0.705822478407\n",
"\ttrain_y_row_norms_mean: 0.347877919902\n",
"\ttrain_y_row_norms_min: 0.0778698200526\n",
"\ttraining_seconds_this_epoch: 204.065674\n",
"\tvalid_h0_col_norms_max: 6.24084816402\n",
"\tvalid_h0_col_norms_mean: 3.8330191565\n",
"\tvalid_h0_col_norms_min: 2.07277098332\n",
"\tvalid_h0_max_x_max_u: 0.999862677883\n",
"\tvalid_h0_max_x_mean_u: 0.930968702242\n",
"\tvalid_h0_max_x_min_u: 0.640350398959\n",
"\tvalid_h0_mean_x_max_u: 0.856678213378\n",
"\tvalid_h0_mean_x_mean_u: 0.477206979816\n",
"\tvalid_h0_mean_x_min_u: 0.178252463979\n",
"\tvalid_h0_min_x_max_u: 0.360575213125\n",
"\tvalid_h0_min_x_mean_u: 0.0527645351181\n",
"\tvalid_h0_min_x_min_u: 0.000214541742634\n",
"\tvalid_h0_range_x_max_u: 0.997314378604\n",
"\tvalid_h0_range_x_mean_u: 0.878204167123\n",
"\tvalid_h0_range_x_min_u: 0.521885364154\n",
"\tvalid_h0_row_norms_max: 5.89793230281\n",
"\tvalid_h0_row_norms_mean: 2.99315610991\n",
"\tvalid_h0_row_norms_min: 0.00719804802815\n",
"\tvalid_objective: 0.334305279604\n",
"\tvalid_y_col_norms_max: 2.78234106666\n",
"\tvalid_y_col_norms_mean: 2.59701149502\n",
"\tvalid_y_col_norms_min: 2.3810874603\n",
"\tvalid_y_max_max_class: 0.999953240738\n",
"\tvalid_y_mean_max_class: 0.84881891044\n",
"\tvalid_y_min_max_class: 0.193943289318\n",
"\tvalid_y_misclass: 0.0947\n",
"\tvalid_y_nll: 0.334305279604\n",
"\tvalid_y_row_norms_max: 0.705822478407\n",
"\tvalid_y_row_norms_mean: 0.347877919902\n",
"\tvalid_y_row_norms_min: 0.0778698200526\n",
"Saving to ./mlp_best.pkl...\n",
"Saving to ./mlp_best.pkl done. Time elapsed: 0.323518 seconds\n"
]
}
],
"source": [
"train = yaml_parse.load(train)\n",
"train.main_loop()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"with open(\"mlp_tutorial_part_3.yaml\", 'r') as f:\n",
" train = f.read()\n",
"hyper_params = {'train_stop' : 50000,\n",
" 'valid_stop' : 60000,\n",
" 'dim_h0' : 500,\n",
" 'max_epochs' : 1, # 10000\n",
" 'save_path' : '.'}\n",
"train = train % (hyper_params)\n",
"print train\n",
"train = yaml_parse.load(train)\n",
"train.main_loop()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"with open(\"mlp_tutorial_part_4.yaml\", 'r') as f:\n",
" train = f.read()\n",
"hyper_params = {'train_stop' : 50000,\n",
" 'valid_stop' : 60000,\n",
" 'dim_h0' : 500,\n",
" 'max_epochs' : 1, # 10000\n",
" 'save_path' : '.'}\n",
"train = train % (hyper_params)\n",
"print train\n",
"train = yaml_parse.load(train)\n",
"train.main_loop()"
]
}
],
"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
}
| bsd-2-clause |
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