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codeparrot

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github-jupyter

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CodeParrot 117

NEONScience/NEON-Data-Skills

tutorials/Python/RGB-camera/intro-rgb-camera/plot-neon-rgb-camera-data/plot-neon-rgb-camera-data.ipynb

1

15753

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "syncID: 67a5e95e1b7445aca7d7750b75c0ee98\n", "title: \"Plotting a NEON RGB Camera Image (GeoTIFF) in Python\"\n", "description: \"This lesson is a brief introduction to RGB camera images and the GeoTIFF raster format in Python.\"\n", "dateCreated: 2018-06-30\n", "authors: Bridget Hass, \n", "contributors: Donal O'Leary\n", "estimatedTime: 0.5 hour\n", "packagesLibraries: \n", "topics: data-analysis, data-visualization, spatial-data-gis \n", "languagesTool: python\n", "dataProduct: DP3.30010.001\n", "code1: https://raw.githubusercontent.com/NEONScience/NEON-Data-Skills/main/tutorials/Python/RGB-camera/intro-rgb-camera/plot-neon-rgb-camera-data/plot-neon-rgb-camera-data.ipynb\n", "tutorialSeries: jupyter-notebooks\n", "urlTitle: plot-neon-rgb-py\n", "---" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "This tutorial introduces NEON RGB camera images and functions to read in and plot GeoTIFF rasters in Python. In this tutorial, we will read in an RGB camera tile of the NEON Smithsonian Environmental Research Center (SERC) site. We will run the user-defined functions `RGBraster2array` and `plotRGBimage` to read in the image as an array, plot an RGB image of this raster, and plot a histogram of the intensities of one of the three bands. \n", "\n", "### Objectives\n", "\n", "After completing this tutorial, you will be able to: \n", "\n", "* Plot a NEON RGB Camera Tile (Data Product \n", "* Plot a histogram of a single band of an RGB Camera Tile\n", "\n", "### Download the Data \n", "\n", "Download the NEON GeoTiFF file of the \n", "<a href=\"https://ndownloader.figshare.com/files/27465803\">camera (RGB) imagery tile</a>\n", "collected over the Smithsonian Environmental Research Station (SERC) NEON field site. Place this data in a location where you know where it is. You will need to know the file path to this data. \n", "\n", "### Background\n", "\n", "As part of the \n", "<a href=\"https://www.neonscience.org/data-collection/airborne-remote-sensing\" target=\"_blank\"> NEON Airborne Operation Platform's</a> \n", "suite of remote sensing instruments, the digital camera producing high-resolution (10 cm) photographs of the earth’s surface. The camera records light energy that has reflected off the ground in the visible part (red, green and blue) of the light spectrum. Often the camera images are used to provide context for the hyperspectral and LiDAR data. \n", "\n", "**Note:** Don't worry about understanding everything in the `raster2array` function at this point. If you are curious, we encourage you to read the docstrings, but we will go into more detail during the data institute. \n", "\n", "**Data Tip:** To run a cell you can either select `Cell > Run Cells` with your cursor in the cell you want to run, or use the shortcut key `Shift + Enter`. For more handy shortcuts, refer to the tab `Help > Keyboard Shortcuts`. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Set up Enviornment\n", "First, make sure that you are running the Python 3.5 environment by running the code in the cell below:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import sys\n", "sys.version" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Data Institue Participants**: You should be running `3.5.x`. If this is not the case, close this console (both the notebook and Home page), and shut down your command prompt that is running your Jupyter notebook. Re-open your command prompt, navigate to your workking directory, and activate your p35 environment by typing `activate p35` in Windows or `source activate p35` in Mac if you followed the pre-institute computer set-up instructions. Once you see `(p35)` at the beginning of your command prompt, you can type `jupyter notebook` to run your notebook.\n", "\n", "**Other tutorial users**: Jupyter Notebooks is not required to complete this tutorial. This tutorial was processed using Python version 3.7.7." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now that you are in the right environment, first we will import the gdal package, which contains tools for programming and manipulating the Geospatial Data Abstraction Library (GDAL). For more information on GDAL, please refer to <a href=\"http://www.gdal.org/\" target=\"_blank\">gdal.org</a>." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import gdal" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If you get the following message:\n", " `ModuleNotFoundError: No Module Named GDAL`\n", "\n", "**Troubleshooting steps** --> try one of the following:\n", "- from a Jupyter Python cell, run the command:\n", "`!conda install gdal`\n", "- from a Command Prompt (Windows) or Terminal (Mac), activate the appropriate environment" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next we will import the `numpy` and `matplotlib` packages. Numpy stands for **Num**erical **Py**thon This is a standard package that comes with the Anaconda installation of Python, so you should not need to do any additional steps to install it. " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "import warnings\n", "warnings.filterwarnings('ignore')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def RGBraster2array(RGB_geotif):\n", " \"\"\"RGBraster2array reads in a NEON AOP geotif file and returns \n", " a numpy array, and header containing associated metadata with spatial information.\n", " --------\n", " Parameters\n", " RGB_geotif -- full or relative path and name of reflectance hdf5 file\n", " --------\n", " Returns \n", " --------\n", " array:\n", " numpy array of geotif values\n", " metadata:\n", " dictionary containing the following metadata (all strings):\n", " array_rows\n", " array_cols\n", " bands\n", " driver\n", " projection\n", " geotransform\n", " pixelWidth\n", " pixelHeight\n", " extent\n", " noDataValue\n", " scaleFactor\n", " --------\n", " Example Execution:\n", " --------\n", " RGB_geotif = '2017_SERC_2_368000_4306000_image.tif'\n", " RGBcam_array, RGBcam_metadata = RGBraster2array(RGB_geotif) \"\"\"\n", " \n", " metadata = {}\n", " dataset = gdal.Open(RGB_geotif)\n", " metadata['array_rows'] = dataset.RasterYSize\n", " metadata['array_cols'] = dataset.RasterXSize\n", " metadata['bands'] = dataset.RasterCount\n", " metadata['driver'] = dataset.GetDriver().LongName\n", " metadata['projection'] = dataset.GetProjection()\n", " metadata['geotransform'] = dataset.GetGeoTransform()\n", " \n", " mapinfo = dataset.GetGeoTransform()\n", " metadata['pixelWidth'] = mapinfo[1]\n", " metadata['pixelHeight'] = mapinfo[5]\n", " \n", " metadata['ext_dict'] = {}\n", " metadata['ext_dict']['xMin'] = mapinfo[0]\n", " metadata['ext_dict']['xMax'] = mapinfo[0] + dataset.RasterXSize/mapinfo[1]\n", " metadata['ext_dict']['yMin'] = mapinfo[3] + dataset.RasterYSize/mapinfo[5]\n", " metadata['ext_dict']['yMax'] = mapinfo[3]\n", " \n", " metadata['extent'] = (metadata['ext_dict']['xMin'],metadata['ext_dict']['xMax'],\n", " metadata['ext_dict']['yMin'],metadata['ext_dict']['yMax'])\n", " \n", " raster = dataset.GetRasterBand(1)\n", " array_shape = raster.ReadAsArray(0,0,metadata['array_cols'],metadata['array_rows']).astype(np.float).shape\n", " metadata['noDataValue'] = raster.GetNoDataValue()\n", " metadata['scaleFactor'] = raster.GetScale()\n", "\n", " array = np.zeros((array_shape[0],array_shape[1],dataset.RasterCount),'uint8') #pre-allocate stackedArray matrix\n", " for i in range(1, dataset.RasterCount+1):\n", " band = dataset.GetRasterBand(i).ReadAsArray(0,0,metadata['array_cols'],metadata['array_rows']).astype(np.float)\n", " band[band==metadata['noDataValue']]=np.nan\n", " band = band/metadata['scaleFactor']\n", " array[...,i-1] = band\n", "\n", " return array, metadata" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "After running this cell, we can call the function, as below. Note that you need to specify the relative path (as shown here with the `./`, indicating that file is saved in your working directory) or the absolute path (eg. `D:\\\\RSDI_2018\\\\data`) - you'll need to use double slashes to indicate that you are pointing to a directory. Please use the correct file path to where you saved the GeoTIFF file downloaded at the befining of the lesson. " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "RGB_geotif = '/Users/olearyd/Git/data/2017_SERC_2_368000_4306000_image.tif'\n", "SERC_RGBcam_array, SERC_RGBcam_metadata = RGBraster2array(RGB_geotif)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can look at the dimensions of this tile by using the `.shape` method:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "SERC_RGBcam_array.shape" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can list the metadata information as follows:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#Display information stored in header\n", "for key in sorted(SERC_RGBcam_metadata.keys()):\n", " print(key)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next, we'll define a function to plot the array data. Run the cell below:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def plot_band_array(band_array,\n", " refl_extent,\n", " colorlimit,\n", " ax=plt.gca(),\n", " title='',\n", " cbar ='on',\n", " cmap_title='',\n", " colormap='spectral'):\n", " \n", " '''plot_band_array reads in and plots a single band or an rgb band combination of a reflectance array\n", " --------\n", " Parameters\n", " --------\n", " band_array: flightline array of reflectance values, created from h5refl2array function\n", " refl_extent: extent of reflectance data to be plotted (xMin, xMax, yMin, yMax) - use metadata['extent'] from h5refl2array function\n", " colorlimit: range of values to plot (min,max). Best to look at the histogram of reflectance values before plotting to determine colorlimit.\n", " ax: optional, default = current axis\n", " title: string, optional; plot title\n", " cmap_title: string, optional; colorbar title\n", " colormap: string, optional; see https://matplotlib.org/examples/color/colormaps_reference.html for list of colormaps\n", " --------\n", " Returns \n", " plots array of single band or RGB if given a 3-band \n", " --------\n", " Example:\n", " --------\n", " plot_band_array(SERC_RGBcam_array, \n", " SERC_RGBcam_metadata['extent'],\n", " (1,255),\n", " title='SERC RGB Camera Tile',\n", " cbar='off')'''\n", " \n", " plot = plt.imshow(band_array,extent=refl_extent,clim=colorlimit); \n", " if cbar == 'on':\n", " cbar = plt.colorbar(plot,aspect=40); plt.set_cmap(colormap); \n", " cbar.set_label(cmap_title,rotation=90,labelpad=20)\n", " plt.title(title); ax = plt.gca(); \n", " ax.ticklabel_format(useOffset=False, style='plain'); #do not use scientific notation #\n", " rotatexlabels = plt.setp(ax.get_xticklabels(),rotation=90); #rotate x tick labels 90 degrees" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Defining the function above will initially produce a blank plotting area (that's ok!).\n", "\n", "Now run this function using the inputs you defined earlier:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "plot_band_array(SERC_RGBcam_array,\n", " SERC_RGBcam_metadata['extent'],\n", " (1,255),\n", " title='SERC RGB Camera Tile',\n", " cbar='off') " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Lastly, we can plot a histogram of the first band (red), which we can extract by using `splicing`. Since Python is 0-based, to extract all values of the first band, we can use: `SERC_RGBcam_array[:,:,0]`. *Notes*: It speeds up the algorithm to flatten the 2-D array into one dimension using `numpy.ravel`; `20` specifies the number of bins. " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "plt.hist(np.ravel(SERC_RGBcam_array[:,:,0]),20);\n", "plt.title('Histogram of SERC Camera Red Band')\n", "plt.xlabel('Brightness'); plt.ylabel('Frequency')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Exercises: \n", "Now that you've followed along to read in and plot an RGB camera image and band, try the following exercises on your own:\n", "\n", "1. **Plot histograms of the green and blue bands**\n", "\n", "2. **Explore the data** to see what you can learn about the `SERC_RGBcam_array` and associated `SERC_RGBcam_metadata` \n", "\n", " a. Determine the minimum and maximum reflectance for each band. Print these values with a print statement. *HINT*: Use the `numpy` functions `np.amin()` and `np.amax()`\n", " \n", " b. What UTM zone is this data in? *HINT: Print out* `SERC_RGBcam_metadata['projection']`\n", " \n", " c. Use the `plot_band_array` function to plot each band of the camera image separately. *HINT*: Use splicing to extract each band (eg. `SERC_RGBcam_array[:,:,0]`). " ] } ], "metadata": { "kernelspec": { "display_name": "py37", "language": "python", "name": "py37" }, "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 }

agpl-3.0

sophie63/FlyLFM

Notebooks/.ipynb_checkpoints/100632_FlyLFMpaper-checkpoint.ipynb

1

2233448

bsd-2-clause

kenfeldmann/6_12_16_carpentry_class

completed_lesson_part_2.ipynb

2

81180

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Analyzing Data from Multiple Files" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import glob\n", "\n", "# The glob library contains a single function, also called glob, \n", "# that finds files whose names match a pattern." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['data/inflammation-01.csv', 'data/inflammation-02.csv', 'data/inflammation-03.csv', 'data/inflammation-04.csv', 'data/inflammation-05.csv', 'data/inflammation-06.csv', 'data/inflammation-07.csv', 'data/inflammation-08.csv', 'data/inflammation-09.csv', 'data/inflammation-10.csv', 'data/inflammation-11.csv', 'data/inflammation-12.csv']\n" ] } ], "source": [ "print(glob.glob('data/inflammation*.csv'))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is has created a list with values of filenames that matched the glob above." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Lets load two libraries\n", "import numpy # Fundamental package for scientific computing with Python.\n", "import matplotlib.pyplot # Provides a MATLAB-like plotting framework.\n", "# this allows plots to appear directly in the notebook\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [], "source": [ "filenames = glob.glob('data/inflammation*.csv')\n", "filenames = filenames[0:3]" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "['data/inflammation-01.csv',\n", " 'data/inflammation-02.csv',\n", " 'data/inflammation-03.csv']" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "filenames" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "data/inflammation-01.csv\n", "data/inflammation-02.csv\n", "data/inflammation-03.csv\n" ] } ], "source": [ "for f in filenames:\n", " print(f)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "data/inflammation-01.csv\n" ] }, { "data": { "image/png": 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1sIizJDk1bdrY777Skl1JRkROxPVIvkpEBonIoPDCym62kly+atXcCl7J1eQl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\nlNmSJckmkyxJrqIPPnCbo/qUe6i1MdH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"text/plain": [ "<matplotlib.figure.Figure at 0x10b6b6fd0>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "data/inflammation-02.csv\n" ] }, { "data": { "image/png": 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XgH2APbP5RhEZJCIzReTTEretLyLDRWSKiLy24opSvvn2W7uic+ONoSPJb9df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\n4qLgSXJ+8SS5gKjaC3Dv3ta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"text/plain": [ "<matplotlib.figure.Figure at 0x106ec0f98>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "data/inflammation-03.csv\n" ] }, { "data": { "image/png": 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vK6XdYuNG93f89Kfhmmvy+10MwzDKTVyR/KiIfAMYICJnArcD9xRzYFV9TlVP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\nrqgMw6hH6lYk33NPPD+yUR34M8l33um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04GoAETlKRBZAznF6AzBeRJYCtwAfL3P8hpFa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\nSkAAAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x109e22860>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "for f in filenames:\n", " print(f)\n", " \n", " data = numpy.loadtxt(fname=f, delimiter=',') # load the data in to into the data variable\n", " fig = matplotlib.pyplot.figure(figsize=(10.0, 3.0)) # create a plotting figure\n", " \n", " axes1 = fig.add_subplot(1, 3, 1) \n", " axes2 = fig.add_subplot(1, 3, 2)\n", " axes3 = fig.add_subplot(1, 3, 3)\n", "\n", " axes1.set_ylabel('average')\n", " axes1.plot(data.mean(axis=0))\n", "\n", " axes2.set_ylabel('max')\n", " axes2.plot(data.max(axis=0))\n", "\n", " axes3.set_ylabel('min')\n", " axes3.plot(data.min(axis=0))\n", "\n", " fig.tight_layout()\n", " matplotlib.pyplot.show()" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "### Creating Functions" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def fahr_to_kelvin(temp):\n", " return ((temp - 32) * (5/9)) + 273.15" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "310.92777777777775" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fahr_to_kelvin(100)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "288.7055555555555" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fahr_to_kelvin(60)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def kelvin_to_celsius(temp_k):\n", " return temp_k - 273.15" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "37.77777777777777" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "kelvin_to_celsius(310.92777777777775)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def print_hello_world():\n", " print(\"Hello World\")\n", " print(\"Hello everybody\")" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Hello World\n", "Hello everybody\n" ] } ], "source": [ "print_hello_world()" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def print_hello_name(name):\n", " print(\"Hello %s\" % name)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Hello Andre\n" ] } ], "source": [ "print_hello_name(\"Andre\")" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def get_value_from_input(value):\n", " if value < 10:\n", " return value\n", " else:\n", " return value * 10" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "5" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x = get_value_from_input(5)\n", "x" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": true }, "outputs": [], "source": [ "y = get_value_from_input(15)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "150" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "y" ] }, { "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

Aggieyixin/cjc2016

Homework7.ipynb

1

941313

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# 张艺馨 15210130100\n", "## 1.UserCF&ItemCF代码" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "critics={'Lisa Rose': {'Lady in the Water': 2.5, 'Snakes on a Plane': 3.5,\n", " 'Just My Luck': 3.0, 'Superman Returns': 3.5, 'You, Me and Dupree': 2.5,\n", " 'The Night Listener': 3.0},\n", " 'Gene Seymour': {'Lady in the Water': 3.0, 'Snakes on a Plane': 3.5,\n", " 'Just My Luck': 1.5, 'Superman Returns': 5.0, 'The Night Listener': 3.0,\n", " 'You, Me and Dupree': 3.5},\n", " 'Michael Phillips': {'Lady in the Water': 2.5, 'Snakes on a Plane': 3.0,\n", " 'Superman Returns': 3.5, 'The Night Listener': 4.0},\n", " 'Claudia Puig': {'Snakes on a Plane': 3.5, 'Just My Luck': 3.0,\n", " 'The Night Listener': 4.5, 'Superman Returns': 4.0,\n", " 'You, Me and Dupree': 2.5},\n", " 'Mick LaSalle': {'Lady in the Water': 3.0, 'Snakes on a Plane': 4.0,\n", " 'Just My Luck': 2.0, 'Superman Returns': 3.0, 'The Night Listener': 3.0,\n", " 'You, Me and Dupree': 2.0},\n", " 'Jack Matthews': {'Lady in the Water': 3.0, 'Snakes on a Plane': 4.0,\n", " 'The Night Listener': 3.0, 'Superman Returns': 5.0, 'You, Me and Dupree': 3.5},\n", " 'Toby': {'Snakes on a Plane':4.5,'You, Me and Dupree':1.0,'Superman Returns':4.0}}" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "2.5" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "critics['Lisa Rose']['Lady in the Water']" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "critics['Toby']['Snakes on a Plane']=4.5" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "{'Snakes on a Plane': 4.5, 'Superman Returns': 4.0, 'You, Me and Dupree': 1.0}" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "critics['Toby']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 1.1 User-based filtering" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "3.1622776601683795" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import numpy as np\n", "np.sqrt(np.power(5-4, 2) + np.power(4-1, 2))" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0.2402530733520421" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "1.0 /(1 + np.sqrt(np.power(5-4, 2) + np.power(4-1, 2)) )" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def sim_distance(prefs,person1,person2):\n", " si={}\n", " for item in prefs[person1]:\n", " if item in prefs[person2]:\n", " si[item]=1\n", " if len(si)==0: return 0\n", " sum_of_squares=np.sum([np.power(prefs[person1][item]-prefs[person2][item],2)\n", " for item in prefs[person1] if item in prefs[person2]])\n", " return 1/(1+sum_of_squares)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0.14814814814814814" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sim_distance(critics, 'Lisa Rose','Gene Seymour')" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def sim_pearson(prefs,p1,p2):\n", " si={}\n", " for item in prefs[p1]:\n", " if item in prefs[p2]: si[item]=1\n", " n=len(si)\n", " if n==0: return 0\n", " sum1=np.sum([prefs[p1][it] for it in si])\n", " sum2=np.sum([prefs[p2][it] for it in si])\n", " sum1Sq=np.sum([np.power(prefs[p1][it],2) for it in si])\n", " sum2Sq=np.sum([np.power(prefs[p2][it],2) for it in si])\n", " pSum=np.sum([prefs[p1][it]*prefs[p2][it] for it in si])\n", " num=pSum-(sum1*sum2/n)\n", " den=np.sqrt((sum1Sq-np.power(sum1,2)/n)*(sum2Sq-np.power(sum2,2)/n))\n", " if den==0: return 0\n", " return num/den" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0.39605901719066977" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sim_pearson(critics, 'Lisa Rose','Gene Seymour')" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def topMatches(prefs,person,n=5,similarity=sim_pearson):\n", " scores=[(similarity(prefs,person,other),other)\n", " for other in prefs if other!=person]\n", " scores.sort( )\n", " scores.reverse( )\n", " return scores[0:n]" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[(0.99124070716192991, 'Lisa Rose'),\n", " (0.92447345164190486, 'Mick LaSalle'),\n", " (0.89340514744156474, 'Claudia Puig')]" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "topMatches(critics,'Toby',n=3) " ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def getRecommendations(prefs,person,similarity=sim_pearson):\n", " totals={}\n", " simSums={}\n", " for other in prefs:\n", " if other==person: continue\n", " sim=similarity(prefs,person,other)\n", " if sim<=0: continue\n", " for item in prefs[other]: \n", " if item not in prefs[person] or prefs[person][item]==0:\n", " totals.setdefault(item,0)\n", " totals[item]+=prefs[other][item]*sim\n", " simSums.setdefault(item,0)\n", " simSums[item]+=sim\n", " rankings=[(total/simSums[item],item) for item,total in totals.items()]\n", " rankings.sort()\n", " rankings.reverse()\n", " return rankings" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[(3.3477895267131013, 'The Night Listener'),\n", " (2.8325499182641614, 'Lady in the Water'),\n", " (2.5309807037655645, 'Just My Luck')]" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "getRecommendations(critics,'Toby')" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[(3.5002478401415877, 'The Night Listener'),\n", " (2.7561242939959363, 'Lady in the Water'),\n", " (2.4619884860743739, 'Just My Luck')]" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "getRecommendations(critics,'Toby',similarity=sim_distance)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 1.2 Item-based filtering" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def transformPrefs(prefs):\n", " result={}\n", " for person in prefs:\n", " for item in prefs[person]:\n", " result.setdefault(item,{})\n", " result[item][person]=prefs[person][item]\n", " return result\n", "\n", "movies = transformPrefs(critics)" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[(0.65795169495976946, 'You, Me and Dupree'),\n", " (0.48795003647426888, 'Lady in the Water'),\n", " (0.11180339887498941, 'Snakes on a Plane'),\n", " (-0.17984719479905439, 'The Night Listener'),\n", " (-0.42289003161103106, 'Just My Luck')]" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "topMatches(movies,'Superman Returns')" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[(4.0, 'Michael Phillips'), (3.0, 'Jack Matthews')]" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "getRecommendations(movies,'Just My Luck')" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[(3.1637361366111816, 'Michael Phillips')]" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "getRecommendations(movies, 'You, Me and Dupree')" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "{'Just My Luck': [(0.22222222222222221, 'Lady in the Water'),\n", " (0.18181818181818182, 'You, Me and Dupree'),\n", " (0.15384615384615385, 'The Night Listener'),\n", " (0.10526315789473684, 'Snakes on a Plane'),\n", " (0.064516129032258063, 'Superman Returns')],\n", " 'Lady in the Water': [(0.40000000000000002, 'You, Me and Dupree'),\n", " (0.2857142857142857, 'The Night Listener'),\n", " (0.22222222222222221, 'Snakes on a Plane'),\n", " (0.22222222222222221, 'Just My Luck'),\n", " (0.090909090909090912, 'Superman Returns')],\n", " 'Snakes on a Plane': [(0.22222222222222221, 'Lady in the Water'),\n", " (0.18181818181818182, 'The Night Listener'),\n", " (0.16666666666666666, 'Superman Returns'),\n", " (0.10526315789473684, 'Just My Luck'),\n", " (0.05128205128205128, 'You, Me and Dupree')],\n", " 'Superman Returns': [(0.16666666666666666, 'Snakes on a Plane'),\n", " (0.10256410256410256, 'The Night Listener'),\n", " (0.090909090909090912, 'Lady in the Water'),\n", " (0.064516129032258063, 'Just My Luck'),\n", " (0.053333333333333337, 'You, Me and Dupree')],\n", " 'The Night Listener': [(0.2857142857142857, 'Lady in the Water'),\n", " (0.18181818181818182, 'Snakes on a Plane'),\n", " (0.15384615384615385, 'Just My Luck'),\n", " (0.14814814814814814, 'You, Me and Dupree'),\n", " (0.10256410256410256, 'Superman Returns')],\n", " 'You, Me and Dupree': [(0.40000000000000002, 'Lady in the Water'),\n", " (0.18181818181818182, 'Just My Luck'),\n", " (0.14814814814814814, 'The Night Listener'),\n", " (0.053333333333333337, 'Superman Returns'),\n", " (0.05128205128205128, 'Snakes on a Plane')]}" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "def calculateSimilarItems(prefs,n=10):\n", " result={}\n", " itemPrefs=transformPrefs(prefs)\n", " c=0\n", " for item in itemPrefs:\n", " c+=1\n", " if c%100==0: print \"%d / %d\" % (c,len(itemPrefs))\n", " scores=topMatches(itemPrefs,item,n=n,similarity=sim_distance)\n", " result[item]=scores\n", " return result\n", "\n", "itemsim=calculateSimilarItems(critics) \n", "itemsim" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[(3.182634730538922, 'The Night Listener'),\n", " (2.5983318700614575, 'Just My Luck'),\n", " (2.4730878186968837, 'Lady in the Water')]" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "def getRecommendedItems(prefs,itemMatch,user):\n", " userRatings=prefs[user]\n", " scores={}\n", " totalSim={}\n", " # Loop over items rated by this user\n", " for (item,rating) in userRatings.items( ):\n", " # Loop over items similar to this one\n", " for (similarity,item2) in itemMatch[item]:\n", " # Ignore if this user has already rated this item\n", " if item2 in userRatings: continue\n", " # Weighted sum of rating times similarity\n", " scores.setdefault(item2,0)\n", " scores[item2]+=similarity*rating\n", " # Sum of all the similarities\n", " totalSim.setdefault(item2,0)\n", " totalSim[item2]+=similarity\n", " # Divide each total score by total weighting to get an average\n", " rankings=[(score/totalSim[item],item) for item,score in scores.items( )]\n", " # Return the rankings from highest to lowest\n", " rankings.sort( )\n", " rankings.reverse( )\n", " return rankings\n", "\n", "getRecommendedItems(critics,itemsim,'Toby')\n" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import os\n", "import random\n", "\n", "class Graph:\n", " def __init__(self):\n", " self.G = dict()\n", " \n", " def addEdge(self, p, q):\n", " if p not in self.G: self.G[p] = dict()\n", " if q not in self.G: self.G[q] = dict()\n", " self.G[p][q] = 1\n", " self.G[q][p] = 1\n", "\n", " def getGraphMatrix(self):\n", " return self.G" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['A', 'a', 'c', 'B', 'd', 'C', 'b']\n" ] } ], "source": [ "graph = Graph()\n", "graph.addEdge('A', 'a')\n", "graph.addEdge('A', 'c')\n", "graph.addEdge('B', 'a')\n", "graph.addEdge('B', 'b')\n", "graph.addEdge('B', 'c')\n", "graph.addEdge('B', 'd')\n", "graph.addEdge('C', 'c')\n", "graph.addEdge('C', 'd')\n", "G = graph.getGraphMatrix()\n", "print(G.keys())\n" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "{'A': {'a': 1, 'c': 1},\n", " 'B': {'a': 1, 'b': 1, 'c': 1, 'd': 1},\n", " 'C': {'c': 1, 'd': 1},\n", " 'a': {'A': 1, 'B': 1},\n", " 'b': {'B': 1},\n", " 'c': {'A': 1, 'B': 1, 'C': 1},\n", " 'd': {'B': 1, 'C': 1}}" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "G" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def PersonalRank(G, alpha, root, max_step):\n", " rank = dict()\n", " rank = {x:0.0 for x in G.keys()}\n", " rank[root] = 1.0\n", " for k in range(max_step):\n", " tmp = {x:0.0 for x in G.keys()}\n", " for i,ri in G.items():\n", " for j,wij in ri.items():\n", " if j not in tmp: tmp[j] = 0.0\n", " tmp[j] += alpha * rank[i] / (len(ri)*1.0)\n", " if j == root: tmp[j] += 1.0 - alpha\n", " rank = tmp\n", " print(k, rank)\n", " return rank" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(0, {'A': 0.3999999999999999, 'a': 0.4, 'c': 0.4, 'B': 0.0, 'd': 0.0, 'C': 0.0, 'b': 0.0})\n", "(1, {'A': 0.6666666666666666, 'a': 0.15999999999999998, 'c': 0.15999999999999998, 'B': 0.2666666666666667, 'd': 0.0, 'C': 0.10666666666666669, 'b': 0.0})\n", "(2, {'A': 0.5066666666666666, 'a': 0.32, 'c': 0.3626666666666667, 'B': 0.10666666666666665, 'd': 0.09600000000000003, 'C': 0.04266666666666666, 'b': 0.053333333333333344})\n", "(3, {'A': 0.624711111111111, 'a': 0.22399999999999998, 'c': 0.24106666666666665, 'B': 0.30577777777777787, 'd': 0.03839999999999999, 'C': 0.13511111111111113, 'b': 0.02133333333333333})\n", "(4, {'A': 0.5538844444444444, 'a': 0.31104, 'c': 0.36508444444444443, 'B': 0.1863111111111111, 'd': 0.11520000000000002, 'C': 0.07964444444444443, 'b': 0.061155555555555574})\n", "(5, {'A': 0.6217718518518518, 'a': 0.258816, 'c': 0.29067377777777775, 'B': 0.31677629629629633, 'd': 0.06911999999999999, 'C': 0.14343585185185187, 'b': 0.03726222222222222})\n", "(6, {'A': 0.5810394074074073, 'a': 0.312064, 'c': 0.3694383407407408, 'B': 0.2384971851851852, 'd': 0.12072960000000002, 'C': 0.1051610074074074, 'b': 0.06335525925925926})\n", "(7, {'A': 0.6233424908641975, 'a': 0.2801152, 'c': 0.322179602962963, 'B': 0.322318538271605, 'd': 0.08976384000000001, 'C': 0.14680873086419757, 'b': 0.047699437037037044})\n", "(8, {'A': 0.5979606407901235, 'a': 0.313800704, 'c': 0.372524196345679, 'B': 0.27202572641975314, 'd': 0.12318720000000004, 'C': 0.12182009679012348, 'b': 0.06446370765432101})\n", "(9, {'A': 0.6248600672921809, 'a': 0.2935894016, 'c': 0.34231744031604944, 'B': 0.32570591341563787, 'd': 0.10313318400000002, 'C': 0.14861466569218107, 'b': 0.05440514528395063})\n", "(10, {'A': 0.6087204113909463, 'a': 0.31508520959999997, 'c': 0.3745310758768724, 'B': 0.29349780121810704, 'd': 0.12458704896, 'C': 0.13253792435094652, 'b': 0.06514118268312757})\n", "(11, {'A': 0.6259090374071659, 'a': 0.3021877248, 'c': 0.3552028945403786, 'B': 0.3278568031376681, 'd': 0.11171472998400003, 'C': 0.14970977315116596, 'b': 0.05869956024362141})\n", "(12, {'A': 0.6155958617974342, 'a': 0.31593497559039996, 'c': 0.37581888485086634, 'B': 0.3072414019859315, 'd': 0.125455269888, 'C': 0.13940666387103434, 'b': 0.06557136062753362})\n", "(13, {'A': 0.6265923595297243, 'a': 0.30768662511615996, 'c': 0.3634492906645737, 'B': 0.32923155598695125, 'd': 0.11721094594560004, 'C': 0.15040047724876437, 'b': 0.0614482803971863})\n", "(14, {'A': 0.6199944608903503, 'a': 0.31648325500928, 'c': 0.37664344590878573, 'B': 0.3160374635863394, 'd': 0.126006502096896, 'C': 0.14380418922212634, 'b': 0.06584631119739025})\n", "(15, {'A': 0.6270315542460547, 'a': 0.311205277073408, 'c': 0.36872695276225853, 'B': 0.3301112040427255, 'd': 0.12072916840611841, 'C': 0.1508408530811013, 'b': 0.06320749271726787})\n", "(16, {'A': 0.6228092982326321, 'a': 0.316834862506967, 'c': 0.37717120373940755, 'B': 0.3216669597688938, 'd': 0.12635858204098563, 'C': 0.14661885476571632, 'b': 0.0660222408085451})\n", "(17, {'A': 0.6273129326666287, 'a': 0.3134571112468316, 'c': 0.37210465315311814, 'B': 0.3306741581298591, 'd': 0.1229809338600653, 'C': 0.15112242048023627, 'b': 0.06433339195377877})\n", "(18, {'A': 0.6246107520062307, 'a': 0.3170600046926233, 'c': 0.3775089728847178, 'B': 0.32526983911327995, 'd': 0.12658379981806633, 'C': 0.1484202810515243, 'b': 0.06613483162597182})\n", "(19, {'A': 0.627493061312974, 'a': 0.3148982686251483, 'c': 0.37426638104575805, 'B': 0.3310344465409781, 'd': 0.1244220802432657, 'C': 0.15130257936315128, 'b': 0.06505396782265599})\n", "{'A': 0.627493061312974, 'a': 0.3148982686251483, 'c': 0.37426638104575805, 'B': 0.3310344465409781, 'd': 0.1244220802432657, 'C': 0.15130257936315128, 'b': 0.06505396782265599}\n" ] } ], "source": [ "print(PersonalRank(G, 0.8, 'A', 20))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 2. 使用graphlab对于音乐数据或电影数据构建推荐系统" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "A newer version of GraphLab Create (v1.9) is available! Your current version is v1.8.5.\n", "\n", "You can use pip to upgrade the graphlab-create package. For more information see https://dato.com/products/create/upgrade.\n", "2016-05-21 10:54:24,093 [INFO] graphlab.cython.cy_server, 176: GraphLab Create v1.8.5 started. Logging: /tmp/graphlab_server_1463799260.log\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "This non-commercial license of GraphLab Create is assigned to [email protected] and will expire on May 20, 2017. For commercial licensing options, visit https://dato.com/buy/.\n" ] } ], "source": [ "import graphlab as gl\n", "gl.canvas.set_target('ipynb')\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": false }, "outputs": [ { "data": { "application/javascript": [ "$(\"head\").append($(\"<link/>\").attr({\n", " rel: \"stylesheet\",\n", " type: \"text/css\",\n", " href: \"//cdnjs.cloudflare.com/ajax/libs/font-awesome/4.1.0/css/font-awesome.min.css\"\n", "}));\n", "$(\"head\").append($(\"<link/>\").attr({\n", " rel: \"stylesheet\",\n", " type: \"text/css\",\n", " href: \"//dato.com/files/canvas/1.8.5/css/canvas.css\"\n", "}));\n", "\n", " (function(){\n", "\n", " var e = null;\n", " if (typeof element == 'undefined') {\n", " var scripts = document.getElementsByTagName('script');\n", " var thisScriptTag = scripts[scripts.length-1];\n", " var parentDiv = thisScriptTag.parentNode;\n", " e = 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\"music_id\", \"rating\"], \"view\": null}, \"view_components\": [\"Summary\", \"Table\", \"Bar Chart\", \"BoxWhisker Plot\", \"Line Chart\", \"Scatter Plot\", \"Heat Map\", \"Plots\"], \"type\": \"SFrame\", \"columns\": [{\"dtype\": \"str\", \"name\": \"user_id\"}, {\"dtype\": \"str\", \"name\": \"music_id\"}, {\"dtype\": \"int\", \"name\": \"rating\"}], \"column_identifiers\": [\"rating\", \"music_id\", \"user_id\"]}, \"columns\": [{\"dtype\": \"str\", \"name\": \"user_id\"}, {\"dtype\": \"str\", \"name\": \"music_id\"}, {\"dtype\": \"int\", \"name\": \"rating\"}]}, e);\n", " });\n", " })();\n", " " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "train_file = '/Users/zhangyixin/Desktop/cjc2016-gh-pages/10000.txt'\n", "sf = gl.SFrame.read_csv(train_file, header=False, delimiter='\\t', verbose=False)\n", "sf.rename({'X1':'user_id', 'X2':'music_id', 'X3':'rating'}).show()" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "collapsed": true }, "outputs": [], "source": [ "(train_set, test_set) = sf.random_split(0.8, seed=1)" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<pre>Recsys training: model = popularity</pre>" ], "text/plain": [ "Recsys training: model = popularity" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<pre>Preparing data set.</pre>" ], "text/plain": [ "Preparing data set." ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<pre> Data has 1599753 observations with 76085 users and 10000 items.</pre>" ], "text/plain": [ " Data has 1599753 observations with 76085 users and 10000 items." ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<pre> Data prepared in: 2.29914s</pre>" ], "text/plain": [ " Data prepared in: 2.29914s" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<pre>1599753 observations to process; with 10000 unique items.</pre>" ], "text/plain": [ "1599753 observations to process; with 10000 unique items." ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "popularity_model = gl.popularity_recommender.create(train_set, 'user_id', 'music_id', target = 'rating')" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<pre>Recsys training: model = item_similarity</pre>" ], "text/plain": [ "Recsys training: model = item_similarity" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<pre>Preparing data set.</pre>" ], "text/plain": [ "Preparing data set." ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<pre> Data has 1599753 observations with 76085 users and 10000 items.</pre>" ], "text/plain": [ " Data has 1599753 observations with 76085 users and 10000 items." ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<pre> Data prepared in: 2.51362s</pre>" ], "text/plain": [ " Data prepared in: 2.51362s" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<pre>Computing item similarity statistics:</pre>" ], "text/plain": [ "Computing item similarity statistics:" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<pre>Computing most similar items for 10000 items:</pre>" ], "text/plain": [ "Computing most similar items for 10000 items:" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<pre>+-----------------+-----------------+</pre>" ], "text/plain": [ "+-----------------+-----------------+" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<pre>| Number of items | Elapsed Time |</pre>" ], "text/plain": [ "| Number of items | Elapsed Time |" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<pre>+-----------------+-----------------+</pre>" ], "text/plain": [ "+-----------------+-----------------+" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<pre>| 1000 | 3.24894 |</pre>" ], "text/plain": [ "| 1000 | 3.24894 |" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<pre>| 2000 | 3.39865 |</pre>" ], "text/plain": [ "| 2000 | 3.39865 |" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<pre>| 3000 | 3.55544 |</pre>" ], "text/plain": [ "| 3000 | 3.55544 |" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<pre>| 4000 | 3.67706 |</pre>" ], "text/plain": [ "| 4000 | 3.67706 |" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<pre>| 5000 | 3.78196 |</pre>" ], "text/plain": [ "| 5000 | 3.78196 |" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<pre>| 6000 | 3.89232 |</pre>" ], "text/plain": [ "| 6000 | 3.89232 |" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<pre>| 7000 | 4.00233 |</pre>" ], "text/plain": [ "| 7000 | 4.00233 |" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<pre>| 8000 | 4.09852 |</pre>" ], "text/plain": [ "| 8000 | 4.09852 |" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<pre>| 9000 | 4.21172 |</pre>" ], "text/plain": [ "| 9000 | 4.21172 |" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<pre>| 10000 | 4.39043 |</pre>" ], "text/plain": [ "| 10000 | 4.39043 |" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<pre>+-----------------+-----------------+</pre>" ], "text/plain": [ "+-----------------+-----------------+" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<pre>Finished training in 4.85052s</pre>" ], "text/plain": [ "Finished training in 4.85052s" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<pre>Finished prediction in 1.6265s</pre>" ], "text/plain": [ "Finished prediction in 1.6265s" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "item_sim_model = gl.item_similarity_recommender.create(train_set, 'user_id', 'music_id', target = 'rating', \n", " similarity_type='cosine')" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<pre>Recsys training: model = factorization_recommender</pre>" ], "text/plain": [ "Recsys training: model = factorization_recommender" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<pre>Preparing data set.</pre>" ], "text/plain": [ "Preparing data set." ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<pre> Data has 1599753 observations with 76085 users and 10000 items.</pre>" ], "text/plain": [ " Data has 1599753 observations with 76085 users and 10000 items." ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<pre> Data prepared in: 3.07978s</pre>" ], "text/plain": [ " Data prepared in: 3.07978s" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<pre>Training factorization_recommender for recommendations.</pre>" ], "text/plain": [ "Training factorization_recommender for recommendations." ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<pre>+--------------------------------+--------------------------------------------------+----------+</pre>" ], "text/plain": [ "+--------------------------------+--------------------------------------------------+----------+" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<pre>| Parameter | Description | Value |</pre>" ], "text/plain": [ "| Parameter | Description | Value |" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<pre>+--------------------------------+--------------------------------------------------+----------+</pre>" ], "text/plain": [ "+--------------------------------+--------------------------------------------------+----------+" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<pre>| num_factors | Factor Dimension | 8 |</pre>" ], "text/plain": [ "| num_factors | Factor Dimension | 8 |" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<pre>| regularization | L2 Regularization on Factors | 1e-08 |</pre>" ], "text/plain": [ "| regularization | L2 Regularization on Factors | 1e-08 |" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<pre>| solver | Solver used for training | sgd |</pre>" ], "text/plain": [ "| solver | Solver used for training | sgd |" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<pre>| linear_regularization | L2 Regularization on Linear Coefficients | 1e-10 |</pre>" ], "text/plain": [ "| linear_regularization | L2 Regularization on Linear Coefficients | 1e-10 |" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<pre>| max_iterations | Maximum Number of Iterations | 50 |</pre>" ], "text/plain": [ "| max_iterations | Maximum Number of Iterations | 50 |" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<pre>+--------------------------------+--------------------------------------------------+----------+</pre>" ], "text/plain": [ "+--------------------------------+--------------------------------------------------+----------+" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<pre> Optimizing model using SGD; tuning step size.</pre>" ], "text/plain": [ " Optimizing model using SGD; tuning step size." ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<pre> Using 199969 / 1599753 points for tuning the step size.</pre>" ], "text/plain": [ " Using 199969 / 1599753 points for tuning the step size." ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<pre>+---------+-------------------+------------------------------------------+</pre>" ], "text/plain": [ "+---------+-------------------+------------------------------------------+" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<pre>| Attempt | Initial Step Size | Estimated Objective Value |</pre>" ], "text/plain": [ "| Attempt | Initial Step Size | Estimated Objective Value |" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<pre>+---------+-------------------+------------------------------------------+</pre>" ], "text/plain": [ "+---------+-------------------+------------------------------------------+" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<pre>| 0 | 25 | No Decrease (223.075 >= 36.5392) |</pre>" ], "text/plain": [ "| 0 | 25 | No Decrease (223.075 >= 36.5392) |" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<pre>| 1 | 6.25 | No Decrease (215.831 >= 36.5392) |</pre>" ], "text/plain": [ "| 1 | 6.25 | No Decrease (215.831 >= 36.5392) |" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<pre>| 2 | 1.5625 | No Decrease (186.436 >= 36.5392) |</pre>" ], "text/plain": [ "| 2 | 1.5625 | No Decrease (186.436 >= 36.5392) |" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<pre>| 3 | 0.390625 | No Decrease (84.5942 >= 36.5392) |</pre>" ], "text/plain": [ "| 3 | 0.390625 | No Decrease (84.5942 >= 36.5392) |" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<pre>| 4 | 0.0976562 | 12.0584 |</pre>" ], "text/plain": [ "| 4 | 0.0976562 | 12.0584 |" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<pre>| 5 | 0.0488281 | 8.67429 |</pre>" ], "text/plain": [ "| 5 | 0.0488281 | 8.67429 |" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<pre>| 6 | 0.0244141 | 20.4528 |</pre>" ], "text/plain": [ "| 6 | 0.0244141 | 20.4528 |" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<pre>+---------+-------------------+------------------------------------------+</pre>" ], "text/plain": [ "+---------+-------------------+------------------------------------------+" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<pre>| Final | 0.0488281 | 8.67429 |</pre>" ], "text/plain": [ "| Final | 0.0488281 | 8.67429 |" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<pre>+---------+-------------------+------------------------------------------+</pre>" ], "text/plain": [ "+---------+-------------------+------------------------------------------+" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<pre>Starting Optimization.</pre>" ], "text/plain": [ "Starting Optimization." ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<pre>+---------+--------------+-------------------+-----------------------+-------------+</pre>" ], "text/plain": [ "+---------+--------------+-------------------+-----------------------+-------------+" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<pre>| Iter. | Elapsed Time | Approx. 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8.39s | 15.9942 | 3.99825 | 0.00436732 |</pre>" ], "text/plain": [ "| 25 | 8.39s | 15.9942 | 3.99825 | 0.00436732 |" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<pre>| 30 | 9.84s | 14.5492 | 3.81322 | 0.00380916 |</pre>" ], "text/plain": [ "| 30 | 9.84s | 14.5492 | 3.81322 | 0.00380916 |" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<pre>| 35 | 11.18s | 13.4488 | 3.66606 | 0.00339327 |</pre>" ], "text/plain": [ "| 35 | 11.18s | 13.4488 | 3.66606 | 0.00339327 |" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<pre>| 40 | 12.52s | 12.6294 | 3.55252 | 0.00306991 |</pre>" ], "text/plain": [ "| 40 | 12.52s | 12.6294 | 3.55252 | 0.00306991 |" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<pre>| 45 | 13.80s | 11.9692 | 3.45831 | 0.00281035 |</pre>" ], "text/plain": [ "| 45 | 13.80s | 11.9692 | 3.45831 | 0.00281035 |" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<pre>| 50 | 15.14s | 10.6591 | 3.26338 | 0.00183623 |</pre>" ], "text/plain": [ "| 50 | 15.14s | 10.6591 | 3.26338 | 0.00183623 |" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<pre>+---------+--------------+-------------------+-----------------------+-------------+</pre>" ], "text/plain": [ "+---------+--------------+-------------------+-----------------------+-------------+" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<pre>Optimization Complete: Maximum number of passes through the data reached.</pre>" ], "text/plain": [ "Optimization Complete: Maximum number of passes through the data reached." ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<pre>Computing final objective value and training RMSE.</pre>" ], "text/plain": [ "Computing final objective value and training RMSE." ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<pre> Final objective value: 9.46694</pre>" ], "text/plain": [ " Final objective value: 9.46694" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<pre> Final training RMSE: 3.0753</pre>" ], "text/plain": [ " Final training RMSE: 3.0753" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "factorization_machine_model = gl.recommender.factorization_recommender.create(train_set, 'user_id', 'music_id',\n", " target='rating')" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "compare_models: using 6871 users to estimate model performance\n", "PROGRESS: Evaluate model M0\n" ] }, { "data": { "text/html": [ "<pre>recommendations finished on 1000/6871 queries. users per second: 2266.21</pre>" ], "text/plain": [ "recommendations finished on 1000/6871 queries. users per second: 2266.21" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<pre>recommendations finished on 2000/6871 queries. users per second: 2103.55</pre>" ], "text/plain": [ "recommendations finished on 2000/6871 queries. users per second: 2103.55" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<pre>recommendations finished on 3000/6871 queries. users per second: 1950.33</pre>" ], "text/plain": [ "recommendations finished on 3000/6871 queries. users per second: 1950.33" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<pre>recommendations finished on 4000/6871 queries. users per second: 1929.12</pre>" ], "text/plain": [ "recommendations finished on 4000/6871 queries. users per second: 1929.12" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<pre>recommendations finished on 5000/6871 queries. users per second: 1855.33</pre>" ], "text/plain": [ "recommendations finished on 5000/6871 queries. users per second: 1855.33" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<pre>recommendations finished on 6000/6871 queries. users per second: 1745</pre>" ], "text/plain": [ "recommendations finished on 6000/6871 queries. users per second: 1745" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Precision and recall summary statistics by cutoff\n", "+--------+-------------------+-------------------+\n", "| cutoff | mean_precision | mean_recall |\n", "+--------+-------------------+-------------------+\n", "| 1 | 0.000436617668462 | 5.40574256191e-05 |\n", "| 2 | 0.000291078445641 | 0.000126827037029 |\n", "| 3 | 0.000339591519915 | 0.000245684068999 |\n", "| 4 | 0.000363848057051 | 0.000273252556399 |\n", "| 5 | 0.00049483335759 | 0.000412897271069 |\n", "| 6 | 0.000436617668462 | 0.000449282076774 |\n", "| 7 | 0.00039503503337 | 0.000522051688185 |\n", "| 8 | 0.000363848057051 | 0.000551159532749 |\n", "| 9 | 0.000420446643704 | 0.000856277368066 |\n", "| 10 | 0.000378401979333 | 0.000856277368066 |\n", "+--------+-------------------+-------------------+\n", "[10 rows x 3 columns]\n", "\n", "('\\nOverall RMSE: ', 5.720865259296598)\n", "\n", "Per User RMSE (best)\n", "+-------------------------------+-------+-----------------+\n", "| user_id | count | rmse |\n", "+-------------------------------+-------+-----------------+\n", "| 83629183d2b25913d3aeed7668... | 1 | 0.0160714285714 |\n", "+-------------------------------+-------+-----------------+\n", "[1 rows x 3 columns]\n", "\n", "\n", "Per User RMSE (worst)\n", "+-------------------------------+-------+---------------+\n", "| user_id | count | rmse |\n", "+-------------------------------+-------+---------------+\n", "| e48e5aeb5a3d9e1425ea541d65... | 3 | 151.583917343 |\n", "+-------------------------------+-------+---------------+\n", "[1 rows x 3 columns]\n", "\n", "\n", "Per Item RMSE (best)\n", "+--------------------+-------+------+\n", "| music_id | count | rmse |\n", "+--------------------+-------+------+\n", "| SOAQJRX12A6701F999 | 2 | 0.0 |\n", "+--------------------+-------+------+\n", "[1 rows x 3 columns]\n", "\n", "\n", "Per Item RMSE (worst)\n", "+--------------------+-------+---------------+\n", "| music_id | count | rmse |\n", "+--------------------+-------+---------------+\n", "| SOXRHKP12A58A7F404 | 1 | 249.654320988 |\n", "+--------------------+-------+---------------+\n", "[1 rows x 3 columns]\n", "\n", "PROGRESS: Evaluate model M1\n" ] }, { "data": { "text/html": [ "<pre>recommendations finished on 1000/6871 queries. users per second: 528.71</pre>" ], "text/plain": [ "recommendations finished on 1000/6871 queries. users per second: 528.71" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<pre>recommendations finished on 2000/6871 queries. users per second: 540.911</pre>" ], "text/plain": [ "recommendations finished on 2000/6871 queries. users per second: 540.911" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<pre>recommendations finished on 3000/6871 queries. users per second: 566.361</pre>" ], "text/plain": [ "recommendations finished on 3000/6871 queries. users per second: 566.361" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<pre>recommendations finished on 4000/6871 queries. users per second: 599.767</pre>" ], "text/plain": [ "recommendations finished on 4000/6871 queries. users per second: 599.767" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<pre>recommendations finished on 5000/6871 queries. users per second: 633.891</pre>" ], "text/plain": [ "recommendations finished on 5000/6871 queries. users per second: 633.891" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<pre>recommendations finished on 6000/6871 queries. users per second: 649.185</pre>" ], "text/plain": [ "recommendations finished on 6000/6871 queries. users per second: 649.185" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Precision and recall summary statistics by cutoff\n", "+--------+-------------------+-------------------+\n", "| cutoff | mean_precision | mean_recall |\n", "+--------+-------------------+-------------------+\n", "| 1 | 0.000436617668462 | 7.40537810234e-05 |\n", "| 2 | 0.000654926502692 | 0.000259672757767 |\n", "| 3 | 0.000630669965556 | 0.000343221025905 |\n", "| 4 | 0.000509387279872 | 0.000364012343451 |\n", "| 5 | 0.000465725513026 | 0.000389864705399 |\n", "| 6 | 0.000436617668462 | 0.000412979758435 |\n", "| 7 | 0.000415826350916 | 0.000460280005852 |\n", "| 8 | 0.000400232862757 | 0.000542752232117 |\n", "| 9 | 0.000388104594188 | 0.000724676260642 |\n", "| 10 | 0.000436617668462 | 0.000978330334701 |\n", "+--------+-------------------+-------------------+\n", "[10 rows x 3 columns]\n", "\n" ] }, { "data": { "text/html": [ "<pre>Finished prediction in 0.282154s</pre>" ], "text/plain": [ "Finished prediction in 0.282154s" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "('\\nOverall RMSE: ', 6.658554396106699)\n", "\n", "Per User RMSE (best)\n", "+-------------------------------+-------+------+\n", "| user_id | count | rmse |\n", "+-------------------------------+-------+------+\n", "| a9be282eaf672c22949799d898... | 1 | 0.0 |\n", "+-------------------------------+-------+------+\n", "[1 rows x 3 columns]\n", "\n", "\n", "Per User RMSE (worst)\n", "+-------------------------------+-------+---------------+\n", "| user_id | count | rmse |\n", "+-------------------------------+-------+---------------+\n", "| 972cce803aa7beceaa7d0039e4... | 19 | 152.416108472 |\n", "+-------------------------------+-------+---------------+\n", "[1 rows x 3 columns]\n", "\n", "\n", "Per Item RMSE (best)\n", "+--------------------+-------+------+\n", "| music_id | count | rmse |\n", "+--------------------+-------+------+\n", "| SOURPHM12A67021876 | 1 | 0.0 |\n", "+--------------------+-------+------+\n", "[1 rows x 3 columns]\n", "\n", "\n", "Per Item RMSE (worst)\n", "+--------------------+-------+---------------+\n", "| music_id | count | rmse |\n", "+--------------------+-------+---------------+\n", "| SOZTVXP12AF72A760B | 2 | 468.813602358 |\n", "+--------------------+-------+---------------+\n", "[1 rows x 3 columns]\n", "\n", "PROGRESS: Evaluate model M2\n" ] }, { "data": { "text/html": [ "<pre>recommendations finished on 1000/6871 queries. users per second: 1777.53</pre>" ], "text/plain": [ "recommendations finished on 1000/6871 queries. users per second: 1777.53" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<pre>recommendations finished on 2000/6871 queries. users per second: 1859.51</pre>" ], "text/plain": [ "recommendations finished on 2000/6871 queries. users per second: 1859.51" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<pre>recommendations finished on 3000/6871 queries. users per second: 1911.33</pre>" ], "text/plain": [ "recommendations finished on 3000/6871 queries. users per second: 1911.33" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<pre>recommendations finished on 4000/6871 queries. users per second: 1954.23</pre>" ], "text/plain": [ "recommendations finished on 4000/6871 queries. users per second: 1954.23" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<pre>recommendations finished on 5000/6871 queries. users per second: 2001.15</pre>" ], "text/plain": [ "recommendations finished on 5000/6871 queries. users per second: 2001.15" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<pre>recommendations finished on 6000/6871 queries. users per second: 2013.4</pre>" ], "text/plain": [ "recommendations finished on 6000/6871 queries. users per second: 2013.4" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Precision and recall summary statistics by cutoff\n", "+--------+-------------------+-------------------+\n", "| cutoff | mean_precision | mean_recall |\n", "+--------+-------------------+-------------------+\n", "| 1 | 0.000436617668462 | 4.60874205598e-05 |\n", "| 2 | 0.000509387279872 | 0.000169795759957 |\n", "| 3 | 0.000630669965556 | 0.000400637138375 |\n", "| 4 | 0.000654926502692 | 0.000473686632907 |\n", "| 5 | 0.000785911803231 | 0.000650759354005 |\n", "| 6 | 0.000679183039829 | 0.000656823488289 |\n", "| 7 | 0.000727696114103 | 0.000915218612788 |\n", "| 8 | 0.00070950371125 | 0.000979411805711 |\n", "| 9 | 0.000711525089345 | 0.00109168492046 |\n", "| 10 | 0.000713142191821 | 0.00116742144715 |\n", "+--------+-------------------+-------------------+\n", "[10 rows x 3 columns]\n", "\n", "('\\nOverall RMSE: ', 7.555521250194041)\n", "\n", "Per User RMSE (best)\n", "+-------------------------------+-------+------------------+\n", "| user_id | count | rmse |\n", "+-------------------------------+-------+------------------+\n", "| 85d4cca3251f1c9b466d346cbe... | 1 | 0.00368622415654 |\n", "+-------------------------------+-------+------------------+\n", "[1 rows x 3 columns]\n", "\n", "\n", "Per User RMSE (worst)\n", "+-------------------------------+-------+---------------+\n", "| user_id | count | rmse |\n", "+-------------------------------+-------+---------------+\n", "| e48e5aeb5a3d9e1425ea541d65... | 3 | 145.876884342 |\n", "+-------------------------------+-------+---------------+\n", "[1 rows x 3 columns]\n", "\n", "\n", "Per Item RMSE (best)\n", "+--------------------+-------+-------------------+\n", "| music_id | count | rmse |\n", "+--------------------+-------+-------------------+\n", "| SOLUHDM12A6701BEDA | 1 | 5.07580577498e-05 |\n", "+--------------------+-------+-------------------+\n", "[1 rows x 3 columns]\n", "\n", "\n", "Per Item RMSE (worst)\n", "+--------------------+-------+---------------+\n", "| music_id | count | rmse |\n", "+--------------------+-------+---------------+\n", "| SOXRHKP12A58A7F404 | 1 | 238.802020677 |\n", "+--------------------+-------+---------------+\n", "[1 rows x 3 columns]\n", "\n" ] } ], "source": [ "result = gl.recommender.util.compare_models(test_set, [popularity_model, item_sim_model, factorization_machine_model],\n", " user_sample=.1, skip_set=train_set)" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "collapsed": true }, "outputs": [], "source": [ "K = 10\n", "users = gl.SArray(sf['user_id'].unique().head(100))" ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\"><table frame=\"box\" rules=\"cols\">\n", " <tr>\n", " <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">user_id</th>\n", " <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">music_id</th>\n", " <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">score</th>\n", " <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">rank</th>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">c66c10a9567f0d82ff31441a9<br>fd5063e5cd9dfe8 ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">SOFCGSE12AF72A674F</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">20.686440678</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">c66c10a9567f0d82ff31441a9<br>fd5063e5cd9dfe8 ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">SOACBLB12AB01871C7</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">17.8421052632</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">c66c10a9567f0d82ff31441a9<br>fd5063e5cd9dfe8 ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">SOZPMJT12AAF3B40D1</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">16.4245283019</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">3</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">c66c10a9567f0d82ff31441a9<br>fd5063e5cd9dfe8 ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">SOGSDHY12AB017BF39</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">14.7</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">4</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">c66c10a9567f0d82ff31441a9<br>fd5063e5cd9dfe8 ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">SOJSXJY12A8C13E32E</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">14.07960199</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">c66c10a9567f0d82ff31441a9<br>fd5063e5cd9dfe8 ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">SOANOQW12A58A793D2</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">13.8804347826</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">6</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">c66c10a9567f0d82ff31441a9<br>fd5063e5cd9dfe8 ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">SOFWKCI12A8C13A22A</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">13.495049505</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">7</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">c66c10a9567f0d82ff31441a9<br>fd5063e5cd9dfe8 ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">SOAFPSO12AF72A4521</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">12.5774647887</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">8</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">c66c10a9567f0d82ff31441a9<br>fd5063e5cd9dfe8 ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">SONDKOF12A6D4F7D70</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">12.1647398844</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">9</td>\n", " </tr>\n", " <tr>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">c66c10a9567f0d82ff31441a9<br>fd5063e5cd9dfe8 ...</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">SOQBUFQ12A6D4F7F4C</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">11.8518518519</td>\n", " <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">10</td>\n", " </tr>\n", "</table>\n", "[10 rows x 4 columns]<br/>\n", "</div>" ], "text/plain": [ "Columns:\n", "\tuser_id\tstr\n", "\tmusic_id\tstr\n", "\tscore\tfloat\n", "\trank\tint\n", "\n", "Rows: 10\n", "\n", "Data:\n", "+-------------------------------+--------------------+---------------+------+\n", "| user_id | music_id | score | rank |\n", "+-------------------------------+--------------------+---------------+------+\n", "| c66c10a9567f0d82ff31441a9f... | SOFCGSE12AF72A674F | 20.686440678 | 1 |\n", "| c66c10a9567f0d82ff31441a9f... | SOACBLB12AB01871C7 | 17.8421052632 | 2 |\n", "| c66c10a9567f0d82ff31441a9f... | SOZPMJT12AAF3B40D1 | 16.4245283019 | 3 |\n", "| c66c10a9567f0d82ff31441a9f... | SOGSDHY12AB017BF39 | 14.7 | 4 |\n", "| c66c10a9567f0d82ff31441a9f... | SOJSXJY12A8C13E32E | 14.07960199 | 5 |\n", "| c66c10a9567f0d82ff31441a9f... | SOANOQW12A58A793D2 | 13.8804347826 | 6 |\n", "| c66c10a9567f0d82ff31441a9f... | SOFWKCI12A8C13A22A | 13.495049505 | 7 |\n", "| c66c10a9567f0d82ff31441a9f... | SOAFPSO12AF72A4521 | 12.5774647887 | 8 |\n", "| c66c10a9567f0d82ff31441a9f... | SONDKOF12A6D4F7D70 | 12.1647398844 | 9 |\n", "| c66c10a9567f0d82ff31441a9f... | SOQBUFQ12A6D4F7F4C | 11.8518518519 | 10 |\n", "+-------------------------------+--------------------+---------------+------+\n", "[10 rows x 4 columns]" ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" } ], "source": [ "recs = item_sim_model.recommend(users=users, k=K)\n", "recs.head()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.11" } }, "nbformat": 4, "nbformat_minor": 0 }

mit

wrightaprilm/squamates

.ipynb_checkpoints/Change_counter-checkpoint.ipynb

1

6325

{ "metadata": { "name": "", "signature": "sha256:c0ab8633fad774129a2241a8aa4402022a06d6ba3d69e5082378fafbfdc37f29" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "import dendropy\n", "import pandas as pd" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 1 }, { "cell_type": "code", "collapsed": false, "input": [ "taxa = dendropy.TaxonSet()\n", "mle = dendropy.Tree.get_from_path('vec_tree', 'newick', taxon_set=taxa, preserve_underscores=True) " ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 119 }, { "cell_type": "code", "collapsed": false, "input": [ "data = pd.read_csv('PyronParityData.csv', index_col=0, header=False)\n", "data" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>col</th>\n", " </tr>\n", " <tr>\n", " <th>tax</th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>a</th>\n", " <td> 0</td>\n", " </tr>\n", " <tr>\n", " <th>b</th>\n", " <td> 1</td>\n", " </tr>\n", " <tr>\n", " <th>c</th>\n", " <td> 0</td>\n", " </tr>\n", " <tr>\n", " <th>d</th>\n", " <td> 0</td>\n", " </tr>\n", " <tr>\n", " <th>e</th>\n", " <td> 1</td>\n", " </tr>\n", " <tr>\n", " <th>f</th>\n", " <td> 1</td>\n", " </tr>\n", " <tr>\n", " <th>g</th>\n", " <td> 1</td>\n", " </tr>\n", " <tr>\n", " <th>h</th>\n", " <td> 1</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "metadata": {}, "output_type": "pyout", "prompt_number": 113, "text": [ " col\n", "tax \n", "a 0\n", "b 1\n", "c 0\n", "d 0\n", "e 1\n", "f 1\n", "g 1\n", "h 1" ] } ], "prompt_number": 113 }, { "cell_type": "code", "collapsed": false, "input": [ "for idx, nd in enumerate(mle.postorder_node_iter()):\n", " if nd.label is None:\n", " lookup = '{}'.format(nd.taxon)\n", " nd.label = int(data.ix[lookup])\n", " else: \n", " pass" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 120 }, { "cell_type": "code", "collapsed": false, "input": [ "putative_c = []\n", "putative_co = []\n", "total = []\n", "childs = []\n", "for index, node in enumerate(mle.postorder_node_iter()):\n", " total.append(index)\n", " if node.parent_node is None:\n", " pass\n", " elif .5 < float(node.label) < 1 or float(node.label) == 0: #Is likely oviparous \n", " if float(node.parent_node.label) < .5 : #List of nodes that demonstrate change away from oviparity. \n", " if node.taxon is not None :\n", " putative_co.append([node.parent_node.label, node.taxon])\n", " else:\n", " putative_co.append(node.parent_node.label)\n", " for nd in node.child_nodes():\n", "# print nd.taxon\n", " pass\n", " elif 0 < float(node.label) < .5 or float(node.label) == 1: \n", " print node.label\n", " if float(node.parent_node.label) > .5: \n", " putative_c.append([node.parent_node.label,node.taxon]) \n", "\n", " \n", "print len(putative_c), 'changes to viviparity' \n", "print len(putative_co), 'reversions to oviparity' " ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "1\n", "1\n", "1\n", ".1\n", "1\n", "1\n", ".1\n", ".1\n", "1 changes to viviparity\n", "1 reversions to oviparity\n" ] } ], "prompt_number": 121 }, { "cell_type": "code", "collapsed": false, "input": [ "Copyright (c) <2014> <April Wright, [email protected]>\n", "\n", "\n", "Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the \"Software\"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:\n", "\n", "The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.\n", "\n", "THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE." ], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }

mit

lwahedi/CurrentPresentation

talks/MDI2/Scraping+Lecture.ipynb

1

164898

{ "cells": [ { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Collecting and Using Data in Python\n", "\n", "## Laila A. Wahedi\n", "### Massive Data Institute Postdoctoral Fellow <br>McCourt School of Public Policy<br>\n", "\n", "### Follow along: Wahedi.us, Current Presentation" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Agenda for today:\n", "* More on manipulating data\n", "* Scrape data\n", "* Merge data into a data frame \n", "* Run a basic model on the data" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Packages to Import For Today\n", "* Should all be included with your Anaconda Python Distribution\n", " * Raise your hand for help if you have trouble\n", "* Our plots will use matplotlib, similar to plotting in matlab\n", "* %matplotlib inline tells Jupyter Notebooks to display your plots\n", "* from allows you to import part of a package" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/law98/anaconda/envs/p3env/lib/python3.6/site-packages/statsmodels/compat/pandas.py:56: FutureWarning: The pandas.core.datetools module is deprecated and will be removed in a future version. Please use the pandas.tseries module instead.\n", " from pandas.core import datetools\n" ] } ], "source": [ "import pandas as pd\n", "import numpy as np\n", "import pickle\n", "import statsmodels.api as sm\n", "from sklearn import cluster\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "from bs4 import BeautifulSoup as bs\n", "import requests\n", "import time\n", "# from ggplot import *" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Other Useful Packages (not used today)\n", "* ggplot: the familiar ggplot2 you know and love from R\n", "* seaborn: Makes your plots prettier\n", "* plotly: makes interactive visualizations, similar to shiny\n", "* gensim: package for doing natural language processing\n", "* scipy: used with numpy to do math. Generates random numbers from distributions, does matrix operations, etc. " ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Data Manipulation \n", "\n", "* Download the .csv file at: <br>\n", "https://data.chhs.ca.gov/dataset/asthma-emergency-department-visit-rates-by-zip-code\n", " * OR: https://tinyurl.com/y79jbxlk\n", "* Move it to the same directory as your notebook" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "slideshow": { "slide_type": "slide" } }, "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>Year</th>\n", " <th>ZIP code</th>\n", " <th>Age Group</th>\n", " <th>Number of Visits</th>\n", " <th>Age-adjusted rate</th>\n", " <th>County Fips code</th>\n", " <th>County</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>2015</td>\n", " <td>90004\\n(34.07646, -118.309453)</td>\n", " <td>Children (0-17)</td>\n", " <td>117.0</td>\n", " <td>91.7</td>\n", " <td>6037</td>\n", " <td>LOS ANGELES</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>2015</td>\n", " <td>90011\\n(34.007055, -118.258872)</td>\n", " <td>Children (0-17)</td>\n", " <td>381.0</td>\n", " <td>102.8</td>\n", " <td>6037</td>\n", " <td>LOS ANGELES</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>2015</td>\n", " <td>90002\\n(33.949079, -118.247877)</td>\n", " <td>Children (0-17)</td>\n", " <td>227.0</td>\n", " <td>123.8</td>\n", " <td>6037</td>\n", " <td>LOS ANGELES</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>2013</td>\n", " <td>90004\\n(34.07646, -118.309453)</td>\n", " <td>Children (0-17)</td>\n", " <td>169.0</td>\n", " <td>129.4</td>\n", " <td>6037</td>\n", " <td>LOS ANGELES</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>2014</td>\n", " <td>90012\\n(34.064406, -118.239532)</td>\n", " <td>Children (0-17)</td>\n", " <td>22.0</td>\n", " <td>69.1</td>\n", " <td>6037</td>\n", " <td>LOS ANGELES</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Year ZIP code Age Group Number of Visits \\\n", "0 2015 90004\\n(34.07646, -118.309453) Children (0-17) 117.0 \n", "1 2015 90011\\n(34.007055, -118.258872) Children (0-17) 381.0 \n", "2 2015 90002\\n(33.949079, -118.247877) Children (0-17) 227.0 \n", "3 2013 90004\\n(34.07646, -118.309453) Children (0-17) 169.0 \n", "4 2014 90012\\n(34.064406, -118.239532) Children (0-17) 22.0 \n", "\n", " Age-adjusted rate County Fips code County \n", "0 91.7 6037 LOS ANGELES \n", "1 102.8 6037 LOS ANGELES \n", "2 123.8 6037 LOS ANGELES \n", "3 129.4 6037 LOS ANGELES \n", "4 69.1 6037 LOS ANGELES " ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "asthma_data = pd.read_csv('asthma-emergency-department-visit-rates-by-zip-code.csv')\n", "asthma_data.head()" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "### Look at those zip codes!" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Clean Zip Code\n", "* We don't need the latitude and longitude\n", "* Create two variables by splitting the zip code variable: \n", " * index the data frame to the zip code variable\n", " * split it in two: https://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.str.split.html\n", " * assign it to another two variables\n", "* Remember: can't run this cell twice without starting over" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "slideshow": { "slide_type": "fragment" } }, "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>Year</th>\n", " <th>Age Group</th>\n", " <th>Number of Visits</th>\n", " <th>Age-adjusted rate</th>\n", " <th>County Fips code</th>\n", " <th>County</th>\n", " <th>zip</th>\n", " <th>coordinates</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>2015</td>\n", " <td>Children (0-17)</td>\n", " <td>117.0</td>\n", " <td>91.7</td>\n", " <td>6037</td>\n", " <td>LOS ANGELES</td>\n", " <td>90004</td>\n", " <td>(34.07646, -118.309453)</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>2015</td>\n", " <td>Children (0-17)</td>\n", " <td>381.0</td>\n", " <td>102.8</td>\n", " <td>6037</td>\n", " <td>LOS ANGELES</td>\n", " <td>90011</td>\n", " <td>(34.007055, -118.258872)</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Year Age Group Number of Visits Age-adjusted rate \\\n", "0 2015 Children (0-17) 117.0 91.7 \n", "1 2015 Children (0-17) 381.0 102.8 \n", "\n", " County Fips code County zip coordinates \n", "0 6037 LOS ANGELES 90004 (34.07646, -118.309453) \n", "1 6037 LOS ANGELES 90011 (34.007055, -118.258872) " ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "asthma_data[['zip','coordinates']] = asthma_data.loc[:,'ZIP code'].str.split(\n", " pat='\\n',expand=True)\n", "asthma_data.drop('ZIP code', axis=1,inplace=True)\n", "asthma_data.head(2)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Rearrange The Data: Group By\n", "* Make child and adult separate columns rather than rows. \n", "* Must specify how to aggregate the columns <br>\n", "https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.groupby.html\n" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "slideshow": { "slide_type": "fragment" } }, "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></th>\n", " <th>Number of Visits</th>\n", " <th>Age-adjusted rate</th>\n", " <th>County Fips code</th>\n", " </tr>\n", " <tr>\n", " <th>Year</th>\n", " <th>zip</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th rowspan=\"4\" valign=\"top\">2009</th>\n", " <th>90001</th>\n", " <td>818.0</td>\n", " <td>226.074245</td>\n", " <td>18111</td>\n", " </tr>\n", " <tr>\n", " <th>90002</th>\n", " <td>836.0</td>\n", " <td>265.349315</td>\n", " <td>18111</td>\n", " </tr>\n", " <tr>\n", " <th>90003</th>\n", " <td>1542.0</td>\n", " <td>369.202131</td>\n", " <td>18111</td>\n", " </tr>\n", " <tr>\n", " <th>90004</th>\n", " <td>580.0</td>\n", " <td>145.538276</td>\n", " <td>18111</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Number of Visits Age-adjusted rate County Fips code\n", "Year zip \n", "2009 90001 818.0 226.074245 18111\n", " 90002 836.0 265.349315 18111\n", " 90003 1542.0 369.202131 18111\n", " 90004 580.0 145.538276 18111" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "asthma_grouped = asthma_data.groupby(by=['Year','zip']).sum()\n", "asthma_grouped.head(4)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "### Lost Columns! Fips summed!" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Group by: Cleaning Up\n", " * Lost columns you can't sum\n", " * took sum of fips\n", " * Must add these back in\n", " * Works because temp table has same index" ] }, { "cell_type": "code", "execution_count": 43, "metadata": { "slideshow": { "slide_type": "fragment" } }, "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></th>\n", " <th>Number of Visits</th>\n", " <th>Age-adjusted rate</th>\n", " <th>fips</th>\n", " <th>county</th>\n", " <th>coordinates</th>\n", " </tr>\n", " <tr>\n", " <th>Year</th>\n", " <th>zip</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\">2009</th>\n", " <th>90001</th>\n", " <td>409.0</td>\n", " <td>226.074245</td>\n", " <td>6037</td>\n", " <td>LOS ANGELES</td>\n", " <td>(33.973252, -118.249154)</td>\n", " </tr>\n", " <tr>\n", " <th>90002</th>\n", " <td>418.0</td>\n", " <td>265.349315</td>\n", " <td>6037</td>\n", " <td>LOS ANGELES</td>\n", " <td>(33.949079, -118.247877)</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Number of Visits Age-adjusted rate fips county \\\n", "Year zip \n", "2009 90001 409.0 226.074245 6037 LOS ANGELES \n", " 90002 418.0 265.349315 6037 LOS ANGELES \n", "\n", " coordinates \n", "Year zip \n", "2009 90001 (33.973252, -118.249154) \n", " 90002 (33.949079, -118.247877) " ] }, "execution_count": 43, "metadata": {}, "output_type": "execute_result" } ], "source": [ "asthma_grouped.drop('County Fips code',axis=1,inplace=True)\n", "temp_grp = asthma_data.groupby(by=['Year','zip']).first()\n", "asthma_grouped[['fips','county','coordinates']]=temp_grp.loc[:,['County Fips code',\n", " 'County',\n", " 'coordinates']]\n", "asthma_grouped.loc[:,'Number of Visits']=asthma_grouped.loc[:,'Number of Visits']/2\n", "asthma_grouped.head(2)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Aside on Copying\n", "* Multiple variables can point to the same data in Python. Saves memory\n", "* If you set one variable equal to another, then change the first variable, the second changes.\n", "* Causes warnings in Pandas all the time. \n", "* Solution: \n", " * Use proper slicing-- .loc[] --for the right hand side\n", " * Use copy\n" ] }, { "cell_type": "code", "execution_count": 60, "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[5, 6]\n" ] } ], "source": [ "A = [5]\n", "B = A\n", "A.append(6)\n", "print(B)" ] }, { "cell_type": "code", "execution_count": 61, "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[5]\n" ] } ], "source": [ "import copy\n", "A = [5]\n", "B = A.copy()\n", "A.append(6)\n", "print(B)" ] }, { "cell_type": "code", "execution_count": 62, "metadata": { "collapsed": true, "slideshow": { "slide_type": "fragment" } }, "outputs": [], "source": [ "asthma_grouped[['fips','county','coordinates']]=temp_grp.loc[:,['County Fips code',\n", " 'County',\n", " 'coordinates']].copy()" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Rearrange The Data: Pivot\n", "* Use pivot and melt to to move from row identifiers to column identifiers and back <br>\n", "https://pandas.pydata.org/pandas-docs/stable/reshaping.html#reshaping-by-melt\n", "* Tell computer what to do with every cell:\n", " * Index: Stays the same\n", " * Columns: The column containing the new column labels\n", " * Values: The column containing values to insert\n", "<img src='pivot.png'>" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Rearrange The Data: Pivot\n", "* Tell computer what to do with every cell:\n", " * Index: Stays the same\n", " * Columns: The column containing the new column labels\n", " * Values: The column containing values to insert\n" ] }, { "cell_type": "code", "execution_count": 48, "metadata": { "slideshow": { "slide_type": "fragment" } }, "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>Age Group</th>\n", " <th>Year</th>\n", " <th>zip</th>\n", " <th>County</th>\n", " <th>coordinates</th>\n", " <th>County Fips code</th>\n", " <th>Adults (18+)</th>\n", " <th>All Ages</th>\n", " <th>Children (0-17)</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>2009</td>\n", " <td>90001</td>\n", " <td>LOS ANGELES</td>\n", " <td>(33.973252, -118.249154)</td>\n", " <td>6037</td>\n", " <td>206.0</td>\n", " <td>409.0</td>\n", " <td>203.0</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>2009</td>\n", " <td>90002</td>\n", " <td>LOS ANGELES</td>\n", " <td>(33.949079, -118.247877)</td>\n", " <td>6037</td>\n", " <td>204.0</td>\n", " <td>418.0</td>\n", " <td>214.0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ "Age Group Year zip County coordinates \\\n", "0 2009 90001 LOS ANGELES (33.973252, -118.249154) \n", "1 2009 90002 LOS ANGELES (33.949079, -118.247877) \n", "\n", "Age Group County Fips code Adults (18+) All Ages Children (0-17) \n", "0 6037 206.0 409.0 203.0 \n", "1 6037 204.0 418.0 214.0 " ] }, "execution_count": 48, "metadata": {}, "output_type": "execute_result" } ], "source": [ "asthma_unstacked = asthma_data.pivot_table(index = ['Year',\n", " 'zip',\n", " 'County',\n", " 'coordinates',\n", " 'County Fips code'], \n", " columns = 'Age Group', \n", " values = 'Number of Visits')\n", "asthma_unstacked.reset_index(drop=False,inplace=True)\n", "asthma_unstacked.head(2)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Rename Columns, Subset Data" ] }, { "cell_type": "code", "execution_count": 49, "metadata": { "slideshow": { "slide_type": "fragment" } }, "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>Age Group</th>\n", " <th>Year</th>\n", " <th>Zip</th>\n", " <th>County</th>\n", " <th>Coordinates</th>\n", " <th>Fips</th>\n", " <th>Adults</th>\n", " <th>Incidents</th>\n", " <th>Children</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>4693</th>\n", " <td>2015</td>\n", " <td>90001</td>\n", " <td>LOS ANGELES</td>\n", " <td>(33.973252, -118.249154)</td>\n", " <td>6037</td>\n", " <td>229.0</td>\n", " <td>441.0</td>\n", " <td>212.0</td>\n", " </tr>\n", " <tr>\n", " <th>4694</th>\n", " <td>2015</td>\n", " <td>90002</td>\n", " <td>LOS ANGELES</td>\n", " <td>(33.949079, -118.247877)</td>\n", " <td>6037</td>\n", " <td>249.0</td>\n", " <td>476.0</td>\n", " <td>227.0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ "Age Group Year Zip County Coordinates Fips Adults \\\n", "4693 2015 90001 LOS ANGELES (33.973252, -118.249154) 6037 229.0 \n", "4694 2015 90002 LOS ANGELES (33.949079, -118.247877) 6037 249.0 \n", "\n", "Age Group Incidents Children \n", "4693 441.0 212.0 \n", "4694 476.0 227.0 " ] }, "execution_count": 49, "metadata": {}, "output_type": "execute_result" } ], "source": [ "asthma_unstacked.rename(columns={\n", " 'zip':'Zip',\n", " 'coordinates':'Coordinates',\n", " 'County Fips code':'Fips',\n", " 'Adults (18+)':'Adults',\n", " 'All Ages':'Incidents',\n", " 'Children (0-17)': 'Children'\n", " },\n", " inplace=True)\n", "asthma_2015 = asthma_unstacked.loc[asthma_unstacked.Year==2015,:]\n", "asthma_2015.head(2)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Save Your Data\n", "* No saving your workspace like in R or STATA\n", "* Save specific variables, models, or results using Pickle\n", " * wb: write binary. Tells computer to save the file\n", " * rb: read binary. Tells computer to read the file\n", " * If you mix them up, you may write over your data and lose it\n", "* Write your data to a text file to read later" ] }, { "cell_type": "code", "execution_count": 287, "metadata": { "collapsed": true, "slideshow": { "slide_type": "fragment" } }, "outputs": [], "source": [ "pickle.dump(asthma_unstacked,open('asthma_unstacked.p','wb'))\n", "asthma_unstacked.to_csv('asthma_unstacked.csv')" ] }, { "cell_type": "code", "execution_count": 288, "metadata": { "collapsed": true, "slideshow": { "slide_type": "fragment" } }, "outputs": [], "source": [ "asthma_unstacked = pickle.load(open('asthma_unstacked.p','rb'))" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Scraping\n" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# How the Internet Works\n", "\n", "* Code is stored on servers\n", "* Web addresses point to the location of that code \n", "\n", "\n", "1. Going to an address or clicking a button sends requests to the server for data, \n", "2. The server returns the requested content\n", "3. Your web browser interprets the code to render the web page \n", "\n", "<img src='Internet.png'>" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Scraping: \n", "* Collect the website code by emulating the process:\n", " * Can haz cheezburger?\n", " <img src='burger.png'>\n", "* Extract the useful information from the scraped code:\n", " * Where's the beef? \n", " <img src='beef.png'>" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# API\n", "### Application Programming Interface \n", "* The set of rules that govern communication between two pieces of code\n", "* Code requires clear expected inputs and outputs\n", "* APIs define required inputs to get the outputs in a format you can expect. \n", "* Easier than scraping a website because gives you exactly what you ask for\n", "\n", "<img src = \"beef_direct.png\">" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# API Keys\n", "## APIs often require identification\n", "* Go to https://docs.airnowapi.org \n", "* Register and get a key\n", "* Log in to the site\n", "* Select web services\n", "\n", "## DO NOT SHARE YOUR KEY\n", "* It will get stolen and used for malicious activity" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Requests to a Server\n", "\n", "\n", "<div style=\"float: left;width:50%\">\n", "<h3> GET</h3>\n", "<ul><li>Requests data from the server</li>\n", "<li> Encoded into the URL</li></ul>\n", "<img src = 'get.png'>\n", "</div>\n", "<div style=\"float: left;width:50%\">\n", "<h3>POST</h3>\n", "<ul><li>Submits data to be processed by the server</li>\n", "<li>For example, filter the data</li>\n", "<li>Can attach additional data not directly in the url</li></ul>\n", "<img src = 'post.png'>\n", "</div>" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Using an API\n", "<img src = 'api.png'>" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Requests encoded in the URL\n", "### Parsing a URL\n", "\n", "### <font color=\"blue\">http://www.airnowapi.org/aq/observation/zipCode/historical/</font><font color=\"red\">?</font><br><font color=\"green\">format</font>=<font color=\"purple\">application/json</font><font color=\"orange\">&<br></font><font color=\"green\">zipCode</font>=<font color=\"purple\">20007</font><font color=\"orange\">&</font><br><font color=\"green\">date</font>=<font color=\"purple\">2017-09-05T00-0000</font><font color=\"orange\">&</font><br><font color=\"green\">distance</font>=<font color=\"purple\">25</font><font color=\"orange\">&</font><br><font color=\"green\">API_KEY</font>=<font color=\"purple\">D9AA91E7-070D-4221-867CC-XXXXXXXXXXX</font>\n", "\n", "* The base URL or endpoint is:<br>\n", "<font color=\"blue\">http://www.airnowapi.org/aq/observation/zipCode/historical/</font>\n", "\n", "* <font color=\"red\">?</font> tells us that this is a query. \n", "* <font color=\"orange\">&</font> separates name, value pairs within the request.\n", "\n", "* Five <font color=\"green\"><strong>name</strong></font>, <font color=\"purple\"><strong>value</strong></font> pairs POSTED \n", " * format, zipCode, date, distance, API_KEY" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Request from Python\n", "### prepare the url\n", "* List of attributes\n", "* Join them with \"&\" to form a string" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "format=application/json&zipCode=20007&date=2017-09-05T00-0000&distance=25&API_KEY=39DC3727-09BD-48C4-BBD8-XXXXXXXXXXXX\n" ] } ], "source": [ "base_url = \"http://www.airnowapi.org/aq/observation/zipCode/historical/\"\n", "attributes = [\"format=application/json\",\n", " \"zipCode=20007\",\n", " \"date=2017-09-05T00-0000\",\n", " \"distance=25\",\n", " \"API_KEY=39DC3727-09BD-48C4-BBD8-XXXXXXXXXXXX\"\n", " ]\n", "post_url = '&'.join(attributes)\n", "print(post_url)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Requests from Python\n", "* Use requests package\n", "* Requested json format\n", " * Returns list of dictionaries\n", " * Look at the returned keys" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{'DateObserved': '2017-09-05 ', 'HourObserved': 0, 'LocalTimeZone': 'EST', 'ReportingArea': 'Metropolitan Washington', 'StateCode': 'DC', 'Latitude': 38.919, 'Longitude': -77.013, 'ParameterName': 'OZONE', 'AQI': 47, 'Category': {'Number': 1, 'Name': 'Good'}}\n" ] } ], "source": [ "ingredients=requests.get(base_url, post_url)\n", "ingredients = ingredients.json()\n", "print(ingredients[0])" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# View Returned Data:\n", "* Each list gives a different parameter for zip code and date we searched" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "For Location Metropolitan Washington the AQI for OZONE is 47\n", "For Location Metropolitan Washington the AQI for PM2.5 is 61\n", "For Location Metropolitan Washington the AQI for PM10 is 13\n" ] } ], "source": [ "for item in ingredients:\n", " AQIType = item['ParameterName']\n", " City=item['ReportingArea']\n", " AQIValue=item['AQI']\n", " print(\"For Location \", City, \" the AQI for \", AQIType, \"is \", AQIValue)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Ethics\n", "* Check the websites terms of use\n", "* Don't hit too hard:\n", " * Insert pauses in your code to act more like a human\n", " * Scraping can look like an attack\n", " * Server will block you without pauses\n", "* APIs often have rate limits\n", "* Use the time package to pause for a second between hits" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "slideshow": { "slide_type": "fragment" } }, "outputs": [], "source": [ "time.sleep(1)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Collect Our Data\n", "### Python helps us automate repetitive tasks. Don't download each datapoint you want separately\n", "* Get a list of zip codes we want\n", " * take a subset to demo, so it doesn't take too long and so we don't all hit too hard from the same ip\n", "* Request the data for those zipcodes on a day in 2015 (you pick, fire season July-Oct)\n", " * Be sure to sleep between requests\n", "* Store that data as you go into a dictionary\n", " * Key: zip code\n", " * Value: Dictionary of the air quality parameters and their value" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "slideshow": { "slide_type": "slide" } }, "outputs": [], "source": [ "base_url = \"http://www.airnowapi.org/aq/observation/zipCode/historical/\"\n", "zips = asthma_2015.Zip.unique()\n", "zips = zips[:450]\n", "date =\"date=2015-09-01T00-0000\"\n", "api_key = \"API_KEY=39DC3727-09BD-48C4-BBD8-XXXXXXXXXXXX\"\n", "return_format = \"format=application/json\"\n", "zip_str = \"zipCode=\"\n", "post_url = \"&\".join([date,api_key,return_format,zip_str])\n", "data_dict = {}\n", "for zipcode in zips:\n", " time.sleep(1)\n", " zip_post = post_url + str(zipcode)\n", " ingredients = requests.get(base_url, zip_post)\n", " ingredients = ingredients.json()\n", " zip_data = {}\n", " for data_point in ingredients:\n", " AQIType = data_point['ParameterName']\n", " AQIVal = data_point['AQI']\n", " zip_data[AQIType] = AQIVal\n", " data_dict[zipcode]= zip_data" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Scraping: Parsing HTML\n", "* What about when you don't have an API that returns dictionaries?\n", "* HTML is a markup language that displays data (text, images, etc)\n", "* Puts content within nested tags to tell your browser how to display it\n", "\n", "***\n", "\n", "### &lt;Section_tag>\n", "### &emsp; &lt;tag> Content &lt;/tag>\n", "### &emsp; &lt;tag> Content &lt;/tag>\n", "### &lt; /Section_tag>\n", "### &lt;Section_tag>\n", "### &emsp; &lt;tag> <font color=\"red\">Beef</font> &lt;/tag>\n", "### &lt; /Section_tag>" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Find the tags that identify the content you want:\n", "* First paragraph of wikipedia article: \n", "https://en.wikipedia.org/wiki/Data_science\n", "* Inspect the webpage: \n", " * Windows: ctrl+shift+i\n", " * Mac: ctrl+alt+i\n", " \n", "<img src = \"wikipedia_scrape.png\">\n" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Parsing HTML with Beautiful Soup\n", "### Beautiful Soup takes the raw html and parses the tags so you can search through them. \n", "* text attribute returns raw html text from requests\n", "* Ignore the warning, default parser is fine\n", "* We know it's the first paragraph tag in the body tag, so: \n", " * Can find first tag of a type using <strong>.</strong>\n", "* But it's not usually that easy..." ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "<p><b>Data science</b>, also known as <b>data-driven science</b>, is an interdisciplinary field of scientific methods, processes, and systems to extract <a href=\"/wiki/Knowledge\" title=\"Knowledge\">knowledge</a> or insights from <a href=\"/wiki/Data\" title=\"Data\">data</a> in various forms, either structured or unstructured,<sup class=\"reference\" id=\"cite_ref-:0_1-0\"><a href=\"#cite_note-:0-1\">[1]</a></sup><sup class=\"reference\" id=\"cite_ref-2\"><a href=\"#cite_note-2\">[2]</a></sup> similar to <a href=\"/wiki/Data_mining\" title=\"Data mining\">data mining</a>.</p>\n" ] } ], "source": [ "ingredients = requests.get(\"https://en.wikipedia.org/wiki/Data_science\")\n", "soup = bs(ingredients.text)\n", "print(soup.body.p)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Use Find Feature to Narrow Your Search\n", "* Find the unique div we identified\n", " * Remember the underscore: \"class_\"\n", "* Find the p tag within the resulting html\n", "* Use an index to return just the first paragraph tag\n", "* Use the text attribute to ignore all the formatting and link tags\n", "* Next: Use a for loop and scrape the first paragraph from a bunch of wikipedia articles\n", "* Learn More: http://web.stanford.edu/~zlotnick/TextAsData/Web_Scraping_with_Beautiful_Soup.html" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "<p><b>Data science</b>, also known as <b>data-driven science</b>, is an interdisciplinary field of scientific methods, processes, and systems to extract <a href=\"/wiki/Knowledge\" title=\"Knowledge\">knowledge</a> or insights from <a href=\"/wiki/Data\" title=\"Data\">data</a> in various forms, either structured or unstructured,<sup class=\"reference\" id=\"cite_ref-:0_1-0\"><a href=\"#cite_note-:0-1\">[1]</a></sup><sup class=\"reference\" id=\"cite_ref-2\"><a href=\"#cite_note-2\">[2]</a></sup> similar to <a href=\"/wiki/Data_mining\" title=\"Data mining\">data mining</a>.</p>\n", "*****************************************\n", "Data science, also known as data-driven science, is an interdisciplinary field of scientific methods, processes, and systems to extract knowledge or insights from data in various forms, either structured or unstructured,[1][2] similar to data mining.\n" ] } ], "source": [ "parser_div = soup.find(\"div\", class_=\"mw-parser-output\")\n", "wiki_content = parser_div.find_all('p')\n", "print(wiki_content[0])\n", "print('*****************************************')\n", "print(wiki_content[0].text)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Back To Our Data\n", "* If it's still running, go ahead and stop it by pushing the square at the top of the notebook: \n", "<img src=\"interrupt.png\">\n", "* Save what you collected, don't want to hit them twice!" ] }, { "cell_type": "code", "execution_count": 306, "metadata": { "collapsed": true, "slideshow": { "slide_type": "fragment" } }, "outputs": [], "source": [ "pickle.dump(data_dict,open('AQI_data_raw.p','wb'))" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Subset down to the data we have:\n", "* use the isin() method to include only those zip codes we've already collected" ] }, { "cell_type": "code", "execution_count": 307, "metadata": { "collapsed": true, "slideshow": { "slide_type": "fragment" } }, "outputs": [], "source": [ "collected = list(data_dict.keys())\n", "asthma_2015_sub = asthma_2015.loc[asthma_2015.Zip.isin(collected),:]" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Create a dataframe from the new AQI data" ] }, { "cell_type": "code", "execution_count": 308, "metadata": { "slideshow": { "slide_type": "fragment" } }, "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>Zip</th>\n", " <th>OZONE</th>\n", " <th>PM2.5</th>\n", " <th>PM10</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>90001</td>\n", " <td>36.0</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>90002</td>\n", " <td>36.0</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>90003</td>\n", " <td>36.0</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>90004</td>\n", " <td>54.0</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>90005</td>\n", " <td>54.0</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Zip OZONE PM2.5 PM10\n", "0 90001 36.0 NaN NaN\n", "1 90002 36.0 NaN NaN\n", "2 90003 36.0 NaN NaN\n", "3 90004 54.0 NaN NaN\n", "4 90005 54.0 NaN NaN" ] }, "execution_count": 308, "metadata": {}, "output_type": "execute_result" } ], "source": [ "aqi_data = pd.DataFrame.from_dict(data_dict, orient='index')\n", "aqi_data.reset_index(drop=False,inplace=True)\n", "aqi_data.rename(columns={'index':'Zip'},inplace=True)\n", "aqi_data.head()" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Combine The Data\n", "https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.merge.html\n", "* Types of merges:\n", " * Left: Use only rows from the dataframe you are merging into\n", " * Right: use only rows from the dataframe you are inserting, (the one in the parentheses)\n", " * Inner: Use only rows that match between both\n", " * Outer: Use all rows, even if they only appear in one of the dataframes\n", "* On: The variables you want to compare\n", " * Specify right_on and left_on if they have different names" ] }, { "cell_type": "code", "execution_count": 309, "metadata": { "slideshow": { "slide_type": "fragment" } }, "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>Year</th>\n", " <th>Zip</th>\n", " <th>County</th>\n", " <th>Coordinates</th>\n", " <th>Fips</th>\n", " <th>Adults</th>\n", " <th>Incidents</th>\n", " <th>Children</th>\n", " <th>OZONE</th>\n", " <th>PM2.5</th>\n", " <th>PM10</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>2015</td>\n", " <td>90001</td>\n", " <td>LOS ANGELES</td>\n", " <td>(33.973252, -118.249154)</td>\n", " <td>6037</td>\n", " <td>229.0</td>\n", " <td>441.0</td>\n", " <td>212.0</td>\n", " <td>36.0</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>2015</td>\n", " <td>90002</td>\n", " <td>LOS ANGELES</td>\n", " <td>(33.949079, -118.247877)</td>\n", " <td>6037</td>\n", " <td>249.0</td>\n", " <td>476.0</td>\n", " <td>227.0</td>\n", " <td>36.0</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Year Zip County Coordinates Fips Adults \\\n", "0 2015 90001 LOS ANGELES (33.973252, -118.249154) 6037 229.0 \n", "1 2015 90002 LOS ANGELES (33.949079, -118.247877) 6037 249.0 \n", "\n", " Incidents Children OZONE PM2.5 PM10 \n", "0 441.0 212.0 36.0 NaN NaN \n", "1 476.0 227.0 36.0 NaN NaN " ] }, "execution_count": 309, "metadata": {}, "output_type": "execute_result" } ], "source": [ "asthma_aqi = asthma_2015_sub.merge(aqi_data,how='outer',on='Zip')\n", "asthma_aqi.head(2)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Look At The Data: Histogram\n", "* 20 bins" ] }, { "cell_type": "code", "execution_count": 310, "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x7f6f595cb128>" ] }, "execution_count": 310, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f6f595a97b8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "asthma_aqi.Incidents.plot.hist(20)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Look At The Data: Smoothed Distribution" ] }, { "cell_type": "code", "execution_count": 311, "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x7f6f594f8e80>" ] }, "execution_count": 311, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f6f5969e860>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "asthma_aqi.loc[:,['Incidents','OZONE']].plot.density()" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Look at particulates\n", "* There is a lot of missingness in 2015\n", "* Try other variables, such as comparing children and adults" ] }, { "cell_type": "code", "execution_count": 312, "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x7f6f595d20b8>" ] }, "execution_count": 312, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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gPOAsYHy5x0fEvIhoioimurq61pzazMxaodx7EA3A6Ij4dwBJdwHLIuLzbTjnHwKvR0Rz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\nEGZmlsoBYWZmqRwQZmaWygFhZmap/j9LzkjWOXatAwAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f6f5966de80>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "asthma_aqi.loc[:,['PM2.5','PM10']].plot.hist()" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Scatter Plot\n", "* Try some other combinations\n", "* Our data look clustered, but we'll ignore that for now" ] }, { "cell_type": "code", "execution_count": 327, "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x7f6f5940bdd8>" ] }, "execution_count": 327, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f6f59339a20>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "asthma_aqi.plot.scatter('OZONE','PM2.5')" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Run a regression:\n", "* Note: statsmodels supports equation format like R <br>\n", "http://www.statsmodels.org/dev/example_formulas.html" ] }, { "cell_type": "code", "execution_count": 320, "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " OLS Regression Results \n", "==============================================================================\n", "Dep. Variable: Incidents R-squared: 0.021\n", "Model: OLS Adj. R-squared: 0.018\n", "Method: Least Squares F-statistic: 5.896\n", "Date: Thu, 15 Feb 2018 Prob (F-statistic): 0.00293\n", "Time: 10:23:19 Log-Likelihood: -3521.4\n", "No. Observations: 544 AIC: 7049.\n", "Df Residuals: 541 BIC: 7062.\n", "Df Model: 2 \n", "Covariance Type: nonrobust \n", "==============================================================================\n", " coef std err t P>|t| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", "OZONE 0.5115 0.266 1.923 0.055 -0.011 1.034\n", "PM2.5 1.6237 0.691 2.348 0.019 0.265 2.982\n", "c 70.3427 28.976 2.428 0.016 13.423 127.263\n", "==============================================================================\n", "Omnibus: 225.881 Durbin-Watson: 1.535\n", "Prob(Omnibus): 0.000 Jarque-Bera (JB): 870.231\n", "Skew: 1.921 Prob(JB): 1.08e-189\n", "Kurtosis: 7.862 Cond. No. 288.\n", "==============================================================================\n", "\n", "Warnings:\n", "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n" ] } ], "source": [ "y =asthma_aqi.loc[:,'Incidents']\n", "x =asthma_aqi.loc[:,['OZONE','PM2.5']]\n", "x['c'] = 1\n", "ols_model1 = sm.OLS(y,x,missing='drop')\n", "results = ols_model1.fit()\n", "print(results.summary())\n", "pickle.dump([results,ols_model1],open('ols_model_results.p','wb'))" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Clustering Algorithm\n", "* Learn more about clustering here: <br>\n", "http://scikit-learn.org/stable/modules/clustering.html\n", "* Use sklearn, a package for data mining and machine learing\n", "* Drop rows with missing values first\n", "* Standardize the data so they're all on the same scale" ] }, { "cell_type": "code", "execution_count": 325, "metadata": { "collapsed": true, "slideshow": { "slide_type": "fragment" } }, "outputs": [], "source": [ "model_df = asthma_aqi.loc[:,['OZONE','PM2.5','Incidents',]]\n", "model_df.dropna(axis=0,inplace=True)\n", "model_df = (model_df - model_df.mean()) / (model_df.max() - model_df.min())\n", "asthma_air_clusters=cluster.KMeans(n_clusters = 3)\n", "asthma_air_clusters.fit(model_df)\n", "model_df['clusters3']=asthma_air_clusters.labels_\n" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Look At Clusters\n", "* Our data are very closely clustered, OLS was probably not appropriate. " ] }, { "cell_type": "code", "execution_count": 326, "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "data": { "text/plain": [ "Text(0.5,0,'Incidents')" ] }, "execution_count": 326, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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1NUVWVhZut5s///HnXHaRjk3rz+ONo2buuv0XfP1bN8RVeU1NTWXt2rVMTU1x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aOjo0qzt3V1deHk5ISePXsiNjYWDx48aFD9tKSkBDweDx07dgSHw1HISRIRQkJCMHPmTKxYsQI/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\npMd5ZdZ3UAAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f6f5920d898>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from mpl_toolkits.mplot3d import Axes3D\n", "fig = plt.figure(figsize=(4, 3))\n", "ax = Axes3D(fig, rect=[0, 0, .95, 1], elev=48, azim=134)\n", "\n", "labels = asthma_air_clusters.labels_\n", "\n", "ax.scatter(model_df.loc[:, 'PM2.5'], model_df.loc[:, 'OZONE'], model_df.loc[:, 'Incidents'],\n", " c=labels.astype(np.float), edgecolor='k')\n", "\n", "ax.set_xlabel('Particulates')\n", "ax.set_ylabel('Ozone')\n", "ax.set_zlabel('Incidents')\n" ] } ], "metadata": { "celltoolbar": "Slideshow", "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.1" } }, "nbformat": 4, "nbformat_minor": 2 }

mit

dennis5943/-sklearn-SVC-Classification-Sample

文章特徵(k 最佳化最測試).ipynb

1

102106

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "新聞數量: 220\n", "前兩筆資料: [{'id': '6553', 'title': '【採訪】家的色彩任你調！ Dulux 得利塗料首推VR 體驗塗刷樂趣', 'description': '有裝修經驗的人應該都會同意色彩絕對是左右居家氣氛與風格的一大要素， 以豐富電腦調色漆滿足屋主對居家色彩需求的Dulux 得利塗料今年正好是推出十周年，特地選在...', 'type': '居家'}, {'id': '6481', 'title': '20歲人妻自爆「有無限性慾」\\u3000抖M看到雞雞抓狂...嘴巴被硬塞老二超爽！', 'description': '\\n南港7條蛇／VC15文\\n\\n有看過片的男人都曉得，創意無限的SOD「都是真的」，且更強大的是', 'type': '正妹'}]\n" ] } ], "source": [ "import requests\n", "from bs4 import BeautifulSoup\n", "import json\n", "from sklearn.feature_extraction.text import TfidfVectorizer\n", "from sklearn.feature_extraction.text import CountVectorizer\n", "import jieba\n", "\n", "headers = { \"User-Agent\":\"Mozilla/4.0 (compatible; MSIE 7.0; Windows NT 6.2; WOW64; Trident/6.0; .NET4.0E; .NET4.0C; Tablet PC 2.0; .NET CLR 3.5.30729; .NET CLR 2.0.50727; .NET CLR 3.0.30729; McAfee; MAARJS)\"\n", " ,\"Referer\": \"http://deviltrigger.oo.gd\"}\n", "\n", "res = requests.post(url = \"https://deviltrigger.oo.gd/everything/trainingdata.php\",headers = headers)\n", "\n", "data = json.loads(res.text)['data']['ClassificationData']\n", "print('新聞數量:',len(data))\n", "print('前兩筆資料:',[data[x] for x in range(min(len(data),2))])\n", "\n", "#將訓練資料中的title / description兩個欄位都串在一起做為訓練資料\n", "corpus = [','.join(filter(None,(t['title'] ,t['description']))) for t in data]" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Building prefix dict from the default dictionary ...\n", "Dumping model to file cache /var/folders/xk/jzfb6tps7kz7xv_j3tjr0h7c0000gn/T/jieba.cache\n", "Loading model cost 1.106 seconds.\n", "Prefix dict has been built succesfully.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "資料筆數: 220\n", "idfkeyword total cnt(keyword 數量): 844\n", "X.toarray() [[ 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.]]\n" ] } ], "source": [ "words = []\n", "#tags = jieba.analyse.extract_tags('-->'.join([x for x in (td['title'],td['description']) if x]) , 10)\n", "for i in [jieba.cut_for_search(w) for w in corpus]: \n", " words.append('|'.join([w for w in i]))\n", "#print(words)\n", "\n", "corpus = words\n", "\n", "vectorizer = TfidfVectorizer(max_df=0.5, max_features=1000, ngram_range=(1,1), min_df=2,use_idf=True)\n", "#vectorizer = TfidfVectorizer(min_df=1)\n", "X = vectorizer.fit_transform(corpus)\n", "idf = vectorizer.idf_\n", "\n", "idfkeyword = dict(zip(vectorizer.get_feature_names(), idf))\n", "#print(idfkeyword)\n", "\n", "print('資料筆數: ',len(corpus))\n", "print('idfkeyword total cnt(keyword 數量):',len(idfkeyword))\n", "print('X.toarray()' , X.toarray())\n", "#print(vectorizer.get_feature_names ())" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "k: 176\n", "bestK Score: -27.70549063263765\n" ] } ], "source": [ "#一般的kmean(一次性)\n", "from sklearn.cluster import MiniBatchKMeans\n", "\n", "k = int(len(corpus) * 0.8)\n", "print('k:',k)\n", "bestK = MiniBatchKMeans(n_clusters= k, init='k-means++', max_iter=10000, n_init=10, verbose=False)\n", "bestK.fit(X)\n", "\n", "score = bestK.score(X)\n", "print('bestK Score:',score)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "center: rows: 176 cols: 844 [[ 0. 0. 0. ..., 0. 0. 0. ]\n", " [ 0. 0. 0.1161942 ..., 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. ]]\n", "size of labels: 220\n", "maplist: 220 [123 50 5 5 79 26 150 80 142 70 63 113 30 118 107 134 64 54\n", " 139 173 99 164 66 40 175 120 87 0 5 5 74 41 67 92 163 73\n", " 102 97 5 5 162 42 46 151 161 104 116 91 94 171 5 33 84 109\n", " 68 6 16 7 16 124 7 127 169 130 56 115 148 22 18 72 174 59\n", " 53 156 48 7 69 58 12 78 38 49 168 85 57 39 22 110 154 5\n", " 5 62 89 83 18 44 37 51 24 128 81 117 12 12 126 103 23 119\n", " 5 5 5 11 157 43 43 5 5 5 131 106 21 93 5 90 10 8\n", " 140 1 5 166 5 32 10 165 122 100 20 133 20 145 28 172 170 28\n", " 153 55 61 146 5 95 96 3 88 71 129 152 9 29 159 15 86 160\n", " 45 132 143 29 5 36 34 60 15 9 5 114 121 137 17 5 149 105\n", " 147 47 35 25 101 75 136 65 4 13 31 125 52 144 76 112 5 158\n", " 111 167 14 135 5 5 141 138 14 98 108 19 5 24 27 155 25 36\n", " 82 2 13 77]\n" ] } ], "source": [ "from sklearn.metrics.pairwise import pairwise_distances_argmin\n", "\n", "print('center:','rows:',len(bestK.cluster_centers_),'cols:',len(bestK.cluster_centers_[0]),bestK.cluster_centers_)\n", "print('size of labels:',len(bestK.labels_))\n", "\n", "maplist = pairwise_distances_argmin(X,bestK.cluster_centers_)\n", "\n", "print('maplist:',len(maplist),maplist)\n", "\n", "#X_Grp = dict(zip(corpus))\n", "#Center 雖然有向量內容，但不知道是X中的哪幾個??\n", "\n" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/plain": [ "array([0, 1, 2])" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from sklearn.metrics.pairwise import pairwise_distances_argmin\n", "\n", "xx = [[1,1],[2,2],[3,3]]\n", "yy = [[1,1],[2,2],[3,4],[5,5]]\n", "pairwise_distances_argmin(xx,yy)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/plain": [ "[('玩遊戲|太|認真|！|男生|全力|衝刺|「|向前|頂|」|力道|太|勇猛|！|女生|最後竟|濕|了|…|,|\\n|文|／|金真露|\\n|\\n|昨天|的|直播|節目|讓|我|看|得欲罷|不能|R|，|真的|4|太|精采|了|！|所以|我|趕|緊來|找|看看',\n", " 0),\n", " (' |經營|模擬|手機|遊戲|《|全民|百貨|》|封測|進行|中| |等級|上限|開放至| |LV| |28|,|\\n|經營|模擬|手機|遊戲|《|全民|百貨|》|今|（|11|）|日雙|平台|封測|登場|，|等級|上限|開放至| |LV28|，|時尚|盛宴| |4F| |露天|派|對|開|放入|場|，|全新|精品|名牌|包開放|設櫃|以及|櫃姐|蘇菲亞|報到|，|娛樂|設施|、|各式|裝飾|道具|等|新品|上市|。|改版|期間|多項|限定|活動|進行|中|。|\\xa0|&|nbs|...|\\n',\n", " 1),\n", " ('《|自|製|魔|物|獵|人|重|弩|》|可收|可擊發|完成|完成度|太高|啦|,|最近|關於|MH|的|消息|，|我|想|最|夯|的|就是|「|魔物|獵人| |世界|」|這款|遊戲|了|吧|，|顛覆過|往|大家|所有|的|認知|，|打臉|那些|認為|不會|平台|跨平台|的|玩家|，|這個|遊戲|從|釋出|消息|到試|玩|影片|出來|無|不|吸引|大家|的|注意|，|不過|阿漆|前面|會|這麼|有感|而發|，|...',\n", " 2),\n", " ('【|模型|】|ALTER|《|偶像|大師| |姑娘|灰姑娘|女孩|》|鷺澤文香| |Bright| |Memories| |明年| |1| |月|預定|推出|,|\\n|《|偶像|大師| |姑娘|灰姑娘|女孩|》|是|以|街|機用|模擬|遊戲|《|偶像|大師|》|世界|觀|的|元素|構成|，|開發|在|智慧|智慧型|電話|等|行動|裝置|上|的|一款|網頁|形式|免費|社交|網路|遊戲|，|由|DeNA|營運|和|提供|服務|、|運行|於|Mobage|平台|。|此|作品|動畫|於|2015|年|1|月|9|日|在|日本|首播|。|鷺澤文香|Brig|...|\\n',\n", " 3),\n", " ('手機|遊戲|《|盜賊|聯盟| |Gangster| |League|》|正式|開|放下|載|,|\\n|盜賊|聯盟| |Gangster| |League| |在|Android|及|IOS|已|正式|開|放下|載|，|共| |7| |種語|言|可供|玩家|選擇|，|包括|繁體|中文|、|簡體|中文|、|英文|、|日文|、|德文|、|西班牙|、|葡萄|葡萄牙|等等|。|玩家|扮演|一位|普通|的|平民|，|帶著|神秘|的|面具|，|穿起|時尚|的|西裝|，|慢慢|踏上|盜賊|...|\\n',\n", " 4),\n", " ('短|髮|妹超|正|！|「|俏麗理|財員|」|用辣照|報外匯|…|讓|人|不住|忍不住|掏錢給|她|保管|,|\\n|【|文|/|傲嬌|女王|】|\\n| |\\n|在|這個|薪水|永遠|趕不上|物價|的|時代|，|理財|就|顯得|相當|重要|了|，|這幾|天女',\n", " 5),\n", " ('急滅|火|！|正妹|鏡頭|前換|上|「|比基|基尼|比基尼|」|...|旁人|血液|沸騰|不住|忍不住|下水|讓|「|兄弟|」|冷靜|,|\\n|文|／|金真露|\\n|\\n|昨天|我|又|在|韓國|最大|的|直播|網|站上|看片|惹|，|夏天|到|了|大家|都|想|盡|辦法來|消暑',\n", " 5),\n", " ('長|榮|空姐|各個|都|是|「|隱乳系|正妹|」|，|甜美|短|髮|搭配|海邊|比基|基尼|比基尼|嫩|乳|，|乳照|實在|美到|太|犯規|！|,|\\n|短|髮|控|福音|來|囉|~|這次|還是|個|長|榮|空姐|呢|！|水亮|大|眼|加上|甜甜|甜甜的|笑容|整個|超|勾魂|，|不|小心',\n", " 5),\n", " ('爆奶|車模|「|中路|大開|」|秒|殺|現場|粉絲|！|「|襯衫|美乳|」|若|隱若現|真的|好|驚人|…|,|\\n|文|｜|Hanabi|\\n|\\n|台灣|的|SG|不管|是|身材|或是|美貌|都|是|出了名|的|搶眼|，|前陣子|在|台灣|知名|車行|的',\n", " 5),\n", " ('台妹|唱|Freestyle|爆紅|！|\\u3000|20|秒|乳搖畫|面好|震撼|，|豐滿|E|奶|快要|蹦出|來|了|！|,|\\n|文|／|雪倫|小姐|\\n|\\n|最近|節目|「|中國|有嘻哈|」|爆紅|，|評審|吳亦凡講|的|「|請問|你|有|Freestyle|嗎|？',\n", " 5),\n", " ('【|白兔|小白兔|精選|】|H|罩杯|D|槽|女神|「|麻美|ゆ|ま|」| |經典|OL|作品|帶領|四名|巨乳|在|辦|公室|打團戰|！|,|\\n|今天|要來|分享|的|女|優是|「|麻美|ゆ|ま|」|(|麻美|由|真|)|！|看到|封面|和|名字|相信|已經|又|不少|人|和|小',\n", " 5),\n", " ('大陸|政府|要求|電信商|在|明年|二月|前|封禁|個|人用| |VPN|？|,|「|翻牆|」|正變|得|越來|越難|...', 5),\n", " ('搶先|上市|！|山寨|搶先|開箱|蘋果| |iPhone| |8|～|但|規格|不|太|一樣|喔|,|地表|最強|研發|中心|的|最新|力作|已經|登場|了|！|沒錯|，|就是| |iPhone| |8|，|山寨|版|再次|超前|蘋果|，|搶先|亮相|開箱|。|正面|無| |HOME| |鍵|、|背後二顆|垂直|排列|的|雙鏡頭|，|在|這次|的|山寨|版| |iPhone| |8| |裡面|都|有|見|到|，| |...',\n", " 5),\n", " ('迎戰|AMD| |EPYC| |Intel| |Xeon| |Scalable| |28|核心|處|理器|發表|,|Intel|近日|針對|伺服|伺服器|市場|再度|推出|了|全新|的|Xeon| |Scalable|系列|處|理器|，|其中|最高|階|的|版本|不僅|單顆|處|理器|核心|數量|規格|可達|到|28|核心|，|還能夠|支援|56|執行|...',\n", " 5),\n", " ('[|F8C1|]| |[| |傷痕|纍纍|的|惡|魔| |]| |[', 5),\n", " ('[|EA84|]| |[| |風|雲|高手| |]| |[', 5),\n", " ('[|C6C6|]| |[| |奇幻|同學會| |See| |You| |Again| |]| |[', 5),\n", " ('[|A146|]| |[| |鐵甲|戰神| |Revolt| |]| |[', 5),\n", " ('[|EA68|]| |[| |惡女| |악|녀| |]| |[', 5),\n", " ('[|6B11|]| |[|最|後|的|武林| |The| |Last| |WuLin| |]| |[', 5),\n", " ('[|1200|]| |[| |29|+|1| |]| |[', 5),\n", " ('【|試片|】|《|騎士|＆|魔法|》|銀|髮|正太|的|異|世界|機人|狂想|狂想曲|,|\\n|你|是否|曾|經著|魔|似的|購買|機器|人|模型|、|對|機器|人|喜歡|到|無|可|自拔|呢|？|這部|在|「|成為|小|說家|吧|」|眾多|的|作品|中異|軍|突起|的|《|騎士|＆|魔法|》|或|許正|對|你|這個|蘿|蔔|迷|的|胃口|喔|？|這部|作品|原作|是|由|天酒|之|瓢|撰|寫|的|網路|小|說|，|雖然|是|常見|的|轉生|題材|，|但|本作|以|其|對|機器|人|的|愛|突破|了類|...|\\n',\n", " 5),\n", " ('《|魔女|異聞錄|：|伊絲|塔利|亞傳|說|》|七夕|祭開|跑| |與織|女共譜|銀河|戀曲|,|\\n|由|大宇|資訊|旗下|星宇|互娛|發行|之|《|魔女|異聞錄|：|伊絲|塔利|亞傳|說|》|準備|迎接|七夕|慶典|，|官方|宣布|今|（|11|）|日為|慶賀|牛郎|與織|女|重逢|的|這別|具感動|的|七月|，|這對|銀河|戀人|將以|全新|卡牌|姿態|降臨|《|魔女|異聞錄|：|伊絲|塔利|亞傳|說|》|，|活動|期間|有|機會|從|活動|中|或|白金|召喚|中|獲取|。|...|\\n',\n", " 5),\n", " ('【|CJ| |17|】|中國|最大|數位|互動|娛樂展| |ChinaJoy| |公布|各大峰|會演|講嘉賓名|單及|日程|,|\\n|2017| |年度|中國國際|數位|互動|娛樂|展覽會|（|簡稱|：|ChinaJoy|）|即將|在| |7| |月| |27| |日至| |30| |日|在|中國|上海|新國際|博覽|中心|舉辦|。|近日|，|主辦|單位|漢|威信|恒|公布|了|\\xa0|2017| |年| |ChinaJoy|\\xa0|系列|大會|，|參與|各大峰|會|的|演|...|\\n',\n", " 5),\n", " ('虛擬|實境|裝置| |Oculus| |Rift| |宣布|限期|大降價| |以| |399| |美元|提供|頭戴|裝置|與|體感|控制|控制器|組合|,|\\n|虛擬|實境|裝置|廠商| |Oculus| |VR| |於|美國|時間| |7| |月| |10| |日|宣布|，|旗下|主力|銷售|的| |VR| |虛擬|實境頭|戴裝置|「|Oculus| |Rift|」|將|即日|即日起|自即日起| |6| |週展|開降價|促銷|活動|，|包含| |Oculus| |Rift| |頭戴|裝置|、|2| |組感|應器|與| |2| |組體|感控|...|\\n',\n", " 5),\n", " ('受|感染|的|上將|「|斯杜|科夫|」|正式|加入|《|暴雪|英霸|》| |HGC| |台灣|站|第三|三季|第三季|八強賽|今晚|登場|,|\\n|受|感染|的|上將|「|斯杜|科夫|」|於|今|（|12|）|日|正式|加入|《|暴雪|英霸|》|，|斯杜|科夫|能|散播|並|控制|病毒|，|利用|病毒|治療|盟友|並對|敵方|造成|傷害|；|精準|掌控|場上|人員|位置|將能|使斯杜|科夫|發揮|最大|作用|，|橫掃千|軍|。|另外|，|《|暴雪|英霸|》|HGC| |台灣|站|第三|三季|第三季|八強賽|於| |12|、|13| |日|...|\\n',\n", " 5),\n", " ('【|模型|】|AOSHIMA|《|艦隊| |Collection|》|齊|柏林|伯爵|號| |預定| |7| |月|發售|,|\\n|是|由|角川遊戲|開發|、|DMM|.|com|提供|及營運|的|網頁|遊戲|，|簡稱|《|艦|Colle|》|（|艦|こ|れ|）|；|由|於|角川遊戲|與|DMM|所|使用|的|名稱|不統|一|，|因此|有|時會|以|《|艦隊|こ|れ|く|し|ょ|ん| |〜|艦|こ|れ|〜|》|作為|標題|。|該遊戲|以|第二|二次|第二次|世界|大戰|時期|的|大|日本|帝國海|軍軍艦|為|題材|，|而遊戲|...|\\n',\n", " 5),\n", " ('《|YURI|!|!|!|on| |ICE|》|接管|池袋|太陽城|王子|飯店| |被|滑冰|選手們|環繞|的|住宿|體驗|報告|,|\\n|透過|與|太陽城|王子|飯店|這個|不可|思議|的|合作|企劃|「|Hotle|!|!|!| |On| |ICE|」|，|《|YURI|!|!|!|on| |ICE|》|的|粉絲|可以|再|一次|沉浸|在|這|令人|難|忘|的|世界|中|。|從| |7| |月| |1| |日到| |9| |月| |30| |日|，|房間|將根據|《|YURI|!|!|!|on| |ICE|》|風格|進行裝|...|\\n',\n", " 5),\n", " ('【|模型|】|「|黏土|人| |小智|＆|皮卡丘|」|13| |日|開放|預購| |歡慶|黏土|人| |800| |號|＆|動畫|問世| |20| |周年|紀念|,|\\n|Good| |Smile| |Company| |官方|部落|格今|（|12|）|日|發表|，|以|迎接|誕生| |20| |周年|的|經典|遊戲|改|編動畫|《|精靈|寶可夢|（|ポ|ケ|ッ|ト|モ|ン|ス|タ|ー|）|》|代表|代表性|搭檔|「|小智|＆|皮卡丘|」|為|題材|的|黏土|人|系列|模型|「|黏土|人| |小智|＆|皮卡丘|（|ね|ん|ど|ろ|い|ど| |サ|ト|シ|＆|ピ|カ|...|\\n',\n", " 5),\n", " ('《|BLADE| |刀鋒|戰記|》|開發商|近日|將於|韓國|推出| |RPG| |新作|《|五|大王|國之傳|說|》|,|\\n|曾開|發過|《|BLADE| |刀鋒|戰記|》|及|系列|作|的|韓國|遊戲|廠商| |4|:|33| |近期|計畫|推出|新作|《|五|大王|國之傳|說|（|暫譯|，|原名|：|The| |Tale| |of| |Five| |Kingdoms|）|》|，|預計將|在| |7| |月| |27| |日|於|韓國|的| |App| |Store| |及| |Google|...|\\n',\n", " 5),\n", " ('像素|風格|策略|遊戲|《|逃脫者| |2|》|8| |月底|上市| |扮演|罪犯|逃離|巨大|監獄|,|\\n|由| |Mouldy| |Toof| |Studios| |開發|、|Team17| |發行|的|《|逃脫者| |2|（|The| |Escapists| |2|）|》|是|款|像素|風格|策略|遊戲|，|近期|官方|宣布|遊戲將|在| |8| |月| |23| |日|推出|。|\\xa0|\\xa0|《|逃脫者| |2|》|是|一款|越獄|題材|遊戲|...|\\n',\n", " 5),\n", " ('小米|旗艦|新機|揭曉|：|小米| |Note| |2| |新規格|版本|,|日前|小米|預告|會|於| |7| |月| |11| |日|發表|一款|旗艦|新機|，|將會|集結| |Snapdragon| |8XX| |處|理器|、|6GB| |RAM|、|4|,|000mAh| |電池|等|規格|。|而|外界|先前|預測|這款|新機|可能|會|是|小米| |6| |Plus| |或是|新|版本|的|小米| |Note| |2| |...',\n", " 6),\n", " ('LG| |Q6| |系列|發表|：|5.5| |吋| |18|：|9| |FullVision| |螢幕|中階機|,|LG| |正式|發表|了| |Q6| |系列|新機|，|共有| |Q6|+|、|Q6|、|Q6|α| |等|三種|不同|版本|，|共通|特色|是|都|搭載| |5.5| |吋|的| |18|：|9| |比例| |FullVision| |螢幕|，|然|後|配置|高通| |S435| |處|理器|以及| |Android| |7.1|.|1| |系統|，|但|都|不具| |...',\n", " 7),\n", " ('LG| |Q6| |登場|，|把| |FullVision| |螢幕|帶|到|中階機|了|！|,|（|應該|）|能用|更|低價|享受|到| |18|:|9| |規格|螢幕|了|。',\n", " 7),\n", " ('入門|FullVision|全|螢幕|！|LG|發表|Q6|、|Q6|+|、|Q6| |α|,|2017|下半|半年|下半年|全|螢幕|手機|將會|大量|爆發|，|LG|發表|Q6|、|Q6|+|、|Q6|α|三款|入門機|，|最大|的|特色|就是|跟|G6|一樣|，|擁有|FullVision|全面|螢幕|。|LG| |Q6|、|Q6|+|與|Q6|...',\n", " 7),\n", " ('《|Rewrite| |IgnisMemoria|》|推出|期間|限定|新|活動|「|其煙火|，|在|夜空|描繪|愛|」|,|\\n|《|Rewrite| |IgnisMemoria|》|期間|限定|活動|「|其煙火|，|在|夜空|描繪|愛|」|舉辦|中|！|遊戲|就|在| |7| |月| |10| |日維修|完|後|，|推出|了|新|的|期間|限定|活動|與|轉蛋|「|其煙火|，|在|夜空|描繪|愛|」|。|■|活動|詳細|◆|活動|舉辦|日期|&|時間|◆|「|其煙火|，|在|夜空|描繪|愛|」|活動|：|...|\\n',\n", " 8),\n", " ('《|爐石|戰記|》|HCT| |春季|冠軍賽|花絮| |卡牌|角色|台詞|紙板|受|現場|玩家|歡迎|,|\\n|《|爐石|戰記|》|全球|巡|迴|賽|春季|冠軍賽|週末|在|中國|上海|落幕|，|現場|最受|玩家|歡迎|的|小|贈品|，|應該|就是|《|爐石|戰記|》|中|的|角色|台詞|紙板|。|\\xa0|為|了|與|《|爐石|戰記|》|全球|巡|迴|賽|春季|冠軍賽|到場|玩家|同樂|，|Blizzard| |在|比賽|現場|準備|了|一些|趣味|內容|，|讓|玩家|可以|趁|...|\\n',\n", " 9),\n", " ('BlizzCon| |爐石電|競將|有|「|好玩|」|的|事|？|《|爐石|戰記|》|電競|經理|分享|官方|賽事|未來|規劃|,|\\n|《|爐石|戰記|》|全球|巡|迴|賽|春季|冠軍賽|剛剛|落幕|，|比賽|現場|聚集|上千|玩家|觀賞|賽事|、|熱情|交流|，|《|爐石|戰記|》|電子|競技|經理|周祺傑針|對|《|爐石|戰記|》|現今|賽事|包括|全球|巡|迴|賽|（|HCT|)|、|世界|大賽|（|HGG|）|與|開放|模式|對決|定位|加以|解析|，|同時|透露|今年|《|爐石|戰記|》|電競|在| |...|\\n',\n", " 9),\n", " ('PS4| |Pro| |新色|登場|！|SIEA| |宣布|推出|《|天命| |2|》|冰河|白|配色|款式| |PS4| |Pro| |主機同|捆組|,|\\n|美國|索尼|互動|娛樂|（|SIEA|）|10| |日|宣布|，|將於| |9| |月| |6| |日|配合| |Activison|\\xa0|大型|多人線|上|第一|人稱|動作|遊戲|《|天命|\\xa0|2|（|Destiny| |2|）|》|的|上市|，|同步|推出| |PlayStation| |4| |Pro|（|PS4| |Pro|）|...|\\n',\n", " 10),\n", " ('PS4| |Pro| |新|配色|「|冰河|白|」|9| |月| |6| |日|在|日本|限量|推出|,|\\n|索尼|互動|娛樂|日本|亞洲|（|SIEJA|）|今|（|11|）|日|宣布|，|將於| |9| |月| |6| |日|在|日本|推出| |PlayStation| |4| |Pro|（|PS4| |Pro|）|主機|的|新|配色|款式|「|冰河|白|」|，|採|限量|提供|，|價格| |4| |萬| |4980| |日圓|（|未稅|）|。|\\xa0|\\xa0|繼|...|\\n',\n", " 10),\n", " ('[|c120|]|[|67fd|]| |2017| |六人|晚餐| |Youth| |Dinner| |[', 11),\n", " ('[|出門|]| |討好|爸爸|別|忘|了|媽咪|！|百靈|推|刮|鬍|刀|與|Dyson|吸塵器|組合包|,|07|/|12|/|2017', 12),\n", " ('[|Android|]| |[|iOS|]|『|Relux|』|嚴選|日本|好|飯店|，|偶爾|當個|貴婦|不為過|吧|?|!|,|07|/|12|/|2017',\n", " 12),\n", " ('[|快訊|]| |各位|太太|注意|!|!|!|傳出|ZenFone| |4|系列|手機|亞洲區|代言|代言人|可能|會|是|最帥|大叔|?|!|,|07|/|12|/|2017',\n", " 12),\n", " ('《|MOBIUS| |FINAL| |FANTASY|》|與|《|FFXII|》|合作|區域|「|光之空|賊|」|正式|登場|,|\\n|由| |SQUARE| |ENIX| |開發|營運|的|手機|遊戲|《|MOBIUS| |FINAL| |FANTASY|》|中文|文版|中文版|宣布|，|為|記念|「|FINAL| |FANTASY| |XII| |The| |Zodiac| |Age|」|發售|，|該|作品|與|「|MOBIUS| |FF|」|的|特別|合作|活動|「|FFXII|：|...|\\n',\n", " 13),\n", " ('N3DS| |免費|遊戲|《|Pictlogica| |Final| |Fantasy|≒|》|即日|釋出| |挑戰|《|FF|》|系列|圖片|益智|猜|謎|,|\\n|SQUARE| |ENIX| |製|作|的|基本|遊玩|免費| |N3DS| |下載|專用|軟體|《|Pictlogica| |Final| |Fantasy|≒|（|ピ|ク|ト|ロ|ジ|カ| |フ|ァ|イ|ナ|ル|フ|ァ|ン|タ|ジ|ー|）|》|，|已經|於| |7| |月| |12| |日|正式|開|放下|載|。|\\xa0|\\xa0|《|Pictlog|...|\\n',\n", " 13),\n", " ('《|勇者|鬥惡龍| |怪物|狂歡|派|對|》|推出|繁中|繁中版|限定|「|怪物|特技|強化|」|「|聯盟|命令|」|等|功能|,|\\n|日本| |Sony| |集團|旗下|之|網際|網路|服務事業| |So|-|net| |代理|的| |RPG| |遊戲|《|勇者|鬥惡龍| |怪物|狂歡|派|對|》|，|於|今|（|12|）|日|進行|改版|，|營運團隊|即日|即日起|陸續|推出|繁中|繁中版|限定|「|怪物|特技|強化|」|、|「|聯盟|命令|」|等|多項|改版|功能|，|並優化|遊戲|內容|，|詳細|改版|內容|請|...|\\n',\n", " 14),\n", " ('《|勇者|鬥惡龍| |怪物|仙境| |SL|》|推出| |S| |級|「|地獄|帝王|艾|斯塔|塔克|斯塔克|」|「|邪惡|艾|斯塔|塔克|斯塔克|」|等|怪物|,|\\n| |RPG| |手機|遊戲|《|勇者|鬥惡龍| |怪物|仙境|SUPER| |LIGHT|》|今|（|12|）|日|宣布|，|推出|艾|斯塔|塔克|斯塔克|精選|，|S| |級|「|艾|斯塔|塔克|斯塔克|、|死亡|機械兵|、|薔薇|女王|、|末扎特|」|機率| |UP|，|「|地獄|帝王|艾|斯塔|塔克|斯塔克|」|「|邪惡|艾|斯塔|塔克|斯塔克|」|等|新生|轉生|追加|。|「|DQ| |嘉年華| |勇者|鬥惡龍| |...|\\n',\n", " 14),\n", " ('《|薩爾達|傳說|：|荒野|之息|》|官方|非官方|中文|文字|字幕|文字幕|中文字幕|製|作|花絮|影片|,|\\n|薩爾達|傳說|：|荒野|之息| |-| |製|作|花絮|(|官方|非官方|中文|文字|字幕|文字幕|中文字幕|)|[|開發|的|起點|]|[|開|放空|間|]|[|故事|與|角色|]|[|特別|篇|]|所有|影片|皆|為|原創|翻譯|，|轉載|請|附|原文|網址|影片|來源|：|THE| |MAKING| |OF|...|\\n',\n", " 15),\n", " ('香港|天堂|任天堂|開設|《|薩爾達|傳說|：|荒野|之息|》|中文|官方|網站| |透露|新動|向|徵兆|？|,|\\n|香港|天堂|任天堂|官方|網站|於|方才|正式|開設|了| |Nintendo| |Switch| |版|《|薩爾達|傳說|：|荒野|之息|（|The| |Legend| |of| |Zelda|:| |Breath| |of| |the| |Wild|）|》|的|中文|官方|網站|，|提供|遊戲|背景|與|角色|的|基本|介紹|。|\\xa0|&|nb|...|\\n',\n", " 15),\n", " ('Leica| |TL2| |無反|性能|大躍進|：|4K| |錄影|、|20fps| |連拍|上身|,|算是|大升級|。', 16),\n", " ('【|快訊|】|Leica| |TL2|正式|發表| |搭載|4K|錄影|與|20fps|連拍|！|,|Leica|從|以前|一路|追趕|日本|相機廠|的|規格|，|到現|在|各種|方面|已經|「|超|C|趕|S|」|，|萊卡|這間|歷史|上|最|有名|的|相機|廠商|很|努力|讓|自家|產品|走|在|最|前面|。|講求|現代感|、|機背|全觸|控|的|T|...',\n", " 16),\n", " ('【|試片|】|《|徒然|喜歡|你|！|》|濃縮式|閃光彈|五連發| |今夏|最強|戀愛|群像|劇|,|\\n|在| |2012| |年|，|一部|名為|《|徒然|喜歡|你|》|的|四格|漫畫|在| |pixiv| |及| |niconico| |橫空|出世|。|這部|作品|以|接近|十天|一篇|的|速度|穩定|的|連載|，|其|短篇|戀愛小說|般的|獨特節|奏|瞬間|吸引|了|大量|的|讀者|。|作者|以|精巧|的|手法|，|在|四格|內特過|多個|一回|完結|的|短篇|演繹|各|...|\\n',\n", " 17),\n", " ('[|快訊|]| |LG| |G6|今天|（|7|/|11| |)| |正式|推出|迷你|迷你版|『|LG| |Q6|』|,|07|/|11|/|2017',\n", " 18),\n", " ('2017| |年款| |Pixel| |XL| |機身|說|不定|也|能|擠|,|而且|會用|上|類似| |LG| |G6| |的|長|條螢|幕|。',\n", " 18),\n", " ('《|星海|爭霸| |2|》|2017| |GSL| |三十|十二|三十二|強| |B| |組| |INnoVation| |率先|取得| |16| |強資格|,|\\n|【|以下|內容|為|廠商|提供|資料|原文|】|B| |組是|第二|個|擁有|非|韓選手|的|小組|。|繼波蘭蟲族|選手| |Elazer| |遭到|小組|淘汰|後|近年|在| |WCS| |與| |GSL| |都|拿下|不少|積分|的|加拿|加拿大|蟲后| |Scarlett| |是否|能|晉級|呢|？|\\xa0|INnoVation|、|Ryung|...|\\n',\n", " 19),\n", " ('【|模型|】|《|女神|異聞錄| |5|》|吉祥|吉祥物|「|摩爾加納|」|黏土|人化|登場|！| |重現|總|攻擊|煞氣|樣貌|,|\\n|Good| |Smile| |Company| |今|（|11|）|日|發表|，|將於| |2017| |年| |12| |月|推出|以| |ATLUS| |PS4| |/| |PS3| |人氣|角色|扮演|角色扮演|遊戲|《|女神|異聞錄| |5|》|登場|角色|「|摩爾加納|」|為|題材|的|黏土|人|系列|模型|新產品|「|黏土|人| |摩爾加納|（|ね|ん|ど|ろ|い|ど| |...|\\n',\n", " 20),\n", " ('【|模型|】|GSC|《|女神|異聞錄| |5|》|摩爾加納|黏土|人|模型|預定| |12| |月|推出|,|\\n|《|女神|異聞錄|5|》|是|Atlus|開發並|發行|的|電子|角色|扮演|角色扮演|遊戲|，|對應|PlayStation| |3|和|PlayStation| |4| |。|本作|是|《|女神|異聞錄|系列|》|的|第五|五部|第五部|主要|作品|，|也|是|首次|在|標題|上|取消|了|「|真|·|女神|轉生|」|的|名字|。|本作|遊戲|的|主題|顏色|為|「|紅色|」|。|遊|...|\\n',\n", " 20),\n", " ('[|0EE9|]|[| |夢幻|飛琴| |The| |Flying| |Machine|]|[', 21),\n", " ('原生| |Android| |介面|入門機|，|Nokia| |3| |提早|提早到|貨開|賣|,|先前|\\xa0|HMD| |Global| |表示| |Nokia| |5|、|Nokia| |3| |分別|會|於|\\xa0|7|/|1| |與| |7|/|15| |在|台|上市|，|結果|兩款|手機|都|提早|提早到|貨開|賣|。|先前| |Nokia| |5| |提早|於| |6| |月| |30| |日到貨|，|而| |Nokia| |3| |則是|於|今日|、|也|就是| |7| |月| |11| | |...',\n", " 22),\n", " ('Apple| |將在|大陸|貴州|新建|一座|數據|中心|,|為|了|遵守|當地|新|施行|的|《|網路|安全|安全法|》|。', 23),\n", " ('宏碁|在|台|推出|夏日|電競|筆電|新品| |Triton| |700| |與| |Helios| |300|,|並且|宣|佈|與| |Garena| |結盟|，|以| |Predator| |品牌|冠名|《|英雄|聯盟|》|校際|盃|比賽|。',\n", " 24),\n", " ('宏碁|電競|品牌| |Predator| |冠名|贊助| |2018|《|英雄|聯盟|》|校際|盃|\\u3000|結|合金|校獎|、|Cosplay| |等|內容|,|\\n|宏碁|與|遊戲|廠商| |Garena| |今日|宣布|正式|結盟|，|以|宏碁|旗下|電競|品牌|「|Predator|」|冠名|贊助| |2018| |年|《|英雄|聯盟|》|校際|盃|，|除了|提供|學生賽|事|所|需要|的|電競|桌機|、|筆電|與|螢幕|、|獎品|外|，|同時|合作|舉辦|多樣化|活動|，|包括| |Cosplay| |線|上賽|、|電競|...|\\n',\n", " 24),\n", " ('《|鬥陣|特攻|》|2017| |角旗|盃|校際|電子|競賽|甲|組冠軍賽| |14| |日|決戰| |Blizzard| |Estadium|,|\\n|《|鬥陣|特攻|》|2017| |TeSL| |角旗|盃|校園|電子|競賽|進入|最後階段|賽事|，|總冠|軍賽將|於|明|（|14|）|日|在| |Blizzard| |Estadium| |開打|，|由|東泰|高中|東泰太陽隊|對戰|立志|中學|立志|猩勢力|。|\\xa0|歷經|三個|月|的|賽事|，|《|鬥陣|特攻|》|2017| |TeS|...|\\n',\n", " 25),\n", " ('《|鬥陣|特攻|》|職業電|競聯賽|下半|半年|下半年|展開| |洛杉磯|、|首爾|、|上海|等|七支|代表|隊|宣布|成立|,|\\n|Activision| |Blizzard| |今| |(|12|)| |日|宣布|，|首批|《|鬥陣|特攻|》|職業電|競聯賽|國際|各|大城|城市|大城市|代表|隊|已|由七個|傳統|運動|及|電競|的|企業家|與|龍頭|買|下|，|《|鬥陣|特攻|》|職業電|競聯賽|是|全球|首創|以|城市|為主場|的|職業|電子|競技|聯賽|，|今日|同步|曝光|新隊伍|擁有者|...|\\n',\n", " 25),\n", " ('19|歲|少女|奪冠|《|中國|最美|臀部|》|這|屁屁|太圓潤|惹|,|說|到|美|臀|~|之前|如夢有|介紹過|《|日本|第一|美|臀|》|，|除了|飲食|要|控制|之外|，|運動|更是|照|三餐|練習|，|超級|辛苦|的|(|;|´|༎|ຶ|Д|༎|ຶ|`|)|倫倫|也|跟|很多|女生|(|幹嘛|脫別|人|下水|)|一樣|，|有|久坐|變成|大|屁屁|的|症頭|，|所以|要|多學學|這位|《|中國|...',\n", " 26),\n", " ('《|三生|三世|擇仙記|》|將進|軍|台港|港澳|台港澳| |故事|背景|搶先|揭開|,|\\n|揚源|國際今|（|12|）|日|宣布|，|取得|由|上海|靈玩|研發|的|線|上|遊戲|《|三生|三世|擇仙記|》|台港|港澳|台港澳|地區|代理|權|。|遊戲|預定|於| |20| |日|進行|刪檔|封測|，|官方|今日|搶先|揭開|故事|背景|與|遊戲|情境|。|\\xa0|【|以下|內容|為|廠商|提供|資料|原文|】|\\xa0|故事|背景|\\xa0|五百|百年|五百年|...|\\n',\n", " 27),\n", " ('《|英雄|聯盟|》|釋|出新|英雄|「|席達|．|慨影|」|宣傳|影片| |「|烏爾|加特|」|將進行|重|製|,|\\n|Riot| |曝光|《|英雄|聯盟|》|新|英雄|「|席達|．|慨影|」|宣傳|影片|，|玩家|是|要|化身|詭影|刺客|還是|冥血|族裔|？|另外|，|官方|日前|也|宣布|英雄|「|烏爾|加特|」|將進行|重|製|。|\\xa0|《|英雄|聯盟|》|新|英雄|「|席達|．|慨影|（|Shieda| |Kayn|）|」|揮舞|有|著|自我|意識|、|被|稱|作勒|哈斯|斯特|哈斯特|...|\\n',\n", " 28),\n", " ('《|英雄|聯盟|》|研發商| |Riot| |在|北美|狀告|疑似|山寨|手機|遊戲| |引發|中國|媒體|猜|測動機|,|\\n|Riot| |上週|在|加州|法院|提起|訴訟|，|認為|中國|上海|沐瞳|科技|（|Moontoon| |Technology|）|旗下|遊戲|包括|《|Mobile| |Legends| |無盡|對決|》|、|《|魔法|英雄|》|運用|了| |Riot| |旗下|知名|遊戲|《|英雄|聯盟|》|大量|元素|，|與|包括|但|不限|於|《|英雄|聯盟|...|\\n',\n", " 28),\n", " ('《|英雄|聯盟|》|韓國|選手| |Smeb| |分享|參與亞洲|對|抗賽|心情| |認為| |FW| |MMD| |攻擊|實力|有|提升|,|\\n|《|英雄|聯盟|》|韓國|隊伍| |KT| |上路|選手| |Smeb| |於|日前|在|台灣|高雄|展覽館|舉辦|《|英雄|聯盟|》|洲際|系列|賽|（|Rift|\\xa0|Rivals|）|亞洲|對|抗賽|中|接受|巴哈|哈姆|巴哈姆|特| |GNN| |編輯|訪問|，|他|對|於|現|在|能|有|越來|越|多|的|國際賽|事|感到|很|開心|，|同時|也|分享|了|對|於| |L|...|\\n',\n", " 29),\n", " ('《|英雄|聯盟|》|ahq| |選手| |Westdoor| |認為|亞洲|對|抗賽|賽制|很酷| |分享|近期|心路|歷程|與|心情|,|\\n|《|英雄|聯盟|》|ahq| |知名|選手| |Westdoor| |於|上|週|在|台灣|高雄|舉辦|的|亞洲|對|抗賽|中|接受|巴哈|哈姆|巴哈姆|特| |GNN| |等|媒體|訪問|，|他|分享|對|於|此次|參加|國際賽|、|5| |Ban| |的|改動|與|此次|賽制|模式|的|看法|，|他|認為|這樣|的|模式|很|有酷|，|雖然會|互相|交流|，|但|又|不敢|太多|說太多|。|&...|\\n',\n", " 29),\n", " ('好|想|嚐嚐|！|「|椰汁|撞奶|」|看起|來|好|可口|，|金|髮|辣妹|「|裸身|」|獻上|兩粒|椰子|…|,|\\n|文|／|金真露|\\n|\\n|疑疑|？|想|說|我|怎麼|開始|介紹|起|西洋|妹子|了|嗎|？|當然|不是|啦|～|看|韓妹|才|4|我|的|最',\n", " 30),\n", " ('《|為|了|誰|的|鍊|金術師|》|繁中|繁中版| |x|《|殺戮|魅影|》|x|《|勇者|前線|》|聯動|合作| |推出|原創|劇情|關卡|,|\\n|由| |DeNA| |代理|的| |3D| |RPG| |手機|遊戲|《|為|了|誰|的|鍊|金術師|》|繁體|中文|文版|中文版|，|於|今日|正式|展開|與|《|殺戮|魅影|》|＋|《|勇者|前線|》|的|三方|聯動|合作|企劃|，|此次|聯動|為|期三週|，|期間|內將|開放|一系|系列|一系列|的|原|創劇|情關|卡|與|聯動|新角|、|專屬|武具|等|。|\\xa0|\\xa0|為|了|...|\\n',\n", " 31),\n", " ('《|英雄|聯盟|》|選手| |Faker| |認為|隊伍| |FW| |在| |MSI| |後|有|成長| |透露|目前|仍會|以|選手|的|身分|努力|,|\\n|《|英雄|聯盟|》|LCK|，|LPL| |賽區|隊伍|於|上|週來|台|參加|在|台灣|高雄|舉辦|的|亞洲|對|抗賽|中|，|韓國|隊伍| |SKT| |知名|中路|選手| |Faker| |在|現場|接受|巴哈|哈姆|巴哈姆|特| |GNN| |的|訪問|，|他|認為|隊伍| |FW| |和| |WE| |在| |MSI| |季中|邀請賽|後|有所|成長|，|同時|也|透露|目前|仍會|...|\\n',\n", " 32),\n", " ('多卡|擴充|一次|滿足|！|微星|MSI| |X299| |SLI| |Plus|主機板|,|微星|近來|在|X299|系列|產品線|中|推出|了|一款|X299| |SLI| |PLUS|主機板|，|雖然|外觀|設計|不|像|自家|Gaming|系列|產品|那麼|醒目|，|但|在|規格|上|仍然|提供|了|相當|充足|的|擴充|能力|，|不僅|直接|提供|了|4|組|PCIex16|擴充|插槽|，|讓|使用|用者|使用者|可以|輕|鬆|擴|充|多|張|顯|示|卡|或是|更|多|PCIe|裝置|，|還具備|了|雙網路|與雙|M|...',\n", " 33),\n", " ('PS4|遊戲|《|白色|情人|節|：|恐怖|學校|》|不是|吧|都|嚇死|了|還要|談戀|愛|…|,|馬|上|就是|白色|情人|節了|，|鼓起|勇氣|，|把|禮物|送給|暗戀|的|她|吧|，|校園|的|戀愛|，|是|微酸|帶點|甜|…|…|等等|！|這畫|風不太|對|XD|竟然|是|恐怖|遊戲|啊|媽媽|，|都|快|嚇死|了|還要|繼續|拚|好感|好感度|（|淚笑|）|。|將於|8|月|24|日|發售|中英|英文|中英文|合版|的|恐怖|探索|...',\n", " 34),\n", " ('《|奇|蹟| |MU| |王者|歸來|》|全新|「|神識|系統|」|登場| |升級|戰盟|聯賽|及|時|裝系統|,|\\n|由|韓國| |Webzen| |（|網禪|）|公司|正式|授權|的| |ARPG| |手機|遊戲|《|奇|蹟| |MU|\\xa0|王者|歸來|》|，|於|今|（|12|）|宣布|將開放|全新|「|神識|系統|」|，|除此|之外|除此之外|，|升級|版戰盟|聯賽|及|新|增加|的|時|裝系統|也|已|在|遊戲|中|登場|。|\\xa0|\\xa0|【|以下|內容|為|廠商|...|\\n',\n", " 35),\n", " ('WWE| |電玩遊戲|最新|作|《|WWE| |2K18|》|將在| |Nintendo| |Switch| |平台|發售|,|\\n|2K| |今|（|11|）|日|宣布|，|WWE| |電玩遊戲|系列|最新|力作|《|WWE| |2K18|》|將在|今年|秋天|於| |Nintendo| |Switch| |平台|發售|。|這讓|《|WWE| |2K18|》|成為|近|五年|內|首款|登上| |Nintendo| |平台|的| |WWE| |電玩遊戲|。|\\xa0|&|nbs|...|\\n',\n", " 36),\n", " ('《|WWE| |2K18|》|釋出|「|SURVIVOR|」|最新|宣傳|影片| |名人|名人堂|巨星| |Kurt| |Angle| |重返|虛擬|擂台|,|\\n|2K| |Games| |宣布|，|預定| |10| |月| |17| |日|推出|的| |PS4| |/| |Xbox| |One| |/| |NS| |摔角|遊戲|《|WWE| |2K18|》|，|現已|釋出|「|SURVIVOR|」|最新|宣傳|影片|，|宣傳|由|奧運|金牌|得主|金牌得主|、|WWE| |名人|名人堂|巨星|暨| |Raw| |總經理| |Kurt| |An|...|\\n',\n", " 36),\n", " ('【|採訪|】|Acer| |公|佈| |MAX|-|Q| |架構|輕薄電|競筆電| |Triton| |700| |售價|與|上市|價格|,|Acer| |在|今日|（|7|/|12|）|將|四月|全球|新品|發表會|上|發表|的|系列|新品|，|正式|在|台灣|發表|，|其中|最受|關注|的|就是|採用| |NVIDIA| |MAX|-|Q| |架構|的|輕薄電|競筆電| |Predator|...',\n", " 37),\n", " ('[|Android|]| |[|iOS|]| |歐美大|咖的|演唱|會|行程|列表|『|Bandsintown|』|不|讓|你|錯過|愛團|的|演出|行程|!|,|07|/|12|/|2017',\n", " 38),\n", " ('玩| |Switch| |也|可以|方便|打字|聊天|！|日本|推出| |Switch| |專用|鍵盤|,|上次|小編|才|為|大家|介紹|了|可用| |Switch| |來|寫|遊戲|程式|的|軟體|，|現在|就|有|兩家|日本|硬|體|製|造|商|推出|了| |Switch| |用|的|鍵盤|，|可以|直接|與| |Switch| |的|手把|相連|，|要|拿|來|在|遊戲|中|聊天|真是|超|方便|的|啦|！',\n", " 39),\n", " ('帶著|滿滿|母乳|22|歲|高顏值|人妻|，|太久|沒有|生活|性生活|....|只好|毅然|決然|投身|AV|界|！|,|\\n|太早|結婚|的|壞|處|就是|這樣|？|可能|因為|年|輕|所以|對|生活|有|許多|憧憬|，|當然|因為|新婚|加上|年紀',\n", " 40),\n", " ('狂|！|爆乳正妹|「|咖骨嫩|Q|」|…|裸身|彎腰|180|度|抱|雙腿|！|男人|小|宇宙|大|爆發|,|\\n|【|文|/|傲嬌|女王|】|\\n| |\\n|什麼|叫做|爆乳|？|！|穿著|毛衣|還能|胸型|立體|，|這才|叫做|「|爆|」|。|今天|要介',\n", " 41),\n", " ('大人|式|的|性|愛|！|員工|旅遊|好|刺激|，|喝|了|點酒|就|不自|自禁|不自禁|情不自禁|的|對|主管|獻身|...|不住|忍不住|又|害怕|被|發現|,|\\n|什麼|叫做|大人|式|的|性|愛|？|顧名|思義|就是|大人|之間|所|期待|的|性|愛|模式|，|好比|說|職場|之間|，|或',\n", " 42),\n", " ('[|cf02|]| |2017| |神鬼|傳奇| |The| |Mummy| |[', 43),\n", " ('[|3ECC|]|[| |神鬼|傳奇| |The| |Mummy|]|[', 43),\n", " ('GoPro| |將把| |Fusion| |360| |VR| |相機|搶|先|提供|特定|合作|夥伴|進行|測試|,|拿來|轉播|運動賽|事|肯定|超|刺激|！',\n", " 44),\n", " ('知名| |3D| |動畫|《|RWBY|》|傳將|推出|改編格|鬥遊戲| |詳情將|於|「|EVO| |2017|」|揭曉|,|\\n|由美國| |Rooster| |Teeth| |Productions| |製|作|、|推出|後|廣受|全球|動畫|迷喜|愛的| |3D| |CG| |動畫|作品|《|RWBY|》|，|現傳出|將改|編為|格鬥遊戲|的|消息|，|詳情|很|可能|會|在|本|週五| |7| |月| |14| |日|開幕|的|全球|格鬥遊|戲盛會|「|EVO| |2017|」|中|...|\\n',\n", " 45),\n", " ('日本|青山|大學|選美|《|4|號|井口|綾子|超可愛|》|意外|被|網友|發現|天然|呆|特質|…|…|,|以選美|比賽|聞名|的|日本|青山|學院|大學|（|青山|学院|大学|）|最近|展開|了|2017|年|的|選美|投票|活動|，|參賽者|們|的|推特|、|Instagram|理所|當然|成為|大家|關注|的|焦點|，|其中|編號|第|4|號的|井口|綾子|呼聲|非常|高|！|她|的|照片|被|網友們|瘋|...',\n", " 46),\n", " ('Nintendo| |Switch| |推出| |niconico| |專用| |App| |外出|在家|無縫|接軌|收看|精彩|影片|,|\\n|日本|動畫|共享|平台| |niconico| |宣布|，|將於| |7| |月| |13| |日釋|出|天堂|任天堂|新主機| |Nintendo| |Switch| |專用|的| |niconico| |收視| |App|「|niconico|」|，|供|日本|地區|玩家|免費|下載|，|使用| |niconico| |的|串流|影片|服務|。|&...|\\n',\n", " 47),\n", " ('中華電信|推出|「|中華|日本|通|」|6|天|999|元上|網吃|到|飽|,|中華電信|看好|暑假|遊|日本|的|需求|，|推出| |「|中華|日本|通|」|當|地上|網卡|，|其實|就是|EZNippon|日本|通|的|上網|吃|到|飽產品|。|透過|線|上|預約|開通|，|以|專屬|網路頻|寬|及|良好|品質|管控|...',\n", " 48),\n", " ('[|快訊|]| |iOS|版|的|『|Google|日|曆|』|正式|成為|Widget|一員|!|!|一分|鐘|快速|教|你|怎麼|用|,|07|/|12|/|2017',\n", " 49),\n", " ('20|歲|人妻|自爆|「|有|無|限性|慾|」|\\u3000|抖|M|看到|雞雞|抓狂|...|嘴巴|被|硬|塞|老二|超爽|！|,|\\n|南港|7|條蛇|／|VC15|文|\\n|\\n|有|看|過片|的|男人|都曉得|，|創意|無限|的|SOD|「|都|是|真的|」|，|且|更|強大|的|是',\n", " 50),\n", " ('以|後| |Android| |應用|再|卡住|，|連按|返回|鍵|就|能|把|它|直接|關掉|了|,|Google| |在| |Android| |7.1| |Nougat| |中|悄悄|加入|了|這個|功能|。',\n", " 51),\n", " ('《|星際大戰|：|戰場|前線| |II|》|多人|連線| |Beta| |測試| |10| |月|登場|,|\\n|Electronic| |Arts| |近日|宣布|，|PC|、|PS4|、|Xbox| |One|《|星際大戰|：|戰場|前線| |II|》|多人|連線| |Beta| |測試將|在| |10| |月初|起開|跑|。|\\xa0|《|星際大戰|：|戰場|前線| |II|》|是| |2015| |年|推出|的|《|星際大戰|：|戰場|前線|》|的|續|篇|新|...|\\n',\n", " 52),\n", " ('Audi| |帶來|搭載|半自動|系統|的|新| |A8|,|號稱|是|全球|首款|達到| |Level|-|3| |自動|駕駛|的|量|產車|。',\n", " 53),\n", " ('不住|忍不住|在|賣場|發展|SOD|！|捕獲|賣場|野生|妹子|，|超空靈|眼神|加|一件|短熱褲|，|完全|擄獲|我心|....|,|\\n|今天|要|跟|大家|分享|的|是|在|賣場|購物|的|空靈系|正妹|，|空靈系|女生|必備|的|一頭|黑直|髮|，|加上|無',\n", " 54),\n", " (\"《|Grand| |Summoners|》|與|《|拳皇|'|98|》|將進行|跨界|合作| |試圖|重現|原作|動作|,|\\n|NextNinja| |與| |GOOD| |SMILE| |COMPANY| |公開|了|旗下|營運中|的|智慧|智慧型|手機| |RPG|《|Grand| |Summoners|（|グ|ラ|ン|ド|サ|マ|ナ|ー|ズ|）|》|（|iOS|／|Android|）|與| |SNK| |的|對|戰格|鬥遊戲|《|拳皇|'|98|》|跨界|合作|活動|...|\\n\",\n", " 55),\n", " ('白色|版| |PS4| |Pro| |以|《|天命| |2|》|同|梱|包|登|場|,|總算|不是|只有|黑色|而已|了|。', 56),\n", " ('6| |吋|螢幕|、|雙主|相機|，|LG| |V30| |全新|產品|概念|圖現|身|,|日前|爆料|達人| |OnLeaks| |釋出|了|新|一批|的| |V30| |產品|概念|圖|，|可發現| |V30| |的|整體|風格|確實|和| |G6| |更為|相似|，|同樣|採用|包含|高|佔|比|的|螢幕|設計|，|機背|也|保有|雙相機|配置|，|不過|配置|的|樣式|和| |G6| |有點|不|太| |...',\n", " 57),\n", " ('【|海外|消息|】|Adobe|認為|Lightroom|運作實|在|太慢|：|「|增加|速度|是|首要|目標|」|,|你|也|曾|經覺|得|，|覺得|不管|你|的|CPU|時脈|衝到|多|快|、|記憶體|加到|多少|條|、|硬碟|換了|超快|的|SSD|，|甚至|連繪|圖卡|都|用|了|卡|王等級|，|但|電腦裡|的|Lightroom|永遠|都|是|「|卡卡|的|...',\n", " 58),\n", " ('[|快訊|]| |是|當模擬|市民|在|玩膩|？|FB|預計|為員工|在|矽谷|打造|真的|社群|住宅|,|07|/|11|/|2017', 59),\n", " ('《|天命| |2|》|NVIDIA| |GeForce| |GTX| |1080| |Ti| |運行| |4K| |實機試|玩|影片|曝光|,|\\n|由遊|戲開|發商| |Activision| |Blizzard| |及| |Bungie| |製|作|的|大型|多人線|上|第一|人稱|動作|遊戲|《|天命| |2|（|Destiny| |2|）|》|將由| |Activision| |Blizzard| |旗下|研發|團隊| |Vicarious| |Visions| |共同|...|\\n',\n", " 60),\n", " ('《|放逐|選舉|》|繁體|中文|文版|中文版|發售|日|決定| |中文|官方|網站|即日|即日起|同步|開張|,|\\n|SEGA| |Games| |宣布|，|日本|一| |Software| |開發|的| |PS4| |冒險|遊戲|《|放逐|選舉|》|繁體|中文|文版|中文版|已|決定|於| |9| |月| |28| |日|發售|。|另外|，|刊登|本遊戲|詳細|資訊|的|繁體|中文|文版|中文版|官方|網站|也|正式|開張|。|\\xa0|繁體|中文|文版|中文版|官方|網站|首頁|《|放逐|選舉|》|是|以|《|...|\\n',\n", " 61),\n", " ('Waymo| |要|讓|自動|駕駛車|懂得|對緊|急救|護車輛|做出|反應|,|將會|自動|靠邊|停|或是|讓|道|。', 62),\n", " ('G|乳正妹|泡|溫泉|大膽|「|放送|福利|」|畫面|好|迷人|！|「|白嫩|大湯圓|」|簡直|最強|女體|誘惑|…|,|\\n|文|｜|Hanabi|\\n|\\n|日本|超強|的|G|奶寫|真|女星|真的|豁出|出去|豁出去|了|！|在|拍|攝節|目大膽|在|溫泉邊|放送|乳量',\n", " 63),\n", " ('「|遊艇|大奶妹|」|開船|好|盡興|！|「|巨大|雪乳|」|快|蹦出|衣服|，|比|海面|還要|波濤|洶湧|！|,|\\n|文|／|薇|薇|WEI|\\n|\\n|天氣|越來|越熱|，|許多人|都|開始|往|有|水|的|地方|跑|！|今天|就|看到|一個|正妹|在|水上',\n", " 64),\n", " ('《|文明|爭戰|》|聯合|戰線|更新| |強化|公會系|統並|新增|公會|貨幣|單位|「|紅寶石|」|,|\\n|由| |Nexon| |營運| |Big| |Huge| |Games| |開發|的|手機|戰略|遊戲|《|文明|爭戰|》|（|DomiNations|）|，|宣布|推出|聯合|戰線|更新|，|並且|進行|強化|公會|系統|與|多項|內容|更新|，|以下|為|本次|更新|內容|說明|。|\\xa0|\\xa0|【|以下|內容|為|廠商|提供|資料|...|\\n',\n", " 65),\n", " ('跪求|臉書|！|台北|醫大|「|巨乳|小|隻|馬|」|乳量|有夠|兇爆|！|E|罩杯|「|火辣|身材|」|引|line|瘋傳|…|,|\\n|主廚|今天|一|回到|家|，|就|發現|萬惡|的|line|群組|被|瘋狂|洗版|…| |稍微|看|了|一下|後|發現|原來|4|一位',\n", " 66),\n", " ('超大|膽|！|直播|女王|「|體驗|玩具|」|畫面|太|煽情|！|大|尺度|「|搓揉|畫面|」|讓|人|不了|受不了|…|,|\\n|玩好|大|啊|！|昨晚|主廚|的|朋友|傳來|一段|超猛|的|直播|片段|，|搞|得|我整|晚上|都|差點|睡|不|著覺|..| |只',\n", " 67),\n", " ('好禮|連續|發|!|!|!| |Sony| |Mobile| |行動|娛樂館|專屬|限定|生日|禮開箱|!|,|Hi| |大家|好|~|今天|要|大家|介紹|一樣|東西|，|Sony| |mobile| |在|今年| |7| |月|開始針|對|過往|購買| |Xperia| |高階手機|的|消費者|有|一個|生日|禮的|兌換|活動|，|從|這個|月|開始|便|可以|收到|一則|簡訊|，|其|上面|有|一組|兌換|資料| |...',\n", " 68),\n", " ('全面|螢幕|顏值|更|高| |華為|Mate| |10|渲染|圖|曝光|,|華為|近年|來|都|是|雙旗|艦|系列|並行|，|包括|上半|半年|上半年|主打|時尚|的|P|系列|，|以及|下半|半年|下半年|主打|商務|的|Mate|系列|。|一代|新一代|Mate| |10|可能|跟上|全|螢幕|風潮|，|採用|6|吋|的|18|:|9| |LCD|...',\n", " 69),\n", " ('超養眼|！|鄉村|奶妹|「|美乳|上工|」|姿勢|有夠|邪|惡|！|胸前|「|兩顆|大|肉包|」|若|隱若現|…|,|\\n|文| |/| |深夜|大主廚|\\n|\\n|不曉得|各位|對主廚|日前|分享|過的|「|鄉村|奶妹|系列|」|有|沒|有|印象|？|最近|哥',\n", " 70),\n", " ('《|A|.|V|.|A| |戰地|之王|》|今日|推出|新|模式|「|躲貓貓| |2|：|詭影|無|蹤|」| | |進階|玩法|提升|難度|,|\\n|《|A|.|V|.|A| |戰地|之王|》|於|今|（|11|）|日|進行|改版|，|將|推出|「|躲貓貓| |2|：|詭影|無|蹤|」|新|模式|，|以|原本|受歡|迎|的|「|躲貓貓|模式|」|為|基礎|加入|更|多|不同|的|元素|，|提升|遊戲|難度|。|\\xa0|【|以下|內容|為|廠商|提供|資料|原文|】|\\xa0|\\xa0|\\xa0|「|躲貓貓|...|\\n',\n", " 71),\n", " ('Juno| |探測器|完成|飛越|大紅斑|的|歷史性|任務|,|照片|和|數據|大約|這週末|開始|傳回|地球|。', 72),\n", " ('少女|穿|回小學史|庫水|！|安價|直播|「|長|大|的|D|奶|」|！|網友|要求|都|好|過分|...|,|\\n|文|／|JUN|\\n|\\n|日本|少女|真的|很喜|歡在網|路上|安價|直播|裸體|，|只要|是|網友|提出|，|她|們|都|會|達|成願',\n", " 73),\n", " ('2017|上半|半年|上半年|「|AV|銷售|排行|」|出爐|！|挑|這些|女優|看準|沒錯|！|「|待|看|名單|」|好長|一串|...|,|\\n|文|／|JUN|\\n|\\n|\\n|\\n|時間|真快|過真快|，|明明|才剛|跨|年|，|怎麼|2017|年|已|經過|一半|了|？|歲|月|不|饒|人|啊|（|突然',\n", " 74),\n", " ('《|英雄|聯盟|》|王者|回歸邀|請賽| |8| |月|香港|電競|音樂節|開戰| |聚集| |Toyz|、|MiSTakE| |等|知名|前選手|,|\\n|香港|夏日|嘉年華|「|工銀亞洲|香港|電競|音樂節|」|將於| |8| |月| |4| |日至| |6| |日一連|三日|在紅|磡|香港|體育館|登場|，|現場|將舉|辦|《|英雄|聯盟|》|王者|回歸|世界|邀請賽|，|屆|時將|聚集| |20| |位分|別來|自|歐洲|、|中國內|地|、|台港|港澳|台港澳|、|北美|地區|的|前|《|英雄|聯盟|》|職業|聯賽|戰隊|知名|選手|，|...|\\n',\n", " 75),\n", " (' |《|魔龍|世界|》|正式|於雙|平台|展開|不|刪檔|封測| |為|了|信仰|奮力|一戰|,|\\n|以|末日|遠征| |3D| |龍戰為|主題|的| |MMORPG| |手機|遊戲|《|魔龍|世界|》|，|今|（|12|）|日將|於|台港|港澳|台港澳| |Google| |Play| |和| |App| |Store| |上市|，|正式|展開|不|刪檔|封測|。|官方|表示|，|遊戲|以| |Unity| |3D| |引擎|打造|戰鬥|效果|，|戰法|牧鐵|組隊|拓荒|、|多|...|\\n',\n", " 76),\n", " ('來|真的|《|銀魂|PS4|/|PSV|王道|動作|遊戲|開發|中|》|代號|「|Last| |Game|」', 77),\n", " ('Louis| |Vuitton| |也|來|推出|自己|的| |Android| |Wear| |手|錶|,|比| |TAG| |Heuer| |賣得|還貴|。',\n", " 78),\n", " ('內衣|反穿|的|最高|境界|！|只|遮住|「|葡萄|葡萄乾|」|胸部|擠壓|變形|，|看起|來|都|要|喘|不|過氣|了|：|好想|解開|,|\\n|把|內衣|反穿|到底|是|一個|怎麼樣|的|畫面|？|沒|想到|其實|也|蠻|有|創意|看|起來|邪|惡敢|更|高|！|最近|悠',\n", " 79),\n", " ('超|反差|！|「|女友|系正妹|」|隱乳技|一流|…|比基|基尼|比基尼|意外|洩漏|「|長|輩|」|大有|來頭|,|\\n|【|文|/|傲嬌|女王|】|\\n| |\\n|這幾天|真的|是|熱到|坐在|辦|公室|看|向|窗外|，|女王|心裡|想|的|盡|是|陽光|、|沙灘',\n", " 80),\n", " ('小米|將推|全新|手機|品牌|，|名稱|叫做|「|藍米|」|？|,|說|到|小米|手機|的|剛開始|的|銷售|特色|，|大概|就是|手機|產品|具備|高| |C|/|P| |值|，|然後以|線|上|販售|為|最|主要|的|銷售|策略|。|不過|近年|小米|在|中國|市場|的|佔|有率|，|已經|被|其他|中國|品牌|所|超越|，|因此|有|消息|指出|小米| |...',\n", " 81),\n", " ('旅美|台灣|選手|鍋貼|談|《|爐石|戰記|》|春季|冠軍賽|收|穫| |未來|持續|增強|自己|、|不|希望|再|失誤|,|\\n|旅美|台灣|選手|鍋貼|（|Kuonet|）|在剛|落幕|的|《|爐石|戰記|》|全球|巡|迴|賽|春季|冠軍戰|中|獲得|晉級|前八強|的|成績|，|他|表示|，|這次|來|參加|春季|冠軍賽|收|穫|很多|，|不僅|跟|這些|知名|好手|交流|學習|到|很多|經驗|，|同時|也|增加|了|在|國際大賽|參戰|經驗|、|尤其|是|如何|在|這樣|的|國際|舞台|上要|保持|...|\\n',\n", " 82),\n", " ('【|贈票|】|諾蘭導演|最新|史詩片|《|大行|敦克爾克大行動|》| |小惡|魔|網友|搶|先|看|,|即將|於|本月| |20| |日|上映|的|史|詩電影|《|大行|敦克爾克大行動|》|，|是|由|《|全面|啟動|》|、|《|星際|效應|》|導演|克里|里斯|克里斯|克里斯多|福|&|middot|;|諾蘭|執導|的|最新|作品|，|演員陣容|包含|與|諾蘭|合作|過|...',\n", " 83),\n", " ('[|新品|消息|]|3000MB|/|s|極速|傳輸| |Corsair|新款|Neutron| |NX500| |SSD|上市|,|Corsair|在|SSD|產品線|中|再度|推出|了|一款|採用|PCIe3|.|0x4|介面|介面卡|設計|的|Neutron| |NX500|，|並採用|了|Phison| |E7|控制|控制器|搭配|MLC|儲存顆|粒|...',\n", " 84),\n", " ('Sony| |粉絲|獨享|！|Xperia| |lounge| |小驚喜|大放|放送|大放送|,|你|還在|認為|登錄| |Xperia| |lounge| |只能|參加|抽獎|、|或|贈|送|免費手|機主題|嗎|？|那|你|就|錯|了|~|現在|、|立刻|、|馬|上|、|很快|的|登入| |Xperia| |lounge|，|獲取|專屬|序號|，|就|可以|到|全家|兌換|美式|咖啡|一杯|。',\n", " 85),\n", " ('Narrator| |遊|戲|製|作|社|團| |宣布|將|製|作|音|樂|動|畫|《|戀華|系列|》|,|\\n|Narrator| |續|《|結戀|》|以及|姊妹|社| |Stroia| |為|希萌|創意|所|製|作|的|《|東津|萌米|》|在| |STEAM| |上|發布|後|，|宣布|將|推出|新作|《|戀華|系列|》|的|消息|。|主| |LOGO| |將與|首篇|主題|有所|關聯|【|以下|內容|為|廠商|提供|資料|原文|】|這次|的|作品|預計以|音樂動畫|的|方式|呈|...|\\n',\n", " 86),\n", " ('記憶|中|十大|零嘴| |回憶過|去| |變老|的|事實|不能|忘|~|,|\\n|說道|台灣|的|國民|零嘴|，|你|想到|的|是|什麼|呢|?|小時候|在|鄉|下|就是|去|柑|仔店|，|長|大之後去|超商',\n", " 87),\n", " ('《|漂流|教室|》|盒裝|典藏|典藏版|在|台|上市| |收|錄|楳|圖|一|雄|燙|印|簽|名|與|日本|文庫|版|沒|有|的| |181| |頁|內容|,|\\n|日本|恐怖|漫畫|大|師|楳|圖|一|雄|經|典|作品|《|漂流|教室|》|，|於| |1975| |年|獲得|小學館|漫畫賞|後|，|接連改|編成|小|說|、|電影|、|電視劇|、|獨立|影集|及|舞台|劇|，|在|台灣|發行|單行本|的| |21| |年|之|後|，|於|本月|在|台|推出|盒裝|典藏|典藏版|，|除|放大|開本|跟|磁|釦|收藏|書盒|外|，|更收|錄|楳|圖|一|雄|燙|印|簽|名|...|\\n',\n", " 88),\n", " ('小米|盒子|在|台|銷量|達| |4.5| |萬台|，|官方|將推限時|降價|優惠|,|小米|於| |2016| |年| |11| |月|正式|在|台灣|市場|開賣|小米|盒子|國際版|，|而|官方|宣布|小米|盒子|國際版|至今|在|台灣|的|銷售量|已|經達|到|了| |4.5| |萬台|。|為|了|慶祝|小米|盒子|國際版|的|熱賣|，|小米|台灣|官網|和| |PChome24h| | |...',\n", " 89),\n", " ('[|FD0A|]|[| |妓女|的|榮耀| |/| |我|賣身|但|不賣|尊嚴|(|港|)| |]|[', 90),\n", " ('性|慾|超|爆發|！|巨乳|癡|女|「|Julia|」|大玩特玩|男人|，|只要|有|錢|想|Julia|想|怎麼|玩|都行|！|,|\\n|這次|的|情節|是|把|Julia|塑造|造成|塑造成|一個|巨乳|癡|女|的|形象|，|欲求|不滿|的|人妻|，|仗|著|自己|有|錢|所以',\n", " 91),\n", " ('21|歲|正妹|「|美乳|員工|」|自願|下海|！|甜笑|說|「|想|跟|男|優來|一發|」|...|在|車上|就|不住|忍不住|了|！|,|\\n|文|／|薇|薇|WEI|\\n|\\n|想必|素人|系列|大家|看得|也|不少|！|但|今天|的|主題|讓|我|覺得|有點|小興奮|！|21|歲|的',\n", " 92),\n", " ('冰|與|火之歌|《|瓊恩雪諾|試鏡|影片|》|流出|？|總覺|得|好像|有|哪裡|不|對|勁|......|,|平常|這時候|《|冰|與|火之歌|：|權力|遊戲|》|都|不多|差不多|是|一季|播完|，|觀眾|開始|哀號|的|日子|了|，|不過|今年|由|於|配合|戲裡|季節|需要|，|劇組|選擇|延後開|拍|，|自然|也|比|平常|還要|晚上|映|，|已經來|到|倒數|第二|二季|第二季|的|第七|季|《|冰|與|火之歌|》|將於|7|月|17|...',\n", " 93),\n", " ('《|理想|身材|的|日本|女星|》|男生|喜歡|的|身材|果然|還是|要|大|胸部|？|,|男生|跟|女生|看|事情|的|角度|可以|說|完全|不一|不一樣|，|以|《|理想|身材|的|日本|女星|》|為例|，|女孩|孩子|女孩子|在乎|的|可能|是|比例|、|曲線|跟|健康|健康美|，|但|若|換成|男孩|孩子|男孩子|的|視點|，|也許|就|變成|漂亮|、|性感|加大|奶|了|，|今天|這篇|分享|給|大家|的|網友票|選|，|就是|由|男性|...',\n", " 94),\n", " ('《|王者|榮耀|》|在|中國將|限制| |12| |歲|以下|玩家|晚上| |9| |時以|後|不能|玩|,|\\n|根據|中國|中央|電視台|報導|，|騰訊|旗下|手機|遊戲|《|王者|榮耀|》|將於| |7| |月| |18| |日起|禁止|未滿| |12| |歲|以下|玩家|，|於|晚上| |9| |時|以後玩|該款|遊戲|。|\\xa0|《|王者|榮耀|》|日前|遭到|中國|官方|媒體|批判|其|對|社會|的|負面|影響|如|孩童|遊玩|時間|過長|、|影響|未成|成年|未成年|成年人|未成年人|歷史觀|等|...|\\n',\n", " 95),\n", " ('《|LINE| |TOUCH| |舞力|全開|》|推出|全新|版本|「|超級|寶貝|」|追加|多種|不同|功能|,|\\n|LINE| |GAME| |旗下|音樂節|奏|跳舞|遊戲|《|LINE| |TOUCH| |舞力|全開|》|於|今|（|11|）|日|宣布|，|將|推出|暑期|大|改版|「|超級|寶貝|」|。|官方|指出|，|本次|改版|最大|特色|將|推出|第二|波寶貝|計畫|，|進化|多種|全新|寶寶養|成功|能|，|包括|寶寶換裝|、|寶寶|翅膀|、|寶寶|打工|、|寶寶職業|...|\\n',\n", " 96),\n", " ('JKF|電音|泳池|VIP|派|對|！|今夏|狂歡|的|好|去|處|！|,|\\n|【|2017| |JKF| |VIP| |POOL| |PARTY|】|\\n|炎炎|酷暑|，|就是|屬|於|比基|基尼|比基尼|辣妹|的|日子|！|今年|夏天|今年夏天|最奢華',\n", " 97),\n", " ('《|加勒|加勒比|加勒比海|盜| |:| |戰爭|之潮|》|大型|更新| |追加|第二|二章|第二章| |「|幽冥|飛船|的|回歸| |」|與|傑克|船長|一同|冒險|,|\\n|JOYCITY| |今| |（|12|）|日宣|佈|旗下|營運|，|NDREAM| |開發|的|手機|戰爭|策略|遊戲|《|加勒|加勒比|加勒比海|盜| |:| |戰爭|之潮| |(|PiratesoftheCaribbean|:|TidesofWar|)|》|進行|大規模|改版|，|於|此次|更新|中|追加|了|第二|二章|第二章|的|劇情|故事|以及|新增|了許|...|\\n',\n", " 98),\n", " ('把妹|把|到|老|闆|女兒|！|貼心|拌|飯|「|巨乳|爆出|」|\\u3000|超兇|脾氣|太|可口|：|想|吃|妳|,|\\n|南港|7|條蛇|／|金|x5|文|\\n|\\n|最近|不|知道|為|什麼|，|海中|腦海中|不斷|浮現|「|拉|麵|」|兩字|，|大概|是|新聞|瘋',\n", " 99),\n", " ('《|少年|三國志|》|將進行|縱橫|天下|改版| |蚩尤|、|青女|來|襲助陣|,|\\n|由|艾肯|娛樂|所|代理|的|手機|遊戲|《|少年|三國志|》|，|營運團隊|今|（|11|）|日|表示|，|將釋|出|最新|改版|內容|，|包含|：|將| |SLG| |策略|元素|與|卡牌|相結合|的|不同|玩法|【|縱橫|天下|】|以及|兩|隻|金寵|【|武神| |‧| |蚩尤|】|與|【|雪神| |‧| |青|女|】|的|駕到|。|\\xa0|《|少年|三國志|》|全新|玩法|...|\\n',\n", " 100),\n", " ('西尾維新|《|十二|十二大|戰|》|電視動畫|主要|角色|草稿|與|設定畫|、|參演聲|優情|報公開|,|\\n|由西尾維新|所著|、|中村|中村光|擔綱|封面|繪|製|的|小|說|《|十二|十二大|戰|》|將|推出|電視動畫|，|官方|也|於|近日|陸續|發表|了|動畫|的|主要|角色|設定|圖|、|聲優|等|情報|。|\\xa0|\\xa0|《|十二|十二大|戰|》|描述| |12| |位背|負著|十二|生肖|十二生肖|之名|的|戰士|，|參與|了|一場|「|只要|獲勝|就|能|實現|一個|願望|」|的|...|\\n',\n", " 101),\n", " ('我|老婆|《|IU|從|女孩|到|女人|》|年紀|跟|身材|一起|長|大|惹|,|IU|從|八年|前出|道|之|後|，|給|大家|的|印象|就是|國民|妹妹|的|印象|，|但是|一直|到|近|兩年|，|跟|越來|越|多|歌手|合作|之|後|，|漸漸|的|叛逆|(|?|)|的|一面|也|顯現|出來|惹|，|所以|《|IU|從|女孩|到|女人|》|的|過程|，|讓|倫倫有種|~|阿|~|這|女孩|長|大|的|感覺|了|阿|...',\n", " 102),\n", " ('羅|技準備|以| |8|,|500| |萬|美元|買下|遊戲|耳|機|製|造|商| |Astro| |Gaming|,|未來|似乎|會成|為羅技|旗下|遊戲|主機|耳機|週邊|的|品牌|。',\n", " 103),\n", " ('菜乃花|PO|「|日光|日光浴|曬|奶照|」|，|夾腿|坐姿|好|邪|惡|，|I|級|美乳|快要|看光|！|,|\\n|文|／|雪倫|小姐|\\n|\\n|日本|寫|真|偶像|「|菜乃花|」|擁有|I|罩杯|的|巨乳|，|名號|相當響|亮|，|是|相當受|歡迎',\n", " 104),\n", " ('格鬥手|機遊戲|《|天武覺|醒|》|Android| |不|刪檔|封測|今日|啟動| |體驗|橫掃|戰場|的|快感|,|\\n|由|成都|凱瑞互|娛研發|、|咪|兔|數位|代理|營運|的|格鬥手|機遊戲|《|天武覺|醒|》|，|今|（|11|）|日|正式|啟動| |Android| |不|刪檔|封測|。|官方|表示|，|玩家|可以|立即|下載|遊戲|，|召喚|三國|傳奇|武將|、|自組|三人小隊|輪番|上陣|，|體驗|橫掃|戰場|的|格鬥|快感|。|\\xa0|\\xa0|【|以下|...|\\n',\n", " 105),\n", " ('[|7FE1|]| |[|他們|的|美好|時光| |Their| |Finest| |]| |[', 106),\n", " ('不|小心|喝醉|！|私立|學校|「|美乳|高材|高材生|」|震個|不停|！|初次|「|體驗|拍|攝|」|畫面|好|刺激|喔|…|,|\\n|又|到|了|主廚|萬惡|的|AV|時間|了|，|今天|要來|跟|各位|分享|的|是|我|個|人|認為|還蠻|新奇|的|片子|！|相信',\n", " 107),\n", " ('《|新楓|之谷|》|12| |週年|重啟|「|地球|防衛|總部|」|副本|並加|開新|劇情|地圖|,|\\n|遊戲|橘子|旗下|《|新楓|之谷|》|邁入| |12| |週年|，|並|於|即日|即日起|重啟|台港|港澳|台港澳|地區|玩家|共同|的|回憶|副本|「|地球|防衛|總部|」|，|並加開|全新|劇情|地圖|，|邀|玩家|協助|「|楓葉|戰士|」|潛|入邪|惡|外星|外星人|「|葛雷|博士|」|的| |UFO|、|奪回|光線|槍|，|拯救|「|地球|防衛|總部|」|危機|，|並|找回|初心|的|冒險|旅程|...|\\n',\n", " 108),\n", " ('一堆|裝置|要|充電|？|FlePow| |5| |PORT| |充電站|提供|良好|充電|環境|,|每個|台灣|人|平均|擁有| |2.7| |個|行動|裝置|，|每個|人|的|辦公|桌上|總有|個|地方|擺放著|平板|、|手機|，|或是|筆電|正在|充電|。|許多種|不同|規格|的|充電線|線|要|如何|整理|，|才能|營造|出|一個|好|的|充電|環境|，|一直|是|每個|人| |...',\n", " 109),\n", " ('身價|高貴|，|LV| |推| |Tambour| |Horizon| |精品|智慧|錶|,|日前|知名|的|時尚|精品|品牌| |Louis| |Vuitton| |(|以下|一律|簡稱|為| |LV|)| |也|正式|推出|旗下|首款| |Android| |Wear| |智慧|錶|：|Tambour| |Horizon|，|並且|已經|開始|在| |LV| |的|官網|販售|，|入手|價從|\\xa0|2|,|450|\\xa0|美元|起跳|，|折| |...',\n", " 110),\n", " ('【|試片|】|這個|天竺|還真遠|《|最遊記| |RELOAD| |BLAST|》|睽|違| |13| |年動畫|本傳|再|出發|,|\\n|《|最遊記|》|是|日本|漫畫家| |峰倉|和|也|於|一|迅社|從| |1997| |年|開始|連載|的|漫畫|作品|，|故事|改編|套用|自我|們|所|熟悉|的|神怪|小說|《|西遊記|》|敘述|三藏|法師|一行|受到|觀音|的|委|託|前往|西天|取經|，|途中|遭遇|妖魔|的|故事|。|\\xa0|主要|大綱|與|出場|人物|基本|本相|相同|基本相同|，|但|本作|與|原作|不同|...|\\n',\n", " 111),\n", " ('《|星之後裔|》|新增|「|蒼穹|的|掠|奪者|－|赫雷斯|」|組隊|副本|及寶石|轉移|系統|,|\\n|韓國|遊戲|廠商| |Gamevil| |發行|的|《|星之後裔|》|（|iOS| |/| |Android|）|將於| |今|（|12|）|日|進行|更新|，|這次|開放|了|新|的|組隊|副本|以及|寶石|轉移|系統|，|以下|為|本次|改版|內容|介紹|。|\\xa0|4|-|4| |組隊|副本| |-| |赫雷斯|\\xa0|【|以下|內容|為|廠|商提|...|\\n',\n", " 112),\n", " ('KTV|包廂|驚見|「|白皙|長|腿|妹|」|！|換上|泳衣|後|的|「|雪色|巨乳|」|太兇狠|了|啦|！|,|\\n|文|／|薇|薇|WEI|\\n|\\n|你們|有|沒|有|想過|其實|KTV|也|是|個|正妹|聚集|聚集地|？|許多|妹子|把|唱歌|當成|一項|約|會還',\n", " 113),\n", " ('【|模型|】|FREEing|《|Re|：|從|零開始|的|異|世界|生活|》|雷姆|、|拉姆|泳裝| |Ver|.| |12| |月|預定|推出|,|\\n|《|Re|:|從|零開始|的|異|世界|生活|》|是|長|月|達|所作|平所作|的|日本|輕小說|作品|，|簡稱|《|Re|:|Zero|》|。|電視動畫|於|2016|年|4|月|3|日起|東京|電視台|、|AT|-|X|與|臺|灣的|巴哈|哈姆|巴哈姆|特動畫|瘋|等|於|每週|的|星期|星期一|在|電視|台上|播放|。|因|作品|動畫化|而|人氣|急升|，|在|2016|年|9|月|17|日成|為|...|\\n',\n", " 114),\n", " ('【|新訊|】|Nikon| |正式|發表| |AF|-|P| |NIKKOR| |70|-|300mm| |f|/|4.5|-|5.6|E| |ED|...|,|稍早|Nikon|在|日本|官網|正式|發表|「|AF|-|P| |NIKKOR| |70|-|300mm| |f|/|4.5|-|5.6|E| |ED| |VR|」|中望|遠變|焦鏡頭|，|採用步|進式|對|焦馬達|，|可以|實現|高速|...',\n", " 115),\n", " ('搭訕|一臉|正經|身體|卻|異常|飢渴|「|E|奶|接待|員|」| |喜歡|嘗試|高難度|體位|…|哪|都|能|做|,|\\n|文|\\\\|大星|派大星|\\n|\\n|有|一陣子|愛迪達|廣告|標語|「|Impossible| |is| |Nothing|」|(|沒有|不|可能|)|超紅|的|，|該',\n", " 116),\n", " ('大陸|工信部|：|沒說|要|禁個|人| |VPN|，|只是|要|規範無許|可|服務商|,|但是|，|很多|大陸|官方|機構|開始|否認|的|東西|，|最後結果|都|跟|傳聞|一樣|啊|…',\n", " 117),\n", " ('處|男|哥哥|在|風俗|店|遇到|親妹|，|直接|被|撲|上|...|過程|好|刺激|喔|！|,|\\n|文|／|雪倫|小姐|\\n|\\n|人氣|AV|女優|湊莉久|，|曾|獲得|「|最優|秀女|優賞|」|，|表現|相當|優秀|，|而|她|在|8|月',\n", " 118),\n", " ('分手|後|反目|，|Vizio| |向樂視索|賠| |1.1| |億|美元|,|對現|在|的|樂視來|說|真的|是|雪上|雪上加霜|。', 119),\n", " ('猛|！|「|G|奶|女星|」|上|節目|穿|鏤|空衣|…|側看|乳峰|險|曝光|\\u3000|來賓|：|腦袋|壞|了|？|,|\\n|【|文|/|傲嬌|女王|】|\\n| |\\n|日本|人氣|寫|真|女星|《|森咲智美|》|相信|不用|女王|多|做|介紹|，|各位|也|早已|因',\n", " 120),\n", " ('《|夢幻|誅|仙手|機版|》|改版|新|增加|「|子女|養育|系統|」|可協助|玩家|一同|戰鬥|,|\\n|由| |Efun| |遊戲|平台|所|代理|營運|的|手機|遊戲|《|夢幻|誅|仙手|機版|》|今|（|12|）|宣布|推出|改版|，|模擬|真實|人生|的|「|子女|養育|系統|」|從懷|胎生|子到|嬰兒|養育|，|再|到|子女|加入|戰鬥|等|一系|系列|一系列|功能|玩法|情報|，|供有|興趣|的|玩家|參考|。|\\xa0|《|夢幻|誅|仙手|機版|》|子女|宣傳|主視覺|【|...|\\n',\n", " 121),\n", " ('【|試片|】|《|單蠢|女孩|》|香蕉|的|美味| |足以|讓|少女|美少女|滿腦|只|剩|香蕉|,|\\n|《|單蠢|女孩|（|ア|ホ|ガ|ー|ル|）|》|的|原作|，|是|由|《|漫畫家|與|助手|》|作者| |ヒ|ロ|ユ|キ|所|繪|製|的|搞笑|漫畫|。|敘述|一名|空前|絕後的|笨蛋|少女|花畑佳子|，|將以|青梅|竹馬|的|資優生|阿久津明|為|首|的|友人|們|不斷|捲|入|各種|麻煩|的|搞笑|故事|。|\\xa0|《|單蠢|女孩|》|的|監督|，|是|曾|執導|《|魔法|少女|...|\\n',\n", " 122),\n", " ('【|採訪|】|家|的|色彩|任|你|調|！| |Dulux| |得利|塗料|首推|VR| |體驗|塗刷樂趣|,|有裝|修經驗|的|人|應該|都|會|同意|色彩|絕對|是|左右|居家|氣氛|與|風格|的|一大|要素|，| |以豐富|電腦調|色漆|滿足|屋主|對|居家|色彩|需求|的|Dulux| |得利|塗料|今年|正好|是|推出|十周|周年|十周年|，|特地|選在|...',\n", " 123),\n", " ('簡化|介面|、|導入|聲|控機|能|，|Spotify| |測試|駕駛|模式|,|相信|不少|人|都|喜歡|一邊|開車|一邊|聽歌|，|而|有|的|人|選擇|聽|廣播|、|有|的|人愛聽|自己|準備|的| |CD|，|也|有|不少|人會|選擇|有|龐大|歌曲|庫|的| |Spotify|。|但| |Spotify| |可能|不會|首歌|每首歌|你|都|愛聽| |(|除非|你|用心|製|作|了|歌| |...',\n", " 124),\n", " ('《|我|的|世界|》|預付|卡|支付|正式|登台| |提供|多元|支付|選擇|,|\\n|台灣|微軟|宣布|，|全球|熱賣|超過| |1| |億| |2000| |萬套|、|月|活躍|玩家|超過| |5500| |萬名|的|模擬|沙盒|遊戲|《|我|的|世界|》|將|即日|即日起|自即日起|在|全台|指定|通路|推出|專屬|預付|卡|，|零售|價新|台幣| |800| |元|，|提供|玩家|除了|信用|信用卡|外|，|更|多元|便利|的|官方|支付|管道|。|\\xa0|&|nb|...|\\n',\n", " 125),\n", " ('Google| |設立|創投|公司|扶持| |AI| |新|創團隊|,|Gradient| |Ventures| |要|幫|初涉| |AI| |領域|的|小|公司|開一個|好頭|。',\n", " 126),\n", " ('[|開箱|]| |5mm|超薄|邊框|『|技嘉|AERO| |15|』|文書|兼|打|Game|兩用|沒|問題|，|開學|挑|筆電|就|~|是|~|你|!|,|07|/|11|/|2017',\n", " 127),\n", " ('[|快訊|]| |LINE7|.|7.0| |動態|消息|『|新|介面|』|小|更新|，|趁機|關心|一下|平常|很少|使用|的|Line|動態|消息|牆|,|07|/|12|/|2017',\n", " 128),\n", " ('【|試片|】|《|戀愛與|謊言|》|在|禁止|戀愛的|世界|中|追|尋戀情|,|\\n|《|戀愛與|謊言|（|恋|と|嘘|）|》|原作|是|由| |ム|サ|ヲ|所|繪|製|的|同名|漫畫|。|敘述|在|未來|的|日本|，|為|了|對|應|逐年|惡化|的|少子|化環境|，|政府|推出|了|超|少子|化對策|基本|本法|基本法|，|通稱|「|緣法|」|，|禁止| |16| |歲|以上|少年|少女|的|自由|戀愛|，|並透過|國民|遺傳|情報|決定|最|優秀|的|結婚|伴侶|。|一名|即將|年|滿|...|\\n',\n", " 129),\n", " ('Ericsson| |預測| |LTE| |成主|流通|訊|連網|技術|，|5G| |將以|更|快|速度|普及|,|根據| |Ericsson| |新一波|行動|趨勢|報告|預測|，|行動|網路|從| |2018| |年|開始|將會|以| |LTE| |技術|為|主要|基礎|，|同時|在| |2020| |年|的|行動|網路|服務用|戶人|數將|新增| |26| |億|，|平均|每天|增加|超過| |100| |萬名|，|而|透|過行| |...',\n", " 130),\n", " ('[|E0DE|]|[|三個|老槍手| |Going| |in| |Style| |]|[', 131),\n", " ('從手|機遊戲|中|體驗線|上|版本|的|精|隨| |《|御龍|在|天|》|遊|戲|製|作|人|獨|家|分享|遊戲|開發|歷程|與|理念|,|\\n|承襲|並延續|了|線|上|遊戲|國戰|玩法|的|手機|遊戲|《|御龍|在|天|》|即將|在|台灣|正式|推出|，|而|巴哈|哈姆|巴哈姆|特| |GNN| |獨家|專訪遊|戲|製|作|人丁|曉成並|透露|，|玩家|將|可以|在|《|御龍|在|天|》|手機|遊戲|中體驗|到|線|上|遊戲|的|精|隨|。|\\xa0|以三國|為|題材|的|手機|遊戲|《|御龍|在|天|》|是|騰訊|遊戲|旗下|天|...|\\n',\n", " 132),\n", " ('《|BFB| |Champions| |2.0|》| |將與|「|熱血|系列|」| |進行|合作| |兩大|主角|正式|參戰|,|\\n|足球|手機|遊戲|《|BFB| |Champions| |2.0|》|於|今|（|11|）|日|宣布|，|將與|《|熱血|足球|》|系列|首度|進行|合作|，|當中|兩|大人|人物|大人物|國|夫君|（|港譯|：|國雄|）|、|亞力|君|（|港譯|：|阿力|）|，|會以| |3D| |球員|形式|登場|，|還有|一系|系列|一系列|主題|活動|，|有興趣|的|玩家|不妨|持續|關注|相關|報導|。|...|\\n',\n", " 133),\n", " ('女神|回歸|！|絕代|少女|美少女|「|麻倉|憂|」|重返|AV|界| |首支|作品|就|參加|轟|趴|被|「|大鍋|炒|」|！|,|\\n|前|幾天|白兔|小白兔|說過|了|，|少女|美少女|界|的|一代|傳奇|「|麻倉|憂|」|睽|違將|近|一年|再度|回歸|發片|，|不好',\n", " 134),\n", " ('傳奇|網路|手機|新作|《|星界|：|王冠|》|於|今日|展開|事前|登錄| |釋出|多項|特色|系統|介紹|,|\\n|傳奇|網路|於|今|（|12|）|日|宣布|，|旗下|最新|手機|遊戲|《|星界|：|王冠|》|事前|登錄|活動|正式|展開|，|只要|前往|活動頁|面|完成|事前|登錄|，|即可|在|遊戲|正式|上市|時|獲得|贈禮|。|\\xa0|製|作|團|隊|表示|，|《|星界|：|王冠|》|是|一款|著|重國家|之間|戰爭|的|角色|扮演|角色扮演|遊戲|，|玩家|們|身處|在|不同|王國|之|...|\\n',\n", " 135),\n", " ('即時|戰略手|機遊戲|《|空城|計|》|今日|預約|開跑| |隨心|所欲|的|攻城|攻城掠地|,|\\n|松崗|集團| |Ucube| |旗下|即時|戰略手|機遊戲|《|空城|計|》|今日|啟動|預|註|冊|活|動|，|官方|表示|將|提供|「|金卡|」|級|以上|三國|武將|卡牌|，|贈送給|玩家|，|並|希望|於|正式|版本|上市|時|，|能|讓|玩家|暢快|遊玩|。|\\xa0|\\xa0|【|以下|內容|為|廠商|提供|資料|原文|】|\\xa0|《|空城|...|\\n',\n", " 136),\n", " ('【|試玩|】|獨立|研發|團隊|打造|《|For| |The| |King|》|描繪|三個|冒險者|的|故事|,|\\n|▲|遊戲|內建|中文|語言|，|在|哪裡換|中文|卻|是|每|一位|朋友|最先|苦惱|的|問題|For| |The| |King|是|一款|戰鬥|與|探索|節奏|都|非常|快速|的|遊戲|玩家|要|選擇|三名|角色|與|他們|的|職業並|投身|一個|隨機|產生|、|但|有|固定|主線|的|奇幻|世界|中|除了|自己|單機遊|玩|之外|，|也|可以|透過|線|上|合作|，|每人|...|\\n',\n", " 137),\n", " ('神|祕|少女|團體| |ClariS| |宣布|推出|「|illusion| |～|被|光芒|所擁|～|」|寫|真集|,|\\n|過去|演唱|多首|動畫|主題|曲|的|神|祕|少女|團體|\\xa0|ClariS|，|宣布|將於| |9| |月|推出|首本|寫|真集|「|illusion| |～|被|光芒|所擁|～|（|illusion| |～|ひ|か|り|に|包|ま|れ|て|～|）|」|的|消息|。|\\xa0|\\xa0|ClariS| |是|由| |2| |名|少女|所|組成|的|...|\\n',\n", " 138),\n", " ('「|煎|餃妹|」|夢幻|童顏|讓|人戀|愛|，|臉書|「|解放|巨乳|照|」|好|殺|，|乳溝|快要|蹦出|來|了|！|,|\\n|文|／|雪倫|小姐|\\n|\\n|「|Misa|煎|餃|」|是|近期|人氣|急速|竄|升|的|網路|正妹|，|長|相|甜美|可愛|，|擁有|一雙',\n", " 139),\n", " (' | |卡牌|對|戰遊戲|《|闇|影詩章| |Shadowverse|》|1000| |萬下載|突破|紀念|活動|正式|開跑|,|\\n|由|日本| |Cygames| |所|推出|的|卡牌|對|戰遊戲|《|闇|影詩章| |Shadowverse|》|，|下載數|正式|突破| |1000| |萬|人次|。|官方|表示|，|為|感謝|玩家|支持|，|《|闇|影詩章| |Shadowverse|》|於| |7| |月| |10| |日|開始|舉辦| |1000| |萬下載|突破|紀念|特別|登入|...|\\n',\n", " 140),\n", " ('《|SD| |鋼彈| |G| |世代| |革命|》|於|台港|港澳|台港澳|上架|！|海外|海外版|製|作|人|分享|遊戲|特色|所在|,|\\n|萬代南夢宮|娛樂|股份|有限|公司|有限公司|宣布|，|「|G| |世代|」|系列|最新|智慧|智慧型|手機|遊戲|應用|程式|《|SD| |鋼彈| |G| |世代| |革命|》|已自今|（|12|）|日起|於| |App| |Store|／|Google| |Play| |開|放下|載|。|為|了|紀念|遊戲|上架|・|事前|登錄數|突破| |20| |萬件|，|現在|正|於|遊戲|中|...|\\n',\n", " 141),\n", " ('大|尺度|正妹|Coser|《|ゆ|と|り|》|變身|7|月|新番|巨乳|人馬|少女|★|現身|秋葉原|,|充滿人|外娘|的|夏季|新番|動畫|《|半獸|人|的|煩惱|》|（|セ|ン|ト|ー|ル|の|悩|み|）|已經|在|7|月|8|日|正式|開播|囉|，|日本|七夕|在|秋葉|原舉|辦|宣傳|活動時|，|乍見|大|尺度|Coser|ゆ|と|り|變身|成動畫|中|的|主角|女主角|—|—|人馬|高中|中生|高中生|「|君原姫|乃|」|在|街頭|散步|...',\n", " 142),\n", " ('韓|製|手|機| |ARPG|《|Lost| |Kingdom| |-| |末日|終戰|》|於|雙系統|正式|上市|,|\\n|由|韓國|遊戲|廠商| |4|:|33| |研發|、|台灣|酷樂|代理|，|曾|獲得| |2016| |韓國手|機遊戲|優秀賞|的| |3D| |動作手|機遊戲|《|Lost| |Kingdom| |-| |末日|終戰|》|今|（|11|）|日|於| |Android| |與| |iOS| |雙系統|正式|上市|。|根據|官方|介紹|，|本|作主|打|精|緻|的|物件|質|...|\\n',\n", " 143),\n", " ('【|模型|】|GSC|《|鬥陣|特攻|》|慈悲| |黏土|人| |預定| |2018| |年| |1| |月|發售|,|\\n|日本|模型|廠商| |Good| |Smile| |Company| |今|（|12|）|日|宣布|，|繼|先前|的|閃光|以及|小美|後|，|將要|推出| |Blizzard| |Entertainment|®| |旗下|遊戲|《|鬥陣|特攻|》|第| |3| |隻|黏土|人|「|慈悲| |經典|造型|版|」|的|消息|。|\\xa0|「|英|...|\\n',\n", " 144),\n", " ('《|星辰|訣|》|正式|於雙|平台|上線| |帶領|玩家|體驗|夢幻|情緣|,|\\n|【|以下|內容|為|廠商|提供|資料|原文|】|\\xa0|由| |Yahgame| |平台|代理|營運|的|電影級|夢幻|情緣|手遊|《|星辰|訣|》|於|今|（|11|）|日|正式|上線|，|推出| |iOS| |與| |Android| |雙|平台|版本|。|遊戲|為|真實|重現|櫻花|樹下|仙侶|情緣|的|愛恨|情仇|歷程|，|加入|眾多|戰鬥|玩法|讓|玩|...|\\n',\n", " 145),\n", " ('動畫|《|馭|刀|巫女|》|公開|主視覺|圖及|新增| |7| |名|角色|設定|,|\\n|由|し|ず|ま|よ|し|の|り|擔任|角色|原案|與|動畫|公司| |Studio| |五組|宣布|將|推出|原|創動畫|作品|《|馭|刀|巫女|》|，|官方|公開|主視|覺圖|、|新增|七名|角色|設定將|自古|以來|威脅|人世|的|異形|存在|·|荒魂|，|以御|刀|祓|除|的|神|薙|之|巫女|。|以|制服|佩刀|為|主要|裝束|的|她|們|，|被|稱作|「|刀|使|」|。|正式|名稱|...|\\n',\n", " 146),\n", " ('《|RO| |仙境|傳說| |Online|》|改版|副本|介紹|,|\\n| | | | | | | | |《|RO|仙境|傳說|Oniline|》|在|去年|6|月|1|日時|，|回歸|原廠|韓商|格雷|維蒂|（|GRAVITY|）|直營|，|並在|7|月|4|日系|統維護|時|進行|『|幻色|童話|』|版本|更新|，|隨著|版本|更新|，|除了|許多|小|地方|有|做|調|整外|，|還有|開啟|一些|新|副本|與|一連串|的|任務給|玩家|體驗|，|...|\\n',\n", " 147),\n", " ('Faraday| |Future| |在|內華達|的|十億|美元|建廠|計畫|已|被|擱置|,|原因|當然|是|賈老|闆|不出|拿不出|錢|了|。',\n", " 148),\n", " ('【|模型|】|ThreeA|《|變形|金剛| |5|：|最終|騎士|》|OPTIMUS| |PRIME| |可動|模型| |明年| |2| |月|推出|,|\\n|出自|《|變形|金剛|》|系列|最新|作|《|變形|金剛| |5|：|最終|騎士|》|，|「|OPTIMUS| |PRIME|」|以全|高約| |48cm| |的|高品質|全可動|模型|登場|。|模型|將其以|騎士|為|形象|打造|的|姿態|，|透過|細節|部分|的|精密|造型|與|帶|有|舊化|效果|的|真實|感塗裝|重現|。|全身|可動|達| |99| |處|，|眼睛|...|\\n',\n", " 149),\n", " ('又|是|只有|天木|じ|ゅ|ん|才行|！|「|胸部|陀螺|」|轉|不停|！|巨乳|果然|可以|當|桌子|...|,|\\n|文|／|JUN|\\n|\\n|\\n|\\n|最愛跟|流行|的|天木|じ|ゅ|ん|（|天木純|）|，|每次|網|路上|開始|流行|什麼|，|她|就|會|（|帶',\n", " 150),\n", " ('被|揉|了|啦|！|F|罩杯|「|乳揉妹|」|整顆|快|掉|出來|！|白嫩|大湯圓|「|揉|乳畫面|」|形狀|好|犯規|...|,|\\n|在|日本|擁有|「|第一|美乳|」|稱號|的|寫|真|女星|森咲智美|，|近期|因為|在|IG|上|分享|大|尺度|影片|，|引',\n", " 151),\n", " ('《|境界|之詩| |Tactics|》|推出|長|篇|活動|「|反戰|的|餘暉|」|篇| |白朗|島巡|邏隊|軍官|菲斯|傑羅|加入|戰局|,|\\n|由希娜|科技|藝術|團隊|所|自|製|營|運|的|手|機|遊|戲|《|境界|之詩| |Tactics|》|（|iOS| |/| |Android|）|，|今|（|11|）|日|宣布|「|瀆神|魔法|再臨|」|長|篇|活動|第二|波|「|反戰|的|餘暉篇|」|開跑|，|來|到|深淵|大地|的|精靈|與|人類|將面臨|嚴峻|考驗|，|全新|五星|角色|「|白朗|島巡|邏隊|軍官|．|菲|...|\\n',\n", " 152),\n", " ('劇場|版動畫|《|魔法|少女|☆|伊|莉雅| |雪下|的|誓言|》|釋出|第三|波宣傳|影片|,|\\n|預計|於| |2017| |年| |8| |月| |26| |日|上映|的|劇場|版動畫|《|Fate|/|kaleid| |liner| |魔法|少女|☆|伊|莉雅| |雪下|的|誓言|》|（|日本|地區|）|，|官網|就|在| |10| |日|這天釋|出|了|第三|波宣傳|影片|。|在|這次|所公開|的|宣傳|影片|中|，|也|可以|首次|聽到|由| |ChouCho| |...|\\n',\n", " 153),\n", " ('微軟|正式|結束|對| |Windows| |Phone| |8.1| |的|支援|,|手機|還能|繼續|用|，|但|就|不會|再有|更新|了|。',\n", " 154),\n", " ('《|俠盜|獵車|手| |5|》|線上|模式|推出|古羅帝|經典|獵豹|、|全新|「|加時|大亂鬥|」|模式|及|最新|獎勵|活動|,|\\n|Rockstar| |Games| |宣布|，|營運中|的|《|俠盜|獵車|手| |5|》|線上|模式|現已|推出|超級|跑車|「|古羅帝|經典|獵豹|」|、|全新|「|加時|大亂鬥|」|模式|、|辦|公室|折扣|以及|最新|獎勵|活動|，|供線|上|玩家|使用|與|體驗|。|\\xa0|【|以下|內容|為|廠商|提供|資料|原文|】|\\xa0|GTA|...|\\n',\n", " 155),\n", " ('「|Jump|」|將|提供|獨立開|發遊戲|玩|到|飽|服務|,|用類|似| |Netflix| |的|月|費制|來|幫|你|發現|不|知名|的|小遊戲',\n", " 156),\n", " ('《|大行|敦克爾克大行動|》|評價|出爐| |影評給|正面|還是|負面|評價|呢|？|,|雖然|有諾蘭|編劇|與|執導|(|實際|上|他|還是|監|製|)|的|光環|，|但|除了|軍武迷|與|諾蘭粉|絲|，|似乎|《|大行|敦克爾克大行動|(|Dunkirk|)|》|的|預告|在|一般|觀眾|眼中|是|比|較|無感|的|。|「|敦克爾克大|撤退|」|這段|歷史|對|那些|對軍事|與|歷史|沒興趣|的|人|...',\n", " 157),\n", " ('《|天堂| |2|：|革命|》|團結|才能|取得|勝利| |血盟|副本|全新|首領|「|巨蟻|女王|」|來襲|,|\\n|網石|遊戲| |（|Netmarble| |Games|）|今|（|12|）|日|宣布|旗下| |MMORPG| |手機|遊戲|《|天堂| |2|：|革命|》|再迎|改版|，|不僅|提高|傲慢|之塔樓|層|，|更|推出| |136| |等級|的|野外|首領|、|野外|寶箱|和|全新|的|血盟|副本|。|未來|血盟|成員將|能|攜手|挑戰|難度|更|高|的| |BOSS|...|\\n',\n", " 158),\n", " ('《|劇場|版| |無敵|鐵金剛|》|將於|明年| |1| |月| |13| |日|在|日本|上映| |實體化|企劃|進行|中|,|\\n|以永井豪|經典|機器|人|作品|《|無敵|鐵金剛|》|為|原作|改編|，|最新|電|影版|《|劇場|版| |無敵|鐵金剛|（|暫稱|）|》|今日|正式|宣布|將於| |2018| |年| |1| |月| |13| |日|在|日本|上映|，|同時|也|公開|了|最新|的|主視|覺圖|。|\\xa0|\\xa0|《|劇場|版| |無敵|鐵金剛|》|將會|以|電視版| |10|...|\\n',\n", " 159),\n", " ('《|為|美好|的|世界|獻上|祝福|！| |-| |為強|欲|的|遊戲獻上|審判|！|-|》|公開|遊戲| |OP|,|\\n|由| |5pb|.| |預定|於|今年| |9| |月| |7| |日|發行|的| |PlayStation| |4| |/| |PlayStation| |Vita| |遊戲|《|為|美好|的|世界|獻上|祝福|！| |-| |為強|欲|的|遊戲獻上|審判|！|-|》|於|官網|公開|了|遊戲| |OP|。|\\xa0|\\xa0|\\xa0|此外|，|...|\\n',\n", " 160),\n", " ('就是|要|「|選奶|大|的|」|？|\\u3000|大|胸部|女生|好困|擾|…|常|被|男生|偷看|胸部|QQ|,|\\n|小|百合|/|文|\\n|\\n|最近|天氣|很|夏天|，|悶悶|熱熱|又|帶點|濕氣|，|有|時候|中午|和|同事|下樓|買|巴克|星巴克|，',\n", " 161),\n", " ('深藏|不露|深藏不露|！|台灣|嫩|奶正妹|海邊|比基|基尼|比基尼|大秀|「|渾圓白|乳|」|太|誘人|，|紅到|香港|去|！|,|\\n|文|／|薇|薇|WEI|\\n|\\n|平時|穿衣|衣服|穿衣服|包緊|緊|不出|看不出|來|，|一旦|到|了|海邊|就是|驗明|正身|的|時候|！|許多|妹子',\n", " 162),\n", " ('不及|等不及|！|「|嫩白|圓乳|」|中路|大開|畫面|讓|人|窒息|，|好想|將頭|「|一把|塞|」|享受|包覆|感|,|\\n|文|／|金真露|\\n|這名|韓國|妹子|수|지|，|可是|男性|雜誌|上常|出現|的|寫|真|模特|，|你|怎麼|能|錯過|她|～|胸',\n", " 163),\n", " ('【|好|硬|日報|】|長|榮短|髮|空姐|「|阿|C|」|、|新生|新生代|偶像|「|武田玲奈|」|、|無敵|顏值|「|Mamu| |Maeda|」|,|\\n|晚安|！|今天|是|7|月|11|日|，|不|只有|Seven|-|Eleven|真好|，|有|【|好|硬|日報|】|肯定|是|更好|！|今天|小白',\n", " 164),\n", " ('《|依露娜|戰紀|Online|》| |夏季|活動|及|創設|公會|活動|實施|中|,|\\n|由| |ASOBIMO|,|Inc|.| |所|製|作|的|多|人|線|上| |RPG| |手機|遊戲|「|依露娜|戰紀| |Online|」|，|已展|開期間|限定|的|「|夏季|活動|」|。|任何|使用|用者|使用者|均|可|至| |GooglePlay| |與| |AppStore| |免費|下載|遊玩|。|\\xa0|\\xa0|【|以下|內容|為|廠商|...|\\n',\n", " 165),\n", " ('《|封神|召喚師|》|今日|開放|事前|登錄| |台灣|原創| |2D| |動畫|同步|曝光|,|\\n|由遊戲|種子|娛樂|代理|的|卡牌|手機|遊戲|《|封神|召喚師|》|即將|在|台|上市|，|今日|率先|展開|事前|登錄|，|官方|同步|曝光|全盤|由|台灣團|隊|製|作|的|２|Ｄ|宣傳動畫|，|替|封神|故事|揭開|神秘|序章|。|\\xa0|\\xa0|【|以下|內容|為|廠商|提供|資料|原文|】|\\xa0|MIT| |動畫| |證明|台灣|...|\\n',\n", " 166),\n", " ('《|Pokemon| |GO|》|日本|人|平均|課金額|為|美國| |3| |倍|以上| |台灣|消費|排行|世界|第| |8|,|\\n|由| |Niantic| |所|出品|的|《|Pokemon| |GO|》|至今|為止|在|世界|全世界|的|營收|已|接近| |12| |億|美元|（|約|新|台幣| |366| |億元|）|，|專門|分析|智慧|智慧型|手機|應用|程式|市場|的|市調|公司| |Sensor| |Tower| |於|近日|公布|針對|《|Pokemon| |GO|》|的|調查|報告|，|...|\\n',\n", " 167),\n", " ('消息|指稱|中國|政府|將進|一步|限制|個|人|用戶|「|翻牆|」|上網|,|相關|消息|指出|，|中國|政府|預計|從|明年| |2| |月| |1| |日起將|要求|中國|移動|、|中國|電信|、|中國|聯通|三大電|信業者|禁止|個|人|用戶|透過| |VPN| |形式|連網|，|意味|日後要|在|中國境|內|上|網將|面臨|更|大內容|監管|及|限制|。',\n", " 168),\n", " ('[|快訊|]| |Amazon|也|有|血拼|日|！|美國|Amazon| |Prime| |Day|倒數|30|小時|啟動|,|07|/|11|/|2017',\n", " 169),\n", " ('劇場|版動畫|《|NO| |GAME| |NO| |LIFE| |遊戲|人生| |ZERO|》|同時|釋出|三支|電視|廣告|影片|,|\\n| | | | | | |預計|在| |2017| |年| |7| |月| |15| |日|上映|的|劇場|版動畫|《|NO| |GAME| |NO| |LIFE| |遊戲|人生| |ZERO|》|（|日本|地區|）|，|官方|就|在| |7| |月| |7| |日|這天|同時|公開|劇場|版|的|三支|電視|廣告|影片|。| | | | | | |這是|一個|禁止|一切|爭端|、|一切|都|由遊|...|\\n',\n", " 170),\n", " ('[|快訊|]| |摔不壞|的|Nokia| |3310|『|川普|ｘ|普丁|』|首次|見面|特別|版|...|兩人|堅韌|不|摧|的|情誼|要價|8|萬台幣|(|?|)|,|07|/|11|/|2017',\n", " 171),\n", " ('《|龍牙| |Dragon| |Fang| |》|事前|登錄|活動|即日|開跑| |共|闖|隨機|迷宮|,|\\n|由| |nxTomo| |Games| |營運|，|Toydea| |研發|的|隨機|迷宮|手機|遊戲|《|龍牙| |Dragon| |Fang|》|，|繁體|中文|文版|中文版|預定|於|七月|下旬|，|於|台港|港澳|台港澳|雙|平台|上線|，|遊戲|事前|登錄|活動|即日|開跑|。|\\xa0|\\xa0|【|以下|內容|為|廠商|提供|資料|原文|】|&|nbs|...|\\n',\n", " 172),\n", " ('超|反差|！|越樸素|的|妹子|反差|感越|大|，|未成|成年|未成年|不能|做|的|事情|成年|之|後|一次|做好|做滿|！|,|\\n|超|反差|妹子|就是|惹人|喜歡|！|看看|這樣|簡單|的|內衣|，|怎麼樣|也|不出|看不出|來會|穿|這種|款式|內衣',\n", " 173),\n", " ('Ubuntu| |已進|駐| |Windows| |Store|,|如果|是| |Insider| |的|話|可以|開|始|嚐|鮮|了|。',\n", " 174),\n", " ('《|凡爾賽|少女|美少女|》|Ruiiia|的|甜美|笑容|讓|人|融化|,|原汁|原味|原汁原味|的|內容|在|這裡| |話|說|從|《|這個|女孩|怎麼|這麼|可|愛|》|開始|，|阿漆|又|發現|了|不少|來|自大|陸|的|可|愛|美眉|，|其實|我|自己|是|很少|從|微博|找妹|的|，|但|隨著|IG|越來|越|普及|，|很多|妹子|開始|使用|之|後|往往|影響|的|層面|就|會|大|很多|，|今天|我們|...',\n", " 175)]" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#輸出文章與Label的Dictionary\n", "c = dict(zip(corpus,bestK.labels_))\n", "\n", "c = sorted(c.items(), key=lambda x:x[1])\n", "\n", "c" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "http://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html\n", "\t\n", "n_clusters : int, optional, default: 8\n", " 中心向量的數量\n", " \n", "max_iter : int, default: 300\n", " Maximum number of iterations of the k-means algorithm for a single run.\n", " \n", "n_init : int, default: 10\n", " Number of time the k-means algorithm will be run with different centroid seeds. The final results will be the best output of n_init consecutive runs in terms of inertia.\n", "\n", "tol : float, default: 1e-4\n", " Relative tolerance with regards to inertia to declare convergence\n", " \n", "n_jobs : int\n", " The number of jobs to use for the computation. This works by computing each of the n_init runs in parallel.\n", "If -1 all CPUs are used. If 1 is given, no parallel computing code is used at all, which is useful for debugging. For n_jobs below -1, (n_cpus + 1 + n_jobs) are used. Thus for n_jobs = -2, all CPUs but one are used.\n", "\n", "random_state : integer or numpy.RandomState, optional\n", "The generator used to initialize the centers. If an integer is given, it fixes the seed. Defaults to the global numpy random number generator.\n", "\n", "verbose : int, default 0\n", " Verbosity mode.\n", "copy_x : boolean, default True\n", " When pre-computing distances it is more numerically accurate to center the data first. If copy_x is True, then the original data is not modified. If False, the original data is modified, and put back before the function returns, but small numerical differences may be introduced by subtracting and then adding the data mean.\n", "\n", "Attributes:\t\n", "\n", "cluster_centers_ : array, [n_clusters, n_features]\n", " Coordinates of cluster centers\n", "labels_ : :\n", "Labels of each point\n", "inertia_ : float\n", " Sum of distances of samples to their closest cluster cente" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "{'algorithm': 'auto',\n", " 'batch_size': 100,\n", " 'cluster_centers_': array([[ 0. , 0. , 0. , ..., 0. , 0. ,\n", " 0. ],\n", " [ 0. , 0. , 0.1161942, ..., 0. , 0. ,\n", " 0. ],\n", " [ 0. , 0. , 0. , ..., 0. , 0. ,\n", " 0. ],\n", " ..., \n", " [ 0. , 0. , 0. , ..., 0. , 0. ,\n", " 0. ],\n", " [ 0. , 0. , 0. , ..., 0. , 0. ,\n", " 0. ],\n", " [ 0. , 0. , 0. , ..., 0. , 0. ,\n", " 0. ]]),\n", " 'compute_labels': True,\n", " 'copy_x': True,\n", " 'counts_': array([ 11, 15, 10, 23, 9, 413, 48, 56, 12, 30, 45, 30, 58,\n", " 34, 38, 36, 35, 29, 36, 27, 30, 19, 41, 16, 42, 32,\n", " 26, 20, 28, 41, 20, 19, 10, 18, 21, 13, 32, 12, 17,\n", " 21, 12, 14, 19, 39, 25, 15, 21, 21, 20, 18, 13, 11,\n", " 21, 18, 20, 9, 11, 20, 15, 14, 23, 15, 21, 17, 20,\n", " 17, 20, 20, 17, 15, 20, 20, 18, 23, 30, 16, 17, 10,\n", " 14, 23, 22, 21, 18, 17, 20, 17, 26, 13, 21, 14, 22,\n", " 13, 32, 10, 19, 15, 18, 11, 19, 18, 21, 16, 17, 21,\n", " 12, 19, 13, 12, 21, 20, 21, 22, 13, 27, 14, 13, 18,\n", " 22, 20, 15, 16, 15, 21, 20, 19, 16, 19, 16, 13, 19,\n", " 24, 16, 19, 16, 24, 15, 18, 18, 16, 24, 20, 16, 18,\n", " 17, 19, 11, 19, 17, 23, 21, 24, 21, 18, 21, 16, 12,\n", " 13, 15, 13, 11, 17, 8, 19, 23, 16, 29, 17, 24, 22,\n", " 23, 19, 19, 15, 18, 7, 15], dtype=int32),\n", " 'inertia_': 27.705490632637652,\n", " 'init': 'k-means++',\n", " 'init_size': None,\n", " 'init_size_': 220,\n", " 'labels_': array([123, 50, 5, 5, 79, 26, 150, 80, 142, 70, 63, 113, 30,\n", " 118, 107, 134, 64, 54, 139, 173, 99, 164, 66, 40, 175, 120,\n", " 87, 0, 5, 5, 74, 41, 67, 92, 163, 73, 102, 97, 5,\n", " 5, 162, 42, 46, 151, 161, 104, 116, 91, 94, 171, 5, 33,\n", " 84, 109, 68, 6, 16, 7, 16, 124, 7, 127, 169, 130, 56,\n", " 115, 148, 22, 18, 72, 174, 59, 53, 156, 48, 7, 69, 58,\n", " 12, 78, 38, 49, 168, 85, 57, 39, 22, 110, 154, 5, 5,\n", " 62, 89, 83, 18, 44, 37, 51, 24, 128, 81, 117, 12, 12,\n", " 126, 103, 23, 119, 5, 5, 5, 11, 157, 43, 43, 5, 5,\n", " 5, 131, 106, 21, 93, 5, 90, 10, 8, 140, 1, 5, 166,\n", " 5, 32, 10, 165, 122, 100, 20, 133, 20, 145, 28, 172, 170,\n", " 28, 153, 55, 61, 146, 5, 95, 96, 3, 88, 71, 129, 152,\n", " 9, 29, 159, 15, 86, 160, 45, 132, 143, 29, 5, 36, 34,\n", " 60, 15, 9, 5, 114, 121, 137, 17, 5, 149, 105, 147, 47,\n", " 35, 25, 101, 75, 136, 65, 4, 13, 31, 125, 52, 144, 76,\n", " 112, 5, 158, 111, 167, 14, 135, 5, 5, 141, 138, 14, 98,\n", " 108, 19, 5, 24, 27, 155, 25, 36, 82, 2, 13, 77], dtype=int32),\n", " 'max_iter': 10000,\n", " 'max_no_improvement': 10,\n", " 'n_clusters': 176,\n", " 'n_init': 10,\n", " 'n_iter_': 37,\n", " 'n_jobs': 1,\n", " 'precompute_distances': 'auto',\n", " 'random_state': None,\n", " 'reassignment_ratio': 0.01,\n", " 'tol': 0.0,\n", " 'verbose': False}" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "bestK.__dict__" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191]\n", "bestK Score: 1.2212453270876722e-15\n" ] } ], "source": [ "from sklearn.cluster import MiniBatchKMeans\n", "\n", "print([x for x in range(0,len(corpus))])\n", "\n", "kmList = [MiniBatchKMeans(n_clusters= k, init='k-means++', max_iter=10000, n_init=10, verbose=False) for k in range(1,len(corpus))]\n", "\n", "for k in kmList:\n", " k.fit(X)\n", "\n", "kmDict = {k:k.score(X) for k in kmList}\n", "\n", "bestK = max([k for k,v in kmDict.items()], key = lambda key:kmDict[key])\n", "print('bestK Score:',kmDict[bestK])\n", "\n" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.0" } }, "nbformat": 4, "nbformat_minor": 2 }

apache-2.0

dynaryu/rmtk

rmtk/vulnerability/derivation_fragility/batch_facility.ipynb

3

133075

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# RMTK batch facility\n", "\n", "The risk Modellers toolkit Batch Facility allows users to derive fragility models for a set of building classes sequenteally. This way, it is not necessary to run each building class at the time, but simply to provide the location of the folder containing the necessary ground motion records and capacity curves.<br />\n", "\n", "In the following figure, a set of fragility models calculated using the N2 method and the batch facility are presented: \n", "<img src=\"../../../figures/fragility_batch.png\" height=\"400\" width=\"800\" align=\"middle\">\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##Load capacity curves\n", "\n", "In order to use this methodology, it is necessary to provide one (or a group) of capacity curves, defined according to the format established on the [RMTK manual](../../../../../rmtk-docs.pdf). Please provide the location of the folder containing this input using the parameter input_folder." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Models loaded:\n", "Masonry\n", "RC\n", "Steel\n" ] } ], "source": [ "from rmtk.vulnerability.common import utils\n", "%matplotlib inline \n", "files_folder = '../../../../rmtk_data/portfolio_Italy'\n", "models = utils.read_set_models(files_folder)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Load ground motion records\n", "\n", "The demand in this methodology is input by a set of ground motion records, which should follow the format established in the [RMTK manual](../../../../../rmtk-docs.pdf). Please provide the location of this folder using the parameter gmrs_folder. It is also possible to plot the corresponding response spectra (displacement, pseudo-acceleration, and Sa-Sd), for which the minimum and maximum periods of interest (minT and maxT) should also be provided." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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YjdKyKJyybVqf7Jm/Xme/ie01K+jBudMaadkqYXcv0Wx9Lctt3hblTAohWkRD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R2Sf89Y3UvdlbVWOPQrFrNQnYVOQ/x2lVXDa5bHKcpiMie4vIa1Kr5mAuzJFTU/M7wvL1wNmp\nYxwYFm/AvAji+mlhcSA1niufbbD3f23oGSsa8TGwCJgtInuG3+8vtJGIvFpV71HVHGa42TX89TYR\n2TZ4GLwbM+Tk1+dFVR0SkWMx13fF3NNPjgqiiGwbti92LbKwCJghIoeHfceLyH4l9lmLuY8XYiFm\ncKqEvRnpueCMgit2rcV1jHwJ58TFUfa5DbPiICJ7Y1b0RzHf5b8O61+P+TAjItthXd5XAV/Fxrbk\ncwfwvrB8WjhWKW4Nx/o4iXtEPrtFIRHqdluR7aYCL4TBuB8gaQitwwRbGiURpq8O6z4Y6pOVEQIn\nCN0FqvpvWK/FPti9+UhwY0BEdhGRGWwpUNOWt30wlwLHaXdcNhkumxyncUwFfiwiC4L74WuxsVmR\nbcP6s4C/DevOBg4RCySyAPibsP6fw/bzRWQuNt4W4PvAPBG5lLxATqr6EDbG71HM9ftPo1U2uFB+\nArg2uG8uTx0vfewYJGQ+cLuqzgv/3QP8AhsXe6WqPpDal1CHQ4LHwAcx+YCqPgL8C3BrOLf/KHEt\n0sfMXw6H1AHgr4BzwzEfJHF33eLUw/xmYL9CwVOC18arRGRq1joABIV6k6q+WKRsJx9tgYF+3TgB\nawusmwR8D3NNephk0O2IAamYy868sDwRc5WKAQqOTh3rp1gwgF8Ad2KNmwMwd504AP8dBeqxGxYM\nIfpx71qoHgX2Ox+z2KSDFzzFyAAFl5IEKJiU3ia1z5lh3d3AecCPwvojgAWh/ntiY1feE/47jiRA\nwQ+A8fnHxnz1/1Ck7vOAqWH5PKzR8xAmSOOxzg7bzcMsaXuE9R8K28+NdQ3r7ycMvPbJp3aZXDa5\nbPLJp1afsOAp05tdjxqezxnA+c2uR53P8XPYuORy9vlb4MPNrns7TRIunON0NSLyOSzy1A9rdLw5\nWFS/00tu7DiOUwSXTY6zJSLyFHCIhjGk7U4ICnOwqp5dcuM2JQTPOllVLytjnzOAS9XGEzsZcMXO\ncahM4JQ43luBx1T1mVocz3Gc7sRlk+M4jpMVV+wcGHgmEwAAAFFJREFUx3Ecx3Ecx3HaHA+e4jiO\n4ziO4ziO0+a4Yuc4juM4juM4jtPmuGLnOI7jOI7jOI7T5rhi5ziO4ziO4ziO0+a4Yuc4juM4juM4\njtPm/D8PirUWkwU6gQAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x10ac64c50>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "gmrs_folder = '../../../../rmtk_data/accelerograms'\n", "gmrs = utils.read_gmrs(gmrs_folder)\n", "minT = 0.1\n", "maxT = 2\n", "utils.plot_response_spectra(gmrs,minT,maxT)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Masonry.csv\n" ] }, { "data": { "image/png": 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gGER6DYFNJcO/0HxRgnYjlf8+FVgWuBnIypUoCILBpq6SkPR9MztC0m+7OWxmtl8T5Qra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\nguoS2qpcn8CbRBXrF/WHRpR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/Abvxh5fpKLkY35VYU44kJElNjiQkSU12EpKk\nJjsJSVKTnYQkqclOQpLUZCchSWr6BapPLOAkfNvzAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x10ae7da10>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "5%\n", "10%\n", "15%\n", "20%\n", "25%\n", "30%\n", "35%\n", "40%\n", "45%\n", "50%\n", "55%\n", "60%\n", "65%\n", "70%\n", "75%\n", "80%\n", "85%\n", "90%\n", "95%\n", "100%\n" ] }, { "ename": "TypeError", "evalue": "plot_fragility_model() takes exactly 4 arguments (3 given)", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-3-6c23ee788a2a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 10\u001b[0m \u001b[0mPDM\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mSds\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mN2Method\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcalculate_fragility\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcapacity_curves\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mgmrs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mdamage_model\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mdamping\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 11\u001b[0m \u001b[0mfragility_model\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mutils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcalculate_mean_fragility\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgmrs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mPDM\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mdamping\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'Sa'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mdamage_model\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 12\u001b[0;31m \u001b[0mutils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot_fragility_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfragility_model\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0.01\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;31mTypeError\u001b[0m: plot_fragility_model() takes exactly 4 arguments (3 given)" ] } ], "source": [ "import N2Method\n", "damage_model = utils.read_damage_model('../../../../rmtk_data/damage_model.csv')\n", "damping = 0.05\n", "T = 2.0\n", "\n", "for imodel in range(len(models['name'])):\n", " print models['name'][imodel]\n", " capacity_curves = utils.read_capacity_curves(models['location'][imodel])\n", " utils.plot_capacity_curves(capacity_curves) \n", " PDM, Sds = N2Method.calculate_fragility(capacity_curves,gmrs,damage_model,damping)\n", " fragility_model = utils.calculate_mean_fragility(gmrs,PDM,T,damping,'Sa',damage_model)\n", " utils.plot_fragility_model(fragility_model,0.01,2)" ] }, { "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 }

agpl-3.0

fhmartinezs/Dinamicos_UD

notebooks/04_Bode.ipynb

1

5425

{ "cells": [ { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "# Analysis of Dynamic Systems" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## Schedule:" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "- Getting started\n", "- Introduction\n", "- Mathematical bases\n", "- Bode diagrams\n", "- Modeling with linear elements\n", "- State variables\n", "- Block diagrams\n", "- Time response\n", "- Frequency response\n", "- Stability\n", "- Root Locus\n", "- Final project\n", "- Course evaluation" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## Mathematical bases" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "- Complex Variable Theory.\n", "- Differential equations.\n", "- Laplace transform.\n", "- Theory of matrices.\n", "- Bode diagrams." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "http://pinkwink.kr/930" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### Bode plots" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from IPython.display import Image\n", "Image(filename='img/bode1.png')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from IPython.display import Image\n", "Image(filename='img/bode2.png')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from IPython.display import Image\n", "Image(filename='img/bode3.png')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from IPython.display import Image\n", "Image(filename='img/bode4.png')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from IPython.display import Image\n", "Image(filename='img/bode5.png')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from IPython.display import Image\n", "Image(filename='img/bode6.png')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from IPython.display import Image\n", "Image(filename='img/bode7.png')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Whit Python:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import sympy\n", "from sympy import *\n", "\n", "sympy.init_printing()\n", "s = Symbol('s')\n", "t = Symbol('t', positive=True)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "G = ((10)*(s+10))/((s)*(s+2)*(s+5))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "G" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "G.expand()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import numpy as np\n", "from scipy import signal\n", "import matplotlib.pyplot as plt\n", "\n", "s1 = signal.lti([10, 100], [1, 7, 10])\n", "w, mag, phase = signal.bode(s1, np.arange(0.01, 100.0, 0.01).tolist())\n", "\n", "plt.figure(figsize=(15,8))\n", "plt.subplot(2,1,1)\n", "plt.semilogx(w, mag, lw=5) # Bode magnitude plot\n", "\n", "plt.ylim([-30, 30])\n", "plt.xlabel('Hz')\n", "plt.ylabel('Magnitude (dB)')\n", "plt.grid(True)\n", "\n", "plt.subplot(2,1,2)\n", "plt.semilogx(w, phase, lw=5, label=\"real bode plot\") # Bode phase plot\n", "\n", "plt.xlabel('Hz')\n", "plt.ylim([-120, 10])\n", "plt.ylabel('Phase (deg)')\n", "plt.grid(True)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.6" } }, "nbformat": 4, "nbformat_minor": 0 }

apache-2.0

freininghaus/adventofcode

2020/day09-haskell.ipynb

1

7666

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Day 9: Encoding Error\n", "https://adventofcode.com/2020/day/9" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "inputLines = lines <$> readFile \"input/day09.txt\"\n", "inputNumbers = map read <$> inputLines" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Part 1" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The complexity of this function is quadratic in the length of the input list, but this should be acceptable because we know that the length is at most 25." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "isValidNext :: Int -> [Int] -> Bool\n", "isValidNext nextNumber numbers = not . null $ pairs\n", " where\n", " pairs = [(a, b) | a <- numbers, b <- numbers, a + b == nextNumber, a /= b]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Verify given examples." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[True,True,False,False]" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "map (`isValidNext` [1..25]) [26, 49, 100, 50]" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[True,False,True,True]" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "map (`isValidNext` ([1..19] ++ [21..25] ++ [45])) [26, 65, 64, 66]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Find the first invalid number in a list of numbers for a given preamble length." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [], "source": [ "firstInvalid :: Int -> [Int] -> Int\n", "firstInvalid preambleLength numbers = f firstPreamble firstRest\n", " where\n", " firstPreamble = take preambleLength numbers\n", " firstRest = drop preambleLength numbers\n", " \n", " f preamble rest = \n", " if isValidNext nextNumber preamble then\n", " f (tail preamble ++ [nextNumber]) (tail rest)\n", " else\n", " head rest\n", " where\n", " nextNumber = head rest" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "testNumbers = [ 35\n", " , 20\n", " , 15\n", " , 25\n", " , 47\n", " , 40\n", " , 62\n", " , 55\n", " , 65\n", " , 95\n", " , 102\n", " , 117\n", " , 150\n", " , 182\n", " , 127\n", " , 219\n", " , 299\n", " , 277\n", " , 309\n", " , 576 ]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Verify given example." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "127" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "firstInvalid 5 testNumbers" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true }, "outputs": [], "source": [ "solution1 = firstInvalid 25" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Solution, part 1" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "466456641" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "solution1 <$> inputNumbers" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Part 2" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Find a contiguous set of numbers in the given list whose sum is the target number.\n", "\n", "The idea is to start with an empty set, whose sum is zero. Then we go through the list and do the following:\n", "* If the sum of the current set is equal to the target, we are done.\n", "* If the sum of the current set too small, add the next number to the set.\n", "* If the sum is too large, drop the first number from the set." ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [], "source": [ "contiguousSet :: Int -> [Int] -> [Int]\n", "contiguousSet target numbers = f 0 [] numbers\n", " where\n", " f currentSum currentNumbers remainingNumbers\n", " | currentSum == target = currentNumbers\n", " | currentSum > target = f (currentSum - head currentNumbers) (tail currentNumbers) remainingNumbers\n", " | currentSum < target = f (currentSum + firstRemaining) (currentNumbers ++ [firstRemaining]) (tail remainingNumbers)\n", " where firstRemaining = head remainingNumbers" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Verify given example." ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[15,25,47,40]" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "contiguousSet 127 testNumbers" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [], "source": [ "solution2 preambleLength numbers = minimum cs + maximum cs\n", " where\n", " invalidNumber = firstInvalid preambleLength numbers\n", " cs = contiguousSet invalidNumber numbers" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "62" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "solution2 5 testNumbers" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Solution, part 2" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "55732936" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "solution2 25 <$> inputNumbers" ] } ], "metadata": { "kernelspec": { "display_name": "Haskell", "language": "haskell", "name": "haskell" }, "language_info": { "codemirror_mode": "ihaskell", "file_extension": ".hs", "name": "haskell", "version": "7.10.2" } }, "nbformat": 4, "nbformat_minor": 0 }

mit

flohorovicic/pynoddy

docs/notebooks/Untitled0.ipynb

1

5181

{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Rayleigh formula for free thermal convection, formulae taken from Kuhn and Gessner 2009, Surveys in Geophysics\n", "\n", "def Rayleigh():\n", " \"\"\" Properties of pure water at 20 degrees K \"\"\"\n", " # values of pure water taken from http://people.ucsc.edu/~bkdaniel/WaterProperties.html\n", " alpha =((0.207 + 0.385 + 0.523) / 3) * 1*10**-3 # thermal expansivity of water\n", " print alpha\n", " rho = 998.0 # Density of pure water\n", " c = 4179.0 # Fluid heat capacity\n", " g = 9.812\n", " mu = ((1.004 + 0.658 + 0.475) / 3) * 1*10**-3 #dynamic viscosity of water\n", " \"\"\" Mean Values \"\"\" \n", " WLFM0_xyz = 2.7, #np.array(S1_out.get_array_as_xyz_structure(\"WLFM0\"))\n", " WLFM0_mean = 2.7 #np.mean(WLFM0_xyz[:, :, :]) # Mean of medium's thermal expansivity\n", " #print WLFM0_mean\n", " \n", " perm_xyz = 1E-12 # np.array(S1_out.get_array_as_xyz_structure(\"PERM\"))\n", " mean_perm = 1E-12 # np.mean(perm_xyz[:,:,:]) # Mean of permeability field\n", " #mean_perm = 5.835e-12\n", " #print \"%0.4g\" %mean_perm\n", " \n", " #temp_xyz = np.array(S1_out.get_array_as_xyz_structure(\"TEMP\")) # determine temperature gradient at the subplot\n", " T2 = 170. #temp_xyz[0:, :, 0]\n", " T1 = 20. # temp_xyz[0:, :, -1]\n", " # z_axis = temp_xyz[0, :, 0:]\n", " # z_axis_list = list(z_axis[:,:].ravel(order = \"F\"))\n", " length = 1000. # len(z_axis_list) * 100.0\n", " #print length\n", " Value = (T2 - T1) / length\n", " dtemp_value = Value # np.mean(Value[:, :])\n", " \n", " print dtemp_value\n", " \n", " Ra = ((T2 - T1) * mean_perm * length * alpha * (rho**2) * g * c) / (WLFM0_mean * mu)\n", " return Ra\n", "print \"%0.4g\" %Rayleigh()" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from vtk import *\n", "\n", "import matplotlib\n", "matplotlib.use('Agg')\n", "from matplotlib.figure import Figure\n", "from matplotlib.backends.backend_agg import FigureCanvasAgg\n", "import pylab as p\n", "\n", "# The vtkImageImporter will treat a python string as a void pointer\n", "importer = vtkImageImport()\n", "importer.SetDataScalarTypeToUnsignedChar()\n", "importer.SetNumberOfScalarComponents(4)\n", "\n", "# It's upside-down when loaded, so add a flip filter\n", "imflip = vtkImageFlip()\n", "imflip.SetInputData(importer.GetOutput())\n", "imflip.SetFilteredAxis(1)\n", "\n", "# Map the plot as a texture on a cube\n", "cube = vtkCubeSource()\n", "\n", "cubeMapper = vtkPolyDataMapper()\n", "cubeMapper.SetInputData(cube.GetOutput())\n", "\n", "cubeActor = vtkActor()\n", "cubeActor.SetMapper(cubeMapper)\n", "\n", "# Create a texture based off of the image\n", "cubeTexture = vtkTexture()\n", "cubeTexture.InterpolateOn()\n", "cubeTexture.SetInputData(imflip.GetOutput())\n", "cubeActor.SetTexture(cubeTexture)\n", "\n", "ren = vtkRenderer()\n", "ren.AddActor(cubeActor)\n", "\n", "renWin = vtkRenderWindow()\n", "renWin.AddRenderer(ren)\n", "\n", "iren = vtkRenderWindowInteractor()\n", "iren.SetRenderWindow(renWin)\n", "\n", "# Now create our plot\n", "fig = Figure()\n", "canvas = FigureCanvasAgg(fig)\n", "ax = fig.add_subplot(111)\n", "ax.grid(True)\n", "ax.set_xlabel('Hello from VTK!', size=16)\n", "ax.bar(xrange(10), p.rand(10))\n", "\n", "# Powers of 2 image to be clean\n", "w,h = 1024, 1024\n", "dpi = canvas.figure.get_dpi()\n", "#fig.set_figsize_inches(w / dpi, h / dpi)\n", "canvas.draw() # force a draw\n", "\n", "# This is where we tell the image importer about the mpl image\n", "extent = (0, w - 1, 0, h - 1, 0, 0)\n", "importer.SetWholeExtent(extent)\n", "importer.SetDataExtent(extent)\n", "# importer.SetImportVoidPointer(canvas.buffer_rgba(0,0), 1)\n", "importer.Update()\n", "\n", "iren.Initialize()\n", "iren.Start()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "imflip.SetInputData(importer.GetOutput())" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.11" } }, "nbformat": 4, "nbformat_minor": 0 }

gpl-2.0

jphall663/GWU_data_mining

02_analytical_data_prep/src/housing.ipynb

1

589763

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Imports And Setup\n", "\n", "You may see a lot of posts online telling you to set up an individual sparkcontext variable. Please note those are from versions ~1.6 and lower and are no longer relevent in 2.0. Now you should only make one `SparkSession` and access spark context from `spark.sparkContext`." ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# imports\n", "import pandas as pd\n", "import numpy as np\n", "import time\n", "import os\n", "from tabulate import tabulate\n", "\n", "import sys\n", "from operator import add\n", "from pyspark import SparkContext\n", "from pyspark.sql import SparkSession\n", "from pyspark.sql import SQLContext\n", "from pyspark.sql import functions as F #https://stackoverflow.com/questions/39504950/python-pyspark-get-sum-of-a-pyspark-dataframe-column-values\n", "from pyspark.sql.functions import monotonically_increasing_id\n", "\n", "from DataPreperation import DataPreperation\n", "\n", "\n", "#.config('spark.executor.cores','6') \\\n", "spark = SparkSession.builder \\\n", " .appName(\"App\") \\\n", " .getOrCreate()\n", " # .master(\"local[*]\") \\\n", " # .config('spark.cores.max','16')\n", " #.master(\"local\") \\\n", " # .config(\"spark.some.config.option\", \"some-value\") \\\n", "\n", "spark.sparkContext.setLogLevel('WARN') #Get rid of all the junk in output\n", "\n", "Y = 'SalePrice'\n", "ID_VAR = 'Id'\n", "DROPS = [ID_VAR]\n", "\n", "original_train = spark.read.format('com.databricks.spark.csv').options(header='true', inferschema='true').load('data_sets/kaggle_house/train.csv')\n", "original_test = spark.read.format('com.databricks.spark.csv').options(header='true', inferschema='true').load('data_sets/kaggle_house/test.csv')\n", "\n", "\n", "#add an id column for row reference\n", "# original_train.withColumn(\"id\", monotonically_increasing_id())\n", "# original_test.withColumn(\"id\", monotonically_increasing_id())\n", "\n", "\n", "#this needs to be done for h2o glm.predict() bug (which needs same number of columns)\n", "# test = test.withColumn(Y,test[ID_VAR])\n", "\n", "\n", "# (train,valid) = original_train.randomSplit([0.7,0.3], seed=123)\n", "\n", "# train.describe().show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Data types\n", "Lets see which variables are categorical and which are numeric. We will need to handle the numeric data later." ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Numeric = ['MSSubClass', 'LotArea', 'OverallQual', 'OverallCond', 'YearBuilt', 'YearRemodAdd', 'BsmtFinSF1', 'BsmtFinSF2', 'BsmtUnfSF', 'TotalBsmtSF', '1stFlrSF', '2ndFlrSF', 'LowQualFinSF', 'GrLivArea', 'BsmtFullBath', 'BsmtHalfBath', 'FullBath', 'HalfBath', 'BedroomAbvGr', 'KitchenAbvGr', 'TotRmsAbvGrd', 'Fireplaces', 'GarageCars', 'GarageArea', 'WoodDeckSF', 'OpenPorchSF', 'EnclosedPorch', '3SsnPorch', 'ScreenPorch', 'PoolArea', 'MiscVal', 'MoSold', 'YrSold']\n", "\n", "Categorical = ['MSZoning', 'LotFrontage', 'Street', 'Alley', 'LotShape', 'LandContour', 'Utilities', 'LotConfig', 'LandSlope', 'Neighborhood', 'Condition1', 'Condition2', 'BldgType', 'HouseStyle', 'RoofStyle', 'RoofMatl', 'Exterior1st', 'Exterior2nd', 'MasVnrType', 'MasVnrArea', 'ExterQual', 'ExterCond', 'Foundation', 'BsmtQual', 'BsmtCond', 'BsmtExposure', 'BsmtFinType1', 'BsmtFinType2', 'Heating', 'HeatingQC', 'CentralAir', 'Electrical', 'KitchenQual', 'Functional', 'FireplaceQu', 'GarageType', 'GarageYrBlt', 'GarageFinish', 'GarageQual', 'GarageCond', 'PavedDrive', 'PoolQC', 'Fence', 'MiscFeature', 'SaleType', 'SaleCondition']\n" ] } ], "source": [ "numerics, categoricals = DataPreperation.get_type_lists(frame=original_train,rejects=[ID_VAR,Y],frame_type='spark')" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "## Dealing with Outliers\n", "Lets look a possible outlier. It may not be an outlier and it may be best to keep the column as is, but lets just pretend it is actually an outlier." ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<script>requirejs.config({paths: { 'plotly': ['https://cdn.plot.ly/plotly-latest.min']},});if(!window.Plotly) {{require(['plotly'],function(plotly) {window.Plotly=plotly;});}}</script>" ], "text/vnd.plotly.v1+html": [ "<script>requirejs.config({paths: { 'plotly': ['https://cdn.plot.ly/plotly-latest.min']},});if(!window.Plotly) {{require(['plotly'],function(plotly) {window.Plotly=plotly;});}}</script>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.plotly.v1+json": { "data": [ { "mode": "markers", "type": "scatter", "x": [ 856, 1262, 920, 756, 1145, 796, 1686, 1107, 952, 991, 1040, 1175, 912, 1494, 1253, 832, 1004, 0, 1114, 1029, 1158, 637, 1777, 1040, 1060, 1566, 900, 1704, 1484, 520, 649, 1228, 1234, 1398, 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'markers'\n", ")\n", "data = [trace]\n", "\n", "# Plot and embed in ipython notebook!\n", "iplot(data)#, filename='basic-scatter')" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "## Winsorize for Outliers" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "For TotalBsmtSF the lower limit is 0.0\n", "For TotalBsmtSF the upper limit is 3206.0\n" ] } ], "source": [ "original_train = DataPreperation.winsorize_columns(original_train,['TotalBsmtSF'],\\\n", " winzerize_type='percentile',limits =0.1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## New Chart\n", "After winsorizing the new chart moved all the values > 3200 are now = 3200" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<script>requirejs.config({paths: { 'plotly': 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\"Export to plot.ly\", \"showLink\": true})});</script>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import plotly.graph_objs as go\n", "from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot\n", "init_notebook_mode(connected=True)\n", "\n", "original_train.select('TotalBsmtSF',Y).toPandas().head()\n", "trace = go.Scatter(\n", " x = original_train.select('TotalBsmtSF').rdd.flatMap(list).collect(),\n", " y = original_train.select(Y).rdd.flatMap(list).collect(),\n", " mode = 'markers'\n", ")\n", "data = [trace]\n", "\n", "# Plot and embed in ipython notebook!\n", "iplot(data)#, filename='basic-scatter')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Label Encoding\n", "When you have an algorithm like an SVM or decision tree that doesn't always numerical values as greater then one another. Or you have an ordinal variable label encoding is a good choice.\n", "\n", "(example XGBoost requires you to do this)\n", "\n", "convert not likely, likely, very likey into lets say 1,2,3\n", "\n", "Note: this must be done before you split the data unlike other data prep techniques" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Column before encoding...\n", "['Gable', 'Gable', 'Gable', 'Gable', 'Gable', 'Gable', 'Gable', 'Gable', 'Gable', 'Gable', 'Hip', 'Hip', 'Hip', 'Gable', 'Hip', 'Gable', 'Gable', 'Gable', 'Gable', 'Hip', 'Gable', 'Gable', 'Hip', 'Gable', 'Gable', 'Gable', 'Gable', 'Gable', 'Gable', 'Gable', 'Gambrel', 'Gable', 'Gable', 'Hip', 'Hip', 'Gable', 'Gable', 'Hip', 'Gable', 'Gable', 'Gable', 'Gable', 'Gable', 'Hip', 'Gable', 'Hip', 'Gable', 'Gable', 'Gable']\n", "\n", "\n", "Numeric = ['MSSubClass', 'LotArea', 'OverallQual', 'OverallCond', 'YearBuilt', 'YearRemodAdd', 'BsmtFinSF1', 'BsmtFinSF2', 'BsmtUnfSF', 'TotalBsmtSF', '1stFlrSF', '2ndFlrSF', 'LowQualFinSF', 'GrLivArea', 'BsmtFullBath', 'BsmtHalfBath', 'FullBath', 'HalfBath', 'BedroomAbvGr', 'KitchenAbvGr', 'TotRmsAbvGrd', 'Fireplaces', 'GarageCars', 'GarageArea', 'WoodDeckSF', 'OpenPorchSF', 'EnclosedPorch', '3SsnPorch', 'ScreenPorch', 'PoolArea', 'MiscVal', 'MoSold', 'YrSold', 'RoofStyle_encoded']\n", "\n", "Categorical = ['MSZoning', 'LotFrontage', 'Street', 'Alley', 'LotShape', 'LandContour', 'Utilities', 'LotConfig', 'LandSlope', 'Neighborhood', 'Condition1', 'Condition2', 'BldgType', 'HouseStyle', 'RoofStyle', 'RoofMatl', 'Exterior1st', 'Exterior2nd', 'MasVnrType', 'MasVnrArea', 'ExterQual', 'ExterCond', 'Foundation', 'BsmtQual', 'BsmtCond', 'BsmtExposure', 'BsmtFinType1', 'BsmtFinType2', 'Heating', 'HeatingQC', 'CentralAir', 'Electrical', 'KitchenQual', 'Functional', 'FireplaceQu', 'GarageType', 'GarageYrBlt', 'GarageFinish', 'GarageQual', 'GarageCond', 'PavedDrive', 'PoolQC', 'Fence', 'MiscFeature', 'SaleType', 'SaleCondition']\n", "\n", "Column after encoding...\n", "[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 3.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0]\n" ] } ], "source": [ "print('Column before encoding...')\n", "print(original_train.select('RoofStyle').rdd.flatMap(list).collect()[0:49])\n", "print()\n", "original_train = DataPreperation.label_encoder(original_train,['RoofStyle'])\n", "print()\n", "numerics, categoricals = DataPreperation.get_type_lists(frame=original_train,rejects=[ID_VAR,Y],frame_type='spark')\n", "print()\n", "print('Column after encoding...')\n", "print(original_train.select('RoofStyle_encoded').rdd.flatMap(list).collect()[0:49])" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "## Feature interaction \n", "Feature interaction is multiplying two variables together (example columns $x$ and $y$ -> $xy$)" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#Here is how to do polynomical expansion\n", "train_corr = DataPreperation.get_top_correlations(original_train,numerics)" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "data": [ { "colorscale": [ [ 0, "#00083e" ], [ 0.5, "#ededee" ], [ 1, "#ffffff" ] ], "hoverinfo": "none", "opacity": 0.75, "showscale": false, "type": "heatmap", "z": [ [ 0, 0, 0 ], [ 0.5, 0.5, 0.5 ], [ 1, 1, 1 ], [ 0.5, 0.5, 0.5 ], [ 1, 1, 1 ], [ 0.5, 0.5, 0.5 ], [ 1, 1, 1 ], [ 0.5, 0.5, 0.5 ], [ 1, 1, 1 ], [ 0.5, 0.5, 0.5 ], [ 1, 1, 1 ], [ 0.5, 0.5, 0.5 ], [ 1, 1, 1 ], [ 0.5, 0.5, 0.5 ], [ 1, 1, 1 ], [ 0.5, 0.5, 0.5 ], [ 1, 1, 1 ], [ 0.5, 0.5, 0.5 ], [ 1, 1, 1 ], [ 0.5, 0.5, 0.5 ], [ 1, 1, 1 ], [ 0.5, 0.5, 0.5 ], [ 1, 1, 1 ], [ 0.5, 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\"#000000\"}}], \"yaxis\": {\"gridwidth\": 2, \"dtick\": 1, \"showticklabels\": false, \"tick0\": 0.5, \"autorange\": \"reversed\", \"zeroline\": false, \"ticks\": \"\"}, \"height\": 3080}, {\"linkText\": \"Export to plot.ly\", \"showLink\": true})});</script>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# https://plot.ly/python/figure-factory/table/\n", "import plotly.figure_factory as ff\n", "\n", "corr_df = pd.DataFrame(columns=['columns', 'correlation', 'correlation_abs'])\n", "for idx, d in enumerate(train_corr):\n", " corr_df.loc[idx] = [d['columns'],d['correlation'],d['correlation_abs']]\n", " \n", "table = ff.create_table(corr_df)\n", "iplot(table, filename='pandas_table')" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Combining: GarageArea & GarageCars (1/1)...\n", "DONE combining features.\n", "Combining: GarageArea & 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" original_train = DataPreperation.feature_combiner(original_train,columns=corr_df.loc[idx]['columns'])\n", " original_test = DataPreperation.feature_combiner(original_test,columns=corr_df.loc[idx]['columns'])\n", "#show the results \n", "table = ff.create_table(original_train.select('GarageArea','GarageCars','GarageArea|GarageCars').toPandas().sample(10))\n", "table.layout.width=1000\n", "iplot(table, filename='pandas_table')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Polynomial Expansion\n", "Polynomial expansion is taking a variable and adding polynomial terms such as $x^2$$ $$x ^3$ etc. This can be very helpful especially in regression based models." ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " Id 1stFlrSF 1stFlrSF_^2 1stFlrSF_^3\n", "924 925 1686 2842596.0 4.792617e+09\n", "649 650 630 396900.0 2.500470e+08\n" ] }, { "data": { 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DataPreperation.polynomial_expansion(original_test,['1stFlrSF'],degree=3)\n", "\n", "#show the results \n", "print(original_train.select(ID_VAR,'1stFlrSF','1stFlrSF_^2','1stFlrSF_^3').toPandas().sample(2))\n", "table = ff.create_table(original_train.select(ID_VAR,'1stFlrSF','1stFlrSF_^2','1stFlrSF_^3').toPandas().sample(10))\n", "table.layout.width=1000\n", "iplot(table, filename='pandas_table')" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "# Perturbed Rate-by-Level with Shrunken Averages\n", "This algorithm is good for hanlding any kind of categorical column when the algoithm needs a continuous column. For this slgorithm you <b>MUST</b> split the data <b>BEFORE</b> putting it in other wise you will have feature leakage and will overfit very very very very badly. You also want to perturb the data in insert random noise to further prevent overfitting.\n", "\n", "Formula:\n", "$$(1 − λ) * levelmean + λ * overallmean*purtubedamount$$" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Encoding numberic variables...\n", "Encoding: MSZoning (1/46) ...\n" ] }, { "data": { "application/vnd.plotly.v1+json": { "data": [ { "colorscale": [ [ 0, "#00083e" ], [ 0.5, "#ededee" ], [ 1, "#ffffff" ] ], "hoverinfo": "none", "opacity": 0.75, "showscale": false, "type": "heatmap", "z": [ [ 0, 0 ], [ 0.5, 0.5 ], [ 1, 1 ], [ 0.5, 0.5 ], [ 1, 1 ], [ 0.5, 0.5 ], [ 1, 1 ], [ 0.5, 0.5 ], [ 1, 1 ], [ 0.5, 0.5 ], [ 1, 1 ], [ 0.5, 0.5 ], [ 1, 1 ], [ 0.5, 0.5 ], [ 1, 1 ], [ 0.5, 0.5 ] ] } ], "layout": { "annotations": [ { "align": "left", "font": { "color": "#ffffff" }, "showarrow": false, "text": "<b>MSZoning</b>", "x": -0.45, "xanchor": "left", "xref": "x1", "y": 0, 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original_train.randomSplit([0.7,0.3], seed=123)\n", "\n", "print(\"Encoding numberic variables...\")\n", "for i, var in enumerate(['MSZoning']):\n", " total = len(categoricals)\n", "\n", " print('Encoding: ' + var + ' (' + str(i+1) + '/' + str(total) + ') ...')\n", " train,valid, original_test = DataPreperation.shrunken_averages_encoder(train, valid_frame = valid,test_frame=original_test,\\\n", " x=var, y=Y, lambda_=0.15, perturb_range=0.05,threshold=150,\\\n", " test=False, frame_type='spark',test_does_have_y=False,id_col=ID_VAR) \n", "table = ff.create_table(train.select('MSZoning','MSZoning_Tencode').toPandas().sample(15))\n", "iplot(table, filename='pandas_table')" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "## Dimensionality Reduction PCA\n", "This is a way to make your feature set less wide and make a smaller number of features out of a hopefully large number of features. The most common and historic algorithm to do this is Principal Component Analysis (PCA).\n", "\n", "Note n_comp will set the number of eigen vectors to return. If its 1 it'll pick the top 1 of all the eigen vectors. Below we can use an n_comp value of 1 or 2 b/c we have two features that we're feeding in. 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inferschema='true').load('data_sets/kaggle_house/test.csv')\n", "# (train,valid) = original_train.randomSplit([0.7,0.3], seed=123)\n", "\n", "#PCA does not handle null values and there was some in test\n", "# train.na.drop()\n", "# valid.na.drop()\n", "# original_test.na.drop()\n", "# original_test.GarageArea.cast('float')\n", "# original_test.GarageCars.cast('float')\n", "\n", "for idx, row in corr_df.iterrows():\n", " if(corr_df.loc[idx]['correlation_abs'] >.7 and corr_df.loc[idx]['correlation_abs'] != 1): #Set a cutoff only combine values greater then .7\n", " print('Doing PCA for', corr_df.loc[idx]['columns'])\n", " #The test data was messy so i couldnt include test it has 'NA' which made for errors\n", " train,valid = DataPreperation.dimensionality_reduction(train, valid_frame = valid,test_frame=None,\\\n", " columns=corr_df.loc[idx]['columns'],n_comp=2,\\\n", " random_seed=420,decompositions_to_run=['PCA'],\\\n", " frame_type='spark',test_does_have_y=False,\\\n", " only_return_decompositions=False,id_col=ID_VAR,\\\n", " column_name=corr_df.loc[idx]['columns'][0]+'&'+corr_df.loc[idx]['columns'][1])#show the results \n", "\n", " \n", " \n", "table = ff.create_table(train.select('GarageArea','GarageCars','GarageArea&GarageCars_pca_1','1stFlrSF&TotalBsmtSF_pca_2').toPandas()[0:10])\n", "# table = ff.create_table(train.select('1stFlrSF','TotalBsmtSF','1stFlrSF&TotalBsmtSF_pca_1','1stFlrSF&TotalBsmtSF_pca_2').toPandas()[0:10])\n", "table.layout.width=1000\n", "iplot(table, filename='pandas_table')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Dimensionality Reduction SVD (cont.)\n", "SVD's are a nother type of decomposition. Many people claim they work better on large datasets compared to PCA.\n", "\n", "\"Singular value decomposition is often preferred over eigendecomposition of the covariance matrix because the calculation of the covariance matrix is a source of error. In singular value decomposition, with such a large dataset, we are much more robust to errors due to dynamic range of numbers or computational error.\"\n", "- https://blog.dominodatalab.com/pca-on-very-large-neuroimaging-datasets-using-pyspark/" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Doing SVD for ['GarageArea', 'GarageCars']\n", "Doing SVD for ['1stFlrSF', 'TotalBsmtSF']\n" ] }, { "data": { "application/vnd.plotly.v1+json": { "data": [ { "colorscale": [ [ 0, "#00083e" ], [ 0.5, "#ededee" ], [ 1, "#ffffff" ] ], "hoverinfo": "none", "opacity": 0.75, "showscale": false, "type": "heatmap", "z": [ [ 0, 0, 0, 0 ], [ 0.5, 0.5, 0.5, 0.5 ], [ 1, 1, 1, 1 ], [ 0.5, 0.5, 0.5, 0.5 ], [ 1, 1, 1, 1 ], [ 0.5, 0.5, 0.5, 0.5 ], [ 1, 1, 1, 1 ], [ 0.5, 0.5, 0.5, 0.5 ], [ 1, 1, 1, 1 ], [ 0.5, 0.5, 0.5, 0.5 ], [ 1, 1, 1, 1 ] ] } ], "layout": { "annotations": [ { "align": "left", "font": { 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"output_type": "display_data" } ], "source": [ "# original_train = spark.read.format('com.databricks.spark.csv').options(header='true', inferschema='true').load('data_sets/kaggle_house/train.csv')\n", "# original_test = spark.read.format('com.databricks.spark.csv').options(header='true', inferschema='true').load('data_sets/kaggle_house/test.csv')\n", "# (train,valid) = original_train.randomSplit([0.7,0.3], seed=123)\n", "\n", "#PCA does not handle null values and there was some in test\n", "# train.na.drop()\n", "# valid.na.drop()\n", "# original_test.na.drop()\n", "# original_test.GarageArea.cast('float')\n", "# original_test.GarageCars.cast('float')\n", "\n", "for idx, row in corr_df.iterrows():\n", " if(corr_df.loc[idx]['correlation_abs'] >.7 and corr_df.loc[idx]['correlation_abs'] != 1): #Set a cutoff only combine values greater then .7\n", " print('Doing SVD for', corr_df.loc[idx]['columns'])\n", " #The test data was messy so i couldnt include test it has 'NA' which made for errors\n", " train,valid = DataPreperation.dimensionality_reduction(train, valid_frame = valid,test_frame=None,\\\n", " columns=corr_df.loc[idx]['columns'],n_comp=2,\\\n", " random_seed=420,decompositions_to_run=['SVD'],\\\n", " frame_type='spark',test_does_have_y=False,\\\n", " only_return_decompositions=False,id_col=ID_VAR,\\\n", " column_name=corr_df.loc[idx]['columns'][0]+'&'+corr_df.loc[idx]['columns'][1])#show the results \n", "\n", " \n", "table = ff.create_table(train.select('GarageArea','GarageCars','GarageArea&GarageCars_svd_1','1stFlrSF&TotalBsmtSF_svd_2').toPandas()[0:10])\n", "# table = ff.create_table(train.select('1stFlrSF','TotalBsmtSF','1stFlrSF&TotalBsmtSF_pca_1','1stFlrSF&TotalBsmtSF_pca_2').toPandas()[0:10])\n", "table.layout.width=1000\n", "iplot(table, filename='pandas_table') " ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [conda root]", "language": "python", "name": "conda-root-py" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 2 }

apache-2.0

sindrerb/VecDiSCS

notebooks/ZnO/Tight Binding Class.ipynb

1

126083

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import pythtb as tb\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from scipy.optimize import minimize\n", "\n", "class Orbital(object):\n", " \"\"\" Orbital object with a onsite energy \"\"\"\n", " def __init__(self, onsite, label):\n", " \"\"\"\n", " Initialize an instance of orbital\n", " \n", " :type onsite: float\n", " :param onsite: The onsite energy of the orbital\n", " \n", " :type label: string\n", " :param label: The name of the orbital (examples: s,p,d,f)\n", " \"\"\"\n", " self.label = label\n", " self.onsite = onsite\n", "\n", " def __repr__(self):\n", " \"\"\" Representation of the orbital object \"\"\"\n", " return \"{} with onsite {}\".format(self.label, self.onsite)\n", " \n", "class Atom(object):\n", " \"\"\" Atom object hosting orbitals \"\"\"\n", " def __init__(self, position, number):\n", " \"\"\" Initialize an instance of atom\n", " \n", " :type position: ndarray, list, tuple\n", " :param position: The onsite energy of the orbital\n", " \n", " :type number: int\n", " :param number: The atomic number of the atom (Z)\n", " \"\"\"\n", " position = np.asarray(position)\n", " self.number = number\n", " self.position = position\n", " self.orbitals = []\n", " \n", " def add_orbital(self, orbital):\n", " \"\"\" Add an orbital to the hosting atom\n", " :type orbital: orbital object\n", " :param orbital: an orbital in the atom\n", " \"\"\"\n", " self.orbitals.append(orbital)\n", " \n", " def number_of_orbitals(self):\n", " \"\"\" Returns the number of orbitals in the atom \n", " :returns: the number of orbitals in the atom\n", " \"\"\"\n", " return len(self.orbitals) \n", "\n", " \n", " def __repr__(self):\n", " \"\"\" Representation of the Atom object \"\"\"\n", "\n", " return \"Atom with Z={} at {}\".format(self.number, self.position)\n", " \n", "class Oxygen(Atom):\n", " \"\"\" A special case of Atom designed for Oxygen\"\"\"\n", " def __init__(self, position):\n", " Atom.__init__(self, position, 8)\n", " self.add_orbital(Orbital(-19.046, \"S\"))\n", " self.add_orbital(Orbital( 4.142, \"Pz\"))\n", " self.add_orbital(Orbital( 4.142, \"Px\"))\n", " self.add_orbital(Orbital( 4.142, \"Py\"))\n", " \n", " def __repr__(self):\n", " return \"Oxygen at {}\".format(self.position)\n", " \n", "class Zinc(Atom):\n", " \"\"\" A special case of Atom designed for Zinc\"\"\"\n", " def __init__(self, position):\n", " \n", " Atom.__init__(self, position, 30)\n", " self.add_orbital(Orbital( 1.666, \"S\"))\n", " self.add_orbital(Orbital( 12.368, \"Pz\"))\n", " self.add_orbital(Orbital( 12.368, \"Px\"))\n", " self.add_orbital(Orbital( 12.368, \"Py\"))\n", " \n", " def __repr__(self):\n", " return \"Zinc at {}\".format(self.position)\n", " \n", "\n", "class Gallium(Atom):\n", " \"\"\" A special case of Atom designed for Gallium\"\"\"\n", " def __init__(self, position):\n", " \n", " Atom.__init__(self, position, 30)\n", " self.add_orbital(Orbital( 1.438, \"S\"))\n", " self.add_orbital(Orbital( 10.896, \"Pz\"))\n", " self.add_orbital(Orbital( 10.896, \"Px\"))\n", " self.add_orbital(Orbital( 10.896, \"Py\"))\n", " \n", " def __repr__(self):\n", " return \"Gallium at {}\".format(self.position)\n", " \n", "class Nitrogen(Atom):\n", " \"\"\" A special case of Atom designed for Nitrogen\"\"\"\n", " def __init__(self, position):\n", " \n", " Atom.__init__(self, position, 30)\n", " self.add_orbital(Orbital(-11.012, \"S\"))\n", " self.add_orbital(Orbital( 0.005, \"Pz\"))\n", " self.add_orbital(Orbital( 0.005, \"Px\"))\n", " self.add_orbital(Orbital( 0.005, \"Py\"))\n", " \n", " def __repr__(self):\n", " return \"Nitrogen at {}\".format(self.position) " ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "class Crystal:\n", " \n", " def __init__(self, lattice):\n", " \"\"\" Initialize an instance of crystal with lattice hosting atom objects\n", " \n", " :type lattice: ndarray\n", " :param lattice: a 3x3 array with lattice parameters [a,b,c]\n", " \"\"\"\n", " self.lattice = lattice\n", " self.atoms = []\n", " \n", " def add_atom(self,atom):\n", " \"\"\" Add an atom object to the crystal\n", " :type atom: atom object\n", " :param atom: the instance of atom to be placed in the crystal\n", " \"\"\"\n", " existing = False\n", " for existing_atom in self.atoms:\n", " if np.all(atom.position == existing_atom.position):\n", " existing = True\n", " if not existing:\n", " self.atoms.append(atom)\n", " return \"Placed atom at {}\".format(atom.position)\n", " else:\n", " raise Warning(\"An atom is already at\").format(atom.position)\n", " \n", " def __repr__(self):\n", " \"\"\" Representation of the crystal object \"\"\"\n", " string = \"Lattice:\\n{}\\nAtoms:\\n\".format(self.lattice)\n", " \n", " for i, atom in enumerate(self.atoms):\n", " string += \"{}: {}\\n\".format(i,atom)\n", " return string" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "class TransitionMatrix:\n", " \"\"\" A matrix containing all hopping parameters between to atoms\"\"\"\n", " def __init__(self, initial_atom, final_atom, equivalent_final_cells=[[0, 0, 0]]):\n", " \"\"\" Create an instance of TransitionMatrix based on an initial and final atom\n", " \n", " :type initial_atom: atom object\n", " :param initial_atom: the initial atom in the transition matrix\n", " \n", " :type final_atom: atom object\n", " :param final_atom: the final atom in the transition matrix\n", " \n", " :type equivalent_final_cells: list\n", " :param equivalent_final_cells: list of int-coordinates to neighbouring cells\n", " \"\"\"\n", " \n", " self.set_transitions( \n", " np.zeros((\n", " initial_atom.number_of_orbitals(),\n", " final_atom.number_of_orbitals())\n", " ) \n", " )\n", " \n", " self.set_equivalent_final_cells(equivalent_final_cells)\n", " self.initial_atom = initial_atom\n", " self.final_atom = final_atom\n", " \n", " if initial_atom == final_atom:\n", " self.label = \"Onsite element at {}\\n\".format(initial_atom)\n", " else:\n", " self.label = \"From {} to {}\\n\".format(initial_atom,final_atom)\n", " \n", " def set_transitions(self, transition_matrix):\n", " \"\"\" Replace the full transition matrix\n", " \n", " :type transition_matrix: ndarray\n", " :param transition_matrix: the new transition matrix\n", " \"\"\"\n", " self.transitions = transition_matrix\n", " \n", " def set_equivalent_final_cells(self, equivalent_final_cells):\n", " \"\"\" set the list of equivalent final cells\n", " \n", " :type equivalent_final_cells: list\n", " :param equivalent_final_cells: the new list of int-coordinates to neighbouring cells\n", " \"\"\"\n", " self.equivalent_final_cells = equivalent_final_cells\n", " \n", " def __getitem__(self, index):\n", " \"\"\" Get the hopping parameter by the index of initial and final orbital [i][f]\"\"\"\n", " return self.transitions[index]\n", " \n", " def __repr__(self):\n", " \"\"\" Represent the transition matrix object\"\"\"\n", " string = self.label\n", " \n", " for final_orbital in self.final_atom.orbitals:\n", " string += \"\\t|{}>\".format(final_orbital.label)\n", " string += \"\\n\"\n", " for i, initial_orbital in enumerate(self.initial_atom.orbitals):\n", " string += \"<{}|\\t\".format(initial_orbital.label)\n", " \n", " for f, final_orbital in enumerate(self.final_atom.orbitals):\n", " string += \"{}\\t\".format(self.transitions.round(2)[i,f])\n", " string += \"\\n\"\n", " string += \"\\n\"\n", " string += \"Equivalent cells: {}\".format(self.equivalent_final_cells)\n", " return string" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "class AtomMatrix:\n", " \"\"\" A matrix containing all transition matrices between atoms \"\"\"\n", " def __init__(self, crystal):\n", " \"\"\" Create instance of atom matrix\n", " :type crystal: crystal object\n", " :param crystal: a crystal containing atoms\n", " \"\"\"\n", " self.atom_matrix = []\n", " \n", " for i,initial_atom in enumerate(crystal.atoms):\n", " atom_matrix_row = [] \n", "\n", " for final_atom in crystal.atoms: \n", " transition_matrix = TransitionMatrix(initial_atom, final_atom)\n", "\n", " if initial_atom == final_atom:\n", " for i, orbital in enumerate(initial_atom.orbitals):\n", " transition_matrix[i][i] = orbital.onsite\n", " \n", " atom_matrix_row.append(transition_matrix)\n", " \n", " self.atom_matrix.append(atom_matrix_row)\n", "\n", " def __getitem__(self, index):\n", " \"\"\" Get a transition matrix by the index of initial and final atom [i][f]\"\"\"\n", " return self.atom_matrix[index]\n", " \n", " def __repr__(self):\n", " \"\"\" Representation of the atom matrix object\"\"\"\n", " string = \"Atom matrix:\\n\"\n", " for i, atom_matrix_row in enumerate(self.atom_matrix):\n", " for f, transition_matrix in enumerate(atom_matrix_row):\n", " string += \"[{},{}]\".format(i,f)\n", " string += \"\\n\"\n", " string += \"\\n\"\n", " for i, atom_matrix_row in enumerate(self.atom_matrix):\n", " for f, transition_matrix in enumerate(atom_matrix_row[i:]):\n", " string += \"[{},{}]\\n{}\\n\\n\".format(i,f,transition_matrix)\n", " return string" ] }, { "cell_type": "code", "execution_count": 58, "metadata": {}, "outputs": [], "source": [ "class TightBinding:\n", " \"\"\" Tight binding class constructed around the pythTB package \"\"\"\n", " def __init__(self, crystal):\n", " \"\"\" Create instance of the Tight binding model \n", " \n", " :type crystal: crystal object\n", " :param crystal: a crystal object containing atoms\n", " \"\"\"\n", " self.crystal = crystal\n", " \n", " self._orbital_positons = []\n", " for atom in crystal.atoms:\n", " for orbital in atom.orbitals:\n", " self._orbital_positons.append(atom.position)\n", " \n", " \n", " self.model = tb.tb_model(3,3,self.crystal.lattice, self._orbital_positons)\n", " \n", " \n", " \"\"\"#############################################################333\"\"\"\n", " \n", " def get_hopping_parameters(self):\n", " \"\"\" Get a list of all hopping parameters in the model\"\"\"\n", " return [self.Vss, self.Vxx, self.Vxy, self.Vsapc, self.Vpasc ]\n", " \n", " \n", " def set_hopping_parameters(self, Vss, Vxx, Vxy, Vsapc, Vpasc):\n", " \"\"\" Set all hopping parameters in the model\n", " \n", " :type Vss: float\n", " :param Vss: The hopping parameter from s to s\n", " \n", " :type Vxx: float\n", " :param Vxx: The hopping parameter from px of the anion to px of the cation\n", " \n", " :type Vxy: float\n", " :param Vxy: The hopping parameter from px of the anion to py of the cation\n", " \n", " :type Vsapc: float\n", " :param Vsapc: The hopping parameter from s of the anion to p of the cation\n", " \n", " :type Vpasc: float\n", " :param Vpasc: The hopping parameter from p of the anion to s of the cation\n", " \"\"\"\n", " self.Vss = Vss\n", " self.Vxx = Vxx\n", " self.Vxy = Vxy\n", " self.Vsapc = Vsapc\n", " self.Vpasc = Vpasc\n", " \n", " \"\"\"############### Vertical bonding system ##################\"\"\" \n", " self.UVss = 0.25*Vss\n", " self.UVzz = 0.25*(Vxx+2*Vxy)\n", " self.UVxx = 0.25*(Vxx-Vxy)\n", " self.UVsz = -0.25*np.sqrt(3)*Vsapc\n", " self.UVzs = 0.25*np.sqrt(3)*Vpasc\n", " \n", " \n", " \"\"\"############### Horizontal bonding system ##################\"\"\"\n", " self.UHss = self.UVss\n", " \n", " self.UHyy = self.UVxx\n", " self.UHzz = 1/9 * (8*self.UVxx + self.UVzz)\n", " self.UHxx = 1/9 * ( self.UVxx + 8*self.UVzz)\n", " \n", " self.UHsz = -1/3 * self.UVsz\n", " self.UHzs = -1/3 * self.UVzs\n", " \n", " self.UHsx = -2*np.sqrt(2)/3 * self.UVsz\n", " self.UHxs = -2*np.sqrt(2)/3 * self.UVzs\n", " \n", " self.UHzx = 2*np.sqrt(2)/9 * (self.UVzz-self.UVxx)\n", " self.UHxz = 2*np.sqrt(2)/9 * (self.UVzz-self.UVxx)\n", "\n", " \"\"\"##############################################################\"\"\"\n", "\n", " \"\"\" ONSITE \"\"\"\n", "\n", " for i, initial_atom in enumerate(self.crystal.atoms):\n", " for io, initial_orbital in enumerate(initial_atom.orbitals):\n", " self.model.set_onsite(initial_orbital.onsite, ind_i=(i*len(self.crystal.atoms)+io), mode='reset')\n", " \n", "\n", "\n", " \"\"\" HOPPING \"\"\"\n", " \n", " self._M03(0,3)\n", " self._M12(1,2)\n", " \n", " self._M13(1,3)\n", " self._M02(0,2)\n", " \n", " \n", " def _M03(self, i, f):\n", " \"\"\" The M14 transition matrix designed by Kobayashi et Al.\"\"\"\n", " self.model.set_hop(self.UVss, ind_i=i*4+0, ind_j=f*4+0, ind_R=[0, 0, -1], mode='reset')\n", " self.model.set_hop(self.UVsz, ind_i=i*4+0, ind_j=f*4+1, ind_R=[0, 0, -1], mode='reset')\n", " self.model.set_hop(self.UVzs, ind_i=i*4+1, ind_j=f*4+0, ind_R=[0, 0, -1], mode='reset')\n", " self.model.set_hop(self.UVzz, ind_i=i*4+1, ind_j=f*4+1, ind_R=[0, 0, -1], mode='reset')\n", " self.model.set_hop(self.UVxx, ind_i=i*4+2, ind_j=f*4+2, ind_R=[0, 0, -1], mode='reset')\n", " self.model.set_hop(self.UVxx, ind_i=i*4+3, ind_j=f*4+3, ind_R=[0, 0, -1], mode='reset') \n", " \n", " def _M12(self, i, f):\n", " \"\"\" The M14 transition matrix designed by Kobayashi et Al.\"\"\"\n", " self.model.set_hop(self.UVss, ind_i=i*4+0, ind_j=f*4+0, ind_R=[0, 0, 0], mode='reset')\n", " self.model.set_hop(self.UVsz, ind_i=i*4+0, ind_j=f*4+1, ind_R=[0, 0, 0], mode='reset')\n", " self.model.set_hop(self.UVzs, ind_i=i*4+1, ind_j=f*4+0, ind_R=[0, 0, 0], mode='reset')\n", " self.model.set_hop(self.UVzz, ind_i=i*4+1, ind_j=f*4+1, ind_R=[0, 0, 0], mode='reset')\n", " self.model.set_hop(self.UVxx, ind_i=i*4+2, ind_j=f*4+2, ind_R=[0, 0, 0], mode='reset')\n", " self.model.set_hop(self.UVxx, ind_i=i*4+3, ind_j=f*4+3, ind_R=[0, 0, 0], mode='reset') \n", " \n", " def _M02(self, i, f):\n", " \"\"\" The M13 transition matrix designed by Kobayashi et Al.\"\"\"\n", " #s-s\n", " self.model.set_hop(self.UHss, ind_i=i*4+0, ind_j=f*4+0, ind_R=[0, 0, 0], mode='reset')\n", " self.model.set_hop(self.UHss, ind_i=i*4+0, ind_j=f*4+0, ind_R=[-1, 0, 0], mode='reset')\n", " self.model.set_hop(self.UHss, ind_i=i*4+0, ind_j=f*4+0, ind_R=[-1, -1, 0], mode='reset')\n", " #s-z\n", " self.model.set_hop(self.UHsz, ind_i=i*4+0, ind_j=f*4+1, ind_R=[0, 0, 0], mode='reset')\n", " self.model.set_hop(self.UHsz, ind_i=i*4+0, ind_j=f*4+1, ind_R=[-1, 0, 0], mode='reset')\n", " self.model.set_hop(self.UHsz, ind_i=i*4+0, ind_j=f*4+1, ind_R=[-1, -1, 0], mode='reset')\n", " #s-x\n", " self.model.set_hop( self.UHsx, ind_i=i*4+0, ind_j=f*4+2, ind_R=[0, 0, 0], mode='reset')\n", " self.model.set_hop(-0.5*self.UHsx, ind_i=i*4+0, ind_j=f*4+2, ind_R=[-1, 0, 0], mode='reset')\n", " self.model.set_hop(-0.5*self.UHsx, ind_i=i*4+0, ind_j=f*4+2, ind_R=[-1, -1, 0], mode='reset')\n", " #s-y\n", " self.model.set_hop( np.sqrt(3)/2*self.UHsx, ind_i=i*4+0, ind_j=f*4+3, ind_R=[-1, 0, 0], mode='reset')\n", " self.model.set_hop(-np.sqrt(3)/2*self.UHsx, ind_i=i*4+0, ind_j=f*4+3, ind_R=[-1, -1, 0], mode='reset')\n", " \"\"\"##########################################\"\"\"\n", " #z-s\n", " self.model.set_hop(self.UHzs, ind_i=i*4+1, ind_j=f*4+0, ind_R=[0, 0, 0], mode='reset')\n", " self.model.set_hop(self.UHzs, ind_i=i*4+1, ind_j=f*4+0, ind_R=[-1, 0, 0], mode='reset')\n", " self.model.set_hop(self.UHzs, ind_i=i*4+1, ind_j=f*4+0, ind_R=[-1, -1, 0], mode='reset')\n", " #z-z\n", " self.model.set_hop(self.UHzz, ind_i=i*4+1, ind_j=f*4+1, ind_R=[0, 0, 0], mode='reset')\n", " self.model.set_hop(self.UHzz, ind_i=i*4+1, ind_j=f*4+1, ind_R=[-1, 0, 0], mode='reset')\n", " self.model.set_hop(self.UHzz, ind_i=i*4+1, ind_j=f*4+1, ind_R=[-1, -1, 0], mode='reset')\n", " #z-x\n", " self.model.set_hop( self.UHzx, ind_i=i*4+1, ind_j=f*4+2, ind_R=[0, 0, 0], mode='reset')\n", " self.model.set_hop(-0.5*self.UHzx, ind_i=i*4+1, ind_j=f*4+2, ind_R=[-1, 0, 0], mode='reset')\n", " self.model.set_hop(-0.5*self.UHzx, ind_i=i*4+1, ind_j=f*4+2, ind_R=[-1, -1, 0], mode='reset')\n", " #z-y\n", " self.model.set_hop( np.sqrt(3)/2*self.UHzx, ind_i=i*4+1, ind_j=f*4+3, ind_R=[-1, 0, 0], mode='reset')\n", " self.model.set_hop(-np.sqrt(3)/2*self.UHzx, ind_i=i*4+1, ind_j=f*4+3, ind_R=[-1, -1, 0], mode='reset')\n", " \"\"\"#########################################\"\"\"\n", " #x-s\n", " self.model.set_hop( self.UHxs, ind_i=i*4+2, ind_j=f*4+0, ind_R=[0, 0, 0], mode='reset')\n", " self.model.set_hop(-0.5*self.UHxs, ind_i=i*4+2, ind_j=f*4+0, ind_R=[-1, 0, 0], mode='reset')\n", " self.model.set_hop(-0.5*self.UHxs, ind_i=i*4+2, ind_j=f*4+0, ind_R=[-1, -1, 0], mode='reset')\n", " #x-z\n", " self.model.set_hop( self.UHxz, ind_i=i*4+2, ind_j=f*4+1, ind_R=[0, 0, 0], mode='reset')\n", " self.model.set_hop(-0.5*self.UHxz, ind_i=i*4+2, ind_j=f*4+1, ind_R=[-1, 0, 0], mode='reset')\n", " self.model.set_hop(-0.5*self.UHxz, ind_i=i*4+2, ind_j=f*4+1, ind_R=[-1, -1, 0], mode='reset')\n", " #x-x\n", " self.model.set_hop( self.UHxx, ind_i=i*4+2, ind_j=f*4+2, ind_R=[0, 0, 0], mode='reset')\n", " self.model.set_hop(-0.5*self.UHxx, ind_i=i*4+2, ind_j=f*4+2, ind_R=[-1, 0, 0], mode='reset')\n", " self.model.set_hop(-0.5*self.UHxx, ind_i=i*4+2, ind_j=f*4+2, ind_R=[-1, -1, 0], mode='reset')\n", " self.model.set_hop(3/4*(self.UHxx+self.UHyy), ind_i=i*4+2, ind_j=f*4+2, ind_R=[-1, 0, 0], mode='add')\n", " self.model.set_hop(3/4*(self.UHxx+self.UHyy), ind_i=i*4+2, ind_j=f*4+2, ind_R=[-1, -1, 0], mode='add') \n", " #x-y\n", " self.model.set_hop(-np.sqrt(3)/4*(self.UHxx-self.UHyy), ind_i=i*4+2, ind_j=f*4+3, ind_R=[-1, 0, 0], mode='reset')\n", " self.model.set_hop( np.sqrt(3)/4*(self.UHxx-self.UHyy), ind_i=i*4+2, ind_j=f*4+3, ind_R=[-1, -1, 0], mode='reset')\n", " \"\"\"###########################################\"\"\"\n", " #y-s\n", " self.model.set_hop( np.sqrt(3)/2*self.UHxs, ind_i=i*4+3, ind_j=f*4+0, ind_R=[-1, 0, 0], mode='reset')\n", " self.model.set_hop(-np.sqrt(3)/2*self.UHxs, ind_i=i*4+3, ind_j=f*4+0, ind_R=[-1, -1, 0], mode='reset')\n", " #y-z\n", " self.model.set_hop( np.sqrt(3)/2*self.UHxz, ind_i=i*4+3, ind_j=f*4+1, ind_R=[-1, 0, 0], mode='reset')\n", " self.model.set_hop(-np.sqrt(3)/2*self.UHxz, ind_i=i*4+3, ind_j=f*4+1, ind_R=[-1, -1, 0], mode='reset')\n", " #y-x\n", " self.model.set_hop(-np.sqrt(3)/4*(self.UHxx-self.UHyy), ind_i=i*4+3, ind_j=f*4+2, ind_R=[-1, 0, 0], mode='reset')\n", " self.model.set_hop( np.sqrt(3)/4*(self.UHxx-self.UHyy), ind_i=i*4+3, ind_j=f*4+2, ind_R=[-1, -1, 0], mode='reset') \n", " #y-y\n", " self.model.set_hop( self.UHyy, ind_i=i*4+3, ind_j=f*4+3, ind_R=[0, 0, 0], mode='reset')\n", " self.model.set_hop(-0.5*self.UHyy, ind_i=i*4+3, ind_j=f*4+3, ind_R=[-1, 0, 0], mode='reset')\n", " self.model.set_hop(-0.5*self.UHyy, ind_i=i*4+3, ind_j=f*4+3, ind_R=[-1, -1, 0], mode='reset')\n", " self.model.set_hop(3/4*(self.UHxx+self.UHyy), ind_i=i*4+3, ind_j=f*4+3, ind_R=[-1, 0, 0], mode='add')\n", " self.model.set_hop(3/4*(self.UHxx+self.UHyy), ind_i=i*4+3, ind_j=f*4+3, ind_R=[-1, -1, 0], mode='add') \n", "\n", " def _M13(self, i, f):\n", " \"\"\" The M24 transition matrix designed by Kobayashi et Al.\"\"\"\n", " #s-s\n", " self.model.set_hop(self.UHss, ind_i=i*4+0, ind_j=f*4+0, ind_R=[0, 0, 0], mode='reset')\n", " self.model.set_hop(self.UHss, ind_i=i*4+0, ind_j=f*4+0, ind_R=[1, 0, 0], mode='reset')\n", " self.model.set_hop(self.UHss, ind_i=i*4+0, ind_j=f*4+0, ind_R=[1, 1, 0], mode='reset')\n", " #s-z\n", " self.model.set_hop(self.UHsz, ind_i=i*4+0, ind_j=f*4+1, ind_R=[0, 0, 0], mode='reset')\n", " self.model.set_hop(self.UHsz, ind_i=i*4+0, ind_j=f*4+1, ind_R=[1, 0, 0], mode='reset')\n", " self.model.set_hop(self.UHsz, ind_i=i*4+0, ind_j=f*4+1, ind_R=[1, 1, 0], mode='reset')\n", " #s-x\n", " self.model.set_hop( -self.UHsx, ind_i=i*4+0, ind_j=f*4+2, ind_R=[0, 0, 0], mode='reset')\n", " self.model.set_hop(0.5*self.UHsx, ind_i=i*4+0, ind_j=f*4+2, ind_R=[1, 0, 0], mode='reset')\n", " self.model.set_hop(0.5*self.UHsx, ind_i=i*4+0, ind_j=f*4+2, ind_R=[1, 1, 0], mode='reset')\n", " #s-y\n", " self.model.set_hop(-np.sqrt(3)/2*self.UHsx, ind_i=i*4+0, ind_j=f*4+3, ind_R=[1, 0, 0], mode='reset')\n", " self.model.set_hop( np.sqrt(3)/2*self.UHsx, ind_i=i*4+0, ind_j=f*4+3, ind_R=[1, 1, 0], mode='reset')\n", " \"\"\"##########################################\"\"\"\n", " #z-s\n", " self.model.set_hop(self.UHzs, ind_i=i*4+1, ind_j=f*4+0, ind_R=[0, 0, 0], mode='reset')\n", " self.model.set_hop(self.UHzs, ind_i=i*4+1, ind_j=f*4+0, ind_R=[1, 0, 0], mode='reset')\n", " self.model.set_hop(self.UHzs, ind_i=i*4+1, ind_j=f*4+0, ind_R=[1, 1, 0], mode='reset')\n", " #z-z\n", " self.model.set_hop(self.UHzz, ind_i=i*4+1, ind_j=f*4+1, ind_R=[0, 0, 0], mode='reset')\n", " self.model.set_hop(self.UHzz, ind_i=i*4+1, ind_j=f*4+1, ind_R=[1, 0, 0], mode='reset')\n", " self.model.set_hop(self.UHzz, ind_i=i*4+1, ind_j=f*4+1, ind_R=[1, 1, 0], mode='reset')\n", " #z-x\n", " self.model.set_hop( -self.UHzx, ind_i=i*4+1, ind_j=f*4+2, ind_R=[0, 0, 0], mode='reset')\n", " self.model.set_hop(0.5*self.UHzx, ind_i=i*4+1, ind_j=f*4+2, ind_R=[1, 0, 0], mode='reset')\n", " self.model.set_hop(0.5*self.UHzx, ind_i=i*4+1, ind_j=f*4+2, ind_R=[1, 1, 0], mode='reset')\n", " #z-y\n", " self.model.set_hop(-np.sqrt(3)/2*self.UHzx, ind_i=i*4+1, ind_j=f*4+3, ind_R=[1, 0, 0], mode='reset')\n", " self.model.set_hop( np.sqrt(3)/2*self.UHzx, ind_i=i*4+1, ind_j=f*4+3, ind_R=[1, 1, 0], mode='reset')\n", " \"\"\"#########################################\"\"\"\n", " #x-s\n", " self.model.set_hop( -self.UHxs, ind_i=i*4+2, ind_j=f*4+0, ind_R=[0, 0, 0], mode='reset')\n", " self.model.set_hop(0.5*self.UHxs, ind_i=i*4+2, ind_j=f*4+0, ind_R=[1, 0, 0], mode='reset')\n", " self.model.set_hop(0.5*self.UHxs, ind_i=i*4+2, ind_j=f*4+0, ind_R=[1, 1, 0], mode='reset')\n", " #x-z\n", " self.model.set_hop( -self.UHxz, ind_i=i*4+2, ind_j=f*4+1, ind_R=[0, 0, 0], mode='reset')\n", " self.model.set_hop(0.5*self.UHxz, ind_i=i*4+2, ind_j=f*4+1, ind_R=[1, 0, 0], mode='reset')\n", " self.model.set_hop(0.5*self.UHxz, ind_i=i*4+2, ind_j=f*4+1, ind_R=[1, 1, 0], mode='reset')\n", " #x-x\n", " self.model.set_hop( self.UHxx, ind_i=i*4+2, ind_j=f*4+2, ind_R=[0, 0, 0], mode='reset')\n", " self.model.set_hop(-0.5*self.UHxx, ind_i=i*4+2, ind_j=f*4+2, ind_R=[1, 0, 0], mode='reset')\n", " self.model.set_hop(-0.5*self.UHxx, ind_i=i*4+2, ind_j=f*4+2, ind_R=[1, 1, 0], mode='reset')\n", " self.model.set_hop(3/4*(self.UHxx+self.UHyy), ind_i=i*4+2, ind_j=f*4+2, ind_R=[1, 0, 0], mode='add')\n", " self.model.set_hop(3/4*(self.UHxx+self.UHyy), ind_i=i*4+2, ind_j=f*4+2, ind_R=[1, 1, 0], mode='add') \n", " #x-y\n", " self.model.set_hop(-np.sqrt(3)/4*(self.UHxx-self.UHyy), ind_i=i*4+2, ind_j=f*4+3, ind_R=[1, 0, 0], mode='reset')\n", " self.model.set_hop( np.sqrt(3)/4*(self.UHxx-self.UHyy), ind_i=i*4+2, ind_j=f*4+3, ind_R=[1, 1, 0], mode='reset')\n", " \"\"\"###########################################\"\"\"\n", " #y-s\n", " self.model.set_hop(-np.sqrt(3)/2*self.UHxs, ind_i=i*4+3, ind_j=f*4+0, ind_R=[1, 0, 0], mode='reset')\n", " self.model.set_hop( np.sqrt(3)/2*self.UHxs, ind_i=i*4+3, ind_j=f*4+0, ind_R=[1, 1, 0], mode='reset')\n", " #y-z\n", " self.model.set_hop(-np.sqrt(3)/2*self.UHxz, ind_i=i*4+3, ind_j=f*4+1, ind_R=[1, 0, 0], mode='reset')\n", " self.model.set_hop( np.sqrt(3)/2*self.UHxz, ind_i=i*4+3, ind_j=f*4+1, ind_R=[1, 1, 0], mode='reset')\n", " #y-x\n", " self.model.set_hop(-np.sqrt(3)/4*(self.UHxx-self.UHyy), ind_i=i*4+3, ind_j=f*4+2, ind_R=[1, 0, 0], mode='reset')\n", " self.model.set_hop( np.sqrt(3)/4*(self.UHxx-self.UHyy), ind_i=i*4+3, ind_j=f*4+2, ind_R=[1, 1, 0], mode='reset') \n", " #y-y\n", " self.model.set_hop( self.UHyy, ind_i=i*4+3, ind_j=f*4+3, ind_R=[0, 0, 0], mode='reset')\n", " self.model.set_hop(-0.5*self.UHyy, ind_i=i*4+3, ind_j=f*4+3, ind_R=[1, 0, 0], mode='reset')\n", " self.model.set_hop(-0.5*self.UHyy, ind_i=i*4+3, ind_j=f*4+3, ind_R=[1, 1, 0], mode='reset')\n", " self.model.set_hop(3/4*(self.UHxx+self.UHyy), ind_i=i*4+3, ind_j=f*4+3, ind_R=[1, 0, 0], mode='add')\n", " self.model.set_hop(3/4*(self.UHxx+self.UHyy), ind_i=i*4+3, ind_j=f*4+3, ind_R=[1, 1, 0], mode='add') \n", " \n", " def f0(self,conjugate=False):\n", " \n", " conjugate = -2*conjugate+1\n", "\n", " f = np.array([\n", " [ 0, 0, 0],\n", " [-1, 0, 0],\n", " [-1, -1, 0]\n", " ])*conjugate\n", "\n", " w = np.array([\n", " 1, \n", " 1, \n", " 1\n", " ])\n", " return f,w\n", " \n", " def f1(self,conjugate=False):\n", " \n", " conjugate = -2*conjugate+1\n", "\n", " f = np.array([\n", " [ 0, 0, 0],\n", " [-1, 0, 0],\n", " [-1, -1, 0]\n", " ])*conjugate\n", "\n", " w = np.array([\n", " 1, \n", " -1/2, \n", " -1/2\n", " ])\n", " return f,w\n", " \n", " def f2(self,conjugate=False):\n", " \n", " conjugate = -2*conjugate+1\n", "\n", " f = np.array([\n", " [ 0, 0, 0],\n", " ])*conjugate\n", "\n", " w = np.array([\n", " 1\n", " ])\n", " return f,w\n", " \n", " def fplus(self,conjugate=False):\n", " \n", " conjugate = -2*conjugate+1\n", "\n", " f = np.array([\n", " [-1, 0, 0],\n", " [-1, -1, 0],\n", " ])*conjugate\n", "\n", " w = np.array([\n", " 1, \n", " 1\n", " ])\n", " return f,w\n", " \n", " \n", " def fminus(self,conjugate=False):\n", " \n", " conjugate = -2*conjugate+1\n", "\n", " f = np.array([\n", " [-1, 0, 0],\n", " [-1, -1, 0],\n", " ])*conjugate\n", "\n", " w = np.array([\n", " 1, \n", " -1\n", " ])\n", " return f,w\n", " \n", "\n", " \"\"\"#####################################################################\"\"\"\n", "\n", " \"\"\" \n", " def update(self):\n", " index_initial = 0\n", " for i, initial_atom in enumerate(self.crystal.atoms):\n", " index_final = index_initial\n", " for f, final_atom in enumerate(self.crystal.atoms[i:]):\n", " f = f+i\n", "\n", " for io, initial_orbital in enumerate(initial_atom.orbitals):\n", " for fo, final_orbital in enumerate(initial_atom.orbitals):\n", "\n", " # Set onsite elements\n", " if initial_atom == final_atom:\n", " if initial_orbital == final_orbital:\n", " self.model.set_onsite(self.atom_matrix[i][f][io][fo], ind_i=(index_initial+io), mode='reset')\n", " \n", " # Set hopping parameters\n", " else:\n", " for final_cell in self.atom_matrix[i][f].equivalent_final_cells:\n", " self.model.set_hop(self.atom_matrix[i][f][io][fo], ind_i=(index_initial+io), ind_j=(index_final+fo), ind_R=final_cell, mode='reset')\n", "\n", " \n", " index_final += final_atom.number_of_orbitals() \n", " index_initial += initial_atom.number_of_orbitals()\n", " \"\"\"\n", " \n", " def display_pythtb(self):\n", " \"\"\" Displat the info from pythTB \"\"\"\n", " self.model.display()\n", " \n", " def calculate(self, k_grid, eig_vectors = False):\n", " \"\"\" Calculate band energies for the given k-points \n", " \n", " :type k_grid: ndarray, list\n", " :param k_grid: an array/list of 3D k-points\n", " \n", " :type eig_vectors: bool\n", " :param eig_vectors: if eigen vectors are returned\n", " \"\"\"\n", " return self.model.solve_all(k_grid,eig_vectors=eig_vectors)\n", "\n", " \n", " def bandstructure(self, ylim=(None,None), color=None, ax=None):\n", " \"\"\" Plot a representation of the band structure\n", " \n", " :type ylim: tuple, list\n", " :param ylim: lower and upper limit of y-values (ymin,ymax)\n", " \"\"\"\n", "\n", " \"\"\" seekpath automatic lines\"\"\"\n", " #path = sp.get_explicit_k_path((lattice, positions, numbers), False, recipe=\"hpkot\", threshold=1e-5,reference_distance=1)\n", " #expath = path['explicit_kpoints_abs'][:5]\n", " #labels = path['explicit_kpoints_labels'][:5]\n", "\n", " \"\"\" manual lines\"\"\"\n", " path=[[0.0,0.0,0.5],[0.5,0.0,0.5],[0.5,0,0.0],[0.0,0.0,0.0],[0,0,0.5],[2./3.,1./3.,0.5],[2./3.,1./3.,0.0],[0,0,0]]\n", " label=(r'$A $', r'$L$', r'$M$', r'$\\Gamma$', r'$A $', r'$H$', r'$K$',r'$\\Gamma $')\n", "\n", " \n", " # call function k_path to construct the actual path\n", " (k_vec,k_dist,k_node)=self.model.k_path(path,301,report=False)\n", "\n", " evals =self.model.solve_all(k_vec)\n", " \n", " fig = None\n", " if not ax:\n", " fig, ax = plt.subplots(figsize=(8,6))\n", " fig.tight_layout()\n", "\n", " ax.set_title(\"Bandstructure for Zno based on Kobayashi\")\n", " ax.set_ylabel(\"Band energy\")\n", "\n", " # specify horizontal axis details\n", " ax.set_xlim([0,k_node[-1]])\n", " # put tickmarks and labels at node positions\n", " ax.set_xticks(k_node)\n", " ax.set_xticklabels(label)\n", " # add vertical lines at node positions\n", "\n", " for n in range(len(k_node)):\n", " if label[n] == r'$\\Gamma$':\n", " ax.axvline(x=k_node[n],linewidth=1, color='k')\n", " else:\n", " ax.axvline(x=k_node[n],linewidth=0.5, color='k')\n", " \n", " for band in evals:\n", " ax.plot(k_dist, band, color=color)\n", "\n", " if not fig:\n", " return ax\n", " else:\n", " ax.set_ylim(ylim)\n", " return ax, fig\n", "\n", "# def __repr__(self):\n", "# return \"Tight binding model for: \\n \\n {} \\n {}\".format(self.crystal, self.atom_matrix)" ] }, { "cell_type": "code", "execution_count": 59, "metadata": {}, "outputs": [], "source": [ "class TbFitter():\n", " mu = 0\n", " T = 1\n", " def __init__(self, model, fitting_k, fitting_E, band_range=(0,-1), monitor=False, tolerance=None):\n", " self.model = model\n", " self.monitor = monitor\n", " self.fitting_k = fitting_k\n", " self.fitting_E = fitting_E\n", " self.tolerance = tolerance\n", " self.band_range = band_range\n", " \n", " def fit(self):\n", " initial_args = self.model.atom_matrix.get_hopping_parameters()\n", " \n", " return minimize(self.fit_function, initial_args, tol=self.tolerance)\n", " \n", " def fit_function(self, args):\n", " Vss, Vxx, Vxy, Vsapc, Vpasc = args\n", " self.model.set_hopping_parameters(Vss, Vxx, Vxy, Vsapc, Vpasc)\n", " E =self.model.calculate(self.fitting_k)[self.band_range[0]:self.band_range[1]]\n", " \n", " \n", "\n", " diff=self.fitting_E-E\n", "\n", " diff=abs(diff)**2\n", " \n", " val=sum((diff*self.weightfun(self.fitting_E,E)).ravel())\n", " \n", " if self.monitor:\n", " print(val)\n", " \n", " self.lastval=val\n", " self.weightsum=sum((self.weightfun(self.fitting_E,E)).ravel())\n", " return val\n", " \n", " def weightfun(self,E1,E2):\n", " w1=1./np.cosh((E1-self.mu)/self.T)**2/self.T/8\n", " w2=1./np.cosh((E2-self.mu)/self.T)**2/self.T/8\n", " return w1+w2" ] }, { "cell_type": "code", "execution_count": 60, "metadata": { "scrolled": false }, "outputs": [], "source": [ "ZnO = Crystal(lattice = np.array([\n", " [ np.sqrt(3)/2, -0.5, 0.0],\n", " [ 0.0, 1.0, 0.0],\n", " [ 0.0, 0.0, 1.65]])*3.28)\n", "\n", "ZnO.add_atom(Oxygen([0,0,0]))\n", "ZnO.add_atom(Oxygen([2/3, 1/3, 1/2]))\n", "ZnO.add_atom(Zinc([2/3, 1/3, 1/8]))\n", "ZnO.add_atom(Zinc([0.0, 0.0, 5/8]))\n", "\n", "\n", "ZnO_tb = TightBinding(ZnO)\n", "\n", "Vss = -6.043\n", "Vxx = 7.157\n", "Vxy = 10.578\n", "Vsapc = 4.703\n", "Vpasc = 8.634\n", "\n", "ZnO_tb.set_hopping_parameters(Vss, Vxx, Vxy, Vsapc, Vpasc)" ] }, { "cell_type": "code", "execution_count": 71, "metadata": {}, "outputs": [ { "data": { "image/png": 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dvPzyy5BIJPj5559RVlYGDw8PPP/883j22Wc77LGtq6vD+fPnkZiYiMTERCQn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\nHAEAAHj+P6GppKwCDeL3AAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f63b622eb38>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ax, fig = ZnO_tb.bandstructure(color=\"black\",ylim=(None,None))\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 92, "metadata": {}, "outputs": [], "source": [ "fitting_k=np.array([\n", " [ 0.5, 0.0, 0.5],\n", " [ 0.0, 0.0, 0.0],\n", " [ 0.0, 0.0, 0.5],\n", " [2./3., 1./3., 0.5],\n", " [2./3., 1./3., 0.0]])\n", "\n", "fitting_E = np.array([\n", " [-5.78, -5.85, -3.63, -5.82, -5.63], #2\n", " [-5.78, -1.52, -3.63, -5.82, -5.63], #3\n", " [-2.44, -1.52, -0.79, -3.13, -3.90], #4\n", " [-2.44, 0.00, -0.79, -3.13, -2.63], #5\n", " [-2.34, 0.00, -0.79, -2.30, -2.63], #6\n", " [-2.34, 0.00, -0.79, -2.30, -2.30], #7 \n", " [ 9.05, 3.30, 6.11, 9.74, 10.55], #8 \n", "])\n", "\n", "\n", "\n", "fitting_k = fitting_k[3:]\n", "\n", "\n", "fitting_E = fitting_E[:,3:]\n", "\n", "band_range = (2,10)" ] }, { "cell_type": "code", "execution_count": 96, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[ -5.82 -5.63]\n", " [ -5.82 -5.63]\n", " [ -3.13 -3.9 ]\n", " [ -3.13 -2.63]\n", " [ -2.3 -2.63]\n", " [ -2.3 -2.3 ]\n", " [ 9.74 10.55]]\n", "[[-5.14654924 -5.81770938]\n", " [-5.14654924 -4.27114878]\n", " [-2.30128942 -3.47985341]\n", " [-2.30128942 -2.7243382 ]\n", " [-1.9722264 -1.19709911]\n", " [-1.9722264 -1.16022449]]\n" ] } ], "source": [ "print(fitting_E)\n", "print(ZnO_tb.calculate(fitting_k)[2:8])\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "ZnO_fitter = TbFitter(model=ZnO_tb, fitting_k=fitting_k, fitting_E=fitting_E, band_range=band_range, monitor=False)" ] }, { "cell_type": "code", "execution_count": 81, "metadata": {}, "outputs": [], "source": [ "\n", "\n", "bands, waves = ZnO_tb.calculate([[0, 0, 0]],True)" ] }, { "cell_type": "code", "execution_count": 85, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(array([[ -5.85021301e+00],\n", " [ -1.51993352e+00],\n", " [ -1.51993352e+00],\n", " [ 3.39619342e-04],\n", " [ 3.39619342e-04],\n", " [ 3.39619342e-04],\n", " [ 3.30018703e+00],\n", " [ 7.38985695e+00]]),\n", " array([[[ 4.60156348e-02+0.j, -4.89242107e-01+0.j, 1.66533454e-16+0.j,\n", " 1.11022302e-16+0.j, -4.60156348e-02+0.j, 4.89242107e-01+0.j,\n", " -2.40457479e-17+0.j, -2.69760010e-17+0.j, -4.68208634e-01+0.j,\n", " -1.98255889e-01+0.j, -9.41114618e-17+0.j, -1.15183043e-16+0.j,\n", " 4.68208634e-01+0.j, 1.98255889e-01+0.j, 1.99421395e-17+0.j,\n", " 2.13472683e-17+0.j]],\n", " \n", " [[ 2.73566479e-18+0.j, -1.29259798e-16+0.j, -5.63327865e-01+0.j,\n", " 1.94561682e-01+0.j, -2.77555756e-17+0.j, -5.55111512e-17+0.j,\n", " 5.63327865e-01+0.j, -1.94561682e-01+0.j, -6.84966767e-17+0.j,\n", " 1.14606573e-17+0.j, 3.59687050e-01+0.j, -1.24228397e-01+0.j,\n", " 1.29823440e-16+0.j, 4.40504939e-17+0.j, -3.59687050e-01+0.j,\n", " 1.24228397e-01+0.j]],\n", " \n", " [[ 7.67992195e-18+0.j, -2.44298317e-17+0.j, -1.94561682e-01+0.j,\n", " -5.63327865e-01+0.j, -7.63278329e-17+0.j, 5.20417043e-17+0.j,\n", " 1.94561682e-01+0.j, 5.63327865e-01+0.j, -8.30221386e-17+0.j,\n", " 1.10912403e-16+0.j, 1.24228397e-01+0.j, 3.59687050e-01+0.j,\n", " 1.59676089e-16+0.j, 1.11132202e-16+0.j, -1.24228397e-01+0.j,\n", " -3.59687050e-01+0.j]],\n", " \n", " [[ 2.74944061e-17+0.j, 4.58862131e-01+0.j, 1.29339482e-01+0.j,\n", " -3.83774465e-01+0.j, 2.08166817e-17+0.j, 4.58862131e-01+0.j,\n", " 1.29339482e-01+0.j, -3.83774465e-01+0.j, -1.11022302e-16+0.j,\n", " -2.65537391e-01+0.j, -7.48470322e-02+0.j, 2.22085161e-01+0.j,\n", " 3.05311332e-16+0.j, -2.65537391e-01+0.j, -7.48470322e-02+0.j,\n", " 2.22085161e-01+0.j]],\n", " \n", " [[ 6.48081543e-18+0.j, 1.42720154e-01+0.j, 4.91114833e-01+0.j,\n", " 3.36159449e-01+0.j, 5.55111512e-17+0.j, 1.42720154e-01+0.j,\n", " 4.91114833e-01+0.j, 3.36159449e-01+0.j, 6.93889390e-17+0.j,\n", " -8.25902486e-02+0.j, -2.84201600e-01+0.j, -1.94530986e-01+0.j,\n", " -2.01227923e-16+0.j, -8.25902486e-02+0.j, -2.84201600e-01+0.j,\n", " -1.94530986e-01+0.j]],\n", " \n", " [[ 2.02497722e-17-0.j, 3.79001978e-01+0.j, -3.41531134e-01+0.j,\n", " 3.38053224e-01+0.j, -1.38777878e-17+0.j, 3.79001978e-01+0.j,\n", " -3.41531134e-01+0.j, 3.38053224e-01+0.j, 0.00000000e+00+0.j,\n", " -2.19323386e-01+0.j, 1.97639509e-01+0.j, -1.95626889e-01+0.j,\n", " 5.55111512e-17+0.j, -2.19323386e-01+0.j, 1.97639509e-01+0.j,\n", " -1.95626889e-01+0.j]],\n", " \n", " [[ -1.84589888e-01+0.j, 1.11022302e-16+0.j, 1.11022302e-16+0.j,\n", " 2.77555756e-17+0.j, -1.84589888e-01+0.j, -8.32667268e-17+0.j,\n", " 1.74948464e-17+0.j, 1.11030744e-17+0.j, 6.82588143e-01+0.j,\n", " -5.55111512e-17+0.j, -5.72717948e-17+0.j, -5.24887412e-17+0.j,\n", " 6.82588143e-01+0.j, 0.00000000e+00+0.j, -7.95146072e-18+0.j,\n", " -6.76934794e-18+0.j]],\n", " \n", " [[ 1.13245922e-01+0.j, 3.29808141e-01+0.j, 8.32667268e-17+0.j,\n", " 1.11022302e-16+0.j, -1.13245922e-01+0.j, -3.29808141e-01+0.j,\n", " 1.31427208e-17+0.j, 8.15571535e-18+0.j, -4.90619332e-01+0.j,\n", " 3.71072260e-01+0.j, -1.03913628e-16+0.j, -1.02443166e-16+0.j,\n", " 4.90619332e-01+0.j, -3.71072260e-01+0.j, -5.76447980e-18+0.j,\n", " -7.62905814e-18+0.j]]]))" ] }, "execution_count": 85, "metadata": {}, "output_type": "execute_result" } ], "source": [ "bands[2:10],waves[2:10]" ] }, { "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": "3.6.3" } }, "nbformat": 4, "nbformat_minor": 2 }

gpl-3.0

amirziai/learning

machine-learning/naive-bayes-from-scratch.ipynb

1

7682

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Naive Bayes from scratch" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "def get_accuracy(x: pd.DataFrame, y: pd.Series, y_hat: pd.Series):\n", " correct = y_hat == y\n", " acc = np.sum(correct) / len(y)\n", " cond = y == 1\n", " y1 = len(y[cond])\n", " y0 = len(y[~cond])\n", "\n", " print(f'Class 0: tested {y0}, correctly classified {correct[~cond].sum()}')\n", " print(f'Class 1: tested {y1}, correctly classified {correct[cond].sum()}')\n", " print(f'Overall: tested {len(y)}, correctly classified {correct.sum()}')\n", " print(f'Accuracy = {acc:.2f}')" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "class Classifier:\n", " def __init__(self, dataset: str = None, mle: bool=True):\n", " if dataset:\n", " x_train, y_train = reader(f'datasets/{dataset}-train.txt')\n", " x_test, y_test = reader(f'datasets/{dataset}-test.txt')\n", " self.train(x_train, y_train, mle)\n", " print('Training accuracy')\n", " print('=' * 10)\n", " self.accuracy(x_train, y_train)\n", " print('Test accuracy')\n", " print('=' * 10)\n", " self.accuracy(x_test, y_test)\n", " \n", " def accuracy(self, x: pd.DataFrame, y: pd.DataFrame) -> None:\n", " y_hat = self.predict(x)\n", " get_accuracy(x, y, y_hat)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "class NB(Classifier):\n", " def __init__(self, dataset: str = None, mle: bool=True):\n", " self.prior = None\n", " self.p_xi_given_y = {0: {}, 1: {}}\n", " self.prior_x = {}\n", " self.cols = None\n", " super().__init__(dataset, mle)\n", " \n", " def train(self, x: pd.DataFrame, y: pd.Series, mle: bool=True):\n", " adj_den = 0 if mle else 2\n", " adj_num = 0 if mle else 1\n", " self.prior = y.value_counts().to_dict()\n", " for c in [0, 1]:\n", " self.prior[c] += adj_num\n", " self.prior[c] /= (len(y) + adj_den)\n", " \n", " self.cols = x.columns\n", " for col in x.columns:\n", " self.prior_x[col] = (x[col].value_counts() / len(y)).to_dict()\n", " \n", " cond = y == 1\n", " y1 = np.sum(cond)\n", " y0 = len(y) - y1\n", " y1 += adj_den\n", " y0 += adj_den\n", " x_pos = x[cond]\n", " x_neg = x[~cond]\n", " for cls in [0, 1]:\n", " for col in x.columns:\n", " x_cls = x_pos if cls == 1 else x_neg\n", " y_cls = y1 if cls == 1 else y0\n", " x1 = len(x_cls.query(f'{col} == 1'))\n", " x0 = len(x_cls.query(f'{col} == 0'))\n", " \n", " x1 += adj_num\n", " x0 += adj_num\n", " \n", " self.p_xi_given_y[cls][col] = {\n", " 0: x0 / y_cls,\n", " 1: x1 / y_cls\n", " }\n", " \n", " def predict(self, x: pd.DataFrame) -> pd.Series:\n", " out = []\n", " for _, row in x.iterrows():\n", " m = {}\n", " for cls in [0, 1]:\n", " m[cls] = np.log([self.prior[0]] + [\n", " self.p_xi_given_y[cls][col][row[col]]\n", " for col in x.columns\n", " ]).sum()\n", " out.append(1 if m[1] >= m[0] else 0)\n", " return pd.Series(out)\n", " \n", " def _get_ind(self, col):\n", " num = self.prior_x[col][0] * self.p_xi_given_y[1][col][1]\n", " den = self.prior_x[col][1] * self.p_xi_given_y[1][col][0]\n", " return num / den\n", " \n", " def most_indicative(self):\n", " return pd.Series({\n", " col: self._get_ind(col)\n", " for col in self.cols\n", " }).sort_values(ascending=False)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "x = pd.DataFrame({'x1': [0, 0, 1, 1], 'x2': [0, 1, 0, 1]})\n", "y = pd.Series([0, 0, 1, 1])" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>x1</th>\n", " <th>x2</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>0</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>1</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>1</td>\n", " <td>1</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " x1 x2\n", "0 0 0\n", "1 0 1\n", "2 1 0\n", "3 1 1" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Class 0: tested 2, correctly classified 2\n", "Class 1: tested 2, correctly classified 2\n", "Overall: tested 4, correctly classified 4\n", "Accuracy = 1.00\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:50: RuntimeWarning: divide by zero encountered in log\n" ] } ], "source": [ "nb = NB()\n", "nb.train(x, y)\n", "nb.accuracy(x, y)" ] } ], "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": "3.7.2" } }, "nbformat": 4, "nbformat_minor": 2 }

mit

harish-garg/Machine-Learning

udacity/enron/ud120-projects-master/final_project/Enron Data Analysis.ipynb

2

29264

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# EDA\n", "## Import the dataset, explore and summarize it" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# load the necessary python modules\n", "import matplotlib.pyplot as plt\n", "import matplotlib\n", "import pickle\n", "import pandas as pd \n", "import numpy as np\n", "from IPython.display import display\n", "%matplotlib notebook" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": true }, "outputs": [], "source": [ "### Load the dictionary containing the dataset. This code taken from poi_id.py script provided by udacity. \n", "with open(\"final_project_dataset.pkl\", \"r\") as data_file:\n", " data_dict = pickle.load(data_file)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Total Number of persons: 146\n", "Total Number of features: 21\n", "Total Number of POIs: 18\n" ] } ], "source": [ "# get some initial stats for the project report.\n", "print(\"Total Number of persons: %d\"%len(data_dict.keys()))\n", "print(\"Total Number of features: %d\"%len(list(data_dict.values())[0]))\n", "print(\"Total Number of POIs: %d\"%sum([1 if x['poi'] else 0 for x in data_dict.values()])) " ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['METTS MARK', 'BAXTER JOHN C', 'ELLIOTT STEVEN', 'CORDES WILLIAM R', 'HANNON KEVIN P', 'MORDAUNT KRISTINA M', 'MEYER ROCKFORD G', 'MCMAHON JEFFREY', 'HORTON STANLEY C', 'PIPER GREGORY F', 'HUMPHREY GENE E', 'UMANOFF ADAM S', 'BLACHMAN JEREMY M', 'SUNDE MARTIN', 'GIBBS DANA R', 'LOWRY CHARLES P', 'COLWELL WESLEY', 'MULLER MARK S', 'JACKSON CHARLENE R', 'WESTFAHL RICHARD K', 'WALTERS GARETH W', 'WALLS JR ROBERT H', 'KITCHEN LOUISE', 'CHAN RONNIE', 'BELFER ROBERT', 'SHANKMAN JEFFREY A', 'WODRASKA JOHN', 'BERGSIEKER RICHARD P', 'URQUHART JOHN A', 'BIBI PHILIPPE A', 'RIEKER PAULA H', 'WHALEY DAVID A', 'BECK SALLY W', 'HAUG DAVID L', 'ECHOLS JOHN B', 'MENDELSOHN JOHN', 'HICKERSON GARY J', 'CLINE KENNETH W', 'LEWIS RICHARD', 'HAYES ROBERT E', 'MCCARTY DANNY J', 'KOPPER MICHAEL J', 'LEFF DANIEL P', 'LAVORATO JOHN J', 'BERBERIAN DAVID', 'DETMERING TIMOTHY J', 'WAKEHAM JOHN', 'POWERS WILLIAM', 'GOLD JOSEPH', 'BANNANTINE JAMES M', 'DUNCAN JOHN H', 'SHAPIRO RICHARD S', 'SHERRIFF JOHN R', 'SHELBY REX', 'LEMAISTRE CHARLES', 'DEFFNER JOSEPH M', 'KISHKILL JOSEPH G', 'WHALLEY LAWRENCE G', 'MCCONNELL MICHAEL S', 'PIRO JIM', 'DELAINEY DAVID W', 'SULLIVAN-SHAKLOVITZ COLLEEN', 'WROBEL BRUCE', 'LINDHOLM TOD A', 'MEYER JEROME J', 'LAY KENNETH L', 'BUTTS ROBERT H', 'OLSON CINDY K', 'MCDONALD REBECCA', 'CUMBERLAND MICHAEL S', 'GAHN ROBERT S', 'MCCLELLAN GEORGE', 'HERMANN ROBERT J', 'SCRIMSHAW MATTHEW', 'GATHMANN WILLIAM D', 'HAEDICKE MARK E', 'BOWEN JR RAYMOND M', 'GILLIS JOHN', 'FITZGERALD JAY L', 'MORAN MICHAEL P', 'REDMOND BRIAN L', 'BAZELIDES PHILIP J', 'BELDEN TIMOTHY N', 'DURAN WILLIAM D', 'THORN TERENCE H', 'FASTOW ANDREW S', 'FOY JOE', 'CALGER CHRISTOPHER F', 'RICE KENNETH D', 'KAMINSKI WINCENTY J', 'LOCKHART EUGENE E', 'COX DAVID', 'OVERDYKE JR JERE C', 'PEREIRA PAULO V. FERRAZ', 'STABLER FRANK', 'SKILLING JEFFREY K', 'BLAKE JR. NORMAN P', 'SHERRICK JEFFREY B', 'PRENTICE JAMES', 'GRAY RODNEY', 'PICKERING MARK R', 'THE TRAVEL AGENCY IN THE PARK', 'NOLES JAMES L', 'KEAN STEVEN J', 'TOTAL', 'FOWLER PEGGY', 'WASAFF GEORGE', 'WHITE JR THOMAS E', 'CHRISTODOULOU DIOMEDES', 'ALLEN PHILLIP K', 'SHARP VICTORIA T', 'JAEDICKE ROBERT', 'WINOKUR JR. HERBERT S', 'BROWN MICHAEL', 'BADUM JAMES P', 'HUGHES JAMES A', 'REYNOLDS LAWRENCE', 'DIMICHELE RICHARD G', 'BHATNAGAR SANJAY', 'CARTER REBECCA C', 'BUCHANAN HAROLD G', 'YEAP SOON', 'MURRAY JULIA H', 'GARLAND C KEVIN', 'DODSON KEITH', 'YEAGER F SCOTT', 'HIRKO JOSEPH', 'DIETRICH JANET R', 'DERRICK JR. JAMES V', 'FREVERT MARK A', 'PAI LOU L', 'BAY FRANKLIN R', 'HAYSLETT RODERICK J', 'FUGH JOHN L', 'FALLON JAMES B', 'KOENIG MARK E', 'SAVAGE FRANK', 'IZZO LAWRENCE L', 'TILNEY ELIZABETH A', 'MARTIN AMANDA K', 'BUY RICHARD B', 'GRAMM WENDY L', 'CAUSEY RICHARD A', 'TAYLOR MITCHELL S', 'DONAHUE JR JEFFREY M', 'GLISAN JR BEN F']\n" ] } ], "source": [ "print data_dict.keys()" ] }, { "cell_type": "code", "execution_count": 15, "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>bonus</th>\n", " <th>deferral_payments</th>\n", " <th>deferred_income</th>\n", " <th>director_fees</th>\n", " <th>exercised_stock_options</th>\n", " <th>expenses</th>\n", " <th>from_messages</th>\n", " <th>from_poi_to_this_person</th>\n", " <th>from_this_person_to_poi</th>\n", " <th>loan_advances</th>\n", " <th>long_term_incentive</th>\n", " <th>other</th>\n", " <th>restricted_stock</th>\n", " <th>restricted_stock_deferred</th>\n", " <th>salary</th>\n", " <th>shared_receipt_with_poi</th>\n", " <th>to_messages</th>\n", " <th>total_payments</th>\n", " <th>total_stock_value</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>count</th>\n", " <td>8.100000e+01</td>\n", " <td>3.800000e+01</td>\n", " <td>4.800000e+01</td>\n", " <td>16.000000</td>\n", " <td>1.010000e+02</td>\n", " <td>94.000000</td>\n", " <td>86.000000</td>\n", " <td>86.000000</td>\n", " <td>86.000000</td>\n", " <td>3.000000e+00</td>\n", " <td>6.500000e+01</td>\n", " <td>9.100000e+01</td>\n", " <td>1.090000e+02</td>\n", " <td>1.700000e+01</td>\n", " <td>9.400000e+01</td>\n", " <td>86.000000</td>\n", " <td>86.000000</td>\n", " <td>1.230000e+02</td>\n", " <td>1.250000e+02</td>\n", " </tr>\n", " <tr>\n", " <th>mean</th>\n", " <td>1.201773e+06</td>\n", " <td>8.416025e+05</td>\n", " <td>-5.810498e+05</td>\n", " <td>89822.875000</td>\n", " <td>2.959559e+06</td>\n", " <td>54192.010638</td>\n", " <td>608.790698</td>\n", " <td>64.895349</td>\n", " <td>41.232558</td>\n", " <td>2.797500e+07</td>\n", " <td>7.464912e+05</td>\n", " <td>4.664105e+05</td>\n", " <td>1.147424e+06</td>\n", " <td>6.218928e+05</td>\n", " <td>2.840875e+05</td>\n", " <td>1176.465116</td>\n", " <td>2073.860465</td>\n", " <td>2.641806e+06</td>\n", " <td>3.352073e+06</td>\n", " </tr>\n", " <tr>\n", " <th>std</th>\n", " <td>1.441679e+06</td>\n", " <td>1.289323e+06</td>\n", " <td>9.420764e+05</td>\n", " <td>41112.700735</td>\n", " <td>5.499450e+06</td>\n", " <td>46108.377454</td>\n", " <td>1841.033949</td>\n", " <td>86.979244</td>\n", " <td>100.073111</td>\n", " <td>4.638256e+07</td>\n", " <td>8.629174e+05</td>\n", " <td>1.397376e+06</td>\n", " <td>2.249770e+06</td>\n", " <td>3.845528e+06</td>\n", " <td>1.771311e+05</td>\n", " <td>1178.317641</td>\n", " <td>2582.700981</td>\n", " <td>9.524694e+06</td>\n", " <td>6.532883e+06</td>\n", " </tr>\n", " <tr>\n", " <th>min</th>\n", " <td>7.000000e+04</td>\n", " <td>-1.025000e+05</td>\n", " <td>-3.504386e+06</td>\n", " <td>3285.000000</td>\n", " <td>3.285000e+03</td>\n", " <td>148.000000</td>\n", " <td>12.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>4.000000e+05</td>\n", " <td>6.922300e+04</td>\n", " <td>2.000000e+00</td>\n", " <td>-2.604490e+06</td>\n", " <td>-1.787380e+06</td>\n", " <td>4.770000e+02</td>\n", " <td>2.000000</td>\n", " <td>57.000000</td>\n", " <td>1.480000e+02</td>\n", " <td>-4.409300e+04</td>\n", " </tr>\n", " <tr>\n", " <th>25%</th>\n", " <td>4.250000e+05</td>\n", " <td>7.964450e+04</td>\n", " <td>-6.112092e+05</td>\n", " <td>83674.500000</td>\n", " <td>5.067650e+05</td>\n", " <td>22479.000000</td>\n", " <td>22.750000</td>\n", " <td>10.000000</td>\n", " <td>1.000000</td>\n", " <td>1.200000e+06</td>\n", " <td>2.750000e+05</td>\n", " <td>1.203000e+03</td>\n", " <td>2.520550e+05</td>\n", " <td>-3.298250e+05</td>\n", " <td>2.118020e+05</td>\n", " <td>249.750000</td>\n", " <td>541.250000</td>\n", " <td>3.969340e+05</td>\n", " <td>4.941360e+05</td>\n", " </tr>\n", " <tr>\n", " <th>50%</th>\n", " <td>7.500000e+05</td>\n", " <td>2.210635e+05</td>\n", " <td>-1.519270e+05</td>\n", " <td>106164.500000</td>\n", " <td>1.297049e+06</td>\n", " <td>46547.500000</td>\n", " <td>41.000000</td>\n", " <td>35.000000</td>\n", " <td>8.000000</td>\n", " <td>2.000000e+06</td>\n", " <td>4.221580e+05</td>\n", " <td>5.158700e+04</td>\n", " <td>4.410960e+05</td>\n", " <td>-1.402640e+05</td>\n", " <td>2.587410e+05</td>\n", " <td>740.500000</td>\n", " <td>1211.000000</td>\n", " <td>1.101393e+06</td>\n", " <td>1.095040e+06</td>\n", " </tr>\n", " <tr>\n", " <th>75%</th>\n", " <td>1.200000e+06</td>\n", " <td>8.672112e+05</td>\n", " <td>-3.792600e+04</td>\n", " <td>112815.000000</td>\n", " <td>2.542813e+06</td>\n", " <td>78408.500000</td>\n", " <td>145.500000</td>\n", " <td>72.250000</td>\n", " <td>24.750000</td>\n", " <td>4.176250e+07</td>\n", " <td>8.318090e+05</td>\n", " <td>3.319830e+05</td>\n", " <td>9.850320e+05</td>\n", " <td>-7.241900e+04</td>\n", " <td>3.086065e+05</td>\n", " <td>1888.250000</td>\n", " <td>2634.750000</td>\n", " <td>2.087530e+06</td>\n", " <td>2.606763e+06</td>\n", " </tr>\n", " <tr>\n", " <th>max</th>\n", " <td>8.000000e+06</td>\n", " <td>6.426990e+06</td>\n", " <td>-8.330000e+02</td>\n", " <td>137864.000000</td>\n", " <td>3.434838e+07</td>\n", " <td>228763.000000</td>\n", " <td>14368.000000</td>\n", " <td>528.000000</td>\n", " <td>609.000000</td>\n", " <td>8.152500e+07</td>\n", " <td>5.145434e+06</td>\n", " <td>1.035973e+07</td>\n", " <td>1.476169e+07</td>\n", " <td>1.545629e+07</td>\n", " <td>1.111258e+06</td>\n", " <td>5521.000000</td>\n", " <td>15149.000000</td>\n", " <td>1.035598e+08</td>\n", " <td>4.911008e+07</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " bonus deferral_payments deferred_income director_fees \\\n", "count 8.100000e+01 3.800000e+01 4.800000e+01 16.000000 \n", "mean 1.201773e+06 8.416025e+05 -5.810498e+05 89822.875000 \n", "std 1.441679e+06 1.289323e+06 9.420764e+05 41112.700735 \n", "min 7.000000e+04 -1.025000e+05 -3.504386e+06 3285.000000 \n", "25% 4.250000e+05 7.964450e+04 -6.112092e+05 83674.500000 \n", "50% 7.500000e+05 2.210635e+05 -1.519270e+05 106164.500000 \n", "75% 1.200000e+06 8.672112e+05 -3.792600e+04 112815.000000 \n", "max 8.000000e+06 6.426990e+06 -8.330000e+02 137864.000000 \n", "\n", " exercised_stock_options expenses from_messages \\\n", "count 1.010000e+02 94.000000 86.000000 \n", "mean 2.959559e+06 54192.010638 608.790698 \n", "std 5.499450e+06 46108.377454 1841.033949 \n", "min 3.285000e+03 148.000000 12.000000 \n", "25% 5.067650e+05 22479.000000 22.750000 \n", "50% 1.297049e+06 46547.500000 41.000000 \n", "75% 2.542813e+06 78408.500000 145.500000 \n", "max 3.434838e+07 228763.000000 14368.000000 \n", "\n", " from_poi_to_this_person from_this_person_to_poi loan_advances \\\n", "count 86.000000 86.000000 3.000000e+00 \n", "mean 64.895349 41.232558 2.797500e+07 \n", "std 86.979244 100.073111 4.638256e+07 \n", "min 0.000000 0.000000 4.000000e+05 \n", "25% 10.000000 1.000000 1.200000e+06 \n", "50% 35.000000 8.000000 2.000000e+06 \n", "75% 72.250000 24.750000 4.176250e+07 \n", "max 528.000000 609.000000 8.152500e+07 \n", "\n", " long_term_incentive other restricted_stock \\\n", "count 6.500000e+01 9.100000e+01 1.090000e+02 \n", "mean 7.464912e+05 4.664105e+05 1.147424e+06 \n", "std 8.629174e+05 1.397376e+06 2.249770e+06 \n", "min 6.922300e+04 2.000000e+00 -2.604490e+06 \n", "25% 2.750000e+05 1.203000e+03 2.520550e+05 \n", "50% 4.221580e+05 5.158700e+04 4.410960e+05 \n", "75% 8.318090e+05 3.319830e+05 9.850320e+05 \n", "max 5.145434e+06 1.035973e+07 1.476169e+07 \n", "\n", " restricted_stock_deferred salary shared_receipt_with_poi \\\n", "count 1.700000e+01 9.400000e+01 86.000000 \n", "mean 6.218928e+05 2.840875e+05 1176.465116 \n", "std 3.845528e+06 1.771311e+05 1178.317641 \n", "min -1.787380e+06 4.770000e+02 2.000000 \n", "25% -3.298250e+05 2.118020e+05 249.750000 \n", "50% -1.402640e+05 2.587410e+05 740.500000 \n", "75% -7.241900e+04 3.086065e+05 1888.250000 \n", "max 1.545629e+07 1.111258e+06 5521.000000 \n", "\n", " to_messages total_payments total_stock_value \n", "count 86.000000 1.230000e+02 1.250000e+02 \n", "mean 2073.860465 2.641806e+06 3.352073e+06 \n", "std 2582.700981 9.524694e+06 6.532883e+06 \n", "min 57.000000 1.480000e+02 -4.409300e+04 \n", "25% 541.250000 3.969340e+05 4.941360e+05 \n", "50% 1211.000000 1.101393e+06 1.095040e+06 \n", "75% 2634.750000 2.087530e+06 2.606763e+06 \n", "max 15149.000000 1.035598e+08 4.911008e+07 " ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# converting the dictionary dataset to a pandas dataframe\n", "enron_df = pd.DataFrame.from_dict(data_dict)\n", "# Removing entries belonging to Total and THE TRAVEL AGENCY IN THE PARK as they are non persons\n", "del enron_df['TOTAL']\n", "del enron_df['THE TRAVEL AGENCY IN THE PARK']\n", "enron_df = enron_df.transpose()\n", "\n", "enron_df_num = enron_df.apply(pd.to_numeric, errors='coerce')\n", "# Removing the email_address from the dataset as it's non-numeric feature and won't seem to have much use right now.\n", "del enron_df_num['email_address']\n", "\n", "enron_df_num.describe()" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "144" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(enron_df_num)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We are left with 144 records now in our dataframe." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Also, the summary of the data sets shows some shows a very large standard deviation for some of the features\n", "and some missing data for others. We will drop some of these features as below." ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": true }, "outputs": [], "source": [ "del enron_df_num['loan_advances']\n", "del enron_df_num['restricted_stock_deferred']\n", "del enron_df_num['director_fees']" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Correlations between features to POI:\n", " bonus 0.302384\n", "deferral_payments -0.098428\n", "deferred_income -0.265698\n", "exercised_stock_options 0.503551\n", "expenses 0.060292\n", "from_messages -0.074308\n", "from_poi_to_this_person 0.167722\n", "from_this_person_to_poi 0.112940\n", "long_term_incentive 0.254723\n", "other 0.120270\n", "poi 1.000000\n", "restricted_stock 0.224814\n", "salary 0.264976\n", "shared_receipt_with_poi 0.228313\n", "to_messages 0.058954\n", "total_payments 0.230102\n", "total_stock_value 0.366462\n", "Name: poi, dtype: float64\n" ] } ], "source": [ "# Feature selections\n", "data_corr_list = enron_df_num.corr()\n", "print('\\nCorrelations between features to POI:\\n ' +str(data_corr_list['poi']))" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "Features ‘exercised_stock_options’, ‘total_stock_value’, and ‘bonus’ have the highest correlation to POI, in descending order. " ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#Get rid of label\n", "del enron_df_num['poi']\n", "poi = enron_df['poi']\n", "\n", "#Create new features\n", "enron_df_num['stock_sum'] = enron_df_num['exercised_stock_options'] +\\\n", " enron_df_num['total_stock_value'] +\\\n", " enron_df_num['restricted_stock'] \n", "enron_df_num['stock_ratio'] = enron_df_num['exercised_stock_options']/enron_df_num['total_stock_value']\n", "enron_df_num['money_total'] = enron_df_num['salary'] +\\\n", " enron_df_num['bonus'] -\\\n", " enron_df_num['expenses']\n", "enron_df_num['money_ratio'] = enron_df_num['bonus']/enron_df_num['salary'] \n", "enron_df_num['email_ratio'] = enron_df_num['from_messages']/(enron_df_num['to_messages']+enron_df_num['from_messages'])\n", "enron_df_num['poi_email_ratio_from'] = enron_df_num['from_poi_to_this_person']/enron_df_num['to_messages']\n", "enron_df_num['poi_email_ratio_to'] = enron_df_num['from_this_person_to_poi']/enron_df_num['from_messages']\n", "\n", "#Feel in NA values with 'marker' value outside range of real values\n", "enron_df_num = enron_df_num.fillna(enron_df_num.mean())\n", "\n", "#Scale to 1-0\n", "enron_df_num = (enron_df_num-enron_df_num.min())/(enron_df_num.max()-enron_df_num.min())" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Feature exercised_stock_options has value 29.133390\n", "Feature total_stock_value has value 21.477343\n", "Feature stock_sum has value 15.039523\n", "Feature poi_email_ratio_to has value 13.360475\n", "Feature bonus has value 11.437118\n", "Feature money_total has value 10.334752\n", "Feature salary has value 9.398674\n", "Feature total_payments has value 7.734639\n", "Feature restricted_stock has value 6.853888\n", "Feature long_term_incentive has value 5.964237\n", "Feature shared_receipt_with_poi has value 5.730789\n", "Feature deferred_income has value 5.610048\n", "Feature money_ratio has value 3.895578\n", "Feature from_poi_to_this_person has value 3.036263\n", "Feature email_ratio has value 2.035016\n", "Feature other has value 1.908430\n", "Feature from_this_person_to_poi has value 1.360849\n", "Feature poi_email_ratio_from has value 1.161332\n", "Feature from_messages has value 0.585913\n", "Feature expenses has value 0.478571\n", "Feature deferral_payments has value 0.380285\n", "Feature to_messages has value 0.368235\n", "Feature stock_ratio has value 0.013267\n" ] } ], "source": [ "from sklearn.feature_selection import SelectKBest\n", "selector = SelectKBest()\n", "selector.fit(enron_df_num,poi.tolist())\n", "scores = {enron_df_num.columns[i]:selector.scores_[i] for i in range(len(enron_df_num.columns))}\n", "sorted_features = sorted(scores,key=scores.get, reverse=True)\n", "for feature in sorted_features:\n", " print('Feature %s has value %f'%(feature,scores[feature]))" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Best classifier found is DecisionTreeClassifier(class_weight=None, criterion='entropy', max_depth=None,\n", " max_features=0.25, max_leaf_nodes=50, min_samples_leaf=1,\n", " min_samples_split=4, min_weight_fraction_leaf=0.0,\n", " presort=False, random_state=None, splitter='best') \n", " with score (recall+precision)/2 of 0.420000\n", " and feature set ['exercised_stock_options', 'total_stock_value', 'stock_sum', 'poi_email_ratio_to', 'bonus', 'money_total']\n" ] } ], "source": [ "from sklearn.naive_bayes import GaussianNB\n", "from sklearn.svm import SVC\n", "from sklearn.grid_search import RandomizedSearchCV, GridSearchCV\n", "from sklearn.tree import DecisionTreeClassifier\n", "from sklearn.metrics import precision_score, recall_score, accuracy_score\n", "from sklearn.cross_validation import StratifiedShuffleSplit\n", "import scipy\n", "import warnings\n", "warnings.filterwarnings('ignore')\n", "\n", "gnb_clf = GridSearchCV(GaussianNB(),{})\n", "#No params to tune for for linear bayes, use for convenience\n", " \n", "svc_clf = SVC()\n", "svc_search_params = {'C': scipy.stats.expon(scale=1), \n", " 'gamma': scipy.stats.expon(scale=.1),\n", " 'kernel': ['linear','poly','rbf'],\n", " 'class_weight':['balanced',None]}\n", "svc_search = RandomizedSearchCV(svc_clf, \n", " param_distributions=svc_search_params, \n", " n_iter=25)\n", "\n", "tree_clf = DecisionTreeClassifier()\n", "tree_search_params = {'criterion':['gini','entropy'],\n", " 'max_leaf_nodes':[None,25,50,100,1000],\n", " 'min_samples_split':[2,3,4],\n", " 'max_features':[0.25,0.5,0.75,1.0]}\n", "tree_search = GridSearchCV(tree_clf, \n", " tree_search_params,\n", " scoring='recall')\n", "\n", "search_methods = [gnb_clf,svc_search,tree_search]\n", "average_accuracies = [[0],[0],[0]]\n", "average_precision = [[0],[0],[0]]\n", "average_recall = [[0],[0],[0]]\n", "\n", "num_splits = 10\n", "train_split = 0.9\n", "indices = list(StratifiedShuffleSplit(poi.tolist(),\n", " num_splits,\n", " test_size=1-train_split, \n", " random_state=0))\n", "\n", "best_features = None\n", "max_score = 0\n", "best_classifier = None\n", "num_features = 0\n", "for num_features in range(1,len(sorted_features)+1):\n", " features = sorted_features[:num_features]\n", " feature_df = enron_df_num[features]\n", " for classifier_idx in range(3): \n", " sum_values = [0,0,0]\n", " #Only do parameter search once, too wasteful to do a ton\n", " search_methods[classifier_idx].fit(feature_df.iloc[indices[0][0],:],\n", " poi[indices[0][0]].tolist())\n", " classifier = search_methods[classifier_idx].best_estimator_\n", " for split_idx in range(num_splits): \n", " train_indices, test_indices = indices[split_idx]\n", " train_data = (feature_df.iloc[train_indices,:],poi[train_indices].tolist())\n", " test_data = (feature_df.iloc[test_indices,:],poi[test_indices].tolist())\n", " classifier.fit(train_data[0],train_data[1])\n", " predicted = classifier.predict(test_data[0])\n", " sum_values[0]+=accuracy_score(predicted,test_data[1])\n", " sum_values[1]+=precision_score(predicted,test_data[1])\n", " sum_values[2]+=recall_score(predicted,test_data[1])\n", " avg_acc,avg_prs,avg_recall = [val/num_splits for val in sum_values]\n", " average_accuracies[classifier_idx].append(avg_acc)\n", " average_precision[classifier_idx].append(avg_prs)\n", " average_recall[classifier_idx].append(avg_recall)\n", " \n", " score = (avg_prs+avg_recall)/2\n", " if score>max_score and avg_prs>0.3 and avg_recall>0.3:\n", " max_score = score\n", " best_features = features\n", " best_classifier = search_methods[classifier_idx].best_estimator_\n", "print('Best classifier found is %s \\n\\\n", " with score (recall+precision)/2 of %f\\n\\\n", " and feature set %s'%(str(best_classifier),max_score,best_features))" ] }, { "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 }

mit

tensorflow/hub

examples/colab/semantic_approximate_nearest_neighbors.ipynb

1

31805

{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "ACbjNjyO4f_8" }, "source": [ "##### Copyright 2019 The TensorFlow Hub Authors.\n", "\n", "Licensed under the Apache License, Version 2.0 (the \"License\");" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "MCM50vaM4jiK" }, "outputs": [], "source": [ "# Copyright 2018 The TensorFlow Hub Authors. All Rights Reserved.\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", "# ==============================================================================" ] }, { "cell_type": "markdown", "metadata": { "id": "9qOVy-_vmuUP" }, "source": [ "# Semantic Search with Approximate Nearest Neighbors and Text Embeddings\n" ] }, { "cell_type": "markdown", "metadata": { "id": "MfBg1C5NB3X0" }, "source": [ "\u003ctable class=\"tfo-notebook-buttons\" align=\"left\"\u003e\n", " \u003ctd\u003e\n", " \u003ca target=\"_blank\" href=\"https://www.tensorflow.org/hub/tutorials/semantic_approximate_nearest_neighbors\"\u003e\u003cimg src=\"https://www.tensorflow.org/images/tf_logo_32px.png\" /\u003eView on TensorFlow.org\u003c/a\u003e\n", " \u003c/td\u003e\n", " \u003ctd\u003e\n", " \u003ca target=\"_blank\" href=\"https://colab.research.google.com/github/tensorflow/hub/blob/master/examples/colab/semantic_approximate_nearest_neighbors.ipynb\"\u003e\u003cimg src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" /\u003eRun in Google Colab\u003c/a\u003e\n", " \u003c/td\u003e\n", " \u003ctd\u003e\n", " \u003ca target=\"_blank\" href=\"https://github.com/tensorflow/hub/blob/master/examples/colab/semantic_approximate_nearest_neighbors.ipynb\"\u003e\u003cimg src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" /\u003eView on GitHub\u003c/a\u003e\n", " \u003c/td\u003e\n", " \u003ctd\u003e\n", " \u003ca href=\"https://storage.googleapis.com/tensorflow_docs/hub/examples/colab/semantic_approximate_nearest_neighbors.ipynb\"\u003e\u003cimg src=\"https://www.tensorflow.org/images/download_logo_32px.png\" /\u003eDownload notebook\u003c/a\u003e\n", " \u003c/td\u003e\n", " \u003ctd\u003e\n", " \u003ca href=\"https://tfhub.dev/google/universal-sentence-encoder/2\"\u003e\u003cimg src=\"https://www.tensorflow.org/images/hub_logo_32px.png\" /\u003eSee TF Hub models\u003c/a\u003e\n", " \u003c/td\u003e\n", "\u003c/table\u003e" ] }, { "cell_type": "markdown", "metadata": { "id": "7Hks9F5qq6m2" }, "source": [ "This tutorial illustrates how to generate embeddings from a [TensorFlow Hub](https://tfhub.dev) (TF-Hub) module given input data, and build an approximate nearest neighbours (ANN) index using the extracted embeddings. The index can then be used for real-time similarity matching and retrieval. \n", "\n", "When dealing with a large corpus of data, it's not efficient to perform exact matching by scanning the whole repository to find the most similar items to a given query in real-time. Thus, we use an approximate similarity matching algorithm which allows us to trade off a little bit of accuracy in finding exact nearest neighbor matches for a significant boost in speed. \n", "\n", "In this tutorial, we show an example of real-time text search over a corpus of news headlines to find the headlines that are most similar to a query. Unlike keyword search, this captures the semantic similarity encoded in the text embedding.\n", "\n", "The steps of this tutorial are:\n", "1. Download sample data.\n", "2. Generate embeddings for the data using a TF-Hub module\n", "3. Build an ANN index for the embeddings\n", "4. Use the index for similarity matching\n", "\n", "We use [Apache Beam](https://beam.apache.org/documentation/programming-guide/) with [TensorFlow Transform](https://www.tensorflow.org/tfx/tutorials/transform/simple) (TF-Transform) to generate the embeddings from the TF-Hub module. We also use Spotify's [ANNOY](https://github.com/spotify/annoy) library to build the approximate nearest neighbours index. You can find benchmarking of ANN framework in this [Github repository](https://github.com/erikbern/ann-benchmarks).\n", "\n", "This tutorial uses TensorFlow 1.0 and works only with TF1 [Hub modules](https://www.tensorflow.org/hub/tf1_hub_module) from TF-Hub. See the updated [TF2 version of this tutorial](https://github.com/tensorflow/hub/blob/master/examples/colab/tf2_semantic_approximate_nearest_neighbors.ipynb)." ] }, { "cell_type": "markdown", "metadata": { "id": "Q0jr0QK9qO5P" }, "source": [ "## Setup" ] }, { "cell_type": "markdown", "metadata": { "id": "whMRj9qeqed4" }, "source": [ "Install the required libraries." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "qmXkLPoaqS--" }, "outputs": [], "source": [ "!pip install -q apache_beam\n", "!pip install -q 'scikit_learn~=0.23.0' # For gaussian_random_matrix.\n", "!pip install -q annoy" ] }, { "cell_type": "markdown", "metadata": { "id": "A-vBZiCCqld0" }, "source": [ "Import the required libraries" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "6NTYbdWcseuK" }, "outputs": [], "source": [ "import os\n", "import sys\n", "import pathlib\n", "import pickle\n", "from collections import namedtuple\n", "from datetime import datetime\n", "\n", "import numpy as np\n", "import apache_beam as beam\n", "import annoy\n", "from sklearn.random_projection import gaussian_random_matrix\n", "\n", "import tensorflow.compat.v1 as tf\n", "import tensorflow_hub as hub" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "_GF0GnLqGdPQ" }, "outputs": [], "source": [ "# TFT needs to be installed afterwards\n", "!pip install -q tensorflow_transform==0.24\n", "import tensorflow_transform as tft\n", "import tensorflow_transform.beam as tft_beam" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "tx0SZa6-7b-f" }, "outputs": [], "source": [ "print('TF version: {}'.format(tf.__version__))\n", "print('TF-Hub version: {}'.format(hub.__version__))\n", "print('TF-Transform version: {}'.format(tft.__version__))\n", "print('Apache Beam version: {}'.format(beam.__version__))" ] }, { "cell_type": "markdown", "metadata": { "id": "P6Imq876rLWx" }, "source": [ "## 1. Download Sample Data\n", "\n", "[A Million News Headlines](https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/SYBGZL#) dataset contains news headlines published over a period of 15 years sourced from the reputable Australian Broadcasting Corp. (ABC). This news dataset has a summarised historical record of noteworthy events in the globe from early-2003 to end-2017 with a more granular focus on Australia. \n", "\n", "**Format**: Tab-separated two-column data: 1) publication date and 2) headline text. We are only interested in the headline text.\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "OpF57n8e5C9D" }, "outputs": [], "source": [ "!wget 'https://dataverse.harvard.edu/api/access/datafile/3450625?format=tab\u0026gbrecs=true' -O raw.tsv\n", "!wc -l raw.tsv\n", "!head raw.tsv" ] }, { "cell_type": "markdown", "metadata": { "id": "Reeoc9z0zTxJ" }, "source": [ "For simplicity, we only keep the headline text and remove the publication date" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "INPWa4upv_yJ" }, "outputs": [], "source": [ "!rm -r corpus\n", "!mkdir corpus\n", "\n", "with open('corpus/text.txt', 'w') as out_file:\n", " with open('raw.tsv', 'r') as in_file:\n", " for line in in_file:\n", " headline = line.split('\\t')[1].strip().strip('\"')\n", " out_file.write(headline+\"\\n\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "5-oedX40z6o2" }, "outputs": [], "source": [ "!tail corpus/text.txt" ] }, { "cell_type": "markdown", "metadata": { "id": "ls0Zh7kYz3PM" }, "source": [ "## Helper function to load a TF-Hub module" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "vSt_jmyKz3Xp" }, "outputs": [], "source": [ "def load_module(module_url):\n", " embed_module = hub.Module(module_url)\n", " placeholder = tf.placeholder(dtype=tf.string)\n", " embed = embed_module(placeholder)\n", " session = tf.Session()\n", " session.run([tf.global_variables_initializer(), tf.tables_initializer()])\n", " print('TF-Hub module is loaded.')\n", "\n", " def _embeddings_fn(sentences):\n", " computed_embeddings = session.run(\n", " embed, feed_dict={placeholder: sentences})\n", " return computed_embeddings\n", "\n", " return _embeddings_fn" ] }, { "cell_type": "markdown", "metadata": { "id": "2AngMtH50jNb" }, "source": [ "## 2. Generate Embeddings for the Data.\n", "\n", "In this tutorial, we use the [Universal Sentence Encoder](https://tfhub.dev/google/universal-sentence-encoder/2) to generate embeddings for the headline data. The sentence embeddings can then be easily used to compute sentence level meaning similarity. We run the embedding generation process using Apache Beam and TF-Transform." ] }, { "cell_type": "markdown", "metadata": { "id": "F_DvXnDB1pEX" }, "source": [ "### Embedding extraction method" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "yL7OEY1E0A35" }, "outputs": [], "source": [ "encoder = None\n", "\n", "def embed_text(text, module_url, random_projection_matrix):\n", " # Beam will run this function in different processes that need to\n", " # import hub and load embed_fn (if not previously loaded)\n", " global encoder\n", " if not encoder:\n", " encoder = hub.Module(module_url)\n", " embedding = encoder(text)\n", " if random_projection_matrix is not None:\n", " # Perform random projection for the embedding\n", " embedding = tf.matmul(\n", " embedding, tf.cast(random_projection_matrix, embedding.dtype))\n", " return embedding\n" ] }, { "cell_type": "markdown", "metadata": { "id": "_don5gXy9D59" }, "source": [ "### Make TFT preprocess_fn method" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "fwYlrzzK9ECE" }, "outputs": [], "source": [ "def make_preprocess_fn(module_url, random_projection_matrix=None):\n", " '''Makes a tft preprocess_fn'''\n", "\n", " def _preprocess_fn(input_features):\n", " '''tft preprocess_fn'''\n", " text = input_features['text']\n", " # Generate the embedding for the input text\n", " embedding = embed_text(text, module_url, random_projection_matrix)\n", " \n", " output_features = {\n", " 'text': text, \n", " 'embedding': embedding\n", " }\n", " \n", " return output_features\n", " \n", " return _preprocess_fn" ] }, { "cell_type": "markdown", "metadata": { "id": "SQ492LN7A-NZ" }, "source": [ "### Create dataset metadata" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "d2D4332VA-2V" }, "outputs": [], "source": [ "def create_metadata():\n", " '''Creates metadata for the raw data'''\n", " from tensorflow_transform.tf_metadata import dataset_metadata\n", " from tensorflow_transform.tf_metadata import schema_utils\n", " feature_spec = {'text': tf.FixedLenFeature([], dtype=tf.string)}\n", " schema = schema_utils.schema_from_feature_spec(feature_spec)\n", " metadata = dataset_metadata.DatasetMetadata(schema)\n", " return metadata" ] }, { "cell_type": "markdown", "metadata": { "id": "5zlSLPzRBm6H" }, "source": [ "### Beam pipeline" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "jCGUIB172m2G" }, "outputs": [], "source": [ "def run_hub2emb(args):\n", " '''Runs the embedding generation pipeline'''\n", "\n", " options = beam.options.pipeline_options.PipelineOptions(**args)\n", " args = namedtuple(\"options\", args.keys())(*args.values())\n", "\n", " raw_metadata = create_metadata()\n", " converter = tft.coders.CsvCoder(\n", " column_names=['text'], schema=raw_metadata.schema)\n", "\n", " with beam.Pipeline(args.runner, options=options) as pipeline:\n", " with tft_beam.Context(args.temporary_dir):\n", " # Read the sentences from the input file\n", " sentences = ( \n", " pipeline\n", " | 'Read sentences from files' \u003e\u003e beam.io.ReadFromText(\n", " file_pattern=args.data_dir)\n", " | 'Convert to dictionary' \u003e\u003e beam.Map(converter.decode)\n", " )\n", "\n", " sentences_dataset = (sentences, raw_metadata)\n", " preprocess_fn = make_preprocess_fn(args.module_url, args.random_projection_matrix)\n", " # Generate the embeddings for the sentence using the TF-Hub module\n", " embeddings_dataset, _ = (\n", " sentences_dataset\n", " | 'Extract embeddings' \u003e\u003e tft_beam.AnalyzeAndTransformDataset(preprocess_fn)\n", " )\n", "\n", " embeddings, transformed_metadata = embeddings_dataset\n", " # Write the embeddings to TFRecords files\n", " embeddings | 'Write embeddings to TFRecords' \u003e\u003e beam.io.tfrecordio.WriteToTFRecord(\n", " file_path_prefix='{}/emb'.format(args.output_dir),\n", " file_name_suffix='.tfrecords',\n", " coder=tft.coders.ExampleProtoCoder(transformed_metadata.schema))" ] }, { "cell_type": "markdown", "metadata": { "id": "uHbq4t2gCDAG" }, "source": [ "### Generaring Random Projection Weight Matrix\n", "\n", "[Random projection](https://en.wikipedia.org/wiki/Random_projection) is a simple, yet powerfull technique used to reduce the dimensionality of a set of points which lie in Euclidean space. For a theoretical background, see the [Johnson-Lindenstrauss lemma](https://en.wikipedia.org/wiki/Johnson%E2%80%93Lindenstrauss_lemma).\n", "\n", "Reducing the dimensionality of the embeddings with random projection means less time needed to build and query the ANN index.\n", "\n", "In this tutorial we use [Gaussian Random Projection](https://en.wikipedia.org/wiki/Random_projection#Gaussian_random_projection) from the [Scikit-learn](https://scikit-learn.org/stable/modules/random_projection.html#gaussian-random-projection) library." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "T1aYPeOUCDIP" }, "outputs": [], "source": [ "def generate_random_projection_weights(original_dim, projected_dim):\n", " random_projection_matrix = None\n", " if projected_dim and original_dim \u003e projected_dim:\n", " random_projection_matrix = gaussian_random_matrix(\n", " n_components=projected_dim, n_features=original_dim).T\n", " print(\"A Gaussian random weight matrix was creates with shape of {}\".format(random_projection_matrix.shape))\n", " print('Storing random projection matrix to disk...')\n", " with open('random_projection_matrix', 'wb') as handle:\n", " pickle.dump(random_projection_matrix, \n", " handle, protocol=pickle.HIGHEST_PROTOCOL)\n", " \n", " return random_projection_matrix" ] }, { "cell_type": "markdown", "metadata": { "id": "CHxZX2Z3Nk64" }, "source": [ "### Set parameters\n", "If you want to build an index using the original embedding space without random projection, set the `projected_dim` parameter to `None`. Note that this will slow down the indexing step for high-dimensional embeddings." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "cellView": "form", "id": "feMVXFL0NlIM" }, "outputs": [], "source": [ "module_url = 'https://tfhub.dev/google/universal-sentence-encoder/2' #@param {type:\"string\"}\n", "projected_dim = 64 #@param {type:\"number\"}" ] }, { "cell_type": "markdown", "metadata": { "id": "On-MbzD922kb" }, "source": [ "### Run pipeline" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "Y3I1Wv4i21yY" }, "outputs": [], "source": [ "import tempfile\n", "\n", "output_dir = pathlib.Path(tempfile.mkdtemp())\n", "temporary_dir = pathlib.Path(tempfile.mkdtemp())\n", "\n", "g = tf.Graph()\n", "with g.as_default():\n", " original_dim = load_module(module_url)(['']).shape[1]\n", " random_projection_matrix = None\n", "\n", " if projected_dim:\n", " random_projection_matrix = generate_random_projection_weights(\n", " original_dim, projected_dim)\n", "\n", "args = {\n", " 'job_name': 'hub2emb-{}'.format(datetime.utcnow().strftime('%y%m%d-%H%M%S')),\n", " 'runner': 'DirectRunner',\n", " 'batch_size': 1024,\n", " 'data_dir': 'corpus/*.txt',\n", " 'output_dir': output_dir,\n", " 'temporary_dir': temporary_dir,\n", " 'module_url': module_url,\n", " 'random_projection_matrix': random_projection_matrix,\n", "}\n", "\n", "print(\"Pipeline args are set.\")\n", "args" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "iS9obmeP4ZOA" }, "outputs": [], "source": [ "!rm -r {output_dir}\n", "!rm -r {temporary_dir}\n", "\n", "print(\"Running pipeline...\")\n", "%time run_hub2emb(args)\n", "print(\"Pipeline is done.\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "JAwOo7gQWvVd" }, "outputs": [], "source": [ "!ls {output_dir}" ] }, { "cell_type": "markdown", "metadata": { "id": "HVnee4e6U90u" }, "source": [ "Read some of the generated embeddings..." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "-K7pGXlXOj1N" }, "outputs": [], "source": [ "import itertools\n", "\n", "embed_file = os.path.join(output_dir, 'emb-00000-of-00001.tfrecords')\n", "sample = 5\n", "record_iterator = tf.io.tf_record_iterator(path=embed_file)\n", "for string_record in itertools.islice(record_iterator, sample):\n", " example = tf.train.Example()\n", " example.ParseFromString(string_record)\n", " text = example.features.feature['text'].bytes_list.value\n", " embedding = np.array(example.features.feature['embedding'].float_list.value)\n", " print(\"Embedding dimensions: {}\".format(embedding.shape[0]))\n", " print(\"{}: {}\".format(text, embedding[:10]))\n" ] }, { "cell_type": "markdown", "metadata": { "id": "agGoaMSgY8wN" }, "source": [ "## 3. Build the ANN Index for the Embeddings\n", "\n", "[ANNOY](https://github.com/spotify/annoy) (Approximate Nearest Neighbors Oh Yeah) is a C++ library with Python bindings to search for points in space that are close to a given query point. It also creates large read-only file-based data structures that are mmapped into memory. It is built and used by [Spotify](https://www.spotify.com) for music recommendations." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "UcPDspU3WjgH" }, "outputs": [], "source": [ "def build_index(embedding_files_pattern, index_filename, vector_length, \n", " metric='angular', num_trees=100):\n", " '''Builds an ANNOY index'''\n", "\n", " annoy_index = annoy.AnnoyIndex(vector_length, metric=metric)\n", " # Mapping between the item and its identifier in the index\n", " mapping = {}\n", "\n", " embed_files = tf.gfile.Glob(embedding_files_pattern)\n", " print('Found {} embedding file(s).'.format(len(embed_files)))\n", "\n", " item_counter = 0\n", " for f, embed_file in enumerate(embed_files):\n", " print('Loading embeddings in file {} of {}...'.format(\n", " f+1, len(embed_files)))\n", " record_iterator = tf.io.tf_record_iterator(\n", " path=embed_file)\n", "\n", " for string_record in record_iterator:\n", " example = tf.train.Example()\n", " example.ParseFromString(string_record)\n", " text = example.features.feature['text'].bytes_list.value[0].decode(\"utf-8\")\n", " mapping[item_counter] = text\n", " embedding = np.array(\n", " example.features.feature['embedding'].float_list.value)\n", " annoy_index.add_item(item_counter, embedding)\n", " item_counter += 1\n", " if item_counter % 100000 == 0:\n", " print('{} items loaded to the index'.format(item_counter))\n", "\n", " print('A total of {} items added to the index'.format(item_counter))\n", "\n", " print('Building the index with {} trees...'.format(num_trees))\n", " annoy_index.build(n_trees=num_trees)\n", " print('Index is successfully built.')\n", " \n", " print('Saving index to disk...')\n", " annoy_index.save(index_filename)\n", " print('Index is saved to disk.')\n", " print(\"Index file size: {} GB\".format(\n", " round(os.path.getsize(index_filename) / float(1024 ** 3), 2)))\n", " annoy_index.unload()\n", "\n", " print('Saving mapping to disk...')\n", " with open(index_filename + '.mapping', 'wb') as handle:\n", " pickle.dump(mapping, handle, protocol=pickle.HIGHEST_PROTOCOL)\n", " print('Mapping is saved to disk.')\n", " print(\"Mapping file size: {} MB\".format(\n", " round(os.path.getsize(index_filename + '.mapping') / float(1024 ** 2), 2)))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "AgyOQhUq6FNE" }, "outputs": [], "source": [ "embedding_files = \"{}/emb-*.tfrecords\".format(output_dir)\n", "embedding_dimension = projected_dim\n", "index_filename = \"index\"\n", "\n", "!rm {index_filename}\n", "!rm {index_filename}.mapping\n", "\n", "%time build_index(embedding_files, index_filename, embedding_dimension)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "Ic31Tm5cgAd5" }, "outputs": [], "source": [ "!ls" ] }, { "cell_type": "markdown", "metadata": { "id": "maGxDl8ufP-p" }, "source": [ "## 4. Use the Index for Similarity Matching\n", "Now we can use the ANN index to find news headlines that are semantically close to an input query." ] }, { "cell_type": "markdown", "metadata": { "id": "_dIs8W78fYPp" }, "source": [ "### Load the index and the mapping files" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "jlTTrbQHayvb" }, "outputs": [], "source": [ "index = annoy.AnnoyIndex(embedding_dimension)\n", "index.load(index_filename, prefault=True)\n", "print('Annoy index is loaded.')\n", "with open(index_filename + '.mapping', 'rb') as handle:\n", " mapping = pickle.load(handle)\n", "print('Mapping file is loaded.')\n" ] }, { "cell_type": "markdown", "metadata": { "id": "y6liFMSUh08J" }, "source": [ "### Similarity matching method" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "mUxjTag8hc16" }, "outputs": [], "source": [ "def find_similar_items(embedding, num_matches=5):\n", " '''Finds similar items to a given embedding in the ANN index'''\n", " ids = index.get_nns_by_vector(\n", " embedding, num_matches, search_k=-1, include_distances=False)\n", " items = [mapping[i] for i in ids]\n", " return items" ] }, { "cell_type": "markdown", "metadata": { "id": "hjerNpmZja0A" }, "source": [ "### Extract embedding from a given query" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "a0IIXzfBjZ19" }, "outputs": [], "source": [ "# Load the TF-Hub module\n", "print(\"Loading the TF-Hub module...\")\n", "g = tf.Graph()\n", "with g.as_default():\n", " embed_fn = load_module(module_url)\n", "print(\"TF-Hub module is loaded.\")\n", "\n", "random_projection_matrix = None\n", "if os.path.exists('random_projection_matrix'):\n", " print(\"Loading random projection matrix...\")\n", " with open('random_projection_matrix', 'rb') as handle:\n", " random_projection_matrix = pickle.load(handle)\n", " print('random projection matrix is loaded.')\n", "\n", "def extract_embeddings(query):\n", " '''Generates the embedding for the query'''\n", " query_embedding = embed_fn([query])[0]\n", " if random_projection_matrix is not None:\n", " query_embedding = query_embedding.dot(random_projection_matrix)\n", " return query_embedding" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "kCoCNROujEIO" }, "outputs": [], "source": [ "extract_embeddings(\"Hello Machine Learning!\")[:10]" ] }, { "cell_type": "markdown", "metadata": { "id": "nE_Q60nCk_ZB" }, "source": [ "### Enter a query to find the most similar items" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "cellView": "form", "id": "wC0uLjvfk5nB" }, "outputs": [], "source": [ "#@title { run: \"auto\" }\n", "query = \"confronting global challenges\" #@param {type:\"string\"}\n", "print(\"Generating embedding for the query...\")\n", "%time query_embedding = extract_embeddings(query)\n", "\n", "print(\"\")\n", "print(\"Finding relevant items in the index...\")\n", "%time items = find_similar_items(query_embedding, 10)\n", "\n", "print(\"\")\n", "print(\"Results:\")\n", "print(\"=========\")\n", "for item in items:\n", " print(item)" ] }, { "cell_type": "markdown", "metadata": { "id": "wwtMtyOeDKwt" }, "source": [ "## Want to learn more?\n", "\n", "You can learn more about TensorFlow at [tensorflow.org](https://www.tensorflow.org/) and see the TF-Hub API documentation at [tensorflow.org/hub](https://www.tensorflow.org/hub/). Find available TensorFlow Hub modules at [tfhub.dev](https://tfhub.dev/) including more text embedding modules and image feature vector modules.\n", "\n", "Also check out the [Machine Learning Crash Course](https://developers.google.com/machine-learning/crash-course/) which is Google's fast-paced, practical introduction to machine learning." ] } ], "metadata": { "colab": { "collapsed_sections": [ "ls0Zh7kYz3PM", "_don5gXy9D59", "SQ492LN7A-NZ" ], "name": "Semantic Search with Approximate Nearest Neighbors and Text Embeddings from TF-Hub [TF1]", "private_outputs": true, "provenance": [], "toc_visible": true }, "kernelspec": { "display_name": "Python 3", "name": "python3" } }, "nbformat": 4, "nbformat_minor": 0 }

apache-2.0

harpolea/pyro2

multigrid/variable_coeff_elliptic.ipynb

2

48752

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Variable Coefficient Poisson" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Derive the form of a test variable-coefficient elliptic equation with periodic boundary conditions for testing the variable-coefficient multigrid solver.\n", "\n", "We want to solve an equation of the form $\\nabla \\cdot (\\alpha \\nabla \\phi) = f$\n", "\n", "Note: it is important for solvability that the RHS, f, integrate to zero over our domain. It seems sufficient to ensure that phi integrates to zero to have this condition met." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Populating the interactive namespace from numpy and matplotlib\n" ] } ], "source": [ "%pylab inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "IPython console for SymPy 1.0 (Python 2.7.12-64-bit) (ground types: gmpy)\n", "\n", "These commands were executed:\n", ">>> from __future__ import division\n", ">>> from sympy import *\n", ">>> x, y, z, t = symbols('x y z t')\n", ">>> k, m, n = symbols('k m n', integer=True)\n", ">>> f, g, h = symbols('f g h', cls=Function)\n", ">>> init_printing()\n", "\n", "Documentation can be found at http://docs.sympy.org/1.0/\n" ] } ], "source": [ "from sympy import init_session\n", "init_session()" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "Variable coefficient elliptic problem, using periodic boundary conditions all around." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "alpha = 2.0 + cos(2*pi*x)*cos(2*pi*y)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [], "source": [ "phi = sin(2*pi*x)*sin(2*pi*y)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "we want to compute $\\nabla \\cdot (\\alpha \\nabla \\phi)$" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [], "source": [ "phi_x = diff(phi, x)\n", "phi_y = diff(phi, y)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [], "source": [ "f = diff(alpha*phi_x, x) + diff(alpha*phi_y, y)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/latex": [ "$$- 16.0 \\pi^{2} \\left(\\cos{\\left (2 \\pi x \\right )} \\cos{\\left (2 \\pi y \\right )} + 1\\right) \\sin{\\left (2 \\pi x \\right )} \\sin{\\left (2 \\pi y \\right )}$$" ], "text/plain": [ " 2 \n", "-16.0⋅π ⋅(cos(2⋅π⋅x)⋅cos(2⋅π⋅y) + 1)⋅sin(2⋅π⋅x)⋅sin(2⋅π⋅y)" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "f = simplify(f)\n", "f" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "-16.0*pi**2*(cos(2*pi*x)*cos(2*pi*y) + 1)*sin(2*pi*x)*sin(2*pi*y)\n" ] } ], "source": [ "print(f)" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "boundary conditions check" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAAoAAAAOBAMAAADkjZCYAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAv3aB7AAAAD3RSTlMAEJmJZjLNVN0i77ur\nRHZ72Yd1AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAVElEQVQIHWNgEDIxZWBgSGeQmMDAsoCBOYGB\n+wAD+0cG/gMMvN8Z5BUYeP8xzDdgYP3MMF8BREJEgLLs3xm4NzCwfATpYkpgYGhnkApgYBB+d5QB\nAPogE3QldevOAAAAAElFTkSuQmCC\n", "text/latex": [ "$$0$$" ], "text/plain": [ "0" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "phi.subs(x, 0)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAAoAAAAOBAMAAADkjZCYAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAv3aB7AAAAD3RSTlMAEJmJZjLNVN0i77ur\nRHZ72Yd1AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAVElEQVQIHWNgEDIxZWBgSGeQmMDAsoCBOYGB\n+wAD+0cG/gMMvN8Z5BUYeP8xzDdgYP3MMF8BREJEgLLs3xm4NzCwfATpYkpgYGhnkApgYBB+d5QB\nAPogE3QldevOAAAAAElFTkSuQmCC\n", "text/latex": [ "$$0$$" ], "text/plain": [ "0" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "phi.subs(x, 1)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAAoAAAAOBAMAAADkjZCYAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAv3aB7AAAAD3RSTlMAEJmJZjLNVN0i77ur\nRHZ72Yd1AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAVElEQVQIHWNgEDIxZWBgSGeQmMDAsoCBOYGB\n+wAD+0cG/gMMvN8Z5BUYeP8xzDdgYP3MMF8BREJEgLLs3xm4NzCwfATpYkpgYGhnkApgYBB+d5QB\nAPogE3QldevOAAAAAElFTkSuQmCC\n", "text/latex": [ "$$0$$" ], "text/plain": [ "0" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "phi.subs(y, 0)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAAoAAAAOBAMAAADkjZCYAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAv3aB7AAAAD3RSTlMAEJmJZjLNVN0i77ur\nRHZ72Yd1AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAVElEQVQIHWNgEDIxZWBgSGeQmMDAsoCBOYGB\n+wAD+0cG/gMMvN8Z5BUYeP8xzDdgYP3MMF8BREJEgLLs3xm4NzCwfATpYkpgYGhnkApgYBB+d5QB\nAPogE3QldevOAAAAAElFTkSuQmCC\n", "text/latex": [ "$$0$$" ], "text/plain": [ "0" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "phi.subs(y, 1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# General Elliptic" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Derive the form of a test variable-coefficient elliptic equation with periodic boundary conditions for testing the variable-coefficient multigrid solver.\n", "\n", "We are solving\n", "\n", "$$\\alpha \\phi + \\nabla \\cdot (\\beta \\nabla \\phi ) + \\gamma \\cdot \\nabla \\phi = f$$\n", "\n", "Note: it is important for solvability that the RHS, f, integrate to zero over our domain. It seems sufficient to ensure that phi integrates to zero to have this condition met." ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [], "source": [ "phi = sin(2*pi*x)*sin(2*pi*y)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAKAAAAAUBAMAAAD4uit9AAAAMFBMVEX///8AAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAv3aB7AAAAD3RSTlMAMnZmzRC73UTvIomZ\nVKu7zOipAAAACXBIWXMAAA7EAAAOxAGVKw4bAAAC5UlEQVQ4EZWVTUgUYRzGn5nd2dFZd9ekS5Du\npEG0iS3WrUPbrTzkJNElyM1CyEMuBV2iXLpEdFAEqShiQtKQoI06RAptGV2UEA+F0GH6kCAP9sGW\nqbg97+zn7OylF97l/z7z+z8z7//9WKCiadEKgUOP6daAKmQBlIZLCWMkZ3vrS4KIvhWGW/RCBJB8\n+OZkaSyiPCitleQO4AT8KyVBREcLw3CqEAEdUCy8SJYERkWwqHrSwCJwqyjYgV93jsWIZNBA7ZDj\niRtUTeAGsOycs+JMsz1I1iYQ/OEwzIPapqI6zqjTqDTEwTxwr/Qikt5MpSFBqaU9NraBzXca2nRm\nteYyBww5y5ZGV9uDXdSnKDfs2a71W9Khl82fBJQj1Qx2E1xH1ytoIwI8BiRwGLiga6vETgkWNWuY\nbIy0T0BJec6pMWAvoMShwmtB7cEjk0yOHEz5H8+PRkykFiB/F+A1AzrOAmeAX8QusQOqJc35zW2A\n3/BnAibwni8ZgQyfBd8QQlEyOfIyGvFcroc29xq+uADV3xEIw9PAT2I32YFZdp9YbQXeuBDC7AM9\npm0YQ0jn0Cbr0oziXG0Fw7YchvKUlaJhb5mhLLAmloLNTrYNj3eu24bxMkNRSMlS+VuzijBXLIz7\n8KyUGdoT+QDNxDQywlBQwFWeHwP7k2LKeUNBBtKYQF00ZAB1Mey0wXngc5mhKLWUhmwqGfC4BIzd\nkIlzUQI6gtEyQ0FuBSLoNkJJVsgSLgQXDPSJ+eanLPbH+OTUV8gJ3AYG5/oxSmmGhiPwmfy8wheS\n1C5Ovk1gB2pT/MJ4jVjUGVxv35dsyi42ZT+++2vZc0NnNvsHviiWeKybu6aj5A6wSE9anknLG0eW\nNxr771JiFbzcgQnunyANlaVWse0IOps4eu6mJdyai2QV4QY9aXcqt6PuVivIL+hOVgX73KnVbiVS\nTvI8eqi5ry9xbbqbMzf/3Eleaa2nXgWscrH/11/AP1oYyg26RXJyAAAAAElFTkSuQmCC\n", "text/latex": [ "$$\\sin{\\left (2 \\pi x \\right )} \\sin{\\left (2 \\pi y \\right )}$$" ], "text/plain": [ "sin(2⋅π⋅x)⋅sin(2⋅π⋅y)" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "phi" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [], "source": [ "alpha = 1.0\n", "beta = 2.0 + cos(2*pi*x)*cos(2*pi*y)\n", "gamma_x = sin(2*pi*x)\n", "gamma_y = sin(2*pi*y)\n" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAABkAAAAPBAMAAADjSHnWAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAv3aB7AAAAD3RSTlMAVO8Qq5l2zWbdiTIi\nu0T8UsK3AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAdklEQVQIHWNggALmqK4NQCajMpjPLsA8h4HB\nJOQzmNfEwLAcyGCD8K4wMMgbwHlfGBjeC8B4zF+BvAQYj+cXA8P+BXAeUA7BQ1XJADRFHm4Kw10G\nhn6EDUDbw6G22xcwcAowazMwsDp992DgdGBgXpayASiHDACNXiNTPqhPAgAAAABJRU5ErkJggg==\n", "text/latex": [ "$$1.0$$" ], "text/plain": [ "1.0" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "alpha" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAANsAAAAUBAMAAAD2E1USAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA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\n", "text/latex": [ "$$\\cos{\\left (2 \\pi x \\right )} \\cos{\\left (2 \\pi y \\right )} + 2.0$$" ], "text/plain": [ "cos(2⋅π⋅x)⋅cos(2⋅π⋅y) + 2.0" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "beta" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAE4AAAAUBAMAAADRkRa/AAAAMFBMVEX///8AAAAAAAAAAAAAAAAA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\n", "text/latex": [ "$$\\sin{\\left (2 \\pi x \\right )}$$" ], "text/plain": [ "sin(2⋅π⋅x)" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "gamma_x" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAE0AAAAUBAMAAAA6pq28AAAAMFBMVEX///8AAAAAAAAAAAAAAAAA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\nYaQ9KwAAAABJRU5ErkJggg==\n", "text/latex": [ "$$\\sin{\\left (2 \\pi y \\right )}$$" ], "text/plain": [ "sin(2⋅π⋅y)" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "gamma_y" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [], "source": [ "phi_x = diff(phi, x)\n", "phi_y = diff(phi, y)" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [], "source": [ "f = alpha*phi + diff(beta*phi_x, x) + diff(beta*phi_y, y) + gamma_x*phi_x + gamma_y*phi_y" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAA1wAAAAcBAMAAACHe6MGAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA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\nAElFTkSuQmCC\n", "text/latex": [ "$$\\left(- 16.0 \\pi^{2} \\cos{\\left (2 \\pi x \\right )} \\cos{\\left (2 \\pi y \\right )} + 2.0 \\pi \\cos{\\left (2 \\pi x \\right )} + 2.0 \\pi \\cos{\\left (2 \\pi y \\right )} - 16.0 \\pi^{2} + 1.0\\right) \\sin{\\left (2 \\pi x \\right )} \\sin{\\left (2 \\pi y \\right )}$$" ], "text/plain": [ "⎛ 2 \n", "⎝- 16.0⋅π ⋅cos(2⋅π⋅x)⋅cos(2⋅π⋅y) + 2.0⋅π⋅cos(2⋅π⋅x) + 2.0⋅π⋅cos(2⋅π⋅y) - 16.0⋅\n", "\n", " 2 ⎞ \n", "π + 1.0⎠⋅sin(2⋅π⋅x)⋅sin(2⋅π⋅y)" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "f = simplify(f)\n", "f" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(-16.0*pi**2*cos(2*pi*x)*cos(2*pi*y) + 2.0*pi*cos(2*pi*x) + 2.0*pi*cos(2*pi*y) - 16.0*pi**2 + 1.0)*sin(2*pi*x)*sin(2*pi*y)\n" ] } ], "source": [ "print(f)" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1.0\n" ] } ], "source": [ "print(alpha)" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "cos(2*pi*x)*cos(2*pi*y) + 2.0\n" ] } ], "source": [ "print(beta)" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "sin(2*pi*x)\n" ] } ], "source": [ "print(gamma_x)" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "sin(2*pi*y)\n" ] } ], "source": [ "print (gamma_y)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Inhomogeneous BCs" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": false }, "outputs": [], "source": [ "phi = cos(Rational(1,2)*pi*x)*cos(Rational(1,2)*pi*y)" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/latex": [ "$$\\cos{\\left (\\frac{\\pi x}{2} \\right )} \\cos{\\left (\\frac{\\pi y}{2} \\right )}$$" ], "text/plain": [ " ⎛π⋅x⎞ ⎛π⋅y⎞\n", "cos⎜───⎟⋅cos⎜───⎟\n", " ⎝ 2 ⎠ ⎝ 2 ⎠" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "phi" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": false }, "outputs": [], "source": [ "alpha = 10\n", "gamma_x = 1\n", "gamma_y = 1\n", "beta = x*y + 1" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": false }, "outputs": [], "source": [ "phi_x = diff(phi, x)\n", "phi_y = diff(phi, y)\n", "f = alpha*phi + diff(beta*phi_x, x) + diff(beta*phi_y, y) + gamma_x*phi_x + gamma_y*phi_y" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/latex": [ "$$- \\frac{\\pi x}{2} \\sin{\\left (\\frac{\\pi y}{2} \\right )} \\cos{\\left (\\frac{\\pi x}{2} \\right )} - \\frac{\\pi y}{2} \\sin{\\left (\\frac{\\pi x}{2} \\right )} \\cos{\\left (\\frac{\\pi y}{2} \\right )} - \\frac{\\pi^{2}}{2} \\left(x y + 1\\right) \\cos{\\left (\\frac{\\pi x}{2} \\right )} \\cos{\\left (\\frac{\\pi y}{2} \\right )} - \\frac{\\pi}{2} \\sin{\\left (\\frac{\\pi x}{2} \\right )} \\cos{\\left (\\frac{\\pi y}{2} \\right )} - \\frac{\\pi}{2} \\sin{\\left (\\frac{\\pi y}{2} \\right )} \\cos{\\left (\\frac{\\pi x}{2} \\right )} + 10 \\cos{\\left (\\frac{\\pi x}{2} \\right )} \\cos{\\left (\\frac{\\pi y}{2} \\right )}$$" ], "text/plain": [ " ⎛π⋅y⎞ ⎛π⋅x⎞ ⎛π⋅x⎞ ⎛π⋅y⎞ 2 ⎛π⋅x⎞ ⎛π⋅\n", " π⋅x⋅sin⎜───⎟⋅cos⎜───⎟ π⋅y⋅sin⎜───⎟⋅cos⎜───⎟ π ⋅(x⋅y + 1)⋅cos⎜───⎟⋅cos⎜──\n", " ⎝ 2 ⎠ ⎝ 2 ⎠ ⎝ 2 ⎠ ⎝ 2 ⎠ ⎝ 2 ⎠ ⎝ 2\n", "- ───────────────────── - ───────────────────── - ────────────────────────────\n", " 2 2 2 \n", "\n", "y⎞ ⎛π⋅x⎞ ⎛π⋅y⎞ ⎛π⋅y⎞ ⎛π⋅x⎞ \n", "─⎟ π⋅sin⎜───⎟⋅cos⎜───⎟ π⋅sin⎜───⎟⋅cos⎜───⎟ \n", " ⎠ ⎝ 2 ⎠ ⎝ 2 ⎠ ⎝ 2 ⎠ ⎝ 2 ⎠ ⎛π⋅x⎞ ⎛π⋅y⎞\n", "── - ─────────────────── - ─────────────────── + 10⋅cos⎜───⎟⋅cos⎜───⎟\n", " 2 2 ⎝ 2 ⎠ ⎝ 2 ⎠" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "f" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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OUOTQsLXVfLji/14Qy2pqWWTMUlyLE55JrQI5iTijxx9r/UcQC5iRUvvDSyaX8J6aUlwd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\nrF6kEuz/AbkqEqYfVUk1AAAAAElFTkSuQmCC\n", "text/latex": [ "$$- \\frac{x y}{2} \\pi^{2} \\cos{\\left (\\frac{\\pi x}{2} \\right )} \\cos{\\left (\\frac{\\pi y}{2} \\right )} - \\frac{\\pi x}{2} \\sin{\\left (\\frac{\\pi y}{2} \\right )} \\cos{\\left (\\frac{\\pi x}{2} \\right )} - \\frac{\\pi y}{2} \\sin{\\left (\\frac{\\pi x}{2} \\right )} \\cos{\\left (\\frac{\\pi y}{2} \\right )} - \\frac{\\pi}{2} \\sin{\\left (\\pi \\left(\\frac{x}{2} + \\frac{y}{2}\\right) \\right )} - \\frac{\\pi^{2}}{2} \\cos{\\left (\\frac{\\pi x}{2} \\right )} \\cos{\\left (\\frac{\\pi y}{2} \\right )} + 10 \\cos{\\left (\\frac{\\pi x}{2} \\right )} \\cos{\\left (\\frac{\\pi y}{2} \\right )}$$" ], "text/plain": [ " 2 ⎛π⋅x⎞ ⎛π⋅y⎞ ⎛π⋅y⎞ ⎛π⋅x⎞ ⎛π⋅x⎞ ⎛π⋅y⎞ \n", " π ⋅x⋅y⋅cos⎜───⎟⋅cos⎜───⎟ π⋅x⋅sin⎜───⎟⋅cos⎜───⎟ π⋅y⋅sin⎜───⎟⋅cos⎜───⎟ π\n", " ⎝ 2 ⎠ ⎝ 2 ⎠ ⎝ 2 ⎠ ⎝ 2 ⎠ ⎝ 2 ⎠ ⎝ 2 ⎠ \n", "- ──────────────────────── - ───────────────────── - ───────────────────── - ─\n", " 2 2 2 \n", "\n", " ⎛ ⎛x y⎞⎞ 2 ⎛π⋅x⎞ ⎛π⋅y⎞ \n", "⋅sin⎜π⋅⎜─ + ─⎟⎟ π ⋅cos⎜───⎟⋅cos⎜───⎟ \n", " ⎝ ⎝2 2⎠⎠ ⎝ 2 ⎠ ⎝ 2 ⎠ ⎛π⋅x⎞ ⎛π⋅y⎞\n", "─────────────── - ──────────────────── + 10⋅cos⎜───⎟⋅cos⎜───⎟\n", " 2 2 ⎝ 2 ⎠ ⎝ 2 ⎠" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "simplify(f)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### boundary conditions" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAFIAAAAmBAMAAAClsdF/AAAAMFBMVEX///8AAAAAAAAAAAAAAAAA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\nYJckbu3t/ZOAtSn9B6LSlWrnCBM+AAAAAElFTkSuQmCC\n", "text/latex": [ "$$\\cos{\\left (\\frac{\\pi y}{2} \\right )}$$" ], "text/plain": [ " ⎛π⋅y⎞\n", "cos⎜───⎟\n", " ⎝ 2 ⎠" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "phi.subs(x,0)" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAAoAAAAOBAMAAADkjZCYAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAv3aB7AAAAD3RSTlMAEJmJZjLNVN0i77ur\nRHZ72Yd1AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAVElEQVQIHWNgEDIxZWBgSGeQmMDAsoCBOYGB\n+wAD+0cG/gMMvN8Z5BUYeP8xzDdgYP3MMF8BREJEgLLs3xm4NzCwfATpYkpgYGhnkApgYBB+d5QB\nAPogE3QldevOAAAAAElFTkSuQmCC\n", "text/latex": [ "$$0$$" ], "text/plain": [ "0" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "phi.subs(x,1)" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"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 }

bsd-3-clause

magwenelab/mini-term-2016

Intro-Numerical-Computing-Python.ipynb

1

392080

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "<center>\n", "\n", "<br><br>\n", "<font size=6>\n", "First Steps with<br><br>\n", "Numerical Computing in Python\n", "</font>\n", "</b>\n", "<br><br>\n", "<font size=3>\n", "Paul M. Magwene\n", "<br>\n", "Spring 2016\n", "</font>\n", "</center>\n", "\n", "\n", "# How to use IPython notebooks\n", "\n", "This document was written in an IPython notebook. IPython notebooks allow us to\n", "weave together explantory text, code, and figures.\n", "\n", "## Don't copy and paste!\n", "Learning to program has similarities to learning a foreign language. You need to\n", "practice writing your own code, not just copying and pasting it from another\n", "document (that's why I'm providing this as a PDF rather than a notebook itself,\n", "to make the copy-and-paste process less convenient).\n", "\n", "Part of the practice of learning to program is making mistakes (bugs). Learning\n", "to find and correct bugs in your own code is vital.\n", "\n", "## Code cells\n", "Each of the grey boxes below that has `In [n]:` to the left shows a so called\n", "\"code cell\". The text in the code cells is what you should type into the code\n", "cells of your own notebook. The regions starting with `Out [n]:` show you the\n", "result of the code you type in the proceeding input cell(s).\n", "\n", "## Evaluating code cells\n", "After you type Python code into a code cell, hit `Shift-Enter` (hold down the\n", "`Shift` key while you press the `Enter` (or `Return`) key) to evaluate the code\n", "cell. If you type valid code you'll usually get some sort of output (at least in\n", "these first few examples). If you make a mistake and get an error message, click\n", "the input code cell and try and correct your mistake(s).\n", "\n", "## Try your own code\n", "Test your understanding of the examples I've provided by writing additional code\n", "to illustrate the same principle or concept. Don't be afraid to make mistakes.\n", "\n", "## Help and Documentation\n", "\n", "A key skill for becoming an efficient programmer is learning to efficiently\n", "navigate documentation resources. The Python standard library is very well\n", "documented, and can be quickly accessed from the IPython notebook help menu or\n", "online at the http://python.org website. Similar links to some of the more\n", "commonly used scientific and numeric libraries are also found in the Ipython\n", "help menu.\n", "\n", "In addition, there are several ways to access abbreviated versions of the\n", "documentation from the interpetter itself." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Help on built-in function min in module builtins:\n", "\n", "min(...)\n", " min(iterable, *[, default=obj, key=func]) -> value\n", " min(arg1, arg2, *args, *[, key=func]) -> value\n", " \n", " With a single iterable argument, return its smallest item. The\n", " default keyword-only argument specifies an object to return if\n", " the provided iterable is empty.\n", " With two or more arguments, return the smallest argument.\n", "\n" ] } ], "source": [ "help(min)" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "?min # this will pop-up a documentation window in the ipython notebook" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Gee-whiz!\n", "\n", "Let's kick off our tour of Python with some nice visualizations. In this first\n", "section I'm not going to explain any of the code in detail, I'm simply going to\n", "generate some figures to show of some of what Python is capable of. However,\n", "once you work your way through this notebook you should be able to come back to\n", "this first section and understand most of the code written here." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%matplotlib inline\n", "from numpy import *\n", "from scipy import stats\n", "from matplotlib.pyplot import *" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x107f3ff60>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# this is a comment\n", "x = array([1,2,3,4,5,6,7,8,9,10])\n", "plot(x,x**2, color='red', marker='o')\n", "xlabel(\"Length\")\n", "ylabel(\"Area\")\n", "title(\"Length vs Area for Squares\")\n", "pass" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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2q8vHQb58rHtNK/PsgUnvuEZ2NaIAD706nTub7kL1huBU/h5YKwcOAHfvcg6G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\n55FH+DVlitmSeMfw4cDgwUCY/MwE6yG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"text/plain": [ "<matplotlib.figure.Figure at 0x107f7d748>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "x = linspace(0, 10, 100) # generate 100 evenly space points\n", " # between 0 and 10\n", "sinx = sin(x)\n", "sinsqrx = sinx * sinx\n", "\n", "plot(x, sinx, color='red', label='sin(x)')\n", "plot(x, sinsqrx, color='blue', label='sin^2(x)')\n", "legend(loc='best') # add optional legend to plot\n", "pass" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x108140470>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# draw 1000 random samples from a normal distribution\n", "# with mean = 1000, sd = 15\n", "mean = 1000\n", "sd = 15\n", "samples = random.normal(mean, sd, size=1000)\n", "\n", "# draw a histogram\n", "# normed means to make the total area under the\n", "# histogram sum to 1 (i.e. a density histogram)\n", "hist(samples, bins=50, normed=True, color='steelblue')\n", "\n", "# draw probability density function for a normal \n", "# distribution with the same parameters\n", "x = linspace(940,1080,250)\n", "y = stats.norm.pdf(x, loc=mean, scale=sd)\n", "plot(x, y, color='firebrick', linestyle='dashed', linewidth=3)\n", "\n", "# label axes\n", "xlabel(\"x\")\n", "ylabel(\"density\")\n", "\n", "pass" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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FTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x108332780>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# the function of 2 variables we want to plot\n", "def f(x,y):\n", " return cos(radians(x)) * sin(radians(y))\n", "\n", "# generate a grid of x,y points at 10 step\n", "# intervals from 0 to 360\n", "x,y = meshgrid(arange(0, 361, 10), arange(0, 361, 10))\n", "\n", "# calculate a function over the grid\n", "z = f(x,y)\n", "\n", "# draw a contour plot representing the function f(x,y)\n", "contourf(x, y, z, cmap='inferno')\n", "\n", "title(\"A contour plot\\nof z = cos(x)*sin(x)\")\n", "\n", "pass" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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GMWpoPF99+ycd27bgtdff5rrrr0cUy6tC+EvDVt3W2L9h4fzlavEFusYRypc0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\nELzEggAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x107f7e390>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# function from previous plot, now represented in 3D\n", "from mpl_toolkits.mplot3d import Axes3D\n", "\n", "fig = figure()\n", "ax = Axes3D(fig)\n", "ax.plot_surface(x, y, z, rstride=2, cstride=2, cmap='inferno')\n", "\n", "# setup axis labels\n", "ax.set_xlabel(\"x (degrees)\")\n", "ax.set_ylabel(\"y (degrees)\")\n", "ax.set_zlabel(\"z\")\n", "\n", "# set elevation and azimuth for viewing\n", "ax.view_init(68, -11)\n", "\n", "title(\"A 3D representation\\nof z = cos(x)*sin(x)\")\n", "\n", "pass" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Numeric data types\n", "\n", "One of the simplest ways to use the Python interpretter is as a fancy\n", "calculator. We'll illustrate this below and use this as an opportunity to\n", "introduce the core numeric data types that Python supports." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "12" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# this is a comment, the interpretter ignores it\n", "# you can use comments to add short notes or explanation\n", "\n", "2 + 10 # add two integers (whole numbers)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "12.0" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "2.0 + 10.0 # add two floating point numbers (real (decimal) numbers)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "12.0" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "2 + 10.0 # operations that mix integers and floats return floats" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "20" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "2 * 10 # multiplication of integers" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "20.0" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "2.0 * 10.0 # multiplication of floats" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0.2" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "1.0/5.0 # division" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0.4" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "2/5 # in Python 2 this used to default to integer division\n", " # in Python 3 division always returns a float" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "1" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "10 % 3 # The % (modulo) operator yields the remainder after division" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "1024" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "2**10 # exponentiation -- 2 raised to the power 10" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "1.4142135623730951" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "2**0.5 # exponentiation with fractional powers\n", " # **0.5 = square root, **(1/3.) = cube root" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "-12.0" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "(10+2)/(4-5) # numerical operators differ in their precedence\n", " # contrast the output of this line with the line below" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "-2.0" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "(10+2)/4-5 # it is a good habit to use parentheses to disambiguate \n", " # potentially confusing calculations" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(1+1j)" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "(1 + 1j) # complex numbers; we won't use these in the course\n", " # but you might occasionally find the need for them \n", " # in biological research" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(4+3j)" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "(1 + 1j) + (3 + 2j) # adding complex numbers" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(-1+0j)" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "(0 + 1j) * (1j) # complex multiplication" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Querying objects for their type\n", "\n", "There is a built-in Python function called `type` that we can use to query a\n", "variable for it's data type." ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "int" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "type(2)" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "float" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "type(2.0)" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "float" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "type(2 + 10.0) # when adding variables of two numeric types, the outcome\n", " # is always the more general type" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Booleans\n", "\n", "Python has a data type to represent True and False values (Boolean variables)\n", "and supports standard Boolean operators like \"and\", \"or\", and \"not\"" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": true }, "outputs": [], "source": [ "x = True\n", "y = False" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "False" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "not x" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "False" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "y" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "not y # if True return False, if False return True" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "False" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x and y # if both arguments are True return true, else return False" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x and (not y)" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x or y # if either argument is True, return True, else return False" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Comparison operators\n", "\n", "Python supports comparison operators on numeric data types. When you carry out a\n", "comparison you get back a Boolean (True,False) value." ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "4 < 5 # less than" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "False" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ "4 > 5 # greater than" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ "5 <= 5.0 # less than or equal to" ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" } ], "source": [ "5 == 5 # tests equality" ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "False" ] }, "execution_count": 39, "metadata": {}, "output_type": "execute_result" } ], "source": [ "5 == (5**0.5)**2 # the results of this comparison might surprise you" ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "5.000000000000001" ] }, "execution_count": 40, "metadata": {}, "output_type": "execute_result" } ], "source": [ "(5**0.5)**2 # the problem is that sqrt(5) can not be represented \n", " # exactly with floating point numbers. This is not a \n", " # limitation of only Python but is generally true\n", " # for all programming languages" ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 41, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# here's one way to test approximate equality when you suspect\n", "# a floating point calculation might be imprecise\n", "\n", "epsilon = 0.0000001\n", "(5 - epsilon) <= ((5**0.5)**2) <= (5 + epsilon)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Variable assignment\n", "A value or the result of a calculation can be given a name, and then reused in a\n", "different context by referring to that name. This is called variable assignment." ] }, { "cell_type": "code", "execution_count": 42, "metadata": { "collapsed": true }, "outputs": [], "source": [ "pi = 3.141592654\n", "radius = 4.0\n", "area_circ = pi * radius**2\n", "# notice that you don't get any output from this code cell" ] }, { "cell_type": "code", "execution_count": 43, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "50.265482464" ] }, "execution_count": 43, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# however, once you evaluate this code cell you will see\n", "# the results of your calculation\n", "area_circ " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Functions\n", "\n", "A \"function\" is a named sequence of statements that performs a computation.\n", "Functions allow us to encapsulate or abstract away the steps required to perform\n", "a useful operation or calculation.\n", "\n", "There are a number of Python funtions that are always available to you:" ] }, { "cell_type": "code", "execution_count": 44, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "1" ] }, "execution_count": 44, "metadata": {}, "output_type": "execute_result" } ], "source": [ "min(1,2) # find the minimum of its input" ] }, { "cell_type": "code", "execution_count": 45, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "11" ] }, "execution_count": 45, "metadata": {}, "output_type": "execute_result" } ], "source": [ "max(10, 9, 11) # find maximum of inputs" ] }, { "cell_type": "code", "execution_count": 46, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "99" ] }, "execution_count": 46, "metadata": {}, "output_type": "execute_result" } ], "source": [ "abs(-99) # return absolute value of numerical input" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There are many other built-in functions, and we'll see more examples of these\n", "below. See the Python documentation on [\"Built-in\n", "Functions\"](https://docs.python.org/3.5/library/functions.html) for more\n", "details.\n", "\n", "## Defining functions\n", "\n", "You can write your own functions. The general form of a function definition in Python is:\n", "\n", "```\n", "def func_name(arg1, arg2, ...):\n", " body of function\n", " return result\n", "```\n", "\n", "Note:\n", " * Python is white space sensitive, body of a function must be indented\n", "(idiomatic style is to indent by 4 spaces NOT tabs)\n", " * Use a Python aware editor/environment to help get indenting correct. Jupyter\n", "will help you get the indentation correct" ] }, { "cell_type": "code", "execution_count": 47, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# a function that carries out a simple mathematical calculation\n", "\n", "def area_of_circle(radius):\n", " \"\"\"radius of circle --> area of circle\"\"\" \n", " return 3.141592654 * radius**2" ] }, { "cell_type": "code", "execution_count": 48, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "3.141592654" ] }, "execution_count": 48, "metadata": {}, "output_type": "execute_result" } ], "source": [ "area_of_circle(1)" ] }, { "cell_type": "code", "execution_count": 49, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "201.061929856" ] }, "execution_count": 49, "metadata": {}, "output_type": "execute_result" } ], "source": [ "area_of_circle(8)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Importing Functions\n", "\n", "Python has a mechanism to allow you to build libraries of code, which can then\n", "be \"imported\" as needed. Python libraries are usually referred to as \"modules\".\n", "\n", "Here’s how we would make functions and various definitions from the `math`\n", "module available for use." ] }, { "cell_type": "code", "execution_count": 50, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import math" ] }, { "cell_type": "code", "execution_count": 51, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "1.0" ] }, "execution_count": 51, "metadata": {}, "output_type": "execute_result" } ], "source": [ "math.cos(2 * 3.141592654) # cosine" ] }, { "cell_type": "code", "execution_count": 52, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "3.141592653589793" ] }, "execution_count": 52, "metadata": {}, "output_type": "execute_result" } ], "source": [ "math.pi # a constant defined in math" ] }, { "cell_type": "code", "execution_count": 53, "metadata": { "collapsed": true }, "outputs": [], "source": [ "pi = math.pi" ] }, { "cell_type": "code", "execution_count": 54, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "1.0" ] }, "execution_count": 54, "metadata": {}, "output_type": "execute_result" } ], "source": [ "math.cos(2 * pi)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If you get tired of writing the module name, you can import all the functions\n", "from a module by writing `from math import *`. You have to be careful with this\n", "though, as any functions or constants imported this way wil overwrite any\n", "variables/names in your current environment that already exits.\n", "\n", "At the beginning of this notebook I imported a library for numerical computing\n", "called [NumPy](http://www.numpy.org) as well as a library for plotting called\n", "[Matplotlib](http://matplotlib.org)." ] }, { "cell_type": "code", "execution_count": 55, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from numpy import *\n", "from matplotlib.pyplot import *" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Numpy includes most of the functions defined in the math module so we didn't\n", "really need to add the `math.` prefix." ] }, { "cell_type": "code", "execution_count": 56, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "2.7182818284590451" ] }, "execution_count": 56, "metadata": {}, "output_type": "execute_result" } ], "source": [ "exp(1) # e^1" ] }, { "cell_type": "code", "execution_count": 57, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "1.0" ] }, "execution_count": 57, "metadata": {}, "output_type": "execute_result" } ], "source": [ "log(e) # natural logarithm of e" ] }, { "cell_type": "code", "execution_count": 58, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "2.0" ] }, "execution_count": 58, "metadata": {}, "output_type": "execute_result" } ], "source": [ "log10(100) # log base 10 of 100" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Lists\n", "\n", "Lists are the simplest \"data structure\". Data structures are computational\n", "objects for storing, accessing, and operating on data.\n", "\n", "List represent ordered collections of arbitrary objects. We'll begin by working\n", "with lists of numbers." ] }, { "cell_type": "code", "execution_count": 59, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[1, 2, 3, 4, 5]" ] }, "execution_count": 59, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x = [1,2,3,4,5] # a list with the numbers 1..5\n", "x" ] }, { "cell_type": "code", "execution_count": 60, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[2.0, 4, 6, 8, 10.0, 11, (1+1j), 3.14159]" ] }, "execution_count": 60, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# a list with floats and ints and complex numbers\n", "y = [2.0, 4, 6, 8, 10.0, 11, (1+1j), 3.14159] \n", "y" ] }, { "cell_type": "code", "execution_count": 61, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "5" ] }, "execution_count": 61, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# lists of a length. We can use the `len` function to get this\n", "len(x)" ] }, { "cell_type": "code", "execution_count": 62, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "8" ] }, "execution_count": 62, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(y)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Indexing lists\n", "\n", "Accessing the elements of a list is called \"indexing\". In Python lists are\n", "\"zero-indexed\" which means when you can access lists elements, the first element\n", "has the index `0`, the second element has the index `1`, ..., and the last\n", "element has the index `len(x)-1`." ] }, { "cell_type": "code", "execution_count": 63, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "2" ] }, "execution_count": 63, "metadata": {}, "output_type": "execute_result" } ], "source": [ "z = [2, 4, 6, 8, 10]\n", "z[0] # first element" ] }, { "cell_type": "code", "execution_count": 64, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "8" ] }, "execution_count": 64, "metadata": {}, "output_type": "execute_result" } ], "source": [ "z[3] # fourth element" ] }, { "cell_type": "code", "execution_count": 65, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "5" ] }, "execution_count": 65, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(z)" ] }, { "cell_type": "code", "execution_count": 66, "metadata": { "collapsed": false }, "outputs": [ { "ename": "IndexError", "evalue": "list index out of range", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mIndexError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-66-7557a87402ea>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mz\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;31m## this generates an error -- why?\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;31mIndexError\u001b[0m: list index out of range" ] } ], "source": [ "z[5] ## this generates an error -- why?" ] }, { "cell_type": "code", "execution_count": 67, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "10" ] }, "execution_count": 67, "metadata": {}, "output_type": "execute_result" } ], "source": [ "z[4] # last element of z" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can use negative indexing to get elements from the end of a list." ] }, { "cell_type": "code", "execution_count": 68, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "10" ] }, "execution_count": 68, "metadata": {}, "output_type": "execute_result" } ], "source": [ "z[-1] # last element" ] }, { "cell_type": "code", "execution_count": 69, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "8" ] }, "execution_count": 69, "metadata": {}, "output_type": "execute_result" } ], "source": [ "z[-2] # second to last element" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Indexing can be used to get, set, and delete items in a list." ] }, { "cell_type": "code", "execution_count": 70, "metadata": { "collapsed": true }, "outputs": [], "source": [ "m = [1, 2, 4, 6, 8, \"hike\"]" ] }, { "cell_type": "code", "execution_count": 71, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[1, 2, 4, 6, 8, 'learning python is so great!']" ] }, "execution_count": 71, "metadata": {}, "output_type": "execute_result" } ], "source": [ "m[-1] = \"learning python is so great!\" # set the last element\n", "m" ] }, { "cell_type": "code", "execution_count": 72, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[2, 4, 6, 8, 'learning python is so great!']" ] }, "execution_count": 72, "metadata": {}, "output_type": "execute_result" } ], "source": [ "del m[0]\n", "m" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can append and delete list elements as well as concatenate two lists" ] }, { "cell_type": "code", "execution_count": 73, "metadata": { "collapsed": true }, "outputs": [], "source": [ "x = [1,2,3]\n", "y = ['a', 'b', 'c', 'd']" ] }, { "cell_type": "code", "execution_count": 74, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[1, 2, 3, 4]" ] }, "execution_count": 74, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x.append(4)\n", "x" ] }, { "cell_type": "code", "execution_count": 75, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[1, 2, 3, 4, 'a', 'b', 'c', 'd']" ] }, "execution_count": 75, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x + y" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Slicing lists\n", "\n", "Python lists support the notion of ‘slices’ - a continuous sublist of a larger\n", "list. The following code illustrates this concept." ] }, { "cell_type": "code", "execution_count": 76, "metadata": { "collapsed": true }, "outputs": [], "source": [ "c = ['a','b','c','d','e','f']" ] }, { "cell_type": "code", "execution_count": 77, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "['a', 'b', 'c']" ] }, "execution_count": 77, "metadata": {}, "output_type": "execute_result" } ], "source": [ "c[0:3] # get the elements of from index 0 up to \n", " # but not including the element at index 3" ] }, { "cell_type": "code", "execution_count": 78, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "['a', 'b', 'c']" ] }, "execution_count": 78, "metadata": {}, "output_type": "execute_result" } ], "source": [ "c[:3] # same as above, first index implied" ] }, { "cell_type": "code", "execution_count": 79, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "['c', 'd', 'e']" ] }, "execution_count": 79, "metadata": {}, "output_type": "execute_result" } ], "source": [ "c[2:5] # from element 2 up to 5" ] }, { "cell_type": "code", "execution_count": 80, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "['d', 'e', 'f']" ] }, "execution_count": 80, "metadata": {}, "output_type": "execute_result" } ], "source": [ "c[3:] # from index three to end (last index implied)" ] }, { "cell_type": "code", "execution_count": 81, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 81, "metadata": {}, "output_type": "execute_result" } ], "source": [ "c[-1:0] # how come this returned an empty list?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "List slices support a \"step\" specified by a third colon" ] }, { "cell_type": "code", "execution_count": 82, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "['a', 'c', 'e']" ] }, "execution_count": 82, "metadata": {}, "output_type": "execute_result" } ], "source": [ "c[0:5:2] # c from 0 to 5, step by 2" ] }, { "cell_type": "code", "execution_count": 83, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "['f', 'e', 'd', 'c', 'b']" ] }, "execution_count": 83, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# you can you a negative step to walk backward over a list\n", "# note where the output stops (why didn't we get 'a'?)\n", "c[-1:0:-1]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As with single indexing, the slice notation can be used to set elements of a\n", "list." ] }, { "cell_type": "code", "execution_count": 84, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "['a', 'b', 'C', 'D', 'e', 'f']" ] }, "execution_count": 84, "metadata": {}, "output_type": "execute_result" } ], "source": [ "c[2:4] = ['C', 'D']\n", "c" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally, there are a number of useful methods associated with list objects, such\n", "as `reverse()` and `sort()`." ] }, { "cell_type": "code", "execution_count": 85, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[1, 1, 3, 3, 4, 5, 11]" ] }, "execution_count": 85, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d = [1, 5, 3, 4, 1, 11, 3]\n", "d.sort() # sort in place\n", "d" ] }, { "cell_type": "code", "execution_count": 86, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[11, 5, 4, 3, 3, 1, 1]" ] }, "execution_count": 86, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d.reverse() # reverse in place\n", "d" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# NumPy arrays\n", "\n", "NumPy is an extension package for Python that provides many facilities for numerical computing. There is also a related package called SciPy that provides even more facilities for scientific computing. Both NumPy and SciPy can be downloaded from http://www.scipy.org/. NumPy does not come with the standard Python distribution, but it does come as an included package if you use the Anaconda Python distribution. The NumPy package comes with documentation and a tutorial. You can access the documentation here: http://docs.scipy.org/doc/.\n", "\n", "The basic data structure in NumPy is the array, which you've already seen in several examples above. As opposed to lists, all the elements in a NumPy array must be of the same type (but this type can differ between different arrays). Arrays are commonly used to represent matrices (2D-arrays) but can be used to represent arrays of arbitrary dimension ($n$-dimensional arrays). " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Arithmetic operations on NumPy arrays" ] }, { "cell_type": "code", "execution_count": 87, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from numpy import *" ] }, { "cell_type": "code", "execution_count": 88, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 2, 4, 6, 8, 10])" ] }, "execution_count": 88, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x = array([2,4,6,8,10]) \n", "x" ] }, { "cell_type": "code", "execution_count": 89, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "numpy.ndarray" ] }, "execution_count": 89, "metadata": {}, "output_type": "execute_result" } ], "source": [ "type(x)" ] }, { "cell_type": "code", "execution_count": 90, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ -2, -4, -6, -8, -10])" ] }, "execution_count": 90, "metadata": {}, "output_type": "execute_result" } ], "source": [ "-x" ] }, { "cell_type": "code", "execution_count": 91, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 4, 16, 36, 64, 100])" ] }, "execution_count": 91, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x**2" ] }, { "cell_type": "code", "execution_count": 92, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 6.28318531, 12.56637061, 18.84955592, 25.13274123, 31.41592654])" ] }, "execution_count": 92, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x * pi" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Notice how all the arithmetic operations operate elementwise on arrays. You can also perform arithmetic operations between arrays, which also operate element wise" ] }, { "cell_type": "code", "execution_count": 93, "metadata": { "collapsed": false }, "outputs": [], "source": [ " y = array([0, 1, 3, 5, 9])" ] }, { "cell_type": "code", "execution_count": 94, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 2, 5, 9, 13, 19])" ] }, "execution_count": 94, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x + y" ] }, { "cell_type": "code", "execution_count": 95, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 0, 4, 18, 40, 90])" ] }, "execution_count": 95, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x * y" ] }, { "cell_type": "code", "execution_count": 96, "metadata": { "collapsed": true }, "outputs": [], "source": [ "z = array([1, 4, 7, 11])" ] }, { "cell_type": "code", "execution_count": 97, "metadata": { "collapsed": false }, "outputs": [ { "ename": "ValueError", "evalue": "operands could not be broadcast together with shapes (5,) (4,) ", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-97-f1bfe8206f1c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mx\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mz\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;31mValueError\u001b[0m: operands could not be broadcast together with shapes (5,) (4,) " ] } ], "source": [ "x + z" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The last example above shows that the lengths of the two arrays have to be the same in order to do element-wise operations.\n", "\n", "By default,most operations on arrays work element-wise. However there are a variety of functions for doing array-wise operations such as matrix multiplication or matrix inversion. Here are a few examples of using NumPy arrays to represent matrices:" ] }, { "cell_type": "code", "execution_count": 98, "metadata": { "collapsed": true }, "outputs": [], "source": [ "m = np.array([[1,2],\n", " [3,4]])" ] }, { "cell_type": "code", "execution_count": 99, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[1, 2],\n", " [3, 4]])" ] }, "execution_count": 99, "metadata": {}, "output_type": "execute_result" } ], "source": [ "m" ] }, { "cell_type": "code", "execution_count": 100, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[1, 3],\n", " [2, 4]])" ] }, "execution_count": 100, "metadata": {}, "output_type": "execute_result" } ], "source": [ "m.transpose()" ] }, { "cell_type": "code", "execution_count": 101, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[-2. , 1. ],\n", " [ 1.5, -0.5]])" ] }, "execution_count": 101, "metadata": {}, "output_type": "execute_result" } ], "source": [ "linalg.inv(m)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Indexing and Slicing NumPy arrays\n", "\n", "Like the built-in lists, NumPy arrays are zero-indexed." ] }, { "cell_type": "code", "execution_count": 102, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 2, 4, 6, 8, 10])" ] }, "execution_count": 102, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x" ] }, { "cell_type": "code", "execution_count": 103, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "2" ] }, "execution_count": 103, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x[0]" ] }, { "cell_type": "code", "execution_count": 104, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "4" ] }, "execution_count": 104, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x[1]" ] }, { "cell_type": "code", "execution_count": 105, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "10" ] }, "execution_count": 105, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x[4]" ] }, { "cell_type": "code", "execution_count": 106, "metadata": { "collapsed": false }, "outputs": [ { "ename": "IndexError", "evalue": "index 5 is out of bounds for axis 0 with size 5", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mIndexError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-106-e8c2945f243d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;31mIndexError\u001b[0m: index 5 is out of bounds for axis 0 with size 5" ] } ], "source": [ "x[5]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Again, you can use negative indexing to get elements from the end of the vector and slicing to get subsets of the array." ] }, { "cell_type": "code", "execution_count": 107, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "10" ] }, "execution_count": 107, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x[-1]" ] }, { "cell_type": "code", "execution_count": 108, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "8" ] }, "execution_count": 108, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x[-2]" ] }, { "cell_type": "code", "execution_count": 109, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 6, 8, 10])" ] }, "execution_count": 109, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x[2:]" ] }, { "cell_type": "code", "execution_count": 110, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([2, 8])" ] }, "execution_count": 110, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x[::3] # every third element of x" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Comparison operators on arrays\n", "\n", "NumPy arrays support the comparison operators, returning arrays of Booleans." ] }, { "cell_type": "code", "execution_count": 111, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 2, 4, 6, 8, 10])" ] }, "execution_count": 111, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x" ] }, { "cell_type": "code", "execution_count": 112, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ True, True, False, False, False], dtype=bool)" ] }, "execution_count": 112, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x < 5" ] }, { "cell_type": "code", "execution_count": 113, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([False, False, True, True, True], dtype=bool)" ] }, "execution_count": 113, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x >= 6" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Combining indexing and comparison on arrays\n", "\n", "NumPy arrays allows us to combine the comparison operators with indexing. This facilitates data filtering and subsetting." ] }, { "cell_type": "code", "execution_count": 114, "metadata": { "collapsed": true }, "outputs": [], "source": [ "x = array([2, 4, 6, 10, 8, 7, 9, 2, 11])" ] }, { "cell_type": "code", "execution_count": 115, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 6, 10, 8, 7, 9, 11])" ] }, "execution_count": 115, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x[x > 5]" ] }, { "cell_type": "code", "execution_count": 116, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 4, 6, 10, 8, 7, 9, 11])" ] }, "execution_count": 116, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x[x != 2]" ] }, { "cell_type": "code", "execution_count": 117, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 2, 10, 9, 2, 11])" ] }, "execution_count": 117, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x[logical_or(x <4, x > 8)]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In the first example we retrieved all the elements of `x` that are larger than 5 (read \"x where x is greater than 5\"). In the second example we retrieved those elements of `x` that did not equal six. The third example is slightly more complicated. We combined the `logical_or` function with comparison and indexing. This allowed us to return those elements of the array `x` that are either less than four *or* greater than six. Combining indexing and comparison is a powerful concept. See the numpy documentation on [logical functions](http://docs.scipy.org/doc/numpy/reference/routines.logic.html) for more information." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Generating Regular Sequences\n", "\n", "Creating sequences of numbers that are separated by a specified value or that follow a particular patterns turns out to be a common task in programming. Python and NumPy have functions to simplify this task." ] }, { "cell_type": "code", "execution_count": 118, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])" ] }, "execution_count": 118, "metadata": {}, "output_type": "execute_result" } ], "source": [ "arange(10)" ] }, { "cell_type": "code", "execution_count": 119, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 3, 7, 11])" ] }, "execution_count": 119, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# generate numbers from 3 to 12 (non-inclusive) stepping by 4\n", "arange(3, 12, 4) " ] }, { "cell_type": "code", "execution_count": 120, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 1. , 1.5, 2. , 2.5, 3. , 3.5, 4. , 4.5, 5. , 5.5, 6. ,\n", " 6.5, 7. , 7.5, 8. , 8.5, 9. , 9.5])" ] }, "execution_count": 120, "metadata": {}, "output_type": "execute_result" } ], "source": [ "arange(1,10,0.5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can also do some fancy tricks on lists to generate repeating patterns." ] }, { "cell_type": "code", "execution_count": 121, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[True, True, False, True, True, False, True, True, False]" ] }, "execution_count": 121, "metadata": {}, "output_type": "execute_result" } ], "source": [ "[True,True,False]*3" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Mathematical functions applied to arrays\n", "\n", "Most of the standard mathematical functions can be applied to numpy arrays however you must use the functions defined in the NumPy module." ] }, { "cell_type": "code", "execution_count": 122, "metadata": { "collapsed": true }, "outputs": [], "source": [ "x = array([2, 4, 6, 8])" ] }, { "cell_type": "code", "execution_count": 123, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([-0.41614684, -0.65364362, 0.96017029, -0.14550003])" ] }, "execution_count": 123, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cos(x)" ] }, { "cell_type": "code", "execution_count": 124, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 0.90929743, -0.7568025 , -0.2794155 , 0.98935825])" ] }, "execution_count": 124, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sin(x)" ] }, { "cell_type": "code", "execution_count": 125, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 0.69314718, 1.38629436, 1.79175947, 2.07944154])" ] }, "execution_count": 125, "metadata": {}, "output_type": "execute_result" } ], "source": [ "log(x)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Plots with Matplotlib\n", "\n", "[Matplotlib](http://matplotlib.org) is a Python library for making nice 2D and\n", "3D plots. There are a number of other plotting libraries available for Python\n", "but matplotlib has probably the most active developer community and is capable\n", "of producing publication quality figures.\n", "\n", "Matplotlib plots can be generated in a variety of ways but the easiest way to\n", "get quick plots is to use the functions defined in the [`matplotlib.pyplot`](http://matplotlib.org/api/pyplot_summary.html)\n", "module." ] }, { "cell_type": "code", "execution_count": 126, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# this tells Jupyter to draw plots in the notebook itself\n", "%matplotlib inline \n", "\n", "# import all the plotting functions from matplotlib.pyplot\n", "from matplotlib.pyplot import *" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Commonly used functions from `matplotlib.pyplot` include `plot`, `scatter`, `imshow`, `savefig` among others. We explored a decent numbers of plotting functionality at the beginning of this notebook. Here are a few more examples." ] }, { "cell_type": "code", "execution_count": 127, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x108639ac8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "x = arange(1, 10, 0.25)\n", "y = x + random.normal(size=len(x))\n", "scatter(x,y,color='black')\n", "pass" ] }, { "cell_type": "code", "execution_count": 128, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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2cncux6RchsWxTnqjxUdvV3hrTnDbpLHVFaX/XOBDGZKLASQSCdx99934h3/4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\nXULDUjpuLkdr2gQHFD5A6wItSR8bMHF5uTQ0UjSXLGj7dFk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sz7rO1m/B9TwDEwd85x3U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\nXm3+fVz+HYDrURT9K/nsmeuvc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"text/plain": [ "<matplotlib.figure.Figure at 0x1085c67f0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# see http://matplotlib.org/users/image_tutorial.html\n", " \n", "import matplotlib.image as mpimg # required for loading images\n", "img = mpimg.imread(\"http://matplotlib.org/_images/stinkbug.png\")\n", "imshow(img)\n", "\n", "pass" ] }, { "cell_type": "code", "execution_count": 129, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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vtYHt6xwAd+GXOn5TU8DmpnIMqVy5oub7Tp5kx49h9iXNeWDyVmDitLvjl873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DXbYIx5LnyjpGP2Y8cvCvnSOu+I4S7uXTfAWyVhlZWnx9KobbisngGKn0MZc+/4GVo93YmffsQu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\n1DnIage68wFWT79dN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"text/plain": [ "<matplotlib.figure.Figure at 0x108d152e8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# demonstrating subplots\n", "\n", "fig, (ax1, ax2, ax3) = subplots(nrows=1, ncols=3)\n", "fig.set_size_inches(15,5)\n", "\n", "x = linspace(1, 100, 200)\n", "y = log(x**2) - sqrt(x) + sin(x)\n", "ax1.plot(x, y, color='blue')\n", "ax1.set_xlabel(\"x\")\n", "ax1.set_ylabel(\"y\")\n", "\n", "z = sqrt(x) * sin(x) - exp(1/x**2)\n", "ax2.plot(x, z, color='orange')\n", "ax2.set_xlabel(\"x\")\n", "ax2.set_ylabel(\"z\")\n", "\n", "ax3.plot(x, y*z,color='purple')\n", "ax3.set_xlabel(\"x\")\n", "ax3.set_ylabel(\"y * z\")\n", "\n", "pass" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Strings\n", "\n", "Strings aren't numerical data, but working with strings comes up often\n", "enough in numerical computing that it's worth mentioning them here.\n", "\n", "Strings represent textual information, data or input. String are an interesting\n", "data type because they share properties with *data structures* like lists (data\n", "structures will be introduced in the next handout)." ] }, { "cell_type": "code", "execution_count": 130, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Beware the Jabberwock, my son!\n" ] } ], "source": [ "# strings can be enclosed in double quotes\n", "s1 = \"Beware the Jabberwock, my son!\" \n", "print(s1)" ] }, { "cell_type": "code", "execution_count": 131, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "str" ] }, "execution_count": 131, "metadata": {}, "output_type": "execute_result" } ], "source": [ "type(s1) # what type are you, s1?" ] }, { "cell_type": "code", "execution_count": 132, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The jaws that bite, the claws that catch!\n" ] } ], "source": [ "# OR in single quotes\n", "s2 = 'The jaws that bite, the claws that catch!' \n", "print(s2)" ] }, { "cell_type": "code", "execution_count": 133, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "'Twas brillig, and the slithy toves\n" ] } ], "source": [ "# If the string you want to write has a quote character\n", "# you need to wrap it in the other type of quote\n", "\n", "# note the single quote at the beginning of 'Twas\n", "s3 = \"'Twas brillig, and the slithy toves\" \n", "print(s3)" ] }, { "cell_type": "code", "execution_count": 134, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "abcdef\n" ] } ], "source": [ "# Concatenating (adding) string\n", "s4 = \"abc\"\n", "s5 = \"def\"\n", "print(s4 + s5)" ] }, { "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

edjdavid/adventures

python/rpy2 DataFrames.ipynb

1

11283

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "import rpy2.robjects as ro\n", "from rpy2.robjects import pandas2ri\n", "from rpy2.robjects.packages import importr\n", "from rpy2.robjects.conversion import localconverter\n", "from IPython.display import display" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "base = importr('base')\n", "df = pd.DataFrame(np.random.random((10, 3)), columns=list('abc'))\n", "r_df = ro.DataFrame({'int_values': ro.IntVector([1,2,3]),\n", " 'str_values': ro.StrVector(['abc', 'def', 'ghi'])})" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## rpy2 doesn't convert pd.DataFrames by default" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Conversion 'py2rpy' not defined for objects of type '<class 'pandas.core.frame.DataFrame'>'\n" ] } ], "source": [ "try:\n", " base.summary(df)\n", "except NotImplementedError as e:\n", " print(e)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Do not use pd.DataFrame on R DataFrame, the results are transposed and not indexed correctly" ] }, { "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>0</th>\n", " <th>1</th>\n", " <th>2</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>1</td>\n", " <td>2</td>\n", " <td>3</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>abc</td>\n", " <td>def</td>\n", " <td>ghi</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " 0 1 2\n", "0 1 2 3\n", "1 abc def ghi" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pd.DataFrame(r_df)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Convert only on a local scope\n", "https://rpy2.github.io/doc/latest/html/generated_rst/pandas.html" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array(['Min. :0.007936 ', '1st Qu.:0.181851 ', 'Median :0.371897 ',\n", " 'Mean :0.427459 ', '3rd Qu.:0.671669 ', 'Max. :0.951857 ',\n", " 'Min. :0.1517 ', '1st Qu.:0.4284 ', 'Median :0.4989 ',\n", " 'Mean :0.4820 ', '3rd Qu.:0.6079 ', 'Max. :0.6450 ',\n", " 'Min. :0.03876 ', '1st Qu.:0.15384 ', 'Median :0.37714 ',\n", " 'Mean :0.42752 ', '3rd Qu.:0.61826 ', 'Max. :0.95349 '],\n", " dtype='<U18')" ] }, "metadata": {}, "output_type": "display_data" }, { "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>int_values</th>\n", " <th>str_values</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>1</th>\n", " <td>1</td>\n", " <td>abc</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>2</td>\n", " <td>def</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>3</td>\n", " <td>ghi</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " int_values str_values\n", "1 1 abc\n", "2 2 def\n", "3 3 ghi" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Conversion 'py2rpy' not defined for objects of type '<class 'pandas.core.frame.DataFrame'>'\n" ] } ], "source": [ "with localconverter(ro.default_converter + pandas2ri.converter):\n", " display(base.summary(df))\n", " display(ro.conversion.rpy2py(r_df))\n", " \n", "try:\n", " print(base.summary(df))\n", "except NotImplementedError as e:\n", " print(e)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Set pandas converter globally" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "ro.conversion.set_conversion(ro.default_converter + pandas2ri.converter)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array(['Min. :0.007936 ', '1st Qu.:0.181851 ', 'Median :0.371897 ',\n", " 'Mean :0.427459 ', '3rd Qu.:0.671669 ', 'Max. :0.951857 ',\n", " 'Min. :0.1517 ', '1st Qu.:0.4284 ', 'Median :0.4989 ',\n", " 'Mean :0.4820 ', '3rd Qu.:0.6079 ', 'Max. :0.6450 ',\n", " 'Min. :0.03876 ', '1st Qu.:0.15384 ', 'Median :0.37714 ',\n", " 'Mean :0.42752 ', '3rd Qu.:0.61826 ', 'Max. :0.95349 '],\n", " dtype='<U18')" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "base.summary(df)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>int_values</th>\n", " <th>str_values</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>1</th>\n", " <td>1</td>\n", " <td>abc</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>2</td>\n", " <td>def</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>3</td>\n", " <td>ghi</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " int_values str_values\n", "1 1 abc\n", "2 2 def\n", "3 3 ghi" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ro.conversion.rpy2py(r_df)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Return to default converter" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "ro.conversion.set_conversion(ro.default_converter)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " <span>R/rpy2 DataFrame (3 x 2)</span>\n", " <table>\n", " <thead>\n", " <tr>\n", " \n", " <th>int_values</th>\n", " \n", " <th>str_values</th>\n", " \n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " \n", " <td>\n", " ...\n", " </td>\n", " \n", " <td>\n", " ...\n", " </td>\n", " \n", " </tr>\n", " \n", " </tbody>\n", " </table>\n", " " ], "text/plain": [ "R object with classes: ('data.frame',) mapped to:\n", "[IntSexpVector, StrSexpVector]\n", " int_values: <class 'rpy2.rinterface.IntSexpVector'>\n", " <rpy2.rinterface.IntSexpVector object at 0x7f23a4b6a320> [RTYPES.INTSXP]\n", " str_values: <class 'rpy2.rinterface_lib.sexp.StrSexpVector'>\n", " <rpy2.rinterface_lib.sexp.StrSexpVector object at 0x7f23a4b69be0> [RTYPES.STRSXP]" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Conversion 'py2rpy' not defined for objects of type '<class 'pandas.core.frame.DataFrame'>'\n" ] } ], "source": [ "try:\n", " display(ro.conversion.rpy2py(r_df))\n", " print(base.summary(df))\n", "except NotImplementedError as e:\n", " print(e)" ] } ], "metadata": { "kernelspec": { "display_name": "Python [conda env:rpy]", "language": "python", "name": "conda-env-rpy-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": "3.7.7" } }, "nbformat": 4, "nbformat_minor": 4 }

mit

mbway/Bayesian-Optimisation

demos/2D-Demo-2-Copy1.ipynb

1

6060

{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%load_ext autoreload\n", "%autoreload 2\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "import seaborn as sns; sns.set() # prettify matplotlib\n", "\n", "import numpy as np\n", "import sklearn.gaussian_process as gp\n", "import GPy" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# local modules\n", "import turbo as tb\n", "import turbo.modules as tm\n", "import turbo.plotting as tp\n", "import turbo.gui.jupyter as tg" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# make deterministic\n", "np.random.seed(100)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Function to optimize:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "xmin, xmax = -6, 6\n", "ymin, ymax = -5, 5\n", "x = np.linspace(xmin, xmax, 100)\n", "y = np.linspace(ymin, ymax, 100)\n", "f = lambda x,y: 1.5 * (np.sin(0.5*x)**2 * np.cos(y) + 0.1*x + 0.2*y) + \\\n", " np.random.normal(0, 0.2, size=None if isinstance(x, float) else x.shape)\n", "X, Y = np.meshgrid(x, y)\n", "Z = f(X,Y)\n", "best_z = np.min(Z)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "tp.surface_3D(X,Y,Z)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "bounds = [('x', xmin, xmax), ('y', ymin, ymax)]\n", "\n", "op = tb.Optimiser(f, 'min', bounds, pre_phase_trials=5, settings_preset='default')\n", "\n", "op.pre_phase_select = tm.random_selector()\n", "op.acquisition = tm.EI(xi=0.01)\n", "\n", "kernel = GPy.kern.Matern52(input_dim=2)\n", "#kernel.variance.set_prior(GPy.priors.Gamma.from_EV(8, 6))\n", "#kernel.lengthscale.set_prior(GPy.priors.Gamma.from_EV(1, 2))\n", "op.surrogate = tm.GPySurrogate(model_params={'normalizer': True, 'kernel': kernel}, training_iterations=5)\n", "'''\n", "op.surrogate_factory = tm.SciKitGPSurrogate.Factory(gp_params=dict(\n", " kernel = gp.kernels.ConstantKernel(constant_value_bounds=(0.1, 5)) * gp.kernels.RBF(length_scale_bounds=(0.1, 5)) + gp.kernels.WhiteKernel(noise_level_bounds=(1e-05, 0.5)),\n", " #kernel = gp.kernels.ConstantKernel() * gp.kernels.RBF() + gp.kernels.WhiteKernel(),\n", "), variable_iterations=lambda trial_num: [10,5,2][(trial_num-4) % 3])\n", "'''\n", "rec = tb.Recorder(op)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "tg.OptimiserProgressBar(op)\n", "op.run(max_trials=30);" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#tp.plot_error(rec, true_best=best_z);\n", "tp.plot_overview(rec)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "op.get_incumbent()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "tp.plot_surrogate_hyper_params_2D(rec, param_indices=(0,1), size_limits=(30,30), use_param_bounds=True);" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "tp.interactive_plot_trial_1D(rec);" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "tp.interactive_plot_trial_2D(rec, true_objective=f)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Try optimising the same function with random search" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "ra = tb.Optimiser(f, 'min', bounds, pre_phase_trials=1, settings_preset='random_search')\n", "recr = tb.Recorder(ra)\n", "ra.run(max_trials=1000)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "tp.plot_error(recr, true_best=best_z);" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "ra.get_incumbent()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "tp.interactive_plot_trial_2D(recr, true_objective=f)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.3" }, "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": 2 }

gpl-3.0

ashkamath/VQA

VQA/LSTM files/LSTM_VGG_VQA.ipynb

2

38861

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Visual Question Answering with LSTM and VGG features\n", "\n", "In this notebook, we build a VQA model with LSTM as the language model and the VGG-19 as our visual model. Since the full dataset is quite large, we load and play with a small portion of it on our local machine. " ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Using TensorFlow backend.\n" ] } ], "source": [ "# don't re-inventing the wheel\n", "import h5py, json, spacy\n", "\n", "import numpy as np\n", "import cPickle as pickle\n", "\n", "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "\n", "from model import LSTMModel\n", "from utils import prepare_ques_batch, prepare_im_batch" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Word Embeddings\n", "For word embeddings, we use the pre-trained `word2vec` provided by the `spacy` package" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# run `python -m spacy.en.download` to collect the embeddings (1st time only)\n", "embeddings = spacy.en.English()\n", "word_dim = 300" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Loading Tiny Dataset\n", "Here we load a tiny dataset of 300 question/answer pairs and 100 images which is prepared using the script in `Dataset Handling.ipynb`" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Loading tiny dataset of 100 image features and 300 question/answer pairs for training.\n" ] } ], "source": [ "h5_img_file_tiny = h5py.File('data/vqa_data_img_vgg_train_tiny.h5', 'r')\n", "fv_im_tiny = h5_img_file_tiny.get('/images_train')\n", "\n", "with open('data/qa_data_train_tiny.pkl', 'rb') as fp:\n", " qa_data_tiny = pickle.load(fp)\n", "\n", "json_file = json.load(open('data/vqa_data_prepro.json', 'r'))\n", "ix_to_word = json_file['ix_to_word']\n", "ix_to_ans = json_file['ix_to_ans']\n", "\n", "vocab_size = len(ix_to_word)\n", "print \"Loading tiny dataset of %d image features and %d question/answer pairs for training.\" % (len(fv_im_tiny), len(qa_data_tiny)) " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this dataset, one image associates with muiltiple question/answer pairs (3 in this case). Therefore, we need to hand-binding the question/answer pairs with the corresponding image feature for training." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [], "source": [ "questions, ques_len, im_ix, ans = zip(*qa_data_tiny)\n", "\n", "nb_classes = 1000\n", "max_ques_len = 26\n", "\n", "X_ques = prepare_ques_batch(questions, ques_len, max_ques_len, embeddings, word_dim, ix_to_word)\n", "X_im = prepare_im_batch(fv_im_tiny, im_ix)\n", "y = np.zeros((len(ans), nb_classes))\n", "y[np.arange(len(ans)), ans] = 1" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Overfit LSTM + VGG\n", "Finally, we are getting to the fun part! Let's build our model..." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "____________________________________________________________________________________________________\n", "Layer (type) Output Shape Param # Connected to \n", "====================================================================================================\n", "bidirectional_1 (Bidirectional) (None, 1024) 3330048 \n", "____________________________________________________________________________________________________\n", "maxpooling2d_1 (MaxPooling2D) (None, 7, 7, 512) 0 \n", "____________________________________________________________________________________________________\n", "flatten_1 (Flatten) (None, 25088) 0 \n", "____________________________________________________________________________________________________\n", "dense_1 (Dense) (None, 4096) 102764544 \n", "____________________________________________________________________________________________________\n", "batchnormalization_1 (BatchNormal(None, 4096) 8192 \n", "____________________________________________________________________________________________________\n", "dense_2 (Dense) (None, 4096) 16781312 \n", "____________________________________________________________________________________________________\n", "batchnormalization_2 (BatchNormal(None, 4096) 8192 \n", "____________________________________________________________________________________________________\n", "dense_3 (Dense) (None, 4096) 16781312 \n", "____________________________________________________________________________________________________\n", "batchnormalization_3 (BatchNormal(None, 4096) 8192 \n", "____________________________________________________________________________________________________\n", "batchnormalization_4 (BatchNormal(None, 5120) 10240 merge_1[0][0] \n", "____________________________________________________________________________________________________\n", "dense_4 (Dense) (None, 2014) 10313694 batchnormalization_4[0][0] \n", "____________________________________________________________________________________________________\n", "batchnormalization_5 (BatchNormal(None, 2014) 4028 dense_4[0][0] \n", "____________________________________________________________________________________________________\n", "dropout_1 (Dropout) (None, 2014) 0 batchnormalization_5[0][0] \n", "____________________________________________________________________________________________________\n", "dense_5 (Dense) (None, 2014) 4058210 dropout_1[0][0] \n", "____________________________________________________________________________________________________\n", "batchnormalization_6 (BatchNormal(None, 2014) 4028 dense_5[0][0] \n", "____________________________________________________________________________________________________\n", "dropout_2 (Dropout) (None, 2014) 0 batchnormalization_6[0][0] \n", "____________________________________________________________________________________________________\n", "dense_6 (Dense) (None, 1000) 2015000 dropout_2[0][0] \n", "====================================================================================================\n", "Total params: 156086992\n", "____________________________________________________________________________________________________\n" ] } ], "source": [ "model = LSTMModel()\n", "model.build()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Since the dataset we are using is *tiny*, we can fit the whole dataset to the convenience `fit` method and specify the `batch_size`. Note that this already ate up a lot of memory and it won't work for the large dataset." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch 1/50\n", "300/300 [==============================] - 73s - loss: 7.9907 - acc: 0.0100 \n", "Epoch 2/50\n", "300/300 [==============================] - 50s - loss: 4.6486 - acc: 0.2500 \n", "Epoch 3/50\n", "300/300 [==============================] - 55s - loss: 2.7681 - acc: 0.5133 \n", "Epoch 4/50\n", "300/300 [==============================] - 49s - loss: 1.7249 - acc: 0.6600 \n", "Epoch 5/50\n", "300/300 [==============================] - 48s - loss: 1.2428 - acc: 0.6800 \n", "Epoch 6/50\n", "300/300 [==============================] - 60s - loss: 1.0568 - acc: 0.7200 \n", "Epoch 7/50\n", "300/300 [==============================] - 59s - loss: 0.7550 - acc: 0.7800 \n", "Epoch 8/50\n", "300/300 [==============================] - 51s - loss: 0.6932 - acc: 0.8200 \n", "Epoch 9/50\n", "300/300 [==============================] - 43s - loss: 0.5585 - acc: 0.8267 \n", "Epoch 10/50\n", "300/300 [==============================] - 48s - loss: 0.5268 - acc: 0.8533 \n", "Epoch 11/50\n", "300/300 [==============================] - 52s - loss: 0.4691 - acc: 0.8667 \n", "Epoch 12/50\n", "300/300 [==============================] - 51s - loss: 0.3992 - acc: 0.8733 \n", "Epoch 13/50\n", "300/300 [==============================] - 54s - loss: 0.4250 - acc: 0.8800 \n", "Epoch 14/50\n", "300/300 [==============================] - 46s - loss: 0.3734 - acc: 0.9033 \n", "Epoch 15/50\n", "300/300 [==============================] - 48s - loss: 0.3020 - acc: 0.8933 \n", "Epoch 16/50\n", "300/300 [==============================] - 44s - loss: 0.2327 - acc: 0.9333 \n", "Epoch 17/50\n", "300/300 [==============================] - 45s - loss: 0.2192 - acc: 0.9333 \n", "Epoch 18/50\n", "300/300 [==============================] - 54s - loss: 0.2253 - acc: 0.9267 \n", "Epoch 19/50\n", "300/300 [==============================] - 43s - loss: 0.2484 - acc: 0.9267 \n", "Epoch 20/50\n", "300/300 [==============================] - 45s - loss: 0.1667 - acc: 0.9400 \n", "Epoch 21/50\n", "300/300 [==============================] - 45s - loss: 0.1572 - acc: 0.9533 \n", "Epoch 22/50\n", "300/300 [==============================] - 41s - loss: 0.1706 - acc: 0.9500 \n", "Epoch 23/50\n", "300/300 [==============================] - 61s - loss: 0.1498 - acc: 0.9500 \n", "Epoch 24/50\n", "300/300 [==============================] - 49s - loss: 0.1316 - acc: 0.9633 \n", "Epoch 25/50\n", "300/300 [==============================] - 60s - loss: 0.2232 - acc: 0.9467 \n", "Epoch 26/50\n", "300/300 [==============================] - 51s - loss: 0.1945 - acc: 0.9633 \n", "Epoch 27/50\n", "300/300 [==============================] - 51s - loss: 0.3117 - acc: 0.9100 \n", "Epoch 28/50\n", "300/300 [==============================] - 51s - loss: 0.2019 - acc: 0.9400 \n", "Epoch 29/50\n", "300/300 [==============================] - 45s - loss: 0.1497 - acc: 0.9767 \n", "Epoch 30/50\n", "300/300 [==============================] - 59s - loss: 0.0780 - acc: 0.9833 \n", "Epoch 31/50\n", "300/300 [==============================] - 54s - loss: 0.0476 - acc: 0.9900 \n", "Epoch 32/50\n", "300/300 [==============================] - 44s - loss: 0.1042 - acc: 0.9800 \n", "Epoch 33/50\n", "300/300 [==============================] - 43s - loss: 0.0800 - acc: 0.9833 \n", "Epoch 34/50\n", "300/300 [==============================] - 44s - loss: 0.0958 - acc: 0.9800 \n", "Epoch 35/50\n", "300/300 [==============================] - 44s - loss: 0.0866 - acc: 0.9800 \n", "Epoch 36/50\n", "300/300 [==============================] - 44s - loss: 0.0398 - acc: 0.9933 \n", "Epoch 37/50\n", "300/300 [==============================] - 40s - loss: 0.0473 - acc: 0.9867 \n", "Epoch 38/50\n", "300/300 [==============================] - 41s - loss: 0.0438 - acc: 0.9900 \n", "Epoch 39/50\n", "300/300 [==============================] - 43s - loss: 0.0566 - acc: 0.9833 \n", "Epoch 40/50\n", "300/300 [==============================] - 48s - loss: 0.1059 - acc: 0.9767 \n", "Epoch 41/50\n", "300/300 [==============================] - 44s - loss: 0.0663 - acc: 0.9900 \n", "Epoch 42/50\n", "300/300 [==============================] - 42s - loss: 0.0786 - acc: 0.9867 \n", "Epoch 43/50\n", "300/300 [==============================] - 45s - loss: 0.0495 - acc: 0.9867 \n", "Epoch 44/50\n", "300/300 [==============================] - 49s - loss: 0.0850 - acc: 0.9733 \n", "Epoch 45/50\n", "300/300 [==============================] - 47s - loss: 0.1627 - acc: 0.9700 \n", "Epoch 46/50\n", "300/300 [==============================] - 44s - loss: 0.0636 - acc: 0.9800 \n", "Epoch 47/50\n", "300/300 [==============================] - 44s - loss: 0.1548 - acc: 0.9667 \n", "Epoch 48/50\n", "300/300 [==============================] - 42s - loss: 0.1278 - acc: 0.9633 \n", "Epoch 49/50\n", "300/300 [==============================] - 43s - loss: 0.0255 - acc: 0.9933 \n", "Epoch 50/50\n", "300/300 [==============================] - 46s - loss: 0.0556 - acc: 0.9800 \n" ] } ], "source": [ "loss = model.fit(X_ques, X_im, y, nb_epoch=50, batch_size=50)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.legend.Legend at 0x257be2650>" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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H2AT+0KGwZk2wSyEiIhKewibwzz4b/vUv8PHAQyIiIp1CWAW+tbByZbBLIiIi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\ngg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x256161e90>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(loss.history['loss'], label='train_loss')\n", "plt.plot(loss.history['acc'], label='train_acc')\n", "plt.legend(loc='best')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's see how far we can get with this overfitted model..." ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Loading tiny dataset of 100 image features and 300 question/answer pairs for testing\n" ] } ], "source": [ "h5_img_file_test_tiny = h5py.File('data/vqa_data_img_vgg_test_tiny.h5', 'r')\n", "fv_im_test_tiny = h5_img_file_test_tiny.get('/images_test')\n", "\n", "with open('data/qa_data_test_tiny.pkl', 'rb') as fp:\n", " qa_data_test_tiny = pickle.load(fp)\n", " \n", "print \"Loading tiny dataset of %d image features and %d question/answer pairs for testing\" % (len(fv_im_test_tiny), len(qa_data_test_tiny)) " ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [], "source": [ "questions, ques_len, im_ix, ans = zip(*qa_data_test_tiny)\n", "\n", "X_ques_test = prepare_ques_batch(questions, ques_len, max_ques_len, embeddings, word_dim, ix_to_word)\n", "X_im_test = prepare_im_batch(fv_im_test_tiny, im_ix)\n", "y_test = np.zeros((len(ans), nb_classes))\n", "y_test[np.arange(len(ans)), [494 if a > 1000 else a for a in ans]] = 1" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "300/300 [==============================] - 5s \n" ] } ], "source": [ "loss, acc = model.evaluate(X_ques_test, X_im_test, y_test)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "4.7170976003 0.246666664879\n" ] } ], "source": [ "print loss, acc" ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [conda root]", "language": "python", "name": "conda-root-py" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.12" } }, "nbformat": 4, "nbformat_minor": 1 }

mit

sripaladugu/sripaladugu.github.io

ipynb/.ipynb_checkpoints/Pandas-checkpoint.ipynb

1

144651

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "### Series" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import pandas as pd" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A Series is like a cross between a list and a dictionary. The items are stored in an order and there are labels \n", "with which you can retrieve them. A Series object also has a name attribute." ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0 Lion\n", "1 Tiger\n", "2 Monkey\n", "3 None\n", "dtype: object\n", "The name of this Series: None\n" ] } ], "source": [ "animals = [\"Lion\", \"Tiger\", \"Monkey\", None]\n", "s = pd.Series(animals)\n", "print(s)\n", "print(\"The name of this Series: \", s.name)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0 1.0\n", "1 2.0\n", "2 3.0\n", "3 NaN\n", "dtype: float64" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "numbers = [1, 2, 3, None]\n", "pd.Series(numbers)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "False" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import numpy as np\n", "np.NaN == None" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "False" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.NaN == np.NaN" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.isnan(np.NaN)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Cricket India\n", "Football America\n", "Soccer Brazil\n", "dtype: object" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sports = {'Cricket': 'India', 'Football': 'America', 'Soccer': 'Brazil'}\n", "s = pd.Series(sports)\n", "s" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Index(['India', 'America', 'Brazil'], dtype='object')" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s.index" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "India Cricket\n", "America Football\n", "Brazil Soccer\n", "dtype: object" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s = pd.Series(['Cricket', 'Football', 'Soccer'], index = [ 'India', 'America', 'Brazil'])\n", "s" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Querying a Series" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A pandas Series can be queried either by the index position or the index label. As we saw if you don't give \n", "an index to the series, the position and the label are effectively the same values. To query by numeric location, \n", "starting at zero, use the iloc attribute. To query by the index label, you can use the loc attribute." ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'Cricket'" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s.iloc[0]" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'Football'" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s.loc['America']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "iloc and loc are not methods, they are attributes." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Okay, so now we know how to get data out of the series. Let's talk about working with the data. A common task is to want to consider all of the values inside of a series and want to do some sort of operation. This could be trying to find a certain number, summarizing data or transforming the data in some way. A typical programmatic approach to this would be to iterate over all the items in the series, and invoke the operation one is interested in. For instance, we could create a data frame of floating point values. Let's think of these as prices for different products. We could write a little routine which iterates over all of the items in the series and adds them together to get a total. \n", "This works, but it's slow. Modern computers can do many tasks simultaneously, especially, but not only, tasks involving mathematics. Pandas and the underlying NumPy libraries support a method of computation called vectorization. Vectorization works with most of the functions in the NumPy library, including the sum function. " ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0 651\n", "1 125\n", "2 498\n", "3 710\n", "4 610\n", "dtype: int64" ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s = pd.Series(np.random.randint(0,1000,10000))\n", "s.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Magic functions begin with a percentage sign. If we type % sign and then hit the Tab key, we can see a list of the available magic functions. You could write your own magic functions too, but that's a little bit outside of the scope of this course. We're actually going to use what's called a cellular magic function. These start with two percentage signs and modify a raptor code in the current Jupyter cell. The function we're going to use is called timeit. And as you may have guessed from the name, this function will run our code a few times to determine, on average, how long it takes. " ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "100 loops, best of 3: 1.41 ms per loop\n" ] } ], "source": [ "%%timeit -n 100\n", "summary = 0\n", "for item in s:\n", " summary += item" ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "100 loops, best of 3: 143 µs per loop\n" ] } ], "source": [ "%%timeit -n 100\n", "np.sum(s)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Related feature in Pandas and NumPy is called broadcasting. With broadcasting, you can apply an operation to every value in the series, changing the series. For instance, if we wanted to increase every random variable by 2, we could do so quickly using the += operator directly on the series object. " ] }, { "cell_type": "code", "execution_count": 44, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "10 loops, best of 3: 27.3 ms per loop\n" ] } ], "source": [ "%%timeit -n 10\n", "s = pd.Series(np.random.randint(0,1000,10000))\n", "for label, value in s.iteritems():\n", " s.set_value(label, value + 2)" ] }, { "cell_type": "code", "execution_count": 42, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "10 loops, best of 3: 838 ms per loop\n" ] } ], "source": [ "%%timeit -n 10\n", "s = pd.Series(np.random.randint(0,1000,10000))\n", "for label, value in s.iteritems():\n", " s.loc[label] = value + 2" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "But if you find yourself iterating through a series, you should question whether you're doing things in the best possible way. Here's how we would do this using the series set value method." ] }, { "cell_type": "code", "execution_count": 43, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "10 loops, best of 3: 456 µs per loop\n" ] } ], "source": [ "%%timeit -n 10\n", "s = pd.Series(np.random.randint(0,1000,10000))\n", "s += 2" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Amazing. Not only is it significantly faster, but it's more concise and maybe even easier to read too. The typical mathematical operations you would expect are vectorized, and the NumPy documentation outlines what it takes to create vectorized functions of your own. One last note on using the indexing operators to access series data. The .loc attribute lets you not only modify data in place, but also add new data as well. If the value you pass in as the index doesn't exist, then a new entry is added. And keep in mind, indices can have mixed types. While it's important to be aware of the typing going on underneath, Pandas will automatically change the underlying NumPy types as appropriate. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Mixed types are also possible" ] }, { "cell_type": "code", "execution_count": 48, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0 2\n", "1 1\n", "2 2\n", "Animal Bear\n", "dtype: object" ] }, "execution_count": 48, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s = pd.Series([2,1,2])\n", "s.loc['Animal'] = 'Bear'\n", "s" ] }, { "cell_type": "code", "execution_count": 51, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Archery Bhutan\n", "Golf Scotland\n", "Sumo Japan\n", "Cricket Australia\n", "Cricket India\n", "Cricket England\n", "dtype: object" ] }, "execution_count": 51, "metadata": {}, "output_type": "execute_result" } ], "source": [ "original_sports = pd.Series({'Archery':'Bhutan',\n", " 'Golf': 'Scotland',\n", " 'Sumo': 'Japan'})\n", "cricket_loving_countries = pd.Series(['Australia', 'India', 'England'], index=['Cricket','Cricket','Cricket'])\n", "all_countries = original_sports.append(cricket_loving_countries)\n", "all_countries" ] }, { "cell_type": "code", "execution_count": 52, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Archery Bhutan\n", "Golf Scotland\n", "Sumo Japan\n", "dtype: object" ] }, "execution_count": 52, "metadata": {}, "output_type": "execute_result" } ], "source": [ "original_sports" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There are a couple of important considerations when using append. First, Pandas is going to take your series and try to infer the best data types to use. In this example, everything is a string, so there's no problems here. Second, the append method doesn't actually change the underlying series. It instead returns a new series which is made up of the two appended together. We can see this by going back and printing the original series of values and seeing that they haven't changed. This is actually a significant issue for new Pandas users who are used to objects being changed in place. So watch out for it, not just with append but with other Pandas functions as well. " ] }, { "cell_type": "code", "execution_count": 53, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Cricket Australia\n", "Cricket India\n", "Cricket England\n", "dtype: object" ] }, "execution_count": 53, "metadata": {}, "output_type": "execute_result" } ], "source": [ "all_countries['Cricket']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally, we see that when we query the appended series for those who have cricket as their national sport, we don't get a single value, but a series itself. This is actually very common, and if you have a relational database background, this is very similar to every table query resulting in a return set which itself is a table. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### The DataFrame Data Structure" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can create a DataFrame in many different ways, some of which you might expect. For instance, you can use a group of series, where each series represents a row of data. Or you could use a group of dictionaries, where each dictionary represents a row of data. " ] }, { "cell_type": "code", "execution_count": 83, "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>Cost</th>\n", " <th>Item purchased</th>\n", " <th>Name</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>Store1</th>\n", " <td>22.5</td>\n", " <td>Dog Food</td>\n", " <td>Kasi</td>\n", " </tr>\n", " <tr>\n", " <th>Store1</th>\n", " <td>21.5</td>\n", " <td>Cat Food</td>\n", " <td>Pradeep</td>\n", " </tr>\n", " <tr>\n", " <th>Store2</th>\n", " <td>5.5</td>\n", " <td>Bird Food</td>\n", " <td>Sri</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Cost Item purchased Name\n", "Store1 22.5 Dog Food Kasi\n", "Store1 21.5 Cat Food Pradeep\n", "Store2 5.5 Bird Food Sri" ] }, "execution_count": 83, "metadata": {}, "output_type": "execute_result" } ], "source": [ "purchase_1 = pd.Series({'Name':'Kasi',\n", " 'Item purchased': 'Dog Food',\n", " 'Cost': 22.50})\n", "purchase_2 = pd.Series({'Name':'Pradeep',\n", " 'Item purchased': 'Cat Food',\n", " 'Cost': 21.50})\n", "purchase_3 = pd.Series({'Name':'Sri',\n", " 'Item purchased': 'Bird Food',\n", " 'Cost': 5.50})\n", "df = pd.DataFrame([purchase_1, purchase_2, purchase_3], index=['Store1','Store1','Store2'])\n", "df" ] }, { "cell_type": "code", "execution_count": 84, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Cost 5.5\n", "Item purchased Bird Food\n", "Name Sri\n", "Name: Store2, dtype: object\n" ] }, { "data": { "text/plain": [ "pandas.core.series.Series" ] }, "execution_count": 84, "metadata": {}, "output_type": "execute_result" } ], "source": [ "print(df.loc['Store2'])\n", "type(df.loc['Store2'])" ] }, { "cell_type": "code", "execution_count": 86, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " Cost Item purchased Name\n", "Store1 22.5 Dog Food Kasi\n", "Store1 21.5 Cat Food Pradeep\n" ] }, { "data": { "text/plain": [ "pandas.core.frame.DataFrame" ] }, "execution_count": 86, "metadata": {}, "output_type": "execute_result" } ], "source": [ "print(df.loc['Store1'])\n", "type(df.loc['Store1'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "What if we want to do column, for example we want to get a list of all the costs?" ] }, { "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", " <th></th>\n", " <th>Store1</th>\n", " <th>Store1</th>\n", " <th>Store2</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>Cost</th>\n", " <td>22.5</td>\n", " <td>21.5</td>\n", " <td>5.5</td>\n", " </tr>\n", " <tr>\n", " <th>Item purchased</th>\n", " <td>Dog Food</td>\n", " <td>Cat Food</td>\n", " <td>Bird Food</td>\n", " </tr>\n", " <tr>\n", " <th>Name</th>\n", " <td>Kasi</td>\n", " <td>Pradeep</td>\n", " <td>Sri</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Store1 Store1 Store2\n", "Cost 22.5 21.5 5.5\n", "Item purchased Dog Food Cat Food Bird Food\n", "Name Kasi Pradeep Sri" ] }, "execution_count": 88, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.T # This essential turns your column names into indicies" ] }, { "cell_type": "code", "execution_count": 89, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Store1 22.5\n", "Store1 21.5\n", "Store2 5.5\n", "Name: Cost, dtype: object" ] }, "execution_count": 89, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.T.loc['Cost'] # We can then use the loc method" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Since iloc and loc are used for row selection, the Panda's developers reserved indexing operator directly on the DataFrame for column selection. In a Panda's DataFrame, columns always have a name. So this selection is always label based, not as confusing as it was when using the square bracket operator on the series objects. For those familiar with relational databases, this operator is analogous to column projection. " ] }, { "cell_type": "code", "execution_count": 85, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Store1 Dog Food\n", "Store1 Cat Food\n", "Store2 Bird Food\n", "Name: Item purchased, dtype: object\n" ] }, { "data": { "text/plain": [ "pandas.core.series.Series" ] }, "execution_count": 85, "metadata": {}, "output_type": "execute_result" } ], "source": [ "print(df['Item purchased'])\n", "type(df['Item purchased'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally, since the result of using the indexing operator is the DataFrame or series, you can chain operations together. For instance, we could have rewritten the query for all Store 1 costs as " ] }, { "cell_type": "code", "execution_count": 90, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Store1 22.5\n", "Store1 21.5\n", "Name: Cost, dtype: float64" ] }, "execution_count": 90, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.loc['Store1']['Cost']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**This looks pretty reasonable and gets us the result we wanted. But chaining can come with some costs and is best avoided if you can use another approach. In particular, chaining tends to cause Pandas to return a copy of the DataFrame instead of a view on the DataFrame. For selecting a data, this is not a big deal, though it might be slower than necessary. If you are changing data though, this is an important distinction and can be a source of error.** " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here's another method. As we saw, .loc does row selection, and it can take two parameters, the row index and the list of column names. .loc also supports slicing. If we wanted to select all rows, we can use a column to indicate a full slice from beginning to end. And then add the column name as the second parameter as a string. In fact, if we wanted to include multiply columns, we could do so in a list. And Pandas will bring back only the columns we have asked for. " ] }, { "cell_type": "code", "execution_count": 91, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Name</th>\n", " <th>Cost</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>Store1</th>\n", " <td>Kasi</td>\n", " <td>22.5</td>\n", " </tr>\n", " <tr>\n", " <th>Store1</th>\n", " <td>Pradeep</td>\n", " <td>21.5</td>\n", " </tr>\n", " <tr>\n", " <th>Store2</th>\n", " <td>Sri</td>\n", " <td>5.5</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Name Cost\n", "Store1 Kasi 22.5\n", "Store1 Pradeep 21.5\n", "Store2 Sri 5.5" ] }, "execution_count": 91, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.loc[:, ['Name','Cost']]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "So that's selecting and projecting data from a DataFrame based on row and column labels. The key concepts to remember are that the rows and columns are really just for our benefit. Underneath this is just a two axis labeled array, and transposing the columns is easy. Also, consider the issue of chaining carefully, and try to avoid it, it can cause unpredictable results. Where your intent was to obtain a view of the data, but instead Pandas returns to you a copy. In the Panda's world, friends don't let friends chain calls. So if you see it, point it out, and share a less ambiguous solution. " ] }, { "cell_type": "code", "execution_count": 92, "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>Cost</th>\n", " <th>Item purchased</th>\n", " <th>Name</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>Store2</th>\n", " <td>5.5</td>\n", " <td>Bird Food</td>\n", " <td>Sri</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Cost Item purchased Name\n", "Store2 5.5 Bird Food Sri" ] }, "execution_count": 92, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.drop('Store1')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It's easy to delete data in series and DataFrames, and we can use the drop function to do so. This function takes a single parameter, which is the index or roll label, to drop. This is another tricky place for new users to pandas. The drop function doesn't change the DataFrame by default. And instead, returns to you a copy of the DataFrame with the given rows removed. We can see that our original DataFrame is still intact. This is a very typical pattern in Pandas, where in place changes to a DataFrame are only done if need be, usually on changes involving indices. So it's important to be aware of. Drop has two interesting optional parameters. The first is called in place, and if it's set to true, the DataFrame will be updated in place, instead of a copy being returned. The second parameter is the axis, which should be dropped. By default, this value is 0, indicating the row axis. But you could change it to 1 if you want to drop a column." ] }, { "cell_type": "code", "execution_count": 93, "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>Item purchased</th>\n", " <th>Name</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>Store1</th>\n", " <td>Dog Food</td>\n", " <td>Kasi</td>\n", " </tr>\n", " <tr>\n", " <th>Store1</th>\n", " <td>Cat Food</td>\n", " <td>Pradeep</td>\n", " </tr>\n", " <tr>\n", " <th>Store2</th>\n", " <td>Bird Food</td>\n", " <td>Sri</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Item purchased Name\n", "Store1 Dog Food Kasi\n", "Store1 Cat Food Pradeep\n", "Store2 Bird Food Sri" ] }, "execution_count": 93, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.drop('Cost',axis=1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There is a second way to drop a column, however. And that's directly through the use of the indexing operator, using the del keyword. This way of dropping data, however, takes immediate effect on the DataFrame and does not return a view." ] }, { "cell_type": "code", "execution_count": 94, "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>Cost</th>\n", " <th>Name</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>Store1</th>\n", " <td>22.5</td>\n", " <td>Kasi</td>\n", " </tr>\n", " <tr>\n", " <th>Store1</th>\n", " <td>21.5</td>\n", " <td>Pradeep</td>\n", " </tr>\n", " <tr>\n", " <th>Store2</th>\n", " <td>5.5</td>\n", " <td>Sri</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Cost Name\n", "Store1 22.5 Kasi\n", "Store1 21.5 Pradeep\n", "Store2 5.5 Sri" ] }, "execution_count": 94, "metadata": {}, "output_type": "execute_result" } ], "source": [ "del df['Item purchased']\n", "df" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally, adding a new column to the DataFrame is as easy as assigning it to some value. For instance, if we wanted to add a new location as a column with default value of none, we could do so by using the assignment operator after the square brackets. This broadcasts the default value to the new column immediately. " ] }, { "cell_type": "code", "execution_count": 95, "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>Cost</th>\n", " <th>Name</th>\n", " <th>Location</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>Store1</th>\n", " <td>22.5</td>\n", " <td>Kasi</td>\n", " <td>None</td>\n", " </tr>\n", " <tr>\n", " <th>Store1</th>\n", " <td>21.5</td>\n", " <td>Pradeep</td>\n", " <td>None</td>\n", " </tr>\n", " <tr>\n", " <th>Store2</th>\n", " <td>5.5</td>\n", " <td>Sri</td>\n", " <td>None</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Cost Name Location\n", "Store1 22.5 Kasi None\n", "Store1 21.5 Pradeep None\n", "Store2 5.5 Sri None" ] }, "execution_count": 95, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df['Location'] = None\n", "df" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The common work flow is to read your data into a DataFrame then reduce this DataFrame to the particular columns or rows that you're interested in working with. As you've seen, the Panda's toolkit tries to give you views on a DataFrame. This is much faster than copying data and much more memory efficient too. But it does mean that if you're manipulating the data you have to be aware that any changes to the DataFrame you're working on may have an impact on the base data frame you used originally. Here's an example using our same purchasing DataFrame from earlier. We can create a series based on just the cost category using the square brackets. " ] }, { "cell_type": "code", "execution_count": 96, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Store1 22.5\n", "Store1 21.5\n", "Store2 5.5\n", "Name: Cost, dtype: float64" ] }, "execution_count": 96, "metadata": {}, "output_type": "execute_result" } ], "source": [ "costs = df['Cost']\n", "costs" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Then we can increase the cost in this series using broadcasting." ] }, { "cell_type": "code", "execution_count": 97, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Store1 24.5\n", "Store1 23.5\n", "Store2 7.5\n", "Name: Cost, dtype: float64" ] }, "execution_count": 97, "metadata": {}, "output_type": "execute_result" } ], "source": [ "costs += 2\n", "costs" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now if we look at our original DataFrame, we see those costs have risen as well. This is an important consideration to watch out for. If you want to explicitly use a copy, then you should consider calling the copy method on the DataFrame for it first. " ] }, { "cell_type": "code", "execution_count": 98, "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>Cost</th>\n", " <th>Name</th>\n", " <th>Location</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>Store1</th>\n", " <td>24.5</td>\n", " <td>Kasi</td>\n", " <td>None</td>\n", " </tr>\n", " <tr>\n", " <th>Store1</th>\n", " <td>23.5</td>\n", " <td>Pradeep</td>\n", " <td>None</td>\n", " </tr>\n", " <tr>\n", " <th>Store2</th>\n", " <td>7.5</td>\n", " <td>Sri</td>\n", " <td>None</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Cost Name Location\n", "Store1 24.5 Kasi None\n", "Store1 23.5 Pradeep None\n", "Store2 7.5 Sri None" ] }, "execution_count": 98, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A common workflow is to read the dataset in, usually from some external file. We saw previously how you can do this using Python, and lists, and dictionaries. You can imagine how you might use those dictionaries to create a Pandas DataFrame. Thankfully, Pandas has built-in support for delimited files such as CSV files as well as a variety of other data formats including relational databases, Excel, and HTML tables. I've saved a CSV file called olympics.csv, which has data from Wikipedia that contains a summary list of the medal various countries have won at the Olympics. We can take a look at this file using the shell command cat. Which we can invoke directly using the exclamation point. What happens here is that when the Jupyter notebook sees a line beginning with an exclamation mark, it sends the rest of the line to the operating system shell for evaluation. So cat works on Linux and Macs." ] }, { "cell_type": "code", "execution_count": 142, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15\r\n", ",№ Summer,01 !,02 !,03 !,Total,№ Winter,01 !,02 !,03 !,Total,№ Games,01 !,02 !,03 !,Combined Total\r\n", " Afghanistan ,14,0,0,2,2,0,0,0,0,0,14,0,0,2,2\r\n", " Algeria ,13,5,4,8,17,3,0,0,0,0,16,5,4,8,17\r\n", " Argentina ,24,21,25,28,74,18,0,0,0,0,42,21,25,28,74\r\n", " Armenia ,6,2,5,7,14,6,0,0,0,0,12,2,5,7,14\r\n", " Australasia ,2,3,4,5,12,0,0,0,0,0,2,3,4,5,12\r\n", " Australia ,26,147,163,187,497,18,5,3,4,12,44,152,166,191,509\r\n", " Austria ,27,18,33,36,87,22,59,78,81,218,49,77,111,117,305\r\n", " Azerbaijan ,6,7,11,25,43,5,0,0,0,0,11,7,11,25,43\r\n", " Bahamas ,16,6,2,6,14,0,0,0,0,0,16,6,2,6,14\r\n", " Bahrain ,9,1,1,1,3,0,0,0,0,0,9,1,1,1,3\r\n", " Barbados ,12,0,0,1,1,0,0,0,0,0,12,0,0,1,1\r\n", " Belarus ,6,12,27,38,77,6,6,4,5,15,12,18,31,43,92\r\n", " Belgium ,26,40,53,55,148,20,1,1,3,5,46,41,54,58,153\r\n", " Bermuda ,18,0,0,1,1,7,0,0,0,0,25,0,0,1,1\r\n", " Bohemia ,3,0,1,3,4,0,0,0,0,0,3,0,1,3,4\r\n", " Botswana ,10,0,1,0,1,0,0,0,0,0,10,0,1,0,1\r\n", " Brazil ,22,30,36,63,129,7,0,0,0,0,29,30,36,63,129\r\n", " British West Indies ,1,0,0,2,2,0,0,0,0,0,1,0,0,2,2\r\n", " Bulgaria ,20,51,86,80,217,19,1,2,3,6,39,52,88,83,223\r\n", " Burundi ,6,1,1,0,2,0,0,0,0,0,6,1,1,0,2\r\n", " Cameroon ,14,3,1,1,5,1,0,0,0,0,15,3,1,1,5\r\n", " Canada ,26,63,102,136,301,22,62,56,52,170,48,125,158,188,471\r\n", " Chile ,23,2,7,4,13,16,0,0,0,0,39,2,7,4,13\r\n", " China ,10,224,164,153,541,10,12,22,19,53,20,236,186,172,594\r\n", " Colombia ,19,5,8,14,27,1,0,0,0,0,20,5,8,14,27\r\n", " Costa Rica ,15,1,1,2,4,6,0,0,0,0,21,1,1,2,4\r\n", " Côte d'Ivoire ,13,1,1,1,3,0,0,0,0,0,13,1,1,1,3\r\n", " Croatia ,7,11,10,12,33,7,4,6,1,11,14,15,16,13,44\r\n", " Cuba ,20,77,68,75,220,0,0,0,0,0,20,77,68,75,220\r\n", " Cyprus ,10,0,1,0,1,10,0,0,0,0,20,0,1,0,1\r\n", " Czech Republic ,6,15,17,23,55,6,7,9,8,24,12,22,26,31,79\r\n", " Czechoslovakia ,16,49,49,45,143,16,2,8,15,25,32,51,57,60,168\r\n", " Denmark ,27,45,74,75,194,13,0,1,0,1,40,45,75,75,195\r\n", " Djibouti ,8,0,0,1,1,0,0,0,0,0,8,0,0,1,1\r\n", " Dominican Republic ,14,3,2,2,7,0,0,0,0,0,14,3,2,2,7\r\n", " Ecuador ,14,1,1,0,2,0,0,0,0,0,14,1,1,0,2\r\n", " Egypt ,22,7,9,13,29,1,0,0,0,0,23,7,9,13,29\r\n", " Eritrea ,5,0,0,1,1,0,0,0,0,0,5,0,0,1,1\r\n", " Estonia ,12,9,9,16,34,9,4,2,1,7,21,13,11,17,41\r\n", " Ethiopia ,13,22,10,21,53,2,0,0,0,0,15,22,10,21,53\r\n", " Fiji ,14,1,0,0,1,3,0,0,0,0,17,1,0,0,1\r\n", " Finland ,25,101,85,117,303,22,42,62,57,161,47,143,147,174,464\r\n", " France ,28,212,241,262,715,22,31,31,47,109,50,243,272,309,824\r\n", " Gabon ,10,0,1,0,1,0,0,0,0,0,10,0,1,0,1\r\n", " Georgia ,6,8,7,17,32,6,0,0,0,0,12,8,7,17,32\r\n", " Germany ,16,191,192,232,615,11,78,78,53,209,27,269,270,285,824\r\n", " United Team of Germany ,3,28,54,36,118,3,8,6,5,19,6,36,60,41,137\r\n", " East Germany ,5,153,129,127,409,6,39,36,35,110,11,192,165,162,519\r\n", " West Germany ,5,56,67,81,204,6,11,15,13,39,11,67,82,94,243\r\n", " Ghana ,14,0,1,3,4,1,0,0,0,0,15,0,1,3,4\r\n", " Great Britain ,28,263,295,289,847,22,10,4,12,26,50,273,299,301,873\r\n", " Greece ,28,33,43,40,116,18,0,0,0,0,46,33,43,40,116\r\n", " Grenada ,9,1,1,0,2,0,0,0,0,0,9,1,1,0,2\r\n", " Guatemala ,14,0,1,0,1,1,0,0,0,0,15,0,1,0,1\r\n", " Guyana ,17,0,0,1,1,0,0,0,0,0,17,0,0,1,1\r\n", " Haiti ,15,0,1,1,2,0,0,0,0,0,15,0,1,1,2\r\n", " Hong Kong ,16,1,1,1,3,4,0,0,0,0,20,1,1,1,3\r\n", " Hungary ,26,175,147,169,491,22,0,2,4,6,48,175,149,173,497\r\n", " Iceland ,20,0,2,2,4,17,0,0,0,0,37,0,2,2,4\r\n", " India ,24,9,7,12,28,9,0,0,0,0,33,9,7,12,28\r\n", " Indonesia ,15,7,12,11,30,0,0,0,0,0,15,7,12,11,30\r\n", " Iran ,16,18,21,29,68,10,0,0,0,0,26,18,21,29,68\r\n", " Iraq ,14,0,0,1,1,0,0,0,0,0,14,0,0,1,1\r\n", " Ireland ,21,9,10,12,31,6,0,0,0,0,27,9,10,12,31\r\n", " Israel ,16,1,1,7,9,6,0,0,0,0,22,1,1,7,9\r\n", " Italy ,27,206,178,193,577,22,37,34,43,114,49,243,212,236,691\r\n", " Jamaica ,17,22,33,22,77,7,0,0,0,0,24,22,33,22,77\r\n", " Japan ,22,142,135,162,439,20,10,17,18,45,42,152,152,180,484\r\n", " Jordan ,10,1,0,0,1,0,0,0,0,0,10,1,0,0,1\r\n", " Kazakhstan ,6,14,21,25,60,6,1,3,3,7,12,15,24,28,67\r\n", " Kenya ,14,31,38,31,100,3,0,0,0,0,17,31,38,31,100\r\n", " Kosovo ,1,1,0,0,1,0,0,0,0,0,1,1,0,0,1\r\n", " North Korea ,10,16,15,23,54,8,0,1,1,2,18,16,16,24,56\r\n", " South Korea ,17,90,85,89,264,17,26,17,10,53,34,116,102,99,317\r\n", " Kuwait ,12,0,0,2,2,0,0,0,0,0,12,0,0,2,2\r\n", " Kyrgyzstan ,6,0,1,2,3,6,0,0,0,0,12,0,1,2,3\r\n", " Latvia ,11,3,11,5,19,10,0,4,3,7,21,3,15,8,26\r\n", " Lebanon ,17,0,2,2,4,16,0,0,0,0,33,0,2,2,4\r\n", " Liechtenstein ,17,0,0,0,0,18,2,2,5,9,35,2,2,5,9\r\n", " Lithuania ,9,6,7,12,25,8,0,0,0,0,17,6,7,12,25\r\n", " Luxembourg ,23,1,1,0,2,8,0,2,0,2,31,1,3,0,4\r\n", " Macedonia ,6,0,0,1,1,5,0,0,0,0,11,0,0,1,1\r\n", " Malaysia ,13,0,7,4,11,0,0,0,0,0,13,0,7,4,11\r\n", " Mauritius ,9,0,0,1,1,0,0,0,0,0,9,0,0,1,1\r\n", " Mexico ,23,13,24,30,67,8,0,0,0,0,31,13,24,30,67\r\n", " Moldova ,6,0,2,4,6,6,0,0,0,0,12,0,2,4,6\r\n", " Mongolia ,13,2,10,14,26,13,0,0,0,0,26,2,10,14,26\r\n", " Montenegro ,3,0,1,0,1,2,0,0,0,0,5,0,1,0,1\r\n", " Morocco ,14,6,5,12,23,6,0,0,0,0,20,6,5,12,23\r\n", " Mozambique ,10,1,0,1,2,0,0,0,0,0,10,1,0,1,2\r\n", " Namibia ,7,0,4,0,4,0,0,0,0,0,7,0,4,0,4\r\n", " Netherlands ,26,85,92,108,285,20,37,38,35,110,46,122,130,143,395\r\n", " Netherlands Antilles ,13,0,1,0,1,2,0,0,0,0,15,0,1,0,1\r\n", " New Zealand ,23,46,27,44,117,15,0,1,0,1,38,46,28,44,118\r\n", " Niger ,12,0,1,1,2,0,0,0,0,0,12,0,1,1,2\r\n", " Nigeria ,16,3,9,12,24,0,0,0,0,0,16,3,9,12,24\r\n", " Norway ,25,56,49,47,152,22,118,111,100,329,47,174,160,147,481\r\n", " Pakistan ,17,3,3,4,10,2,0,0,0,0,19,3,3,4,10\r\n", " Panama ,17,1,0,2,3,0,0,0,0,0,17,1,0,2,3\r\n", " Paraguay ,12,0,1,0,1,1,0,0,0,0,13,0,1,0,1\r\n", " Peru ,18,1,3,0,4,2,0,0,0,0,20,1,3,0,4\r\n", " Philippines ,21,0,3,7,10,4,0,0,0,0,25,0,3,7,10\r\n", " Poland ,21,66,85,131,282,22,6,7,7,20,43,72,92,138,302\r\n", " Portugal ,24,4,8,12,24,7,0,0,0,0,31,4,8,12,24\r\n", " Puerto Rico ,18,1,2,6,9,6,0,0,0,0,24,1,2,6,9\r\n", " Qatar ,9,0,1,4,5,0,0,0,0,0,9,0,1,4,5\r\n", " Romania ,21,89,95,122,306,20,0,0,1,1,41,89,95,123,307\r\n", " Russia ,6,147,126,154,427,6,49,40,35,124,12,196,166,189,551\r\n", " Russian Empire ,3,1,4,3,8,0,0,0,0,0,3,1,4,3,8\r\n", " Soviet Union ,9,395,319,296,1,010,9,78,57,59,194,18,473,376,355,1,204\r\n", " Unified Team ,1,45,38,29,112,1,9,6,8,23,2,54,44,37,135\r\n", " Saudi Arabia ,11,0,1,2,3,0,0,0,0,0,11,0,1,2,3\r\n", " Samoa ,9,0,1,0,1,0,0,0,0,0,9,0,1,0,1\r\n", " Senegal ,14,0,1,0,1,5,0,0,0,0,19,0,1,0,1\r\n", " Serbia ,4,3,6,6,15,2,0,0,0,0,6,3,6,6,15\r\n", " Serbia and Montenegro ,3,2,4,3,9,3,0,0,0,0,6,2,4,3,9\r\n", " Singapore ,16,1,2,2,5,0,0,0,0,0,16,1,2,2,5\r\n", " Slovakia ,6,9,11,8,28,6,2,2,1,5,12,11,13,9,33\r\n", " Slovenia ,7,5,8,10,23,7,2,4,9,15,14,7,12,19,38\r\n", " South Africa ,19,25,32,29,86,6,0,0,0,0,25,25,32,29,86\r\n", " Spain ,23,45,63,41,149,19,1,0,1,2,42,46,63,42,151\r\n", " Sri Lanka ,17,0,2,0,2,0,0,0,0,0,17,0,2,0,2\r\n", " Sudan ,12,0,1,0,1,0,0,0,0,0,12,0,1,0,1\r\n", " Suriname ,12,1,0,1,2,0,0,0,0,0,12,1,0,1,2\r\n", " Sweden ,27,145,170,179,494,22,50,40,54,144,49,195,210,233,638\r\n", " Switzerland ,28,50,75,67,192,22,50,40,48,138,50,100,115,115,330\r\n", " Syria ,13,1,1,1,3,0,0,0,0,0,13,1,1,1,3\r\n", " Chinese Taipei ,14,5,7,12,24,11,0,0,0,0,25,5,7,12,24\r\n", " Tajikistan ,6,1,1,2,4,4,0,0,0,0,10,1,1,2,4\r\n", " Tanzania ,13,0,2,0,2,0,0,0,0,0,13,0,2,0,2\r\n", " Thailand ,16,9,8,14,31,3,0,0,0,0,19,9,8,14,31\r\n", " Togo ,10,0,0,1,1,1,0,0,0,0,11,0,0,1,1\r\n", " Tonga ,9,0,1,0,1,1,0,0,0,0,10,0,1,0,1\r\n", " Trinidad and Tobago ,17,3,5,11,19,3,0,0,0,0,20,3,5,11,19\r\n", " Tunisia ,14,4,2,7,13,0,0,0,0,0,14,4,2,7,13\r\n", " Turkey ,22,39,27,28,94,16,0,0,0,0,38,39,27,28,94\r\n", " Uganda ,15,2,3,2,7,0,0,0,0,0,15,2,3,2,7\r\n", " Ukraine ,6,35,30,55,120,6,2,1,4,7,12,37,31,59,127\r\n", " United Arab Emirates ,9,1,0,1,2,0,0,0,0,0,9,1,0,1,2\r\n", " United States ,27,1,022,794,704,2,520,22,96,102,84,282,49,1,118,896,788,2,802\r\n", " Uruguay ,21,2,2,6,10,1,0,0,0,0,22,2,2,6,10\r\n", " Uzbekistan ,6,9,6,17,32,6,1,0,0,1,12,10,6,17,33\r\n", " Venezuela ,18,2,3,10,15,4,0,0,0,0,22,2,3,10,15\r\n", " Vietnam ,15,1,3,0,4,0,0,0,0,0,15,1,3,0,4\r\n", " Virgin Islands ,12,0,1,0,1,7,0,0,0,0,19,0,1,0,1\r\n", " Yugoslavia ,16,26,29,28,83,14,0,3,1,4,30,26,32,29,87\r\n", " Zambia ,13,0,1,1,2,0,0,0,0,0,13,0,1,1,2\r\n", " Zimbabwe ,13,3,4,1,8,1,0,0,0,0,14,3,4,1,8\r\n", " Independent Olympic Athletes ,3,1,0,1,2,0,0,0,0,0,3,1,0,1,2\r\n", " Independent Olympic Participants ,1,0,1,2,3,0,0,0,0,0,1,0,1,2,3\r\n", " Mixed team ,3,8,5,4,17,0,0,0,0,0,3,8,5,4,17\r\n" ] } ], "source": [ "!cat olympics.csv" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We see from the cat output that there seems to be a numeric list of columns followed by a bunch of column identifiers. The column identifiers have some odd looking characters in them. This is the unicode numero sign, which means number of. Then we have rows of data, all columns separated. We can read this into a DataFrame by calling the read_csv function of the module. When we look at the DataFrame we see that the first cell has an NaN in it since it's an empty value, and the rows have been automatically indexed for us. " ] }, { "cell_type": "code", "execution_count": 144, "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", " <th>4</th>\n", " <th>5</th>\n", " <th>6</th>\n", " <th>7</th>\n", " <th>8</th>\n", " <th>9</th>\n", " <th>10</th>\n", " <th>11</th>\n", " <th>12</th>\n", " <th>13</th>\n", " <th>14</th>\n", " <th>15</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>NaN</td>\n", " <td>№ Summer</td>\n", " <td>01 !</td>\n", " <td>02 !</td>\n", " <td>03 !</td>\n", " <td>Total</td>\n", " <td>№ Winter</td>\n", " <td>01 !</td>\n", " <td>02 !</td>\n", " <td>03 !</td>\n", " <td>Total</td>\n", " <td>№ Games</td>\n", " <td>01 !</td>\n", " <td>02 !</td>\n", " <td>03 !</td>\n", " <td>Combined Total</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>Afghanistan</td>\n", " <td>14</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>2</td>\n", " <td>2</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>14</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>2</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>Algeria</td>\n", " <td>13</td>\n", " <td>5</td>\n", " <td>4</td>\n", " <td>8</td>\n", " <td>17</td>\n", " <td>3</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>16</td>\n", " <td>5</td>\n", " <td>4</td>\n", " <td>8</td>\n", " <td>17</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>Argentina</td>\n", " <td>24</td>\n", " <td>21</td>\n", " <td>25</td>\n", " <td>28</td>\n", " <td>74</td>\n", " <td>18</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>42</td>\n", " <td>21</td>\n", " <td>25</td>\n", " <td>28</td>\n", " <td>74</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>Armenia</td>\n", " <td>6</td>\n", " <td>2</td>\n", " <td>5</td>\n", " <td>7</td>\n", " <td>14</td>\n", " <td>6</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>12</td>\n", " <td>2</td>\n", " <td>5</td>\n", " <td>7</td>\n", " <td>14</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " 0 1 2 3 4 5 6 7 8 \\\n", "0 NaN № Summer 01 ! 02 ! 03 ! Total № Winter 01 ! 02 ! \n", "1 Afghanistan 14 0 0 2 2 0 0 0 \n", "2 Algeria 13 5 4 8 17 3 0 0 \n", "3 Argentina 24 21 25 28 74 18 0 0 \n", "4 Armenia 6 2 5 7 14 6 0 0 \n", "\n", " 9 10 11 12 13 14 15 \n", "0 03 ! Total № Games 01 ! 02 ! 03 ! Combined Total \n", "1 0 0 14 0 0 2 2 \n", "2 0 0 16 5 4 8 17 \n", "3 0 0 42 21 25 28 74 \n", "4 0 0 12 2 5 7 14 " ] }, "execution_count": 144, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = pd.read_csv('olympics.csv')\n", "df.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It seems pretty clear that the first row of data in the DataFrame is what we really want to see as the column names. It also seems like the first column in the data is the country name, which we would like to make an index. Read csv has a number of parameters that we can use to indicate to Pandas how rows and columns should be labeled. For instance, we can use the index call to indicate which column should be the index and we can also use the header parameter to indicate which row from the data file should be used as the header." ] }, { "cell_type": "code", "execution_count": 145, "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>№ Summer</th>\n", " <th>01 !</th>\n", " <th>02 !</th>\n", " <th>03 !</th>\n", " <th>Total</th>\n", " <th>№ Winter</th>\n", " <th>01 !.1</th>\n", " <th>02 !.1</th>\n", " <th>03 !.1</th>\n", " <th>Total.1</th>\n", " <th>№ Games</th>\n", " <th>01 !.2</th>\n", " <th>02 !.2</th>\n", " <th>03 !.2</th>\n", " <th>Combined Total</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>Afghanistan</th>\n", " <td>14</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>2</td>\n", " <td>2</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>14</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>2</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>Algeria</th>\n", " <td>13</td>\n", " <td>5</td>\n", " <td>4</td>\n", " <td>8</td>\n", " <td>17</td>\n", " <td>3</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>16</td>\n", " <td>5</td>\n", " <td>4</td>\n", " <td>8</td>\n", " <td>17</td>\n", " </tr>\n", " <tr>\n", " <th>Argentina</th>\n", " <td>24</td>\n", " <td>21</td>\n", " <td>25</td>\n", " <td>28</td>\n", " <td>74</td>\n", " <td>18</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>42</td>\n", " <td>21</td>\n", " <td>25</td>\n", " <td>28</td>\n", " <td>74</td>\n", " </tr>\n", " <tr>\n", " <th>Armenia</th>\n", " <td>6</td>\n", " <td>2</td>\n", " <td>5</td>\n", " <td>7</td>\n", " <td>14</td>\n", " <td>6</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>12</td>\n", " <td>2</td>\n", " <td>5</td>\n", " <td>7</td>\n", " <td>14</td>\n", " </tr>\n", " <tr>\n", " <th>Australasia</th>\n", " <td>2</td>\n", " <td>3</td>\n", " <td>4</td>\n", " <td>5</td>\n", " <td>12</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>2</td>\n", " <td>3</td>\n", " <td>4</td>\n", " <td>5</td>\n", " <td>12</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " № Summer 01 ! 02 ! 03 ! Total № Winter 01 !.1 02 !.1 \\\n", " Afghanistan 14 0 0 2 2 0 0 0 \n", " Algeria 13 5 4 8 17 3 0 0 \n", " Argentina 24 21 25 28 74 18 0 0 \n", " Armenia 6 2 5 7 14 6 0 0 \n", " Australasia 2 3 4 5 12 0 0 0 \n", "\n", " 03 !.1 Total.1 № Games 01 !.2 02 !.2 03 !.2 \\\n", " Afghanistan 0 0 14 0 0 2 \n", " Algeria 0 0 16 5 4 8 \n", " Argentina 0 0 42 21 25 28 \n", " Armenia 0 0 12 2 5 7 \n", " Australasia 0 0 2 3 4 5 \n", "\n", " Combined Total \n", " Afghanistan 2 \n", " Algeria 17 \n", " Argentina 74 \n", " Armenia 14 \n", " Australasia 12 " ] }, "execution_count": 145, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = pd.read_csv('olympics.csv', index_col=0, skiprows=1)\n", "df.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now this data came from the all time Olympic games medal table on Wikipedia. If we head to the page we could see that instead of running gold, silver and bronze in the pages, these nice little icons with a one, a two, and a three in them In our csv file these were represented with the strings 01 !, 02 !, and so on. We see that the column values are repeated which really isn't good practice. Panda's recognize this in a panda.1 and .2 to make things more unique. But this labeling isn't really as clear as it could be, so we should clean up the data file. We can of course do this just by going and editing the CSV file directly, but we can also set the column names using the Pandas name property. Panda stores a list of all of the columns in the .columns attribute." ] }, { "cell_type": "code", "execution_count": 146, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Index(['№ Summer', '01 !', '02 !', '03 !', 'Total', '№ Winter', '01 !.1',\n", " '02 !.1', '03 !.1', 'Total.1', '№ Games', '01 !.2', '02 !.2', '03 !.2',\n", " 'Combined Total'],\n", " dtype='object')" ] }, "execution_count": 146, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.columns" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " We can change the values of the column names by iterating over this list and calling the rename method of the data frame. Here we just iterate through all of the columns looking to see if they start with a 01, 02, 03 or numeric character. If they do, we can call rename and set the column parameters to a dictionary with the keys being the column we want to replace and the value being the new value we want. Here we'll slice some of the old values in two, since we don't want to lose the unique appended values. We'll also set the ever-important in place parameter to true so Pandas knows to update this data frame directly. " ] }, { "cell_type": "code", "execution_count": 125, "metadata": { "collapsed": true }, "outputs": [], "source": [ "df.rename?" ] }, { "cell_type": "code", "execution_count": 147, "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># Summer</th>\n", " <th>Gold</th>\n", " <th>Silver</th>\n", " <th>Bronze</th>\n", " <th>Total</th>\n", " <th># Winter</th>\n", " <th>Gold.1</th>\n", " <th>Silver.1</th>\n", " <th>Bronze.1</th>\n", " <th>Total.1</th>\n", " <th># Games</th>\n", " <th>Gold.2</th>\n", " <th>Silver.2</th>\n", " <th>Bronze.2</th>\n", " <th>Combined Total</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>Afghanistan</th>\n", " <td>14</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>2</td>\n", " <td>2</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>14</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>2</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>Algeria</th>\n", " <td>13</td>\n", " <td>5</td>\n", " <td>4</td>\n", " <td>8</td>\n", " <td>17</td>\n", " <td>3</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>16</td>\n", " <td>5</td>\n", " <td>4</td>\n", " <td>8</td>\n", " <td>17</td>\n", " </tr>\n", " <tr>\n", " <th>Argentina</th>\n", " <td>24</td>\n", " <td>21</td>\n", " <td>25</td>\n", " <td>28</td>\n", " <td>74</td>\n", " <td>18</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>42</td>\n", " <td>21</td>\n", " <td>25</td>\n", " <td>28</td>\n", " <td>74</td>\n", " </tr>\n", " <tr>\n", " <th>Armenia</th>\n", " <td>6</td>\n", " <td>2</td>\n", " <td>5</td>\n", " <td>7</td>\n", " <td>14</td>\n", " <td>6</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>12</td>\n", " <td>2</td>\n", " <td>5</td>\n", " <td>7</td>\n", " <td>14</td>\n", " </tr>\n", " <tr>\n", " <th>Australasia</th>\n", " <td>2</td>\n", " <td>3</td>\n", " <td>4</td>\n", " <td>5</td>\n", " <td>12</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>2</td>\n", " <td>3</td>\n", " <td>4</td>\n", " <td>5</td>\n", " <td>12</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " # Summer Gold Silver Bronze Total # Winter Gold.1 \\\n", " Afghanistan 14 0 0 2 2 0 0 \n", " Algeria 13 5 4 8 17 3 0 \n", " Argentina 24 21 25 28 74 18 0 \n", " Armenia 6 2 5 7 14 6 0 \n", " Australasia 2 3 4 5 12 0 0 \n", "\n", " Silver.1 Bronze.1 Total.1 # Games Gold.2 Silver.2 \\\n", " Afghanistan 0 0 0 14 0 0 \n", " Algeria 0 0 0 16 5 4 \n", " Argentina 0 0 0 42 21 25 \n", " Armenia 0 0 0 12 2 5 \n", " Australasia 0 0 0 2 3 4 \n", "\n", " Bronze.2 Combined Total \n", " Afghanistan 2 2 \n", " Algeria 8 17 \n", " Argentina 28 74 \n", " Armenia 7 14 \n", " Australasia 5 12 " ] }, "execution_count": 147, "metadata": {}, "output_type": "execute_result" } ], "source": [ "for col in df.columns:\n", " if col[:2]=='01': # if the first two letters are '01'\n", " df.rename(columns={col:'Gold'+col[4:]}, inplace=True) #mapping changes labels\n", " if col[:2]=='02':\n", " df.rename(columns={col:'Silver'+col[4:]}, inplace=True)\n", " if col[:2]=='03':\n", " df.rename(columns={col:'Bronze'+col[4:]}, inplace=True)\n", " if col[:1]=='№':\n", " df.rename(columns={col:'#'+col[1:]}, inplace=True)\n", "df.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Querying a DataFrame" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Boolean masking is the heart of fast and efficient querying in NumPy. It's analogous a bit to masking used in other computational areas. A Boolean mask is an array which can be of one dimension like a series, or two dimensions like a DataFrame, where each of the values in the array are either true or false. This array is essentially overlaid on top of the data structure that we're querying. And any cell aligned with the true value will be admitted into our final result, and any sign aligned with a false value will not. Boolean masking is powerful conceptually and is the cornerstone of efficient NumPy and pandas querying. This technique is well used in other areas of computer science, for instance, in graphics. But it doesn't really have an analogue in other traditional relational databases, so I think it's worth pointing out here. Boolean masks are created by applying operators directly to the pandas series or DataFrame objects. For instance, in our Olympics data set, you might be interested in seeing only those countries who have achieved a gold medal at the summer Olympics. To build a Boolean mask for this query, we project the gold column using the indexing operator and apply the greater than operator with a comparison value of zero. This is essentially broadcasting a comparison operator, greater than, with the results being returned as a Boolean series." ] }, { "cell_type": "code", "execution_count": 149, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ " Afghanistan False\n", " Algeria True\n", " Argentina True\n", " Armenia True\n", " Australasia True\n", " Australia True\n", " Austria True\n", " Azerbaijan True\n", " Bahamas True\n", " Bahrain True\n", " Barbados False\n", " Belarus True\n", " Belgium True\n", " Bermuda False\n", " Bohemia False\n", " Botswana False\n", " Brazil True\n", " British West Indies False\n", " Bulgaria True\n", " Burundi True\n", " Cameroon True\n", " Canada True\n", " Chile True\n", " China True\n", " Colombia True\n", " Costa Rica True\n", " Côte d'Ivoire True\n", " Croatia True\n", " Cuba True\n", " Cyprus False\n", " ... \n", " Sri Lanka False\n", " Sudan False\n", " Suriname True\n", " Sweden True\n", " Switzerland True\n", " Syria True\n", " Chinese Taipei True\n", " Tajikistan True\n", " Tanzania False\n", " Thailand True\n", " Togo False\n", " Tonga False\n", " Trinidad and Tobago True\n", " Tunisia True\n", " Turkey True\n", " Uganda True\n", " Ukraine True\n", " United Arab Emirates True\n", " United States True\n", " Uruguay True\n", " Uzbekistan True\n", " Venezuela True\n", " Vietnam True\n", " Virgin Islands False\n", " Yugoslavia True\n", " Zambia False\n", " Zimbabwe True\n", " Independent Olympic Athletes True\n", " Independent Olympic Participants False\n", " Mixed team True\n", "Name: Gold, dtype: bool" ] }, "execution_count": 149, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df['Gold']>0" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The resultant series is indexed where the value of each cell is either true or false depending on whether a country has won at least one gold medal, and the index is the country name. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "So this builds us the Boolean mask, which is half the battle. What we want to do next is overlay that mask on the DataFrame. We can do this using the where function. The where function takes a Boolean mask as a condition, applies it to the DataFrame or series, and returns a new DataFrame or series of the same shape. Let's apply this Boolean mask to our Olympics data and create a DataFrame of only those countries who have won a gold at a summer games. " ] }, { "cell_type": "code", "execution_count": 151, "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># Summer</th>\n", " <th>Gold</th>\n", " <th>Silver</th>\n", " <th>Bronze</th>\n", " <th>Total</th>\n", " <th># Winter</th>\n", " <th>Gold.1</th>\n", " <th>Silver.1</th>\n", " <th>Bronze.1</th>\n", " <th>Total.1</th>\n", " <th># Games</th>\n", " <th>Gold.2</th>\n", " <th>Silver.2</th>\n", " <th>Bronze.2</th>\n", " <th>Combined Total</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>Afghanistan</th>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>Algeria</th>\n", " <td>13.0</td>\n", " <td>5.0</td>\n", " <td>4.0</td>\n", " <td>8.0</td>\n", " <td>17.0</td>\n", " <td>3.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>16.0</td>\n", " <td>5.0</td>\n", " <td>4.0</td>\n", " <td>8.0</td>\n", " <td>17.0</td>\n", " </tr>\n", " <tr>\n", " <th>Argentina</th>\n", " <td>24.0</td>\n", " <td>21.0</td>\n", " <td>25.0</td>\n", " <td>28.0</td>\n", " <td>74.0</td>\n", " <td>18.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>42.0</td>\n", " <td>21.0</td>\n", " <td>25.0</td>\n", " <td>28.0</td>\n", " <td>74.0</td>\n", " </tr>\n", " <tr>\n", " <th>Armenia</th>\n", " <td>6.0</td>\n", " <td>2.0</td>\n", " <td>5.0</td>\n", " <td>7.0</td>\n", " <td>14.0</td>\n", " <td>6.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>12.0</td>\n", " <td>2.0</td>\n", " <td>5.0</td>\n", " <td>7.0</td>\n", " <td>14.0</td>\n", " </tr>\n", " <tr>\n", " <th>Australasia</th>\n", " <td>2.0</td>\n", " <td>3.0</td>\n", " <td>4.0</td>\n", " <td>5.0</td>\n", " <td>12.0</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>2.0</td>\n", " <td>3.0</td>\n", " <td>4.0</td>\n", " <td>5.0</td>\n", " <td>12.0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " # Summer Gold Silver Bronze Total # Winter Gold.1 \\\n", " Afghanistan NaN NaN NaN NaN NaN NaN NaN \n", " Algeria 13.0 5.0 4.0 8.0 17.0 3.0 0.0 \n", " Argentina 24.0 21.0 25.0 28.0 74.0 18.0 0.0 \n", " Armenia 6.0 2.0 5.0 7.0 14.0 6.0 0.0 \n", " Australasia 2.0 3.0 4.0 5.0 12.0 0.0 0.0 \n", "\n", " Silver.1 Bronze.1 Total.1 # Games Gold.2 Silver.2 \\\n", " Afghanistan NaN NaN NaN NaN NaN NaN \n", " Algeria 0.0 0.0 0.0 16.0 5.0 4.0 \n", " Argentina 0.0 0.0 0.0 42.0 21.0 25.0 \n", " Armenia 0.0 0.0 0.0 12.0 2.0 5.0 \n", " Australasia 0.0 0.0 0.0 2.0 3.0 4.0 \n", "\n", " Bronze.2 Combined Total \n", " Afghanistan NaN NaN \n", " Algeria 8.0 17.0 \n", " Argentina 28.0 74.0 \n", " Armenia 7.0 14.0 \n", " Australasia 5.0 12.0 " ] }, "execution_count": 151, "metadata": {}, "output_type": "execute_result" } ], "source": [ "only_gold = df.where(df['Gold']>0)\n", "only_gold.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We see that the resulting DataFrame keeps the original indexed values, and only data from countries that met the condition are retained. All of the countries which did not meet the condition have NaN data instead. This is okay. Most statistical functions built into the DataFrame object ignore values of NaN. " ] }, { "cell_type": "code", "execution_count": 153, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "151" ] }, "execution_count": 153, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df['Gold'].count()" ] }, { "cell_type": "code", "execution_count": 154, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "109" ] }, "execution_count": 154, "metadata": {}, "output_type": "execute_result" } ], "source": [ "only_gold['Gold'].count()" ] }, { "cell_type": "code", "execution_count": 156, "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># Summer</th>\n", " <th>Gold</th>\n", " <th>Silver</th>\n", " <th>Bronze</th>\n", " <th>Total</th>\n", " <th># Winter</th>\n", " <th>Gold.1</th>\n", " <th>Silver.1</th>\n", " <th>Bronze.1</th>\n", " <th>Total.1</th>\n", " <th># Games</th>\n", " <th>Gold.2</th>\n", " <th>Silver.2</th>\n", " <th>Bronze.2</th>\n", " <th>Combined Total</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>Algeria</th>\n", " <td>13.0</td>\n", " <td>5.0</td>\n", " <td>4.0</td>\n", " <td>8.0</td>\n", " <td>17.0</td>\n", " <td>3.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>16.0</td>\n", " <td>5.0</td>\n", " <td>4.0</td>\n", " <td>8.0</td>\n", " <td>17.0</td>\n", " </tr>\n", " <tr>\n", " <th>Argentina</th>\n", " <td>24.0</td>\n", " <td>21.0</td>\n", " <td>25.0</td>\n", " <td>28.0</td>\n", " <td>74.0</td>\n", " <td>18.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>42.0</td>\n", " <td>21.0</td>\n", " <td>25.0</td>\n", " <td>28.0</td>\n", " <td>74.0</td>\n", " </tr>\n", " <tr>\n", " <th>Armenia</th>\n", " <td>6.0</td>\n", " <td>2.0</td>\n", " <td>5.0</td>\n", " <td>7.0</td>\n", " <td>14.0</td>\n", " <td>6.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>12.0</td>\n", " <td>2.0</td>\n", " <td>5.0</td>\n", " <td>7.0</td>\n", " <td>14.0</td>\n", " </tr>\n", " <tr>\n", " <th>Australasia</th>\n", " <td>2.0</td>\n", " <td>3.0</td>\n", " <td>4.0</td>\n", " <td>5.0</td>\n", " <td>12.0</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>2.0</td>\n", " <td>3.0</td>\n", " <td>4.0</td>\n", " <td>5.0</td>\n", " <td>12.0</td>\n", " </tr>\n", " <tr>\n", " <th>Australia</th>\n", " <td>26.0</td>\n", " <td>147.0</td>\n", " <td>163.0</td>\n", " <td>187.0</td>\n", " <td>497.0</td>\n", " <td>18.0</td>\n", " <td>5.0</td>\n", " <td>3.0</td>\n", " <td>4.0</td>\n", " <td>12.0</td>\n", " <td>44.0</td>\n", " <td>152.0</td>\n", " <td>166.0</td>\n", " <td>191.0</td>\n", " <td>509.0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " # Summer Gold Silver Bronze Total # Winter Gold.1 \\\n", " Algeria 13.0 5.0 4.0 8.0 17.0 3.0 0.0 \n", " Argentina 24.0 21.0 25.0 28.0 74.0 18.0 0.0 \n", " Armenia 6.0 2.0 5.0 7.0 14.0 6.0 0.0 \n", " Australasia 2.0 3.0 4.0 5.0 12.0 0.0 0.0 \n", " Australia 26.0 147.0 163.0 187.0 497.0 18.0 5.0 \n", "\n", " Silver.1 Bronze.1 Total.1 # Games Gold.2 Silver.2 \\\n", " Algeria 0.0 0.0 0.0 16.0 5.0 4.0 \n", " Argentina 0.0 0.0 0.0 42.0 21.0 25.0 \n", " Armenia 0.0 0.0 0.0 12.0 2.0 5.0 \n", " Australasia 0.0 0.0 0.0 2.0 3.0 4.0 \n", " Australia 3.0 4.0 12.0 44.0 152.0 166.0 \n", "\n", " Bronze.2 Combined Total \n", " Algeria 8.0 17.0 \n", " Argentina 28.0 74.0 \n", " Armenia 7.0 14.0 \n", " Australasia 5.0 12.0 \n", " Australia 191.0 509.0 " ] }, "execution_count": 156, "metadata": {}, "output_type": "execute_result" } ], "source": [ "only_gold = only_gold.dropna()\n", "only_gold.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Often we want to drop those rows which have no data. To do this, we can use the drop NA function. You can optionally provide drop NA the axis it should be considering. Remember that the axis is just an indicator for the columns or rows and that the default is zero, which means rows. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "When you find yourself talking about pandas and saying phrases like, often I want to, it's quite likely the developers have included a shortcut for this common operation. For instance, in this example, we don't actually have to use the where function explicitly. The pandas developers allow the indexing operator to take a Boolean mask as a value instead of just a list of column names." ] }, { "cell_type": "code", "execution_count": 158, "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># Summer</th>\n", " <th>Gold</th>\n", " <th>Silver</th>\n", " <th>Bronze</th>\n", " <th>Total</th>\n", " <th># Winter</th>\n", " <th>Gold.1</th>\n", " <th>Silver.1</th>\n", " <th>Bronze.1</th>\n", " <th>Total.1</th>\n", " <th># Games</th>\n", " <th>Gold.2</th>\n", " <th>Silver.2</th>\n", " <th>Bronze.2</th>\n", " <th>Combined Total</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>Algeria</th>\n", " <td>13</td>\n", " <td>5</td>\n", " <td>4</td>\n", " <td>8</td>\n", " <td>17</td>\n", " <td>3</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>16</td>\n", " <td>5</td>\n", " <td>4</td>\n", " <td>8</td>\n", " <td>17</td>\n", " </tr>\n", " <tr>\n", " <th>Argentina</th>\n", " <td>24</td>\n", " <td>21</td>\n", " <td>25</td>\n", " <td>28</td>\n", " <td>74</td>\n", " <td>18</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>42</td>\n", " <td>21</td>\n", " <td>25</td>\n", " <td>28</td>\n", " <td>74</td>\n", " </tr>\n", " <tr>\n", " <th>Armenia</th>\n", " <td>6</td>\n", " <td>2</td>\n", " <td>5</td>\n", " <td>7</td>\n", " <td>14</td>\n", " <td>6</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>12</td>\n", " <td>2</td>\n", " <td>5</td>\n", " <td>7</td>\n", " <td>14</td>\n", " </tr>\n", " <tr>\n", " <th>Australasia</th>\n", " <td>2</td>\n", " <td>3</td>\n", " <td>4</td>\n", " <td>5</td>\n", " <td>12</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>2</td>\n", " <td>3</td>\n", " <td>4</td>\n", " <td>5</td>\n", " <td>12</td>\n", " </tr>\n", " <tr>\n", " <th>Australia</th>\n", " <td>26</td>\n", " <td>147</td>\n", " <td>163</td>\n", " <td>187</td>\n", " <td>497</td>\n", " <td>18</td>\n", " <td>5</td>\n", " <td>3</td>\n", " <td>4</td>\n", " <td>12</td>\n", " <td>44</td>\n", " <td>152</td>\n", " <td>166</td>\n", " <td>191</td>\n", " <td>509</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " # Summer Gold Silver Bronze Total # Winter Gold.1 \\\n", " Algeria 13 5 4 8 17 3 0 \n", " Argentina 24 21 25 28 74 18 0 \n", " Armenia 6 2 5 7 14 6 0 \n", " Australasia 2 3 4 5 12 0 0 \n", " Australia 26 147 163 187 497 18 5 \n", "\n", " Silver.1 Bronze.1 Total.1 # Games Gold.2 Silver.2 \\\n", " Algeria 0 0 0 16 5 4 \n", " Argentina 0 0 0 42 21 25 \n", " Armenia 0 0 0 12 2 5 \n", " Australasia 0 0 0 2 3 4 \n", " Australia 3 4 12 44 152 166 \n", "\n", " Bronze.2 Combined Total \n", " Algeria 8 17 \n", " Argentina 28 74 \n", " Armenia 7 14 \n", " Australasia 5 12 \n", " Australia 191 509 " ] }, "execution_count": 158, "metadata": {}, "output_type": "execute_result" } ], "source": [ "only_gold = df[df['Gold']>0]\n", "only_gold.head()" ] }, { "cell_type": "code", "execution_count": 159, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "110" ] }, "execution_count": 159, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#To get the no of countries who recieved at least one gold in Summer or Winter Olympics\n", "len(df[(df['Gold']>0) | df['Gold.1']>0])" ] }, { "cell_type": "code", "execution_count": 163, "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># Summer</th>\n", " <th>Gold</th>\n", " <th>Silver</th>\n", " <th>Bronze</th>\n", " <th>Total</th>\n", " <th># Winter</th>\n", " <th>Gold.1</th>\n", " <th>Silver.1</th>\n", " <th>Bronze.1</th>\n", " <th>Total.1</th>\n", " <th># Games</th>\n", " <th>Gold.2</th>\n", " <th>Silver.2</th>\n", " <th>Bronze.2</th>\n", " <th>Combined Total</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>Liechtenstein</th>\n", " <td>17</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>18</td>\n", " <td>2</td>\n", " <td>2</td>\n", " <td>5</td>\n", " <td>9</td>\n", " <td>35</td>\n", " <td>2</td>\n", " <td>2</td>\n", " <td>5</td>\n", " <td>9</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " # Summer Gold Silver Bronze Total # Winter Gold.1 \\\n", " Liechtenstein 17 0 0 0 0 18 2 \n", "\n", " Silver.1 Bronze.1 Total.1 # Games Gold.2 Silver.2 \\\n", " Liechtenstein 2 5 9 35 2 2 \n", "\n", " Bronze.2 Combined Total \n", " Liechtenstein 5 9 " ] }, "execution_count": 163, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Are there any countries which won a gold in winter olympics but never in summer olympics\n", "df[(df['Gold']==0) & (df['Gold.1']>0)]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Extremely important, and often an issue for new users, is to remember that each Boolean mask needs to be encased in parenthesis because of the order of operations. This can cause no end of frustration if you're not used to it, so be careful. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Indexing DataFrames" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The index is essentially a row level label, and we know that rows correspond to axis zero. In our Olympics data, we indexed the data frame by the name of the country. Indices can either be inferred, such as when we create a new series without an index, in which case we get numeric values, or they can be set explicitly, like when we use the dictionary object to create the series, or when we loaded data from the CSV file and specified the header. Another option for setting an index is to use the set_index function. This function takes a list of columns and promotes those columns to an index. Set index is a destructive process, it doesn't keep the current index. If you want to keep the current index, you need to manually create a new column and copy into it values from the index attribute. Let's go back to our Olympics DataFrame. Let's say that we don't want to index the DataFrame by countries, but instead want to index by the number of gold medals that were won at summer games. First we need to preserve the country information into a new column. We can do this using the indexing operator or the string that has the column label. Then we can use the set_index to set index of the column to summer gold medal wins. " ] }, { "cell_type": "code", "execution_count": 164, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th># Summer</th>\n", " <th>Silver</th>\n", " <th>Bronze</th>\n", " <th>Total</th>\n", " <th># Winter</th>\n", " <th>Gold.1</th>\n", " <th>Silver.1</th>\n", " <th>Bronze.1</th>\n", " <th>Total.1</th>\n", " <th># Games</th>\n", " <th>Gold.2</th>\n", " <th>Silver.2</th>\n", " <th>Bronze.2</th>\n", " <th>Combined Total</th>\n", " <th>country</th>\n", " </tr>\n", " <tr>\n", " <th>Gold</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>14</td>\n", " <td>0</td>\n", " <td>2</td>\n", " <td>2</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>14</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>2</td>\n", " <td>2</td>\n", " <td>Afghanistan</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>13</td>\n", " <td>4</td>\n", " <td>8</td>\n", " <td>17</td>\n", " <td>3</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>16</td>\n", " <td>5</td>\n", " <td>4</td>\n", " <td>8</td>\n", " <td>17</td>\n", " <td>Algeria</td>\n", " </tr>\n", " <tr>\n", " <th>21</th>\n", " <td>24</td>\n", " <td>25</td>\n", " <td>28</td>\n", " <td>74</td>\n", " <td>18</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>42</td>\n", " <td>21</td>\n", " <td>25</td>\n", " <td>28</td>\n", " <td>74</td>\n", " <td>Argentina</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>6</td>\n", " <td>5</td>\n", " <td>7</td>\n", " <td>14</td>\n", " <td>6</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>12</td>\n", " <td>2</td>\n", " <td>5</td>\n", " <td>7</td>\n", " <td>14</td>\n", " <td>Armenia</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>2</td>\n", " <td>4</td>\n", " <td>5</td>\n", " <td>12</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>2</td>\n", " <td>3</td>\n", " <td>4</td>\n", " <td>5</td>\n", " <td>12</td>\n", " <td>Australasia</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " # Summer Silver Bronze Total # Winter Gold.1 Silver.1 Bronze.1 \\\n", "Gold \n", "0 14 0 2 2 0 0 0 0 \n", "5 13 4 8 17 3 0 0 0 \n", "21 24 25 28 74 18 0 0 0 \n", "2 6 5 7 14 6 0 0 0 \n", "3 2 4 5 12 0 0 0 0 \n", "\n", " Total.1 # Games Gold.2 Silver.2 Bronze.2 Combined Total \\\n", "Gold \n", "0 0 14 0 0 2 2 \n", "5 0 16 5 4 8 17 \n", "21 0 42 21 25 28 74 \n", "2 0 12 2 5 7 14 \n", "3 0 2 3 4 5 12 \n", "\n", " country \n", "Gold \n", "0 Afghanistan \n", "5 Algeria \n", "21 Argentina \n", "2 Armenia \n", "3 Australasia " ] }, "execution_count": 164, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df['country'] = df.index\n", "df = df.set_index('Gold')\n", "df.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You'll see that when we create a new index from an existing column it appears that a new first row has been added with empty values. This isn't quite what's happening. And we know this in part because an empty value is actually rendered either as a none or an NaN if the data type of the column is numeric. What's actually happened is that the index has a name. Whatever the column name was in the Jupiter notebook has just provided this in the output. We can get rid of the index completely by calling the function reset_index. This promotes the index into a column and creates a default numbered index. " ] }, { "cell_type": "code", "execution_count": 165, "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>Gold</th>\n", " <th># Summer</th>\n", " <th>Silver</th>\n", " <th>Bronze</th>\n", " <th>Total</th>\n", " <th># Winter</th>\n", " <th>Gold.1</th>\n", " <th>Silver.1</th>\n", " <th>Bronze.1</th>\n", " <th>Total.1</th>\n", " <th># Games</th>\n", " <th>Gold.2</th>\n", " <th>Silver.2</th>\n", " <th>Bronze.2</th>\n", " <th>Combined Total</th>\n", " <th>country</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>0</td>\n", " <td>14</td>\n", " <td>0</td>\n", " <td>2</td>\n", " <td>2</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>14</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>2</td>\n", " <td>2</td>\n", " <td>Afghanistan</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>5</td>\n", " <td>13</td>\n", " <td>4</td>\n", " <td>8</td>\n", " <td>17</td>\n", " <td>3</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>16</td>\n", " <td>5</td>\n", " <td>4</td>\n", " <td>8</td>\n", " <td>17</td>\n", " <td>Algeria</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>21</td>\n", " <td>24</td>\n", " <td>25</td>\n", " <td>28</td>\n", " <td>74</td>\n", " <td>18</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>42</td>\n", " <td>21</td>\n", " <td>25</td>\n", " <td>28</td>\n", " <td>74</td>\n", " <td>Argentina</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>2</td>\n", " <td>6</td>\n", " <td>5</td>\n", " <td>7</td>\n", " <td>14</td>\n", " <td>6</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>12</td>\n", " <td>2</td>\n", " <td>5</td>\n", " <td>7</td>\n", " <td>14</td>\n", " <td>Armenia</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>3</td>\n", " <td>2</td>\n", " <td>4</td>\n", " <td>5</td>\n", " <td>12</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>2</td>\n", " <td>3</td>\n", " <td>4</td>\n", " <td>5</td>\n", " <td>12</td>\n", " <td>Australasia</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Gold # Summer Silver Bronze Total # Winter Gold.1 Silver.1 \\\n", "0 0 14 0 2 2 0 0 0 \n", "1 5 13 4 8 17 3 0 0 \n", "2 21 24 25 28 74 18 0 0 \n", "3 2 6 5 7 14 6 0 0 \n", "4 3 2 4 5 12 0 0 0 \n", "\n", " Bronze.1 Total.1 # Games Gold.2 Silver.2 Bronze.2 Combined Total \\\n", "0 0 0 14 0 0 2 2 \n", "1 0 0 16 5 4 8 17 \n", "2 0 0 42 21 25 28 74 \n", "3 0 0 12 2 5 7 14 \n", "4 0 0 2 3 4 5 12 \n", "\n", " country \n", "0 Afghanistan \n", "1 Algeria \n", "2 Argentina \n", "3 Armenia \n", "4 Australasia " ] }, "execution_count": 165, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = df.reset_index()\n", "df.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "One nice feature of pandas is that it has the option to do multi-level indexing. This is similar to composite keys in relational database systems. To create a multi-level index, we simply call set index and give it a list of columns that we're interested in promoting to an index. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Pandas will search through these in order, finding the distinct data and forming composite indices. A good example of this is often found when dealing with geographical data which is sorted by regions or demographics. Let's change data sets and look at some census data for a better example. This data is stored in the file census.csv and comes from the United States Census Bureau. In particular, this is a breakdown of the population level data at the US county level. It's a great example of how different kinds of data sets might be formatted when you're trying to clean them. For instance, in this data set there are two summarized levels, one that contains summary data for the whole country. And one that contains summary data for each state, and one that contains summary data for each county." ] }, { "cell_type": "code", "execution_count": 169, "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>SUMLEV</th>\n", " <th>REGION</th>\n", " <th>DIVISION</th>\n", " <th>STATE</th>\n", " <th>COUNTY</th>\n", " <th>STNAME</th>\n", " <th>CTYNAME</th>\n", " <th>CENSUS2010POP</th>\n", " <th>ESTIMATESBASE2010</th>\n", " <th>POPESTIMATE2010</th>\n", " <th>...</th>\n", " <th>RDOMESTICMIG2011</th>\n", " <th>RDOMESTICMIG2012</th>\n", " <th>RDOMESTICMIG2013</th>\n", " <th>RDOMESTICMIG2014</th>\n", " <th>RDOMESTICMIG2015</th>\n", " <th>RNETMIG2011</th>\n", " <th>RNETMIG2012</th>\n", " <th>RNETMIG2013</th>\n", " <th>RNETMIG2014</th>\n", " <th>RNETMIG2015</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>40</td>\n", " <td>3</td>\n", " <td>6</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>Alabama</td>\n", " <td>Alabama</td>\n", " <td>4779736</td>\n", " <td>4780127</td>\n", " <td>4785161</td>\n", " <td>...</td>\n", " <td>0.002295</td>\n", " <td>-0.193196</td>\n", " <td>0.381066</td>\n", " <td>0.582002</td>\n", " <td>-0.467369</td>\n", " <td>1.030015</td>\n", " <td>0.826644</td>\n", " <td>1.383282</td>\n", " <td>1.724718</td>\n", " <td>0.712594</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>50</td>\n", " <td>3</td>\n", " <td>6</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>Alabama</td>\n", " <td>Autauga County</td>\n", " <td>54571</td>\n", " <td>54571</td>\n", " <td>54660</td>\n", " <td>...</td>\n", " <td>7.242091</td>\n", " <td>-2.915927</td>\n", " <td>-3.012349</td>\n", " <td>2.265971</td>\n", " <td>-2.530799</td>\n", " <td>7.606016</td>\n", " <td>-2.626146</td>\n", " <td>-2.722002</td>\n", " <td>2.592270</td>\n", " <td>-2.187333</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>50</td>\n", " <td>3</td>\n", " <td>6</td>\n", " <td>1</td>\n", " <td>3</td>\n", " <td>Alabama</td>\n", " <td>Baldwin County</td>\n", " <td>182265</td>\n", " <td>182265</td>\n", " <td>183193</td>\n", " <td>...</td>\n", " <td>14.832960</td>\n", " <td>17.647293</td>\n", " <td>21.845705</td>\n", " <td>19.243287</td>\n", " <td>17.197872</td>\n", " <td>15.844176</td>\n", " <td>18.559627</td>\n", " <td>22.727626</td>\n", " <td>20.317142</td>\n", " <td>18.293499</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>50</td>\n", " <td>3</td>\n", " <td>6</td>\n", " <td>1</td>\n", " <td>5</td>\n", " <td>Alabama</td>\n", " <td>Barbour County</td>\n", " <td>27457</td>\n", " <td>27457</td>\n", " <td>27341</td>\n", " <td>...</td>\n", " <td>-4.728132</td>\n", " <td>-2.500690</td>\n", " <td>-7.056824</td>\n", " <td>-3.904217</td>\n", " <td>-10.543299</td>\n", " <td>-4.874741</td>\n", " <td>-2.758113</td>\n", " <td>-7.167664</td>\n", " <td>-3.978583</td>\n", " <td>-10.543299</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>50</td>\n", " <td>3</td>\n", " <td>6</td>\n", " <td>1</td>\n", " <td>7</td>\n", " <td>Alabama</td>\n", " <td>Bibb County</td>\n", " <td>22915</td>\n", " <td>22919</td>\n", " <td>22861</td>\n", " <td>...</td>\n", " <td>-5.527043</td>\n", " <td>-5.068871</td>\n", " <td>-6.201001</td>\n", " <td>-0.177537</td>\n", " <td>0.177258</td>\n", " <td>-5.088389</td>\n", " <td>-4.363636</td>\n", " <td>-5.403729</td>\n", " <td>0.754533</td>\n", " <td>1.107861</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>5 rows × 100 columns</p>\n", "</div>" ], "text/plain": [ " SUMLEV REGION DIVISION STATE COUNTY STNAME CTYNAME \\\n", "0 40 3 6 1 0 Alabama Alabama \n", "1 50 3 6 1 1 Alabama Autauga County \n", "2 50 3 6 1 3 Alabama Baldwin County \n", "3 50 3 6 1 5 Alabama Barbour County \n", "4 50 3 6 1 7 Alabama Bibb County \n", "\n", " CENSUS2010POP ESTIMATESBASE2010 POPESTIMATE2010 ... \\\n", "0 4779736 4780127 4785161 ... \n", "1 54571 54571 54660 ... \n", "2 182265 182265 183193 ... \n", "3 27457 27457 27341 ... \n", "4 22915 22919 22861 ... \n", "\n", " RDOMESTICMIG2011 RDOMESTICMIG2012 RDOMESTICMIG2013 RDOMESTICMIG2014 \\\n", "0 0.002295 -0.193196 0.381066 0.582002 \n", "1 7.242091 -2.915927 -3.012349 2.265971 \n", "2 14.832960 17.647293 21.845705 19.243287 \n", "3 -4.728132 -2.500690 -7.056824 -3.904217 \n", "4 -5.527043 -5.068871 -6.201001 -0.177537 \n", "\n", " RDOMESTICMIG2015 RNETMIG2011 RNETMIG2012 RNETMIG2013 RNETMIG2014 \\\n", "0 -0.467369 1.030015 0.826644 1.383282 1.724718 \n", "1 -2.530799 7.606016 -2.626146 -2.722002 2.592270 \n", "2 17.197872 15.844176 18.559627 22.727626 20.317142 \n", "3 -10.543299 -4.874741 -2.758113 -7.167664 -3.978583 \n", "4 0.177258 -5.088389 -4.363636 -5.403729 0.754533 \n", "\n", " RNETMIG2015 \n", "0 0.712594 \n", "1 -2.187333 \n", "2 18.293499 \n", "3 -10.543299 \n", "4 1.107861 \n", "\n", "[5 rows x 100 columns]" ] }, "execution_count": 169, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = pd.read_csv('census.csv')\n", "df.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "I often find that I want to see a list of all the unique values in a given column. In this DataFrame, we see that the possible values for the sum level are using the unique function on the DataFrame. This is similar to the SQL distinct operator. Here we can run unique on the sum level of our current DataFrame and see that there are only two different values, 40 and 50. " ] }, { "cell_type": "code", "execution_count": 170, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([40, 50])" ] }, "execution_count": 170, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df['SUMLEV'].unique() #40 belongs to state level data and 50 belongs to county level data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's get rid of all of the rows that are summaries at the state level and just keep the county data." ] }, { "cell_type": "code", "execution_count": 171, "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>SUMLEV</th>\n", " <th>REGION</th>\n", " <th>DIVISION</th>\n", " <th>STATE</th>\n", " <th>COUNTY</th>\n", " <th>STNAME</th>\n", " <th>CTYNAME</th>\n", " <th>CENSUS2010POP</th>\n", " <th>ESTIMATESBASE2010</th>\n", " <th>POPESTIMATE2010</th>\n", " <th>...</th>\n", " <th>RDOMESTICMIG2011</th>\n", " <th>RDOMESTICMIG2012</th>\n", " <th>RDOMESTICMIG2013</th>\n", " <th>RDOMESTICMIG2014</th>\n", " <th>RDOMESTICMIG2015</th>\n", " <th>RNETMIG2011</th>\n", " <th>RNETMIG2012</th>\n", " <th>RNETMIG2013</th>\n", " <th>RNETMIG2014</th>\n", " <th>RNETMIG2015</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>1</th>\n", " <td>50</td>\n", " <td>3</td>\n", " <td>6</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>Alabama</td>\n", " <td>Autauga County</td>\n", " <td>54571</td>\n", " <td>54571</td>\n", " <td>54660</td>\n", " <td>...</td>\n", " <td>7.242091</td>\n", " <td>-2.915927</td>\n", " <td>-3.012349</td>\n", " <td>2.265971</td>\n", " <td>-2.530799</td>\n", " <td>7.606016</td>\n", " <td>-2.626146</td>\n", " <td>-2.722002</td>\n", " <td>2.592270</td>\n", " <td>-2.187333</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>50</td>\n", " <td>3</td>\n", " <td>6</td>\n", " <td>1</td>\n", " <td>3</td>\n", " <td>Alabama</td>\n", " <td>Baldwin County</td>\n", " <td>182265</td>\n", " <td>182265</td>\n", " <td>183193</td>\n", " <td>...</td>\n", " <td>14.832960</td>\n", " <td>17.647293</td>\n", " <td>21.845705</td>\n", " <td>19.243287</td>\n", " <td>17.197872</td>\n", " <td>15.844176</td>\n", " <td>18.559627</td>\n", " <td>22.727626</td>\n", " <td>20.317142</td>\n", " <td>18.293499</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>50</td>\n", " <td>3</td>\n", " <td>6</td>\n", " <td>1</td>\n", " <td>5</td>\n", " <td>Alabama</td>\n", " <td>Barbour County</td>\n", " <td>27457</td>\n", " <td>27457</td>\n", " <td>27341</td>\n", " <td>...</td>\n", " <td>-4.728132</td>\n", " <td>-2.500690</td>\n", " <td>-7.056824</td>\n", " <td>-3.904217</td>\n", " <td>-10.543299</td>\n", " <td>-4.874741</td>\n", " <td>-2.758113</td>\n", " <td>-7.167664</td>\n", " <td>-3.978583</td>\n", " <td>-10.543299</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>50</td>\n", " <td>3</td>\n", " <td>6</td>\n", " <td>1</td>\n", " <td>7</td>\n", " <td>Alabama</td>\n", " <td>Bibb County</td>\n", " <td>22915</td>\n", " <td>22919</td>\n", " <td>22861</td>\n", " <td>...</td>\n", " <td>-5.527043</td>\n", " <td>-5.068871</td>\n", " <td>-6.201001</td>\n", " <td>-0.177537</td>\n", " <td>0.177258</td>\n", " <td>-5.088389</td>\n", " <td>-4.363636</td>\n", " <td>-5.403729</td>\n", " <td>0.754533</td>\n", " <td>1.107861</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>50</td>\n", " <td>3</td>\n", " <td>6</td>\n", " <td>1</td>\n", " <td>9</td>\n", " <td>Alabama</td>\n", " <td>Blount County</td>\n", " <td>57322</td>\n", " <td>57322</td>\n", " <td>57373</td>\n", " <td>...</td>\n", " <td>1.807375</td>\n", " <td>-1.177622</td>\n", " <td>-1.748766</td>\n", " <td>-2.062535</td>\n", " <td>-1.369970</td>\n", " <td>1.859511</td>\n", " <td>-0.848580</td>\n", " <td>-1.402476</td>\n", " <td>-1.577232</td>\n", " <td>-0.884411</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>5 rows × 100 columns</p>\n", "</div>" ], "text/plain": [ " SUMLEV REGION DIVISION STATE COUNTY STNAME CTYNAME \\\n", "1 50 3 6 1 1 Alabama Autauga County \n", "2 50 3 6 1 3 Alabama Baldwin County \n", "3 50 3 6 1 5 Alabama Barbour County \n", "4 50 3 6 1 7 Alabama Bibb County \n", "5 50 3 6 1 9 Alabama Blount County \n", "\n", " CENSUS2010POP ESTIMATESBASE2010 POPESTIMATE2010 ... \\\n", "1 54571 54571 54660 ... \n", "2 182265 182265 183193 ... \n", "3 27457 27457 27341 ... \n", "4 22915 22919 22861 ... \n", "5 57322 57322 57373 ... \n", "\n", " RDOMESTICMIG2011 RDOMESTICMIG2012 RDOMESTICMIG2013 RDOMESTICMIG2014 \\\n", "1 7.242091 -2.915927 -3.012349 2.265971 \n", "2 14.832960 17.647293 21.845705 19.243287 \n", "3 -4.728132 -2.500690 -7.056824 -3.904217 \n", "4 -5.527043 -5.068871 -6.201001 -0.177537 \n", "5 1.807375 -1.177622 -1.748766 -2.062535 \n", "\n", " RDOMESTICMIG2015 RNETMIG2011 RNETMIG2012 RNETMIG2013 RNETMIG2014 \\\n", "1 -2.530799 7.606016 -2.626146 -2.722002 2.592270 \n", "2 17.197872 15.844176 18.559627 22.727626 20.317142 \n", "3 -10.543299 -4.874741 -2.758113 -7.167664 -3.978583 \n", "4 0.177258 -5.088389 -4.363636 -5.403729 0.754533 \n", "5 -1.369970 1.859511 -0.848580 -1.402476 -1.577232 \n", "\n", " RNETMIG2015 \n", "1 -2.187333 \n", "2 18.293499 \n", "3 -10.543299 \n", "4 1.107861 \n", "5 -0.884411 \n", "\n", "[5 rows x 100 columns]" ] }, "execution_count": 171, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = df[df['SUMLEV']==50]\n", "df.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Also while this data set is interesting for a number of different reasons, let's reduce the data that we're going to look at to just the total population estimates and the total number of births. We can do this by creating a list of column names that we want to keep then project those and assign the resulting DataFrame to our df variable. " ] }, { "cell_type": "code", "execution_count": 173, "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>STNAME</th>\n", " <th>CTYNAME</th>\n", " <th>BIRTHS2010</th>\n", " <th>BIRTHS2011</th>\n", " <th>BIRTHS2012</th>\n", " <th>BIRTHS2013</th>\n", " <th>BIRTHS2014</th>\n", " <th>BIRTHS2015</th>\n", " <th>POPESTIMATE2010</th>\n", " <th>POPESTIMATE2011</th>\n", " <th>POPESTIMATE2012</th>\n", " <th>POPESTIMATE2013</th>\n", " <th>POPESTIMATE2014</th>\n", " <th>POPESTIMATE2015</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>1</th>\n", " <td>Alabama</td>\n", " <td>Autauga County</td>\n", " <td>151</td>\n", " <td>636</td>\n", " <td>615</td>\n", " <td>574</td>\n", " <td>623</td>\n", " <td>600</td>\n", " <td>54660</td>\n", " <td>55253</td>\n", " <td>55175</td>\n", " <td>55038</td>\n", " <td>55290</td>\n", " <td>55347</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>Alabama</td>\n", " <td>Baldwin County</td>\n", " <td>517</td>\n", " <td>2187</td>\n", " <td>2092</td>\n", " <td>2160</td>\n", " <td>2186</td>\n", " <td>2240</td>\n", " <td>183193</td>\n", " <td>186659</td>\n", " <td>190396</td>\n", " <td>195126</td>\n", " <td>199713</td>\n", " <td>203709</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>Alabama</td>\n", " <td>Barbour County</td>\n", " <td>70</td>\n", " <td>335</td>\n", " <td>300</td>\n", " <td>283</td>\n", " <td>260</td>\n", " <td>269</td>\n", " <td>27341</td>\n", " <td>27226</td>\n", " <td>27159</td>\n", " <td>26973</td>\n", " <td>26815</td>\n", " <td>26489</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>Alabama</td>\n", " <td>Bibb County</td>\n", " <td>44</td>\n", " <td>266</td>\n", " <td>245</td>\n", " <td>259</td>\n", " <td>247</td>\n", " <td>253</td>\n", " <td>22861</td>\n", " <td>22733</td>\n", " <td>22642</td>\n", " <td>22512</td>\n", " <td>22549</td>\n", " <td>22583</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>Alabama</td>\n", " <td>Blount County</td>\n", " <td>183</td>\n", " <td>744</td>\n", " <td>710</td>\n", " <td>646</td>\n", " <td>618</td>\n", " <td>603</td>\n", " <td>57373</td>\n", " <td>57711</td>\n", " <td>57776</td>\n", " <td>57734</td>\n", " <td>57658</td>\n", " <td>57673</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " STNAME CTYNAME BIRTHS2010 BIRTHS2011 BIRTHS2012 BIRTHS2013 \\\n", "1 Alabama Autauga County 151 636 615 574 \n", "2 Alabama Baldwin County 517 2187 2092 2160 \n", "3 Alabama Barbour County 70 335 300 283 \n", "4 Alabama Bibb County 44 266 245 259 \n", "5 Alabama Blount County 183 744 710 646 \n", "\n", " BIRTHS2014 BIRTHS2015 POPESTIMATE2010 POPESTIMATE2011 POPESTIMATE2012 \\\n", "1 623 600 54660 55253 55175 \n", "2 2186 2240 183193 186659 190396 \n", "3 260 269 27341 27226 27159 \n", "4 247 253 22861 22733 22642 \n", "5 618 603 57373 57711 57776 \n", "\n", " POPESTIMATE2013 POPESTIMATE2014 POPESTIMATE2015 \n", "1 55038 55290 55347 \n", "2 195126 199713 203709 \n", "3 26973 26815 26489 \n", "4 22512 22549 22583 \n", "5 57734 57658 57673 " ] }, "execution_count": 173, "metadata": {}, "output_type": "execute_result" } ], "source": [ "columns_to_keep = ['STNAME',\n", " 'CTYNAME',\n", " 'BIRTHS2010',\n", " 'BIRTHS2011',\n", " 'BIRTHS2012',\n", " 'BIRTHS2013',\n", " 'BIRTHS2014',\n", " 'BIRTHS2015',\n", " 'POPESTIMATE2010',\n", " 'POPESTIMATE2011',\n", " 'POPESTIMATE2012',\n", " 'POPESTIMATE2013',\n", " 'POPESTIMATE2014',\n", " 'POPESTIMATE2015'\n", " ]\n", "df = df[columns_to_keep]\n", "df.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The US Census data breaks down estimates of population data by state and county. We can load the data and set the index to be a combination of the state and county values and see how pandas handles it in a DataFrame. We do this by creating a list of the column identifiers we want to have indexed. And then calling set index with this list and assigning the output as appropriate. We see here that we have a dual index, first the state name and then the county name." ] }, { "cell_type": "code", "execution_count": 174, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th></th>\n", " <th>BIRTHS2010</th>\n", " <th>BIRTHS2011</th>\n", " <th>BIRTHS2012</th>\n", " <th>BIRTHS2013</th>\n", " <th>BIRTHS2014</th>\n", " <th>BIRTHS2015</th>\n", " <th>POPESTIMATE2010</th>\n", " <th>POPESTIMATE2011</th>\n", " <th>POPESTIMATE2012</th>\n", " <th>POPESTIMATE2013</th>\n", " <th>POPESTIMATE2014</th>\n", " <th>POPESTIMATE2015</th>\n", " </tr>\n", " <tr>\n", " <th>STNAME</th>\n", " <th>CTYNAME</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th rowspan=\"5\" valign=\"top\">Alabama</th>\n", " <th>Autauga County</th>\n", " <td>151</td>\n", " <td>636</td>\n", " <td>615</td>\n", " <td>574</td>\n", " <td>623</td>\n", " <td>600</td>\n", " <td>54660</td>\n", " <td>55253</td>\n", " <td>55175</td>\n", " <td>55038</td>\n", " <td>55290</td>\n", " <td>55347</td>\n", " </tr>\n", " <tr>\n", " <th>Baldwin County</th>\n", " <td>517</td>\n", " <td>2187</td>\n", " <td>2092</td>\n", " <td>2160</td>\n", " <td>2186</td>\n", " <td>2240</td>\n", " <td>183193</td>\n", " <td>186659</td>\n", " <td>190396</td>\n", " <td>195126</td>\n", " <td>199713</td>\n", " <td>203709</td>\n", " </tr>\n", " <tr>\n", " <th>Barbour County</th>\n", " <td>70</td>\n", " <td>335</td>\n", " <td>300</td>\n", " <td>283</td>\n", " <td>260</td>\n", " <td>269</td>\n", " <td>27341</td>\n", " <td>27226</td>\n", " <td>27159</td>\n", " <td>26973</td>\n", " <td>26815</td>\n", " <td>26489</td>\n", " </tr>\n", " <tr>\n", " <th>Bibb County</th>\n", " <td>44</td>\n", " <td>266</td>\n", " <td>245</td>\n", " <td>259</td>\n", " <td>247</td>\n", " <td>253</td>\n", " <td>22861</td>\n", " <td>22733</td>\n", " <td>22642</td>\n", " <td>22512</td>\n", " <td>22549</td>\n", " <td>22583</td>\n", " </tr>\n", " <tr>\n", " <th>Blount County</th>\n", " <td>183</td>\n", " <td>744</td>\n", " <td>710</td>\n", " <td>646</td>\n", " <td>618</td>\n", " <td>603</td>\n", " <td>57373</td>\n", " <td>57711</td>\n", " <td>57776</td>\n", " <td>57734</td>\n", " <td>57658</td>\n", " <td>57673</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " BIRTHS2010 BIRTHS2011 BIRTHS2012 BIRTHS2013 \\\n", "STNAME CTYNAME \n", "Alabama Autauga County 151 636 615 574 \n", " Baldwin County 517 2187 2092 2160 \n", " Barbour County 70 335 300 283 \n", " Bibb County 44 266 245 259 \n", " Blount County 183 744 710 646 \n", "\n", " BIRTHS2014 BIRTHS2015 POPESTIMATE2010 \\\n", "STNAME CTYNAME \n", "Alabama Autauga County 623 600 54660 \n", " Baldwin County 2186 2240 183193 \n", " Barbour County 260 269 27341 \n", " Bibb County 247 253 22861 \n", " Blount County 618 603 57373 \n", "\n", " POPESTIMATE2011 POPESTIMATE2012 POPESTIMATE2013 \\\n", "STNAME CTYNAME \n", "Alabama Autauga County 55253 55175 55038 \n", " Baldwin County 186659 190396 195126 \n", " Barbour County 27226 27159 26973 \n", " Bibb County 22733 22642 22512 \n", " Blount County 57711 57776 57734 \n", "\n", " POPESTIMATE2014 POPESTIMATE2015 \n", "STNAME CTYNAME \n", "Alabama Autauga County 55290 55347 \n", " Baldwin County 199713 203709 \n", " Barbour County 26815 26489 \n", " Bibb County 22549 22583 \n", " Blount County 57658 57673 " ] }, "execution_count": 174, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = df.set_index(['STNAME','CTYNAME'])\n", "df.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "An immediate question which comes up is how we can query this DataFrame. For instance, we saw previously that the loc attribute of the DataFrame can take multiple arguments. And it could query both the row and the columns. When you use a MultiIndex, you must provide the arguments in order by the level you wish to query. Inside of the index, each column is called a level and the outermost column is level zero. For instance, if we want to see the population results from Washtenaw County, you'd want to the first argument as the state of Michigan." ] }, { "cell_type": "code", "execution_count": 175, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "BIRTHS2010 977\n", "BIRTHS2011 3826\n", "BIRTHS2012 3780\n", "BIRTHS2013 3662\n", "BIRTHS2014 3683\n", "BIRTHS2015 3709\n", "POPESTIMATE2010 345563\n", "POPESTIMATE2011 349048\n", "POPESTIMATE2012 351213\n", "POPESTIMATE2013 354289\n", "POPESTIMATE2014 357029\n", "POPESTIMATE2015 358880\n", "Name: (Michigan, Washtenaw County), dtype: int64" ] }, "execution_count": 175, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.loc['Michigan', 'Washtenaw County']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You might be interested in just comparing two counties. For instance, Washtenaw and Wayne County which covers Detroit. To do this, we can pass the loc method, a list of tuples which describe the indices we wish to query. Since we have a MultiIndex of two values, the state and the county, we need to provide two values as each element of our filtering list." ] }, { "cell_type": "code", "execution_count": 176, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th></th>\n", " <th>BIRTHS2010</th>\n", " <th>BIRTHS2011</th>\n", " <th>BIRTHS2012</th>\n", " <th>BIRTHS2013</th>\n", " <th>BIRTHS2014</th>\n", " <th>BIRTHS2015</th>\n", " <th>POPESTIMATE2010</th>\n", " <th>POPESTIMATE2011</th>\n", " <th>POPESTIMATE2012</th>\n", " <th>POPESTIMATE2013</th>\n", " <th>POPESTIMATE2014</th>\n", " <th>POPESTIMATE2015</th>\n", " </tr>\n", " <tr>\n", " <th>STNAME</th>\n", " <th>CTYNAME</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th rowspan=\"2\" valign=\"top\">Michigan</th>\n", " <th>Washtenaw County</th>\n", " <td>977</td>\n", " <td>3826</td>\n", " <td>3780</td>\n", " <td>3662</td>\n", " <td>3683</td>\n", " <td>3709</td>\n", " <td>345563</td>\n", " <td>349048</td>\n", " <td>351213</td>\n", " <td>354289</td>\n", " <td>357029</td>\n", " <td>358880</td>\n", " </tr>\n", " <tr>\n", " <th>Wayne County</th>\n", " <td>5918</td>\n", " <td>23819</td>\n", " <td>23270</td>\n", " <td>23377</td>\n", " <td>23607</td>\n", " <td>23586</td>\n", " <td>1815199</td>\n", " <td>1801273</td>\n", " <td>1792514</td>\n", " <td>1775713</td>\n", " <td>1766008</td>\n", " <td>1759335</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " BIRTHS2010 BIRTHS2011 BIRTHS2012 BIRTHS2013 \\\n", "STNAME CTYNAME \n", "Michigan Washtenaw County 977 3826 3780 3662 \n", " Wayne County 5918 23819 23270 23377 \n", "\n", " BIRTHS2014 BIRTHS2015 POPESTIMATE2010 \\\n", "STNAME CTYNAME \n", "Michigan Washtenaw County 3683 3709 345563 \n", " Wayne County 23607 23586 1815199 \n", "\n", " POPESTIMATE2011 POPESTIMATE2012 POPESTIMATE2013 \\\n", "STNAME CTYNAME \n", "Michigan Washtenaw County 349048 351213 354289 \n", " Wayne County 1801273 1792514 1775713 \n", "\n", " POPESTIMATE2014 POPESTIMATE2015 \n", "STNAME CTYNAME \n", "Michigan Washtenaw County 357029 358880 \n", " Wayne County 1766008 1759335 " ] }, "execution_count": 176, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.loc[[('Michigan','Washtenaw County'),('Michigan','Wayne County')]]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Okay so that's how hierarchical indices work in a nutshell. They're a special part of the pandas library which I think can make management and reasoning about data easier. Of course hierarchical labeling isn't just for rows. For example, you can transpose this matrix and now have hierarchical column labels. And projecting a single column which has these labels works exactly the way you would expect it to." ] } ], "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

TheImmigrant/Python

Credit card validator.ipynb

1

3593

{ "cells": [ { "cell_type": "code", "execution_count": 186, "metadata": {}, "outputs": [], "source": [ "def credit():\n", " print \"Please enter Visa or Amex credit card: \"\n", " card=raw_input()\n", " card_rev=card[::-1]\n", " total = 0\n", " \n", " for i in card_rev[1::2]:\n", " #print \"Printing i %s\" %i\n", " x= int(i)*2\n", " if len(str(x)) == 2:\n", " for a in str(x):\n", " #print \"Printing a %s\" %a\n", " total += int(a)\n", " #print \"Printing subtotal %s\" %total\n", " else:\n", " total +=int(x)\n", " #print \"Printing total %s\" %total\n", " #print total\n", " \n", " for i in card_rev[::2]:\n", " total += int(i)\n", " #print total\n", " \n", " \n", " #print total\n", " \n", " if ((len(card) == 16 or len(card) == 13 ) and int(card[0]) == 4 and (total % 10 == 0 )) or \\\n", " (len(card) == 15 and (int(card[:2]) == 34 or int(card[:2]) == 37) and (total % 10 == 0 )):\n", " \n", " print \"This is a valid Credit Card\"\n", " \n", " else:\n", " print \"Invalid credit card\"\n", " " ] }, { "cell_type": "code", "execution_count": 187, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Please enter Visa or Amex credit card: \n", "4111111111111111\n", "This is a valid Credit Card\n" ] } ], "source": [ "credit()" ] }, { "cell_type": "code", "execution_count": 126, "metadata": {}, "outputs": [], "source": [ "test='4111111111111111'\n" ] }, { "cell_type": "code", "execution_count": 139, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'0241687'" ] }, "execution_count": 139, "metadata": {}, "output_type": "execute_result" } ], "source": [ "test[::-1][1::2]" ] }, { "cell_type": "code", "execution_count": 183, "metadata": { "collapsed": true }, "outputs": [], "source": [ "test_2='371449635398431'" ] }, { "cell_type": "code", "execution_count": 162, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'40555093'" ] }, "execution_count": 162, "metadata": {}, "output_type": "execute_result" } ], "source": [ "test_2[::-1][::2]" ] }, { "cell_type": "code", "execution_count": 167, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'0241687'" ] }, "execution_count": 167, "metadata": {}, "output_type": "execute_result" } ], "source": [ "test_2[::-1][1::2]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.13" } }, "nbformat": 4, "nbformat_minor": 2 }

mit

hsgui/interest-only

deeplearning/jupyter-notebook/toy_example_LSTM.ipynb

1

10740

{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "'''\n", "http://iamtrask.github.io/2015/11/15/anyone-can-code-lstm/\n", "'''" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import copy, numpy as np" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "np.random.seed(0)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def sigmoid(x):\n", " output = 1 / (1 + np.exp(-x))\n", " return output\n", "\n", "def sigmoid_output_to_derivative(output):\n", " return output * (1 - output)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# training dataset generation\n", "int2binary = {}\n", "binary_dim = 8\n", "\n", "largest_number = pow(2, binary_dim)\n", "binary = np.unpackbits(np.array([range(largest_number)], dtype=np.uint8).T, axis = 1)\n", "for i in range(largest_number):\n", " int2binary[i] = binary[i]" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "256" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pow(2, binary_dim)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,\n", " 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25,\n", " 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38,\n", " 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51,\n", " 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64,\n", " 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77,\n", " 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90,\n", " 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103,\n", " 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116,\n", " 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129,\n", " 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142,\n", " 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155,\n", " 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168,\n", " 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181,\n", " 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194,\n", " 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207,\n", " 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220,\n", " 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233,\n", " 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246,\n", " 247, 248, 249, 250, 251, 252, 253, 254, 255]], dtype=uint8)" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.array([range(256)], dtype=np.uint8)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[0, 0, 0, ..., 0, 0, 0],\n", " [0, 0, 0, ..., 0, 0, 1],\n", " [0, 0, 0, ..., 0, 1, 0],\n", " ..., \n", " [1, 1, 1, ..., 1, 0, 1],\n", " [1, 1, 1, ..., 1, 1, 0],\n", " [1, 1, 1, ..., 1, 1, 1]], dtype=uint8)" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.unpackbits(np.array([range(256)], dtype=np.uint8).T, axis = 1)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# input variables\n", "alpha = 0.1\n", "input_dim = 2\n", "hidden_dim = 16\n", "output_dim = 1" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# initialize neural network weights\n", "syn_0 = 2 * np.random.random((input_dim, hidden_dim)) - 1\n", "syn_1 = 2 * np.random.random((hidden_dim, output_dim)) - 1\n", "syn_h = 2 * np.random.random((hidden_dim, hidden_dim)) - 1" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": true }, "outputs": [], "source": [ "syn_0_update = np.zeros_like(syn_0)\n", "syn_1_update = np.zeros_like(syn_1)\n", "syn_h_update = np.zeros_like(syn_h)" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Error: 1.04679338382\n", "Pred: [0 0 0 0 0 0 0 1]\n", "True: [0 1 1 1 1 0 0 1]\n", "43 + 78 = 1\n", "----------\n", "Error: 1.11273823275\n", "Pred: [0 1 1 1 1 1 0 1]\n", "True: [1 0 0 1 0 1 0 0]\n", "22 + 126 = 125\n", "----------\n", "Error: 0.847013893305\n", "Pred: [0 1 0 1 0 0 0 0]\n", "True: [0 1 0 1 1 0 0 1]\n", "41 + 48 = 80\n", "----------\n", "Error: 0.230761729471\n", "Pred: [1 0 0 0 0 0 1 0]\n", "True: [1 0 0 0 0 0 1 0]\n", "95 + 35 = 130\n", "----------\n", "Error: 0.0128427033786\n", "Pred: [0 1 1 1 0 1 1 0]\n", "True: [0 1 1 1 0 1 1 0]\n", "9 + 109 = 118\n", "----------\n", "Error: 0.0037268223704\n", "Pred: [0 1 0 1 0 1 1 0]\n", "True: [0 1 0 1 0 1 1 0]\n", "81 + 5 = 86\n", "----------\n" ] } ], "source": [ "# training logic:\n", "for j in range(10001):\n", " # generate a simple addition problem (a + b = c)\n", " a_int = np.random.randint(largest_number / 2)\n", " a = int2binary[a_int] # binary encoding\n", " \n", " b_int = np.random.randint(largest_number / 2)\n", " b = int2binary[b_int]\n", " \n", " c_int = a_int + b_int\n", " c = int2binary[c_int]\n", " \n", " # where we'll store our best guess (binary encoded)\n", " d = np.zeros_like(c)\n", " \n", " overallError = 0\n", " \n", " layer_2_deltas = list()\n", " layer_1_values = list()\n", " layer_1_values.append(np.zeros(hidden_dim))\n", " \n", " # moving along the positions in the binary encoding\n", " for position in range(binary_dim):\n", " # generate input and output\n", " X = np.array([[a[binary_dim - position - 1], b[binary_dim - position - 1]]])\n", " y = np.array([[c[binary_dim - position - 1]]]).T\n", " \n", " # hidden layer (input + prev_hidden)\n", " layer_1 = sigmoid(np.dot(X, syn_0) + np.dot(layer_1_values[-1], syn_h))\n", " \n", " #output layer\n", " layer_2 = sigmoid(np.dot(layer_1, syn_1))\n", " \n", " # error\n", " cost = np.sum(np.square(y - layer_2))/2\n", " layer_2_error = layer_2 - y\n", " layer_2_delta = layer_2_error * sigmoid_output_to_derivative(layer_2)\n", " layer_2_deltas.append(layer_2_delta)\n", " overallError += cost\n", " \n", " # decode estimate so we could print it out\n", " d[binary_dim - position - 1] = np.round(layer_2[0][0])\n", " \n", " #store hidden layer so we could use it in the ndex timestamp\n", " layer_1_values.append(copy.deepcopy(layer_1))\n", " \n", " future_layer_1_delta = np.zeros(hidden_dim)\n", " \n", " for position in range(binary_dim):\n", " \n", " X = np.array([[a[position], b[position]]])\n", " layer_1 = layer_1_values[-1 - position]\n", " pre_layer_1 = layer_1_values[-1 -position -1]\n", " \n", " # error at output layer\n", " layer_2_delta = layer_2_deltas[-1 - position]\n", " \n", " # error at hidden layer\n", " # 1. hidden layer (layer_1) passed to output layer\n", " # 2. hidden layer (layer_1) also passed to hidden layer itself in next timestamp\n", " layer_1_delta = (layer_2_delta.dot(syn_1.T) + future_layer_1_delta.dot(syn_h.T)) * sigmoid_output_to_derivative(layer_1)\n", " \n", " syn_1_update += np.atleast_2d(layer_1).T.dot(layer_2_delta)\n", " \n", " syn_h_update += np.atleast_2d(pre_layer_1).T.dot(layer_1_delta)\n", " syn_0_update += X.T.dot(layer_1_delta)\n", " \n", " future_layer_1_delta = layer_1_delta\n", " \n", " syn_0 -= syn_0_update * alpha\n", " syn_1 -= syn_1_update * alpha\n", " syn_h -= syn_h_update * alpha\n", " \n", " syn_0_update *= 0\n", " syn_1_update *= 0\n", " syn_h_update *= 0\n", " \n", " # print out progress\n", " if (j % 2000 == 0):\n", " print(\"Error: \", overallError)\n", " print(\"Pred: \", d)\n", " print(\"True: \", c)\n", " out = 0\n", " for index, x in enumerate(reversed(d)):\n", " out += x * pow(2, index)\n", " print(a_int, \" + \", b_int, \" = \", out)\n", " print(\"----------\")" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 2 }

gpl-2.0

hpparvi/PyTransit

notebooks/contamination/example_1a.ipynb

1

839950

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Contamination example 1a\n", "## No contamination and uninformative priors on orbital parameters\n", "\n", "*[Hannu Parviainen](mailto:[email protected])<br>\n", "Instituto de Astrofísica de Canarias*\n", "\n", "Last modified: 15.7.2019\n", "\n", "Here we use the `pytransit.contamination` module to estimate the *true planet to star radius ratio* robustly using multicolour photometry in the presence of possible flux contamination from an unresolved source in the photometry aperture, as detailed in Parviainen et al. 2019 (submitted). This can be used in the validation of transiting planet candidates, where, e.g., blended eclipsing binaries are a significant source of false positives.\n", "\n", "**Light curves:** We don't use real data here, but create simulated multicolour photometry lightcurves using the `MockLC` class found in `src.mocklc`. The code is the same that was used for the simulations in Parviainen et al. (2019).\n", "\n", "**Log posterior function:** The log posterior function is defined by `MockLPF` class found in `src.blendlpf.MockLPF`. The class inherits `pytransit.lpf.PhysContLPF` and overrides the `_init_instrument` method to define the instrument and the contamination model (amongst other things to make running a variety of simulations smooth).\n", "\n", "**Parametrisation:** As discussed in the paper, the contamination is parametrised by the *apparent area ratio* ($k_\\mathrm{True}^2$), *true area ratio* ($k_\\mathrm{App}^2$), and the effective temperatures of the host and contaminant stars. The *apparent area ratio* defines how deep the transit is in a single single passband and can be wavelength dependent (if the host and contaminant are of different spectral type), while the *true area ratio* stands for the unblended true geometric planet-star area ratio.\n", "\n", "The *true radius ratio* ($k_\\mathrm{True}$) is the main quantity of interest in transiting planet candidate validation), since it together with a stellar radius estimate gives the true absolute planetary radius. " ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Populating the interactive namespace from numpy and matplotlib\n" ] } ], "source": [ "%pylab inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "import sys\n", "from corner import corner\n", "sys.path.append('.')" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "from src.mocklc import MockLC, SimulationSetup\n", "from src.blendlpf import MockLPF\n", "import src.plotting as pl " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Create a mock light curve" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 936x288 with 3 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "lc = MockLC(SimulationSetup('M', 0.1, 0.0, 0.0, 'short_transit', cteff=5500))\n", "lc.create(wnsigma=[0.001, 0.001, 0.001, 0.001], rnsigma=0.00001, rntscale=0.5, nights=1);\n", "lc.plot();" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Initialize the log posterior function" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "lpf = MockLPF('Example_1', lc)" ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " 0 |G| tc [-inf .. inf]\t 1 |G| pr [0.00 .. inf]\n", " 2 |G| rho [0.00 .. inf]\t 3 |G| b [0.00 .. 1.00]\n", " 4 |P| k2_app [0.00 .. 0.06]\t 5 |G| k2_true [0.00 .. inf]\n", " 6 |G| teff_h [2500.00 .. 12000.00]\t 7 |G| teff_c [2500.00 .. 12000.00]\n", " 8 |P| q1_0 [0.00 .. 1.00]\t 9 |P| q2_0 [0.00 .. 1.00]\n", " 10 |P| q1_1 [0.00 .. 1.00]\t 11 |P| q2_1 [0.00 .. 1.00]\n", " 12 |P| q1_2 [0.00 .. 1.00]\t 13 |P| q2_2 [0.00 .. 1.00]\n", " 14 |P| q1_3 [0.00 .. 1.00]\t 15 |P| q2_3 [0.00 .. 1.00]\n", " 16 |L| loge_0 [-4.00 .. 0.00]\t 17 |L| loge_1 [-4.00 .. 0.00]\n", " 18 |L| loge_2 [-4.00 .. 0.00]\t 19 |L| loge_3 [-4.00 .. 0.00]\n" ] } ], "source": [ "lpf.print_parameters(columns=2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Optimize" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "60df54dfeaab4177a0c40d28f58ec419", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=0, description='Global optimisation', max=1000, style=ProgressStyle(descripti…" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\r" ] }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 936x144 with 5 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "lpf.optimize_global(1000)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 936x360 with 4 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "lpf.plot_light_curves()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Estimate the posterior\n", "\n", "The contamination parameter space is a bit difficult to sample (especially if the signal to noise ratio is low), so the sampling should be continued at least for 10000 iterations." ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "5f801b9af5334693b49bacb1eb13fa73", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=0, description='MCMC sampling', max=2, style=ProgressStyle(description_width=…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=0, description='Run 1/2', max=5000, style=ProgressStyle(description_width='in…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=0, description='Run 2/2', max=5000, style=ProgressStyle(description_width='in…" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "lpf.sample_mcmc(5000, reset=True, repeats=2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Analysis\n", "\n", "We plot the main results below. It is clear that a single good-quality four-colour light curve still allows for contamination from a source of similar spectral type as the host star. However, in this example, the maximum allowed level of contamination is not sufficient to take the transiting object out of the planetary regime.\n", "\n", "Also, the joint posterior plots clearly show that any significant contamination must come from a source of a similar spectral type as the host. Combining this information with prior knowledge about the probability of having such a system without colour variations can be used in probabilistic planet candidate validation.\n", "\n", "### Plot the basic joint posterior" ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [], "source": [ "df = lpf.posterior_samples()" ] }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 936x234 with 10 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "pl.joint_radius_ratio_plot(df, fw=13, clim=(0.099, 0.12), htelim=(3570, 3630), ctelim=(2400,3800), blim=(0, 0.5), rlim=(3.8, 5.2));" ] }, { "cell_type": "code", "execution_count": 45, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 936x234 with 10 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "pl.joint_contamination_plot(df, fw=13, clim=(0, 0.4), htelim=(3570, 3630), ctelim=(2400,3800), blim=(0, 0.5), rlim=(3.8, 5.2));" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Plot the apparent and true radius ratio posteriors" ] }, { "cell_type": "code", "execution_count": 46, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 504x360 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "pl.marginal_radius_ratio_plot(df, bins=60, klim=(0.097, 0.12), figsize=(7,5));" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Make a corner plot to have a good overview to the posterior space" ] }, { "cell_type": "code", "execution_count": 48, "metadata": { "scrolled": false }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 1000.8x1000.8 with 36 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "corner(df.iloc[:,2:-3]);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "<center>&copy; 2019 Hannu Parviainen</center>" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.3" } }, "nbformat": 4, "nbformat_minor": 1 }

gpl-2.0

kastnerkyle/pylearn2

pylearn2/scripts/tutorials/jobman_integration.ipynb

5

28320

{ "metadata": { "name": "jobman_integration" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Random hyperparameter search using Jobman" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Prerequisites\n", "\n", "For this tutorial we assume the reader is familiar with Jobman and its `jobdispatch` helper script. For more information, see the [jobman documentation](http://deeplearning.net/software/jobman/).\n", "\n", "## Problem overview\n", "\n", "Suppose you have a yaml file describing an experiment for which you'd like to do hyperparameter optimization by random search:\n", "\n", "```\n", "!obj:pylearn2.train.Train {\n", " dataset: &train !obj:pylearn2.datasets.mnist.MNIST {\n", " which_set: 'train',\n", " one_hot: 1,\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.sgd.SGD {\n", " batch_size: 100,\n", " learning_rate: 1e-3,\n", " learning_rule: !obj:pylearn2.training_algorithms.learning_rule.Momentum {\n", " init_momentum: 0.5,\n", " },\n", " monitoring_batches: 10,\n", " monitoring_dataset : *train,\n", " termination_criterion: !obj:pylearn2.termination_criteria.EpochCounter {\n", " max_epochs: 1\n", " },\n", " },\n", " save_path: \"mlp.pkl\",\n", " save_freq : 5\n", "}\n", "```\n", "\n", "Here's how you can do it using Pylearn2 and Jobman:\n", "\n", "* Consider your yaml file as a string and adapt it by replacing hyperparameter values with statements of the form `%(hyperparameter_name)x`, just like you'd do for string substitution\n", "* Write an extraction method which takes a `Train` object as input and returns results extracted from it as output\n", "* Write a Jobman-compatible experiment method which will do string substitution on the yaml string using a dictionary of your hyperparameters, instantiate a `Train` object, train it and extract results by calling the extraction \n", " method on the `Train` object\n", "* Write a separate Jobman-compatible configuration file containing your yaml file in a string representation, your hyperparameters and the name of a method which will be used to extract results\n", "* Call the `jobman` executable with the experiment method and the configuration file\n", "\n", "Let's now break it down a little." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Adapting an existing yaml file\n", "\n", "Very little has to be done: just replace all desired hyperparameter values by a string substitution statement.\n", "\n", "For example, if you want to optimize the learning rate and the momentum coefficient, here's how your yaml file would look like:\n", "\n", "```\n", "!obj:pylearn2.train.Train {\n", " dataset: &train !obj:pylearn2.datasets.mnist.MNIST {\n", " which_set: 'train',\n", " one_hot: 1,\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.sgd.SGD {\n", " batch_size: 100,\n", " learning_rate: %(learning_rate)f,\n", " learning_rule: !obj:pylearn2.training_algorithms.learning_rule.Momentum {\n", " init_momentum: %(init_momentum)f,\n", " },\n", " monitoring_batches: 10,\n", " monitoring_dataset : *train,\n", " termination_criterion: !obj:pylearn2.termination_criteria.EpochCounter {\n", " max_epochs: 1\n", " },\n", " },\n", " save_path: \"mlp.pkl\",\n", " save_freq : 5\n", "}\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Writing the experiment method\n", "\n", "Luckily for you, there's already an experiment method built in Pylearn2: `pylearn2.scripts.jobman.experiment.train_experiment`.\n", "\n", "Like all methods compatible with Jobman, it expects two arguments (`state` and `channel`) and returns `channel.COMPLETE` when done. Furthermore, it expects `state` to contain at least:\n", "\n", "* a `yaml_template` key pointing to the (not yet complete) yaml string describing the experiment, \n", "* a `hyper_parameters` key pointing to a `DD` object containing all variable hyperparameters for the experiment, and\n", "* an `extract_results` key pointing to a string\n", "\n", "Here's the method's implementation:\n", "\n", " def train_experiment(state, channel):\n", " \"\"\"\n", " Train a model specified in state, and extract required results.\n", " \n", " This function builds a YAML string from ``state.yaml_template``, taking\n", " the values of hyper-parameters from ``state.hyper_parameters``, creates\n", " the corresponding object and trains it (like train.py), then run the\n", " function in ``state.extract_results`` on it, and store the returned values\n", " into ``state.results``.\n", " \n", " To know how to use this function, you can check the example in tester.py\n", " (in the same directory).\n", " \"\"\"\n", " yaml_template = state.yaml_template\n", " \n", " # Convert nested DD into nested ydict.\n", " hyper_parameters = expand(flatten(state.hyper_parameters), dict_type=ydict)\n", " \n", " # This will be the complete yaml string that should be executed\n", " final_yaml_str = yaml_template % hyper_parameters\n", " \n", " # Instantiate an object from YAML string\n", " train_obj = pylearn2.config.yaml_parse.load(final_yaml_str)\n", " \n", " try:\n", " iter(train_obj)\n", " iterable = True\n", " except TypeError:\n", " iterable = False\n", " if iterable:\n", " raise NotImplementedError(\n", " ('Current implementation does not support running multiple '\n", " 'models in one yaml string. Please change the yaml template '\n", " 'and parameters to contain only one single model.'))\n", " else:\n", " # print \"Executing the model.\"\n", " train_obj.main_loop()\n", " # This line will call a function defined by the user and pass train_obj\n", " # to it.\n", " state.results = jobman.tools.resolve(state.extract_results)(train_obj)\n", " return channel.COMPLETE\n", "\n", "It simply builds a dictionary out of `state.hyper_parameters` does string substitution on `state.yaml_template` with it.\n", "\n", "It then instantiates the `Train` object as described in the yaml string and calls its `main_loop` method.\n", "\n", "Finally, when the method returns, it calls the method referenced in the `state.extract_results` string by passing it the `Train` object as argument. This method is responsible to extract any relevant results from the `Train` object and returning them, either as is or in a `DD` object. The return value is stored in `state.results`.\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Writing the extraction method\n", "\n", "Your extraction method should accept a `Train` object instance and return either a single value (`float`, `int`, `str`, etc.) or a `DD` object containing your values.\n", "\n", "For the purpose of this tutorial, let's write a simple method which extracts the misclassification rate and the NLL from the model's monitor:\n", "\n", " def results_extractor(train_obj):\n", " channels = train_obj.model.monitor.channels\n", " train_y_misclass = channels['y_misclass'].val_record[-1]\n", " train_y_nll = channels['y_nll'].val_record[-1]\n", " \n", " return DD(train_y_misclass=train_y_misclass,\n", " train_y_nll=train_y_nll)\n", "\n", "Here we extract misclassification rate and NLL values at the last training epoch from their respective channels of the model's monitor and return a `DD` object containing those values." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Writing the configuration file\n", "\n", "Your configuration file should contain\n", "\n", "* `yaml_template`: a yaml string representing your experiment\n", "* `hyper_parameters.[name]`: the value of the `[name]` hyperparameter. You must have at least one such item, but you can have as many as you want.\n", "* `extract_results`: a string of the `module.method` form representing the result extraction method which is to be used\n", "\n", "Here's how a configuration file could look for our experiment:\n", "\n", " yaml_template:=@__builtin__.open('mlp.yaml').read()\n", " \n", " hyper_parameters.learning_rate:[email protected]_uniform(1e-5, 1e-1)\n", " hyper_parameters.init_momentum:[email protected]_uniform(0.5, 1.0)\n", "\n", " extract_results = \"sheldon.code.pylearn2.scripts.jobman.extraction.trivial_extractor\"\n", "\n", "Notice how we're using the key:=@method statement. This serves two purposes:\n", "\n", "1. We don't have to copy the yaml file to the configuration file as a long, hard to edit string.\n", "2. We don't have to hard-code hyperparameter values, which means every time `jobman` is called with this configuration file, it'll get different hyperparameters.\n", "\n", "For reference, here's `utils.log_uniform`'s implementation:\n", "\n", " def log_uniform(low, high):\n", " \"\"\"\n", " Generates a number that's uniformly distributed in the log-space between\n", " `low` and `high`\n", " \n", " Parameters\n", " ----------\n", " low : float\n", " Lower bound of the randomly generated number\n", " high : float\n", " Upper bound of the randomly generated number\n", " \n", " Returns\n", " -------\n", " rval : float\n", " Random number uniformly distributed in the log-space specified by `low`\n", " and `high`\n", " \"\"\"\n", " log_low = numpy.log(low)\n", " log_high = numpy.log(high)\n", " \n", " log_rval = numpy.random.uniform(log_low, log_high)\n", " rval = float(numpy.exp(log_rval))\n", " \n", " return rval\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Running the whole thing\n", "\n", "Here's how you would call `jobman` to train your model:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "!jobman cmdline -g numbered pylearn2.scripts.jobman.experiment.train_experiment workid_prefix=jobman_demo jobman_demo/mlp.conf" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "/opt/lisa/os/epd-7.1.2/lib/python2.7/site-packages/scikits/__init__.py:1: UserWarning: Module jobman was already imported from /data/lisa/exp/dumouliv/jobman/__init__.pyc, but /opt/lisa/os/lib64/python2.7/site-packages is being added to sys.path\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "The working directory is: /data/lisa/exp/dumouliv/Pylearn2/pylearn2/scripts/tutorials/jobman1\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "/data/lisa/exp/dumouliv/Pylearn2/pylearn2/models/mlp.py:44: UserWarning: MLP changing the recursion limit.\r\n", " warnings.warn(\"MLP changing the recursion limit.\")\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "/data/lisa/exp/dumouliv/Pylearn2/pylearn2/space/__init__.py:272: UserWarning: It looks like the <class 'pylearn2.space.CompositeSpace'>subclass of Space does not call the superclass __init__ method. Currently this is a warning. It will become an error on or after 2014-06-17.\r\n", " \"subclass of Space does not call the superclass __init__ \"\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Parameter and initial learning rate summary:\r\n", "\th0_W: 0.000205\r\n", "\th0_b: 0.000205\r\n", "\tsoftmax_b: 0.000205\r\n", "\tsoftmax_W: 0.000205\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Compiling sgd_update...\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Compiling sgd_update done. Time elapsed: 3.680988 seconds\r\n", "compiling begin_record_entry...\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "compiling begin_record_entry done. Time elapsed: 0.094591 seconds\r\n", "Monitored channels: \r\n", "\th0_col_norms_max\r\n", "\th0_col_norms_mean\r\n", "\th0_col_norms_min\r\n", "\th0_max_x_max_u\r\n", "\th0_max_x_mean_u\r\n", "\th0_max_x_min_u\r\n", "\th0_mean_x_max_u\r\n", "\th0_mean_x_mean_u\r\n", "\th0_mean_x_min_u\r\n", "\th0_min_x_max_u\r\n", "\th0_min_x_mean_u\r\n", "\th0_min_x_min_u\r\n", "\th0_range_x_max_u\r\n", "\th0_range_x_mean_u\r\n", "\th0_range_x_min_u\r\n", "\th0_row_norms_max\r\n", "\th0_row_norms_mean\r\n", "\th0_row_norms_min\r\n", "\tlearning_rate\r\n", "\tmomentum\r\n", "\tmonitor_seconds_per_epoch\r\n", "\tobjective\r\n", "\ty_col_norms_max\r\n", "\ty_col_norms_mean\r\n", "\ty_col_norms_min\r\n", "\ty_max_max_class\r\n", "\ty_mean_max_class\r\n", "\ty_min_max_class\r\n", "\ty_misclass\r\n", "\ty_nll\r\n", "\ty_row_norms_max\r\n", "\ty_row_norms_mean\r\n", "\ty_row_norms_min\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Compiling accum...\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "graph size: 116\r\n", "Compiling accum done. Time elapsed: 1.706080 seconds\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Monitoring step:\r\n", "\tEpochs seen: 0\r\n", "\tBatches seen: 0\r\n", "\tExamples seen: 0\r\n", "\th0_col_norms_max: 6.23503405999\r\n", "\th0_col_norms_mean: 3.82355643971\r\n", "\th0_col_norms_min: 2.06193996111\r\n", "\th0_max_x_max_u: 0.999166448331\r\n", "\th0_max_x_mean_u: 0.835546521642\r\n", "\th0_max_x_min_u: 0.484522687851\r\n", "\th0_mean_x_max_u: 0.898498338615\r\n", "\th0_mean_x_mean_u: 0.477264282788\r\n", "\th0_mean_x_min_u: 0.14077357946\r\n", "\th0_min_x_max_u: 0.502200790533\r\n", "\th0_min_x_mean_u: 0.13427006672\r\n", "\th0_min_x_min_u: 0.000345345510862\r\n", "\th0_range_x_max_u: 0.982607199247\r\n", "\th0_range_x_mean_u: 0.701276454921\r\n", "\th0_range_x_min_u: 0.212314451878\r\n", "\th0_row_norms_max: 5.89326124667\r\n", "\th0_row_norms_mean: 2.98549156744\r\n", "\th0_row_norms_min: 0.0\r\n", "\tlearning_rate: 0.000205\r\n", "\tmomentum: 0.583961\r\n", "\tmonitor_seconds_per_epoch: 0.0\r\n", "\tobjective: 2.30258509299\r\n", "\ty_col_norms_max: 0.0\r\n", "\ty_col_norms_mean: 0.0\r\n", "\ty_col_norms_min: 0.0\r\n", "\ty_max_max_class: 0.1\r\n", "\ty_mean_max_class: 0.1\r\n", "\ty_min_max_class: 0.1\r\n", "\ty_misclass: 0.903\r\n", "\ty_nll: 2.30258509299\r\n", "\ty_row_norms_max: 0.0\r\n", "\ty_row_norms_mean: 0.0\r\n", "\ty_row_norms_min: 0.0\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Time this epoch: 8.862564 seconds\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Monitoring step:\r\n", "\tEpochs seen: 1\r\n", "\tBatches seen: 500\r\n", "\tExamples seen: 50000\r\n", "\th0_col_norms_max: 6.23503528894\r\n", "\th0_col_norms_mean: 3.82355957684\r\n", "\th0_col_norms_min: 2.0619418242\r\n", "\th0_max_x_max_u: 0.999166295949\r\n", "\th0_max_x_mean_u: 0.835562596825\r\n", "\th0_max_x_min_u: 0.484561123332\r\n", "\th0_mean_x_max_u: 0.898487726921\r\n", "\th0_mean_x_mean_u: 0.47726432182\r\n", "\th0_mean_x_min_u: 0.140795730801\r\n", "\th0_min_x_max_u: 0.502187137372\r\n", "\th0_min_x_mean_u: 0.134253984725\r\n", "\th0_min_x_min_u: 0.000345513621585\r\n", "\th0_range_x_max_u: 0.982615216558\r\n", "\th0_range_x_mean_u: 0.701308612099\r\n", "\th0_range_x_min_u: 0.212336533604\r\n", "\th0_row_norms_max: 5.89326388489\r\n", "\th0_row_norms_mean: 2.98549391807\r\n", "\th0_row_norms_min: 9.32520084457e-07\r\n", "\tlearning_rate: 0.000205\r\n", "\tmomentum: 0.583961\r\n", "\tmonitor_seconds_per_epoch: 8.862564\r\n", "\tobjective: 2.21611916125\r\n", "\ty_col_norms_max: 0.0675190825355\r\n", "\ty_col_norms_mean: 0.0446384068231\r\n", "\ty_col_norms_min: 0.0272975089866\r\n", "\ty_max_max_class: 0.134260376897\r\n", "\ty_mean_max_class: 0.114756053537\r\n", "\ty_min_max_class: 0.104770463666\r\n", "\ty_misclass: 0.57\r\n", "\ty_nll: 2.21611916125\r\n", "\ty_row_norms_max: 0.0153922340481\r\n", "\ty_row_norms_mean: 0.0060778646612\r\n", "\ty_row_norms_min: 0.00163779261751\r\n", "Saving to mlp.pkl...\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Saving to mlp.pkl done. Time elapsed: 0.421483 seconds\r\n", "The experiment returned value is None\r\n" ] } ], "prompt_number": 2 }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Multiple runs using `jobdispatch`\n", "\n", "Launching 10 hyperoptimization jobs is as easy as" ] }, { "cell_type": "code", "collapsed": false, "input": [ "!jobdispatch --local --repeat_jobs=10 /opt/lisa/os/bin/jobman cmdline -g numbered pylearn2.scripts.jobman.experiment.train_experiment workdir_prefix=jobman_demo/ jobman_demo/mlp.conf" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "\r\n", "\r\n", "The jobs will be launched on the system: Local\r\n", "With options: ['repeat_jobs:10', \"tasks_filename:['nb0', 'compact']\", 'launch_cmd:Local']\r\n", "We generate the DBI object with 10 command\r\n", "Fri Jan 10 15:41:21 2014\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "[DBI] The Log file are under LOGS.NOBACKUP/jobman_cmdline_-g_numbered_pylearn2.scripts.jobman.experiment.train_experiment_workdir_prefix_jobman_demo__jobman_demo_mlp.conf_2014-01-10_15-41-21.548948\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "[DBI,1/10,Fri Jan 10 15:41:22 2014] /opt/lisa/os/bin/jobman cmdline -g numbered pylearn2.scripts.jobman.experiment.train_experiment workdir_prefix=jobman_demo/ jobman_demo/mlp.conf\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "[DBI,2/10,Fri Jan 10 15:41:43 2014] /opt/lisa/os/bin/jobman cmdline -g numbered pylearn2.scripts.jobman.experiment.train_experiment workdir_prefix=jobman_demo/ jobman_demo/mlp.conf\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "[DBI,3/10,Fri Jan 10 15:42:04 2014] /opt/lisa/os/bin/jobman cmdline -g numbered pylearn2.scripts.jobman.experiment.train_experiment workdir_prefix=jobman_demo/ jobman_demo/mlp.conf\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "[DBI,4/10,Fri Jan 10 15:42:25 2014] /opt/lisa/os/bin/jobman cmdline -g numbered pylearn2.scripts.jobman.experiment.train_experiment workdir_prefix=jobman_demo/ jobman_demo/mlp.conf\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "[DBI,5/10,Fri Jan 10 15:42:45 2014] /opt/lisa/os/bin/jobman cmdline -g numbered pylearn2.scripts.jobman.experiment.train_experiment workdir_prefix=jobman_demo/ jobman_demo/mlp.conf\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "[DBI,6/10,Fri Jan 10 15:43:06 2014] /opt/lisa/os/bin/jobman cmdline -g numbered pylearn2.scripts.jobman.experiment.train_experiment workdir_prefix=jobman_demo/ jobman_demo/mlp.conf\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "[DBI,7/10,Fri Jan 10 15:43:27 2014] /opt/lisa/os/bin/jobman cmdline -g numbered pylearn2.scripts.jobman.experiment.train_experiment workdir_prefix=jobman_demo/ jobman_demo/mlp.conf\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "[DBI,8/10,Fri Jan 10 15:43:48 2014] /opt/lisa/os/bin/jobman cmdline -g numbered pylearn2.scripts.jobman.experiment.train_experiment workdir_prefix=jobman_demo/ jobman_demo/mlp.conf\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "[DBI,9/10,Fri Jan 10 15:44:08 2014] /opt/lisa/os/bin/jobman cmdline -g numbered pylearn2.scripts.jobman.experiment.train_experiment workdir_prefix=jobman_demo/ jobman_demo/mlp.conf\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "[DBI,10/10,Fri Jan 10 15:44:29 2014] /opt/lisa/os/bin/jobman cmdline -g numbered pylearn2.scripts.jobman.experiment.train_experiment workdir_prefix=jobman_demo/ jobman_demo/mlp.conf\r\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "[DBI,Fri Jan 10 15:44:50 2014] left running: 0/10\r\n", "[DBI] 10 jobs. finished: 10, running: 0, waiting: 0, init: 0\r\n", "[DBI] jobs unfinished (starting at 1): []\r\n", "[DBI] The Log file are under LOGS.NOBACKUP/jobman_cmdline_-g_numbered_pylearn2.scripts.jobman.experiment.train_experiment_workdir_prefix_jobman_demo__jobman_demo_mlp.conf_2014-01-10_15-41-21.548948\r\n" ] } ], "prompt_number": 3 }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Parsing results using `jobman.tools.find_conf_files`\n", "\n", "Once all relevant results have been extracted, you'll probably want to find the best set of hyperparameters.\n", "\n", "One way to do that is to call `jobman.tools.find_conf_files` on the directory containing your experiment directories; the method will return a list of `DD` objects for all experiment files present in that directory and in its subdirectories. You can then go through that list and quickly extract the best hyperparameters:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "import numpy\n", "from jobman import tools\n", "\n", "def parse_results(cwd):\n", " optimal_dd = None\n", " optimal_measure = numpy.inf\n", "\n", " for tup in tools.find_conf_files(cwd):\n", " dd = tup[1]\n", " if 'results.train_y_misclass' in dd:\n", " if dd['results.train_y_misclass'] < optimal_measure:\n", " optimal_measure = dd['results.train_y_misclass']\n", " optimal_dd = dd\n", " \n", " print \"Optimal \" + \"results.train_y_misclass\" + \": \" + str(optimal_measure)\n", " for key, value in optimal_dd.items():\n", " if 'hyper_parameters' in key:\n", " print key + \": \" + str(value)\n", "\n", "parse_results(\"jobman_demo/\")" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "WARNING: jobman_demo/__init__.pyc/current.conf file not found. Skipping it" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "WARNING: jobman_demo/mlp.conf/current.conf file not found. Skipping it\n", "WARNING: jobman_demo/__init__.py/current.conf file not found. Skipping it\n", "WARNING: jobman_demo/mlp.pkl/current.conf file not found. Skipping it\n", "WARNING: jobman_demo/utils.py/current.conf file not found. Skipping it\n", "WARNING: jobman_demo/utils.pyc/current.conf file not found. Skipping it\n", "WARNING: jobman_demo/mlp.yaml/current.conf file not found. Skipping it\n", "Optimal results.train_y_misclass: 0.217\n", "hyper_parameters.learning_rate: 0.00191878940445\n", "hyper_parameters.init_momentum: 0.782112604517\n" ] } ], "prompt_number": 4 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }

bsd-3-clause

huajianmao/learning

coursera/deep-learning/2.deep-neural-network/week1/pa.3.Gradient Checking.ipynb

1

25840

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Gradient Checking\n", "\n", "Welcome to the final assignment for this week! In this assignment you will learn to implement and use gradient checking. \n", "\n", "You are part of a team working to make mobile payments available globally, and are asked to build a deep learning model to detect fraud--whenever someone makes a payment, you want to see if the payment might be fraudulent, such as if the user's account has been taken over by a hacker. \n", "\n", "But backpropagation is quite challenging to implement, and sometimes has bugs. Because this is a mission-critical application, your company's CEO wants to be really certain that your implementation of backpropagation is correct. Your CEO says, \"Give me a proof that your backpropagation is actually working!\" To give this reassurance, you are going to use \"gradient checking\".\n", "\n", "Let's do it!" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "# Packages\n", "import numpy as np\n", "from testCases import *\n", "from gc_utils import sigmoid, relu, dictionary_to_vector, vector_to_dictionary, gradients_to_vector" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 1) How does gradient checking work?\n", "\n", "Backpropagation computes the gradients $\\frac{\\partial J}{\\partial \\theta}$, where $\\theta$ denotes the parameters of the model. $J$ is computed using forward propagation and your loss function.\n", "\n", "Because forward propagation is relatively easy to implement, you're confident you got that right, and so you're almost 100% sure that you're computing the cost $J$ correctly. Thus, you can use your code for computing $J$ to verify the code for computing $\\frac{\\partial J}{\\partial \\theta}$. \n", "\n", "Let's look back at the definition of a derivative (or gradient):\n", "$$ \\frac{\\partial J}{\\partial \\theta} = \\lim_{\\varepsilon \\to 0} \\frac{J(\\theta + \\varepsilon) - J(\\theta - \\varepsilon)}{2 \\varepsilon} \\tag{1}$$\n", "\n", "If you're not familiar with the \"$\\displaystyle \\lim_{\\varepsilon \\to 0}$\" notation, it's just a way of saying \"when $\\varepsilon$ is really really small.\"\n", "\n", "We know the following:\n", "\n", "- $\\frac{\\partial J}{\\partial \\theta}$ is what you want to make sure you're computing correctly. \n", "- You can compute $J(\\theta + \\varepsilon)$ and $J(\\theta - \\varepsilon)$ (in the case that $\\theta$ is a real number), since you're confident your implementation for $J$ is correct. \n", "\n", "Lets use equation (1) and a small value for $\\varepsilon$ to convince your CEO that your code for computing $\\frac{\\partial J}{\\partial \\theta}$ is correct!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 2) 1-dimensional gradient checking\n", "\n", "Consider a 1D linear function $J(\\theta) = \\theta x$. The model contains only a single real-valued parameter $\\theta$, and takes $x$ as input.\n", "\n", "You will implement code to compute $J(.)$ and its derivative $\\frac{\\partial J}{\\partial \\theta}$. You will then use gradient checking to make sure your derivative computation for $J$ is correct. \n", "\n", "<img src=\"images/1Dgrad_kiank.png\" style=\"width:600px;height:250px;\">\n", "<caption><center> <u> **Figure 1** </u>: **1D linear model**<br> </center></caption>\n", "\n", "The diagram above shows the key computation steps: First start with $x$, then evaluate the function $J(x)$ (\"forward propagation\"). Then compute the derivative $\\frac{\\partial J}{\\partial \\theta}$ (\"backward propagation\"). \n", "\n", "**Exercise**: implement \"forward propagation\" and \"backward propagation\" for this simple function. I.e., compute both $J(.)$ (\"forward propagation\") and its derivative with respect to $\\theta$ (\"backward propagation\"), in two separate functions. " ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "# GRADED FUNCTION: forward_propagation\n", "\n", "def forward_propagation(x, theta):\n", " \"\"\"\n", " Implement the linear forward propagation (compute J) presented in Figure 1 (J(theta) = theta * x)\n", " \n", " Arguments:\n", " x -- a real-valued input\n", " theta -- our parameter, a real number as well\n", " \n", " Returns:\n", " J -- the value of function J, computed using the formula J(theta) = theta * x\n", " \"\"\"\n", " \n", " ### START CODE HERE ### (approx. 1 line)\n", " J = x * theta\n", " ### END CODE HERE ###\n", " \n", " return J" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": "J = 8\n" } ], "source": [ "x, theta = 2, 4\n", "J = forward_propagation(x, theta)\n", "print (\"J = \" + str(J))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Expected Output**:\n", "\n", "<table style=>\n", " <tr>\n", " <td> ** J ** </td>\n", " <td> 8</td>\n", " </tr>\n", "</table>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Exercise**: Now, implement the backward propagation step (derivative computation) of Figure 1. That is, compute the derivative of $J(\\theta) = \\theta x$ with respect to $\\theta$. To save you from doing the calculus, you should get $dtheta = \\frac { \\partial J }{ \\partial \\theta} = x$." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "# GRADED FUNCTION: backward_propagation\n", "\n", "def backward_propagation(x, theta):\n", " \"\"\"\n", " Computes the derivative of J with respect to theta (see Figure 1).\n", " \n", " Arguments:\n", " x -- a real-valued input\n", " theta -- our parameter, a real number as well\n", " \n", " Returns:\n", " dtheta -- the gradient of the cost with respect to theta\n", " \"\"\"\n", " \n", " ### START CODE HERE ### (approx. 1 line)\n", " dtheta = x\n", " ### END CODE HERE ###\n", " \n", " return dtheta" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": "dtheta = 2\n" } ], "source": [ "x, theta = 2, 4\n", "dtheta = backward_propagation(x, theta)\n", "print (\"dtheta = \" + str(dtheta))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Expected Output**:\n", "\n", "<table>\n", " <tr>\n", " <td> ** dtheta ** </td>\n", " <td> 2 </td>\n", " </tr>\n", "</table>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Exercise**: To show that the `backward_propagation()` function is correctly computing the gradient $\\frac{\\partial J}{\\partial \\theta}$, let's implement gradient checking.\n", "\n", "**Instructions**:\n", "- First compute \"gradapprox\" using the formula above (1) and a small value of $\\varepsilon$. Here are the Steps to follow:\n", " 1. $\\theta^{+} = \\theta + \\varepsilon$\n", " 2. $\\theta^{-} = \\theta - \\varepsilon$\n", " 3. $J^{+} = J(\\theta^{+})$\n", " 4. $J^{-} = J(\\theta^{-})$\n", " 5. $gradapprox = \\frac{J^{+} - J^{-}}{2 \\varepsilon}$\n", "- Then compute the gradient using backward propagation, and store the result in a variable \"grad\"\n", "- Finally, compute the relative difference between \"gradapprox\" and the \"grad\" using the following formula:\n", "$$ difference = \\frac {\\mid\\mid grad - gradapprox \\mid\\mid_2}{\\mid\\mid grad \\mid\\mid_2 + \\mid\\mid gradapprox \\mid\\mid_2} \\tag{2}$$\n", "You will need 3 Steps to compute this formula:\n", " - 1'. compute the numerator using np.linalg.norm(...)\n", " - 2'. compute the denominator. You will need to call np.linalg.norm(...) twice.\n", " - 3'. divide them.\n", "- If this difference is small (say less than $10^{-7}$), you can be quite confident that you have computed your gradient correctly. Otherwise, there may be a mistake in the gradient computation. \n" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "# GRADED FUNCTION: gradient_check\n", "\n", "def gradient_check(x, theta, epsilon=1e-7):\n", " \"\"\"\n", " Implement the backward propagation presented in Figure 1.\n", " \n", " Arguments:\n", " x -- a real-valued input\n", " theta -- our parameter, a real number as well\n", " epsilon -- tiny shift to the input to compute approximated gradient with formula(1)\n", " \n", " Returns:\n", " difference -- difference (2) between the approximated gradient and the backward propagation gradient\n", " \"\"\"\n", " \n", " # Compute gradapprox using left side of formula (1). epsilon is small enough, you don't need to worry about the limit.\n", " ### START CODE HERE ### (approx. 5 lines)\n", " thetaplus = theta + epsilon\n", " thetaminus = theta - epsilon\n", " Jplus = forward_propagation(x, thetaplus)\n", " Jminus = forward_propagation(x, thetaminus)\n", " gradapprox = (Jplus - Jminus) / 2 / epsilon\n", " ### END CODE HERE ###\n", " \n", " # Check if gradapprox is close enough to the output of backward_propagation()\n", " ### START CODE HERE ### (approx. 1 line)\n", " grad = backward_propagation(x, theta)\n", " ### END CODE HERE ###\n", " \n", " ### START CODE HERE ### (approx. 1 line)\n", " difference = np.linalg.norm(grad - gradapprox) / (np.linalg.norm(grad) + np.linalg.norm(gradapprox))\n", " ### END CODE HERE ###\n", " \n", " if difference < 1e-7:\n", " print(\"The gradient is correct!\")\n", " else:\n", " print(\"The gradient is wrong!\")\n", " \n", " return difference" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": "The gradient is correct!\ndifference = 2.919335883291695e-10\n" } ], "source": [ "x, theta = 2, 4\n", "difference = gradient_check(x, theta)\n", "print(\"difference = \" + str(difference))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Expected Output**:\n", "The gradient is correct!\n", "<table>\n", " <tr>\n", " <td> ** difference ** </td>\n", " <td> 2.9193358103083e-10 </td>\n", " </tr>\n", "</table>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Congrats, the difference is smaller than the $10^{-7}$ threshold. So you can have high confidence that you've correctly computed the gradient in `backward_propagation()`. \n", "\n", "Now, in the more general case, your cost function $J$ has more than a single 1D input. When you are training a neural network, $\\theta$ actually consists of multiple matrices $W^{[l]}$ and biases $b^{[l]}$! It is important to know how to do a gradient check with higher-dimensional inputs. Let's do it!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 3) N-dimensional gradient checking" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "The following figure describes the forward and backward propagation of your fraud detection model.\n", "\n", "<img src=\"images/NDgrad_kiank.png\" style=\"width:600px;height:400px;\">\n", "<caption><center> <u> **Figure 2** </u>: **deep neural network**<br>*LINEAR -> RELU -> LINEAR -> RELU -> LINEAR -> SIGMOID*</center></caption>\n", "\n", "Let's look at your implementations for forward propagation and backward propagation. " ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "def forward_propagation_n(X, Y, parameters):\n", " \"\"\"\n", " Implements the forward propagation (and computes the cost) presented in Figure 3.\n", " \n", " Arguments:\n", " X -- training set for m examples\n", " Y -- labels for m examples \n", " parameters -- python dictionary containing your parameters \"W1\", \"b1\", \"W2\", \"b2\", \"W3\", \"b3\":\n", " W1 -- weight matrix of shape (5, 4)\n", " b1 -- bias vector of shape (5, 1)\n", " W2 -- weight matrix of shape (3, 5)\n", " b2 -- bias vector of shape (3, 1)\n", " W3 -- weight matrix of shape (1, 3)\n", " b3 -- bias vector of shape (1, 1)\n", " \n", " Returns:\n", " cost -- the cost function (logistic cost for one example)\n", " \"\"\"\n", " \n", " # retrieve parameters\n", " m = X.shape[1]\n", " W1 = parameters[\"W1\"]\n", " b1 = parameters[\"b1\"]\n", " W2 = parameters[\"W2\"]\n", " b2 = parameters[\"b2\"]\n", " W3 = parameters[\"W3\"]\n", " b3 = parameters[\"b3\"]\n", "\n", " # LINEAR -> RELU -> LINEAR -> RELU -> LINEAR -> SIGMOID\n", " Z1 = np.dot(W1, X) + b1\n", " A1 = relu(Z1)\n", " Z2 = np.dot(W2, A1) + b2\n", " A2 = relu(Z2)\n", " Z3 = np.dot(W3, A2) + b3\n", " A3 = sigmoid(Z3)\n", "\n", " # Cost\n", " logprobs = np.multiply(-np.log(A3), Y) + np.multiply(-np.log(1 - A3), 1 - Y)\n", " cost = 1. / m * np.sum(logprobs)\n", " \n", " cache = (Z1, A1, W1, b1, Z2, A2, W2, b2, Z3, A3, W3, b3)\n", " \n", " return cost, cache" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now, run backward propagation." ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [], "source": [ "def backward_propagation_n(X, Y, cache):\n", " \"\"\"\n", " Implement the backward propagation presented in figure 2.\n", " \n", " Arguments:\n", " X -- input datapoint, of shape (input size, 1)\n", " Y -- true \"label\"\n", " cache -- cache output from forward_propagation_n()\n", " \n", " Returns:\n", " gradients -- A dictionary with the gradients of the cost with respect to each parameter, activation and pre-activation variables.\n", " \"\"\"\n", " \n", " m = X.shape[1]\n", " (Z1, A1, W1, b1, Z2, A2, W2, b2, Z3, A3, W3, b3) = cache\n", " \n", " dZ3 = A3 - Y\n", " dW3 = 1. / m * np.dot(dZ3, A2.T)\n", " db3 = 1. / m * np.sum(dZ3, axis=1, keepdims=True)\n", " \n", " dA2 = np.dot(W3.T, dZ3)\n", " dZ2 = np.multiply(dA2, np.int64(A2 > 0))\n", " dW2 = 1. / m * np.dot(dZ2, A1.T) # Should not multiply by 2\n", " db2 = 1. / m * np.sum(dZ2, axis=1, keepdims=True)\n", " \n", " dA1 = np.dot(W2.T, dZ2)\n", " dZ1 = np.multiply(dA1, np.int64(A1 > 0))\n", " dW1 = 1. / m * np.dot(dZ1, X.T)\n", " db1 = 1. / m * np.sum(dZ1, axis=1, keepdims=True) # Should not multiply by 4\n", " \n", " gradients = {\"dZ3\": dZ3, \"dW3\": dW3, \"db3\": db3,\n", " \"dA2\": dA2, \"dZ2\": dZ2, \"dW2\": dW2, \"db2\": db2,\n", " \"dA1\": dA1, \"dZ1\": dZ1, \"dW1\": dW1, \"db1\": db1}\n", " \n", " return gradients" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "You obtained some results on the fraud detection test set but you are not 100% sure of your model. Nobody's perfect! Let's implement gradient checking to verify if your gradients are correct." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**How does gradient checking work?**.\n", "\n", "As in 1) and 2), you want to compare \"gradapprox\" to the gradient computed by backpropagation. The formula is still:\n", "\n", "$$ \\frac{\\partial J}{\\partial \\theta} = \\lim_{\\varepsilon \\to 0} \\frac{J(\\theta + \\varepsilon) - J(\\theta - \\varepsilon)}{2 \\varepsilon} \\tag{1}$$\n", "\n", "However, $\\theta$ is not a scalar anymore. It is a dictionary called \"parameters\". We implemented a function \"`dictionary_to_vector()`\" for you. It converts the \"parameters\" dictionary into a vector called \"values\", obtained by reshaping all parameters (W1, b1, W2, b2, W3, b3) into vectors and concatenating them.\n", "\n", "The inverse function is \"`vector_to_dictionary`\" which outputs back the \"parameters\" dictionary.\n", "\n", "<img src=\"images/dictionary_to_vector.png\" style=\"width:600px;height:400px;\">\n", "<caption><center> <u> **Figure 2** </u>: **dictionary_to_vector() and vector_to_dictionary()**<br> You will need these functions in gradient_check_n()</center></caption>\n", "\n", "We have also converted the \"gradients\" dictionary into a vector \"grad\" using gradients_to_vector(). You don't need to worry about that.\n", "\n", "**Exercise**: Implement gradient_check_n().\n", "\n", "**Instructions**: Here is pseudo-code that will help you implement the gradient check.\n", "\n", "For each i in num_parameters:\n", "- To compute `J_plus[i]`:\n", " 1. Set $\\theta^{+}$ to `np.copy(parameters_values)`\n", " 2. Set $\\theta^{+}_i$ to $\\theta^{+}_i + \\varepsilon$\n", " 3. Calculate $J^{+}_i$ using to `forward_propagation_n(x, y, vector_to_dictionary(`$\\theta^{+}$ `))`. \n", "- To compute `J_minus[i]`: do the same thing with $\\theta^{-}$\n", "- Compute $gradapprox[i] = \\frac{J^{+}_i - J^{-}_i}{2 \\varepsilon}$\n", "\n", "Thus, you get a vector gradapprox, where gradapprox[i] is an approximation of the gradient with respect to `parameter_values[i]`. You can now compare this gradapprox vector to the gradients vector from backpropagation. Just like for the 1D case (Steps 1', 2', 3'), compute: \n", "$$ difference = \\frac {\\| grad - gradapprox \\|_2}{\\| grad \\|_2 + \\| gradapprox \\|_2 } \\tag{3}$$" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [], "source": [ "# GRADED FUNCTION: gradient_check_n\n", "\n", "def gradient_check_n(parameters, gradients, X, Y, epsilon=1e-7):\n", " \"\"\"\n", " Checks if backward_propagation_n computes correctly the gradient of the cost output by forward_propagation_n\n", " \n", " Arguments:\n", " parameters -- python dictionary containing your parameters \"W1\", \"b1\", \"W2\", \"b2\", \"W3\", \"b3\":\n", " grad -- output of backward_propagation_n, contains gradients of the cost with respect to the parameters. \n", " x -- input datapoint, of shape (input size, 1)\n", " y -- true \"label\"\n", " epsilon -- tiny shift to the input to compute approximated gradient with formula(1)\n", " \n", " Returns:\n", " difference -- difference (2) between the approximated gradient and the backward propagation gradient\n", " \"\"\"\n", " \n", " # Set-up variables\n", " parameters_values, _ = dictionary_to_vector(parameters)\n", " grad = gradients_to_vector(gradients)\n", " num_parameters = parameters_values.shape[0]\n", " J_plus = np.zeros((num_parameters, 1))\n", " J_minus = np.zeros((num_parameters, 1))\n", " gradapprox = np.zeros((num_parameters, 1))\n", " \n", " # Compute gradapprox\n", " for i in range(num_parameters):\n", " \n", " # Compute J_plus[i]. Inputs: \"parameters_values, epsilon\". Output = \"J_plus[i]\".\n", " # \"_\" is used because the function you have to outputs two parameters but we only care about the first one\n", " ### START CODE HERE ### (approx. 3 lines)\n", " parametersplus = np.copy(parameters_values)\n", " parametersplus[i] += epsilon\n", " J_plus[i], _ = forward_propagation_n(X, Y, vector_to_dictionary(parametersplus))\n", " ### END CODE HERE ###\n", " \n", " # Compute J_minus[i]. Inputs: \"parameters_values, epsilon\". Output = \"J_minus[i]\".\n", " ### START CODE HERE ### (approx. 3 lines)\n", " parametersminus = np.copy(parameters_values)\n", " parametersminus[i] -= epsilon\n", " J_minus[i], _ = forward_propagation_n(X, Y, vector_to_dictionary(parametersminus))\n", " ### END CODE HERE ###\n", " \n", " # Compute gradapprox[i]\n", " ### START CODE HERE ### (approx. 1 line)\n", " gradapprox[i] = (J_plus[i] - J_minus[i]) / 2 / epsilon\n", " ### END CODE HERE ###\n", " \n", " # Compare gradapprox to backward propagation gradients by computing difference.\n", " ### START CODE HERE ### (approx. 1 line)\n", " difference = np.linalg.norm(grad - gradapprox) / (np.linalg.norm(grad) + np.linalg.norm(gradapprox))\n", " ### END CODE HERE ###\n", "\n", " if difference > 1e-7:\n", " print(\"\\033[93m\" + \"There is a mistake in the backward propagation! difference = \" + str(difference) + \"\\033[0m\")\n", " else:\n", " print(\"\\033[92m\" + \"Your backward propagation works perfectly fine! difference = \" + str(difference) + \"\\033[0m\")\n", " \n", " return difference" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": "\u001b[93mThere is a mistake in the backward propagation! difference = 1.1890913023330276e-07\u001b[0m\n" } ], "source": [ "X, Y, parameters = gradient_check_n_test_case()\n", "\n", "cost, cache = forward_propagation_n(X, Y, parameters)\n", "gradients = backward_propagation_n(X, Y, cache)\n", "difference = gradient_check_n(parameters, gradients, X, Y)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Expected output**:\n", "\n", "<table>\n", " <tr>\n", " <td> ** There is a mistake in the backward propagation!** </td>\n", " <td> difference = 0.285093156781 </td>\n", " </tr>\n", "</table>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It seems that there were errors in the `backward_propagation_n` code we gave you! Good that you've implemented the gradient check. Go back to `backward_propagation` and try to find/correct the errors *(Hint: check dW2 and db1)*. Rerun the gradient check when you think you've fixed it. Remember you'll need to re-execute the cell defining `backward_propagation_n()` if you modify the code. \n", "\n", "Can you get gradient check to declare your derivative computation correct? Even though this part of the assignment isn't graded, we strongly urge you to try to find the bug and re-run gradient check until you're convinced backprop is now correctly implemented. \n", "\n", "**Note** \n", "- Gradient Checking is slow! Approximating the gradient with $\\frac{\\partial J}{\\partial \\theta} \\approx \\frac{J(\\theta + \\varepsilon) - J(\\theta - \\varepsilon)}{2 \\varepsilon}$ is computationally costly. For this reason, we don't run gradient checking at every iteration during training. Just a few times to check if the gradient is correct. \n", "- Gradient Checking, at least as we've presented it, doesn't work with dropout. You would usually run the gradient check algorithm without dropout to make sure your backprop is correct, then add dropout. \n", "\n", "Congrats, you can be confident that your deep learning model for fraud detection is working correctly! You can even use this to convince your CEO. :) \n", "\n", "<font color='blue'>\n", "**What you should remember from this notebook**:\n", "- Gradient checking verifies closeness between the gradients from backpropagation and the numerical approximation of the gradient (computed using forward propagation).\n", "- Gradient checking is slow, so we don't run it in every iteration of training. You would usually run it only to make sure your code is correct, then turn it off and use backprop for the actual learning process. " ] } ], "metadata": { "coursera": { "course_slug": "deep-neural-network", "graded_item_id": "n6NBD", "launcher_item_id": "yfOsE" }, "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": 1 }

mit

computational-class/computational-communication-2016

code/0.common_questions.ipynb

1

3422

{ "cells": [ { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "#### 在anaconda 环境中运行jupyter notebook\n", "***\n", "***\n", "***\n", "# 问题及其解决方法\n", "***\n", "***\n", "***" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# 在terminal里成功安装第三方的包，结果发现在notebook里无法import\n", "> 这个问题多出现于mac用户，因为mac有一个系统自带的python，成功安装的第三方包都被安装到了系统自带的python里。因此需要确保我们使用的是conda自己的pip，即需要指定pip的路径名，比如我的pip路径名在：/Users/chengjun/anaconda/bin/pip,那么在terminal里输入：\n", "# /Users/chengjun/anaconda/bin/pip install package_name" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "slideshow": { "slide_type": "slide" } }, "source": [ "# 如何查看anaconda自带的包和已经安装的包？\n", "> 打开terminal，输入： conda list" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# windows用户安装graphlab-create出错：unistall tornado, permission denied： tornado/speedup.pdy, 解决方法：\n", "- 首先，卸载tornado：\n", "> conda remove tornado\n", "- 然后，重新运行：\n", "> pip install -U graphlab-create" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# 如何让graphlab在notebook中展示所有的结果（不另外打开新的窗口）\n", "> 运行以下代码" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false, "slideshow": { "slide_type": "slide" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "A newer version of GraphLab Create (v1.9) is available! Your current version is v1.8.5.\n", "\n", "You can use pip to upgrade the graphlab-create package. For more information see https://dato.com/products/create/upgrade.\n" ] } ], "source": [ "import graphlab as gl\n", "from IPython.display import display\n", "from IPython.display import Image\n", "\n", "gl.canvas.set_target('ipynb')" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# 如何卸载一个包\n", "> conda remove package_name" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "slideshow": { "slide_type": "slide" } }, "outputs": [], "source": [] } ], "metadata": { "celltoolbar": "Slideshow", "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.11" } }, "nbformat": 4, "nbformat_minor": 0 }

mit

AllenDowney/ProbablyOverthinkingIt

ess3.ipynb

1

702693

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Internet use and religion in Europe, part three\n", "-----------------------------------------\n", "\n", "This notebook presents explorations of the association between Internet use and religion in Europe, using data from the European Social Survey (http://www.europeansocialsurvey.org).\n", "\n", "Copyright 2015 Allen Downey\n", "\n", "MIT License: http://opensource.org/licenses/MIT" ] }, { "cell_type": "code", "execution_count": 100, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from __future__ import print_function, division\n", "\n", "import string\n", "import random\n", "\n", "import numpy as np\n", "import pandas as pd\n", "import statsmodels.formula.api as smf\n", "\n", "import thinkstats2\n", "import thinkplot\n", "import matplotlib.pyplot as plt\n", "\n", "import ess\n", "\n", "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Read all data" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 13.9 s, sys: 1.58 s, total: 15.4 s\n", "Wall time: 15.4 s\n" ] } ], "source": [ "%time cycles = ess.read_all_cycles()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Clean the data" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 12 ms, sys: 0 ns, total: 12 ms\n", "Wall time: 11.2 ms\n", "CPU times: user 8 ms, sys: 0 ns, total: 8 ms\n", "Wall time: 9.02 ms\n", "CPU times: user 4 ms, sys: 4 ms, total: 8 ms\n", "Wall time: 8.55 ms\n", "CPU times: user 8 ms, sys: 4 ms, total: 12 ms\n", "Wall time: 9.56 ms\n", "CPU times: user 8 ms, sys: 0 ns, total: 8 ms\n", "Wall time: 9.35 ms\n" ] } ], "source": [ "for cycle in cycles:\n", " %time ess.clean_cycle(cycle)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Resample" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 336 ms, sys: 52 ms, total: 388 ms\n", "Wall time: 388 ms\n", "22 countries\n", "25 countries\n", "23 countries\n", "29 countries\n", "27 countries\n" ] } ], "source": [ "%time cycle_maps = [ess.resample(cycle) for cycle in cycles]\n", "for cycle_map in cycle_maps:\n", " print(len(cycle_map), 'countries')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Remove a few countries from a few cycles where they are missing data." ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 0 ns, sys: 0 ns, total: 0 ns\n", "Wall time: 574 µs\n" ] } ], "source": [ "%time ess.remove_missing(cycle_maps)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Replace income and education with ranks" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 148 ms, sys: 0 ns, total: 148 ms\n", "Wall time: 147 ms\n", "CPU times: user 160 ms, sys: 0 ns, total: 160 ms\n", "Wall time: 162 ms\n", "CPU times: user 160 ms, sys: 0 ns, total: 160 ms\n", "Wall time: 160 ms\n", "CPU times: user 248 ms, sys: 0 ns, total: 248 ms\n", "Wall time: 248 ms\n", "CPU times: user 200 ms, sys: 0 ns, total: 200 ms\n", "Wall time: 201 ms\n" ] } ], "source": [ "for cycle_map in cycle_maps:\n", " %time ess.replace_with_ranks(cycle_map)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Fill missing values" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 280 ms, sys: 84 ms, total: 364 ms\n", "Wall time: 365 ms\n", "CPU times: user 296 ms, sys: 100 ms, total: 396 ms\n", "Wall time: 396 ms\n", "CPU times: user 284 ms, sys: 116 ms, total: 400 ms\n", "Wall time: 402 ms\n", "CPU times: user 384 ms, sys: 116 ms, total: 500 ms\n", "Wall time: 502 ms\n", "CPU times: user 356 ms, sys: 120 ms, total: 476 ms\n", "Wall time: 478 ms\n" ] } ], "source": [ "for cycle_map in cycle_maps:\n", " %time ess.fill_vars_by_country(cycle_map)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Contatenate the countries within each cycle" ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 72 ms, sys: 16 ms, total: 88 ms\n", "Wall time: 88.2 ms\n", "37937\n", "43709\n", "43000\n", "56752\n", "52458\n" ] } ], "source": [ "%time dfs = [ess.concat_groups(cycle_map) for cycle_map in cycle_maps]\n", "for df in dfs:\n", " print(len(df))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Contatenate the cycles" ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 16 ms, sys: 12 ms, total: 28 ms\n", "Wall time: 24.9 ms\n", "(233856, 32)\n" ] } ], "source": [ "%time df = pd.concat(dfs, ignore_index=True)\n", "print(df.shape)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Group by country" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "34" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "grouped = df.groupby('cntry')\n", "len(grouped)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The previous steps are now in two functions. The first reads and cleans the data:" ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%time cycles = ess.read_and_clean()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The second resamples and fills" ] }, { "cell_type": "code", "execution_count": 44, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "resampling\n", "removing\n", "replacing\n", "Round 1\n", "HU hinctnta\n", "IE hinctnta\n", "Round 2\n", "EE hinctnta\n", "UA hinctnta\n", "Round 3\n", "HU hinctnta\n", "EE hinctnta\n", "UA hinctnta\n", "Round 4\n", "BG hinctnta\n", "CY hinctnta\n", "SK hinctnta\n", "Round 5\n", "PT hinctnta\n", "filling\n", "concating\n", "CPU times: user 3 s, sys: 628 ms, total: 3.62 s\n", "Wall time: 3.64 s\n" ] } ], "source": [ "%time df = ess.resample_and_fill(cycles)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "I run the resampling process a few hundred times and store the results in HDF" ] }, { "cell_type": "code", "execution_count": 45, "metadata": { "collapsed": false }, "outputs": [], "source": [ "store = pd.HDFStore('ess.resamples.h5')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It's pretty fast, considering the size of these DataFrames.\n" ] }, { "cell_type": "code", "execution_count": 46, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 124 ms, sys: 56 ms, total: 180 ms\n", "Wall time: 182 ms\n" ] } ], "source": [ "%time store.put('df', df)" ] }, { "cell_type": "code", "execution_count": 48, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 32 ms, sys: 24 ms, total: 56 ms\n", "Wall time: 54.5 ms\n" ] } ], "source": [ "%time df = store.get('df')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "I store the dataframe using random keys." ] }, { "cell_type": "code", "execution_count": 91, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'KzFWeo'" ] }, "execution_count": 91, "metadata": {}, "output_type": "execute_result" } ], "source": [ "def random_name():\n", " t = [random.choice(string.ascii_letters) for i in range(6)]\n", " return ''.join(t)\n", "\n", "random_name()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Add dataframes to the store" ] }, { "cell_type": "code", "execution_count": 378, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def add_frames(n):\n", " for i in range(n):\n", " name = random_name()\n", " print(name)\n", " df = ess.resample_and_fill(cycles)\n", " store.put(name, df)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "How many dataframes in the store?" ] }, { "cell_type": "code", "execution_count": 379, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "204" ] }, "execution_count": 379, "metadata": {}, "output_type": "execute_result" } ], "source": [ "keys = store.keys()\n", "len(keys)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Pick one at random" ] }, { "cell_type": "code", "execution_count": 199, "metadata": { "collapsed": false }, "outputs": [], "source": [ "key = random.choice(keys)\n", "df = store.get(key)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And run the logit model" ] }, { "cell_type": "code", "execution_count": 201, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<table class=\"simpletable\">\n", "<caption>Logit Regression Results</caption>\n", "<tr>\n", " <th>Dep. Variable:</th> <td>hasrelig_f</td> <th> No. Observations: </th> <td>233856</td> \n", "</tr>\n", "<tr>\n", " <th>Model:</th> <td>Logit</td> <th> Df Residuals: </th> <td>233847</td> \n", "</tr>\n", "<tr>\n", " <th>Method:</th> <td>MLE</td> <th> Df Model: </th> <td> 8</td> \n", "</tr>\n", "<tr>\n", " <th>Date:</th> <td>Wed, 18 Nov 2015</td> <th> Pseudo R-squ.: </th> <td>0.03130</td> \n", "</tr>\n", "<tr>\n", " <th>Time:</th> <td>13:25:33</td> <th> Log-Likelihood: </th> <td>-1.4810e+05</td>\n", "</tr>\n", "<tr>\n", " <th>converged:</th> <td>True</td> <th> LL-Null: </th> <td>-1.5289e+05</td>\n", "</tr>\n", "<tr>\n", " <th> </th> <td> </td> <th> LLR p-value: </th> <td> 0.000</td> \n", "</tr>\n", "</table>\n", "<table class=\"simpletable\">\n", "<tr>\n", " <td></td> <th>coef</th> <th>std err</th> <th>z</th> <th>P>|z|</th> <th>[95.0% Conf. Int.]</th> \n", "</tr>\n", "<tr>\n", " <th>Intercept</th> <td> 1.1474</td> <td> 0.017</td> <td> 68.390</td> <td> 0.000</td> <td> 1.114 1.180</td>\n", "</tr>\n", "<tr>\n", " <th>inwyr07_f</th> <td> 0.0511</td> <td> 0.002</td> <td> 32.995</td> <td> 0.000</td> <td> 0.048 0.054</td>\n", "</tr>\n", "<tr>\n", " <th>yrbrn60_f</th> <td> -0.0084</td> <td> 0.000</td> <td> -30.239</td> <td> 0.000</td> <td> -0.009 -0.008</td>\n", "</tr>\n", "<tr>\n", " <th>edurank_f</th> <td> -0.0107</td> <td> 0.017</td> <td> -0.631</td> <td> 0.528</td> <td> -0.044 0.023</td>\n", "</tr>\n", "<tr>\n", " <th>hincrank_f</th> <td> 0.0810</td> <td> 0.016</td> <td> 5.093</td> <td> 0.000</td> <td> 0.050 0.112</td>\n", "</tr>\n", "<tr>\n", " <th>tvtot_f</th> <td> -0.0200</td> <td> 0.002</td> <td> -8.940</td> <td> 0.000</td> <td> -0.024 -0.016</td>\n", "</tr>\n", "<tr>\n", " <th>rdtot_f</th> <td> -0.0158</td> <td> 0.002</td> <td> -9.419</td> <td> 0.000</td> <td> -0.019 -0.013</td>\n", "</tr>\n", "<tr>\n", " <th>nwsptot_f</th> <td> -0.0317</td> <td> 0.004</td> <td> -8.825</td> <td> 0.000</td> <td> -0.039 -0.025</td>\n", "</tr>\n", "<tr>\n", " <th>netuse_f</th> <td> -0.1130</td> <td> 0.002</td> <td> -64.205</td> <td> 0.000</td> <td> -0.116 -0.110</td>\n", "</tr>\n", "</table>" ], "text/plain": [ "<class 'statsmodels.iolib.summary.Summary'>\n", "\"\"\"\n", " Logit Regression Results \n", "==============================================================================\n", "Dep. Variable: hasrelig_f No. Observations: 233856\n", "Model: Logit Df Residuals: 233847\n", "Method: MLE Df Model: 8\n", "Date: Wed, 18 Nov 2015 Pseudo R-squ.: 0.03130\n", "Time: 13:25:33 Log-Likelihood: -1.4810e+05\n", "converged: True LL-Null: -1.5289e+05\n", " LLR p-value: 0.000\n", "==============================================================================\n", " coef std err z P>|z| [95.0% Conf. Int.]\n", "------------------------------------------------------------------------------\n", "Intercept 1.1474 0.017 68.390 0.000 1.114 1.180\n", "inwyr07_f 0.0511 0.002 32.995 0.000 0.048 0.054\n", "yrbrn60_f -0.0084 0.000 -30.239 0.000 -0.009 -0.008\n", "edurank_f -0.0107 0.017 -0.631 0.528 -0.044 0.023\n", "hincrank_f 0.0810 0.016 5.093 0.000 0.050 0.112\n", "tvtot_f -0.0200 0.002 -8.940 0.000 -0.024 -0.016\n", "rdtot_f -0.0158 0.002 -9.419 0.000 -0.019 -0.013\n", "nwsptot_f -0.0317 0.004 -8.825 0.000 -0.039 -0.025\n", "netuse_f -0.1130 0.002 -64.205 0.000 -0.116 -0.110\n", "==============================================================================\n", "\"\"\"" ] }, "execution_count": 201, "metadata": {}, "output_type": "execute_result" } ], "source": [ "formula = ('hasrelig_f ~ inwyr07_f + yrbrn60_f + edurank_f + hincrank_f +'\n", " 'tvtot_f + rdtot_f + nwsptot_f + netuse_f')\n", "res = ess.run_model(df, formula)\n", "res.summary()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "I use Country objects to collect data associated with each country." ] }, { "cell_type": "code", "execution_count": 318, "metadata": { "collapsed": false }, "outputs": [], "source": [ "class Country:\n", " def __init__(self, code, nobs):\n", " self.code = code\n", " self.name = ess.country_name(code)\n", " self.nobs = nobs\n", " self.mean_map = {}\n", " self.param_map = {}\n", "\n", " def add_mean(self, means):\n", " self.mean_seq.append(means)\n", " \n", " def add_params(self, params):\n", " self.param_seq.append(params)\n", " \n", " def add_params2(self, params):\n", " self.param2_seq.append(params)\n", " \n", " def get_means(self, varname):\n", " t = [mean[varname] for mean in self.mean_seq]\n", " return np.array(t)\n", "\n", " def get_params(self, varname):\n", " t = [params[varname] for params in self.param_seq]\n", " return np.array(t)\n", "\n", " def get_params2(self, varname):\n", " t = [params[varname] for params in self.param2_seq]\n", " return np.array(t)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Make the country objects" ] }, { "cell_type": "code", "execution_count": 290, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Austria\n", "Belgium\n", "Bulgaria\n", "Switzerland\n", "Cyprus\n", "Czech Republic\n", "Germany\n", "Denmark\n", "Estonia\n", "Spain\n", "Finland\n", "France\n", "United Kingdom\n", "Greece\n", "Croatia\n", "Hungary\n", "Ireland\n", "Israel\n", "Iceland\n", "Italy\n", "Lithuania\n", "Luxembourg\n", "Latvia\n", "Netherlands\n", "Norway\n", "Poland\n", "Portugal\n", "Romania\n", "Russia\n", "Sweden\n", "Slovenia\n", "Slovakia\n", "Turkey\n", "Ukraine\n" ] } ], "source": [ "keys = store.keys()\n", "key = random.choice(keys)\n", "df = store.get(key)\n", "\n", "grouped = df.groupby('cntry')\n", "country_map = {}\n", "\n", "for code, group in grouped:\n", " country_map[code] = Country(code, len(group))\n", " print(country_map[code].name)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For each resampled frame, run both models and store the results in the Country objects" ] }, { "cell_type": "code", "execution_count": 291, "metadata": { "collapsed": false }, "outputs": [], "source": [ "formula1 = ('hasrelig_f ~ inwyr07_f + yrbrn60_f + '\n", " 'edurank_f + hincrank_f +'\n", " 'tvtot_f + rdtot_f + nwsptot_f + netuse_f')\n", "\n", "formula2 = ('rlgdgr_f ~ inwyr07_f + yrbrn60_f + '\n", " 'edurank_f + hincrank_f +'\n", " 'tvtot_f + rdtot_f + nwsptot_f + netuse_f')\n", "\n", "def process_frame(df):\n", " grouped = df.groupby('cntry')\n", " for code, group in grouped:\n", " country = country_map[code]\n", " country.add_mean(group.mean())\n", " \n", " model = smf.logit(formula1, data=group) \n", " results = model.fit(disp=False)\n", " country.add_params(results.params)\n", " \n", " model = smf.ols(formula2, data=group) \n", " results = model.fit(disp=False)\n", " country.add_params2(results.params)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Process the dataframes in the store" ] }, { "cell_type": "code", "execution_count": 292, "metadata": { "collapsed": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "/AAVZWa\n", "/ADbUvD\n", "/AJEDdF\n", "/AOacJP\n", "/AsSyrK\n", "/BIXejR\n", "/Blwttj\n", "/BytXnJ\n", "/CuiQgF\n", "/CxkVBv\n", "/DOKcxz\n", "/DSSzPM\n", "/DdpHTg\n", "/EBHNWn\n", "/EHuhuk\n", "/EIaigX\n", "/EOOBpB\n", "/EdeAYH\n", "/EiftYh\n", "/EoHBcy\n", "/Evkitq\n", "/FJboqX\n", "/FWawby\n", "/GIKXkG\n", "/GPBBMj\n", "/GYhuaT\n", "/GdTLTY\n", "/GeUlsB\n", "/GeolrR\n", "/GkMwBV\n", "/GownbC\n", "/GrCTmE\n", "/HGSBFA\n", "/HemGKU\n", "/HujYDN\n", "/IKLjEu\n", "/IORbkE\n", "/IXYMov\n", "/InEXbB\n", "/JKBolS\n", "/JVSJPq\n", "/JofMZK\n", "/JomohW\n", "/JznRlw\n", "/KEthFz\n", "/KFwczR\n", "/KUVnJc\n", "/KnKXTR\n", "/KuGUhG\n", "/KudtCP\n", "/LaUmLC\n", "/LissvE\n", "/LmraEV\n", "/MCmopN\n", "/MIdmWa\n", "/MgSdJx\n", "/NJjQrX\n", "/NfzPAX\n", "/OJZEtt\n", "/Oaksmf\n", "/OdhAjf\n", "/PJETsk\n", "/PXxSpS\n", "/PiWfGA\n", "/PptHII\n", "/PvfGpy\n", "/QTTYTa\n", "/QbhbQt\n", "/QoHLXF\n", "/QskeUe\n", "/QtkeEX\n", "/RHVBHl\n", "/RRpxwc\n", "/RYtpJo\n", "/RuCVox\n", "/RwJMYt\n", "/SHnJcB\n", "/ScbnLb\n", "/TOcaLi\n", "/TRVSRU\n", "/TaHTXL\n", "/UKzbGY\n", "/UVvNeb\n", "/UfXGIO\n", "/VHIVpS\n", "/VcRwRL\n", "/VgqgVe\n", "/VlUfcv\n", "/VzZAXk\n", "/WczOWP\n", "/WkLtrX\n", "/WkfCQW\n", "/WlHtRg\n", "/WwTDDj\n", "/WxWlWp\n", "/XGmIIH\n", "/XOxJQN\n", "/XhgvtL\n", "/YMsFSK\n", "/YeASVz\n", "/YoxGxL\n", "/YvdfEk\n", "/ZEEBve\n", "/ZXovwc\n", "/ZgSZAY\n", "/ZjGafB\n", "/ZxNahg\n", "/bJIOjl\n", "/bNKOFy\n", "/bOZZkd\n", "/blIIdK\n", "/btCIZx\n", "/bvntaM\n", "/cEzhky\n", "/cYiUkH\n", "/cgKsnt\n", "/czQkEF\n", "/dcjEvm\n", "/dqGBQR\n", "/dyuBXv\n", "/dzpDVu\n", "/eDVvJf\n", "/ewfhTI\n", "/fBFUGB\n", "/fEkGRW\n", "/ggYbXH\n", "/gnJSCF\n", "/hIQegI\n", "/hOQHWV\n", "/hTfXDB\n", "/hlJZff\n", "/hmmXxf\n", "/iABzcU\n", "/iGVZEK\n", "/iWltCV\n", "/iaTUMA\n", "/iiSwHC\n", "/ilWxnR\n", "/jNqZpZ\n", "/kVTeXb\n", "/kfAnDn\n", "/kuZzaN\n", "/lHDxRr\n", "/lfUmXq\n", "/lsXAWo\n", "/mbTfIj\n", "/mtyzJg\n", "/nOsmSf\n", "/ncvQcP\n", "/neEVfl\n", "/nnERGx\n", "/nnUXHn\n", "/nvZcGU\n", "/olGyuX\n", "/pezcXZ\n", "/qRQqmc\n", "/qWZfql\n", "/qchGUz\n", "/qfZVHF\n", "/qqYojL\n", "/rCrtjG\n", "/rSravW\n", "/rUWSeP\n", "/rYeOLP\n", "/rnYDRv\n", "/sPKzmv\n", "/shBLMW\n", "/siHRLd\n", "/ssTuqu\n", "/tIBOEC\n", "/tJaytt\n", "/taosRR\n", "/tazMjo\n", "/teuTPZ\n", "/tiUoXr\n", "/tjiIAT\n", "/tjjdLV\n", "/tuLFXm\n", "/uHhvHu\n", "/uOAidw\n", "/uWhsWV\n", "/uZGRbW\n", "/vPnlcH\n", "/vktdCG\n", "/vqFufP\n", "/vqxuWx\n", "/wGQrTR\n", "/wahSMf\n", "/wsvHYt\n", "/xfKUcU\n", "/xgAfWd\n", "/xjbtHM\n", "/xtqhXa\n", "/xwCakd\n", "/xxGtEc\n", "/yImumW\n", "/yKuLlN\n", "/ybmXrn\n", "/yfOjqX\n", "/ylSawW\n", "/zBUKWF\n", "/zYbdcJ\n", "/zvRkow\n", "/zyIeFR\n" ] } ], "source": [ "for key in store.keys():\n", " print(key)\n", " df = store.get(key)\n", " process_frame(df)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Check a country" ] }, { "cell_type": "code", "execution_count": 294, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "204" ] }, "execution_count": 294, "metadata": {}, "output_type": "execute_result" } ], "source": [ "varname = 'netuse_f'\n", "country = country_map['BE']\n", "params = country.get_params2(varname)\n", "len(params)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Extract the given parameters for each country" ] }, { "cell_type": "code", "execution_count": 380, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def extract_params(country_map, param_func, varname):\n", " \"\"\"Extracts parameters.\n", " \n", " country_map: map from country code to Country\n", " param_func: function that takes country and returns param list\n", " varname: name of variable to get the mean of\n", " \n", " returns: list of (code, name, param, low, high, mean) tuple\n", " \"\"\"\n", " t = []\n", " for code, country in sorted(country_map.items()):\n", " name = country.name\n", "\n", " params = param_func(country)\n", " param = np.median(params)\n", " low = np.percentile(params, 2.5)\n", " high = np.percentile(params, 97.5)\n", " \n", " means = country.get_means(varname)\n", " mean = np.median(means)\n", " \n", " t.append((code, name, param, low, high, mean))\n", " \n", " t.sort(key=lambda x: x[2])\n", " return t" ] }, { "cell_type": "code", "execution_count": 384, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def extract_vars(country_map, exp_var, dep_var):\n", " def param_func(country):\n", " return country.get_params(exp_var)\n", "\n", " t = extract_params(country_map, param_func, dep_var)\n", " return t\n", "\n", "\n", "def extract_vars2(country_map, exp_var, dep_var):\n", " def param_func(country):\n", " return country.get_params2(exp_var)\n", "\n", " t = extract_params(country_map, param_func, dep_var)\n", " return t" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Plot confidence intervals for estimated parameters" ] }, { "cell_type": "code", "execution_count": 320, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def plot_cis(t):\n", " plt.figure(figsize=(8,8))\n", "\n", " n = len(t)\n", " ys = n - np.arange(n)\n", " codes, names, params, lows, highs, means = zip(*t)\n", " plt.hlines(ys, lows, highs, color='blue', linewidth=2, alpha=0.5)\n", " plt.plot(params, ys, 'ws', markeredgewidth=0, markersize=15)\n", "\n", " for param, y, code in zip(params, ys, codes):\n", " plt.text(param, y, code, fontsize=10, color='blue', \n", " horizontalalignment='center',\n", " verticalalignment='center')\n", "\n", " plt.vlines(0, 0, n+1, color='gray', alpha=0.5)\n", " plt.yticks(ys, names)" ] }, { "cell_type": "code", "execution_count": 469, "metadata": { "collapsed": true }, "outputs": [], "source": [ "plot_counter = 1\n", "\n", "def save_plot(flag=False):\n", " global plot_counter\n", " if flag:\n", " root = 'ess3.%2.2d' % plot_counter\n", " thinkplot.Save(root=root, formats=['png'])\n", " plot_counter += 1" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Make a plot showing confidence interval for the given parameters" ] }, { "cell_type": "code", "execution_count": 470, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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XXoHnn4c992zZudZ2/J2XV70HJ+tJuhVYmyzZ3wu5Y/lFY/4MHEoWnHwDKLUm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\nZ1bSsMEJ0Au4StKjkqYAG5IS9gEQES8DPwFGAZOBCRFxR97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"text/plain": [ "<matplotlib.figure.Figure at 0x7f52aaf91d50>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "t = extract_vars(country_map, 'inwyr07_f', 'hasrelig_f')\n", "plot_cis(t)\n", "thinkplot.Config(title='Interview year',\n", " xlabel='log odds ratio interview year')\n", "save_plot()" ] }, { "cell_type": "code", "execution_count": 471, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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yAiPpEElXpuOr0v2PSZotaaik0ZKeKtSxyvaf/2R7r5iZWeWq9hGV7pKmkm11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\nYBRwqqQXgQdJuXweLRyUtCpwFrAZ8CEwStJewK2kVWtPylX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"text/plain": [ "<matplotlib.figure.Figure at 0x7f52a2d048d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "t = extract_vars(country_map, 'yrbrn60_f', 'hasrelig_f') \n", "plot_cis(t)\n", "thinkplot.Config(title='Year born',\n", " xlabel='log odds ratio year born')\n", "save_plot()" ] }, { "cell_type": "code", "execution_count": 472, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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PTsF/J5wAf/oTTJgAjzySvpt1RfU6crID8ElZSOBk4DBJe5baJF0naQ9JgyUN\nz4F+f5f0i7y9j6QXJF0DTCZNaz+rcPy+kq7Oy9+RNEXS05Ie6qwPama1ZdQoWGwxGDKkoa1fPzj5\nZHj55bT9qKPgt79Ns8iadUV1OXICbESac6TcH0jBfsMlLQNsBRwIHEQK6tsQmA08Jelu4B1SRs+B\nETEW0lwlhfMVJ2U7BdglIt6S1KvjP5JVG08tbu3RuzcMGDBvuwS/+x3ssAPstRdss828+3TV/+c6\nIKrFFlBn/79Xr8VJc7O4PizpMknLA/sCt0TEZ/mOzX2lsD5JtwHbAHcAr5cKk2aUJpd5FLgmBwbe\n1tzODv4z69rUwnRUG28MffvCkUd2Xn/MOlJXDP5ri2dIxUdTriWNluxH49ycIpGCAGHe4MBi4dPj\n88aIH0naHNgVGC9pQETMKD+xg//qh/9TWns8+CDcckvz2xdZJH01pSv+Pzd6NPjfcLWjywX/tUUO\nCVxc0mGlNkn9JG0DDAN+knaL5wuHfV3SFyT1APYkjYQ09W+ctyWtn6erH1Q4/1oRMTYiTiXl/ny5\nwz+YmdW8HXeEjz+Gq65qaJs8OT0Aa2ZJXRYn2SBg5/wq8VTgLOCtiPg38CxwdWHfAMYCtwKTSLd7\nJhS2Ff0vcBepeHmzsP03kiZLmgI8mh/ANTObx+23w/33p1eJN9ooPQy7yiqV7pVZ9ehywX+SliS9\nedM/It7PbYOBARGxUEPMHfxnZtY2Dv6rbQ7+awVJO5NGTS4uFSZZ8a0bMzMzq6B6fSC2SRFxP9Cn\nifZrgGs6vUNmZmY2jy41cmJmZmbVz8WJmZmZVZW6LU4k7SXpM0nrtfP4PSV9pYXth0s6sP09NLOu\n7o470pwmL7wAW24J/fvD6qvDiium5f794Y03Kt1Ls85Xz8+c7E965Xd/YGg7jh8EjACeK98gqVtE\nXLFAvTOzLu8vf4Hddkvfn3gitV1zDYwfDxdfXNm+mVVSXY6cSOoJbAEcRZoJFkkDJY0o7HOppIPz\n8tmSnpE0SdI5krYCdgfOkTRB0pqSRku6QNJTwLGSTpV0fD7+MEljc+jfLXkiNzOzZs2aBU8+CZde\nCjfe2NAekb7MurJ6HTnZE7gnIt6QNF3Spsz7qnAAIak3sFdErA8gqVdEvCfpTmBERNyW2wPoHhFf\nzeunFs51a0RcldvPAA4FLl2YH9DMKmdBppEvHTt8OHzzm7DaarDCCjBhAmy6acvZO+29blec9t5q\nW70WJ/sOSoSzAAAgAElEQVQDF+Tlm2m4xdOU/wIfSfpD3qe4X/mviRtpWl9JZwLLAD2Be5vrmIP/\nzAzSrZyf/jQtf+c7aX3TTT1qYrXNwX/NyCMhOwAb5dGObqRRkuE0vo21BGmG3Lk5sG8nUljgUXkZ\n5h1taS4EcBiwR0RMybeKBjbXPwf/mdW+Bf1jPGMGjBoFU6emkZK5c9ODseecs3Cva7awOfivefsC\n10ZEn4hYIyJWA14lfdYNJC0maVlSARKSlgKWjYi/AccBG+fzvA/0ms+1SiMrPYFpkroDP+jgz2Nm\ndeaWW+Cgg+C11+DVV9MbOX36wJgxLd/WMesq6rE4+R5we1nbrbn9JmAq6fZMKdhvaWCEpEnAGCAP\ntHID8P8kjZe0ZjPXKo2cnAI8CTxCervHA7Nm1qwbboBBgxq37bNPurUDLlDMulzwXyU5+M/MrG0c\n/FfbHPxnZmZmdcHFiZmZmVUVFydmZmZWVeqyOJG0sqQbJL0kaZykuyWts4DnXF3S/oX1AZIuWvDe\nmllX1LNn4/Vhw+Doo9Py0KHw5S+nbJ2+feG22zq7d2aVVXfFiSSR3tZ5MCLWjojNgP8DVirs0575\nXdYADiitRMT4iDh2QftrZl1T+Rs5xXUJjjsOJk6E22+HIUM6t29mlVZ3xQlpArZPIuLKUkNETAa6\nSRojaTgwVdLikq6WNDnn5wwEkNRH0sP5FeLxOWcH4GxgW0kTJf2kmNUjaXNJj+XzPCpp3c79yGZW\n68pf5Cutr702dO8O06d3fp/MKqXuZogFNgLGN9EuoD+wYUS8nkP75kZEP0nrAfflouJt4OsR8XG+\nFXQ98FXgZ8AJEbE7pCDBwrmfA7bNs83uDPySNBmcmVmTZs9Ot21KZsyAPfecd7/x46FbN1h++c7r\nm1ml1WNx0tJEImMj4vW8/DXgYoCIeEHS68A6wD+ASyVtDMzNbTBvzk7RssC1ktbO1+++AP03sw5Q\n7VO99+iRbtuUXHMNjBuXliPgggvg6qvh+efTMyfF2z6V+mzV/jO1+lGPxckzND9qUZ6NU15wiDRD\n7FsRcaCkbsBHrbjmGcADETFI0urA6OZ2dPCfmTWleFun9MzJccfBiBFw6qmw++6eOdaqn4P/mhER\nD0r6paTDIuIqAEn9gG3Ldh0DfB8YlW/nrAa8QMrT+Wfe5yBScCCkrJ2lm7lsL+DNvHxIS/1z8J9Z\n56j2P2rnndf8toiGYmX33eEPf0hT2x+QH8mv9s9mXZeD/1o2CNg5v0o8FTgLeIvGt3wuAxaRNJmU\no3NwRHyS2w+W9DSwHjAr7z8JmCvpaUk/yecqne83wK8kTaAhBdnMrFlNva1TaisuA/ziF3DWWZ3X\nN7NKc7ZOJ3K2jplZ2zhbp7Y5W8fMzMzqgosTMzMzqyouTszMzKyquDgxMzOzqlKTxYmkWWXrgyVd\nUqn+mJktqJaCAAcPhltvbXl/s3pSk8UJ876qW7FXYNoZImhm1sj8ggBb2m5Wb2q1OCn3+R9TScMk\n7VNYn5W/D5Q0WtLNkp6T9OfCPt/ObeMkXTy/QL88UnOnpAeA+yVdI2nPwvmuk7RHJ3xuM6tTzQUB\nmnUFtfqv/h6SCqkU9AaG5+WWRlU2ATYgTcj2qKStgQnA5aTgvtclXV84pqVAv/5A34iYKWk70rT3\nwyUtA2wFHNgRH9TMakd7Z24dOrT1QYALek3PLmu1oFaLk9kR8fkfY0kHA5u14rixEfFmPuZpYA3g\nQ+CVQiDgX4Ahebk80K/487ovImYCRMTDki6TtDypeLklIj5rqgPO1jGzprQUBNjULRzf1rFq5Gyd\nxop/TD8l366StAiwWGHbx4XluaTPXz7SUjxXS4F+H5Yddy1ptGQ/YHBzHXW2jln96sg/3sXbOMst\nB+++27A+YwYsv3zHX9NsQTlbp3mvAQPy8h5A9xb2DVLY35q5+IBUXJR+LbQ60A8YBvwEiIh4vm1d\nNjNr3sCBcOONMGdOWh82DHbcsZI9Mlu4anXkpKnnSkptV5Ge/XgauIeG4L6mjiMiPpJ0JHCPpA+A\np2gc6HeNpJ8Ddxfao/xcEfFvSc8Ct7f7U5lZl9VSEOCuu8L48TBgAHTrBmuvDZdf3vl9NOssDv4D\nJC0VER/k5d8Cf4+Ii9p4jiWByUD/iHi/mX0c/Gdm1gYO/qttDv5bMIdJmijpGdKtnCvacnB+k+dZ\n4OLmChMzMzNrnVq9rdOhIuJC4MIFOP5+oE+HdcjMzKwL88iJmZmZVRUXJ2ZmZlZVaro4kXSypKmS\nJuVnRjbPU9QPmP/RHXL9wyV5Jlgzq7izzoKNNoKNN04zzY4dm15BHj++0j0za7uafeZE0lbArqS3\nY+ZI6g0sThOv+S4sEdGmB2fNzBaGxx+Hu+9OM8x2754mafv446YDA81qQS2PnKwM/Cci5gBExIyI\neKu4g6T9JU2WNEXS2bntCEm/KewzWNIlefkHkp7MozCX5xlmkTRL0pmSnpb0uKQVc/tQScfn5cMk\njc373CKpR6f8FMysy5s2Lc0Y2z1POdm7N6yySmX7ZLYganbkBLgP+IWkF4D7gRsj4uHSRklfBM4G\nNgVmAvfl5OBbgMeBE/Ou3wXOlPSVvLx1Dvq7DPg+8CdgSeDxiPi5pF8DhwFn0XiE5taIuCpf+wzg\nUODShfPRzayWdcSU88Vz7LILnH46rLce7Lwz7LcfbLfdwrm+p8u3zlCzxUlEfJCfLdkW2AG4UdL/\n5s0CvgqMjoh3ACRdB2wXEcMlvSJpC+AlYP2IeEzSUaRp78cpjYP2AKbl830SEXfn5fHA15voUl9J\nZwLLAD2Be5vqt4P/zKyjLbVUerZkzBgYNSoVJ2efXeleWVfk4D8gJ/8+BDwkaQpwcHFz2e7FO683\nkEZJngduK7RfExEnNXGpOYXlz2j8cytdZxiwR0RMySnJA5vqs4P/zGxh/BpYZBHYfvv01bdvSjWG\nxgGCC/P6ZuDgPyStK2mdQlN/4PW8HMBYYHtJy0nqBnyPhlTh24G9gP1JhQrAA8C+klbI5+8tabX5\ndYOGoqcnME1Sd+AH7f5gZmZt9Pe/w4svNqxPnAirr978/mbVrpZHTnoCl0haFvgUeBE4nPRMCREx\nLd/mGUUqIO6KiBF528wc0veViBiX257LAX/35Qdh5wBHAm/QeBSm+DZQcfkU4Elgev7ec6F8ajOz\nMrNmwdFHw8yZsOiisM46cMUVsO++flvHapOD/zqRg//MzNrGwX+1zcF/ZmZmVhdcnJiZmVlVcXFi\nZmZmVcXFiZmZmVWVTi1OJH0m6dzC+gmSTp3PMdvnHJ3S+jBJ+yxgP17LWTwLTNKsjjiPmVl7LbII\nnHBCw/q550JpeomhQ+G88yrSLbN26+yRk0+AQZKWy+uteXVlB2Drwnq7X3dRssiCnKMJfv3GzCpq\nscXg9tvhnXfSevH1Yb9KbLWos4uTOcCVwE/LN0haIQfmjc1fW0tanTR3yU8lTZC0Td59O0mPSnq5\nOIoi6f/lYydJGprb+kh6QdI1wBTgy2XXvV3SOElTJR1WaG8u7G+NvD45T1df2n8VSQ/n0MAphb6a\nmS1U3bvDkCFwwQWV7olZx6jEJGyXAZOLycDZRcAFEfFonpn1nojYQNLlwPsRcT6ApP8BVo6Ir+Ww\nvjuBWyXtAqwdEZvn0ZHhkrYF/gGsDRwYEWPzOYrX/WFEvJtThMdKuiUi3qX5sL+LgN9GxJ8lHVk4\nzwG5z79UusBSHfYTM7MuqTXTzJf2OfJI6NcPTjyxxd3bfP627GfWUTq9OImI9yVdCxwDzC5s2hn4\nSqFwWFpS6S/4YjURwB35XM9JWim37wLsImliXl+KVJT8A3i9VJg04VhJe+XlVYF1SFPfNxf2tzUw\nKC//Gfh1Xh4L/DFPX39HRExq6mIO/jOzhWHppeGgg+Dii6FHj0r3xrqqWg/+uxCYAFxdaBOwRUR8\nUtxRTd8wLe5T3OFXEXFl2fF9gA+aOomkgcBOwJYR8ZGkUcASeXNLYX/ziIgxeaRmN2CYpPMj4k/l\n+zn4z8xaq62/Ln7yE9h0UzjkkIVzfrP5qengv3zb5CbgUBoeKL2PNJoCgKRN8uL7wNKtOO29wA9L\noy2SvlQK8WtBL+DdXJisD2zZius8SgoRBPh+ob+rAdMj4vfA70lBhGZmneYLX4Dvfhf+8IeGB2Gd\nmGG1qLOLk+Ifk/OA5QvrxwCb5YdZnwGG5PYRpDd8ig/ElgfxEREjgeuBxyVNJhU/PZvYv7h+D7Bo\nDgH8FfB4M30tBvwdC/w4X+OLhfYdgKclTQC+S3o2xcxsoSsOMB9/PPznP423nXkmrLpq+lptflnr\nZlXAwX+dyMF/ZmZt4+C/2ubgPzMzM6sLLk7MzMysqrg4MTMzs6ri4sTMzMyqSrvmOZG0Mmmuks2A\nmcDbwE8i4sUF7VCedv79iGgxqkrSa8B7pDlI/gMcFBFvLuj1m7jGphExo7k+SjoNeDgiHujIa5uZ\ndYRp09L8J+PGwbLLwkorwTe+AVcXZpn69FN45hl47jlYb73K9dWspM3FSZ6a/Xbg6oj4Xm7rB6wE\nLHBxQuuD9AIYGBEzcrHwf8DRHXD98ms09ZTx532MiBZTlc3MKiUCBg1Kk7LdcENqmzwZ3nsPjjmm\nYb+TToL+/V2YWPVoz22dHUhTu38+E2tETI6IRySdloPvJkr6l6Q/Akj6gaQnc/vlOfsGSd+UND6H\n640sXGMDSaNysF9rCo4ngLXyOecJEMztQyX9SdJjkv6eM3qQNFDSiNKJJF0q6eDCuU/MIX9PSlqr\n/MKShpXCByV9NQcSPp3371m+v5lZZxk1KiUWDxnS0NavH2xTiCV9+GG4+Wa47LLO759Zc9pzW2cj\nUtbMPPIowqmSlgHGAJfkcL7vAltHxFxJlwHfl3QPKaF424h4XdKy+TQC1gcGkmZwfUHSZRExt4lL\nlkY1vglMzcvzBAgCGxT6viVpcraJku5mXsUJ1wBmRkQ/SQeSbmXt3tT+khYDbgC+GxHjc2EyGzOz\nBdTeaeZ794YBA5rfPnNmGlX585+hZ9k/pRwKaJXUnuKkxdsu+bbPdcB5ETFR0lHAAGBczslZApgG\nbEF6VuN1gIiYWTj/XRExB3hH0r9Jt4yaep5klKTewKekwgOaDxAMYHhEfAx8nHN0Nic9M9OSv+Tv\nNwDNBZILWA94KyLG588zq6kdHfxnZp2l6WiyBkcckcICt9qqc/pj9a+SwX/PAPu2sH0o8EZEXFNo\nuyYiTiruJGm3Fs5RDPabS/P9HAj8l1QMHUYqHtoSIPgZqbAp3t5qKc9znmnzW1hvkoP/zKyt2vtr\n48EH4ZZbmt52zTXwj3/A9dd37DWta6tY8F9EPAgsLumwUpukfpK2kbQ7KeX32MIhDwD7lkL4JPXO\nt1ueALbLqcHkEZA2y7d7fgIcn2+lNBcgKGBPSYtLWo5U2DwFvEF6xmWxfGtpx8LpBeyXl/cDHiu0\nF6udAF4AVpG0Wb7u0pK6teczmZl1hB13hI8/hquuamibPBkeeghOPjndzlnEE0pYFWrXq8TAIOBC\nST8DPgJeBX4KnE4KwxubRyqGR8RQST8H7ssPws4BjoyIsZKGALfl9reBb+Tzt2YUovjGzDRJtwE/\nJhUmv5U0KX++h4Aj8/6TgVGkwMHTI2IagKSbSM+svApMKLvGF/K5PgL2L7Q36mNEzJG0H+k5mx7A\nh8DXgQ9a8VnMzBaK229PrxL/+tewxBLQpw989BHMng17791430svha99rSLdNGukywT/SToVmDW/\n+VMWch8c/Gdm1gYO/qttDv5rHVcGZmZmVa69t3VqTkS076kcMzMz61RdbeTEzMzMqlxNFyeSTpY0\nVdKkPPvs5pJGS2ph2qF2XWeeOUskfVHSzR15HTOzznDWWbDRRrDxxmna+rFjYeBAGJ+n13z1VVh3\nXRg5ssXTmC00NXtbR9JWwK5A//ymTG9gcZp4k6YDzHO+HDL4nQ6+jpnZQvX443D33TBxInTvDjNm\npNeNpfT1z3/Ct74F558PX/96pXtrXVUtj5ysDPwnzyRLRMyIiLeKO0jaP+fiTJF0dm47QtJvCvsM\nlnRJXr5D0rg8GnMYZSQtn7N5viWpj6Spub2PpIdzTtD4XDiZmVWdadNg+eVTYQJpivtVVknL//pX\nSiz+5S9ht5amyTRbyGp25IQ02dovJL0A3A/cGBEPlzZK+iJwNrApaYr6+yTtCdwCPA6cmHf9LnBm\nXj4kIt7N85SMlXRLRLybz7cicCdwckQ8kCePK42ovA18PSI+lrQOcD3w1YX0uc2si2vv7K1Dh8Iu\nu8Dpp6cE4p13hv32g+22SwnGgwenWz7l858syDUX9Fjrmmq2OImID/KzJduSkpJvlPS/ebNIxcHo\niHgHQNJ1wHYRMVzSK5K2AF4C1o+I0syvx0raKy+vCqwDjAUWI810e2REjGmiO4sBl0ramDTd/rrN\n9dvZOmZWSUstlZ4tGTMmpRbvtx+cfXa6pbPzzvCnP8HBB0OPloI8zJrRUdk6dTMJm6R9gIOBpYET\ngC8B+0TEwXn7ocAGEXG8pENIQYHPA+tFxAmSBgJnkEZAPsrBgKdGxMP5gdibgTcj4uR8vj7AiIjo\nK2kosGREnJinrP8oIro30UdPwmZmVeXWW1POzvvvw7nnpuLkpZdg+HDoVgUBHJ6ErbZ1uUnYJK2b\nb6GU9Adez8tBGvHYXtJyuWD4HjA6b78d2Is0Hf0Nua0X8G4uTNYHtiycO4AfAutLOpF59SIlLQMc\nBFTBH2kzs3n9/e/w4osN6xMnwuqrp2UJLrwQevWCQw+tTP/MoIaLE6AnMEzSMzn7Zn1SIjKQ8naA\n/yVl6TwNjIuIEXnbTOBZYLWIGJcPuQdYVNKzwK9Iz6UUThdBKmZ2lHQEjd8Kugw4WNLTwHrAPK8e\nm5lVg1mz0rMlG26YXiV+/vl5nwm55hp46y342c8q0UOzOrqtUwt8W8fMrG18W6e2dbnbOmZmZlaf\nXJyYmZlZVXFxYmZmZlXFxYmZmZlVlbooTvL08VPK2oZKOl7SqAUJApR0mqSdFryXZmbV6bXXoG/f\nxm1Dh8J556XlTz+FFVaA//u/zu6ZdVV1UZw0I5pZnoekZn8OEXFqRDzQYb0yM6sBKrxfMXIkDBiQ\nJmwz6wz1XJw0ImkRScMknZ7XZ0k6N89NspWkUySNzSGBVxSOG5Znn0XSa3lEZnwOFFwvty8l6Y+S\nnpQ0QdIeFfmQZmYdqFSg/OUv8KMfwZprplRjs4WtZrN12qg7cB0wOSJ+lduWBJ6IiBMAJD0bEWfk\n5Wsl7RYRd9F4srUApkfEAEk/Ik2TfxhwMvBARPxQ0rLAk5Luj4gPO+0TmpkVtCVsb/Dg5rd99FHK\n4Pn97+Gdd1KhslUhd72toX4OAbTWqJfipLnbNqX2K0ipxb8qbJsLFAcpd5T0/0hFS29gKnBXE+e8\nLX+fAJSyO3cBdpd0Ql5fnBQc+EL5wQ7+M7NqoxamyLrrLhg4EBZbDPbaKxUXF13U8jHWdTn4r0BS\nT+D5iPhyoe0iYDxwCPAcKWF4t4j4OG9/PyKWzstLAK8BAyLiX5JOJU1Zf7qkq0kBf7dJejXvM0PS\nZsA5EbGDpHHA/hFRSKxosp+eIdbMqs6sWbD++vDPfza0HXtses5k+HB49NGGlOLp0+GOO1KCcWfw\nDLG1rUvPEBsRs4C3JO0AIKk38E3gkbzLH4C/AjflEMByS+Tv7+RC5ztt7MK9wDGlFUn923i8mVnF\n9OwJq6ySbt8AzJgB99wDm2wCjzwC//gHvPpq+rr00nRrx2xhqoviJDsIOEXSROABYGhEvJK3RURc\nAEwErpUkCreCchDgVaRbOfcAT7biesVnUc4AuueHZKcCp3XEBzIz6yzXXgtnnAH9+8NOO6XbN08/\nnZa7d2/Yb4890q2eOXMq1lXrAuritk6t8G0dM7O28W2d2talb+uYmZlZ/XBxYmZmZlXFxYmZmZlV\nFRcnZmZmVlW6ZHEi6WRJUyVNkjRR0ubtOMfukn62MPpnZlYtunVLb/CUvn7zm9R+112w6abpdeMN\nN4Qrr6xsP62+1MsMsa0maStgV6B/RMzJc6Is3tbzRMQIYERH98/MrJosuSRMnNi4bc4cOPxweOop\n+OIX0/qrr1amf1afuuLIycrAfyJiDkBEzIiIt3Ko36/zXCVPSloLPh8heSIH+o2UtGJuHyzpkrw8\nTNJFkh6V9HIpKNDMrB69/z58+in07p3Wu3eHddetbJ+svnS5kRPgPuAXkl4A7idl7jxMmlBtZkT0\nk3QgcCGwOzAmIrYEkPQ/wImkwL/yCUtWjoivSfoKcCeNc3vMzDpNR4XrDR0Ks2en2zklJ50E3/lO\nmoxt9dXTJG277Qb77984b8eBgLYgulxxEhEfSBoAbAvsANwo6f/y5tKkzDcAF+TlVSXdRBpxWQwo\nzTpbnFQmgDvy+Z+TtFJz13fwn5nVkh495r2tA3DVVSl/5/774dxzYeRIuPrqzu+fVRcH/3WQfAtm\nMLARsENEvCapO/BmRKwgaTRwbkTcJWl70rT4O0gaTAoBPDqHA94VEbfmc34eKlh2Lc8Qa2Y1Zeml\n022clrzzDqyxBrz3Xsdf3zPE1jbPENtKktaVtE6hqT8pkRhgv8L3x/JyL+DNvDx4YffPzKzaffAB\nFP9xPHEi9OlTqd5YPepyt3WAnsAlkpYFPgVeBA4HdgO+IGkS8BGwf95/KHCzpHeBB4HVc3sx+I8W\nls3Malb5Myff+lZ67uScc+CII9Jtn549YdiwinXR6lCXv61TIulV0m2aGQvxGr6tY2bWBr6tU9t8\nW2fBuWowMzOrAl3xtk6TImLNSvfBzMzMPHJiZmZmVcbFiZmZmVWVmilOJM3NIX1TJT0t6ThJbX7I\nZmGRNKvSfTAz62il4L+NNkohf+efD6Xn+kePht13b9j35z9Pb/N88klFump1pJaeOfkwIvoDSFoB\nuJ40B8nQSnYKQNIi+IFaM6tDxeC/6dPhgAPSZGvl082feSY8/jj89a+w2GKd3k2rMzUzclIUEdOB\nIcBRAJK6STpH0lhJkyQNye0DJY2WdLOk5yT9uXSOHPT3yzwaM07SppLuk/SSpMPzPj0l3S9pfA4E\n3CO395H0gqRrJE0Bvlw47/KSHpP0rU78kZiZLXQrrABXXgmXXtq4/bzz4N57YcQIWLzNGe9m86ql\nkZNGIuLVXJSsCOxFCu3bXNLiwCOS7su7bgJsALwFPCpp64h4jDTS8XpE9Jd0PjAM2AroAUwFrgBm\nA4Mi4n1JywOPk0L9ANYGDoyIsZDe5c59uRM4OSIeWOg/BDOzFiyMML011oC5c9MoCsAjj8ALL8CE\nCWmUpb3Xd/CfFdVscVJmF6CvpH3zei9S8TAHGBsRbwJIehroQ8PU9KVCYwqwVER8AHwg6WNJvUjF\nya8kbQt8BnwxFyCQCpuxhT4sBjwAHBkRY5rrqIP/zKyerLMOzJwJ990He+9d6d5YpXVU8F/NFieS\n1gTmRsS/83OxR0XEyLJ9BgIfF5rm0vgzl7Z9BhQf4foM6A7sDSwPbBoRc/MsskvkfT4o69IcYBzw\nTaBVxYmZ2cLUEb9uzjuv8forr6SHZFdYIa2vtBJcdx3stBP07g3Ff2/5113XU/6P7tNOO61d56nJ\nZ07yA7GXA5fkpnuBIyUtmrevK2nJ5o5v6pTNtPcC/p0Lkx1oyNVpSgA/BNaXdGIbrm1mVhOmT095\nOkcf3bh9nXXgttvgBz+ASZMq0zerL7U0ctJD0kTSiManwLXABXnb70m3aybk14v/DQxi3nC+5jQV\n4hfAdcAISZNJoyLPle3T6BwREZL2B+6U9F5EXN6Gz2dmVnVKwX9z5sCii8JBB8Fxx6VtUvoC2Gwz\nuPpq2GOP9IrxGmtUrMtWBxz814kc/Gdm1jYO/qttDv4zMzOzuuDixMzs/7d33/F2VOX+xz9fAkhI\nIYSONxLpHQ6hhJ4QyRUvASnCBZSil/JDBJGmonKCiNJEISAiCAEEKQE0oBgUIiEQkPQIQYwE6dJJ\nIEEMz++PWZsz2dn7tJyc3b7v12u/9syaNTNrzoTDc9aaWY+ZVRUHJ2ZmZlZV6iY4kfSRpItz66dL\nOqeSbTIzqwfLLQenn96yfvHFkH9D9OqrYbPNss9OO8HEid3fRqsvdROckM1TcoCk1dJ6h548ldSj\n65tkZlb7VlwR7roL3ngjW8+nXL3nniw4mTgRnnoKrroqy7/z6quVaavVh3oKTj4ErgZOLd6QcuE8\nkPLu/FHSgFR+vaSrJE0CLkz5c/oq84akL6V6N0j6jKT1JD2Ucu1MlrRz2j5a0v658/2qkIfHzKzW\nrbACHHccXHrpktsuuCDrSenfP1tvaoKjjoIrrujeNlp9qaV5TtrjSmCGpAuLyi8HrouIGyUdA1xG\nNg8KwLrAzmmOkp8BuwH/BOak5RuBwcDxqf7eEfGBpI3IMiPvAFxLFhT9RtIqZDl6vrSsLtLMrDOW\nZsbWE0+ErbeGM9MUk4XekyefhEGDFq+7/fYwenTHzunZZC2vroKTlKDvBuBksrw4BYPJkgMC3AQU\ngpcAbs9NPjIB2AN4DvgZcJykdYG3ImJBCjxGSdqGbCr8jdN5H5J0ZUoOeDBwR0R8VKqNzq1jZrWo\nT59sArbLLoOePaG1KZs8nVPj6qrcOnUzCZukeRHRR9KqwBTgOrLrGynpNWCdiPiPpBWAlyJiDUnX\nAfdExJh0jP8CbgPmAmcDPwX+CAyIiDMkNQMrR8SZ6RmVhRGxQtr3TLKhpUOBoyNidok2ehI2M6s5\nffrAvHnw1luw3XZwzDFZAHLOObD77nDuuTB0aEv9730v61npZFqVxXgSttrmSdiSiHiLLMD4Ci0P\nxT4C/G9aPgJ4qMy+L5Al+tswIp4FHgZOz9XvC7ySlo8E8g/RXg98PTvMkoGJmVmtW3VVOOQQuPba\nlg0Ghs4AACAASURBVGGdM8+Es86CN9/M1qdNy4Z0Tjyxcu202ldPwzr5LolLgJNy618DrpN0Blne\nnWPK7AcwiZag7WHg/PQN2TMtYyQdCdwHzP/4IFl25CeBu5byOszMqkr+7ZzTToNRo1rWR4yAF1+E\nXXbJ6vXtm2UpXmut7m+n1Y+6GdaptJQFeQbQFBHzytTxsI6ZWQd4WKe2eVingiR9BngSuKxcYGJm\nZmbtU0/DOhUTEX8EBla6HWZmZvXAPSdmZmZWVRycmJmZWVWp2+BE0vy2ay1Wf6CkmV107iGSxnbF\nsczMqknv3tn33LnZZGxNTS2fm26qaNOsjtTzMydLvBYjafmI+E8lGmNmVg/yrxVvuCFMnVq5tlj9\nqtuek4LUizFB0m+AWZKWk3SRpMdTIsDjSuwzsEyCvyGSxku6XdJTkm7K7fPZVDaZlrw9ZmZm1kH1\n3HOS1wRsERHPpWDk7YjYUdIngIcljSuq/yqlE/wBbAtsDrwMTJS0C9l0+VcDQyNijqRbKdFzY2ZW\nKUuTWK/cvnPmZMM5BaNGwa67dv68Tv5nBY0SnDweEc+l5eHAVpIOTut9gQ2Bv+fqr8jiCf42KjrW\nSwCSpgGfBt4Hno2IOanOTcASPTLgxH9mVj822MDDOra4rkr81yjByXtF6ydFxP35AkkDc6unAi9H\nxJcKCf5y2z7ILS8i+xkW95KUnQ2v2X8amFkFVOpXj3/lNZbiP7pHdjL7Y90/c1LCH4ATJS0PIGnj\nNPV8XmsJ/ooFMBsYKGn9VHZYF7bXzMysodRzcBJllq8hm2p+Snp1+Ge0BB+FelcCR6Vhm03IJfij\nxLMkEfEB2TDOvemB2FdL1TMzq3X5t3UKz5wUPvmEgGZLw4n/upET/5mZdYwT/9U2J/4zMzOzuuDg\nxMzMzKqKgxMzMzOrKg5OzMzMrKo0ZHDS0aSAnTh+s6TTluU5zMyqRT4Z4FZbVbQpVicaMjihA6/5\nSurMz8iv5JhZw1CH38Uwa12jBicASFonJfibKmmmpF1T+XxJF6d5TnaW9N2UKHCmpJ/n9t9A0u8l\nPZGOs0nFLsbMzKxONMr09eUcDtwXEeenHpLCTLErA5Mi4nQASU9GxPfT8g2S9o2Ie8iS/R0fEX+X\ntBPZ5G3Duv8yzKyeVeMU8MsikWA5XZCqpSp/hlZeowcnjwO/lLQCcHdETE/li4AxuXp7STqDLGjp\nD8yS9CCwC3C7Wvo0V2zrhE78Z2Zm9aqrEv815AyxkuZFRJ+0vDawL/BV4McRcWPR9pWAucCgiHhR\n0jlkz5RcCjwdEeuWOP45wPyIuKSo3DPEmlnd6dMH5s3LHogdMQJmzuy6Y3uG2NrmGWI7QdKngNci\n4hrgWqCpRLWV0vcbknoDXwCIiHnAs5IOTseSpK27odlmZmZ1rVGDk0L3xVBgmqQpZEHHT4u2ExFv\nA78AZgH3AY/ljnME8JX04OwsYL8S5zAzq2v5t3WefhoGDGj5jBlTfj+zchpyWKdSPKxjZtYxHtap\nbR7WMTMzs7rg4MTMzMyqioMTMzMzqyoOTszMzKyq1HRwImlRmnp+mqTJknZuxz7jJQ3qovMPkvTT\ntmuamdWvHj2gqQm23RYGDYJHH23Z9vjjMGQIbLxxtm3ffWHWrIo11WpErc8Q+35ENAFIGg78EBjS\nxj5BF7zmK2n5iJgMTF7aY5mZ1bKVV4apU7PlcePgW9/Kppx/9VU49FC45RYYPDjbPnEizJkDW25Z\nseZaDajpnpMiqwBvAkgaImlsYYOkUZKOKt5B0lckPS3pMUm/kHR5Kh8haZKkKZLul7RmKm+WdKOk\nh4EbJO1ZOI+kHSU9kvaZKGnj7rhoM7Nq8s470L9/tjxqFBx9dEtgArDrrrD//hVpmtWQWu856Slp\nKtksruuQTapWyhK9JZLWBb5DNivsfOABYFraPCEiBqd6/wecCZyetm0K7BYRH0gakjvkU8DuEbFI\n0meA84GDl+7yzMw6rjuT3DU3w4IF2bDOwoXw8svw4IPZtiefzIKT1vZtj/amanFyv/pR68HJgtyw\nzmDgRqA9nYUCdgT+nGaARdLtQKG3Y4Ck24C1yZL5/SOVB/DbiPigxDH7kfWmbJjqrVDqxE78Z2b1\npmfPlmGdSZPgS19qea4kP+/kTjtlOXiGD4ef/KT722nLXlcl/qv14ORjETFJ0uqSVgf+w+JDVj1L\n7VK0np/B7nLg4oi4R9KeQHNu2/tlmvB94E8RcYCk9YDxpSo1O7Q3s2Wskr9mBg+G11+H116DLbaA\nKVNgv5TY47HHsuns77knW29PO8ePzx6otdpQ/Ef3yJEjO3WcunnmRNKmQA/gDeA5YHNJK0rqB+xV\nVD2AvwB7SuonaXngIFoClr7AS2n56PxpWmlCfp9jOnsdZma1bPZsWLQIVl8dvvpVuP76xd/eee+9\nxXPxmJVS6z0nhWdOIAscjkzJa55PwzKzgGeBKcU7RsRLks4HHid7kHY28E7a3AzcLuktsmdR1ivs\nxuI9Lvn1C4HRkr4D3IsT/5lZgyg8cwLZMM4NN2QByFprwa23wllnwYsvwpprwhprwPe+V9n2WvVr\n6MR/knpFxHup5+RO4NqI+M0yPJ8T/5mZdYAT/9U2J/7rnObU8zIT+MeyDEzMzMysfWp9WGepRMQZ\nlW6DmZmZLa7Re07MzMysytRMcCJpfomy4yV9KS0fLWmd3La5kvov4zZ9fH4zM2td794ty4MHZw/R\nrrde9qBsU1P2+ec/K9c+qx61NKyzxJOkEfHz3OpRZM+OvJyrv0xfWCs6v5mZtSL/CvGkSdn36NEw\neTJcdlll2mTVqWZ6TkpJuW5Ok3QQsD3wq5TbZqVU5WspW/EMSZvk98kdY5akT6XluyQ9kcqOzdWZ\nL+m8lP340aJcO6el5WMlPZ7q3CGp1MRvZmaWE7H4LLJmUOPBCWmekYgYAzwBHB4R20XEwrT9tYgY\nBPyMltw4xf8Z5Ne/HBHbAzsAJ0taNZWvDDwaEdsCDwHHlth3TETsmOo8BXylC67PzKyueUI2K6WW\nhnXao/if+Z3pewpwYDv2P0XS59PyAGAjskna/h0R96byycDeJfbdStJ5ZNmRewN/6EjDzawx1XtG\ni6W5vsK+XZCqZZmr9/vY3eotOCnuFSkk6FtEy7UW591ZCSBlGB4GDI6IhZIeLGwDPszV/4jFf26F\nc14P7BcRMyUdBQwp1UAn/jMzs3rlxH8tCr0l88jy27RlLrAvgKTtgE+n8r7AWykw2RQY3M5zF87f\nG3hF0grAF4EXSu3gxH9mltfovxJae96kudmJ/2pNVyX+q6XgZGVJz+fWf5y+8z0XV0l6H9ilaN98\nDpwxwJGSZgGPAU+n8vuAEyQ9mcoeLdq/1LHyy99Nx3stfedemjMzs/ffhwEDWta/8Q3o39/PndiS\nGjq3Tndzbh0zs45xbp3a5tw6ZmZmVhccnJiZmVlVcXBiZmZmVcXBiZmZmVWVug9OJC2SNDVNYX+n\npC57i0bSLyRt1lXHMzOrVz16ZIn9tt4aDjwQ5qdUruPHw4gRi9c9+mgYM6a7W2jVpO6DE+D9iGiK\niK2Bd4Hju+rAEXFsRDzVVcczM6tXK68MU6fCjBnQty/8vJW0qZJfL250jRCc5E0CNgCQNF7SoLS8\nuqRn0/IWkh5LvS3TJW0gqZeke1NSv5mSvpA7xnZp+UpJf0lJA5src3lmZtVv551hzpzW63jWhcZW\nS5OwLRVJPchy4vwpFeUnUMs7AfhpRNwsaXmyn9H/AC9GxP+kY/XNHaPg7Ih4K53nj5K2ioiZy+Ja\nzMyqRXtmuM3XWbQIxo2DYcPad/zx49vOrdPos+zWo0YITnpKmgp8kmzq+qvaqP8IcLak/wLujIi/\nS5oBXCzpR8A9EfFwif0OlXQs2c90HWBzYIngxLl1zKwRLViQPXPy4oswcCCccEJWXm74xsM6tcm5\nddpvQUQ0SepJlil4f+AuFk8AWEjwR0TcImkSWf6d30k6PiIelNRE1oNynqQ/RcT3C/tI+jRwGrB9\nRLwj6br8MfOcW8fM6kl7f6X17Jk9c7JgAfz3f8NvfgMHHACrrQZvvbV43TffhDXWyJaHDHFunVrS\nVbl1GuaZk4hYAJwM/ECSyHpRtk+bDy7Uk7R+RDwbEZcDvwG2lrQOsDAifgVcDDQVHb4v8B7wrqS1\ngH0oPWRkZtbQevaEyy6Ds8/OnivZaCN46SWYPTvb/txzMH06bLttZdtpldUIPScfBwkRMU3S34FD\nyIKM2yQdB9ybq3eIpC8CHwIvAz8AdgQukvRRKj9hsRNETE9DR7OB54FSwz5mZg0rP0yz7baw4YZw\n221w6KFw001wzDGwcCGssAJcey306VO5tlrlOfFfN3LiPzOzjnHiv9rmxH9mZmZWF9xz0o3cc2Jm\nZo3EPSdmZmZWFxycmJmZWVWpu7d1JC0CZuSKbomIC8vU3R/4W2fz46Tp74+MiFM6s7+ZWSPq0SNL\nAFhw2GFw5pnZfCavvJK9bgzZa8a33VaRJlqF1V1wQkr01866BwBjgU4FJxExGZjcmX3NzBpVIQlg\nMQluvhm2267722TVpWGGdST9SNJfUzK/iyTtDIwgm79kqqT1JW0raVKqc6ekfmnf8Wn/xyQ9LWm3\nVD5E0ti0vKOkRyRNkTRR0saVu1ozs9rkdwYM6rPnpJBLp+B84AHg8xGxKWSJ+yLiXUm/BcZGxJ2p\nfAbw1YiYIGkkcA5wKtkEbT0iYidJ+6TyvYvO+xSwe0QskvSZdN6DMTOrI0uTgaOwbyHPTsG3vw1f\n+EIWmBxxRMuwzvDhcMEFS3/ecu2w6lWPwcmC4mGdlCl4oaRrgXvS5+PNqc4qwCoRMSGVjwZuz9W7\nM31PAQaWOG8/4AZJG5IFMyuUapwT/5lZoyvk2SnmYZ3a11WJ/+punhNJ8yJiiYmPJa0IDCPrzRgY\nEcNSgr6xEXFnCk5mRMR6qf4GwG0RMUjSg8BpETFF0urAXyLi05KGpPIRkq4HnoiIUZLWA8ZHxKeL\n2uB5Tsys4fXpA/PmLVk+dChccomDk3rS2XlO6rHnZAmSegG9IuL3kh4B5qRN88iS9pGyCb8labeI\neBj4EjC+A6fpC7yUlo/pmpabmTUW//1mUJ/BSfEzJ78HLgN+I2klsmGcU9O2XwO/kPQ14AvAUcBV\nklYmC2DKBRlRYvlCYLSk77B4IkEzM8spfuZkn33g/POz5fwzJ2usAePGdX/7rPLqblinmnlYx8zM\nGomnrzczM7O64ODEzMzMqoqDEzMzM6sqDk7MzMysqjg4SSTNT9/rSTqsHfUHSpq57FtmZlafeveG\nWbOyN3eammC11WD99bPl4cMr3TqrpHp8lbizCq/RfBo4HLilgm0xM6t7Emy5ZctsscccAyNGwIEH\nVrZdVnnuOVnSj4DdUzLAU1JPykOSJqfPzsU7pO3b5NYflrRVt7bazKwOeLYFA/eclHIWcHpEjACQ\n1BPYOyI+kLQRcDOwQ9E+1wBHA6embMSfiAgP+ZhZh9V7UrquSBzY6Brh5+DgZEnFk8WsCIxKPSOL\ngI1L7HMH8F1JZwBfBq4rd3An/jMzs3rlxH9drJAwMJ/ML5U3AytHxJmF7MYRsYKkgWRJA7dK9a4E\nHgAuALaLiHdKnMMzxJqZJcUJAI85BvbdFw46qHJtsq7lxH9dZx6Qz2rcF3ghLR8J9Ciz3zXAPcCf\nSwUmZmZm1j5+ILZFoUtjOrBI0jRJpwBXAkdJmgZsAswvsQ8RMQV4h1aGdMzMrIVK/D1dqswaj4d1\nuoikdYEHI2KTVup4WMfMzBqGE/9VkKQjgUnAtyvdFjMzs1rnnpNu5J4TMzNrJO45MTMzs7pQ12/r\nSFoEzMgV7R8R/6xUe8zMbEk9esDWW7es3303PPss7L9/lmvngw+yKe3PO69ybbTuVdfBCfB+RDSV\n2iBlz4R7nMXMrLJWXrklv07Bs8/CHnvA2LGwcGGWDPCAA2DQoMq00bpXQw3rpEzCT0saDcwEBki6\nUtJfJM1KE64V6s6V1Jzy6cyQtEkq7y3pulQ2XdKBqXy4pEdS/dsk9arIRZqZ1ZmVVoJtt4V//KPS\nLbHuUu/BSc+UwG+qpDFk85JsCFwREVumIZ6zI2IHYBtgT0lbpn0DeC0iBgE/A05P5d8F3oqIrSNi\nG+ABSasDZwPDUv3JwDe67SrNzGrYggVZz0hTU+nZYd98Ex5/HDbfvPvbZpVR78M6C/LDOmnK+eci\n4vFcnUMlHUv2s1gH2ByYlbbdmb6nAIUk3sOAQws7R8TbkvZN+z2SRotWBB7p6osxM6smXZGArrkZ\nevZcclgHYMKErMfkmWfghBNgiy2W7vyNkDCvXtR7cFLKe4UFSZ8GTgO2j4h3JF0HrJSr+0H6XsTi\nP6tSr0XdHxGHt3VyJ/4zM2uf3XfPnjmZOxeGDoWvfx0GDKh0q6w1TvzXDoVkfrn1gSyerG8bYDTQ\nBKxJNnX9mRFxg6RngUER8aak7YGLImKopB8CK0XEqekY/cgCl8nAXhExJz1vsm5EPFPUHj9/a2ZW\npDgBIMD48XDJJVlwAvCTn8CTT8LVV3d782wpeJ6T0kpFAvl8ONOBqcBs4FfAw60cp7DfecCqkmam\nfDtDIuJ14GjgFknTyYZ0yk5jb2ZmLcrl2MmXn3AC3HcfvPDCknWt/tR1z0m1cc+JmZk1EvecmJmZ\nWV1wcGJmZmZVxcGJmZmZVRUHJ2ZmZlZV6jI4kbQoNzPsFEnrSZrYjv3GS+qSzA1p+vv+XXEsM7NG\n0qNHy4yxTU3w3HPZq8UjRlS6ZdZd6nUStlIJ/3Ztx375V4aXll/LMTPrhHKJAK1x1GXPSSmS5qfv\nIamH5HZJT0m6qUz9jiYEXE3SuFT/F5SeRdbMzMzaUK89Jz0lFeLuf0TEQSzek7EtWS6cl4GJknaJ\niOJcOGdHxFuSegB/lLRlRMwilxBQ0v8jSwh4LHAO8FBEnCfpc8BXluH1mZlVpc7mr8nvV0gECLD+\n+jBmTNee0zl2ql+9BicLSgzr5D0eES8BpFleB7Jkor6OJgTcHTgAICJ+J+mtUid2bh0zs9aVSwRo\n1a+rcuvUa3DSlg9yy8VJ/bo6IeBimh2ym1kdq8SvOP9arR7Ff3SPHDmyU8dpmGdOOqgvWfbidyWt\nBezTjn0eAg4HkLQPsOqya56ZmVn9qteek1YT/pXZ3rIxYnp6ZmU28DztSwg4kizx32FkQ0TPdajF\nZmYGtC8RoNU3J/7rRk78Z2ZmjcSJ/8zMzKwuODgxMzOzquL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"text/plain": [ "<matplotlib.figure.Figure at 0x7f52ab466d90>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "t = extract_vars(country_map, 'edurank_f', 'hasrelig_f') \n", "plot_cis(t)\n", "thinkplot.Config(title='Education rank',\n", " xlabel='log odds ratio education rank')\n", "save_plot()" ] }, { "cell_type": "code", "execution_count": 473, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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zs5rg4MTMzMyqioMTMzMzqyoOTszMzKyq1OzbOpKWAx7Mu18A5gBvkd7K2SQi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\nNAv4dO66a6YkgHtV8ZGYWRlrrQWXXAIjR7YuP/dcuP32bMX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"text/plain": [ "<matplotlib.figure.Figure at 0x7f52ab310f50>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "t = extract_vars(country_map, 'hincrank_f', 'hasrelig_f') \n", "plot_cis(t)\n", "thinkplot.Config(title='Income rank',\n", " xlabel='log odds ratio income rank')\n", "save_plot()" ] }, { "cell_type": "code", "execution_count": 474, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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qCFLMrLp1pEBlaUmvFb5OAOqAmyWNBaZSH1wMA/aRNEHSNrntRlJ15BsbXjgi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\nvC3wfWAyMIl0j8pS+y871g3AP4C5wFjgwY46GTPrWUpXpf/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"text/plain": [ "<matplotlib.figure.Figure at 0x7f52a2ab8e10>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "t = extract_vars(country_map, 'tvtot_f', 'hasrelig_f') \n", "plot_cis(t)\n", "thinkplot.Config(title='Television',\n", " xlabel='log odds ratio television')\n", "save_plot()" ] }, { "cell_type": "code", "execution_count": 475, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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6EfgDcC9wbc5gewzwleb76FaJljfV/cYbpyy1s2en/TFjlkz+du+9qU9T7uG0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\nr51tDxkCTz1V61aaWa018j0qRwM3pZ9NHTj+EOBG4OHyHZJ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"text/plain": [ "<matplotlib.figure.Figure at 0x7f5270b73390>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "t = extract_vars(country_map, 'rdtot_f', 'hasrelig_f') \n", "plot_cis(t)\n", "thinkplot.Config(title='Radio',\n", " xlabel='log odds ratio radio')\n", "save_plot()" ] }, { "cell_type": "code", "execution_count": 476, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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zHsDDNARt95OKB96f1y8EbpB0EPA3YO4nF4n4r6THgZsW8TnM2qz4ds4xx8AF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\nAf1IicrJkvbP2/UD1gMmAp9GxL25fRKwR4mYNpV0LrAiacba+0sF7to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ggtKDMnOp/yXc\n3BNH0Wi5sD6M9B5NI40UHJXbR5Mud8wkJYrPN3Gscs9zDvDrfLllCdJIyZD82jhgt4j4RNIjpHt6\nCsnGYOB0SQtyX4/M7R8CW+f3/U3gkNx+Vo53dv7euyiWV0kJ3gqk8vafSrqKdOlpstIb+hZwAC2/\nn2btQvWXiM2sq5C0XER8mEcPngC2z/el1LR8j8W8iLiw0rFUI0lzI6Kle2OKt78GuDuPiplVDY+c\nmHVN9yg9Grw0afSg5hOTIv6Lqml+b6wmeOTEzMzMqopviDUzM7Oq4uTEzMzMqoqTEzMzM6sqTk7M\nzMysqjg5MTMzs6ry/wGtjwdWNnUf8AAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f51e489dc10>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "t = extract_vars(country_map, 'nwsptot_f', 'hasrelig_f') \n", "plot_cis(t)\n", "thinkplot.Config(title='Hours of newspaper per week',\n", " xlabel='log odds ratio per hour newspaper')\n", "save_plot()" ] }, { "cell_type": "code", "execution_count": 477, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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zcJqk7wHfonEOo0aclNDMzGpVWyQl9Mq0maS5EdErJyo8qdCjUg+sGBGnSOoK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\nmNzEfgr1LgA+I2mOpJnA0Ij4JzAcuFHSLLLLPl9sl3dgZlV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"text/plain": [ "<matplotlib.figure.Figure at 0x7f52ac04c810>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "t = extract_vars(country_map, 'netuse_f', 'hasrelig_f') \n", "plot_cis(t)\n", "thinkplot.Config(title='Internet',\n", " xlabel='log odds ratio Internet', \n", " xlim=[-0.15, 0.1])\n", "save_plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Make a scatter plot of fraction who have religion versus odds ratio of netuse." ] }, { "cell_type": "code", "execution_count": 478, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def plot_scatter(t):\n", " plt.figure(figsize=(8,8))\n", "\n", " codes, names, params, lows, highs, means = zip(*t)\n", "\n", " for param, mean, code in zip(params, means, codes):\n", " plt.text(param, mean, code, fontsize=10, color='blue', \n", " horizontalalignment='center',\n", " verticalalignment='center')\n", " \n", " corr = np.corrcoef(params, means)[0][1]\n", " print(corr)" ] }, { "cell_type": "code", "execution_count": 479, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "-0.38248029366\n" ] }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f52abfd3a50>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "t = extract_vars(country_map, 'netuse_f', 'hasrelig_f')\n", "plot_scatter(t)\n", "thinkplot.Config(title='',\n", " xlabel='log odds ratio Internet',\n", " ylabel='fraction with affiliation',\n", " xlim=[-0.15, 0.05])\n", "save_plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Make similar figures for the second model, with degree of religiosity as the dependent variable." ] }, { "cell_type": "code", "execution_count": 480, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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fC/xNkPQvSb8DkPT1XBxwgqSrJS2V278saZykiZKGFe6xuaThuWhfWwKOx4EN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\nnXpKFi1KA2PPP7+izTMrq61y61TjY50DgGsjYsOI2CgivgT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"text/plain": [ "<matplotlib.figure.Figure at 0x7f52a9675a50>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "t = extract_vars2(country_map, 'inwyr07_f', 'rlgdgr_f')\n", "plot_cis(t)\n", "thinkplot.Config(title='Interview year',\n", " xlabel='linear coefficient interview year')\n", "save_plot()" ] }, { "cell_type": "code", "execution_count": 481, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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Aw4C/AlOAfhHx73w9ETG40fOcmbbGde+egpKiAQPSKMqVV8LFF8NWW1Wmb9Zx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\nE7iblHCw4MvAMfmm25nAAWWuYWZmnVjxUz/PPAN9+jS9brqp+eOsMrr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"text/plain": [ "<matplotlib.figure.Figure at 0x7f52abcef710>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "t = extract_vars2(country_map, 'yrbrn60_f', 'rlgdgr_f')\n", "plot_cis(t)\n", "thinkplot.Config(title='Year born',\n", " xlabel='linear coefficient year born')\n", "save_plot()" ] }, { "cell_type": "code", "execution_count": 482, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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BNgHGNnPsKGBmRGwlaWngCUkPpmP9gE0i4o2Uon4zsoRt7wGvA1en634AnAD8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\nSLqMND3/ncAsYNOIeDHvT0ScVnY+zxBr1oI8Q2x9GTgwBSJFhx2Weks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"text/plain": [ "<matplotlib.figure.Figure at 0x7f52a2a62c10>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "t = extract_vars2(country_map, 'edurank_f', 'rlgdgr_f')\n", "plot_cis(t)\n", "thinkplot.Config(title='Education rank',\n", " xlabel='linear coefficient educational rank')\n", "save_plot()" ] }, { "cell_type": "code", "execution_count": 483, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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8FhGXpyJ/90n6K/CIpB6S/piK/U2StDuApC6Szk+F/KZKOrxwLUmnpGKCUyT9NrWtI+kv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\nrCrpYeD7wP2Fc0WOKg4HPinpeJZ/K+gi4GhJc4AtgBavHpt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"text/plain": [ "<matplotlib.figure.Figure at 0x7f52ab154cd0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "t = extract_vars2(country_map, 'hincrank_f', 'rlgdgr_f')\n", "plot_cis(t)\n", "thinkplot.Config(title='Income rank',\n", " xlabel='linear coefficient income rank')\n", "save_plot()" ] }, { "cell_type": "code", "execution_count": 484, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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+CvxU0jhJa9K80szJr4DHgYdJb/d48tmsjf76V9hnn6Zt++2XlnbAAYpZZ+DC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\nm0eA/XKRv5WAAbn9eaC3pDX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4GSAi3s61dOZHAMPy9iRgSkQ8k8fyDNAbmCM4ceE/s45j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\nZp2pqSkLaf/hh/DZz8KvfpUlB4QsaWBTE6yxRhbQbfJkWH7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"text/plain": [ "<matplotlib.figure.Figure at 0x7f527103a090>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "t = extract_vars2(country_map, 'tvtot_f', 'rlgdgr_f')\n", "plot_cis(t)\n", "thinkplot.Config(title='Television',\n", " xlabel='linear coefficient television')\n", "save_plot()" ] }, { "cell_type": "code", "execution_count": 485, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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BevWa/7yBA2Hw4LJ00czaUNleT861dr4J9I2ITyWtCiy/qOsi4pzC7veBPwNz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\nMltdVTuiAtwNfE/Sk8A9wE0pgSAAkjYBRpIteb8IuFvSocB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"text/plain": [ "<matplotlib.figure.Figure at 0x7f5280894cd0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "t = extract_vars2(country_map, 'rdtot_f', 'rlgdgr_f')\n", "plot_cis(t)\n", "thinkplot.Config(title='Radio',\n", " xlabel='linear coefficient radio')\n", "save_plot()" ] }, { "cell_type": "code", "execution_count": 486, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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AD4ED8/ENwA2SZgH3A2vm7eXF/ZpbNltk5XNOdt89zTv55S/h6KPTsE/v3jByZNWaaGbW\nbrr8sE6JpJdJwzRvd+A9PKxjVgUe1jGrDg/rLDpHDWZmZjWgKw7rNCki1q52G8zMzMw9J2ZmZlZj\nHJyYmZlZTenUwUlTBfwkjc1J1ipx/6NyNlmzqjrrLNh0U9hss/RWz/jxMHgwTJhQ7ZaZmS28Tjvn\npIUCfuWv+HaYiPhtJe5j1pJHH4U770x5Tnr0gLffho8+SmnstdBz5M3Mqq8z95w0WcCveICkA3Mh\nv6mSzsnbjpZ0buGYYgG/b+eif5Mk/UZSt7x9jqQzJT0p6dFC8b8GSSfl5SMkjc/H3CipZ0W+Bevy\npk+HFVZIgQmkQoCrrFLdNpmZLYpO23NC8wX8AJC0KnAOsAXwDnCPpL2AG4FHSQX8AL4JnJkL9n0T\n+HJEzJV0GXAwcDXQC3g0In4i6RfAEcBZzNtDMzoirsj3/hlwODCiYx7dakU1iuCV33O33eCnP4UN\nNoBdd4UDDoAdd2zduR3dNjOztui0wUkzBfx+mHcL+BIwNiJmAkj6E7BjRNwq6SVJWwMvABtGxCOS\njgW2BJ5Q6gvvCUzP1/s4Iu7MyxOArzbRpAGSzgSWJmWh/UtT7XbhP2tvSy2V5paMGwdjxqTg5Jxz\nqt0qM+uK2qvwX6cNTgAi4jPgAeABSVOBocXdZYcXR9+vJfWSPAvcVNg+KiJ+1MStPiksf8a831vp\nPiOBPSNiqqShwOCm2tzgXy3rSq38cXbrBjvtlD4DBsCoUWl7eULiWmmvmdWn8l+6hw8f3qbrdNo5\nJ80U8Hs1LwcwHthJ0vKSugPfAsbm/TcDe5Pq51ybt/0V2E9Sv3z95SStsaBm0Bj09Aam54rG327z\ng5ktpL//HZ5/vnF90iRYc83mjzczq3WdueekuQJ+NwJExPQ8zDOGFEDcERG3533vSHoa2Cginsjb\nnpH0E9LclG6k3pLvAq8xf1G/aGL5NOBxYEb+2btDntqszJw5cNxx8M47sNhisN568Nvfwn77+W0d\nM+ucXPivglz4z6w6XPjPrDpc+M/MzMzqgoMTMzMzqykOTszMzKymODgxMzOzmtKZ39ZpkqS5wJTC\npj9HxLnNHLsX8PeIeKaN99oSODQijm/L+WaV0L07DBzYuH7ggXDKKakw4PTp0DMXWlhvPbj++qo0\n0cxsHnUXnADvR8SgVh67D3A70KbgJCImkDLGmtWsXr1S7pNyElxzDWyxReXbZGbWki4zrCPpHElP\nSZos6Ze5qvEQ4Je50N/akjaX9Fg+5qacQwVJY/P5j0t6TtL2eftgSbfn5a0kPSJpoqSHJa1fvac1\nax2/2W5mtagee056Sir+nng2cD+wd0RsCCCpb0S8K+k24PaIuClvnwIcExHjJA0HzgBOICVa6x4R\nW0vaPW8vr6/zDLBDLhq4a77vfh34nFaH2ju9fEMDfPABDCr0Jf7oR7D//ikwOfjgxmGd3XaDX/yi\nY9ritPlmtjDqMTj5oHxYJ6ev/1DS74E78ufz3fmYpYGlI2Jc3j4KuKFwXKkGz0SgfxP3XQa4StK6\npGCmR1ONc+E/q7SePT2sY2aV4cJ/CyH3ZmwF7ELqzTg2L8P8BQJLyjPafZR/zqXp7+1nwF8jYh9J\na9JYx2ceLvxnLamlvx611BYz6xzaq/BflwhOJC0FLBURd0l6BHgx75oN9AWIiP9ImiVp+4h4CDiE\nZgKMZvQF3sjLh7VPy806luecmFktqsfgpHzOyV3AJcCtkpYk9YickPddC1wh6Thgf2Ao8BtJvUgB\nTHNBRnkhQIBzgVG5eOCdNN8jY1ZR5XNOdt8dzj47LRfnnPTrB/fcU/n2mZmVc+G/CnLhP7PqcOE/\ns+pw4T8zMzOrCw5OzMzMrKY4ODEzM7OaUpfBiaSVJV0r6QVJT0i6U9J6i3jNNSUdWFjfUtLFi95a\ns8rp3Xve9ZEj4bjj0nJDA3zhC2ny7IABcNNN5WebmVVG3QUnkgTcDNwfEetGxBeB/wVWKhzTlreU\n1gIOKq1ExAQX/LPORmp+XYITT0wJ226+GY48srJtMzMrqbvgBNgZ+DgiLi9tiIgpQHdJ4yTdCkyT\ntISkKyVNyfVwBgNI6i/pQUkT8mfbfJlzgB1yHZ7vu66O1YPyl8dK6+uuCz16wIwZlW+TmVk95jnZ\nlKYrBQsYBGwSEa9KOgmYGxEDJW0A3JODireAr0bER3ko6BrgS8CpwMkRMQRS0b/CtV1XxzqF8pwn\nb78Ne+01/3ETJkD37rDCCpVrm5lZST0GJy0lEhkfEa/m5e1IydmIiOckvQqsB/wDGCFpM1Kq+tJc\nlZbe025VXR3rmmolDXxDw/x1dkaNgieeSMsRcOGFcOWV8Oyzac5JadinVp4BaqstZtYx6jE4eYrm\ney3eK1svDzhK2WPfjIhDSgUDW3HPVtXVARf+s9pSHNYpzTk58US4/XY44wwYMmT+eSpmZs1x4b9m\nRMT9ks6WdEREXAEgaSCwQ9mh44CDgTF5OGcN4DlSjZx/5mMOBbrn5dlAn2Zu2+q6Oi781/V0lj/y\niMZgZcgQ+P3v4c9/hoMO6jzPYGbV1V6F/+pxQizAPsCu+VXiacBZwJvMO+RzGdBN0hRSjZ2hEfFx\n3j5U0pPABsCcfPxkYK6kJyV9P1+rWFfn55ImkoIZ56i3mtTU2zqlbcVlgNNPh7POqlzbzMxKXFun\nglxbx6w6XFvHrDpcW8fMzMzqgoMTMzMzqykOTszMzKymODgxMzOzmtIlgxNJcxZ81CJdvyFnoDWr\neaVigK+8kgr+mZlVW5cMTliIV30lteU78is51mk4yZqZ1ZquGpwAIGmVXORvkqSpkrbL2+dIOi/n\nOtlW0mmSxudjfls4fx1Jd0l6Il9ng6o9jJmZWZ2ouwyxC+kg4O6IODv3kPTK23sBj0XEyQCSno6I\nn+XlqyTtERF3AJcDR0XEC5K2JiVw26Xyj2FdQXtmaV2Ua7V3tlhnnzWzcl09OBkP/EFSD+CWiJic\nt88FRheO+4qkH5CCluWAaZLGAF8GblBjv/jiC7qha+uYmVm9aq/aOl0yQ6yk2RHRJy+vDOwBHANc\nEBFXl+1fEngF2DIiXpd0BmlOyYXAcxGxahPXPwOYExHnl213hlirOX36wOzZaULskCEwdWq1W9T+\nnCHWrDqcIbYNJK0BzIiI3wG/BwY1cdiS+edMSb2B/QEiYjbwsqT98rWUCwyamZnZIuiqwUmp+2Jn\n4MlcsG9/4OKy/UTEO8AVwDTgbuDxwnUOBg7PE2enAXs2cQ+zmlZ8W+e552D11Rs/o0c3f56ZWUfp\nksM61eJhHbPq8LCOWXV4WMfMzMzqgoMTMzMzqykOTszMzKymODgxMzOzmlI3wYmkzySdV1g/Oecb\nMbM26tYNTj65cf2882D48Mb1yy+HjTZKn623hocfrnwbzaz+1E1wAnwM7CNp+by+UK/FSOre/k0y\n69wWXxxuvhlmzkzrxdeO77gjBScPPwzPPAO/+Q0cdBC89VZ12mpm9aOegpNPSLVuTijfIam/pPsl\nTZZ0n6TV8/aRkn4j6THgXElTJPXNCdVmSjokH3eVpF0lrZkL/E3In23z/lGS9irc70+S9ixvh1ln\n06MHHHkkXHjh/Pt+8YvUk7Lccml90CAYOhR+9avKttHM6k+91da5DJgi6dyy7ZcCV+bU9IcBlwD7\n5H2rAttGREj6NbA98BrwYl6+GtgGOCof/9WI+EjSesA1wJdI2WVPAG6VtDSwLXBIRz2kWUkliuZ9\n97swcCCcckpaL/WePP00bLnlvMd+8YswalTl2uaigWb1qa6Ck4iYLekq4HvAB4Vd2wB75+U/AqXg\nJYAbCpnRxgE7Aq8CvwaOlLQqMCsiPsiBxwhJm5GKA66f7/ugpMskrQDsB9wYEZ811UYX/rPOpk8f\nOPRQuOQS6NkTWsoj6ByDZl2bC/+VKRXrk7QsMBG4kvR8wyXNAFaJiE9zBeI3IqKfpCuBOyJidL7G\nF4DrSYX+fkxKZ38fsHpE/EBSA9ArIk7Jc1Q+jIge+dxTSENLBwDDIuLZJtroDLHWqZSKAs6aBVts\nAYcdlgKQM86AHXaAn/4Udt658fjTT089K8VJs7XAGWLNqsMZYrOImEUKMA6ncVLsI8C38vLBwIPN\nnPtPYAVg3Yh4GXgIOLlwfF9gel4+FChOoh0JfD9dZv7AxKwzW3ZZ+OY34fe/bxzWOeUUOPVUePvt\ntP7kk2lI57vfrV47zaw+1NOwTrFL4nzg2ML6ccCVkn4A/As4rJnzAB6jMWh7CDg7/4Q0p2W0pENJ\nRQDnfH6RiH9Jehq4eRGfw6xmFN/OOekkGDGicX3IEHj9dfjyl9NxffvCn/4EK61U+XaaWX2pm2Gd\napPUC5gCDIqI2c0c42EdsyrwsI5ZdXhYp4ok7Qo8DVzSXGBiZmZmrVNPwzpVExH3Af2r3Q4zM7N6\n4J4TMzMzqykOTszMzKymVDQ4aUtxPkk7ldLE5/WRkvZdxHa8Imm5RblG4VpzFnyUWf1oqRhgQwOc\nf35VmmVmdaTSPSdtKc63M/DlwnqbX3fJNXO6Lco1muDXb6xLaakYoBZ6Tr6Z2fwqHZy0VJyvn6Qb\nJY3Pny9LWpNU0+YESRMlbZ8P31HSw5JeLPaiSPpBPndyzuZaKvr3nKRRwFTgC2X3vVnSE5KmSTqi\nsH2OpDMlPSnpUUkr5u1r5fUpks4sHL9KLgo4SdLUQlvN6kpLxQDNzNpDNd7Waa4438XAhRHxsKQ1\ngLsjYmNJvwFmR8QFAJL+B1g5IraTtBFwGykx2m6kzK5b5d6RWyXtAPwDWBc4JCLG52sU7/udiJgl\nqScwXtKNOctsL+DRiPiJpF8ARwBn5Xb+KiL+KKmYC/Og3OazlW6wVLt9Y2aLoD2L45WuVV4MsFrt\nMLP6VPHgpIXifLsCGxUChz6SSv+DL0YTAdySr/WMpFI+yt2A3SRNyutLkYKSfwCvlgKTJhwvqVQU\ncHVgPWA88HFE3Jm3TwC+mpe/TGNF4z8Cv8jL44E/5No9t0TE5KZu5sJ/Vg/KiwGamUH7Ff6rVp6T\ni2gszlciYOuI+Lh4oJoexC4eUzzg5xFxedn5/YH3mrqIpMHALsA2EfGhpDHAknn3J4VDP2MB31VE\njMs9NXsAIyVdEBFXlx/X4F/5rMI66q/c97/fWAywmu0ws9pR/kv38DZWAa3Kq8TNFOe7h9SbAoCk\nzfPibKBPKy77F+A7pd4WSatJ6reAc/oCs3JgsiGwTSvu8zDzFhEstXcNYEZE/A74HTCoFdcy67Sa\nKgbo6gxm1h4qHZyUF+dbobD+PeCLeTLrU8CRefvtpDd8ihNii9cJgIi4F7gGeFTSFFLw07uJ44vr\ndwOL5YJ9PwcebaatUVg/Hjgm32PVwvadgSclTQS+SZqbYlZ3yosB/vvf8+4780xYffX0WWONyrfP\nzDo/F/6rIBf+M6sOF/4zqw4X/jMzM7O64ODEzMzMaoqDEzMzM6spdRmcSFpJ0jU5g+wTkh4p5DIx\ns3bw1ltw0EGwzjrwxS/Cl78Mt9wCY8fC0kvDoEGw8cbwk59Uu6Vm1tnUXXCSs7PeAoyNiHUi4ouk\nV3/L09ZXK8eLWacXAXvvDYMHw4svwhNPwLXXwj//md7Y2XFHmDQJJk6E0aNhwoRqt9jMOpO6C06A\nrwAfFZOxRcRrETFC0jBJt0n6K3CvpF6S/iDp8fyq8p4AkrpL+mWhTk/ptWYknZrr6jwp6ed52zqS\n7sq9NA9K2qDSD21WSfffD0sskWrslKyxBhx77Ly5TpZcEjbfHF56qfJtNLPOqx57DzYhZZ9tziBg\nQES8I+ls4K8R8R1JywCPS7oP+DbwTq7TswTwkKR7gI2APYGtcuK2ZfI1LweOiogXJG1Nqh+0Swc9\nn1nVPfVUyg67IG+/DePHe2jHzBZOPQYn8yQSkfQrYDtSyvtfAfdGxDt5927AEEkn5/UlgDXy9gGS\n9svb+5Jq7uwC/CEiPgTIAU5vYFvghkKq/cU74sHMFlV7pZBffvl51489Fh56CBZfHH75Sxg3LvWY\nPP88HH00bLJJ29rglPdmXVM9BidPAfuWViLiGEnLA0/kTeV1dr4REc8XN+Qg49icdba4/WvMW8sH\n0tDYOxElhjUgAAAgAElEQVTRqnT1Lvxn9WCTTdJckpIRI2DmzDQxFmCHHeD22+GVV2DnnVMdntVX\nr0pTzayC2qvwX11miJX0GDAyIn6T19cAHgAagC9GxHF5+1lA38L6oIiYJOkI4L+B/SPiU0nrA/8E\ndgBOB3aNiA8kLRsRsyQ9DFwYETfmCbkDImJKE+1yhlirG9tsA8OGpZ4RgNdeg512gpEj4bzzUnAC\ncNFF8PTTcPnlzV2p4zlDrFl1OEPsvPYGdpL0kqTHgZHAKXlfMTr4GdAjT3CdBpTKJ/4OeBqYKGkq\n8Guge0T8BbgNeELSJOCkfPzBwOGSngSmkealmNW1W26BBx6AtdeGrbdOgcq556Z9xfo7Rx8Nd9+d\n3uQxM2uNuuw5qVXuOTGrDvecmFWHe07MzMysLjg4MTMzs5ri4MTMzMxqioMTMzMzqyl1G5xIWlnS\ntZJeyGnl75S0XrXbZVZvpk+Hb30L1l035Tn5+tdhtdVSYcCSY46Bc86pXhvNrHOpxyRspeJ/NwNX\nRsS38raBwErA8y2du4Br4tdtzBpFwD77wGGHpcJ/AFOmwG23wcknw9VXp+J/Dz2UfpqZtUa99pzs\nDHxcVvxvCnCEpL1K2yT9SdKeuSDgrZLGSPq7pNPz/v6SnpM0CpgCrC5pTuH8/SRdmZf3lzQ1FwR8\noFIPalZNY8aklPXFAoADB8KPf5yqFY8Zk1Lb/+pX0L179dppZp1LXfacAJsCTRVp/z1wAnCrpKVJ\nNXEOAQ4FvkQqGvgB8DdJdwIzgXWBQyJiPKRcJYXrBY1J3U4DdouINyX1bf9HMmu9StSkaWiAadNg\nyy3n3yfBr3+dUtfvvTdsv33Hts81eMzqS70GJ00OvUTEg5Iuk7QCsB9wY0R8lkds7omIWQCSbgK2\nB24BXi0FJs0oJZd5GBgl6XrgpuYOdm0dqydqIbXSZpvBgAHw3e9Wrj1mVl3tVVunXoOTp0jBR1Ou\nIvWWHAAMa+YYAZ/l5fJCgcXAp+fnGyP+n6StgK8DEyRtGRFvl1+4wb/iWQVU6q/ZJpvAjTc2v79b\nt/Qp5/8MzOpT+S/dw4cPb/7gFtTlnJOIuB9YIhfwA9KEWEnbk+rsfD8dFs8WTvuqpGUl9QT2IvWE\nNPV74VuSNpTUDdincP11ImJ8RJwBzAC+0O4PZlZjvvIV+OgjuOKKxm1TpqQJsGZmbVWXwUm2D7Br\nfpV4GnAW8GZE/ItU1O/KwrEBjAdGA5NJwz0TC/uKfgjcQQpe3ijsPzcXEJwKPNxUVWKzenTzzXDf\nfelV4k03TZNhV1ml2q0ys86syxX+k9SL9ObNoIiYnbcNA7aMiOM6+N5+E9msClz4z6w6XPivFSTt\nSuo1uaQUmGTFt27MzMysiup1QmyTIuI+oH8T20cBoyreIDMzM5tPl+o5MTMzs9rn4MTMzMxqSlWD\nE0k/ljRN0mRJk3KekAWdM1zSV/Ly9/Orv+3RlgZJJ7XTtUZK2rc9rmXWmTVVFPD551NytqKGBjj/\n/Ko00cxqUNXmnEjalpSwbFBEfCJpOWCJBZ2X84iUHA9cTUo5vyhtWYz2nRDrCbbW5TVVFHDq1Hmr\nFZe0lGnWzLqeavacrAz8OyI+AcjZVFeTNBpA0l6S3pe0mKQlJb2Yt4+UtK+k44BVgTGS7pc0JPe+\nTMrF+l7Kx28paaykJyTdLWnlvH2spAsl/Q34XrFhko6QND4X8bux1DuT732xpIclvVjqHVEyQtKz\nku4FVqTpBG5mXUZTRQEHDIAvNJGe0G/Ym1lRNd/WuQc4XdJzwH3AdcAjwOZ5/w7AVGAroAfwWN4e\npOyul0o6ERhcSBN/O4Ck64CxuUfkUmBIRMyUdAApGdvh+To9IuJL+Zxij8zoiLgib/9ZPn5E3rdy\nRGwnaSPgNlLitn2A9YGNSEHX06Qig2Y1qyNTyLdUFBBSxeJBgxrXp0+HH/ygY9tVXu7DKfTNalfV\ngpOIeE/SlqQgZGdScPJD4EVJG5KqBF8A7Ah0B8a15rqSTgHej4hfS9qUVGn4vlzcrzspq2vJdc1c\nZoCkM4Glgd7A3aVmk4oBEhHPSFopb98RuCZnWHtT0v3Ntc+F/6yraGmoZp11YNKkxvXhw917YlYP\n6qLwX0R8BjwAPJDTvg/N6/8NfAL8lZR/pBtw8oKul5Os7UsKFiANrTwVEV9u5pTmivqNBPaMiKmS\nhgKDC8d8XLxl4bxWDeO48J/Vio7+q7igooDN6Yh2jR0L/j3ArON1+sJ/ktaXtF5h0yDgFeAhUmG+\nRyLi38DywPoR8VQTl5kN9M3XWxP4FfDNiPgo738O6Cdpm3xMD0kbt9Ss/LM3MF1SD+DbLHhy64PA\nAZK6SVqF1BNk1qU1VxTwH/+oXpvMrHOoZs9Jb+BSScsAnwLPA0eS3rxZkfQ/fEiF+FZq8gpwOXC3\npDeAscBywC15COf1iNhD0n7AJZKWJj3vhaQ5IU0pBSGnAY+Tqgs/nttafsznyxFxc369+WngNdLc\nGbMu7+ab4fvfh1/8ApZcEtZaCy68sOkhH7+xY2YlXa7wXzW58J9Zdbjwn1l1uPCfmZmZ1QUHJ2Zm\nZlZTHJyYmZlZTXFwYmZmZjWlUwcnTRUOzGnpm8lL2eb7zGli26qSbmjP+5h1FWedBZtuCpttljLF\njh+f8pBMmJD2v/wyrL8+3HtvVZtpZlVS1SRsi6KFwoEdUXRvvutFxBvA/u18H7O69+ijcOedKUNs\njx7w9tspH4qUPv/8J+y+O1xwAXz1q9VurZlVQ2fuOZmvcGBEvFk8QNKBkqZImirpnLztaEnnFo4Z\nJunSvHxLLhA4TdIR5TeUtIKkRyTtLqm/pGl5e39JD0qakD/bduBzm3Vq06fDCiukwARgueVglVXS\n8uuvw9e+BmefDXvsUb02mll1ddo8J5KWImWT7UUuHBgRD0oaA5wETAceBbYA3iEVGrwEeBh4NCLW\ny9f5P+DMiHhE0rIRMStXIR4P7JjXZwPrkAr9/Tgi/iqpP3B7RAzIx38WER/lrLfXlAoKlrXZeU6s\nplS6mkJDA7z3Hmy/Pbz/Puy6KxxwAOy4YxrWmTo1DfkcfXR7t3Us81ahaK/rmllL2prnpNMO6zRV\nOFDSD/NukQoHjo2ImQCS/kQKNm6V9JKkrYEXgA0jopTR9XhJe+fl1YH1SEHK4qQ6P9+NiKYKEC4O\njJC0GTCXVKG4SS78Z13dUkuluSXjxsGYMSk4OeecNKSz665w9dUwdCj07FntlprZwmqvwn+dtuek\nnKR9SYUD+5CKBK4G7BsRQ/P+w4GNI+IkSYcBmwLPAhtExMmSBgM/A74aER/mHpgzcm/MHOAG4I2I\n+HG+Xn8ae04agF4RcYqk7sCHEdGjiTa658SszOjRMGoUzJ4N552XgpMXXoBbb4Xu3dvnHs4Qa1Yd\nXS5DbDOFA1/Ny0Hq8dhJ0vI5YPgWqW8X4GZgb+BA4Nq8rS8wKwcmGwLbFK4dwHeADSWd0kRz+pKG\nkQAOBdrpn1Sz+vP3v8PzzzeuT5oEa66ZliW46CLo2xcOP7w67TOz6uu0wQmpGN9ISU9JmgxsCDSU\ndkbEdOCHwBjgSeCJiLg973uHVKRvjYh4Ip9yN7CYpKeBn5PmqxQuF0EKZr4i6WjmfSvoMmCopCeB\nDYD5Xj02s2TOHBg2DDbZJL1K/Oyz88/7GDUK3nwTTj21Gi00s2qrm2GdzsDDOmbV4WEds+rocsM6\nZmZmVp8cnJiZmVlNcXBiZmZmNcXBiZmZmdWUugtOmirS18rzGiSd1E5tGJnzrphZG/TunX5+9hl8\n73swYAAMHAhbbQWvvFLVpplZBXTaDLEtaOvrMO35Gk1HFB806zKU5/Zfd116pXjq1LT+xhvQq1f1\n2mVmlVF3PSdFkk7Nhf+elPTzvG0dSXflAn8PStqgifOOkDQ+n3djrp1T6hG5WNLDkl4s9Y4oGSHp\nWUn3AiuSUuib2SKYPr2xKCDAqqvCMstUrz1mVhn12HMCgKTdgT2BrXLW19I/aZcDR0XEC7m+zmXA\nLmWnj46IK/J1fgYcDozI+1aOiO0kbUQqBDga2IdUT2cjUrXkp4Hfd9zT1ScXYOvamvrz/+Y3U5HA\nceNgl13g29+GzTdv3bnl2qHch5Xxf7PWUeo2OAF2Bf4QER9CygorqTewLXCD9HnHxuJNnDtA0pnA\n0qRMtHfn7QHckq/3jKSV8vYdSZWIA3hT0v3NNcqF/8xab7XV4Lnn4P7702eXXeCGG+ArX6l2y8ys\nKS781wxJsyOij6TzgGcj4neFfX3ztlWbOO8MYHZEXCDpZWDPiJgqaSgwOCIOk3QlcEdEjC6714XA\nlIi4Mm8fDfwpIm4qu4czxJq1Qp8+qRBgufPPh1dfhUsuWbjrOUOsWXU4Q+z87gUOK8wXWTYi3gVe\nlrRf3iZJAwvnlL7A3sB0ST2Ab7Pgya0PAgdI6iZpFWDn9nwQs65q0qQ0CRbSmzuTJ0P//lVtkplV\nQD0GJwEQEX8hzQl5QtIkoPSa8MHA4blI3zTSvJR5zgVOAx4HHgKeaer6Zfe6GXieNNdkFPBIez2M\nWVdUGnX9179gzz3Tq8SbbQaLLw7HHlvdtplZx6u7YZ1a5mEds+rwsI5ZdXhYx8zMzOqCgxMzMzOr\nKQ5OzMzMrKZ06uBE0lxJk3Im1wmStm3FOQusvSPpipxkzcyqoHt3GDQoJVzbckt49NG0/ZVXoGfP\ntK/0+eMfq9pUM+sAnT0J2/sRMQhA0m7Az4HBCzhngTNSI+KIRW+ambVVr17pNWKAe+6B//3fxgyv\n667buM/M6lOn7jkpszTwdmlF0g9yfZzJkhrKD845SS6T9IykeyTdWaiVM1bSFnl5TuGc/XIitlKd\nncskPZrr7AyWNErS06VjzGzR/ec/sNxy1W6FmVVSZ+856ZlzmCwJfJ78LPeirBsRW0nqBtwmaYeI\nGFc49xvAmhGxUU5D/wyN9XDmy2XSxDLAMhGxraQ9STlVtiXlOvmbpM0iYnI7PadZzWrv+ioNDfDB\nB2nI5sMPU1Xi+wsFIV58Me0rGTECtttuwW1pTUZt14oxqw2dPTj5oDCssw1wNbApsBuwWw5cAJYC\n1gWKwcn2wPUAEfGWpDELee8Abs/L04DpEfFUbstTQH9gvuDEtXXMFqxnz8ahm8ceg0MPhWnT0vo6\n63hYx6xWtVdtnc4enHwuIh6TtIKkfnnTzyPi8pZOoTFdfYuXLiz3LNv3cf75GfBRYftnNPPdNvhX\nM6szHf1Xeptt4N//Tp+2tmXsWPDvAWYdr/yX7uHDh7fpOnUz50TShqTn+TfwF+A7kpbK+1YrBC0l\nDwP75vo6K9H8RNq3JG2Yh4f2oRUTas2s/Tz7LMydC8svX+2WmFmldPaek56FoRsBQ3N++Hvzq8CP\nKhXpmEOqqTODxuBiNLALaY7IP4CJwH+auMcPgTvyuU+QhohKWpqP4iDGrI1Kc04AIuCqqxrr7ZTP\nOTn8cNfbMas3Xbq2jqSlIuI9ScuTCv19OSL+1YH3c20dsypwbR2z6mhrbZ3O3nOyqO6QtAywOPDT\njgxMzMzMrHW6dHASETtXuw1mZmY2r7qZEGtmZmb1wcGJmZmZ1ZROHZy0sfDfWElbttP9t5R0cXtc\ny8waNVf4D2D8+JSzZP3107499mhM0GZm9aGzzzlpa+G/RX5lRtJiETEBmLCo1zKzeTVX+O+tt+CA\nA+DPf07J2QAefji9XrzpplVrrpm1s07dc1Lm88J/uQhfKbU8kkZIGlp+gqTDJT0n6XFJV0i6NG8f\nIukxSRMl3Stpxby9QdLVkh4CrpK0U+k+kraS9Eg+52FJ61fioc3qXbHw34gRMGxYY2ACqa7OXntV\npWlm1kE6e89Jk4X/mjBfb4mkVYGfAINISdruB57Mu8dFxDb5uP8BTgFOzvs2BLaPiI8kDS5c8hlg\nh4iYK2lX4Gxgv0V7PLPa1NEp68sL/43Jla+efjoFJ21pW1vLfbjihFnldfbgpLnCfwsiYCvggYh4\nJ59/A1Dq7Vhd0vXAyqQcKC/l7QHcFhEfMb9lSL0p6+bjejR1Yxf+M1uw8sJ/hxzSOK+kmMdw661h\n9mzYbTe46KLKt9PM5uXCf2UKhf9WAD5l3iGr8oJ9MP+8k2IGu0uB8yLiDkk7AQ2Ffe8304SfAX+N\niH0krQmMbeogF/6zetDRf43PP79xuVT4b8YM2GQTmDgR9twz7Xv8cRg9Gu64o+W2ufCfWWW48F+Z\nXPivOzATeBXYWNLiOQPsV8oOD+BvwE6SlpG0GLAvjQFLX+CNvDyseJsWmlA857C2PoeZzatU+G+F\nFeCYY2DkyHnf3nnvvca6O2ZWHzp7z0l54b9Dc/Gaf+RhmWnAy6SifvOIiDcknQ2MJ02kfZbGwn8N\nwA2SZpHmoqxZOo35i/2V1s8FRkn6CXAnLvxn1mbNFf5baSW47jo49VR4/XVYcUXo1w9OP7267TWz\n9uXCf6nw32LATcDvI+LWDryfC/+ZVYEL/5lVR1sL/9XNsE4bNeSel6nASx0ZmJiZmVnrdOmek0pz\nz4mZmXUl7jkxMzOzuuDgxMzMzGpKpwpOJK0k6RpJL0p6IqeL37va7TKz6isVC9x001Qw8IILGhO2\njR0LSy+d9pc+999f1eaaWQs6zavEkgTcAlwZEQflbWsAe7by/MUi4tMObKKZVVGxWOCMGXDQQfDu\nu41J2XbaCW67rWrNM7OF0Jl6Tr4CfBQRl5c2RMRrETFCUndJv5Q0XtJkSUfC5wUAx0m6FXgqF+p7\nQNItufflHEmH5POmSFo7n9dS4b8/SBqTzz8ubx8u6fhSuySdJel7lfxyzKxRv35w+eWpUGCJ56Kb\ndR6dpucE2IQmkqllhwPvRMRWkpYAHpJ0T943CNgkIl7NhfoGkor3zSIlaLsin/c94DjgBFou/Lc+\nqcBgX+A5SZcBfyDlSblYUjfgAOBL7ffoZvWro1Lhr7VWyiw7Y0ZaHzeuMbEbwE03pWM6og2uUmG2\naDpTcFJeVfhXwHbAx6R09QMllaoA9wXWJdXYGR8RrxZO/VtEvJWv8QLwl7x9Go1VjVsq/HdnRHwC\nzJT0L2ClHPjMlLR5PmdiRMxq6iFc+M+sOnbYAW6/vdqtMKtvXbHw31Ok+jcARMQxkpYHniAFJ8dG\nxL3FE3JPyXtl1ylWFP6ssP4Zjd9HS4X/Pi4szy2c8ztSTZ2VSD0pTXLhP7N5tdd/EsVigQAvvZQm\nyfbrV7k2mHV1Xa7wX0TcDywp6ejC5qXyz78A381p6JG0vqRei3C7thT+uxn4L+CLNPbGmFkVzJgB\nRx8Nxx1X7ZaYWVt0pp4TgL2BCyWdAswg9YqcAtwIrAVMzG/1/AvYh5YL9ZUr7mugdYX/Gk+O+ETS\n/cAsp4E1q7xSscBPPoHFFoNDD4UTT0z7pPnnnJx2GnzjG9Vpq5m1zOnr20meCDsB2C8iXmzmGMct\nZmbWZTh9fRVJ2hh4HrivucDEzMzMWsc9JxXknhMzM+tK3HNiZmZmdcHBiZmZmdWUigUnkuZU6l61\n3AYz63i9e8+7/sorMGDAvNsaGubPjWJmtaGSPSe1MNmi3dtQyq1iZrVDrRjhbs0xZlYdVR3WkTRW\n0pZ5eQVJL+flEyT9Pi8PkDRV0pKS1pF0l6QnJD0oaYN8zEhJl0l6NBfkGyxplKSnJV1Zds8LJE2T\ndJ+kFfK2zXOhv8mSbpK0zALaN0zSbZL+Ctwrqaek6yU9lc9/rHSemZmZLZxq/9bfXFKzi4CxkvYB\nfgQcGREfSrocOCoiXpC0NXAZsEs+Z5mI2FbSnsBtwLbA08DfJA2MiCmkjLJ/i4gTJZ0GnEEq9ncV\ncExEjJM0PG8/oYX2QSooOCAi3pF0MjAzIjaRtAnwZAvnmXUplUwNvyj3qvUU9rXePrP2VO3gpEkR\nEZKGAVOBX0fEo5J6kwKOG9TYH7t46RSgVNJrGjA9Ip4CkPQU0B+YQqqfc10+7o/ATZL6AktHxLi8\nfRRwQyuaeW9EvJOXtyMFVETEU5KmNHeSC/+ZVV5zQzge2jFrX/VS+O9TGoeWlizbtz4wG1gtr3cD\n3omIQTStVJCvWMyvtN7Uc4qmezeK/1y11L7ygoKt+mfOhf+sq6mFv/LLLw+zyuqEz5wJa6/duF4L\n7TTr7Oql8N8rpEJ5APuVNkpaGrgY2AFYXtK+EfEu8LKk/fIxkjRwIe/XDdg/Lx8EjMvXnSVp+7z9\nEGBsS+1rwsPAN3O7NgYGtHCsmVVY796wyiowZkxaf/tt+MtfYPvtWz7PzKqjkj0nvST9o7B+PnAe\ncL2kI4E7aezJuAAYkeeWHA6MkfQAcDDwa0k/AXoAfyYN18D8Bf6a8h6wVT7/LeCAvH0o8JtcyfhF\n4LC8vbn2lc9FuQwYlYeQngWeAv6zoC/EzDrG++/D6qs3rp90Elx1FRxzTGMxwIYGWGutqjTPzBbA\n6evbQS761yMiPpK0DnAvsH5EfFp2nNPXm5lZl9HW9PXVnnNSL5YC7pfUgzT35P+VByZmZmbWOu45\nqSD3nJiZWVfiwn9mZmZWF+o2OJG0t6TPSllk23D+XpI2amH/UZIOaXsLzayj3XILdOsGzz0H22wD\ngwbBmmvCiium5UGD4LXXqt1KMytXt8M6kq4DegITI6KhDeePBG6PiNFN7OseEXPbcE0P65hV0AEH\nwAcfwBZbNOYxGTUKJkyASy6patPMugQP6xTkbLJbA8eSXxfO9XZuLxwzQtLQvHxOroszWdIvJW0L\nDAF+KWmipLVznZ0LJf0NOF7SGZJOyucfIWm8pCcl3SipZ6Wf2czmNWcOPP44jBgB113XuD0ifcys\ndtXr2zp7AXdHxGuSZkjagvlznwQQkpYD9o6IDQEk9Y2IdyXdRuo5uSlvD9Lrwl/K62cUrjU6Iq7I\n238GHA6M6MgHNOtsKpWBtXSfW2+F//ovWGMN6NcPJk5MPSgtpayvdBvNrGn1GpwcCFyYl2/I63c0\nc+x/gA9zFeQ7yo4r/2fsOpo2QNKZwNJAb+AvzTXMtXXMKuPPf4YTTkjL+++f1rfYwr0mZh2pvWrr\n1N2ck9wT8g9gBql3pHv+eTDwvxHx9XzcFcBDETFK0uKk6sb7Af0jYhdJVzJvz8kY4KSImJjXzwBm\nR8QFkl4G9oyIqXmoaHBEHEYZzzkxq4y3304ZYvv1Sz0lc+emibGvvAIjR6Y5J5deWu1WmtU/zzlp\ntB9wVUT0j4i1ImIN4GXSs24saXFJy5CCkZC0FLBMRNwFnAhslq8zG+i7gHuVvvDewPSchO3b7fw8\nZraQbrwRDj00BSMvv5zeyOnfH8aNcyVis86gHoOTbwE3l20bnbdfD0wjDc9MzPv6ALdLmgyMA3JH\nMNcCP5A0QdLaNK3UDXIa8DjwEPAMzdf2MbMKuPZa2Gefebftu28a2gEHKGa1ru6GdWqZh3XMzKwr\n8bCOmZmZ1QUHJ2ZmZlZTHJyYmZlZTXFwYmZmZjWl7oMTSXMlTZI0RdJNObV9e137ipaKA5pZbeje\nPRX5GzgQvvGNlNoeYOxYGDJk3mOHDYPR81XUMrNKqvvgBHg/IgZFxEDgXeCo9rpwRBwREc+01/XM\nrGP06gWTJsGUKdC3L/z2t80fK/lVY7Nq6wrBSdFjwDoAuZDflnl5hZzlFUmbSHo897ZMlrSOpKUk\n3ZkL+02VtH/hGlvk5csk/U3SNEkN1Xk8M1uQbbeFF19s+Ri/8W9WXfVaW2c+kroDXwX+mjcFTSdL\nOxq4OCKukbQY6Tv6OvB6IfV938I1Sn4cEbPyfe6TNCAipnbEs5jVu/YsjFe81ty5cM89sMsulW2H\nC/2ZLZyuEJz0lDQJWA14BfjNAo5/BPixpC8AN0XEC5KmAP+/vTuPt6qq/z/+eosgIDjglJWFU9qA\ngaA5i5qm5pxpWg4NmplDpdX3m/YT1L6Njpk5lWLOUypqKhkEOBHI6FQqmLPkFAgi4uf3x1rHezic\nO3LuPcN9Px+P+7h7r7323msduPC5a+29Pr+R9AvgjoiYWOa8gyUdRfpM1wU+BSwTnDjxn1nXW7gw\nPXPywgtpGftjjknlzU3feFrHrGOc+K+NJM2LiP6S+pCyBZ8TEX+WNIaUCHByDkQmRMT6+Zz1gb2A\n44FvR8TYnI/ni8BRwH0RcUYhGSDwBnAvMCwi3spJA8dFxKiStniFWLMq6N8f5s1LQcoXvpCyFe+/\nP8yalQKViUW/buy7L5x8Mmy/ffXaa9YovEJsKyJiIXAC8DNJIo2iDMuHDyzUk7RBRMyOiN8CtwGb\nSVoXeCcirgZ+AwwpufwqwNvAfyWtA+yB8+uY1Zw+feD88+GUU9JzJRtvDC++CE88kY4/+yxMnw6D\nB1e3nWbdXXeY1vkgSIiIaZKeAg4iBRk3SDoauLOo3kGSvgYsBl4CfgZsCfxa0vu5/JilbhAxPU8d\nPQE8R0oAaGY1oniaZvBg2GgjuOEGOPhguOoq+PrX4Z13oGdP+MMf0kiLmVVPw0/r1BJP65iZWXfi\naR0zMzNrCA5OzMzMrKY4ODEzM7Oa4uDEzBpeJdZdMLOu06G3dSR9CDiX9Crum8ArwPci4l/L26C8\n9Pu8iDirlXpzSLly3gf+AxweES8u7/3L3GPziHi9uTZKGgmMj4j7ylzCzGrIyy/D974HkyfDaqvB\nOuukdU8uv7ypznvvwaOPwuOPwyabVK+tZt1Zu4OTvEbIn4HLI+IruWwzYB1guYMT2r4+SADDI+L1\nHCz8L2nRtEoKoNxTxsWvJ59W4XuaWSeISAuvff3rcN11qWzGDPjvf+GEE5rq/eQnaTVZByZm1dOR\naZ2dgHcj4pJCQUTMiIiJkkbmhHlTJb0g6Y8Akr5WlEzvIkkr5PLdJU3JCfXGFN3jU5LGSnpaUlsC\njuKEfmtJuknSpPy1TS4fIelPkh6Q9E9J38rlwyWNLlxI0gWSjii69o8kzcjt37D0xpKukPSlvL2F\npG4HLgMAACAASURBVPtzfx6W1K+Nn6mZdbKxY6FXLzj66KayzTaD7bZr2h8/Hm68ES68sOvbZ2ZN\nOjKt8xlgSrkDeRThNEmrAhOA30r6JGnRs20iYomkC4GvSrobuATYPiKezcvDQxqp2BQYTlp59UlJ\nF0bEkjK3LIxq7A7MytvnkZaov1/Sx4C7SXluCm3fCugHTJV0Z7lusPTozZsRsZmkw0hTWXuXqy+p\nF3AdcFBETMmBycJyn5OZdX0yvAEDYOjQ5o+/+WYaVbnqKuhX8mtFZ7XVCQHNyutIcNLitEue9rka\nOCsipko6DhgKTE6H6A28DHyO9KzGswAR8WbR9e+IiMXAa5JeJU0ZlXueZKykAcB7pMAD4PPAJ9W0\nJGR/SSvn694WEYuARTkvzpakZ2Zacm3+fh1wTnPdBjYBXoqIKbk/88tVdOI/s+poLZnfMcfA4YfD\n1lt3TXvMGlGlEv91JDh5lKJcNGWMAP5dkvRuVET8pLiSpL1auMa7RdtLaL6dw4G3SMHQUaTgQcDn\nIqL4Gqj8v0zvkwKb4umtPi20K5rZLrdf1gj/qmQGdO2owbhx8P77cNNN5Y+PGgXPPQfXXFP+uH9s\nzdqm9JfukSNHdug67X7mJCL+Bqwk6ahCmaTNJG0naW9gF+DEolPuAw6UtFauOyBPtzwE7CBpYKG8\nIx3I0z3fA07KUyn3khL8FdpWSOElYF9JK0lagxTY/AP4N+kZl155amnnossLODhvHww8UFReHO0E\n8CSwrqRh+b79JfXoSJ/MrPJ23hkWLYJLL20qmzED/v73lAjwqqtgBS+uYFYTOpr4b3/gXEk/Bt4B\nZgPfB04HPgxMyiMVt0XECEmnAvfmB2EXA8dGxKScdO+WXP4K8IV8/baMQhS/MfOypFuA75ICk99J\nmp7793fg2Fx/BjAWWBM4PSJeBpB0A+mZldnAIyX3WD1f6x3gkKLypdoYEYslHUx6zqYPsADYlZSt\n2MxqwJ//nF4l/uUvoXdvGDgwJfxbuBAOOGDpuhdcANtuW5VmmnV73Sbxn6TTgPmtrZ/SyW1w4j+z\nKhg3bpyf7zKrAif+axtHBmZmZjWuo9M6dSciOvZUjpmZmXWp7jZyYmZmZjXOwYmZmZnVlLoJTiQt\nycvfz8rLw/9AzSxeUg2Syi66Zma1p0ePlD/nM5+BwYPh7LNT7h1Ia6LsXbQO9Kmnwh57wLvvlr2U\nmXWCenrmZEFEDIGUPwe4hrS8/YhqNgogvwrth23N6kTfvjB1atqeOxcOPTQlACxdbO3MM+HBB+Gu\nu1JeHjPrGnUzclIsIuYCRwPHAUjqIenXOdHf9Lx+SiGp3zhJN0p6XNJVhWtImiPp//JozGRJm0u6\nV9JTkr6d6/ST9NecnHCGpH1y+UBJT0oaJWkm8NGi666Zkwvu0YUfiZl10FprwSWXpHVNip11Ftxz\nD4weDSutVJ22mXVX9TRyspSImJ2DkrWB/UgJ+raUtBIwUdK9uepgUuK/l4D7JW0TEQ+QRjqejYgh\nks4GrgC2Ji1fPwu4mJS4b/+ImCdpTeBB4PZ83Y2AwyJiEqR3uXNbbgdOiYj7Ov1DMGtQnbFcfEvp\nPtZfH5YsSaMoABMnwpNPwiOPpFGWzm5bZ17XrB7VbXBSYjdgkKRCzp9VSMHDYmBSRLwIIGkaMJCm\nZegLgcZMYOWIeBt4W9IiSauQgpOfS9qelIfnwzkAgRTYTCpqQy/SUv3HRsSE5hrqxH9mtW/jjVOW\n4nvvXXblWDNrXjUT/9UESRsASyLi1fxc7HERMaakznBgUVFRaRLBwrH3WTrZ4PtAT+AA0lL3m0fE\nEkmzSVmVYdll6RcDk4HdgTYFJ2ZWXqV/TMaNg+LfA84qWSf6mWfSQ7JrrZX211kHrr4adtkFBgxY\n+lz/CJs1r2qJ/2pBfiD2IuC3uege4FhJK+bjn5DUt7nzy12ymfJVgFdzYLIT8PEWrhHAN4BNJf2o\nHfc2syqaOxeOOQaOP37p8o03hltuga99DaZPr07bzLqreho56SNpKmlE4z3gSuCcfOwy0nTNI/n1\n4ldJyQmXSdDXjNJ6hf2rgdGSZpBGRR4vqbPUNSIiJB0C3C7pvxFxUTv6Z2ZdZOHC9Crx4sWw4opw\n+OHwgx+kY1L6Ahg2DC6/HPbZJ42+rL9+1Zps1q10m8R/tcCJ/8yqw4n/zKrDif/MzMysITg4MTMz\ns5ri4MTMzMxqSsMGJ+3NdZNXfZ1ZoXsPlzS6Etcys67Tr1/6PmcO9OmTHpotfF11VYunmlkF1dPb\nOu21zJOnklaMiPeq0Rgzq33FqUQ32qgp/46Zda2GHTkpyKMYEyTdBsyStEK5PDwl5wyUND7n1Jki\naeuiazWXq2f3XDaF9BqzmZmZdUAjj5wUGwJ8OiKezcFIc3l4Cl4Bdo2IRZI2JmVA3iIfWyZXD/AI\ncAmwU0Q8Lel6nKXYrK49/XSazim44ALYdtvqtcesO+kuwcmkiHg2bzeXh+epovq9gAskfZa05P3G\nJdcqztWzPrAAmB0RT+c6V5GyJptZlRWWm28u3Udzy9FvuGHr0zptWcrey92btV93CU5K8+CUy8Mz\nsGj3+8BLEXGYpB7AO0XHyuXqKR0laXbBGSf+MzOzRtXtE/8th0IenrER8Z6kTwDPl9RZpajscKBH\nC9cL4AlgoKQNIuIZ4JDmKjvxn1nXGjFi2cR/lby2mTXp1on/2qg0V07BZcBjpDw8M4Hf0xR8FOpd\nCByRp202AYpfS17mWZKIWESaxrkzPxD7Srl6Zlbbit/WKTxzUvi64ILqtcusu3FunS7k3Dpm1eHc\nOmbV4dw6ZmZm1hAcnJiZmVlNcXBiZmZmNcXBiZmZmdWUhg5OJC2RNFXSTEk3SOrTQt0jJf22Qvcd\nIemkSlzLzLpWjx7p7ZxBg+Cgg2DhwlReSApoZp2voYMTYEFEDImIQcC7wDEt1K3kazR+JcesTvXt\nm1aGnTkTevWCiy5K5Wr3+wZm1lGNHpwUmwhsJGl1SbfmpH8PShpUWlHS3pIekvSIpDGS1s7lIyT9\nUdJYSU9LOr7onFMkPSlpAmltFDOrc9ttl9Y7MbOu1S1WiJW0IrA78BfgdGBKROwnaSfgSlJiwOLf\niyZExFb53G8BPwJOzsc+AexEWkX2SUkXkpIBHgx8FuhJSgQ4ubP7ZWbNK129tS0rahef89578Je/\nwJ57dux+7eGVZs2W1ujBSR9JhdRd44E/Ag8DBwBExFhJa0jqX3LeepJuAD5ESgL4TC4P4M6IWAy8\nJunVXGd74JaIeAd4R9LtNJNfx7l1zGrbwoVN2Yh32AG++c3qtsesnlQqt05DrxAraV5E9C8pewT4\nUkTMzvv/Bj4FHAgMjYjjJY0DfhMRd0jaERgRETtJOg2YHxFn5XNnAnsB+wEDIuK0XH428EKhXtG9\nvUKsWRW0Z4XY/v1h3ry2l5tZ87xCbNtNAL4KIGk4MDci5pfUWQV4MW8fWVRe7gMO0qjMfpJ651GY\nvfBDsWZmZh3S6MFJuQBhBDBU0nTg/4AjiupGUZ0bJU0G5haVF9dpuknEVOB6YDpwFzCpMs03s67W\n3Fs5CxbAeus1fZ17bte2y6w7aehpnVrjaR2z6nDiP7Pq8LSOmZmZNQQHJ2ZmZlZTHJyYmZlZTXFw\nYmZmZjWlxeBE0sC8lkdxWatJ7SQNlXRe3t5R0tbtbZikOZIGtFSe7/OMpMF5yfkft/c+zdx7uKTR\nlbiWmdWXV16BQw+FDTeEYcNgm23g1lvTCrOrrpoWaPvsZ2HXXWHu3Gq31qwxdWTkpNXXTSJiSkSc\nmHd3Arap4H0CQNJmwI3AQRExLSJGR8QvO3AfMzMAImC//WD48JRTZ/JkuO46eP759IrxDjukpIDT\np8MWW8DvflftFps1po5O6xQChHGSfiHp4Zz0brtcPlzSaEkfB74NfF/SVEnbSlpL0k2SJuWvbfI5\na0i6V9IsSZfSzPLv2aeBPwNfi4jJ+fwjJf02b18h6TxJ9+cEfV/K5StIulDS4/ledxYd2z2XTwH2\nL9xI0oByiQLzCNIoSePzaM4Bkn4jaYakv+R8PmZWR/72N1hpJTj66Kayj30MjjsuBS4FEfDf/8KA\nZcZ2zawSlvc/0AB6RMTnJO0BnAbs+sHBiGclXQTMi4izASRdA5wTEfdL+hhwN2n5+NOA8RFxpqQ9\ngeYyWgi4FfhqRDxQ0pZiH4qIbSV9ErgduJmUU+fjEfFJSesAjwN/kNQbuATYKSKelnR90fVGUj5R\nIMD6pJGhTwMPAftHxMmSbgG+CNzWxs/RzCqoXCK9tqT7GDAANt+8+eMTJqRpnddeg3794Oc/b/me\n7WmfmTVpLThpcWoluyV/fwQY2Ez94lGQzwOfVNMyjP0lrUxKnrc/QETcJemNFu49BjhK0r0R8X4z\ndW7N13o8ByIA2wE35PJXJI3N5ZsCsyOikBz9KqDwu9O2lE8UGMBfImKJpFnAChFxTz5nZnOfhRP/\nmdWu0tVhjzsOJk6EXr3g17+G7beH0flptF/9Cn70I/j977u+nWa1qlKJ/1oLTl4DVi8pW4OmLL0A\ni/L3JW24HqRA5XMR8e5ShelfhbauIncccDFwIXBMM3WKr1+4bjRzj9IgrLROc+16FyAi3pe0uKj8\nfZr5LEb4VyazTlf6YzZuXHqOpDV/+xvcfHPT/gUXpFGSYcOWrbv33nDggc3f06w7Kv2le+TIkR26\nTovPnOSEeC/l6QzyWzJfACa24x7zgOLMwPcCJxR2JH02b44HDs1le7BsUFTs/Vx3U0mFnrclsLkf\n+JKSdYDhufwJYKCkDfL+IUXnlEsUOK+N9zOzOrLzzvDOO3DRRU1lb79dvu7EibDRRl3TLrPupi0j\nHYcDv5N0dt4fERGzm6kbZbZHAzdJ2pc04nFCvt70fP+/A8eSnu24VtIhwAPAsy3dIyIWSdoH+Luk\nV4C3m7l/8fbNwC7AY8BzpKmot/K1jgbulLSAFJCsXOgv8Mfc3rcpnyiw9H7l9s2sDtx6K3z/+2na\nZq21YOWV0zY0PXMSAautBpddVt22mjWqbpf4T9LKEfG2pDWAh4FtIuLVLrq3E/+ZVYET/5lVR0cT\n/3XH113vkLQa0As4vasCEzMzM2ubbhecRMRO1W6DmZmZNc+5dczMzKymODgxMzOzmlKXwYmkJXk5\n/BmSbpHUr0rt+Lakw6pxbzPrOj16pLd0NtsMDjgA5s9vOvboo+kV5E03hU98As48s3rtNGsUdRmc\nAAsiYkhEbAb8l5S/p8tFxMUR8adq3NvMuk7fvinh34wZsMoqcPHFqXzhQth3X/jJT+CJJ1JCwAce\ngAsvrG57zepdvQYnxR4ENgSQNFjSQzlJ3y35rZxCgsKzJf0jJ/fbQtKfJf1T0hmFC+WyyTn54FFF\n5fMlnSlpWk7+t3YuHyHppLx9VE5kOC0nNuzTpZ+CmXWJrbZKGYsBrrkGttsOPv/5tN+nT1pV9he/\nqF77zBpBXb+tI6kHsBtwXy66EvhuREzIK8eeBnyftCDaoojYQtIJpIR8Q4A3gKclnR0RbwDfiIg3\ncmAxSdJNubwv8GBEnCrpl8BRwM9YeqG1myPi0tyuM0iJCy/o3E/ArHuoxNLwFUj3wZIlMGYM7LJL\n2n/sMRg6dOk6G2yQpn3mz0/JAWttWftaa49ZOfUanPSRNBX4CDAHuEjSqsCqETEh1xkF3Fh0zu35\n+yxgVkS8AiDpGWA9UqByoqT9cr31gI2BScC7EXFnLp9CUeblIoMknQmsCvQD7ilTx4n/zOrQwoXp\nmZMXXoCBA+GYooxeXlfRrElXJf6rVQsjYkge4bgH2Jem0ZOC0hXpCgkK3y/aLuyvmPPm7AJsFRHv\n5IzFvXOdlpL6Ff5pugLYJyJmSjqCprw9S3HiP7P2W94fm7Ym/mvOWWelZ04WLoQvfAFuuw323x8+\n9SkYP37pus88k0ZM+uXH9P0jb91JlyT+q3URsZCUq+dnpASDb0jaLh8+DBjXxksJWAV4IwcmmwJb\ntfG8QhDUD3hZUk/ga228r5nVkT594Pzz4ZRT0ojJoYemBID35V+NFi6EE06AH/+4uu00q3f1Gpx8\nMJAaEdOAp4CDSEn5fp2T9G0GnN7MueWS9N1NGkF5DPg56UHbZe5Xcn7x9k9JuXomAo+XuYeZ1SkV\njcMOHpyyEd9wQwpWbrstvT686abpVePPfQ6++93qtdWsEXS7xH/V5MR/ZtXhxH9m1dHRxH/1OnJi\nZmZmDcrBiZmZmdUUBydmZmZWUxomOJF0Sl7ZdXrOu7NlBa89v/VaZtbd/Oxn8JnPwGc/m9ZBmTQp\nvbK86aZpf8gQOOigarfSrP7U6zonS5G0NfBFYEhELJY0AFipgrfwU6xmtpQHH4Q770zrn/TsCa+/\nDosWpTd7rrkGNt+82i00q1+NMnLyIeA/EbEYICJeBz4i6WYASftKWiBpRUm9JT2dyzeU9JecT2e8\npE1y+fo5h86MvOrrByT9MOfQmS5pRC4bmHP2XJJHb+6R1Bsza1gvvwxrrpkCE4ABA2DdddO2X8oz\nWz6NEpzcC6wn6UlJv5O0AzANGJyPbw/MBLYEPgc8lMsvAY6PiGHAD4FCLtHzgN/lrMcvFm4iaTdg\no4jYkpSbZ6ik7fPhjYALIuIzwJvAlzqnq2ZWC3bbDZ57DjbZJK1rUlgpNgK++tWmaR0vyGbWfg0x\nrRMRb0saSgpCdgKuB/6HlNRvU2AL4GxgB6AHMEHSysA2wI1qWmGpV/6+DbB/3r4K+GXe3g3YLef1\nAViZFJQ8B8yOiBm5fAowsMLdNLMWtLZMfCUS/xXfa+WVYcoUmDABxo6Fgw9O2Yhbm9bpzOXsvVS+\nNYqGCE4AIuJ94O/A3yXNJK0W+3dgT1JunPtIyQBXAE4mBSlvRMSQdt7q5xFxSXGBpIEsna9nCdCn\n3MlO/GfWOFZYAXbcMX0NGgSjRlW7RWbVVanEfw2xQqykTwAREf/K+2eScuXcDPwJuCIi/p+kh4C1\nImLDXO9+4JyIuElp+GRQRMyQdBtwQ0RcLek7wK8ior+kXYEzgF3yaM1HgHdJIyijI2JQvu5JQL+I\nGFnSTq8Qa1YFnbFC7D//mUZJNt447Z96Krz1FsyaBb/5DQwdWtHbmdWljq4Q2ygjJ/2A30paDXgP\n+BdwNLAQWBso5A2dDqxTdN5Xgd9LOhXoCVwLzABOBK6R9GPgNvLbOhExRtIngQfzVNA8UpK/5vL1\nmFmDmj8fjj8e3nwTVlwxBSkXXwwHHpieOemTx07XWgvuvbe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"text/plain": [ "<matplotlib.figure.Figure at 0x7f52a2f56990>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "t = extract_vars2(country_map, 'nwsptot_f', 'rlgdgr_f')\n", "plot_cis(t)\n", "thinkplot.Config(title='Newspaper',\n", " xlabel='linear coefficient newspaper')\n", "save_plot()" ] }, { "cell_type": "code", "execution_count": 487, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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b8MYbaVHtjTfCZZfB2LEwdy4MHAgPPZRS69uSnJm2djgzbTMi4kFgdUnDiprX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\nSXpS0i9yx0ZK+pekqen15e5twaJ6dLYdbZbvahW2YQ1J4yU9IWmWpON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"text/plain": [ "<matplotlib.figure.Figure at 0x7f52abcefad0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "t = extract_vars2(country_map, 'netuse_f', 'rlgdgr_f')\n", "plot_cis(t)\n", "thinkplot.Config(title='Internet',\n", " xlabel='linear coefficient Internet')\n", "save_plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here's the scatter plot of effect size on rlgdgr versus mean value of rlgdgr\n", "\n", "rlgdgr is on a 0 to 10 scale, so it is mildly astonishing that national means vary as much as they do, from 2.5 to 7. " ] }, { "cell_type": "code", "execution_count": 488, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "-0.156647270319\n" ] }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f52a9675b10>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "t = extract_vars2(country_map, 'netuse_f', 'rlgdgr_f')\n", "plot_scatter(t)\n", "thinkplot.Config(title='',\n", " xlabel='log odds ratio Internet',\n", " ylabel='degree of religosity',\n", " xlim=[-0.35, 0.1], ylim=[1, 8])\n", "save_plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The correlation is dragged down by the outliers; without them, it is more like we saw above." ] }, { "cell_type": "code", "execution_count": 489, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "-0.361069845352\n" ] } ], "source": [ "codes, names, params, lows, highs, means = zip(*t[2:])\n", "corr = np.corrcoef(params, means)[0][1]\n", "print(corr)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The following is preliminary work on the next step, showing effect sizes in terms of probability of religious affiliation." ] }, { "cell_type": "code", "execution_count": 490, "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>cntry</th>\n", " <th>inwyr</th>\n", " <th>tvtot</th>\n", " <th>tvpol</th>\n", " <th>rdtot</th>\n", " <th>rdpol</th>\n", " <th>nwsptot</th>\n", " <th>nwsppol</th>\n", " <th>netuse</th>\n", " <th>rlgblg</th>\n", " <th>...</th>\n", " <th>hasrelig_f</th>\n", " <th>rlgdgr_f</th>\n", " <th>yrbrn60_f</th>\n", " <th>edurank_f</th>\n", " <th>hincrank_f</th>\n", " <th>tvtot_f</th>\n", " <th>rdtot_f</th>\n", " <th>nwsptot_f</th>\n", " <th>netuse_f</th>\n", " <th>inwyr07_f</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>BE</td>\n", " <td>2002</td>\n", " <td>7</td>\n", " <td>2</td>\n", " <td>6</td>\n", " <td>0</td>\n", " <td>4</td>\n", " <td>4</td>\n", " <td>0</td>\n", " <td>2</td>\n", " <td>...</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>-27</td>\n", " <td>0.311326</td>\n", " <td>0.486886</td>\n", " <td>7</td>\n", " <td>6</td>\n", " <td>4</td>\n", " <td>0</td>\n", " <td>-4.587777</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>BE</td>\n", " <td>2002</td>\n", " <td>6</td>\n", " <td>3</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>4</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>2</td>\n", " <td>...</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>-12</td>\n", " <td>0.575953</td>\n", " <td>0.167451</td>\n", " <td>6</td>\n", " <td>1</td>\n", " <td>4</td>\n", " <td>1</td>\n", " <td>-4.644231</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>BE</td>\n", " <td>2002</td>\n", " <td>4</td>\n", " <td>2</td>\n", " <td>7</td>\n", " <td>2</td>\n", " <td>2</td>\n", " <td>2</td>\n", " <td>6</td>\n", " <td>2</td>\n", " <td>...</td>\n", " <td>0</td>\n", " <td>5</td>\n", " <td>5</td>\n", " <td>0.479871</td>\n", " <td>0.691997</td>\n", " <td>4</td>\n", " <td>7</td>\n", " <td>2</td>\n", " <td>6</td>\n", " <td>-5.449223</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>BE</td>\n", " <td>2002</td>\n", " <td>5</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>7</td>\n", " <td>2</td>\n", " <td>...</td>\n", " <td>0</td>\n", " <td>2</td>\n", " <td>24</td>\n", " <td>0.407944</td>\n", " <td>0.381305</td>\n", " <td>5</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>7</td>\n", " <td>-5.010420</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>BE</td>\n", " <td>2002</td>\n", " <td>2</td>\n", " <td>2</td>\n", " <td>2</td>\n", " <td>2</td>\n", " <td>2</td>\n", " <td>2</td>\n", " <td>6</td>\n", " <td>1</td>\n", " <td>...</td>\n", " <td>1</td>\n", " <td>6</td>\n", " <td>6</td>\n", " <td>0.867955</td>\n", " <td>0.911231</td>\n", " <td>2</td>\n", " <td>2</td>\n", " <td>2</td>\n", " <td>6</td>\n", " <td>-5.010001</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>5 rows × 32 columns</p>\n", "</div>" ], "text/plain": [ " cntry inwyr tvtot tvpol rdtot rdpol nwsptot nwsppol netuse rlgblg \\\n", "0 BE 2002 7 2 6 0 4 4 0 2 \n", "1 BE 2002 6 3 1 1 4 1 1 2 \n", "2 BE 2002 4 2 7 2 2 2 6 2 \n", "3 BE 2002 5 0 1 0 1 0 7 2 \n", "4 BE 2002 2 2 2 2 2 2 6 1 \n", "\n", " ... hasrelig_f rlgdgr_f yrbrn60_f edurank_f hincrank_f tvtot_f \\\n", "0 ... 0 0 -27 0.311326 0.486886 7 \n", "1 ... 0 0 -12 0.575953 0.167451 6 \n", "2 ... 0 5 5 0.479871 0.691997 4 \n", "3 ... 0 2 24 0.407944 0.381305 5 \n", "4 ... 1 6 6 0.867955 0.911231 2 \n", "\n", " rdtot_f nwsptot_f netuse_f inwyr07_f \n", "0 6 4 0 -4.587777 \n", "1 1 4 1 -4.644231 \n", "2 7 2 6 -5.449223 \n", "3 1 1 7 -5.010420 \n", "4 2 2 6 -5.010001 \n", "\n", "[5 rows x 32 columns]" ] }, "execution_count": 490, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.head()" ] }, { "cell_type": "code", "execution_count": 491, "metadata": { "collapsed": false }, "outputs": [], "source": [ "grouped = df.groupby('cntry')\n", "group = grouped.get_group('NL')" ] }, { "cell_type": "code", "execution_count": 492, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.427779488759\n", "2.0 7.0\n", "0.424357421451\n", "0.468580999574\n", "0.391422353132\n" ] } ], "source": [ "formula1 = ('hasrelig_f ~ inwyr07_f + yrbrn60_f + '\n", " 'edurank_f + hincrank_f +'\n", " 'tvtot_f + rdtot_f + nwsptot_f + netuse_f')\n", "\n", "model = smf.logit(formula1, data=group) \n", "results = model.fit(disp=False)\n", "\n", "mean = group.mean()\n", "low_net = np.percentile(group['netuse_f'], 25)\n", "high_net = np.percentile(group['netuse_f'], 75)\n", "\n", "def prob_hasrelig(results, df):\n", " return results.predict(mean)[0]\n", "\n", "print(mean['hasrelig_f'])\n", "print(low_net, high_net)\n", "print(prob_hasrelig(results, mean)) \n", "mean.netuse_f = low_net\n", "print(prob_hasrelig(results, mean)) \n", "mean.netuse_f = high_net\n", "print(prob_hasrelig(results, mean)) " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.10" } }, "nbformat": 4, "nbformat_minor": 0 }

mit

jteehan/cfme_tests

notebooks/Interaction with Timelines Notebook.ipynb

10

4414

{ "cells": [ { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "vm name: test-tl-x0dr\n" ] } ], "source": [ "# adding provider and the rest of staff necessary for working with timelines\n", "from utils.generators import random_vm_name\n", "from utils.providers import get_crud\n", "from cfme.infrastructure.virtual_machines import Vm\n", "\n", "prov = get_crud('vsphere6-nested')\n", "if not prov.exists:\n", " prov.create()\n", "\n", "vm = Vm(name=random_vm_name(\"tl\", max_length=16), provider=prov)\n", "print(\"vm name: {}\".format(vm.name))\n", "if vm.exists:\n", " vm.delete_from_provider()\n", "vm.create_on_provider()" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "# generating some power activity management events\n", "prov.mgmt.wait_vm_running(vm.name)\n", "prov.mgmt.stop_vm(vm.name)\n", "prov.mgmt.wait_vm_stopped(vm.name)\n", "prov.mgmt.start_vm(vm.name)\n", "prov.mgmt.wait_vm_running(vm.name)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(u'test-tl-x0dr', u'VmPoweredOnEvent', u'VC')\n", "(u'test-tl-x0dr', u'VmPoweredOffEvent', u'VC')\n", "(u'test-tl-x0dr', u'VmPoweredOnEvent', u'VC')\n" ] } ], "source": [ "# timelines filter setup and obtaining all displayed power activity events\n", "from utils.appliance.implementations.ui import navigate_to\n", "tl_view = navigate_to(vm, 'Timelines')\n", "tl_view.filter.event_type.select_by_visible_text('Management Events')\n", "tl_view.filter.event_category.fill('Power Activity')\n", "tl_view.filter.time_position.select_by_visible_text('centered')\n", "tl_view.filter.apply.click()\n", "\n", "all_vm_events = tl_view.chart.get_events('Power Activity')\n", "# checking which events we have there\n", "for evt in all_vm_events:\n", " print(evt.source_vm, evt.event_type, evt.event_source)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(u'test-tl-x0dr', u'VmPoweredOnEvent', u'VC')\n", "(u'test-tl-x0dr', u'VmPoweredOffEvent', u'VC')\n", "(u'test-tl-x0dr', u'VmPoweredOnEvent', u'VC')\n" ] } ], "source": [ "# obtaining the same vm events from provider entity\n", "tl_view = navigate_to(prov, 'Timelines')\n", "# Management Events and Power Activity are already chosen by default\n", "tl_view.filter.time_position.select_by_visible_text('centered')\n", "tl_view.filter.apply.click()\n", "\n", "prov_events = tl_view.chart.get_events()\n", "vm_related_events = [evt for evt in prov_events if hasattr(evt, 'source_vm') and evt.source_vm==vm.name]\n", "for evt in vm_related_events:\n", " print(evt.source_vm, evt.event_type, evt.event_source)\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "#to be continued\n", "# todo: policy events" ] } ], "metadata": { "celltoolbar": "Edit 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 }

gpl-2.0

gip/quant

ripple/OrderbooksRipple.ipynb

2

275252

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "![RippleLabs](https://raw.githubusercontent.com/gip/techtrading/master/img/Logo_Ripple.png \"Ripple\")\n", "\n", "<br />\n", "<br />\n", "[Gilles Pirio](mailto:[email protected]) @ Ripple Research & Data Team\n", "\n", "August 20, 2015\n", "\n", "# Visualizing order books on Ripple\n", "\n", "[Ripple](https://ripple.com/) is a distributed ledger that is not limited to one currency. The Ripple protocol allows users or entities to define their store of value (and define what that value is as it is not only limited to fiat currencies). A native trading engine is implemented in [rippled](https://github.com/ripple/rippled), the Ripple daemon. The Ripple protocol also defines a native currency designed to improve market liquidity: XRP. The Ripple consensus ledger therefore includes all the building blocks necessary for a market to function and enable trading for users, gateways, market makers, financial institutions, ...\n", "\n", "XRP has a central place in the protocol: beyond bringing security and reducing spam, it is also designed to increase the overall liquidity. XRP often acts as a bridge between the different currencies - if trading between two currencies is not possible because no (or limited) offers is available, the Ripple protocol will often be able to find a path when both currencies can be traded to/from XRP.\n", "\n", "We introduce a tool used to visualize order books and explore how XRP is indeed increasing liquidity between currencies.\n", "\n", "## Getting and plotting order books\n", "\n", "We start by importing useful modules from [matplotlib](http://matplotlib.org/) (the plotting package) and [pyripple](https://github.com/gip/pyripple). We then create a feed object to get data from Ripple. " ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "from pyripple.feed import syncfeed\n", "feed = syncfeed.SyncFeed()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Given one currency pair, the `getOrderbook()` method will return the order book, either locally if cached, or by connecting to a data provider. [Bitstamp](https://www.bitstamp.net/) is a major gateway on Ripple, and we will pull the USD@Bitstamp / XRP order book. The issuer account for Bitstamp can be found [here](https://www.bitstamp.net/ripple.txt). The `showInfo()` method prints basic information about the order book." ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Orderbook USDXRP in ledger 15380994\n", " Close date: None\n", " Currency: USD@rvYAfWj5gh67oV6fW32ZzP3Aw4Eubs59B\n", " Counter currency: XRP\n", " Spread: 0.074365 (0.058606 %)\n", " Best ask/bid: 126.888406 / 126.814041\n", " Through: []\n" ] } ], "source": [ "# Bitstamp issuer address\n", "Bitstamp = 'rvYAfWj5gh67oV6fW32ZzP3Aw4Eubs59B'\n", "# Getting USD@Bitstamp / XRP order book\n", "USDXRP_Bitstamp = feed.getOrderbook(('USD', Bitstamp), ('XRP', None))\n", "# Show some information\n", "USDXRP_Bitstamp.showInfo()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We use the `plot()` function to visualize the order book." ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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z8yUjI0OmTZsmXl5eanPeWbNmSffu3SUlJUVu3rwpTz75pFafZzc3N61mriXX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"text/plain": [ "<matplotlib.figure.Figure at 0x10d81ec90>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "USDXRP_Bitstamp.plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It is also possible to add a weighted average plot. The weighted average represents the global exchange rate as a function of the amount of currency exchanged. " ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.legend.Legend at 0x10dcd40d0>" ] }, "execution_count": 41, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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/icsXFRWRvr4+nTlzRrI/1WOWG2p/RFW3xMpkMnGSsZr++usv0tbWpt9++40q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lAAAg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\neS4sG2OUn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"text/plain": [ "<matplotlib.figure.Figure at 0x10c5fc090>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Normal orderbook plot\n", "USDXRP_Bitstamp.plot()\n", "# Add a weighted plot (newfig is set to False so we don't create a new plot)\n", "USDXRP_Bitstamp.plotWeighted(10e6, newfig= False, styleask= 'b-.', stylebid='r-.', label='Weighted')\n", "# Set y and x limit as well as the legend\n", "plt.gca().set_ylim((100, 150))\n", "plt.gca().set_xlim((0, 10e6))\n", "plt.legend()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As expected, the bigger the amount exchanged, the worst the actual exchange rate. Methods to compute weighted average are available. For instance:" ] }, { "cell_type": "code", "execution_count": 42, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The gloabal echange rate to buy 10 XRP could be 126.888406\n", "The gloabal echange rate to buy 100 XRP could be 126.888406\n", "The gloabal echange rate to buy 1000 XRP could be 126.888717\n", "The gloabal echange rate to buy 10000 XRP could be 126.893595\n", "The gloabal echange rate to buy 100000 XRP could be 126.894293\n", "The gloabal echange rate to buy 1000000 XRP could be 126.950319\n", "The gloabal echange rate to buy 10000000 XRP could be 141.458890\n", "The global echange rate to sell 10 XRP could be 126.814041\n", "The global echange rate to sell 100 XRP could be 126.814041\n", "The global echange rate to sell 1000 XRP could be 126.814041\n", "The global echange rate to sell 10000 XRP could be 126.814041\n", "The global echange rate to sell 100000 XRP could be 126.814041\n", "The global echange rate to sell 1000000 XRP could be 126.080724\n", "The global echange rate to sell 10000000 XRP could be 117.663124\n" ] } ], "source": [ "for i in [1,2,3,4,5,6,7]:\n", " print('The gloabal echange rate to buy %8i XRP could be %f' % (10**i, USDXRP_Bitstamp.weigtedAverageA(10**i)))\n", "for i in [1,2,3,4,5,6,7]:\n", " print('The global echange rate to sell %8i XRP could be %f' % (10**i, USDXRP_Bitstamp.weigtedAverageB(10**i)))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that there is simply no guarantees this exchange rates could be achieved if a real transaction were to be executed on Ripple. That is because other players on the market could also place competing buy/sell orders and alter the actual exchange rates significantly.\n", "\n", "## The role of XRP\n", "\n", "We are now going to visualize the USD to USD direct conversion rate between two gateways on the Ripple network, [Bitstamp](https://www.bitstamp.net/) and [Gatehub](https://gatehub.net). " ] }, { "cell_type": "code", "execution_count": 43, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Orderbook USDUSD in ledger 15381016\n", " Close date: None\n", " Currency: USD@rvYAfWj5gh67oV6fW32ZzP3Aw4Eubs59B\n", " Counter currency: USD@rhub8VRN55s94qWKDv6jmDy1pUykJzF3wq\n", " Spread: 0.007477 (0.740703 %)\n", " Best ask/bid: 1.009486 / 1.002009\n", " Through: []\n" ] } ], "source": [ "# Gatehub issuer address\n", "Gatehub = 'rhub8VRN55s94qWKDv6jmDy1pUykJzF3wq'\n", "# Getting USD@Bitstamp to USD@Gatehub orderbook\n", "USD_Bitstamp_Gatehub = feed.getOrderbook(('USD', Bitstamp), ('USD', Gatehub)) # Getting the order book\n", "# Showing some info\n", "USD_Bitstamp_Gatehub.showInfo()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We want to explore the crossing of the USD@Bitstamp / USD@Gethub currency pair with another currency, XRP. This method is eavily used on the Forex market, where it is often called 'triangulation'.\n", "\n", "To do so, we pull the following two books: USD@Bitstamp / XRP and XRP / USD@Gatehub." ] }, { "cell_type": "code", "execution_count": 44, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Getting USD@Bitstamp to XRP orderbook\n", "USDXRP_Bitstamp = feed.getOrderbook(('USD', Bitstamp), ('XRP', None))\n", "# Getting XRP to USD@Gatehub\n", "XRPUSD_Gatehub = feed.getOrderbook(('XRP', None), ('USD', Gatehub))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can do the product of the two previous order book. The result will be a synthetic order book that simulates a trade going through the USD@Bitstamp / XRP and XRP / USD@Gatehub order books." ] }, { "cell_type": "code", "execution_count": 45, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Orderbook USDUSD in ledger 15381028\n", " Close date: None\n", " Currency: USD@rvYAfWj5gh67oV6fW32ZzP3Aw4Eubs59B\n", " Counter currency: USD@rhub8VRN55s94qWKDv6jmDy1pUykJzF3wq\n", " Spread: 0.003110 (0.311187 %)\n", " Best ask/bid: 0.999528 / 0.996417\n", " Through: [('XRP', None)]\n" ] } ], "source": [ "# The product of two order book produces a synthetic order book simulating a trade going through both initial books\n", "USD_Bitstamp_Gatehub_Through_XRP = USDXRP_Bitstamp * XRPUSD_Gatehub\n", "# Showing some info\n", "USD_Bitstamp_Gatehub_Through_XRP.showInfo()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Both `USD_Bitstamp_Gatehub_Through_XRP` and `USD_Bitstamp_Gatehub` have the same currency pair. Only difference is that the former describe a trade going through XRP as an intermediary currency. We now plot the weighted average for both books." ] }, { "cell_type": "code", "execution_count": 46, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.legend.Legend at 0x10dbbe910>" ] }, "execution_count": 46, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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Jnj17SoXx888/w8jICCYmJti2bRvXo0hBQQEODg5cfp2VlYXKykp4e3vzliUmJvIq20SE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\nw1xyySVUrlyZOnXqUK9ePe6///4c2wcPHkx8fDwNGjRg586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76CSSJEkqARyJlXT0vvgC\nzjsP0tKgSZOg00iSJKkI8ppYSUXDzp2R62CfeMICK0mSpELjSKyko3PDDfDTT/Dvf0MoFHQaSZIk\nFVGFPhLbq1cvEhISaNasWa779OnThwYNGtCiRQvmz5+f/XxmZiZXXHEFjRo1onHjxsyaNSs6qSUF\na+RImD4dhg61wEqSJKlQHbbEXnvttUyePDnX7ampqaxevZpVq1aRkpLCzTffnL2tb9++dOjQgWXL\nlrFo0SIaNWoUndSSgrNyJfTtC2+9BXFxQaeRJElSCXPYEtumTRvi4+Nz3T5u3Dh69OgBQOvWrcnM\nzGTDhg1s3bqVGTNm0KtXLwDKlClDlSpVohRbUiB274bkZHjoIWjRIug0kiRJKoHyvbDTunXrSExM\nzH5cu3Zt1q5dy9dff021atW49tprOeOMM7jhhhvYuXNnfk8nKUh/+xvUrg033RR0EkmSJJVQUVmd\n+NcX6YZCIfbs2cO8efP4y1/+wrx58zj22GN5/PHHo3E6SUFIS4ss4vTKK14HK0mSpMCUye8BatWq\nRXp6evbjtWvXUqtWLcLhMLVr1+ass84C4Iorrsi1xA4cODD756SkJJKSkvIbS1I0bdkC11wDr74K\n1aoFnUaSJElFWFpaGmlpaQV2/HyX2E6dOvHiiy/SpUsXZs2axXHHHUdCQgIAiYmJrFy5koYNGzJl\nyhSa5HIvyf1LrKQiJhyOTB++9FJo3z7oNJIkSSrifj0wOWjQoKge/7AlNjk5mWnTprF582YSExMZ\nNGgQWVlZANx444106NCB1NRU6tevz7HHHsuwYcOyX/vCCy/QrVs3du/eTb169XJskxQj/v1vWLoU\nhg8POokkSZJEKBzNu84eTYAo3/hWUhR99RW0bg1Tp0Lz5kGnkSRJUgyKdueLysJOkoqhvXvh6qvh\n3nstsJIkSSoyLLGSDm7wYKhQAfr2DTqJJEmSlM3pxJIOtGQJtG0Lc+bASScFnUaSJEkxzOnEkgpW\nVlbkdjqPPWaBlSRJUpFjiZWU06OPQo0acN11QSeRJEmSDuB0Ykn/M29e5F6wCxZAzZpBp5EkSVIx\n4HRiSQVj167INOJnnrHASpIkqchyJFZSxN13w4oV8M47EAoFnUaSJEnFRLQ7X5moHUlS7PrsMxg+\nHBYutMBKkiSpSHM6sVTS7d4dWcTpuecgISHoNJIkSdIhWWKlYmTKV1P4NP3TI3vR44/DySfDVVcV\nTChJkiQpiiyxUjHy2vzXWPH9iry/4Isv4IUXYOhQpxFLkiQpJlhipWJk/nfzaVmjZd523rsXrr8e\nHnoIatcu2GCSJElSlFhipWJix+4dfJP5DY2rNc7bC156CUqXhhtvLNhgkiRJUhS5OrFUTCzasIjG\n1RpTtnTZw+/8zTcwaBB8/DGU8rssSZIkxQ4/vUrFxPyM+Zxx4hmH3zEchptugjvugNNOK/hgkiRJ\nUhRZYqViYl7GvLxdDztyJKxfD/37F3woSZIkKcossVIxMf+7PIzEbtoE/frBq69C2TxMO5YkSZKK\nGEusVAzs3rub5ZuX0zyhee477d0L114L3bvDmWcWXjhJkiQpiiyxUjGwdONSTok/hQplKxy4ce9e\nePddOO88+PFHePTRwg8oSZIkRYmrE0vFwLyMeQdOJf75Z3j9dRg8GOLj4c474fLLoYz/7CVJkhS7\n/DQrFQPzv5v/v0WdtmyBoUPhhRfgN7+BV16BNm0gFAo2pCRJkhQFTieWioF5GfM4J1w7Mtparx6s\nXAkffAATJsC551pgJUmSVGw4EivFuL0rV3Dj0M9p9fWNcG0vWLgQEhODjiVJkiQVCEusFKtWroS/\n/53whHFsbRVHqQ++jFz7KkmSJBVjTieWYs2yZdCtG/zud9CgAWPHP8WMXudbYCVJklQiWGKlWPHF\nF9ClCyQlQbNm8OWXcP/9fLZjOWfUOOOwL5ckSZKKA0usVNStWQM9ekDbtnDGGZHyevfdULky8N+V\niU9sGWxGSZIkqZBYYqWiasMG6NMncpuck06KXAPbvz9UqpS9SzgcZn7GfrfXkSRJkoo5S6xU1Gzd\nCg88AI0bR26Ns2wZPPQQVKlywK7fbP2GCmUrkFApIYCgkiRJUuGzxEpFxe7d8Mwz0KABpKfD3Lnw\n/PNQvXquL1nw3QJOr3F6IYaUJEmSguUtdqSghcPw7rtw111w6qnw0UfQpEmeXrrwu4WcnmCJlSRJ\nUslhiZWCNH8+3HknbNoEL70E7dod0csXbFhA16ZdCyicJEmSVPQ4nVgKQkYG9OoFF10EV10FCxYc\ncYGFyHTiFjVaFEBASZIkqWiyxEqFKSsLnn46cp/XqlVhxQq46SYoc+STIjJ/zmTzzs3Ui69XAEEl\nSZKkosnpxFJhmT4dbrkFTjwRPvkEGjbM1+EWbVhEs+rNKF2qdJQCSpIkSUWfJVYqaBs2RBZt+ugj\nePZZuPzyyK1z8mnBdwtokeBUYkmSJJUsTieWCsrevfDii9C0KdSoEbnf6xVXRKXAwn9XJvb2OpIk\nSSphHImVCsLSpXDddVC+PKSl5fmWOUdiwYYF3PCbG6J+XEmSJKkocyRWiqbdu2HQIEhKgmuvPaJ7\nvh6JrL1ZLNu0jGbVm0X92JIkSVJR5kisFC2zZ0dGX+vWjdz/tXbtAjvViu9XUKdKHY4td2yBnUOS\nJEkqiiyxUn7t3AkPPAAjR0YWbrrqqqhd95ob7w8rSZKkksrpxFJ+fPYZnH46fPcdLF4MXboUeIGF\nSIk9PcFFnSRJklTyWGKlo5GVBQMHwp//DI8+CiNGQNWqhXb6hRsWOhIrSZKkEsnpxNKRWrECuneH\nE06IXPt64omFevpwOBwZifX2OpIkSSqBHImV8iochn/8A37/+8jKw6mphV5gATJ2ZBAixImVCv/c\nkiRJUtAciZXyYtMm6NEDNm+Gjz+GU08NLMovizqFCuHaW0mSJKmocSRWOpxp06BlS2jRAmbODLTA\ngos6SZIkqWRzJFbKzd69kUWbXnoJhg2D9u2DTgREFnXq1LBT0DEkSZKkQDgSKx1MRga0awcffghz\n5xaZAgveI1aSJEklmyVW+rVp0+A3v4E2bWDKFKhZM+hE2X7c/SPpW9M59YRgpzRLkiRJQXE6sfSL\ncBiGDIlMIf73vyMjsUXM4o2LaVStEWVLlw06iiRJkhQIS6wEsHMn9O4NS5fCrFlw8slBJzooF3WS\nJElSSed0Yunrr+F3v4v8PHNmkS2wAAu/W+j1sJIkSSrRLLEq2aZOhbPPhp49I1OIK1YMOtEhLdq4\niOYJzYOOIUmSJAXG6cQquVJS4IEHYNQoaNs26DSHFQ6HWbxhMc2qNws6iiRJkhQYS6xKnr17oX9/\nGD8eZsyAhg2DTpQn32z9hsrlK3NCxROCjiJJkiQFxhKrkmXHDujWDbZujSzgdPzxQSfKs0UbFtEs\nwVFYSZIklWxeE6uSY+3ayL1fq1aF//wnpgoswOINi2le3ethJUmSVLJZYlUyLFkC55wDXbrAK69A\nuXJBJzpiLuokSZIkWWJVEnz8MZx/Pjz+OAwYAKFQ0ImOitOJJUmSJK+JVXE3fjxcd13k9jkXXhh0\nmqP2856fWZO5htOqnhZ0FEmSJClQjsSq+HrtNejdGyZMiOkCC/DFpi+of3x9ypWOvWnQkiRJUjQ5\nEqviJxyOTB1OSYFp02LmFjqHsmiD18NKkiRJYIlVcRMOw733RqYRz5wJNWsGnSgqXJlYkiRJinA6\nsYqPcBjuvBPefx/S0opNgYXIysQu6iRJkiQ5EqviYt8+uPVWmDsXpk6F+PigE0XV4g2LnU4sSZIk\nYYlVcbB3b2QBpxUr4IMPoHLloBNF1YYdG9i9dze14moFHUWSJEkKnCVWsW3fPujVC9LTYfJkqFQp\n6ERRt3jjYpolNCMUo/e3lSRJkqLJEqvYtW8f3HgjrFkDkyZBxYpBJyoQLuokSZIk/Y8LOyk2hcPQ\npw8sXRq5D2wxLbDgok6SJEnS/iyxij3hMPz1r/DZZ5ER2Li4oBMVKBd1kiRJkv7H6cSKPfffH1mB\n+MMPoUqVoNMUqD379vDFpi9oWr1p0FEkSZKkIsESq9gyeDCMHQvTpsHxxwedpsCt/mE1J8adSKVy\nxW/BKkmSJOloWGIVO15/HV54AT7+GKpVCzpNoXAqsSRJkpSTJVaxYeJE6N8fPvoIEhODTlNoFm1Y\n5MrEkiRJ0n5c2ElF3yefQM+e8N570KhR0GkK1S/3iJUkSZIUYYlV0bZ0KVx6KbzxBrRuHXSaQrdo\nwyKnE0uSJEn7scSq6NqwAS6+GJ5+Gi68MOg0hW77ru1s+HED9eLrBR1FkiRJKjIssSqafvoJOnWK\nTCO++uqg0wTii01fcFrV0yhdqnTQUSRJkqQi47AltlevXiQkJNCsWe7X5fXp04cGDRrQokUL5s+f\nn2Pb3r17admyJR07dsx/WpUM+/bBNddA/frwt78FnSYwSzYu8f6wkiRJ0q8ctsRee+21TJ48Odft\nqamprF69mlWrVpGSksLNN9+cY/vzzz9P48aNCYVC+U+rkuH++yEjA159FUrw783STUtpWs0SK0mS\nJO3vsCW2TZs2xMfH57p93Lhx9OjRA4DWrVuTmZnJhg0bAFi7di2pqalcf/31hMPhKEVWsTZsGLz1\nFrz7LhxzTNBpArVk4xKaVG8SdAxJkiSpSMn3NbHr1q0jcb/7dtauXZt169YBcMcdd/DUU09RqpSX\n3ioPPvkEBgyACROgatWg0wTO6cSSJEnSgaLSLn89yhoOh5kwYQLVq1enZcuWjsLq8Navh86dYfhw\nOO20oNME7oeffmDH7h0kVk48/M6SJElSCVImvweoVasW6enp2Y/Xrl1LrVq1GDNmDOPGjSM1NZWf\nf/6Zbdu2cc011/D6668fcIyBAwdm/5yUlERSUlJ+YymW7NoFV1wBN98MHToEnaZIWLpxKU2qN/Fa\nckmSJMWctLQ00tLSCuz4oXAehknXrFlDx44dWbx48QHbUlNTefHFF0lNTWXWrFncfvvtzJo1K8c+\n06ZNY/DgwYwfP/7AAKGQI7Ul3U03wcaN8P/+Hzj1HIChnw9lXsY8Xu70ctBRJEmSpHyJduc77Ehs\ncnIy06ZNY/PmzSQmJjJo0CCysrIAuPHGG+nQoQOpqanUr1+fY489lmHDhuUaXDrAyy/D9Onw2WcW\n2P0s3bTURZ0kSZKkg8jTSGyBBnAktuT67DPo2BE+/hgaNgw6TZGSNDyJ+8+9nz+e8sego0iSJEn5\nEu3O59CXgrFlC1x1FaSkWGB/JRwOuzKxJEmSlAtLrApfOAy9esEll0T+KIeNP24kTJiEYxOCjiJJ\nkiQVOflenVg6Yi+8AGvXwujRQScpkn4ZhfU6ckmSJOlAllgVrjlz4JFHYNYsKFcu6DRF0tJNS2lS\nzUWdJEmSpINxOrEKz9atketg//EPOOWUoNMUWV4PK0mSJOXOEqvCEQ5D795w4YVw5ZVBpynSLLGS\nJElS7pxOrMIxYgQsXRqZTqxchcNhpxNLkiRJh2CJVcH79lu48054/3045pig0xRpa7etpWLZipxQ\n8YSgo0iSJElFktOJVbD27YOePeH226Fly6DTFHlOJZYkSZIOzRKrgjVkCPz8M/TvH3SSmOBUYkmS\nJOnQnE6sgvPFF/+7nU4Zf9XyYsnGJfy+zu+DjiFJkiQVWY7EqmDs3g3du8Ojj0L9+kGniRlLNy11\nOrEkSZJ0CJZYFYzHH4eEBLjhhqCTxIx94X18sekLGldrHHQUSZIkqchyjqeib+lSeOEFmDcPQqGg\n08SMr7d8TdWKValcvnLQUSRJkqQiy5FYRdfevXD99fDww5CYGHSamOKiTpIkSdLhWWIVXS++COXK\nQe/eQSeJOUs2LrHESpIkSYfhdGJFz9dfR0ZgP/kESvn9yJFavHExF9W/KOgYkiRJUpFm01B0hMNw\n001w113QsGHQaWLSko1LaFa9WdAxJEmSpCLNEqvoeOstyMiAO+8MOklM2r13N6t/WE2jao2CjiJJ\nkiQVaU4nVv5t2wb9+sHo0VC2bNBpYtKKzSs4qcpJHFPmmKCjSJIkSUWaI7HKv4EDoV07+N3vgk4S\ns5ZsXEKzBKcSS5IkSYfjSKzyZ9EieOONyL1hddQWb1zs9bCSJElSHjgSq6O3bx/cfHNkReJq1YJO\nE9MWb1xM0+pNg44hSZIkFXmWWB29f/8bsrLg+uuDThLzXJlYkiRJyhunE+vo7NgB994LY8dC6dJB\np4lp23dtZ9OPmzgl/pSgo0iSJElFniOxOjqPPw7nnw+tWgWdJOYt2rCIRtUaUbqUXwZIkiRJh+NI\nrI7cN9/A0KGwcGHQSYqFZ2Y9w+WNLg86hiRJkhQTLLE6cv37Q9++ULt20Eli3oxvZjBn/RzeuPSN\noKNIkiRJMcESqyPz8cfw6acwbFjQSWLevvA++v2nH4/+4VEqlK0QdBxJkiQpJnhNrPJu3z64447I\n9bAVKwadJua9ufhNwoRJbpYcdBRJkiQpZjgSq7x76y0IhSDZ0pVfP+/5mXs/vJc3Ln2DUiG/S5Ik\nSZLyyhKrvNm9G+6/H1JSIkVW+fKP2f+gZY2WtDmpTdBRJEmSpJhiiVXevPoq1KsHf/hD0EliXubP\nmTwx8wnSeqYFHUWSJEmKOaFwOBwONEAoRMARdDg//ggNGsCECXDGGUGniXn3Tr2X73Z8x2t/fi3o\nKJIkSVKBi3bncyRWh/fcc3DuuRbYKFi/fT3/N+f/WHiT99iVJEmSjoYjsTq077+HU0+FWbOgfv2g\n08S8mybcRFy5OJ5q91TQUSRJkqRC4UisCtfjj0PnzhbYKFj5/UrGLBvDiltXBB1FkiRJilmOxCp3\nGzdCo0awaBHUqhV0mpjX+e3OtKzRknva3BN0FEmSJKnQOBKrwvPUU9C1qwU2Cj5f9zkz02cy/JLh\nQUeRJEmSYpolVge3cWPktjqLFwedJOaFw2Hunno3D573IBXLVgw6jiRJkhTTSgUdQEWUo7BRM+Wr\nKazdtpZeLXsFHUWSJEmKeY7E6kC/jMIuWhR0kpgXDod54KMHeCjpIcqU8p+bJEmSlF+OxOpAgwdD\ncjLUrh10kpg3afUkduzewZVNrgw6iiRJklQsODSknDZtgldegYULg04S88LhMH/76G8MShpEqZDf\nF0mSJEnR4Cdr5fTCC3DFFZCYGHSSmDduxTj27NvDpY0uDTqKJEmSVGw4Eqv/2bEDhg6FmTODThLz\n9oX38WDag47CSpIkSVHmp2v9zyuvwHnnQcOGQSeJeWOXjaVMqTJ0OrVT0FEkSZKkYsWRWEVkZcEz\nz8CYMUEniXm/jMI+ecGThEKhoONIkiRJxYojsYp4802oXx/OOivoJDHv7aVvU6lcJS6qf1HQUSRJ\nkqRix5FYwb598OST8PTTQSeJefvC+xg0bRDPXviso7CSJElSAXAkVpCaCmXLQrt2QSeJee8se4fK\n5SvTrp5/l5IkSVJBsMQKnnoK+vcHRw7zJRwO88j0R7j/3PsdhZUkSZIKiCW2pFuwAL76KnJvWOXL\nxFUTAfhTgz8FnESSJEkqviyxJd2QIfCXv0SmE+uoOQorSZIkFQ4XdirJNm2CsWNh1aqgk8S8qV9P\nZduubVzW6LKgo0iSJEnFmiOxJVlKClx+OVStGnSSmPfI9Ee4t829lAr5T0qSJEkqSI7EllRZWfDS\nSzBpUtBJYt6Mb2aQvi2dLk27BB1FkiRJKvYcNiqpxoyBhg2hefOgk8S8v8/4O/f8/h7KlPI7IUmS\nJKmg+am7pHr+eRgwIOgUMW/O+jks3bSUcS3GBR1FkiRJKhEciS2JPv8cvvsOOnYMOknMe3Lmk/Q7\npx/lSpcLOookSZJUIlhiS6J//hNuuglKlw46SUxb/cNqPlrzEdefcX3QUSRJkqQSw+nEJc3WrZHr\nYZcvDzpJzHvm02e48Tc3UqlcpaCjSJIkSSWGJbakGTECLrgAEhKCThLTNv24iVFLRrHslmVBR5Ek\nSZJKFKcTlyThcGQqce/eQSeJeS/OfpHOTTqTUMkvAyRJkqTC5EhsSTJ7Nvz4I/zhD0EniWk/7v6R\noXOGMrPXzKCjSJIkSSWOI7ElSUoK3HADlPK/9vx4bf5rtDmpDQ1OaBB0FEmSJKnECYXD4XCgAUIh\nAo5QMmzdCnXrwooVUL160Gli1p59e6g/pD6jrxhN69qtg44jSZIkFXnR7nwOyZUUvyzoZIHNl7eX\nvs1Jx51kgZUkSZICYoktKVJSXNApn8LhME/MfIK7fntX0FEkSZKkEssSWxIsWACZmS7olE+TVk8i\nTJg/NfhT0FEkSZKkEssSWxK8/jp07+6CTvn02MePcffv7iYUCgUdRZIkSSqxvMVOcZeVBSNHwvTp\nQSeJaTO+mUHG9gyubHJl0FEkSZKkEs2hueLu/ffhlFOgYcOgk8S0v8/4O/1/158ypfzeR5IkSQqS\nJba4e/116NEj6BQxbc76OSzdtJQeLfx7lCRJkoLmfWKLsy1b4OST4euvIT4+6DQx69LRl9K2blv6\ntO4TdBRJkiQp5nifWOXdW29Bu3YW2HxYsnEJn6Z/yvVnXB90FEmSJElYYou3f/3LqcT59OiMR7nj\n7DuoWLZi0FEkSZIk4XTi4mvlSjj3XEhPh7Jlg04Tk1Z9v4rfvvZbvuzzJZXLVw46jiRJkhSTnE6s\nvBkxApKTLbD58PjHj3PLWbdYYCVJkqQixPuFFEfhMIweHZlOrKPy7dZveXfFu6y6bVXQUSRJkiTt\n57Ajsb169SIhIYFmzZrluk+fPn1o0KABLVq0YP78+QCkp6fTtm1bmjRpQtOmTRkyZEj0UuvQFi6E\nXbugVaugk8SsJ2c+yfUtr+f4CscHHUWSJEnSfg5bYq+99lomT56c6/bU1FRWr17NqlWrSElJ4eab\nbwagbNmyPPvssyxdupRZs2bxj3/8g2XLlkUvuXI3ejRcdRWEQkEniUkbdmxgxOIR3HnOnUFHkSRJ\nkvQrhy2xbdq0If4Qt2gZN24cPf67Am7r1q3JzMxkw4YN1KhRg9NPPx2ASpUq0ahRI9avXx+l2MrV\nL1OJr7oq6CQx68XZL3JVk6tIqJQQdBRJkiRJv5Lva2LXrVtHYmJi9uPatWuzdu1aEhL+VwDWrFnD\n/Pnzad26dX5Pp8OZMwfKlIH/foGgI/Pj7h/5v7n/xye9Pgk6iiRJkqSDiMrqxL9eLjm03zTWHTt2\ncMUVV/D8889TqVKlaJxOhzJqlFOJ8+G1+a/Rpk4bGpzQIOgokiRJkg4i3yOxtWrVIj09Pfvx2rVr\nqVWrFgBZWVlcfvnlXH311VxyySW5HmPgwIHZPyclJZGUlJTfWCXTvn3w1lswaVLQSWLSnn17eGbW\nM4y8bGTQUSRJkqSYlZaWRlpaWoEdP98ltlOnTrz44ot06dKFWbNmcdxxx5GQkEA4HOa6666jcePG\n3H777Yc8xv4lVvnw6adQuTI0bRp0kpg05osx1IqrxTmJ5wQdRZIkSYpZvx6YHDRoUFSPf9gSm5yc\nzLRp09i8eTOJiYkMGjSIrKwsAG688UY6dOhAamoq9evX59hjj2XYsGEAzJw5kzfeeIPmzZvTsmVL\nAB577DHat28f1Teg/YweDV26BJ0iJoXDYZ765Cn+dt7fgo4iSZIk6RBC4V9f0FrYAUKhA66p1VHY\nuxdq14Zp06Bhw6DTxJyPvv6Iv6T+haV/WUqpUFQuFZckSZJE9Dufn9aLi88+g2rVLLDsg/jRAAAg\nAElEQVRH6alPnqLfOf0ssJIkSVIR5yf24uLdd+EQi2cpd0s2LmH+d/O5uvnVQUeRJEmSdBiW2OIg\nHIaxYy2xR+nZT5/llrNu4ZgyxwQdRZIkSdJh5Ht1YhUBy5bBrl3w3wW0lHebftzEO8vfYeWtK4OO\nIkmSJCkPHIktDn6ZShwKBZ0k5qTMTeGy0y6j2rHVgo4iSZIkKQ8sscWB18Melay9Wbw05yX6tO4T\ndBRJkiRJeWSJjXVr18KXX0KbNkEniTljlo2hwfENaFGjRdBRJEmSJOWRJTbWjRsHf/oTlC0bdJKY\n8/xnz9O3dd+gY0iSJEk6ApbYWOdU4qMye91svtvxHZ1O7RR0FEmSJElHwBIbyzIz4bPP4MILg04S\nc4Z8NoRbz7qV0qVKBx1FkiRJ0hGwxMay1FRISoJjjw06SUzJ2J7BxFUT6dWyV9BRJEmSJB0hS2ws\nGzcOOjkd9kgNnTOU5KbJxFeIDzqKJEmSpCMUCofD4UADhEIEHCE27dkD1avDkiVQs2bQaWLG9l3b\nqf9Cfab1nMZpVU8LOo4kSZJU7EW78zkSG6tmzYK6dS2wR+i5Wc/xx1P+aIGVJEmSYlSZoAP8f/bu\nPLqq8tzj+DeEeVQBEQgICAlzCAIRBQVBoFRUFBWVWgXHWsWhWrV6F15XnVqtA1otDmgt4IRiBRFR\nwiiTDIqAqBBGlUGCQJiS7PvHvkRjAjIk2Rm+n7XOCufsnX1+J7y2eXje9906QhMmQN++UacoVpZu\nWsqTc59kzlVzoo4iSZIk6QjZiS2uLGIPy+6M3Vzy1iU82ONBmhzbJOo4kiRJko6Qa2KLo/XroW1b\n2LgRYr1FzKG4ZeItrPlxDW9e+CYxMTFRx5EkSZJKjfyu+ZxOXBy9/354b1gL2EMy8euJvLnsTRZf\nt9gCVpIkSSrmnE5cHDmV+JBt3LmRweMG88p5r3BcpeOijiNJkiTpKDmduLjZuze8tc5XX0Ht2lGn\nKdKCIOD8188n/rh4Hj7r4ajjSJIkSaWS04lLu5kzISHBAvYQvPrZq3z9w9eMuWBM1FEkSZIk5ROL\n2OJm0iTo1SvqFEXeuh/XceukW/lg0AdUKFsh6jiSJEmS8olrYoubDz+Es86KOkWRFgQBQ94dwo2d\nbqR93fZRx5EkSZKUjyxii5MtW8K1sKecEnWSIm3EghFsSd/CXV3uijqKJEmSpHzmdOLi5KOPoGtX\nKF8+6iRF1sqtK7n7o7uZesVUysWWizqOJEmSpHxmJ7Y4cSrxQWUFWQx5dwh3drmTVse3ijqOJEmS\npAJgEVtcBIFF7K94YcELpO9L55ZTbok6iiRJkqQC4nTi4uLrr2HfPmjRIuokRdK327/l7o/v5uPL\nPya2TGzUcSRJkiQVEDuxxcWHH4a31omJiTpJkXTj+zdy7cnX0qZOm6ijSJIkSSpAdmKLiw8/hAsv\njDpFkfT2srf5fOPnvHr+q1FHkSRJklTAYoIgCCINEBNDxBGKvsxMqFULli+HOnWiTlOkbNu9jVbP\ntGLUBaM4/cTTo44jSZIk6Rfyu+ZzOnFxsGgR1K9vAZuHOyffyW+b/dYCVpIkSSolnE5cHKSkQLdu\nUacocmasmcG7K97liz98EXUUSZIkSYXETmxxMGWKRewvZGRl8Ifxf+CxXo9xTMVjoo4jSZIkqZBY\nxBZ1GRkwYwac7nTZn3tm3jPUrlKbi1pdFHUUSZIkSYXI6cRF3aJFEBcHxx8fdZIi47sd33H/tPuZ\ndsU0YrzlkCRJklSq2Ikt6lwPm8sdH97B4HaDaVG7RdRRJEmSJBUyO7FF3ZQpMHhw1CmKjGmrpzEl\ndQrLblgWdRRJkiRJEbATW5S5HjaHfZn7uGHCDTzW6zGqlq8adRxJkiRJEbCILcoWLoSGDaF27aiT\nFAlPzX2KE6qewICWA6KOIkmSJCkiRWI68V8++gsZWRlHdY2AIJ/SFB1nvD6XY04qx7gP74g6SuS2\n79nOO1++w9QrprqZkyRJklSKFYkitmLZipSLLUcMR1eclLTiJnFOKnMGdadW5VpRR4lcrcq1mDl4\nJk2ObRJ1FEmSJEkRigmCINIWZkxMDBFHKJq+/hpOOw3WrYNy5aJOI0mSJElHJL9rPtfEFlXPPw+X\nXmoBK0mSJEk/Yye2KNqyBeLjYcECOPHEqNNIkiRJ0hGzE1sa/OMfcP75FrCSJEmS9At2YouarVuh\naVOYPx8aN446jSRJkiQdFTuxJd3jj8N551nASpIkSVIe7MQWJWlpYRd2zhw46aSo00iSJEnSUbMT\nW5I99xz85jcWsJIkSZJ0AHZii4o9e6BJE5gwARITo04jSZIkSfnCTmxJNWoUtG5tAStJkiRJB1E2\n6gACsrLg73+HJ56IOokkSZIkFWl2YouC99+H8uWhR4+ok0iSJElSkWYRWxT8/e9w++0QExN1EkmS\nJEkq0tzYKWqffQZ9+8KqVVCuXNRpJEmSJClfubFTSfPUU3DddRawkiRJknQI7MRG6YcfwnvCfvkl\nHH981GkkSZIkKd/ZiS1JXngB+vWzgJUkSZKkQ2QnNiqZmdC0Kbz+OnTsGHUaSZIkSSoQdmJLivfe\ngzp1LGAlSZIk6TBYxEblqafgxhujTiFJkiRJxYrTiaOwdCn06AGrV0P58lGnkSRJkqQC43TikuDZ\nZ+GqqyxgJUmSJOkw2YktbLt3Q1wczJ8PjRpFnUaSJEmSCpSd2OLu7behfXsLWEmSJEk6Ahaxhe35\n58OpxJIkSZKkw+Z04sL0zTdwyimwbh1UqBB1GkmSJEkqcE4nLs5eegl+9zsLWEmSJEk6QnZiC0tG\nBpx4IkyaBK1aRZ1GkiRJkgqFndji6vXX4aSTLGAlSZIk6SiUjTpAibd3L0yfDvfdB088EXUaSZIk\nSSrWLGILwqZN8P778N578OGHkJAAV18NvXtHnUySJEmSijXXxOaHIIAvvoD//jcsXJcsgR49oF8/\n6NsX6tSJOqEkSZIkRSK/az6L2CO1Zw+kpIRF63vvhYVsv37h44wz3IFYkiRJksj/ms/pxIfj++9h\n/PiwaP3oI2jTBs4+O+zAtmoFMTFRJ5QkSZKkEs1O7MEEASxe/NM04RUroFevsHD9zW+gVq2oE0qS\nJElSkeZ04oK2axd8/PFP04QrVPhpmnCXLlC+fNQJJUmSJKnYcDpxQfj225+K1ilTICkp7Lbu31nY\nacKSJEmSVCSU3k7sV1/B22+Hj+XLw9vf9OsXThM+7rjCzyNJkiRJJZDTiY/U3r0wfXp4/9YJEyAt\nDc49F/r3h27dnCYsSZIkSQUgv2u+Mr92wuDBg6lTpw5t2rQ54Dk33XQTzZo1IzExkYULF2a/PnHi\nRJo3b06zZs14+OGH8yfx4Vi3DkaMCAvV2rXhL3+B6tXh5ZfDY//8Z7hRkwWsJEmSJBULv9qJnT59\nOlWrVuXyyy/n888/z3V8woQJDB8+nAkTJjBnzhyGDh3K7NmzyczMJCEhgcmTJ1O/fn06duzI6NGj\nadGiRc4A+VmVZ2XB7Nnw7rtht3XDhnCacN++YbFau3b+vI8kSZIk6ZAU+sZOXbt2JTU19YDH3333\nXX7/+98DkJycTFpaGt999x2rVq2iadOmNGrUCICBAwcybty4XEXsUduxA2bMCKcJv/UWHHNM2Hn9\n17+gY0eIjc3f95MkSZIkReaodydev349DRo0yH4eFxfH+vXr2bBhQ67X58yZk+c19mbupXzsYUzp\nXb487LZOnAjz5kH79tCzJ0yaBC1bHvFnkSRJkiQVbflyi52jbQ1Xf7A65WPLUyamTPbjb2f9jSuT\nrgxP2L493JRp8WJSX32aSmu/ZXzrCnzcvDwze1QkvcJyHupxBVfmUcDeOflORi4amev1B3s8+NP1\nC/n8lxe/nOf5V7S7osif/5eP/sK/P/s3Mf9/26EYYoiJieH+7vczqO2gXOcPSxnG6CWjieH/z4+J\nIYYY/ueM/2Fg64G5zv/rtL/y+tLXiSGGCmUr0KB6AxrWaMhlbS7j5Hon5zpfkiRJUulySLsTp6am\n0q9fvzzXxF533XV069aNgQPDgqR58+ZMnTqVVatWMWzYMCZOnAjAgw8+SJkyZfjzn/+cM0BMDHff\nczdZQRYBAad2PZXOXTpTJX0fld+fHE4RnjIFOnSA9u1JP7Uj28/sAmVz1t/VKlSjcrnKufJt272N\nXRm7cr1evUL1yM5P35ee5/lVylcp8uf/sOsHtu/ZDkBAkP0PGDUr16R6heq5zv9ux3ek7U4Lzw8C\nAsLzT6h6AsdVyn0ro3U/rmNz+mYA0vels+7HdazZtoYzG59J+7rtc53/91l/Z+mmpTSs0TDH48Qa\nJ1KhbIVc50uSJEkqWCkpKaSkpGQ/v++++wr/FjsHK2J/vrHT7Nmzufnmm5k9ezYZGRkkJCTw0Ucf\nUa9ePTp16nTgjZ06dMh50cxM+PprOPNMuOCC8P6txxxzdJ9UJdL8DfNZ9N0i1mxbk+Px5G+epG+z\nvrnOn7NuDrFlYmlzfBuLXEmSJKkQFPrGTpdccglTp05l8+bNNGjQgPvuu499+/YBcO2119K3b18m\nTJhA06ZNqVKlCi+99FJ44bJlGT58OL179yYzM5MhQ4YceFOnZ57J/Vrz5lCt2pF/MpUKHep1oEO9\nDr9+4v8b9+U4xn81npVbV5JcP5kzTjyDbo26cUrcKZSLLVeASSVJkiTlh0PqxBZogHyuyqVDkbY7\njRlrZpCSmkJKagrjBo6jfvX6UceSJEmSSpz8rvksYqUDyMjK4N6P7yU5LpnOcZ2pU7VO1JEkSZKk\nYqfQpxNLpdXujN1ULleZ5z59jivHXclxlY6jc1xnejTukefO05IkSZIKnp1Y6RBkBVks37ycT9Z+\nwtbdW/nTqX/KdU5GVgaxMbHZtx+SJEmS5HRiqcgau2wsV717FclxyTx39nM0rNEw6kiSJElS5Cxi\npSLsux3f8eD0B9m8azP/Of8/UceRJEmSImcRKxVxO/buoNlTzZg0aBJt6rSJOo4kSZIUqfyu+crk\n25UkAVC1fFVuTr6Zh2c+HHUUSZIkqcSxEysVgG27tzEldQrnNT8v6iiSJElSpJxOLEmSJEkqNpxO\nLEmSJEkqtSxiJUmSJEnFRtEoYk84IfdjxIioU0mSJEmSipiyUQcAYNGinM8zM6FChbzPDQKIiSn4\nTFI++XHPjyzdtJRT4k6JOookSZJU7BW/jZ26dIGmTeHCC6FbN6hSpcCySflhycYl9HylJ6uGrqJS\nuUpRx5EkSZIKlRs7jR0LbdvC3/4WTjvu2TP8c2Zm1MmkPLU+vjWd6nfipUUvRR1FkiRJKvaKXyf2\n57ZvhylTYP58+N//zd9gUj6avW42F795MV/d+BXlY8tHHUeSJEkqNN4n9nBs3gzlykGNGgVzfekw\n9H61NwNaDODqk6+OOookSZJUaJxOfDjefx8aNoTzzoMxY2DnzqgTqRT7n9P/hwdmPEBGVkbUUSRJ\nkqRiq2R3YgG2bYN33gmL2E8+gd69YdgwaNGi4N5TOoDF3y0m8YTEqGNIkiRJhcbpxEdj8+ZwY6he\nvaBRo8J5T0mSJEkqxSxiC0oQwIwZ0LkzlC0at8+VJEmSpOLONbEFZdMmuPlmOP54uPRSGD0a0tKi\nTiVJkiRJ+hmL2P2OPx4+/RQ+/xzOOAP+859wU6ibboo6mSRJkiTp/zmd+GDS02HDBmjaNOokKmH2\nZu7l4RkPc3fXu4ktExt1HEmSJKnAOJ24MFWufOAC9p57oH9/GDEC1q8v3Fwq9sqVKceklZMY9fmo\nqKNIkiRJxYqd2CO1aRN88AFMmBB+bdAAfvtb+OMfoW7dqNOpGJiaOpXB7w5m+Q3LKRdbLuo4kiRJ\nUoGwE1tU1K4NgwbBqFHw/fcwfDhkZYUP6RCc0egMmhzbhJGLRkYdRZIkSSo27MQWhowMuO8+6NED\nTj0VypePOpGKiNnrZnPRGxex4sYVVCxbMeo4kiRJUr6zE1sc7d4dfr399rCDO2AAvPYa7NwZbS5F\n7pS4U0iqm0RKakrUUSRJkqRiwU5sYdu4Ed57LyxiK1aEceOiTqSIZWRlULZM2ahjSJIkSQUiv2s+\ni9goZWZCrLdXkSRJklRyOZ24JDlQAXvrrfC738E778CuXYWbSZIkSZKKMDuxRdGGDTB2LLz1Fixc\nCL17w/nnw7nnhlOQJUmSJKmYcDpxabNxY7hu9u234dVX4bjjok4kSZIkSYfMIlaSJEmSVGy4JlaS\nJEmSVGpZxBZne/bAWWfBvHlRJ5EkSZKkQmERW5xVqADXXw99+8Jzz4HTsiVJkiSVcK6JLQlWrIAL\nLoD27eHpp6Fq1agTSZIkSRLgmljlJT4eZs8O7zublATbtkWdSJIkSZIKhJ3Ykmb+fOjQIeoUkiRJ\nkgR4ix1JkiRJUjHidGIdGf+hQJIkSVIJYBFbGnzzDXTsCHPnRp1EkiRJko6KRWxp0KQJ3HornHsu\nDB0KaWlRJ5IkSZKkI2IRWxrExMCll8KSJZCeDs2bh/eVzcyMOpkkSZIkHRaL2NKkZk0YMQLefx/e\nfRc2bYo6kSRJkiQdFncnliRJkiQVGHcnVsFyirEkSZKkIswiVjmdfz5cdx2sXh11EkmSJEnKxSJW\nOb3wAhxzDLRvD4MHw1dfRZ1IkiRJkrJZxCqnWrXgoYfC4vXEE+HUU+Hmm6NOJUmSJEmAGzvp1/z4\nIyxbBsnJUSeRJEmSVAzld81nEStJkiRJKjDuTqyiISsLLroIXn0V9u6NOo0kSZKkUsIiVkcmJgau\nuAJeegmaNIGHH4YtW6JOJUmSJKmEs4jVkYmJgb594aOP4L33YOlSOOkkGDYs6mSSJEmSSjDXxCr/\nbNkC69ZBYmLUSSRJkiQVEW7spOLpm2+gUSOIjY06iSRJkqRC5MZOKp5uvDG87+yf/wxLlkSdRpIk\nSVIxZRGrwjFhAnzwQbiWtk8faN8enngC7MJLkiRJOgxOJ1bhy8yElBSYPz/szEqSJEkqsVwTq5Jv\n5UrIyID4+KiTSJIkSTpKrolVyTd3LnTvDgkJcOut8PHHsHdv1KkkSZIkFQF2YlU0BQEsXAjjx4f3\nof3yS3jrLejRI+pkkiRJkg6D04lVOn3/PVSuDNWq5T62bx+UK1f4mSRJkiT9KotY6eeysqBhQ2ja\nFHr2DB8dOkDZslEnkyRJkoRrYqWcypSB5cvhjjvghx/g2muhVi244AJv3yNJkiSVQHZiVfJs3AjL\nlsEZZ+Q+lp4Oe/bAsccWfi5JkiSpFHI6sXQ0pk6Fs8+GRo3glFOgU6fw0aqVU5AlSZKkAmARKx2t\nfftg0aLwVj77H2eeCU8/HXUySZIkqcSxiJUKQlZWuL72l0aPDm/1064dJCVBfDzExhZ+PkmSJKmY\ncmMnqSDkVcACNGsGNWrA2LHQrx9Urw7JyfDxx4WbT5IkSRJgJ1Y6PD/+CIsXQ+PGEBeX+/jo0ZCZ\nGa6xbd4cKlUq/IySJElSEWInVopS9erQtWveBSzA9u3w3//C734Hxx0XdnLPOw9WrSrcnJIkSVIJ\nZSdWKij79sFXX8EXX0CvXuG05F8aNix8PT4+fDRqBOXKFXZSSZIkqcC4sZNUkjz3XFjkrlgRPjZs\nCAvZefOgWrWo00mSJElHzSJWKsl274aVK6FFC4iJyXls375wevKJJ0KTJj89GjeGzp1zny9JkiQV\nARaxUmmVlQVr1oRF7sqV4TrblSthyxaYNCn3+bt3w5tvQsOG4aN+facqS5IkqdBZxEo6NJs3w003\nhYXvmjXw3XdQuzZ06gRvv537/Kys8OuBbjckSZIkHYFC35144sSJNG/enGbNmvHwww/nOr5161b6\n9+9PYmIiycnJfPHFF9nHHnzwQVq1akWbNm249NJL2bNnT74Fl/QratWCUaNgxoywiE1Ph9mz4b77\n8j7/88+hYsWwa5ucDP37ww03wL/+Vbi5JUmSpIM4aCc2MzOThIQEJk+eTP369enYsSOjR4+mRYsW\n2efcfvvtVK9enXvvvZcvv/ySG264gcmTJ5OamsqZZ57JsmXLqFChAhdffDF9+/bl97//fc4AdmKl\nomPPnrBju2EDfPtt+LVCBbj66tznfvop3Hgj1KsXPurWhTp1ICEBTjut8LNLkiSpSMrvmq/swQ7O\nnTuXpk2b0qhRIwAGDhzIuHHjchSxy5Yt48477wQgISGB1NRUNm3aRPXq1SlXrhzp6enExsaSnp5O\n/fr18y24pAJQoUK4cdSJJ/76ufHx8MgjYaG7v+j98ktYvTrvInbuXHj44bDQPf748FGnTrhZVdu2\n+f9ZJEmSVCIdtIhdv349DRo0yH4eFxfHnDlzcpyTmJjI2LFj6dKlC3PnzmX16tWsW7eOpKQkbrvt\nNho2bEilSpXo3bs3PXv2LJhPIanwVasGXboc+vknnggDB8LGjeHjs8/Cr61b513EzpgBw4dDzZrh\n1OiaNcNHixbQvn3+fQ5JkiQVKwctYmMO4ZYdd955J0OHDiUpKYk2bdqQlJREbGws33zzDY8//jip\nqanUqFGDCy+8kP/85z9cdtll+RZeUjFSpw5ceOGhn9+oEZx7brj78ubN4X10t2wJO755FbHvvQeP\nPfZT0bu/8D35ZOjaNd8+hiRJkqJ10CK2fv36rF27Nvv52rVriYuLy3FOtWrVePHFF7OfN27cmCZN\nmjB+/HhOPfVUatasCcD555/PrFmz8ixihw0blv3nbt260a1btyP5LJJKkrg4uOSSQz+/Y0e4++6w\n0N1f+K5cCccem3cR+8IL8Je/hMePOSb8euyx0Lcv5PWPbVu3wvbt4TlVq3pfXkmSpANISUkhJSWl\nwK5/0I2dMjIySEhI4KOPPqJevXp06tQp18ZO27Zto1KlSpQvX54RI0Ywc+ZMRo4cyaJFixg0aBDz\n5s2jYsWKXHHFFXTq1IkbbrghZwA3dpIUhb17w0J361ZISwu/bt0KjRvnPU365ZfDonfr1nADrOrV\noUYNuO46+POfc5//6acwZ85P5+3/Wr9+eKsjSZKkUqJQN3YqW7Ysw4cPp3fv3mRmZjJkyBBatGjB\nc889B8C1117L0qVLueKKK4iJiaF169a88MILALRr147LL7+cDh06UKZMGdq3b88111yTb8El6aiU\nL//TzsqH4ve/Dx8QFsDbt8O2bVCpUt7n//gjLFkSnvPjjz99vfRSuOOO3Of/+99hoVy9evioWjVc\nd9yzJ/Tokfv8TZvCa1arFp5bubLdYUmSVCoctBNbKAHsxEoSrF0b7u68v9jdsSN8dOqUdxH7z3/C\n3/8eFtM7doTd4SpVwm5xXp3h8eNh8uSfit79RXL79tCqVe7z9+wJi+Jy5SyOJUnSUcnvms8iVpJK\ngszMsJiNjQ0L1F+aOzfc8XnHjp8K3+3b4bzzYMCA3Offfz/87/9CEITFceXK4dc//SmcQv1LEybA\ntGk/nbf/a4cO0LJl7vN37QqvXamSRbIkSSWcRawkqfDs2wc7d0J6evj1mGPyXtM7fTrMnPnTufvP\nv+CCcJfpXxo2LLzP8O7dULFiWPRWrgz33AN5LT15801ISfnpvP2Prl0hMTG/P7UkScpHFrGSpJIj\nKyssZPcXvtWqhTtA/9Inn8D8+T+dl54ednPPOQd69Sr83JIk6ZBZxEqS9GteeAF69w5v1SRJkiKV\n3zVfmXy7kiRJRUFmJixaBG3bwoUXhmt1/cdSSZJKDDuxkqSS6ccf4ZVXYPjw8JZKd90Fl1wSdSpJ\nkkodpxNLknQ4giC8vdDmzRaxkiRFwCJWkiRJklRsuCZWkqT81LMn3HYbfPll1EkkSdIhsIiVJJVu\nzz4LZcvC6adD9+4wZgzs2RN1KkmSdABOJ5YkCcLC9Z134Lnnwh2Op06NOpEkSSWCa2IlSSpoO3dC\nlSpRp5AkqURwTawkSQXtQAXsO+9431lJkiJmEStJ0qFKS4Prr4dmzeCvf4V166JOJElSqWMRK0nS\nobriCliyBEaNgrVroW1b6NMHdu2KOpkkSaWGa2IlSTpS6emQkgJ9+0adRJKkIsuNnSRJKg7WrIHd\nuyE+PuokkiRFyo2dJEkqDhYsgDPOgORkeOop2Lgx6kSSJJUIFrGSJBWE884L183+7//CnDlhR/bs\ns2HZsqiTSZJUrDmdWJKkwrBjB7z9NvToAfXqRZ1GkqRC45pYSZJKmqys8P6zXbtCbGzUaSRJyleu\niZUkqaT5/nv4058gLg5uvBFmzgwLW0mSlItFrCRJUatbF+bPD7uxderAtddCo0bw7LNRJ5Mkqchx\nOrEkSUXRkiXhOtpTTok6iSRJR8U1sZIklXZjx4abQ3XqBGWcVCVJKtpcEytJUmm3Zg0MGQING8JN\nN8HUqZCZGXUqSZIKhZ1YSZKKq2XL4K23wsf338M330ClSlGnkiQpB6cTS5Kk3DZs8P6zkqQiyenE\nkiQptwMVsB9/DOecAy+8EHZrJUkq5ixiJUkqydq3h4svhg8+gIQEOPVUePhhWLUq6mSSJB0RpxNL\nklRa7NkDKSkwbhx07w4XXhh1IklSKeCaWEmSVHDmzYPmzaFataiTSJJKCNfESpKkgvP3v4fra3v2\nhMcegy+/BP+xWZJUhNiJlSRJOe3YEW4INX48TJgQdmU//xxiY6NOJkkqhpxOLEmSCk8QQGoqNG6c\n97GYmEKPJEkqXpxOLEmSCk9MTN4FLMC770KrVnD77TBlCuzdW7jZJEmlkkWsJEk6Mv36wUsvQZUq\n8Oc/Q61acPbZ8OGHUSeTJJVgTieWJEn5Y8sW+OgjaNAAOneOOo0kqYhwTawkSSqe7rgDKlSA3r0h\nORnKlYs6kSSpELgmVpIkFU/9+kFGBgwdCrVrw3nnwTPPQHp61MkkScWInVhJklT4Nm6EyZPD6cfP\nPBN2aCVJJZLTiSVJUsm3cSP8z//AmWdC9+5h51aSVCw5nViSJJV8ZctCQgL8+/Uye4sAACAASURB\nVN/QtCkkJsItt4S38pEklWp2YiVJUtGWkQHz58PHH0PlynDzzVEnkiQdBqcTS5Ik/dJ//gOLF8Pp\np0OXLnDMMVEnkiT9P6cTS5Ik/VLr1lC1Kjz+eHif2qSkcBfkL7+MOpkkKZ+VjTqAJEnSUUtMDB8A\ne/fCp5/C1KnhVGRJUonidGJJklT69OkD9evDGWeEU5AbNYo6kSSVWK6JlSRJOlqffx52aqdNCx8V\nKoTF7IsvQrlyUaeTpBLFIlaSJCk/BQGsWBFOQb700qjTSFKJYxErSZJUmD7/HD75BC6+GGrUiDqN\nJBU77k4sSZJUmLKyYNIkOPHEsFP7wQeQmRl1KkkqtezESpIkHYotW2DMGBg5Er79FkaPhq5do04l\nSUWe04klSZKitmQJ1K0LNWtGnUSSijynE0uSJEWtdeu8C9jMTHj3Xdizp/AzSVIpYRErSZKUXzZt\ngn/8A+rVg6uvhpSUcE2tJCnfWMRKkiTllxNOgClTYNEiaNYMhg4NN4QaMSLqZJJUYrgmVpIkqSAt\nWQI7d0JyctRJJCkSbuwkSZJUUsyYAS1bwnHHRZ1EkgqMGztJkiSVFK++Co0bw29/C6+8Atu2RZ1I\nkoo8O7GSJElR2r4d/vtfeO21cCOos86C11+HMvYaJJUMTieWJEkqqdLSYO5c6NUr6iSSlG8sYiVJ\nkkqjhQth9Wro0wcqVow6jSQdMtfESpIklUZpafDEE1C3Llx+OYwfD3v2RJ1KkgqdnVhJkqTiZMMG\nePNNeOON8PY948bB6adHnUqSDsjpxJIkSQp9+y1UqwZVq0adRJIOyOnEkiRJCtWtm3cBu2cPXHAB\nvPgibNlS+LkkqQBZxEqSJJVEF1wQrptt0gR69oR//hO++y7qVJJ01JxOLEmSVJLt3AkffABvvQWx\nsfDKK1EnklTKuCZWkiRJ+SstDapXhzJO0pOU/1wTK0mSpPx1//1Qvz5cc004BXn37qgTSdIB2YmV\nJEkSfPUVvPtueMuexYvDdbSPPw4NGkSdTFIx53RiSZIkFaxNm8KO7IAB3r5H0lGziJUkSVJ0tm8P\npx/37QunnQblykWdSFIR55pYSZIkRWffPqhUCf70Jzj+eLjoInj5Zdi4MepkkkoJO7GSJEk6Mt9+\nC++/H049rl0bnn026kSSiiCnE0uSJKn4WL0aatZ0ba1UijmdWJIkScXHiy9C3brQq1e42/GKFWAD\nQ9JRsBMrSZKkgrV9O0yeHE47njgRypcPpyEnJESdTFIhcDqxJEmSiq8ggC++gGbNoEKFvI/HxBR+\nLkkFptCnE0+cOJHmzZvTrFkzHn744VzHt27dSv/+/UlMTCQ5OZkvvvgi+1haWhoDBgygRYsWtGzZ\nktmzZ+dbcEmSJBVDMTHQunXeBezWrVCvHgwaBK++6o7HkvJ00CI2MzOTP/7xj0ycOJGlS5cyevRo\nli1bluOcBx54gPbt27N48WJeeeUVhg4dmn1s6NCh9O3bl2XLlvHZZ5/RokWLgvkUkiRJKv6OPRY+\n+QS6doWxYyE+Hk4+Gf7xj6iTSSpCDlrEzp07l6ZNm9KoUSPKlSvHwIEDGTduXI5zli1bRvfu3QFI\nSEggNTWVTZs2sW3bNqZPn87gwYMBKFu2LDVq1CigjyFJkqQSoVEjuPbasIjdtCncDKpRo6hTSSpC\nDlrErl+/ngYNGmQ/j4uLY/369TnOSUxMZOzYsUBY9K5evZp169axatUqateuzZVXXkn79u25+uqr\nSU9PL4CPIEmSpBKpXLmwK9u/f97HR4+Gu++GGTMgI6Nws0mKzEGL2JhDWFR/5513kpaWRlJSEsOH\nDycpKYnY2FgyMjJYsGABf/jDH1iwYAFVqlThoYceyrfgkiRJKuVatQrX2P7xj1C/Ptx0E8yd6y18\npBKu7MEO1q9fn7Vr12Y/X7t2LXFxcTnOqVatGi+++GL288aNG9OkSRN27NhBXFwcHTt2BGDAgAEH\nLGKHDRuW/edu3brRrVu3w/0ckiRJKm3atg0ff/0rfP01jBr106ZQnTpFnU4qtVJSUkhJSSmw6x/0\nFjsZGRkkJCTw0UcfUa9ePTp16sTo0aNzbNC0bds2KlWqRPny5RkxYgQzZ85k5MiRAJx++uk8//zz\nxMfHM2zYMHbt2pVrh2NvsSNJkqR8s//3Sm/TIxUZ+V3zHbQTW7ZsWYYPH07v3r3JzMxkyJAhtGjR\ngueeew6Aa6+9lqVLl3LFFVcQExND69ateeGFF7K//6mnnuKyyy5j7969nHTSSbz00kv5FlySJEnK\n5UDFa2oq3Hhj2Knt1w8qVy7UWJLyz0E7sYUSwE6sJEmSCtrOneGOx6++Gq6bPe+8sKDt1g1iY6NO\nJ5Vo+V3zWcRKkiSpdPn2WxgzJixozz4b7rsv6kRSiWYRK0mSJOWXjAwoe9AVdpKOUn7XfAe9xY4k\nSZJUoh2ogL34Yrj/flixonDzSPpVFrGSJEnSL910E2zaBGecASefDI88AqtXR51KEk4nliRJkg4s\nMxOmTQvX0C5ZAjNmePse6TC5JlaSJEmKQhDkXcAe6HVJgGtiJUmSpGgcqFB96CE480x45plw52NJ\nBcpOrCRJknQ0du2CSZPgzTfhvfegTRsYMAAuuwxq1ow6nRQ5pxNLkiRJRdWePTB5cljQ3n47tGwZ\ndSIpchaxkiRJUnEVBJCaCo0bR51EKjSuiZUkSZKKq++/h1NOgaQk+Otf4csvo04kFTsWsZIkSVJh\nOeEE2LABHn8cvvsu3BCqZUt49tmok0nFhtOJJUmSpKhkZcG8ebBjB/ToEXUaqUC4JlaSJEkqLV57\nDWJjoU8fqFo16jTSEXFNrCRJklRalCkDzz8P9erB2WfDCy/Axo1Rp5IiZSdWkiRJKuq2bYMJE+Cd\nd+CDD2DZMqhbN+pU0iFxOrEkSZJUmu3dC+XL5349Kyt8lC1b+Jmkg3A6sSRJklSa5VXAAnz+ebj7\n8e9+B6+/HnZvpRLIIlaSJEkqCRITYcECOPVUGDkS4uLCHY/feivqZFK+cjqxJEmSVBLt3AmTJ0PF\nitC7d9RpVIq5JlaSJEnS0Rs58qcC99hjo06jEsw1sZIkSZKOXsWK8OqrcOKJcMYZ8NBD8NlnYINJ\nRZydWEmSJKk0S0+HlBR4/32YOBFmzoTjj486lUoQpxNLkiRJKlx798Ly5dCmDcTERJ1GxYzTiSVJ\nkiQVrjVroH9/aNAArroq3PHYW/goIhaxkiRJkg6uaVP4+mv4+OOwGztiRHgLn7vuijqZSiGnE0uS\nJEk6fOnpsHkzNGyY+1hmJsTGFn4mFUmuiZUkSZJUtF1zDcydC716hY8uXcLdkFUqWcRKkiRJKtoy\nMsIidtKk8PH553DaafD003DSSVGnUyGziJUkSZJUvKSlhetpe/SAGjWiTqNCZhErSZIkqeRIT4fu\n3cPHWWeFHVunHpco3mJHkiRJUslRoQI89lj49Z57oHbtsGM7fHjUyVRE2YmVJEmSVHT8+CNMmwab\nNsGVV0adRvnA6cSSJEmSSq/XXoO33w67tT16QOPGEBMTdSodhNOJJUmSJJVep58e3rZn6tRw/Wzj\nxjBkCHz6adTJVEjsxEqSJEkqnoIAli0Ldz4+5RTo0CHqRMqD04klSZIk6VBddRUce2y4+3HXrlCt\nWtSJSh2nE0u/IiUlJ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"text/plain": [ "<matplotlib.figure.Figure at 0x10e0ec8d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Direct orderbook in green\n", "USD_Bitstamp_Gatehub.plotWeighted(6000, styleask='g', stylebid='g--', label='Direct')\n", "# Through XRP in red\n", "USD_Bitstamp_Gatehub_Through_XRP.plotWeighted(6000, stylebid='r--', styleask='r', newfig= False, label='Through XRP')\n", "# Setting limits\n", "plt.gca().set_ylim((0.95, 1.1))\n", "plt.legend()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First, it is noticeable that the exchange rate for USD issued by Gatehub and Bitstamp may not be exactly 1.0. One of the reason may be may be related to the amount of trust the market places in these two gateways.\n", "\n", "Second, directly trading USD@Bitstamp to USD@Gatehub has some limitation:\n", "* The order book is limited in size - meaning that directly selling/buying Gatehub USD for Bitstamp USD won't be possible for amount in excess of ~USD 1500.\n", "* The rate offered by the direct order book is not always competitive. In some cases, going through XRP gives a better trade in terms of available size (payment in excess of USD 10k can easily be processed) and rate.\n", "\n", "**The good news is that on the Ripple network, exchanges will usually use the cheapest rate available**. More specifically, a trade on Ripple will use the best price out of: the direct path and the path going through XRP as an intermediary currency. Alternatively, it is also possible to specify a custom path, though that would require a custom pathfinding engine." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Comparing orderbook\n", "\n", "We are now going to visualize and compare the USDXRP books for 3 USD gateways on Ripple: [Bitstamp](https://www.bitstamp.net/), [Snapswap](https://snapswap.us) and [Gatehub](https://gatehub.net)." ] }, { "cell_type": "code", "execution_count": 48, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Issuer address for Snapswap\n", "Snapswap = 'rMwjYedjc7qqtKYVLiAccJSmCwih4LnE2q'\n", "# Pull the 3 order books\n", "USDXRP_Bitstamp = feed.getOrderbook(('USD', Bitstamp), ('XRP', None))\n", "USDXRP_Snapswap = feed.getOrderbook(('USD', Snapswap), ('XRP', None))\n", "USDXRP_Gatehub = feed.getOrderbook(('USD', Gatehub), ('XRP', None))" ] }, { "cell_type": "code", "execution_count": 49, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.legend.Legend at 0x10d780e90>" ] }, "execution_count": 49, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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1BAIBtLS0UF5ejl27dvESBl1dXbx69YpraRMKhRg/fjx++ukn7idb4uPjubG1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\ndWwAAJxGebnZE5ySI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"text/plain": [ "<matplotlib.figure.Figure at 0x10db88450>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Plotting the order books on the same graph\n", "USDXRP_Bitstamp.plotWeighted(10e6, newfig= True, styleask='b', stylebid='b--', label='Bitstamp')\n", "USDXRP_Snapswap.plotWeighted(10e6, newfig= False, styleask='g', stylebid='g--', label='Snapswap')\n", "USDXRP_Gatehub.plotWeighted(10e6, newfig= False, styleask='r', stylebid='r--', label='Gatehub')\n", "plt.gca().set_ylim((100,150))\n", "plt.legend()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The plot above clearly shows huge variations in terms of:\n", "* Best rate available\n", "* Initial bid/ask spread\n", "* Evolution of the spread and the rate as size of the exchange grows\n", "\n", "These parameters are key metrics when considering which gateway to do business with and extend trust to." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Conclusion\n", "\n", "We introduced a visualization package for order books. It can be used to explore how trading between arbitrary currency pairs works on Ripple.\n", "\n", "Please contact [Gilles Pirio](mailto:[email protected]) for any comment or feedback.\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": "2.7.9" } }, "nbformat": 4, "nbformat_minor": 0 }

mit

ageron/ml-notebooks

09_up_and_running_with_tensorflow.ipynb

2

199488

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "**Chapter 9 – Up and running with TensorFlow**" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "_This notebook contains all the sample code and solutions to the exercises in chapter 9._\n", "\n", "<table align=\"left\">\n", " <td>\n", " <a target=\"_blank\" href=\"https://colab.research.google.com/github/ageron/handson-ml/blob/master/09_up_and_running_with_tensorflow.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n", " </td>\n", "</table>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Warning**: this is the code for the 1st edition of the book. Please visit https://github.com/ageron/handson-ml2 for the 2nd edition code, with up-to-date notebooks using the latest library versions. In particular, the 1st edition is based on TensorFlow 1, while the 2nd edition uses TensorFlow 2, which is much simpler to use." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Setup" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First, let's make sure this notebook works well in both python 2 and 3, import a few common modules, ensure MatplotLib plots figures inline and prepare a function to save the figures:" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "# To support both python 2 and python 3\n", "from __future__ import division, print_function, unicode_literals\n", "\n", "# Common imports\n", "import numpy as np\n", "import os\n", "\n", "try:\n", " # %tensorflow_version only exists in Colab.\n", " %tensorflow_version 1.x\n", "except Exception:\n", " pass\n", "\n", "# to make this notebook's output stable across runs\n", "def reset_graph(seed=42):\n", " tf.reset_default_graph()\n", " tf.set_random_seed(seed)\n", " np.random.seed(seed)\n", "\n", "# To plot pretty figures\n", "%matplotlib inline\n", "import matplotlib\n", "import matplotlib.pyplot as plt\n", "plt.rcParams['axes.labelsize'] = 14\n", "plt.rcParams['xtick.labelsize'] = 12\n", "plt.rcParams['ytick.labelsize'] = 12\n", "\n", "# Where to save the figures\n", "PROJECT_ROOT_DIR = \".\"\n", "CHAPTER_ID = \"tensorflow\"\n", "IMAGES_PATH = os.path.join(PROJECT_ROOT_DIR, \"images\", CHAPTER_ID)\n", "os.makedirs(IMAGES_PATH, exist_ok=True)\n", "\n", "def save_fig(fig_id, tight_layout=True, fig_extension=\"png\", resolution=300):\n", " path = os.path.join(IMAGES_PATH, fig_id + \".\" + fig_extension)\n", " print(\"Saving figure\", fig_id)\n", " if tight_layout:\n", " plt.tight_layout()\n", " plt.savefig(path, format=fig_extension, dpi=resolution)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Creating and running a graph" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "import tensorflow as tf\n", "\n", "reset_graph()\n", "\n", "x = tf.Variable(3, name=\"x\")\n", "y = tf.Variable(4, name=\"y\")\n", "f = x*x*y + y + 2" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<tf.Tensor 'add_1:0' shape=() dtype=int32>" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "f" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "42\n" ] } ], "source": [ "sess = tf.Session()\n", "sess.run(x.initializer)\n", "sess.run(y.initializer)\n", "result = sess.run(f)\n", "print(result)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "sess.close()" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "with tf.Session() as sess:\n", " x.initializer.run()\n", " y.initializer.run()\n", " result = f.eval()" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "42" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "result" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "init = tf.global_variables_initializer()\n", "\n", "with tf.Session() as sess:\n", " init.run()\n", " result = f.eval()" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "42" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "result" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "init = tf.global_variables_initializer()" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "42\n" ] } ], "source": [ "sess = tf.InteractiveSession()\n", "init.run()\n", "result = f.eval()\n", "print(result)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "sess.close()" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "42" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "result" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Managing graphs" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "reset_graph()\n", "\n", "x1 = tf.Variable(1)\n", "x1.graph is tf.get_default_graph()" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "graph = tf.Graph()\n", "with graph.as_default():\n", " x2 = tf.Variable(2)\n", "\n", "x2.graph is graph" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "False" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x2.graph is tf.get_default_graph()" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "10\n", "15\n" ] } ], "source": [ "w = tf.constant(3)\n", "x = w + 2\n", "y = x + 5\n", "z = x * 3\n", "\n", "with tf.Session() as sess:\n", " print(y.eval()) # 10\n", " print(z.eval()) # 15" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "10\n", "15\n" ] } ], "source": [ "with tf.Session() as sess:\n", " y_val, z_val = sess.run([y, z])\n", " print(y_val) # 10\n", " print(z_val) # 15" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Linear Regression" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Using the Normal Equation" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "from sklearn.datasets import fetch_california_housing\n", "\n", "reset_graph()\n", "\n", "housing = fetch_california_housing()\n", "m, n = housing.data.shape\n", "housing_data_plus_bias = np.c_[np.ones((m, 1)), housing.data]\n", "\n", "X = tf.constant(housing_data_plus_bias, dtype=tf.float32, name=\"X\")\n", "y = tf.constant(housing.target.reshape(-1, 1), dtype=tf.float32, name=\"y\")\n", "XT = tf.transpose(X)\n", "theta = tf.matmul(tf.matmul(tf.matrix_inverse(tf.matmul(XT, X)), XT), y)\n", "\n", "with tf.Session() as sess:\n", " theta_value = theta.eval()" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[-3.7185181e+01],\n", " [ 4.3633747e-01],\n", " [ 9.3952334e-03],\n", " [-1.0711310e-01],\n", " [ 6.4479220e-01],\n", " [-4.0338000e-06],\n", " [-3.7813708e-03],\n", " [-4.2348403e-01],\n", " [-4.3721911e-01]], dtype=float32)" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "theta_value" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Compare with pure NumPy" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[-3.69419202e+01]\n", " [ 4.36693293e-01]\n", " [ 9.43577803e-03]\n", " [-1.07322041e-01]\n", " [ 6.45065694e-01]\n", " [-3.97638942e-06]\n", " [-3.78654265e-03]\n", " [-4.21314378e-01]\n", " [-4.34513755e-01]]\n" ] } ], "source": [ "X = housing_data_plus_bias\n", "y = housing.target.reshape(-1, 1)\n", "theta_numpy = np.linalg.inv(X.T.dot(X)).dot(X.T).dot(y)\n", "\n", "print(theta_numpy)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Compare with Scikit-Learn" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[-3.69419202e+01]\n", " [ 4.36693293e-01]\n", " [ 9.43577803e-03]\n", " [-1.07322041e-01]\n", " [ 6.45065694e-01]\n", " [-3.97638942e-06]\n", " [-3.78654265e-03]\n", " [-4.21314378e-01]\n", " [-4.34513755e-01]]\n" ] } ], "source": [ "from sklearn.linear_model import LinearRegression\n", "lin_reg = LinearRegression()\n", "lin_reg.fit(housing.data, housing.target.reshape(-1, 1))\n", "\n", "print(np.r_[lin_reg.intercept_.reshape(-1, 1), lin_reg.coef_.T])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Using Batch Gradient Descent" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Gradient Descent requires scaling the feature vectors first. We could do this using TF, but let's just use Scikit-Learn for now." ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [], "source": [ "from sklearn.preprocessing import StandardScaler\n", "scaler = StandardScaler()\n", "scaled_housing_data = scaler.fit_transform(housing.data)\n", "scaled_housing_data_plus_bias = np.c_[np.ones((m, 1)), scaled_housing_data]" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ 1.00000000e+00 6.60969987e-17 5.50808322e-18 6.60969987e-17\n", " -1.06030602e-16 -1.10161664e-17 3.44255201e-18 -1.07958431e-15\n", " -8.52651283e-15]\n", "[ 0.38915536 0.36424355 0.5116157 ... -0.06612179 -0.06360587\n", " 0.01359031]\n", "0.11111111111111005\n", "(20640, 9)\n" ] } ], "source": [ "print(scaled_housing_data_plus_bias.mean(axis=0))\n", "print(scaled_housing_data_plus_bias.mean(axis=1))\n", "print(scaled_housing_data_plus_bias.mean())\n", "print(scaled_housing_data_plus_bias.shape)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Manually computing the gradients" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch 0 MSE = 9.161542\n", "Epoch 100 MSE = 0.7145004\n", "Epoch 200 MSE = 0.56670487\n", "Epoch 300 MSE = 0.5555718\n", "Epoch 400 MSE = 0.5488112\n", "Epoch 500 MSE = 0.5436363\n", "Epoch 600 MSE = 0.5396291\n", "Epoch 700 MSE = 0.5365092\n", "Epoch 800 MSE = 0.53406775\n", "Epoch 900 MSE = 0.5321473\n" ] } ], "source": [ "reset_graph()\n", "\n", "n_epochs = 1000\n", "learning_rate = 0.01\n", "\n", "X = tf.constant(scaled_housing_data_plus_bias, dtype=tf.float32, name=\"X\")\n", "y = tf.constant(housing.target.reshape(-1, 1), dtype=tf.float32, name=\"y\")\n", "theta = tf.Variable(tf.random_uniform([n + 1, 1], -1.0, 1.0, seed=42), name=\"theta\")\n", "y_pred = tf.matmul(X, theta, name=\"predictions\")\n", "error = y_pred - y\n", "mse = tf.reduce_mean(tf.square(error), name=\"mse\")\n", "gradients = 2/m * tf.matmul(tf.transpose(X), error)\n", "training_op = tf.assign(theta, theta - learning_rate * gradients)\n", "\n", "init = tf.global_variables_initializer()\n", "\n", "with tf.Session() as sess:\n", " sess.run(init)\n", "\n", " for epoch in range(n_epochs):\n", " if epoch % 100 == 0:\n", " print(\"Epoch\", epoch, \"MSE =\", mse.eval())\n", " sess.run(training_op)\n", " \n", " best_theta = theta.eval()" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[ 2.0685523 ],\n", " [ 0.8874027 ],\n", " [ 0.14401656],\n", " [-0.34770882],\n", " [ 0.36178368],\n", " [ 0.00393811],\n", " [-0.04269556],\n", " [-0.6614529 ],\n", " [-0.6375279 ]], dtype=float32)" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "best_theta" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Using autodiff" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Same as above except for the `gradients = ...` line:" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [], "source": [ "reset_graph()\n", "\n", "n_epochs = 1000\n", "learning_rate = 0.01\n", "\n", "X = tf.constant(scaled_housing_data_plus_bias, dtype=tf.float32, name=\"X\")\n", "y = tf.constant(housing.target.reshape(-1, 1), dtype=tf.float32, name=\"y\")\n", "theta = tf.Variable(tf.random_uniform([n + 1, 1], -1.0, 1.0, seed=42), name=\"theta\")\n", "y_pred = tf.matmul(X, theta, name=\"predictions\")\n", "error = y_pred - y\n", "mse = tf.reduce_mean(tf.square(error), name=\"mse\")" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [], "source": [ "gradients = tf.gradients(mse, [theta])[0]" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch 0 MSE = 9.161542\n", "Epoch 100 MSE = 0.71450037\n", "Epoch 200 MSE = 0.56670487\n", "Epoch 300 MSE = 0.5555718\n", "Epoch 400 MSE = 0.54881126\n", "Epoch 500 MSE = 0.5436363\n", "Epoch 600 MSE = 0.53962916\n", "Epoch 700 MSE = 0.5365092\n", "Epoch 800 MSE = 0.53406775\n", "Epoch 900 MSE = 0.5321473\n", "Best theta:\n", "[[ 2.0685523 ]\n", " [ 0.8874027 ]\n", " [ 0.14401656]\n", " [-0.3477088 ]\n", " [ 0.36178365]\n", " [ 0.00393811]\n", " [-0.04269556]\n", " [-0.66145283]\n", " [-0.6375278 ]]\n" ] } ], "source": [ "training_op = tf.assign(theta, theta - learning_rate * gradients)\n", "\n", "init = tf.global_variables_initializer()\n", "\n", "with tf.Session() as sess:\n", " sess.run(init)\n", "\n", " for epoch in range(n_epochs):\n", " if epoch % 100 == 0:\n", " print(\"Epoch\", epoch, \"MSE =\", mse.eval())\n", " sess.run(training_op)\n", " \n", " best_theta = theta.eval()\n", "\n", "print(\"Best theta:\")\n", "print(best_theta)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "How could you find the partial derivatives of the following function with regards to `a` and `b`?" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [], "source": [ "def my_func(a, b):\n", " z = 0\n", " for i in range(100):\n", " z = a * np.cos(z + i) + z * np.sin(b - i)\n", " return z" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "-0.21253923284754914" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "my_func(0.2, 0.3)" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [], "source": [ "reset_graph()\n", "\n", "a = tf.Variable(0.2, name=\"a\")\n", "b = tf.Variable(0.3, name=\"b\")\n", "z = tf.constant(0.0, name=\"z0\")\n", "for i in range(100):\n", " z = a * tf.cos(z + i) + z * tf.sin(b - i)\n", "\n", "grads = tf.gradients(z, [a, b])\n", "init = tf.global_variables_initializer()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's compute the function at $a=0.2$ and $b=0.3$, and the partial derivatives at that point with regards to $a$ and with regards to $b$:" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "-0.21253741\n", "[-1.1388495, 0.19671397]\n" ] } ], "source": [ "with tf.Session() as sess:\n", " init.run()\n", " print(z.eval())\n", " print(sess.run(grads))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Using a `GradientDescentOptimizer`" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [], "source": [ "reset_graph()\n", "\n", "n_epochs = 1000\n", "learning_rate = 0.01\n", "\n", "X = tf.constant(scaled_housing_data_plus_bias, dtype=tf.float32, name=\"X\")\n", "y = tf.constant(housing.target.reshape(-1, 1), dtype=tf.float32, name=\"y\")\n", "theta = tf.Variable(tf.random_uniform([n + 1, 1], -1.0, 1.0, seed=42), name=\"theta\")\n", "y_pred = tf.matmul(X, theta, name=\"predictions\")\n", "error = y_pred - y\n", "mse = tf.reduce_mean(tf.square(error), name=\"mse\")" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [], "source": [ "optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)\n", "training_op = optimizer.minimize(mse)" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch 0 MSE = 9.161542\n", "Epoch 100 MSE = 0.7145004\n", "Epoch 200 MSE = 0.56670487\n", "Epoch 300 MSE = 0.5555718\n", "Epoch 400 MSE = 0.54881126\n", "Epoch 500 MSE = 0.5436363\n", "Epoch 600 MSE = 0.53962916\n", "Epoch 700 MSE = 0.5365092\n", "Epoch 800 MSE = 0.53406775\n", "Epoch 900 MSE = 0.5321473\n", "Best theta:\n", "[[ 2.0685523 ]\n", " [ 0.8874027 ]\n", " [ 0.14401656]\n", " [-0.3477088 ]\n", " [ 0.36178365]\n", " [ 0.00393811]\n", " [-0.04269556]\n", " [-0.66145283]\n", " [-0.6375278 ]]\n" ] } ], "source": [ "init = tf.global_variables_initializer()\n", "\n", "with tf.Session() as sess:\n", " sess.run(init)\n", "\n", " for epoch in range(n_epochs):\n", " if epoch % 100 == 0:\n", " print(\"Epoch\", epoch, \"MSE =\", mse.eval())\n", " sess.run(training_op)\n", " \n", " best_theta = theta.eval()\n", "\n", "print(\"Best theta:\")\n", "print(best_theta)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Using a momentum optimizer" ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [], "source": [ "reset_graph()\n", "\n", "n_epochs = 1000\n", "learning_rate = 0.01\n", "\n", "X = tf.constant(scaled_housing_data_plus_bias, dtype=tf.float32, name=\"X\")\n", "y = tf.constant(housing.target.reshape(-1, 1), dtype=tf.float32, name=\"y\")\n", "theta = tf.Variable(tf.random_uniform([n + 1, 1], -1.0, 1.0, seed=42), name=\"theta\")\n", "y_pred = tf.matmul(X, theta, name=\"predictions\")\n", "error = y_pred - y\n", "mse = tf.reduce_mean(tf.square(error), name=\"mse\")" ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [], "source": [ "optimizer = tf.train.MomentumOptimizer(learning_rate=learning_rate,\n", " momentum=0.9)" ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [], "source": [ "training_op = optimizer.minimize(mse)\n", "\n", "init = tf.global_variables_initializer()" ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Best theta:\n", "[[ 2.068558 ]\n", " [ 0.82962847]\n", " [ 0.11875335]\n", " [-0.26554456]\n", " [ 0.3057109 ]\n", " [-0.00450249]\n", " [-0.03932662]\n", " [-0.8998645 ]\n", " [-0.8705207 ]]\n" ] } ], "source": [ "with tf.Session() as sess:\n", " sess.run(init)\n", "\n", " for epoch in range(n_epochs):\n", " sess.run(training_op)\n", " \n", " best_theta = theta.eval()\n", "\n", "print(\"Best theta:\")\n", "print(best_theta)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Feeding data to the training algorithm" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Placeholder nodes" ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[6. 7. 8.]]\n" ] } ], "source": [ "reset_graph()\n", "\n", "A = tf.placeholder(tf.float32, shape=(None, 3))\n", "B = A + 5\n", "with tf.Session() as sess:\n", " B_val_1 = B.eval(feed_dict={A: [[1, 2, 3]]})\n", " B_val_2 = B.eval(feed_dict={A: [[4, 5, 6], [7, 8, 9]]})\n", "\n", "print(B_val_1)" ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[ 9. 10. 11.]\n", " [12. 13. 14.]]\n" ] } ], "source": [ "print(B_val_2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Mini-batch Gradient Descent" ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [], "source": [ "n_epochs = 1000\n", "learning_rate = 0.01" ] }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [], "source": [ "reset_graph()\n", "\n", "X = tf.placeholder(tf.float32, shape=(None, n + 1), name=\"X\")\n", "y = tf.placeholder(tf.float32, shape=(None, 1), name=\"y\")" ] }, { "cell_type": "code", "execution_count": 45, "metadata": {}, "outputs": [], "source": [ "theta = tf.Variable(tf.random_uniform([n + 1, 1], -1.0, 1.0, seed=42), name=\"theta\")\n", "y_pred = tf.matmul(X, theta, name=\"predictions\")\n", "error = y_pred - y\n", "mse = tf.reduce_mean(tf.square(error), name=\"mse\")\n", "optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)\n", "training_op = optimizer.minimize(mse)\n", "\n", "init = tf.global_variables_initializer()" ] }, { "cell_type": "code", "execution_count": 46, "metadata": {}, "outputs": [], "source": [ "n_epochs = 10" ] }, { "cell_type": "code", "execution_count": 47, "metadata": {}, "outputs": [], "source": [ "batch_size = 100\n", "n_batches = int(np.ceil(m / batch_size))" ] }, { "cell_type": "code", "execution_count": 48, "metadata": {}, "outputs": [], "source": [ "def fetch_batch(epoch, batch_index, batch_size):\n", " np.random.seed(epoch * n_batches + batch_index) # not shown in the book\n", " indices = np.random.randint(m, size=batch_size) # not shown\n", " X_batch = scaled_housing_data_plus_bias[indices] # not shown\n", " y_batch = housing.target.reshape(-1, 1)[indices] # not shown\n", " return X_batch, y_batch\n", "\n", "with tf.Session() as sess:\n", " sess.run(init)\n", "\n", " for epoch in range(n_epochs):\n", " for batch_index in range(n_batches):\n", " X_batch, y_batch = fetch_batch(epoch, batch_index, batch_size)\n", " sess.run(training_op, feed_dict={X: X_batch, y: y_batch})\n", "\n", " best_theta = theta.eval()" ] }, { "cell_type": "code", "execution_count": 49, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[ 2.0703337 ],\n", " [ 0.8637145 ],\n", " [ 0.12255152],\n", " [-0.31211877],\n", " [ 0.38510376],\n", " [ 0.00434168],\n", " [-0.0123295 ],\n", " [-0.83376896],\n", " [-0.8030471 ]], dtype=float32)" ] }, "execution_count": 49, "metadata": {}, "output_type": "execute_result" } ], "source": [ "best_theta" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Saving and restoring a model" ] }, { "cell_type": "code", "execution_count": 50, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch 0 MSE = 9.161542\n", "Epoch 100 MSE = 0.7145004\n", "Epoch 200 MSE = 0.56670487\n", "Epoch 300 MSE = 0.5555718\n", "Epoch 400 MSE = 0.54881126\n", "Epoch 500 MSE = 0.5436363\n", "Epoch 600 MSE = 0.53962916\n", "Epoch 700 MSE = 0.5365092\n", "Epoch 800 MSE = 0.53406775\n", "Epoch 900 MSE = 0.5321473\n" ] } ], "source": [ "reset_graph()\n", "\n", "n_epochs = 1000 # not shown in the book\n", "learning_rate = 0.01 # not shown\n", "\n", "X = tf.constant(scaled_housing_data_plus_bias, dtype=tf.float32, name=\"X\") # not shown\n", "y = tf.constant(housing.target.reshape(-1, 1), dtype=tf.float32, name=\"y\") # not shown\n", "theta = tf.Variable(tf.random_uniform([n + 1, 1], -1.0, 1.0, seed=42), name=\"theta\")\n", "y_pred = tf.matmul(X, theta, name=\"predictions\") # not shown\n", "error = y_pred - y # not shown\n", "mse = tf.reduce_mean(tf.square(error), name=\"mse\") # not shown\n", "optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate) # not shown\n", "training_op = optimizer.minimize(mse) # not shown\n", "\n", "init = tf.global_variables_initializer()\n", "saver = tf.train.Saver()\n", "\n", "with tf.Session() as sess:\n", " sess.run(init)\n", "\n", " for epoch in range(n_epochs):\n", " if epoch % 100 == 0:\n", " print(\"Epoch\", epoch, \"MSE =\", mse.eval()) # not shown\n", " save_path = saver.save(sess, \"/tmp/my_model.ckpt\")\n", " sess.run(training_op)\n", " \n", " best_theta = theta.eval()\n", " save_path = saver.save(sess, \"/tmp/my_model_final.ckpt\")" ] }, { "cell_type": "code", "execution_count": 51, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[ 2.0685523 ],\n", " [ 0.8874027 ],\n", " [ 0.14401656],\n", " [-0.3477088 ],\n", " [ 0.36178365],\n", " [ 0.00393811],\n", " [-0.04269556],\n", " [-0.66145283],\n", " [-0.6375278 ]], dtype=float32)" ] }, "execution_count": 51, "metadata": {}, "output_type": "execute_result" } ], "source": [ "best_theta" ] }, { "cell_type": "code", "execution_count": 52, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Restoring parameters from /tmp/my_model_final.ckpt\n" ] } ], "source": [ "with tf.Session() as sess:\n", " saver.restore(sess, \"/tmp/my_model_final.ckpt\")\n", " best_theta_restored = theta.eval() # not shown in the book" ] }, { "cell_type": "code", "execution_count": 53, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 53, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.allclose(best_theta, best_theta_restored)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If you want to have a saver that loads and restores `theta` with a different name, such as `\"weights\"`:" ] }, { "cell_type": "code", "execution_count": 54, "metadata": {}, "outputs": [], "source": [ "saver = tf.train.Saver({\"weights\": theta})" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "By default the saver also saves the graph structure itself in a second file with the extension `.meta`. You can use the function `tf.train.import_meta_graph()` to restore the graph structure. This function loads the graph into the default graph and returns a `Saver` that can then be used to restore the graph state (i.e., the variable values):" ] }, { "cell_type": "code", "execution_count": 55, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Restoring parameters from /tmp/my_model_final.ckpt\n" ] } ], "source": [ "reset_graph()\n", "# notice that we start with an empty graph.\n", "\n", "saver = tf.train.import_meta_graph(\"/tmp/my_model_final.ckpt.meta\") # this loads the graph structure\n", "theta = tf.get_default_graph().get_tensor_by_name(\"theta:0\") # not shown in the book\n", "\n", "with tf.Session() as sess:\n", " saver.restore(sess, \"/tmp/my_model_final.ckpt\") # this restores the graph's state\n", " best_theta_restored = theta.eval() # not shown in the book" ] }, { "cell_type": "code", "execution_count": 56, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 56, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.allclose(best_theta, best_theta_restored)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This means that you can import a pretrained model without having to have the corresponding Python code to build the graph. This is very handy when you keep tweaking and saving your model: you can load a previously saved model without having to search for the version of the code that built it." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Visualizing the graph" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "TensorBoard is a great tool to visualize TensorFlow graphs, training curves, and much more. Our TensorFlow code will write various files in a log directory, and the TensorBoard server will regularly read these files and produce nice interactive visualizations. It can plot graphs, learning curves (i.e., how the loss evaluated on the training set or test set evolves as a function of the epoch number), profiling data to identify performance bottlenecks, and more. In short, it helps keep track of everything. Here's the overall picture:\n", "\n", "`TensorFlow writes logs to ===> log directory ===> TensorBoard reads data and displays visualizations`" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If we want to visualize different graphs, or learning curves for different training runs, we don't want the log files to get all mixed up. So we will need one log subdirectory per graph, or per run. Let's use a root log directory that we will call `tf_logs`, and a sub-directory that we will call `run-` followed by the current timestamp (you can use any other name you prefer in your own code):" ] }, { "cell_type": "code", "execution_count": 57, "metadata": {}, "outputs": [], "source": [ "from datetime import datetime\n", "\n", "now = datetime.utcnow().strftime(\"%Y%m%d%H%M%S\")\n", "root_logdir = \"tf_logs\"\n", "logdir = \"{}/run-{}/\".format(root_logdir, now)" ] }, { "cell_type": "code", "execution_count": 58, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'tf_logs/run-20210325095200/'" ] }, "execution_count": 58, "metadata": {}, "output_type": "execute_result" } ], "source": [ "logdir" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In fact, let's create a function that will generate such a subdirectory path every time we need one:" ] }, { "cell_type": "code", "execution_count": 59, "metadata": {}, "outputs": [], "source": [ "def make_log_subdir(run_id=None):\n", " if run_id is None:\n", " run_id = datetime.utcnow().strftime(\"%Y%m%d%H%M%S\")\n", " return \"{}/run-{}/\".format(root_logdir, run_id)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now let's save the default graph to our log subdirectory using `tf.summary.FileWriter()`:" ] }, { "cell_type": "code", "execution_count": 60, "metadata": {}, "outputs": [], "source": [ "file_writer = tf.summary.FileWriter(logdir, graph=tf.get_default_graph())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now the root log directory contains one subdirectory:" ] }, { "cell_type": "code", "execution_count": 61, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['run-20210325095200']" ] }, "execution_count": 61, "metadata": {}, "output_type": "execute_result" } ], "source": [ "os.listdir(root_logdir)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And this subdirectory contains one log file (called a \"TF events\" file) for the graph:" ] }, { "cell_type": "code", "execution_count": 62, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['events.out.tfevents.1616665937.kiwimac']" ] }, "execution_count": 62, "metadata": {}, "output_type": "execute_result" } ], "source": [ "os.listdir(logdir)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "However, the actual graph data may still be in the OS's file cache, so we need to `flush()` or `close()` the `FileWriter` to be sure that it's well written to disk:" ] }, { "cell_type": "code", "execution_count": 63, "metadata": {}, "outputs": [], "source": [ "file_writer.close()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Okay, now let's start TensorBoard! It runs as a web server in a separate process, so we first need to start it. One way to do that is to run the `tensorboard` command in a terminal window. Another is to use the `%tensorboard` Jupyter extension, which takes care of starting TensorBoard, and it allows us to view TensorBoard's user interface directly within Jupyter. Let's load this extension now:" ] }, { "cell_type": "code", "execution_count": 64, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The tensorboard extension is already loaded. To reload it, use:\n", " %reload_ext tensorboard\n" ] } ], "source": [ "%load_ext tensorboard" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next, let's use the `%tensorboard` extension to start the TensorBoard server. We need to point it to the root log directory:" ] }, { "cell_type": "code", "execution_count": 65, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/html": [ "\n", " <iframe id=\"tensorboard-frame-9bb87ff5937c48c\" width=\"100%\" height=\"800\" frameborder=\"0\">\n", " </iframe>\n", " <script>\n", " (function() {\n", " const frame = document.getElementById(\"tensorboard-frame-9bb87ff5937c48c\");\n", " const url = new URL(\"/\", window.location);\n", " url.port = 6006;\n", " frame.src = url;\n", " })();\n", " </script>\n", " " ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%tensorboard --logdir {root_logdir}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Great! We can now visualize graphs. :)\n", "\n", "In fact, let's make this easy by creating a `save_graph()` function that will automatically create a new log subdir and save the given graph (by default `tf.get_default_graph()`) to this directory:" ] }, { "cell_type": "code", "execution_count": 66, "metadata": {}, "outputs": [], "source": [ "def save_graph(graph=None, run_id=None):\n", " if graph is None:\n", " graph = tf.get_default_graph()\n", " logdir = make_log_subdir(run_id)\n", " file_writer = tf.summary.FileWriter(logdir, graph=graph)\n", " file_writer.close()\n", " return logdir" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's see if it works:" ] }, { "cell_type": "code", "execution_count": 67, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "'tf_logs/run-20210325095244/'" ] }, "execution_count": 67, "metadata": {}, "output_type": "execute_result" } ], "source": [ "save_graph()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now let's look at TensorBoard again. Note that this will reuse the existing TensorBoard server since we're reusing the same root log directory:" ] }, { "cell_type": "code", "execution_count": 68, "metadata": { "scrolled": false }, "outputs": [ { "data": { "text/plain": [ "Reusing TensorBoard on port 6006 (pid 43590), started 0:00:45 ago. (Use '!kill 43590' to kill it.)" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", " <iframe id=\"tensorboard-frame-6698e989b3fca11b\" width=\"100%\" height=\"800\" frameborder=\"0\">\n", " </iframe>\n", " <script>\n", " (function() {\n", " const frame = document.getElementById(\"tensorboard-frame-6698e989b3fca11b\");\n", " const url = new URL(\"/\", window.location);\n", " url.port = 6006;\n", " frame.src = url;\n", " })();\n", " </script>\n", " " ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%tensorboard --logdir {root_logdir}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Notice that you can switch between runs by picking the log subdirectory you want from the \"Run\" dropdown list (at the top left)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Visualizing Learning Curves\n", "\n", "Now let's see how to visualize learning curves:" ] }, { "cell_type": "code", "execution_count": 69, "metadata": {}, "outputs": [], "source": [ "reset_graph()\n", "\n", "n_epochs = 1000\n", "learning_rate = 0.01\n", "\n", "X = tf.placeholder(tf.float32, shape=(None, n + 1), name=\"X\")\n", "y = tf.placeholder(tf.float32, shape=(None, 1), name=\"y\")\n", "theta = tf.Variable(tf.random_uniform([n + 1, 1], -1.0, 1.0, seed=42), name=\"theta\")\n", "y_pred = tf.matmul(X, theta, name=\"predictions\")\n", "error = y_pred - y\n", "mse = tf.reduce_mean(tf.square(error), name=\"mse\")\n", "optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)\n", "training_op = optimizer.minimize(mse)\n", "\n", "init = tf.global_variables_initializer()" ] }, { "cell_type": "code", "execution_count": 70, "metadata": {}, "outputs": [], "source": [ "logdir = make_log_subdir()" ] }, { "cell_type": "code", "execution_count": 71, "metadata": {}, "outputs": [], "source": [ "mse_summary = tf.summary.scalar('MSE', mse)\n", "file_writer = tf.summary.FileWriter(logdir, tf.get_default_graph())" ] }, { "cell_type": "code", "execution_count": 72, "metadata": {}, "outputs": [], "source": [ "n_epochs = 10\n", "batch_size = 100\n", "n_batches = int(np.ceil(m / batch_size))" ] }, { "cell_type": "code", "execution_count": 73, "metadata": {}, "outputs": [], "source": [ "with tf.Session() as sess: # not shown in the book\n", " sess.run(init) # not shown\n", "\n", " for epoch in range(n_epochs): # not shown\n", " for batch_index in range(n_batches):\n", " X_batch, y_batch = fetch_batch(epoch, batch_index, batch_size)\n", " if batch_index % 10 == 0:\n", " summary_str = mse_summary.eval(feed_dict={X: X_batch, y: y_batch})\n", " step = epoch * n_batches + batch_index\n", " file_writer.add_summary(summary_str, step)\n", " sess.run(training_op, feed_dict={X: X_batch, y: y_batch})\n", "\n", " best_theta = theta.eval() # not shown" ] }, { "cell_type": "code", "execution_count": 74, "metadata": {}, "outputs": [], "source": [ "file_writer.close()" ] }, { "cell_type": "code", "execution_count": 75, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[ 2.07033372],\n", " [ 0.86371452],\n", " [ 0.12255151],\n", " [-0.31211874],\n", " [ 0.38510373],\n", " [ 0.00434168],\n", " [-0.01232954],\n", " [-0.83376896],\n", " [-0.80304712]], dtype=float32)" ] }, "execution_count": 75, "metadata": {}, "output_type": "execute_result" } ], "source": [ "best_theta" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now let's look at TensorBoard. Try going to the SCALARS tab:" ] }, { "cell_type": "code", "execution_count": 76, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Reusing TensorBoard on port 6006 (pid 43590), started 0:02:08 ago. (Use '!kill 43590' to kill it.)" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", " <iframe id=\"tensorboard-frame-6549bb5697dded37\" width=\"100%\" height=\"800\" frameborder=\"0\">\n", " </iframe>\n", " <script>\n", " (function() {\n", " const frame = document.getElementById(\"tensorboard-frame-6549bb5697dded37\");\n", " const url = new URL(\"/\", window.location);\n", " url.port = 6006;\n", " frame.src = url;\n", " })();\n", " </script>\n", " " ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%tensorboard --logdir {root_logdir}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Name scopes" ] }, { "cell_type": "code", "execution_count": 77, "metadata": {}, "outputs": [], "source": [ "reset_graph()\n", "\n", "n_epochs = 1000\n", "learning_rate = 0.01\n", "\n", "X = tf.placeholder(tf.float32, shape=(None, n + 1), name=\"X\")\n", "y = tf.placeholder(tf.float32, shape=(None, 1), name=\"y\")\n", "theta = tf.Variable(tf.random_uniform([n + 1, 1], -1.0, 1.0, seed=42), name=\"theta\")\n", "y_pred = tf.matmul(X, theta, name=\"predictions\")" ] }, { "cell_type": "code", "execution_count": 78, "metadata": {}, "outputs": [], "source": [ "with tf.name_scope(\"loss\") as scope:\n", " error = y_pred - y\n", " mse = tf.reduce_mean(tf.square(error), name=\"mse\")" ] }, { "cell_type": "code", "execution_count": 79, "metadata": {}, "outputs": [], "source": [ "optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)\n", "training_op = optimizer.minimize(mse)\n", "\n", "init = tf.global_variables_initializer()\n", "\n", "mse_summary = tf.summary.scalar('MSE', mse)\n", "\n", "logdir = make_log_subdir()\n", "file_writer = tf.summary.FileWriter(logdir, tf.get_default_graph())" ] }, { "cell_type": "code", "execution_count": 80, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Best theta:\n", "[[ 2.07033372]\n", " [ 0.86371452]\n", " [ 0.12255151]\n", " [-0.31211874]\n", " [ 0.38510373]\n", " [ 0.00434168]\n", " [-0.01232954]\n", " [-0.83376896]\n", " [-0.80304712]]\n" ] } ], "source": [ "n_epochs = 10\n", "batch_size = 100\n", "n_batches = int(np.ceil(m / batch_size))\n", "\n", "with tf.Session() as sess:\n", " sess.run(init)\n", "\n", " for epoch in range(n_epochs):\n", " for batch_index in range(n_batches):\n", " X_batch, y_batch = fetch_batch(epoch, batch_index, batch_size)\n", " if batch_index % 10 == 0:\n", " summary_str = mse_summary.eval(feed_dict={X: X_batch, y: y_batch})\n", " step = epoch * n_batches + batch_index\n", " file_writer.add_summary(summary_str, step)\n", " sess.run(training_op, feed_dict={X: X_batch, y: y_batch})\n", "\n", " best_theta = theta.eval()\n", "\n", "file_writer.flush()\n", "file_writer.close()\n", "print(\"Best theta:\")\n", "print(best_theta)" ] }, { "cell_type": "code", "execution_count": 81, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "loss/sub\n" ] } ], "source": [ "print(error.op.name)" ] }, { "cell_type": "code", "execution_count": 82, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "loss/mse\n" ] } ], "source": [ "print(mse.op.name)" ] }, { "cell_type": "code", "execution_count": 83, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "a\n", "a_1\n", "param/a\n", "param_1/a\n" ] } ], "source": [ "reset_graph()\n", "\n", "a1 = tf.Variable(0, name=\"a\") # name == \"a\"\n", "a2 = tf.Variable(0, name=\"a\") # name == \"a_1\"\n", "\n", "with tf.name_scope(\"param\"): # name == \"param\"\n", " a3 = tf.Variable(0, name=\"a\") # name == \"param/a\"\n", "\n", "with tf.name_scope(\"param\"): # name == \"param_1\"\n", " a4 = tf.Variable(0, name=\"a\") # name == \"param_1/a\"\n", "\n", "for node in (a1, a2, a3, a4):\n", " print(node.op.name)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Modularity" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "An ugly flat code:" ] }, { "cell_type": "code", "execution_count": 84, "metadata": {}, "outputs": [], "source": [ "reset_graph()\n", "\n", "n_features = 3\n", "X = tf.placeholder(tf.float32, shape=(None, n_features), name=\"X\")\n", "\n", "w1 = tf.Variable(tf.random_normal((n_features, 1)), name=\"weights1\")\n", "w2 = tf.Variable(tf.random_normal((n_features, 1)), name=\"weights2\")\n", "b1 = tf.Variable(0.0, name=\"bias1\")\n", "b2 = tf.Variable(0.0, name=\"bias2\")\n", "\n", "z1 = tf.add(tf.matmul(X, w1), b1, name=\"z1\")\n", "z2 = tf.add(tf.matmul(X, w2), b2, name=\"z2\")\n", "\n", "relu1 = tf.maximum(z1, 0., name=\"relu1\")\n", "relu2 = tf.maximum(z1, 0., name=\"relu2\") # Oops, cut&paste error! Did you spot it?\n", "\n", "output = tf.add(relu1, relu2, name=\"output\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Much better, using a function to build the ReLUs:" ] }, { "cell_type": "code", "execution_count": 85, "metadata": {}, "outputs": [], "source": [ "reset_graph()\n", "\n", "def relu(X):\n", " w_shape = (int(X.get_shape()[1]), 1)\n", " w = tf.Variable(tf.random_normal(w_shape), name=\"weights\")\n", " b = tf.Variable(0.0, name=\"bias\")\n", " z = tf.add(tf.matmul(X, w), b, name=\"z\")\n", " return tf.maximum(z, 0., name=\"relu\")\n", "\n", "n_features = 3\n", "X = tf.placeholder(tf.float32, shape=(None, n_features), name=\"X\")\n", "relus = [relu(X) for i in range(5)]\n", "output = tf.add_n(relus, name=\"output\")" ] }, { "cell_type": "code", "execution_count": 86, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'tf_logs/run-relu1/'" ] }, "execution_count": 86, "metadata": {}, "output_type": "execute_result" } ], "source": [ "save_graph(run_id=\"relu1\")" ] }, { "cell_type": "code", "execution_count": 87, "metadata": { "scrolled": false }, "outputs": [ { "data": { "text/plain": [ "Reusing TensorBoard on port 6006 (pid 43590), started 0:04:52 ago. (Use '!kill 43590' to kill it.)" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", " <iframe id=\"tensorboard-frame-88e2356596cb12df\" width=\"100%\" height=\"800\" frameborder=\"0\">\n", " </iframe>\n", " <script>\n", " (function() {\n", " const frame = document.getElementById(\"tensorboard-frame-88e2356596cb12df\");\n", " const url = new URL(\"/\", window.location);\n", " url.port = 6006;\n", " frame.src = url;\n", " })();\n", " </script>\n", " " ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%tensorboard --logdir {root_logdir}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Even better using name scopes:" ] }, { "cell_type": "code", "execution_count": 88, "metadata": {}, "outputs": [], "source": [ "reset_graph()\n", "\n", "def relu(X):\n", " with tf.name_scope(\"relu\"):\n", " w_shape = (int(X.get_shape()[1]), 1) # not shown in the book\n", " w = tf.Variable(tf.random_normal(w_shape), name=\"weights\") # not shown\n", " b = tf.Variable(0.0, name=\"bias\") # not shown\n", " z = tf.add(tf.matmul(X, w), b, name=\"z\") # not shown\n", " return tf.maximum(z, 0., name=\"max\") # not shown" ] }, { "cell_type": "code", "execution_count": 89, "metadata": {}, "outputs": [], "source": [ "n_features = 3\n", "X = tf.placeholder(tf.float32, shape=(None, n_features), name=\"X\")\n", "relus = [relu(X) for i in range(5)]\n", "output = tf.add_n(relus, name=\"output\")" ] }, { "cell_type": "code", "execution_count": 90, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'tf_logs/run-relu2/'" ] }, "execution_count": 90, "metadata": {}, "output_type": "execute_result" } ], "source": [ "save_graph(run_id=\"relu2\")" ] }, { "cell_type": "code", "execution_count": 91, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Reusing TensorBoard on port 6006 (pid 43590), started 0:05:43 ago. (Use '!kill 43590' to kill it.)" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", " <iframe id=\"tensorboard-frame-57e36dbbc0430494\" width=\"100%\" height=\"800\" frameborder=\"0\">\n", " </iframe>\n", " <script>\n", " (function() {\n", " const frame = document.getElementById(\"tensorboard-frame-57e36dbbc0430494\");\n", " const url = new URL(\"/\", window.location);\n", " url.port = 6006;\n", " frame.src = url;\n", " })();\n", " </script>\n", " " ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%tensorboard --logdir {root_logdir}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Sharing Variables" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Sharing a `threshold` variable the classic way, by defining it outside of the `relu()` function then passing it as a parameter:" ] }, { "cell_type": "code", "execution_count": 92, "metadata": {}, "outputs": [], "source": [ "reset_graph()\n", "\n", "def relu(X, threshold):\n", " with tf.name_scope(\"relu\"):\n", " w_shape = (int(X.get_shape()[1]), 1) # not shown in the book\n", " w = tf.Variable(tf.random_normal(w_shape), name=\"weights\") # not shown\n", " b = tf.Variable(0.0, name=\"bias\") # not shown\n", " z = tf.add(tf.matmul(X, w), b, name=\"z\") # not shown\n", " return tf.maximum(z, threshold, name=\"max\")\n", "\n", "threshold = tf.Variable(0.0, name=\"threshold\")\n", "X = tf.placeholder(tf.float32, shape=(None, n_features), name=\"X\")\n", "relus = [relu(X, threshold) for i in range(5)]\n", "output = tf.add_n(relus, name=\"output\")" ] }, { "cell_type": "code", "execution_count": 93, "metadata": {}, "outputs": [], "source": [ "reset_graph()\n", "\n", "def relu(X):\n", " with tf.name_scope(\"relu\"):\n", " if not hasattr(relu, \"threshold\"):\n", " relu.threshold = tf.Variable(0.0, name=\"threshold\")\n", " w_shape = int(X.get_shape()[1]), 1 # not shown in the book\n", " w = tf.Variable(tf.random_normal(w_shape), name=\"weights\") # not shown\n", " b = tf.Variable(0.0, name=\"bias\") # not shown\n", " z = tf.add(tf.matmul(X, w), b, name=\"z\") # not shown\n", " return tf.maximum(z, relu.threshold, name=\"max\")" ] }, { "cell_type": "code", "execution_count": 94, "metadata": {}, "outputs": [], "source": [ "X = tf.placeholder(tf.float32, shape=(None, n_features), name=\"X\")\n", "relus = [relu(X) for i in range(5)]\n", "output = tf.add_n(relus, name=\"output\")" ] }, { "cell_type": "code", "execution_count": 95, "metadata": {}, "outputs": [], "source": [ "reset_graph()\n", "\n", "with tf.variable_scope(\"relu\"):\n", " threshold = tf.get_variable(\"threshold\", shape=(),\n", " initializer=tf.constant_initializer(0.0))" ] }, { "cell_type": "code", "execution_count": 96, "metadata": {}, "outputs": [], "source": [ "with tf.variable_scope(\"relu\", reuse=True):\n", " threshold = tf.get_variable(\"threshold\")" ] }, { "cell_type": "code", "execution_count": 97, "metadata": {}, "outputs": [], "source": [ "with tf.variable_scope(\"relu\") as scope:\n", " scope.reuse_variables()\n", " threshold = tf.get_variable(\"threshold\")" ] }, { "cell_type": "code", "execution_count": 98, "metadata": {}, "outputs": [], "source": [ "reset_graph()\n", "\n", "def relu(X):\n", " with tf.variable_scope(\"relu\", reuse=True):\n", " threshold = tf.get_variable(\"threshold\")\n", " w_shape = int(X.get_shape()[1]), 1 # not shown\n", " w = tf.Variable(tf.random_normal(w_shape), name=\"weights\") # not shown\n", " b = tf.Variable(0.0, name=\"bias\") # not shown\n", " z = tf.add(tf.matmul(X, w), b, name=\"z\") # not shown\n", " return tf.maximum(z, threshold, name=\"max\")\n", "\n", "X = tf.placeholder(tf.float32, shape=(None, n_features), name=\"X\")\n", "with tf.variable_scope(\"relu\"):\n", " threshold = tf.get_variable(\"threshold\", shape=(),\n", " initializer=tf.constant_initializer(0.0))\n", "relus = [relu(X) for relu_index in range(5)]\n", "output = tf.add_n(relus, name=\"output\")" ] }, { "cell_type": "code", "execution_count": 99, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'tf_logs/run-relu6/'" ] }, "execution_count": 99, "metadata": {}, "output_type": "execute_result" } ], "source": [ "save_graph(run_id=\"relu6\")" ] }, { "cell_type": "code", "execution_count": 100, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Reusing TensorBoard on port 6006 (pid 43590), started 0:06:21 ago. (Use '!kill 43590' to kill it.)" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", " <iframe id=\"tensorboard-frame-f55ce8541b1e56b0\" width=\"100%\" height=\"800\" frameborder=\"0\">\n", " </iframe>\n", " <script>\n", " (function() {\n", " const frame = document.getElementById(\"tensorboard-frame-f55ce8541b1e56b0\");\n", " const url = new URL(\"/\", window.location);\n", " url.port = 6006;\n", " frame.src = url;\n", " })();\n", " </script>\n", " " ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%tensorboard --logdir {root_logdir}" ] }, { "cell_type": "code", "execution_count": 101, "metadata": {}, "outputs": [], "source": [ "reset_graph()\n", "\n", "def relu(X):\n", " with tf.variable_scope(\"relu\"):\n", " threshold = tf.get_variable(\"threshold\", shape=(), initializer=tf.constant_initializer(0.0))\n", " w_shape = (int(X.get_shape()[1]), 1)\n", " w = tf.Variable(tf.random_normal(w_shape), name=\"weights\")\n", " b = tf.Variable(0.0, name=\"bias\")\n", " z = tf.add(tf.matmul(X, w), b, name=\"z\")\n", " return tf.maximum(z, threshold, name=\"max\")\n", "\n", "X = tf.placeholder(tf.float32, shape=(None, n_features), name=\"X\")\n", "with tf.variable_scope(\"\", default_name=\"\") as scope:\n", " first_relu = relu(X) # create the shared variable\n", " scope.reuse_variables() # then reuse it\n", " relus = [first_relu] + [relu(X) for i in range(4)]\n", "output = tf.add_n(relus, name=\"output\")" ] }, { "cell_type": "code", "execution_count": 102, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'tf_logs/run-relu8/'" ] }, "execution_count": 102, "metadata": {}, "output_type": "execute_result" } ], "source": [ "save_graph(run_id=\"relu8\")" ] }, { "cell_type": "code", "execution_count": 103, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Reusing TensorBoard on port 6006 (pid 43590), started 0:06:45 ago. (Use '!kill 43590' to kill it.)" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", " <iframe id=\"tensorboard-frame-41cda150228bada\" width=\"100%\" height=\"800\" frameborder=\"0\">\n", " </iframe>\n", " <script>\n", " (function() {\n", " const frame = document.getElementById(\"tensorboard-frame-41cda150228bada\");\n", " const url = new URL(\"/\", window.location);\n", " url.port = 6006;\n", " frame.src = url;\n", " })();\n", " </script>\n", " " ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%tensorboard --logdir {root_logdir}" ] }, { "cell_type": "code", "execution_count": 104, "metadata": {}, "outputs": [], "source": [ "reset_graph()\n", "\n", "def relu(X):\n", " threshold = tf.get_variable(\"threshold\", shape=(),\n", " initializer=tf.constant_initializer(0.0))\n", " w_shape = (int(X.get_shape()[1]), 1) # not shown in the book\n", " w = tf.Variable(tf.random_normal(w_shape), name=\"weights\") # not shown\n", " b = tf.Variable(0.0, name=\"bias\") # not shown\n", " z = tf.add(tf.matmul(X, w), b, name=\"z\") # not shown\n", " return tf.maximum(z, threshold, name=\"max\")\n", "\n", "X = tf.placeholder(tf.float32, shape=(None, n_features), name=\"X\")\n", "relus = []\n", "for relu_index in range(5):\n", " with tf.variable_scope(\"relu\", reuse=(relu_index >= 1)) as scope:\n", " relus.append(relu(X))\n", "output = tf.add_n(relus, name=\"output\")" ] }, { "cell_type": "code", "execution_count": 105, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'tf_logs/run-relu9/'" ] }, "execution_count": 105, "metadata": {}, "output_type": "execute_result" } ], "source": [ "save_graph(run_id=\"relu9\")" ] }, { "cell_type": "code", "execution_count": 106, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Reusing TensorBoard on port 6006 (pid 43590), started 0:07:06 ago. (Use '!kill 43590' to kill it.)" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", " <iframe id=\"tensorboard-frame-5f6a27b4563df923\" width=\"100%\" height=\"800\" frameborder=\"0\">\n", " </iframe>\n", " <script>\n", " (function() {\n", " const frame = document.getElementById(\"tensorboard-frame-5f6a27b4563df923\");\n", " const url = new URL(\"/\", window.location);\n", " url.port = 6006;\n", " frame.src = url;\n", " })();\n", " </script>\n", " " ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%tensorboard --logdir {root_logdir}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Extra material" ] }, { "cell_type": "code", "execution_count": 107, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "x0: my_scope/x\n", "x1: my_scope/x_1\n", "x2: my_scope/x_2\n", "x3: my_scope/x\n", "x4: my_scope_1/x\n", "x5: my_scope/x\n", "True\n" ] } ], "source": [ "reset_graph()\n", "\n", "with tf.variable_scope(\"my_scope\"):\n", " x0 = tf.get_variable(\"x\", shape=(), initializer=tf.constant_initializer(0.))\n", " x1 = tf.Variable(0., name=\"x\")\n", " x2 = tf.Variable(0., name=\"x\")\n", "\n", "with tf.variable_scope(\"my_scope\", reuse=True):\n", " x3 = tf.get_variable(\"x\")\n", " x4 = tf.Variable(0., name=\"x\")\n", "\n", "with tf.variable_scope(\"\", default_name=\"\", reuse=True):\n", " x5 = tf.get_variable(\"my_scope/x\")\n", "\n", "print(\"x0:\", x0.op.name)\n", "print(\"x1:\", x1.op.name)\n", "print(\"x2:\", x2.op.name)\n", "print(\"x3:\", x3.op.name)\n", "print(\"x4:\", x4.op.name)\n", "print(\"x5:\", x5.op.name)\n", "print(x0 is x3 and x3 is x5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The first `variable_scope()` block first creates the shared variable `x0`, named `my_scope/x`. For all operations other than shared variables (including non-shared variables), the variable scope acts like a regular name scope, which is why the two variables `x1` and `x2` have a name with a prefix `my_scope/`. Note however that TensorFlow makes their names unique by adding an index: `my_scope/x_1` and `my_scope/x_2`.\n", "\n", "The second `variable_scope()` block reuses the shared variables in scope `my_scope`, which is why `x0 is x3`. Once again, for all operations other than shared variables it acts as a named scope, and since it's a separate block from the first one, the name of the scope is made unique by TensorFlow (`my_scope_1`) and thus the variable `x4` is named `my_scope_1/x`.\n", "\n", "The third block shows another way to get a handle on the shared variable `my_scope/x` by creating a `variable_scope()` at the root scope (whose name is an empty string), then calling `get_variable()` with the full name of the shared variable (i.e. `\"my_scope/x\"`)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Strings" ] }, { "cell_type": "code", "execution_count": 108, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[b'Do' b'you' b'want' b'some' b'caf\\xc3\\xa9?']\n" ] } ], "source": [ "reset_graph()\n", "\n", "text = np.array(\"Do you want some café?\".split())\n", "text_tensor = tf.constant(text)\n", "\n", "with tf.Session() as sess:\n", " print(text_tensor.eval())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Autodiff" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note: the autodiff content was moved to the [extra_autodiff.ipynb](extra_autodiff.ipynb) notebook." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Exercise solutions" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 1. to 11." ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "See appendix A." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 12. Logistic Regression with Mini-Batch Gradient Descent using TensorFlow" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First, let's create the moons dataset using Scikit-Learn's `make_moons()` function:" ] }, { "cell_type": "code", "execution_count": 109, "metadata": {}, "outputs": [], "source": [ "from sklearn.datasets import make_moons\n", "\n", "m = 1000\n", "X_moons, y_moons = make_moons(m, noise=0.1, random_state=42)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's take a peek at the dataset:" ] }, { "cell_type": "code", "execution_count": 110, "metadata": {}, "outputs": [ { "data": { "image/png": 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i7qQkM9ZfgJHABygh0YrCmHsnarJaERDAR5pGet263F6vntsktO/4cYL27EHr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\nrQDSUMbD3M2dNI65HtK/SCf90nSbN+CQO8ijSRmGaEQoLTArzqTN1Jy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4/ADDzh+GvEBS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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f96d104cef0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(X_moons[y_moons == 1, 0], X_moons[y_moons == 1, 1], 'go', label=\"Positive\")\n", "plt.plot(X_moons[y_moons == 0, 0], X_moons[y_moons == 0, 1], 'r^', label=\"Negative\")\n", "plt.legend()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We must not forget to add an extra bias feature ($x_0 = 1$) to every instance. For this, we just need to add a column full of 1s on the left of the input matrix $\\mathbf{X}$:" ] }, { "cell_type": "code", "execution_count": 111, "metadata": {}, "outputs": [], "source": [ "X_moons_with_bias = np.c_[np.ones((m, 1)), X_moons]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's check:" ] }, { "cell_type": "code", "execution_count": 112, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[ 1. , -0.05146968, 0.44419863],\n", " [ 1. , 1.03201691, -0.41974116],\n", " [ 1. , 0.86789186, -0.25482711],\n", " [ 1. , 0.288851 , -0.44866862],\n", " [ 1. , -0.83343911, 0.53505665]])" ] }, "execution_count": 112, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X_moons_with_bias[:5]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Looks good. Now let's reshape `y_train` to make it a column vector (i.e. a 2D array with a single column):" ] }, { "cell_type": "code", "execution_count": 113, "metadata": { "collapsed": true }, "outputs": [], "source": [ "y_moons_column_vector = y_moons.reshape(-1, 1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now let's split the data into a training set and a test set:" ] }, { "cell_type": "code", "execution_count": 114, "metadata": { "collapsed": true }, "outputs": [], "source": [ "test_ratio = 0.2\n", "test_size = int(m * test_ratio)\n", "X_train = X_moons_with_bias[:-test_size]\n", "X_test = X_moons_with_bias[-test_size:]\n", "y_train = y_moons_column_vector[:-test_size]\n", "y_test = y_moons_column_vector[-test_size:]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Ok, now let's create a small function to generate training batches. In this implementation we will just pick random instances from the training set for each batch. This means that a single batch may contain the same instance multiple times, and also a single epoch may not cover all the training instances (in fact it will generally cover only about two thirds of the instances). However, in practice this is not an issue and it simplifies the code:" ] }, { "cell_type": "code", "execution_count": 115, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def random_batch(X_train, y_train, batch_size):\n", " rnd_indices = np.random.randint(0, len(X_train), batch_size)\n", " X_batch = X_train[rnd_indices]\n", " y_batch = y_train[rnd_indices]\n", " return X_batch, y_batch" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's look at a small batch:" ] }, { "cell_type": "code", "execution_count": 116, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[ 1. , 1.93189866, 0.13158788],\n", " [ 1. , 1.07172763, 0.13482039],\n", " [ 1. , -1.01148674, -0.04686381],\n", " [ 1. , 0.02201868, 0.19079139],\n", " [ 1. , -0.98941204, 0.02473116]])" ] }, "execution_count": 116, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X_batch, y_batch = random_batch(X_train, y_train, 5)\n", "X_batch" ] }, { "cell_type": "code", "execution_count": 117, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[1],\n", " [0],\n", " [0],\n", " [1],\n", " [0]])" ] }, "execution_count": 117, "metadata": {}, "output_type": "execute_result" } ], "source": [ "y_batch" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Great! Now that the data is ready to be fed to the model, we need to build that model. Let's start with a simple implementation, then we will add all the bells and whistles." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First let's reset the default graph." ] }, { "cell_type": "code", "execution_count": 118, "metadata": { "collapsed": true }, "outputs": [], "source": [ "reset_graph()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The _moons_ dataset has two input features, since each instance is a point on a plane (i.e., 2-Dimensional):" ] }, { "cell_type": "code", "execution_count": 119, "metadata": { "collapsed": true }, "outputs": [], "source": [ "n_inputs = 2" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now let's build the Logistic Regression model. As we saw in chapter 4, this model first computes a weighted sum of the inputs (just like the Linear Regression model), and then it applies the sigmoid function to the result, which gives us the estimated probability for the positive class:\n", "\n", "$\\hat{p} = h_\\boldsymbol{\\theta}(\\mathbf{x}) = \\sigma(\\boldsymbol{\\theta}^T \\mathbf{x})$\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Recall that $\\boldsymbol{\\theta}$ is the parameter vector, containing the bias term $\\theta_0$ and the weights $\\theta_1, \\theta_2, \\dots, \\theta_n$. The input vector $\\mathbf{x}$ contains a constant term $x_0 = 1$, as well as all the input features $x_1, x_2, \\dots, x_n$.\n", "\n", "Since we want to be able to make predictions for multiple instances at a time, we will use an input matrix $\\mathbf{X}$ rather than a single input vector. The $i^{th}$ row will contain the transpose of the $i^{th}$ input vector $(\\mathbf{x}^{(i)})^T$. It is then possible to estimate the probability that each instance belongs to the positive class using the following equation:\n", "\n", "$ \\hat{\\mathbf{p}} = \\sigma(\\mathbf{X} \\boldsymbol{\\theta})$\n", "\n", "That's all we need to build the model:" ] }, { "cell_type": "code", "execution_count": 120, "metadata": { "collapsed": true }, "outputs": [], "source": [ "X = tf.placeholder(tf.float32, shape=(None, n_inputs + 1), name=\"X\")\n", "y = tf.placeholder(tf.float32, shape=(None, 1), name=\"y\")\n", "theta = tf.Variable(tf.random_uniform([n_inputs + 1, 1], -1.0, 1.0, seed=42), name=\"theta\")\n", "logits = tf.matmul(X, theta, name=\"logits\")\n", "y_proba = 1 / (1 + tf.exp(-logits))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In fact, TensorFlow has a nice function `tf.sigmoid()` that we can use to simplify the last line of the previous code:" ] }, { "cell_type": "code", "execution_count": 121, "metadata": { "collapsed": true }, "outputs": [], "source": [ "y_proba = tf.sigmoid(logits)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As we saw in chapter 4, the log loss is a good cost function to use for Logistic Regression:\n", "\n", "$J(\\boldsymbol{\\theta}) = -\\dfrac{1}{m} \\sum\\limits_{i=1}^{m}{\\left[ y^{(i)} \\log\\left(\\hat{p}^{(i)}\\right) + (1 - y^{(i)}) \\log\\left(1 - \\hat{p}^{(i)}\\right)\\right]}$\n", "\n", "One option is to implement it ourselves:" ] }, { "cell_type": "code", "execution_count": 122, "metadata": {}, "outputs": [], "source": [ "epsilon = 1e-7 # to avoid an overflow when computing the log\n", "loss = -tf.reduce_mean(y * tf.log(y_proba + epsilon) + (1 - y) * tf.log(1 - y_proba + epsilon))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "But we might as well use TensorFlow's `tf.losses.log_loss()` function:" ] }, { "cell_type": "code", "execution_count": 123, "metadata": { "collapsed": true }, "outputs": [], "source": [ "loss = tf.losses.log_loss(y, y_proba) # uses epsilon = 1e-7 by default" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The rest is pretty standard: let's create the optimizer and tell it to minimize the cost function:" ] }, { "cell_type": "code", "execution_count": 124, "metadata": { "collapsed": true }, "outputs": [], "source": [ "learning_rate = 0.01\n", "optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)\n", "training_op = optimizer.minimize(loss)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "All we need now (in this minimal version) is the variable initializer:" ] }, { "cell_type": "code", "execution_count": 125, "metadata": { "collapsed": true }, "outputs": [], "source": [ "init = tf.global_variables_initializer()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And we are ready to train the model and use it for predictions!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There's really nothing special about this code, it's virtually the same as the one we used earlier for Linear Regression:" ] }, { "cell_type": "code", "execution_count": 126, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch: 0 \tLoss: 0.792602\n", "Epoch: 100 \tLoss: 0.343463\n", "Epoch: 200 \tLoss: 0.30754\n", "Epoch: 300 \tLoss: 0.292889\n", "Epoch: 400 \tLoss: 0.285336\n", "Epoch: 500 \tLoss: 0.280478\n", "Epoch: 600 \tLoss: 0.278083\n", "Epoch: 700 \tLoss: 0.276154\n", "Epoch: 800 \tLoss: 0.27552\n", "Epoch: 900 \tLoss: 0.274912\n" ] } ], "source": [ "n_epochs = 1000\n", "batch_size = 50\n", "n_batches = int(np.ceil(m / batch_size))\n", "\n", "with tf.Session() as sess:\n", " sess.run(init)\n", "\n", " for epoch in range(n_epochs):\n", " for batch_index in range(n_batches):\n", " X_batch, y_batch = random_batch(X_train, y_train, batch_size)\n", " sess.run(training_op, feed_dict={X: X_batch, y: y_batch})\n", " loss_val = loss.eval({X: X_test, y: y_test})\n", " if epoch % 100 == 0:\n", " print(\"Epoch:\", epoch, \"\\tLoss:\", loss_val)\n", "\n", " y_proba_val = y_proba.eval(feed_dict={X: X_test, y: y_test})" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note: we don't use the epoch number when generating batches, so we could just have a single `for` loop rather than 2 nested `for` loops, but it's convenient to think of training time in terms of number of epochs (i.e., roughly the number of times the algorithm went through the training set)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For each instance in the test set, `y_proba_val` contains the estimated probability that it belongs to the positive class, according to the model. For example, here are the first 5 estimated probabilities:" ] }, { "cell_type": "code", "execution_count": 127, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[ 0.54895616],\n", " [ 0.70724374],\n", " [ 0.51900256],\n", " [ 0.9911136 ],\n", " [ 0.50859052]], dtype=float32)" ] }, "execution_count": 127, "metadata": {}, "output_type": "execute_result" } ], "source": [ "y_proba_val[:5]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To classify each instance, we can go for maximum likelihood: classify as positive any instance whose estimated probability is greater or equal to 0.5:" ] }, { "cell_type": "code", "execution_count": 128, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[ True],\n", " [ True],\n", " [ True],\n", " [ True],\n", " [ True]], dtype=bool)" ] }, "execution_count": 128, "metadata": {}, "output_type": "execute_result" } ], "source": [ "y_pred = (y_proba_val >= 0.5)\n", "y_pred[:5]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Depending on the use case, you may want to choose a different threshold than 0.5: make it higher if you want high precision (but lower recall), and make it lower if you want high recall (but lower precision). See chapter 3 for more details." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's compute the model's precision and recall:" ] }, { "cell_type": "code", "execution_count": 129, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.86274509803921573" ] }, "execution_count": 129, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from sklearn.metrics import precision_score, recall_score\n", "\n", "precision_score(y_test, y_pred)" ] }, { "cell_type": "code", "execution_count": 130, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.88888888888888884" ] }, "execution_count": 130, "metadata": {}, "output_type": "execute_result" } ], "source": [ "recall_score(y_test, y_pred)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's plot these predictions to see what they look like:" ] }, { "cell_type": "code", "execution_count": 131, "metadata": {}, "outputs": [ { "data": { "image/png": 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o6W/luUSgtKyUgfcOZFH6IoqzilmUvoiB9w6ktKw02qLFHuFolki8qGczh0R+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\ng3iptOmU07Gw0RXANVCWWxa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"text/plain": [ "<matplotlib.figure.Figure at 0x7f96d0e11f98>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "y_pred_idx = y_pred.reshape(-1) # a 1D array rather than a column vector\n", "plt.plot(X_test[y_pred_idx, 1], X_test[y_pred_idx, 2], 'go', label=\"Positive\")\n", "plt.plot(X_test[~y_pred_idx, 1], X_test[~y_pred_idx, 2], 'r^', label=\"Negative\")\n", "plt.legend()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Well, that looks pretty bad, doesn't it? But let's not forget that the Logistic Regression model has a linear decision boundary, so this is actually close to the best we can do with this model (unless we add more features, as we will show in a second)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now let's start over, but this time we will add all the bells and whistles, as listed in the exercise:\n", "* Define the graph within a `logistic_regression()` function that can be reused easily.\n", "* Save checkpoints using a `Saver` at regular intervals during training, and save the final model at the end of training.\n", "* Restore the last checkpoint upon startup if training was interrupted.\n", "* Define the graph using nice scopes so the graph looks good in TensorBoard.\n", "* Add summaries to visualize the learning curves in TensorBoard.\n", "* Try tweaking some hyperparameters such as the learning rate or the mini-batch size and look at the shape of the learning curve." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Before we start, we will add 4 more features to the inputs: ${x_1}^2$, ${x_2}^2$, ${x_1}^3$ and ${x_2}^3$. This was not part of the exercise, but it will demonstrate how adding features can improve the model. We will do this manually, but you could also add them using `sklearn.preprocessing.PolynomialFeatures`." ] }, { "cell_type": "code", "execution_count": 132, "metadata": { "collapsed": true }, "outputs": [], "source": [ "X_train_enhanced = np.c_[X_train,\n", " np.square(X_train[:, 1]),\n", " np.square(X_train[:, 2]),\n", " X_train[:, 1] ** 3,\n", " X_train[:, 2] ** 3]\n", "X_test_enhanced = np.c_[X_test,\n", " np.square(X_test[:, 1]),\n", " np.square(X_test[:, 2]),\n", " X_test[:, 1] ** 3,\n", " X_test[:, 2] ** 3]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is what the \"enhanced\" training set looks like:" ] }, { "cell_type": "code", "execution_count": 133, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[ 1.00000000e+00, -5.14696757e-02, 4.44198631e-01,\n", " 2.64912752e-03, 1.97312424e-01, -1.36349734e-04,\n", " 8.76459084e-02],\n", " [ 1.00000000e+00, 1.03201691e+00, -4.19741157e-01,\n", " 1.06505890e+00, 1.76182639e-01, 1.09915879e+00,\n", " -7.39511049e-02],\n", " [ 1.00000000e+00, 8.67891864e-01, -2.54827114e-01,\n", " 7.53236288e-01, 6.49368582e-02, 6.53727646e-01,\n", " -1.65476722e-02],\n", " [ 1.00000000e+00, 2.88850997e-01, -4.48668621e-01,\n", " 8.34348982e-02, 2.01303531e-01, 2.41002535e-02,\n", " -9.03185778e-02],\n", " [ 1.00000000e+00, -8.33439108e-01, 5.35056649e-01,\n", " 6.94620746e-01, 2.86285618e-01, -5.78924095e-01,\n", " 1.53179024e-01]])" ] }, "execution_count": 133, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X_train_enhanced[:5]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Ok, next let's reset the default graph:" ] }, { "cell_type": "code", "execution_count": 134, "metadata": { "collapsed": true }, "outputs": [], "source": [ "reset_graph()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now let's define the `logistic_regression()` function to create the graph. We will leave out the definition of the inputs `X` and the targets `y`. We could include them here, but leaving them out will make it easier to use this function in a wide range of use cases (e.g. perhaps we will want to add some preprocessing steps for the inputs before we feed them to the Logistic Regression model)." ] }, { "cell_type": "code", "execution_count": 135, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def logistic_regression(X, y, initializer=None, seed=42, learning_rate=0.01):\n", " n_inputs_including_bias = int(X.get_shape()[1])\n", " with tf.name_scope(\"logistic_regression\"):\n", " with tf.name_scope(\"model\"):\n", " if initializer is None:\n", " initializer = tf.random_uniform([n_inputs_including_bias, 1], -1.0, 1.0, seed=seed)\n", " theta = tf.Variable(initializer, name=\"theta\")\n", " logits = tf.matmul(X, theta, name=\"logits\")\n", " y_proba = tf.sigmoid(logits)\n", " with tf.name_scope(\"train\"):\n", " loss = tf.losses.log_loss(y, y_proba, scope=\"loss\")\n", " optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)\n", " training_op = optimizer.minimize(loss)\n", " loss_summary = tf.summary.scalar('log_loss', loss)\n", " with tf.name_scope(\"init\"):\n", " init = tf.global_variables_initializer()\n", " with tf.name_scope(\"save\"):\n", " saver = tf.train.Saver()\n", " return y_proba, loss, training_op, loss_summary, init, saver" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's create a little function to get the name of the log directory to save the summaries for Tensorboard:" ] }, { "cell_type": "code", "execution_count": 136, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from datetime import datetime\n", "\n", "def log_dir(prefix=\"\"):\n", " now = datetime.utcnow().strftime(\"%Y%m%d%H%M%S\")\n", " root_logdir = \"tf_logs\"\n", " if prefix:\n", " prefix += \"-\"\n", " name = prefix + \"run-\" + now\n", " return \"{}/{}/\".format(root_logdir, name)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next, let's create the graph, using the `logistic_regression()` function. We will also create the `FileWriter` to save the summaries to the log directory for Tensorboard:" ] }, { "cell_type": "code", "execution_count": 137, "metadata": {}, "outputs": [], "source": [ "n_inputs = 2 + 4\n", "logdir = log_dir(\"logreg\")\n", "\n", "X = tf.placeholder(tf.float32, shape=(None, n_inputs + 1), name=\"X\")\n", "y = tf.placeholder(tf.float32, shape=(None, 1), name=\"y\")\n", "\n", "y_proba, loss, training_op, loss_summary, init, saver = logistic_regression(X, y)\n", "\n", "file_writer = tf.summary.FileWriter(logdir, tf.get_default_graph())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "At last we can train the model! We will start by checking whether a previous training session was interrupted, and if so we will load the checkpoint and continue training from the epoch number we saved. In this example we just save the epoch number to a separate file, but in chapter 11 we will see how to store the training step directly as part of the model, using a non-trainable variable called `global_step` that we pass to the optimizer's `minimize()` method.\n", "\n", "You can try interrupting training to verify that it does indeed restore the last checkpoint when you start it again." ] }, { "cell_type": "code", "execution_count": 138, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch: 0 \tLoss: 0.629985\n", "Epoch: 500 \tLoss: 0.161224\n", "Epoch: 1000 \tLoss: 0.119032\n", "Epoch: 1500 \tLoss: 0.0973292\n", "Epoch: 2000 \tLoss: 0.0836979\n", "Epoch: 2500 \tLoss: 0.0743758\n", "Epoch: 3000 \tLoss: 0.0675021\n", "Epoch: 3500 \tLoss: 0.0622069\n", "Epoch: 4000 \tLoss: 0.0580268\n", "Epoch: 4500 \tLoss: 0.054563\n", "Epoch: 5000 \tLoss: 0.0517083\n", "Epoch: 5500 \tLoss: 0.0492377\n", "Epoch: 6000 \tLoss: 0.0471673\n", "Epoch: 6500 \tLoss: 0.0453766\n", "Epoch: 7000 \tLoss: 0.0438187\n", "Epoch: 7500 \tLoss: 0.0423742\n", "Epoch: 8000 \tLoss: 0.0410892\n", "Epoch: 8500 \tLoss: 0.0399709\n", "Epoch: 9000 \tLoss: 0.0389202\n", "Epoch: 9500 \tLoss: 0.0380107\n", "Epoch: 10000 \tLoss: 0.0371557\n" ] } ], "source": [ "n_epochs = 10001\n", "batch_size = 50\n", "n_batches = int(np.ceil(m / batch_size))\n", "\n", "checkpoint_path = \"/tmp/my_logreg_model.ckpt\"\n", "checkpoint_epoch_path = checkpoint_path + \".epoch\"\n", "final_model_path = \"./my_logreg_model\"\n", "\n", "with tf.Session() as sess:\n", " if os.path.isfile(checkpoint_epoch_path):\n", " # if the checkpoint file exists, restore the model and load the epoch number\n", " with open(checkpoint_epoch_path, \"rb\") as f:\n", " start_epoch = int(f.read())\n", " print(\"Training was interrupted. Continuing at epoch\", start_epoch)\n", " saver.restore(sess, checkpoint_path)\n", " else:\n", " start_epoch = 0\n", " sess.run(init)\n", "\n", " for epoch in range(start_epoch, n_epochs):\n", " for batch_index in range(n_batches):\n", " X_batch, y_batch = random_batch(X_train_enhanced, y_train, batch_size)\n", " sess.run(training_op, feed_dict={X: X_batch, y: y_batch})\n", " loss_val, summary_str = sess.run([loss, loss_summary], feed_dict={X: X_test_enhanced, y: y_test})\n", " file_writer.add_summary(summary_str, epoch)\n", " if epoch % 500 == 0:\n", " print(\"Epoch:\", epoch, \"\\tLoss:\", loss_val)\n", " saver.save(sess, checkpoint_path)\n", " with open(checkpoint_epoch_path, \"wb\") as f:\n", " f.write(b\"%d\" % (epoch + 1))\n", "\n", " saver.save(sess, final_model_path)\n", " y_proba_val = y_proba.eval(feed_dict={X: X_test_enhanced, y: y_test})\n", " os.remove(checkpoint_epoch_path)\n", "\n", "file_writer.close()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Once again, we can make predictions by just classifying as positive all the instances whose estimated probability is greater or equal to 0.5:" ] }, { "cell_type": "code", "execution_count": 139, "metadata": { "collapsed": true }, "outputs": [], "source": [ "y_pred = (y_proba_val >= 0.5)" ] }, { "cell_type": "code", "execution_count": 140, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.97979797979797978" ] }, "execution_count": 140, "metadata": {}, "output_type": "execute_result" } ], "source": [ "precision_score(y_test, y_pred)" ] }, { "cell_type": "code", "execution_count": 141, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.97979797979797978" ] }, "execution_count": 141, "metadata": {}, "output_type": "execute_result" } ], "source": [ "recall_score(y_test, y_pred)" ] }, { "cell_type": "code", "execution_count": 142, "metadata": {}, "outputs": [ { "data": { "image/png": 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yeiSL5vpNOhscnoN8OIO7zDy9SZfZZ7TKQWorJYB0r5E0dfbUesUA0BTKzy9n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\nOdVQChswFEMrkrLvkgFRDkL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"text/plain": [ "<matplotlib.figure.Figure at 0x7f96d29e95f8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "y_pred_idx = y_pred.reshape(-1) # a 1D array rather than a column vector\n", "plt.plot(X_test[y_pred_idx, 1], X_test[y_pred_idx, 2], 'go', label=\"Positive\")\n", "plt.plot(X_test[~y_pred_idx, 1], X_test[~y_pred_idx, 2], 'r^', label=\"Negative\")\n", "plt.legend()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now that's much, much better! Apparently the new features really helped a lot." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's open tensorboard, find the latest run and look at the learning curve:" ] }, { "cell_type": "code", "execution_count": 143, "metadata": {}, "outputs": [], "source": [ "%tensorboard --logdir {root_logdir}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now you can play around with the hyperparameters (e.g. the `batch_size` or the `learning_rate`) and run training again and again, comparing the learning curves. You can even automate this process by implementing grid search or randomized search. Below is a simple implementation of a randomized search on both the batch size and the learning rate. For the sake of simplicity, the checkpoint mechanism was removed." ] }, { "cell_type": "code", "execution_count": 144, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Iteration 0\n", " logdir: tf_logs/logreg-run-20170606195328/\n", " batch size: 19\n", " learning_rate: 0.00443037524522\n", " training: .....................\n", " precision: 0.979797979798\n", " recall: 0.979797979798\n", "Iteration 1\n", " logdir: tf_logs/logreg-run-20170606195605/\n", " batch size: 80\n", " learning_rate: 0.00178264971514\n", " training: .....................\n", " precision: 0.969696969697\n", " recall: 0.969696969697\n", "Iteration 2\n", " logdir: tf_logs/logreg-run-20170606195646/\n", " batch size: 73\n", " learning_rate: 0.00203228544324\n", " training: .....................\n", " precision: 0.969696969697\n", " recall: 0.969696969697\n", "Iteration 3\n", " logdir: tf_logs/logreg-run-20170606195730/\n", " batch size: 6\n", " learning_rate: 0.00449152382514\n", " training: .....................\n", " precision: 0.980198019802\n", " recall: 1.0\n", "Iteration 4\n", " logdir: tf_logs/logreg-run-20170606200523/\n", " batch size: 24\n", " learning_rate: 0.0796323472178\n", " training: .....................\n", " precision: 0.980198019802\n", " recall: 1.0\n", "Iteration 5\n", " logdir: tf_logs/logreg-run-20170606200726/\n", " batch size: 75\n", " learning_rate: 0.000463425058329\n", " training: .....................\n", " precision: 0.912621359223\n", " recall: 0.949494949495\n", "Iteration 6\n", " logdir: tf_logs/logreg-run-20170606200810/\n", " batch size: 86\n", " learning_rate: 0.0477068184194\n", " training: .....................\n", " precision: 0.98\n", " recall: 0.989898989899\n", "Iteration 7\n", " logdir: tf_logs/logreg-run-20170606200851/\n", " batch size: 87\n", " learning_rate: 0.000169404470952\n", " training: .....................\n", " precision: 0.888888888889\n", " recall: 0.808080808081\n", "Iteration 8\n", " logdir: tf_logs/logreg-run-20170606200932/\n", " batch size: 61\n", " learning_rate: 0.0417146119941\n", " training: .....................\n", " precision: 0.980198019802\n", " recall: 1.0\n", "Iteration 9\n", " logdir: tf_logs/logreg-run-20170606201026/\n", " batch size: 92\n", " learning_rate: 0.000107429229684\n", " training: .....................\n", " precision: 0.882352941176\n", " recall: 0.757575757576\n" ] } ], "source": [ "from scipy.stats import reciprocal\n", "\n", "n_search_iterations = 10\n", "\n", "for search_iteration in range(n_search_iterations):\n", " batch_size = np.random.randint(1, 100)\n", " learning_rate = reciprocal(0.0001, 0.1).rvs(random_state=search_iteration)\n", "\n", " n_inputs = 2 + 4\n", " logdir = log_dir(\"logreg\")\n", " \n", " print(\"Iteration\", search_iteration)\n", " print(\" logdir:\", logdir)\n", " print(\" batch size:\", batch_size)\n", " print(\" learning_rate:\", learning_rate)\n", " print(\" training: \", end=\"\")\n", "\n", " reset_graph()\n", "\n", " X = tf.placeholder(tf.float32, shape=(None, n_inputs + 1), name=\"X\")\n", " y = tf.placeholder(tf.float32, shape=(None, 1), name=\"y\")\n", "\n", " y_proba, loss, training_op, loss_summary, init, saver = logistic_regression(\n", " X, y, learning_rate=learning_rate)\n", "\n", " file_writer = tf.summary.FileWriter(logdir, tf.get_default_graph())\n", "\n", " n_epochs = 10001\n", " n_batches = int(np.ceil(m / batch_size))\n", "\n", " final_model_path = \"./my_logreg_model_%d\" % search_iteration\n", "\n", " with tf.Session() as sess:\n", " sess.run(init)\n", "\n", " for epoch in range(n_epochs):\n", " for batch_index in range(n_batches):\n", " X_batch, y_batch = random_batch(X_train_enhanced, y_train, batch_size)\n", " sess.run(training_op, feed_dict={X: X_batch, y: y_batch})\n", " loss_val, summary_str = sess.run([loss, loss_summary], feed_dict={X: X_test_enhanced, y: y_test})\n", " file_writer.add_summary(summary_str, epoch)\n", " if epoch % 500 == 0:\n", " print(\".\", end=\"\")\n", "\n", " saver.save(sess, final_model_path)\n", "\n", " print()\n", " y_proba_val = y_proba.eval(feed_dict={X: X_test_enhanced, y: y_test})\n", " y_pred = (y_proba_val >= 0.5)\n", " \n", " print(\" precision:\", precision_score(y_test, y_pred))\n", " print(\" recall:\", recall_score(y_test, y_pred))\n", "\n", "file_writer.close()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The `reciprocal()` function from SciPy's `stats` module returns a random distribution that is commonly used when you have no idea of the optimal scale of a hyperparameter. See the exercise solutions for chapter 2 for more details. " ] }, { "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": "3.7.10" }, "nav_menu": { "height": "603px", "width": "616px" }, "toc": { "navigate_menu": true, "number_sections": true, "sideBar": true, "threshold": 6, "toc_cell": false, "toc_section_display": "block", "toc_window_display": true } }, "nbformat": 4, "nbformat_minor": 1 }

apache-2.0

luwei0917/awsemmd_script

notebook/Optimization/hybrid_simulation_analysis_aug05.ipynb

1

6304589

mit

cliburn/sta-663-2017

worksheet/Mock Midterms 2.ipynb

1

5242

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Midterm exams\n", "\n", "This is a \"closed book\" examination - in particular, you are not to use any resources outside of this notebook (except possibly pen and paper). You may consult help from within the notebook using ? but not any online references. You should turn wireless off or set your laptop in \"Airplane\" mode prior to taking the exam. \n", "\n", "You have 2 hours to complete the exam." ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Q1 (10 points)**. \n", "\n", "1. Read the `data/iris.csv` data set into a Pandas DataFrame. Dispaly the first 4 lines of the DataFrame. (2 points)\n", "2. Create a new DataFrame showing the mean `SepalLength`, `SepalWidth`, `PetalLength` and `PetalWidth` for the 3 different types of irises. (4 points)\n", "3. Make a scatter plot of `SepalLength` against `PetalLength` where each species is assigned a different color. (4 points)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Q2 (10 points)**\n", "\n", "Write a function `peek(df, n)` to display a random selection of $n$ rows of any dataframe (without repetition). Use it to show 5 random rows from the iris data set. The function should take as inputs a dataframe and an integer. Do not use the `pandas sample` method." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Q3 (10 points)** \n", "\n", "Write a function that when given $m$ vectors of length $k$ and another $n$ vectors of length $k$, returns an $m \\times n$ matrix of the cosine distance between each pair of vectors. Take the cosine distance to be \n", "$$\n", "\\frac{A \\cdot B}{\\|A\\} \\|B\\|}\n", "$$\n", "for any two vectors $A$ and $B$. \n", "\n", "Do not use the `scipy.spatial.distance.cosine` function or any functions from `np.linalg` or `scipy.llnalg`." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Q4 (10 points)**\n", "\n", "Consider the following matrix $A$ with dimensions (4,6), to be interpreted as 4 rows of the measurements of 6 features.\n", "```python\n", "np.array([[5, 5, 2, 6, 2, 0],\n", " [8, 6, 7, 8, 9, 7],\n", " [9, 5, 0, 4, 6, 8],\n", " [8, 7, 9, 3, 6, 1]])\n", "```\n", "\n", "1. Add 1 to the first row, 2 to the second row, 3 to the third row and 4 to the fourth row using a vector `v = np.array([1,2,3,4])` and broadcasting. (2 points)\n", "2. Normalize A so that its row means are all 0 and call it A1. (2 points)\n", "3. What are the singular values of A1? (2 points)\n", "4. What are the eigenvalues of the covariance matrix of A1? (2 points)\n", "5. Find the least squares solution vector $x$ if $Ax = y$ where `y = np.array([1,2,3,4]).T` (2 points)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Q10 (10 points)**\n", "\n", "We want to calculate the first 100 Catalan numbers. The $n^\\text{th}$ Catalan number is given by \n", "$$\n", "C_n = \\prod_{k=2}^n \\frac{n+k}{k}\n", "$$\n", "for $n \\ge 0$. \n", "\n", "1. Use `numpy` to find the first 100 Catalan number - the function should take a single argument $n$ and return an array ```[Catalan(1), Catalan(2), ..., Catalan(n)]``` (4 points).\n", "2. Use `numba` to find the first 100 Catalan numbers (starting from 1) fast using a JIT compilation 4 points)\n", "3. Use `cython` to find the first 100 Catalan numbers (starting from 1) fast both AOT compilation (4 points)\n", "\n", "In each case, code readability and efficiency is important." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" }, "latex_envs": { "bibliofile": "biblio.bib", "cite_by": "apalike", "current_citInitial": 1, "eqLabelWithNumbers": true, "eqNumInitial": 0 } }, "nbformat": 4, "nbformat_minor": 2 }

mit

cgranade/qutip-notebooks

examples/example-control-grape-iswap.ipynb

2

314040

{ "metadata": { "name": "", "signature": "sha256:0fd4068f5084b4ef2e2291329a317c15ccdd1edd60f3b632699305aa832483b0" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "GRAPE calculation of control fields for iSWAP implementation" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Robert Johansson ([email protected])" ] }, { "cell_type": "code", "collapsed": false, "input": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "import time\n", "import numpy as np" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 1 }, { "cell_type": "code", "collapsed": false, "input": [ "from qutip import *\n", "from qutip.control import *" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 2 }, { "cell_type": "code", "collapsed": false, "input": [ "T = 1\n", "times = np.linspace(0, T, 100)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 3 }, { "cell_type": "code", "collapsed": false, "input": [ "U = iswap()\n", "R = 50\n", "H_ops = [#tensor(sigmax(), identity(2)),\n", " #tensor(sigmay(), identity(2)),\n", " #tensor(sigmaz(), identity(2)),\n", " #tensor(identity(2), sigmax()),\n", " #tensor(identity(2), sigmay()),\n", " #tensor(identity(2), sigmaz()),\n", " tensor(sigmax(), sigmax()),\n", " tensor(sigmay(), sigmay()),\n", " tensor(sigmaz(), sigmaz())]\n", "\n", "H_labels = [#r'$u_{1x}$',\n", " #r'$u_{1y}$',\n", " #r'$u_{1z}$',\n", " #r'$u_{2x}$',\n", " #r'$u_{2y}$',\n", " #r'$u_{2z}$',\n", " r'$u_{xx}$',\n", " r'$u_{yy}$',\n", " r'$u_{zz}$',\n", " ]" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 4 }, { "cell_type": "code", "collapsed": false, "input": [ "H0 = 0 * np.pi * (tensor(sigmaz(), identity(2)) + tensor(identity(2), sigmaz()))" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 5 }, { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "GRAPE" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from qutip.control.grape import plot_grape_control_fields, _overlap, grape_unitary_adaptive, cy_grape_unitary" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 6 }, { "cell_type": "code", "collapsed": false, "input": [ "from scipy.interpolate import interp1d\n", "from qutip.ui.progressbar import TextProgressBar" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 7 }, { "cell_type": "code", "collapsed": false, "input": [ "u0 = np.array([np.random.rand(len(times)) * (2 * np.pi / T) * 0.01 for _ in range(len(H_ops))])\n", "\n", "u0 = [np.convolve(np.ones(10)/10, u0[idx, :], mode='same') for idx in range(len(H_ops))]" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 8 }, { "cell_type": "code", "collapsed": false, "input": [ "result = cy_grape_unitary(U, H0, H_ops, R, times, u_start=u0, eps=2*np.pi/T,\n", " progress_bar=TextProgressBar())" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "10.0%. Run time: 3.90s. Est. time left: 00:00:00:35\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "20.0%. Run time: 7.49s. Est. time left: 00:00:00:29\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "30.0%. Run time: 11.07s. Est. time left: 00:00:00:25\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "40.0%. Run time: 14.66s. Est. time left: 00:00:00:21\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "50.0%. Run time: 18.23s. Est. time left: 00:00:00:18\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "60.0%. Run time: 21.79s. Est. time left: 00:00:00:14\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "70.0%. Run time: 25.35s. Est. time left: 00:00:00:10\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "80.0%. Run time: 28.97s. Est. time left: 00:00:00:07\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "90.0%. Run time: 32.54s. Est. time left: 00:00:00:03\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Total run time: 35.46s\n" ] } ], "prompt_number": 9 }, { "cell_type": "code", "collapsed": false, "input": [ "#result = grape_unitary(U, H0, H_ops, R, times, u_start=u0, eps=2*np.pi/T,\n", "# progress_bar=TextProgressBar())" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 10 }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Plot control fields for iSWAP gate in the presense of single-qubit tunnelling" ] }, { "cell_type": "code", "collapsed": false, "input": [ "plot_grape_control_fields(times, result.u / (2 * np.pi), H_labels, uniform_axes=True);" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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NjscvBn4W+GJEPNP3XW13VxWXxe4DzgK2Abex747HK4GPFh2P9zttMX2ljse5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\ngP7K+nrg3QCSVgJPR0SFd+T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"text": [ "<matplotlib.figure.Figure at 0x7f9f9eb69518>" ] } ], "prompt_number": 11 }, { "cell_type": "code", "collapsed": false, "input": [ "# compare to the analytical results\n", "np.mean(result.u[-1,0,:]), np.mean(result.u[-1,1,:]), np.pi/(4 * T)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 12, "text": [ "(-0.74176269364089276, -0.74159807970319103, 0.7853981633974483)" ] } ], "prompt_number": 12 }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Fidelity" ] }, { "cell_type": "code", "collapsed": false, "input": [ "U" ], "language": "python", "metadata": {}, "outputs": [ { "latex": [ "Quantum object: dims = [[2, 2], [2, 2]], shape = [4, 4], type = oper, isherm = False\\begin{equation*}\\left(\\begin{array}{*{11}c}1.0 & 0.0 & 0.0 & 0.0\\\\0.0 & 0.0 & 1.0j & 0.0\\\\0.0 & 1.0j & 0.0 & 0.0\\\\0.0 & 0.0 & 0.0 & 1.0\\\\\\end{array}\\right)\\end{equation*}" ], "metadata": {}, "output_type": "pyout", "prompt_number": 13, "text": [ "Quantum object: dims = [[2, 2], [2, 2]], shape = [4, 4], type = oper, isherm = False\n", "Qobj data =\n", "[[ 1.+0.j 0.+0.j 0.+0.j 0.+0.j]\n", " [ 0.+0.j 0.+0.j 0.+1.j 0.+0.j]\n", " [ 0.+0.j 0.+1.j 0.+0.j 0.+0.j]\n", " [ 0.+0.j 0.+0.j 0.+0.j 1.+0.j]]" ] } ], "prompt_number": 13 }, { "cell_type": "code", "collapsed": false, "input": [ "result.U_f.tidyup(1e-2)" ], "language": "python", "metadata": {}, "outputs": [ { "latex": [ "Quantum object: dims = [[2, 2], [2, 2]], shape = [4, 4], type = oper, isherm = False\\begin{equation*}\\left(\\begin{array}{*{11}c}1.000 & 0.0 & 0.0 & 0.0\\\\0.0 & 0.093 & 0.996j & 0.0\\\\0.0 & 0.996j & 0.093 & 0.0\\\\0.0 & 0.0 & 0.0 & 1.000\\\\\\end{array}\\right)\\end{equation*}" ], "metadata": {}, "output_type": "pyout", "prompt_number": 14, "text": [ "Quantum object: dims = [[2, 2], [2, 2]], shape = [4, 4], type = oper, isherm = False\n", "Qobj data =\n", "[[ 0.99999759+0.j 0.00000000+0.j 0.00000000+0.j\n", " 0.00000000+0.j ]\n", " [ 0.00000000+0.j 0.09332763+0.j 0.00000000+0.99563304j\n", " 0.00000000+0.j ]\n", " [ 0.00000000+0.j 0.00000000+0.99563304j 0.09332763+0.j\n", " 0.00000000+0.j ]\n", " [ 0.00000000+0.j 0.00000000+0.j 0.00000000+0.j\n", " 0.99999759+0.j ]]" ] } ], "prompt_number": 14 }, { "cell_type": "code", "collapsed": false, "input": [ "_overlap(U, result.U_f).real" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 15, "text": [ "0.9978153151159737" ] } ], "prompt_number": 15 }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Test numerical integration of GRAPE pulse" ] }, { "cell_type": "code", "collapsed": false, "input": [ "c_ops = []" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 16 }, { "cell_type": "code", "collapsed": false, "input": [ "U_f_numerical = propagator(result.H_t, times[-1], c_ops, args={})" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 17 }, { "cell_type": "code", "collapsed": false, "input": [ "U_f_numerical" ], "language": "python", "metadata": {}, "outputs": [ { "latex": [ "Quantum object: dims = [[2, 2], [2, 2]], shape = [4, 4], type = oper, isherm = False\\begin{equation*}\\left(\\begin{array}{*{11}c}(1.000+0.014j) & 0.0 & 0.0 & (-1.462\\times10^{-04}+0.010j)\\\\0.0 & (0.059-8.591\\times10^{-04}j) & (0.014+0.998j) & 0.0\\\\0.0 & (0.014+0.998j) & (0.059-8.591\\times10^{-04}j) & 0.0\\\\(-1.462\\times10^{-04}+0.010j) & 0.0 & 0.0 & (1.000+0.014j)\\\\\\end{array}\\right)\\end{equation*}" ], "metadata": {}, "output_type": "pyout", "prompt_number": 18, "text": [ "Quantum object: dims = [[2, 2], [2, 2]], shape = [4, 4], type = oper, isherm = False\n", "Qobj data =\n", "[[ 9.99844252e-01 +1.44723227e-02j 0.00000000e+00 +0.00000000e+00j\n", " 0.00000000e+00 +0.00000000e+00j -1.46188200e-04 +1.00996554e-02j]\n", " [ 0.00000000e+00 +0.00000000e+00j 5.93512842e-02 -8.59120229e-04j\n", " 1.44460622e-02 +9.98132255e-01j 0.00000000e+00 +0.00000000e+00j]\n", " [ 0.00000000e+00 +0.00000000e+00j 1.44460622e-02 +9.98132255e-01j\n", " 5.93512842e-02 -8.59120229e-04j 0.00000000e+00 +0.00000000e+00j]\n", " [ -1.46188200e-04 +1.00996554e-02j 0.00000000e+00 +0.00000000e+00j\n", " 0.00000000e+00 +0.00000000e+00j 9.99844252e-01 +1.44723227e-02j]]" ] } ], "prompt_number": 18 }, { "cell_type": "code", "collapsed": false, "input": [ "_overlap(U, U_f_numerical).real" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 19, "text": [ "0.9989882532493792" ] } ], "prompt_number": 19 }, { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "Process tomography" ] }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Ideal iSWAP gate" ] }, { "cell_type": "code", "collapsed": false, "input": [ "op_basis = [[qeye(2), sigmax(), sigmay(), sigmaz()]] * 2\n", "op_label = [[\"i\", \"x\", \"y\", \"z\"]] * 2" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 20 }, { "cell_type": "code", "collapsed": false, "input": [ "fig = plt.figure(figsize=(8,6))\n", "\n", "U_ideal = spre(U) * spost(U.dag())\n", "\n", "chi = qpt(U_ideal, op_basis)\n", "\n", "fig = qpt_plot_combined(chi, op_label, fig=fig, threshold=0.001)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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tseiYxY0+rLtD+VHNZhNLS0swDMNpoBjlWGqlF4mYvFrhM/PLLPq2RK2mivJZ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RMUw4ahePNNmDYNGjSA8+fh118RLl/+g8wA6PX4+fnJTTYNBgPZ2dnk5OSg0+lkF+SiUH5Co8PL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\nodYTAY0gCIIgCLWeCGgEQRAEQaj1/j8nmXfl2uNyHwAAAABJRU5ErkJggg==\n", "text": [ "<matplotlib.figure.Figure at 0x7f9faa2c0978>" ] } ], "prompt_number": 21 }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "iSWAP gate calculated using GRAPE" ] }, { "cell_type": "code", "collapsed": false, "input": [ "fig = plt.figure(figsize=(8,6))\n", "\n", "U_ideal = to_super(result.U_f)\n", "\n", "chi = qpt(U_ideal, op_basis)\n", "\n", "fig = qpt_plot_combined(chi, op_label, fig=fig, threshold=0.001)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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bSFrBEidPxZ0royhKatExjRt9VHeH8qPa7TYWFxeh67rdQDHOsZyVXiRiimqF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ZXN+eLzr4iIiCAjI4Px48fj5eXFrl27aN++PaGhobRu3Zp58+Zx48YNsrOzZW+M0WhEq9WSmJjI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\nUdVyXkKdIZacCoiARqj1NBoNc+fOJSMjg++++67U+0+dOpVTp07RrFkzpRe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"text": [ "<matplotlib.figure.Figure at 0x7f9f964b8be0>" ] } ], "prompt_number": 22 }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Versions" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from qutip.ipynbtools import version_table\n", "\n", "version_table()" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "<table><tr><th>Software</th><th>Version</th></tr><tr><td>Cython</td><td>0.21.2</td></tr><tr><td>Numpy</td><td>1.9.1</td></tr><tr><td>OS</td><td>posix [linux]</td></tr><tr><td>SciPy</td><td>0.14.1</td></tr><tr><td>matplotlib</td><td>1.4.2</td></tr><tr><td>QuTiP</td><td>3.1.0</td></tr><tr><td>Python</td><td>3.4.0 (default, Apr 11 2014, 13:05:11) \n", "[GCC 4.8.2]</td></tr><tr><td>IPython</td><td>2.3.1</td></tr><tr><td colspan='2'>Tue Jan 13 13:44:55 2015 JST</td></tr></table>" ], "metadata": {}, "output_type": "pyout", "prompt_number": 23, "text": [ "<IPython.core.display.HTML at 0x7f9fb8b18e10>" ] } ], "prompt_number": 23 } ], "metadata": {} } ] }

lgpl-3.0

r-uhlig/ProjMRI

k-space.ipynb

1

1203848

gpl-3.0

darkomen/TFG

medidas/13082015/.ipynb_checkpoints/Análisis de datos Ensayo 2-Copy1-checkpoint.ipynb

1

471086

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Análisis de los datos obtenidos " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Uso de ipython para el análsis y muestra de los datos obtenidos durante la producción.Se implementa un regulador experto. Los datos analizados son del día 13 de Agosto del 2015\n", "\n", "Los datos del experimento:\n", "* Hora de inicio: 12:06\n", "* Hora final : 12:26\n", "* Filamento extruido: 314Ccm\n", "* $T: 150ºC$\n", "* $V_{min} tractora: 1.5 mm/s$\n", "* $V_{max} tractora: 5.3 mm/s$\n", "* Los incrementos de velocidades en las reglas del sistema experto son distintas:\n", " * En los caso 3 y 5 se mantiene un incremento de +2.\n", " * En los casos 4 y 6 se reduce el incremento a -1.\n", " \n", "Este experimento dura 20min por que a simple vista se ve que no aporta ninguna mejora, de hecho, añade más inestabilidad al sitema.\n", "Se opta por añadir más reglas al sistema, e intentar hacer que la velocidad de tracción no llegue a los límites." ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#Importamos las librerías utilizadas\n", "import numpy as np\n", "import pandas as pd\n", "import seaborn as sns" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Numpy v1.9.2\n", "Pandas v0.16.2\n", "Seaborn v0.6.0\n" ] } ], "source": [ "#Mostramos las versiones usadas de cada librerías\n", "print (\"Numpy v{}\".format(np.__version__))\n", "print (\"Pandas v{}\".format(pd.__version__))\n", "print (\"Seaborn v{}\".format(sns.__version__))" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#Abrimos el fichero csv con los datos de la muestra\n", "datos = pd.read_csv('ensayo2.CSV')" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Populating the interactive namespace from numpy and matplotlib\n" ] } ], "source": [ "%pylab inline" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#Almacenamos en una lista las columnas del fichero con las que vamos a trabajar\n", "columns = ['Diametro X','Diametro Y', 'RPM TRAC']" ] }, { "cell_type": "code", "execution_count": 24, "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>Diametro X</th>\n", " <th>Diametro Y</th>\n", " <th>RPM TRAC</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>count</th>\n", " <td>750.000000</td>\n", " <td>750.000000</td>\n", " <td>750.000000</td>\n", " </tr>\n", " <tr>\n", " <th>mean</th>\n", " <td>1.436204</td>\n", " <td>1.381896</td>\n", " <td>2.500000</td>\n", " </tr>\n", " <tr>\n", " <th>std</th>\n", " <td>0.363320</td>\n", " <td>0.373249</td>\n", " <td>1.387489</td>\n", " </tr>\n", " <tr>\n", " <th>min</th>\n", " <td>0.014000</td>\n", " <td>0.000342</td>\n", " <td>1.700000</td>\n", " </tr>\n", " <tr>\n", " <th>25%</th>\n", " <td>1.172458</td>\n", " <td>1.138152</td>\n", " <td>1.700000</td>\n", " </tr>\n", " <tr>\n", " <th>50%</th>\n", " <td>1.344506</td>\n", " <td>1.287561</td>\n", " <td>1.700000</td>\n", " </tr>\n", " <tr>\n", " <th>75%</th>\n", " <td>1.723012</td>\n", " <td>1.617986</td>\n", " <td>3.300000</td>\n", " </tr>\n", " <tr>\n", " <th>max</th>\n", " <td>2.319446</td>\n", " <td>2.459850</td>\n", " <td>5.300000</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Diametro X Diametro Y RPM TRAC\n", "count 750.000000 750.000000 750.000000\n", "mean 1.436204 1.381896 2.500000\n", "std 0.363320 0.373249 1.387489\n", "min 0.014000 0.000342 1.700000\n", "25% 1.172458 1.138152 1.700000\n", "50% 1.344506 1.287561 1.700000\n", "75% 1.723012 1.617986 3.300000\n", "max 2.319446 2.459850 5.300000" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Mostramos un resumen de los datos obtenidoss\n", "datos[columns].describe()\n", "#datos.describe().loc['mean',['Diametro X [mm]', 'Diametro Y [mm]']]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Representamos ambos diámetro y la velocidad de la tractora en la misma gráfica" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.collections.LineCollection at 0xfb27590>" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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QX3YbxZE+/8iN8hxPl53zI3Jt8KlE9Zono17Pp72+YuDQ1kfwuY62H2my5mpy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xYlaHUinL1pN7bU5rGpMoeIRYUMyDtRoYq/uT2fG2S7zSqf6jgmIVDzRMUGK59CaTKVzyAOxloZ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\n4y+e+jzef/t7RnqNw0YjvIZbB128afsmfOIzp/HFx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1cwkvx4KFjIiArXYUCJzxjB7dZK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\nu44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rx2Y+I9JPEWljxY2dwmzGwR2VLPCZz6TZRPTCQRV3iJnP9GX7yIOH8G//wQMx2cF0\nRHYLhF2wLOn1fES2W8r3v7f8vdqK262bKD5LRhEUVFz8XZ/PfMqcf3m+65jFZ9AJ5+wgL9BaHTuV\n9QRYaDHRbBCqotsDnGD9+D5Fnk7jzpnbcLZ2Hi/XLsW+T5Y3xQtePvjOY1pGLT45M91KxK1MmeMV\nn+nM5/DxNguFeVFsjPp6OHSV/R7hdK1ypoTtCfxCvZWGkKGp4jPkmLQoAz4Dk5S/R8Evg1mGQ7kB\nrrNp6NoeTEkjk4M3NIsG+/mtRI4lzxtOld26w0StvDLcbpNulBwyNosb7nH3dxmqRQPEYO/rtFnF\nj9/2w3j/nT8GSilaHQc6mItuWoHXctoo6gXx2UaLhNlqiuy2HcpuAUBR5bNO5XyY3yg7J251pu7Z\nqzWcvVoXjeuNZrjesphPSmkwt7fr18EYko78HDKJ9k6BqxeG/cy470GW7LbjsXWellHM15lDBhWf\nbL2utdl9TRbvxR3qN4Y8E4HsteOKhuwkFZ8qbJvdM/eK2y1vWB5ciDfa8qaGX/7fX4e3PXAQUyUT\nl1ZaeOrb7J70tZdW8eePngU0tiZPzR0ECLAwN7hByU3Zjg9TfAb1VFrWp0HY80j2UvFJKQ1cucIO\njS6RgwJRKZu8+NxoWJgum1AUIh50WfbWXsj5BPpzm9brwQUnYtl9tBKasuQzQmJl0PX+91kUnwnm\nUxSfB7IDawfBMJikKcn4RTuYRPEEM5iEP4TsVobpRBHCi92sNUBIyCvLbNL5xr4V2a2duPSXEjEX\nvOMrk926A5x/02AmLn9RdtD3KTo9t09mHMX+uSIUnUV//Js/+Ar+3795EQDw6Neu4hd+73HcmX8Q\nAPCN+lMAIHKmTkmkIKL4DJ7pFWH9PRpTyHNYk1mfZaMIjahDy25dn4KQdPfYUbq8+0vhrPO+4uJQ\nvz8JPWA++cwnl8dwMyB/yOiX7N+h9D2PkxZlIIrPjHXZdtos79RXRSZyEoNmL9PAIioyzICCM6Vk\n5mN/bxQ5LYdeStE7VM6nP/leBcMgaXjGkZewWfxClim7LZogRg8azBi72LWYDD9HAmWHJXfKbzmd\nmIIjWiQI5tOPy25584IzUmnFZymvA0HxKTsnSvqtld3+3bPXAADvf+Q2ABCGeQBj7wF5ASFGOCYk\nLoODn3tugojISyTaOwVefLaHnNOtt20Uc5o0t5yDNxMdS/7+i/Wp2plxU3y9bnblubJAVAU2QvHJ\n147kd4/rur+dMHR2z3RcPyK7nXTmM1B5zKSrPOaqOViOB59S3H9yDpQCz5xegVpoQ1c0zOcZ4zmM\nU/231k8DAE4EJlRZ4E3ftOJTQ7C3qaO9x7u621ieBdt3Yq5cQg6a2HDm8yxHUMZ8up6PWsvCqcCN\nk3cvZTKmvZDzCQAH5ovQNQXnrjdQb1l4+vQq5qdy2DcXP4h/9Q2/iEuNy5gyR2MlQ+lnhPk05czn\njaAATnZlRoWphReP6EUr1sFUvC253cpgqDpKehGbQRFysXEFAHCgvC/z+37tAw/jpcs1HFro7yCG\nzOf4hkN9M5+JTnjodnsLmc9AOsMvfxWjDIUo2OjV0O45oOifcY1CIQSK7sDt5mE7Pp5+cRU/9MY2\nnj93E5QC3/gGxZHjh3CpeR4ktx8/8ba70eo5uPPIdN/P4mZjovhsB8XniLEklYCpbSTiVhSiYCo3\nNRLzmSWpHTZqBQDeeeRtmMvNQFM0PLh4/1C/Pwk9MfPJ53NbgvkMmNotFp++T+F6vlALRKMMdtpN\nUgZezJUymM+W00ZOzaMNks58Dpi9TEPP9mJNvyT4mVLK5QE4fQw8wArfhtXo+3cgVCFkGQ6JUYE9\nbjgkZLcJR2KZlJKPnCxlMJ+Vgg5idqF5cUcx7n5dUIuoQR7T5vguum4Xh8sHxL/xu4NCwiY2V6gY\nqo4psypc94vB85jm7VEuGIASsEbSCI1bF2tEKcXpy5uolgy86ug0KkUjFssQzi3LpJPj+QdsN/hn\nUUuMcOQHuEdvJ7jsdlg38HrLzmQ92c9in7/dVWP7MAd/NqA5yKdpvBHKbht2E9D6c2WB0IRvc8iG\nLBCOPcjuBe4IZ+JOQewxrh+R3U4283ljfbDK4++/9RSeOb2KckHHI689hEefuYrz7TN4AW28ZuGB\nmFN9Vvv+cvMqzmy+jNumT8ay2tOgqQpKeT1VdqvR4A6s7aHiM2k2BLCLNkH/zKeh6pjJTQtpXhS1\nlgVKQ9kB1yjvZeZTUxUc31fBmSs1fOIrF+F6Pt71PUf6LsdVs4x75+8a+ecLt9vYzKc8T/XGRgfT\nZTN1XmT438nnfeKfbayDqbowUjayYWS3aZjOTWG5vQJKKc7XWN7kIL37/FQ+NjsahSg+x2A+Q8lz\nQnabyH3jci/ZDGIY+zHaZyKYTzt0y50yq9i0auElP6P4dHwXvuKCuga0IKbjU09cwsvX2cX6hQub\nuL10HMhfgXHqWZx2Pbz/vh+WShbFnKndxJ+/+Jd47iaL51nIj1Z8ZlmBT5tVnK2dh+O7A7NwPZ+m\nznsCEeZ9iPVXNct46+HvHfh1WdC0aNQKUNSC9WHHZbdbLT4Btg+I4nMXowxk4OHWydnrKFpOB0Wt\nhHUgI+eTRRmMznx6qVEfQHimVHjxKWE+82oOtu9IY6zsIQyH9oJL+zBIYz657LYbaUQub7RRKRqZ\nhb9ueiCqB2LF92neACjprHEoKz6bkvsH30sqRZabSAhTRHAsFRZE8WlyuZkiZz51TUE+T5nLrOS+\nUdDyICC3RHa7Vu+h3rLx4B0LIIRgpmzi6loblFIQQjKlk5M4tweEqpmNhrz47Ho7X3zyxlXP67Gs\n14wzxfV8tLoODs5n76G8kKWugWbHEa+bg5nA5dDRbOQzfh+//3a8FqDJmc+ZxJjNMOCKItm9YNj4\nsZ1E9I5j7hnmM4iUmko/Z47vr8AvrONvLz6K//KCgvfd84N44cWXgDrw9sNvxgsBm/mRlz4mGlsA\nU7f94Invx0fOfBwdp4ubPTYT/MjhNw/991WLBjab8qatGhhl+cpoTd1dLj4Z8xF9SAgh0DWlj/kE\nWNzKixtnYHl27BDmmxN/+CoFA4vTeRxZ6s9X4J2rvXCInzxYxUtXavj8s9dQKeh4w91Lg79pSMgK\nIG6iEz2oe7aLjYYlZa5GRei0mpDdBhcOlaigqgcTacXneLJbAJgyq7jSvIa228G5+kXkVBP7i+O/\nn7fPnMQTN56Odc2HRdrMZxgmzWW36TmfLmUX7JHdbiWXv2lzCufrF1Frs8M8e7YuNHZ5/yOn8Jmv\nXsHj32IzUAfni7i21sZLz+dgvqoKpVTHE6tP4MH9d+OOmVN9PyvKsH/lxtMAgFNTx0cu6EMr8P7N\njx+2das+ML7F92nqvCew85e0pFSLm0Px5oSPrRefRqT4zAdv+24aesgwqCni+R66bheLOSZvTis+\nCSHIqeZIjInr+XBcP7PxxpnPci4PQhrSzNnohbmkxC+jluODEPlzzmF7NhSiTBTDMA5Si8+E67br\n+bhZ74ls4TS0PLaH+Fb80sY/g6pZBqxQZRGFrPnN9xI+56SrSiwLe6Ewj9ObZ9lrCQzA0mS3AJAv\nAB3IDYdURUVBy9+STN2zQZwVV3/NVnK4uNxEs+OgUjQyDYdGUXTsJAxdRaWgxxhcYHeZz6hZWdvp\nZGaF86JtEPPZFk7dBhptu6/4BJjZXFfroKCn70OmrqJaMtAhNeS1vHQUq6QXoSnaSDOf4dhD/+sQ\nCqwRm+DbiegeY+QnP2olGimVpbyilOIjZz4u4qQ2ejVca93A3bN3Yn9pCW2nA03RcKV1ve97LzWv\niu8D2D3rzpnbhv4bK0UD1262pcw8dVVQn8BT91Tx2b/5A/I5JCA+u7cUcZDkwauzgexAUQh+9Z88\nJGVbLM+GStSRDXp2A8ykhRm3vP3BQ5myrFGhqUofw5zTTJiqETuoVzbYZSBLDgAwp1Kr0YLm51MP\nMVPMmcqZz5JWRs2rw0i5hHljzjkC4Rq70VrGSmcNd0yf2tJhe9v0SfzqG39xrO/VU9xukwYUvOMu\nu5Ry+/nRZbeS4jNXBa1TrLaYG3AW88kLn7fccxRvvuMACCH4k0+zjts7X3cY95+aw59/9gyefOEh\nkGIdubuexGcvPdZXfHq+19fx/4UHPjgwc0qGSil9IF6YTfVqA4vPQcynRz0oRNmSwc8oSH7u03m2\nhlvO9jCfHLsZZSCDYLFSmiJ8/+DNszS3WwCZxj8yDIqoAELmM6eaKOV1qew2Jy7MVp9LtB2MIWSt\nq2TDda/CSHG7NVQdmqKJNcfVTLMZjDMAEb1mt+MXdv4ZTOcqgCVnPnnzK5rbzFVT/P+qqhLLmYyO\nBPBZJ0Iyis88RQeATtKcTAtDzw8CrEHGC+timf1fx/Vw+hJ7H7gpIG/Erzd6qBQNdLOYzwlkrzhm\nKrkYgwuE8vlRFQy3AtGYpkHF5zAxK/znAABcPfiefsbSVPKAVsvchwDg6EEDZ4wODhZOSfcTQgim\nzWqM+eRrKm+o0jtmq2tD1xShlrM9h5FEija0A/xOIhrnxAv/UWS3js/czXeqRmi0bXQtdyDB8+LG\nGVxr3cCr5+/BSmdNFJOPHHkLAODU9HH85vf+e1CE+1HNquP/fvI/4VrrBop6Ab/80L+BoepQSfZ5\nk4Qwdew6mCrF17Pl+oBrwFFHex4npPiMz9TpmtI3DwfEzVGixSfvjPENF0h3gLR9e884Bp48UAEB\nM+r5vteMzrBlgTPMyQKoYpRjB/UNPgidYXe/3F7Frzz1G6CgeGjpQfzkq35c+nVJp1UOvhEW1TLq\nSg1ayqocN14ECItPLu0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w+n2vxYHSvpF/jgy8OEjOfSpEQUHPDyUH8YaQ3e4kOxD9HDjz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b0JGwN0AM8Lm9exduI8EpngTDm25OXtW3jm7rPYmmzZuWpHhRQCK0ujqYFDL93dAcYLyOV2\nrey1ivk8XDhxb6V8D5zT2+75m2FgU54rDBJhy5KfvBX2fbqyW55nj5rVsszWfD8GPZ+1stv99Xye\nW9XLP/X5F1EohW9428ONfYADqT2CSTHBuJhgKA/iGXSbS2WP57Prz+Hs4hkMj2BG9YVyHZoSrA+z\nZYmQ48y9iE8FAGmafnuapt9X9nn+IIDfBPD7AH4hy7Jrc1hHcghcuXAKCsBv//GXAABXL4R3LGzP\n52QX6+NNnjxJtAzL/sbxxAmtQik3t6+80bJbOMFihUlHQyIGif7vynMnwMaTAoOB/oowPUrm4sn8\nvjnZsj2QZxb0+JGXtm/ht575GADgzRffMP+V34PVUnw2icuX7mxDjUdQorCiYRob400M5dCKjf2y\nfHKIpcVBMNMR0EJ/kEg8sHQRJwaLeOL2F4LlO/kOEpFY8UGOjstrS8GhbwKIAE8cKt0jbuc47tHX\nt7Q4sEFf51YW8JbXNIfjGLE5LiaYFBN+Hmbg93i+Ye3Lj2YdTupxMC9s1LM3XVgjez5Jv9nXWSzL\nsqcAPF7+/sve878O4NfnsmbkUHnsyio++sln8fQLdzFIJB6+VBWfzvlcH28ceoQ5IW1R5gpBFarx\neVEmFBaBWClHrcx75Y4I63zmnvM5KTAsnzfJ1uue82nu7Jsy3OXREgYiwVN3nsHtnTu4cupBvObs\nqw9l/WexsjTCU8/fxdZObpO9DTfvbEON9bntzu5dnBxOD3LZyXfuWXgCurfz0tmTeOr5u5jkhdvW\nkwInF4eQQuLR1YfxFzcz3Nm9a0OQdvJdup7HhBMLA1y9cMql3XriMzHni7JqYGN3f6PIhBA4t7KI\nazc38bVvfch+LqpUnU+Kz+n41yXvu/quI1mHUTLC2cUzU5xPc2OCN+9JvzlI2S3pEI9dcbHljz6w\njOEg/GiYL9Jn7n4Ju/kuVkezwzkIOa6Y8lpfXBYK1tWUVnw6IeZe2U356cRn6Hya84AJGdrwnE8z\n+N6k0EohcXrxNG7t3IaCwtc89N5jMU7AlFlXE44B4O7mOBCfs9ie7FhX/F65dO4k8kLhxq0t+9yk\nLLsFgFeu6tmFT956yi7fyXeYdHuMeOyy+45cHDkBmAhdSWGcz3sprXzV5VWcX13Ee143fZTYQITO\nJ8tup3P51IMYySHecvErbPr2UXBp6QJu797B3d1gQiGdT0JKKD4JAODM8gLOlz0ovhA1PLKiU9t+\n90u6rffR1YdqryEkBqSsi08oZcvqrDiFLz5VsKxrDMptUnU+R7bstnQ+vVmf40KPDvHduTMLuu/z\n3OIZvPHC6+a70vvk4ll9oVctewV0r68al+NY9hKf+fb9i0+zDl7fpym7BYBXrj4MwI2nAeh8Hjce\nu9oc5i+EgBQSuXE+xxuQQu7rs/Ld3/BX8BPf/3YsjKbPwTViczffhYKi8zmDpeFJfPBdP4rvfO3f\nOtL1eMXKIwCAJ73jGQDWS1d8aUTnk/Qbik9iedWV1eCnz/kTZ7EyWrYzzI46SISQ+8WKS29kpVLO\n05TladHvEXS/d1R8DqaU3Rrns7xT7zuf41w7Pf7F8NlF3ff5VQ+9B4mcfkF9mDxwTq/7tZsbtWVa\nfO7tfBaq0GLwPspuAeDS2TJ06GUnPnXgkN6+D69cRSISfOG2Cx3S4pPO53GhOoPTJxEJcqVTb9fH\nGzg1XNqX6y+EmFpua/92eRyZnmQ6n7NZHCzY6pWj4pWny5tJXiUD4DufFJ+k3/AsRizf9I5HcHZ5\nEV/+aH2YuxA6lfFTN/4UUkg8vHK14S8Qcvwx1yW+86ngAofMz6aAmm5KT+d8BoFDuROfJtl6t3Q7\nAed8jqRLlHz35XdgcbCIxx9469zXeb9cKtNmrzU5n0oBRRlAlY9ryw27ZfLs/TqfS2Wv6c5ubt83\nL1zZ7SgZ4eTwBDbLm3uFKjAuxhgdQVonaebsyiK+/asfw+XzdeHgi8+N8SZOL+w58nzfSCExEAm2\nS/FJ5/P48/DKQ5BC1p1Pik9CAFB8Eo/L55fwbV/5yqnLX7n6MD51409xdfkyx6yQaHHOp+9swiu7\nLXs+G8puuyo/k9J9GZfOp1IqCBwyfW15kdt/Y8SnfzH86OpDx64k//zKIgaJxLWbU8SnaijDrrCd\n6zEai3uMz5iG+WyZt8jL7Zx4rpeEsJ8zN2aFzudx4mve3HzTNZESuSqQFzk2J1u4fOqBVt93IAfW\n+aT4PP4sJCNcPXW5zMhwN5E2xvtLQiak67Dsluyb9OxjEBB47TFIsCTkfpENzqZfdmtmeRaV5f6/\n7RrG4TTOpym/Nc8PpAlV8cWnLrs97u6clAIXz57A8y9t1txsVcCJT+9mQxUzw/F+xaB108vHJthp\n6IlPIaRdv53cvB9v8sVAIhIUpfAE2ne2BnKArZziMyYeXrmCXOVB6u3mZAsDkfDmPek9PIuRfXP5\n1AP4kbf9IM6dOHfUq0LIfWMDh4JRK67stjntdrow6QJJJXDIzEAdDrToTMrEzb2cz+PKA2dP4ks3\nNnBrfRdnlp2A1DcYzM2I6fvYiMH7Lbt1zmco7pMkvJnhxCedz5hIRIKJyl3SbcuBMgM5wKT82zEc\nbwQYlu0IhXfDrlA55DHphSfkKKHzSe6JS0sXGXhAosbM8/S1p/+7c0brYuQ4jA6ZB9U5n058mrLb\n0hltcj7l8XY+AT3qBKiHDhXFPstujfN5n4FDro9YPzbOpx82IyCsM2qdzwEdkhhIZIK8yG0a9KlB\nu2WVvuDk928kmBtO3qCuQikbaEdIn+FRQAjpFY2jVtDQ89mYdttNTPCNEUVV8SmFhIDAxHc+c+N8\nHn/xeeqEFnFbO3nwfDjrdUbZ7UGdz/Jn1fk0QU+AFqi253NC5zMmTODQxmQ+ozR8wWnmfpLjTVNq\neqGKzrZuEHIvUHwSQnpFc+CQl3ZryjBRF5yio6dM63yW82fGlZ5PIYR2dzznc1I6n8MIxKe73gv3\naaEAta+eT91vt3hQ57N8bMXnoOJ8lhequ4URn3Q+YyAROnBoY3c+aaZ0PuPFP+No8dnN7xBC7gUe\nBYSQXmGEQDVQyM75bOj5LMpLiK7etHZlt6X4GYfiEzAX2E58mrErMVwMO+cxfN4vu53lbrff81mW\n3Uo/cKiedjuSFJ8xYG7M2J7PlsVn4HxGcLyR5pFdBYrOtm4Qci9QfBJCeoULHHLPKaA25zMowywv\nIERHR63YsttJs/MJ6HI/P3DIOp/HPO0WqDuPBj9waD9lt/dbBitrPZ/1wCEBd6Fq+o3pksSBSbt1\ncxxb7vkUFJ+xIRsqaNjzSYiGRwEhpFdUXSjze33Op7f80NbuaKiV3ZqeTy8QJ5EJJmpiH+/mYwgI\nDMTxT29s2ucAoPYZOLQzOdicT4N5DzPSxhf3vvNp1pMuSRwkQmKicjvHkWW3BI0jvVh2SwhA8UkI\n6RlyStmtXW6DInwnrNtiwIjPfErgEFCGqnh28aSYYCAHUWyTatqsoVC++Myr/8yydUDn026iqvPp\nBw5BWvFpbnzIjjrtXSMpBeFmGTh00JsUVVh2Gx+uSqbifFJ8EkLxSQjpF9PSbo0obRan3S67lVJA\nCH/UihZio4FzNauBQ+NiHI0LU02bNYTjdvZ2Pk/cZ+DQtLLbcNSKX3bb7ZsdXcOMIjIpxW0LxAHF\nZ3Q46cm0W0KqUHwSQnqFMZvClk6XOCQaAofM5UOXLxsGiZw65xMABiKxfZ6AEZ/Hv98T8MpuK88H\ncz5npd225HwaR7Nxzqdfdotu3+zoGklZem6CotouRaf4jI+maosCLLslBKD4JIT0jGllt+ZCv2nU\niuqB/NTic0bZrUyQe4J8XEzicT4b+q+AsOx2nmm3mOp8+mW3wqpjOp9xkUgtNrfzHQgI+7gt2PMZ\nH43fI0rZm5uE9BkeBYSQXuHSbkNxWQsc6lHPJ6CFkHU+G9Judc9nxfmMIOkW2GPUyn7SbifbkELe\nt+skKytggp2qzmdB5zNKbNltvjMXZ5LOZ3y4hO1K2S2PaUIoPgkh/WK68xkur6bhAl32Pfcuu01E\ngonf85nH0/NpdlxVfCoFKLUP8ZnvYDFZuO+bD258j348mehfkorzyZ7POPHLbuchDoPAIRHJMdd7\nGuZ8MnCIEAAUn4SQnmG++/2wGX/OZ5Pz6V7aXTGgnU/9X7rb1PMpExSqgFIKSqmy7DYO51M2uBBA\nJe12xkCd7cnOgRJMq6NepjmfYNptlJgyW+18tj96KCi7TSg+Y6BxzicYOEQIQPFJCOkZ1vksKs6m\nCRwyZZjwxWf3nahBIpFXnc8kdD4BIFc5cpVDQUUjPg1V5zP3ym7VDOdzJ9/BQjK67/etlv3aOZ+V\ntFtbdkvnMyrMsVGoYi7OJJ3PCGmcJ12w55MQADyLEUJ6RVPZLZTryxNCQEDUlgPd7sFLpAscmljn\nMxy1AgC5KqwrHIsLM81tCNJuZwQOTVR+oHLKav+XnfMZlN1Kq07dzQ5eqMZA4qXbzqMUnYFD8dEU\nOFQoZedIE9JneBYjhPQKEzikCv+iAPBLaoUQ4R1rGwDTXYLAoSmjVgAgLya293MQi/NpRp1UBKby\ny25nOJ/qgBeNruxW/2ya86nDbivOZ6c/cd0hkX55OgOHSHPVAud8EqLhLRhCSK+QlfAXjUu71a+R\nYdmtUQ0dvnAYDKTX86nF5agyagXQLuA416m3sbgwdrc1pt2Wv8+Y86nL5e5/31dDrJrmfEo/cKgH\nZd5dwnc+59HzORS++Izkhk/Pse0bXoiYAgOHCAEoPgkhPUPawKFK2q0vPiEaewC7LAUGUpcaF4Wa\nOmoFAPIix7gY6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"text/plain": [ "<matplotlib.figure.Figure at 0xfb21750>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "datos.ix[:, \"Diametro X\":\"Diametro Y\"].plot(figsize=(16,10),ylim=(0.5,3)).hlines([1.85,1.65],0,3500,colors='r')\n", "#datos['RPM TRAC'].plot(secondary_y='RPM TRAC')" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x1395f730>" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x1397b8d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "datos.ix[:, \"Diametro X\":\"Diametro Y\"].boxplot(return_type='axes')" ] }, { "cell_type": "markdown", "metadata": { "collapsed": false }, "source": [ "Aumentando la velocidad se ha conseguido que disminuya el valor máxima, sin embargo ha disminuido el valor mínimo. Para la siguiente iteracción, se va a volver a las velocidades de 1.5- 3.4 y se van a añadir más reglas con unos incrementos de velocidades menores, para evitar saturar la velocidad de traccción tanto a nivel alto como nivel bajo." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Comparativa de Diametro X frente a Diametro Y para ver el ratio del filamento" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.collections.PathCollection at 0x13c0ae70>" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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0RlpbP4xh9GEYKzB7+m+xe/da4C3MZQs/wtmz+zlw4LMA7N3bEedq6uw8wo4d\nPrzeOjZs6Cccvt5ulzN6xYwGMvcNBmupqnLZ4ZRmzp1e4Bw7d97Ajh3Hk04Sc6ZiAOyH5eHD6XPT\nd3b62LYNoI4tW34dF5GULE9Pso5S4jKFmXSmUi2JmOm+cwkR/RmkUMStECmE6+H3r2Dhwre4dOkq\nWluj1Nd3sHatOVBrGIbd4wazJ93b28+OHT527TpPS0swbgKWM5zSzJnvo7U1HHs43Jpwzk8yOnol\nhjGG2+0GjmKKvHMyUxRw0dt7Ova9j+9//2d2aoc/+qM3GRn5CDBGZWUf9fU1VFW57HNYmKt+GTzx\nRC8ez6rYtrG4Bdy93jqi0fiJVIm5hZzuL+d8gx/84CW+8IVbk75ZWH/19dZchfjVuDLN05NsQFbI\nHInTnwEkTn/myXWQ8HOf64wlUjuFz/cB//f/fo4jR47bGTXLy6O43ebSz6OjS7l0yUNl5Rna2srj\nRNFcOnEZCxe+z6VLtYDbjs1PjAn/9rf384//OI958zw88MAw3/zmeSKRZZhC3wlcBbiACm655T1e\necUL/DHgxed7h1tvPcWBAw1AHfPmjfDQQ+/z9a+vT3oNHnlklz2n4NFHf015eRl///eeWG9+sf2g\nsh4Azu9Ouy0s0b3tNnOiGJymstLL4cPmw3K857407phU13/8jeG6pA+OVKRaNMb528zmN1Es7h2J\n0y8RiuUHOVvk83q+YMHHMIx/Z3i4gXD4KrR+LzZwGQL6MIwobvc8DMPg0iUXMMamTWGamppsV4fp\n3jHFy+Opw+frwuNZBpQTCPQQiYTs/bq6utm+PczIyPWMjAD8Gp/v44TD/05j46/p6KhjbGwJZs+/\nF3iPsrIPMzxs/usaxhhXXrkYGMB8Q7iRnTuvobFxYru7urp5+umFQA3l5VEaG9eyZEk5jz32nj3o\nm+jKsqKYWlt7OHjQfBAkixzaubMnFq1UB5jJ5BLXynWmmE7WQ7f87InnmOz+ZZr5sxAiwwoBEf0Z\nIDEWOh/mur9xNrHcBoHASrZsif/XaGsrZ+PGhXg8blpbwxw92sV3vzuAYfTQ319jH28J2eHD19HZ\neYT+/rPU1FxJfX15zF/ex/DwCB6P2UHr7DzC8PA7wBnmz19Cf/8oFy92EYmM8ZOfLOWrX13Ea6/9\nnPPn/ze//W0zr7zyee677whvvPE0R48uZv78m1izppyFCxdgGNcyMnKWoaFoUveIGVpqDqJu2XKZ\nhoZPceojvB7eAAAVoUlEQVTUaUc+m/IJ18S5CLzTX5/Y8WhqWoffb8W9j/fSrbVygbiUE2CmSYhE\n+uxwWOfvuVBnBc8FRPSnmVSx0ML0kc/YieV3tgTs/vsXE42ep6UFO9Kkt7c/NsDaA1zP7t1uamr2\nU19f4wjjDLJ9exnhcBjo47776ohErubSpV8wOvo5AL75zf/JK6/4gbsBDyMjr7J79x9ihmwuY3S0\nn927a4BPcNdd/4cDBzzAGE89VcvIyFrMNw9zEpYp+gbz5g3i8XiBctrbO+IiU/z+FVRVnQOuiqWD\nNrFi3f3+xRNi+pPl4knV8UgcGHX23AF27jxn25GOyTo2iTOOS21yVb6I6BcZMhicGfleG+v4SOQD\nYAy/fwUtLVYaBWvAdjxJbX//WVpbP0wotACf7016e31cvPgzYD3wFrt3/xqf70N4vTcyOmoK9U9/\n+slYHda4WqJ7dsw+x+HD/4GFC0/xF3/Rx/e+9/FYeZQHHrhMU9OfOlxLjfYiL0NDUSorj9PWNh6W\neOhQfPtWr14Vt3JWIlYPPlX5ZDiPSTy3FdKabN9MiET62LKlBo/nfNHlzJlNZCB3Bvje935EKDTM\n17++fvKdi5BCHMidCswQwl8SjUbZtCnMzp3XxNwQ4wOeVrbN+voa7r23DHAzb14vCxcuIBT6N8bG\nqoCPYg6y/oRFixpZv/4XADz77KcIhY7y0Y/+gl/+sha320U0ehVut4u//MtRzp49z4ED12Lm5KkE\nruW++97n6ac/xOhoP3/zN1VJf1NW6OPQUJSysp/i9f4+8+fPS5qMzbp32Q5ypopvT9wv0zoTB2Iz\nce/Ej0EkF/25+tu0kIHcAqS9vYP/9t9qAaiv70i6ZilIr71Q8XqvYnT0tzFXTZSqKrOXbrkonCJV\nVtbB8PCVjI7W4XK9zdjYWsxFVhYAfYyNBYlE3mb//k/i8XhYt+4lDhxo4J13vgq8iWFEGX+LMCNw\nDh7cRTT6B5hZPUfYt+/fGR1dC9RSX3/RtjMxNPLQISvL5ypCIZdtd6JrpLLSRzAYTpmULZHx4yvj\n5i2k3m/yOtvbOxzZR83/kUzSKVhuOOu7kBmyiMoskusCHsLM0NCwko6OOtraylmwoJ7KyjO0tpqT\nsRLvW0PDSh58sAKP56eUlb1OU9MA0A+cBSKApqxsDR6PuTjK5ctv8uKL6zDFvA/4ABjEFP3lfPe7\nl+jsPEI06swQOYbHU0lZ2THmzfuJ/ZaR7HfU0LCSxsa1cXY7hdEwDJqbT/JnfxaluXmoaFelSjXB\nSkiN9PSnmaamdTz77M8JBsNZ50ARcmeq3qBWr15Fbe3y2MCuuUh4INDD5csnY2GYi+3zPfFEL4bx\nRQwDfvSj72P2qc5x/fUv8pnP3MiaNVUcPdoV88kvY968c8BS/P4XeOedvwT68XhGMQxPbOLUMD7f\nfyAcPoPb/S7z51+Jx7OK0VGD0dHVbNtmUF/fETcw6lxMxZkKwroOgUAPGzb0xwadzWgar7fOMXEs\n+aItzuuZyZhSNmNPTU3r2Ls3O/eOkDvi058B0vkV58KPu9D8plO5KIbT533HHecwjDEMo49Ll2oo\nKzvOSy+tsydYbdsWxIyn78N068wD3gSWAefwehcTiczD7N2Pcd99/4+nn14Yy8R5FTBKWdkvcbmW\n4nZ/wIIFH7NTOoM5szcS6WN01GBk5ENAhEcf7bUnYk3m4453o5gTxY4efZsrrqiiqWld2slRqSZm\nTQfTcf/mKrn49MW9M8vMpdfTrq7uOeOmSmxLZ+cRhobeJxQai03K8jI8PMgPfvAS7e0dsYlHNzB/\n/pvcdVcXZgqF94GPAR8CXnMI/nuUlb3DmjUNsdprgT683nfweKrxeOpibxHm2IEZQbOCaLQfj8fD\nzTf/AjMfz5kcWzdmu4Yef/xqtm6ttB8aweAygsHx5RctIpG+WA79HtuV1N7eMWfudykh7p0ZYCon\nZxUqmS6+MRPkG9Yav1yiee927rwGMPB4nsUwbsZMj/ARdu++Gp/vFB6Ph8rKs7S1fZSmpvuAv+fA\ngfeALwMGfr9BIACmWB/lwQfX2dfov/7XdxgevpKyso8RjXbhdntoayvH74/v5bpcNYyMRGKx/TcA\nBtu3nwX2x9IUV9LSEkwZVdPUtI5HH93P9u1l7Nq1ivr6Hpwhk+Nx/OPXwarH5arBMMbo7f0NYE4y\n27ixGo/nnB2Kmev1TkTCkqeXvERfKXUT8LjW+g8Ttt8GPIw5gvWU1npvPucpZkppcpZz9mY2i2tM\nB1N9bjOrJqxf72f37gHgM5hx9W8xNnYlDzwwTH19je0meeml64HPAv+PW255jy9+8V7uvfcscAVl\nZWtjOfKxe/Fmz/oSW7d+DGCC4IO5MEo0+i5wM9DHvHknCIdX8g//0MWiRdfEZtaWp217Y+Nadu48\nh2EY+P0r6Ogoj+uQHDrkTK52Pi67ZijkYscOH21tQXp7w2zbZuYH6uw8kjY3fiEtkCPkIfpKqa3A\nl4BQwvZ5QBuwBrgIvK6Uatda5/ouKhQBDQ0rk87eLFY2bDhBfX0Nq1evYmDgQiw9Qw9+/63U1Bzh\n8cfh8mUPsIRIpJsnnnBh5sYhlvq3Drf7/zA2topXXvkEX/ziRR591KC//6zt1nHGu1u9/sQQROfq\nXACLFq3jwQdN244eXcTu3bWMjl7F2rUH+dnP/pStW+elzZ9v5v4ZwuPxYI47xDMutvGLpGzceNJO\n2+z3L8bvX8GOHab/f3zJx3Gcbh9JG1JY5NPT7wY+DzydsP1aoFtrPQSglHoNs2tyII9zFS1m2N/p\nOe/egfxnbxYC4xObPkJl5RnWrj1hL6w93vtdS3//S+zeXQtchWFYsfN1bNhgsGtXD5HIScbGPorp\nzx/j6NEu9u//JJFIGfv3+4BzRCJ9hMO1VFYeT5qH3ukyi0ZDuFw1tLQEE1ISu4ExXnttJcPD4/H4\nzuMBu8e+eXOIcNjMoR8I9HDXXYsnvIU63StgifYq2tqCcW8gzuRozvsePxcgPg2zMPvkLPpa6x8q\npT6cpKgSGHJ8vwBU5XqeuYDVWywFilXss8XsrZ8HTvH5zy/iQEKXxhyIXQb8lrKyk9TU1DM0FAWi\nLFwYweOZGEORTKTj6/Tg96+we9FWqGNvbz+7dn2c+fP7WL/+PQIB800i2bKPXm8dPt+bbNzow+9f\nCwRJRuKbAjBhrCCTTJhmXpz0+wgzS14hmzHR36+1/pRj2w2Yfv4/iX1vA17TWv8wTVUFETcqCGAO\nvGv9HkpdzerVqyZss9D6PQDuvvsW7rnn7/jBD6qYP7+Wf/7n5Sh1tV1uHfPpT7/IyMhv8HpvZP78\neezZs8wuX716FceOnWDduj4AOjrq7G1WPRb33z8vbh/Lvh//+N9is7/H8PkGKCur5xvfeI+rr67j\n7rtvAeC5517hr/7KjdfroaNj3A1n1ZPqeky2z1QcI+REQaRheBdYpZS6Aghjuna+NdlBc7knXAKx\nwnOqfbW1y22XDkBn5xuxHriHlpbjjlj4pTQ0rGTfvsM888wqYDmjo27uvbefw4eX0th4k11HV1c3\nLtd1jI5ey+joaebPX0Z19VK799vZ+QYABw5U2jYMDFxgcDDEffd5uHz5TVyuBtzuATsV8eBgyL7u\ntbXLueIK5wv1GJHIaGyBFA/V1W/Ecu0sxeM5TzQ6xuBgiMbGG+nsfIPOzjdS9sSta5HNPc7lmOlg\nrv02E6mursj6mKkQ/SiAUmo9UK613qOU2gz8C6bDcZ/W+tQUnEcQCpg64C0WLrwSr/fqpHuYLp0o\nPt8gra2LaWgwHwqTTUYyff9LABdVVTX27NnE/ZwzW/3+G2JROPGD6onhkMeOnZCB1hJDZuTOAKXQ\n25ir7UuWhTJZCKKVJdJKZZBMPFNlp5xM9NvbO9iyxUc02k9bW3lWcyBSLSNocerUaW6+2XQfHTq0\ndM6J/lz+bUJuM3JF9GeAEvjhzdn2zVTbpmOlqEzSGZw6dZrf/30z1bMz9cJcYS7/NkFSKwtC0ZJJ\nKuFEpipv01yYVyFkjoi+IBQhmeSrzySdwWQrZwlzDxF9QZjDZCLkIvalhYi+IBQhkpRMyBURfUEo\nUkTshVyQfPqCkAHTuVbAXFqHQCh8RPQFYRKmcy1jWSdZmGlE9AVBEEoI8ekLwiRM56CpDMgKM42I\nviBkwHQKsoi9MJOIe0cQBKGEENEXhCwppmibYrJVmBlE9AUhC4op2qaYbBVmDhF9QRCEEkIGcgUh\nC4op2qaYbBVmDhF9QciSYhLQYrJVmBnEvSMIglBCiOgLgiCUECL6giAIJYSIviAIQgkhoi8IglBC\niOgLgiCUECL6gpADkt5AKFZyitNXSrmBfwJWA5eBe7XWAUf5JuArwEBs09e01r/K01ZBKAis9AYA\nBw92Syy8UFTkOjnrDmC+1vrTSqmbgNbYNouPA/dord/I10BBECbHeuvI5AGUzb7C3CNX987vAS8D\naK1/DqxJKP8E8JBS6l+VUt/Iwz5BKDjM9AaLOXhwcUEIZzaJ1drbO7j99uOShK2EyVX0K4Gg47sR\nc/lY7Ae+BqwDPqOU+pMczyMIBUlDw8qCEPxs6OrqZssWH8HgMiKRvtk2R5glcnXvBIEKx3e31nrM\n8f1JrXUQQCn1InAj8GK6CqurK9IVFz3SvuKl0NvW2Hgjr756AoDVq1el3G/JknK83iBVVQZ79tTQ\n2HgjUPjty5e53r5syVX0XwduA55TSv0ucMwqUEpVAW8rpa4FLmL29vdNVuHAwIUcTSl8qqsrpH1F\nSrG0rbZ2OZD+/6i2djkHDoQA801lYOBC0bQvV0qhfdmSq+j/CPisUur12PcvK6XWA+Va6z1KqYeA\nVzEje17RWr+c43kEQZhCis0lJUw9OYm+1joK3J+w+VeO8meAZ/KwSxAEQZgGZHKWIAhCCSGiLwiC\nUEKI6AtCASHpHYTpRkRfEAqEbCZZCUKuiOgLgiCUELIwuiAUCGZ6B8mLI0wvIvqCUECI2AvTjbh3\nBEEQSggRfUEQhBJCRF8QBKGEENEXBEEoIUT0BaGEOXbshMwJKDFE9AWhROnq6mbduj6ZDFZiiOgL\ngiCUEBKnLwglSkPDSjo6TjM4GJL5ASWEiL4glDCrV6+a0ytLCRMR944gCEIJIaIvCCWGpG8ubUT0\nBaGEkPTNgoi+IAhCCSEDuYJQQkj6ZkFEXxAywHKFzIZQTvW5RexLG3HvCMIkzKYfXHzwwlQjoi8I\nglBC5OTeUUq5gX8CVgOXgXu11gFH+W3Aw0AEeEprvXcKbBWEWWE2/eDigxemmlx9+ncA87XWn1ZK\n3QS0xrahlJoHtAFrgIvA60qpdq31makwWBBmg9kUXBF7YSrJ1b3ze8DLAFrrn2MKvMW1QLfWekhr\nPQq8Btycl5WCUKAkTnSa7HuqbTNpo3P7sWMnZswOoTDItadfCQQd3w2llFtrPRYrG3KUXQCqcjyP\nIBQs1iArYLtg0n1vaFg54Zjp7sWnOp+13eUKcuCAJFwrJXIV/SBQ4fhuCT6Ygu8sqwA+mKzC6uqK\nyXYpaqR9xUuqti1ZUo7LFbQ/A2m/V1dXTDhmuq9bqvPNtB2zyVxuWy7kKvqvA7cBzymlfhc45ih7\nF1illLoCCGO6dr41WYVzOdNfdXWFtK9ISde22trlHDgQsj8Dab8PDFyYcMx0X7dU57O2L1lSPiN2\nzBZz+bcJuT3QXNFoNOuDlFIuxqN3AL4MfAIo11rvUUp9DngEc8xgn9b6O5NUGZ3rN0baV5zM5baB\ntK/Yqa6ucGV7TE49fa11FLg/YfOvHOUvAC/kUrcgCIIwfcjkLEEQhBJCRF8QBKGEENEXBEEoIUT0\nBUEQSggRfUEQhBJCRF8QBKGEENEXBEEoIUT0BUEQSggRfUEQhBJCRF8QBKGEENEXBEEoIUT0BUEQ\nSggRfUEQhBJCRF8QBKGEENEXBEEoIUT0BUEQSggRfUEQhBJCRF8QBKGEENEXBEEoIUT0BUEQSggR\nfUEQhBJCRF8QBKGEENEXBEEoIbzZHqCUWgg8A1QDF4C/0FqfTdjnSeD3YuVR4A6tdTB/cwVBEIR8\nyFr0gfuBt7TW/10p9QXg74CNCft8HPhPWuvBfA0UBEEQpo5c3Du/B7wc+/wycIuzUCnlBlYBe5RS\nrymlvpyfiYIgCMJUkbanr5T6ChN78f2A5aq5AFQllC8C/gfQFqv/VaXUUa312/mbKwiCIORDWtHX\nWu8D9jm3KaUOAhWxrxXA+YTDLgL/Q2s9HNu/A/gdQERfEARhlsnFp/868MfAEeBW4KcJ5Qr4vlLq\nRsADfAb47iR1uqqrKybZpbiR9hUvc7ltIO0rNXIR/e8A31NK/StwGfgvAEqpTUC31vp5pdTTwM+A\nUeB7WutfTpXBgiAIQu64otHobNsgCIIgzBAyOUsQBKGEENEXBEEoIUT0BUEQSggRfUEQhBIil+id\nvJmL+XtiM5H/CViNGdV0r9Y64Ci/DXgYiABPaa33zoqhOZJB+zYBXwEGYpu+prX+1YwbmgdKqZuA\nx7XWf5iwvajvnUWa9s2FezcPeAq4GlgAPKa1ft5RXrT3MIO2ZXX/ZkX0mZv5e+4A5mutPx3752qN\nbbNuWhuwBnPy2utKqXat9ZlZszZ7UrYvxseBe7TWb8yKdXmilNoKfAkIJWyfC/cuZftiFPW9i/Hn\nwIDW+h6l1BXAm8DzMCfuYcq2xcjq/s2We2cu5u+x26S1/jnmD8ziWsw5DENa61HgNeDmmTcxL9K1\nD+ATwENKqX9VSn1jpo2bArqBzwOuhO1z4d5B6vZB8d87gOeAR2Kf3Zg9eotiv4fp2gZZ3r9pF32l\n1FeUUm87/zDz9WSSv+fPgf8M/LVS6obptjVPKhlvE4ARe3hZZUOOsmRtLnTStQ9gP/A1YB3wGaXU\nn8ykcfmitf4hE/+ZYG7cu3TtgyK/dwBa67DWOqSUqsAUyb91FBf1PZykbZDl/Zt2904J5e8JMt4m\nALfWeiz2eSihrAL4YKYMmyLStQ/gSWvMRSn1InAj8OIM2jddzIV7Nxlz4t4ppeqBHwLf1lp/31FU\n9PcwTdsgy/s3Wz796cjfM9u8DtwGPKeU+l3gmKPsXWBVzB8Xxny1/NbMm5gXKdunlKoC3lZKXYv5\nwF5HwoO+iJkL9y4lc+XeKaVqgB8Df621fjWhuKjvYbq25XL/Zkv052L+nh8Bn1VKvR77/mWl1Hqg\nXGu9Rym1GfgXTJfaPq31qdkyNEcma99DwKuY9/MVrfXLqSoqcKIAc+zeOUnWvrlw7x7CdNk8opSy\n/N97AN8cuIeTtS2r+ye5dwRBEEoImZwlCIJQQojoC4IglBAi+oIgCCWEiL4gCEIJIaIvCIJQQojo\nC4IglBAi+oIgCCWEiL4gCEIJ8f8BUNn/71NjAUcAAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x1399d110>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.scatter(x=datos['Diametro X'], y=datos['Diametro Y'], marker='.')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#Filtrado de datos\n", "Las muestras tomadas $d_x >= 0.9$ or $d_y >= 0.9$ las asumimos como error del sensor, por ello las filtramos de las muestras tomadas." ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": false }, "outputs": [], "source": [ "datos_filtrados = datos[(datos['Diametro X'] >= 0.9) & (datos['Diametro Y'] >= 0.9)]" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#datos_filtrados.ix[:, \"Diametro X\":\"Diametro Y\"].boxplot(return_type='axes')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##Representación de X/Y" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.collections.PathCollection at 0x13c4d110>" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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TUyMuXT4r8lojCSLRjAMfAZ502DaK8Z03DxSAVAV9ofG4ZZN0y/KXJeKc/zOIM3Gqq4UO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\nrd1GhwYlyvVmIVz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"text/plain": [ "<matplotlib.figure.Figure at 0x13c23170>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.scatter(x=datos_filtrados['Diametro X'], y=datos_filtrados['Diametro Y'], marker='.')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#Analizamos datos del ratio" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "count 717.000000\n", "mean 1.052009\n", "std 0.204253\n", "min 0.616384\n", "25% 0.916724\n", "50% 1.030142\n", "75% 1.140996\n", "max 1.689048\n", "dtype: float64" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ratio = datos_filtrados['Diametro X']/datos_filtrados['Diametro Y']\n", "ratio.describe()" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x13c36130>" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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mTvulzIgP/EFtMEcpTXVUspNGAwa4mOTY1btQBOF9KEDg2AaNWaAxPXbKADcP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\n3Ie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"text/plain": [ "<matplotlib.figure.Figure at 0x13c5b2d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "rolling_mean = pd.rolling_mean(ratio, 50)\n", "rolling_std = pd.rolling_std(ratio, 50)\n", "rolling_mean.plot(figsize=(12,6))\n", "# plt.fill_between(ratio, y1=rolling_mean+rolling_std, y2=rolling_mean-rolling_std, alpha=0.5)\n", "ratio.plot(figsize=(12,6), alpha=0.6, ylim=(0.5,1.5))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#Límites de calidad" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Calculamos el número de veces que traspasamos unos límites de calidad. \n", "$Th^+ = 1.85$ and $Th^- = 1.65$ " ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "collapsed": true }, "outputs": [], "source": [ "Th_u = 1.85\n", "Th_d = 1.65" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "collapsed": false }, "outputs": [], "source": [ "data_violations = datos[(datos['Diametro X'] > Th_u) | (datos['Diametro X'] < Th_d) |\n", " (datos['Diametro Y'] > Th_u) | (datos['Diametro Y'] < Th_d)]" ] }, { "cell_type": "code", "execution_count": 35, "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>Tmp Husillo</th>\n", " <th>Tmp Nozzle</th>\n", " <th>Diametro X</th>\n", " <th>Diametro Y</th>\n", " <th>MARCHA</th>\n", " <th>PARO</th>\n", " <th>RPM EXTR</th>\n", " <th>RPM TRAC</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>count</th>\n", " <td>719.000000</td>\n", " <td>719.000000</td>\n", " <td>719.000000</td>\n", " <td>719.000000</td>\n", " <td>719</td>\n", " <td>719</td>\n", " <td>719</td>\n", " <td>719.000000</td>\n", " </tr>\n", " <tr>\n", " <th>mean</th>\n", " <td>66.321280</td>\n", " <td>151.304172</td>\n", " <td>1.421605</td>\n", " <td>1.364943</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>2.442142</td>\n", " </tr>\n", " <tr>\n", " <th>std</th>\n", " <td>0.200433</td>\n", " <td>0.891735</td>\n", " <td>0.363879</td>\n", " <td>0.371820</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>1.360563</td>\n", " </tr>\n", " <tr>\n", " <th>min</th>\n", " <td>65.900000</td>\n", " <td>149.500000</td>\n", " <td>0.014000</td>\n", " <td>0.000342</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>0</td>\n", " <td>1.700000</td>\n", " </tr>\n", " <tr>\n", " <th>25%</th>\n", " <td>66.100000</td>\n", " <td>150.600000</td>\n", " <td>1.172458</td>\n", " <td>1.138152</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>1.700000</td>\n", " </tr>\n", " <tr>\n", " <th>50%</th>\n", " <td>66.400000</td>\n", " <td>151.200000</td>\n", " <td>1.321566</td>\n", " <td>1.264575</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>1.700000</td>\n", " </tr>\n", " <tr>\n", " <th>75%</th>\n", " <td>66.500000</td>\n", " <td>152.000000</td>\n", " <td>1.631253</td>\n", " <td>1.563394</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>1.700000</td>\n", " </tr>\n", " <tr>\n", " <th>max</th>\n", " <td>66.600000</td>\n", " <td>153.200000</td>\n", " <td>2.319446</td>\n", " <td>2.459850</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>0</td>\n", " <td>5.300000</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Tmp Husillo Tmp Nozzle Diametro X Diametro Y MARCHA PARO RPM EXTR \\\n", "count 719.000000 719.000000 719.000000 719.000000 719 719 719 \n", "mean 66.321280 151.304172 1.421605 1.364943 1 1 0 \n", "std 0.200433 0.891735 0.363879 0.371820 0 0 0 \n", "min 65.900000 149.500000 0.014000 0.000342 True True 0 \n", "25% 66.100000 150.600000 1.172458 1.138152 1 1 0 \n", "50% 66.400000 151.200000 1.321566 1.264575 1 1 0 \n", "75% 66.500000 152.000000 1.631253 1.563394 1 1 0 \n", "max 66.600000 153.200000 2.319446 2.459850 True True 0 \n", "\n", " RPM TRAC \n", "count 719.000000 \n", "mean 2.442142 \n", "std 1.360563 \n", "min 1.700000 \n", "25% 1.700000 \n", "50% 1.700000 \n", "75% 1.700000 \n", "max 5.300000 " ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data_violations.describe()" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([<matplotlib.axes._subplots.AxesSubplot object at 0x13E92C30>,\n", " <matplotlib.axes._subplots.AxesSubplot object at 0x13EEE2F0>,\n", " <matplotlib.axes._subplots.AxesSubplot object at 0x13F179B0>,\n", " <matplotlib.axes._subplots.AxesSubplot object at 0x13F3C8F0>,\n", " <matplotlib.axes._subplots.AxesSubplot object at 0x13F6B050>,\n", " <matplotlib.axes._subplots.AxesSubplot object at 0x13F89770>,\n", " <matplotlib.axes._subplots.AxesSubplot object at 0x13FB8070>,\n", " <matplotlib.axes._subplots.AxesSubplot object at 0x13FCB950>], dtype=object)" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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GP8YSLNsfQ9AXERFOqVKls51FsCBJf2Y557b3i4gIt4tg2bA3NeSPWUprPZ4dz3hm2cnJ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\njgOBAHV19VlJmjkX1uZ6/t2NVBMC+VJZGd9H2v/LzHJ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"text/plain": [ "<matplotlib.figure.Figure at 0x13ebc630>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "data_violations.plot(subplots=True, figsize=(12,12))" ] }, { "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": "3.4.2" } }, "nbformat": 4, "nbformat_minor": 0 }

cc0-1.0

MIT-LCP/critical-data-book

part_ii/chapter_16/jupyter/Ch5e.ipynb

1

43205

{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "Rmd_chunk_options": "echo=FALSE", "autoscroll": false, "collapsed": true }, "outputs": [], "source": [ "dat <- read.csv(\"full_cohort_data.csv\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "\\doublespacing\n", "\n", "# Chapter Goals\n", "\n", "In this subchapter, we will work through a case study, and discuss the data analysis components which should be included in an original research article suitable for an clinical journal. We will also discuss some approaches for model and feature selection.\n", "\n", "# Introduction\n", "\n", "\\doublespacing\n", "We will now use what we learned in the previous sections to examine if indwelling arterial catheters (IAC) have any effect on patient mortality [@hsu2015association]. As reiterated throughout, clearly identifying a study objective is important for a smooth data analysis. In our case, we'd like to estimate the effect of IAC on mortality, but acknowledge a few potential problem areas. First, the groups who receive IAC and and those who don't are likely different in many respects, and many of these differences likely also have some effect on mortality. Second, we would like to be able to limit ourselves on mortality events which occur in close proximity to the ICU admission. The dataset includes 28 day mortality, so that would seem to be in close proximity to the ICU admission. As for the first issue, we also have many covariates which capture some of the features we may be concerned with, including severity of illness (`sapsi_first` and `sofa_first`), age (`age`), patient gender (`gender_num`) and co-morbidities (`chf_flg`, `afib_flg`, `renal_flg`, etc).\n", "\n", "With all these in mind, we should have a good start on determining our study objective. In our case, it might be,\n", "\n", "> To estimate the effect that administration of IAC during an ICU admission has on 28 day mortality in patients within the MIMIC II study who received mechanical ventilation, while adjusting for age, gender, severity of illness and comorbidities.\n", "\n", "For now, this describes our outcome and covariates quite well. One of the first things that is often done is to describe our population by computing summary statistics of all or a subset of variables collected in the study. This description allows the reader to understand how well the study would generalize to other populations. We have made available an `R` package on GitHub that will allow one to construct preliminary forms of such a table quite quickly. To install the `R` package, first install and load the `devtools` package:\n", "\n", "\\singlespacing\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "Rmd_chunk_options": "echo=TRUE, eval=FALSE", "autoscroll": false, "collapsed": true }, "outputs": [], "source": [ "install.package(\"devtools\")\n", "library(devtools)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "\\doublespacing\n", "\n", "and then install and load our package by using the `install_github` function.\n", "\n", "\\singlespacing\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "Rmd_chunk_options": "echo=TRUE, eval=FALSE", "autoscroll": false, "collapsed": true }, "outputs": [], "source": [ "install_github(\"jraffa/MIMICbook\")\n", "library(MIMICbook);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "\\doublespacing\n", "\n", "Before we do any in depth analysis, let's make sure we are using the original dataset, first by removing and then reloading the `dat` data frame. In order to ensure our research is reproducible, it's a good idea to make sure the entire process of doing the analysis is documented. By starting from the original copy of the dataset, we are able to present precisely what methods we used in an analysis.\n", "\n", "\\singlespacing\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "Rmd_chunk_options": "echo=FALSE,eval=TRUE", "autoscroll": false, "collapsed": true }, "outputs": [], "source": [ "rm(dat);\n", "dat <- read.csv(\"full_cohort_data.csv\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "Rmd_chunk_options": "echo=TRUE,tidy=TRUE, eval=FALSE", "autoscroll": false, "collapsed": true }, "outputs": [], "source": [ "rm(dat);\n", "dat <- read.csv(url)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "\\doublespacing\n", "\n", "As mentioned before, recoding binary encoded variables (ones which are 0s and 1s) to the `R` data class `factor` can sometimes make interpreting the `R` output easier. The following piece of code cycles through all the columns in `dat` and converts any binary variables to a `factor`.\n", "\n", "\\singlespacing\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "Rmd_chunk_options": "echo=TRUE,tidy=TRUE", "autoscroll": false, "collapsed": true }, "outputs": [], "source": [ "# Identify which columns are binary coded\n", "bincols <- colMeans((dat==1 | dat==0),na.rm=T)==1\n", "for(i in 1:length(bincols)){ #Turn the binary columns into a factor\n", " if(bincols[i]) {\n", " dat[[i]] <- as.factor(dat[[i]]);\n", " }\n", "}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "\\doublespacing\n", "\n", "We are now ready to generate a summary of the patient characteristics in our study. The `MIMICbook` package has a `produce.table1` function. This generates a summary table of the data frame you pass to it, using an appropriate summary for continuous variables (average and standard deviation) and categorical variables (number and percentages) for each variable. In its most simple form, `produce.table1` can be passed a data frame as an argument, which we do (passing it the `dat` data frame). This output is not very nice, and we can make it look nicer by using a powerful `R` package called `knitr`, which provides many tools to assist in performing reproducible research. You can find out more about `knitr` by running `?knitr` on the `R` console after loading it. We will be using the `kable` command, which will take our `tab1` variable -- a summary table we generated using the `produce.table1` function, and make it look a little nicer.\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "Rmd_chunk_options": "echo=FALSE,tidy=TRUE", "autoscroll": false, "collapsed": true }, "outputs": [], "source": [ "produce.table1 <- function(x,labels=NULL) { # May throw this in an R package on github so I can omit from the book.\n", " out <- matrix(NA,nr=length(x[1,]))\n", " rrn <- NULL;\n", " for(i in 1:length(x[1,])) {\n", " if(is.factor(x[,i])) {\n", " if(is.null(labels[i])) {\n", " tmp<- table(x[,i])\n", " most.prev.name <- names(which.max(tmp))\n", " } else {\n", " if(is.na(labels[i])) {\n", " tmp<- table(x[,i])\n", " most.prev.name <- names(which.max(tmp))\n", " } else {\n", " most.prev.name <- labels[i];\n", " }\n", " }\n", " if(sum(is.na(x[,i]))==0) {\n", " out[i,] <- paste0(sum(x[,i]==most.prev.name,na.rm=T), \" (\", round(100*mean(x[,i]==most.prev.name,na.rm=T),1), \"%)\")\n", " } else {\n", " out[i,] <- paste0(sum(x[,i]==most.prev.name,na.rm=T), \" (\", round(100*mean(x[,i]==most.prev.name,na.rm=T),1), \"%)\", \" [Missing: \", sum(is.na(x[,i])), \"]\")\n", "\n", " }\n", " rrn[i] <- paste0(names(x)[i], \"==\", most.prev.name);\n", " labels[i] <- most.prev.name;\n", "\n", " } else {\n", " if(sum(is.na(x[,i]))==0) {\n", " out[i,] <- paste0(round(mean(x[,i],na.rm=T),1), \" (\" , round(sd(x[,i],na.rm=T),1), \")\")\n", " } else {\n", " out[i,] <- paste0(round(mean(x[,i],na.rm=T),1), \" (\" , round(sd(x[,i],na.rm=T),1), \")\", \" [Missing: \", sum(is.na(x[,i])), \"]\")\n", " }\n", " rrn[i] <- paste0(names(x)[i]);\n", " }\n", "\n", " }\n", " rownames(out) <- rrn;\n", " colnames(out) <- \"Average (SD), or N (%)\";\n", " attr(out,\"labels\") <- labels;\n", " return(out)\n", "}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "\\singlespacing\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "Rmd_chunk_options": "echo=TRUE,eval=FALSE", "autoscroll": false, "collapsed": true }, "outputs": [], "source": [ "tab1 <- produce.table1(dat);\n", "library(knitr);\n", "kable(tab1,caption = \"Overall patient characteristics\")\n", "" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "Rmd_chunk_options": "echo=FALSE,eval=TRUE", "autoscroll": false, "collapsed": true }, "outputs": [], "source": [ "tab1 <- produce.table1(dat);\n", "library(knitr);\n", "kable(tab1,caption = \"Overall patient characteristics\",format=\"latex\")\n", "" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "\\doublespacing\n", "\n", "The row descriptors are not very informative, and what we have produced would not be usable for final publication, but it suits our purposes for now. `knitr` allows one to output such tables in HTML, \\LaTeX\\ or even a Word document, which you can edit and make the table more informative. The results are contained in Table 1.\n", "\n", "A couple things we may notice from the baseline characteristics are:\n", "\n", "1. Some variables have a lot of missing observations (e.g., `bmi`, `po2_first`, `iv_day_1`).\n", "2. None of the patients have sepsis.\n", "\n", "Both of these points are important, and illustrates why it is always a good idea to perform basic descriptive analyses before beginning any modeling. The missing data is primarily related to weight/BMI, or lab values. For the purpose of this chapter, we are going to ignore both of these classes of variables. While we would likely want to adjust for some of these covariates in a final version of the paper, and Chapter 2.3 gives some useful techniques for dealing with such a situation, we are going to focus on the set of covariates we had identified in our study objective, which do not include these variables. The issue related to sepsis is also of note. Sepsis certainly would contribute to higher rates of mortality when compared to patients without sepsis, but since we do not have any patients with sepsis, we cannot and do not need to adjust for this covariate per se. What we do need to do is acknowledge this fact by revising our study objective. We originally identified our population as patients within MIMIC, but because this is a subset of MIMIC -- those without sepsis, we should revise the study objective to:\n", "\n", "> To estimate the effect that administration of IAC during an ICU admission has on 28 day mortality in patients without sepsis who received mechanical ventilation within MIMIC II, while adjusting for age, gender, severity of illness and comorbidities.\n", "\n", "We will also *not* want to include the `sepsis_flg` variable as a covariate in any of our models, as there is no patients with sepsis within this study to estimate the effect of sepsis. Now that we have examined the basic overall characteristics of the patients, we can begin the next steps in the analysis.\n", "\n", "The next steps will vary slightly, but it is often useful to put yourself in the shoes of a peer reviewer. What problems will a reviewer likely find with your study and how can you address them? Usually, the reviewer will want to see how the population differs for different values of the covariate of interest. In our case study, if the treated group (IAC) differed substantially from the untreated group (no IAC), then this may account for any effect we demonstrate. We can do this by summarizing the two groups in a similar fashion as was done for Table 1. We can reuse the `produce.table1` function, but we pass it the two groups separately by splitting the `dat` data frame into two using the `split` function (by the `aline_flg` variable), later combining them into one table using `cbind` to yield Table 2. It's important to ensure that the same reference groups are used across the two study groups, and that's what the labels argument is used for (see `?produce.table1` for more details).\n", "\n", "\\singlespacing\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "Rmd_chunk_options": "echo=TRUE,tidy=TRUE,eval=FALSE", "autoscroll": false, "collapsed": true }, "outputs": [], "source": [ "datby.aline <- split(dat,dat$aline_flg)\n", "reftable <- produce.table1(datby.aline[[1]]);\n", "tab2 <- cbind(produce.table1(datby.aline[[1]],labels=attr(reftable,\"labels\")),\n", " produce.table1(datby.aline[[2]],labels=attr(reftable,\"labels\")))\n", "colnames(tab2) <- paste0(\"Average (SD), or N (%)\",c(\", No-IAC\", \", IAC\"))\n", "kable(tab2, caption=\"Patient characteristics stratified by IAC administration\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "Rmd_chunk_options": "echo=FALSE,eval=TRUE", "autoscroll": false, "collapsed": true }, "outputs": [], "source": [ "datby.aline <- split(dat,dat$aline_flg)\n", "reftable <- produce.table1(datby.aline[[1]]);\n", "tab2 <- cbind(produce.table1(datby.aline[[1]],labels=attr(reftable,\"labels\")),\n", " produce.table1(datby.aline[[2]],labels=attr(reftable,\"labels\")))\n", "colnames(tab2) <- paste0(\"Average (SD), or N (%)\",c(\", No-IAC\", \", IAC\"))\n", "kable(tab2, caption=\"Patient characteristics stratified by IAC administration\",format=\"latex\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "\n", "\\doublespacing\n", "\n", "As you can see in Table 2, the IAC group differs in many respects to the non-IAC group. Patients who were given IAC tended to have higher severity of illness at baseline (`sapsi_first` and `sofa_first`), slightly older, less likely to be from the MICU, and have slightly different co-morbidity profiles when compared to the non-IAC group.\n", "\n", "Next, we can see how the covariates are distributed among the different outcomes (death within 28 days versus alive at 28 days). This will give us an idea of which covariates may be important for affecting the outcome. The code to generate this is nearly identical to that used to produce Table 2, but instead, we replace `aline_flg` with `day_28_flg` (the outcome) to get Table 3.\n", "\n", "\\singlespacing\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "Rmd_chunk_options": "echo=TRUE,tidy=TRUE,eval=FALSE", "autoscroll": false, "collapsed": true }, "outputs": [], "source": [ "datby.28daymort <- split(dat,dat$day_28_flg)\n", "reftablemort <- produce.table1(datby.28daymort[[1]]);\n", "tab3 <- cbind(produce.table1(datby.28daymort[[1]],labels=attr(reftablemort,\"labels\")),\n", " produce.table1(datby.28daymort[[2]],labels=attr(reftablemort,\"labels\")))\n", "colnames(tab3) <- paste0(\"Average (SD), or N (%)\",c(\",Alive\", \",Dead\"))\n", "kable(tab3,caption=\"Patient characteristics stratified by 28 day mortality\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "Rmd_chunk_options": "echo=FALSE,eval=TRUE", "autoscroll": false, "collapsed": true }, "outputs": [], "source": [ "datby.28daymort <- split(dat,dat$day_28_flg)\n", "reftablemort <- produce.table1(datby.28daymort[[1]]);\n", "tab3 <- cbind(produce.table1(datby.28daymort[[1]],labels=attr(reftablemort,\"labels\")),\n", " produce.table1(datby.28daymort[[2]],labels=attr(reftablemort,\"labels\")))\n", "colnames(tab3) <- paste0(\"Average (SD), or N (%)\",c(\", Alive\", \", Dead\"))\n", "kable(tab3,caption=\"Patient characteristics stratified by 28 day mortality\",format=\"latex\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "\\doublespacing\n", "\n", "As can be seen in Table 3, those patients who died within 28 days differ in many ways with those who did not. Those who died had higher SAPS and SOFAS scores, were on average older, and had different co-morbidity profiles.\n", "\n", "# Logistic Regression Analysis\n", "\n", "In Table 3, we see that of the 984 subjects receiving IAC, 170 (17.2%) died within 28 days, whereas 113 of 792 (14.2%) died in the no-IAC group. In a univariate analysis we can assess if the lower rate of mortality is statistically significant, by fitting a single covariate `aline_flg` logistic regression.\n", "\n", "\n", "\n", "\\singlespacing\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "Rmd_chunk_options": "echo=TRUE,message=FALSE", "autoscroll": false, "collapsed": true }, "outputs": [], "source": [ "uvr.glm <- glm(day_28_flg ~ aline_flg,data=dat,family=\"binomial\")\n", "exp(uvr.glm$coef[-1])\n", "exp(confint(uvr.glm)[-1,]);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "\\doublespacing\n", "\n", "Those who received IAC had over a 25\\% increase in odds of 28 day mortality when compared to those who did not receive IAC. The confidence interval includes one, so we would expect the p-value would be >0.05. Running the `summary` function, we see that this is the case.\n", "\n", "\\singlespacing\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "Rmd_chunk_options": "echo=FALSE", "autoscroll": false, "collapsed": true }, "outputs": [], "source": [ "summary(uvr.glm)$coef" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "\\doublespacing\n", "\n", "Indeed, the p-value for `aline_flg` is about 0.09. As we saw in Table 2, there are likely several important covariates that differed among those who received IAC and those who did not. These may serve as confounders, and the possible association we observed in the univariate analysis may be stronger, non-existent or in the opposite direction (i.e., IAC having lower rates of mortality) depending on the situation. Our next step would be to adjust for these confounders. This is an exercise in what is known as model building, and there are several ways people do this in the literature. A common approach is to fit all univariate models (one covariate at a time, as we did with `aline_flg`, but separately for each covariate and without `aline_flg`), and perform a hypothesis test on each model. Any variables which had statistical significance under the univariate models would then be included in a multivariable model. Another approach begins with the model we just fit (`uvr.glm` which only has `aline_flg` as a covariate), and then sequentially adds variables one at a time. This approach is often called *step-wise forward selection*. We will make a choice to do *step-wise backwards selection*, which is as it sounds -- the opposite direction of step-wise forward selection. Model selection is a challenging task in data analysis, and there are many other methods [see @dash1997feature;@harrell2015regression;@friedman2009elements;@james2013introduction] we couldn't possibly describe in full detail here. As an overall philosophy, it is important to outline and describe the process by which you will do model selection before you actually do it and stick with the process.\n", "\n", "In our stepwise backwards elimination procedure, we are going to fit a model containing IAC (`aline_flg`), age (`age`), gender, (`gender_num`), disease severity (`sapsi_first` and `sofa_first`), service type (`service_unit`), and comorbidities (`chf_flg`, `afib_flg`, `renal_flg`, `liver_flg`, `copd_flg`, `cad_flg`, `stroke_flg`, `mal_flg` and `resp_flg`). This is often called the *full model*, and is fit below. From the full model, we will proceed by eliminating one variable at a time, until we are left with a model where we are left with only statistically significant covariates. Because `aline_flg` is the covariate of interest, it will remain in the model regardless of its statistical significance. At each step we need to come up with a criteria to choose which variable we will eliminate. There are several ways of doing this, but one way we can make this decision is performing a hypothesis test for each covariate, and choosing to eliminate the covariate with the largest p-value, unless all p-values are <0.05 or the largest p-value is `aline_flg`, in which case we would stop or eliminate the next largest p-value, respectively.\n", "\n", "Most of the covariates are binary or categorical in nature, and we've already converted them to factors. The disease severity scores (SAPS and SOFA) are continuous. We could add them as we did age, but this assumes a linear trend in the odds of death as these scores change. This may or may not be appropriate (see Figure 1). Indeed, when we plot the log odds of 28 day death by SOFA score, we note that while the log odds of death generally increase as the SOFA score increases the relationship may not be linear (Figure 1).\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "Rmd_chunk_options": "echo=FALSE,message=FALSE,warning=FALSE,fig.cap=\"Plot of log-odds of mortality for each of the SOFA groups. Error bars represent 95% confidence intervals for the log odds\",results=\"hide\"", "autoscroll": false, "collapsed": true }, "outputs": [], "source": [ "library(Hmisc)\n", "postscript(\"FigE1.eps\")\n", "#tmp <- prop.table(table(cut2(dat$age,g=5), dat$day_28_flg),1)\n", "tmp.glm <- glm(day_28_flg ~ cut2(sofa_first,c(1:14)),data=dat,family=\"binomial\")\n", "tmp <- tmp.glm$coef\n", "tmp <- tmp[1] + c(0,tmp[2:15])\n", "names(tmp) <- levels(cut2(dat$sofa_first,c(1:14)));\n", "names(tmp)[15] <- \"14-17\"\n", "#names(tmp)[2:3] <- c(\"[5]\", \"[6]\")\n", "library(ggplot2)\n", "se <- sqrt(diag(summary(tmp.glm)$cov.unscaled) + c(0,diag(summary(tmp.glm)$cov.unscaled)[-1]) + 2*c(0,summary(tmp.glm)$cov.unscaled[1,2:15]))\n", "limits <- aes(ymax = tmp + se, ymin=tmp - se)\n", "qplot((names(tmp)),tmp) + xlab(\"SOFA Group\") + ylab(\"Log Odds of 28 Day Mortality\") + geom_errorbar(limits, width=0.12) + scale_x_discrete(limits=names(tmp))\n", "dev.off()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "Rmd_chunk_options": "echo=FALSE,message=FALSE,warning=FALSE,fig.cap=\"Plot of log-odds of mortality for each of the SOFA groups. Error bars represent 95% confidence intervals for the log odds\"", "autoscroll": false, "collapsed": true }, "outputs": [], "source": [ "#tmp <- prop.table(table(cut2(dat$age,g=5), dat$day_28_flg),1)\n", "tmp.glm <- glm(day_28_flg ~ cut2(sofa_first,c(1:14)),data=dat,family=\"binomial\")\n", "tmp <- tmp.glm$coef\n", "tmp <- tmp[1] + c(0,tmp[2:15])\n", "names(tmp) <- levels(cut2(dat$sofa_first,c(1:14)));\n", "names(tmp)[15] <- \"14-17\"\n", "#names(tmp)[2:3] <- c(\"[5]\", \"[6]\")\n", "library(ggplot2)\n", "se <- sqrt(diag(summary(tmp.glm)$cov.unscaled) + c(0,diag(summary(tmp.glm)$cov.unscaled)[-1]) + 2*c(0,summary(tmp.glm)$cov.unscaled[1,2:15]))\n", "limits <- aes(ymax = tmp + se, ymin=tmp - se)\n", "qplot((names(tmp)),tmp) + xlab(\"SOFA Group\") + ylab(\"Log Odds of 28 Day Mortality\") + geom_errorbar(limits, width=0.12) + scale_x_discrete(limits=names(tmp))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "\\doublespacing\n", "\n", "What can be done in this situation is to turn a continuous covariate into a discrete one. A quick way of doing this is using the `cut2` function in the `Hmisc` package[^Hmisc]. Applying `cut2(sofa_first,g=5)` turns the `sofa_first` variable into five approximately equal sized groups by SOFA score. For illustration, SOFA breaks down into the following sized groups by SOFA scores:\n", "\n", "\n", "\\singlespacing\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "Rmd_chunk_options": "echo=TRUE", "autoscroll": false, "collapsed": true }, "outputs": [], "source": [ "library(Hmisc)\n", "table(cut2(dat$sofa_first,g=5))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "\n", "\\doublespacing\n", "\n", "with not quite equal groups, due to the already discretized nature of SOFA to beginning with. We will treat both SAPS and SOFA in this way in order to avoid any model misspecification that may occur as a result of assuming a linear relationship.\n", "\n", "[^Hmisc]: You may need to install `Hmisc`, which can be done by running `install.packages(\"Hmisc\")` from the `R` command prompt.\n", "\n", "Returning to fitting the full model, we use these new disease severity scores, along with the other covariates we identified to include in the full model.\n", "\n", "\\singlespacing\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "Rmd_chunk_options": "echo=TRUE,tidy=TRUE", "autoscroll": false, "collapsed": true }, "outputs": [], "source": [ "mva.full.glm <- glm(day_28_flg ~ aline_flg + age + gender_num + cut2(sapsi_first,g=5) + cut2(sofa_first,g=5) + service_unit + chf_flg + afib_flg + renal_flg + liver_flg + copd_flg + cad_flg + stroke_flg + mal_flg + resp_flg,data=dat,family=\"binomial\")\n", "summary(mva.full.glm)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "\\doublespacing\n", "\n", "The `summary` output show that some of the covariates are very statistically significant, while others may be expendable as they are not important. Ideally, we would like as simple of a model as possible that can explain as much of the variation in the outcome as possible. We will attempt to remove our first covariate by the procedure we outlined above. For each of the variables we consider removing, we could fit a logistic regression model without that covariate, and then test it against the current model. `R` has a useful function that automates this process for us, called `dropl`. We pass to `dropl` our logistic regression object (`mva.full.glm`) and the type of test you would like to do. If you recall from the logistic regression section, we used `test=\"Chisq\"`, and this is what we will pass the `drop1` function as well.\n", "\n", "\\singlespacing\n", "\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "Rmd_chunk_options": "echo=TRUE", "autoscroll": false, "collapsed": true }, "outputs": [], "source": [ "drop1(mva.full.glm,test=\"Chisq\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "\\doublespacing\n", "\n", "As you see from the output, each covariate is listed, along with a p-value (`Pr(>Chi)`). Each row represents a hypothesis test with the bigger (alternative model) being the full model (`mva.full.glm`), and each null being the full model without the row's covariate. The p-values here should match those output if you were to do this exact test with `anova`. As we can see from the listed p-values, `aline_flg` has the largest p-value, but we stipulated in our model selection plan that we would retain this covariate as it's our covariate of interest. We will then go to the next largest p-value which is the `cad_flg` variable (coronary artery disease). We will update our model, and repeat the backwards elimination step on the updated model. We could just cut and paste the `mva.full.glm` command and remove `+ cad_flg`, but an easier way less prone to errors is to use the `update` command. The `update` can take a `glm` or `lm` object, and alter one of the covariates. To do a backwards elimination, the second argument is `.~. - variable`. The `.~.` part indicates keep the outcome and the rest of the variables the same, and the `- variable` indicates to fit the model without the variable called `variable`. Hence, to fit a new model from the full model, but without the `cad_flg` variable, we would run:\n", "\n", "\\singlespacing\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "Rmd_chunk_options": "echo=TRUE", "autoscroll": false, "collapsed": true }, "outputs": [], "source": [ "mva.tmp.glm <- update(mva.full.glm, .~. - cad_flg)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "\\doublespacing\n", "\n", "We then repeat the `drop1` step:\n", "\n", "\\singlespacing\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "Rmd_chunk_options": "echo=TRUE", "autoscroll": false, "collapsed": true }, "outputs": [], "source": [ "drop1(mva.tmp.glm,test=\"Chisq\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "\\doublespacing\n", "\n", "and see that `aline_flg` still has the largest p-value, but `chf_flag` has the second largest, so we'll choose to remove it next. To update the new model, and run another elimination step, we would run:\n", "\n", "\\singlespacing\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "Rmd_chunk_options": "echo=TRUE", "autoscroll": false, "collapsed": true }, "outputs": [], "source": [ "mva.tmp.glm2 <- update(mva.tmp.glm, .~. - chf_flg)\n", "drop1(mva.tmp.glm2,test=\"Chisq\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "\\doublespacing\n", "\n", "where again `aline_flg` has the largest p-value, and `gender_num` has the second largest. We continue, eliminating `gender_num`, `copd_flg`, `liver_flg`, `cut2(sofa_first, g = 5)`, `renal_flg`, and `service_unit`, in that order (results omitted). The table produced by `drop1` from our final model is as follows:\n", "\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "Rmd_chunk_options": "echo=FALSE,results=\"hide\"", "autoscroll": false, "collapsed": true }, "outputs": [], "source": [ "mva.tmp.glm3 <- update(mva.tmp.glm2, .~. - gender_num)\n", "drop1(mva.tmp.glm3,test=\"Chisq\")\n", "mva.tmp.glm4 <- update(mva.tmp.glm3, .~. - copd_flg)\n", "drop1(mva.tmp.glm4,test=\"Chisq\")\n", "mva.tmp.glm5 <- update(mva.tmp.glm4, .~. - liver_flg)\n", "drop1(mva.tmp.glm5,test=\"Chisq\")\n", "mva.tmp.glm6 <- update(mva.tmp.glm5, .~. - cut2(sofa_first, g = 5))\n", "drop1(mva.tmp.glm6,test=\"Chisq\")\n", "mva.tmp.glm7 <- update(mva.tmp.glm6, .~. - renal_flg)\n", "drop1(mva.tmp.glm7,test=\"Chisq\")\n", "mva.tmp.glm8 <- update(mva.tmp.glm7, .~. - service_unit)\n", "drop1(mva.tmp.glm8,test=\"Chisq\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "\\singlespacing\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "Rmd_chunk_options": "echo=TRUE", "autoscroll": false, "collapsed": true }, "outputs": [], "source": [ "drop1(mva.tmp.glm8,test=\"Chisq\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "\\doublespacing\n", "\n", "All variables are statistically significant at the 0.05 significance level. Looking at the `summary` output, we see that `aline_flg` is not statistically significant (p=0.98), but all other terms are statistically significant, with the exception of the `cut2(sapsi_first, g = 5)[12,14)`, which suggest that the second to lowest SAPS group may not be statistically significantly different than the baseline (lowest SAPS group).\n", "\n", "\n", "\\singlespacing\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "Rmd_chunk_options": "echo=TRUE,message=FALSE,warning=FALSE", "autoscroll": false, "collapsed": true }, "outputs": [], "source": [ "mva.final.glm <- mva.tmp.glm8;\n", "summary(mva.final.glm)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "\\doublespacing\n", "\n", "We would call this model our final model, and would present it in a table similar to Table 4. Since the effect of IAC was of particular focus, we will highlight it by say that it is not associated with 28 day mortality with an estimated adjusted odds ratio of 1.01 (95% CI: 0.71-1.43, p=0.98). We may conclude that after adjusting for the other potential confounders found in Table 4, we do not find any impact of using IAC on mortality.\n", "\n", "\\singlespacing\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "Rmd_chunk_options": "echo=FALSE,message=FALSE,warning=FALSE,eval=TRUE", "autoscroll": false, "collapsed": true }, "outputs": [], "source": [ "library(sjPlot);\n", "out <- sjt.glm(mva.final.glm,no.output=TRUE,emph.p=FALSE)\n", "final.table <- out$data[-1,-6];\n", "colnames(final.table) <- c(\"Covariate\", \"AOR\", \"Lower 95% CI\", \"Upper 95% CI\", \"p-value\")\n", "final.table[,1] <- c(\"IAC\", \"Age (per year increase)\", \"SAPSI [12-14)* (relative to SAPSI<12)\", \"SAPSI [14-16)*\", \"SAPSI [16-19)*\", \"SAPSI [19-32]*\", \"Atrial Fibrillation\", \"Stroke\", \"Malignancy\", \"Non-COPD Respiratory disease \")\n", "final.table[,5] <- gsub(\"&lt;\", \"<\",final.table[,5])\n", "kable(final.table,caption=\"Multivariable logistic regression analysis for mortality at 28 days outcome (Final Model)\",format=\"latex\");" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "\n", "\\doublespacing\n", "\n", "# Conclusion and Summary\n", "\n", "\n", "This brief overview of the modeling techniques for health data has provided you with the foundation to perform the most common types of analyses in health studies. We have cited how important having a clear study objective before conducting data analysis, as it identifies all the important aspects you need to plan and execute your analysis. In particular by identifying the outcome, you should be able to determine what analysis methodology would be most appropriate. Often you will find that you will be using multiple analysis techniques for different study objectives within the same study. Table 5 summarizes some of the important aspects of each analysis approach.\n", "\n", "\\singlespacing\n", "\n", "\n", "\\begin{table}[]\n", "\\centering\n", "\\caption{Summary of different methods}\n", "\\label{my-label}\n", "\\begin{tabular}{|l|l|l|l|}\n", "\\hline\n", " & Linear Regression & Logistic Regression & \\begin{tabular}[c]{@{}l@{}}Cox Proportional\\\\ Hazards Model\\end{tabular} \\\\ \\hline\n", "\\begin{tabular}[c]{@{}l@{}}Outcome \\\\ Data Type\\end{tabular} & Continuous & Binary & \\begin{tabular}[c]{@{}l@{}}Time to an Event\\\\ (possibly censored)\\end{tabular} \\\\ \\hline\n", "\\begin{tabular}[c]{@{}l@{}}Useful\\\\ Preliminary \\\\ Analysis\\end{tabular} & Scatterplot & Contingency \\& 2x2 Tables & \\begin{tabular}[c]{@{}l@{}}Kaplan-Meier Survivor\\\\ Function Estimate\\end{tabular} \\\\ \\hline\n", "\\begin{tabular}[c]{@{}l@{}}Presentation\\\\ Output\\end{tabular} & Coefficient & Odds Ratio & Hazard Ratio \\\\ \\hline\n", "R Output & Coefficient & Log Odds Ratio & Log Hazard Ratio \\\\ \\hline\n", "\\begin{tabular}[c]{@{}l@{}}Presentation \\\\ Interpretation\\end{tabular} & \\begin{tabular}[c]{@{}l@{}}An estimate of the expected\\\\ change in the outcome per one \\\\ unit increase in the covariate,\\\\ while keeping all other\\\\ covariates constant\\end{tabular} & \\begin{tabular}[c]{@{}l@{}}An estimate of the fold\\\\ change in the odds of \\\\ the outcome per unit\\\\ increase in the covariate,\\\\ while keeping all other\\\\ covariates constant.\\end{tabular} & \\begin{tabular}[c]{@{}l@{}}An estimate of the fold \\\\ change in the hazards of \\\\ the outcome per unit\\\\ increase in the covariate,\\\\ while keeping all other \\\\ covariates constant.\\end{tabular} \\\\ \\hline\n", "\\end{tabular}\n", "\\end{table}\n", "\n", "\n", "\n", "\n", "<!-- || | Linear Regression | Logistic Regression | Cox Regression\n", "|-----------|-------------------|---------------------|-------------------\n", "| Outcome Data Type | Continuous | Binary | Time to Event\n", "|------------------:|------------------:|--------------------:|------------\n", "| Presentation Output | Coefficient | Odds Ratio | Hazard Ratio\n", "| Output Interpretation | An estimate of the expected change | An estimate of the expected change | An estimate of the expected change\n", "| |in the outcome per one unit increase | in the (log) odds of the outcome per one unit increase | in the (log) hazard of the outcome per one unit increase\n", "| | in the covariate, while keeping all | in the covariate, while keeping all |in the covariate, while keeping all\n", "| | other covariates constant | other covariates constant | other covariates constant ||\n", "-->\n", "\n", "\\doublespacing\n", "\n", "Fortunately, `R`'s framework for conducting these analyses is very similar across the different types of techniques, and this framework will often extend more generally to other more complex models (including machine learning algorithms) and data structures (including dependent/correlated data such as longitudinal data). We have highlighted some areas of concern that careful attention should be paid to including missing data, colinearity, model misspecification, and outliers. These items will be looked at more closely in Chapters 2.6\n", "\n", "\n", "# References\n", "\n", "\n" ] } ], "metadata": { "Rmd_header": { "author": "Jesse D. Raffa, Marzyeh Ghassemi, Tristan Naumann, Mengling Feng and Douglas Hsu", "bibliography": "bib.bib", "csl": "ieee.csl", "date": "\\today", "header-includes": null, "output": { "pdf_document": { "fig_caption": true, "includes": { "in_header": "/media/veracrypt1/MIT-book/preamble.tex" }, "keep_tex": true, "number_sections": true } }, "title": "Section 2: Chapter 5e -- Data Analysis: Case Study and Summary" }, "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" } }, "nbformat": 4, "nbformat_minor": 0 }

mit

cesarcontre/Simulacion2017

Modulo2/Clase15_EvolucionPrecios.ipynb

2

262639

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Aplicando Python para análisis de precios: simulación de escenarios futuros de precios\n", "\n", "<img style=\"float: right; margin: 0px 0px 15px 15px;\" src=\"https://upload.wikimedia.org/wikipedia/commons/d/d7/Philippine-stock-market-board.jpg\" width=\"400px\" height=\"125px\" />\n", "\n", "> En la clase anterior vimos como importar datos de activos de la base de datos de Yahoo Finance, tanto descargándolos como archivos separados por comas (.csv), como usando el paquete pandas-datareader. En esta clase, veremos como pronosticar escenarios de evolución de precios, suponiendo que los rendimientos diarios se distribuyen normalmente.\n", "\n", "**Referencias:**\n", "- http://pandas.pydata.org/\n", "- http://www.learndatasci.com/python-finance-part-yahoo-finance-api-pandas-matplotlib/" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 1. Motivación\n", "\n", "Hace menos de una década, los instrumentos financieros estaban en la cúspide de la popularidad. Las instituciones financieras de todo el mundo estaban negociando miles de millones de dólares de estos instrumentos a diario, y los analistas cuantitativos estaban modelándolos utilizando el cálculo estocástico y el poderoso `C++`.\n", "\n", "Sin embargo, el avance en los últimos años ha sido impresionante y las cosas han cambiado. Por una parte, la [crisis financiera del 2008](https://es.wikipedia.org/wiki/Crisis_financiera_de_2008) fue producida por los instrumentos financieros llamados *derivados*. Por otra parte, los volúmenes transaccionales han bajado y la demanda de modelado con `C++` se ha marchitado con ellos. Además, un nuevo jugador entró en la competencia... `¡Python!`\n", "\n", "`Python` ha estado ganando muchos seguidores en la industria financiera en los últimos años y con razón. No en vano, junto a `R` son los lenguajes de programación más utilizados en cuanto a análisis financiero." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 2. Recordemos como descargar datos..." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Antes que nada, para poder hacer simular escenarios de predicción de precios, vamos a recordar lo que hicimos en la clase pasada de descargar los datos de Yahoo Finance, utilizando el paquete `data` de la librería `pandas_datareader`.\n", "\n", "Esta vez, utilizaremos los datos de precios de cierre ajustados de activos de la compañía Apple en el año 2016 para nuestra aplicación." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Importamos librerías\n", "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "from pandas_datareader import data\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Descargamos datos...\n", "# Instrumento: Apple\n", "ticker = ['AAPL']\n", "# Fuente: Yahoo Finance\n", "data_source = 'yahoo'\n", "# Fechas de interés (inicio y fin): 2016\n", "start_date = '2016-01-04'\n", "end_date = '2016-12-31'\n", "# Función DataReader\n", "panel_data = data.DataReader(ticker, data_source, start_date, end_date)" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Solo nos interesa los precios de cierre ajustados...\n", "closes = panel_data.loc['Adj Close']\n", "# Generamos todos los días del 2016 en orden\n", "all_weekdays = pd.date_range(start_date, end_date)\n", "# Reindizamos\n", "closes = closes.reindex(all_weekdays)\n", "# Llenamos huecos en los precios de cierre\n", "closes = closes.fillna(method='ffill')" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "image/png": 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P4UuPH855P845ZmIK2l02uG2zpW9CGl2zDjwBtIays8HZjLrDPVt+NrLlfHup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\nJZM5MeqxyWRDyWFaegepjoVysqgdt6N3etZYvBZ1ncaocyJ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"text/plain": [ "<matplotlib.figure.Figure at 0x7ff2a7c9abe0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Graficamos\n", "closes.plot(figsize=(8,6));" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 3. Rendimientos diarios\n", "\n", "Los rendimientos diarios se pueden calcular con los precios de cierre de la siguiente manera:\n", "\n", "$$r_i=\\frac{p_i-p_{i-1}}{p_{i-1}},$$\n", "\n", "donde $r_i$ es el rendimiento en el día $i$ y $p_i$ es el precio de cierre ajustado en el día $i$." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Ver en el pizarrón:** aproximación con logaritmo, además mencionar validez estadística suponiendo distribución lognormal." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Entonces, los rendimientos diarios se pueden calcular como:" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Función para calcular los rendimientos diarios a partir de los precios de cierre...\n", "def calc_daily_returns(closes):\n", " return np.log(closes/closes.shift(1))[1:]" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "image/png": 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TEdEUsSvUjgVP7HI+XGhW/CxKrh/7/mZ8+bF9dZ1DScLNQavnXsx1VGJ0zzekC0yK13VF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"text/plain": [ "<matplotlib.figure.Figure at 0x7ff2a7d97c18>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Calcular rendimientos diarios y graficarlos\n", "daily_returns = calc_daily_returns(closes)\n", "daily_returns.plot(figsize=(8,6));" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Entonces, suponemos que la diferencia logaritmica de los precios (rendimientos diarios) tiene una distribución normal.\n", "\n", "¿Cómo se caracteriza una [distribución normal](https://es.wikipedia.org/wiki/Distribuci%C3%B3n_normal)?" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(0.00032252606319594702, 0.012278733453628314)" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mu=daily_returns.mean().AAPL\n", "sigma=daily_returns.std().AAPL\n", "mu, sigma" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 4. Simulación de varios escenarios\n", "\n", "Habiendo caracterizado los rendimientos diarios como una variable aleatoria normal con la media y la varianza muestral obtenida de los datos del 2016, podemos generar números aleatorios con estas características para simular el comportamiento de los precios de las acciones en el 2017.\n", "\n", "Sin embargo, cada simulación que hagamos nos conducirá a distintos resultados (los precios siguen evolucionando aleatoriamente). Entonces, lo que haremos es simular varios escenarios para así ver alguna tendencia y tomar decisiones (próxima clase)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Pero, ¿cómo generar vectores de números aleatorios que distribuyen normalmente con una media y varianza dadas?" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Help on built-in function randn:\n", "\n", "randn(...) method of mtrand.RandomState instance\n", " randn(d0, d1, ..., dn)\n", " \n", " Return a sample (or samples) from the \"standard normal\" distribution.\n", " \n", " If positive, int_like or int-convertible arguments are provided,\n", " `randn` generates an array of shape ``(d0, d1, ..., dn)``, filled\n", " with random floats sampled from a univariate \"normal\" (Gaussian)\n", " distribution of mean 0 and variance 1 (if any of the :math:`d_i` are\n", " floats, they are first converted to integers by truncation). A single\n", " float randomly sampled from the distribution is returned if no\n", " argument is provided.\n", " \n", " This is a convenience function. If you want an interface that takes a\n", " tuple as the first argument, use `numpy.random.standard_normal` instead.\n", " \n", " Parameters\n", " ----------\n", " d0, d1, ..., dn : int, optional\n", " The dimensions of the returned array, should be all positive.\n", " If no argument is given a single Python float is returned.\n", " \n", " Returns\n", " -------\n", " Z : ndarray or float\n", " A ``(d0, d1, ..., dn)``-shaped array of floating-point samples from\n", " the standard normal distribution, or a single such float if\n", " no parameters were supplied.\n", " \n", " See Also\n", " --------\n", " random.standard_normal : Similar, but takes a tuple as its argument.\n", " \n", " Notes\n", " -----\n", " For random samples from :math:`N(\\mu, \\sigma^2)`, use:\n", " \n", " ``sigma * np.random.randn(...) + mu``\n", " \n", " Examples\n", " --------\n", " >>> np.random.randn()\n", " 2.1923875335537315 #random\n", " \n", " Two-by-four array of samples from N(3, 6.25):\n", " \n", " >>> 2.5 * np.random.randn(2, 4) + 3\n", " array([[-4.49401501, 4.00950034, -1.81814867, 7.29718677], #random\n", " [ 0.39924804, 4.68456316, 4.99394529, 4.84057254]]) #random\n", "\n" ] } ], "source": [ "help(np.random.randn)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Generamos un data frame de rendimientos diarios proyectados (10 trayectorias)" ] }, { "cell_type": "code", "execution_count": 33, "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>0</th>\n", " <th>1</th>\n", " <th>2</th>\n", " <th>3</th>\n", " <th>4</th>\n", " <th>5</th>\n", " <th>6</th>\n", " <th>7</th>\n", " <th>8</th>\n", " <th>9</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>2017-01-01</th>\n", " <td>0.001635</td>\n", " <td>-0.009805</td>\n", " <td>-0.017782</td>\n", " <td>0.009676</td>\n", " <td>-0.004888</td>\n", " <td>0.002540</td>\n", " <td>0.008746</td>\n", " <td>0.012359</td>\n", " <td>0.013965</td>\n", " <td>-0.011846</td>\n", " </tr>\n", " <tr>\n", " <th>2017-01-02</th>\n", " <td>0.026196</td>\n", " <td>-0.004280</td>\n", " <td>-0.000222</td>\n", " <td>-0.014662</td>\n", " <td>-0.015916</td>\n", " <td>0.003292</td>\n", " <td>0.015374</td>\n", " <td>0.002333</td>\n", " <td>-0.010639</td>\n", " <td>0.022012</td>\n", " </tr>\n", " <tr>\n", " <th>2017-01-03</th>\n", " <td>0.001157</td>\n", " <td>0.011639</td>\n", " <td>0.004739</td>\n", " <td>-0.004335</td>\n", " <td>-0.005158</td>\n", " <td>-0.030909</td>\n", " <td>-0.029466</td>\n", " <td>0.003240</td>\n", " <td>0.004610</td>\n", " <td>0.005462</td>\n", " </tr>\n", " <tr>\n", " <th>2017-01-04</th>\n", " <td>0.014833</td>\n", " <td>0.011974</td>\n", " <td>0.003052</td>\n", " <td>0.012743</td>\n", " <td>0.011755</td>\n", " <td>0.018015</td>\n", " <td>0.004187</td>\n", " <td>0.002654</td>\n", " <td>0.000491</td>\n", " <td>-0.021645</td>\n", " </tr>\n", " <tr>\n", " <th>2017-01-05</th>\n", " <td>-0.009603</td>\n", " <td>-0.029719</td>\n", " <td>0.023247</td>\n", " <td>0.007783</td>\n", " <td>0.007477</td>\n", " <td>0.010282</td>\n", " <td>0.000222</td>\n", " <td>-0.005886</td>\n", " <td>-0.004087</td>\n", " <td>0.020534</td>\n", " </tr>\n", " <tr>\n", " <th>2017-01-06</th>\n", " <td>0.004296</td>\n", " <td>0.010370</td>\n", " <td>0.005441</td>\n", " <td>0.021967</td>\n", " <td>0.001707</td>\n", " <td>-0.002056</td>\n", " <td>0.021941</td>\n", " <td>0.004952</td>\n", " <td>-0.002793</td>\n", " <td>0.014643</td>\n", " </tr>\n", " <tr>\n", " <th>2017-01-07</th>\n", " <td>-0.001130</td>\n", " <td>0.017210</td>\n", " <td>0.018686</td>\n", " <td>-0.005758</td>\n", " <td>0.012695</td>\n", " <td>-0.026993</td>\n", " <td>-0.004309</td>\n", " <td>-0.003507</td>\n", " <td>0.021019</td>\n", " <td>0.009641</td>\n", " </tr>\n", " <tr>\n", " <th>2017-01-08</th>\n", " <td>-0.017076</td>\n", " <td>0.003059</td>\n", " <td>0.006499</td>\n", " <td>-0.004242</td>\n", " <td>0.017326</td>\n", " <td>0.000968</td>\n", " <td>0.003835</td>\n", " <td>-0.011881</td>\n", " <td>-0.016687</td>\n", " <td>-0.004275</td>\n", " </tr>\n", " <tr>\n", " <th>2017-01-09</th>\n", " <td>0.013656</td>\n", " <td>0.014024</td>\n", " <td>0.016472</td>\n", " <td>0.013918</td>\n", " <td>-0.012474</td>\n", " <td>0.011353</td>\n", " <td>-0.011358</td>\n", " <td>0.001590</td>\n", " <td>0.006316</td>\n", " <td>0.017131</td>\n", " </tr>\n", " <tr>\n", " <th>2017-01-10</th>\n", " <td>0.017973</td>\n", " <td>0.012252</td>\n", " <td>-0.003146</td>\n", " <td>0.000181</td>\n", " <td>0.006700</td>\n", " <td>0.012226</td>\n", " <td>-0.006764</td>\n", " <td>0.003978</td>\n", " <td>0.005186</td>\n", " <td>0.003637</td>\n", " </tr>\n", " <tr>\n", " <th>2017-01-11</th>\n", " <td>0.011986</td>\n", " <td>-0.006766</td>\n", " <td>-0.002177</td>\n", " <td>-0.008032</td>\n", " <td>0.003820</td>\n", " <td>-0.019233</td>\n", " <td>-0.003324</td>\n", " <td>0.003967</td>\n", " <td>-0.013692</td>\n", " <td>-0.006229</td>\n", " </tr>\n", " <tr>\n", " <th>2017-01-12</th>\n", " <td>-0.019501</td>\n", " <td>0.000312</td>\n", " <td>-0.014076</td>\n", " <td>0.004491</td>\n", " <td>-0.004520</td>\n", " <td>0.008112</td>\n", " <td>-0.006794</td>\n", " <td>0.029027</td>\n", " <td>-0.006161</td>\n", " <td>0.002583</td>\n", " </tr>\n", " <tr>\n", " <th>2017-01-13</th>\n", " <td>0.010174</td>\n", " <td>-0.002632</td>\n", " <td>-0.003349</td>\n", " <td>0.018712</td>\n", " <td>0.012899</td>\n", " <td>0.015045</td>\n", " <td>0.006782</td>\n", " <td>-0.000629</td>\n", " <td>-0.026083</td>\n", " <td>0.004304</td>\n", " </tr>\n", " <tr>\n", " <th>2017-01-14</th>\n", " <td>-0.002599</td>\n", " <td>-0.011145</td>\n", " <td>0.011550</td>\n", " <td>-0.004617</td>\n", " <td>0.001870</td>\n", " <td>0.015886</td>\n", " <td>0.019708</td>\n", " <td>0.009834</td>\n", " <td>0.012653</td>\n", " <td>0.009668</td>\n", " </tr>\n", " <tr>\n", " <th>2017-01-15</th>\n", " <td>-0.015208</td>\n", " <td>-0.000832</td>\n", " <td>0.014985</td>\n", " <td>0.014446</td>\n", " <td>-0.015531</td>\n", " <td>-0.016650</td>\n", " <td>0.000757</td>\n", " <td>0.007868</td>\n", " <td>-0.003239</td>\n", " <td>0.010871</td>\n", " </tr>\n", " <tr>\n", " <th>2017-01-16</th>\n", " <td>0.012466</td>\n", " <td>-0.001309</td>\n", " <td>0.007323</td>\n", " <td>-0.014597</td>\n", " <td>0.010634</td>\n", " <td>0.001879</td>\n", " <td>-0.015696</td>\n", " <td>0.001069</td>\n", " <td>-0.002184</td>\n", " <td>0.014792</td>\n", " </tr>\n", " <tr>\n", " <th>2017-01-17</th>\n", " <td>0.002546</td>\n", " <td>-0.011002</td>\n", " <td>-0.006634</td>\n", " <td>-0.011102</td>\n", " <td>-0.001530</td>\n", " <td>-0.007483</td>\n", " <td>0.000989</td>\n", " <td>0.012210</td>\n", " <td>0.000114</td>\n", " <td>0.017578</td>\n", " </tr>\n", " <tr>\n", " <th>2017-01-18</th>\n", " <td>0.006659</td>\n", " <td>-0.017985</td>\n", " <td>0.004048</td>\n", " <td>-0.003895</td>\n", " <td>-0.012751</td>\n", " <td>0.001868</td>\n", " <td>-0.001618</td>\n", " <td>-0.001637</td>\n", " <td>-0.005030</td>\n", " <td>0.021775</td>\n", " </tr>\n", " <tr>\n", " <th>2017-01-19</th>\n", " <td>0.013234</td>\n", " <td>0.014969</td>\n", " <td>0.005220</td>\n", " <td>-0.015106</td>\n", " <td>0.019113</td>\n", " <td>0.018295</td>\n", " <td>0.030389</td>\n", " <td>0.019503</td>\n", " <td>0.002661</td>\n", " <td>-0.004833</td>\n", " </tr>\n", " <tr>\n", " <th>2017-01-20</th>\n", " <td>-0.013008</td>\n", " <td>0.000460</td>\n", " <td>-0.006082</td>\n", " <td>0.001730</td>\n", " <td>0.005206</td>\n", " <td>0.025067</td>\n", " <td>0.001559</td>\n", " <td>-0.009832</td>\n", " <td>-0.016421</td>\n", " <td>0.008000</td>\n", " </tr>\n", " <tr>\n", " <th>2017-01-21</th>\n", " <td>0.005161</td>\n", " <td>-0.001504</td>\n", " <td>0.003882</td>\n", " <td>0.010115</td>\n", " <td>0.002668</td>\n", " <td>0.011422</td>\n", " <td>-0.026077</td>\n", " <td>0.010940</td>\n", " <td>-0.009660</td>\n", " <td>0.004939</td>\n", " </tr>\n", " <tr>\n", " <th>2017-01-22</th>\n", " <td>-0.009079</td>\n", " <td>-0.018114</td>\n", " <td>-0.003165</td>\n", " <td>-0.002671</td>\n", " <td>-0.007284</td>\n", " <td>0.010555</td>\n", " <td>0.020110</td>\n", " <td>0.000251</td>\n", " <td>0.008224</td>\n", " <td>-0.011758</td>\n", " </tr>\n", " <tr>\n", " <th>2017-01-23</th>\n", " <td>0.005465</td>\n", " <td>0.003654</td>\n", " <td>-0.004430</td>\n", " <td>-0.000569</td>\n", " <td>0.006121</td>\n", " <td>0.021166</td>\n", " <td>-0.007490</td>\n", " <td>0.009120</td>\n", " <td>0.012208</td>\n", " <td>0.005613</td>\n", " </tr>\n", " <tr>\n", " <th>2017-01-24</th>\n", " <td>0.001611</td>\n", " <td>-0.000328</td>\n", " <td>0.013454</td>\n", " <td>0.003264</td>\n", " <td>-0.010833</td>\n", " <td>-0.007116</td>\n", " <td>-0.013008</td>\n", " <td>-0.009764</td>\n", " <td>0.037963</td>\n", " <td>-0.002116</td>\n", " </tr>\n", " <tr>\n", " <th>2017-01-25</th>\n", " <td>0.015743</td>\n", " <td>0.013642</td>\n", " <td>-0.001552</td>\n", " <td>0.001998</td>\n", " <td>0.019844</td>\n", " <td>-0.008015</td>\n", " <td>-0.017601</td>\n", " <td>-0.004407</td>\n", " <td>-0.000271</td>\n", " <td>0.019447</td>\n", " </tr>\n", " <tr>\n", " <th>2017-01-26</th>\n", " <td>-0.014317</td>\n", " <td>0.008864</td>\n", " <td>0.012776</td>\n", " <td>0.019172</td>\n", " <td>0.000217</td>\n", " <td>-0.008575</td>\n", " <td>0.017964</td>\n", " <td>0.000417</td>\n", " <td>0.021091</td>\n", " <td>-0.014189</td>\n", " </tr>\n", " <tr>\n", " <th>2017-01-27</th>\n", " <td>0.016546</td>\n", " <td>-0.006578</td>\n", " <td>-0.001065</td>\n", " <td>-0.003888</td>\n", " <td>0.000021</td>\n", " <td>-0.020072</td>\n", " <td>0.012768</td>\n", " <td>0.016908</td>\n", " <td>-0.004452</td>\n", " <td>-0.026215</td>\n", " </tr>\n", " <tr>\n", " <th>2017-01-28</th>\n", " <td>-0.003563</td>\n", " <td>0.011343</td>\n", " <td>-0.023083</td>\n", " <td>0.009566</td>\n", " <td>0.001271</td>\n", " <td>-0.000712</td>\n", " <td>0.011130</td>\n", " <td>-0.003756</td>\n", " <td>-0.009444</td>\n", " <td>-0.006764</td>\n", " </tr>\n", " <tr>\n", " <th>2017-01-29</th>\n", " <td>-0.009841</td>\n", " <td>0.004236</td>\n", " <td>0.000059</td>\n", " <td>-0.001138</td>\n", " <td>0.008783</td>\n", " <td>-0.011061</td>\n", " <td>0.003415</td>\n", " <td>0.001657</td>\n", " <td>-0.016416</td>\n", " <td>0.001230</td>\n", " </tr>\n", " <tr>\n", " <th>2017-01-30</th>\n", " <td>-0.004130</td>\n", " <td>0.008675</td>\n", " <td>-0.015378</td>\n", " <td>-0.009291</td>\n", " <td>0.011943</td>\n", " <td>0.001601</td>\n", " <td>0.017542</td>\n", " <td>-0.019090</td>\n", " <td>-0.005568</td>\n", " <td>-0.001849</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", " </tr>\n", " <tr>\n", " <th>2017-11-27</th>\n", " <td>0.007556</td>\n", " <td>-0.011841</td>\n", " <td>0.009909</td>\n", " <td>0.004305</td>\n", " <td>-0.011312</td>\n", " <td>-0.015307</td>\n", " <td>0.010652</td>\n", " <td>-0.003567</td>\n", " <td>-0.006457</td>\n", " <td>0.002033</td>\n", " </tr>\n", " <tr>\n", " <th>2017-11-28</th>\n", " <td>-0.014438</td>\n", " <td>-0.007346</td>\n", " <td>-0.022481</td>\n", " <td>0.000631</td>\n", " <td>0.009699</td>\n", " <td>0.003930</td>\n", " <td>0.006814</td>\n", " <td>0.003454</td>\n", " <td>0.009840</td>\n", " <td>-0.008614</td>\n", " </tr>\n", " <tr>\n", " <th>2017-11-29</th>\n", " <td>-0.014526</td>\n", " <td>-0.003500</td>\n", " <td>0.024960</td>\n", " <td>-0.006093</td>\n", " <td>-0.000635</td>\n", " <td>0.003847</td>\n", " <td>-0.016097</td>\n", " <td>0.000096</td>\n", " <td>-0.001193</td>\n", " <td>0.002449</td>\n", " </tr>\n", " <tr>\n", " <th>2017-11-30</th>\n", " <td>-0.016050</td>\n", " <td>0.025307</td>\n", " <td>0.002347</td>\n", " <td>0.020145</td>\n", " <td>-0.018988</td>\n", " <td>0.002838</td>\n", " <td>-0.006594</td>\n", " <td>0.026106</td>\n", " <td>-0.008405</td>\n", " <td>-0.016111</td>\n", " </tr>\n", " <tr>\n", " <th>2017-12-01</th>\n", " <td>0.011158</td>\n", " <td>-0.001659</td>\n", " <td>-0.000366</td>\n", " <td>-0.014142</td>\n", " <td>-0.006841</td>\n", " <td>0.011968</td>\n", " <td>-0.004281</td>\n", " <td>0.008911</td>\n", " <td>-0.011338</td>\n", " <td>0.011404</td>\n", " </tr>\n", " <tr>\n", " <th>2017-12-02</th>\n", " <td>0.018667</td>\n", " <td>0.009638</td>\n", " <td>-0.010837</td>\n", " <td>-0.002440</td>\n", " <td>0.000109</td>\n", " <td>0.000715</td>\n", " <td>0.013715</td>\n", " <td>-0.007542</td>\n", " <td>-0.006603</td>\n", " <td>-0.010332</td>\n", " </tr>\n", " <tr>\n", " <th>2017-12-03</th>\n", " <td>0.009941</td>\n", " <td>0.014621</td>\n", " <td>-0.019942</td>\n", " <td>0.005058</td>\n", " <td>-0.003502</td>\n", " <td>0.005676</td>\n", " <td>-0.005833</td>\n", " <td>0.008129</td>\n", " <td>-0.015009</td>\n", " <td>0.004568</td>\n", " </tr>\n", " <tr>\n", " <th>2017-12-04</th>\n", " <td>0.001559</td>\n", " <td>-0.008312</td>\n", " <td>-0.011307</td>\n", " <td>0.005147</td>\n", " <td>-0.019394</td>\n", " <td>-0.013827</td>\n", " <td>0.009281</td>\n", " <td>0.008066</td>\n", " <td>0.001154</td>\n", " <td>0.005752</td>\n", " </tr>\n", " <tr>\n", " <th>2017-12-05</th>\n", " <td>0.005204</td>\n", " <td>-0.005849</td>\n", " <td>-0.006936</td>\n", " <td>0.015428</td>\n", " <td>0.002972</td>\n", " <td>0.004435</td>\n", " <td>-0.003698</td>\n", " <td>0.003228</td>\n", " <td>0.008106</td>\n", " <td>-0.003678</td>\n", " </tr>\n", " <tr>\n", " <th>2017-12-06</th>\n", " <td>0.019090</td>\n", " <td>-0.008847</td>\n", " <td>0.005056</td>\n", " <td>-0.007084</td>\n", " <td>0.003637</td>\n", " <td>-0.012323</td>\n", " <td>0.015219</td>\n", " <td>0.003825</td>\n", " <td>0.002709</td>\n", " <td>0.018603</td>\n", " </tr>\n", " <tr>\n", " <th>2017-12-07</th>\n", " <td>-0.002101</td>\n", " <td>0.001946</td>\n", " <td>0.002757</td>\n", " <td>-0.016052</td>\n", " <td>-0.001441</td>\n", " <td>-0.010461</td>\n", " <td>0.004290</td>\n", " <td>0.005848</td>\n", " <td>0.009367</td>\n", " <td>-0.014643</td>\n", " </tr>\n", " <tr>\n", " <th>2017-12-08</th>\n", " <td>0.000135</td>\n", " <td>0.014434</td>\n", " <td>-0.006115</td>\n", " <td>-0.014371</td>\n", " <td>0.025172</td>\n", " <td>0.018693</td>\n", " <td>0.015314</td>\n", " <td>0.003224</td>\n", " <td>-0.010508</td>\n", " <td>-0.007256</td>\n", " </tr>\n", " <tr>\n", " <th>2017-12-09</th>\n", " <td>-0.003299</td>\n", " <td>-0.003622</td>\n", " <td>-0.005240</td>\n", " <td>-0.015726</td>\n", " <td>-0.000399</td>\n", " <td>-0.001157</td>\n", " <td>0.027594</td>\n", " <td>0.009925</td>\n", " <td>0.003248</td>\n", " <td>-0.010676</td>\n", " </tr>\n", " <tr>\n", " <th>2017-12-10</th>\n", " <td>-0.012277</td>\n", " <td>0.014550</td>\n", " <td>-0.004148</td>\n", " <td>-0.032955</td>\n", " <td>0.012771</td>\n", " <td>0.009280</td>\n", " <td>0.002404</td>\n", " <td>0.010939</td>\n", " <td>0.002052</td>\n", " <td>-0.001851</td>\n", " </tr>\n", " <tr>\n", " <th>2017-12-11</th>\n", " <td>0.000301</td>\n", " <td>0.012803</td>\n", " <td>0.023424</td>\n", " <td>-0.011778</td>\n", " <td>0.017059</td>\n", " <td>-0.013463</td>\n", " <td>-0.006297</td>\n", " <td>0.030367</td>\n", " <td>-0.008242</td>\n", " <td>0.008615</td>\n", " </tr>\n", " <tr>\n", " <th>2017-12-12</th>\n", " <td>0.019381</td>\n", " <td>-0.011218</td>\n", " <td>-0.011620</td>\n", " <td>-0.006458</td>\n", " <td>-0.007801</td>\n", " <td>-0.001653</td>\n", " <td>-0.009326</td>\n", " <td>-0.008934</td>\n", " <td>0.019987</td>\n", " <td>0.003965</td>\n", " </tr>\n", " <tr>\n", " <th>2017-12-13</th>\n", " <td>0.013960</td>\n", " <td>0.008187</td>\n", " <td>-0.004795</td>\n", " <td>-0.010864</td>\n", " <td>-0.007743</td>\n", " <td>-0.012740</td>\n", " <td>0.008036</td>\n", " <td>0.016236</td>\n", " <td>0.002573</td>\n", " <td>0.005652</td>\n", " </tr>\n", " <tr>\n", " <th>2017-12-14</th>\n", " <td>-0.013984</td>\n", " <td>0.001737</td>\n", " <td>0.022147</td>\n", " <td>0.002454</td>\n", " <td>0.011253</td>\n", " <td>0.026602</td>\n", " <td>0.013948</td>\n", " <td>0.013234</td>\n", " <td>-0.002109</td>\n", " <td>0.005229</td>\n", " </tr>\n", " <tr>\n", " <th>2017-12-15</th>\n", " <td>0.006357</td>\n", " <td>-0.006640</td>\n", " <td>-0.005520</td>\n", " <td>0.001869</td>\n", " <td>-0.012127</td>\n", " <td>-0.020225</td>\n", " <td>-0.001572</td>\n", " <td>-0.011891</td>\n", " <td>0.001727</td>\n", " <td>0.011958</td>\n", " </tr>\n", " <tr>\n", " <th>2017-12-16</th>\n", " <td>0.007895</td>\n", " <td>0.016826</td>\n", " <td>0.013639</td>\n", " <td>0.022095</td>\n", " <td>0.015367</td>\n", " <td>-0.006510</td>\n", " <td>-0.000359</td>\n", " <td>-0.005531</td>\n", " <td>-0.008543</td>\n", " <td>0.005717</td>\n", " </tr>\n", " <tr>\n", " <th>2017-12-17</th>\n", " <td>0.023831</td>\n", " <td>-0.008335</td>\n", " <td>-0.002538</td>\n", " <td>-0.013271</td>\n", " <td>0.004330</td>\n", " <td>0.018406</td>\n", " <td>0.016581</td>\n", " <td>0.011025</td>\n", " <td>0.017743</td>\n", " <td>-0.002915</td>\n", " </tr>\n", " <tr>\n", " <th>2017-12-18</th>\n", " <td>-0.009995</td>\n", " <td>-0.011326</td>\n", " <td>0.015698</td>\n", " <td>0.000619</td>\n", " <td>0.013311</td>\n", " <td>-0.007340</td>\n", " <td>0.002196</td>\n", " <td>0.023186</td>\n", " <td>0.020098</td>\n", " <td>-0.003940</td>\n", " </tr>\n", " <tr>\n", " <th>2017-12-19</th>\n", " <td>0.042686</td>\n", " <td>-0.000483</td>\n", " <td>0.010491</td>\n", " <td>0.021548</td>\n", " <td>0.002753</td>\n", " <td>0.006897</td>\n", " <td>-0.006826</td>\n", " <td>0.021217</td>\n", " <td>0.003688</td>\n", " <td>-0.014165</td>\n", " </tr>\n", " <tr>\n", " <th>2017-12-20</th>\n", " <td>0.013124</td>\n", " <td>0.009801</td>\n", " <td>0.009783</td>\n", " <td>0.011403</td>\n", " <td>-0.002770</td>\n", " <td>0.010050</td>\n", " <td>-0.011645</td>\n", " <td>0.017482</td>\n", " <td>0.003255</td>\n", " <td>0.000536</td>\n", " </tr>\n", " <tr>\n", " <th>2017-12-21</th>\n", " <td>-0.023554</td>\n", " <td>0.031473</td>\n", " <td>0.005506</td>\n", " <td>-0.000976</td>\n", " <td>-0.018106</td>\n", " <td>0.003397</td>\n", " <td>0.001772</td>\n", " <td>-0.003138</td>\n", " <td>-0.017946</td>\n", " <td>-0.015982</td>\n", " </tr>\n", " <tr>\n", " <th>2017-12-22</th>\n", " <td>0.001204</td>\n", " <td>-0.017023</td>\n", " <td>0.009463</td>\n", " <td>0.017953</td>\n", " <td>0.014322</td>\n", " <td>0.011161</td>\n", " <td>0.001696</td>\n", " <td>0.017773</td>\n", " <td>0.015775</td>\n", " <td>0.006945</td>\n", " </tr>\n", " <tr>\n", " <th>2017-12-23</th>\n", " <td>-0.010608</td>\n", " <td>0.019001</td>\n", " <td>0.012828</td>\n", " <td>-0.023677</td>\n", " <td>-0.023595</td>\n", " <td>0.003758</td>\n", " <td>-0.017105</td>\n", " <td>-0.000509</td>\n", " <td>0.006760</td>\n", " <td>-0.005219</td>\n", " </tr>\n", " <tr>\n", " <th>2017-12-24</th>\n", " <td>0.012809</td>\n", " <td>0.006851</td>\n", " <td>0.006450</td>\n", " <td>0.004138</td>\n", " <td>0.011743</td>\n", " <td>0.011899</td>\n", " <td>-0.011566</td>\n", " <td>-0.004584</td>\n", " <td>-0.000184</td>\n", " <td>0.001979</td>\n", " </tr>\n", " <tr>\n", " <th>2017-12-25</th>\n", " <td>0.000524</td>\n", " <td>-0.003675</td>\n", " <td>-0.003900</td>\n", " <td>-0.016397</td>\n", " <td>0.008392</td>\n", " <td>0.003099</td>\n", " <td>0.002630</td>\n", " <td>0.009348</td>\n", " <td>0.000496</td>\n", " <td>-0.004760</td>\n", " </tr>\n", " <tr>\n", " <th>2017-12-26</th>\n", " <td>0.007524</td>\n", " <td>0.013518</td>\n", " <td>0.022000</td>\n", " <td>-0.009571</td>\n", " <td>-0.028486</td>\n", " <td>0.017769</td>\n", " <td>0.024975</td>\n", " <td>-0.019487</td>\n", " <td>-0.005518</td>\n", " <td>-0.003951</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>360 rows × 10 columns</p>\n", "</div>" ], "text/plain": [ " 0 1 2 3 4 5 \\\n", "2017-01-01 0.001635 -0.009805 -0.017782 0.009676 -0.004888 0.002540 \n", "2017-01-02 0.026196 -0.004280 -0.000222 -0.014662 -0.015916 0.003292 \n", "2017-01-03 0.001157 0.011639 0.004739 -0.004335 -0.005158 -0.030909 \n", "2017-01-04 0.014833 0.011974 0.003052 0.012743 0.011755 0.018015 \n", "2017-01-05 -0.009603 -0.029719 0.023247 0.007783 0.007477 0.010282 \n", "2017-01-06 0.004296 0.010370 0.005441 0.021967 0.001707 -0.002056 \n", "2017-01-07 -0.001130 0.017210 0.018686 -0.005758 0.012695 -0.026993 \n", "2017-01-08 -0.017076 0.003059 0.006499 -0.004242 0.017326 0.000968 \n", "2017-01-09 0.013656 0.014024 0.016472 0.013918 -0.012474 0.011353 \n", "2017-01-10 0.017973 0.012252 -0.003146 0.000181 0.006700 0.012226 \n", "2017-01-11 0.011986 -0.006766 -0.002177 -0.008032 0.003820 -0.019233 \n", "2017-01-12 -0.019501 0.000312 -0.014076 0.004491 -0.004520 0.008112 \n", "2017-01-13 0.010174 -0.002632 -0.003349 0.018712 0.012899 0.015045 \n", "2017-01-14 -0.002599 -0.011145 0.011550 -0.004617 0.001870 0.015886 \n", "2017-01-15 -0.015208 -0.000832 0.014985 0.014446 -0.015531 -0.016650 \n", "2017-01-16 0.012466 -0.001309 0.007323 -0.014597 0.010634 0.001879 \n", "2017-01-17 0.002546 -0.011002 -0.006634 -0.011102 -0.001530 -0.007483 \n", "2017-01-18 0.006659 -0.017985 0.004048 -0.003895 -0.012751 0.001868 \n", "2017-01-19 0.013234 0.014969 0.005220 -0.015106 0.019113 0.018295 \n", "2017-01-20 -0.013008 0.000460 -0.006082 0.001730 0.005206 0.025067 \n", "2017-01-21 0.005161 -0.001504 0.003882 0.010115 0.002668 0.011422 \n", "2017-01-22 -0.009079 -0.018114 -0.003165 -0.002671 -0.007284 0.010555 \n", "2017-01-23 0.005465 0.003654 -0.004430 -0.000569 0.006121 0.021166 \n", "2017-01-24 0.001611 -0.000328 0.013454 0.003264 -0.010833 -0.007116 \n", "2017-01-25 0.015743 0.013642 -0.001552 0.001998 0.019844 -0.008015 \n", 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"2017-12-05 0.005204 -0.005849 -0.006936 0.015428 0.002972 0.004435 \n", "2017-12-06 0.019090 -0.008847 0.005056 -0.007084 0.003637 -0.012323 \n", "2017-12-07 -0.002101 0.001946 0.002757 -0.016052 -0.001441 -0.010461 \n", "2017-12-08 0.000135 0.014434 -0.006115 -0.014371 0.025172 0.018693 \n", "2017-12-09 -0.003299 -0.003622 -0.005240 -0.015726 -0.000399 -0.001157 \n", "2017-12-10 -0.012277 0.014550 -0.004148 -0.032955 0.012771 0.009280 \n", "2017-12-11 0.000301 0.012803 0.023424 -0.011778 0.017059 -0.013463 \n", "2017-12-12 0.019381 -0.011218 -0.011620 -0.006458 -0.007801 -0.001653 \n", "2017-12-13 0.013960 0.008187 -0.004795 -0.010864 -0.007743 -0.012740 \n", "2017-12-14 -0.013984 0.001737 0.022147 0.002454 0.011253 0.026602 \n", "2017-12-15 0.006357 -0.006640 -0.005520 0.001869 -0.012127 -0.020225 \n", "2017-12-16 0.007895 0.016826 0.013639 0.022095 0.015367 -0.006510 \n", "2017-12-17 0.023831 -0.008335 -0.002538 -0.013271 0.004330 0.018406 \n", "2017-12-18 -0.009995 -0.011326 0.015698 0.000619 0.013311 -0.007340 \n", "2017-12-19 0.042686 -0.000483 0.010491 0.021548 0.002753 0.006897 \n", "2017-12-20 0.013124 0.009801 0.009783 0.011403 -0.002770 0.010050 \n", "2017-12-21 -0.023554 0.031473 0.005506 -0.000976 -0.018106 0.003397 \n", "2017-12-22 0.001204 -0.017023 0.009463 0.017953 0.014322 0.011161 \n", "2017-12-23 -0.010608 0.019001 0.012828 -0.023677 -0.023595 0.003758 \n", "2017-12-24 0.012809 0.006851 0.006450 0.004138 0.011743 0.011899 \n", "2017-12-25 0.000524 -0.003675 -0.003900 -0.016397 0.008392 0.003099 \n", "2017-12-26 0.007524 0.013518 0.022000 -0.009571 -0.028486 0.017769 \n", "\n", " 6 7 8 9 \n", "2017-01-01 0.008746 0.012359 0.013965 -0.011846 \n", "2017-01-02 0.015374 0.002333 -0.010639 0.022012 \n", "2017-01-03 -0.029466 0.003240 0.004610 0.005462 \n", "2017-01-04 0.004187 0.002654 0.000491 -0.021645 \n", "2017-01-05 0.000222 -0.005886 -0.004087 0.020534 \n", "2017-01-06 0.021941 0.004952 -0.002793 0.014643 \n", "2017-01-07 -0.004309 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0.027594 0.009925 0.003248 -0.010676 \n", "2017-12-10 0.002404 0.010939 0.002052 -0.001851 \n", "2017-12-11 -0.006297 0.030367 -0.008242 0.008615 \n", "2017-12-12 -0.009326 -0.008934 0.019987 0.003965 \n", "2017-12-13 0.008036 0.016236 0.002573 0.005652 \n", "2017-12-14 0.013948 0.013234 -0.002109 0.005229 \n", "2017-12-15 -0.001572 -0.011891 0.001727 0.011958 \n", "2017-12-16 -0.000359 -0.005531 -0.008543 0.005717 \n", "2017-12-17 0.016581 0.011025 0.017743 -0.002915 \n", "2017-12-18 0.002196 0.023186 0.020098 -0.003940 \n", "2017-12-19 -0.006826 0.021217 0.003688 -0.014165 \n", "2017-12-20 -0.011645 0.017482 0.003255 0.000536 \n", "2017-12-21 0.001772 -0.003138 -0.017946 -0.015982 \n", "2017-12-22 0.001696 0.017773 0.015775 0.006945 \n", "2017-12-23 -0.017105 -0.000509 0.006760 -0.005219 \n", "2017-12-24 -0.011566 -0.004584 -0.000184 0.001979 \n", "2017-12-25 0.002630 0.009348 0.000496 -0.004760 \n", "2017-12-26 0.024975 -0.019487 -0.005518 -0.003951 \n", "\n", "[360 rows x 10 columns]" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ndays = 360\n", "ntraj=10\n", "dates=pd.date_range('20170101',periods=ndays)\n", "simret = pd.DataFrame(sigma*np.random.randn(ndays,ntraj)+mu,index=dates)\n", "simret" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Con los rendimientos, calculamos los precios de cierre... (explicar en el tablero)" ] }, { "cell_type": "code", "execution_count": 34, "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>0</th>\n", " <th>1</th>\n", " <th>2</th>\n", " <th>3</th>\n", " <th>4</th>\n", " <th>5</th>\n", " <th>6</th>\n", " <th>7</th>\n", " <th>8</th>\n", " <th>9</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>2017-01-01</th>\n", " <td>114.583898</td>\n", " <td>113.280617</td>\n", " <td>112.380542</td>\n", " <td>115.508992</td>\n", " <td>113.838944</td>\n", " <td>114.687720</td>\n", " <td>115.401667</td>\n", " <td>115.819331</td>\n", " <td>116.005530</td>\n", " <td>113.049609</td>\n", " </tr>\n", " <tr>\n", " <th>2017-01-02</th>\n", " <td>117.625225</td>\n", " <td>112.796831</td>\n", " <td>112.355545</td>\n", " <td>113.827773</td>\n", " <td>112.041371</td>\n", " <td>115.065837</td>\n", " <td>117.189527</td>\n", " <td>116.089826</td>\n", " <td>114.777848</td>\n", " <td>115.565620</td>\n", " </tr>\n", " <tr>\n", " <th>2017-01-03</th>\n", " <td>117.761349</td>\n", " <td>114.117323</td>\n", " <td>112.889297</td>\n", " <td>113.335412</td>\n", " <td>111.464991</td>\n", " <td>111.563680</td>\n", " <td>113.786846</td>\n", " <td>116.466586</td>\n", " <td>115.308251</td>\n", " <td>116.198584</td>\n", " </tr>\n", " <tr>\n", " <th>2017-01-04</th>\n", " <td>119.521093</td>\n", " <td>115.492011</td>\n", " <td>113.234327</td>\n", " <td>114.788845</td>\n", " <td>112.782940</td>\n", " <td>113.591702</td>\n", " <td>114.264306</td>\n", " <td>116.776120</td>\n", " <td>115.364829</td>\n", " <td>113.710535</td>\n", " </tr>\n", " <tr>\n", " <th>2017-01-05</th>\n", 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<td>112.535364</td>\n", " <td>148.214980</td>\n", " <td>120.002584</td>\n", " <td>103.409812</td>\n", " <td>98.067598</td>\n", " <td>210.266364</td>\n", " </tr>\n", " <tr>\n", " <th>2017-12-06</th>\n", " <td>130.308028</td>\n", " <td>157.945073</td>\n", " <td>148.064027</td>\n", " <td>124.333869</td>\n", " <td>112.945391</td>\n", " <td>146.399791</td>\n", " <td>121.842930</td>\n", " <td>103.806145</td>\n", " <td>98.333652</td>\n", " <td>214.214638</td>\n", " </tr>\n", " <tr>\n", " <th>2017-12-07</th>\n", " <td>130.034479</td>\n", " <td>158.252752</td>\n", " <td>148.472823</td>\n", " <td>122.353976</td>\n", " <td>112.782738</td>\n", " <td>144.876251</td>\n", " <td>122.366724</td>\n", " <td>104.414979</td>\n", " <td>99.259081</td>\n", " <td>211.100704</td>\n", " </tr>\n", " <tr>\n", " <th>2017-12-08</th>\n", " <td>130.052099</td>\n", " <td>160.553608</td>\n", " <td>147.567627</td>\n", " <td>120.608176</td>\n", " <td>115.657704</td>\n", " <td>147.609825</td>\n", " <td>124.255098</td>\n", " <td>104.752152</td>\n", " <td>98.221529</td>\n", " <td>209.574436</td>\n", " </tr>\n", " <tr>\n", " <th>2017-12-09</th>\n", " <td>129.623759</td>\n", " <td>159.973105</td>\n", " <td>146.796354</td>\n", " <td>118.726311</td>\n", " <td>115.611525</td>\n", " <td>147.439134</td>\n", " <td>127.731511</td>\n", " <td>105.796946</td>\n", " <td>98.541059</td>\n", " <td>207.348885</td>\n", " </tr>\n", " <tr>\n", " <th>2017-12-10</th>\n", " <td>128.042111</td>\n", " <td>162.317672</td>\n", " <td>146.188763</td>\n", " <td>114.877472</td>\n", " <td>117.097427</td>\n", " <td>148.813666</td>\n", " <td>128.038931</td>\n", " <td>106.960607</td>\n", " <td>98.743511</td>\n", " <td>206.965471</td>\n", " </tr>\n", " <tr>\n", " <th>2017-12-11</th>\n", " <td>128.080640</td>\n", " <td>164.409264</td>\n", " <td>149.653569</td>\n", " <td>113.532411</td>\n", " <td>119.112091</td>\n", " <td>146.823661</td>\n", " <td>127.235147</td>\n", " <td>110.258537</td>\n", " <td>97.932987</td>\n", " <td>208.756215</td>\n", " </tr>\n", " <tr>\n", " <th>2017-12-12</th>\n", " <td>130.587202</td>\n", " <td>162.575196</td>\n", " <td>147.924600</td>\n", " <td>112.801578</td>\n", " <td>118.186507</td>\n", " <td>146.581221</td>\n", " <td>126.054102</td>\n", " <td>109.277874</td>\n", " <td>99.910105</td>\n", " <td>209.585576</td>\n", " </tr>\n", " <tr>\n", " <th>2017-12-13</th>\n", " <td>132.422969</td>\n", " <td>163.911703</td>\n", " <td>147.216976</td>\n", " <td>111.582754</td>\n", " <td>117.274901</td>\n", " <td>144.725663</td>\n", " <td>127.071183</td>\n", " <td>111.066623</td>\n", " <td>100.167459</td>\n", " <td>210.773608</td>\n", " </tr>\n", " <tr>\n", " <th>2017-12-14</th>\n", " <td>130.584059</td>\n", " <td>164.196657</td>\n", " <td>150.513755</td>\n", " <td>111.856888</td>\n", " <td>118.602069</td>\n", " <td>148.627358</td>\n", " <td>128.856043</td>\n", " <td>112.546272</td>\n", " <td>99.956429</td>\n", " <td>211.878703</td>\n", " </tr>\n", " <tr>\n", " <th>2017-12-15</th>\n", " <td>131.416834</td>\n", " <td>163.110050</td>\n", " <td>149.685151</td>\n", " <td>112.066127</td>\n", " <td>117.172524</td>\n", " <td>145.651533</td>\n", " <td>128.653665</td>\n", " <td>111.215888</td>\n", " <td>100.129161</td>\n", " <td>214.427521</td>\n", " </tr>\n", " <tr>\n", " <th>2017-12-16</th>\n", " <td>132.458526</td>\n", " <td>165.877714</td>\n", " <td>151.740738</td>\n", " <td>114.569777</td>\n", " <td>118.986980</td>\n", " <td>144.706418</td>\n", " <td>128.607536</td>\n", " <td>110.602453</td>\n", " <td>99.277374</td>\n", " <td>215.656986</td>\n", " </tr>\n", " <tr>\n", " <th>2017-12-17</th>\n", " <td>135.653070</td>\n", " <td>164.500858</td>\n", " <td>151.356125</td>\n", " <td>113.059328</td>\n", " <td>119.503363</td>\n", " <td>147.394558</td>\n", " <td>130.757791</td>\n", " <td>111.828603</td>\n", " <td>101.054553</td>\n", " <td>215.029276</td>\n", " </tr>\n", " <tr>\n", " <th>2017-12-18</th>\n", " <td>134.303981</td>\n", " <td>162.648287</td>\n", " <td>153.750824</td>\n", " <td>113.129310</td>\n", " <td>121.104725</td>\n", " <td>146.316623</td>\n", " <td>131.045191</td>\n", " <td>114.451750</td>\n", " <td>103.106103</td>\n", " <td>214.183718</td>\n", " </tr>\n", " <tr>\n", " <th>2017-12-19</th>\n", " <td>140.160964</td>\n", " <td>162.569794</td>\n", " <td>155.372278</td>\n", " <td>115.593452</td>\n", " <td>121.438550</td>\n", " <td>147.329188</td>\n", " <td>130.153762</td>\n", " <td>116.905963</td>\n", " <td>103.487086</td>\n", " <td>211.171203</td>\n", " </tr>\n", " <tr>\n", " <th>2017-12-20</th>\n", " <td>142.012567</td>\n", " <td>164.170949</td>\n", " <td>156.899676</td>\n", " <td>116.919098</td>\n", " <td>121.102676</td>\n", " <td>148.817321</td>\n", " <td>128.646966</td>\n", " <td>118.967733</td>\n", " <td>103.824534</td>\n", " <td>211.284344</td>\n", " </tr>\n", " <tr>\n", " <th>2017-12-21</th>\n", " <td>138.706719</td>\n", " <td>169.420100</td>\n", " <td>157.765919</td>\n", " <td>116.805091</td>\n", " <td>118.929722</td>\n", " <td>149.323650</td>\n", " <td>128.875156</td>\n", " <td>118.595002</td>\n", " <td>101.977882</td>\n", " <td>207.934538</td>\n", " </tr>\n", " <tr>\n", " <th>2017-12-22</th>\n", " <td>138.873774</td>\n", " <td>166.560431</td>\n", " <td>159.265982</td>\n", " <td>118.921014</td>\n", " <td>120.645266</td>\n", " <td>150.999621</td>\n", " <td>129.093897</td>\n", " <td>120.721588</td>\n", " <td>103.599359</td>\n", " <td>209.383685</td>\n", " </tr>\n", " <tr>\n", " <th>2017-12-23</th>\n", " <td>137.408365</td>\n", " <td>169.755458</td>\n", " <td>161.322220</td>\n", " <td>116.138354</td>\n", " <td>117.831938</td>\n", " <td>151.568158</td>\n", " <td>126.904477</td>\n", " <td>120.660152</td>\n", " <td>104.302103</td>\n", " <td>208.293801</td>\n", " </tr>\n", " <tr>\n", " <th>2017-12-24</th>\n", " <td>139.179739</td>\n", " <td>170.922512</td>\n", " <td>162.366049</td>\n", " <td>116.619988</td>\n", " <td>119.223750</td>\n", " <td>153.382386</td>\n", " <td>125.445170</td>\n", " <td>120.108256</td>\n", " <td>104.282886</td>\n", " <td>208.706495</td>\n", " </tr>\n", " <tr>\n", " <th>2017-12-25</th>\n", " <td>139.252659</td>\n", " <td>170.295485</td>\n", " <td>161.733997</td>\n", " <td>114.723332</td>\n", " <td>120.228436</td>\n", " <td>153.858393</td>\n", " <td>125.775499</td>\n", " <td>121.236304</td>\n", " <td>104.334582</td>\n", " <td>207.715421</td>\n", " </tr>\n", " <tr>\n", " <th>2017-12-26</th>\n", " <td>140.304357</td>\n", " <td>172.613186</td>\n", " <td>165.331655</td>\n", " <td>113.630569</td>\n", " <td>116.851923</td>\n", " <td>156.616690</td>\n", " <td>128.956342</td>\n", " <td>118.896691</td>\n", " <td>103.760495</td>\n", " <td>206.896268</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>360 rows × 10 columns</p>\n", "</div>" ], "text/plain": [ " 0 1 2 3 4 \\\n", "2017-01-01 114.583898 113.280617 112.380542 115.508992 113.838944 \n", "2017-01-02 117.625225 112.796831 112.355545 113.827773 112.041371 \n", "2017-01-03 117.761349 114.117323 112.889297 113.335412 111.464991 \n", "2017-01-04 119.521093 115.492011 113.234327 114.788845 112.782940 \n", "2017-01-05 118.378846 112.110228 115.897468 115.685727 113.629372 \n", "2017-01-06 118.888501 113.278886 116.529735 118.255151 113.823533 \n", "2017-01-07 118.754185 115.245287 118.727703 117.576149 115.277680 \n", "2017-01-08 116.743571 115.598358 119.501769 117.078472 117.292441 \n", "2017-01-09 118.348698 117.230916 121.486529 118.719393 115.838434 \n", "2017-01-10 120.494987 118.676055 121.104893 118.740843 116.617167 \n", "2017-01-11 121.947919 117.875811 120.841522 117.790952 117.063462 \n", "2017-01-12 119.592829 117.912639 119.152433 118.321117 116.535561 \n", "2017-01-13 120.815761 117.602758 118.754033 120.555989 118.048483 \n", "2017-01-14 120.502143 116.299354 120.133653 120.000658 118.269460 \n", "2017-01-15 118.683438 116.202626 121.947411 121.746786 116.446795 \n", "2017-01-16 120.172264 116.050563 122.843735 119.982608 117.691722 \n", "2017-01-17 120.478648 114.780756 122.031470 118.657913 117.511751 \n", "2017-01-18 121.283559 112.734873 122.526408 118.196615 116.022835 \n", "2017-01-19 122.899299 114.435090 123.167656 116.424521 118.261747 \n", "2017-01-20 121.310950 114.487723 122.420851 116.626084 118.879005 \n", "2017-01-21 121.938664 114.315635 122.896980 117.811790 119.196563 \n", "2017-01-22 120.836570 112.263540 122.508593 117.497482 118.331471 \n", "2017-01-23 121.498807 112.674550 121.967086 117.430593 119.058044 \n", "2017-01-24 121.694711 112.637650 123.619098 117.814505 117.775243 \n", "2017-01-25 123.625686 114.184732 123.427422 118.050128 120.135659 \n", "2017-01-26 121.868345 115.201349 125.014459 120.335163 120.161686 \n", "2017-01-27 123.901580 114.446079 124.881412 119.868204 120.164200 \n", "2017-01-28 123.460893 115.751607 122.031812 121.020374 120.317065 \n", "2017-01-29 122.251836 116.242997 122.038991 120.882768 121.378407 \n", "2017-01-30 121.748024 117.255764 120.176600 119.764800 122.836751 \n", "... ... ... ... ... ... \n", "2017-11-27 127.650523 155.741106 154.030467 122.280864 116.728320 \n", "2017-11-28 125.820727 154.601278 150.606341 122.358071 117.865942 \n", "2017-11-29 124.006251 154.061087 154.412838 121.614832 117.791080 \n", "2017-11-30 122.031865 158.009704 154.775711 124.089648 115.575541 \n", "2017-12-01 123.401165 157.747748 154.719080 122.347081 114.787578 \n", "2017-12-02 125.726317 159.275489 153.051425 122.048882 114.800065 \n", "2017-12-03 126.982426 161.621307 150.029455 122.667804 114.398727 \n", "2017-12-04 127.180561 160.283475 148.342621 123.300744 112.201416 \n", "2017-12-05 127.844095 159.348652 147.317349 125.217785 112.535364 \n", "2017-12-06 130.308028 157.945073 148.064027 124.333869 112.945391 \n", "2017-12-07 130.034479 158.252752 148.472823 122.353976 112.782738 \n", "2017-12-08 130.052099 160.553608 147.567627 120.608176 115.657704 \n", "2017-12-09 129.623759 159.973105 146.796354 118.726311 115.611525 \n", "2017-12-10 128.042111 162.317672 146.188763 114.877472 117.097427 \n", "2017-12-11 128.080640 164.409264 149.653569 113.532411 119.112091 \n", "2017-12-12 130.587202 162.575196 147.924600 112.801578 118.186507 \n", "2017-12-13 132.422969 163.911703 147.216976 111.582754 117.274901 \n", "2017-12-14 130.584059 164.196657 150.513755 111.856888 118.602069 \n", "2017-12-15 131.416834 163.110050 149.685151 112.066127 117.172524 \n", "2017-12-16 132.458526 165.877714 151.740738 114.569777 118.986980 \n", "2017-12-17 135.653070 164.500858 151.356125 113.059328 119.503363 \n", "2017-12-18 134.303981 162.648287 153.750824 113.129310 121.104725 \n", "2017-12-19 140.160964 162.569794 155.372278 115.593452 121.438550 \n", "2017-12-20 142.012567 164.170949 156.899676 116.919098 121.102676 \n", "2017-12-21 138.706719 169.420100 157.765919 116.805091 118.929722 \n", "2017-12-22 138.873774 166.560431 159.265982 118.921014 120.645266 \n", "2017-12-23 137.408365 169.755458 161.322220 116.138354 117.831938 \n", "2017-12-24 139.179739 170.922512 162.366049 116.619988 119.223750 \n", "2017-12-25 139.252659 170.295485 161.733997 114.723332 120.228436 \n", "2017-12-26 140.304357 172.613186 165.331655 113.630569 116.851923 \n", "\n", " 5 6 7 8 9 \n", "2017-01-01 114.687720 115.401667 115.819331 116.005530 113.049609 \n", "2017-01-02 115.065837 117.189527 116.089826 114.777848 115.565620 \n", "2017-01-03 111.563680 113.786846 116.466586 115.308251 116.198584 \n", "2017-01-04 113.591702 114.264306 116.776120 115.364829 113.710535 \n", "2017-01-05 114.765689 114.289629 116.090813 114.894347 116.069630 \n", "2017-01-06 114.530020 116.824953 116.667111 114.573887 117.781684 \n", "2017-01-07 111.479893 116.322661 116.258629 117.007592 118.922709 \n", "2017-01-08 111.587856 116.769599 114.885484 115.071322 118.415427 \n", "2017-01-09 112.861959 115.450870 115.068355 115.800450 120.461535 \n", "2017-01-10 114.250306 114.672570 115.526976 116.402551 120.900411 \n", "2017-01-11 112.073968 114.292003 115.986187 114.819610 120.149653 \n", "2017-01-12 112.986866 113.518168 119.402306 114.114343 120.460405 \n", "2017-01-13 114.699576 114.290627 119.327169 111.176395 120.979941 \n", "2017-01-14 116.536253 116.565463 120.506379 112.592065 122.155268 \n", "2017-01-15 114.612018 116.653720 121.458253 112.227965 123.490401 \n", "2017-01-16 114.827523 114.837052 121.588100 111.983181 125.330644 \n", "2017-01-17 113.971515 114.950692 123.081813 111.995960 127.553145 \n", "2017-01-18 114.184654 114.764832 122.880543 111.434047 130.361126 \n", "2017-01-19 116.292910 118.305928 125.300640 111.731021 129.732675 \n", "2017-01-20 119.244884 118.490542 124.074691 109.911218 130.774716 \n", "2017-01-21 120.614661 115.440658 125.439481 108.854580 131.422182 \n", "2017-01-22 121.894446 117.785692 125.470947 109.753440 129.885986 \n", "2017-01-23 124.502015 116.906820 126.620432 111.101553 130.617064 \n", "2017-01-24 123.619149 115.395939 125.390113 115.400330 130.341031 \n", "2017-01-25 122.632294 113.382665 124.838709 115.369048 132.900557 \n", "2017-01-26 121.585264 115.437861 124.890772 117.828087 131.028172 \n", "2017-01-27 119.169126 116.921238 127.020398 117.304683 127.637915 \n", "2017-01-28 119.084357 118.229860 126.544163 116.202102 126.777462 \n", "2017-01-29 117.774470 118.634303 126.754077 114.310114 126.933515 \n", "2017-01-30 117.963200 120.733796 124.357282 113.675432 126.698999 \n", "... ... ... ... ... ... \n", "2017-11-27 145.340958 120.808242 98.322406 100.394299 213.350798 \n", "2017-11-28 145.913218 121.634273 98.662648 101.387045 211.520796 \n", "2017-11-29 146.475627 119.692050 98.672115 101.266119 212.039352 \n", "2017-11-30 146.891979 118.905390 101.281935 100.418506 208.650506 \n", "2017-12-01 148.660557 118.397496 102.188480 99.286380 211.043621 \n", "2017-12-02 148.766912 120.032522 101.420683 98.632981 208.874241 \n", "2017-12-03 149.613654 119.334460 102.248451 97.163684 209.830607 \n", "2017-12-04 147.559116 120.447158 103.076513 97.275861 211.041114 \n", "2017-12-05 148.214980 120.002584 103.409812 98.067598 210.266364 \n", "2017-12-06 146.399791 121.842930 103.806145 98.333652 214.214638 \n", "2017-12-07 144.876251 122.366724 104.414979 99.259081 211.100704 \n", "2017-12-08 147.609825 124.255098 104.752152 98.221529 209.574436 \n", "2017-12-09 147.439134 127.731511 105.796946 98.541059 207.348885 \n", "2017-12-10 148.813666 128.038931 106.960607 98.743511 206.965471 \n", "2017-12-11 146.823661 127.235147 110.258537 97.932987 208.756215 \n", "2017-12-12 146.581221 126.054102 109.277874 99.910105 209.585576 \n", "2017-12-13 144.725663 127.071183 111.066623 100.167459 210.773608 \n", "2017-12-14 148.627358 128.856043 112.546272 99.956429 211.878703 \n", "2017-12-15 145.651533 128.653665 111.215888 100.129161 214.427521 \n", "2017-12-16 144.706418 128.607536 110.602453 99.277374 215.656986 \n", "2017-12-17 147.394558 130.757791 111.828603 101.054553 215.029276 \n", "2017-12-18 146.316623 131.045191 114.451750 103.106103 214.183718 \n", "2017-12-19 147.329188 130.153762 116.905963 103.487086 211.171203 \n", "2017-12-20 148.817321 128.646966 118.967733 103.824534 211.284344 \n", "2017-12-21 149.323650 128.875156 118.595002 101.977882 207.934538 \n", "2017-12-22 150.999621 129.093897 120.721588 103.599359 209.383685 \n", "2017-12-23 151.568158 126.904477 120.660152 104.302103 208.293801 \n", "2017-12-24 153.382386 125.445170 120.108256 104.282886 208.706495 \n", "2017-12-25 153.858393 125.775499 121.236304 104.334582 207.715421 \n", "2017-12-26 156.616690 128.956342 118.896691 103.760495 206.896268 \n", "\n", "[360 rows x 10 columns]" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "simdata=(closes.loc['2016-12-30',:].AAPL)*np.exp(simret.cumsum())\n", "simdata" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Concatenamos y graficamos..." ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "data": { "image/png": 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UQghx3Gp3GxnZshmFXLqwnLX1nWxpSM/ANh7qYcWc9KIZJ2pGrSORxOtwe/uw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FAAAg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\nuHjYaAEAAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7ff2a7a49518>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "simul=pd.concat([closes.T, simdata.T]).T\n", "simul.plot(figsize=(8,6),legend=False);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "___\n", "Entonces, ya aprendimos a bajar datos con pandas-datareader. En específico, a partir de los precios de cierre ajustados obtuvimos los rendimientos diarios.\n", "\n", "Suponiendo que los rendimientos diarios son un proceso estocástico estacionario de distribución normal, pudimos caracaterizarlo y proyectar varios escenarios de evolución de los precios (montecarlo).\n", "\n", "La próxima clase veremos cómo tomar decisiones estableciendo un umbral de precio." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Anuncios parroquiales...\n", "\n", "1. Viernes 28 de Octubre y Martes 21 de Noviembre no hay clase.\n", "2. Clase de reposición: próximo Miércoles 18 de Octubre de 16:00-18:00, aula D-206 (Clase de repaso).\n", "3. Grupos de proyectos: hoja con integrantes.\n", "4. Para el próximo viernes: proyecto definido, subir a moodle un cuaderno con nombre del proyecto, objetivos y una breve introducción.\n", "5. Les sigo debiendo una tarea de la parte de integración." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<script>\n", " $(document).ready(function(){\n", " $('div.prompt').hide();\n", " $('div.back-to-top').hide();\n", " $('nav#menubar').hide();\n", " $('.breadcrumb').hide();\n", " $('.hidden-print').hide();\n", " });\n", "</script>\n", "\n", "<footer id=\"attribution\" style=\"float:right; color:#808080; background:#fff;\">\n", "Created with Jupyter by Esteban Jiménez Rodríguez.\n", "</footer>" ] } ], "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

SothanaV/smartmushroombox

sql/Untitled.ipynb

1

5007

{ "cells": [ { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import sqlite3\n", "conn = sqlite3.connect('Test.db')" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "c = conn.cursor()\n", "\n", "# Create table\n", "c.execute('''CREATE TABLE stocks\n", " (date text, trans text, symbol text, qty real, price real)''')\n", "\n", "# Insert a row of data\n", "c.execute(\"INSERT INTO stocks VALUES ('2006-01-05','BUY','RHAT',100,35.14)\")\n", "\n", "# Save (commit) the changes\n", "conn.commit()\n", "\n", "# We can also close the connection if we are done with it.\n", "# Just be sure any changes have been committed or they will be lost.\n", "conn.close()" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import sqlite3\n", "conn = sqlite3.connect('example.db')\n", "c = conn.cursor()" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(u'2006-01-05', u'BUY', u'RHAT', 100.0, 35.14)\n" ] }, { "data": { "text/plain": [ "<sqlite3.Cursor at 0x4453420>" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Never do this -- insecure!\n", "symbol = 'RHAT'\n", "c.execute(\"SELECT * FROM stocks WHERE symbol = '%s'\" % symbol)\n", "\n", "# Do this instead\n", "t = ('RHAT',)\n", "c.execute('SELECT * FROM stocks WHERE symbol=?', t)\n", "print c.fetchone()\n", "\n", "# Larger example that inserts many records at a time\n", "purchases = [('2006-03-28', 'BUY', 'IBM', 1000, 45.00),\n", " ('2006-04-05', 'BUY', 'MSFT', 1000, 72.00),\n", " ('2006-04-06', 'SELL', 'IBM', 500, 53.00),\n", " ]\n", "c.executemany('INSERT INTO stocks VALUES (?,?,?,?,?)', purchases)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(u'2006-01-05', u'BUY', u'RHAT', 100.0, 35.14)\n", "(u'2006-03-28', u'BUY', u'IBM', 1000.0, 45.0)\n", "(u'2006-04-06', u'SELL', u'IBM', 500.0, 53.0)\n", "(u'2006-04-05', u'BUY', u'MSFT', 1000.0, 72.0)\n" ] }, { "data": { "text/plain": [ "(u'2006-04-05', u'BUY', u'MSFT', 1000, 72.0)" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ ">>> for row in c.execute('SELECT * FROM stocks ORDER BY price'):\n", " print row\n", "\n", "(u'2006-01-05', u'BUY', u'RHAT', 100, 35.14)\n", "(u'2006-03-28', u'BUY', u'IBM', 1000, 45.0)\n", "(u'2006-04-06', u'SELL', u'IBM', 500, 53.0)\n", "(u'2006-04-05', u'BUY', u'MSFT', 1000, 72.0)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(u'2006-04-05', u'BUY', u'MSFT', 1000.0, 72.0)\n" ] } ], "source": [ "print row" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [], "source": [ "x = \"Naii\"\n", "file = open(\"newfile.txt\", \"w\")\n", "file.write(\"hello world in the new file\")\n", "file.write(\"and another line\")\n", "file.write(x)\n", "file.close()" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "hello world in the new fileand another lineNaii\n" ] } ], "source": [ "file = open('newfile.txt', 'r')\n", "\n", "print file.read()" ] }, { "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 }

bsd-2-clause

utensil/julia-playground

dl/hello_mx_gpu.ipynb

1

24500

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "It works on a Windows 10 PC which has an old GPU: GeForce GTX 550 Ti.\n", "\n", "Simply run the following command (not using docker) in the root of this project:\n", "\n", "```\n", "jupyter notebook\n", "```\n", "\n", "Also run the following in a separate terminal:\n", "\n", "```\n", "tensorboard --logdir=./dl/logs --host=127.0.0.1 --port=8889\n", "```" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Requirement already satisfied: tensorflow-gpu in c:\\coding\\anaconda3\\lib\\site-packages (1.8.0)\n", "Requirement already satisfied: termcolor>=1.1.0 in c:\\coding\\anaconda3\\lib\\site-packages (from tensorflow-gpu) (1.1.0)\n", "Requirement already satisfied: protobuf>=3.4.0 in c:\\coding\\anaconda3\\lib\\site-packages (from tensorflow-gpu) (3.6.0)\n", "Requirement already satisfied: six>=1.10.0 in c:\\coding\\anaconda3\\lib\\site-packages (from tensorflow-gpu) (1.11.0)\n", "Requirement already satisfied: absl-py>=0.1.6 in c:\\coding\\anaconda3\\lib\\site-packages (from tensorflow-gpu) (0.2.2)\n", "Requirement already satisfied: astor>=0.6.0 in c:\\coding\\anaconda3\\lib\\site-packages (from tensorflow-gpu) (0.6.2)\n", "Requirement already satisfied: wheel>=0.26 in c:\\coding\\anaconda3\\lib\\site-packages (from tensorflow-gpu) (0.31.1)\n", "Requirement already satisfied: tensorboard<1.9.0,>=1.8.0 in c:\\coding\\anaconda3\\lib\\site-packages (from tensorflow-gpu) (1.8.0)\n", "Requirement already satisfied: grpcio>=1.8.6 in c:\\coding\\anaconda3\\lib\\site-packages (from tensorflow-gpu) (1.12.1)\n", "Requirement already satisfied: numpy>=1.13.3 in c:\\coding\\anaconda3\\lib\\site-packages (from tensorflow-gpu) (1.14.3)\n", "Requirement already satisfied: gast>=0.2.0 in c:\\coding\\anaconda3\\lib\\site-packages (from tensorflow-gpu) (0.2.0)\n", "Requirement already satisfied: setuptools in c:\\coding\\anaconda3\\lib\\site-packages (from protobuf>=3.4.0->tensorflow-gpu) (39.1.0)\n", "Requirement already satisfied: markdown>=2.6.8 in c:\\coding\\anaconda3\\lib\\site-packages (from tensorboard<1.9.0,>=1.8.0->tensorflow-gpu) (2.6.11)\n", "Requirement already satisfied: werkzeug>=0.11.10 in c:\\coding\\anaconda3\\lib\\site-packages (from tensorboard<1.9.0,>=1.8.0->tensorflow-gpu) (0.14.1)\n", "Requirement already satisfied: bleach==1.5.0 in c:\\coding\\anaconda3\\lib\\site-packages (from tensorboard<1.9.0,>=1.8.0->tensorflow-gpu) (1.5.0)\n", "Requirement already satisfied: html5lib==0.9999999 in c:\\coding\\anaconda3\\lib\\site-packages (from tensorboard<1.9.0,>=1.8.0->tensorflow-gpu) (0.9999999)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "distributed 1.21.8 requires msgpack, which is not installed.\n" ] } ], "source": [ "!pip install tensorflow-gpu" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "env: CUDA_DEVICE_ORDER=PCI_BUS_ID\n", "env: CUDA_VISIBLE_DEVICES=0,1\n" ] } ], "source": [ "%env CUDA_DEVICE_ORDER=PCI_BUS_ID\n", "%env CUDA_VISIBLE_DEVICES=0,1" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "PCI_BUS_ID\n", "0,1\n" ] } ], "source": [ "import os\n", "print(os.environ[\"CUDA_DEVICE_ORDER\"])\n", "print(os.environ[\"CUDA_VISIBLE_DEVICES\"])" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\coding\\Anaconda3\\lib\\site-packages\\h5py\\__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n", " from ._conv import register_converters as _register_converters\n" ] } ], "source": [ "import tensorflow as tf" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[name: \"/device:CPU:0\"\n", " device_type: \"CPU\"\n", " memory_limit: 268435456\n", " locality {\n", " }\n", " incarnation: 8034297251726998311]" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from tensorflow.python.client import device_lib\n", "\n", "device_lib.list_local_devices()" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "''" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tf.test.gpu_device_name()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "From https://mxnet.incubator.apache.org/install/index.html :" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "nvcc: NVIDIA (R) Cuda compiler driver\n", "Copyright (c) 2005-2017 NVIDIA Corporation\n", "Built on Fri_Sep__1_21:08:32_Central_Daylight_Time_2017\n", "Cuda compilation tools, release 9.0, V9.0.176\n" ] } ], "source": [ "!nvcc --version" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Requirement already satisfied: mxnet-cu90 in c:\\coding\\anaconda3\\lib\\site-packages (1.2.0)\n", "Requirement already satisfied: requests in c:\\coding\\anaconda3\\lib\\site-packages (from mxnet-cu90) (2.18.4)\n", "Requirement already satisfied: numpy in c:\\coding\\anaconda3\\lib\\site-packages (from mxnet-cu90) (1.14.3)\n", "Requirement already satisfied: graphviz in c:\\coding\\anaconda3\\lib\\site-packages (from mxnet-cu90) (0.8.3)\n", "Requirement already satisfied: chardet<3.1.0,>=3.0.2 in c:\\coding\\anaconda3\\lib\\site-packages (from requests->mxnet-cu90) (3.0.4)\n", "Requirement already satisfied: idna<2.7,>=2.5 in c:\\coding\\anaconda3\\lib\\site-packages (from requests->mxnet-cu90) (2.6)\n", "Requirement already satisfied: urllib3<1.23,>=1.21.1 in c:\\coding\\anaconda3\\lib\\site-packages (from requests->mxnet-cu90) (1.22)\n", "Requirement already satisfied: certifi>=2017.4.17 in c:\\coding\\anaconda3\\lib\\site-packages (from requests->mxnet-cu90) (2018.4.16)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "distributed 1.21.8 requires msgpack, which is not installed.\n" ] } ], "source": [ "!pip install mxnet-cu90" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "timestamp, name, pci.bus_id, driver_version, pstate, pcie.link.gen.max, pcie.link.gen.current, temperature.gpu, utilization.gpu [%], utilization.memory [%], memory.total [MiB], memory.free [MiB], memory.used [MiB]\n", "2018/06/29 09:51:58.754, GeForce GTX 550 Ti, 00000000:01:00.0, 385.54, P0, [Not Supported], [Not Supported], 42, [Not Supported], [Not Supported], 1024 MiB, 448 MiB, 576 MiB\n" ] } ], "source": [ "# From https://stackoverflow.com/questions/49076092/is-there-a-way-to-check-if-mxnet-uses-my-gpu/49079940#49079940\n", "# https://developer.download.nvidia.com/compute/DCGM/docs/nvidia-smi-367.38.pdf\n", "!\"C:\\Program Files\\NVIDIA Corporation\\NVSMI\\nvidia-smi\" --query-gpu=timestamp,name,pci.bus_id,driver_version,pstate,pcie.link.gen.max,pcie.link.gen.current,temperature.gpu,utilization.gpu,utilization.memory,memory.total,memory.free,memory.used --format=csv" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "import mxnet as mx \n", "def gpu_device(gpu_number=0):\n", " try:\n", " _ = mx.nd.array([1, 2, 3], ctx=mx.gpu(gpu_number))\n", " except mx.MXNetError:\n", " return None\n", " return mx.gpu(gpu_number)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "gpu(0)" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "gpu_device()" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "gpu(0)" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mx.gpu(0)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "From https://gluon.mxnet.io/chapter03_deep-neural-networks/mlp-gluon.html :" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "from __future__ import print_function\n", "import numpy as np\n", "import mxnet as mx\n", "from mxnet import nd, autograd, gluon" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "data_ctx = mx.cpu()\n", "model_ctx = mx.cpu()\n", "# model_ctx = mx.gpu(0)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "batch_size = 64\n", "num_inputs = 784\n", "num_outputs = 10\n", "num_examples = 60000\n", "def transform(data, label):\n", " return data.astype(np.float32)/255, label.astype(np.float32)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [], "source": [ "train_data = mx.gluon.data.DataLoader(mx.gluon.data.vision.MNIST(train=True, transform=transform),\n", " batch_size, shuffle=True)\n", "test_data = mx.gluon.data.DataLoader(mx.gluon.data.vision.MNIST(train=False, transform=transform),\n", " batch_size, shuffle=False)" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [], "source": [ "class MLP(gluon.Block):\n", " def __init__(self, **kwargs):\n", " super(MLP, self).__init__(**kwargs)\n", " with self.name_scope():\n", " self.dense0 = gluon.nn.Dense(64)\n", " self.dense1 = gluon.nn.Dense(64)\n", " self.dense2 = gluon.nn.Dense(10)\n", "\n", " def forward(self, x):\n", " x = nd.relu(self.dense0(x))\n", " x = nd.relu(self.dense1(x))\n", " x = self.dense2(x)\n", " return x" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [], "source": [ "net = MLP()\n", "net.collect_params().initialize(mx.init.Normal(sigma=.01), ctx=model_ctx)" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "\n", "[[-5.2642502e-04 -4.8494569e-04 -9.1017238e-05 -1.0700601e-03\n", " 9.5340359e-04 1.2931204e-03 -3.8861975e-04 -6.4619188e-04\n", " 1.3646495e-04 -1.7153830e-03]]\n", "<NDArray 1x10 @cpu(0)>" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data = nd.ones((1,784))\n", "net(data.as_in_context(model_ctx))" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Hidden Representation 1: \n", "[[0. 0. 0.0257028 0.41763663 0. 0.\n", " 0. 0. 0. 0.03712562 0.16054314 0.35507876\n", " 0. 0.12578698 0. 0. 0. 0.30374664\n", " 0.292567 0.35357708 0. 0.07809136 0.21969806 0.2177984\n", " 0. 0.3457912 0.13206203 0.01624641 0.27534354 0.22952288\n", " 0.2202207 0. 0.00258669 0.06395139 0.68015635 0.\n", " 0. 0. 0.1652459 0.18695295 0.25243065 0.01728743\n", " 0.06471729 0. 0. 0.2552151 0. 0.\n", " 0.03300378 0.33107045 0.6453747 0.04547642 0. 0.\n", " 0. 0.19542485 0.02424754 0. 0. 0.04300808\n", " 0.16542053 0.13203493 0. 0. ]]\n", "<NDArray 1x64 @cpu(0)>\n", "Hidden Representation 2: \n", "[[0.0000000e+00 0.0000000e+00 4.8457514e-03 0.0000000e+00 2.4975553e-02\n", " 0.0000000e+00 9.2384806e-03 1.1846514e-02 0.0000000e+00 1.5087268e-02\n", " 0.0000000e+00 1.3427198e-02 1.6015759e-02 0.0000000e+00 0.0000000e+00\n", " 0.0000000e+00 0.0000000e+00 0.0000000e+00 2.7162414e-02 4.1979598e-05\n", " 0.0000000e+00 1.8946800e-02 3.0578913e-03 0.0000000e+00 0.0000000e+00\n", " 2.7754948e-02 7.5642066e-04 0.0000000e+00 0.0000000e+00 1.9757828e-02\n", " 1.7670706e-02 0.0000000e+00 4.0669916e-03 1.0265570e-02 7.5005908e-03\n", " 1.5555882e-02 0.0000000e+00 0.0000000e+00 0.0000000e+00 2.8156085e-02\n", " 0.0000000e+00 0.0000000e+00 2.0807199e-02 0.0000000e+00 0.0000000e+00\n", " 0.0000000e+00 5.2651879e-04 0.0000000e+00 0.0000000e+00 3.6671013e-02\n", " 1.6886523e-02 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00\n", " 1.5089142e-02 1.0638590e-02 9.0155248e-03 1.8627236e-02 1.4041221e-02\n", " 0.0000000e+00 0.0000000e+00 0.0000000e+00 1.2555162e-02]]\n", "<NDArray 1x64 @cpu(0)>\n", "Network output: \n", "[[-1.1785791e-03 1.9014490e-04 8.1118196e-04 -3.8255830e-04\n", " 4.7956721e-04 -1.2719276e-04 3.3852040e-05 -2.3284566e-04\n", " 7.1805023e-04 1.1753932e-03]]\n", "<NDArray 1x10 @cpu(0)>\n" ] }, { "data": { "text/plain": [ "\n", "[[-1.1785791e-03 1.9014490e-04 8.1118196e-04 -3.8255830e-04\n", " 4.7956721e-04 -1.2719276e-04 3.3852040e-05 -2.3284566e-04\n", " 7.1805023e-04 1.1753932e-03]]\n", "<NDArray 1x10 @cpu(0)>" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "class MLP(gluon.Block):\n", " def __init__(self, **kwargs):\n", " super(MLP, self).__init__(**kwargs)\n", " with self.name_scope():\n", " self.dense0 = gluon.nn.Dense(64, activation=\"relu\")\n", " self.dense1 = gluon.nn.Dense(64, activation=\"relu\")\n", " self.dense2 = gluon.nn.Dense(10)\n", "\n", " def forward(self, x):\n", " x = self.dense0(x)\n", " print(\"Hidden Representation 1: %s\" % x)\n", " x = self.dense1(x)\n", " print(\"Hidden Representation 2: %s\" % x)\n", " x = self.dense2(x)\n", " print(\"Network output: %s\" % x)\n", " return x\n", "\n", "net = MLP()\n", "net.collect_params().initialize(mx.init.Normal(sigma=.01), ctx=model_ctx)\n", "net(data.as_in_context(model_ctx))" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [], "source": [ "num_hidden = 64\n", "net = gluon.nn.HybridSequential()\n", "with net.name_scope():\n", " net.add(gluon.nn.Dense(num_hidden, activation=\"relu\"))\n", " net.add(gluon.nn.Dense(num_hidden, activation=\"relu\"))\n", " net.add(gluon.nn.Dense(num_outputs))\n", "net.hybridize()" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [], "source": [ "net.collect_params().initialize(mx.init.Normal(sigma=.1), ctx=model_ctx)" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [], "source": [ "softmax_cross_entropy = gluon.loss.SoftmaxCrossEntropyLoss()" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [], "source": [ "trainer = gluon.Trainer(net.collect_params(), 'sgd', {'learning_rate': .01})" ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [], "source": [ "def evaluate_accuracy(data_iterator, net):\n", " acc = mx.metric.Accuracy()\n", " for i, (data, label) in enumerate(data_iterator):\n", " data = data.as_in_context(model_ctx).reshape((-1, 784))\n", " label = label.as_in_context(model_ctx)\n", " output = net(data)\n", " predictions = nd.argmax(output, axis=1)\n", " acc.update(preds=predictions, labels=label)\n", " return acc.get()[1]" ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Requirement already satisfied: mxboard in c:\\coding\\anaconda3\\lib\\site-packages (0.1.0)\n", "Requirement already satisfied: protobuf>=3.0.0 in c:\\coding\\anaconda3\\lib\\site-packages (from mxboard) (3.6.0)\n", "Requirement already satisfied: Pillow in c:\\coding\\anaconda3\\lib\\site-packages (from mxboard) (5.1.0)\n", "Requirement already satisfied: six in c:\\coding\\anaconda3\\lib\\site-packages (from mxboard) (1.11.0)\n", "Requirement already satisfied: numpy in c:\\coding\\anaconda3\\lib\\site-packages (from mxboard) (1.14.3)\n", "Requirement already satisfied: setuptools in c:\\coding\\anaconda3\\lib\\site-packages (from protobuf>=3.0.0->mxboard) (39.1.0)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "distributed 1.21.8 requires msgpack, which is not installed.\n" ] } ], "source": [ "!pip install mxboard" ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Requirement already satisfied: tensorboard in c:\\coding\\anaconda3\\lib\\site-packages (1.8.0)\n", "Requirement already satisfied: html5lib==0.9999999 in c:\\coding\\anaconda3\\lib\\site-packages (from tensorboard) (0.9999999)\n", "Requirement already satisfied: bleach==1.5.0 in c:\\coding\\anaconda3\\lib\\site-packages (from tensorboard) (1.5.0)\n", "Requirement already satisfied: protobuf>=3.4.0 in c:\\coding\\anaconda3\\lib\\site-packages (from tensorboard) (3.6.0)\n", "Requirement already satisfied: werkzeug>=0.11.10 in c:\\coding\\anaconda3\\lib\\site-packages (from tensorboard) (0.14.1)\n", "Requirement already satisfied: six>=1.10.0 in c:\\coding\\anaconda3\\lib\\site-packages (from tensorboard) (1.11.0)\n", "Requirement already satisfied: wheel>=0.26; python_version >= \"3\" in c:\\coding\\anaconda3\\lib\\site-packages (from tensorboard) (0.31.1)\n", "Requirement already satisfied: markdown>=2.6.8 in c:\\coding\\anaconda3\\lib\\site-packages (from tensorboard) (2.6.11)\n", "Requirement already satisfied: numpy>=1.12.0 in c:\\coding\\anaconda3\\lib\\site-packages (from tensorboard) (1.14.3)\n", "Requirement already satisfied: setuptools in c:\\coding\\anaconda3\\lib\\site-packages (from protobuf>=3.4.0->tensorboard) (39.1.0)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "distributed 1.21.8 requires msgpack, which is not installed.\n" ] } ], "source": [ "!pip install tensorboard" ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [], "source": [ "from mxboard import SummaryWriter\n", "sw = SummaryWriter(logdir='logs', flush_secs=5)" ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch 0. Loss: 1.2356886094411215, Train_acc 0.8395166666666667, Test_acc 0.8474\n", "Epoch 1. Loss: 0.46565542084376016, Train_acc 0.88465, Test_acc 0.8912\n", "Epoch 2. Loss: 0.3715192502895991, Train_acc 0.901, Test_acc 0.9029\n", "Epoch 3. Loss: 0.32939287207126616, Train_acc 0.91, Test_acc 0.9112\n", "Epoch 4. Loss: 0.30172612600326537, Train_acc 0.91625, Test_acc 0.9184\n", "Epoch 5. Loss: 0.2804558823386828, Train_acc 0.9217333333333333, Test_acc 0.921\n", "Epoch 6. Loss: 0.2626380964756012, Train_acc 0.9262666666666667, Test_acc 0.9252\n", "Epoch 7. Loss: 0.24708882774909338, Train_acc 0.9300333333333334, Test_acc 0.9296\n", "Epoch 8. Loss: 0.23393845278819403, Train_acc 0.9341833333333334, Test_acc 0.9332\n", "Epoch 9. Loss: 0.22262413431803385, Train_acc 0.93545, Test_acc 0.9352\n" ] } ], "source": [ "epochs = 10\n", "smoothing_constant = .01\n", "\n", "# collect parameter names for logging the gradients of parameters in each epoch\n", "params = net.collect_params()\n", "param_names = params.keys()\n", "global_step = 0\n", "\n", "for e in range(epochs):\n", " cumulative_loss = 0\n", " for i, (data, label) in enumerate(train_data):\n", " data = data.as_in_context(model_ctx).reshape((-1, 784))\n", " label = label.as_in_context(model_ctx)\n", " with autograd.record():\n", " output = net(data)\n", " loss = softmax_cross_entropy(output, label)\n", " \n", " sw.add_scalar(tag='cross_entropy', value=loss.mean().asscalar(), global_step=global_step)\n", " if i == 0:\n", " sw.add_image('minist_first_minibatch', data.reshape((batch_size, 1, 28, 28)), e)\n", " if e == 0:\n", " sw.add_graph(net)\n", " grads = [i.grad() for i in net.collect_params().values()]\n", " for i, name in enumerate(param_names):\n", " sw.add_histogram(tag=name, values=grads[i], global_step=e, bins=1000)\n", "\n", " global_step += 1\n", " loss.backward()\n", " trainer.step(data.shape[0])\n", " cumulative_loss += nd.sum(loss).asscalar()\n", "\n", " \n", " \n", " test_accuracy = evaluate_accuracy(test_data, net)\n", " train_accuracy = evaluate_accuracy(train_data, net)\n", " sw.add_scalar(tag='accuracy_curves', value=('train_acc', train_accuracy), global_step=e)\n", " sw.add_scalar(tag='accuracy_curves', value=('valid_acc', test_accuracy), global_step=e)\n", " print(\"Epoch %s. Loss: %s, Train_acc %s, Test_acc %s\" %\n", " (e, cumulative_loss/num_examples, train_accuracy, test_accuracy))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "\n", "\n", "\n", "\n", "" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.5" } }, "nbformat": 4, "nbformat_minor": 2 }

mit

the-deep-learners/TensorFlow-LiveLessons

notebooks/first_tensorflow_neurons.ipynb

1

10078

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# First TensorFlow Neurons" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Load dependencies" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import numpy as np\n", "np.random.seed(42)\n", "import tensorflow as tf\n", "tf.set_random_seed(42)\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Set number of neurons" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "n_input = 784\n", "n_dense = 128" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Define placeholder Tensor for simulated MNIST digits" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "x = tf.placeholder(tf.float32, [None, n_input])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Create Variable Tensors for neuron biases `b` and weight matrix `W`" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "b = tf.Variable(tf.zeros([n_dense]))\n", "W = tf.Variable(tf.random_uniform([n_input, n_dense])) # 1.\n", "# W = tf.Variable(tf.random_normal([n_input, n_dense])) # 2.\n", "# W = tf.get_variable('W', [n_input, n_dense], \n", "# initializer=tf.contrib.layers.xavier_initializer())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Design the computational graph" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "z = tf.add(tf.matmul(x, W), b)\n", "a = tf.sigmoid(z) # first with tf.sigmoid(), then stick with tf.tanh() or tf.nn.relu()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Create op for variable initialization" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "initializer_op = tf.global_variables_initializer()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Execute the graph in a session" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true }, "outputs": [], "source": [ "with tf.Session() as session:\n", " session.run(initializer_op)\n", " \n", " layer_output = session.run(a, {x: np.random.random([1, n_input])})" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "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.]], dtype=float32)" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "layer_output" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7fb3307bd470>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "_ = plt.hist(np.transpose(layer_output))" ] }, { "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

telecom-research/crtc-scraper

_code/notebooks/CRTC-Hearing-TextAnalysis.ipynb

1

33477

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# CRTC Hearing Text Analysis" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The purpose of this notebook is to illustrate the method of text analysis using a corpus created from digital content published by the CRTC. This is the second part in a two-part process, the first of which is a description of the code that 'scraped' the CRTC webpage to create the corpus. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Setting Up" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The code below imports the modules that are required to process the text. " ] }, { "cell_type": "code", "execution_count": 63, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# importing code modules\n", "import json\n", "import ijson\n", "from ijson import items\n", "\n", "import pprint\n", "from tabulate import tabulate\n", "\n", "import matplotlib.pyplot as plt\n", "\n", "import re\n", "import csv\n", "import sys\n", "import codecs\n", "\n", "import nltk\n", "import nltk.collocations\n", "import collections\n", "import statistics\n", "from nltk.metrics.spearman import *\n", "from nltk.collocations import *\n", "from nltk.stem import WordNetLemmatizer\n", "\n", "\n", "# This is a function for reading the contents of files\n", "def read_file(filename):\n", " \"Read the contents of FILENAME and return as a string.\"\n", " infile = codecs.open(filename, 'r', 'utf-8')\n", " contents = infile.read()\n", " infile.close()\n", " return contents" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Reading the File\n", "This code loads and then reads the necessary files: the `json` file with all the hearing text, and a `txt` file with a list of stopwords, taken from here: http://www.lextek.com/manuals/onix/stopwords2.html. I've also added a few custom words." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# loading the JSON file\n", "filename = \"../scrapy/hearing_result6.json\"\n", "\n", "# loading the stopwords file\n", "stopwords = read_file('cornellStopWords.txt')\n", "customStopwords = stopwords.split()" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# reads the file and assigns the keys and values to a Python dictionary structure\n", "with open(filename, 'r') as f:\n", " objects = ijson.items(f, 'item')\n", " file = list(objects)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A bit of error checking here to confirm the number of records in the file. We should have 14." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "14\n" ] } ], "source": [ "# checks to see how many records we have\n", "print(len(file))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Changing the number in the code below will print a different record from the file. Please remember that in coding, numbered lists begin at `0`." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# commenting this out to make the github notebook more readable.\n", "# prints all content in a single record. Changing the number shows a different record\n", "file[0]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here is a bit more error checking to confirm the record titles and their urls." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# iterates through each record in the file\n", "for row in file:\n", " # prints the title of each record and its url\n", " print(row['title'], \":\", row['url'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And a bit more processing to make the text more readable. It's printed below." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# appends all of the text items to a single string object (rather than a list)\n", "joined_text = []\n", "for row in file:\n", " joined_text.append(' '.join(row['text']))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# shows the text. Changing the number displays a different record...\n", "# ...changing/removing the second number limits/expands the text shown.\n", "print(joined_text[5][:750])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "-----" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Text Analysis Processing\n", "This is the begining of the first processing for the text analysis. Here we will split all the words apart, make them all lowercase, and remove the punctuation, numbers, and words on the stopword list." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true, "scrolled": true }, "outputs": [], "source": [ "# splits the text string in each record into a list of separate words\n", "token_joined = []\n", "for words in joined_text:\n", " # splits the text into a list of words\n", " text = words.split()\n", " # makes all words lowercase\n", " clean = [w.lower() for w in text if w.isalpha()]\n", " # applies stopword removal\n", " text = [w for w in clean if w not in customStopwords]\n", " token_joined.append(text)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Since a word of interest is `guarantee`, here is a list of how many times that word (and its variations) appear in each record. " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#for title,word in zip(file,token_joined):\n", " # print(title['title'],\"guarantee:\", word.count('guarantee'), \"guarantees:\", \\\n", " # word.count('guarantees'), \"guaranteed:\", word.count('guaranteed'))" ] }, { "cell_type": "code", "execution_count": 132, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Transcript, Hearing April 11, 2016 service: 210 services: 103\n", "Transcript, Hearing April 12, 2016 service: 166 services: 74\n", "Transcript, Hearing April 13, 2016 service: 98 services: 49\n", "Transcript, Hearing April 14, 2016 service: 98 services: 31\n", "Transcript, Hearing April 15, 2016 service: 76 services: 31\n", "Transcript, Hearing April 18, 2016 service: 215 services: 79\n", "Transcript, Hearing April 19, 2016 service: 90 services: 46\n", "Transcript, Hearing April 20, 2016 service: 118 services: 60\n", "Transcript, Hearing April 21, 2016 service: 137 services: 52\n", "Transcript, Hearing April 22, 2016 service: 30 services: 30\n", "Transcript, Hearing April 25, 2016 service: 208 services: 60\n", "Transcript, Hearing April 26, 2016 service: 157 services: 60\n", "Transcript, Hearing April 27, 2016 service: 62 services: 28\n", "Transcript, Hearing April 28, 2016 service: 34 services: 20\n" ] } ], "source": [ "for title,word in zip(file,token_joined):\n", " print(title['title'],\"service:\", word.count('service'),\"services:\", word.count('services'))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Concordance\n", "It looks like record number 5 has the most occurences of the word `guarantee`. The code below isolates the record and creates a concordance based on the selected word." ] }, { "cell_type": "code", "execution_count": 138, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# splits the text from the record into a list of individual words\n", "words = joined_text[0].split()\n", "#assigns NLTK functionality to the text\n", "text = nltk.Text(words)" ] }, { "cell_type": "code", "execution_count": 135, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Displaying 25 of 79 matches:\n", "tion like YouTube with captioning; services supplied online using sign languag\n", " sign language, for example, relay services like video relay interpreting and \n", "want to add that telecommunication services should be recognized as a basic se\n", " catch up. 7012 Broadband internet services must be defined as a basic service\n", "indication display of what type of services would be offered for the deaf comm\n", "’m unaware of any direct frontline services provided in sign language. But I a\n", "us about getting rid of any of the services we have currently. 7072 Now, maybe\n", "one who works at telecommunication services to understand deaf culture and per\n", "d, so that we can provide the best services for our community going forward. 7\n", "oned earlier that the packages and services available to the deaf community we\n", "herwise, they’ll be unaware of any services being offered. 7100 MR. ROOTS: Jim\n", " enhance our understanding of what services are available and see which produc\n", "ailable and see which products and services will meet our needs. 7108 And that\n", "ordable and reliable communication services are increasingly essential, for th\n", "ibility of media and communication services by 2020. 7177 The Access 2020 Coal\n", "eliable, fixed and mobile internet services that are essential for participati\n", "excluding broadband from the basic services framework. 7187 We recognize that \n", "t the affordability and quality of services that are available to them. 7191 E\n", "cess to fixed and mobile broadband services and the need for deploying the wid\n", "ents in improving accessibility of services they deliver or responding to the \n", "es can access basic communications services that are essential to their abilit\n", "ccess and use basic communications services they need. 7206 Even though incumb\n", "her information and communications services and applications. 7207 We submit t\n", "cerns about affordability of basic services for Canadians with low incomes, as\n", "to and use of basic communications services by Canadians with severe or very s\n", "None\n" ] } ], "source": [ "# prints a concordance output for the selected word (shown in green)\n", "print(text.concordance('services', lines=25))" ] }, { "cell_type": "code", "execution_count": 140, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#creates a new file that can be written by the print queue\n", "fileconcord = codecs.open('April11_service_concord.txt', 'w', 'utf-8')\n", "#makes a copy of the empty print queue, so that we can return to it at the end of the function\n", "tmpout = sys.stdout\n", "#stores the text in the print queue\n", "sys.stdout = fileconcord\n", "#generates and prints the concordance, the number pertains to the total number of bytes per line\n", "text.concordance(\"service\", 79, sys.maxsize)\n", "#closes the file\n", "fileconcord.close()\n", "#returns the print queue to an empty state\n", "sys.stdout = tmpout" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Below is what the text looks like after the initial processing, without punctuation, numbers, or stopwords." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# shows the text list for a given record. Changing the first number displays a... \n", "# ...different record, changing/removing the second number limits/expands the text shown\n", "print(token_joined[5][:50])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Lemmatization\n", "Some more preparation for the text processing. The code below works on the all of the records, creating one master list of words which is then lemmatized." ] }, { "cell_type": "code", "execution_count": 141, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# creates a variable for the lemmatizing function\n", "wnl = WordNetLemmatizer()\n", "\n", "# lemmatizes all of the verbs\n", "\n", "lemm = []\n", "for record in token_joined:\n", " for word in record:\n", " lemm.append(wnl.lemmatize(word, 'v'))\n", "'''\n", "lemm = []\n", "for word in token_joined[13]:\n", " lemm.append(wnl.lemmatize(word, 'v'))\n", "'''\n", "\n", "# lemmatizes all of the nouns \n", "lems = []\n", "for word in lemm:\n", " lems.append(wnl.lemmatize(word, 'n'))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here we are checking to make sure the lemmatizer has worked. Now the word `guarantee` only appears in one form." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# just making sure the lemmatizer has worked\n", "#print(\"guarantee:\", lems.count('guarantee'), \"guarantees:\", \\\n", " # lems.count('guarantees'), \"guaranteed:\", lems.count('guaranteed'))" ] }, { "cell_type": "code", "execution_count": 142, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "service: 2435 0\n" ] } ], "source": [ "print(\"service:\", lems.count('service'), lems.count('services'))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Word Frequency\n", "Here is a count of the number of words in each record. While this data isn't terribly useful 'as is', we can make a few assumptions about the text here. Notably that some of the hearings were much longer than others." ] }, { "cell_type": "code", "execution_count": 108, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Transcript, Hearing April 11, 2016 : 16664 words\n", "Transcript, Hearing April 12, 2016 : 12891 words\n", "Transcript, Hearing April 13, 2016 : 8423 words\n", "Transcript, Hearing April 14, 2016 : 8319 words\n", "Transcript, Hearing April 15, 2016 : 4840 words\n", "Transcript, Hearing April 18, 2016 : 13523 words\n", "Transcript, Hearing April 19, 2016 : 10184 words\n", "Transcript, Hearing April 20, 2016 : 8541 words\n", "Transcript, Hearing April 21, 2016 : 12865 words\n", "Transcript, Hearing April 22, 2016 : 3400 words\n", "Transcript, Hearing April 25, 2016 : 14791 words\n", "Transcript, Hearing April 26, 2016 : 11804 words\n", "Transcript, Hearing April 27, 2016 : 7454 words\n", "Transcript, Hearing April 28, 2016 : 6803 words\n" ] } ], "source": [ "# counting the number of words in each record \n", "for name, each in zip(file,token_joined):\n", " print(name['title'], \":\",len(each), \"words\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here we will count the five most common words in each record." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "docfreq = []\n", "for words in token_joined:\n", " docfreq.append(nltk.FreqDist(words))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "for name, words in zip(file_obj, docfreq):\n", " print(name['title'], \":\", words.most_common(5))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "These are the 10 most common word pairs in the text." ] }, { "cell_type": "code", "execution_count": 143, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "service provider; market force; digital literacy; basic service; data\n", "cap; eastern ontario; fix wireless; private sector; low income; rural\n", "remote\n" ] } ], "source": [ "# prints the 10 most common bigrams\n", "colText = nltk.Text(lems)\n", "colText.collocations(10)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Error checking to make sure the code is processing the text properly." ] }, { "cell_type": "code", "execution_count": 115, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[('hear', 'april'),\n", " ('april', 'quebec'),\n", " ('quebec', 'april'),\n", " ('april', 'copyright'),\n", " ('copyright', 'reserve')]" ] }, "execution_count": 115, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# creates a list of bigrams (ngrams of 2), printing the first 5\n", "colBigrams = list(nltk.ngrams(colText, 2)) \n", "colBigrams[:5]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "More error checking." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# error checking. There should be one less bigram than total words\n", "print(\"Number of words:\", len(lems))\n", "print(\"Number of bigrams:\", len(colBigrams))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Below is a frequency plot showing the occurence of the 25 most frequent words." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# frequency plot with stopwords removed\n", "%matplotlib inline\n", "plt.rcParams['figure.figsize'] = (10.0, 10.0)\n", "fd = nltk.FreqDist(colText)\n", "fd.plot(25)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Collocations\n", "Here we are preparing the text to search for bigrams containing the word `guarantee`. This code searches for words appearing before and after `guarantee` with a window size of two words on either side." ] }, { "cell_type": "code", "execution_count": 144, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# loads bigram code from NLTK\n", "bigram_measures = nltk.collocations.BigramAssocMeasures()\n", "# bigrams with a window size of 2 words\n", "finder = BigramCollocationFinder.from_words(lems, window_size = 2)\n", "# ngrams with 'word of interest' as a member\n", "word_filter = lambda *w: 'service' not in w\n", "# only bigrams that contain the 'word of interest'\n", "finder.apply_ngram_filter(word_filter)" ] }, { "cell_type": "code", "execution_count": 145, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# filter results based on statistical test\n", "\n", "# calulates the raw frequency as an actual number and percentage of total words\n", "act = finder.ngram_fd.items()\n", "raw = finder.score_ngrams(bigram_measures.raw_freq)\n", "# log-likelihood ratio\n", "log = finder.score_ngrams(bigram_measures.likelihood_ratio)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Research shows that this is the most reliable statistical test for unreliable data. \n", "\n", "**Log-Likelihood Ratio**\n", "\n", "The Log-likelihood ratio calculates the size and significance between the observed and expected frequencies of bigrams and assigns a score based on the result, taking into account the overall size of the corpus. The larger the difference between the observed and expected, the higher the score, and the more statistically significant the collocate is.\n", "The Log-likelihood ratio is my preferred test for collocates because it does not rely on a normal distribution, and for this reason, it can account for sparse or low frequency bigrams. It does not over-represent low frequency bigrams with inflated scores, as the test is only reporting how much more likely it is that the frequencies are different than they are the same. The drawback to the Log-likelihood ratio is that it cannot be used to compare scores across corpora." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "An important note here that **words will appear twice** in the following list. As the ngrams can appear both before and after the word, care must be taken to identify duplicate occurences in the list below and then combine the totals." ] }, { "cell_type": "code", "execution_count": 118, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Collocate Log-Likelihood\n", "------------------------------- ----------------\n", "('digital', 'literacy') 450.282\n", "('literacy', 'skill') 39.179\n", "('literacy', 'train') 12.498\n", "('iterate', 'literacy') 11.203\n", "('literacy', 'critically') 11.203\n", "('literacy', 'nowadays') 11.203\n", "('literacy', 'telecommunities') 11.203\n", "('literacy', 'mean') 9.784\n", "('literacy', 'conceive') 7.399\n", "('literacy', 'throw') 7.399\n", "('literacy', 'express') 6.726\n", "('literacy', 'factor') 6.726\n", "('literacy', 'free') 6.726\n", "('literacy', 'poverty') 6.228\n", "('literacy', 'primary') 5.833\n", "('literacy', 'actual') 5.505\n", "('literacy', 'reflect') 5.505\n", "('literacy', 'option') 5.226\n", "('literacy', 'potential') 4.983\n", "('literacy', 'benefit') 4.767\n", "('literacy', 'enhance') 4.767\n", "('literacy', 'hand') 4.767\n", "('literacy', 'responsibility') 4.767\n", "('literacy', 'involve') 4.574\n", "('literacy', 'extent') 4.400\n", "('literacy', 'job') 4.400\n", "('literacy', 'couple') 3.832\n", "('literacy', 'essential') 3.832\n", "('literacy', 'guess') 3.221\n", "('literacy', 'comment') 2.982\n", "('literacy', 'ensure') 2.982\n", "('literacy', 'increase') 2.982\n", "('literacy', 'country') 2.839\n", "('literacy', 'move') 2.773\n", "('literacy', 'bite') 2.647\n", "('bite', 'literacy') 2.647\n", "('literacy', 'time') 2.530\n", "('literacy', 'build') 2.370\n", "('literacy', 'kind') 1.826\n", "('literacy', 'part') 1.792\n", "('literacy', 'work') 1.164\n", "('fund', 'literacy') 1.144\n", "('literacy', 'issue') 0.930\n", "('literacy', 'digital') 0.854\n", "('literacy', 'make') 0.797\n", "('literacy', 'people') 0.441\n" ] } ], "source": [ "# prints list of results. \n", "print(tabulate(log, headers = [\"Collocate\", \"Log-Likelihood\"], floatfmt=\".3f\", \\\n", " numalign=\"left\"))" ] }, { "cell_type": "code", "execution_count": 129, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Collocate Actual\n", "------------------------------ --------\n", "('literacy', 'responsibility') 1\n", "('ask', 'literacy') 1\n", "('literacy', 'gap') 1\n", "('bridge', 'literacy') 1\n", "('literacy', 'support') 1\n", "('literacy', 'skill') 1\n", "('literacy', 'objective') 1\n", "('literacy', 'piece') 3\n", "('literacy', 'largely') 1\n", "('literacy', 'problem') 2\n", "('literacy', 'digital') 1\n", "('thought', 'literacy') 1\n", "('literacy', 'huge') 1\n", "('digital', 'literacy') 10\n" ] } ], "source": [ "# prints list of results. \n", "print(tabulate(act, headers = [\"Collocate\", \"Actual\"], floatfmt=\".3f\", \\\n", " numalign=\"left\"))" ] }, { "cell_type": "code", "execution_count": 97, "metadata": { "collapsed": true }, "outputs": [], "source": [ "with open('digital-literacy_collocate_Act.csv','w') as f:\n", " w = csv.writer(f)\n", " w.writerows(act)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here is an example of words appearing twice. Below are both instances of the ngram 'quality'. The first instance appears before 'guarantee' and the second occurs after." ] }, { "cell_type": "raw", "metadata": {}, "source": [ "Collocate Log-Likelihood\n", "---------------------------- ----------------\n", "('quality', 'guarantee') 76.826\n", "('guarantee', 'quality') 3.955\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A bit more processing to clean up the list." ] }, { "cell_type": "code", "execution_count": 146, "metadata": { "collapsed": true }, "outputs": [], "source": [ "##################################################################\n", "############### sorts list of log-likelihood scores ##############\n", "##################################################################\n", "\n", "# group bigrams by first and second word in bigram \n", "prefix_keys = collections.defaultdict(list)\n", "for key, l in log:\n", " # first word\n", " prefix_keys[key[0]].append((key[1], l))\n", " # second word\n", " prefix_keys[key[1]].append((key[0], l))\n", " \n", "# sort bigrams by strongest association \n", "for key in prefix_keys:\n", " prefix_keys[key].sort(key = lambda x: -x[1])\n", "\n", " # prints top 80 results\n", "logkeys = prefix_keys['service'][:80]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here is a list showing **only** the collocates for the word `guarantee`. Again, watch for duplicate words below." ] }, { "cell_type": "code", "execution_count": 147, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Collocate Log-Likelihood\n", "----------------- ----------------\n", "provider 1300.460\n", "basic 1084.240\n", "telecommunication 328.872\n", "internet 267.560\n", "quality 221.506\n", "objective 180.661\n", "provide 164.326\n", "broadband 141.885\n", "telephone 135.467\n", "universal 107.138\n", "social 101.779\n", "minimum 93.220\n", "wireless 90.850\n", "communication 90.108\n", "level 87.269\n", "essential 74.443\n", "telecom 69.281\n", "relay 58.368\n", "quality 55.463\n", "level 47.471\n", "improve 41.829\n", "offer 41.147\n", "include 39.013\n", "rural 37.936\n", "provide 36.162\n", "voice 33.907\n", "deliver 32.591\n", "meg 27.341\n", "agency 27.287\n", "obligation 24.594\n", "high 23.735\n", "discretionary 22.993\n", "offer 22.866\n", "emergency 22.337\n", "satellite 21.426\n", "cell 21.202\n", "product 20.738\n", "cellular 20.349\n", "extend 20.070\n", "improvement 19.911\n", "dsl 18.344\n", "deliver 17.959\n", "delivery 17.797\n", "overbuilt 16.223\n", "telecomm 16.223\n", "migrate 16.122\n", "fund 15.920\n", "canadian 14.736\n", "package 14.663\n", "voip 14.450\n", "definition 13.657\n", "comparable 13.302\n", "hear 12.441\n", "rep 12.439\n", "issue 12.342\n", "area 12.166\n", "purchase 11.911\n", "luxury 11.724\n", "outage 11.724\n", "application 11.496\n", "web 11.414\n", "provision 11.207\n", "bundle 11.192\n", "obtain 10.892\n", "work 10.836\n", "satisfactory 10.747\n", "type 10.657\n", "thing 10.461\n", "fibre 10.412\n", "mko 10.366\n", "directly 10.173\n", "wireline 10.120\n", "subsidy 10.043\n", "canadian 9.799\n", "delivery 9.625\n", "wrap 9.597\n", "point 9.336\n", "enhance 9.282\n", "sort 9.225\n", "extend 9.139\n" ] } ], "source": [ "from tabulate import tabulate\n", "print(tabulate(logkeys, headers = [\"Collocate\", \"Log-Likelihood\"], floatfmt=\".3f\", \\\n", " numalign=\"left\"))" ] }, { "cell_type": "code", "execution_count": 148, "metadata": { "collapsed": true }, "outputs": [], "source": [ "with open('service_collocate_Log.csv','w') as f:\n", " w = csv.writer(f)\n", " w.writerows(logkeys)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# working on a regex to split text by speaker\n", "diced = []\n", "for words in joined_text:\n", " diced.append(re.split('(\\d+(\\s)\\w+[A-Z](\\s|.\\s)\\w+[A-Z]:\\s)', words))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "print(diced[8])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "init_names = []\n", "for words in joined_text:\n", " init_names.append(set(re.findall('[A-Z]{3,}', words)))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "print(init_names)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "with open('initialNames.csv','w') as f:\n", " w = csv.writer(f)\n", " w.writerows(init_names)" ] } ], "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 }

mit

konstantinstadler/pymrio

doc/source/notebooks/autodownload.ipynb

1

22676

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Automatic downloading of MRIO databases" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Pymrio includes functions to automatically download some of the publicly available global EE MRIO databases.\n", "This is currently implemented for [EXIOBASE 3](https://doi.org/10.5281/zenodo.3583070), [OECD](https://www.oecd.org/sti/ind/inter-country-input-output-tables.htm) and [WIOD](http://www.wiod.org)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The functions described here download the raw data files. Thus, they can also be used for post processing by other tools." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## EXIOBASE 3 download" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "EXIOBASE 3 is licensed under the [Creative Commons Attribution 4.0 International-license](http://creativecommons.org/licenses/by/4.0/). Thus you can remix, tweak, and build upon EXIOBASE 3, even commercially, as long as you give credit to the EXIOBASE compilers. The suggested citation for EXIOBASE 3 is [Stadler et al 2018](https://doi.org/10.1111/jiec.12715). You can find more information, links to documentation as well as concordance matrices on the [EXIOBASE 3 Zenodo repository](https://doi.org/10.5281/zenodo.3583070). The download function of pymrio also downloads the files from this repository." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To download, start with:" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import pymrio" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "and define a folder for storing the data:" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "exio3_folder = \"/tmp/mrios/autodownload/EXIO3\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "With that we can start the download with (this might take a moment):" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "exio_meta = pymrio.download_exiobase3(\n", " storage_folder=exio3_folder, system=\"pxp\", years=[2011, 2012]\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The command above will download the latest EXIOBASE 3 tables in the product\n", "by product classification (system='pxp') for the years 2011 and 2012. Both\n", "parameters (system and years) are optional and when omitted the function will\n", "download all available files." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The function returns the meta data for the release (which is stored in ```metadata.json``` in the download folder).\n", "You can inspect the meta data by:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false, "jupyter": { "outputs_hidden": false } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Description: EXIOBASE3 metadata file for pymrio\n", "MRIO Name: EXIO3\n", "System: pxp\n", "Version: 10.5281/zenodo.3583070\n", "File: /tmp/mrios/autodownload/EXIO3/metadata.json\n", "History:\n", "20210223 15:40:31 - FILEIO - Downloaded https://zenodo.org/record/4277368/files/IOT_2012_pxp.zip to IOT_2012_pxp.zip\n", "20210223 15:38:16 - FILEIO - Downloaded https://zenodo.org/record/4277368/files/IOT_2011_pxp.zip to IOT_2011_pxp.zip\n" ] } ], "source": [ "print(exio_meta)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "By default, the download_exiobase3 fetches the latest version of EXIOBASE3\n", "available at the [EXIOBASE 3 Zenodo repository](https://doi.org/10.5281/zenodo.3583070).\n", "To download one of the previous versions specify the DOI with the doi\n", "parameter:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "prev_version_storage = \"/tmp/mrios/autodownload/EXIO3_7\"\n", "exio_meta_37 = pymrio.download_exiobase3(\n", " storage_folder=prev_version_storage,\n", " system=\"ixi\",\n", " years=2004,\n", " doi=\"10.5281/zenodo.3583071\",\n", ")" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Description: EXIOBASE3 metadata file for pymrio\n", "MRIO Name: EXIO3\n", "System: ixi\n", "Version: 10.5281/zenodo.3583071\n", "File: /tmp/mrios/autodownload/EXIO3_7/metadata.json\n", "History:\n", "20210223 15:43:42 - FILEIO - Downloaded https://zenodo.org/record/3583071/files/IOT_2004_ixi.zip to IOT_2004_ixi.zip\n" ] } ], "source": [ "print(exio_meta_37)" ] }, { "cell_type": "markdown", "metadata": { "lines_to_next_cell": 2 }, "source": [ "Currently (Feb 2021), the following versions are available. Please\n", "double-check at the [EXIOBASE 3 Zenodo\n", "repository](https://doi.org/10.5281/zenodo.3583070) (a box at the left\n", "sidebar titled 'Versions')\n", "\n", "- Version 3.7: 10.5281/zenodo.3583071 (only ixi files from 1995 to 2011 are\n", "available)\n", "- Version 3.8: 10.5281/zenodo.4277368" ] }, { "cell_type": "markdown", "metadata": { "lines_to_next_cell": 2 }, "source": [ "## WIOD download" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**DUE TO A RESTRUCTERING OF THE WIOD WEBPAGE THIS IS CURRENTLY BROKEN.**\n", "\n", "\n", "WIOD is licensed under the [Creative Commons Attribution 4.0 International-license](http://creativecommons.org/licenses/by/4.0/). Thus you can remix, tweak, and build upon WIOD, even commercially, as long as you give credit to WIOD. The WIOD web-page suggest to cite [Timmer et al. 2015](http://doi.wiley.com/10.1111/roie.12178) when you use the database. You can find more information on the [WIOD webpage](http://www.wiod.org)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The download function for WIOD currently processes the [2013 release version of WIOD](http://www.wiod.org/database/wiots13)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To download, start with:" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "import pymrio" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Define a folder for storing the data" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "wiod_folder = \"/tmp/mrios/autodownload/WIOD2013\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And start the download with (this will take a couple of minutes):" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "wiod_meta = pymrio.download_wiod2013(storage_folder=wiod_folder)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The function returns the meta data for the release (which is stored in ```metadata.json``` in the download folder).\n", "You can inspect the meta data by:" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false, "jupyter": { "outputs_hidden": false } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Description: WIOD metadata file for pymrio\n", "MRIO Name: WIOD\n", "System: IxI\n", "Version: data13\n", "File: /tmp/mrios/autodownload/WIOD2013/metadata.json\n", "History:\n", "20210223 15:46:26 - FILEIO - Downloaded http://www.wiod.org/protected3/data13/water/wat_may12.zip to wat_may12.zip\n", "20210223 15:46:25 - FILEIO - Downloaded http://www.wiod.org/protected3/data13/materials/mat_may12.zip to mat_may12.zip\n", "20210223 15:46:25 - FILEIO - Downloaded http://www.wiod.org/protected3/data13/land/lan_may12.zip to lan_may12.zip\n", "20210223 15:46:24 - FILEIO - Downloaded http://www.wiod.org/protected3/data13/AIR/AIR_may12.zip to AIR_may12.zip\n", "20210223 15:46:24 - FILEIO - Downloaded http://www.wiod.org/protected3/data13/CO2/CO2_may12.zip to CO2_may12.zip\n", "20210223 15:46:23 - FILEIO - Downloaded http://www.wiod.org/protected3/data13/EM/EM_may12.zip to EM_may12.zip\n", "20210223 15:46:22 - FILEIO - Downloaded http://www.wiod.org/protected3/data13/EU/EU_may12.zip to EU_may12.zip\n", "20210223 15:46:21 - FILEIO - Downloaded http://www.wiod.org/protected3/data13/SEA/WIOD_SEA_July14.xlsx to WIOD_SEA_July14.xlsx\n", "20210223 15:46:20 - FILEIO - Downloaded http://www.wiod.org/protected3/data13/update_sep12/wiot/wiot09_row_sep12.xlsx to wiot09_row_sep12.xlsx\n", "20210223 15:46:15 - FILEIO - Downloaded http://www.wiod.org/protected3/data13/wiot_analytic/wiot04_row_apr12.xlsx to wiot04_row_apr12.xlsx\n", " ... (more lines in history)\n" ] } ], "source": [ "print(wiod_meta)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The WIOD database provide data for several years and satellite accounts.\n", "In the default case, all of them are downloaded. You can, however, specify\n", "years and satellite account." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can specify the years as either int or string (2 or 4 digits):" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "res_years = [97, 2004, \"2005\"]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The available satellite accounts for WIOD are listed in the ```WIOD_CONFIG```.\n", "To get them import this dict by:" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "from pymrio.tools.iodownloader import WIOD_CONFIG" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false, "jupyter": { "outputs_hidden": false } }, "outputs": [ { "data": { "text/plain": [ "{'url_db_view': 'http://www.wiod.org/database/wiots13',\n", " 'url_db_content': 'http://www.wiod.org/',\n", " 'mrio_regex': 'protected.*?wiot\\\\d\\\\d.*?xlsx',\n", " 'satellite_urls': ['http://www.wiod.org/protected3/data13/SEA/WIOD_SEA_July14.xlsx',\n", " 'http://www.wiod.org/protected3/data13/EU/EU_may12.zip',\n", " 'http://www.wiod.org/protected3/data13/EM/EM_may12.zip',\n", " 'http://www.wiod.org/protected3/data13/CO2/CO2_may12.zip',\n", " 'http://www.wiod.org/protected3/data13/AIR/AIR_may12.zip',\n", " 'http://www.wiod.org/protected3/data13/land/lan_may12.zip',\n", " 'http://www.wiod.org/protected3/data13/materials/mat_may12.zip',\n", " 'http://www.wiod.org/protected3/data13/water/wat_may12.zip']}" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "WIOD_CONFIG" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To restrict this list, you can either copy paste the urls or automatically select the accounts:" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "sat_accounts = [\"EU\", \"CO2\"]\n", "res_satellite = [\n", " sat\n", " for sat in WIOD_CONFIG[\"satellite_urls\"]\n", " if any(acc in sat for acc in sat_accounts)\n", "]" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false, "jupyter": { "outputs_hidden": false } }, "outputs": [ { "data": { "text/plain": [ "['http://www.wiod.org/protected3/data13/EU/EU_may12.zip',\n", " 'http://www.wiod.org/protected3/data13/CO2/CO2_may12.zip']" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "res_satellite" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [], "source": [ "wiod_meta_res = pymrio.download_wiod2013(\n", " storage_folder=\"/tmp/foo_folder/WIOD2013_res\",\n", " years=res_years,\n", " satellite_urls=res_satellite,\n", ")" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false, "jupyter": { "outputs_hidden": false } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Description: WIOD metadata file for pymrio\n", "MRIO Name: WIOD\n", "System: IxI\n", "Version: data13\n", "File: /tmp/foo_folder/WIOD2013_res/metadata.json\n", "History:\n", "20210218 15:29:34 - FILEIO - Downloaded http://www.wiod.org/protected3/data13/wiot_analytic/wiot01_row_apr12.xlsx to wiot01_row_apr12.xlsx\n", "20210218 15:29:33 - FILEIO - Downloaded http://www.wiod.org/protected3/data13/wiot_analytic/wiot00_row_apr12.xlsx to wiot00_row_apr12.xlsx\n", "20210218 15:29:32 - FILEIO - Downloaded http://www.wiod.org/protected3/data13/CO2/CO2_may12.zip to CO2_may12.zip\n", "20210218 15:29:31 - FILEIO - Downloaded http://www.wiod.org/protected3/data13/EU/EU_may12.zip to EU_may12.zip\n", "20210218 15:29:30 - FILEIO - Downloaded http://www.wiod.org/protected3/data13/wiot_analytic/wiot04_row_apr12.xlsx to wiot04_row_apr12.xlsx\n", "20210218 15:29:27 - FILEIO - Downloaded http://www.wiod.org/protected3/data13/wiot_analytic/wiot97_row_apr12.xlsx to wiot97_row_apr12.xlsx\n", "20210218 15:29:26 - FILEIO - Downloaded http://www.wiod.org/protected3/data13/wiot_analytic/wiot05_row_apr12.xlsx to wiot05_row_apr12.xlsx\n" ] } ], "source": [ "print(wiod_meta_res)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Subsequent download will only catch files currently not present in the folder, e.g.:" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [], "source": [ "additional_years = [2000, 2001]\n", "wiod_meta_res = pymrio.download_wiod2013(\n", " storage_folder=\"/tmp/foo_folder/WIOD2013_res\",\n", " years=res_years + additional_years,\n", " satellite_urls=res_satellite,\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "only downloads the years given in ```additional_years```, appending these downloads to the meta data file." ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false, "jupyter": { "outputs_hidden": false } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Description: WIOD metadata file for pymrio\n", "MRIO Name: WIOD\n", "System: IxI\n", "Version: data13\n", "File: /tmp/foo_folder/WIOD2013_res/metadata.json\n", "History:\n", "20210218 15:29:34 - FILEIO - Downloaded http://www.wiod.org/protected3/data13/wiot_analytic/wiot01_row_apr12.xlsx to wiot01_row_apr12.xlsx\n", "20210218 15:29:33 - FILEIO - Downloaded http://www.wiod.org/protected3/data13/wiot_analytic/wiot00_row_apr12.xlsx to wiot00_row_apr12.xlsx\n", "20210218 15:29:32 - FILEIO - Downloaded http://www.wiod.org/protected3/data13/CO2/CO2_may12.zip to CO2_may12.zip\n", "20210218 15:29:31 - FILEIO - Downloaded http://www.wiod.org/protected3/data13/EU/EU_may12.zip to EU_may12.zip\n", "20210218 15:29:30 - FILEIO - Downloaded http://www.wiod.org/protected3/data13/wiot_analytic/wiot04_row_apr12.xlsx to wiot04_row_apr12.xlsx\n", "20210218 15:29:27 - FILEIO - Downloaded http://www.wiod.org/protected3/data13/wiot_analytic/wiot97_row_apr12.xlsx to wiot97_row_apr12.xlsx\n", "20210218 15:29:26 - FILEIO - Downloaded http://www.wiod.org/protected3/data13/wiot_analytic/wiot05_row_apr12.xlsx to wiot05_row_apr12.xlsx\n" ] } ], "source": [ "print(wiod_meta_res)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To catch all files, irrespective if present in the storage_folder or not pass ```overwrite_existing=True```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## OECD download" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The OECD Inter-Country Input-Output tables (ICIO) are available on the [OECD webpage.](https://www.oecd.org/sti/ind/inter-country-input-output-tables.htm) There is no specific licence given for the these tables, but the webpage state that \"Data can be downloaded for free\" (per July 2019).\n", "\n", "The download function works for both, the 2016 and 2018 release.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To download the data, we first define the folder for storing the data (these will be created if they do not exist yet):" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [], "source": [ "oecd_folder_v2018 = \"/tmp/mrios/autodownload/OECD_2018\"\n", "oecd_folder_v2016 = \"/tmp/mrios/autodownload/OECD_2016\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Than we can start the download with" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [], "source": [ "meta_2018 = pymrio.download_oecd(storage_folder=oecd_folder_v2018)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Be default, the 2018 release of the OECD - ICIO tables are downloaded.\n", "To retrieve the 2016 version, pass \"version='v2016\".\n", "\n", "As for WIOD, specific years can be specified by passing a list of years:" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [], "source": [ "meta_2016 = pymrio.download_oecd(\n", " storage_folder=oecd_folder_v2016, version=\"v2016\", years=[2003, 2008]\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Both functions return the meta data describing the download progress and MRIO info. Thus:" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Description: OECD-ICIO download\n", "MRIO Name: OECD-ICIO\n", "System: IxI\n", "Version: v2018\n", "File: /tmp/mrios/autodownload/OECD_2018/metadata.json\n", "History:\n", "20210223 16:00:46 - FILEIO - Downloaded http://stats.oecd.org/wbos/fileview2.aspx?IDFile=9f579ef3-4685-45e4-a0ba-d1acbd9755a6 to ICIO2018_2015.zip\n", "20210223 15:59:29 - FILEIO - Downloaded http://stats.oecd.org/wbos/fileview2.aspx?IDFile=0190bd9d-31d0-4171-bd1c-82d96b88e469 to ICIO2018_2014.zip\n", "20210223 15:58:34 - FILEIO - Downloaded http://stats.oecd.org/wbos/fileview2.aspx?IDFile=8c8ac674-1b6c-4c8e-94d1-158f06285659 to ICIO2018_2013.zip\n", "20210223 15:57:23 - FILEIO - Downloaded http://stats.oecd.org/wbos/fileview2.aspx?IDFile=cfd03495-8a90-4449-8097-a30f06853cab to ICIO2018_2012.zip\n", "20210223 15:56:04 - FILEIO - Downloaded http://stats.oecd.org/wbos/fileview2.aspx?IDFile=dc48c8c0-f200-487a-aecb-0c2c17fe3ddf to ICIO2018_2011.zip\n", "20210223 15:54:27 - FILEIO - Downloaded http://stats.oecd.org/wbos/fileview2.aspx?IDFile=16d04830-3c27-47a5-bc03-e429d27f585e to ICIO2018_2010.zip\n", "20210223 15:52:54 - FILEIO - Downloaded http://stats.oecd.org/wbos/fileview2.aspx?IDFile=4cc79090-d1ee-48b6-a252-e75312d32a1c to ICIO2018_2009.zip\n", "20210223 15:51:25 - FILEIO - Downloaded http://stats.oecd.org/wbos/fileview2.aspx?IDFile=1fd2fc03-c140-46f4-818e-9a66b671ff70 to ICIO2018_2008.zip\n", "20210223 15:50:08 - FILEIO - Downloaded http://stats.oecd.org/wbos/fileview2.aspx?IDFile=c4d4c21d-00db-48d8-9f9a-f722fcdca494 to ICIO2018_2007.zip\n", "20210223 15:48:55 - FILEIO - Downloaded http://stats.oecd.org/wbos/fileview2.aspx?IDFile=da62c835-f4fa-4450-bf19-1dd60f88a385 to ICIO2018_2006.zip\n", " ... (more lines in history)\n" ] } ], "source": [ "print(meta_2018)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Eora26 download" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Eora26 requires registration prior to download and therefore an automatic download has not been implemented.\n", "For further information check the download instruction at the [Eora26 example notebook.](working_with_eora26.ipynb#Getting-Eora26)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## EXIOBASE download (previous version 1 and 2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Previous EXIOBASE version requires registration prior to download and therefore an automatic download has not been implemented.\n", "For further information check the download instruction at the [EXIOBASE example notebook.](working_with_exiobase.ipynb#Getting-EXIOBASE)" ] } ], "metadata": { "anaconda-cloud": {}, "jupytext": { "formats": "ipynb,py:light" }, "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": "3.8.5" }, "toc-showmarkdowntxt": false, "toc-showtags": false }, "nbformat": 4, "nbformat_minor": 4 }

gpl-3.0

elizabetht/deep-learning

batch-norm/Batch_Normalization_Solutions.ipynb

1

45926

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Batch Normalization – Solutions" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Batch normalization is most useful when building deep neural networks. To demonstrate this, we'll create a convolutional neural network with 20 convolutional layers, followed by a fully connected layer. We'll use it to classify handwritten digits in the MNIST dataset, which should be familiar to you by now.\n", "\n", "This is **not** a good network for classfying MNIST digits. You could create a _much_ simpler network and get _better_ results. However, to give you hands-on experience with batch normalization, we had to make an example that was:\n", "1. Complicated enough that training would benefit from batch normalization.\n", "2. Simple enough that it would train quickly, since this is meant to be a short exercise just to give you some practice adding batch normalization.\n", "3. Simple enough that the architecture would be easy to understand without additional resources." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This notebook includes two versions of the network that you can edit. The first uses higher level functions from the `tf.layers` package. The second is the same network, but uses only lower level functions in the `tf.nn` package.\n", "\n", "1. [Batch Normalization with `tf.layers.batch_normalization`](#example_1)\n", "2. [Batch Normalization with `tf.nn.batch_normalization`](#example_2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The following cell loads TensorFlow, downloads the MNIST dataset if necessary, and loads it into an object named `mnist`. You'll need to run this cell before running anything else in the notebook." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Successfully downloaded train-images-idx3-ubyte.gz 9912422 bytes.\n", "Extracting MNIST_data/train-images-idx3-ubyte.gz\n", "Successfully downloaded train-labels-idx1-ubyte.gz 28881 bytes.\n", "Extracting MNIST_data/train-labels-idx1-ubyte.gz\n", "Successfully downloaded t10k-images-idx3-ubyte.gz 1648877 bytes.\n", "Extracting MNIST_data/t10k-images-idx3-ubyte.gz\n", "Successfully downloaded t10k-labels-idx1-ubyte.gz 4542 bytes.\n", "Extracting MNIST_data/t10k-labels-idx1-ubyte.gz\n" ] } ], "source": [ "import tensorflow as tf\n", "from tensorflow.examples.tutorials.mnist import input_data\n", "mnist = input_data.read_data_sets(\"MNIST_data/\", one_hot=True, reshape=False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Batch Normalization using `tf.layers.batch_normalization`<a id=\"example_1\"></a>\n", "\n", "This version of the network uses `tf.layers` for almost everything, and expects you to implement batch normalization using [`tf.layers.batch_normalization`](https://www.tensorflow.org/api_docs/python/tf/layers/batch_normalization) " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We'll use the following function to create fully connected layers in our network. We'll create them with the specified number of neurons and a ReLU activation function.\n", "\n", "This version of the function does not include batch normalization." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "\"\"\"\n", "DO NOT MODIFY THIS CELL\n", "\"\"\"\n", "def fully_connected(prev_layer, num_units):\n", " \"\"\"\n", " Create a fully connectd layer with the given layer as input and the given number of neurons.\n", " \n", " :param prev_layer: Tensor\n", " The Tensor that acts as input into this layer\n", " :param num_units: int\n", " The size of the layer. That is, the number of units, nodes, or neurons.\n", " :returns Tensor\n", " A new fully connected layer\n", " \"\"\"\n", " layer = tf.layers.dense(prev_layer, num_units, activation=tf.nn.relu)\n", " return layer" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We'll use the following function to create convolutional layers in our network. They are very basic: we're always using a 3x3 kernel, ReLU activation functions, strides of 1x1 on layers with odd depths, and strides of 2x2 on layers with even depths. We aren't bothering with pooling layers at all in this network.\n", "\n", "This version of the function does not include batch normalization." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "\"\"\"\n", "DO NOT MODIFY THIS CELL\n", "\"\"\"\n", "def conv_layer(prev_layer, layer_depth):\n", " \"\"\"\n", " Create a convolutional layer with the given layer as input.\n", " \n", " :param prev_layer: Tensor\n", " The Tensor that acts as input into this layer\n", " :param layer_depth: int\n", " We'll set the strides and number of feature maps based on the layer's depth in the network.\n", " This is *not* a good way to make a CNN, but it helps us create this example with very little code.\n", " :returns Tensor\n", " A new convolutional layer\n", " \"\"\"\n", " strides = 2 if layer_depth % 3 == 0 else 1\n", " conv_layer = tf.layers.conv2d(prev_layer, layer_depth*4, 3, strides, 'same', activation=tf.nn.relu)\n", " return conv_layer" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Run the following cell**, along with the earlier cells (to load the dataset and define the necessary functions). \n", "\n", "This cell builds the network **without** batch normalization, then trains it on the MNIST dataset. It displays loss and accuracy data periodically while training." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Batch: 0: Validation loss: 0.69066, Validation accuracy: 0.09580\n", "Batch: 25: Training loss: 0.33632, Training accuracy: 0.07812\n", "Batch: 50: Training loss: 0.32540, Training accuracy: 0.10938\n", "Batch: 75: Training loss: 0.32689, Training accuracy: 0.09375\n", "Batch: 100: Validation loss: 0.32562, Validation accuracy: 0.10700\n", "Batch: 125: Training loss: 0.32580, Training accuracy: 0.03125\n", "Batch: 150: Training loss: 0.32605, Training accuracy: 0.10938\n", "Batch: 175: Training loss: 0.32327, Training accuracy: 0.09375\n", "Batch: 200: Validation loss: 0.32601, Validation accuracy: 0.09860\n", "Batch: 225: Training loss: 0.32541, Training accuracy: 0.07812\n", "Batch: 250: Training loss: 0.32626, Training accuracy: 0.07812\n", "Batch: 275: Training loss: 0.32360, Training accuracy: 0.12500\n", "Batch: 300: Validation loss: 0.32589, Validation accuracy: 0.09240\n", "Batch: 325: Training loss: 0.32271, Training accuracy: 0.15625\n", "Batch: 350: Training loss: 0.32303, Training accuracy: 0.12500\n", "Batch: 375: Training loss: 0.32643, Training accuracy: 0.10938\n", "Batch: 400: Validation loss: 0.32498, Validation accuracy: 0.11260\n", "Batch: 425: Training loss: 0.32552, Training accuracy: 0.09375\n", "Batch: 450: Training loss: 0.32669, Training accuracy: 0.10938\n", "Batch: 475: Training loss: 0.32649, Training accuracy: 0.04688\n", "Batch: 500: Validation loss: 0.32516, Validation accuracy: 0.11000\n", "Batch: 525: Training loss: 0.32924, Training accuracy: 0.01562\n", "Batch: 550: Training loss: 0.32491, Training accuracy: 0.20312\n", "Batch: 575: Training loss: 0.32237, Training accuracy: 0.28125\n", "Batch: 600: Validation loss: 0.32532, Validation accuracy: 0.09240\n", "Batch: 625: Training loss: 0.32465, Training accuracy: 0.14062\n", "Batch: 650: Training loss: 0.32736, Training accuracy: 0.07812\n", "Batch: 675: Training loss: 0.32522, Training accuracy: 0.12500\n", "Batch: 700: Validation loss: 0.32503, Validation accuracy: 0.09860\n", "Batch: 725: Training loss: 0.32523, Training accuracy: 0.09375\n", "Batch: 750: Training loss: 0.32533, Training accuracy: 0.14062\n", "Batch: 775: Training loss: 0.32533, Training accuracy: 0.12500\n", "Final validation accuracy: 0.11420\n", "Final test accuracy: 0.12190\n", "Accuracy on 100 samples: 0.13\n" ] } ], "source": [ "\"\"\"\n", "DO NOT MODIFY THIS CELL\n", "\"\"\"\n", "def train(num_batches, batch_size, learning_rate):\n", " # Build placeholders for the input samples and labels \n", " inputs = tf.placeholder(tf.float32, [None, 28, 28, 1])\n", " labels = tf.placeholder(tf.float32, [None, 10])\n", " \n", " # Feed the inputs into a series of 20 convolutional layers \n", " layer = inputs\n", " for layer_i in range(1, 20):\n", " layer = conv_layer(layer, layer_i)\n", "\n", " # Flatten the output from the convolutional layers \n", " orig_shape = layer.get_shape().as_list()\n", " layer = tf.reshape(layer, shape=[-1, orig_shape[1] * orig_shape[2] * orig_shape[3]])\n", "\n", " # Add one fully connected layer\n", " layer = fully_connected(layer, 100)\n", "\n", " # Create the output layer with 1 node for each \n", " logits = tf.layers.dense(layer, 10)\n", " \n", " # Define \n", " model_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=logits, labels=labels))\n", " \n", " train_opt = tf.train.AdamOptimizer(learning_rate).minimize(model_loss)\n", " \n", " correct_prediction = tf.equal(tf.argmax(logits,1), tf.argmax(labels,1))\n", " accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))\n", " \n", " # Train and test the network\n", " with tf.Session() as sess:\n", " sess.run(tf.global_variables_initializer())\n", " for batch_i in range(num_batches):\n", " batch_xs, batch_ys = mnist.train.next_batch(batch_size)\n", "\n", " # train this batch\n", " sess.run(train_opt, {inputs: batch_xs, \n", " labels: batch_ys})\n", " \n", " # Periodically check the validation or training loss and accuracy\n", " if batch_i % 100 == 0:\n", " loss, acc = sess.run([model_loss, accuracy], {inputs: mnist.validation.images,\n", " labels: mnist.validation.labels})\n", " print('Batch: {:>2}: Validation loss: {:>3.5f}, Validation accuracy: {:>3.5f}'.format(batch_i, loss, acc))\n", " elif batch_i % 25 == 0:\n", " loss, acc = sess.run([model_loss, accuracy], {inputs: batch_xs, labels: batch_ys})\n", " print('Batch: {:>2}: Training loss: {:>3.5f}, Training accuracy: {:>3.5f}'.format(batch_i, loss, acc))\n", "\n", " # At the end, score the final accuracy for both the validation and test sets\n", " acc = sess.run(accuracy, {inputs: mnist.validation.images,\n", " labels: mnist.validation.labels})\n", " print('Final validation accuracy: {:>3.5f}'.format(acc))\n", " acc = sess.run(accuracy, {inputs: mnist.test.images,\n", " labels: mnist.test.labels})\n", " print('Final test accuracy: {:>3.5f}'.format(acc))\n", " \n", " # Score the first 100 test images individually, just to make sure batch normalization really worked\n", " correct = 0\n", " for i in range(100):\n", " correct += sess.run(accuracy,feed_dict={inputs: [mnist.test.images[i]],\n", " labels: [mnist.test.labels[i]]})\n", "\n", " print(\"Accuracy on 100 samples:\", correct/100)\n", "\n", "\n", "num_batches = 800\n", "batch_size = 64\n", "learning_rate = 0.002\n", "\n", "tf.reset_default_graph()\n", "with tf.Graph().as_default():\n", " train(num_batches, batch_size, learning_rate)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "With this many layers, it's going to take a lot of iterations for this network to learn. By the time you're done training these 800 batches, your final test and validation accuracies probably won't be much better than 10%. (It will be different each time, but will most likely be less than 15%.)\n", "\n", "Using batch normalization, you'll be able to train this same network to over 90% in that same number of batches.\n", "\n", "# Add batch normalization\n", "\n", "To add batch normalization to the layers created by `fully_connected`, we did the following:\n", "1. Added the `is_training` parameter to the function signature so we can pass that information to the batch normalization layer.\n", "2. Removed the bias and activation function from the `dense` layer.\n", "3. Used `tf.layers.batch_normalization` to normalize the layer's output. Notice we pass `is_training` to this layer to ensure the network updates its population statistics appropriately.\n", "4. Passed the normalized values into a ReLU activation function." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def fully_connected(prev_layer, num_units, is_training):\n", " \"\"\"\n", " Create a fully connectd layer with the given layer as input and the given number of neurons.\n", " \n", " :param prev_layer: Tensor\n", " The Tensor that acts as input into this layer\n", " :param num_units: int\n", " The size of the layer. That is, the number of units, nodes, or neurons.\n", " :param is_training: bool or Tensor\n", " Indicates whether or not the network is currently training, which tells the batch normalization\n", " layer whether or not it should update or use its population statistics.\n", " :returns Tensor\n", " A new fully connected layer\n", " \"\"\"\n", " layer = tf.layers.dense(prev_layer, num_units, use_bias=False, activation=None)\n", " layer = tf.layers.batch_normalization(layer, training=is_training)\n", " layer = tf.nn.relu(layer)\n", " return layer" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To add batch normalization to the layers created by `conv_layer`, we did the following:\n", "1. Added the `is_training` parameter to the function signature so we can pass that information to the batch normalization layer.\n", "2. Removed the bias and activation function from the `conv2d` layer.\n", "3. Used `tf.layers.batch_normalization` to normalize the convolutional layer's output. Notice we pass `is_training` to this layer to ensure the network updates its population statistics appropriately.\n", "4. Passed the normalized values into a ReLU activation function.\n", "\n", "If you compare this function to `fully_connected`, you'll see that – when using `tf.layers` – there really isn't any difference between normalizing a fully connected layer and a convolutional layer. However, if you look at the second example in this notebook, where we restrict ourselves to the `tf.nn` package, you'll see a small difference." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def conv_layer(prev_layer, layer_depth, is_training):\n", " \"\"\"\n", " Create a convolutional layer with the given layer as input.\n", " \n", " :param prev_layer: Tensor\n", " The Tensor that acts as input into this layer\n", " :param layer_depth: int\n", " We'll set the strides and number of feature maps based on the layer's depth in the network.\n", " This is *not* a good way to make a CNN, but it helps us create this example with very little code.\n", " :param is_training: bool or Tensor\n", " Indicates whether or not the network is currently training, which tells the batch normalization\n", " layer whether or not it should update or use its population statistics.\n", " :returns Tensor\n", " A new convolutional layer\n", " \"\"\"\n", " strides = 2 if layer_depth % 3 == 0 else 1\n", " conv_layer = tf.layers.conv2d(prev_layer, layer_depth*4, 3, strides, 'same', use_bias=False, activation=None)\n", " conv_layer = tf.layers.batch_normalization(conv_layer, training=is_training)\n", " conv_layer = tf.nn.relu(conv_layer)\n", "\n", " return conv_layer" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Batch normalization is still a new enough idea that researchers are still discovering how best to use it. In general, people seem to agree to remove the layer's bias (because the batch normalization already has terms for scaling and shifting) and add batch normalization _before_ the layer's non-linear activation function. However, for some networks it will work well in other ways, too. \n", "\n", "Just to demonstrate this point, the following three versions of `conv_layer` show other ways to implement batch normalization. If you try running with any of these versions of the function, they should all still work fine (although some versions may still work better than others). \n", "\n", "**Alternate solution that uses bias in the convolutional layer but still adds batch normalization before the ReLU activation function.**" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def conv_layer(prev_layer, layer_num, is_training):\n", " strides = 2 if layer_num % 3 == 0 else 1\n", " conv_layer = tf.layers.conv2d(prev_layer, layer_num*4, 3, strides, 'same', use_bias=True, activation=None)\n", " conv_layer = tf.layers.batch_normalization(conv_layer, training=is_training)\n", " conv_layer = tf.nn.relu(conv_layer)\n", " return conv_layer" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Alternate solution that uses a bias and ReLU activation function _before_ batch normalization.**" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def conv_layer(prev_layer, layer_num, is_training):\n", " strides = 2 if layer_num % 3 == 0 else 1\n", " conv_layer = tf.layers.conv2d(prev_layer, layer_num*4, 3, strides, 'same', use_bias=True, activation=tf.nn.relu)\n", " conv_layer = tf.layers.batch_normalization(conv_layer, training=is_training)\n", " return conv_layer" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Alternate solution that uses a ReLU activation function _before_ normalization, but no bias.**" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def conv_layer(prev_layer, layer_num, is_training):\n", " strides = 2 if layer_num % 3 == 0 else 1\n", " conv_layer = tf.layers.conv2d(prev_layer, layer_num*4, 3, strides, 'same', use_bias=False, activation=tf.nn.relu)\n", " conv_layer = tf.layers.batch_normalization(conv_layer, training=is_training)\n", " return conv_layer" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To modify `train`, we did the following:\n", "1. Added `is_training`, a placeholder to store a boolean value indicating whether or not the network is training.\n", "2. Passed `is_training` to the `conv_layer` and `fully_connected` functions.\n", "3. Each time we call `run` on the session, we added to `feed_dict` the appropriate value for `is_training`.\n", "4. Moved the creation of `train_opt` inside a `with tf.control_dependencies...` statement. This is necessary to get the normalization layers created with `tf.layers.batch_normalization` to update their population statistics, which we need when performing inference." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Batch: 0: Validation loss: 0.69135, Validation accuracy: 0.09860\n", "Batch: 25: Training loss: 0.58727, Training accuracy: 0.10938\n", "Batch: 50: Training loss: 0.47633, Training accuracy: 0.09375\n", "Batch: 75: Training loss: 0.40283, Training accuracy: 0.17188\n", "Batch: 100: Validation loss: 0.36413, Validation accuracy: 0.11260\n", "Batch: 125: Training loss: 0.34614, Training accuracy: 0.09375\n", "Batch: 150: Training loss: 0.34372, Training accuracy: 0.04688\n", "Batch: 175: Training loss: 0.31415, Training accuracy: 0.25000\n", "Batch: 200: Validation loss: 0.33029, Validation accuracy: 0.20880\n", "Batch: 225: Training loss: 0.37454, Training accuracy: 0.20312\n", "Batch: 250: Training loss: 0.35721, Training accuracy: 0.42188\n", "Batch: 275: Training loss: 0.30789, Training accuracy: 0.46875\n", "Batch: 300: Validation loss: 0.18574, Validation accuracy: 0.71100\n", "Batch: 325: Training loss: 0.22868, Training accuracy: 0.64062\n", "Batch: 350: Training loss: 0.27518, Training accuracy: 0.65625\n", "Batch: 375: Training loss: 0.05569, Training accuracy: 0.90625\n", "Batch: 400: Validation loss: 0.09854, Validation accuracy: 0.85200\n", "Batch: 425: Training loss: 0.04723, Training accuracy: 0.90625\n", "Batch: 450: Training loss: 0.02127, Training accuracy: 0.98438\n", "Batch: 475: Training loss: 0.00786, Training accuracy: 0.98438\n", "Batch: 500: Validation loss: 0.04686, Validation accuracy: 0.93780\n", "Batch: 525: Training loss: 0.02769, Training accuracy: 0.95312\n", "Batch: 550: Training loss: 0.00446, Training accuracy: 1.00000\n", "Batch: 575: Training loss: 0.05585, Training accuracy: 0.92188\n", "Batch: 600: Validation loss: 0.03143, Validation accuracy: 0.95780\n", "Batch: 625: Training loss: 0.00918, Training accuracy: 0.98438\n", "Batch: 650: Training loss: 0.03437, Training accuracy: 0.95312\n", "Batch: 675: Training loss: 0.03991, Training accuracy: 0.96875\n", "Batch: 700: Validation loss: 0.03455, Validation accuracy: 0.95300\n", "Batch: 725: Training loss: 0.03672, Training accuracy: 0.93750\n", "Batch: 750: Training loss: 0.03293, Training accuracy: 0.95312\n", "Batch: 775: Training loss: 0.02039, Training accuracy: 0.96875\n", "Final validation accuracy: 0.97460\n", "Final test accuracy: 0.97450\n", "Accuracy on 100 samples: 0.98\n" ] } ], "source": [ "def train(num_batches, batch_size, learning_rate):\n", " # Build placeholders for the input samples and labels \n", " inputs = tf.placeholder(tf.float32, [None, 28, 28, 1])\n", " labels = tf.placeholder(tf.float32, [None, 10])\n", "\n", " # Add placeholder to indicate whether or not we're training the model\n", " is_training = tf.placeholder(tf.bool)\n", "\n", " # Feed the inputs into a series of 20 convolutional layers \n", " layer = inputs\n", " for layer_i in range(1, 20):\n", " layer = conv_layer(layer, layer_i, is_training)\n", "\n", " # Flatten the output from the convolutional layers \n", " orig_shape = layer.get_shape().as_list()\n", " layer = tf.reshape(layer, shape=[-1, orig_shape[1] * orig_shape[2] * orig_shape[3]])\n", "\n", " # Add one fully connected layer\n", " layer = fully_connected(layer, 100, is_training)\n", "\n", " # Create the output layer with 1 node for each \n", " logits = tf.layers.dense(layer, 10)\n", " \n", " # Define loss and training operations\n", " model_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=logits, labels=labels))\n", " \n", " # Tell TensorFlow to update the population statistics while training\n", " with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):\n", " train_opt = tf.train.AdamOptimizer(learning_rate).minimize(model_loss)\n", " \n", " # Create operations to test accuracy\n", " correct_prediction = tf.equal(tf.argmax(logits,1), tf.argmax(labels,1))\n", " accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))\n", " \n", " # Train and test the network\n", " with tf.Session() as sess:\n", " sess.run(tf.global_variables_initializer())\n", " for batch_i in range(num_batches):\n", " batch_xs, batch_ys = mnist.train.next_batch(batch_size)\n", "\n", " # train this batch\n", " sess.run(train_opt, {inputs: batch_xs, labels: batch_ys, is_training: True})\n", " \n", " # Periodically check the validation or training loss and accuracy\n", " if batch_i % 100 == 0:\n", " loss, acc = sess.run([model_loss, accuracy], {inputs: mnist.validation.images,\n", " labels: mnist.validation.labels,\n", " is_training: False})\n", " print('Batch: {:>2}: Validation loss: {:>3.5f}, Validation accuracy: {:>3.5f}'.format(batch_i, loss, acc))\n", " elif batch_i % 25 == 0:\n", " loss, acc = sess.run([model_loss, accuracy], {inputs: batch_xs, labels: batch_ys, is_training: False})\n", " print('Batch: {:>2}: Training loss: {:>3.5f}, Training accuracy: {:>3.5f}'.format(batch_i, loss, acc))\n", "\n", " # At the end, score the final accuracy for both the validation and test sets\n", " acc = sess.run(accuracy, {inputs: mnist.validation.images,\n", " labels: mnist.validation.labels, \n", " is_training: False})\n", " print('Final validation accuracy: {:>3.5f}'.format(acc))\n", " acc = sess.run(accuracy, {inputs: mnist.test.images,\n", " labels: mnist.test.labels,\n", " is_training: False})\n", " print('Final test accuracy: {:>3.5f}'.format(acc))\n", " \n", " # Score the first 100 test images individually, just to make sure batch normalization really worked\n", " correct = 0\n", " for i in range(100):\n", " correct += sess.run(accuracy,feed_dict={inputs: [mnist.test.images[i]],\n", " labels: [mnist.test.labels[i]],\n", " is_training: False})\n", "\n", " print(\"Accuracy on 100 samples:\", correct/100)\n", "\n", "\n", "num_batches = 800\n", "batch_size = 64\n", "learning_rate = 0.002\n", "\n", "tf.reset_default_graph()\n", "with tf.Graph().as_default():\n", " train(num_batches, batch_size, learning_rate)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "With batch normalization, we now get excellent performance. In fact, validation accuracy is almost 94% after only 500 batches. Notice also the last line of the output: `Accuracy on 100 samples`. If this value is low while everything else looks good, that means you did not implement batch normalization correctly. Specifically, it means you either did not calculate the population mean and variance while training, or you are not using those values during inference.\n", "\n", "# Batch Normalization using `tf.nn.batch_normalization`<a id=\"example_2\"></a>\n", "\n", "Most of the time you will be able to use higher level functions exclusively, but sometimes you may want to work at a lower level. For example, if you ever want to implement a new feature – something new enough that TensorFlow does not already include a high-level implementation of it, like batch normalization in an LSTM – then you may need to know these sorts of things.\n", "\n", "This version of the network uses `tf.nn` for almost everything, and expects you to implement batch normalization using [`tf.nn.batch_normalization`](https://www.tensorflow.org/api_docs/python/tf/nn/batch_normalization).\n", "\n", "This implementation of `fully_connected` is much more involved than the one that uses `tf.layers`. However, if you went through the `Batch_Normalization_Lesson` notebook, things should look pretty familiar. To add batch normalization, we did the following:\n", "1. Added the `is_training` parameter to the function signature so we can pass that information to the batch normalization layer.\n", "2. Removed the bias and activation function from the `dense` layer.\n", "3. Added `gamma`, `beta`, `pop_mean`, and `pop_variance` variables.\n", "4. Used `tf.cond` to make handle training and inference differently.\n", "5. When training, we use `tf.nn.moments` to calculate the batch mean and variance. Then we update the population statistics and use `tf.nn.batch_normalization` to normalize the layer's output using the batch statistics. Notice the `with tf.control_dependencies...` statement - this is required to force TensorFlow to run the operations that update the population statistics.\n", "6. During inference (i.e. when not training), we use `tf.nn.batch_normalization` to normalize the layer's output using the population statistics we calculated during training.\n", "7. Passed the normalized values into a ReLU activation function.\n", "\n", "If any of thise code is unclear, it is almost identical to what we showed in the `fully_connected` function in the `Batch_Normalization_Lesson` notebook. Please see that for extensive comments. " ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def fully_connected(prev_layer, num_units, is_training):\n", " \"\"\"\n", " Create a fully connectd layer with the given layer as input and the given number of neurons.\n", " \n", " :param prev_layer: Tensor\n", " The Tensor that acts as input into this layer\n", " :param num_units: int\n", " The size of the layer. That is, the number of units, nodes, or neurons.\n", " :param is_training: bool or Tensor\n", " Indicates whether or not the network is currently training, which tells the batch normalization\n", " layer whether or not it should update or use its population statistics.\n", " :returns Tensor\n", " A new fully connected layer\n", " \"\"\"\n", "\n", " layer = tf.layers.dense(prev_layer, num_units, use_bias=False, activation=None)\n", "\n", " gamma = tf.Variable(tf.ones([num_units]))\n", " beta = tf.Variable(tf.zeros([num_units]))\n", "\n", " pop_mean = tf.Variable(tf.zeros([num_units]), trainable=False)\n", " pop_variance = tf.Variable(tf.ones([num_units]), trainable=False)\n", "\n", " epsilon = 1e-3\n", " \n", " def batch_norm_training():\n", " batch_mean, batch_variance = tf.nn.moments(layer, [0])\n", "\n", " decay = 0.99\n", " train_mean = tf.assign(pop_mean, pop_mean * decay + batch_mean * (1 - decay))\n", " train_variance = tf.assign(pop_variance, pop_variance * decay + batch_variance * (1 - decay))\n", "\n", " with tf.control_dependencies([train_mean, train_variance]):\n", " return tf.nn.batch_normalization(layer, batch_mean, batch_variance, beta, gamma, epsilon)\n", " \n", " def batch_norm_inference():\n", " return tf.nn.batch_normalization(layer, pop_mean, pop_variance, beta, gamma, epsilon)\n", "\n", " batch_normalized_output = tf.cond(is_training, batch_norm_training, batch_norm_inference)\n", " return tf.nn.relu(batch_normalized_output)" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "The changes we made to `conv_layer` are _almost_ exactly the same as the ones we made to `fully_connected`. However, there is an important difference. Convolutional layers have multiple feature maps, and each feature map uses shared weights. So we need to make sure we calculate our batch and population statistics **per feature map** instead of per node in the layer.\n", "\n", "To accomplish this, we do **the same things** that we did in `fully_connected`, with two exceptions:\n", "1. The sizes of `gamma`, `beta`, `pop_mean` and `pop_variance` are set to the number of feature maps (output channels) instead of the number of output nodes.\n", "2. We change the parameters we pass to `tf.nn.moments` to make sure it calculates the mean and variance for the correct dimensions." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def conv_layer(prev_layer, layer_depth, is_training):\n", " \"\"\"\n", " Create a convolutional layer with the given layer as input.\n", " \n", " :param prev_layer: Tensor\n", " The Tensor that acts as input into this layer\n", " :param layer_depth: int\n", " We'll set the strides and number of feature maps based on the layer's depth in the network.\n", " This is *not* a good way to make a CNN, but it helps us create this example with very little code.\n", " :param is_training: bool or Tensor\n", " Indicates whether or not the network is currently training, which tells the batch normalization\n", " layer whether or not it should update or use its population statistics.\n", " :returns Tensor\n", " A new convolutional layer\n", " \"\"\"\n", " strides = 2 if layer_depth % 3 == 0 else 1\n", " \n", " in_channels = prev_layer.get_shape().as_list()[3]\n", " out_channels = layer_depth*4\n", " \n", " weights = tf.Variable(\n", " tf.truncated_normal([3, 3, in_channels, out_channels], stddev=0.05))\n", " \n", " layer = tf.nn.conv2d(prev_layer, weights, strides=[1,strides, strides, 1], padding='SAME')\n", "\n", " gamma = tf.Variable(tf.ones([out_channels]))\n", " beta = tf.Variable(tf.zeros([out_channels]))\n", "\n", " pop_mean = tf.Variable(tf.zeros([out_channels]), trainable=False)\n", " pop_variance = tf.Variable(tf.ones([out_channels]), trainable=False)\n", "\n", " epsilon = 1e-3\n", " \n", " def batch_norm_training():\n", " # Important to use the correct dimensions here to ensure the mean and variance are calculated \n", " # per feature map instead of for the entire layer\n", " batch_mean, batch_variance = tf.nn.moments(layer, [0,1,2], keep_dims=False)\n", "\n", " decay = 0.99\n", " train_mean = tf.assign(pop_mean, pop_mean * decay + batch_mean * (1 - decay))\n", " train_variance = tf.assign(pop_variance, pop_variance * decay + batch_variance * (1 - decay))\n", "\n", " with tf.control_dependencies([train_mean, train_variance]):\n", " return tf.nn.batch_normalization(layer, batch_mean, batch_variance, beta, gamma, epsilon)\n", " \n", " def batch_norm_inference():\n", " return tf.nn.batch_normalization(layer, pop_mean, pop_variance, beta, gamma, epsilon)\n", "\n", " batch_normalized_output = tf.cond(is_training, batch_norm_training, batch_norm_inference)\n", " return tf.nn.relu(batch_normalized_output)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To modify `train`, we did the following:\n", "1. Added `is_training`, a placeholder to store a boolean value indicating whether or not the network is training.\n", "2. Each time we call `run` on the session, we added to `feed_dict` the appropriate value for `is_training`.\n", "3. We did **not** need to add the `with tf.control_dependencies...` statement that we added in the network that used `tf.layers.batch_normalization` because we handled updating the population statistics ourselves in `conv_layer` and `fully_connected`." ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Batch: 0: Validation loss: 0.68918, Validation accuracy: 0.09860\n", "Batch: 25: Training loss: 0.53546, Training accuracy: 0.07812\n", "Batch: 50: Training loss: 0.41245, Training accuracy: 0.09375\n", "Batch: 75: Training loss: 0.36121, Training accuracy: 0.07812\n", "Batch: 100: Validation loss: 0.33833, Validation accuracy: 0.09900\n", "Batch: 125: Training loss: 0.33615, Training accuracy: 0.06250\n", "Batch: 150: Training loss: 0.36635, Training accuracy: 0.04688\n", "Batch: 175: Training loss: 0.34496, Training accuracy: 0.07812\n", "Batch: 200: Validation loss: 0.34961, Validation accuracy: 0.09900\n", "Batch: 225: Training loss: 0.34695, Training accuracy: 0.17188\n", "Batch: 250: Training loss: 0.50702, Training accuracy: 0.04688\n", "Batch: 275: Training loss: 0.34169, Training accuracy: 0.20312\n", "Batch: 300: Validation loss: 0.36515, Validation accuracy: 0.16300\n", "Batch: 325: Training loss: 0.32103, Training accuracy: 0.29688\n", "Batch: 350: Training loss: 0.31099, Training accuracy: 0.31250\n", "Batch: 375: Training loss: 0.27836, Training accuracy: 0.43750\n", "Batch: 400: Validation loss: 0.32132, Validation accuracy: 0.39140\n", "Batch: 425: Training loss: 0.29945, Training accuracy: 0.45312\n", "Batch: 450: Training loss: 0.28822, Training accuracy: 0.43750\n", "Batch: 475: Training loss: 0.16682, Training accuracy: 0.70312\n", "Batch: 500: Validation loss: 0.14634, Validation accuracy: 0.75320\n", "Batch: 525: Training loss: 0.34097, Training accuracy: 0.45312\n", "Batch: 550: Training loss: 0.15460, Training accuracy: 0.78125\n", "Batch: 575: Training loss: 0.02774, Training accuracy: 0.95312\n", "Batch: 600: Validation loss: 0.03633, Validation accuracy: 0.94900\n", "Batch: 625: Training loss: 0.07376, Training accuracy: 0.90625\n", "Batch: 650: Training loss: 0.05857, Training accuracy: 0.93750\n", "Batch: 675: Training loss: 0.06562, Training accuracy: 0.92188\n", "Batch: 700: Validation loss: 0.05356, Validation accuracy: 0.92300\n", "Batch: 725: Training loss: 0.01616, Training accuracy: 0.98438\n", "Batch: 750: Training loss: 0.02600, Training accuracy: 0.96875\n", "Batch: 775: Training loss: 0.01844, Training accuracy: 0.96875\n", "Final validation accuracy: 0.95780\n", "Final test accuracy: 0.96050\n", "Accuracy on 100 samples: 0.98\n" ] } ], "source": [ "def train(num_batches, batch_size, learning_rate):\n", " # Build placeholders for the input samples and labels \n", " inputs = tf.placeholder(tf.float32, [None, 28, 28, 1])\n", " labels = tf.placeholder(tf.float32, [None, 10])\n", "\n", " # Add placeholder to indicate whether or not we're training the model\n", " is_training = tf.placeholder(tf.bool)\n", "\n", " # Feed the inputs into a series of 20 convolutional layers \n", " layer = inputs\n", " for layer_i in range(1, 20):\n", " layer = conv_layer(layer, layer_i, is_training)\n", "\n", " # Flatten the output from the convolutional layers \n", " orig_shape = layer.get_shape().as_list()\n", " layer = tf.reshape(layer, shape=[-1, orig_shape[1] * orig_shape[2] * orig_shape[3]])\n", "\n", " # Add one fully connected layer\n", " layer = fully_connected(layer, 100, is_training)\n", "\n", " # Create the output layer with 1 node for each \n", " logits = tf.layers.dense(layer, 10)\n", " \n", " # Define loss and training operations\n", " model_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=logits, labels=labels))\n", " train_opt = tf.train.AdamOptimizer(learning_rate).minimize(model_loss)\n", " \n", " # Create operations to test accuracy\n", " correct_prediction = tf.equal(tf.argmax(logits,1), tf.argmax(labels,1))\n", " accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))\n", " \n", " # Train and test the network\n", " with tf.Session() as sess:\n", " sess.run(tf.global_variables_initializer())\n", " for batch_i in range(num_batches):\n", " batch_xs, batch_ys = mnist.train.next_batch(batch_size)\n", "\n", " # train this batch\n", " sess.run(train_opt, {inputs: batch_xs, labels: batch_ys, is_training: True})\n", " \n", " # Periodically check the validation or training loss and accuracy\n", " if batch_i % 100 == 0:\n", " loss, acc = sess.run([model_loss, accuracy], {inputs: mnist.validation.images,\n", " labels: mnist.validation.labels,\n", " is_training: False})\n", " print('Batch: {:>2}: Validation loss: {:>3.5f}, Validation accuracy: {:>3.5f}'.format(batch_i, loss, acc))\n", " elif batch_i % 25 == 0:\n", " loss, acc = sess.run([model_loss, accuracy], {inputs: batch_xs, labels: batch_ys, is_training: False})\n", " print('Batch: {:>2}: Training loss: {:>3.5f}, Training accuracy: {:>3.5f}'.format(batch_i, loss, acc))\n", "\n", " # At the end, score the final accuracy for both the validation and test sets\n", " acc = sess.run(accuracy, {inputs: mnist.validation.images,\n", " labels: mnist.validation.labels, \n", " is_training: False})\n", " print('Final validation accuracy: {:>3.5f}'.format(acc))\n", " acc = sess.run(accuracy, {inputs: mnist.test.images,\n", " labels: mnist.test.labels,\n", " is_training: False})\n", " print('Final test accuracy: {:>3.5f}'.format(acc))\n", " \n", " # Score the first 100 test images individually, just to make sure batch normalization really worked\n", " correct = 0\n", " for i in range(100):\n", " correct += sess.run(accuracy,feed_dict={inputs: [mnist.test.images[i]],\n", " labels: [mnist.test.labels[i]],\n", " is_training: False})\n", "\n", " print(\"Accuracy on 100 samples:\", correct/100)\n", "\n", "\n", "num_batches = 800\n", "batch_size = 64\n", "learning_rate = 0.002\n", "\n", "tf.reset_default_graph()\n", "with tf.Graph().as_default():\n", " train(num_batches, batch_size, learning_rate)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Once again, the model with batch normalization quickly reaches a high accuracy. But in our run, notice that it doesn't seem to learn anything for the first 250 batches, then the accuracy starts to climb. That just goes to show - even with batch normalization, it's important to give your network a bit of time to learn before you decide it isn't working." ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 2 }

mit

dib-lab/kevlar

notebook/bigsim/roc-separate-withscalpel-anddiscosnp.ipynb

1

1018640

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "import re\n", "import sys\n", "import math\n", "import matplotlib\n", "import seaborn\n", "import numpy\n", "from matplotlib import pyplot as plt\n", "from collections import defaultdict\n", "\n", "from evalutils import IntervalForest, populate_index_from_bed, compact\n", "from evalutils import subset_variants_bed, load_kevlar_vcf, load_scalpel_vcf, load_discosnp_vcf, load_gatk_mvf, load_triodenovo_vcf\n", "import kevlar\n", "\n", "seaborn.set_context({'figure.figsize': (22, 11)})\n", "matplotlib.rcParams['axes.labelsize'] = 16\n", "matplotlib.rcParams['xtick.labelsize'] = 14\n", "matplotlib.rcParams['ytick.labelsize'] = 14" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "def roc(calls, index, delta=10, fmt='vcf'):\n", " ncorrect = 0\n", " num_true_calls_per_false_call = list()\n", " for varcall in calls:\n", " if fmt == 'vcf':\n", " valid = index.query(varcall.seqid, varcall.position, delta=delta) != set()\n", " elif fmt == 'mvf':\n", " callindex, call = varcall\n", " valid = index.query(call['CHROM'], call['POS'], delta=delta) != set()\n", " else:\n", " raise ValueError('unknown format \"'+ fmt +'\"')\n", " if valid:\n", " ncorrect += 1\n", " continue\n", " num_true_calls_per_false_call.append(ncorrect)\n", " if len(num_true_calls_per_false_call) == 0 or ncorrect > num_true_calls_per_false_call[-1]:\n", " num_true_calls_per_false_call.append(ncorrect)\n", " return num_true_calls_per_false_call" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "def doplot(axis, data, color, label, linestyle, symbol, msize, xmax):\n", " axis.plot(0, data[0], symbol, markersize=msize, color=color, label=label, linestyle=linestyle)\n", " if len(data) > 1:\n", " axis.plot(range(len(data))[-1], data[-1], symbol, markersize=msize, color=color)\n", " axis.plot(range(len(data)), data, color=color, linestyle=linestyle)\n", " if xmax <= 10:\n", " axis.plot(range(len(data)), data, symbol, markersize=msize, color=color, markevery=1)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "DEBUG cov: 10\n", "\u001b[31mDEBUG multi-mapping contig (callclass=227, mappings=2), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=1036, mappings=5), FALSE call\u001b[0m\n", "\u001b[32mDEBUG multi-mapping contig (callclass=156, mappings=2), TRUE call\u001b[0m\n", "\u001b[32mDEBUG multi-mapping contig (callclass=834, mappings=4), TRUE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=1354, mappings=3), FALSE call\u001b[0m\n", "\u001b[32mDEBUG multi-mapping contig (callclass=1423, mappings=3), TRUE call\u001b[0m\n" ] }, { "data": { "image/png": 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kFop1km6KX8Dh8aseh2/ZFXmA6XiCZ8ojaczhL856R/w6PCDp7lvM7EKCgcm7CfaNXQ60cffDVt4RDF4+QrCS72aC34/HiDodO8JQghd6PQheuv0I9HT3F2OU3RXq++BQfUbwb0Hvo5gBKjnMcnLLjdCbie7uPib0vTLB24Zz3f3riHIfApvd/RYzuxV4ESie9DYttEfJnwQDKKNjtPMYUMbd7zazRcCjoRH8q4An3L1+1t6piIj8XSnWiUhaWXBK8WigkbvPytneiKSdYp2IZBUzmwVUdPeKOdwVyWS5bQ/JtcBGIjYZN7M4gjfESXuLzCUYmY/cSPwCoAjJ9x9Juv4Mgg2UHwol5SFYhgPBwSB5M6/7IiIiR6RYJyIixzvFOhERSVW2L9kObf5dNfQ1D3CqmdUBtrr7j2Y2BHjYzL4j2FPgEYLNjv8D4O4rzGwa8G8z60YwVfffwFR3/z6qLQNGEEzjTdpP5HPgDjNbQbC/Q5afvCUiIn8vinUiInK8U6wTEZGjkRMzJBsQ7E+3GCgEPB76ddIeEAOAQcAwgqPhywLNog7jaE9w4tp0gv0bviHYoyDabQQbJUeeutSfYE+SBcAhtAGqiIhkPsU6ERE53inWiYhIhuXoHpIiIiIiIiIiIiLy95Lb9pAUERERERERERGR41i27yF5rDCz2wiWBlCkSJH6Z5xxRg73SETk+LBw4cLN7l46p/shinUiIllFsS73UKwTEckaRxvrtGQ7DRo0aOALFizI6W6IiBwXzGyhuzfI6X5Icop1IiKZR7Eud1KsExHJPEcb67RkW0RERERERERERLJNjg5ImllJM/vNzKpkc7sDzeyl7GxTRET+nnIw1nU3synZ2aaIiPw96blORETSK6dnSPYD/ufua8zsbDObaGY/mdkeM/vezO43s2R9tEBPM/vOzPaZ2QYzey4i/1Iz+9LMtoTq+c7M+kS1+zzQycwqZ8M9iojI31u6Yp2Z9TczT+FzckS5VOMh8BrQwMwuzsZ7FRGRv6f0xrqKKcS5FrEqN7OLzOygmX0blaXnOhGRY1SOHWpjZoWBrsDVoaT6wCbgZuBH4FyCh6n8wDMRl74AtATuB5YCJYCyEfk7gZdCebuBC4F/m9lud38FwN03mdl04M5QPSIiIpkug7FuIDA8qqo3AXf33yPSUo2H7r7PzP4D3APMyby7EhER+ctRPNcBtAC+ifi+NUb9JYE3gE+B8pF5eq4TETl25eQp21cCh4AvANz99aj8H8ysHtCaUOAys9OBHsBZ7r4iouzipF+4+0JgYUTeWjO7DrgYeCUifUqoXgUuERHJKumOde6+k+DlGgBmdgpBDLs5Iu2I8TBkCjDDzAq7++5MuSMREZHk0h3rImxx941HqH8UMBYw4PoY+XquExE5BuXkgOTFwEJP/Zjv4sC2iO/XAD8ALczsQ4Il57OB+6NmjYSZWV2gIdA/Kms+UN7Mqrj7mozdgkjmOXToED///DO7du3K6a6IZFj+/Pk5+eSTKV68eE53JbfISKyL1gXYDrwTkZbWeLiAINZfQDCzREREJLMdTax718zigFXAYHefHJlpZncB8cANwKMp1K3nOhGRY1BODkieBmxIKTP0Fq0T0D4iuXLoujahPCdY2vaBmV3g7ocirv8ZKE1wj4+7e/Tyt19DPysCClyS4zZv3oyZcfrpp5MnT05v7yqSfu7Onj17+OWXXwA0KBnISKyLzM8D3Aq84e77IrLSFA/dfbeZ7SCIdSIiIlkhI7FuJ9CHYFblQaAV8JaZ3eLu40PXnQn8Ezjf3RPNLKUm9FwnInIMyskByULAb7EyQkvRPgSGuHvkjJA8QEHgZndfGSp7M/A9cA4wL6LsxUBR4HzgeTNb6+7jIvL3RPRDJMdt376dihUrajBSjllmRuHChSlfvjy//vqrBiQDGYl1ka4ATgFGRqWnJx7uQbFORESyTrpjnbtvJtgLOckCMysFPACMN7OCBPsn93H3tUdoX891IiLHoJwc+dgMlIxONLMzgFnAm+7eNyp7A3Aw6eErZBXBW7VTIwu6+1p3X+rurwGDOHzJ9omhn5syegMimSkxMZH8+fPndDdEjlqhQoU4cOBATncjt8hIrIt0G/Cluy+LSk9zPCSId4p1IiKSVY421iWZB1QL/bosUBMYHTpd+yDwGFAr9L1ZxHV6rhMROQbl5IDkYoIgE2ZmNQmC1iR37xXjmi+AfGZWJSKtMsFMz/WptJU0kyRSbeAAwcmkIrlCKktRRI4Z+nOcTEZiXVK5csBVBCeTRktTPAzlxwGLMth/ERGRI8lwrItSh7+Wfv8CnBlKS/oMB1aHfv1lxHV6rhMROQbl5JLtjwmWUp/k7lvMrBbwf8BM4Bkzi08qGHHy2icED1Wvm1nPUNoQgrdpCwDMrAewlmDZGsAlBPuTRJ6wDcGS7jk6dVRERLJQRmJdkluBXcDbMeo9YjwMuRj4wd1XZcrdiIiIHC7dsc7MbiEYRFxMcEL31cDdwIOhcgeAbyMbMbPfgX3uniwdPdeJiByTcmyGpLsvJTgRrU0o6QbgZOAmgjdjkZ+kaw4BLYHfgc8Igt/PwDURB9rkBZ4HEggeyu4G+gL9orrQltizTkREMt33339PfHw8f/75Z7a2O2TIEK677rpsbVP+kpFYB2DBNNMuwIRYD1hpjIegWCciIlkso7EOeITgee3r0LW3uvvgDHRBsU5E5BiU06dnPA7cY2Z53b2/u1usT+QF7r7B3W9w92LufrK7t3f33yLyh7h7LXcv4u4l3L2eu78SdQL3VUAiMDnb7lTkOPXbb7/Rq1cvqlWrRlxcHCeffDINGzZk6NCh7Ny587DygwYNIm/evDz88MPJ0jt06ICZpfjJly9fuNy1116b7Nr333+fQoUK8c9//jNmHw8ePJisruLFi3Puuefy/vvvh8s88sgjycqULFmSxo0bM3/+fAA++eSTVPtnZowfPz7F36e+ffvSo0cPihUrBsDu3bvp1KkTZ555Jvny5aNJkyYxr5s5cyb16tUjLi6OKlWq8Npr6fv/7TvuuIMvv/ySL7/88siFJatkJNa5u1dy97tSqvRI8dDMahMsa3s1y+5MREQkkK5Y5+5j3b1m6JmtuLs3SDpdOyWhemtHpum5TkTk2JWjA5LuPg0YBlTI5qaLAJ3d/WA2tytyXFm3bh316tVj2rRpPPnkkyxatIj/+7//o0+fPnz66adMmTLlsGtGjRpF3759GTNmDImJieH0YcOGsWHDhvCnYMGCvPzyy+Hvv/zyS8w+jBkzhhtuuIF//etfPP7446n2d/To0WzYsIH58+dTs2ZNWrduHR5wBKhVq1a4vTlz5lCqVCmuuOIK9u/fzyWXXJKsfx07duTiiy9Olnb99den+Ps0depUOnXqFE5LTEwkLi6OHj16cMUVV8S8bs2aNVx55ZVccsklLF68mPvvv58777wz2UDqkcTFxdGmTRuGDh2a5mskc+VgrCsHdHT3HdncroiI/M3ouU5ERNIrJ/eQBMDdX8qBNmPtxyVyXNiwAdq0gbfegvj4I5c/GnfeeSd58uRhwYIFFClSJJxeu3ZtrrvuOtw9Wfm5c+eyefNm+vfvz1tvvcVHH31Ey5YtAShRogQlSpRIVr5EiRLEp3ITL7zwAv369WPs2LG0bdv2iP094YQTiI+PJz4+nhEjRvDmm2/ywQcfcO655wKQL1++cHvx8fH069ePSZMmsX79eqpVq5asL4UKFaJAgQKp9i/Jm2++Sd26dSlfvnw4rVixYgwfPhyARYsWsXr16sOue+WVV6hYsSJDhgwBoEaNGnz11VcMHDiQa665ht9++42zzjqLe++9l379gl0pFi9ezPnnn89bb70VnknaqlUrrrrqKvbu3UtcXNwR+yuZL4di3fTsblNERP6+9FwnIiLpkdNLtkUkkz35JHz+efAzK23dupWPP/6Yu+++O9lgZKTo05ZHjhxJmzZtyJ8/Px06dGDkyJEZbv+hhx7iscce4/3330/TYGS0/Pnzky9fPg4cOBAzf+/evYwfP56yZcty6qmnZrifAHPmzKFBgwbpvm7u3Lk0a9YsWVrz5s2ZP38+iYmJlClThtdff53+/fszf/58du/eTbt27ejYsWOyZe3nnHMOe/fuZd68eUd1HyIiIiIiIiKZIcdnSIpIbD17QkJC+q7Ztw/mz4dDh2D4cFi8GAoUSPv1depAaDLeEa1atQp35/TTT0+WXqFCBbZv3w4E+z0mzQLcuXMnb7/9NjNnzgSgY8eOPPPMM2zcuDFNswwjffTRR7z//vtMmzaN5s2bp+tagH379vHss8+ya9cuGjduHE5funQpRYsWBYI9Hk888UTeffddChYsmO42Iq1fv57zzz8/3ddt3LiRMmXKJEsrU6YM+/fvZ+vWrZQuXZqrrrqK2267jfbt29OwYUMOHToUnlGZpFixYhQrVox169Zx6aWXHtW9iIiIiIiIiBwtzZAUOY6sXw9Jq6Tdg+/Zbc6cOSQkJHDuueeyd+/ecPqbb75JhQoVwjMFK1euzDnnnMPYsWPT3cZZZ51FpUqV6N+/Pzt2pH17vLZt21K0aFEKFy7M0KFDGTx4cLIZiKeffjoJCQkkJCSwYMECOnfuTKtWrfjmm2/S3cdIe/bsyfBS6ehZpknL4CPTBw4ciJkxYcIEJkyYEHPGaqFChdizZ0+G+iAiIiIiIiKSmTRDUiSXSutMxSQbNkDlyskHJLdtgzffzJq9JKtWrYqZ8d133yVLr1SpEgCFCxdOlj5y5Ei+//778GnZAIcOHWLTpk08+OCD6Wq7fPnyvPvuuzRq1IimTZsyffp0TjjhhCNeN3jwYJo0aULx4sU5+eSTD8svUKAAVatWDX+vV68e//3vfxkyZAijR49OVx8jlSpVim3btqX7uvj4eDZu3Jgs7ffff6dAgQKULFkynPbDDz/w888/A7B27drDloe7O9u2baN06dIZ6L2IiIiIiIhI5tIMSZHjxJNPBku1IyUmZt1ekieddBLNmjXj5ZdfZufOnamWXbZsGfPmzWP69OnhGYgJCQnMmzePdevW8dlnn6W7/VNOOYXZs2ezY8cOmjRpkqYBv/j4eKpWrRpzMDIlefPmZffu3enuX6S6deuyfPnydF93wQUXMGPGjGRpM2bM4NxzzyVv3rwA7N+/n/bt29O6dWuee+45br/99vDgZJKVK1dy4MAB6tWrl/GbEBEREREREckkGpAUOU7MnQv79ydP278fvvwy69p85ZVXOHToEPXr12fixIksX76clStXMnHiRL755pvwoNnIkSOpW7cuTZo0oXbt2uHPOeecw+WXX57hw23Kly/P7Nmz2b17N5dffjlbtmw5qvs5ePAgGzduZOPGjaxatYrHH3+clStXcs011xxVvc2bN2fu3LkkJiYmS1++fDkJCQls2bKFnTt3kpCQkGx5+J133snatWvp3bs33333HSNGjGDcuHH07t07XKZfv35s27aNYcOG0bt3b+rVq0fHjh2TnXA+Z84cqlevHp69KiIiIiIiIpKTNCApcpxYvDhYph39Wbw469qsXLkyixcvpkWLFjz66KPUrVuXevXqMWjQIO666y6GDBnC/v37GT9+PNdff33MOm644QYmT56crr0gI8XHxzNr1iwOHDhA48aN2bx5c4bvZ9myZZQtW5ayZctSp04d3n33XUaMGEG7du0yXCfA1VdfTd68efnkk0+SpTdr1oy6desyefJk5s2bR926dalfv344v2rVqnz44Yf83//9H2effTbPPfccr7zySvgE7U8//ZQXX3yR8ePHU7x4ccyMN954gyVLljBw4MBwPRMnTqRbt25HdQ8iIiIiIiIimcUiZ9FIbA0aNPAFCxbkdDfkOLdixQpq1KiR092QLPLyyy/zwQcf8PHHH2dru9988w3Nmzdn1apVFCtWLNvaTe3Ps5ktdPcGMTMlxyjWiYhkHsW63EmxTkQk8xxtrNOhNiIi2eCOO+5gx44d/Pnnn9k6MLhhwwbGjRuXrW2KiIiIiIiIpEYDkiIi2SBfvnw8/PDD2d5uixYtsr1NERERERERkdRoD0kRERERERERERHJNhqQFBERERERERGjgtskAAAgAElEQVQRkWyjAUkRERERERERERHJNhqQFBERERERERERkWyjAUkRERERERERERHJNhqQFBERERERERERkWyjAUkRERERERERERHJNhqQFJFsN23aNMyMnTt3HlU9VatW5bnnnsukXuW8TZs2cfLJJ/PTTz9la7uTJ0/mvPPOw92ztV0RERERERH5e9KApIhkiJml+unUqVOK1zZu3JgNGzZQpEiR7OvwMeCJJ56gdevWnHLKKeG0O++8k/r161OwYEHOOOOMmNctXryYiy66iLi4OCpUqMCzzz6brnZbt27Nrl27mDx58lH1X0RERERERCQtNCApcpyZuXYmFYdUZObamVnazoYNG8Kf11577bC0F198MeZ1Bw4coECBAsTHx2NmWdrHY8kff/zBmDFj6NKly2F5t956K+3atYt53datW2natCkVK1Zk4cKFDBw4kKeeeophw4alue2kAeSXXnopw/0XERERERERSSsNSIocR2aunUnLiS1Zv2M9LSe2zNJByfj4+PDnhBNOOCytRIkSfPfdd5gZkyZN4tJLLyUuLo6xY8fGXLL91ltvUbNmTQoUKMBpp53GgAEDkrW3YcMGWrZsSaFChahUqRLjx48/rE9bt26lS5culC5dmuLFi9O4cWMSEhLC+cOHD6dUqVJMmzaNmjVrUrRoUZo0acKPP/6YrJ6hQ4dSuXJlChQoQPXq1Rk7dmw47x//+Aft27dPVv7gwYOUKVOGV199FYA9e/bQvXt3SpcuTVxcHBdeeCFfffVVqr+fU6ZMoVixYjRo0CBZ+quvvsrdd99NlSpVYl43duxYDh06xOjRo6lVqxZt2rShV69eDBw4EIBdu3Zx+umnc9ddd4WvWb9+PSeccAIvv/xyOK1Vq1Z8/vnn/Pzzz6n2U0RERERERORoaUBS5DiRNBi5+8BuAHYf2J3lg5Jp1bdvX3r16sWKFSu48sorD8v/8ssvadu2Le3bt+fbb7/l8ccfp3///uGZlwDt27fn559/ZubMmUyePJnhw4ezYcOGcH5iYiItWrRgy5YtfPTRRyxcuJAGDRrQuHFjNm3aFC73559/MnjwYN544w3mzJnDxo0b6dGjRzh/4sSJ9OnThwceeIBvv/2WO+64g1tvvZUZM2YA0KFDB95//3127doVvmbGjBls27aNG2+8EYCePXvy3nvvMW7cOBYtWkS1atVo0aIFmzdvTvH3aM6cOYcNRqbF3LlzadSoEfnz5w+nNW/enHXr1oWXxU+YMIFRo0YxZcoUEhMT6dChAw0bNqR79+7ha6pVq8YJJ5zA7Nmz090HERERERERkfTIl9MdEJHYek7rScLGhCMXBLbt3ca3v3/LIT+ULH33gd00GdeE2ifXpmRcySPWUye+DkNaDMlQf1Nz3333ce2114a/L1myJFn+Cy+8QIsWLXj44YcBqF69Ot999x3PP/883bp1Y8mSJcycOZMFCxZQv359AEaPHk316tXDdXz88cesXr2azz//nAIFCgAwYMAApkyZwsSJE7nnnnsA2L9/PyNGjOC0004DoFevXtx7773hegYOHEiXLl244447wn2fP38+zz//PE2bNqVly5bkz5+f9957LzxTcsKECVxxxRWcdNJJbNu2jVGjRvGf//yHFi1aAPDaa6/x6aefMnz4cB555JGYv0fr168P9yk9Nm7cSO3atZOllSlTJpxXtmxZGjRowOOPP06XLl1o27YtK1euPOy/gZlRtmxZ1q1bl+4+iIiIiIiIiKSHZkiKHAe+3/z9YYORSQ75Ib7f/H029yi5I838W7FiBRdeeGGytIsuuogffviBvXv3smLFCgoUKEC9evXC+dWqVaNUqVLh7wsXLmTHjh2ceOKJFC1aNPxZvXo1a9asCZcrXrx4soG/cuXKsWvXLnbvDmaWfvfddzH7snz5cgAKFizI9ddfz4QJEwDYvXs37733Hh06dABg1apVJCYmJqsjf/78nHfeeeE6YtmzZw9xcXGp/j6lJHovzqTTsiPTH3jgAapXr87QoUMZOXJkeNAyUqFChdizZ0+G+iAiIiIiIiKSVpohKZJLpWemYvRy7UiF8xdmatupNKrUKDO7ly5HOk3b3WMecJN0YndK+ZEOHTpEhQoV+PTTTw/LK1GiRPjXkUubk9pIuj7WQF50OQiWbTdp0oRNmzYxY8YM8ubNy9VXXx2+l7TUEa1UqVJs27YtxfyUxMfHs3HjxmRpv//+O0CyQcfffvuNlStXkjdvXlavXh2zrq1bt1K6dOl090FEREREREQkPTRDUuQ40KhSI6a2nUrh/IWTpeeGwci0qFmzJp9//nmytM8//5zKlStTsGBBatasyb59+1i8eHE4f82aNcn2ZKxXrx6//vorBQsWpGrVqsk+aR1kMzPOOOOMmH2pWbNm+Psll1xCuXLleOutt5gwYQLXX399eHZj9erVyZs3b7I6Dhw4wLx585LVEa1u3bqpzqBMyQUXXMCsWbM4cOBAOG3GjBlUrFiRsmXLAsEgaadOnTjzzDMZN24cDz300GFLtv/8809+/PHHZLNQRURERERERLKCBiRFjhPRg5LHymAkQJ8+fZg2bRrPPPMMq1atYuzYsbz00ks88MADAJx11llcdtlldO3alXnz5rFo0SI6d+5MoUKFwnVceeWV1KtXj1atWjF9+nTWrVvHl19+ySOPPMK8efPS3Jf777+fUaNGMWLECFatWsWgQYOYNGlSuC8QDFy2a9eOV199lenTp4eXawOULFmSrl270rt3bz7++GOWL19Ot27d+PPPP7n99ttTbLd58+YsWbKEP/74I1n6qlWrSEhIYOPGjezbt4+EhAQSEhI4ePAgAB07dsTM6NKlC8uWLePtt99m0KBB9OnTJ1zHiy++yNdff80bb7xB27ZtufHGG2nfvj179+4Nl/niiy8oUaIE5513Xpp/r0REREREREQyQgOSIseRpEHJ00qcdswMRkIwy2/ixImMHz+eWrVq8eijj9K/f3+6desWLjNhwgTi4+O59NJL+cc//kGXLl0oV65cOD9v3rxMnz6dhg0b0qlTJ6pXr06bNm1Yu3Yt8fHxae5LmzZtGDhwIM899xy1atVi+PDhjBo1iqZNmyYrd/PNN7N8+fJwnyINHjyYVq1a0aFDB+rVq8fKlSuZNm1asj0vo51zzjnUqlWLSZMmHdZO3bp1GTZsGOvWraNu3brUrVs3PDv0pJNOYvr06axZs4b69evTq1cv+vXrx9133w3A0qVL6du3L8OHD6dChQoADBs2jN27d/Pggw+G25k4cSIdO3YMHwgkIiIiIiIiklUsab8zSVmDBg18wYIFOd0NOc6tWLGCGjVq5HQ3JAe99957PProoyxZsuSIe2Zmpl9//ZXatWuzZMmS8KDl0Urtz7OZLXT31E86kmynWCciknkU63InxToRkcxztLFOh9qIiOQS1157LWvXruXXX3+lfPny2dbuunXreO211zJtMFJEREREREQkNRqQFBHJRXr16pXtbTZs2DDb2xQREREREZG/L+0hKSIiIiIiIiIiItlGA5IiIiIiIiIiIiKSbTQgKSIiIiIiIiIiItkm1w1Imtk6M/MYnw9D+f1j5G2MqqOPmf1mZr+bWe+ovLpm9r2ZFcrO+xIREUmiWCciIsc7xToREUlNbjzU5hwgb8T3ssBC4O2ItO+ByyK+Jyb9wszOAp4AWgIGTDWz6e6+1MzyAq8Bd7v7nqzpvoiIyBEp1omIyPFOsU5ERFKU6wYk3X1T5Hcz6wL8AUyKSD7o7snenkU4A1ji7v8Xun5JKG0p0BP41t0/yfSOi4iIpJFinYiIHO8U60REJDW5bkAykpkZ0AUY7+67I7Iqm9kvwH5gHtDP3X8I5S0FqpvZqQRv0qoD35pZRaA70CCbui8iInJEinUiInK8U6wTEZFouW4PyShNgUrAyIi0eUAn4AqgGxAPfGlmJwG4+wqgHzADmA48FEobDjwMXGxmS8zsWzO7NrtuREQypn///tSuXTvT6zUzJk+enGqZAwcOUL16dT777LNMbz81S5cupXz58uzatStb25Uco1gnIiLHO8U6ERFJJrcPSHYDvnb3hKQEd//I3d929yWhKfotCe7jlogyw9399NBnuJl1CGV9AowBbgp9xpjZydl1MyLHm02bNnHXXXdRsWJFChYsSJkyZbj88suZMWNGTnctU4wYMYLy5ctzySWXhNOefvppLrzwQooUKULwsv9wP/74I1dffTVFihShVKlS3HPPPezfvz/N7Z555pmcf/75DBo06KjvQY4JinUiInK8U6wTEZFkcu2AZCigXEOwWXGK3H0nsAyolkI9JwFPAncA5wOr3H2Fuy8DVgHnZWa/RXLa3h17efMfb7J3x94sb6t169bMnz+fUaNGsXLlSqZOncoVV1zBli1bsrzt7DB06FC6dOmSLG3fvn1cd9119OzZM+Y1iYmJXHXVVfz555/MmTOHiRMnMnnyZHr37h2zfEo6d+7Mq6++ysGDBzPcf8n9FOtEROR4p1gnIiKx5NoBSYLp+/uAN1MrZGZxBJsbb0ihyCBgqLuvI7jf/BF5BUh+8pvIMe/7Kd/z/Xvfs/KDlVnazvbt25kzZw7PPfccl19+OaeddhrnnHMOffr0oU2bNuFy+/fvp1+/fpx22mkULFiQypUr89JLLwHB4F2XLl2oVKkShQoVolq1agwYMIBDhw6l2G6nTp1o2bIlTz31FGXKlKFo0aJ07tyZPXv+OmDR3RkwYABVqlShUKFCnHnmmYwfPz5d97dgwQJWrlxJy5Ytk6U/8cQT9O7dm7p168a8bvr06Sxbtoxx48ZRr149mjZtyoABA3jttdf4448/cHeaNm1KkyZNcHcAdu7cSbVq1ejevXu4nmbNmrF161ZmzZqVrn7LMacTinUiInJ864RinYiIRMmVA5KhTY+7Am+6+59ReQPN7FIzq2Rm5wGTgSLA2Bj1NAFqAi+Gkr4GTjezq83sGuB0YH4W3opItlv8+uJkP7NK0aJFKVq0KFOmTGHv3pRnY95yyy288cYbDBo0iBUrVjBq1ChOOOEEAA4dOkT58uV5++23WbFiBU8//TTPPPMMo0ePTrXt2bNn88033/Dpp5/yzjvvMH36dB588MFw/iOPPMKoUaMYNmwYy5cv56GHHuL222/nww8/TPP9zZkzh6pVq4b7mlZz586lRo0anHLKKeG05s2bs2/fPhYuXIiZMXbsWBISEhg4cCAA99xzDwUKFOBf//pX+JoCBQpQp04dZs+ena725dihWCciIsc7xToREUlJbj1l+zKCqfodYuRVACYCpYBNwFfA+e6+PrKQmRUChgFt3D0RwN1/MbM7CDZCNuB2d/81q25C5GhM6zmNjQkb01T2t29+Y+/20KBgaFvDdbPW8bg9DkDcCXGUObvMEeuJrxNPiyEt0tRmvnz5GDNmDN26dWPEiBHUrVuXCy+8kBtuuIHzzgtWzKxatYo333yTjz76iBYtgnorV64criN//vw88cQT4e8VK1Zk0aJFTJw48bCl0pHy5s3L6NGjKVq0KLVr1+b555+nS5cuPPvsswAMGjSI6dOnc/HFFwNQqVIl5s+fz7Bhw7jqqqvSdH/r16+nbNmyaSobaePGjZQpk/z3ulSpUuTNm5eNG4P/nuXKlWPkyJHcdNNN/PHHH0yYMIH58+dTqFChZNeVK1eOdevWpbsPcsy4DMU6ERE5vl2GYp2IiMSQKwck3X0m4WGVw/LaxEqPUW4PwZuy6PSxxHjrJnIsK3FqCfb9sQ8/5OChxNBPy2OUOK1ElrTbunVrrrrqKubMmcPcuXOZNm0aL7zwAk8//TT9+vVj8eLF5MmTh0aNGqVYx/Dhwxk5ciTr169nz549HDhwgNNOOy3Vds866yyKFi0a/n7BBRewf/9+1qxZw759+9i7dy8tWrRIdujMgQMHqFixYprvbc+ePcTFxaW5fKSUDruJTL/22mtp164dTz31FAMGDODss88+rHyhQoWSLUWX44tinYiIHO8U60REJCW5ckBSREjzTMUkK6euZNINkzi4969DUPLF5eOGSTdQvWX1zO5eWFxcHE2bNqVp06Y89thjdO3alf79+9OnT5/wHokpeeutt+jZsycDBw6kYcOGFC9enGHDhvHf//43w/1J2n/ygw8+4NRTT02Wlz9//liXxFSqVCkWL07/svf4+Hi++OKLZGmbN28mMTEx2czJvXv38vXXX5M3b15Wr14ds66tW7emaxBVRERERERE5FiQK/eQzA3M7DYzW2BmCzZt2pTT3RE5or3b95InXx4sj5GvUD4sj5EnX56/lnJnk5o1a3Lw4EH27t1LvXr1OHToEDNnzoxZ9vPPP+e8886je/fu1KtXj6pVq7JmzZojtrF06VJ27doV/v7VV19RoEABqlSpQs2aNSlYsCDr16+natWqyT5HmnkZqW7dunz//fepHrATywUXXMCKFSv4+eefw2kzZsygYMGC1K9fP5x2//33s2/fPmbMmMHo0aN5//33D6vr22+/pV69eulqX0RERET+ouc6EZHcSQOSKXD3Ee7ewN0blC5dOqe7I3JEi0YtYv/u/ZQ5uwxt3m9DmbPLsH/3/iw73GbLli00btyY8ePHs2TJEtauXcukSZMYMGAAl19+OcWLF6datWrceOONdO3alXfeeYe1a9cyZ84cxo0bB0D16tVZtGgRH330EatWreLJJ59M0yEuBw8e5NZbb2XZsmXMmDGDvn370q1bN4oUKUKxYsXo06cPffr04fXXX2f16tUkJCQwfPhwRowYkeb7a9SoEXv37mXJkiXJ0n/88UcSEhLCezsmJCSQkJDAzp07geB07Fq1atGxY0cWL17MJ598wv3330+3bt0oXrw4ANOmTePf//4348ePp1GjRvTv35+uXbuG95gEWLduHb/88gvNmjVLc59F0ksPaSIicrzTc52ISO6kJdsix4m4EnE0+1czzu95PpbHqNS4El8N+Yof5/yYJe0VLVqU888/nxdffJHVq1ezb98+ypcvT7t27XjkkUfC5d544w0effRR7rnnHjZv3kyFChXo1asXALfffjsJCQm0a9cOd6d169b07t2b119/PdW2L730UmrVqkWjRo3YvXs3rVu3ZsCAAeH8J598kjJlyjBw4EDuvPNOihcvTp06dXjggQfSfH8nnXQS1113HRMmTKBOnTrh9Mcee4yxY//arqhu3boAzJw5k8suu4y8efPy4Ycfctddd3HhhRdSqFAh2rVrFz5Re9OmTXTq1IlHHnkkfPhP3759mT59Op07d+Z///sfZsbEiRNp1qxZumZ1iqSXu48ARgA0aNAg9T0WREREREREMokdaY+3LG3crCTwHdDQ3Y+8TjPz2h0IFHD3e9JSvkGDBr5gwYIs7pX83a1YsYIaNWrkdDdyvU6dOrF582amTp2a5W0tW7aMRo0asXr16vDsxuywb98+qlWrxsSJE7nwwguzrd3MlNqfZzNb6O4NsrlLcgSKdSIimUexLndSrBMRyTxHG+tyesl2P+B/SYORZvZiaOnYXjNbF+sCM7vRzBLMbLeZrTez+2OUaRdRZqOZjTez+IgizwOdzKxyVtyUiBwfatWqxcCBA1m7dm22trt+/XoefvjhY3YwUpIzs5Jm9puZVcnmdgea2UvZ2aaIiPw9KdaJiEh65diApJkVBroCoyKS8wBjgTdSuOYK4D8Ey8tqA3cBvcyse0SZC4FxoXpqAdcCNYEJSWXcfRMwHbgz8+5IRI5HHTt25Oyzz87WNqtXr87tt9+erW1Klgq/fDOzs81sopn9ZGZ7zOx7M7vfzJLFYzM708xmh8r8YmaPmZlF5M8yM4/xWRZRjV6+iYhIdsn0iSZmVtbM/mNm35lZopmNiVGNYp2IyDEqJ2dIXgkcAr5ISnD3Hu4+FFiZwjU3Ax+4+yvu/oO7fwg8CzwY8aB2AfCzuw9297Xu/hUwFDgvqq4pQNtMvB8RyQZjxozJluXaIpkhxsu3+sAmgnhWC/gn8BjQN+Ka4sAM4DfgHOAe4H7gvoiqrwPKRnwqAn8CbycV0Ms3ERHJDlk10QQoCGwGngPmxapHsU5E5NiVkwOSFwMLPX2bWBYE9kal7QEqAEknP3wBlDWzqy1QCmgD/C/quvlA+exeViAiIn8ryV6+ufvr7n6Pu88KvVh7E3gVaB1xTXugMHCLu3/r7u8QzAC5L+nlm7tvdfeNSR/gIqAIEH0ilF6+iYhIVsuSiSbuvi4UM8cAW1NpX7FOROQYlJMDkqcBG9J5zcfAtWbWzMzymFl1oHcoryyAu88lCEgTgP0EM1EMuCWqrl9DPyumv+siIiJpkpaXb8WBbRHfLwDmuPueiLSPgXKkHLO6AR+5+09R6Xr5JiIiWS2rJpqklWKdiMgxKCcHJAtxeBA6kteAl4D3CQYbvwLeDOUlAphZzVCZJwmWxrUA4oF/R9WV9KBXKL0dFxERSaNUX76ZWT2gE8EsySTxBMu1I/0WkRddR3XgUoIYGU0v30REJKtlyUSTdFCsExE5BuXkgORmoGR6LvDAg0BRgsAXT/BGDGBd6OdDwHx3/5e7L3H3jwn2JLnZzE6JqO7E0M9NGey/iIjIkaT48s3MTgc+BIaElmVHip5lYimkQzA7ckOormh6+SYiIlktSyaapINinYjIMSgnByQXE5x+nW7unujuv7j7foLl2XPd/fdQdmEOD2JJ3y0irTZwAFiakT6IiIikQcyXb2Z2BjALeNPd+0Zlb+TwmZAnh34mmzlpZgUItiQZ7e4HY7Svl28iIpLVsmqiSVop1omIHINyckDyY6CGmZ2UlGBmVc2sDsE+WQXMrE7oUyCUX8rM7jSzGqH0F4EbgJ4R9X4AXBMqV9nMLiR4+7bI3X+MKHcxwR5du7P4PkVE5O/rsJdvoa1FZgGT3L1XjGvmAhebWVxEWlOCJWnrospeC5Qi+cmmkfTyTUREslpWTTRJK8U6EZFjUI4NSLr7UoK3YG0ikkcSBLReBHuHLA59ykWU6Qh8TXCKWy3gMndPeptG6BS2+4DuwLfAZGAVcE1UF9oSe78tEcllOnXqRMuWLbOs/lWrVlGmTBl27NiRZW3E8vLLL9OqVatsbVOyXbKXb2ZWC5hJMCD5jJnFJ30irvkPsBsYY2a1zew6oC8wKMaBAbcBn7r7Dym0r5dvIiKS1bJqoglJ1xEcAHdi6Hv04KdinYjIMSgnZ0gCPA7cY2Z5Adz9Mne3GJ91ofzN7n6Buxd19yLu3sTd50VX6u5D3b2Wuxd297Lu3s7df07KN7OrCJZxT86e2xQ5PsUaKJw6dSqFCxfm4YcfzqFepV+/fv246667KFGiBAB79+6lU6dOnHXWWeTPn5/LLrss5nWzZ8+mfv36xMXFUblyZYYPH56udrt168aCBQuYM2fO0d6C5FIxXr7dQLD8+iaCfR8jP0nX7CCYEVkOWAAMA14ABkXWbWaVgcak/nJNL99ERCRLZdVEk5Ck6y4Grg79+n9RZRTrRESOQTk6IOnu0wgetCpkc9NFgM4p7LclIhk0btw4WrduzbPPPsvTTz+d091J5sCBAzHTf/rpJ9577z06d+4cTktMTCQuLo7u3btz1VVXxbxu7dq1XHnllTRs2JDFixfz0EMP0aNHD955J/pskpQVLFiQdu3a8dJLL6XvZuRYE3755u79U3jxFrnHMe6+1N0vcfe40Iu1x6NnR7r7D+6ex93fjtWoXr6JiEg2yqqJJrHqqJiUr1gnInLsyukZkrj7S+6+PpvbfDtWwBM55iUmwtSp8OSTwc/E9B5SmHEvvvgiXbt2ZeTIkdx7773J8kaPHk3NmjWJi4ujevXqDB48mEOHDgHQtm1bWrdunaz8oUOHOOWUUxg8eHDMtqZNm8bFF19MyZIlOfHEE2nevDkrVqwI569btw4zY+LEiTRu3JhChQrx73//O2Zdb731FmeeeSannnpqOK1IkSIMHz6c2267jQoVYr8vGT58OOXKlWPo0KHUqFGDbt26ccsttzBw4EAANm3aRNmyZXniiSfC1yxZsoS4uDgmT/7r/5lbtWrFlClT2L1bq4yOV3r5JiIixzvFOhERSa98Od0BEckkiYnQvDnMmwe7dkGRInDeefDxx5A3b5Y2/eijj/LCCy/w7rvvHjaj8LXXXuOxxx5j6NCh1K9fn2+//ZZu3bqRP39+unfvTocOHWjdujXbt2/nhBNOAIKl0Bs2bKBt27Yx29u1axc9e/bkrLPOYs+ePTz11FNcffXVLF++nAIFCoTLPfTQQwwcOJBRo0aRP3/+mHXNmTOHBg0apPue586dS7NmzZKlNW/enLFjx3LgwAFKly7NmDFjuPrqq2natCl16tShbdu2tG3bluuvvz58TYMGDTh48CBz587l8ssvT3c/5Njg7tk+DTalmZMiIiJZQbFORETSQwOSIrlVz56QkJD28lu2wPLlEJp5yM6dMHMm1KkDJ52U+rVJ6tSBIUPS1c0ZM2bw4YcfMnXq1JjLm5988kkGDBgQHoSrVKkSffv25ZVXXqF79+40b96c4sWL884779ClSxcAJkyYwOWXX058fPxh9QGHzagcPXo0xYsXZ/78+Vx00UXh9B49eiQb/Itl/fr11KlTJ133DLBx40aaNGmSLK1MmTIcPHiQzZs3U7ZsWZo3b85dd91F+/btufTSS9m3bx9Dhw5Ndk3hwoUpUaIE69atS3cfRERERERERI5FOb5kW0Qyyc6dfw1GJjl0KEjPQrVr16ZKlSo8/vjjbN++PVnepk2b+Omnn7j99tspWrRo+NO3b1/WrFkDQL58+bjpppuYMGECAPv27eOdd96hQ4cOKba5Zs0a2rVrR5UqVf6fvfuOj6pK/zj+OWkkISGhGSCUhKKCiCAoBkVAAcUFwRV3EQQPfuYAACAASURBVFtQEV0s/HRdCyrg2nWt666iCMoq6q4NUZEiTQQpUqUoJSAtgIQS0pPz++NOwiQkMAmTzEzyfb9eeU3uObc8o8jjfe6551CnTh3i4uIoKChg+/btxfbzZORjZmYm4eHh5f3aABhTbNo/Cqf4c29/9tlnCQsL47333uP9998nKirquPNERESQmZlZoRhEREREREREAo1GSIr4q3KOVGTaNLj22uIFyKgoeO01KLEStjc1btyYqVOncskll9C7d29mzpxJ3bp1AYrmiXzjjTfo1q1bmee4/vrr6datGzt37uTHH38kJyeHq666qsz9BwwYQHx8PG+++Sbx8fGEhITQrl07cnJyiu1Xu3btk8bfoEED0tLSPPmqxTRq1Ig9e/YUa9u7dy8hISHUdxuRmpKSwm+//YYxhi1bttC1a9fjznXgwAEaNmxY7hhEREREREREApFGSIpUF/36OXNGRkWBMc5n165OeyWLj49n7ty5HD16lEsvvZTff/8dcF5hjo+PZ/PmzbRu3fq4n0Jdu3alVatWTJkyhffff59BgwaVOpIQ4Pfff2f9+vU8/PDD9O7dm7Zt23LkyBHy8io2l3mnTp1Yt25duY9LSkpi1qxZxdpmzpxJly5diuarzM3N5brrruPKK6/khRde4I477jhuFOfmzZvJysri3HPPrVD8IiIiIiIiIoFGIyRFqovgYGcBm2++ceae7NjRKUZW8oI2hRo3bszcuXO59NJLueSSS5g9ezYNGjRg7Nix3HXXXcTGxnLFFVeQm5vLTz/9xM6dO3nooYeKjr/uuut4++23SUlJ4bPPPivzOnXr1qVBgwa89dZbNGvWjJ07d3L//fcTElKxv84uu+wyhg0bRl5eXrFzrFu3jpycHPbv3096ejorXfN5Fs43efvtt/PPf/6TUaNGMWLECBYuXMikSZOYMmVK0TkeffRR9u7dy6xZs4iJiWH69OnccMMNzJkzh6Ag53nQggULaNmyJW3atKlQ/CIiIiIiIiKBRiMkRaqT4GDn9exHHnE+q6gYWSguLo45c+YA0KtXL/bu3cutt97KO++8w+TJkznnnHPo3r0748ePJzExsdix119/PRs3biQmJoY+ffqUeY2goCA++ugjVq9eTfv27Rk5ciR///vfqVWrVoVivuKKK4iIiODbb789rr1Tp0589NFHLF++nE6dOtGpU6ei/sTERL7++mvmz59Px44defLJJ3n11VeLFtyZN28e//jHP3jvvfeIjY3FGMOkSZNYv349zz77bNF5pkyZwvDhwysUu4iIiIiIiEggMoWLMEjZunTpYpctW+brMKSaW79+PW3btvV1GDXSG2+8wX//+19mz55dpdddu3Ytl156Kb/88gsxMTFVeu3KdqI/z8aY5dbak684JFVKuU5ExHuU6/yTcp2IiPecaq7TK9siUuMNHz6cAwcOcOjQoSotDO7atYv33nuv2hUjRURERERERE5EBUkRqfGCg4N5+OGHq/y6ffv2rfJrioiIiIiIiPia5pAUERERERERERGRKqOCpIiIiIiIiIiIiFQZFSRFRERERERERESkynhUkDTGXGGMud5tO94YM8cYs88Y8x9jTGTlhSgiIlL5lOtERKQmUL4TERF/4OkIyTFAU7ftl4AzgY+BfsBjXo5LRESkqinXiYhITaB8JyIiPudpQbI1sArAGBMO9AfutdaOBB4CBldOeCIiIlVGuU5ERGoC5TsREfE5TwuSEUCG6/ckIAyY7tpeDzTxclwiIiJVTblORERqAuU7ERHxOU8LktuAC1y/DwB+stamubYbAke8HZiIVC89e/bkzjvv9HUY1UJaWhpxcXFs3ry5Sq87bdo0OnbsSEFBQZVetwop14mISE2gfCciIj7naUFyAvC4MeZ74C5golvfBThP0kSkhklOTsYYgzGG0NBQTjvtNHr16sXrr79Obm5usX0//fRTnn766SqL7e2336ZTp05ERUURExNDhw4deOSRR6rs+pXpqaee4oorrqBVq1ZFbffccw9dunQhPDychISEUo9bs2YNPXr0ICIigvj4eB5//HGstR5ft3///gQHB/P++++f6lfwV8p1IiJSEyjfiYiIz3lUkLTWvgD8BSc5jQTecOtuCLzn/dBEpFxS58DnCc5naduVpHfv3uzevZuUlBRmzJjBgAEDGDNmDN27d+fo0aNF+9WrV4/o6OhKjaXQO++8w913383tt9/OypUrWbRoEY8++igZGRknP/gUJScnM3bsWI/3T0hIYO7cuR7vn5GRwdtvv80tt9xSrL2goICbbrqJG2+8sdTjDh8+TJ8+fYiLi2Pp0qW8+uqrPP/887z44oseXxtg2LBhvPrqq+U6JlAo14mISE2gfCciIv7A0xGSWGsnWGuHW2vHW7chNdbaYdbadyonPBHxSOocmNsfMrY5n+ueK75diUXJWrVq0ahRI+Lj4+nYsSP33nsvc+fO5aeffuK5554r2q/kK9uffvopHTp0ICIignr16tGjRw9SU1OL+r/66iu6du1KREQE9evXZ8CAAWRlZQHOK8s33XQTdevWJSIigt69e/Pzzz8XHTt16lT++Mc/MmLECFq3bk27du245pprihXfxo4dS/v27fnwww9p1aoV0dHRDBo0iP379xftk5ycTP/+/XniiSeIi4sjKiqKYcOGkZmZWSn/LD3x9ddfExQUxIUXXlis/bXXXuOuu+7i9NNPL/W4999/n4yMDN59913at2/P1VdfzQMPPMCLL76ItZasrCzat2/PzTffXHTMrl27aNCgAS+88EJR25VXXsmyZcvYtGlT5XxBH1OuExGRmkD5TkREfM3jgqSI+LFFwyDfNfovPwPWjCu+vWhYlYbTvn17Lr/8cj755JNS+/fs2cOQIUO46aabWL9+PfPnz+eGG24o6p8+fToDBw6kT58+LF++nDlz5tCjR4+iuQuTk5P58ccf+eKLL1iyZAmRkZFcfvnlRYXCRo0asWTJErZs2XLCOFNSUvjoo4/47LPPmDFjBitWrGD06NHF9pk3bx6rVq1i9uzZfPLJJ8yYMYMHHnjgVP7xnJIFCxbQuXNnjDHlOm7RokV0796diIiIorbLLruMXbt2kZKSQnh4OB988AEffPAB//3vf7HWcuONN3LOOedw3333FR3TvHlz4uLimDdvnte+k4iIiIiIiNQsIWV1GGMyAU8nF7PW2treCUlEAFg+CtJWerZvWAxkBAGuxUby3V9NDoKwWJjV8+TnqdsROr9czkBL165dO2bNmlVq365du8jNzWXw4MG0aNECcIqYhf7+978zePBgnnjiiaK2Dh06APDrr78ydepU5s2bx8UXXwzA5MmTad68Oe+//z633norY8aMYdWqVbRq1YrWrVvTtWtX+vbty7XXXktoaGjROfPy8pg0aRIxMTEA3HbbbUyc6D6NEgQHBzNx4kSioqJo3749zz77LLfccgtPP/00tWtX/V9727Zto3HjxuU+bs+ePTRt2rRYW1xcXFFfYmIiHTp04JlnnuG2225j0aJFrFixgtWrVx9X/GzSpAkpKSkV/g7+RLlORERqAuU7ERHxN2UWJIFX8DxpiYgvhdWF2i3g6DaKipIABEHtBKcgWcWstWWO4jvnnHPo3bs37du3p2/fvvTu3ZvBgwfTsGFDAFasWEFycnKpx65fv56goCCSkpKK2mJiYjj77LNZt24dAI0bN2bRokWsXbuWefPm8cMPPzBixAheeuklFi5cSGRkJAAtWrQoKkaCU2jbu3dvset16NCBqKioou2kpCRycnLYvHkzHTp04KmnnuKpp54q6s/OzsYYU+w152+++Ybu3bsD0K9fPxYsWFDUl5GRQb9+/QgODi5qS09PL/W7A2RmZhYVEsur5L+Pwje03Nvvuecepk6dyksvvcTHH39MfHz8ceeJiIjw6WvrXqZcJyIiNYHynYiI+JUyC5LW2gerMhARKaE8IxUL55AsVozE2c7aAxdMgLieXgzu5NatW0fLli1L7QsODmbGjBksXryYGTNmMGHCBB566CHmzZvHOeecc8LznmhV6JIFt/bt29O+fXtGjhzJ999/T/fu3fn444+Lip3uoyULjy98LdxTt99+O3/605+Kth944AHi4+O5++67i9rci3pvv/12sWJez549efbZZ+natatH12vQoAFpaWnlihGc19j37NlTrK2w+Ope4Ny/fz/r168nODi4zHkiDxw4UFQ8DnTKdSIiUhMo34mIiL/RHJIi1YH7HJIAwZHHfs/PgEXJVRrO2rVrmT59OoMHDy5zH2MMSUlJjBkzhqVLl9KkSRM++ugjADp16sTs2bNLPa5du3YUFBSwaNGiorbDhw+zZs0a2rVrV+b1CvtONPqwNGvWrCm2WvjixYsJCwujVatWgLN6eOvWrYt+oqOjj2tzn7cxPj6+WF9ISMhxbSfSqVOnopGg5ZGUlMSCBQuKFgYCmDlzJk2aNCEhIaGo7dZbb6VVq1Z89NFHjBkzhuXLlxc7T1ZWFps3b+bcc88tdwwiIiIiIiIicOI5JP9WjvNYa+3zXohHRCoiaaIzQjI/wylGnj0W1ow9tp008WRnqLDs7Gz27NlDQUEB+/btY/bs2Tz11FN07tyZv/71r6Ues3jxYmbNmsVll11GXFwcK1as4LfffisqGo4ePZoBAwbQunVrhg4dirWWGTNmMGLECNq0acPAgQMZMWIE48ePJzY2ltGjR1OnTh2GDh0KwB133EGTJk245JJLaNq0Kbt37+aJJ54gMjKSvn37luv75eXlcfPNN/PYY4+xa9cuHnzwQYYPH+6T+SPBWYjmgQce4Pfff6d+/fpF7Zs2bSI9PZ1du3aRk5PDypXO/KPt2rUjLCyMoUOHMm7cOJKTk3nkkUf45ZdfeOaZZxgzZkzRyNI33niDuXPnsnLlShITE0lOTmbo0KGsWLGi6DX3xYsXU6tWreNW+Q5UynUiIlITKN+JiIi/OdEcks+U4zwWUNIS8ZW4XtBzmjNSMmmS83p2/S6u7YlOfyWZNWsWjRs3Jjg4mNjYWNq3b8+YMWMYMWIEYWFhpR4TExPDwoULee211zh48CDNmjXj0Ucf5frrrwfgiiuu4LPPPmPcuHE8//zzREdH061bN+644w4AJk6cyKhRo7jyyivJysriwgsvZPr06UUjEfv06cM777zDG2+8wf79+6lXrx6dO3dm5syZnH766eX6fj169OCss86iV69eZGRkcPXVV/Pcc8+dwj+xU3P22Wdz/vnn8+GHHzJy5Mii9ltvvbXYytedOnUCYOvWrSQkJBATE8PMmTMZOXIkXbp0oW7dutx3333ce++9AGzcuJH77ruPf//73yQmJgLw8ssvc+655/J///d/vPnmmwBMmTKF6667rqhAWQ0o14mISE2gfCciIn7FlDUfmzGmVnlOZK3N9kpEfqhLly522bJlvg5Dqrn169fTtm1bX4chbpKTk9m/fz/Tpk3zdSjFTJ8+nXvuuYd169YVWwynsu3bt48zzzyTZcuWFRUty3KiP8/GmOXW2i6VEWN5Kdcdo1wnIuI9/pTrQPmukHKdiIj3nGquO9GiNtUyCYmIBLrLL7+ckSNHsmPHDlq0aFFl1926dSv/+te/TlqMDCTKdSIiUhMo34mIiL850SvbIiLip9xX8a4q559/Pueff36VX1dERERERESqF48LksaYG4E7gDOA8JL91tpqM6GYiAjApEmTfB2CVDHlOhERqQmU70RExNeCPNnJGHMtMAH4FYgFPgG+BPKAHcArlRWgiIhIVVCuExGRmkD5TkRE/IFHBUngPuBZYJhr+yVr7Z+B1kAukOL90ERqnrIWmRIJJAH851i5TkREagLlOxER8TlPC5KnA3OAAsACYQDW2r3A34F7KyU6kRokODiY3NxcX4chcsoyMzMJDQ31dRgVoVwnIiI1gfKdiIj4nKcFySwA6wx72QMkuPUdApp6NyyRmic2NpbU1FQKCgp8HYpIhVhrycjIYOfOnZx22mm+DqciqizXGWPGGmNsiZ89bv3Gtc8uY0ymMWauMeYst/5axpjJxpjDxphfjDG9S5z/bmPMB96KV0REqhXlOxER8TlPF7VZhzOEfzawEHjQGLMRZ56Rx4BfvBWQMWYsMKZEc6q1tpGr37j6bwPqAj8CI621P7v6awFvAwNxEuxfrLWz3M5/N3CBtXaot2IW8YYGDRqwY8cONm7c6OtQRCosNDSUuLg46tSp4+tQKqLKcp3LRqCn23a+2+9/w3mlLtm132PATGPMGdbaIzg5sDOQBPQDPjDGxFlrrTGmGc7oFi2JLiIipVG+ExERn/O0IDmBY0/OHgNmActc2xnAH70blpKW1DxBQUE0b97c12GI1GRVnevyrLV7Sja6HryNAp6x1n7iarsJ2AsMBd4E2gJTrbU/G2O2AM8DDYB9wOvAWNerdyIiIiUp34mIiM95VJC01k52+32Daxh9dyASWGCt3e3luJS0RESkSvkg17U0xuwEcnBG+z9srd0CJAKNgBlu8WQaY+YD3XBy3SrgBmNMBHAZsBvYb4z5E1DbWjvJy7GKiEg1oXwnIiL+wNMRksVYaw8B07wcizslLRER8alKznU/4oz03wCcBjwC/OC6KWzk2ie1xDGpQLzr93eADjiv3e0H/gTEAM8AfY0xY4DrcKYuuc1au6GSvoeIiAQ45TsREfEFjxa1McZcb4wZXUbfw8YYb87HWJi0+gHDcRLVD8aY+pw4aRX2vYNTlFwHjKZ40hphjBnjmhB5vjHmTC/GLSIiAawqc5219htr7cfW2tWueY774+Tkm9x3KxlGYZu1NtdaO9Jam2itPc9a+z3wAjAeOBP4M870Jf8BJuNlc7bOIeHlBOZsnePtU4uISCVTvhMREX/g6Srb9wHpZfQdcfV7hZKWiIj4SJXlupKstenAz0AbnFEecOxBW6HTOP6BHADGmB44ue0F4BLgK9e8yu8DXYwx0d6Kdc7WOfSf0p9th7bRf0p/FSVFRAKP8p2IiPicpwXJ1sCaMvp+dvVXCiUtERGpIj7LdcaYcJyHZruBrTj5rk+J/u7AD6UcWwv4N86rank4uT3U1R3m+gz2RpyFxciM3AwAMnIzVJQUEQk8ynciIuJznhYkC4D6ZfTVL8d5yk1JS0REqkiV5TpjzAvGmB7GmERjTFfgf0Bt4F1rrQVeBh40xvzRGNMemIQzmuWDUk73KPCttXapa/t74GpjTEfgfuBna+3BU425ZDGykIqSIiIBR/lORER8ztNFbZbizOf431L6hgPLvBWQMeYF4EtgO87Ix0dxS1rGmJeB0caYDcAvOBMjlydpvWSMmYQzt6SSloiIFKqyXAc0BaYADYB9wGLgAmvtNlf/c0AE8DpQF2d+5b6uEf5FXDdvfwY6ujV/ivOgbg6wk+JTnlTYsC+GHVeMLJSRm8GwL4aRMirFG5cSEZHKpXwnIiI+52lB8hlgums167dw/sKPB24FLgQu92JMSloiIuILVZbrrLVDTtJvgbGunxPttxZnShP3tgLgHteP10wcOLHUEZIAkaGRTBw40ZuXExEJCFmHsvg8+XMGTRpEeEy4r8PxlPKdiIj4nEcFSWvtLNdqay8B7+IsIGOAXcC1rsVnvEJJS0REfKEqc10g6pXYi2nXTuOy/1xGbkFuUXtkaCTTrp1Gr8RePoxORMQ3Nk7dyMbPN/LLl7/Q4foOvg7HI8p3IiLiDzyeH8Ra+zHQDOiEM4djR6C5tba0of4iIiIBR7nuxHol9iK5Y3LRtoqRIlLTrXhnRbHPQFGT8p0x5jZjzDJjzLJ9+/b5OhwREXHx9JVtoGiE4apKisWvGGNuA24DaN68uY+jERGRqqJcd2LNY5z9mtdpzqRBk1SMFJEa573e77F19lYATLAB4LeFvzHOjAMg8dJEn8VWHjUl31lrxwPjAbp06WJ9HI6IiLhU2urYgc5aO95a28Va26Vhw4a+DkdERMTrKpLr8gryAEgZlaJipIjUSN1Hdy8qRNp8p76Vn5MPQGhkKBc/crHPYhMREQkU5RohKSIiIjVbbn4uwSYYY4yvQxERqTIbPt/AjsU7irZDaoWQl5WHLTg24C4kPITBHw0moWeCDyIUEREJLD4tSBpj6gIbgG7W2s1VeN0XgDBr7d1VdU0REamZqluuyyvIIzQ41JunFBHxayvfXckXyV8QFBJUNDLSGENwWDD5OfkE1womPzufoJAgsg5m+Tha3/BhrrsT6GutvbKqrikiIt7h61e2Hwa+LkxaxphXXBMOZxljUkrubIxpZ4yZY4xJde2zxRjzlDEmzG2fxsaYD4wxG4wx+caYSaVc91kg2RjTspK+l4iISCGv5zrXfkONMSuNMRnGmD3GmP8YYxq57VIpuS6vII+QIL1gISLVX/aRbL4Y9gVf3fEVQSFB3LXpLh7JeoRHsh4h/oJ48nLyiDsnjiFfDCHunDhyMnICbnEbLypvrutpjPnCGLPblcdWG2NuLrHPJGOMLeXnqNtubwFdjDHdK/PLiYiI9/nsjsIYEwncCgxwaw4C3gXOBvqWcliOq38FcBA4BycJhQB/c+1TC9gPPINrov6SrLX7jDEzgDuA+0/1u4iIiJSmsnKdMeZCYDLwV+BzIA74F/A+cClUXq7LLcglNEgjJEWkesvPyeedbu+wd+1e4s+P548f/JHYFrFF/eEx4fR9vi8XjLoAE2RIvCSRxS8vZvuC7T6M2jcqmOu6AWuA54DdwGXAeGNMlrX2A9c+9wAPljhuITC/cMNam22M+QC4G1hw6t9GRESqiscFSWNMHE5SuBioBwy21q4zxvwFWGKtXVbOa18BFOAkFQCstXe5rvVXSklc1tpNwCa3pm3GmJ5Ad7d9UnASEsaYwSe4/lTgKVSQFBERl0DJdUASsMNa+5Jre6sx5jXgtRKn83qu0whJEakJ9m/Yz961e2nRowXJc5OP6x/y+ZBi20HBQXS7rxvd7utWRRGeGi/nu4rkuqdKNP3bGNMLuBr4wLXPIeCQW8wXAi2BG0ocOxWYaYyJtNZmlCNuERHxIY9e2TbGnInzBOsOIAM4Awh3dZ8BjKrAtbsDy6219qR7lh1Xa+ByYF4FDl8CxBtjWlX0+iIiUn0EWK5bCDQ2xgwwjgbAEODrEod7Pdfl5ueqICki1d7BlIMA9Hm+j48j8b5KyHennOtc6gBpJ+gfDvxsrf2hRPsynIE2Sad4fRERqUKeziH5ArAVSMR5Aua+tOZCKvaXfwuc4fnlZoz5wRiTBfwKfI8zZ0l57XJ9JlQkBhERqXYCJtdZaxcB1+K8op0D7HPFe1OJ03g91+VZLWojItVbfm4++zfsByA2IfYkewckb+e7Cue6QsaY/jhTjowvoz8GuAZnCpNiXKMiD6H7OhGRgOJpQbIH8JS19iBQ8snXHqBxBa4dAVR0Gbo/A+cCQ3GS6AMVOEemWxwiIiIBk+uMMe2AV4G/A51xRlA2At4scQ6v5zqNkBSR6iz7SDavJL7CrAdmERYVRmSDSF+HVBm8ne9OJdcVvor9AXC3tXZJGbtdDwTjzJ9cmkx0XyciElDKc0eRX0Z7fY7d8JTHfqBuBY7DWvub69d1xphg4G1jzPPW2rxynKae63NfRWIQEZFqKVBy3UM4c3w979pntWvV0QXGmNFux3o91+UV5GlRGxGplmyBZeb9Mzmy8wgXP3YxzS9sjjHm5AcGJm/muwrnOmPMRTjTjTxmrf33CXYdDnxirT1QRn89Kum+bs7WOQz7YhgTB06kV2KvyriEiEiN5OkIyWUcP3lwoauBxRW49gqgXQWOKykIp7AaXM7j2gO5OPOniIiIBFKui+T4m8nCbfe7Z6/nutwCjZAUkerHFli++stXLH9zOW3+0IZe43rRqm+1nWre2/muQrnOGHMx8A0wzlr78gn2Ox84h1Je13b1t8KZA/On8sZwMnO2zqH/lP5sO7SN/lP6M2frHG9fQkSkxvL0juJJYLox5kuc+aoscLExZgTwJ6Aij4q+BZ41xtS31v4ORRP3RwFNgDBjTEfXvuustTnGmBtwXgdYgzNnVhfgaeB/1trswhO7HVcHKHBt51hr17ldvzuwQCuxiYiISyDlui+Bt4wxd7iu0Rh4GfjJWrvd7fpez3V5BZpDUkSqn/Wfrmf5m8uJTYzlqslX+TqcyubtfFeRXNcT+Ar4F/C+MaaRqz/fWltypONtOPMpl7WQaXdgi7X213LGfUKFxciMXCeFZuRm0H9Kf6ZdO00jJUVEvMCjgqS1dpYx5k84Nzt/cDW/iDNZ/p+stQvLe2Fr7RpjzBKcVUFfdzW/jTOnSaEVrs9EIAUofE2tDc4IkG2uY18qcfoVJbYHuPZNcGu7FhhT3rhFRKR6CqRcZ62dZIyJBu4E/oEzmf8c4G8lQvB6rtMckiJS3RTkFTD74dkA3Lb8NiLqVu+pCL2d7yqY65JxRvv/1fVTqNg9myvXDQEeP8Eq3tdSxujJiipZjCykoqSIiPd4fEdhrf3UGPMZcBZwGvA7sMZaW3AK1x8HvGKMecNam2+t7XmSGKYAUzyI9YSTvRhj/oDzatv/yhFrtaa5UUREAi7XvQa8VlZ/ZeU6zSEpItXNL9N+4cCvB2hzRZtqX4wsVAn5rry5LhmnKHmyOI/gjLQslTGmPdARZ2Sn1wz7YthxxchCGbkZDPtiGCmjUrx5SRGRGsfTOSQBsI611trvrLWrTvEGDWvtdJynaE1P5TwVUBsYVs5FcKotzY0iInKMct2JaQ5JEalutn63FQz86ROv1rT8njfznQ9zXRPgRmvtIW+edOLAiUSGlr7CemRoJBMHTvTm5UREaiSP7ihcQ/pPyFr7cUUCsNa+WpHjTkVFY62ONDeKiIhDuc4zeQV5hAWHVcapRUSqVH5OPt/e9y1rL7GuKgAAIABJREFU/rOGM648g5DwmvGwpbLynY9y3YzKOG+vxF5Mu3baca9tR4ZG6j5JRMRLPM26H5bR7j6Ph4p8AUZzo4iIFKNc54Hc/NwyR42IiASKrd9tZfbDs9n5404atmtIz3E9fR1SVVK+80BhUfKy/1xGbkEuESERuj8SEfEiTwuSbUtpqw/0BwYDN3ktIqkymhtFRKQY5ToPaA5JEQlEC59fyM8f/ly0nbY1DRNk6Hx7Z/7wrz9gzAmnoK9ulO881CuxF1e3vZoPf/6Qr4Z+pWKkiIgXebrK9sYyun4wxuQDdwCLvBaVVKq8gjxeX/I6lyRewqSVk7Acv2Cd5kYRkZpGuc4zmkNSRPzVrIdmsfaDtaX2Hd55mAZnNqBuYl0A6jStQ9J9SbS4uEVVhugXlO/Kp15EPepH1FcxUkTEy7xxRzEH+NQL55Eq8v327xn17aii7ZCgEPIKjq15oLlRRESOo1znkleQp4KkiPiNFRNXMP2e6dgCS+7RXJp1a0b90+sft19QWBC9xvUiqlGZCzaLQ/muhCM5R4iuFe3rMEREqh1v3FF0AUp/71f80s97nddVto3aRnx0PPO3zS+aS1LFSBGRUinXueTm5xIarFe2RcT3cjNyWfrPpYTHhHPWn88iuFYw3f7ajYi6Eb4OLZAp35VwJOcI0WEqSIqIeJunq2z/rZTmMKA9cBXwljeDksrx8uKX2XRgE68vfZ3osGia1WmGMaZowuZhXwxj4sCJKkaKSI2kXHdy1lq2HdpG16ZdfR2KiNRgBzYdIPtINh9e+SGHdxym66iu9H2hr6/DChjKd5777dBvfL7hc7o16+brUEREqh1PR0g+U0pbPrATeAkY57WIpFL8dug3/u/b/wOceVAGnjGw2OTdvRJ7aQEbEanplOtO4utfvyYnP0evbIuIzyx8biGzHphVtH3p05dy7q3n+jCigKR856E7v7kTgGZ1mvk4EhGR6sfTO4rS3nvItdYWeDMYqTwLti8A4KfbfqJT404+jkZExC8p153E5rTNADx44YM+jkREaqIFTy3gu9HfUbdVXXo/25vaDWvXyEVpvED5zkOp6akkxiYyadAkX4ciIlLtnLQgaYwJA8YC/7PWLq/0iKRSLNi2gOiwaDrEdfB1KCIifke5zjN7j+4l2ATTpn4bX4ciIjVExu8ZrP90PUd2HmHeuHmERYeRPDeZOk3r+Dq0gKR8Vz6Hsg/RuUlnwkPCfR2KiEi1c9KCpLU2xxhzD/BNFcQjlWTetnlc1PwigoOCfR2KiIjfUa7zzL6j+2gQ2YAgE+TrUESkBvj1m1/54bkfSJmbAkDt02pz+6rbtVL2KVC+K59DWYeIqRXj6zBERKolT1/ZXgW0A+ZXYixSSeZvm8/6/eu58/w7fR2KiIg/U647ib0Ze2lYu6GvwxCRaiwvK4/vn/me7MPZLH19Kfk5+Vz44IV0vbsr4bHhhEaE+jrE6kD5zkOHslWQFBGpLJ4WJP8GvGeM2WStnXXSvcWvPDH/CU6rfRrDOg7zdSgiIv5Mue4E0jLT+HzD5/RK6OXrUESkmkldk8rilxZjCyyHdxxm6+ythNYOpfZptUmel0zdlnV9HWJ1o3zngdz8XDJyM4gJV0FSRKQyeFqQfAeIBb41xmQAewDr1m+ttWd4OzipmINZBxnyvyGkZaVxOPswG/Zv4NnezxIRWtr81SIi4qJcdwJzUuYA0P609j6ORESqgyO7jjD9nunkZeWRuiaVo6lHqR1XG4CON3dk4ISBPo6wWlO+88Dh7MMAGiEpIlJJPC1ILqd4khI/9uqPr/Lt5m9JappEQmwCnRp14o4ud/g6LBERf6dcdwJ7j+4F4MGLtMK2iJzc0n8vZfXk1WX2p+9J5/Bvh4nrEEdkg0j6PN+Hs645qwojrNGU7zywOW0zAHVqaQElEZHK4FFB0lo7pLIDEe9Iz0nnlR9fYcDpA5h67VRfhyMiEjCU605s39F9ADSIbODjSETE36WnpjPjvhnUaVqH2ITYUvep17oeSfcmcf6d51dxdKJ855lnvn8GgGYxzQDIOpTF58mfM2jSIMJjtOq2iMipKrMgaYzZAlxlrV1VhfHIKZrw0wQOZB5gdPfRvg5FRMTvKdd5bl/GPmLDYwkLDvN1KCLiR/au3ct/Lv8PeZl5RW35OfnkZ+czdNpQ6p9e34fRSSHlu/I7nH2Y+Oh4Lk28FICNUzey8fON/PLlL3S4voOPoxMRCXwnGiGZANSqojjES77e9DVnNTyLrk27+joUEZFAkIBynUdSDqZwWu3TfB2GiPgBay1ZaVlYa1kzZQ1Hdh3hvL+cB+bYPk06N1Ex0r8koHxXLvsz9nNu43MxxvmDveKdFUWfKkiKiJw6T+eQFD+Xm59Lj0k9WLRjkeaLFBERr/r191/58pcv6Rqvh10iNVnWwSyO7j3Kkn8uYclrS4raG3VqxBX/vMKHkYl43++Zv3P5vy9n3NBxAASFBgGw/fvtjDNOW+Klidw460afxSgiEshOVpDUZMcBoMAW0Hl8Z9bsXUO/1v24N+leX4ckIhJIlOtOYlWq84bfyPNG+jgSEalKOUdzSF2dCoAtsPzvz//jyM4jALS+vDWtr2gNQIuLW/gsRikX5TsPWWvZn7EfrofQsaHkZuRSkFsAUPQZGhnKxY9c7MswRUQC2skKkuOMMfs9OI+11t7kjYCk/PpO7suavWu47uzrmHzV5KLXCkRExCPKdSeRcjAFgCvPuNK3gYhIlZp+z3RWTFhRrK3PC32oE1+H1v1aa2GPwKN856GM3Ayy8rKo17Megz4axH+v+S95WcfmSQ0JD2HwR4NJ6JnguyBFRALcyQqSHYFsD86jp20+YK1lT/oeZm+dTbuG7Xh30LsqRoqIlJ9y3UmkHEyhbnhdYsJjinekzoFFwyBpIsT1On5bRALarmW7aHpBU3qM7QFARL0I4s+L93FUcgqU7zy0OnU1AE2im5C1NYugkKCiPhNkCAoJIutglq/CExGpFk5WkBxkrV1ykn3ER6779DqmrJ0CwD/6/oPgoGAfRyQiEpCU607g199/ZfLqyZzV8KziHalzYG5/yM9wPs8eA2vGHdvuOU1FSZEAVpBXwP4N+zn/rvNpfVlrX4cj3qF856HJqycTFRbFwDMG8sk9n5CTkUNEgwgKcguo27Iue1bt0eI2IiKnKOjku4g/SjmYwpS1U+jWrBvj+4+nT8s+vg5JRESqoScXPMnh7MPc3+3+4h2LhjnFR3A+C4uRhduLhlVtoCLiVZu+3UR+dr5GREqN9PO+nzkn7hyia0UTHhNO3+f70ubyNkTUi2D40uH0ea4Ptepo0XIRkVOhgmSAmrN1DgDj+49neOfhGh0pIiKV4vfM32nXsB1Xtb2qeMdZD4Fxe9GisBgJEBwJSZOqJD4RqRyr3l1FVOMozrzqTF+HIlLlNuzfwJkNnD/7Qz4fQtK9SeQczSEsKoyg4CC63deNIZ8P8XGUIiKB7WSvbIufmr99PvUi6tG2YVtfhyIiItXYwayDxNWOK95oLWx6A2ze8QcER8LZYyGuZ1WEJyJe8tPbP7Fp+qai7c0zNnPmoDMJDtVDb6lZ9h3dx96je4sKkoVy0p2CpIiIeEeZBUlrrUZP+qHc/FzW7F3DlDVT6NemH0FG/5pERCpKue7kDmYdpE29NsUbcw5A2kowoWBzi/flZ8CasVD/PBUlRfxYfm4+U2+eyuEdhwH47YffiKgfQUS9CADqJtalY3JHX4YoXqR857kffvsBgAuaXlCsXQVJERHv0gjJAJGansqvB37lH4v+wecbPgegV4IWCxARkcp1MOsgseGxxRuPpjif7sXI4MgSc0gmw6CUKohQRMqrIL+AiRdNZOeSncR3jSekVggJvRK48u0rqdO0jq/DE/Gphb8tJCw4jC5NuhRrz0nPISouykdRiYhUPypIBoh+7/djxZ4VAHSN78q4nuPomdDTt0GJiEi1V2pBMn2r8xkUDgVZx17TXjPWKUYGR0LSxKoOVUQ8YAssE5ImsGvpLtoNbsfgjwdjjPF1WCJ+ISc/h3dXvct5Tc4jPCS8eJ9GSIqIeJUKkgGg8DXtbs268XjPx+natCtRYXo6JyIilSuvII/0nPSyR0he9DEsu8tZwCauJ9Tv4qyunTQR4jSKX8RfHN5xmPGdx5N1MAtrLQW5BU4x8iMVI0Xcvf3T2+w9upebO958XF9Oeg6htUN9EJWISPWkgmQA2Jy2mbyCPEZ0HsGlLS/1dTgiIlJDbD+0HYC64XWLd6SnQGgsNB3g/BSK66XXtEX8TEF+AcvHL+fo3qOcd+d5hEWFEVk/kgv+7wJMkIqRIu52HN4BwGM9HjuuLzcjVwVJEREvUkEyAKxOXQ3AWQ3P8nEkIiJSk7yz4h2CTTD9T+9fvOPoVohK8ElMInJi+zfsJzfj2PyuP77yI6veW0VU4yj6vdpPIyJFTiAtM40GkQ2ICI0o1m6tdQqSkSpIioh4iwqSfmr3kd1M3zQdi+XJBU9SO7Q2HeI6+DosERGpQZbvXk7709qTWDfxWGP6Vtj1NTQd5LvARKRU2xZsY9LFk45rb3t1W7o/3F3FSJGTSMtKO/6tACA/Ox8sKkiKiHiRCpJ+6papt/DNpm+KtgedOYjQYCVAERGpOuv2raN78+7HGqyFmRc5v8e0801QInKco3uPsnXOVjZ8tgETbLjm42sICgkCwAQbEnslqpAi4oG0rDTqRhxfkMzNdEYd678jERHvUUHSD72+5HW+2fQN93S9h3uT7gWgcVRjH0clIiI1SU5+DtsPbef0+qcfazy8HjJ3Qavh0P5R3wUnIsXMvH8mq95bBUBCzwTa/rGtjyMSAWPMQ8AfgTOAbGAx8JC1dq3bPpOAm0oc+qO19gK3fV4EkoGjwIPW2vfd+gYADwDdrbX2VGNOy0yjXkS949oLp0FQQVJExHuCfB1AScaYh4wxS40xh40x+4wxXxpj2pfYZ5Ixxpb4WVxinxeNMQeMMb8ZY64r0TfAGPO98cP3Vo5kH+Gub+4CYFzPcTSPaU7zmOYaHVmTpM6BzxOcz9K2RSTgBUKuy8zNBCA6LPpY49bJgIH2oyE4vCKnFZFTkJ+bz6IXFzF33NxiP79+8yunDzidketHcu20a30dpkihnsC/gG7AJUAeMMsYU7LiNwto7PZzRWGHq+A4FOgL/A142xjTwNUXDbwE3OaNYiTAwayDpY+QVEFSRMTr/HGEZE+cxLUUMMDjOImrnbX2gNt+s4Ab3LZzCn8pkbjaAO8YY7611u53S1xXeitxedOiHYuwWL4a+hUx4TG+DkeqWuocmNsf8jOcz7PHwJpxx7Z7TnNWsRWRQNcTP891mXlOQbJoYn9rYcskZ+7I2i0qckoROUU/vf0TM+6bcVy7CTJ0uKEDDc5s4IOoREpnrb3MfdsYcwNwCLgQ+NKtK9tau6eM07QF5lprlwHLjDEvA4nAfuAp4D/W2nXeirmsOSRVkBQR8T6/K0gGYuLypiU7lwAUn7NLao5Fw5ziIzifhcXIwu1Fw2BQis/CExHvCIRcVzhCMjwkHPKOwvdDIGsPNOlX0VOKSDn8NOEnUr5LKda2eeZmml3YjGHzhzmPMtz44Ys/IiVF47yhl1ai/SJjzF7gIDAPGG2t3evqWwXcZoypC7QEIoBNxpgLgF7Aud4KzlpLWqYKkiIiVcXvCpKl8OvE5W3r96+neUxzomtFn3xnqXxHNsEv/wIKquZ69TtD5k6wec52YTESIDgSkiZVTRwiUtX8Ltdl5WUBEBES4Yze3jUNQmMhvv+pnFZEPHBw20G+uuMrIupGUKtOraL2yAaR9HmuDyZIxUcJSK8AK4FFbm3TgU+BrUAC8ATwnTGms7U221r7rTHmPzhvFGTizDeZDrwJ3A4MM8aMAjKAu6y1P1Q0uCM5R8i3+XplW0SkigRCQdKvE5c37T6ymw/WfMBlrS47+c5SuTJT4ecnnZvww+shJKrqrm1CjhUkCwVHwtljIa5n1cUhIlXJ73Jd4Svb4SHhcNC1/sDArRAWW8GvKCKeSN+Tzltd3sIYw/Blw4lppil8JPC5Fqa5CLjIWptf2G6t/dBttzXGmOXANuAPODkQa+1YYKzbuR7ByZeHcKY86QicDfzXGJNorc2hAtIynWeCGiEpIlI1/LogGQiJy5smrZwEwB/a/MG3gdQ0B9fChhfBuo2CPLQO0pZDrdOgy2vQ5o6qiaVwDsmS8jNgzViof56KkiLVjL/muqIRkqERsH8lRMSrGClSCbbM3sJXd3xFQZ7z/yE56Tlk7M+g7z/6qhgp1YIx5iVgCNDLWrvlRPtaa3cZY3bgzI1c2rlOB24GOuE8iJtvrd0N7DbGhOGs6L2mInGmZbkKkhohKSJSJfy2IBkoicub5m+fT7uG7bir612+DiWw7Z4BWyZ6vv/vyyBzB4THFW8/61HoMNaroZ2U+xyS4IyMLDaHZLLmkBSpRvw51xXOIVnHZsOOT6HlLZ4eKiIeKMgvYNu8bUzuPZnoJtEkXppY1Ne8e3M6D+/sw+hEvMMY8wpOnutprd3gwf4NgHhgdyl9BuctgL9aaw8ZY4KAULe+UCC4orFqhKSISNXyy4JkICUub8kvyGfh9oUMPXuor0PxH3mZsGwk5JScUu0k9n0PBbnHFxjLEhQM54+HxBtOvm9lS5p4bJXtwte014w9tp1UjkKriPg1f891hSMk62Zudf5ObTGkPIeLyAnsWbWHCUkTyMvMwwQbrvnfNTRLaubrsES8yhjzOnADMAhIM8Y0cnWlW2vTjTFROKP8P8HJbQnA08Be4LNSTnkLcNBa+6lr+3vgcWPMRUAHIBfYWNF4TzRCMvtQNkCxOV1FROTU+F1BMtAS16n637r/MWHFBLYd3MaRnCNc3OJiX4Xif1K/c0Y6Rp8OweGeHxfZHDq/AqddVHmxVZa4XtBzmjNSMmmS83p2/S6u7YlOv4gEvEDIdYVzSNbJ+s1piG1fnsNF5ATmPjaXkFohdB/dndaXtaZJlya+DkmkMvzF9Tm7RPs4nByXjzOtyI1ALE6+mwP8yVp7xP0AY0wc8AhwYWGbtXaZMeZpnLx4BLjBWptZ0WAPZh0EIDb8+OlJsg45D+lUkBQR8R6/K0gSYImrogpsAbdOvZWJK50Rb+fHn0/vlr25vPXlVR1K5ctJg8XDIPdw+Y47uh2CQqHfSgiJqJzY/FFcr+KvZZfcFpHqwO9zXeEr27UztjgjzmvVL8/hIlKGtR+tZePUjfR8vCcXj9aDaKm+rLUnXA7elZc8Ws3TWpuK83CuZPvTOA/sTllGrjNNUu3Q2sf1ZR/KJrR2KEHBQd64lIiI4IcFyUBLXBW1du9aJq6cSHRYNHOT53Ju43N9GU7l2vAy7PgCGl4EnPBfb3ERTaDFtTWrGCkiNUIg5LrCV7bD0zdDzFkVPY1IjWWtZevsrWQfyS5qy0rLYuotU6kVU4sL7rnAh9GJSEmFD+IiQyOP68s6lEV4TDne2BIRkZPyu4JkdWetZfiXw5mwYgIAq+9YTUJsgm+D8jZr4cdbIW2Fs314AzQdBBeX9pahiIj4o8JXtkPTN0GcRnGJlNe2+duY3Gfyce0h4SHcNOcmvfop4mcK815E6PGDIbIPZVMrRv/Nioh4kwqSZTDG3AbcBtC8eXOvnPPnvT/Tf0p/Ug6m0LJuS6468ypaxLTwyrn9Sups2PIONLwQwupB7QQ450lfRyUiIiWcKNd9sfELekSAyT8K9arxKH4RLzq84zBrPliDLbBsm78NE2y45YdbCK51bE2pqEZRRMVF+TBKkZrF0/u6jNwMwoLDCDLHv5adfShbIyRFRLxMBckyWGvHA+MBunTpYr1xzicXPEnKwRSGnzucV/u9SnhINUpqGTthXn9nnsjs3yEiHi6ZDcF6kigi4q9OlOu+2/od78aBDauHaf5nn8QnEmi+Hvk1G6ceWz+qdb/WxJ8f78OIRMTT+7rM3EwiSpkqylpL6ppUGnVsVMpRIiJSUSpIVqGlu5ZyTbtrGD9gvK9D8a5fXofVj0LuEWjxZ8BAwnUqRoqIBLgrY6MxjS7VXL4iJRTkFfDdo99xNPXosbbcAjZO3UiPMT246MGLAIqNjBQR/5aZl1nq69o/f/QzR1OPEtng+LklRUSk4vy6IGmMqQtsALpZazd76Zx3An2ttVd643yeKrAFbD+0navOvKoqL1t5dn0Li28Cmwc5ByGmHZwxClrd7OvIRETkFOUV5FEvCGILjkB9LbwhNY+1lumjpnMo5VCp/VkHs9g2fxvRTaIxwcfWqGrSpQkX/N8FhIT79f9ii/iEv9/bZeaVPkJy+ZvLqRVTi74v9D3VS4iIiBt//7+lh4Gv3ROWMeZ64K/AmUC6q/9Gt34D3APcDiQCB4B3rbUPunZ5C3jYGNPdWrugar4GpKankpOfE/hzRqathO/6OEXIiEYQPxhMMJxxN0S38nV0IiIByd9u0rLzskkIdW1EtfRGOCIBZd+6fSx5dQl1W9Ytc/GZs687m6smX4Xzv54i4oFy39u57dcG+Akw1lr3SVjLd2+XewQ+T4CkiRDXC1LnwKJhkDTReWW7lBGSB7cd5IwBZxDVSHO/ioh4k98WJI0xkcCtwAC3truBh4D7gcVABHB6iUP/AfR37bMGiAEaF3Zaa7ONMR8AdwNVVpC8d8a9AIG7onb27/BVe8hKhdA6cMY90OxqaJjk68hERKqDijyA+5PruNOBfcA/rbXPu52zwg/gsvOzSSwqSCZU4OuIBJ6ZD8zkl6m/AJB9JBuAG2ffSGxCrC/DEqkWTuHeDmNMGPAhMB/o4d5X7nu79E2QUQBz+8PZY2DNOMjPgLn9aZ3XnpQSIySttaTvSad2o9rl+8IiInJSfluQBK4ACoCFAMaYWOBpYJC1dqbbfmsKfzHGnAHcBXSw1q5322dFiXNPBWYaYyKttRmVEXxJW9K2AHBpy0ur4nLelzoHsvZA6xFOIbJxH19HJCJSLVTkJs0Y0w8ovAGbDrQF3jLGZFpr/wmn9gAuOy+bMwsLkrUTKvbFRALEoe2HePeSd0nbnEZ813hiWzgFyHpt6qkYKeI95b63c/MssBqYR4mCpIvn93a2wPnMz4DVY6Agq2h7FKv4IfT8YrvnHMkhLzNPoyNFRCqBPxckuwPLrbWFK6H1BYKBOGPMOpyRj0uA+6y1W1z7DAS2AJcbY74CgnAS1/3W2r1u516G892TgNmV/k2A3Ud2c+M5NwbmytrWwm+fQXAkdH4VgsN8HZGISHVSkZu0G4AvrbX/cm1vMcY8DTxgjHndLXdW6AFcTn4ON8dAakRr4sJUkJHq6ciuI0xImsCh7YcIDgvmgnsvoMdjPQiPCcD/VxPxfxW5t8MY8wect9/OBa4u49ye39uZIJyUy7FiJEBwJI8fbUFEWPERkgdTDgKoICkiUgmCfB3ACbQAdrttt8SJ9xHgXuAqIBSY4xpdUrhPC2AIkIxzw3Ym8KUxpui7um7KDgEJlfoNjl2PPel7aBzV+OQ7+5P8LMjYBatGw7YPIGGoipEiIt53wps0Y8xOY8xnxhj3yRxrAVklzpMJNMXJg4Xcb9I8lp2fTfMQ2BfVrjyHiQQEay1zx83lxfgXObT9EN3u78bgjwdz2T8uUzFSpPKU+97OGNMYZ/qRG6y1R8o6cbnu7cLqASXnfTWQeD2fHjhw3KI239z9DQAxzWJOemoRESkffx4hGQGkum0H4SSpu621MwCMMdcBe3Bec/vItU8tnKT1i2ufG4CNwHnAj27ny3Rdo9Lty9hHbkFuYBUkM3bBd5fA4Y3Odkw76PiMb2MSEameTnSTNgpncbbHcG7S2rpuvL4FXjHG9AVmAa2B+1zHNwZSwLlJM8aU+wFcdk46IQZnZLxINWKtZfbDs1n4zEKadGnCxY9ezBlXnuHrsERqgorc2/0H+Le1drEH5/fs3i57fymNFrtpPENCYVeJwReHfztMbEIszS9q7kEIIiJSHv5ckNwP1HXbLrxZW1fYYK09ZIzZBTR32yevsBjp8iuQ59rHvSBZD2cRgEp359d3AtAspllVXO7U5GfB9v/Bohuc7XYPQlQraDoQatX3bWwiItVTRW7S3gJaAV+49j0MvAKMBfJLnL/cD+Bycw8DYEJUkJTAU5BfQMqcFPJzSv6nADt+3MHCZxZSp2kdhkwdQnTjaB9EKFIjVeTe7hKghzFmjGvbAEHGmDzgL9ba8W7nO6V7OwO80ABWXfgA4Pw9sn/9ftK2pNFjTA9MUMlRlSIicqr8uSC5Aue160ILXZ9nADsAjDFROCNBtrntE2KMaeW2UmlLnO9ZuA/GmFZAOPBTZQVfKDsvmy82fkGLmBb0P71/ZV+u4vJz4PA6WPkQ7J4Okc2hy6sQfyUYJWARkUpU7ps01+vdDxhjHgYa4dyEFa6allLi/OW+ScvLUUFSAkv6nnS2zNqCtZadS3ay9J9Ly9w3pnkMw5cNp3ZDrZorUoUqcm93dolzDARGA+cDOwsbvXFvZ4H798NzcR0AmPvYbPY8NZGL2UNiVgPI7w7BwRU9vYiIlMKfC5LfAs8aY+pba3+31v5ijPkC5xW1EUAaMA7YC0xzHTMLJxG9Y4wZ5Wp7GWdk5DK3c3cHtlhrf63sL3H/zPvJyc/hbxf+jTB/nH8xczekp8CmN2Dre05b69ug7f0Q3dqnoYmI1BAVuUkDwFqbj+umzBhzLbDIfRG3it6k5eU6U3UFhahgI4Fh5t9msnry6v9n777jrKjOP45/nu0sHWnSi4qoKEYMYgUVldiNJnbB2GKJ0fjTaIrExDQTE2OMNYoxRomJYkUsESsqoFiwIAooSO/b2/n9cebuDpe7u3fLbbvf9+s1r907c2bmmbu799k5c0rt6yHjh3D47w6PWXaHXXagoJvGihRJsibf2znqvjajAAAgAElEQVTnPgwfwMzGADXR62nKvV1+T/xzwG2OzFs1O/H6yvXkLv0SqqvZ685LOYAl5FEJt82FeY/BrFmqlBQRaUVpWyHpnPvAzN7GT1BzW7D6LOBPwJP4lvWvAYdFZg51ztWY2THAX4BX8N3UngeudM7VhA5/Gr67W9NVV8PMmfDuu7D33jBpUoOJ6X9L/kf/zv353t7fa9bpEso5eP5AKAomsht4MgybAjtOhKzc1MYmItJ+NPkmzcx6AqcAs/FjJ08JXh8SdexmPYCrrioCVCEp6W/zl5t55+/vsHjmYkYcN4Ijbj4CgC4DupCTn7b/5oq0O825t2uC+O/tKjbEio79Kj/j7SeA3/kGGdsMVFVUBG+95e8Bj0njHm8iIhkm3f9T+wX+huwO51x1MLvaecESk3NuJf6mLCYz2wMYDXynydFUV8ORR/qEVFwMHTvC2LHbPC2rrqnm1S9fpayqjCUbl7Bw7UJ+feivyc/Jb/LpWlVVCax9DcL1shUbfGXkiCug3yTotT/o5lNEJKlacJN2NnBTsH0OMN4593bU4Zv1AK660ldIZudqfD1JX5u/3My9B9zLluVbyM7PZs+z9qTH8B6pDktE6tfke7sw59w0YFp4XZPv7cL3QtmFUB2k1XwouiKbTgX3wYwZuEcf3XYu7uJiWLBAFZIiIq0orSsknXPPmtltwACiuqm1QD/gbOfc5ibvOXMmvPmmT0gQ82nZU4ue4oTpJ9TuUphbyJS9p7RG3C2z4BpY9NfY23a6ALrumtx4REQkrEk3ac65dcC4hg7YkgdwNZEKyZxOTd1VJCnm3z2ft255iy3Lt3DULUcx9gdjUx2SiDQiLe7tLAuo8ZWRo6bCB1N9pWQ5vLfbdRxwwFnQvTuVjz1Nniuv269jRxg9upVCFhERSPMKSQDn3F9a+XjPNXvnd9+tq4yMiHpa9t7q9wB48ewXKcwtpG+nvvTt1LfZp2wV1RXw+b2+S/bIH227LbebKiNFRFIsLW7SQmqqfK7LyVMLSUkv7z3wHu9Ne49lry6jsGchR/3lKMZepspIkUyR8nu7TjtBYTmMmwZ9xkO3val64lsU3VXJ0Jcv8mUmTWJlzkD6u+XkVJfX9YqbNKk1QxcRaffSvkIyrey9N+TlQUVF3bqop2Ufr/uYId2GcOjQQ1MQYIhzsHwGVG6Bkq/8k7/Bp0LP/VIbl4iIxJTym7SQmirfhS1HXbYlxZxzzDh7Bqs/WA3A+kXrKexZyNAJQzn+vuPp3E+/oyLSBLmd4YRP617/7kly/lLJp/1hdAc/cqTLyuKBmjM55sRsRu+Fv9drZN4AERFpOlVINkV5ua/oi+jUaZunZWVVZby89GXGDkiDJ/Xr3oBXT6p7nV0AvQ9OXTwiIpIxZiz8F0d0g9y8LqkORdqptR+t5eETHqaiqIKilUUMPngwBd0L2GHnHTj0xkPZYZcdGj+IiEhjvvoKgEtPLmRuMOZ/RVEF1dVQvO8EuPqAVEYnItKmqUIyXitWwMkn++/HjIHjj9/uadlznz/HyqKVXLjPhSkMNLDpA/914hvQoS/kdoV8DfQuIiKNs5oyAHp0GpDiSKS9efPPb/LidS9SU1lDToccdjtlNzr26sihvzqUrJysVIcnIm1NWRlfDO/B+qFda1dtXbEVgM791QJbRCSRVCEZr8jYkTfdBD/8IeRs+9ZV11Rz7YvXkp+dz/gh45MfX8SX//EzZ38908+Y3XM/MGt8PxERkUD3YCB/06Q2kmSLnlpEYc9C9jhtD4ZOGMpOR+2U6pBEpC0rL6ck27FDYV2r681f+qGXuw7qWt9eIiLSClQhGa/KSv910KDtKiOnLZjG9IXT+WjtR0wcNpGCnIIUBAhUboXXvgME3cr7fUuVkSIi0iTllWVM7lzD8ryBDMhXt1hJrrUL17LTUTsx8XcTUx2KiLQHZWWUZFXRs7Bn7SpVSIqIJIcqJOMVqZDMzd1m9ZKNS5jy+BQAxvYfy7NnPpvsyOoULwMc7Hc/DDrZjxspIiLSBMWbPmSnPJjdZSwD9FBLWtnaj9ey9eutMbeVbSqjaFURvUf1TnJUItJulZdTFFUhuWX5FjA0aZaISIKpQjJe9VRIvrLsFQDeu+g9RvUehaXy5q14mf/aeWfIKUxdHCIikrGq1rwOwKbOo1IcibQFNdU1fPbMZ1SVVlFZWsmT5z9JTWVNveVzO+Yy6gz97olIkpSVsdUq6dmhrkKyZF0JBd0KyM7VrNoiIomkCsl4RSoko7prv7LsFXp06MEevfdITWVkxWb48JdQXQpbPvbrOg5OfhwiItImVJZ8DYB1HJjiSKQtWPTkIqafOL32dW5hLqc9cRq5HXNjlu/Svwud+mjsUhFJjpryMooLqrYZQ7JsYxkdundIYVQiIu2DKiTjVV8LyS9f4cBBB5JlKZr58eun4ZM/Ql53sGzYYT8/q7aIiEhTPfUUHT95npodoSCvZ+PlRerhnGPe7fNYOH0huR1zOe/N87Aso8MOHVThKCJpo6a0hLJObNNlu3RjKQXdNfSViEiiqUIyXjEqJDeXbWbxhsWcO/rcFAUFbP4YLAtOXAnZ+amLQ0REMtvad+D6U+g6qgy6w8TZJ0OXp6Hf4amOTDLQV298xTOXPAPAnmfuSe89NC6kiKSfoq3rKd8R+nbsU7tOLSRFRJJDFZLxqqryX0MVkp+s+wSA3XrtloqI/KzaC3/lx4xUZaSIiLREtoNLy7CVQD5YhwqYfQwcNhP6TEh1dJIhXr/pdda8v4b1i9aDwVWrr6Kwp8a1FpH0s75kPbnlFfTqMYBJO0+qXV+6sZQuA7ukMDIRkfYhRf2MM1CMFpIfr/NjNo7sNTIVEcHq2f5r7/GpOb+IiLQt+cAgQv8dlMOcKamLRzJK8dpiXrj6BRbPWkzJuhL2OnsvOvbqmNoJ/0RE6rGhdAP5VTCy/2gKcuq6aJdtLFOXbRGRJFALyXjFqJB8e8XbdMrrxLDuw1ITU2QSm71/n5rzi4hI27PNo8p8GDctRYFIpln2yjIATp1xKgP316RIIpLeyitLya8GCuoqH51zlG4sVZdtEZEkUAvJeEVVSC5cs5Db593O/gP3JycrBfW6Xz4CC66Bgr6Q1y355xcRkbbFRb2szIK9fgF9xqckHMk8S2cvJbdjLv327ZfqUEREGlVRUgSAFdRVPlaWVFJTWaMWkiIiSaAWkvGKqpCc8ckMAC7c58LUxPPJn/3XkVel5vwiItK2RPWqtdwa+PAG6DlWlZKyDeccrsbXYL9wzQu8c887AFQUVTDs8GFk52anMjwRkbhURSokO9RVSJZtLANQC0kRkSRQhWS8oiokP173MQO7DOSkkSelIJYi2DAXRl4NI3+U/POLiEibVUEOeQQTuVWXwJzJcMLSVIYkaaR4TTH3HXSfn7QmMGT8EPrs5WeoHXXGqFSFJiLSJJWlvkIyK7+u8rF0YymAWkiKiCSBKiTjFVUh+cGaD1Izu3Z1OczcG2oqYWAKKkNFRKTtyi7k8ew9OLr8bQrNv2bcfamOSpKofEs5W1Zsiblt6eylPHPxMwDsd8V+FHQvIDs3mzEXjaGgm27eRSSz2Lp1AGR37Fi7rraFZA+1kBQRSTRVSMYrVCE5d8Vc3l/9PlNGJ2Hm0aIvYPYxUFXsX9dUQNkq2OkC341ORESkNWTlwfinefS1O3liywoe2DHHV0b2mZDqyCTB1n68ltL1pTjnePK8J7dp/Rit1269mPDLCYw8aWQSIxQRaX29ZjxPZRYUjz+gdl2khaS6bIuIJJ4qJOMVqpCc+fFMDGPy6MmJO1/5BnjhECheCq4KBp9at63jUNjjZ4k7t4iItD/dRkGf8RRX3MzyrN5wwjupjkgSYOOSjax+f3Xt65J1JTx5/pPbTGp06I2H0n1495j7D9x/IF0Hdk10mCIiCZe7ei1fd4asAQNr10VaSKrLtohI4qlCMl6hCslXv3yVvfruRbeCBM5uvfhO2PwhDP8e9Dkchpza+D4iIiItVFRRRKe8TqkOQ1rR1q+3snjWYnDw8g0vs3nZ5m22d9ihAyf98ySycrLI65zHgLEDUhSpiEjyZJWUUpQL+Tn5tes2fL4BUJdtEZFkUIVkvEIVkovWL+LgwQcn9nwrn4Ue+8DYexJ7HhERkZCiiiJ6FvZMdRjSRM45Fty3gJJ1Jdtt++iRj/h63te1r4+fdjx99uxT+7rzjp3p1FeV0CLSvmSVlFKcB72y8wD4/PnPefVXr9J/bH8KuqqFpIhIoqlCMl5BhWRVtrFiywoGdx3ceseuLoM3zoCvHt12/a5Xtt45RERE4lBSWUJhbmGqw5AmmH/3fJa/sZwF0xbUW2biTRPZ/Tu7k1OQQ8feHestJyLSXmSVlFGSC/nZvoVkZPzcI/90ZCrDEhFpN1QhGa/KSsjK4uviVVS76tapkKyugHeugM/+5l8PPBm67u6/t2wYfm7LzyEiItIEZVVlFOSoZUgmeOfv77Bs9jLe/+f7ZOdl039sf856/iyysrO2KWdZRk6B/uUTEQnLLi2jONRlu7LEN0DpM6pPQ7uJiEgr0X+n8aqshNxcvtj4BQCDug5q2fFKlsPTe0DlZtjxKL/sfCFk6yZQRERSRxWS6WPu3+byxfNfxNzmnGPRU4vI75JP/2/25+wXzyavU16SIxQRyVw5ZeUUF0Je0GU7UiGZ00G3yCIiyaBP23h9+ik4x9rp95FVA/v026d5xyldBZVbYOZevqv26N/BTudDXuzZLEVERJKpvLq8tvuaJF/51nJmnD2DkvUlLJ+znE59O9U7ucLAcQM55T+n0KmPxn8UEWmqnNJyirvWddmuLKkkOz97u1bmIiKSGKqQjMeiRfD++1BZyTHXP8hrgwvp+fMmViCWfA0rZ8FboW7Y3/gz7Hp568YqIiLSAmohmToL/72QF655gU1LNzH4kMEMP3I4x91znCacERFJgJzSCorzICfL3xJXllSSW5ib4qhERNoPVUjGo7gYamoA6FBezehl5TBzJhxzTHz7L38cXjnBf99pJ9j9OuiwI/Q7KkEBi4iINI8qJFOjqqyKWVfOIjsvm0N/fSgHXXtQqkMSEWnT8soqKcvPxswAVUiKiCSbKiTjEVRGRuRXVMOCBfFVSJas8JWR2R3gm3dDr3HQaViCAhUREWm+6ppqqmqqagf4l8Qo31KOc47V763mX0f/i8rSSnDgahxnvXAWww7T/wkiIglTXQ1PPUVuRRWDtuT419nZVJVWqUJSRCSJVCEZj6ysbSolK/JzKBg9uvH9qstg4W/892P/DkNOS1CAIiIiLVdeXQ6gFpIJ4Jxjy1dbePu2t3nj92/Uri/sWcjYy8cC0GVAF4YeOjRVIYqItA9HHgmvvgrAcQur/OtZs9RCUkQkyVQhGY+OHcE5KC6mKNfx9a792GXSpMb3W/hr+Ow2yO8Jg76T+DhFRERaoKyqDECT2rSi0o2lbFi8gc+e+YyXp74MwJAJQ9jl2F0AGHTAIPp/s38KIxQRaUc2b4ZPPoGKCgDyq4G33oKZM1UhKSKSZKqQjMcuu8DUqVS/M59TP5nKuPO/x0+ys+svX10Bq1/yY0fmdYcj3oSsBsqLiIikgfIqtZCMpXhNMSvfWdmsfV+87kVWvbsKgAH7DWDfS/Zl52/tXO/M2SIikkAlJX5+gLDiYliwgMqSfqqQFBFJIlVIxuuYY9h46H48fdNUjixsZIbtL+6DuRf57/f4GXQenvj4REREWijSQlIVktt6fMrjfPbMZ83ef/wvxrPjPjsyYOwACnsWtmJkIiLSJIWF0KkTFBXVrevYEUaPpvLRFXpYJCKSRKqQbILNZZsB6FrQteGCq1+CDv3hkCeh6+5JiExERKTlartst6NJbZxzLJy+kOI1xfWWWTp7KaNOH8U3L/tmk4+fU5BDn7361M7iKiIiKdS1KwweDG+9RU1xEeV52XQYO5YVPfdk1bvz2WHnHVIdoYhIu6EKySZYX7oegB4demy/sWgJrHoRcLByFvQ/FnrsndwARUREWuC1L18D4LP1zW8NmCmKVhUx/675FK0uYt7f5jVafq/JezFgvwFJiExERBJq1iyYOZNb75xC6e4j+PGNs/jylrcBGPP9MSkOTkSk/VCFZBN8uflLAAZ2GVi3snQlfPUYfHY7bP7Qr7Ns2PXyFEQoIiLSPFvLt3LZzMsAuPHVGzlw0IFMGDohxVElzjv3vMPs62eDQe9RvTn7hbPJysmKWTYrN4v8zu2n1aiISJuWnQ3HHMNNi/I5cvgIyM5m3SfrKOxZyJDxQ1IdnYhIuxH7P+8MYWYXm9kSMyszs/lmdlBo281mtsHMvjKzM6L2O9bMXrMm9p9atmkZAIO7Da5bOfdimHeJr4zc93Y44Ss4aQ302KdF1yYiIgLJy3WLNyymvNpPalNeXc4xDx3DS0teatVrSSdrPlxDt6HduL7mer7//vfp2LsjHXp0iLmoMlJEJLGSfV8HsLViK53zOwOwasEqdhih7toiIsmUsRWSZvZd4Bbg18DewBvATDMbZGbHAqcDRwBXA/eYWc9gv87An4ALnHOuKedctnkZXfK70K2gm1+x6UNYPgN2uxZO3gA7XwSFAyA/RpduERGRJkpmrqtxNdu8LqksabOVkms/WsvC6QvpvXvvVIciItLupeK+zjnH1vKtdM7rzKr3VvH13K/Z9cRdW/GqRESkMZncZftKYJpz7u7g9WVmdhTwfWAjMNs5Nw+YZ2Z/BoYC6/CJ7p/OuY+aesJlm5cxqOuguhULfw05nWDkVZDXyMzbIiIiTZf0XBdWUlnClMensPSHS1tymLRQvLaY/3znP5RtLqNopZ9ddfSU0SmOSkRESEGuK6oowuHonN+ZJS8uAWDUaaNa4VJERCReGVkhaWZ5wD7AH6I2PQfsj09OF5hZd2AY0AFYbGb7AROAbzTnvMs2LWNw16C79uK7YdlDMPL/1CJSRERaXapyXVhhbiH3HX9fSw/TIq7G8fAJD7P6vdUtOk5lSSWlG0rZ+Vs702VAF/Y8c09GnjSylaIUEZHmSFWuW1m0EoBeZb147kfP0XVQVzr369y8ixARkWbJyApJoCeQDUTfnawGDnfOzTKzfwJzgVLgHKAIuBO4CJhiZj8ESoDLnHNvxHPSLzd/yQEDD4CSFX7cSAx2vbJ1rkhERGRbSc11WZZFDXXdtgtzC3nqtKcSNrGNc47q8up6t8+8fCYfPPgBrsZRVVrFLsfuQuEOhS06Z/+x/RlzkWZQFRFJIym5r3vikycAWP/RegD2mrxXiy9ERESaJlMrJCOixwqxyDrn3FRgau0Gs58Cc4DNwA3AaGAU8IiZDXXOVTR0oq3lW9lYtpHx+aXw+nfB1cBxn0OHvq12MSIiIjEkJdft1GMnlucup6SyJPGVkTWO/57+XxZOX9hgueFHDqf3Hr0p7FXI/lftT1Z2xg59LSIiDUvafd1LS17iJy/9BICHXnmI4ziOUaeru7aISLJlaoXkOqAaiK4N7M32T9cws12Ac/GDJJ8DvOKcWwmsDLoJjAA+aOiEy7csB2Di5uehfDmMvBo6DW3pdYiIiNQnqbmuc35nnjrtKaY8PoX7jr+v2ZWRNVU1rPtkHa6m/vkF5vxxDgunL2TEcSMYMG5AzDLZ+dnsc8E+5HXMa1YcIiKSEZKa67aWb+WYh46hotrXWeZvyQfg3bJ3mcjEFl+MiIjELyMrJJ1zFWY2H5gIPBLaNBH4b7ismRm+Sf9VzrnNZpYF5Ia25eK7CTRo5qInubcPdCtfDnvdCLtf10pXIyIisr1U5LoJQye0aAKbTcs28dpvX2P+HfMbLTtk/BBO+tdJqnAUEWnHkp3rFm9YTE1l3fAknYo6UZVdxQlPncBTXRLXM0BERLaXkRWSgZuBB8zsbeB1/Bgi/YA7osp9D9jknHs0eP0acIOZHQjsCVQCnzZ0opqN7/H5Gx9yW3//uuyjmynoOQ76KGGJiEhCJS3XAZRtLmPG5BmcMO0ECroWNFh2+ZvL2bJ8S+1r5xzPXPwMJetK2Pnondn73L3r3deyjeETh5NbmNtYSCIi0vYl777O+crI/LJ8ptw7hb5r+rKp6yZKqkqY8viUFj2UExGRpsnYCknn3HQz2wH4KbAj8CHwLefcskgZM+sTbD8gtN88M/sN8BiwFTjLOVfa0LmyXBU3960CoKwKClhP9UuTyJ4ws9FKyZUr4dRTYfp06KvhJkVEpAmSmesA3vrHp3w641NO32cRf3ttz5h5q2xzGe/94z1m/XDWdt2yLcs46cGTGHH8CLV8FBGRuCT1vi6YwG3EpyPou8YnuZmTZlKYW8h9x9/X2pcmIiINMOfqH+NJvDHDzM371fbrS/P60OHkVQ3ue/HFcOedcNFFcNttCQpQRCSDmNl855ymOk4zY8aMcUesvJT8r5exhCF0vPicmHnr6UueZt7f5lHQrYAznj1jm4rH/C75dB3UNYlRi4ikJ+W69DRi1Ai3/LTlfOfO7zDkyyFs6rKJu665K6ETuYmItFUtzXWqkIxDrArJivJcbv7HkTzrnqx3v4oKePNNcA46dIAvvlArSRER3aSlp37Wz32P75NDDTUYWcGEp10GdmG3k3cDfLfsubfNZc+z9mTSXyapFaSISD2U69JTP+vnLuTC2tdVWVXk1PhOg0MPG8rZL5ydqtBERDJOS3NdxnbZTjQzuwC4AOAbgw2oq7itKM/lxRkH8XzluQ2+g8uW1X1fXQ2//KVaSYqISPoI57od2ZEc/NhakcpIB5SuL+Wde96p3afzjp0ZP3W8KiNFRCQjbJPrbMfwbV1tZWRuYS4H//TgVIQnItJuqYVkHGK1kKx0eeQePgv6jI+5z8qVMGwYlJXVrVMrSRERtRpJVztaf3eRv18DoJIcZuSewgtf7qK8JSLSRMp16Wnk0D3ct5eeRi5VtesqyeHYf5zC2LN2SWFkIiKZp6W5Lqs1g2nrKsrrmkPmWgXMmVxv2V/+Empqtl0XaSUpIiKSfowajEpyqMGoIYucqjLlLRERaTM2rHfUkLVdvpt+f1njO4uISKvK+ApJM+tuZqvNbHic5S81syeaep7y8lxu/rQf5eW5wes8GFf/TGxz5vgxJMMqKuCNN5p6ZhERkcQzalhNHx7iVFbTh1wqGO3eVd4SEZGkSfS9nSsuIZeK7fJdxVvvNj9oERFployvkASuA55xzn0OYGaDzOxJMys2s3Vm9hczCw90dTcwxswOivcEzuWQd9QsfvzbZeQeOZOyqr68/sbV0Kf+mdjefddPZhO9vKtcJyIizZDom7SuA7pye/UFfO6G87eq8znyDxM56vh85S0REUmmhN7bdepsHPXHI7bLdwcelp+ASxERkYZk9BiSZlYIrACOdc69ZmbZwAJgPXAlsANwP/Bf59xlof3+AAx2zp0S53nWAsuAnsC61r2KpFHsyZepcYNiT4VMjRuaHvtg51yvRAXTVpnZTUBP59yU4PUg4DbgUKAU+BdwlXOuItieDywBvuucezWO4yvXpZZiT75MjRsUeyoo1yVYMu7tQrkO2s/vYjpR7KmRqbFnatzQfmJvUa7L9ArJk4E78TdozswmAU/j35SvgjJnAvcAvZ1zW4J1BwPPA92dcyVNON+8TB2cWrEnX6bGDYo9FTI1bsjs2DNFsh7ABftk7M9TsadGpsaeqXGDYk+FTI07k+jeLj6ZGjco9lTJ1NgzNW5Q7PHK9C7bBwHzXV2t6jjg40jCCswC8oF9QuvmATlBeRERkXT3LaAGeD14fQSwO3CWc+4d59zzwNXA+WbWJbTfE8BxQYWmiIhIOtO9nYhIO5LpFZKDgZWh132B1VFl1gHVwTYAgidnm4EhCY5PRESkNegmTURE2jrd24mItCOZXiHZASiLWldfH/To9aXB/k1xVxPLpxPFnnyZGjco9lTI1Lghs2PPFMm8Scvkn6diT41MjT1T4wbFngqZGncm0b1dfDI1blDsqZKpsWdq3KDY45LpY0g+COQ6574TvL4B+LZzbvdQmV7AGuBQ59xLofWlwGTn3PQkhy0iItIkZjYLWOqcuzB4fRcw3Dl3WKiMAZXAmc65h0PrVwC/cc79Nclhi4iIxE33diIi7Uumt5B8F9gt9HoOMNLMBoTWTQTKgfmRFWY2HCgA3klGkCIiIi20Dugeer2KUEvIQE8gm+1bTvYA1iYuNBERkVahezsRkXYk0yskZ+GT1A7B6+eAhcA/zGxvMzscuAm4OzILW+Ag4Avn3GfJDVdERKRZdJMmIiJtne7tRETakYyukHTOfQC8DZwavK4GjgZK8DORTgceBa6K2vU04O6mnMvMLjazJWZWZmbzzeyglsbfEmZ2sJk9YWYrzMyZ2eSo7WZmU83sazMrNbPZZrZ7VJnuZvaAmW0OlgfMrFuC477WzOaa2RYzW2tmT5rZHhkS+yVm9n4Q+xYzm2NmR6d73DGu47rgd+avoXVpG3sQl4taVmVI7Dua2f3B73qZmX1kZoeke+xmtjTGe+7M7OlQmQY/E80s38xuNbN1ZlZs/vNqwPZnkzgl5SatsZ9rKpjyXVJjtzaS64I4MibfWQbnuuDcGZfvTLku7STr3q6xn2sqmHKd7u2afx3Kdcp1jcWdvvnOOZfRC3AU8CmQHWf5PfDd2bo24RzfxY/LdT4wErgVKAIGpfC6vwX8GjgZn6QnR22/BtgKfDu45n8DXwOdQ2Vm4m9o98fPwLoQeDLBcc8CpgQxjQIew3c97JEBsR8PTAJ2AnYBbgx+L/ZM57ijrmE/YAnwHvDXdH/Pg/NOBT7Bd0+NLL3SPXagG/AF8A/gm8BQ4DBgZAbE3ivq/d4bqAHOCbY3+pkI3B5cy0TgG8BsYAFxflZriflzmQNcEno9CHgKnwPWBz+H/Kh9ZgE/jvP4aZfrgriU75IYO20g1wUxZFR88/MAACAASURBVFS+I0NzXXDejMx3KNel5UKC7+3i+bmm6LqV63Rv15xrUK5Trosn9rTNd0n5Q0nCL8cPgMFxlj0COLKJx38L3+okvO4z/CQB6XD9RYSSFmD42Vh/ElrXIfjjuDB4PRI/O90BoTIHButGJDH2TvhZYY/NtNiD824ALsyEuIGuwOfAocEHyF8z4T3HJ64P69mWtrHj/6l8vYHtaRt7jFh/AmwCCoPXDX4mBr9rFcAZoe0D8YmvSZ+/WrZ5jxN9k5bWuS6IR/kuNbFnTK4Lzpdx+Y4MzXXBedpEvkO5Lm0WEnhv19jPNR0WlOtSEntw3ozJdyjXJfU9p43kuuCcaZPvMrrLdoRz7i/OuWVxln3OOTcr3mObWR6wD757XNhz+FrtdDQUX/NdG7NzrhR4hbqYx+GT3Ruh/V4HiknudXXGDx2wMXidEbGbWbaZnYpPum+QGXHfBfzHOfe/qPWZEPsw811YlpjZw2Y2LFifzrGfALxlZtPNbI2ZLTCzS83MMiD2WkG83wP+6ZwrifMzcR8gl22v7SvgY9L3czPtOeeeBW4D4u0e0Q842zm3ubGCGZrrIEP+jgIZl+8yNNdB5ua7TMx10AbynXJdeknUvZ1yXVJkXK6DjM13ynXJjTvjcx2kX75rExWSCVbfrKWr2X6G03QRiauhmPsCa11QvQ0QfL+G5F7XLfimvnNCcUGaxm5mo8ysCD9xxB3Aic6Pd5PucZ+P747wsxib0zp2/BObyfguFecH53vD/Fh66Rz7MOBifNP+I/G/678FLgnFFYk1LB1iD5uIT7D3BK/j+Uzsi386vq6BMtIMCXwAl4m5DjLn7wgyKN9laq6DjM53mZrroG3kO+W69kG5LvEyJtdB5uY75TrluhZIq3yX05Kd2xkX9dpirEs3jcUcK/6kXZeZ3Yxvonyg84NWh6Vr7J8Co/FjSHwbuN/MxjcQV8rjNrMR+CbmBznnKhoomnaxAzjnZm5zQrM38YngHODNemJLh9izgHnOuWuD1++a2c74pPXXULl0jD3sfGCuc25B1PrmfCZmwudme5eJuQ7S/O8oA/NdxuU6yOx8l8G5DtpGvlOua1+U6xIgA3MdZGC+U65TrmuhtMp3aiHZuHX42uDomt/ebF+LnC4iM1U1FPMqoHeoiXGk+W4vknBdZvYn/Ix4hzrnvghtSuvYnXMVzrnFzrnIh9EC4Io0j3sc/snHh2ZWZWZVwCHAxcH369M49u0454rwg//uTHq/7yuBj6LWfYyfiCQSF6Rn7JFz9cYP+B2euTKez8RV+CdtPRsoI+klE3MdZMbfUcbluwzNddCG8l0G5TrI8HynXNeuKNclSCbmOsjYfKdcp1zXLOmY71Qh2YjgqcN8fNPWsIls2/c/nSzB/9LUxmxmBcBB1MU8Bz9GxrjQfuOAjiT4uszsFuB0fML6JGpzWsceQxaQT3rHPQM/693o0DIPeDj4flEax76dILZd8Ukhnd/314ERUet2ASLdbdM59ojJ+C4sD0dWxPmZOB8/U1v42gbgB3JO18/Ndi1Dcx2k+d9RG8p3mZDroA3luwzKdZD5+W4yynXtgnJdYrShXAeZke+U65Trmmsy6ZbvXJJm8snkBT8NegVwXvCm34IfjHRwCmPqRN0HUAnw8+D7QcH2a4AtwEn4mVYfJvaU8x8A++H/ED4g8VPO3xbEdSi+Fj6ydAqVSdfYf4v/QBmCTwK/wc8sNSmd467nWmYTzMSW7rEDf8A/9RsKjAWeCmIdnM6xA/viP7h/gh/j5RRgM3BJhrzvhv+H5u4Y2xr9TARuB1YAhwN7Ay/hnzrHNUO0luQv8fxcUxSX8l0SY6cN5bogltlkQL4jQ3NdcN6MzXco17W7JZ6fa4riUq7TvV1LrmU2ynWJfo8zNtcF503LfJfUP5RMXvADmC7F1yjPBw5OcTzj8f31o5dpoV+4qfinDWXAy8AeUcfoAfwz+KPZEnzfLcFxx4rZAVNDZdI19mn4JyDl+IFnXyA0zX26xl3Ptcxm26SVtrGHPsgrgg/B/wK7ZUjsRwPvBXEtAn4AWIbEPiH42/xmPdsb/EwECoBb8d1GSoAngYHJ/l3X0uSfe1rluiCm8SjfJS122lCuC2KZTQbkOzI41wXnzsh8h3Jdu1wa+7mmKKbxKNfp3q751zIb5bpkvM8ZmeuC86ZlvrPg4CIiIiIiIiIiIiIJpzEkRUREREREREREJGlUISkiIiIiIiIiIiJJowpJERERERERERERSRpVSIqIiIiIiIiIiEjSqEJSREREREREREREkkYVkiIiIiIiIiIiIpI0qpAUiZOZTTYzV89yeBOPdV6w34BExZsIZpYTxP3T0LpfmVlVKuMSEZHWoVynXCci0h4o3ynfSerlpDoAkQx0CrA8at1HqQhEREQkQZTrRESkPVC+E0kRVUiKNN0C59ziVAchIiKSQMp1IiLSHijfiaSIumyLtCIz62Bmt5jZQjMrNrOVZvaEmY2IY9+zzGxBsN9mM3vfzM6LKjPBzP5nZkXBMtPMdosztglm9oKZbQnO8Z6ZTQ5tP8PMZpvZWjPbambvmNmZTX4T/LGuNLOPzazUzDaY2VwzO645xxIRkfSiXFd7LOU6EZE2TPmu9ljKd5IQaiEp0nTZZhb+23HOuerg+w7BcgOwCtgBuASYY2a7OufWxDqgmR0C3A/8GfgRkA3sBnQPlTke+C/wBHA6/oHCj4FXzWxP59yK+gI2s28D/wZeAS4A1gF7AINDxYYFZRYDNcB4YJqZFTjn7mnkPQmf6xzgd8AvgNeD92Ov4L0QEZHMoFzXAOU6EZE2Q/muAcp3kkiqkBRpuk+iXr8OHAjgnNuATwoAmFk2MAtYC3wXuLWeY44D1jnnrgytey50HANuAV50zp0UWj8b+AK4Argq1oHNLAufDOcChznnaoJNL4TLOed+GbXPbKA/8H0g7qQVXMu7zrlfhdY904T9RUQk9ZTrGqZcJyLSNijfNUz5ThJGXbZFmu5EYN/Q8r3wRjM71czeNrPNQBVQhH+S1FDT/rlALzP7h5kdbWZdo7bvin/i9aD52dBygid5RcBbwMENHHs3YABwTyhhbcfMRpjZdDNbAVQGy+RG4q7vWvYJujccZmaFTdxfRERST7muYcp1IiJtg/Jdw5TvJGFUISnSdB865+aFlk8jG8zsROAh4EPgNGAsPrFtAArqO6Bz7kX8U7YhwAxgnZk9Z2Z7BEV6B1/vpy6hRJajaLjJfGRb9OxxtcysC/A8sDtwDXBQEPf9DcVdj3uBS4H9g2OuN7P/mtmgJh5HRERSR7muYcp1IiJtg/Jdw5TvJGHUZVukdZ0KfOKcOzeywswKgG6N7eic+zfwbzPrBByKH6tjZvBhvz4odjXwUozdyxs49Lrga/8GyhwADATGOefeDMWe21jc0ZxzDrgduN3MegBHAn/EJ/MDmno8ERFJO8p1ynUiIu2B8p3ynSSQKiRFWlchvil/2Nk0oTWyc64IeMLMdsJ/2HcHPgK+AnZzzt3UxJg+DvY9z8zuDZJKrLjBP5UDwMx2AI5t4rm2EYy78pCZjQPOacmxREQkbSjXhSjXiYi0Wcp3Icp30tpUISnSup4F/mpmfwBm4pvGXwJsaWgnM7sR3/z+JWAlMAjfNH5e8MGPmV0KPBo8lXsE/2StL775/BfOuVtiHds5V2NmPwz2ecHM7sQ/Wdsd6O6cuwE/eHMR/snXVKAz8DNgDb6rQdzM7O/ARmAOfsDnEfiZ455raD8REckYynXKdSIi7YHynfKdJJDGkBRpXXcAv8F/SD+Jb9J+DLC1kf3eAobhZ0x7PjjGi4SeYjnnngAOAboAf8fP8PZb/BgkbzV0cOfco0Es2cB9wBP4AZuXBdtX4Qd0zgMeBW4MruXheC46ymv4ZH0HPlFdix+v5NyGdhIRkYyhXKdcJyLSHijfKd9JAlnsFr4iIiIiIiIiIiIirU8tJEVERERERERERCRpVCEpIiIiIiIiIiIiSaMKSREREREREREREUkaVUiKiIiIiIiIiIhI0qhCUkRERERERERERJJGFZIiIiIiIiIiIiKSNKqQFBERERERERERkaRRhaSIiIiIiIiIiIgkjSokRUREREREREREJGlUISkiIiIiIiIiIiJJowpJERERERERERERSRpVSIqIiIiIiIiIiEjSqEJSREREREREREREkkYVkiIiIiIiIiIiIpI0qpAUERERERERERGRpFGFpIiIiIiIiIiIiCSNKiRFREREREREREQkaVQhKSIiIiIiIiIiIkmjCkkRERERERERERFJGlVIioiIiIiIiIiISNKoQlJEMpqZLTWz2amOQ0REJFHMbLaZLU11HCIiIomkfNe+qEJSUs7MxpuZM7Orota7YPlnPfvNNrOiqHVTQ/s5M6sws7Vm9qaZ/cnM9qznWNOi9ote7gmVnRysO7k1rr8+ZnatmT1iZl8E51vaSPmxZvaCmW01sy1m9qyZjY5RboSZ/cHM/mdmm4JjT21ibGZmZ5rZw2a22MxKzOxLM3vCzMbWs0+WmV1hZp+YWZmZfWVmfzSzjvWU/5aZvWFmxWa2IXgvhjYlThGRdKFcFzOeXczshiDutUH+WmBmP2kgN4wwsxlmtjHID6+a2aExyu1oZjcGuXBtcC3TmhHjOWY2y8yWB7lrrZnNCd6f7Hr2OdvM3jWzUjNbbWb3mFmvesrGlbtFRDKF8l3MeEaY2YNm9rGZbQ7unT4xs5vNbMcG9klavotx3Emh92tMPWWU76RFclIdgEgcTjezPzjnFjRhn58DS4BsoDswGvgecLmZ3Qz8n3POxdjv+0BRjPWLmxhza/g1sAF4B+jWUEEz2w+YDazAXzvApcCrZra/c+6DUPFxwJXA58B8YLvEFod84AFgAfAw/r3eEbgImGNmZzvnov/Z+BPwA+Ax4I/AyOD13mZ2uHOuJnQ9JwH/Ad4D/g/oCvwQeN3Mxjjnvm5GzCIi6aw95rpzgUuAJ4AHgUpgAvAr4Dtmtp9zrjRS2MyGA28AVcDvgc3A+cAsM5vknHshdOwRwHXAV8BcYFIzY/wGsBG4DVgDdAKOBu4DDsK/37XM7ArgZuBl4HJgAD7njjOzbzrnikNlm5K7RUTaivaY7wbg75UeA5bj89go4ALgVDMb7ZxbEymconxXK3goeDv+vetUTxnlO2k555wWLSldgPGAA66KWu+A94EyYFaM/WYDRVHrpgb7jYlRvgfwYrD9mqht04L1PeOId3JQ9uQEvy/DQt9/CCxtoOzbwBagf2hd/2DdczHeh27B92OCa5naxNhygENirO8DrANWA1mh9bsDNcB/o8pfFpz/9NC6XHyyWgZ0Cq0fDVQDd0UdYykwO9W/x1q0aNHS0KJcF/McY4CuMdb/Kjj3pVHr/x3kgdGhdZ2CfPEpYKH1nYFewfc9g+NNa8XYnw7yWt/Qup5AcZCTs0Prjw3Of13UMZqSu2c39H+AFi1atKTLonzXpPfqlODcV0etT2m+wzckWY5vRLLd+698p6W1FnXZlnT3JfA34AgzO6wlB3LObcB/6G8Brq2vO1hrMbMeQVeCz4NuXuvNbL6Z/V+c8X4R53l2AvYFHnHOrQjtvwJ4BDjczPqG1m9wzm1q2tVsF1uVc+7lGOtX45+S9Q6WiNMAA/4ctcvdQAlwZmjdIUA/4B7nXO0TTeefos4GvmtmudHnNrNvmO+GXmS+i/f9ZtY7qkykS8bhQReQZWZWbmbvm9mpTXgLRERaU7vMdc65ec65zTE2TQ++7hE6T0fgOPwDqNpWNUGeuAfYBZ8LI+u3OufWNvOy4rEMn9e6htadABQCtzrnqkOxPAl8QSjXNTV3h/YbZmaPB13+tpjZY2Y2LKpMpLvkZDO7zMwWBT+bRWZ2WatcvYhI87TLfNeAZcHX7qHzpDTfBd2zL8P3TttaTzHlO2kVqpCUTHAjvpn678zMWnKgIHE9hr+BODBGkR5m1jPGkteM0z2Cb4o+E/+hfgP+6dD45kVfr0hCmhNj25v4G6Z9WvmcDRkAVADhSs998S1J3g4XdM6V4bt97xtVFuq/ni74RBx9zhfxCfBq4FHgLOAlMyuMcZzfAafiuyL8HMgDHjKzyQ1fmohIwijX1RkQfF0dWrcnfriQ+nIDbJtLWpWZdQ3eo53N7FJ8d/NFbNvtr7H8tauZdYqzbKzc3RF4CZ9jrwX+DnwLP5zJdjdz+J/Hj4F/BuU3AX8xs+vrvVARkcRrt/nOzAqC8w8wsyOAO4NNz4SKpSzfmVkOvsHIc865/zRQVPlOWoXGkJS055xbb2a/xyev7+LHLGyJ94OvuwCzorZ9Ws8+p+DHNIyLmXXFj814u3Pu0iZH2DT9gq8rYmyLrOuf4BgAPxEN8E3ggaCyMaIfsM45Vx5jtxXA/maW55yrIP7rWRhaPxy4wjlX2wLTzBbixzX5AfDbqOP0BPaMtMwxszvwvxc3m9l0FxqzTEQkGZTrao+ZjX9QVAX8K7Qp1bnuRepumBzwAnBRuGUIjcdoQZlFcZSF7a+nJ3CLc+6HkRVm9gr+IdxU/DjOYbsAI51zy4OytwGvAT81s79H1ouIJFM7z3fnAbeGXi8FznTOvRpal8p89yP8mJQnNVJO+U5ahVpISqb4M/A18CuL0V23ibYEX7vE2PZtYGKM5ZUmnqMUKAfGmtmQZkUZv0gLwFiVfWVRZRLGzHbGT3SzAp/MwgqJHR9sH2NzrmcLvrVj2N+C9SfGOM7t4W6Cwfd34LtLjK8nThGRRFOu8+/BfsDPnXPhG8lU57qL8e/R2fixvXIJdbGLOn88MTb3erZ5wOacewx/w31CjLIPhm/Cgod+f8I3SDg2RnkRkWRpr/luRnD+E/EtLDcB0bNSpyTfBd2hrwd+6Zxb0khx5TtpFUmvkDSzg83sCTNbEenvH7XdzI/t9rX56eNnm9nuUWW6m9kDwXgCm4Pvu4W2DzGzV8ysOPg6JGr/R83sggReprQy51wJ/mnIcLZ/ItJUkWS1Jca2V5xzL8RY1sQoW6/gQ/CH+PGvlpjZQjO7taVjpdSjJPiaH2NbQVSZuJlZtpn1jVq61lN2KHWDSk+KMY5JST3xxYqxOdfzRXTry+D1F8AwtvdxjHUfBV9jlRdpEuU6aY72nuvM7Jf47nB3Oed+E7U5IbkuOG90rusRXcY593bwHj3gnDsV383sFfMzoTYnxuZczybn3KoY5T8G+tj246cp10lCKddJc7XXfOecWx6cf4Zz7nrgHHzX9WtDxVKV7+7Ez2T+hzgOpXwnrSIVLSQ74WcMvhz/pCHa1fjWVZfhxxtYAzxvZp1DZf4FfAM/pf1RwfcPhLb/Ed9KazSwktAflZmdgG8CfHfrXI4k0b3AJ8DPon4fmmrP4Gt9TfhbhXPuDmAIcD7wDnAy8IKZtbRbQrSvg6+xmu5H1sVqIt+Ygfi/n/ByS3Sh4B/Dl/B/2xOdcx/UE2NPM4uViPrju3NXhMqGY48uC9tfj6vnGuoblyZW+RaNYSMSRblOmqtd5jozmwr8FLiP2Denicp1sH2uezSOfe7Ht+iYHFrXWIwuVEa5TtoC5TppiXaZ76KO+T7wLr4VfkTS852ZnQgcjv/7GmxmO5mfjCZSYTkgWBepP1K+k1aR9ApJ59wzzrnrgkFSa8LbzMzwTx5+65z7r3PuQ/xTg87A6UGZkfhkdYFz7g3n3BzgQuAYMxsRHGokcL9z7jNgWvAaM+uCT2oXOOfq+yWXNBWM03Qtvln7Vc05RvAU6ET8QMqvtV50sTnnVjrn7nHOnYUfpP8h/CzRrTkQ8dzg67gY2/bDf0DPb8ZxV7F994bfhwuY2WB8ZWRXfGXkuw3EmIUfXzK8fwH+H8x5UWWh/uvZgh+PJGy4RQ1OHVR+DsW3koy2W4x1I4Ovcc1uLtIQ5TpprvaY68wPOn898A/gvHp+bz/Ad/eqLzfAtrmkKaJzXfSwI7F0CL6GW5c0lL/GAp8Gs6Q2Vra+3N3dYg/mvyuwxjlXHLVeuU4SSrlOWqI95rt6dGDbXJKKfDc4+Hov8FloicxU/VjwOhKn8p20inQbQ3Io0Bd4LrIimFziFWD/YNU4oAh4I7Tf60BxqMx7+Onjs4AjqBvo9rfANOfcJ4m6AEks59wM/M/+SqB3U/YNEtYj+Gb9NwZdBRLCzAotanbnIOlGfhe36w7WXM65xfikdIqZRQYNJvj+FOB/9TR5b+y4ZTG6N0SavkcqI2fjx9A6wjnXUKXndHyy+WHU+vPxLUweDK17Gf/E7jyrm50NM9sLP77jI865yqjjdGHbJ4sEr7vgx2qJ9n0LdT8Pvr8IP47Lyw1ch0hrUK6TBrWnXGdmP8d323sAmOKcq4lVLrixeRIYH+SDyP6d8JMEfIaf7bTJYuS6+cGxc8xsh3p2i9ykvRla9zi+ldil5ifnicR4LL5bYm2ua0Hu/nH4RdCqZQSxc90ZZjYgVDYPuAKoBp6q57pEWotynTSqveS7eirXMLMJ+G7gtbkkFfkOnxNOibE8Emy/Jngd6RavfCetIt1m2Y78oa6OWr+auua8fYG14SdhzjlnZmtC+1+FHwNhKf5D4kIz2x84CJhgZg/iE+Cb+BkSY403IenrGuBV/FOP6KcjEZPMbFd8pXt3YG/807POwE3OuZvq2e9kMyuKsX6Nc+65qHXfDs4R7XP8OBYvm9lj+K4sG4N4v48fm+PVGPttw8zOou5pVS8gz8x+Grxe5pwLd2e5HN9S8VUzi8zcdhn++rdp6RFUvkVupCJJ4eDQsZ8Iug80FFvn4HxD8DPFjQg9yY543jm3GsA594H52c4uNbNHgWfw78cP8BWAtTOpOucqzexyfCXmq2Z2N/4fjSuAtfhWNNE+B643sz3wT9j2Ac7FdwP5S4zy64C3zOxefHP+KcAgfMuchP0zIxJQrpN4tPlcZ2aXAL8AvsTPWn26b1RVa7Vz7vnQ62uBw4DnzOxP+Buj8/F/N0dHt5IK5bXITeSeoXWvOOcam9SgE7A8dH2r8X9/JwBj8GMnh/PXWjP7Gb7L2wtm9lAQ24/w+ejPUcePO3cH1gEnBTdxs4Gd8Q/fVuMrdaMtwue6O4Ct+FZp++InLPiqkWsXaSnlOolXm893wO1mtiPwP2AZfuzEfYBT8Z/P0Z/5Sc13QaXh4uj1wb0V+ErDeaHyynfSOpxzKVvwT8Qmh17vj29FNTCq3H3As8H31wGfxzjWEuDH9ZwnD9/0eX/8H839+MrYf+I/wFL6PrT3Bd/qzQFXRa13wFP17PN4sL0oav3UYH1kqcB/oL2Nn2lrz3qONy1qv+jltVDZyY2UfRbYITjfAnyru1L8h/yfgR3jfF9mN3CO2THKj8PfHBXhP4hnAd+IUW5II/FPjiO2xo7hgPFR+2TjE86n+G4IK4CbgU71nOMY/D+XJfik/x9geIxyS4P36hv4JF8clH8A6BNVNvKzO5y6m+By/D8Wp6f6b0FL21xQrtPilOuaGc/sGPuMDN6XTUF+eA04vJ7jN3TsqXHEl4fvEjo3eH+rgvzyGnAJkFvPfpPxrbrK8GPm3Qv0rqdsvLl7Nj7fDQuuf0tQ/nFgp3p+1ybjH/x9hs91nwGXp/pvQUvbXFCu01L3M4p8Binf1Z3jO8DTwFdBbijFV9zdCgyqZ5+k5bsG4o68/2Pq2T4Z5TstLVgs+EGmRPC04lLn3LTg9TD8E4hvOufmhso9jZ/04hwzOxc/sUYXFwQfjFGyFbjMOXdfjPP8HF8xcYmZvQP8zDn3tJkdDdzgnNsnsVcqIunA/OyP9wETnHOzUxuNtBfKdSKSTGY2Ht8SZUrkc0ck0ZTrRCTZlO8yX7qNIbmEuok0gNpJLw6ibmyROfguNOFBUccBHdl2/JHI/rviB1C+NliVBeQG3+fhW22JiIgki3KdiIi0dcp1IiLSoKSPIRkMxrpT8DILGGRmo4ENzrkvzezPwE/M7BP8WAA/xTfr/ReAc+5jM3sWuNPMzseP/3Ynvvn3p1HnMuAu4EeubjyR14CLzOxj/JgPCZ+NS0RE2hflOhERaeuU60REpCVS0UJyDPBusHTAj+P2LnBDsP33+HHlbsPPxrQjfgbfraFjnIEfq+A5/NgD7wFnxTjXBfiBksMzMU3FjzMwD6gh9qCoIiIiLaFcJyIibZ1ynYiINFtKx5AUERERERERERGR9iXdxpAUERERERERERGRNizpY0hmCjO7AN81gI4dO+6z6667pjgiEZG2Yf78+eucc71SHYco14mIJIpyXfpQrhMRSYyW5jp12Y7DmDFj3Lx581IdhohIm2Bm851zY1Idh2xLuU5EpPUo16Un5ToRkdbT0lynLtsiIiIiIiIiIiKSNCmtkDSz7ma22syGJ/m8fzCzvyTznCIi0j4p14mISFuXwlz3HzO7MpnnFBGR1pHqFpLXAc845z43s15mNsvMvjazcjP7ysxuM7OukcJmVmBm08zsfTOrNLPZ0Qc0s5PM7DkzW2tmW83sLTM7LqrY74DJZjYssZcnIiKyTa7by8weCnJcqZl9amb/Z2a1+djMxpvZ42a20sxKgpx3bvRBzex0M1sQlFllZv80s76hIsp1IiKSLE26rwMws1Fm9nKQD1eY2c/NzGId3MxOMzNnZk9FbfoF8NPoY4uISPpLWYWkmRUC5wF/D1bVAI8BxwK7AJOBw4C7Q7tlA2XAX4Gn6zn0IcD/gKOBvYFngMfM7KBIAefcWuA54PutczUiIiLbi5Hr9gHWAmcBuwPXAz8HfhzabX/gA+BkYA/gduAuMzs9dNwDgAeA+4PjnADsBjwYKaNcJyIiydCc+zoz6wI8D6wG9gV+APwfsF1rx+DB2k3Aq9HbnHMfAF8AZ7bW9YiISHKkcpbtb+GT1esAzrn1wB2h7cvM7G/AtZEVzrli4CIAM9sT6BZ9UOfc/TDL1gAAIABJREFU5VGrfmFmR+Nv1sJJ7Ang1/jEJyIikgjRue7eqO1fmNk3gG/jcxLOuV9HlbndzCYEZf4VrBsHLHfO/Sl4vcTMbgVujdpXuU5ERBKtyfd1wBlAIXCOc64U+NDMRgJXmtnNLph51cxygYeAnwATgJ4xzv8EcBpwW6te1f+zd+fRclZVwsafTUIgBGzDJInMrTJrgKgMIqCIDKK2dDM5ACqIEyIgOAACIoggg0O3giAK2DSC/bUiAiKjgEAYwwwyqJBAkIBkIiTZ3x+nilQq997Uvanp5j6/tWrd1HtOve+p5Vps964zSJJaqpNLtrcB7shejvmOiLHAR4Drm/CsFYCpddduA97Y7n1OJElDSp+xruJ1LByjFtXnJmBMROwWxcrAXpRVAbWMdZKkVhtIXrclcGOlGFl1JTAWWLvm2reBJzPz5308/zbgHRExcgBjlyR1SCcLkmsBk+ovVvbWmgE8DbwM7L84D4mIzwOrU5a21Xqm8nftxbm/JEl96DHWVVVmR+5HWZbdW58PUJa6nVW9lpm3UGaDXAjMpiwDD2Dfuo8b6yRJrTaQvG41ynLtWs/WtBEROwJ7Ulkh14dngKUpxUxJ0iDRyYLkSMp+kPW+DGxGWWK9LnDGQB8QEbtT9hv5aGY+Vddc/TXOX9IkSa3SW6wjItaj7Id8RmZe2kufrSnLtA/OzNtqrm8IfB/4FmVfyp0oCdxP6m5hrJMktdpA87r6GZXVA22yMvP/PMqS7kWtIjDWSdIg1Mk9JJ8HRtdfzMzJwGTgoYj4B3BjRJyQmX/rz80rxcjzgU9k5m966LJi5e+U/g1bkqSG9RjrImJ94Frgosz86kKfKn3eRVmCfUxm1s+g/BpwW2aeUnl/b0RMp8TMb9TETGOdJKnVBpLXTaYyE7LGqpW/z1IOdRsDXF1z8PZSABExB9goMx+uXDfWSdIg1MkZkndRTgTtS3V8y/TnxhGxB3ABsF9mXtJLt42BVyknmUqS1AoLxbrK7MbrgF9l5pd7+lBEvBv4PXBcZva0UmA5YG7dter7qLlmrJMktdpA8rpbgG0iYtmaPu+jLL9+Ergd2AQYV/P6DeWQ0nHAEzWf2xh4JjPrl4BLkrpYJ2dIXgmcHBErZeY/KntkrQTcAUwDNqIst/5zZj5W/VAlkRtBOWFt+YgYB5CZd1fa96LMjDwcuCEiqr+8zc7MF2qevw1lI+UZrfySkqQhrT7WbQRcQ5kdeWJNjKrOJCEitqMs5f5P4MKaPnMzszr747fA2RHx2cozxlCWwt2ZmX+teb6xTpLUagPJ634JfBM4LyJOAN4CfJXyQ1wC04H7ah8SES8CwzNzgeuUWHdFa76aJKlVOjZDMjMnUk5E26tyaRZlw+I/AQ8Cp1MSrl3qPno55Ve4PSn7Zt1VeVUdRCm0nkHZXLn6+nXdffYGzm7Ot5EkaWE9xLr/oCxJ25MFY1TtYQD7UWZAHl7XfnvNfc8DDgW+QEnYLgEeBT5UNwRjnSSppQaS12XmS5QZkWOBCcCPgO8Bp/Xn2ZUZlv+GsU6SBp0oP0B16OEROwFnAhtmZv3Ss1Y+d1fKr3Rvzcw5i+o/fvz4nDBhQusHJklDQETckZnjOz2OdjHWSdLQY6xr23M/D3woM3dspL+xTpKaZ3FjXSf3kCQzr6D8GrZ6mx89Cti/kQRNkqTFYayTJC3pOhjrXgW+2OZnSpKaoJN7SAKQmd/vwDMvbvczJUlDl7FOkrSk61CsO6vdz5QkNUdHZ0hKkiRJkiRJGlosSEqSJEmSJElqGwuSkiRJkiRJktrGgqQkSZIkSZKktrEgKUmSJEmSJKltLEhKkiRJkiRJahsLkpIkSZIkSZLaxoKkJEmSJEmSpLaxIClJkiRJkiSpbSxISpIkSZIkSWobC5KSJEmSJEmS2saCpCRJkiRJkqS2sSApSZIkSZIkqW0sSEqSJEmSJElqGwuSkiRJkiRJktrGgqQkSZIkSZKktrEgKUmSJEmSJKltLEhKkiRJkiRJahsLkpIkSZIkSZLaxoKkJEmSJEmSpLaxIClJkiRJkiSpbSxISpIkSZIkSWobC5KSJEmSJEmS2saCpCRJkiRJkqS2sSApSZIkSZIkqW0sSEqSJEmSJElqGwuSkiRJkiRJktrGgqQkSZIkSZKktrEgKUmSJEmSJKltLEhKkiRJkiRJahsLkpIkSZIkSZLaxoKkJEmSJEmSpLaxIClJkiRJkiSpbSxISpIkSZIkSWobC5KSJLVZRDwZEdnD63eV9mN7aJtcd4/DI+LZiHguIg6ra9s0Ih6OiJHt/F6SJEmS1IiuK0iapEmShoC3A2NqXpsBCVxc0+fhuj6bVBsi4q3A8cDeldcJEbFJpW0YcDbw+cyc2fJvIklSD8zrJEl9Gd7pAfTg7cCwmvdjgDtYOEnbrub93Oo/apK0DwABXBYRV2XmRJM0SVI3yMwpte8j4lPAP4Ff1Vyek5kLJGY11gfuzcxrKp+/t3JtInAIcF9mXt30gUuS1DjzOklSr7quIGmSJkkaSiIigE8BF2TmjJqmdSPiaWA2cCvw9cx8vNI2EXhLRKxJSdLeAtwXEWsDXwDGt2n4kiT1yLxOktSXrluyXWtRSVpEPBERF0XEujVtryVpEbEWCydpC0z1lySpw94HrAP8tObarcB+wM7AAcBqwM0RsRJAZj4IfB34A3AV8LXKtR8D3wC2iYh7I+K+iPhwu76IJEk9Ma+TJNXruhmSdfpK0h4CVgWOoiRpG2XmPzLzwYioJmlQSdIi4grmJ2nHU4qxR2Xm/2vTd5EkqScHALdn5t3VC5n5+9oOEfFn4HFgX+C0Sp8fUwqQ1T4fq/zzauARYEtKrLspIt6Smc+18ktIktQH8zpJ0gK6vSBpkiZJWmJFxKrAh4DP99UvM6dFxP3Am3u5z0rAt4DtgS2ARyszJomIR4F3Ar9t4tAlSeoP8zpJ0gK6dsl2TZJ2dl/9MnMa0EiSdhA1SVpm3g9UkzRJkjphP+AV4KK+OkXEspR9syb10uU04AeZ+SQlti9d0zaCBQ8VkCSpbczrJEk96dqCJCZpkqQlWGU/rU8DF2Xmy3Vtp0bEthGxTkS8E7gEGAX8vIf77ABsCJxZuXQ7sF5E7BYRHwLWA25r4VeRJKkv+2FeJ0mq05VLtheVpFGWnf2VstfI0Sw6Sftk5dJrSRoliJmkSZI6ZTvKLJCP9dC2OvDfwMrAFODPwBaZ+VRtp4gYCfwI2Csz5wJk5tMRcRBliVsAn8nMZ1r1JSRJ6o15nSSpN11ZkMQkTZK0hMvMaymxqKe2vRq8x0xKElZ//ef0kNBJktRm22FeJ0nqQVcWJE3SJEmSJGlwM6+TJPWmm/eQlCRJLRQRB0bEhIiYMGXKlE4PR5IkSdIQYUGyFyZpkqQlXWaelZnjM3P8Kqus0unhSJLUdOZ1ktSdLEj2wiRNkiRJkgY38zpJ6k4WJCVJkiRJkiS1TUcLkhExOiKejYh/bfNzT42I77fzmZKkoamDse4LEfGbdj5TkjQ0mddJkvqr0zMkvw5cnpl/iYi3RcR/R8TfImJmRDwcEV+JiNfGGBEbRsS1lWA3KyIej4gTI2JETzePiHdFxJyIuK+u6WRgv4hYt4XfTZIkqIl1ABFxZmUvq1kR8WR954g4NiKyl9eqlT7LRsR5EXFvRLwaEdf18NyzgfERsU0Lv5skSdD/vG67iPi/iJgUETMq8eyT9TeNiH0i4u5Kn8kRcUFErFbTxbxOkgapjhUkI2I54NPAOZVLmwNTgI8DGwHfBI4BvlrzsdnAz4EdgfWAQ4BPASf0cP/RwC+AP9a3ZeYU4Crgs835NpIkLayHWAcl9v6cEqN6ciowpu51PXBdZj5X6TMMmAX8EPhdTzfJzFeAXwIHL963kCSpdwPM67YCJgL/DmwM/BdwVkTsU3PfrYHzKTFzI+DDwIbAhdU+5nWSNHgN7+CzdwHmATcBZOa5de2PR8RmwO7AiZU+jwGP1fR5KiK2A3qa/XEOJXgFJdDV+03lvl8Z+FeQJKlPC8Q6gMz8IkBEHE75gW0BmTkNmFZ9HxFrUOLcx2v6TAcOqrS/FXh9L8//DfCHiFguM2cs7peRJKkHA8nrTqzr818RsX2lzy8r17YE/p6Zp1fePxERPwB+UPdZ8zpJGoQ6uWR7G+COzMw++rwOmNpbY0S8CdiJMnOk9vrngNXoYeZkjduAN7Z7nxNJ0pDSSKxblE8BLwKXDuCzEyg/Pm65GM+XJKkvi53X9dLnJmBMROwWxcrAXsDldZ8zr5OkQaiTBcm1gEm9NVZ+RduPMn2/vu3miJgFPAr8ibJnSbVtE8qygI9m5tw+nv9M5e/a/R24JEkN6jPWLUplv61PAr+oLMHul8qsyJcw1kmSWmfAeV1Nnw8A7wXOql7LzFuAvSlLtGdTloEHsG/dx83rJGkQ6mRBciRl/6uFRMR6lD2xzsjMnmaE7AlsBuxDWSJwZOVzywAXAYdn5hOLeP7MmnFIktQKvca6Bu0MrAH8dDHuMRNjnSSpdRYnr6vuFflL4ODMvK3m+obA94FvUfal3ImyCu4ndbcwr5OkQaiTe0g+D4yuvxgR6wPXAhdl5lcX+hSQmX+r/POBiBgG/DQiTqFs/L8h8LOI+Fmlz1LltjEH2CUzr6pcX7Hyd0pTvo0kSQvrMdb1w4HAzZl5/2LcY0WMdZKk1hlwXhcR76IswT4mM+tnUH4NuC0zT6m8vzcipgM3RsQ3anJC8zpJGoQ6OUPyLkrx8DWVX8GuA36VmV9u8D5LUQqrw4CngU2AcTWvH1MOwhkH3FzzuY2BVymnu0mS1AoLxbpGRcRYYFfg7IE+vLKf1rLAnQO9hyRJizCgvC4i3g38HjguM8/ooctyQP0WXNX3UXPNvE6SBqFOzpC8Ejg5IlbKzH9ExEbANZRf0U6MiNWqHTNzMkBEfJyyHGAiZR+R8cBJwCU1e2vdV/uQiHgOeCUzF7hO2Xz5Rk8dlSS10AKxDl47kG15YCwwIiLGVfo+kJmzaz77SWA6cHFPN64keyOAlYHlq/fJzLtrum0DPJ6ZjzbxO0mSVGsged12lKXc/wlcWNNnbmZWZzr+Fjg7Ij5becYY4Azgzsz8a83zzeskaRDqWEEyMydGxG2Uk9J+BPwHsCplf8g967pXfwGbQ5m6/+bKtacqnz19AEPYm3L4jSRJLdFDrIOyH+S2Nd3uqvxdB3gSyj4jlNO1L+wjwbqccpBA/X1qZ43szWLMsJQkaVEGmNftR5kBeXjlVfUUlcNpMvO8iFgB+ALwPcohbdcCR9Td07xOkgahyMzOPTxiJ+BMYMNFnIjd7OfuCpwCvDUz5yyq//jx43PChAmtH5gkDQERcUdmju/0ONqlg7FuY+CPwFsy86VF9TfWSVLzGOva9lzzOknqkMWNdZ3cQ5LMvILyK9rqbX70KGD/RoKWJEmLo4OxbizwiUaKkZIkLQ7zOklSf3VyD0kAMvP7HXhmj/txSZLUCh2KdVe1+5mSpKHLvE6S1B8dnSEpSZIkSZIkaWixIClJkiRJkiSpbSxISpIkSZIkSWobC5KSJEmSJEmS2saCpCRJkiRJkqS2sSApSZIkSZIkqW0sSEqSJEmSJElqGwuSkiRJkiRJktrGgqQkSZIkSZKktrEgKUmSJEmSJKltLEhKkiRJkiRJahsLkpIkSZIkSZLapqGCZETsEhEfq3n/xoi4NiKmRMQFEbFc64YoSVLrGeskSUOB8U6S1A0anSH5TWD1mvenA+sDFwM7A8c0eVySJLWbsU6SNBQY7yRJHddoQfJNwD0AEbEs8AHg0Mz8PPA14N9bMzxJktrGWCdJGgqMd5Kkjmu0IDkSmFH595bACOCKyvsHgbFNHpckSe1mrJMkDQXGO0lSxzVakHwK2KLy792AOzNzauX9KsDLzR6YJEltZqyTJA0FxjtJUscNb7DfOcC3I2I34J3AwTVtW1B+SZMkaTAz1kmShgLjnSSp4xoqSGbmqRExlRKgfgGcXdO8SuWaJEmDlrFOkjQUGO8kSd2g0RmSZOY5lF/T6q/v39QRSZLUIcY6SdJQYLyTJHVao3tISpIkSZIkSdJi63WGZETMBLLB+2RmjmrOkCRJag9jnSRpKDDeSZK6TV9Lts+k8aAlSdJgZKyTJA0FxjtJUlfptSCZmV9t50AkSWo3Y50kaSgw3kmSuo17SEqSJEmSJElqm772kDyiH/fJzDylCeORJKltjHWSpKHAeCdJ6jZ97SH5nX7cJwGDliRpsDHWSZKGAuOdJKmr9FWQHNm2UUiS1BnGOknSUGC8kyR1lb4OtXmlnQORJKndjHWSpKHAeCdJ6jYeaiNJkiRJkiSpbRouSEbEJyLiloh4ISJm1L9aOUhJktrBWCdJGgqMd5KkTmuoIBkRewPnAI8CrwcuBX4LzAH+DpzZqgFKktQOxjpJ0lBgvJMkdYNGZ0geBpwM7F95f3pm7gm8CXgVeLL5Q5Mkqa2MdZKkocB4J0nquEYLkm8BrgXmAQmMAMjM54BvAYe2ZHSSJLWPsU6SNBQY7yRJHddoQXIWQGYmMBlYu6btJWD15g5LkqS2a1usi4hjIyLrXpNr2qPS55mImBkR10XERjXty0TE+RHxz4h4JCJ2qLv/wRHxy2aNV5K0RDHeSZI6rtGC5AOUKfwANwFfjYhNI2IT4BjgkWYNyKAlSeqQtsW6ioeBMTWvTWrajqAsqfsi8HbgOeAPEbFCpf1AYHNgS+As4JcREQARsQZldsshTR6vJGnJYLyTJHXc8Ab7ncP8X86OAa4GJlTezwA+0txh8TCwXc37uTX/rgat/Sr9jqEErfUy82UWDFo7U4LWGzIza4LWO5o8XknS4NfuWDcnMyfXX6wkWocA38nMSyvX9qUkafsAPwE2AH6TmfdHxOPAKcDKwBTgR8CxlaV3kiTVM95JkjquoYJkZp5f8++HKjMStwGWA27MzElNHpdBS5LUVh2IdetGxNPAbOBW4OuZ+TiwDrAacFXNeGZGxA3AVpRYdw/w8YgYCbwfmAQ8HxF7AKMy87wmj1WStIQw3kmSukGjMyQXkJkvAZc1eSy1DFqSpI5qcay7lTLT/yFgVeAo4OZKUrhapc+zdZ95Fnhj5d/nAm+lLLt7HtgD+BfgO8COEfFN4KOUvcEOzMyHWvQ9JEmDnPFOktQJDRUkI+JjwFqZ+e0e2r4OPJmZzdqX0aAlSWq7dsa6zPx93f3/DDwO7Av8udqtfhjVa5n5KvD5unv8lLK/1vrAnpTtS/YGzqfsyyVJkvFOktQVGj3U5jBgWi9tL1famyIzf5+ZF2fmvZl5NfAByjj3re1W97EFglZmfj4z18nMt2fmn4BTWThoXUAJWpIkQRtjXb3MnAbcD7yZ8oMZzP8RrmpVFv5BDoCI2JYS204F3gP8rrKv8oXA+JrDASRJMt5Jkjqu0YLkm4CJvbTdz/xT2prOoCVJapOOxbqIWJbyo9kk4AlKvHtfXfs2wM09fHYZ4L8os/7nUGL70pXmEZW/w1o1dknSoGO8kyR1XKMFyXnASr20rdSP+/SbQUuS1CZti3URcWpEbBsR60TEO4FLgFHAzzMzgTOAr0bERyJiY+A8ymyWnpbQHQ1cmZm3V97/Cdg9IsYBXwHuz8wXmzV2SdKgZ7yTJHVco4fa3A4cAPyqh7YDgAnNGlBEnAr8FvgrZebj0dQErYg4A/hGRDwEPELZY7I/Qev0iDiPsrekQUuSVNW2WAesDvw3sDIwhbKP1haZ+VSl/bvASOBHwGjK/so7Vmb4v6aSvO0JjKu5/GvKD3XXAk+z4JYnkiQZ7yRJHddoQfI7wBWV06zPpvwH/43Ap4GtgZ2aOCaDliSpE9oW6zJzr0W0J3Bs5dVXv/soW5rUXpsHfKnykiSpnvFOktRxDRUkM/PqiNgHOB34OeUAmQCeAfauHD7TFAYtSVIntDPWSZLUKcY7SVI3aHSGJJl5cURcAmxC2VvkeeC+SpFPkqRBb6jFuog4EDgQYM011+zwaCRJ7TLU4p0kqfs0XJCE12YY3tOisXQVkzRJGpqGUqzLzLOAswDGjx+fHR6OJKmNhkq8M6+TpO7UstOxB7vMPCszx2fm+FVWWaXTw5EkSZIk9ZN5nSR1JwuSkiS1UESMjohnI+Jf2/zcUyPi++18piRJkiQ1oqMFSZM0SdIQ8HXg8sz8C0BEnBkREyJiVkQ8Wd85IpaNiPMi4t6IeDUiruvr5hHxroiYExH31TWdDOwXEes26XtIktQj8zpJUn91eoZkf5O0tSMie3jtVNdvREQcHxFPRMQrEfHXiDi4potJmiSp5SJiOeDTwDk1l5einGr6i14+NgyYBfwQ+N0i7j+6cp8/1rdl5hTgKuCz/R64JEn90/S8LiI+EhFXRcSUiHg5Im6NiA/W3cq8TpIGqY4VJAeYpFXtBIypeV1T1/7flT4HAusB/wHcW200SZMktckuwDzgpuqFzPxiZv4AeKSnD2Tm9Mw8qHLgzN8Xcf9zKHHzll7afwPs3e9RS5LUoBbmddtW3u8KbApcDvxvRGxT7WBeJ0mDV8MFyYh4Q0ScGBF/iogHImLDyvXPRcT4ATy730lajX9k5uSa1+yace4I7ADskpl/yMwnM/PWzLyu7h4maZKkBbQg1m0D3JGZTT/BOiI+B6wGnNBHt9uAN7Z7CZ0kqbs1Od61JK/LzC9l5ncy87bMfCwzjwPuAD5cdw/zOkkahBoqSEbE+sBEyi9PMyizDpetNK8HHDKAZy9OkvbriHguIm6KiH+va/swcDtwaET8PSIejYjvR8Tydf1M0iRJr2lRrFsLmNSUAdaIiE2AbwIfzcy5fXR9pvJ37WaPQZI0OLUg3rUqr+vJCsDUumvmdZI0CDU6Q/JU4AlgHcovYFHTdhOw5QCePZAkbRpwOLBHZRx/BP4nIj5W02dd4F3A24DdgS9QlgKcV3cvkzRJUq1WxLqRlP0gmyYilgEuAg7PzCcW0X1mzTgkSYLmx7tW5XULiIjPA6sD59c1mddJ0iA0vMF+2wIfy8wXI2JYXdtkyn4f/TUSeLY/H8jM54Hv1VyaEBErA0cAF1SuLQUksE9mvgQQEV8AroyIN2Rm9ZkmaZKkWq2Idc8Doxd7ZAsaA2wI/Cwifla5thQQETGHsmXJVZXrK1b+TmnyGCRJg1ez412r8rrXRMTuwCnAXpn5VF2zeZ0kDUL9OdSmtyVhKzE/CPRHs5K0W4E317yfBDxdLUZWPFj5u2bNNZM0SVK9Zse6uyjFw2Z6GtgEGFfz+jHwWOXfN9f03Rh4lbI0T5KkqmbGu1bldcBrxcjzgU9k5m96+Jx5nSQNQo0WJCcAH++lbXfgzwN4drOStHEsuETgJmBs3Z6Rb6n8rf01zSRNklSrFbHuSmCDiFipeiEi3hQR44CxwIiIGFd5jajps2Glz8rA8tU+AJn5ambeV/sCngNeqbyfVvP8bYAbM3PGAMYuSVoyNTvetSqvIyL2oMyY3C8zL+nlc+Z1kjQINbpk+9vAFRHxW+BCypLod0fEZyj7fmw/gGdfCZwcEStl5j+gJGnA8tQkaZW+D2Tm7IjYlxJs7qKc5LYb8HngyJr7/hI4mrKU7Vjg9cCZwCWZ+VxNP5M0SVKtpse6zJwYEbcBewE/qlz+KWW5XNVdlb/rAE9W/n05ZU+u+j61+3w1Ym/K4TeSJFU1O961JK+LiL0oMyMPB26IiNUqTbMz84Wa55vXSdIg1FBBMjOvrvw6dQawa+XyaZQNhPfIzJv6++DFSNKOoiRpc4FHgE9m5mv7jGTmtIjYAfgB5bTtqcD/A75aNwSTNEnSa1oR6yqOA86MiB9n5tzM3K6Bsazdnwdk5rHAsbXXImJXSqzsbUaJJGkIana8a1VeBxxEyVfPqLyqrge2q3lvXidJg1CjMyTJzF9HxP8CGwGrAv8AJmbmvMV4fr+StMz8OfDzBsb6MLBjb+0maZKknrQi1mXmFRHxI8rJoPUb8bfSKGD/zJzTxmdKkgaBFsS7pud1jfyAZ14nSYNXwwVJgMxM4L5mPdwkTZLUbZod6yr3/H4z79fgMy9u9zMlSYNHM+OdeZ0kqb8aKkhWpvT3aaCJj0maJKkbtDLWSZLULVoV78zrJEn90egMyYt6uZ41/zYYSJIGM2OdJGkoMN5Jkjqu0YLkBj1cWwn4APDvwL5NG5EkSZ1hrJMkDQXGO0lSxzV6yvbDvTTdHBFzgc8CtzRtVJIktZmxTpI0FBjvJEndYKkm3ONa4INNuI8kSd3KWCdJGgqMd5KktmhGQXI8MKMJ95EkqVsZ6yRJQ4HxTpLUFo2esn1ED5dHABsD/wac3cxBSZLUbsY6SdJQYLyTJHWDRg+1+U4P1+YCTwOnA8c1bUSSJHWGsU6SNBQY7yRJHddoQXJkD9dezcx5zRyMJEkdZKyTJA0FxjtJUsctcg/JiBgBHAtsnJmv1LwMWJKkJYKxTpI0FBjvJEndYpEFycycDXwJGNX64UiS1H7GOknSUGC8kyR1i0ZP2b4H2LCVA5EkqcOMdZKkocB4J0nquEYLkkcAR0bEDq0cjCRJHWSskyQNBcY7SVLHNXqozbnA64ErI2IGMBnImvbMzPWaPThJktrIWCdJGgqMd5Kkjmu0IHkHCwYpSZKWNMY6SdJQYLyTJHVcQwXJzNyr1QORJKmTjHWSpKHAeCdJ6ga97iEZEY9HxNvaORhJktrJWCdJGgqMd5KkbtPXoTZrA8u0aRySJHXC2hjrJElLvrUx3kmSukijp2xLkiRJkiRJ0mJbVEHSzY4lSUs6Y50kaSgw3kmSusaiDrU5LiKeb+A+mZn7NmNAkiS1mbFOkjQUGO8kSV1jUQXJccArDdzHX9skSYOVsU6SNBQY7yRJXWNRBckPZ+ZtbRmJJEmdYayTJA0FxjtJUtfwUBtJkiRjvpVaAAAgAElEQVRJkiRJbWNBUpIkSZIkSVLbWJCUJEmSJEmS1Da97iGZmRYrJUlLNGOdJGkoMN5JkrqNgUmSJEmSJElS21iQlCRJkiRJktQ2FiQlSZIkSZIktY0FSUmSJEmSJEltY0FSkiRJkiRJUttYkJQkSZIkSZLUNhYkJUmSJEmSJLWNBUlJktosIr4WEbdHxD8jYkpE/DYiNq7rc15EZN3rz3V9TouIFyLibxHx0bq23SLiTxER7fhOkiRJktSoritImqRJkoaA7YD/BLYC3gPMAa6OiBXr+l0NjKl57VJtiIjdgH2AHYEjgJ9GxMqVthWA04EDMzNb+k0kSeqBeZ0kqS/DOz2AHmxHSdJuBwI4npKkbZiZL9T0uxr4eM372dV/1CVpbwbOjYgrM/P5miTtgyZpkqROyMz3176PiI8DLwFbA7+taXolMyf3cpsNgOsycwIwISLOANYBngdOBC7IzAeaPnhJkhqzHeZ1kqRedF1B0iRNkjQErUBZtTC17vq7IuI54EXgeuAbmflcpe0e4MCIGA2sC4wEHouILYDtgc3aMnJJknpgXidJ6kvXLdnuQZ9JWkQ8EhFnR8SqNW33AOMjYnREbM7CSdqJbRm5JEmNORO4G7il5toVwCeA9wKHAe8AromIZQAy80rgAsrMk/OAfYFpwE+Ag4D9I+LBiLgjIrZq0/eQJKk35nWSpNd03QzJHvSWpP0aeAJYGziBkqRtnpmvZOaVEVFN0mbSc5J2CDAD+GJm3tyuLyNJUq2IOA14F/CuzJxbvZ6ZF9V0mxgRdwBPAbtSYiCZeSxwbM29jqLEy5coS+PGAZsAv4qIdTJzNpIkdYZ5nSTpNV1dkDRJkyQtySLidGAvYPvMfLyvvpn5TET8nbKHVk/3egvwSWBTSsJ2Q2ZOAiZFxAhgPWBiM8cvSVIjzOskSfW6tiBpkiZJWpJFxJmUOLddZj7UQP+VgTcCk3poC8pskcMz86WIWApYuqZtaWBYE4cvSVJDzOskST3pyj0kK0naPsB7mp2kUb6zSZokqWMi4kfA/sDewNSIWK3yWr7SvnxEnBoRW0bE2hGxHeUAgOeA/+3hlp8CXszMX1fe/wl4T0S8C/gs8CrwcGu/lSRJCzKvkyT1putmSFaStI8DH6aSpFWapmXmtEqydixwKSVQrQ2cRP+StOMrSdpbMUmTJLXf5yp//1h3/ThKjJtLWX72CeD1lHh3LbBHZr5c+4GIeANwFOXUUgAyc0JEnESJiy8DH8/Mmc3/GpIk9cy8TpLUl64rSGKSJklawmVmLKJ9JvD+Bu/1LCWJq79+EiWxkySpE8zrJEm96rqCpEmaJEmSJA1u5nWSpL505R6SkiRJkiRJkpZMFiQlSRqiIuLAiJgQEROmTJnS6eFIkiRJGiIsSPbCJE2StKTLzLMyc3xmjl9llVU6PRxJkprOvE6SupMFyV6YpEmSJEnS4GZeJ0ndyYKkJEkdFhGjI+LZiPjXJt7zAxFxd0QY6yVJkiR1la5OUkzQJElDxNeByzPzLwARkT28Dqp2johje+mTEbEqQGZeBswFPtqRbyRJUg1zO0lSrW7/D/drCVpE7NdH8vX26gci4u0RcXVETI2IFyPijxHxjmq7CZokqZtExHLAp4Fz6poOAMbUvH5e03ZqXdsY4Hrgusx8rqbfz4CDWzNySZL6pd+5HUBEfKxSdJwVEc9HxC+qbeZ2kjR4dW1BsocE7X9YOPm6AHgCmFD5zPLAFcAzwFbAlsAk4MqIWKHm9iZokqRusQswD7ip7vqLmTm55jWz2pCZ02rbgKWBbYCz6+7xG2B8RLyplV9AkqS+DCS3q3zuYOAUyg9xGwPbA/9Xd3tzO0kahLq2IEldgpaZM+uSr38CuwE/zcysfGZ9YEXgm5n5YGY+CBwNvB5Yr+beJmiSpG6xDXBHTSyrOrMyE+T2iDhoEcvRPgW8CFxaezEz/wo8C2zb1BFLktQ//c7tIuL1wEnAJzLzgsx8LDMnZualdfc2t5OkQaibC5K9JWhVewCjKL+IVT0MTAE+FRHLRMQylCVvfwXur3YyQZMkdZG1KLP5ax0D7AnsAFwEfI+y1G0hlULlJ4FfZOYrPXR5Bli7WYOVJGkABpLb7QgMA94QEQ9ExNMR8b8RsW7tB83tJGlwGt7pAfShpwSt1oHAZZn5Wp/MfDkitqNM4/9a5fKTwPtql7pVmKBJkrrBSEoi9ZrM/FbN27sjYhjwDeCEHj6/M7AG8NNe7j+z8gxJkjql37kdsC5lAs1RwCHAC5Qf7K6NiA0yc0ZNX3M7SRpkunmG5EhgVk8NEbERZX/Is+uujwTOBW4BtgC2Bu4C/i8iRtXdxgRNktQNngdGL6LPrcDrIuINPbQdCNycmff30AZlK5MpizE+SZIWV79zO0quujRwcGZekZm3UQ6vWZWyvLuWuZ0kDTLdPEOyrwTtQOBvlANsau0D/CuwdWbOBYiIfYCpwL9RNkquMkGTJHWDu4D9FtFnHCWRe7H2YkSMBXalHBSwkIhYlhIX71zsUUqSNHADye2qsyUfqF7IzJci4hlgzbq+5naSNMh08wzJu4AN6y9WkquPA+dm5ry65uWApGyYXDWvcu2172qCJqltZs+GY46B9763/J09u9MjUve5EtggIlYCiIjdIuKAiNg4Iv41Ij4NHA+c1cMekZ8EpgMX93LvLYBXWPgEb0mS2mkguV01dq1X0395yoncT9Xdw9xOkgaZbi5ILpCg1fh34F8oS7Pr/QF4HfCfEbFBZfr/z4C5wDU1/UzQJLXe7Nmw2mrwrW/BNdeUv6utZlFSC8jMicBtwF6VS68Cn6NsP3Iv8CXKnlmH1X4uIoJyuvaFdfto1dp7Ee2SJLVDv3O7zHyEcjbAmRGxdURsSMntngMuq+lqbidJg1DXFiR7SNCqDgCurJymVv+Zhyj7iWxCSeT+BKwO7JyZf6/paoImqfVOOAGmTl3w2tSp5bq0oOOAgyNiWGWfrE0zc4XMHJWZm2TmmZk5p/YDWayTmZ/r6YYRsQol0Tu5DeOXJKlXA8ntKj4O/Bn4LaXguCzw3ro8ztxOkgahri1IVryWoFUvZOa2mblLbx/IzD9k5rsy8/WZOTozt8/Mm6vtzU7QTrvlNIYfP5zTbjmtGbeTtCS5/vqer998c8/XNWRl5hXAjyg/ojXLOsDnMvOJJt5TkqSBGkhu93JmfjozV6zkdrtl5l+q7f74JkmDVzcfakNmXhER1QTtqUX1b1DTErTTbjmNw64qK+iqfw/d8tDFva2kwe6ll+CHP4Tbb++5faut2jseDQqZ+f0m3+82ymwUSZI6rttzO0lSe3V1QRK6N0GrLUZWWZSUhrgXX4Tvfx9OP738e5dd4MYb4eWX5/cZPRqOOqpzY5QkSeqQbs3tJEnt1+1LtrtST8XIqsOuOszl29JQ88IL5QTttdaCb34Ttt0WJkyA3/0Onn8ejj66nLJ99NEweTKMGNHpEUuSJEmS1DFdP0OyGx3xhyMW2e4sSWkIeP75MhvyBz8osyB3373Mfhw3bn6fESPg+OM7N0ZJkiRJkrqMMyQH4Lvv++5itUsa5J57Do48EtZeG046qSzNnjgRLrlkwWKkJEmSJElaiAXJATh0y0P53o7fA+B1L76OQ089lNe9+DoAvrfj95wdKS2pJk+Gww4rhchTT4UPfQjuuw8uugg23rjTo5MkSZIkaVBwyfYAVYuO13zxGl437XVsc+M2vOcH77EYKS2JnnkGvvtd+MlPYPZs+NjH4Otfh/XW6/TIJEmSJEkadCxILoZDtzyUlx8up+e+/eG3W4yUljR/+xucfDL89KcwZw584hOlEPmmN3V6ZJIkSZIkDVoWJAfg5BVPZtbUWQtenAbHxXEALDt6WY584cgOjExSUzz1VNkb8txzIRP23x++9jVYZ51Oj0ySJEmSpEHPPSQH4N1HvbvP9m2P2bZNI5HUVI8/DgccUGZAnnsufOpT8NhjcNZZFiMlSZIkSWoSZ0gOwJaHbsmsF2dxw7duWKjt3Ue/my0O2aIDo5I0YI8+CieeCOefD8OHw0EHwRFHwBprdHpkkiRJkiQtcSxIDtD0KdP7dV1SF3r4Yfj2t+HCC2HECPjCF0ohcuzYTo9MkiRJkqQllku2B+i+i+4DYPjI4bz3pPcyfGSp7d5/0f2dHJakRjzwAOyzD2ywAVx6KXz5y/DEE3DGGRYjJUmSJElqMWdIDtDSyy7NWh9ciz0v3ZOlhi/FFoduwcW7X8ykCZM6PTRJVXPnwu9/D3fdBZtuCquvXpZmX3IJLLdcmQ156KGw6qqdHqkkSZIkSUOGBckBOmzSYQu8Hz5iOPv8dp8OjUbSQubOhfe/H269FaZPh6WWKteWX76cmP3lL8PKK3d6lJIkSZIkDTkWJCUtWV59FR58EM45B264obyHUoxceulyYvbee3d2jJIkSZIkDWEWJCUNXrNnw333wZ13wh13lL/33AOvvNJz/zlz4C9/ae8YJUmSJEnSAixIShocZs2CiRPnFx7vuKO8r86AfN3rYLPNyknZm20G//wnfOUrMG3a/HuMGgXjxnVm/JIkSZIkCbAgKakbzZhRZjpWi4933gn3319mOAKMHg2bb172gdx881KAXHfdsk9k1dy55fCa6h6So0bBO98JO+/cme8kSZIkSZIAC5KSWqX+hOudd4Zhwxbu9/LLcPfdCy67fvBBmDevtK+8cik67rprKTxuvjmstRZE9P38YcPgyivLGO6+u8yM7G0MkjRYzJ4NJ5wAN90EW28NRx0FI0Z0elSSJElSv1iQlNR89SdcV2cnXnwx3HvvgsuuH3kEMsvnVlutFBw/8pH5Mx9XX33RxcfeDBsGH/hAeUnSYDd7dvnv5NSp5f0118APfwiTJ1uUlCRJ0qBiQXIwe/A0uPsIGPdd2ODQhd9LnTBvHlx0EdxyS1l6DWUfx2uugZVWmt9v9dVLwXGffebPfBwzpjNjlqTB4Fvfml+MrJo6tcyYPP74zoxJkiRJGgALkoPVg6fBXYeVf991GEy+Gib9fv57sCip5po7F6ZMgWeegUmT5r/q30+ePP+gmVqZ8N73wuGHlyXcb3hD+7+DJA1GmXDFFXD66T2333xze8cjSZIkLSYLkoPV3Ucs+L5ajKxttyA5NDW6d2PVq6/Cs8/2XmCsvn/22fn7OtZaccUys3HsWFh//fLvF16A888vJ2NXLb88HHII7LRT87+zJC2pbr0VjjwSrr++HOg1ffrCfbbaqv3jkiRJkhaDBcnBZN5cmPk0TH8K1twDnvrv3vtuemr7xqXuUb9348iRsMEGJZl97rmeC45Tpszfw7EqAlZZpRQXx4wpB8JU/10tPo4ZU/YyW2aZnsfx+OOecC1JA/XQQ/CNb8Cvfw2rrlr2itx3X1hzzQWXbY8eXQ62kSRJkgYRC5ID1Yr9G+e9CjP+DtOfLEXHaU/CjMrf6U+Wtpyz6PuM3QXWP2RgY9DglQk//jHceGM5+ADKHo533AF77FHeL7VUWSo9diyssQa84x0LFhirrze8AZZeeuBj8YRrSRqYp5+G446Dc88tPyodfzx8+ctlljmUbTFOOKEs095qK0/ZliRJ0qBkQXIgBrp/49xXYPpfa4qMT80vPk5/ssx+zNolsQEjx8Lya8PKW5W/o9aCUWvDs9fCA9/peXzPXA4PnWFRciiYNQuuuw5+9zu47DJ48smF+0TAZz8LRx9dZj22qyjoCdeS1LipU+Hkk+HMM8ss8y98ocyQXGWVBfuNGOEBNpIkSRr0LEgOxKL2b7zrKzB81ILFxulPwsxJC/aLpWC5NUqR8Q3bzy82Vv8utwYM62XWw3W79D3Guw63ILmkeuaZUoD83e/gD38osyBHjoQddoBdd4Wf/Wz+6dZQlkvvvHNZXi1J6i4zZ5bl2CedBC++CB/9aCk4rrNOp0cmSZIktYwFyYEY9935MyF7NA9uPwiWWrpScFwbxuy0YLFx+bVh5BthqQH+T1A/hrG7lJmRte1aMsybBxMmzJ8Feeed5fqaa8J++5Ui5Pbbl6Lk3Lll3zH3bpTUDK3YnkTFnDnwi1/AN78Jf/97+e/0SSfB297W6ZFJkiRJLWdBciA2OHTBZdq1Xv82GP/DUnBcdgws1aLlsdVE8O4jygE26x9iorgkefnlMvvxssvg8svLCddLLQVbbgknnliWQW+8cVmOXcu9GyU1y0C3J1HfMuE3v4GvfQ0efLDs5Xv++bDddp0emSRJktQ2FiQH4sHTei5GArx4D7wwAVZ9V+vHscGhCyaD9e81uPzlL6UAedllcP318Oqr8C//UgqKu+4KO+0EK6+86Pu4d6OkZljU9iR3H2HM6a8bb4SvfrUcSLPeenDppfBv/7bwj0uSpPabPbscGnbTTbD11h4aJkktZkFyIOqTtHru36hGvPpq+T881SLkww+X6xtsAF/6UikobrXV4p12LUkDtajtSTY9tX1jGezuu6/MiLzsMhg7Fs46C/bfH4b7f8MkqSvMnl32W586tby/5pqyv+/kyRYlJalFlur0AAal+v0Zx+7Sd7tU9fzzZWnennuWk1O33x5+8ANYa61ysupjj8EDD8App8C221qMlNQ5GxwKY3rZf3bsLv7w1oinnip7/b71rWV25He+A48+CgccYDFSkrrJCSfML0ZWTZ1arkuSWsL/NzwQ7t/YnebOLXsn3nUXbLppZ/ZOrB/DTjuVAuNll5VDaW65pewfttpqsPvuZRbkDjvACiu0d5yStCh9bU/yzOXw0BkWJXvz/PNlv98f/agsxz788LJUe8UVOz0ySVJPbrqp5+s339zecUjSEGJBcqDcv7G7zJ0LO+5YTpeeMaOcOL3JJvCTn7SvKDl3LnzmMzBxIsycWZ47bBi88kpp33xzOOaYUoTcbLNySI0kdSu3J+m/6dPhjDPgu9+FadPK7Mhjj4U11uj0yCRJfdl667JMu95WW7V/LJI0RFiQ1OCUCX/7G9xxB9x5Z5mVeMcd89tnzCjFyXHjOjfGOXPKOL/4xbJ32JgxnRuLpEErIj4HfAUYA9wPHJKZN1baTgP2A6YDX83MC2s+txtwJLBNZma/H1y/h+TYXcrMyNp2Fa++CuecA8cdV/Yb+/CH4dvfhg037PTIJEmNOOqosmdk7bLt0aPLdUlSSwzqgmTHkjS1VyY88UQpPFYLkHfeWZbEQZlp2NPp0xFlr8bdd2/POC+5BC6+uIy3at48WHVVi5GSBiQi9gTOBD4H/Kny9/cRsSHwNmAfYEfgzcC5EXFlZj4fESsApwMfHHCcc3uShfW0Lcevf10S1kcfhW22KSdnO6NGkvql43ndiBHlB6UTTijLtLfaylO2JanFBm1BsqNJmlpn3jz4y1/mFx6rf198sbQPHw4bbwwf/GBZAr3ZZuWwgGuugb33LkvkqkaNgo9+tCyRbodlly37RNaPoZOzNCUNdocC52Xm2ZX3X4yInYDPAlOB6zJzAjAhIs4A1gGeB04ELsjMBxbr6ZXtSF7660v8dOz3+PSfP82/7D0EC5FQipHvf3+ZfT99evlv/vDh8PLLJS5ddhnsskv5MUyS1LCuyetGjIDjj1/s20iSGjNoC5J0OknT4ps7Fx55ZMHC4113wT//WdpHjCj7QO6xRyk8br55SfqWXXbhe+28M7zznfMTxVGjyvudezkhthW6YQySlhgRMQLYHDi1rukqYCtKPDswIkYD6wIjgcciYgtge2CzZo3lhhNvYNqkadx40o184L/a9CNPvVYfXJZZll7PnAmzZpW/tf++9tpy6MGsWaX/zJml+PjlL8Mpp7T/EDVJWnJ0Pq+rXwEw1FcESFIbDMqCZDclaapYVKI4Zw489NCCMx/vvrsU7qAUGd/2tjKjsTrzcaONGl8mMWwYXHllGcPdd5dZie0+ZbsbxiBpSbIyMAx4tu76s8AOmXllRFwA3A7MBPYFpgE/AQ4C9o+IQ4AZwBczc8BHhd7/P/eXvxfd3/6C5Jw5ZYuOj3yk/Ld11ixYZhl485vhK1+B2bN7LyLW/+2rbdasMku/v1Zc0f/OS9IAdUVe9+Bp8/dMvuswmHw1TPr9/PdgUVKSWmBQFiTpoiRNLLyMbdSospH/AQeUAuWdd8I995SED2C55Uqx7pOfnD/zcYMNytK3xTFsWFme3a4l2t06BklLmvplaFG9lpnHAse+1hBxFHAL8BJwPDAO2AT4VUSsk5mzG33oySuezKyps1iKOWzDDazJ3/jri2vwrZjGPIaz7OhlOfKFIxu72dy5ZeuNqVPhhRfK3/pXb9dffnnh+82aBRMnwic+sXDb8OEwcmT5oWvkyAX/veyyZc/h2vc99emp7a67ylK+aiwDt+WQpMXX+bzu7iMWfF8tRta2W5CUpKYbrAXJqo4kaa+ZPbtsfHzTTbD11p3Z+LiVS9jmzCmJ4Msvl30Ra//W/vvOO+GGG8pSNyjXb7utvFZYoYzrM5+ZP/NxvfWcTSJJi/Y8MBdYre76qiycuBERbwE+CWxKSdhuyMxJwKTKDJT1gImNPvzdR72bqw+7nMM4lZGUZcrr8ATv5FYu5KNsvefbykFejRQYq1tx9GbkyHKa6ejRZcbhWmuVQl/12m23weWXL3hoWAQcfDAcdtj8omF1X8dW2GEHuPpqt+WQpNboXF437rvzZ0L2ZNP6yZuSpGYYrAXJjiZpQClGrrZaSbSgHKrywx+W09naVZSsn5m43HKl4HfuuTBjRs/Fw56u9db+yisDH1s1UTzttHIKtiSpXzJzdkTcAbwP+FVN0/uAS2v7RkRQZoscnpkvRcRSwNI1bUtTZqA0bMtDt2TuL45i5D2zqD2mZSSv8GnOhR9TXlXLLFOKidUi4uqrl32Aa6/VvmqvL7NM34O57DK4/vqFDw3bYQdYY43+fK2Bc1sOSWqFzud1Gxy64DLtWmN3gfUP6dftJEmNGZQFyU4naUCZGVktRlZNnVpmabRqdka9efPKLMaq6dPhxhvLvlp9GT68zFxcfvkF/6666sLXevt37bU//rHs/dhTomgxUpIWx2nA+f+/vTuPsqyqzz7+feiODC9tgo1xQjRqAoISWXaWaZFZTJaokcQs0CiSKBkUSdKyME4vg1NEbcU5iknQFRETSdIYtBkUjQSBRlA7gK8RIkaZGkVo0ogNv/ePc6rrdnVVdY331K37/bDuKu4Z99116zy99zlnnyRXApfR3J72aLbuCgR4BXBXVZ3Xvv8acHqSZwH7AT8HvjOtHV++msNv/OY20wu4aedlXLn6WI458M9GOxV33nk6m5+ehfLQMIflkKQ5tSDaddevHr8zEuBHF8AN77NTUpLmwUB2SLY6a6QBzW3a43nsY+HFL5725mbkssuaDsixjjqq6SCcqHNxxx2bKxjnypFHLoyGoiQtMlV1bpLlwJuARwHrgedW1fdHlknyiHb+AT3rrUvyDuCfgXuAl1XVJqbh5ItO5ov3PgPYdjiuG+9/Ki+9/aMcs+8HZ/CpZsCrEyVpMeu2XTd2DMmxrjnJDklJmgcD2yHZZSMNaMaM/NKXtp1+7LHNoPf98PnPN2NH9l6ZuOuuzcNi+nn1hg1FSZo3VfVh4MOTzL8NePw4098BvGOm+z3jiDO48J0384z7rmLXB3++ZfrGHX6BtTuu4Iwjfm+mm54Zr06UpEWp83ZdzxiSBVz/7V/hyU+9aXS4kqedMZOPJUnajlSNHT9YY61YsaLWrVu39cT772fz7o9kyT2jt20/sGw3lm7ocAzJkSsT1661M1DSgpXk6qpa0XU5tLXxsm715at53QWv5U1fhQN+AJc9Ft56ELzzue9h1UqfOCpJEzHrFqZx23UA16/mwWtO4qwv780tZx3No4//DK845DvssP+7fcK2JE1gtlnnAH8z9ZCHsGKPWzmdN3MJh3M6b2bFHn3sjITRKxPPOae5KvOcc+yMlCRNWZI/TrIuybo77rhjm/mrVq5i+fr3cOqhcMTL4dRDYfl6OyMlSYvL6rtgyXeLGz/1AgC+96nfYcl3i9V3dVwwSVrEvEJyCsY7k3bttbD//tsu+81vwn779algkjSAvGpkYZo0635zNTznZLjwDPj6KrNOkrbDrFuYxsu60x56WnPDN1AUIVt+ArAMTrn7lD6XVJIWPq+QnCfbu2rkpS8df72XvGSeCyZJUp9sybqvr4LTNzc/MeskSYNje+26tc9cS9FcpDPSCTnysyjWPnNt/worSUPEDskJVNXHqmpFVa14+MMfvs38731v/PUmmi5J0qAx6yRJg2577boXnfIivnLQV7Z0Sm5Zj+IrB32FF53yon4VVZKGih2SM7RpE1Rt+9o0/ee6SZK0XUl2S3JbkidOcfkTkqyZzT7NOknSYrdq5Sp2v/kIoOmEHPkPYPebj3DcZEmaJwPfIdlFA02SpA68Abigqr4HkGTPJOcnuTfJhiTvT9L7ZLWPAyuSHNhJaSVJmqau2nZP+u8dgLB56WYuPvxiNi/dDIQn/rcPC5Wk+TLwD7VJ8i5g96r6w/b9nsCHgMOATcCngZOq6v52/o7ATcDRVfXvU9zHHcD3J1lkd2DDjD/E4mJdNKyHUdZFw3oYtVdVLeu6EIMkyS7AD4HnV9XXkiwBrgXuBFYBy4Gzgc9V1Wt61ns38Liq+v0p7MOsmzrrYpR10bAeRlkXDbNuBua7bTdR1j2CR+y3mc333rnrnRtZxh7cw/8s37h816Us/T+3cdu35uwDDhb/lkdZFw3rYZR10ZhV1i2dy5L0W9tAeyXw/Pb9EuDfaBpoBzLaQAvwGoCq+lmSTwMnAlPqkKyqbQcb2boc63yKXsO6aFgPo6yLhvUwKsm67S+lMZ4LPAhc1r5/DrAvTWfjDwCSnAycleSNVXV3u9wa4KIku1TV/062A7Nu6qyLUdZFw3oYZV00zLrp60fbbuDC4jgAAA0ESURBVHtZ1+7X7zDWQy/romE9jLIuGrPNukG/ZXuiBtrLquobVXURcDJwfJKH9qy3BnhBG3qSJC10BwJX1+htDSuB60c6I1trgR2Bp/dMW0dz8nFlX0opSdLM2baTpCEy6B2SNtAkScPgccAtPe8fCdw2ZpkNwAPtPADaqyJ/Cjx+nssnSdJs2baTpCEy6B2SC6WB9rE52s5iYF00rIdR1kXDehhlXUzfzsB9Y6ZNNAj02Omb2vVny9/bKOtilHXRsB5GWRcN62H6bNstLNbDKOuiYT2Msi4as6qHQe+QXAgNNKrKL2PLumhYD6Osi4b1MMq6mJENwG4972+lpzHW2h1YwraNt4cBd8y2AP7eRlkXo6yLhvUwyrpoWA8zYttuAbEeRlkXDethlHXRmG09DHqHZOcNNEmS+uAaYJ+e95cDT06yR8+0I4CfAVePTEjyRGAn4Bv9KKQkSbNg206Shsigd0jaQJMkDYO1NPm2vH1/IfCfwCeT7J/k2cC7gI/3PGEbmvG4bqyq7/a3uJIkTZttO0kaIoPeIdl5Ay3Jq5LclOS+JFcnOXC22xwkSV6f5Kokdye5I8n5SZ7Sdbm6luQNSSrJB7suSxeSPCrJ2e134r4k1yU5uOty9VuSJUne0nOMuCnJW5Ms7bps8y3JQUnWJPlh+7dw3Jj5SXJqkh8l2ZTk0iT7dlTcBa+qvg1cCRzTvn8AOBL4X5qnkZ4LnAecNGbVFwMfn+3+hz3rwLybiHln3pl1Zt0c6rRtZ9aZdRMx68w6s25+sm6gOyQXQAPtaOBM4O3A/sB/AF9Isudstz1ADgE+DDwTOAzYDFyc5GFdFqpLSX4TOB74Vtdl6UKSX6L5+wvN3+OTgdcAt3dZro68Dng1cCKwN/Dn7fvXd1moPtkVWE/zmTeNM/9k4LU0343foPl+XJRkWd9KOHhOA05MsgSgqm6uqudV1S5VtbyqXlNVPxtZuG1APA34yGx2atZtcQjm3VbMO/OuZdaZdXOiy7adWbfFIZh1WzHrzLqWWTcPWZeqicYJHgxJfpsmPPZpQ2t7yz8FuAT4tar66Sz3fQXwrao6vmfad4F/qqph+GJuI8muNE+5e2FVnd91efotyS/S3C5yPPB/gfVVdUK3peqvJG8HDq6qA7ouS9eSfB64s6pe3jPtbGB5VT2vu5L1V5KNwAlV9fft+wA/Aj5YVW9rp+1ME14nVdXfdFXWhS7JicC/VtX3p7Dsc2hyfu0s92nWjcO8M+/Mu4ZZ1zDr5kZXbTuzbnxmnVln1jXMusZcZ91AXyEJUFVfBD4E7LG9ZVuPBo6dg87IhwBPp7mVoNeFNGeUhtUymu/VT7ouSEc+RvMPly91XZAOvRC4Ism5SW5Pcm2SE9qD1bD5GnBokr0BkuxDc7b5gk5L1b1foRmkfsvxs6o2AV9luI+f21VV759KZ2S77IVz0Blp1k3MvDPvzLuGWTc+s24GumjbmXWTMuvMOrOuYdaNb1ZZtyjud6+q909j2bFBM1MTPeHtNuDZc7SPQXQmcC3NINRDJcnxwJOAl3Vdlo49AXgV8F7gr2luGf1AO2/Yxl15J80/5K5L8gDNMfdtVfXhbovVuZEnZo53/HxMn8uiyZl1EzPvzDvzrmHWjc+sm6EO2nZm3cTMOrPOrGuYdeObVdYtig7Jjo295z3jTBsKSVYDzwKeNZVbLBaTJHvRjDlzYFXd33V5OrYDsK7n9pZrkvwqzRgbwxRaAEcDxwIvoRmU/WnAmUluqqpPdFqyhcHj5+Dwd9XDvDPvWuZdw6ybnMfPweHvqodZZ9a1zLqGWTe5GR0/B/6W7Q5tAB5gtEd4xC+zbe/wopfkvTQDSh9WVTd2XZ4OrKQ5u7o+yeYkm4GDgVe173fstnh9dQtw3Zhp1wPDNig4NE+CfHdVfaaqvl1VnwJWMxyDH0/m1vanx8+Fz6wbw7wz73qYdw2zbnxm3eAw68Yw68y6HmZdw6wb36yyzg7JGWrPlFwNHDFm1hE0T2UbGknOpDlTcFhV3dB1eTryL8BTac6UjLzWAZ9p/3+YzqxdBuw1ZtqvAVMa926R2YXmH7i9HsBj70004bXl+JlkJ+BAhuz4udCZdVsz7wDzrpd51zDrxmfWDQizbmtmHWDW9TLrGmbd+GaVdd6yPTurgU8luZLmD/VPaQZW/minpeqjJB+iGVfjhcBPkoz0jG+sqo3dlay/quou4K7eaUnuBX5cVeu7KVVn3gv8R5I3AucC+wMnAm/otFTdOB/4qyQ30Vzavz+wCvhkp6Xqg/apjE9q3+4A7JnkaTR/EzcneR/wxiQ3AP8PeBOwEfh0JwXWZIY+68C8G2HebcW8a5h1DbNusJl1mHUjzLqtmHUNs64xp1mXqqEdFmNOJHkVcDLwKGA98JdV9dVuS9U/SSb6Ap1WVaf2sywLTZJLgfVVdULXZem3JEfSjLuyF3AzzfgiH6ghO+AkWQa8BTiK5rL1W2jOrJ5eVfd1Wbb5luQQ4MvjzDq7qo5rn8x3CvAnwG7AFcCrh/AfeQNh2LMOzLvJmHfDnXdmnVm3WJh1Zt1kzDqzDrNurFlnnR2SkiRJkiRJkvpm2O93lyRJkiRJktRHdkhKkiRJkiRJ6hs7JCVJkiRJkiT1jR2SkiRJkiRJkvrGDklJkiRJkiRJfWOHpCRJkiRJkqS+sUNSmqIkxyWpCV7Pnua2Xtmut8d8lXc+JFnalvtNPdPemmRzl+WSJM0Ns86sk6RhYN6Zd+re0q4LIA2g3wf+Z8y067ooiCRJ88SskyQNA/NO6ogdktL0XVtV/9V1ISRJmkdmnSRpGJh3Uke8ZVuaQ0l2TnJmkv9Mcm+SW5KsSbLXFNZ9WZJr2/V+muRbSV45ZplDk3wpycb29YUk+0yxbIcmuTjJ3e0+vpnkuJ75f5Dk0iR3JLknyTeSvHTaldBsa1WS65NsSvLjJFclecFMtiVJWljMui3bMuskaREz77Zsy7zTvPAKSWn6liTp/dupqnqg/f+d29fpwK3AcuDVwOVJ9q6q28fbYJKDgbOB9wGvBZYA+wC79SzzO8DngDXAS2hOKPwV8O9J9quqH05U4CS/B3wW+Crwx8AG4CnA43oWe0K7zH8BDwKHAH+fZKeqOms7ddK7r5cD7wROAy5r6+PX27qQJA0Gs24SZp0kLRrm3STMO80nOySl6bthzPvLgGcBVNWPaUIBgCRLgLXAHcDRwAcm2OZKYENVreqZdmHPdgKcCVxSVb/bM/1S4EbgL4GTxttwkh1owvAq4PCqerCddXHvclX1ljHrXAo8BvgzYMqh1X6Wa6rqrT3TLpjG+pKk7pl1kzPrJGlxMO8mZ95p3njLtjR9RwG/0fN6Re/MJMckuTLJT4HNwEaaM0mTXdp/FfDwJJ9McmSSXxwzf2+aM17/kOZpaEvbM3kbgSuAgybZ9j7AHsBZPYG1jSR7JTk3yQ+Bn7ev47ZT7ok+y9Pb2xsOT7LLNNeXJHXPrJucWSdJi4N5NznzTvPGDklp+tZX1bqe13dGZiQ5CjgHWA+8GHgGTbD9GNhpog1W1SU0Z9keD/wLsCHJhUme0i7yy+3PsxkNlJHXbzP5JfMj88Y+PW6LJA8FLgL2BV4HHNiW++zJyj2BvwVOAJ7ZbvPOJJ9Lsuc0tyNJ6o5ZNzmzTpIWB/Nucuad5o23bEtz6xjghqr6o5EJSXYCfml7K1bVZ4HPJtkVOIxmrI4vtAf7O9vFTga+PM7qP5tk0xvan4+ZZJkDgMcCK6vq6z1l/4XtlXusqirgI8BHkjwM+C3gPTRhfsB0tydJWnDMOrNOkoaBeWfeaR7ZISnNrV1oLuXvdSzTuBq5qjYCa5I8ieZgvxtwHfADYJ+qetc0y3R9u+4rk/xtGyrjlRuas3IAJFkOPH+a+9pKO+7KOUlWAi+fzbYkSQuGWdfDrJOkRcu862Heaa7ZISnNrS8CH0zybuALNJfGvxq4e7KVkryN5vL7LwO3AHvSXBq/rj3wk+QE4Lz2rNw/0pxZeyTN5fM3VtWZ4227qh5M8hftOhcn+RuaM2v7ArtV1ek0gzdvpDnzdSqwDHgzcDvNrQZTluQTwE+Ay2kGfN6L5slxF062niRpYJh1Zp0kDQPzzrzTPHIMSWlufRR4B81B+nyaS9qfB9yznfWuAJ5A88S0i9ptXELPWayqWgMcDDwU+ATNE97+mmYMkism23hVndeWZQnwd8AamgGbv9/Ov5VmQOeHAOcBb2s/y2em8qHH+BpNWH+UJqheTzNeyR9NtpIkaWCYdWadJA0D88680zzK+Ff4SpIkSZIkSdLc8wpJSZIkSZIkSX1jh6QkSZIkSZKkvrFDUpIkSZIkSVLf2CEpSZIkSZIkqW/skJQkSZIkSZLUN3ZISpIkSZIkSeobOyQlSZIkSZIk9Y0dkpIkSZIkSZL6xg5JSZIkSZIkSX3z/wHLUnxfpMeihwAAAABJRU5ErkJggg==\n", "text/plain": [ "<Figure size 1584x792 with 6 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "DEBUG cov: 20\n", "\u001b[31mDEBUG multi-mapping contig (callclass=21, mappings=2), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=75, mappings=2), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=209, mappings=2), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=167, mappings=2), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=228, mappings=4), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=240, mappings=2), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=329, mappings=2), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=337, mappings=3), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=387, mappings=2), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=173, mappings=3), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=576, mappings=3), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=999, mappings=3), FALSE call\u001b[0m\n", "\u001b[32mDEBUG multi-mapping contig (callclass=814, mappings=2), TRUE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=834, mappings=2), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=913, mappings=3), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=1381, mappings=3), FALSE call\u001b[0m\n", "\u001b[32mDEBUG multi-mapping contig (callclass=1416, mappings=2), TRUE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=1832, mappings=2), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=811, mappings=2), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=748, mappings=2), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=1184, mappings=2), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=590, mappings=5), FALSE call\u001b[0m\n", "\u001b[32mDEBUG multi-mapping contig (callclass=371, mappings=3), TRUE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=979, mappings=4), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=388, mappings=2), FALSE call\u001b[0m\n", "\u001b[32mDEBUG multi-mapping contig (callclass=1627, mappings=3), TRUE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=628, mappings=4), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=2059, mappings=2), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=822, mappings=3), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=2335, mappings=2), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=1087, mappings=2), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=1412, mappings=2), FALSE call\u001b[0m\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 1584x792 with 6 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "DEBUG cov: 30\n", "\u001b[31mDEBUG multi-mapping contig (callclass=45, mappings=3), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=6, mappings=2), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=43, mappings=2), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=101, mappings=2), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=255, mappings=2), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=11, mappings=2), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=105, mappings=2), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=164, mappings=5), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=122, mappings=2), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=305, mappings=2), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=382, mappings=3), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=384, mappings=3), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=317, mappings=3), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=784, mappings=3), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=170, mappings=4), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=389, mappings=2), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=1382, mappings=2), FALSE call\u001b[0m\n", "\u001b[32mDEBUG multi-mapping contig (callclass=855, mappings=2), TRUE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=1187, mappings=2), FALSE call\u001b[0m\n", "\u001b[32mDEBUG multi-mapping contig (callclass=1396, mappings=2), TRUE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=1394, mappings=2), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=1395, mappings=2), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=1055, mappings=2), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=1003, mappings=2), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=1293, mappings=2), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=1142, mappings=3), FALSE call\u001b[0m\n", "\u001b[32mDEBUG multi-mapping contig (callclass=1085, mappings=3), TRUE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=1373, mappings=2), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=692, mappings=5), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=1668, mappings=2), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=668, mappings=2), FALSE call\u001b[0m\n", "\u001b[32mDEBUG multi-mapping contig (callclass=783, mappings=3), TRUE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=630, mappings=4), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=1328, mappings=4), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=1739, mappings=2), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=773, mappings=3), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=1839, mappings=5), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=1652, mappings=4), FALSE call\u001b[0m\n", "\u001b[32mDEBUG multi-mapping contig (callclass=1540, mappings=3), TRUE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=1073, mappings=5), FALSE call\u001b[0m\n" ] }, { "data": { "image/png": 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xicJinak1i1/AjvGrDzuW+ApdEPUV3DNldY5hx460G0O+35agVNUNInIkLlF5Fa7u7A/AIFXdYWYeLpk5DDfT70Lcn8c/CVt9O8QTuA6+a3CdcDnA9ar6nwjHFnlt/7d3PcH9LLixHiNETYIRP0t2eD0XV6vqGO/9rrjeiD+q6vyQ46YC61V1iIj8FfgP0DLY2+bVOCnAJVRGR7jPP4GOqnqViHwF3OVl+E8GhqvqQbH9pMYYYxoqi3XGmJoStwryaKC/qn7ib2uMqTmLdcaYWBGRT4AeqtrD56aYGEu0GpTZwBpCipaLSFNcD3KwNslcXOY+tDD54UAW29cvCZ6/F64g8+3epjTctB1wC42kR6/5xhhjTLUs1hljjEl1FuuMMcbUStyneHvFxHf33qYB3UXkAGCjquaIyGPAnSKyGFeTYBiuePJ4AFX9UUTeA/4rIn/DDe39LzBFVX8Ku5cAz+KG/QbrkXwGXC4iP+LqQ8R8ZS9jjDENi8U6Y4wxqc5inTHGmGjyYwTlwbj6dguBZsC93vfBGhL/Ah4FnsQtRd8ZOCFscY/zcSu6vY+r//A1rsZBuMtwhZdDV3W6B1fT5EugAiuoaowxJvos1hljjEl1FuuMMcZEja81KI0xxhhjjDHGGGOMMQ1botWgNMYYY4wxxhhjjDHGNCBxr0GZLETkMtxUArKysg7aa6+9fG6RSXQFWwtYsWkFPVr3oEWTFn43x5iEtWDBgvWq2t7vdhiLdcYYEysW6xKHxTpjjImNaMc6m+JdAwcffLB++eWXfjfDJLAZ2TMYOGEgW0q3kJmRyZTBU+jfs7/fzTImIYnIAlU92O92mO1ZrDPGmOixWJeYLNYZY0z0RDvW2QhKYzxlFWWUVZTV+ryZK2Zy+qunU1xWDMCW0i0MnDDQkpTGGGOMMcYYY4wxNeBrglJEdgIWA0eo6rI43vdhoLGqXhuve5rEVaEVPPHFE9z+0e3bkoz1ZUlKY0yQj7HuatxqqX+J1z2NMcY0TPZcZ4wxpr78HkF5B/Cuqi4Tkf2B24A/Ae2AHOB54BFVrQieICICXAdcDvQENgIvqept3v6jgAeAPYFMYCXwvKo+HHLfkcAyEXlMVZfH+DOaKCspK2FzyeaoXCt/az7XTLuG6cumM2D3AfTbpV+tzh/52Ug2bd0Ucd+W0i0MnTyUFdeviEJLjTFJrFaxTkTuAe6u5FodVfVX77gq4yHwHHCHiPRV1U9j8smMMcYYp7axrgeQHeE6A1T1vfCNIvIn4BNgsar2Dtllz3XGGJMifEtQikgmcClwirfpIGAdcCEuiP0R93CVAdwfcuojwEDgZuBboBXQOWR/IfC4t28LcCTwXxHZoqpPAajqOhF5H7jCu45JEl+v+Zpjxx7LhuINUbtm00ZNeeqkp7j84Mtxz/s1d2jXQ7fVngyXmZHJ6FNHR6uZxpgkVMdY9zDwTNilJgIaTE56qoyHqrpVRMYD1wKWoDTGGBMT9XiuAzgR+Drk/cYI198JGAt8BHQN3WfPdcYYkzr8HEF5ElABzAZQ1RfD9i8XkT7AmXiBTET2BK4B9lPVH0OOXRj8RlUXAAtC9mWLyBlAX+CpkO1ve9e1QJYklmxYwgmvnEBmRibD+w9HqF0ysTLH7nosvdr2qtO5/Xv2Z8rgKTskKW2hHGOMp9axTlULcZ1tAIjIzrgYdmHItmrjoedt4AMRyVTVHXtSjDHGmPqrdawLsUFV11Rz/ReAlwABzoqw357rjDEmBfiZoOwLLNCqlxFvCfwW8v5UYDlwoohMBdKAmcDNYaNKthGRA4EjgHvCds0DuorIbvGsk2JqLlAeYMmGJVRoBYWBQga9MQhV5YMLP2DPdnv63bxtwpOUlpw0xoSoS6wLdwmwCZgUsq2m8fBLXKw/HDfyxBhjjIm2+sS6/4lIU+Bn4N+q+kboThG5EugEnA3cVcm17bnOGGNSgJ8Jyl2AvMp2er1sFwPnh2ze1TtvkLdPcVPh3hGRw8NqVa4C2uM+472qGj5dbrX32gOwQJZAPl/1OWO/Hstr37+23VTuVk1aMWPIjIRKTgYFk5RDJw9l9KmjLTlpjAmqS6wL3Z8G/BUYq6pbQ3bVKB6q6hYR2YyLdcYYY0ws1CXWFQI34UZdlgF/AV4VkSGq+op33r64msyHqWp5FaWY7LnOGGNSgJ8JymbA2kg7vKlrU4HHVDV0xEga0AS4UFWXeMdeCPwEHAJ8EXJsX6A5cBgwUkSyVfXlkP3B5ZqbReGzmCgo2FrAde9dx+hFo2nWqBmn7nUqA/cYSLMM91fUp3MferTu4W8jq9C/Z39bEMcYE64usS7UAGBn3OICoWoTD4uxWJcQSjaX8NbFb3HamNNo2qqp380xxphoqXWsU9X1uFrKQV+KSDvgFuAVEWmCq798k6pGWkwnlD3XGWNMCvAzQbke2Cl8o4jsBcwAJoasRBqUB5QFH8Y8P+N63boT8kAWEsi+FZGOuCneoQnKNt7runp8BhMl83Lncd6k88jelM2dfe/k1iNvpUWTFn43yxhj6qsusS7UZcAcVf0+bHuN4yEu3lmsSwA/vf0TP731E0veWcJ+F+znd3OSyozsGTZLwZjEVd9YF/QFMNT7vjOwDzBaRIKrTqa5y0oZcJKqvu9tt+c6Y4xJAWk+3nshLuhsIyL7AJ8Ar6vqDRHOmQ00EpHdQrbtiku0rqziXsGRJqF6A6W4lU8blvJymDIFRoxwr+XlvjZn1spZHDXmKMoqyph58UzuO+Y+S04aY1JFXWJd8LguwMm4lU/D1SgeevubAl/Vsf0mir56wf01LHwxfC0jU5UZ2TMYOGEgKzevZOCEgczInuF3k4wx26tzrAtzAL9PFc8F9vW2Bb+eAZZ6388JOS9hnutKNpcw8fSJlGwu8bspxhiTdPwcQTkdN/W6rapuEJE/AB/jetnuF5FOwQNDVnb7EPeQ9aKIXO9tewzX2/YlgIhcA2TjprkB9MPVNwldwRvcFPBPG9yqpuXl8Oc/wxdfQFERZGXBoYfC9OmQnh735ixYvYCB4wfSo3UPZl08i/ZZ7ePeBmOMiaG6xLqgvwJFwGsRrlttPPT0BZar6s9R+TSm1sYeN5bsj7xJHV75tJzPcrhX7gWg57E9uejDi3xqXeILJie3lLpf17aUbmHghIG2GJ0xiaXWsU5EhuCSigtxK4CfAlwF3OodVwp8F3oTEfkV2Kqq220ngZ7rbKS8McbUnW8jKFX1W9yKa4O8TWcDHYBzcT1noV/BcyqAgcCvwCxcMFwFnBqyQE46MBJYhHtIuwq4DbgjrAmDiTwqJXWpwn33wWefQWGhe19Y6JKV06bFvTmL1y/mxHEn0qZZGz648ANLThpjUk5dYh24+Wu41bvHRXrgqmE8hIYY6xJM3zv7kt7Y6wD01retKHV/RRmZGfQb1s+nlsVXSVkJufm5tfp67fvXOGn8SduSk0HBJKWNpDQmMdQ11gHDcM9r871z/6qq/65DExIm1gVHyNtIeWOMqT1RVf9uLnIi8B9gH1WN2zxjETkZeAjYT1XLqjv+4IMP1i+//LK6wxLb2rXw17/Cu+/uuE8Ehg+HYcPi1pwtpVs46NmD2Fi8kdl/nc3ubXaP272NMf4SkQWqerDf7YgXH2Ndb+AjoJeqbq7u+JSIdQlo5acrGdNvjBs9GfIrV6OmjTj79bPpNbCXX02Lm0k/TOKKqVewbkt0y8Pt0moXW5zOJCyLdXG7r+/PdTuMlFdIb5xOecD9MdhIeWNMqop2rPNzijeq+p6IPAl0o+oaktGWBQytSRBLelu3wv/+B9ddB/n58Le/wbhxsCVkNEJWFhxwQFybdesHt7J4/WI+uPADS04aY1Kaj7GuC3BRTZKTJnbWfuMWts1olkFZSRnpTdIp31pOWqM0SjalXo0yVWX+6vkUBgpRVV5c9CLjvx1Pn859GNF/BGlS88k7i9cvZtT8UQTKAzvsy8zIZPSpoyOcZYzxQ0N+rutxdI/fE5ReR1QwOdmQRsobY0x9+ZqgBFDVx324Z6R6Xslp/Xp4+2347bcd9y1eDG+8AZs2wX77wYwZsNdesHz5jjUoBwyIW5On/TyNUfNHccNhN3DcrsfF7b7GGOMXn2Ld+9UfZWKtYHUBAKXFpXQ6oBPHjTyOD2/9kDVfr2HhiwtTrkbZPZ/cw/BZw7e9b5TWiHuPvpfb/3Q7GekZtb7ewF4Dt6tBCS45aTUojUk8DfG5TiuUb175hpY7t6RobdG2xCS4kfJnvXoWPY7u4V8DjTEmifieoDTVqKiAp5+GzWEDYFTh88/hvfegrJIOw6wsOP10OP98OO44aOT9dU+f7mpOLlrkRk4OGBDzBXLGLBrD6oLVADwx7wl6d+jN/cfeH9N7GmOMMX4rzCukUdNGHPN/x3DY9YchaULPY3ry+WOfk/Npjt/Ni6oZ2TMYMWsEg3oP4oqDrwCge6vu9Gjdo87X7N+zP1MGT9mWpLTkpDEmkSyZuoQNP23gkCsP4euxX1NRVpHyI+WNMSZWLEGZ6ObNg6uvjryvWze44QY47zzYbbcd9zdtChkRRiukp8PAge4rDnI25zB08tBt79tltmPcGeNo2qhpXO5vjDHG+KUwr5AOvTtw+D8O37YtLT2NI248giNuPMLHlkXXuqJ1nP+/8+nVthfPnfIczRs3j9q1g0nKoZOHMvrU0ZacNMYkjM8fms0B7VfRaeoiNhU1o3D/vhz7rxNSeqS8McbEiiUoE91Kr4TLwoWwzz7b78vIcAvcJLjlvy0HYNr50zim5zGkSzrpabEdsWmMMcYkgoK8Alp1b+V3M2JKVRk6eSgbizcy7fxpUU1OBvXv2d8WxDHGJJTcuTn0/fQ+dmm8hrR1JezfuClpbdYix1xGz/l/S8mR8sYYE0s1r1Ru/JHjBbVdd4XGjbf/SoLkJED2b65o9B5t9qBxemNLThpjjGkwCvMKad45+gm7RLLst2VM/Xkqdx91N/t32t/v5hhjTFysuOVpupFLeqAYQd3rvHkwbdq2kfKD3hrkdzONMSZpWIIy0eXkQOvW0LKl3y2ps+xN2aRJGt1bdfe7KcYYY0zclJeWU7SuiBadW/jdlJiauWImAKftdZrPLTHGmPjIX5VPYPYXZFC6/Y6iIlfn3xhjTK1ZgjLR5eRA9+RO7K3YtIJuLbvVafVOY4wxJlkVrS0CJeVHUM5cOZP2me3Zq91efjfFGGPiYsOSDeRpJ7RZs+13ZGW5RUiNMcbUmiUoE10KJCizN2XTs3VPv5thjDHGxFVBXgFA6o+gXDmTfrv0Q5Kk9IwxxtRXoDDAUvagtHcfaOQt69C8ORx6KAwY4G/jjDEmSVmCMtGlQoLyt2x6tO7hdzN8kZcHRx0Fa9b43RJjjDHxVphXCECLLqmboFyxaQU5m3M4apej/G6KMcbETaAogJJG4UuToF8/V5JrwgSYPh3Srd6+McbUhSUoE1lhIWzcmNQJyq1lW1ldsLrBjqAcMQI++8y9GmOMaViCIyhTeYp3sP7kUT0sQWmMaTgChQEAMlo2hbZtoXNnGDjQkpPGGFMPjfxugKnCL7+41yROUOZszkFReu6UGgnKvDyYOBHeew8CgaqPDQTg88+hogJeeAHuugs6dYpPO40xxvivMK8QBJp3TOEE5cqZtGnWht4devvdFGOMiZtggrJx88ZQXAzhtSiNMcbUmiUoE1lOjntN4gRl9qZsgISe4r16Nbz6KkyZAlu2VH5cIOAW5auogN69oU2bqq+bnQ2q7vuKCjeK8skno9duY4wxia1gdQFZ7bNIa5S6E1ZmrZxF3+59SZPU/YzGGBNuW4IyyxKUxhgTLZagTGSpkKD8zSUoE22Kd34+/O9/MG4cfPyxSyDuvz907Fj5OSJw551w3nmwVzULleblwa67/p6gLC2F0aNtFKUxpgEpL4dp02DhQjjwQLdoQAOb+laYV5jS07tz83NZ9tsyrjrkKr+bYowxcRUoDNCoaSPXAVVcDJmZfjfJGGOSniUoE1lOjnuY69zZ75bUWfambDLSMujSoktc77typatT/dZbLhm5Q7uyoaTEJRFrmnSsjREjXNIzVHm5jaI0xjQQ5eXw5z/DF19AURFkZbmVTRt1anqgAAAgAElEQVTY4gEFeQUpvYL3zJVWf9IY0zCVFpW66d3gEpRt2/rbIGOMSQGWoExkOTnQtSs0St6/phWbVtC9VXfS0+LzQBoIwJAhrk4kwGGHuenY4Y4/HgYNcvtFot+OuXN3rFEZCMCcOdG/lzHGJJxp01xystCtYk1hoXs/bZpbRKCBKMwrpOP+VQzNT0J5BXlM/G4i5VrOlCVTaNWkFft33L/+181zcfnVV22mgTEm8QUKA2RkZbg3W7bYFG9jjImC5M18NQQ5OUk9vRvcCMp4LZBTXg4XXeQebm67DS67DHr6NLN84UJ/7muMMQlh4UI3cjJUUZEr5NtAEpQV5RUUri1MuRGUj3/xOA/OfnDb+wv2uyAqnZAjRsBnn9lMA2NMcggUBrYfQWkJSmOMqTdLUCaynBw3JS6JZf+WzWl7nRbz+6jCFVe45ORDD8FNN8X8lsYYYypz4IFuWndwBCW49wcc4F+b4mzL+i1ouaZcDcolG5fQq20vFly2AICsjKwqj1eF776DSZPconSRbNniZj5UVFi9ZmNMcrAEpTHGRJ8lKBNVRQX88gucfbbfLamzwkAh67asi8kK3qowahQMH+4ebFTd7wZ33GHJSWOM8d2AAa6D7eOP3Q/o5s3d+wED/G5Z3BSsLgBIuRGUSzcupVfbXjRvXHXi9ccf4bXXXMfhjz9CWhp06BC5rMrmzW4WBFi9ZmNMcrAEpTHGRJ8lKBPV2rVu6ecknuK9ctNKIDoreKv+/vCybh1ccokrZXbccb8PyOnVCy69tN63MsYYU1/p6W5BnLZtYc893ZC4BraKd2GeGz2aSiMoVZVlG5dxTI9jtm3buBHefBMmT3aJRnBx+scfXTKyXz+45ho480yXoAyXl+cWrAsKBGwUpTEm8ZUWlZLZ1lu52xKUxhgTFZagTFQ5Oe41iROU2ZuyAepdg3LJEldb8osvft/WtCk89RRcfnlsFrkxxhhTT+nprmfpyCMbTN3JUAV53gjKLsk7glIVvv7ajYJ8/30oZC1Ffyli4tO788FNbv+SJVBW5pKMwV9ZdtnFxeezzoIuXaq+x4gRbtJIKBtFaYxJdNtGUJaWuh9alqA0xiSAGdkzGDp5KKNPHU3/nv39bk6tWYIyUaVCgvI3L0FZxxGUqvDCC3DdddCkCQwb5l7T0uCMM2CvvaLZWmOMMVFXUtJgH9q2jaDslFgjKCsqYO5cl3ScOtWVSalMaSls2OByzX37Qoudl7IE2G2n3ejkxeCBA+Gcc6BPn7p1GM6d60ZNhgoEYM6c2l/LGGPiJVAYIKN5hhs9CQ021hljEseM7BkMnDCQLaVbGDhhIFMGT0m6JKUlKBNVCiQoV2xaQbNGzeiQFWFOVxWys2HcOPe1eDEccwy89BJ06xajhhpjjIm+sjL31UAf2gryCmjWphmNmkT3V63iYvfHWls//uiSkq+9BqtWuQ6/P/+5+mnUffq4TsH27WHMoqXMnAxjHt2d3dvUrf3hFi6MznWMMSaeto2gtASlMSYBhCYngaRNUlqCMlHl5ECLFtCqld8tqbPsTdn0aN0DqeGQitJSuPdeeOABN8KjXz+45RYYMsSNmjTGGJNEGvhDW+HqwirrT5aUwJo1NbtWIAAffuiSi7NmuRkGdZGRASeeCA8+CH/5i/s1ozaWblxKuqSzS6td6tYAY4xJAVqhBIosQWmMSQzhycmgZExSWoIyUeXkuNGTSVxgccWmFTVewXvpUjjvPJg/Hy6+GO65x9WwMsYYk6SCD21Nm/rbDp8U5BVUuoJ3UREceCD8/HPtrrn33nD77dCmDqMXO3Z007Fbt679uUFLNy6lR+seZKRn1P0ixhiT5EqLS0GhcZYlKI0x/hs6eegOycmgLaVbGDp5KCuuXxHfRtWRJSgTVTBBmcRW5a/ikC6HVHvcmjVwxBFuytrrr7ui+sYYY5JcA39oK8wrpG2vthH3PfCAS04+9BC0a1ez6x10EPTu7W+/5dKNS9m9ze7+NcAYYxJAaVEpgI2gNMbUi6oy+5fZvPz1yyzesLjO12nVtBVsjrwvMyOT0aeOrvO1480SlIlo40b46Sc47DC/W1JngfIA67aso0uLqpfvVIWhQ6GgABYsgH32iVMDjTHGxFZJiXttgA9tqkrhmsKIK3gvW+YSk+efDzfd5EPj6khVWbpxKYd1S97fTYwxJhoChW5lL5egdAuiNcRYZ4zZ3vvL3ufhOQ8TKA9UfzCwcvNKVmxaQVZGFgd1OYg0qVtduzbN2nDkzkeyIG8BJWUl27ZnZmQm1fRusARlYho+3PXGXX653y2ps7yCPAC6tuxa5XFPPQXvvQdPPmnJSWOMSSkNeFRJYV4h5YFyWnZrucO+G26Axo3hX//yoWH1sKF4A5u3brYRlMaYBm/7BGXDjXXGGKe0vJS7ZtzFyNkj6dG6R41rde/XcT+GHz2c0/c+neaNK69bXlOhtSiTMTkJlqBMPEuWuGzdpZfCvvv63Zo6yy3IBaBri8oTlIsWudEjAwbAFVfEq2XGGGPiogE/tOXOdzGwc5/O27YVF8Po0fDOOzByJHSpeoJBwlm6cSmAJSiNMQ1eMEGZkZXxe6zLzPSxRcYYv6zYtILBkwbz+arPuazPZTx24mM0y/Dnd9/+PfszZfAUhk4eyuhTRyddchIsQZl4brnFLSgwfLjfLamX1QWrAbZN8Z4zB9au9fathokT4bPPoH17ePHFpF4LyBhjTCQNcJGc1193q2xnzs2lmQj/erkTTIBff4WpU39fHOf66/1uae1ZgtIYY5ztRlDmNtzOOGMaukk/TOKSty9BUV4961XO+cM5fjeJ/j37J82COJFYgjKRfPIJTJ4M99/vlttMYrn53gjKll1ZvBiOPHL7/XvvDffdB0OGQKdOPjTQGGNMbDWwGpRPPQVXXQUtWsDZxatpmtaRia+51a6bNYPzzoNzz4WjjoJGSfjb19KNSxGEnq17+t0UY4zxVaDIpngb09CoKmMWjWHCdxOo0AqKy4qZ88sc/tj1j0w8cyI9d7Lfj6IhCX9FTmH/+hd07ZqcQyvC5Bbk0ji9MW2bteXx8ZCW5vKvLVtCVhbstpuNmjTGmJTWgB7ann3WJSdPOQVef035d6dceg/qzZPP+N2y6Fm6cSndW3WnSaMmfjfFGGN8ZTUojWlY8rfm8/cpf2fidxPZq91etG3WFkEY1ncYdx11F43TG/vdxJRhCcpEkZ8PH30E11yTEgFudcFqb3q3MH48HHMM9O3rd6uMMcbETYo/tJWXw8yZMGECPP88nHSSm+JdsGIDWzdvpesfq14kLtks3bjUpncbYwwhCcosS1Aak8o2Fm9k4ncTeWTuI6zctJL/O+b/uPXIW0lPS/e7aSnLEpSJ4r33IBCA007zuyVRkVuQS9cWXZk/H5Ytgzvv9LtFxhhj4irFalAuWuT6EH/5xb3Pz4fffnOzAi69FJ54Apo0gZ/muxrMqZigPGufs/xuhjHG+M5GUBqTOr5c/SX3zryXQHlgu+1by7Yy55c5lFaUsl/H/Zh58UyO7H5kJVcx0WIJykTx1ltuxZjDD/e7JVGRm5/LgZ0PZPx498B2xhl+t8gYY0xcJclDmyqUlla+v7wcHn7YrV3Xrh2ccIIrUdK4MRx/PJx88vaLt+bOyyUjK4N2e7eLfePj5Lfi39hQvMFGUBpjDCGreGd6q3inpUFGhs+tMsbUVsHWAs5+/WwKthawR9s9ttsnCFcdchVDDhjC/h33R6w+XVxYgjIRBAJuec+zz4b05B8urKqsLljNgN1PZuJE9/DWqpXfrTLGGBNXSbBITkEBHH00fPVV9ccOHuxGSbZtW/VxufNy6XJwF9LS06LSxkTwzpJ3AFvB2xhjAEqLSsnIykDSxCUomzWz4vrGJKGb3r+JlZtW8unQT210ZIJIuASliKwAdomw611VPVlE7gHuDtu3VlW3rQUtIjcBNwMCjFTVR0L2HQhMBA5Q1eIoN79uPvnEzRVLkend+VvzKSotouTXrqxd61YuNcYY87sGEesSfIq3Klx5pZu6fccdbqp2ZQ44wNWYrE55oJw1C9dw6HWHRq+hPntr8Vtc8vYlHLHzEfx5tz/73RxjTBJJ1VgXKAy46d3we4LSGJNU3lv6Hs9+9Sw3H3GzJScTSMIlKIFDgNBhhJ2BBcBrIdt+Ao4OeV8e/EZE9gOGAwNxgWyKiLyvqt+KSDrwHHBVwiQnwU3vzsqCY4/1uyX1NmcOPPtmLjSHj97sSsuWNXuoM8aYBib1Y11xsavxkZaYIwlfegleeQXuvRf++c/oXHPtN2spD5TT5ZAu0bmgj1SVNxe/yaA3BnFQ54OYdv40mmXYQ7gxplZSMtYFCgNugRywBKUxSWjJhiVc8vYl7NN+H4b3H+53c0yIhEtQquq60PcicgmQD7wesrlMVddUcom9gG9U9WPv/G+8bd8C1wPfqeqHUW94XVVUwOTJcOKJSR3cSkvdQ94DD0D6HqthMKxe0oXrrkzqj2WMMTHRIGJdcXFCjZ5cvx5mz4ayMigqgquuctO7o7mIW+68XCC5F8hZW7iWUfNGMfH7iSzduJRDuhzC9Aum07JJS7+bZoxJMqka62wEpTHJ65VvXuHyKZfTpFETXjn9FZo2SpzfVU0CJihDiatEegnwiqpuCdm1q4jkAgHgC+AOVV3u7fsW6CUi3XE9bb2A70SkB3A1cHCcml+9oiIYMwZWr07a6d1bt8K0aS4xOW8eDB0Kf7wslyumwzefdWX3Nn630BhjElvKxrqSkoR4aJs0CZ57Dj780C14E9ShA4wbF93Sz7nzcsnqkEWr7slbePnSdy7l3Z/fpX+P/txyxC0M3ncwzRs397tZxpgkl0qxzhKUxiSna969hlHzR9G3e1/Gnzmebi27+d0kAPLyYNAgePVV6NSp+uNTWUInKIHjgZ7A8yHbvgAuBhYDHYBhwBwR+YOqblDVH0XkDuAD7/jbvW3vAXcCfUVkOJAGDFPVt+L0WX5XWgpXX+2ejIqKYO+94ZRT4t4MgBUrYMIE+O672p+7dSt89BFs2uT+I732mlvn5/5P3QiSLi2Sf4qbMcbEQWrGugR4aPvqKzjrLOjZE26+2YXaFi3cvu7do7+A29pv1tK5T+ekXemxQiuYtXIWlx54Kf895b9+N8cYk1pSJtaVFpXSpGUT9yYBYp0xpnozV8xk1PxRXHXIVTx24mM0SkucVNiIEfDZZ+71ySf9bo2/EudvJbK/AfNVdVFwg6pOCz1ARD4HlgNDgEe9Y54Bngk55gLv2w+BJcDhuEA2W0R6qeqvsfwQO5gwAZ59Fi66CC65BP70p5jW6Hr+eTfKMdzq1fD55+77nj1rP4pEBAYOhAsucOUzG3n/mlYXrKZ109ZkZmTWr+HGGNMwpGasS4CHtlGjIDPTJSpbt479/fJ/yU/q6d0/rPuB/K35VizeGBMLKRPrAoUBmnf2RpYnQKwzxlRNVbnlw1vo1rIbDx3/UEIlJz/+2KWGKipg9Gi4666GPYoycf5mwohIB+BU4KqqjlPVQhH5Htijkuu0BUYA/YHDgJ9V9Udv38/AocA7UWx61SoqYORI2HdfN707xqMsFi2Cyy6Dbt12HCmSlQX33w+DB0OPHtG7Z25BLl1bJO8DmjHGxEvKxjrwvQblhg2uP3DIkPgkJ8tKytiyfgstuyVvrcY5v8wB4Iidj/C5JcaYVJJqsW6HKd4dOsT6lsaYenjjhzeYlzuPF//yYkIs+Ld2LYwfD2PHunxNUFmZjaJM2AQlbrj/VmBiVQeJSFNcseQZlRzyKPCEqq4QkQOAjJB9jdl+ZbnYmzIFfvjBTe+OcXJSFW68Edq0gW++ic8DGkBufi5dW1qC0hhjauBiUjHWge+jSl580ZXBvKrKx+Hoyc/NB0j6BGX7zPbsttNufjfFGJNaLiaFYp3VoDQmeZSWl3LHx3fQu0NvLtr/omqPLy93ZexefdV1dkdbfj7MmuXus99+bhZqWZnX1lIbRZmQCUqviPKlwERVLQjb9zCuZywHV6vkLiALeCnCdY4D9gH+6m2aD+wpIqfgpgLsCcyL0cfYkapbTaZHDzjnnJjfbsoUN2T4iSfil5wEN8X7Dx3+EL8bGmNMEkrZWBdUUuLmV/ugvByeegqOOspNWIiH/FWpkaA8YucjkraGpjEm8aRirNsuQblliyUojamhigqYPduNHlyzJnb32Zg5l2XtH6NCAgQabWBD86UctnwKZ71fdR+GKsyf70rhtWoV3VmmQY0awU03uWp/o0bB4sXb7y8vb9ijKBMyQQkcjRvaf0GEfd2ACUA7YB3wOXCYqq4MPUhEmgFPAoNUtRxAVXNF5HJcHRMB/q6qq2P1IXbw6aeu6OOTT/5esDFGSkvdP/w994S//z2mt9pOeUU5awrX2BRvY4yp3tGkYqwLKi6Gtm3jfluAd991i8A99FD87pnsCcp1Rev4eePPXNrnUr+bYoxJLUeTQrGuoryCspIyG0FpjEcVFixwE0RXrqz6uK+/huxsV2putxhM1lAqWNfrX6zZfRjpgTZklHSGMmi79FqKvjmJ5TW4xsEHw4UXurU2Yl2paO5cCAS23xYIwJw5sb1vIkvIBKWqzsAFmkj7BtXwGsW4nrTw7S8RoVcuLh56CNq3h6FDY3aL8nI3avLpp2HJEnj7bcjIqP68aFlbtJZyLbcEpTHGVCNlY12Qjw9tjz8OXbrAqafG757BBGWLri3id9MomrtqLmD1J40x0ZVqsa60qBSAjCzvAcsSlCYFLF/uRu3l59f+3B9/dF9NmsAee1RdxW7PPWH4cDj9dJekjJbS8lKmL5vOI3Mf4dsVn3D2Pmfz3CnP0appq+pP9tHChX63IPEkZIIyJRUXw/vvw9VXxyyI/fADnHAC5OZCy5Zwyy0u8x9Pqwtcx2WXFl3ie2NjjDGJxadFcr74Aj78EB58ML4ddPmr8mnSqglNWjSJ302jaHbObDLSMjio80F+N8UYYxJWoNANd7IRlCZVvPqqW1S3oqJuU5o7dYIbboCzz45vWbmg/375X/75yT/5tehX2mW2478D/8vf+vzNytUkKUtQVkJELgMuA+jevXv9Lxgcv3vssfW/VgSqcO21rgzKG2/AySf7s3hqbn4ugC2SY4wxDV1JiS8PbSNGuJnl8VocJ6hgVUHSTu8GmLNqDn0690mI1S2NMSaaovlct12CsrzcPd9ZgtL47H//g4kTXU6gNjZtcp26hx0GEybEpuZiLG0q2cT106/ngE4H8NwpzzFg9wFkpMexd9pEnSUoK6GqzwLPAhx88MG1/K8ewccfQ3o69O1b70tF8u67brWp//wHzjwzJreokdwCL0FpU7yNMSbhRb0zLpQPo0oWLICpU+H//g+aN4/rrclflZ+0CcpAeYD5ufO58pAr/W6KMcZEXTSf67ZLUJaUuI2WoDQ+2bLFjV589lno2tUt7FIbInDnnXD33fGddRIt478dT0lZCaMGjOKgLjYDJBVYgjJeZsxwFVdbRL82VXBBnF694Ioron75WsnNzyVd0umQ1cHfhhhjjKlW1DvjQvmQoLzvPje96Oqr43pbwCUoO+7fMf43joKFeQvZWr7V6k8aY0w1AkVegjKrsYtzYAlK44ucHDjpJFfm7fbb4d57kzPJWB8vLHyBAzodQJ/OffxuiomSND9vLiI7ichaEYnBGk5V3vdhEXk8bjcsLIR58+CYY2Jy+WefdcvTP/SQ/z+Ulv62lB6te5Celu5vQ4wxxvinvNz1nsWx1sjXX8Nbb8H117s6zPFUHiincG1h0o6gfHL+k6RJGkfufKTfTTHGmIS23QhKS1Aan2zYAH/+M6xaBdOnw/33+58HiLeFeQv5Ku8rLjnwEqs3mUJ8TVACdwDvquoyABH5j4h8KSIlIrIi0gkico6ILBKRLSKyUkRujnDMeSHHrBGRV0SkU8ghI4GLRWTXWHyoHXz2GZSVQf/+Ub90YSHcc4+79CmnRP3ytbZ4/WL2areX380wxpiE0WA640L58ND28MNuksK118btltsU5BWAkpQJyrFfj+Xlb17mrn530blFZ7+bY4xJUg0l1lmC0vitqMitN5GdDe+8A8cf73eL/PHCwhdokt6E8/c93++mmCjybYq3iGQClwKhabU04CVgX+CECOcMAMYD1wLvAXsDz4lIsaqO8o45EngZuAl4C+gIPAWMA44FUNV1IvI+cAWwQ4Iz6j7+2HVpHBn9kQnPPw/r17t6W353HJRXlLNkwxKO37WB/pQ0xpjItnXGicj+wG3An4B2QA7wPPCIqlYETxCRfYFRwB+BjcB/gRGqrvy5iHwCHBXhXj+o6h+870cCy0TkMVVdHpNPVpk41+VauxZeew0uvxx22ikut9xO/qp8IPkSlD+t/4krp15Jv136MazfML+bY4xJbjsMPAGOBHoDa1S1R/gJInKOd14vYB0wSlUfCtnfGXgE6APsAbysqheHXSausW67BOVmS1A2JBUV8NRTMGeOv+346SdYtAgmTYrZ8hYJr7i0mHHfjuPMfc5kp2Y+/OJnYsbPGpQnARXA7OAGVb0GQERuIkKCErgQeEdVn/LeLxeRB4BbReRJ78HtcGCVqv7bOyZbRJ4Angi71tvA/cQjQTljhlsaKzMzqpcNBOCRR+Coo+Dww6N66TrJ2ZxDSVmJjaA0xhhPhM64g3APYRfikpN/BJ4DMnAxCRFpCXwAzAIOAfYExgBFuAc1gDOAxiG3agJ8C7wW3BD3zrhQcR5V8vzzLiZe6dMaLwW5BUByJShLy0sZNGkQTRs1ZdwZ42iUZmXJjTF1E6uBJ7jYth54EG9Bt3DxjnXbJSjXWIKyofj1V7joIjedepddoHHj6s+JlfR0ePFFOO00/9rgp1+LfuWh2Q+xqWQTlxx4id/NMVHm52+jfYEFwdEgNdQEKAnbVgx0A3YBVuASnveLyCnAFKAtMAh4N+y8eUBXEdkt2NMXE5s2wVdfwbDoj0wYP97VnXjuuahfuk4Wr18MYAlKY4z53Xadcar6Ytj+5SLSBzgTL0EJnA9kAkNUtRj4TkT2Bv4hIo+qszH0IiJyPpAFhF8/fp1xoeKYoCwrg2eecVOc9twz5reLKDiCskXX6C+EFytTlkxh0ZpFvHrWq3Rr2c3v5hhjkltMBp6o6gpcAhMROauK+8ct1gUTlBlZGTbFO0WVlsK0aW76dPCv+KOP4Lff4Omn4e9/93/mYkNRVlHGfbPuY+nGpQCs37Kej7I/oqyijAG7D+DoHkf720ATdX4mKHcB8mp5znTgPyJyAvAhsDtwo7evM7BCVeeKyGDclO5muM/4ATAk7FqrvdceQOwSlLNmufHgUV4gp6ICRo6E/fd3BXITgSUojTFmBzXpjGsJ/Bby/nDgUy85GTQdGIGLWdkRrvE3YJqq/hK2PT6dceGCv9HHYZGct992nXVPPhnzW1Uqf1U+GZkZNG0dv0WB6mvsN2Pp1LwTZ+x9ht9NMcYkv1gNPKmpuMW60qJSJE1o1LSRJShTTEmJWwk7WEJtp52gTRu3b/fd3e8Z++3nbxsbElXliilX8PzC5+nZuidpkkaTRk34x2H/4KL9L+IPHf5Q/UVM0vEzQdkMWFvLc54DdgMm46bD5QP/Ae4BygFEZB/gcdyD3HRc4vIhXP2ui0KuFXzwi21EmTPH1Z887LCoXvbtt93K3RMmJE4PzuL1i2nbrC3tMtv53RRjjEkUVXbGeaMnL8aNmgzqBKwKO3RtyL7tEpQi0gtXjzLSZJ/4dMaFi+ND26hRbrrVySfH/FaVyl+VT8tuLZNmFcn1W9YzdclUrj30WpvabYyJhpgMPKnFteIW6wKFARo3b+x+3luCMmUsXgznngvffANnngkXX+wGATW0lbETyT9n/JPnFz7PsL7DGHHMCL+bY+LEz99K1wO1qmjq9crdKiJ34B7S1uEtfMPvQex2YF5IgeVvRKQI+FRE7gwZXeL1h7Cuju2vmcJCt6xokyZRu2RJiZsxvuuucFZVkx3ibPEGW8HbGGPCVNoZJyJ7AlOBx1R1Utju8FEoUsl2cKMn87xrhYtPZ1y4OC2S8/33rszzgw+6mkx+CSYok8XE7yZSWlHKRftfVP3BxhhTvZgMPKmFuMW6YILS3dW7bZTXGTCxVVQEb77p1rEtL3dfb77p/hqnToWTTvK7heaZL5/hvk/v49IDL2V4/+F+N8fEkZ8JyoW4USO1pqrlQC6AN517rqr+6u3OZMegFnwfOrShN1CKW1QgdkpLo971cvvt7qHsvfegUQINfFi8fjGn9Dql+gONMabhiNgZJyJ7ATOAiap6W9juNbhOuFAdvNftHgBFpDGuhMlzqloW4f7x6YwLF6dRJU8+6fr/LvG5Rnr+qnx69u/pbyNqYezXY9m/4/7s19HmqhljoiJWA09qKm6xLlAYcPUnwUZQ+kwVPv8cXnvN1YesicJC9wxdVATt20NWltt+7LGuvmTXrrFrr6mZ73/9nuveu44Buw/g6YFPJ83sFBMdfqa3pgMjRaStqm4AEJHdgeZAF6CxiBzgHfuDqgZEpB1wNvAJrm7JUO/9USHXfQe3AtwV/D7F+zHgK1XNCTmuL67G15ZYfUAg6gnKDz6Axx6Da65JnNqTABuLN/Jr0a82gtIYY7a3Q2ecV4rkY+A1Vb0hwjlzcfGxqaoG63Mdj5vCtiLs2NOAdsALldw/Pp1x4eJQg3LzZhg7FgYPhnY+VhapKK+gYHUBLbolxwI5P677kfmr5/PoCY/63RRjTOqI1cCTmopbrIs4gtISlHE3diyMGAFLl7pfNTp2rNl56enu94aLLoIjj4S0tNi209ROaXkpQ94aQssmLRlz2hgrQ9MA+fY3rqrfisg83ArbwdL2z7N9snGh99qT3x/KLsLVlBTcQ9zRqjov5LpjRKQFcDXwCAXlKT0AACAASURBVLAZN0rllrAmDAbujtbnqVQUE5QbNrh6GHvv7RbISSQ/rf8JsAVyjDEmzHadcSLyB1xycgZwv4hsGympqmu8b8fj4tMYEbkP6AXcBtwbYQGCy4CPVHV5JfePT2dcuDg8tL30khsBcfXVMbtFjRStLULLNWmmeI/9eizpks7gfQf73RRjTOqI1cATQs5rCVR47wOq+kPIYXGLdaVFpZag9Nm4cTBkCBxyCIwe7WpGtkiOPkJTjQc+e4AFeQuYdM4kOmR1qP4Ek3L8TknfiyuO/Iyqlqvq0VUdrKrrcaubVklVnwCeqGy/iJyMm/b9Ru2aWwelpVGZh60Kl18O69bBlCmJFwdtBW9jjNlRhM64s3HTtc/1vkKJd85mETneO/5L3ArfjwDbDXkTkV2BY7xrVyY+nXHhYlyDsqLCTe8+9FA46KCY3KLG8lflAyR8grJCK3jmy2f4zxf/4cTdT6RT8/AqAsYYUzexGngSdl7QKcBK3II4QXGLdYHCAJntvZqTcZgtYLY3fbobsNO/P0ybFtVlHozPFq1ZxIhZIzh/3/M5Y+8z/G6O8YmvCUpVfU9EngS64QJNvGQBQyup1xVdURpBOXYsvPGGWwjgwAOj0K4oW7x+MY3TG9OjdQ+/m2KMMYkmtDPuHtwCAFVS1W+BftUcsxyodHJSXDvjwsV4VMlHH8GSJfDyyzG5fK0kQ4IyNz+XIW8N4aPsjzhhtxN49pRn/W6SMSb1xGrgSZUF6OId6wKFAVr3bO3eFBe75KTVyIuJ8nL48EN45x3X76kKr74KvXu7RW0sOZla7p15Ly2btOTxAY/73RTjI79HUKKqcf8XqKqvxe1mUUhQLl/uprD16wc33RSldkXZ4g2L2aPNHlYnwhhjwjSIzrhwMUxQqrpazO3bw9lnR/3ytZboCcqSshJOGn8SyzYu45mTn+Gygy6zgvPGmKhrKLEuUBigcVbIFO9Em9aWZHJy4KGHfp94EVRaCu+/D3l50Lw5tGrlth9wgBu0E3xvUsOSDUuYvHgyw/oNo02zNtWfYFKWZZNiraysXgnK8nJXxDc93Y0USU+PYtuiaPH6xezbYV+/m2GMMQkp5TvjwsVo2ltuLvztb25a1/DhiTF6IvvjbJru1JTMdpl+NyWiG6ffyDdrv2HqeVM5aY+T/G6OMSaFNYRYFygMkNE8ZBVvS1DWWVkZnHsufPVV5MXuDjnE1Zo8+eTEiPcmdv499980Tm/MVYdc5XdTjM8sQRlr9RxBOXkyzJ4NY8ZA9+7Ra1Y0BcoDLNu4jLP3SYChLMYYY/xXXOxiXxR71aZMgQsvhK1b4Ykn4Moro3bpOlvz9Rp+mvwTR919VEKOSpz0wySe+vIpbjz8RktOGmNMPVWUVbA1fytNW3udb//P3n1HR1VtDxz/nvSEGgIJTekgSBFEBBEpYgdFxacCYnvYnv6wFyxgQfGJiM/yLCigCFjgoaDSBCEKiFIDoXdMIQkB0jOZOb8/7iQkISEzk7nTsj9rZU3unVt2WJqdu+fsc6RAWS2TJ8O6dTBnDtx2ttm0RUBLy0ljxpYZjO42mrjaDi7HLgKWFCjNVs0C5ccfQ/PmMGqUG2Nys33H92HVVlkgRwghhCE/360PbWlpRh5s1Qq++QbatXPbpasl/rV4wuqEcfHYi70dCgDpuem8Hv86x3KOAbBo9yJ6NevF65e/7uXIhBDC/+Ucy0HbNHWa2peMlgKlyxIS4KWXYPhwYxSlqLk+/PND8ovyebzP494ORfgAKVCazWJxOXHt32/MvTFhgu+2dgOsO7oOgK5xXb0ciRBCCJ/g5oe2ceMgJwdmz/ad4uSx7cdInJdIv3H9iIz2/gPqgp0LuH/R/WTmZXJuPaPlomOjjnx101eEBYd5OTohhPB/WUlZAFKgdNCJE/Dtt0YLd3krVkB0NHz4oawxVFPZtI34Q/G8/+f7DGk/RAY7CUAKlOazWKCuaxPnf/opBAXBvfe6OSY3W7BrAefWO1fmoBRCCGEoXtnUDf76Cz77DB57DDp2dMsl3SJ+YjyhUaH0fqy3V+M4kX+CsYvH8sWWL7ig8QUsv2M5XeIkHwshhLtlJdsLlE2kQAmQlGR8cLh//5nvpabCjz8a07JER5/ZUBgZCTNnGgveiZph3dF1fLHlCwCKbEUs27+MgycOUje8Li9d9pKXoxO+QgqUZrNYIMT5f+bCQvj8cxgyxGjx9lU5hTks3beU+3rIqqBCCCHs3PTQZrPBww9DbCyMH++GuNwk80Am2+Zu45KnLiEqxnuL4yzZu4R7f7iXlOwUXrzsRV647AUZLSmEECapcARl/fpejMg7tm6Fp56C5cuNPN2w4ZmjICMjjUXtRo+Gnj1llGRNl5KdwnWzryO/KJ9aobUA6N6kOxMHTWTYecOICvXNhQaF50mB0mwuzkH5ww9w7Bjcf78JMbnR0n1LyS/KZ9h5w7wdihBCCF/hpjkoZ8+GP/4wRlm42IxgiqPrjoKGrqM8O7XJwRMH6ft5X7ILswE4VXCKjg07suC2BfRs2tOjsQghRE2TlZQFCmrFGQUW8vKgSRPvBuVhe/fCFVcYBcfnnzcWr/OVqVeEb9JaM2bhGHItuWy6f5O0couzkgKl2YqKXCpQfvKJsWr3VVeZEJMbLdi1gOiIaPq16OftUIQQQvgKN4ygtFrhlVfgggt8b6G4jF0ZoCCmXYxH7/vb4d9IykringvuoW54XZrVbcbDvR4mIsQ97fRCCCEql5WURa3YWgSH2hcHqGEt3ikpcOWVxqjJ33+H9u29HZHwBzO3zGTR7kW8c9U7UpwUVZICpdlcGEGptfFL/5//9O3FcYpsRSzctZChHYYSEiT/KQkhhLBzw0Pb11/Dnj0wb54xH7MvSd+ZTnSraEIiPJv7EtMSCQkK4aMhHxEa7PyHn0IIIVyXnZx9ur0bIDe3xhQoc3LgmmuMDr8VK6Q4WdNorfnwzw/Zmb7TufPQfLn1S/q36M//Xfx/JkUnAolUlczmQoEyO9vId82amRSTm8QfiiczP5NhHaS9WwghRCnVnJfLaoXXXoPOnWGYD6aY9J3pNDyvocfvuyN9B+1j2ktxUgghvCArKev0AjlQo0ZQTpsGmzfDokXQq5e3oxGeNnPLTB7++WHqhdcjOMi5EVTn1D2H6TdMJ0j52KfNwidJgdJsLhQoU1ON17g4E+JxowU7FxAREsGVba70dihCCCF8STUf2ubNgx07jFGUvjZ6Uts0GbszaHV5K4/fOzEtka5xnp33UgghhCErKYsmF5aac7KGFCiLiuCdd6BfP7juOm9HIzzt8MnDjF08lv4t+rPizhVSaBSmkgKl2apRoGzc2IR4qmnaxml8ufVLADanbObKNldSK6yWl6MSQgjhdakrYe3d0Ge6sUhOs5OwoKWxHTewytP/+AMSEozvp0yBjh3h5pvNDdkVJ4+cpCiviIYdPDuCsqCogL3H93Lb+bd59L5CCCHAarGScyzndIu31jWmQDlvHhw6BP/5j7cjEZ5m0zbu/eFerDarjIIUHiEFSrNZLBDi3D+zL4+g/HTjp+w7vo8ucV3o2bQnj/d+3NshCSGE8LbUlfDrELDmGq+9g+DCI5BbZGwPWHTWIqXNZsxtlZl5et/XX/vmPMzpO9MBPN7ivTtjNzZto1OjTh69rxBCCMhJzQHN6QJlQYHxGuAFSq3hrbeMOSeHDPF2NMKTMnIzeGvNWyzfv5yPh3xMq2jPd46ImsehyplS6lqggdZ6ln27GTAL6AwsAe7TWueaFqU/C7AW7+SsZIa0H8KMYTO8HYoQQriV5LpqWHu3UZwE4/UKoLi4aM013h92sNLTd+wwipPvvgs33QRhYRAba3bQrvFWgTIxLRFACpRCiGqTfOe8rOQsoFSBMi/PeA3wAuXq1bBhA3z8se9NuSLMsT9zP08ufZJFuxdhsVkY3mk4Y3qM8XZYooZw9NfMeKB5qe13gPOAb4BrgJfcHFfgcKFAmZICSkGjRibF5CKbtpGcnUyT2k2qPlgIIfyP5DpX9ZkOwVGnt8NLvRccBX1mnPX0NWuM12uugebNfbc4CUaBMiI6gqhGUVUf7EaJaYkEqSDax8jSqUKIapN856SsJHuBsknNKlBOnmw8k95xh7cjEZ5gsVoY/s1wfjnwC4/0eoTN92/m21u+RSnl7dBEDeFogbItsAVAKRUBDAEe11r/C3gOGG5OeH5Oa2NWYRdGUMbEON0Zbrr03HSKbEU0rdPU26EIIYQZJNe5Km4gdBlftkgJxnaXCRA34Kynr1ljPAC1bWtahG6TsTODhh0aevyP9cT0RNpEtyE8JLzqg4UQ4uwk3zmppEBZg0ZQnjoFP/0E994b0D+mKGVi/EQ2pWxi+g3Tefuqt+nWuJu3QxI1jKMFykigeJh/HyAMWGzf3gFIxaoiVqvx6kKB0hcXyEnOSgaQAqUQIlBJrnNV6kpIePl0m3cxay4kTIDUX896+po1cMklRveAr0vfle7x9m4wRlBKe7cQwk0k3zkpKykLFaSoFWtfHLQGFCh/+82YI/qKK7wdifCEDUkbeG31a4zqOoqbOt7k7XBEDeVogfIQ0Nv+/VBgo9a6eCr7RkCWuwMLCBaL8epCgdIX559MykoCpEAphAhYkutcVXoOSoCCUu9Zc2HtXZWemp4Ou3cbBUpfl38yn+zkbGLOi/HofS1WC7szdkuBUgjhLpLvnJSdnE2tuFoEhdgfn2tAgXLVKuMxtnfvqo8V/i0zL5PRC0YTVzuO/1wty7UL73G0QPkZ8IpS6jfgEWB6qfd6Y3zSJsoL0AJlkzoyB6UQIiBJrnNVn+kQVPyQFgzzAB1m34wy3q/E2rXGqz8UKDN2ZQCeXyBn7/G9FNmKpEAphHAXyXdOykrKOt3eDacLlFGenY/Yk1atgl69AvpHFMDaI2vp/nF3dmfsZvoN04mOjPZ2SKIGc6hAqbWeDDyEkaz+BXxU6u1GwBfuDy0AuFigTEnxzQJlcrbR4i2L5AghApHkumqIGwht7jG+bzEBfgT0ExDVAgYsMt6vxJo1Rpq88EKPRFotJSt4d5AVvIUQ/kvynfOykrJOL5ADkJ1tvNaq5Z2ATJadDX/9Bf37ezsSYRabtvHmb2/Sb3o/lFL8dvdvXNnmSm+HJWo4h5dh0Vp/hvFpW/n9d7s1okBSXKB0YrWb7GzIzfXNOSiTspKIiYyRCfqFEAFLcp2LtA1SlkLDPlD3BuBFiLwQhr1e5alr1kCPHv7RJZe+K52gkCCi23h2dEFiWiIKxXkNz/PofYUQgUvynXOykrJodnGz0ztycozXAC1Q/v67sZzCgAHejkSYITU7ldELRrN031KGdxrOp0M/pX5EfW+HJYTDLd7CFS6MoExNNV59cQRlUlaStHcLIYQ4U/payNoD7R5yal4uiwXWr/eP9m4wVvCObhNNcGiwR++bmJ5Iy/otiQqVPjshhPA0q8VKbKPtXHHJGGNROIDcv2AqULTVq7GZZdUqY4yNv+Rn4ZhcSy6zts6i20fdWH1oNR9d9xHfDP9GipPCZ1Q6tE8plQdoB6+jtdaB+fFRdRQVGa8BUqBMzk6WBXKEEAFFcp2bZG4xXuMGwuG9xvcREVWetnkz5Of7zwNQ+k5ZwVsI4Z8k37kub+dP3P7kbMJCLfDrEOgyHkLeMZrhd/4TmjQ561Qm/mjVKujZM2AHiNY4aTlpPPfLc3yz/RuyCrM4v9H5LLtjGV3iung7NCHKOFvv8bs4nsRERVwYQZmSYrz6YoEyKStJHpCEEIFGcp07nEyE0LoQ2RTyEox9DoygXLPGePWHAqWtyEbGngzaDWnnsXvmFObw3C/PsTV1Kzeed6PH7iuECEiS71wUseNBQsLtz3XWXEh4GZR925YHa++GYQe9Fp+75eQY3Q1PPOHtSIQ7aK256/u7WL5/OSO7jGR0t9Fc1uIygpQ00wrfU2mBUmv9rCcDCUjVaPH2tTkobdpGclYyTWt7YARl6koj0feZbnwaWX5bCCHcRHKdm5xKhLqdQCmnWrxXr4aWLaGpHwzOP3HwBDaLzWMjKDckbeC2ebex9/hexl48lmf6PuOR+wohApPkO9cdKpzAOZaHCCtdpCwWHAV9ZnglLrOsXWs0Asr8k4Hh802f89Oen3j36nf5v4v/z9vhCHFWjq/eIpznYoFSKWjUyKSYXJSWk4ZVW82fgzJ1pdE6Yc093UKR8PLp7SpWgxVCCOEFJxOh6XXG9/n5xmsVBUqrFVasgJtvNjk2NylZwdsDBcqcwhxu/NoYMbnyzpUMaDnA9HsKIYSo2NZlMew/3p9Bt8UTrApOv1EAXDwB4gZ4KTL3sNkgPh5+/dXIzWvXQnAw9O3r7chEdR08cZBHlzzKgJYDeLjXw94OR4gqnW0OyqeduI7WWr/lhngCi8UCoYDaC2m/O3RKrTy4pieEZJobmrNOZezhkgjorE45/LO45LfbT38qac2FrS+BreD0doC1UAghvEtynRsUZEB+KtSzTwHi4AjKDRvgxAm44gqT43OTkgJlB/MLlJN+m8SRU0eIvzueS8+91PT7CSECn+Q71xUdXkb/0asILm7rLhYOJEyAmIv8skhps8Gbb8Inn8DBg8Y+pYzXoUOhTh2vhSbcwKZt3PP9PQBMv2G6tHQLv3C2EZSTnLiOBiSJlWexwPVA7tOwzLFTnupmfDl6vKe0A34/B9jzPOzx4I1tpT6lDMAWCiGE10muq66TO4zX8gXKKhbJWWbPc4MGmRSXm6XvTCeqURSRDapuXa+O/Zn7eWvNW4zsMlKKk0IId5J854QvBn/BgV8OADB26uzT7d1AkSWYkFCrsWHNhbV3+eUAiu+/h3HjjDw8cSIMGwZRUd6OSrjLB+s/YOXBlXw69FNa1m/p7XCEcMjZCpTm/gVeE1gsUA9QtWHAfIdOGfsohIfBv/9tbmjO+nnvz0xZ9w6zhn1JXG2TV/A5/DUcmHVmcbLLBL/8dFII4dMk11XXqUTj1ckRlMuXQ/fuvjelSWUydmV4pL37sSWPERocyr+v8LE/BIQQ/k7ynRP6Pd+Po2uPYsm18P0nw4xVvO1FysQ/u9Dloq2oUJt9AMV0L0frmsmToVUrWLIEQmTit4CyO2M3zyx/hmvaXsO93e/1djhCOOxsi+QUVPaecFBxi3dQbWjiWA/bor+gd2/A5KkenfXX7nUsz4X6rW6BkHDzbpS6Eg7OKVucBPuKeRP8toVCCOGbJNe5wclECKkFUecY2w7MQZmTA7//Do895oH43CR9Zzrn3XieqfdYcWAFP+z6gTcHv0nTOn6wcpAQwm9IvnNOq4GtGP71cL695VsOJrZizuQR3Pyv76hdP4fowY+h/vce9NkKN/rn/Phr18KaNfCf/0hxMtBYbVbuWnAXESERTLt+Gqq4b18IPyC/jsxUVGTMTRJ09ja30lJTIc7kAYquSMpKomFUQ8LNLE6CMcdk+ZXxSs9J6actFEIIEbBOJkLdjlA8t1FenjG7/lmeeFavNj7DGzzYQzFWU256LrnpucR0iDH1Pj/v+Znw4HDGXjzW1PsIIYSoWv6JfIJCglBBiqMH2vHTzCH8Y+zX5GVHwP4IONIXHvC/4iTA229DdDTcfbe3IxHukJCawPe7vkdrza6MXaw9upavbvpKPuwUfsfhAqVSajTwINABOKPiprWWGSvKs1ggDIcLlDk5xpdPFiizkzzzC67P9NOreBe3dSdMOL3tpy0UQgj/ILnOBScTIa7URJJ//w2NG5/1lOXLITwcLvWTKRbTd3lmBe8NyRvoGtfV/A8DhRA1nuS7qm38bCOFuYU07taYwW8O5uisnQBs/eYI7XNyoFkzL0fomn37YP58eO45qF3b29GI6tBa88GfH/DE0icotBaW7L/ngnu4vfPtXoxMCNc4VKBUSt0OfAbMAS4GvsIovV0DpADzzArQrxW3eAc7VqBMTTVefbFAmZyVTJPaHug7jxsIAxYZIyn7zDDauWN62ren+2ULhRDCP0iuc4HlFOT9fXr+SYD9+6F167OetmyZUZysYppKn5GxKwMwt0CptWZj8kZ5oBBCmE7ynWMi6kVw5VtX0vvR3qggRaumXWALENbAGFVSq5a3Q3TJO+9AaCg8/LC3IxGOyLPksXD3QvZknLlS7dqja/lxz49c1+46Pr/hc2IijU6P4KBgT4cphFs4OoLyCeBNYDwwCnhHa71RKRULrAQOmhOenysZQenYE1hKivFaxcATr0jKSqJzbGfP3CxuYNk27vLbQghhDsl1ziq/gjcYBcqz9G6npEBCAkxyZj1ZL0vfmU5wWDD1W9Y37R4HThzgZMFJejTpYdo9hBDCTvKdA25bcFuZ7SDLCVDBDP/2LjjnRb8sUBYWwqxZcOut0MTH1jwQZe07vo9Jv03im8RvOFVwqsJjIkIimHLlFB7t/ajMNSkCgqMFyvbAM4AN0BhlN7TWx5RSrwIvAx+bEqE/s1iMOShDHOuQ8NURlFablZTsFJnDQggR6CTXOSp1pTGyvcU/jG3LKVjQEnp8ZLR4t2lzxil//AFffAHffWdsX32158KtrvSd6TRo14Cg4CDT7rEhaQOAFCiFEJ4g+c4VhceN0ZNK+e0Iyvh4OHkSbr7Z25GIs5mTMIf7F92PVVu5pdMtjO42mr7n9CVIlf07JEgFyWhJEVAc/Us7H0BrrTGG/bcs9d5JoLl7wwoQJS3ejo2g9NUCZVpuGlZt9UyLtxBCeI/Hcp1SaoJSSpf7Sin1vrIfk6SUylNK/aqUOr/U++FKqS+VUqeUUruVUoPLXf//lFKz3RVvGakrjbmCcw/BzndAhcAf9xnbv98InTijxfvECbjkEpg+Hfr3h59+gm7dTInOFOk7002ff3Jj8kZCg0I9160ghKjJJN+5ojATwqKN7/20QLlwoTEHtL8sUlfT7Du+j3u+v4cR80fQJa4LiQ8lMmPYDAa1GkR4SDihwaFlvqQ4KQKNowXKRKCt/fvfgWeVUt2VUl2Al4Dd7goooJJYcYt3iHMFykaNzAvJFclZyQAyglIIEeg8luvsdgFNSn11KfXe0xgteI8AFwHHgGVKqTr29+8DLgT6AJ8As5W9t0cpdQ7wOPCom+M1rL3bWLgMQBeBtoItz76db0RWrkB59CjYbPD55/DNN3DNNaZEZgproZXM/ZnmFyhTNtI5trMskCOE8ATJd64oLlAWFhrPeX5WoNQafvjBKE76WegB7UT+CT7Z8AmXfn4pbd9ry8wtMxl36ThW3bWKFvVbeDs8ITzK0Rbvzzj9ydpLwHLgL/t2LnCTe8NiFzCg1La11PfFSewu+3EvYSSxDlrrLMomsWswklic1lqXSmK93BxvxYqK7AXKqlu8MzNhwwZo2NCYtNiXJGUlAVKgFEIEPE/nuiKtdUr5nfYHr0eBSVrrefZ9d2I8tI3AaLvrCPygtd6ulNoPvAU0BNKAD4AJWutjbo7X0Ge6MYKyuEiJPv2eDoWPLXB72RbvY/ZIfHGO5aoc33ccbdXEdIgx7R5aazYkbWDYecNMu4cQQpQi+c4VhcchPNYYPQl+V+VLTIQDB+CZZ7wdibBYLSzdt5Qvtn7B9zu/p8BawHkNz+ONy99gZJeRnFPvHG+HKIRXOFSg1Fp/Wer7nfYRi/2AKCBea53s5rgCI4lZLFAHCK1d4dv5+fDjj8ZExT/9ZHwYd+ONHonMKcUFyiZ1pMVbCBG4vJDrWiul/gYKgT+AcVrr/UAroDGwtFQ8eUqp1cAlGLluC3CHUioSuApIBtKVUv8AammtZ7g51tPiBkKX8bB1wumRkwDBUXCgBxzedEYrgK9OYeKI9J3pgLkreB85dYSMvAyZf1II4RGS71xUmAl1OpwuUNau+BnPVy1caLwOGeLdOGqKJXuXsDV16xn7j546ytztczmWc4yYyBjuu/A+RncbzYVNLpSFbkSN5+gIyjK01ieBRW6OpbTASGKWQmMEZagxgnLOHFhqjzwnB5YsgVOnjBXU/vUvGDkSevjgs8lfSX9RO6y2zEEphKhRTM51f2B0AuwEYoEXgDX2h8TicYap5c5JBZrZv/8c6IrRppcO/AOoB0wCrlRKjQdGYswtdp/WeqfbIk9dCQkvly1OgjGisulaGHCusYBA6VMCoUDZwbwCZfECORc2udC0ewghRGUk3zmo4DiEN/DbEZQLF8KFF0KzZlUfK1yXa8nlscWP8cnGTyp8PzQolKEdhjK662iuaXcNYcFhHo5QCN/lUIFSKTUKaKG1nljBe+OAg1prd83rGDhJzJJvzPIZWpvt22HUKIiONnJZcDDcdJNRlBw40Nj2RTZt4/td33Ntu2sJDfax3nMhhHAjT+Y6rfXP5a6/DtgP3AmsKz6sfBjF+7TWFuBf5a4xDWN+rvOAWzGmO7kd+BJjXi/3KD0HJRgjJ4u3Q6xwwxkNEKSmGtOXREe7LQqPydiVQZ2mdQiva97ckBuTNxKsguka19W0ewghRDHJdy7QNrCcNOag9MMCZVoarF0L48d7O5LAtv3Ydm797la2p23nmb7PMK7fOIJV2Qf90OBQKUoKUQlHR1A+Acyo5L0s+/uSxMorfmALq8WTT0DdurBrF8SYN42V2/1x9A9Sc1IZ1kHmxRJCBDyP5brytNbZSqntQDtggX13Y+BIqcNiOfMDOgCUUv0xctsDwL+BH7XWWUqpr4CPlVJ17PM0V1/pOSiDo6DLBEiYYGwXAEevO+OU1FSIjT1jYKVfSN2aSqNO5q5etzFlIx0bdSQy1LFF9YQQopok3znLchLQRoHypH8UKHfuNLr3iopg2zZjkZyhQ70dVWDSWvPZps/4v5//jzrhdVgyaglXtrnS22EJ4XccXcW7LZBQyXvbOb0KnNtprbPt92iHMeoRTo+kLOZIEpsMDMKexICvgJ6lVohzP6vR/rZ9dy0WL4YXo1ptXQAAIABJREFUX/Sv4iTAgp0LCA0K5dp213o7FCGEMJvXcp1SKgLjQ7Rk4ABGvrui3Pv9gDUVnBsO/BejK6AI+9h9+9vFH9G7b5x+3EAYsAiiWsCAH6HTU8Z2eHMj08YOOOOU1FT/bO8uKiji2LZjNLnQvClOihfIkfZuIYQHSb5zVsFx+118u8Vba/j8c7j4YujYEcaOhSeegOnTje3u3b0dYeDJs+QxYv4IxiwcQ99z+7LlgS1SnBTCRY4WKG1AZaW1GCeu4zS/TWJQUqD8am4UbdoY80z6E601/9v5Pwa2Gki9iHreDkcIIczmsVynlJqslOqvlGqllLoY+A6oBczUWmtgKvCsUuompVRnjJEu2VQ8ouVFYInW+k/79m/AzUqpC4CngO1a6xPuih0wipTDDkLcgNPbjecak6u0bn3G4f5aoDy27Rg2i83UAmVydjKpOal0byxPjUIIj5F856zCTOPVx1u833gD7r0X8vLg7bfh0CE4edL42rrVPzsZfN2bv7/J3G1zmThoIktGLaFx7fJjqYQQjnK0xftPYAzwbQXvjQH+cldASqnJwELgMMbIyBcplcSUUlOB55VSO4HdGHNUOpPE3lFKzcCYm9K8JAZgKwBgz4FI/v1vCDdv+ipT7EzfyZ7je3is92PeDkUIITzBY7kOaA7MARoCaRhTmPTWWh+yv/9vIBL4AIjGmJ/5yvJta/aHuVuBC0rtno/xwd1K4G+MKVLMt2+f8dqmzRlvHTsGnTt7JAq3St5oLGTbpId5Bcpd6bsA6Bzrh/9AQgh/JfnOWYWlR1DuN773sQLltGnw/PPGugczZ0KQaUOIRLGU7BQmr5nM8E7DGddvnLfDEcLvOVqgnAQstq+W/SlGAmgG/BPoC1ztxpgCI4kB2PIBCA6P5MYbTb2TKRbsNKaFub7D9V6ORAghPMJjuU5rfVsV72tggv3rbMdtw5gCpfQ+GzDW/uU5+/cbQzNatCizW2v/HUGZvDGZ8HrhRLc2b3WffZlGYbdNgzMLu0IIYRLJd87y8RGU//sf3H8/XH210eItxUnPeGXVKxRYC3h90OveDkWIgOBQgVJrvVwpNQJ4B5iJsSCNApKA27XWy90VUMAkMSgpUAaFRPrlcPoFuxZwUdOLaFa3WdUHCyGEn/NkrgtI+/dD8+ZntAucPAmFhX5aoNyQTJMeTVAmJvG9x/cSGhTKOXXPMe0eQghRmuQ7F/hogdJiMVbmnjQJevWCb7+F0NCqzxPVtztjN59s+IT7L7yfdjHtqj5BCFElR0dQorX+Rin1HdAFY26SdGCbvegnKqKNAqUOivJyIM47eOIg6/9ez8RBE70dihBCeIzkumrYv7/C9u5U+xJ2/lagtFqspG5NpdfDvUy9z97je2kd3ZrgIPOmxBZCiPJqUr5TSt0H3Adw7rnnunaRkhbvUgXKKO8+46WkwM03w5o18M9/wrvvej2kgFZkK2LZvmXsPb4XgPk75xMZGslL/V/ycmRCBA6HC5RQMgJxi0mx+BS3JDJtzEFJSKSbovKcF1e+SHhwOKO6jvJ2KEII4VGS61y0bx9ce+0Zu/21QJm+Ix1rgdXU+SfBKFC2bWDagrlCCFGpmpLvtNafAJ8A9OzZU7t0kcJMCI4wnutyciAiAoK9+8HSm2/Cn3/CnDlw21l7EEV1ZORmMDF+IrMTZpOak1rmvbevfJu42n72B44QPsypAmVN4pZEpgsBCAr1rwLln3//yayts3ju0uc4t141H1iFEEL4LLfkOoDcXGMoRyUreIP/FSiTNiQBmLqCt9aavcf3MqDlANPuIYQQwg0KM40FcsAoUPpAe/fKlXDppVKcNNsDPz7Agp0LGNp+KKO7jebScy9FoQgOCqZ+RH1vhydEQJECpansBcow/ylQaq15fOnjxNaK5dlLn/V2OEIIIfzBgQPGawC1eCdvTCasdhgx7WJMu0dqTio5lhwZQSmEEL6u4LjR3g0+UaDMzIStW2HCBK+GEfDWHV3Hd4nfMb7/eCYMmODtcIQIeF5d30spFa2USlVKeXTpSqXUZKXUf8y/k9HiHRTqP5OBzN8xn98O/8arA1+lbnhdb4cjhBB+L/BzHZCcbLw2bXrGW6mpxmqiMebV+UyRsjGFxhc0RgWZu0AOIAVKIYTf82Kue1gp9YPpN/KxEZTx8aA19O/v1TACmtaap5c9TVytOJ7o84S3wxGiRvBqgRIYB/yktd4HoJR6Vyn1l1IqXyl1sPzBSqlOSqmV9uSXr5Tar5R6XSkVVuqYJkqp2UqpnUopq1JqRgX3fRO4Syl1Zi+aOykLACHh/jGCUmvNuBXj6BzbmXu63+PtcIQQIlC4PdfZjxuhlNqslMpVSqUopWYppRqXOsQzuQ4g31gUjsgz892xY9Cwoden6nKKzWojZXOKqe3dIAVKIURAcTbXDVBKfa+USrbnsa1KqXvKHTNDKaUr+MopddinQE+lVD8zfzgKfWsE5apVEB4OF1/s1TAC2qLdi4g/HM/4/uOpE17H2+EIUSN4rUCplIoC/gl8Vmp3EDAT+KKS0wrt718JdAAeBe4FXit1TDjGKnSTgD8quojWOg1YCjzo+k/gCKNAGRrhHwXKdUfXsTtjN0/2eZKQIOn+F0KI6jIr1yml+gJf2o87HxgGdAK+Kj7Gc7kOKLAvChcWdsZbqan+196dsSsDS67FIwvkBKtgWtRrYep9hBDCTC7mukuABGA40Bn4L/CJUmpEqWPGAk3Kfe0Hvik+QGtdAMwG/s8dP0ulCjN9rkB58cXGWj3C/Y7lHOPZX56lfUx7/tnjn94OR4gaw+EqlFIqDiNJXAY0AIZrrROVUg8B67XWfzl572sBG/B78Q6t9SP2ez2J8WBWhtZ6L7C31K5DSqkBQL9SxxzEnqCUUsPPcv8fgNeBp5yM23HKgs2iiIzy9kBVx8xOmE1ESAQ3drzR26EIIYRX+EuuA/oAR7XW79i3Dyil3gPeK3c583MdnC5Qhoef8ZY/FiiTNxot654YQdmyfktCg0NNvY8QQpTn5nznSq57vdyu/yqlBgI3YxQc0VqfBE6Wirkv0Bq4o9y5PwDLlFJRWutcJ+J2XOkW7+xsaNz47Meb6ORJ2LQJXnjBayEEDK01a4+uZduxbQBYrBYW71vMz3t+xqqtfH/b95KjhfAghwqUSqnzgNVAKPAnxoNR8ec1HTA+ARvl5L37ARu01i6vGqqUagtcjZGUnLUeaKaUalPciuB2QUVYC4OJ8oMpKItsRXy9/WuGth8qc08KIWokP8t1vwOvK6WGAouAGOA24Kdyp5uf66DKAmUFa+f4tL///JvQqFAadmho6n32Ze6T9m4hhMeZkO+qnevs6gJHz/L+GGC71npNuf1/YTzX9gF+qWYMZ7JZoCjbZ0ZQ/vYb2Gwy/6SzrDYrC3Yu4HjecQCOnjrKVwlfsS+z7J9HTes05clLnmR0t9F0atTJG6EKUWM5OoJyMnAAuArIpnh5asPvwBsu3LsFkOzCeSil1gA9MNq5P8WY88RZSfbXloApD21aFWEtDPJ2B4BDftn/C2m5aYzoMqLqg4UQIjD5Ta7TWq9VSt2O0dIdiZHPlwF3lruM6bkOCLgRlEnrk2jasylBIeZ1QGit2ZOxh15depl2DyGEqIS7853Lua6YUmoIcDnQt5L36wG3UMFzn9Y6Vyl1EiPXuV9hpvEa7huL5KxaBaGh0Lu310LwOynZKYyaP4pfDpyuXysUg1oN4qX+LzGw5UCCg4zJsuNqxZV8L4TwLEcLlP2BUVrrE0qp8v+3pmDMB+KsSCDVhfMAbgXqAN2At4BncD6R5pWKwxTaj0ZQzt42m3rh9bim7TXeDkUIIbzFb3KdUqoT8B/gVWCJPba3gI+B0aWuYXquAyotUGZnQ26ufxUorYVWkjcl0+sRcwuHx/OOc7LgpIygFEJ4g7vzXXVyXXHr9mzg/7TW6ys5bBQQjDH/ckXyMCvXFRgj7nxlBGXx/JP+8IzpLem56SzctRCbtpFjyWFi/ESyCrL4eMjHXNfuOgCiQqOIjoz2cqRCiNKcWQnFWsn+GE4/ADkjHXDpN4LW+oj920R7Up2mlHpLa13kxGXsH4GR5koMDlFFWAtDiPLx33t5ljzm75jPreffSnjImaNfhBCiBvGXXPccxhxhb9mP2Wpf1TReKfV8qXPNz3UAhfbBN+UKlKn2x1V/KlCmJqRiLbDS7KJmpt5HVvAWQniZO/Ody7lOKXUpxvQkL2mt/3uWQ8cA87TWxyt5vwFm5briEZQ+UKDMyoING+DZZ71ye79gsVq4atZVbEzeWLLv/Ebns2L0Cs6PPd+LkQkhquJogfIvjMmIF1Xw3s3AOhfuvQm4y4XzygvC+DmCAWcKlJ0xltlOcEMMFQuxUlQY5pOfblltVtb/vZ5CayHrjq4juzBb2ruFEDWdP+W6KM58uCzeVqX2mZ/roNIRlMeOGa+xsabe3a3+Xv83AM16SYFSCBGw3J3vXMp1SqnLgB+BCVrrqWc5rhdGN8GjlbzfBmMOzY0VvV9tJQXKBlBUZHwo56UC5ezZYLXC4MFeub1feD3+dTYmb+SLYV8woOUAwJhXUtq2hfB9jhYoJwKLlVILMea70sBlSqn7gX8AA1249xLgTaVUjNY6A0oWAqgNNAXClFIX2I9N1FoXKqXuAPIxHrQKgZ4Y7W7faa0Lii9c6ry6gM2+Xai1Tix1/35AvGkrvQEE2ygqCPHJOSi/3v41I+ePLNluXrc5/VvITMtCiBrNn3LdQuBTpdSDnG7xngps1FofLnV/83MdGAVKpSCk7J8V/jiCMml9ElGNoqjXop6p99l7fC8KRavoVqbeRwghKuDufOdKrhuAUZz8EPhKKVW8LLZVa11+JOR9wB5gVSX37wfs11rvcTJuxxSWavHOyTG+98IDXk4OTJgAffvKAjmV2Zi8kdfiX2Nkl5Hc0a38Yu9CCF/nUIFSa71cKfUPjIef6+y7p2BMvv8PrfXvzt5Ya52glFqPseroB/bd0zDmRCm2yf7aCjiIMWrkOaAdxgiRQ/Zz3yl3+U3ltofaj21Zat/twHhn43ZKiBVLVqhPjqD88+8/iQiJ4McRP6JQtG3QVj5VEkLUaP6U67TWM5RSdYCHgbeBk8BK4OlyIZif68AoUIaHG0XKUvyxQPn3+r9p1qsZqtzP4m57M/dyTr1ziAiJqPpgIYRwI3fnOxdz3V0Y3QBP2r+KlXlms+e624BXzrJK+O0Yi8m5V+pKWHs3NB9qbJ9MhPWDoBNeKVBOmQIpKTBv3hnpVmBMWzb6f6OJrRXLe9e85+1whBAucHgOSq31fKXU/4DzgVggA0jQWtuqcf+XgXeVUh9pra1a6wFVxDAHmONArGf9la2Uug6jFe47J2J1XogNS2GITxYoE44lcH6j8xnUapC3QxFCCJ/hZ7nuPaDSv8A9luvgdIGynOICpb+0eBdkFZC2I43zbzVnjqp1R9fx856fAYg/FC/t3UIIrzEh3zmb6+7CgbZwrXUWxkjMCimlOgMXYIz8dJ/UlfDrELDmwp6PjX2/jwBbnlFODTnk1ttV5dgx+Pe/4cYb4ZJLPHprv7AjbQe3zbuN7Wnb+WnET7L4jRB+yplFcrB/arXNXTfXWi9WSn0ANMf4tMxTagF3O7mojtNUiMZiCfPJFu+EYwlc2+5ab4chhBA+R3KdC85SoGzQAEJDTY/ALZI3JIN2//yT+UX5vLDiBaasnYLm9ACgMT3GuPU+QgjhDHfmOy/muqbAaK31Sbdede3dRnESQFuMV5t97aBwgM8wZl8xn8UCzz8PeXnwhmdu6fMS0xLZkLQBgKSsJF5Z/QpRoVH8NOInrml3jZejE0K4yqECpb0F4Ky01t+4EoDW+j+unFcdrsbqtFAbhQW+t0jOsZxjHMs5RpfYLt4ORZRisVg4evQo+fn53g5FCJcFBwdTv359GjZsSFBQkLfDcYrkumo4S4HS39q7AZpe1LRa17FYLby48kUOnTSe0Tcmb2R3xm4euPAB3rryLWqHVToYSAghTGdWvvNSrltqyoX7TD89grK8AqDRk2fud7MDB2DqVGNhnPR0ePhh6NDB9Nv6NJu2MXXdVJ5d/iwWm6Vk/8CWA5l10yya1qle/hZCeJejIyjnVrK/9DwgnnsQ8hehmsLCUGJ8rECZkGos5ioFSt9y9OhR6tSpQ8uWLU2f+0wIM2itsVgspKamcvToUc4991xvh+QsyXWuKiiAsLAzdqek+F+BMrpNNFHVTNzPr3iet9a8RbsG7VBKUTe8LotHLuaqtle5KVIhhKgWyXdViRsIXcZDwsvlipThMK8AnjW3zzo7Gy6/HP7+G264Ae68E66+2tRb+qQiWxE/7v6R7MJsAOZsm8OPe35k2HnDeH3Q64QFhxEcFEyLei3k+UmIAOBogbJjBftigCHAcOBOt0UUSMKg0OJ7Iyi3HTM6ObrESYHSl+Tn50txUvg1pRRhYWE0a9aMXbt2eTscV0iuc1UFIyj37oW1a+FJ8weZuM3f6//m3EurV1j/ec/PvLXmLR648AH+O+S/bopMCCHcSvJdVVJXVlCcBCiAmwF2AJeadvunnoKDB2HVKujXz7Tb+LQjJ48wcv5I4g/Hl+wLCw7j/Wve56GLHpJnJiECkKOreFf2pLlGKWUFHgTWui2qQKBtqDAoLAz3uTkoE44l0DCqIXG1/GhYSw0hiVYEAn9r7S4mua4aKihQvvGGMajysce8FJOTspKzOHXkVLXmn0zKSmL0gtF0ie3ClKumuDE6IYRwH8l3Dig9ByVAcNTp7XAgZQJgzjzCS5fCRx8ZH/DVxOJkka2IeYnzeOinhyi0FjL9hun0PacvAA0iGxATFePlCIUQZnFqkZxKrATmu+E6gaXImEQ5vyCciAgvx1JOwrEEusR2kWKYEEI4TnLd2ZQrUB44AF98Af/6FzRu7MW4nHB03VEAml3sWoHSarMyav4oci25fD38ayJDI90ZnhBCeIrkOyg7B2VwFHSZAAkTjO0CoNtUU2574gTcey907AivvmrKLXzWoROHeG/9e3yV8BUp2Sn0aNKDuTfPpV1MO2+HJoTwEHcUKHsCFcweXMPlnwKgyBqBL9UBbdrG9mPb+WePf3o7FCGE8CeS686mXIFy0iQICjJa1PzFkTVHCA4PpkmPJi6d/3r866w8uJLPr/+cjo0q6p4UQgi/IPkOjDkoBywyRlL2mQFxAyCmJyy9GSZnwporTbnthAmQlAR//IHPDXIx06ETh7jk80tIy0njuvbXMbrraIa0H0JocKi3QxNCeJCjq3g/XcHuMKAzcCPwqTuDCggFWQAU2c5c1dSbDmQeIMeSIwvkCOFhu3bton///uzZs4c6dep47L5Tp05l9erVzJ8vgyGqIrmuGgoKoH59AA4fhunTYcwYaOZ6t7THHV1zlKY9mxIS7vxnt6sPrWbCqgmM7DKSuy64y/3BCSGEG0m+c1DcQBh2sOz2wccg8SXMmMNr/3748ENjBGXPnm6/vM9Kz03nqllXkVOYw4b7Nsg6CULUYI5OFDapgq/xQG/gHcCPxkh4iL1AadW+9dFXwjH7Ct7yi1+4SWpqKo899hjt2rUjIiKC2NhYLrnkEt577z2ys7PPOH7KlCkEBwfz/PPPl9k/atQolFKVfoWEhJQcN2zYsDLnfv/990RGRjJ+/PgKYywqKipzrbp169KrVy++//77kmNeeOGFMsdER0czaNAg1q9fD8Dy5cvPGp9SilmzZlX67/Tss8/yyCOPlBQnt23bxoABA4iNjSU8PJzWrVvzwgsvYLFYypy3cuVKevToQUREBG3atOHTT517ZnjggQdYs2YNa9asceq8GkpynasKC0tGUE6xT734zDNejMdJRflFJP2VxDmXnOP0uem56YyYN4LW0a3573X/lelThBD+QPKdq7KzjQmWQ9zRiFjWCy8Yl50wwe2X9lmp2akMmT2EgycOsvD2hfKMKkQN52iBMrKCr3CtdUut9bNa6zyzAvRbBfYWb5tvzUGVkJqAQnF+o/O9HYowSXIy9O8PKSnm3+vgwYP06NGDxYsX8+qrr7Jx40ZWrFjBk08+yS+//MIPP/xwxjmfffYZzz77LDNmzMBqtZbs/+CDD0hOTi75Cg8P5/333y/Z/vvvvyuMYcaMGdxyyy289dZbvPzyy2eNd/r06SQnJ7N+/Xo6derEzTffXFKABDj//PNL7hcfH0/Dhg255pprKCws5LLLLisT3+jRo+nXr1+ZfcOHD6/032nRokXcddddJfvCwsK4++67Wb58Obt372bKlCl88sknZYqs+/bt49prr+Wyyy5j06ZNPPXUUzz44INlCqtViYiI4LbbbuO9995z+JwaTHKdq+wt3tnZxujJW26Bc6u3GLZHJW9MxlpodbpAufrQanp92ou03DS+Hv41dcI9NzpaCCGqQfKdq3JyTBk9+ddfMGcOPP44NG3q9sv7nIW7FjJ0zlCaTWnGn0l/Mnf4XPq1qIErAgkhyqjyox+lVBgwAfhOa73B9IgCRYExcsyK742gbB3dmlphPra0uHCbV1+F334zXj/4wNx7PfjggwQFBfHXX39Rq9Qfa507d+amm25Ca13m+LVr15Kens6ECRP4+uuv+fnnnxkyZAgA9erVo169emWOr1evHo3PssLG22+/zbhx45g5cya33357lfHWr1+fxo0b07hxYz755BPmzp3LwoUL6dWrFwAhISEl92vcuDHjxo3j22+/5dChQ7Rr165MLJGRkYSFhZ01vmJz586le/fuNCvV79q+fXvat29fst2iRQtWrFhBfHx8yb4PP/yQli1bMnWqMRF7x44dWbduHZMnT+aGG24gNTWVrl27MnbsWMaNGwfApk2b6N27N19//XXJSNPrr7+e6667jvz8fCJq0oRGTpBcV032AuWsWXDqFDz8sLcDcs6RNUcAaN6nuUPH27SNp5c9zZS1U2gV3YpfRv9CjyY9zAxRCCHcQvJdNZlQoNTa6Dpo2BCerqj5PsAs27eM6+deT7M6zXjykie5s9udMnezEAJwoECptS5USo0FfvZAPIHDkgOAVfnYCMpjCTJ03k88+ihs3uzcOQUFsH492Gzw0UewaZPRheKoCy6AqQ4uSnj8+HGWLFnC66+/XqY4WVr5Vsdp06Zx2223ERoayqhRo5g2bVpJgdJZzz33HP/5z3/4/vvvufrqq50+PzQ0lJCQkDNaqovl5+cza9YsmjRpwrnVHAoWHx9PzyomE9qzZw9LlizhlltuKdm3du1arryy7CTsV111FXfddRdWq5W4uDg+//xzbrzxRgYPHkznzp0ZMWIEo0ePLtMGf9FFF5Gfn88ff/xB//79q/WzBCrJddVUUIAOC+f996FHD+jd29sBOefImiNEt4mmdlxth45ftm8Zb699m3u738vUq6dSO8yx84QQwtsk31WTCQXKzZthxQpjipS6dd16aZ9j0zaeWf4MLeu3ZMe/dhARIh+cCyFOc7TFewvQycxAAo69QGlTUV4O5LT8onz2ZOyRBXIC2KFDxqewYLweOmTevfbs2YPWmg4dOpTZ37x5c2rXrk3t2rV54IEHSvZnZ2fzzTffcMcddwAwevRofvrpJ1Jc6EX/+eefmTRpEvPnz3epOFlQUMDLL79MTk4OgwYNKtmfkJBQEntUVBQzZsxg7ty5hIdXb7GrQ4cO0aRJxSsD9+rVi4iICNq3b8/ll1/OK6+8UvJeSkoKcXFxZY6Pi4ujsLCQ48ePA3Dddddx3333MXLkSB588EFsNlvJiMtiderUoU6dOhw8eLBaP0cNILnOVQUFJGWEs327MXrSn6Zh1FpzZM0Rp9q7NyZvBOCtK96S4qQQwh9JvnOVCQXKH34w8ubIkW69rE+akzCHTSmbeG3ga1KcFEKcwdHZfZ8GvlBK7dVaLzczoIBRaLR424J8p0C5K30XVm2lc2xnb4ciHODoSMZiycnQunXZAmVmJsydCw50IbtNfHw8VquV++67j/z8/JL9c+fOpXnz5iUjCVu3bs1FF13EzJkzecbJ1TS6du1KRkYGEyZMoHfv3me0hlfm9ttvJzg4mLy8POrXr88777xTZoRihw4dSubNPHXqFHPmzOH6669n1apVdOvWzakYS8vLy6u0tXrevHlkZWWxefNmnnrqKVq2bMnTpfp7yo9CLW6bL71/8uTJLF26lK+++op169ZVOKI1MjKSvDyZUqoKkutcVVDApsRwGjSA227zdjDOOXHgBDmpOU4VKLekbqFFvRZER0abGJkQQphG8p2rTChQLlxodB7Exrr1sj6noKiAF1a+QPfG3bm9S9VTMwkhah5HC5SfA/WBJUqpXCAFKD25nNZad6jwzJrKPoKSEN8pUG5P2w4gC+QEqFdfNVq7S7NazZuLsm3btiil2LlzZ5n9rVq1AiAqqux/+9OmTWPXrl0lq3ED2Gw20tLSnC5QNmvWjPnz5zNw4ECuuOIKli5dSv369as875133mHw4MHUrVuX2Ar+CgwLC6Nt27Yl2z169OB///sfU6dOZfr06U7FWFrDhg3JzMys8L1zzjGKIp06daKwsJCHHnqIJ554guDgYBo3bnzGCNNjx44RFhZGdPTpwsj+/fs5evQoAAcOHDijnVxrTWZmJo0aNXL5Z6ghJNe5SBcUkLgvnHufhEjfmtmkSsXzTzpboOzW2PUPLYQQwssk37kqJ8eYLNJN/v4bNmyAN95w2yW9zmqzsuLACnKKn4ftVh1cxcETB/n0jk8JUo42cgohahJHC5QbKJu0RFWKco3XEN9ZjCYxLZFgFUy7mHbeDkWYYO1aKCwsu6+wENasMed+MTExXHnllbz//vs88sgj1K5deZvj9u3b+eOPP1i2bFmZRWXy8vLo27cvq1ev5rLLLnPq/ueccw6rVq1i0KBBDB5C5uiQAAAgAElEQVQ8mGXLlpUp2lWkcePGZQqQjggODiY3N9epc8rr3r07iYmJVR5ns9mwWCwloyT79OnDzz+XnSJq2bJl9OrVi+DgYAAKCwsZOXIkN998M926deP++++nT58+NG9+erGP3bt3Y7FY6NFDFvGoguQ6V2iNKiwknzCuv97bwTjvyJojhNUJo9H5jhXw8yx57M7YzS2dbqn6YCGE8E2S71yVkwMtWrjtcosWGa9Dh7rtkl6ltWbMwjFM31zxB/tD2g9hcOvBHo5KCOEvHCpQaq39rGHLB1jsBY1Q3ypQtotpR1iwE6umCL+xaZPn7/nhhx/St29fLrzwQiZMmEC3bt0ICQlhw4YNbNmypaR9etq0aXTv3p3Bg8/8g+Tyyy9n2rRpThcowRhJWVykvPzyy1m2bBkxMTEu/zxFRUUlIxazsrKYPXs2u3fvZvz48S5fE4yFbR544AGsVmtJYXHmzJnUqlWLzp07ExYWxvr163n++ee59dZbS0aZPvjgg3z44Yc88cQTjBkzhtWrV/Pll1/y7bffllx73LhxZGZm8sEHH1CnTh0WL17M6NGj+eWXX0rawOPj42nfvn3J6FZRMcl1LrJ/MlJAOHXqeDkWFxz5/QjNezcnKNix0Rzbjm3Dpm10i5MRlEII/yT5rhrc3OK9cCG0agWdAmRG0OdXPM/0zdN57tLn+Mf5/zjjfZlqTAhxNpX+Na6U2q+Ukr++XaSLjCHtKsy3WrylvVu4U+vWrdm0aRNXX301L774It27d6dHjx5MmTKFhx56iKlTp1JYWMisWbMYPnx4hde45ZZb+O677zh58qRLMTRu3Jhff/0Vi8XCoEGDSE9Pd/nn2b59O02aNKFJkyZccMEFzJ8/n08++YQRI0a4fE2AoUOHEhwczPLlp6d5Cg4OZuLEiVx00UV07dqV1157jbFjxzJt2rSSY9q2bcuPP/7IihUr6NatG5MmTeLDDz8sWaH7l19+4d1332XWrFnUrVsXpRRffPEFW7duZfLkySXXmTNnDmPGjKnWzxCoJNe5QUGB8UI4Ub6T8hxy4uAJUrem0npwa4fP2ZK6BUBavIUQfkXynZu4sUCZkwPLl8P11/vX4nKVeXfdu7zx2xs8cOEDTBw0kQsaX3DGV0iQow2cQoia6Gy/IVoC1Vu6tgazFeYRDARF+MYIyoKiAvYe38ut59/q7VBEgGncuDHvvvsu7777bqXHpKWlVfrePffcwz333HPG/tIL7JQ2a9asM/bFxsaSkJBQ6T1CQkJK2qYr89prr/Haa6+d9ZjSPvroI4ePDQkJYdy4cUyZMoWrrroKgFGjRjFq1Kgqzx00aBCbKhkee/nll2OxWMrsa9q0aZki7ZYtW9i+fTsLFixwON4apiWS66qnVIHSzesGmC5xnjH1QsebOzp8zpaULdQOq03raMeLmkII4QNaIvmuerSGrCw4y7RGzli+3EihgdDe/fvh33lsyWPc1PEm3r/2/TMWeRRCCEfIRxgmsRbmEVwAYbV8o516d8ZubNpGp0YB0j8ghJ954IEHOHnyJFlZWdTxYB9scnIyX375pUfvKWoYPx5BuWPeDhp3b0yDNg0cPmdz6ma6xnWVCf6FEKKmOXHCmNak1Hzq1bFwIdStC/36ueVyXpNTmMOdC+6kZf2WzLhhBsFBwd4OSQjhp6oqUMrkyS6yWvKhEEKjQr0dCiAreAvhbSEhITz//PMev+/VV1/t8Xv6Icl11VFqDkp/GkF56ugpjq49yqCJgxw+R2vN1tStjOwy0sTIhBDCNJLvqiM52Xht0qTal8rMhHnz4NprIcw3xrO47Jnlz7A/cz8r71xJnXD5QFwI4bqqCpQvK6UcmdBNa63vdEdAgcJmyQMLhNXyjQJlYloiQSqI9jHtvR2KEEL4Gsl11WEfQVkUFE6ob6Q8h+yYvwOATsMd7yw4eOIgpwpOyQI5Qgh/JfmuOtxYoJw0CU6ehGefrfalvGr5/uV88OcHPNb7Mfq37O/tcIQQfq6qAuUFQIED15FP48rR1nwogIg6vvG0lpiWSNsGbQkPkalnhBCiHMl11WEvUKoI/8ovid8lEtsllpj2MQ6fIwvkCCH8nOS76iguUFazxfvIEXj3XRg1Crr5cTqZuXkm//rpX5zX8DwmDpro7XCEEAGgqgLlMK31eo9EEmC0zWjxDqvnGwVKWcFbCCEqJbmuOuwFSsL9p0CZnZLN4d8OM2DCAKfO25KyBYWiS2wXcwITQghzSb6rDjeNoHzpJWO9nVdfdUNMJjh88jA70nac9ZivEr7iy61f0r9Ff7666SsiQyM9FJ0QIpDJIjlmsRWABSJqe/+fuNBayJ6MPdzc8WZvhyKEECLQ2AuUQZH+U6Dc8b8doJ1r7wZjBGW7mHbUCvOjyTaFEEK4R0oKREYaK9u4KCEBZs6Exx+HFi3cGJuLNiRtICMvA4AjJ48wK2EWvx78tcrzglQQE/pP4IXLXpBFcYQQbuP96lmAUhRAoW+0eO/J2INVW2UFbyGEEO7nhwXKbbO30bBjQxp1anTW47TWzE6YzepDqwFYdWgVl7e63BMhCiGE8DXJycboSaVcOn3VKhgxAurXh3Hj3BybC7Yd20bPT3uW2deuQTteHfgqA1oOIFhVXniMqx1H6+jWZocohKhhpEBpEkWBz8xBKSt4CyGEMI29QBkc5R8FyuP7jnP4t8Nc/sbZC43JWcmMWTiGH/f8SIPIBoQFhxEREsFNHW/yUKRCCCF8SnGB0kk2G7zyitHS3bYt/PgjNGhgQnxOmpMwhyAVxOKRi6kVVos6YXXoHNsZ5WIBVgghqqvSAqXWOsiTgQQapQrBApF1vV+glBW8hRCiYpLr3MDPRlBunbUVFHQd1RWtNeN/HU/84fgzjtuSsoW8ojymXjWVRy5+hCAl/6kIIfyX5Ds3SE6GLs7PQTxvHrz8srEozn//C7VrmxCbk7TWfL39awa1GsQVba7wdjhCCAGAJCqTKFUIhd4rUOZZ8vj98O/8dvg3fj/yO62jW8vkxcJnLF68GKUU2dnZ1bpO27ZtmTRpkpui8r60tDRiY2M5cuSIR+/73XffcfHFF6O1LNopXGAvUIbWCvNyIFXTWrP1i620vrw1dZvXZdHuRby6+lUy8zKxaVuZr0GtBrH5/s2M7T1WipNCCCFcHkH500/GiMkZM3yjOAmwMXkj+zL3cev5t3o7FCGE+H/27jysqmp94Ph3MSMoKijgBE6UOOFQOKbgnMPV1NIcQtEG035WmmbZdagsUkvLMrWraV4yrcw0p7xIqDil5ISz4MDgjMp4gP3748ARFBDwHA7o+3kentPee+291uHKXWe9511rGcgnbhOxsNBBGpQz0y7e00On025pO9ovbc+fZ/+kmVszs7RDlLyQcyF4fuFJyLkQk9ajlCrwJyAgIN97/f39iY2NxcFBNprIacaMGfTv35+aNWsCEBMTQ9euXXF3d8fW1pZatWrxf//3f9y+fTvXfQcPHqRdu3bY2dlRo0YNZs2aVaR6+/fvT2JiImvWrDHaexGPkawApZVD6c+gvLDzAjfO3qDJ8CakpKcwfvN4Grg0YN/ofYQGhOb6WfP8Gp5wecLcTRZCCFEaJCXBrVtFDlBqGmzZAp06gWUp2ktm1dFVWFlYybIlQohSRdagNBFLqzRIBVtH8wQo91zaQ+OqjZnbbS4Azd2bm6UdomSFnAuhV3AvknRJ9AruxfrB6/Gr7WeSumJjYw3/vX79ekaPHp3rnL193hm7Op0OGxsb3NzcTNKusurWrVssW7aMkJC7gWVLS0sGDBjAJ598grOzMydOnOC1117jxo0bLF++HIDr16/TpUsXunfvzrfffsvhw4cJDAykQoUKvP7664WqOzugPH/+fAYOHGiS9yceYdkZlI6lP0AZ8X0E1g7WNOjXgNnhszl74yxbhm7B2tL8y7EIIYQoxeLi9K9F/PwaGQkxMdC1qwnaVEyapvHT0Z/oUqcLle1LwWKYQgiRRTIoTcTCUgc6UNYlHwPWNI2IuAja1mxL5zqd6Vyns3Q+j4GcwUnAEKQ0VSalm5ub4adixYr3nXNycuL48eMopVi9ejUdOnTAzs6O77//Ps8p3qtWrcLb2xsbGxs8PDwICgrKVV9sbCy9evXC3t6e2rVr88MPP9zXpuvXrxMYGEiVKlWoUKEC/v7+REREGK4vXLgQFxcXNm3ahLe3N46OjnTu3Jnz58/nes6XX35JnTp1sLGxwcvLi++//95wrV+/fgwZMiRX+fT0dFxdXfnmm28ASE5OZuzYsVSpUgU7Ozvatm3L7t27C/x9rlu3jvLly9Oy5d3dFF1dXXn55Zdp3rw5Hh4edO3alVdeeYWwsLvr5X3//fdkZmaydOlSGjZsyKBBg3jzzTeZPXs2AImJiTzxxBOMGTPGcE90dDQVK1bkq6++Mpzr06cPO3bs4OLFiwW2U4j7lPIAZcrNFG5G3+T66esc++kY3gO8ic+I56Owj+j3ZD9Ze0sIIcSDZX8JX8QMyi1b9K9dSlFXs+fSHqITomV6txCi1JEMSlPIzMDCMpPMNIWFRcnHgM8nnOdmyk183HxKvG5hPOM3jSciLuLBBYEbKTc4cvkImVpmrvNJuiQ6r+hMo6qNqGRX6YHP8XHz4YvuXxSrvQWZPHkyc+bMoWnTptja2nLo0KFc13ft2sXgwYOZOXMmAwcOZNeuXYwZM4ZKlSoxevRoAIYMGcL169cJCQnB2tqa//u//8uVsZmRkUH37t2pVq0aGzduxMnJicWLF+Pv78+JEyeoUqUKALdv3+bzzz9n+fLlWFpaMmzYMMaNG8dvv/0GQHBwMBMmTGDevHn4+/uzfv16Ro4cSbVq1ejSpQtDhw7lpZdeIjEx0TBFfevWrdy4cYPnn38egPHjx7NhwwZWrFhBrVq1CAoKonv37pw+fRoXF5c8f0dhYWG5gpN5uXjxImvXrqVDhw6Gc+Hh4fj5+WFtfTcDrFu3bnz00UfExsbi7u7OypUradu2Ld27d6dnz54MHTqUNm3aMHbsWMM99evXp2LFioSGht4XgBWiQGlpANhWKH0ByqSrSXzh+QW6RJ3hXNPhTfl056dkZGYYZhkIIYQQBSpmgHLrVvDyAg8PE7SpmFYdWYWNpQ19n+xr7qYIIUQukkFpChnJAGSmmefXmx3UkgDl4+PE1RP3BSezZWqZnLh6ooRblNtbb71F3759qV27NtWqVbvv+pw5c+jevTvvvfceXl5eBAQE8MYbb/Dpp58CcOjQIUJCQvjuu+9o1aoVLVq0YOnSpSQlJRmesXnzZk6fPs1PP/1Ey5YtqV+/PkFBQVStWpXg4GBDubS0NBYtWkTLli1p1qwZb775Jtu2bTNcnz17NoGBgbz66qt4eXnx1ltvMXDgQENbevXqhbW1NWvXrjXcs3LlSnr06IGzszM3btzgu+++Y+7cuXTv3h1vb28WL15M+fLlWbhwYb6/o+joaNzz+dDbr18/7O3tqVmzJu7u7ixatMhwLS4uDldX11zls4/jsqYjtWzZkunTpxMYGMibb77JyZMnWbp0aa57lFK4u7sTFRWVbxuFyEtGkj6D0qZ86QtQHl97HF2ijs5Bnenznz48/8vzePp5sj1qO/61/fGs6GnuJgohhCgLihGgTE2F7dtLz/RuTdP4dv+3LPx7IT3r98TJzsncTRJCiFwkg9IUMvRBkwydpVl+wRFxEVgoCxq7NjZD7cJYipLJeO/07pzKWZcz6VqUhfGgzMDIyMj7svbatWtHUFAQKSkpREZGYmNjQ/Pmd9dSrV+/fq5sxL///puEhAQqV869nEFKSgpnzpwxHFeoUAGPHF9jV6tWjcTERJKSkihXrhzHjx/nrbfeuq8tH3/8MQC2trYMGDCAlStXMmTIEJKSkli7dq0h4Hfq1CkyMjJo27at4X5ra2t8fX05duxYvr+D5ORk7Ozs8rz29ddfk5CQQGRkJJMnT2bixInMmzfPcF0plat89m7cOc+/8847/P7773z55ZesW7fuvqAm6NcNTU5OzreNQuRFdycVhcLOsfR9pDi25hiV6laizYQ2hr+Hmyk3OXrlKIMaDTJz64QQQpQZsbFgZQX5zITJy65d+r11SsP07pspN3n595dZfWw1Xet2ZWGv/L80F0IIcyl9o4lHQVYGZYbOPL/eiPgIvJy9KGddziz1i5LnV9uP9YPX3xekLA3BSeCBu3VrmnZfkA3u7hSe3/WcMjMzqVGjRq5syGxOTne/Ic45FTq7juz78wrs3VsOYOjQoXTu3JkrV66wdetWLC0t6d27t+G9FOYZ93JxceHGjRt5XnN3d8fd3Z0nn3yS8uXL06VLF9577z2qVq2Km5ubIVMy2+XLlwFyBSHj4+M5efIklpaWnD59Os96rl+/bpgKL0RhpSemomGLg2PBf6MlLfl6Mue2naP1261z/e3tvqhfD7ZNzTbmapoQQjwWlFLvAs8BTwCpwG7gXU3TjuQoswx46Z5b92ia1ipHmblAAJAITNY0bWWOa72BSUB7LftDmCnExoKrKxRh+a6tW/UxzY4dTdaqQtlzcQ+Dfh7EhYQLfNLpEya2nYiFkomUQojSp9T9P5NS6l2l1D6l1C2l1BWl1O9KqUb3lFmmlNLu+dl9T5m5SqnrSqkLSqkh91zrrZTaoR4U8Siq+BBY6wnx2/XHVcg6Ns0mJfmJiIuQ6d2PoewgZXZgurQEJwvD29ubHTt25Dq3Y8cO6tSpg62tLd7e3qSmpnLw4EHD9TNnznD16lXDcfPmzYmJicHW1pZ69erl+ils0E0pxZNPPplnW7y9vQ3HzzzzDNWqVWPVqlWsXLmSAQMGGLIfvby8sLS0zPUMnU7Hnj17cj3jXs2aNSswwzJbZqZ+Kn9a1rp/rVu3Zvv27eh0d9fY27p1K56enoYp45qmERAQQOPGjVmxYgXvvvvufeuA3r59m/Pnz+fKUhWmU6b7unukJ6aSii3lStl3YifWnSAzPRPvAbn/7nZd2IWFsuDp6k+bqWVCCPHY6Ah8DbQB/IF04E+l1L27d/4JuOf4eTb7QlYA8kWgK/AOsEQp5ZJ1rTzwOfCySYOToN/FuxDTu2/f1mdO7toF69dDq1ZQoYJJW5YvTdP4bOdntFvaDk3TCBsRxqR2kyQ4KYQotUpjBmVH9B3ZPkABM9B3ZN6apl3PUe5PYFiO47Ts/7inI6sP/EcptVnTtKs5OrI+Ru3I4kNgey/99O69rwJg/2wSJEXrz3dcD66mDxTdTLlJ1M0oXm3xqsnrEqVPdpByxG8jWPqvpWUiOAkwYcIE2rZty8cff2zYJGf+/PmGacxNmjShY8eOjBo1im+++QZra2vGjx+Pvb294RnPPvsszZs3p0+fPnz66ad4eXkRExPDH3/8Qe/evfH19S1UWyZOnMhLL72Ej48Pfn5+/P7776xevZpNmzYZyiilePHFF/nmm284efIkW7K3aAQqVarEqFGjePvtt3FycqJmzZoEBQVx+/ZtXnnllXzr7datGzNmzODWrVtUyPoku3btWm7fvk3z5s1xcHDg0KFDTJgwgQ4dOlCjRg0Ahg8fzkcffURgYCCTJk3i6NGjzJ0717BmJsC8efPYt28fhw4dokaNGmzcuJEhQ4awb98+Q2B1586dODk5Ffr3JB5aR8piX5eHjKRU0rHlAYnSJe7YmmM4eTjh3iL3gHLXhV00dW2Ko42jmVomhBCPB03TuuU8VkoNAxKAtsDvOS6lapqWezrIXQ2A7Zqm7Qf2K6W+AGoDV4GPgR80TXvwN7wPKzYWatYssIhOB76+EBl599xHH5m4XQWYEz6Hd/58h/4N+rOkzxIq2lU0X2OEEKIQSl2Assx2ZOEjDGtPkpmib3v2TNKMJP31vlFGrTIv/8T9A8gGOY8zv9p+RI2PMncziqR169YEBwczffp0pk2bhpubG9OmTTPs4A36jWhGjRpFhw4dcHV1ZcaMGcTExBiuW1pasmXLFqZMmUJAQABXr17Fzc2N9u3b4+bmVui2DBo0iCtXrvDJJ58wduxYPD09+e677+hyzwJCw4YN45NPPqFGjRq5dtUG+Pzzz7G0tGTo0KGGAOOmTZvy3cEb4KmnnqJhw4asXr2awMBAQL/e5axZszh+/Dg6nY6aNWsycOBA3nnnHcN9zs7ObNmyhXHjxtGiRQucnZ2ZMmUKr7/+OgCHDx9m8uTJLF++3BDUXLBgAT4+PkyaNMkQBA4ODmb48OHY2NgU+ncliq/M9nV5yEgqfRmUKQkpnN16lqfHPZ1rend6Zjp7Lu3hpab3ziYUQghRAsqjn8F375o27ZRSl4GbQCjwnqZpl7Ou/QO8rJSqBNQB7IHTSqlWgB9QMlM/YmPh6YIz7xcv1gcnP/8cvL3B0hJyLEleoo5ePsp7/3uPvk/2ZfXA1Q9cKkkIIUoDZeps+IellHIHYtCvK7Ij69wyoC/6TJL7OjKlVDdgAfAU+o4sBPBAv/7JEqC5pmlpFFLLli21/fv3F1woZwblvSzLQccN4NqxsFUW27zd8xi/eTxxb8fh6nj/Jhii9IqMjKRBgwbmboYwo7Vr1zJ16lQOHTpUoh8kY2JiaNSokSHD0hgK+veslPpb07SCd056zJSZvi4Pcf4vcjtkH7Ghp3jmmSLfbhKHVh7i16G/EhgeSI1Wd/9NR8RF0OzbZvz3uf8yuPFgM7ZQCPGok77ufkqpn9Bn/LfUNC0j69wgIAk4B3gCHwKWQAtN01KzykwDhgLJwAfAemA/8DrQEBif9YxxmqbtKqgNxerr0tPBxgamToXp0/Mscvs21KsHTz6p37nbnPFAXYaO1t+1JjohmqNjjlLVoar5GiOEeKQZu68rdRmUeZgHRADhOc5tAn4hd0f2P6VUC03TUjVN26yU+gH91Llk9Asv3wG+BV4FRiilCt2RFYqrHzT+NxyenjtIaVkOGk8rkeAk6DfIcXN0k+CkEGVQ3759OXfuHDExMVSvXr3E6o2KimLx4sVGC06KYikbfV0eMlNKRwblqY2nCJ0WipapkXA+gQo1KlD96dx/R7su6H8FskGOEEKUrKyNbtoB7bKDkwCapv2Yo9hhpdTfQDTQE30fiKZp04BpOZ71Pvr+MgH9Eik+QGNgtVKqdlG+nCuU+HjQtALXoJwzBy5fhnXrzBucBJi1YxZ/x/7N6oGrJTgphChTSnWAskx1ZPEh9wcnQX98eBo4P1UiQUrZIEeIsu3NN98s8TrbtJFgjTmVqb4uD1pKKmnYmH0Nyl2f7eL6mevU8K2BQ1UHGg1uhLLIPUrcdWEX1cpXo5ZTLTO1UgghHj9Kqc+BQYCfpmlnCyqraVqMUuoi+kzLvJ7lBYwEmqH/Yu4vTdNigVillA36WQSHjdl+YmP1r/kEKOPiYPZsGDBAvwalOW08tZEZoTMY3GgwA7wHmLcxQghRRKU2QFnmOrKca1AC6akWWNnqd9vVr0EZYPI1KNMy0jh6+Sjd63Y3aT1CCCGMo8z1dXnJyqB0NmMGZeKVRKJDo2k3pR3+M/3zLbfrwi7a1Gwja3EJIUQJUUrNQ9/PddQ07XghyrsA1YHYPK4p9LMEJmialqCUsgCsc1yzRj893LjispaCziNAqdPByJGQmgoff2z0motk98XdDFg9gKZuTVnYa6F5GyOEEMVgYe4G5CWrI3sR8Dd2R4b+PRu/I2u9VD+dG9Asy3H257pkpmX9ei3L6a+bWGhUKLpMnWRQCiFEGVAm+7q8pOkDlObMoDy+9jhapob3AO98y8TejuXczXO0qSEZw0IIURKUUguAEcBg4IZSyi3rxzHruqNSarZSqrVSylMp1RH9RnGXgV/zeGQgcFPTtF+yjncA/kqpdsBrgA44YfQ3kk8GZWYmBAbCxo3w9ddQP8+vD0tG5JVIev63J+6O7vzx4h9UsK1gvsYIIUQxlboAZZntyFz9oON6KOfBreYbuLShOtdWNoRyHvrzrn4PXUV+MrVMZu+aTc//9qR6+er4184/e0QIIYT5ldm+Lq/3kmr+NSgj10RSuV5lXJvkv/7y7ou7AWhds3VJNUsIIR53Y9Dv3L0N/Zdr2T8Tsq5noF+G5DfgJPA9+r6qtaZpt3M+SCnlCrwPjM0+p2nafmAW+n5xAjBM07Rko76DjAz9rjcA+/frj7NMngwrVsCMGTB6tFFrLbJXN7yKlYUVm4dulr0IhBBlVmmc4j0m63XbPeeno19nK7sjGw5URN/JhQDPF9CRtc0+p2nafqVUdkd2G2N2ZK5+0DeKWxfAmvdJu1gF+h4yyqPvtfjvxQQfCQbgStIVjlw+wnMNnmNRr0U4l3M2SZ1CCCGMpuz2dfdQujRSscXe3hRPf7Cka0mc3XaWNhMLnrp97MoxABpXbVxSTRNCiMeapmkFrqeR1S91K+Sz4tFvGHfv+Vnog5TGl5EB3bpBaKj+ePhw/SKTmzcTcdiSzz6DV1+F9983Se2FlpKewu6LuxnvO566leuatzFCCPEQSl2Assx3ZEBSElijw8LG0VRVMHf3XK4nX+cJ5ydwtnfmuz7fMcJnhKyrJYQQZcCj0NdlU7pU0i1tzbZr6Yl1J9AyNLz75z+9G+DEtRPUrFATBxsz7+YjhBCibNi4EfbsgfR0/fGdO/rjjRs5m9YLgFdeMf+u3QdiD5CWkUabmrKEiRCibCt1AcpHQVQUuJBOujLNrzcxLZETV0/wQYcPmNZxmknqEEIIIQrDMj2VTCtbs9UfuSYSJw8n3FvkvbtqtuNXj/Oky5Ml1CohhBBl3sGDkJiY+1xiIkREEFdZH6B0LQWzqXdd2AXIEiZCiLKv1K1BWVoopV5WSu1XSu2/cuVKke5duFCfQXkx3tokbTty+QgammyGI4QQ4qE8TF+XzTI9lQwzBShTbqZwZusZvAd4FziDQNM0jl89zhPOT5Rg64QQQp4Dj5sAACAASURBVJQGxe7rmjXjvh3gHBzAx4f4eH3mZJUqxm1rcey6sIt6letR1aGquZsihBAPRQKU+dA0bZGmaS01TWtZpQg9T2wsbNigD1BeumxNXJzx2xYRFwEgAUrxWJg2bRqNGjUy+nOVUqxZs6bAMjqdDi8vL/766y+j11+Qw4cPU716dRLv/dZeCCMrbl+Xk2V6KpqNeQKUB5ceJFOXScMXGhZYLvZOLLfTbksGpRBCPIaK3df16KFfc9LRUR+NdHTUH/foQXw8ODuDlZnnI2qaxq4Lu2R6txDikSABSiObORM0TR+g1GnWzJxp/Doi4iJwsnXCw8nD+A8XZV5KQgo/9vuRlIQUk9d15coVxowZg6enJ7a2tri6utKpUye2bt1q8rpLwqJFi6hevTrPPPMMAJmZmfTp04datWphZ2eHu7s7Q4cO5dKlS7nuO3/+PL1798bBwQEXFxfeeOMN0tLSCl1v48aNadWqFXPnzjXq+xHCFKwzUsm0LvkApS5Zx66gXdT2r031p6oXWPbEVf0G5hKgFEIIUWiWlrB5MwQH67fqDg7WH1taEh8Pbm7mbiCcu3mO+MR42tSQAKUQouwr1QFKpVQlpVS8Uspo25EppcYqpdYZ63k5xcbC0qX6dZSt0ZGqWbN0KUbPooyIj8DHzUc2xBF5OrHuBCfWnuDk7ydNXlf//v3Zu3cv3333HSdPnmT9+vX06NGDa9eumbzukvDll18SGBiY65y/vz8//fQTJ06c4Oeff+bs2bP069fPcD0jI4OePXty+/ZtwsLCCA4OZs2aNbz99ttFqnvEiBF88803pGcvzC5EKWWVmQq2JR+gPLDkAHfi7vDMB888sOzxq8cBeMJFpngLIYS5lLWxHaAPUvbqpd+qu1cv/TEQH1+61p+UDEohxKOgVAcogSnAH5qmnck+oZQaqpSKUEqlKKWuKqWW57xB6Y1XSh1XSqUqpWKVUp/kKLIYaKmUam/sxs6cCZmZ+v+2RocOazIyMGoWZUZmBofiD8n0bpGvg/85mOvVVG7evElYWBiffPIJnTp1wsPDg6eeeooJEyYwaNAgQ7m0tDSmTJmCh4cHtra21KlTh/nz5wP6YF5gYCC1a9fG3t6e+vXrExQURGb2H1IeAgIC6NWrFx9++CGurq44OjoyYsQIkpOTDWU0TSMoKIi6detib29P48aN+eGHH4r0/vbv38/Jkyfp1auX4ZyFhQXjx4+nVatWeHh40KZNGyZPnsy+fftISdFnrG7ZsoWjR4+yYsUKmjdvTpcuXQgKCmLx4sXcunULTdPo0qULnTt3RtM0AO7cuUP9+vUZO3asoa6uXbty/fp1tm/fXqR2i7KpTA7aADQNGy0NrYQDlOkp6ez8ZCcez3jg2cHzgeWPXz2Og7UD1csXnGkphBDCpIo8tstRrr5S6rZS6s49l0w2titIXFzpCVBWsK2AdxVvczdFCCEeWqndxVspVQ4YBfTOce4N4F1gIrAbsAe87rl1DtArq8xhwAkwbO2paVqqUuq/wBtAmDHbHB4O2bM4swOUaWmwa5fx6jh9/TRJuiQJUD4GNo3fRFxE4dJv4/+JJ+Vm1pTurMTaqO1RTFfTAbCraIdr0wd/inLzcaP7F90LVaejoyOOjo6sW7eOdu3aYWdnl2e5l156ibCwMObNm0ezZs2Ijo7mwoULgH7KdPXq1fnpp5+oUqUKe/fu5eWXX8bZ2fm+zMWcQkNDsbe3Z9u2bVy6dImRI0cyadIkQ+Dz/fffZ82aNSxYsIAnnniC8PBwRo8eTaVKlejZs2eh3l9YWBj16tWjYsWK+Za5fv06K1euxNfX1/D+w8PDadCgATVr1jSU69atG6mpqfz999/4+fnx/fff06RJE2bPns3EiRN54403sLGx4bPPPjPcY2Njg4+PD6GhoXTu3LlQbRZlWp6DNmAC8CRwJ+v68BzXn8+6zwu4AnyladpnOZ65GJiilGqvaZpR+zuDrE5PlXCA8uDSg9yOuU3f5X0LVf74Nf0O3jLzQAghzOMhxnYopWyAH4G/gA45r5lybFeQ0pJBufPCTnyr+2JpYWnupgghxEMrtQFK4FkgE9gJoJSqCMwC+mqalnOBu8PZ/6GUegIYBzTRNC0yR5l7U8nWAVuVUuU0TUsyVoMP5qyloo4xw60YM99YT9eTDXJEXpxqOZF6KxUtUwMt62TWq7JQOHk4Gb1OKysrli1bxujRo1m0aBHNmjWjbdu2DBw4EF9fXwBOnTrFjz/+yMaNG+neXR/4rFOnjuEZ1tbWzJgxw3Ds6enJgQMHCA4OLjBAaWlpydKlS3F0dKRRo0Z8+umnBAYGMmvWLADmzp3Lli1baN9e/2V67dq12bt3LwsWLCh0gDI6Ohp3d/c8r02aNImvvvqKpKQkWrVqxfr16w3X4uLicL3nE6uLiwuWlpbEZa33UK1aNZYsWcILL7zArVu3WLlyJXv37sXe3j7XfdWqVSMqKqpQ7RVlV3EGbUqpHkD2gGwT0ABYrJRK1jTtKyihQVtqKgAWdjYmeXy2zPRMfn/ld06u0y9dkZKQQs02NantX7tQ95+4eoJ2tdqZsolCCCEKVuSxXQ6fAoeAUO4JUGYxydguP3fuQFKS+QOUt1JvcTj+MP069HtwYSGEKANKc4CyPfC3lj0HEroCloCrUuoY+szIvcDbmqadzSrzL+As0F0ptQH9FPZQYKKmaZdzPHs/+vfeGthmktanp4O1tdEfGxEXgbWFtaTxPwYKm8mY7eT6k6weuJr0lLtrFlrZWTFw9UC8et33ZbRR9O/fn549exIWFkZ4eDibNm1izpw5fPTRR0yZMoWDBw9iYWGBn59fvs9YuHAhS5YsITo6muTkZHQ6HR4eBW8A1aRJExwdHQ3HrVu3Ji0tjTNnzpCamkpKSgrdu3fPlS2l0+nw9PQs9HtLTk7ONyt04sSJBAYGEh0dzfTp0xk6dCgbN2401JdfllbO83379uXFF1/kww8/JCgoiKZNm95X3t7ePtfUdfHIKs6gbRjwu6ZpX2cdn1VKzQImKaUW5Og7TTtoyw5Q2psugzIzPZNfh/3KkR+P0GhwI+wq2aGUosXLLQqVEZmkSyI6IZpA5/y/9BBCCGFyxRnboZTqiX52XHOgfz7PNv3YLof4eP2ruTbJuZBwgURdInsv7UVDk/UnhRCPjNIcoPQAYnMc10EfcHwfGA9cBz4AQpRSDbIGXnWy7hsEBKDPIZsN/K6Uaq1pWiaApmlJSqkEwNNkrdfpTBOgjI/Au4o3NpamzVYRZU/KzRQsrCxQFgpLW0syUjOwsLK4O/XbROzs7OjSpQtdunThgw8+YNSoUUybNo0JEyZw9zNo3latWsX48eOZPXs2bdq0oUKFCixYsIBff/212O3JXr/y999/p1atWrmuWRfhb9LFxYWDB/Nex9PFxQUXFxe8vLwM07l37NhB+/btcXNzY+fOnbnKX716lYyMjFyZlSkpKezbtw9LS0tOnz6dZz3Xr18vUlBVlFnFGbTZAvf+cScDNdD3g1FZ50w7aMsKUCoTBSgzMzJZG7CWIz8eoXNQZ9pObFvkZ5y8ps+6lB28hRDCrIo8tlNKuaNfruQ5TdNu5/elVImM7XLIDlCaI4Ny76W9+C7xNRxbWVjhW923gDuEEKLsKM2b5NiTe/BlAVgDb2iatknTtL3AEKAqd6fFWaAftA3TNO2vrDW3hgFPA0/d8/zkrDpMw1QByrgImd4t8nTguwOkJaXh2tSVQb8NwrWpK2lJaSbfLOde3t7epKenk5KSQvPmzcnMzCQkJCTPsjt27MDX15exY8fSvHlz6tWrx5kzZ/Ism9Phw4dJTEw0HO/evRsbGxvq1q2Lt7c3tra2REdHU69evVw/D8rMzKlZs2acOHGiwA174G5ANDUrUNO6dWsiIyO5ePGioczWrVuxtbWlRYsWhnMTJ04kNTWVrVu3snTpUn777bf7nn3kyBGaN29e6DaLMqugQdtbQD/0/V9I1nRwgM1AX6VUV6WUhVLKC8jeKj7nustJgMkGbZkp+jUoLcuZJkAZPiecwysP4/+Rf7GCk6Cf3g0SoBRCCDMrztjuB+AbTdN2F+L5ph3b5ZC1Yo9ZApQr/lmBnZUdK59bSXD/YP4K+AsnO+Mv5SSEEOZQmjMorwKVchxnD96OZZ/QNC1BKRUD1MpRJl3TtJM57jsFpGeV2ZPjfGX0mwoYX0YGaJrRA5Rxd+KIuxMnAUqRJzsnO7p+1pVW41uhLBS1/Wuz+4vdnA87b5L6rl27xsCBAxk5ciRNmjShfPny7N+/n6CgIDp16kSFChWoUKECzz//PKNGjWLevHk0b96cixcvEhUVxbBhw/Dy8mLZsmVs3LiRevXq8eOPPxIaGkqlSpUKrDs9PZ2RI0fywQcfEBMTw+TJkxk9ejQODg4ATJgwwZDB+cwzz3Dnzh12796NhYUFL7/8cqHen5+fHykpKRw6dAgfH/3fXHh4OAcOHKBdu3ZUrFiRM2fOMHXqVDw9PWnXTr++XdeuXWnYsCHDhw9nzpw5XLt2jYkTJzJ69GgqVKgAwKZNm/j2228JCwvD19eXadOmMWrUKHx9fXHLmi8UFRXFpUuX6Nq1a7H+9xFlij0Qn+M456BtC4BSaggQh37Qtgp9Rkld4LessreAecA0IOOe55ts0JZ6KxV7wMoEAcq0xDR2fbaLut3q0n5K8TdnPX71OApFvcr1jNg6IYQQRVScsZ0/0EEp9e+sYwVYKKXSgTGapi3K8TzTje3uYa4MyozMDNZErqFn/Z682PjFkq1cCCFKQGnOoDwI5FxoMXvO5BPZJ5RSjugzRaJzlLFSStXNcV8d9IHY6Bz31QXsgAPGbzb67EkweoDyn7h/ANkgR+Rt0NpBtH6rNcpCP/3FwtKCNm+3YdDaQSapz9HRkVatWjFv3jw6dOhAw4YNmTJlCi+++CKrVq0ylFu+fDkvvvgib7zxBk8++SQBAQEkJCQA8Morr/D888/z4osv8tRTTxEVFcXbb7+dX5UG2fX5+fnRr18//P39CQoKMlyfOXMm06ZNY/bs2TRs2JAuXbrw888/U7t24TbUAHB2dua5555j5cqVhnP29vasWbMGf39/vLy8CAwMpEmTJoSFhRnWq7S0tGTDhg2UK1eOtm3b8sILL/Dcc88xe/ZsAK5cuUJAQADvv/++YTOhyZMn07BhQ0aMGGGYFh8cHEzXrl2LlPUpyqxCDdoAw6BN05sEOKLPwHRDPw0c7k7vzmayQVvyTX3msJWD8QOU+xfuJ+lqEh3+ndd+CIV3/NpxPCt6Ym9dIok1Qggh8lacsV1jwCfHzwfov3TzAVbnuM+0Y7t7ZAcoq1QpidruCjsfRtydOJ5v+HzJViyEECVEPWiNOHNRSjUGIoCqmqZdyzq3FqgHvALcAKajn7rtnbX2iAWwD7iDfi0TgC/QT/tuk70GpVIqAJiqaVrOQGa+WrZsqe3fv7/wjb99GypUgM8+gwkTCn9fHjK1TP6K/ouVh1ayJnINybpkYt+OpZJ9wRlmouyJjIykQYMG5m5GqRcQEMDVq1dz7ZxtKkePHsXPz4/Tp08bsh9LQmpqKvXr1yc4OJi2bYs3rdXcCvr3rJT6W9O0liXcpFJLKTUBCNA0rVHWsRdwAuisadq2rHOOwOWscj/l85zlQD1N09rkOFcXOA14aZp2qqB2FLmvA2LX7sG9Xyu2/N8Gun7xbJHuLYguSce8OvNwbezKsK3DHupZzb5thrujO38M+cNIrRNCiAeTvi634ozt8nhGAPCVpmmOeZwv1NiuOH3dvV57DdasgSslkq+Zo971r7H80HIuT7iMg41DyVYuhBB5MHZfV2ozKDVNO4w+GyRn+tcwYDfwO/pv3eyATtkdWFYAshf6Qdxf6Nfougj8Kzs4mWUw+ulxpmGEDMqT107yztZ38PjCA7/v/fjx6I/09urN/176nwQnhSghDRs2ZPbs2Zw7d65E642Ojua9994rs8FJUWSbgQZKKWeArGVKfgPmKaXaKqW8gaXo+7b1AEopF6XUa0qpBkopH6XUPGAgd7+cy9YeOPug4GRxpd7SZ1BaOxo3g/LvxX+TGJ/IMx8881DPydQyOXntpKw/KYQQZlacsV0RmHZsd4/4+JKf3p2emc7PkT/T26u3BCeFEI+s0rwGJei/RZunlFqoaVqGpmm3gVFZP3nSNC0W/SAtT0qpRuinBZguNz49Xf9qVbxf75nrZ2ixqAUp6Sl0r9edz7p8Rp8n+lDOutyDbxZCGNXw4cNLvE4vLy+8vLxKvF5hHpqmHVZKZQ/aFmSdHgZ8jn7QpoAd3D9oGw58lnU9HOiYtclATiYdtKXd1gcobcobL0CZnpLOzk934tnRE4/2D7fEwRe7vyBJl0TrGq2N1DohhBAPochju5w0TVsGLMt5rkTGdveIiyv5AGXIuRCuJF3hhYYvlGzFQghRgkp1gFLTtE1KqQVADXKsIfmQqgHDs9bzMo2HyKBMz0xn6K9DsbKw4uTYk9SuVPg184R4HCxbtszcTRDCFIo0aNM07SpQYNStJAZt2RmUxgxQXtxzkTuxd+j5Tc+Hes6+S/uY/Odk+j7ZlwHeA4zUOiGEEMVVZsd294iPh6xlxI3mcuJlknT5J45+/8/3lLcpT4/6PYxbsRBClCKlOkAJoGnafCM/b4sxn5enhwhQfhz2Mbsv7ubH/j9KcFIIIR4TZXXQprujD1DaVjBegDL2gH6PoBqtahT7GQkpCQz6eRDu5d35rs93KKWM1TwhhBAPoUyO7e5hrCneN1Nu8tPRn1j+z3J2Xtj5wPLDmgzDzsru4SsWQohSqtQHKMukYgYo91zcw4zQGQxtMpQXGkn6vhBCPE7K4qDNJAHKv2MpX708jq6ODyx79sZZRq0bxfXk67nO30i5waVblwgNCKWyfWWjtU0IIcTjLTFR/+PmVrT74u/Ek5qh7zMPxx9m+aHl/Hb8N1IzUmng0oAP/T6keoXq+d6vUJI9KYR45EmA0hSKGaD8fPfnVLavzFc9vjJBo4QQQgjjSk/UD7bsnIybQene3L1QZcdtHMe+mH10qt0p13lPPJnVaRZta8lGU0IIIYwnPl7/WpQMyrXH19JvVb9c51zKufByi5cZ3nQ4LdxbSKa/EEIgAUrTKGaA8tzNc/i4+eBk52SCRgkhhBDGlR2gtHeyMcrz0hLTuHr8Kg2fb/jAshtObuCPU38wu8ts3m7ztlHqF0IIIQoSF6d/LWyAUtM0Pg77mDqV6vBe+/cAcHN0o3OdzthYGqfvFEKIR4UEKE2hmAHK8wnn6Vn/4TYFEEIIIUpKRnJWgLKicTIo4yLiQAP3FgVnUKampzJ+83iedHmScb7jjFK3EEII8SBFzaDccX4H+2L28fWzXzOy2UjTNUwIIR4BEqA0hfR0/atV4X+9qempxN2Jo5ZTLRM1SgghhDCuzCTjBiizN8h50BTvueFzOX39NFuGbpEMFCGEECWmqAHK2eGzcbZ35iWfl0zXKCGEeERYmLsBj5yMDAgN1f/333/rjwvh4q2LABKgFCIPAQEB9OrVy2TPP3XqFK6uriQkmGyz4zx99dVX9OnTp0TrFMKYMlPSALByMFIG5YE4HKo6UL5a+XzLRF6J5MOwD+n3ZD+61O1ilHqFEEKIwsgOUFat+uCyJ66eYN2Jdbz+1OuUsy5n2oYJIcQjQAKUxpSRAd26wbRp+uOPP9YfFyJIeT7hPCABSlG25BU4XL9+PeXKleO9994zU6uKbsqUKYwZMwYnJ/36r8eOHcPPzw9XV1fs7OyoU6cOU6ZMIS0tLdd9oaGhtGjRwlBm4cKFRap39OjR7N+/n7CwMKO9FyFKUmZyKpmoIs0YKEjM3zG4t3DPd7OAZF0yL6x5AQdrBxY8u8AodQohhBCFFR8Pzs75r+SVlpFG3J044u7EEbQzCFtLW15/+vWSbaQQQpRRMsXbmDZuhD17ICVFf5ycrD/euBEekP0lAUphFBkZ+n9vBw9Cs2bQowdYWpZY9StWrGDUqFEEBQXxf//3fyVWb2HodDqs8/g0eeHCBdauXcucOXMM52xsbHjppZdo1qwZFStW5J9//mH06NGkp6cTFBQEwLlz53j22WcZOXIkP/zwAzt27GDMmDFUqVKF/v37F6pNtra2vPjii8yfP5/27dsb540KUYK01FTSsMXOCLuP6pJ1XDl2hSf6PJFvmbc2v8Xhy4fZOGQj7uULt9O3EEIIYSxxcfdP79Y0jZ0XdrL8n+X8dPQnElLvzsh5ufnLVHUoRLqlEEIIyaA0qoMHITEx97nERIiIeOCt2QHKGhVqmKJl4nGQncE7eDD8+9/610Jm8BrDvHnzGDVqFEuWLLkvOLl06VK8vb2xs7PDy8uLzz//nMzMTAAGDx58X0AvMzOTmjVr8vnnn+dZ16ZNm2jfvj2VKlWicuXKdOvWjcjISMP1qKgolFIEBwfj7++Pvb093377bZ7PWrVqFY0bN6ZWrbtfDtSrV4+AgACaNm2Kh4cHffr0YciQIbkyHRcuXEi1atX48ssvadCgAaNHj+all15i9uzZAFy5cgV3d3dmzJhhuOfQoUPY2dmxZs0aw7k+ffqwbt06kpKSCvz9ClEqpaSSZmGc6d2XD19Gy9DyXX9yzbE1LPx7Ie+0eYfu9bobpU4hhBCiKOLj7wYoz944y/Tt06n3ZT3aL23Pfw//l389+S++fvZrvun5DYt6LWJW51nmbbAQQpQhkkFpTM2agYMD3Llz95yDA/j4PPDW8wnncXVwxc7KzoQNFGXK+PGFCm4bXLsGx45BVuCPO3cgJET/78/ZuXDP8PGBL74oclOnTp3KnDlz+OWXX+jZM/dO9IsXL+aDDz7gyy+/pEWLFhw5coTRo0djbW3N2LFjGTp0KP379+fmzZtUrFgR0E+djo2NZfDgwXnWl5iYyPjx42nSpAnJycl8+OGH9O7dm2PHjmFjc3fDjHfffZfZs2fz3Xff5Zk9CRAWFkbLli0LfH+nT59m06ZNudaLDA8Pp2vXrrnKdevWje+//x6dTkeVKlVYtmwZvXv3pkuXLvj4+DB48GAGDx7MgAEDDPe0bNmS9PR0wsPD6dSpU4HtEKLUSUtFZ6QAZczfMUDeG+Skpqfy5uY3aeHegg/9PzRKfUIIIURRxcfDU0/BsSvHaPZtM3QZOvxr+/PvDv/muQbP4WjjaO4mCiFEmSUBSmPq0QN8ffXTuhMT9cFJX1/9+Qc4f+u8TO8WD+fOnbvByWyZmfrzhQ1QFsPWrVvZsGED69evvy84CTBz5kyCgoIMQbnatWszefJkvv76a8aOHUu3bt2oUKECP//8M4GBgQCsXLmSTp064ebmlmed92ZcLl26lAoVKrB3717atWtnOD9u3LhcwcC8REdH45PPlwht2rThwIEDpKamMnr0aD7++GPDtbi4ODp37pyrvKurK+np6Vy9ehV3d3e6devGmDFjGDJkCB06dCA1NZUvv/wy1z3lypXDycmJqKioAtspRGmkUo0XoIw9EIt9ZXucPJzuu7YsYhkXb13kP33+g7VlPgt/CSGEECaUmgoxMeDmBptPbyYtI40jrx2hYdWG5m6aEEI8EiRAaUyWlrB5s34NwIgIfTZaIdcAPJ9wnoZVpHMTORQ1k3H9ev207pwZvI6O8OWXD1wD9WE0atSIhIQEpk+fTtu2bQ1ZkKCf5nzhwgVeeeUVXnvtNcP59PR0NE0DwMrKihdeeIGVK1cSGBhIamoqP//8M/Pnz8+3zjNnzjB16lT27NnDlStXyMzMJDMzk/Pnz+cq96DMSIDk5GTs7PLOXF61ahW3b9/mn3/+YeLEiXz66ae8++67huv3buSR/Z5ynv/000/ZtGkTy5cvZ9euXTg63v/Nur29PcnJyQ9sqxCljYUulQzLhwtQZqRlcGbLGc5uOYt78/s3yNFl6Ji1YxatarSic53O+TxFCCGEMK21a/VbDPToAYsv7sLDyUOCk0IIYUQSoDQ2S0t9MKgIASFN0zifcJ4e9R6caSlEvh4ig/dhuLu7s27dOvz9/encuTNbt26lUqVKAIZ1JhcuXEibNm3yfcbQoUNp06YNly5dYs+ePaSlpdGvX798y/fu3Zvq1avz7bffUr16daysrPD29r5vl20HB4cHtt/FxYUbN27kea1mzZoAeHt7k5GRwahRo5g4cSJWVla4ubkRFxeXq/zly5exsrLCOUfGalRUFBcuXEApxdmzZ/H19b2vnuvXr1OlSpUHtlWI0sZCl0q6VfEDlBHfR7B5/GZSbqZgV9GOlmPu/1JhxaEVRCdE803Pb/Ld3VsIIYQwte++Aw8P6NRJY8S8XXTw6GDuJgkhxCNFApSlwPXk6yTpkmSKt3g4D5HB+7CqV6/O9u3b8ff3p1OnTmzduhVnZ2dcXV2pXr06Z86cYfjw4fne7+vrS926dQkODiY8PJy+ffvmmWkIcO3aNSIjI1mwYAF+fn4AHDhwgPT09GK1vVmzZhw7duyB5TIzM0lPTycjIwMrKytat27N2rVrc5XZunUrLVu2NKx3qdPpGDJkCH369MHX15fXXnuNtm3b5tqQ58yZM6SkpNC8efNitV8Ic7JITyXTtngByluXbvHH63/g2tiV9u+3p26Xulja5P7/q/TMdD4K+4iW1VrKxjhCCCHMJioKtm6FadPg4u3zxNyOoU3N/L98F0IIUXQSoCwFLty6ACABSvHwipHBayzu7u5s376dTp064e/vz7Zt23BxcWHatGmMGzeOihUr8uyzz6LT6Thw4ACXLl3KNV16yJAhLFmyhKioKH799dd866lUqRIuLi4sXryYmjVrcunSJUNWY3F069aNESNGkJ6ebnjGihUrsLOzo3HjxtjY2LB//37effddBgwYgG1WMObVV1/lq6++Yvz48bzyyivs3LmTBS7djAAAIABJREFUZcuWERwcbHj21KlTuXz5Mn/++SdOTk5s2rSJYcOGERISgoWFBaDfpKdOnTrUr1+/WO0Xwpwsdalc09lgH6dfk6sotk7cSmZ6Js+tfI5KdSrlunYr9Ra/Hf+N5YeWc/bGWT7v9rlkTwohhDCbpUtBKRgxAnZe2AUgAUohhDAyC3M3QOjXnwQJUIqyz9XVlZCQEAD8/Py4fPkyo0aN4j//+Q8rVqygadOmtG/fnkWLFlG7du1c9w4dOpQTJ07g5OREly5d8q3DwsKCVatWcejQIRo1asTrr7/OzJkzDYHDonr22Wext7dn8+bNhnNWVlbMmjWLp556iiZNmjB9+nRef/11li5daihTu3Zt/vjjD/766y98fHz46KOPmD9/vmEDn9DQUObMmcPy5cupWLEiSimWLVtGZGQkn376qeE5wcHBjB49ulhtF8LcLNJTuaOzZebMot0X/Vc0R4KP0PadtvcFJ88nnMfzC0+Grx3OyWsnmd5xOr29ehux1UIIIUThXbwIn34KHTpArVoQfjEcB2sHmrg2MXfThBDikaKyN3UQ+WvZsqW2f/9+kz3/q71fMW7jOOInxFPVoarJ6hGlW2RkJA0aNDB3Mx5LCxcuZPXq1Wzbtq1E6z1y5AidOnXi5MmTODndv3NxWVbQv2el1N+apj14ByNRoora18XGwvlqvtykIv3sN3P2bOGyKDPTM1nUYhHJN5IZe3ws1uVy78o9I3QG07ZP48/hf9LRsyMWSr5LFUKUPdLXlU7FGdf16gUbNkDXrvrVlFouaomTnRPbhpfs50YhhChtjN3Xyaf+UuB8wnlsLW2pUk42yRDCHEaPHk2nTp1ISEgo0XpjYmJYvnz5IxecFI+HGTPAhjRSsSUjg0JnUR5dfZT4Q/F0ndP1vuCkpmks/2c5frX98K/tL8FJIYQQZhUbq1/eHeCvv+DshUQi4iJoU0OmdwshhLHJJ/9S4HzCeWo51ZL1tYQwE0tLS6ZMmVLigcKuXbvSrVu3Eq1TCGOIjYVly8CWVFKxJS1Nvz7XPRvb5+noqqOUr14e7/7e913bdWEXZ26cYXiT/DfVEkIIIUrKzJmQtWw4mZnw1px9ZGgZsv6kEEKYgAQoS4HsAKUQQghRFsycqR+oZQcogUJlUabeTuX0ptM06N8AZXH/l3LL/1lOOety9Pfub4pmCyGEEIUWG6v/8i09XX+clgYbDuk3yGlVo5UZWyaEEI8mCVCWAhKgFEIIUZaEh+sHajkDlGlpsGtXwfed+uMUGakZeWZPpqSnsOroKvo36I+jjaMpmi2EEEIUWvaXcTllVNtFpXRvKtlXyvsmIYQQxSYBSjPTZeiIuR0jAUohhBBlxsGDoKVnUKPCbQKfOoz2+3q09AwOHiz4vsg1kTi4OlCzbc37rq07sY6E1ASGN5Xp3UIIIcwv+8s4A8s0tBo7URdbm61NQgjxKJMApZldun0JDU0ClEIIIcqOjAzo1g1u3YJ9+2DwYP1xRka+t6QlpnHqj1M0eK4BFpa5P35omsaSA0uoXr46fp5+pm69EEII8UAHD4Km3f35+fB6sL/JiinPmbtpQgjxSJIApZncSbtD/J14/on7B0AClEIIIcqOjRthz567x3fu6I+ztzrNw+lNp9El6fAekHt6t6ZpTNw6ka1ntzLu6XFYWliaqtVCCCFEsWV/kdatrmxwKIQQpmBl7gY8jq4kXsFznidJuiTDudoVa5uxRUIIIUQRHDyoD0rmlJgIERHQq1eet0SuiaScSzk8nvEwnNM0jSnbpjAnfA5jnxrLO23fMWWrhRBCiGK5kHCBzWc2M6XdFPkiTQghTEQClGaw7dw2knRJfPDMB7g5ulHVoSp1K9c1d7OEEEKIwmnWDBwdcwcpHRzAxyfP4qm3Ujm5/iSNBjdib9xexm0cR5IuCV2GjlPXT/Fqi1eZ32M+St2/s7cQQghhTiHnQui3qh+ZWiYjmo0wd3OEEOKRJVO8zSDkXAgVbCswtcNUXnvqNfp79zd3k4QwuY4dOzJ27FhzN+ORcOPGDVxdXTlz5kyJ1rt+/Xp8fHzIvHdLS/H46dEDfH31QUql9K++vvrzeQidGUranTTqDq/LwNUDib0di3cVb5q6NeUj/49Y0HOBBCeFEEKUOiHnQuj5354kpCZgoSyIvhlt7iYJIcQjSwKUZvC/qP/RwaMDVhaSwCqMKD4E1nrqX/M6NoGAgACUUiilsLa2pmrVqvj5+bFgwQJ0Ol2usr/88guzZs0yWVvutWTJEpo1a4ajoyNOTk40adKE999/v8TqN6WPP/6YZ599lrp19ZnXV65coVu3blSrVg1bW1tq1qzJ66+/TkJCQq77Dh8+TIcOHbC3t6d69erMmDEDTdMKXW+vXr2wtLRk5cqVRn0/ogyytITNmyE4GGbM0L9u3qw/f4+rx6+y54s9+IzwYdKFScTfiee3Qb+xeuBqVg9czZT2U7BQ8nFECCFE6RJyLoRewb1ITk8GIFPLpFdwL0LOme6ztRBCPM5kRFDCLiRc4PT107JLqTCu+BDY3guSovWvx4JyH5swSNm5c2diY2OJiopiy5Yt9O7dm3//+9+0b9+exMREQ7nKlStTvnx5k7Ujp//85z+88cYbvPrqq0RERBAeHs7UqVNJSkp68M0PKSAggGnTphW6vKenJ9u3by90+aSkJJYsWUJgYKDhnIWFBf369eP333/n5MmTLFu2jG3btjF69GhDmVu3btGlSxdcXV3Zt28f8+fP57PPPmPu3LmFrhtgxIgRzJ8/v0j3iEeUpaV+vcn339e/5hGcTNGl8MuYX7AsZ8mxfsdYf3I9n3X5jBbVWpihwUIIIUThZAcnc+4ZAJCkS5IgpRBCmIgEKEtYSJS+M/Ov7W/mlohHSvgIyMj6AJWRBIen5z4ON916Oba2tri5uVG9enV8fHx466232L59OwcOHCAoKMhQ7t4p3r/88gtNmjTB3t6eypUr06FDB+Lj4w3XN2zYgK+vL/b29jg7O9O7d29SUlIA/RTnl156iUqVKmFvb0/nzp05evSo4d5169bx3HPP8corr1CvXj28vb0ZOHBgrmDctGnTaNSoET/++CN169alfPny9O3bl6tXrxrKBAQE0KtXLz788ENcXV1xdHRkxIgRJCcnm+R3WRh//PEHFhYWtG3b1nDO2dmZV199lRYtWuDh4UGnTp0YM2YMYWFhhjIrV64kKSmJ77//nkaNGtG/f38mTZrE3Llz0TSNlJQUGjVqxMiRIw33xMTE4OLiwuzZsw3n+vTpw/79+zl9+nTJvGFRZt1OvU3vsb2JDYllXZt1TDkwhT5P9OEN3zfM3TQhhBCiQCN+G3FfcDJbki6JEb/JWpRCCGFsMse4hIVEheBs70xj18bmbooo7f4eDzciClfWxgmSLICstQEzcn6gsgCbivBnxwc/p5IPtPiiiA29X6NGjejevTs///wz06dPv+96XFwcgwYNYtasWfTv3587d+6we/duw/VNmzbxr3/9i8mTJ7N06VLS09PZsmWLYe3DgIAATpw4wW+//UalSpV477336N69OydPnsTe3h43Nzf+97//cfbsWerUqZNvO6Oioli1ahW//voriYmJDBo0iPfee49vv/3WUCY0NBR7e3u2bdvGpUuXGDlyJJMmTTJbFmFYWBgtWrQocL2+mJgYfvnlFzp06GA4Fx4eTvv27bG3tzec69atG1OnTiUqKoratWvz3//+l6effpoePXowYMAAhg8fTtOmTXn77bcN99SqVQtXV1dCQ0OpV6+ead6kKPM0TWPsqrE0XtUYi9oWvDbzNcbbjafPE31krUkhhBCl3tJ/Lc0zgxKgnHU5lv5rqRlaJYQQjzYJUJYgTdP437n/0dGzo6y3JYzLphI4eEBiNIYgJQAW4OCpD1CWMG9vb/788888r8XExKDT6RgwYAAeHh6APqiZbebMmQwYMIAPP/zQcK5JkyYAnDp1inXr1hEaGsozz/w/e3ceb1VV/3/89WYScSATBcUQnFGcSUVFQYFfllaW5ZAllpp9NecyDYfMTBscssHMElNzqiwyh4spioom5ABKVAqoKAjOyKh+fn+sfXFzOHfknnuG+34+Hvtx7ll77b3XOnf43LX2WmvvA8D1119Pv379uPHGGznmmGM477zzeOqpp9h8883ZYost2H333Rk1ahSHH344Xbt2XXHO9957j7Fjx9KzZ08AjjvuOK69duV/ODt37sy1117L2muvzaBBg7jkkkv42te+xg9/+EPWWmutNvikWmb27NlstNFGRfcdfvjh/PWvf2Xx4sUceOCBK9Vl7ty5bLLJJivl792794p9AwYMYIcdduDiiy/muOOOY9KkSTzxxBM8/fTTq3QobbzxxsyaNattK2Y15bonr2PZRcvo+W5PvvaPr7HxrhuXu0hmZmbNNnzAcO44/I5VOil7dO3BHYffwfABXq7LzKytuYOyHc18cyYvvPUC397z2+UuilWDloxkrF+DksKnK38AS+bCHr+F3sPasHBNi4gGR0rtuOOOjBgxgkGDBjFq1ChGjBjBIYccwgYbbADAE088wejRo4seO336dDp16sSQIUNWpPXs2ZPtt9+eZ599FoCNNtqISZMmMW3aNB544AEeeeQRvv71r3PZZZfx8MMP06NHDwA23XTTFZ2TkDreXn311ZWut8MOO7D22muveD9kyBCWLVvGc889xw477MBFF13ERRddtGL/0qVLkbTStOi77rqLoUOHAnDAAQesNPV60aJFHHDAAXTOrd+3cOHConUHWLx48YqOxUKXXXYZ5513HjNmzODss8/mlFNOWWk0aOH3o/4BOfn0k08+mXHjxnHZZZdx66230rdv31Wus+aaa5Z1mrtVnuXvL+e6p65j0fJFLH9/OX+56C+MmDGCkZeOdOekmZlVpcJOSndOmpmVljso21H9YsoOatbm8mtQAnTuUbAG5Wj47Kx2LdKzzz7b4PTqzp07U1dXx6OPPkpdXR2//e1vOeuss3jggQfYcccdGz1vY0+dLuyAGzRoEIMGDeKEE07goYceYujQodx6660rOj/zoynrj6+fRt5cxx9/PF/84hdXvD/zzDPp27cvJ5304Tp7+U6+a665ZqXOvWHDhnHJJZew++67N+t6vXr14o033ii6r0+fPvTp04dtttmG9ddfn6FDhzJmzBg+9rGP0adPH+bOnbtS/vrO2HyH54IFC5g+fTqdO3ducJ3J119/fUVnshnADZNvYOyYsXRb1o1OH3Ri+KTh9DugH0NOGdL0wWZmZhWqvpPy6L8ezbWfudbtODOzEvI843Z036z76L1Wbwb2GljuolitGXJt6pSE9Lr9+Su/H9K+6+RMmzaNu+++m0MOOaTBPJIYMmQI5513Ho8//jgbb7wxt9xyCwA777wz//jHP4oet+222/LBBx8wadKkFWlvv/02U6dOZdttt23wevX7GhudWMzUqVNXehr5o48+Srdu3dh8882B9HTyLbbYYsW2zjrrrJKWX/exb9++K+3r0qXLKmmN2XnnnVeMFG1MfUfr0qVLgTTyc+LEiSseNAQwfvx4Nt54Y/r3778i7ZhjjmHzzTfnlltu4bzzzmPKlCkrnXfJkiU899xz7LLLLk2WwTqOu35/FyPvHcm+D+7L0IeH0nub3hx6/aFeb9LMzKre8AHDmXXKLHdOmpmVWFV3UEr6P0kzJS2RNEXS0Ny+SyW9LulFSV8qOO4gSQ+pHVtOS95bwt3/u5uRm490g83aXu/hMOwO6LEpDPs7bPut3Ps70v4SWbp0KXPnzuXll1/mqaee4tJLL2XYsGHsuuuunHHGGUWPefTRR7nwwgt5/PHHeeGFFxg3bhwvvvjiik7E7373u9x2222MGTOGZ599lmeeeYbLLruMRYsWseWWW/KZz3yGr3/960ycOJGpU6dy5JFHsu6663LEEUcA8I1vfIPvf//7PPzww8yePZtHH32Ur3zlK/To0YNRo0a1qH7vvfceX/3qV3nmmWcYP3483/nOdzj22GPLsv4kpAfbTJ8+nddee21F2h133MF1113HtGnTmDVrFn//+985/vjj2WOPPVZ0eB5xxBH06NGD0aNHM23aNP785z9z8cUXc9ppp634m3TVVVcxYcIErr/+ej7/+c8zevRojjjiCBYt+nB07qOPPsoaa6yx0lPErbQqPdbNfnM2bz75JnSFMUvHcN4H53HCtBPosX6PUl7WzMxqSKXHOjMzK72q7aCUdChwBXARsDPwCHCXpH6SDgKOAEYB3waukdQrO24d4DLguGhsrmgb++Ozf+T1xa9z9E5Ht9clraPpPTxN465fa3LF+9Le7b333nvZaKON6NevH/vvvz/jxo3jvPPO48EHH2ywE69nz548/PDDHHjggWy55ZacfvrpnHPOORx55JEAfPKTn+T222/nrrvuYuedd2bffffl/vvvp1On9Cfr2muvZbfdduPTn/40u+22G4sWLeLuu+9eMVJx5MiRPPbYY3zxi19kq6224uCDDwbSiMGtttqqRfXbd9992W677Rg+fDgHH3ww++23Hz/60Y9a+3Gttu23357ddtuNm2++eUVa9+7dueqqq9h7770ZOHAgp556KgcddBB33nnnijw9e/Zk/PjxvPzyywwePJgTTjiB008/ndNOOw2AGTNmcPrpp3PllVcyYMAAAC6//HIkceqpp644z0033cSXvvSlFet4WmlVQ6y74ekb6DunL7127EXnbp2bPsDMzCynGmKdmZmVnqr1b7mkx4CnI+LYXNp/gT8CbwC7RMRhWfo84MCIeFzSlcBrEXF+c681ePDgmDx58mqVd+i1Q5m7cC4zTpzhJ3hbUdOnT2fgQE//rySjR49mwYIF3HHHHeUuykruvvtuTj75ZJ599tmVHq5TavPnz2ebbbZh8uTJKzoxG9LYz7OkKRExuBRlrDWVHuvue/4+PnH9Jzjzh2ey53F7csDPDmjR8WZmtcqxrvkqPdaZmVlxbR3rqrKnTFI3YFegrmBXHbAn8BQwWNJ6knYF1gT+J2kPYDjp7ly7eebVZ3johYc4bpfj3DlpZqvtE5/4BCeccAIvvfRSu1535syZ/PKXv2yyc9LaRqXHuvtn3s8n//BJ1pu3Hl2WduGtAW+V8nJmZlaDKj3WmZlZ+6nW3rJeQGdgXkH6PKBPRNwD3AA8DowFjgIWAr8GjgeOljQ9W99kz1IX9uopV9OtczdG7zS61Jcysw7ipJNOYtNNN23Xa+62224ceuih7XrNDq5iY939M+/nwJsOZOn7S+k7Jz2l/owXz+D+mfe35WXMzKz2VWysMzOz9lWtHZT1Cuenqz4tIs6PiC0iYvuIuB04E5gEvAVcAOwHnAXclt25azP3z7yf/pf35/6Z97N4+WJ+//Tv+dzAz7HBWhu05WXMrMTGjh1bcdO7rUOqqFhX3zm5aHl6eFLfOX1ZssYSXl7nZQ686UB3UpqZWWtUVKwzM7P216XcBWilBcD7QJ+C9A1Z9e4bkrYCvkpadPko4MGIeAV4JQtiWwNT26Jg+YbbgTcdyOgdR/Pmkjc5bpfj2uL0ZmbWcVRkrDv6r0ev6JyE1EE5p+8colOwaPkijv7r0cw6ZdbqXsbMzDqGiox1ZmbW/qpyBGVELAOmACMLdo0kPfVtBUkiTQE4IyLeItW5a25fV9K0gtVWOKpk0fJF/HLyL9l+w+0Z1n9YW1zCaly1PrTKLM8/x22jUmPdtZ+5lh5d01PcuyzvQu95vZnTdw4APbr24NrPXNsWlzEzsw6gUmOdmZm1v6rsoMxcCoyWdIykgZKuADYGrirI9zXgzYj4c/b+IWA/SXsD3wCWAzNWtzD5zsk1lqzBoTcfyhpL1gDgf6//jwmzJqzuJazGde7cmeXLl5e7GGarbfHixXTt2rXcxagVFRXrAIYPGM4dh99Bj6496De7H52iE69u8Co9uvbgjsPvYPiA4W1xGTMz6zgqLtaZmVn7q9Yp3kTELZLWB8YAGwHTgE9GxOz6PJJ6Z/v3yh03WdIPgduBd4AvR8Ti1S1Pfsrb1jO2ZuC/BzJ9xnSe3vFpFr+32FPerEkf+chHmDdvHn379qVTp2q+d2AdVUSwePFi5syZQ+/evctdnJpQabGuXn0n5a8O/hUAPZf1dOekmZm1SqXGOjMza1/yVLymDR48OCZPntxonvwIyqPGHsWAWQOY2X8m142+zqNKrFk++OADXnrpJd59991yF8Ws1bp27cqGG27Iuuuu22AeSVMiYnA7FsuaoTmxrtBFvS5i+WvL6blHT06ZdEqJSmZmVn0c6ypTa2KdmZkV19axrmpHUFaaF499kW//49sAvNf5PQA+9uLHOP/889P++16Ee8tVOqsGnTp1ol+/fuUuhplZo34/4vfM/MfMldIW/msh39P3ABiw/wC+cu9XylE0MzMzMzOrUp5H2gBJx0maLGny/Pnzm8w/9LtD6dojrbnW5f0uK7127dGVfcbsU7rCmpmZtUJLYx2sHO/qvb/sfcDxzszMKk9rYp2ZmbU/d1A2ICKujojBETF4gw02aDL/gOEDOOSWQ+jSfeVBqV26d+GQWw6h/7D+JSqpmZlZ67Q01oHjnZmZVZfWxDozM2t/7qBsQ0veXEKnLp1QJ9FlzS6ok+jUpRNL3lxS7qKZmZm1Gcc7MzMzMzNrS1XfQSlpPUnzJG3ezPwnShpXirL867f/YtmiZfTesTeH/fUweu/Ym2WLlvHE754oxeXMzMzKwvHOzMxKoZLadmZm1r6qvoMSOBu4MyKeA5DUT9LfJL0raYGkn0nqlsv/G2CwpKFtXZDuPbsz6sejOG7ycWw+cnOOffxYRv5oJGusu0ZbX8rMzDqYSmq0Od6ZmVmJVEzbzszM2pciotxlaDVJPYA5wEER8ZCkzsCTwGvAacD6wHXAnyLim7njfgJsGhFfaOZ15gOzW1i8XsCCFh5TLVy36lOr9YLarVut1gtg64hYp9yFqDaSfgz0ioijs/f9gF8A+wGLgT8AZ0TEsmz/GsBM4NCImNiM87cm1kHt/qzWar2gdutWq/UC160aOda1UHu07RzriqrVutVqvaB261ar9YLarVubxrouTWepaJ8EPgAezt6PArYjBagXASR9G7hG0ncj4u0s3zhgvKQeEbGoqYtERItXU5Y0OSIGt/S4auC6VZ9arRfUbt1qtV6Q6lbuMlSbrNF2DHBQ9r4z8HdSo20oHzbaBHwTICKWSvoDcBLQZAdla2JdVpaa/Fmt1XpB7datVusFrls1cqxrlZK37RzrVlWrdavVekHt1q1W6wW1W7e2jnXVPsV7KDAlPhwGOgSYXh/AMvcAawC75tImkzpnh7RLKc3MzFZPQ422L0fEvyJiPPBt4FhJ6+aOGwd8OuvgNDMzq2Ru25mZdWDV3kG5KfBK7n0fYF5BngXA+9k+ALI7a28B/UtcPjMzs7bgRpuZmdU6t+3MzDqwau+gXBNYUpDW0KKahemLs+NL5eoSnrvcXLfqU6v1gtqtW63WC2q7bqVSyY22Wv1+1mq9oHbrVqv1AtetGtVqvUrJbbvyqNW61Wq9oHbrVqv1gtqtW5vWq9ofknMj0DUivpi9vwD4fERsl8uzAfAqsF9E3J9LXwyMjohb2rnYZmZmLSLpHmBWRHw9e381sHlE7J/LI2A5cGRE3JxLnwP8MCJ+3s7FNjMzaza37czMOrZqH0H5BLBt7v0kYKCkTXJpI4GlwJT6BEmbA92Bf7VHIc3MzFbTAmC93Pu55EZKZnoBnVl1ZOVHgfmlK5qZmVmbcNvOzKwDq/YOyntIQWv97H0d8Azwe0k7SxoB/Bj4Te4pb5DW8no+Iv7bvsU1MzNrFTfazMys1rltZ2bWgVV1B2VETAX+CRyWvX8f+BSwiPSk01uAPwNnFBx6OPCbUpVL0v9JmilpiaQpkoaW6lqlIOksSY9LelvSfEl/kzSoII8knS/pZUmLJU2QtF1D56xEks6WFJJ+nkur2npJ2kjSddn3bImkZyXtm9tflXWT1FnS93O/UzMlXSipSy5PVdRN0j6Sxkmak/3sjS7Y32Q9JK0n6XpJb2Xb9ZI+0q4VKdBYvSR1lXSJpKclvSvpFUl/kNSv4BxrSLpS0oIs37iCzreOruIabdUe68DxrlrrVYvxzrHOsc7ctisVx7rqrJdjXWXXq1ZjXVau8sW7iKjqDfgEMAPo3Mz8g0jT33qWqDyHktYAOxYYCFwJLAT6lfuzakEd7gGOzj6r7YHbSdMJP5rLcybwDvD5LN+twMvAOuUufzPruAcwE3gK+Hm11wv4CPA88HtgN2AAsD8wsAbqdjbwOnAQ6UEfnwbeAM6ptroBnwQuAg4h/bM9umB/k/UA7iJ1TO1JejLzM8DfKrVeQE9gfPa3cevs53Mi8CzQJZfvV1ldRwK7ABOAJ5v7t70jbKRRkyfk3vcD7sg+89eyeLNGwTH3AN8pQVmqPtblPh/HuyqqV63GO8c6x7pyf98qZcNtu1LUwbGuyurlWFf59arVWNdU3Uod78r+jW2jD/AkYNNm5h0F/L8SluUx0giWfNp/SQ8oKPtn1co6rU16MuxB2XuRnib73VyeNbNfwK+Xu7zNqE9P4Dlgv+wX5efVXq/sD8jDjeyv5rrdAVxXkHYdcEc11430z+3olnyPSP8YB7BXLs/eWdrW5a5TsXo1kGfbrMzbZ+97AsuAL+XyfAz4oJR/r6tto4IabbUY67I6ON5VeL1qNd451jnWlbtOlbThtl2pP1/Hugqvl2Nd1dWrJmNdsbo1kKfN4l1VT/GuFxE/i4jZzcxbFxH3lKIckroBu5Km3uXVkXrFq9U6pOUA3sjeDyA9nGFFPSNiMfAg1VHPq4E/RsR9BenVXK/PAo9JukXSq5KelHSiJGX7q7luDwHDJW0DIGlb0j9rj8nyAAAgAElEQVQgd2b7q7luec2pxxBSkHgkd9zDwLtUV13XzV7r/6bsCnRl5bq/CEynuupVUhFxN/ALoLnTATcGvhIRb7VlOWo41oHjXTXUq1bjnWOdY51l3LYrOce6yq+XY12mSupVqCPFOmjDeNelsZ3WYg09QXUeMKL9i9NmriANx52Uva9/cmyxevZtr0K1hqRjgS2ALxfZXbX1AjYD/g+4DLgY2Ik0BQXg51R33S4h/SP1rKT3SX+3fhARv8z2V3Pd8ppTjz7A/MhuQwFEREh6lVWf6FyRsn/2f0qavvBSltyHdCd/QUH2eVRJvdpLRPysBXkLG1RtpVZjHTjeVXy9qN1451jnWGeVp1bjnWNdhdcLx7pqq1ehDhHroO3jnTsoSyMK3qtIWlWQdClpqPHekRaqzquqekramjRcfmhELGska1XVK9MJmBwRZ2Xvn5C0JXACKYjVq8a6HQp8BTiCtC7HTsAVkmZGxG9z+aqxbsU0VY9idaqKumYLYN9AWlfn0805hCqoVwdWK79zgONd/aFF0ipNrcY7xzrHOqtctfJ751iXHVokrdI41q2s0uvVkJqNdVCaeFcTU7wryAJST3Fhr/CGrNp7XvEkXUZ6Kt5+EfF8btfc7LXa6jmEdCd0mqT3JL0H7Av8X/b1a1m+aqsXpDUuni1Im056iAZU7/cM0pOJfxIRN0fE1Ii4HrgUqA/Y1Vy3vObUYy6wYW56B9nXG1Dhdc0C2E3ADsD+EfFabvdc0giFXgWHVdv3sKOoqVgHjnc5lV4vqN1451jnWGeVp6binWPdCpVeL3Csq7Z6FarpWAeli3fuoGxD2Z2bKaQnFeWNZOW1BSqepCtIdzb2i4h/F+yeSfqhG5nL3x0YSmXX8y+kJ9ftlNsmAzdnX/+H6qwXpPUqti5I2wqoX7+nWr9nAD1I/xzmvc+Hf7+quW55zanHJNLC5kNyxw0B1qKC6yqpK3ALKYANj4i5BVmmkJ6Qma/7JqTFoyu2Xh1VLcU6cLyrP6BK6gW1G+8c6xzrrMLUUrxzrEuqpF7gWFdt9SpUs7EOShzvSvGkn468kYYtLwOOyb4BV5AWP9203GVrQR1+AbxNWrC2T25bO5fnzCzP50hPir2Z9Bj5dcpd/hbWdQLZk96quV7Ax7M/At8lrcPyBeAt4IQaqNtY4CXgU0B/4GBgPvDTaqsbKQjV/wO1CDg3+7pfc+sB3AVMBfYgBbGppDU/KrJepKVE/gLMAXYp+JuyZu4cv8ryjAB2Bu4nrY/UrCdWe2v373nVx7qsHo53VVavWo13jnWOdeX+vnlr8Pte9fHOsa766uVYV/n1qtVY11TdSh3vyv6NrcWNtKDtLGApqfd4n3KXqYXljwa283N5BJxPGn6+BHgAGFTusreiroVBrGrrlf2hfyor93+AkwBVe91ICylfTrpjuBh4nrTeTPdqqxswrIHfrbHNrQfwUdJaH29n2w3ARyq1XqR/Phr6mzI6d47upMW/XyMFwr8BHyv398xbo9/3qo51WR0c76qwXrUY7xzrHOu8Ve5W7fHOsa466+VYV9n1qtVY11TdSh3vlB1sZmZmZmZmZmZm1u68BqWZmZmZmZmZmZmVjTsozczMzMzMzMzMrGzcQWlmZmZmZmZmZmZl4w5KMzMzMzMzMzMzKxt3UJqZmZmZmZmZmVnZuIPSzMzMzMzMzMzMysYdlGbNJGm0pGhgG9HCcx2THbdJqcpbCpK6ZOUek0u7UNJ75SyXmZm1Dcc6xzozs47A8c7xzipPl3IXwKwKfQF4qSDt2XIUxMzMrEQc68zMrCNwvDOrEO6gNGu5JyPif+UuhJmZWQk51pmZWUfgeGdWITzF26wNSVpT0hWSnpH0rqRXJI2TtHUzjv2ypCez496S9LSkYwryDJd0n6SF2XaXpG2bWbbhku6V9HZ2jackjc7t/5KkCZLmS3pH0r8kHdniDyGd6zRJ0yUtlvS6pMclfbo15zIzs8riWLfiXI51ZmY1zPFuxbkc76xdeASlWct1lpT/3YmIeD/7es1suwCYC6wPnABMkrRNRLxa7ISS9gWuAy4HTgc6A9sC6+XyfAb4EzAOOIJ0g+E7wERJO0TEnIYKLOnzwK3Ag8BxwAJgELBpLttmWZ7/AR8Aw4CxkrpHxDVNfCb5ax0FXAJ8D3g4+zx2zD4LMzOrDo51jXCsMzOrGY53jXC8s/bkDkqzlvt3wfuHgb0BIuJ1UpAAQFJn4B5gPnAocGUD5xwCLIiI03JpdbnzCLgC+EdEfC6XPgF4HjgVOKPYiSV1IgXHx4H9I+KDbNe9+XwR8f2CYyYAfYFvAM0OYlldnoiIC3Npd7bgeDMzKz/HusY51pmZ1QbHu8Y53lm78RRvs5Y7GPh4bvtafqekwyT9U9JbwHvAQtKdpsamAjwObCDp95I+Jalnwf5tSHfEblR62lqX7E7fQuAxYJ9Gzr0tsAlwTS6ArULS1pJukTQHWJ5to5sod0N12TWbDrG/pB4tPN7MzMrPsa5xjnVmZrXB8a5xjnfWbtxBadZy0yJicm6bUb9D0sHATcA04HBgd1Kgex3o3tAJI+IfpLtw/YG/AAsk1UkalGXZMHu9jg8DTP32CRofYl+/r/DpdCtIWhcYD2wHnAkMzcp9XWPlbsDvgBOBPbNzvibpT5L6tfA8ZmZWPo51jXOsMzOrDY53jXO8s3bjKd5mbesw4N8R8dX6BEndgY80dWBE3ArcKmltYD/SWh93ZX/8X8uyfRu4v8jhSxs59YLstW8jefYCPgYMiYhHc2Xv2lS5C0VEAL8CfiXpo8D/A35KCu57tfR8ZmZWcRzrHOvMzDoCxzvHO2tH7qA0a1s9SEP/875CC0YrR8RCYJykLUh//NcDngVeBLaNiB+3sEzTs2OPkfS7LMgUKzeku3YASFofOKiF11pJtm7LTZKGAEetzrnMzKxiONblONaZmdUsx7scxzsrNXdQmrWtu4GfS/oJcBdpKP0JwNuNHSTpB6Th+vcDrwD9SEPpJ2eBAEknAn/O7trdRrrz1oc03P75iLii2Lkj4gNJp2TH3Cvp16Q7b9sB60XEBaTFoBeS7oydD6wDnAO8Spqa0GySfgu8AUwiLSC9NenJdHWNHWdmZlXDsc6xzsysI3C8c7yzduQ1KM3a1lXAD0l/tP9GGgJ/IPBOE8c9BmxGeiLb+Owc/yB3lysixgH7AusCvyU9Qe5i0homjzV28oj4c1aWzsC1wDjSAtCzs/1zSQtEdwP+DPwgq8vNzal0gYdIwfsqUuA6i7TeyVcbO8jMzKqGY51jnZlZR+B453hn7UjFRwSbmZmZmZmZmZmZlZ5HUJqZmZmZmZmZmVnZuIPSzMzMzMzMzMzMysYdlGZmZmZmZmZmZlY27qA0MzMzMzMzMzOzsnEHpZmZmZmZmZmZmZWNOyjNzMzMzMzMzMysbNxBaWZmZmZmZmZmZmXjDkozMzMzMzMzMzMrG3dQmpmZmZmZmZmZWdm4g9LMzMzMzMzMzMzKxh2UZmZmZmZmZmZmVjbuoDQzMzMzMzMzM7OycQelmZmZmZmZmZmZlY07KM3MzMzMzMzMzKxs3EFpZmZmZmZmZmZmZeMOSjMzMzMzMzMzMysbd1CamZmZmZmZmZlZ2biD0szMzMzMzMzMzMrGHZRmZmZmZmZmZmZWNu6gNDMzMzMzMzMzs7JxB6WZVTVJsyRNKHc5zMzMSkXSBEmzyl0OMzOzUnK869jcQWllJ2mYpJB0RkF6ZNsNDRw3QdLCgrTzc8eFpGWS5kt6VNJlknZo4FxjC44r3K7J5R2dpR3SFvVviKSzJN0m6fnserOayL+7pHslvSPpbUl3S9qpSL6tJf1E0n2S3szOfX4LyyZJR0q6WdL/JC2S9IKkcZJ2b+CYTpJOlfRvSUskvSjpp5LWaiD/JyU9IuldSa9nn8WAlpTTzKxSONYVLc9Wki7Iyj0/i19PSvpuI7Fha0l/kfRGFh8mStqvSL6NJP0gi4Xzs7qMbUUZj5J0j6SXstg1X9Kk7PPp3MAxX5H0hKTFkuZJukbSBg3kbVbsNjOrFo53RcuztaQbJU2X9FbWdvq3pEslbdTIMe0W74qc94Dc5zW4gTyOd9amupS7AGbNcISkn0TEky045lxgJtAZWA/YCfgacLKkS4FvRUQUOe4bwMIi6f9rYZnbwkXA68C/gI80llHSHsAEYA6p7gAnAhMl7RkRU3PZhwCnAc8BU4BVAl0zrAFcDzwJ3Ez6rDcCjgcmSfpKRBT+83EZcBJwO/BTYGD2fmdJIyLig1x9Pgf8EXgK+BbQEzgFeFjS4Ih4uRVlNjOrZB0x1n0VOAEYB9wILAeGAxcCX5S0R0Qsrs8saXPgEeA94EfAW8CxwD2SDoiIe3Pn3ho4G3gReBw4oJVl3AV4A/gF8CqwNvAp4FpgKOnzXkHSqcClwAPAycAmpJg7RNJuEfFuLm9LYreZWa3oiPFuE1Jb6XbgJVIc2x44DjhM0k4R8Wp95jLFuxWym4S/In12azeQx/HO2l5EePNW1g0YBgRwRkF6AE8DS4B7ihw3AVhYkHZ+dtzgIvk/Cvwj239mwb6xWXqvZpR3dJb3kBJ/Lpvlvp4GzGok7z+Bt4G+ubS+WVpdkc/hI9nXg7O6nN/CsnUB9i2S3htYAMwDOuXStwM+AP5UkP+b2fWPyKV1JQWv2cDaufSdgPeBqwvOMQuYUO6fY2/evHlrbHOsK3qNwUDPIukXZtc+sSD91iwO7JRLWzuLFzMA5dLXATbIvu6VnW9sG5b971lc65NL6wW8m8Xkzrn0g7Lrn11wjpbE7gmN/R/gzZs3b5WyOd616LP6QnbtbxeklzXekQaWvEQaVLLK5+94561Um6d4W6V7AfglMErS/qtzooh4nRQE3gbOamj6WFuR9NFs6sFz2bSw1yRNkfStZpb3+WZeZwvg48BtETEnd/wc4DZghKQ+ufTXI+LNltVmlbK9FxEPFEmfR7qLtmG21TscEHB5wSG/ARYBR+bS9gU2Bq6JiBV3PCPdZZ0AHCqpa+G1Je2iNG19odKU8OskbViQp34Kx4hsyshsSUslPS3psBZ8BGZmbalDxrqImBwRbxXZdUv2Oih3nbWAT5NuSK0YdZPFiWuArUixsD79nYiY38pqNcdsUlzrmUv7LNADuDIi3s+V5W/A8+RiXUtjd+64zST9NZsi+Lak2yVtVpCnfnrlaEnflPSf7HvzH0nfbJPam5m1ToeMd42Ynb2ul7tOWeNdNp37m6TZa+80kM3xzkrCHZRWDX5AGtZ+iSStzomyQHY7qUGxd5EsH5XUq8jWrRWXu400dP0u0h/5C0h3j4a1rvQNqg9Qk4rse5TUgNq1ja/ZmE2AZUC+E/TjpJEm/8xnjIglpGniHy/ICw3XZ11SYC685j9IAfHbwJ+BLwP3S+pR5DyXAIeRpi6cC3QDbpI0uvGqmZmVjGPdhzbJXufl0nYgLS/SUGyAlWNJm5LUM/uMtpR0Iml6+n9YeZpgU/FrG0lrNzNvsdi9FnA/KcaeBfwW+CRp+ZNVGnek78d3gBuy/G8CP5N0XoMVNTMrvQ4b7yR1z66/iaRRwK+zXXfmspUt3knqQhpAUhcRf2wkq+OdlYTXoLSKFxGvSfoRKZgdSlrzcHU8nb1uBdxTsG9GA8d8gbQmYrNI6kla2/FXEXFii0vYMhtnr3OK7KtP61viMgDpwTbAbsD1WedjvY2BBRGxtMhhc4A9JXWLiGU0vz7P5NI3B06NiBUjNCU9Q1oX5STg4oLz9AJ2qB+5I+kq0s/FpZJuidyaZ2Zm7cGxbsU5O5NuHL0H/CG3q9yx7h982IAK4F7g+PzIEZouo7I8/2lGXli1Pr2AKyLilPoESQ+SbsqdT1oHOm8rYGBEvJTl/QXwEDBG0m/r083M2lMHj3fHAFfm3s8CjoyIibm0csa700lrWn6uiXyOd1YSHkFp1eJy4GXgQhWZ3ttCb2ev6xbZ93lgZJHtwRZeYzGwFNhdUv9WlbL56kcIFuv8W1KQp2QkbUl6cM4cUnDL60Hx8sGqZWxNfd4mjYbM+2WWfnCR8/wqP60w+/oq0vSKYQ2U08ys1Bzr0mewB3BuROQbluWOdf9H+oy+QlobrCu5KXkF129OGVtbn5VuuEXE7aQG+GeL5L0x3yjLbgJeRhqgcFCR/GZm7aWjxru/ZNc/mDQC802g8KnXZYl32fTp84DvR8TMJrI73llJtHsHpaR9JI2TNKd+vYCC/VJaG+5lpcfVT5C0XUGe9SRdn61H8Fb29Udy+/tLelDSu9lr/4Lj/yzpuBJW09pYRCwi3S3ZnFXvmLRUffB6u8i+ByPi3iLbq0XyNij7o3gKaf2smZKekXTl6q610oBF2esaRfZ1L8jTbJI6S+pTsPVsIO8APlyk+oAi66AsaqB8xcrYmvo8Xzg6M3v/PLAZq5peJO3Z7LVYfrMWcayz1ujosU7S90nT566OiB8W7C5JrMuuWxjrPlqYJyL+mX1G10fEYaRpaQ8qPWm1NWVsTX3ejIi5RfJPB3pr1fXXHOuspBzrrLU6aryLiJey6/8lIs4DjiJNdT8rl61c8e7XpCel/6QZp3K8s5IoxwjKtUlPJD6ZdCei0LdJo6++SVqv4FVgvKR1cnn+AOwCHAB8Ivv6+tz+n5JGce0EvELul0zSZ0lDhn/TNtWxdvQ74N/AOQU/Dy21Q/ba0JD/NhERVwH9gWOBfwGHAPdKWt1pDIVezl6LDfWvTys2pL4pHyP9/uS3KwozZf8o3k/63R4ZEVMbKGMvScUCU1/S9O9lubz5shfmhVXrEw3UoaF1bYrlX601cMwKONZZa3XIWCfpfGAMcC3FG6ulinWwaqz7czOOuY404mN0Lq2pMkYuj2Od1QLHOlsdHTLeFZzzaeAJ0ij9eu0e7yQdDIwg/X5tKmkLpYfb1HdgbpKl1fcfOd5ZSbR7B2VE3BkRZ2eLrn6Q3ydJpDsTF0fEnyJiGumuwjrAEVmegaTgdVxEPBIRk4CvAwdK2jo71UDguoj4LzA2e4+kdUlB7riIaOiH3ipUts7TWaRh8Ge05hzZXaKDSQszP9R2pSsuIl6JiGsi4sukRf9vIj2Fui0XNn48ex1SZN8epD/YU1px3rmsOh3iR/kMkjYldU72JHVOPtFIGTuR1qfMH9+d9A/n5IK80HB93iatZ5K3uQoWu846QweQRlEW2rZI2sDstVlPTzdrjGOdtVZHjHVKi9ifB/weOKaBn9uppOlhDcUGWDmWtERhrCtcpqSYNbPX/OiTxuLX7sCM7CmsTeVtKHavp+IPB9gGeDUi3i1Id6yzknKss9XREeNdA9Zk5VhSjni3afb6O+C/ua3+Sdi3Z+/ry+l4ZyVRaWtQDgD6AHX1CdnDKh4E9syShgALgUdyxz0MvJvL8xTpcfWdgFF8uHDuxcDYiPh3qSpgpRURfyF9708DNmzJsVkAu400DeAH2dSCkpDUQwVPj86CcP3P4irTx1orIv5HClJfkFS/CDHZ118A7mtgiHxT511SZDpE/VD5+s7JCaQ1uEZFRGOdoLeQgs8pBenHkkag3JhLe4B0R+8Yffj0NyTtSFof8raIWF5wnnVZ+c4j2ft1SWu9FPqGctPVs6+PJ60D80Aj9TBrC4511qiOFOsknUua5nc9cHREfFAsX9bQ+RswLIsH9cevTXrowH9JT1NtsSKxbkp27i6S1m/gsPpG26O5tL+SRpGdqPSwn/oyHkSaxrgi1q1G7P5O/k026mVrise6L0naJJe3G3Aq8D5wRwP1MmsrjnXWpI4S7xrobEPScNK08RWxpBzxjhQTvlBkuy3bf2b2vn4aveOdlUSlPcW7/hd3XkH6PD4c/tsHmJ+/UxYRIenV3PFnkNZQmEX6o/F1SXsCQ4Hhkm4kBcRHSU9gLLZehVWuM4GJpLsihXdP6h0gaRtSJ/x6wM6ku2vrAD+OiB83cNwhkhYWSX81IuoK0j6fXaPQc6R1MB6QdDtp6ssbWXm/QVrbY2KR41Yi6ct8eDdrA6CbpDHZ+9kRkZ/+cjJpJONESfVPhvsmqf4rjQTJOuPqG1b1QWKf3LnHZdMNGivbOtn1+pOeRLd17k53vfERMQ8gIqYqPU3tREl/Bu4kfR4nkToEVzypNSKWSzqZ1Kk5UdJvSP94nArMJ42yKfQccJ6kQaQ7cLsCXyVNG/lZkfwLgMck/Y40/P9ooB9p5E7J/rkxyzjWWXPUfKyTdALwPeAF0lOxj0iDrlaYFxHjc+/PAvYH6iRdRmooHUv6vflU4SiqXFyrb1TukEt7MCKaekjC2sBLufrNI/3+fRYYTFp7OR+/5ks6hzRF7l5JN2VlO50Ujy4vOH+zY3dmAfC5rFE3AdiSdDNuHqmTt9B/SLHuKuAd0qi1j5MegPBiE3U3W12OddZcNR/vgF9J2gi4D5hNWntxV+Aw0t/nwr/57Rrvsk7E/xWmZ20rSJ2Ik3P5He+sNCKibBvpjtno3Ps9SaOsPlaQ71rg7uzrs4HnipxrJvCdBq7TjTRUek/SL9F1pM7ZG0h/0Mr6OXT0jTQqLoAzCtIDuKOBY/6a7V9YkH5+ll6/LSP9gfsn6UleOzRwvrEFxxVuD+Xyjm4i793A+tn1niSNyltM+qN/ObBRMz+XCY1cY0KR/ENIjaWFpD/M9wC7FMnXv4nyj25G2Zo6RwDDCo7pTApAM0jTFuYAlwJrN3CNA0n/bC4i/RPwR2DzIvlmZZ/VLqSg/26W/3qgd0He+u/dCD5sFC8l/aNxRLl/F7zV5oZjnbdwrGtleSYUOWZg9rm8mcWHh4ARDZy/sXOf34zydSNNIX08+3zfy+LLQ8AJQNcGjhtNGvW1hLTm3u+ADRvI29zYPYEU7zbL6v92lv+vwBYN/KyNJt0I/C8p1v0XOLncvwveanPDsc7bh9+j+r9BjncfXuOLwN+BF7PYsJjUkXcl0K+BY9ot3jVS7vrPf3AD+0fjeOetDTdl39iyyO5mnBgRY7P3m5HuUOwWEY/n8v2d9BCNoyR9lfSgjnUjK3y2xsk7wDcj4toi1zmX1FFxgqR/AedExN8lfQq4ICJ2LW1NzawSKD1d8lpgeERMKG9prKNwrDOz9iRpGGmkytH1f3fMSs2xzszam+Nd7am0NShn8uGDOYAVD9EYyodrk0wiTbnJL7I6BFiLldcvqT9+G9KCzGdlSZ2ArtnX3UijuszMzNqLY52ZmdU6xzozM2uRdl+DMlvcdYvsbSegn6SdgNcj4gVJlwPflfRv0loCY0jDgP8AEBHTJd0N/FrSsaT1435NGi4+o+BaAq4GTo8P1yN5CDhe0nTSmhElf9qXmZl1LI51ZmZW6xzrzMysLZVjBOVg4IlsW5O0DtwTwAXZ/h+R1qX7BelpTxuRnhD8Tu4cXyKtdVBHWrvgKeDLRa51HGnh5fyTns4nrVMwGfiA4ousmpmZrQ7HOjMzq3WOdWZm1mbKugalmZmZmZmZmZmZdWyVtgalmZmZmZmZmZmZdSDtvgZltZB0HGkqAWuttdau22yzTZlLZGZWG6ZMmbIgIjYodznMsc7MrFQc6yqHY52ZWWm0dazzFO9mGDx4cEyePLncxTAzqwmSpkTE4HKXw1bmWGdm1nYc6yqTY52ZWdtp61jnKd5mZmZmZmZmZmZWNmXtoJS0nqR5kjZv5+v+RNLP2vOaZmbWMTnWmZlZrStjrPujpNPa85pmZlYa5R5BeTZwZ0Q8J2kDSfdIelnSUkkvSvqFpJ71mSV1lzRW0tOSlkuaUHhCSZ+TVCdpvqR3JD0m6dMF2S4BRkvarLTVMzMzWynW7SjppizGLZY0Q9K3JK2Ix5KGSfqrpFckLcpi3lcLTyrpCElPZnnmSrpBUp9cFsc6MzNrLy1q1wFI2l7SA1k8nCPpXEkqdnJJh0sKSXcU7PoeMKbw3GZmVn3K1kEpqQdwDPDbLOkD4HbgIGArYDSwP/Cb3GGdgSXAz4G/N3DqfYH7gE8BOwN3ArdLGlqfISLmA3XAN9qmNmZmZqsqEut2BeYDXwa2A84DzgW+kztsT2AqcAgwCPgVcLWkI3Ln3Qu4HrguO89ngW2BG+vzONaZmVl7aE27TtK6wHhgHvBx4CTgW8AqoyGzG20/BiYW7ouIqcDzwJFtVR8zMyuPcj7F+5Ok4PUwQES8BlyV2z9b0i+Bs+oTIuJd4HgASTsAHyk8aUScXJD0PUmfIjXe8kFtHHARKRCamZmVQmGs+13B/ucl7QJ8nhSTiIiLCvL8StLwLM8fsrQhwEsRcVn2fqakK4ErC451rDMzs1JrcbsO+BLQAzgqIhYD0yQNBE6TdGlkT3KV1BW4CfguMBzoVeT644DDgV+0aa3MzKxdlXOK91BgSjTwGHFJGwOfAx5og2utA7xRkPZPoG97r5NiZmYdSqOxLrMuq8aopvI8DGwk6SAlvYDDSLMG8hzrzMys1FrTrhsCTMw6J+vdA2wM9M+l/QCYFRHXNXL9fwK7SVqzFWU3M7MKUc4Oyk2BVwoTs7W5FgFzgHeAo1fnIpJOADYhTYXLezl77b865zczM2tE0VhXLxs9OZo0jbuhPAeSpsZdXZ8WEZNIo0VuBJaRpo0LOKrgcMc6MzMrtda06/qQpnfnzcvtQ9Io4FCyGXSNeBnoSurcNDOzKlXODso1SetJFjoV2IU0JXsz4PLWXkDS50nrlXwpImYX7K6/W+c7bWZmVioNxTokbU1aT/nyiPhTA3n2Ik3rPiki/plL3xb4GfB90rqWnyA16H5dcArHOjMzK7XWtusKR1zWPyAnspkBY0lTwJuaZeBYZ2ZWA8q5BuUCYL3CxIiYC8wF/i3pNWCipAsj4sWWnDzrnLwe+EpEjCuS5aPZ6/yWFdvMzKzZisY6SdsA9wM3R8R3Vjkq5dmbNGX73IgoHGF5FvDPiPhx9v5pSe+SYiBx/l4AACAASURBVOZ3czHTsc7MzEqtNe26uWQjJXM2zF7nkR4StxFwb+7B3p0AJL0HbBcRM7J0xzozsxpQzhGUT5CeONqY+vKt0ZITS/oicAMwOiL+2EC2QcBy0pNSzczMSmGVWJeNfpwA3BYRpxY7SNI+wF3A9yKi2EyCHsD7BWn175VLc6wzM7NSa027bhIwVFL3XJ6RpOnas4DHge2BnXLbONJDT3cCZuaOGwS8HBGFU8bNzKyKlLOD8h5goKT1Ia2xJekoSYMk9c+evH0V8GhE/K/+IEnbStqJ9AS3tSXtlL2v338YaU2u7wAPSuqTbR9lZUNJCzMvKm01zcysAyuMdduRRk5OAC7KxagVo0gkDSN1Tl4F3JjLs0HuvH8DPiPpG5I2y6aC/wz4V0S8kMvnWGdmZqXWmnbdH4BFwNgs3+dI7bdLI3k3IqblN+BN4J3s/bLc9YcCd692LZYtg3PPhf33T6/LljV9jJmZtZmyTfGOiKmS/kl66ugvSOuWHA8MJN1ZexG4Hbi44NA7SQsx13sie60fMXI8qV6Xs/I6Jw8Aw3LvDwfOW916mJmZNaRIrPsCaQrbodmWVx/HRpNGSJ6RbfVmkz3sJiLGSloHOBH4KfAWqePz2wXndKwzM7OSak27LiLekjQyyz8ZeIMUzy5tybWzEZgHA/9vtSqxbBn06QNvZMtd3ncf/PznMHcudOu2Wqc2M7PmKecalADfA66QdFVE3Avc29QBEdG/if3DmjpHdhfvfaCh6d9mZmZtJR/rzgfObyxzRIwmdVI2KiKuBK5saL9jnZmZtaPWtOumAvs09wJZfCz0NeCxiHi0uecp6sILP+ycrPfGGyn9ggtW69RmZtY85ZziTUTcTbprtkk7X3ot4OiIeK+dr2tmZh2MY52ZmdW6Msa65cA3V/ssDz9cPP2RR1b71GZm1jzlHkFJRPysDNe8tb2vaWZmHZdjXY1YtiyNpnn4YdhrLxgzxlP/zMwyZYp1V7fJifbaK03rLrTnnm1yejMza1rZOyjNzMzMKp7XJzMzq11jxqS/6flp3uutl9LNzKxdlHWKt5mZmVlVaGx9MjMzq27duqUbTueck57ifc45vgFlZtbOPILSzMzMrClen8zMrLZ16+YH4piZlZFHUK6OZcvg3HPTXbZzz03vzczMrPbstVfxdK9PZmZmZma22jyCsrW8FpWZmVnHMWYM/OhHsHTph2len8zMzMzMrE14BGVreS0qMzOzjqNbN9hqK+jf3+uTmZmZmZm1MY+gbC2vRWVmZtZxvPIKTJ0KF18MZ55Z7tKYmZmZmdUUj6BsLa9FZWZm1nHce296HTWqvOUwMzMzM6tB7qBsrTFj0tpTeV6LyszMrDbV1cEGG8COO5a7JGZmZmZmNccdlK3VrVtae+qcc7wWlZmZWS374AMYPx5GjoRO/tfJzMzMzKyteQ3K1dGtG1xwQblLYWZmZqU0dSrMm+fp3WZmZmZmJeJhAGZmZmaNqatLryNHlrccZmZmZmY1yh2UZmZmZo2pq4NBg2DjjctdEjMzMzOzmuQOSjMzM7OGLFoEEyd6ereZmZmZWQm5g9LMzMysIRMnwtKl7qA0MzMzMyshd1CamZmZNaSuDtZYA/bZp9wlMTMzMzOrWe6gNDMzM2tIXV3qnFxzzXKXxMzMzMysZrmD0szMzKyYl1+GadM8vdvMzMzMrMTcQWlmZmZWzPjx6dUdlGZmZmZmJeUOSjMzM7Ni7rkHeveG7bcvd0nMzMzMzGqaOyjNzMzMCn3wQRpBOWoUSOUujZmZmZlZTXMHpZmZmVmhJ5+EBQs8vdvMzMzMrB24g9LMzMysUF1deh0xorzlMDMzMzPrANxBaWZmZlaorg523BH69Cl3SczMzMzMap47KM3MzMzy3n0XHnrI07vNzMzMzNqJOyjNzMzM8h54AJYvdwelmZmZmVk7cQelmZmZWV5dHXTvDnvvXe6SmJmZmZl1CO6gXE1vvfAWP934p7z1wlvlLoqZmZm1hbo62Hff1ElpZmZmZmYl5w7K1fTgRQ+y8JWFTPzhxHIXxczMzFbXiy/C9Ome3m1mZmZm1o7cQbmanrnlmfR68zNlLomZmZmttvHj06s7KM3MzMzM2k2XchegGl3y0UtY8saSldKWvLmE7+l7AHRfrztnvn5mOYpmZmZmq6OuDjbaCLbbrtwlMTMzMzPrMDyCshX2GbNPo/v3PXffdiqJmZmZtZn3308jKEeNAqncpTEzMzMz6zDcQdkKQ04bwj7nFO+k3OecfdjjlD3auURmZma22p54Al5/3dO7zczMzMzamTsoW+nd+e+2KN3MzMwqXF1deh0xorzlMDMzMzPrYNxB2UrTbp4GQJc1u7D/D/eny5ppOU8/LMfMzJoiaZakKLL9Pdt/fpF9cwvOcYakeZJelXR6wb6dJc2QtGZ71qvq1dXBzjvDhhuWuyRmZmZmZh1KxXVQVkujrWv3rmz16a046+2z2Ps7e3Pmm2ey5YFb0qW7nztkZmZN+jiwUW7bBQjg1lyeGQV5tq/fIWkH4ALg8Gy7UNL22b7OwG+AEyJicclrUiveeQceecTTu83M2ki1tOvMzKwyVGJv2seBzrn3GwFTWLXRNiz3/v36L3KNtgMBAXdIqouIqW3ZaDv9lZXiI126deGIvx2xOqc0M7MOIiLm599L+hrwNnBbLvm9iFipoZazDfB0RNyXHf90ljYVOAWYFhH3tnnBa9kDD8Dy5e6gNDNrO1XRrjMzs8pQcR2UbrSZmVlHIknA14AbImJRbtdmkuYAy4DHgLMj4vls31RgK0n9SI22rYBpkvoDJwKD26n4taOuDtZcE/baq9wlMTOrCW7XmZlZS1TcFO+8phptkmZKulnSZrl9KxptkjZl1UbbykMfzczMymskMAC4Jpf2GDAaOAA4FugDPCJpfYCImA6cDYwH6oCzsrSrgO8CQyU9LWmapM+2V0WqWl0dDBsGa6xR7pKYmdUct+vMzKwpFTeCskBjjbZ/AxsCY0iNtu0i4rWImC6pvtEGWaNN0t182Gi7gNQ5OyYi/tJOdTEzMyvmWODxiHiyPiEi7spnkPQo8DxwFHBplucqUodkfZ4jsy/vBf4DDCHFuoclbRURr5ayElVt9myYMQOOP77cJTEzq1Vu15mZWaMqvYPSjTYzM6tZkjYEPgOc0Fi+iFgo6RlgywbOsz7wfWA4sAfw32xEJZL+C+wO/K0Ni15bxmdtX68/aWZWKm7XmZlZoyp2ineu0fabxvJFxEKgOY2248k12iLiGaC+0WZmZlYOo4GlwM2NZZLUnbTu1isNZLkUuDIiZpFie9fcvm6s/JACK1RXB337wsCB5S6JmVnNcbvOzMyao2I7KHGjzczMali2HtcxwM0R8U7Bvp9I2lfSAEm7A38E1gKuK3KeEcC2wBVZ0uPA1pIOkvQZYGvgnyWsSnV7/3249940elIqd2nMzGrRaNyuMzOzJlTkFO+mGm2kaWovkNYqOYemG21fzZJWNNpIQc2NNjMzK5dhpFEiRxbZtwlwE9ALmA88CuwREbPzmSStCfwCOCwi3geIiDmSjidNiRPw9Yh4uVSVqHpTpsAbb3h6t5lZCbhdZ2ZmzVWRHZS40WZmZjUuIu4nxaJi+w5r5jkWkxplhenXUaSBZ0XU1aWRkyNGlLskZma1aBhu15mZWTNUZAelG21mZmbWLurqYJddoFevcpfEzKzmuF1nZmbNVclrUJqZmVkJSTpO0mRJk+fPn1/u4rS/t9+GSZM8vdvMzMzMrMzcQdmADt9oMzOzmhcRV0fE4IgYvMEGG5S7OO1vwgR47z13UJqZ1TC368zMqoM7KBvQ4RttZmZmta6uDtZaC4YMKXdJzMysRNyuMzOrDu6gNDMzs46prg6GDYM11ih3SczMzMzMOrSydlBKWk/SPEmbt/N1fyLpZ+15TTMz65jKGOtOlDSuPa9ZVWbOhP/+19O7zczagNt1Zma2uso9gvJs4M6IeE7SjpJukvTi/2fvvuOkLK/+j38Oyy6wIEVEiiDFgqgYVFRADGAXwR4pNkjUJJqoDyJRYwElxAoao7FjR/0ZC8HCWhZEioiCIiKKAqJUC0jdZXfP7497FobZwpbZmXtnv+/Xa57Zue9rrjnzmOR4nbmKmW0xs0Vmdo2ZbY/RzA40s+xI8ttqZt+a2RgzyyiuczPraWZ5ZvZ5zK3bgSFm1qEKv5uIiAhE5ToAM7s3shfWVjNbGtvYzEaamZfw2DPSpq6ZPWFmn5nZNjObUsznPgJ0NbNjqvC7VV9vvx08q0ApIhIP5R3X9Taz18xspZltjuSz38d2amaDzWxepM0qM3vGzFpENdG4TkQkRSStQGlmmcDFwGORS4cDa4ELgIOAm4GbgGuj3pYLPAmcCHQErgL+AIwupv8mwFPAu7H33H0tkAX8OT7fRkREpKhich0EufdJghxVnLuAljGPqcAUd18TaZMGbAX+DbxeXCfungM8B1xRuW+RorKyoE0b6Ngx2ZGIiFRrFRzX9QDmA+cABwP/AR42s8FR/R4NPE2QMw8CzgAOBJ4tbKNxnYhI6qidxM/uCxQA0wHc/fGY+9+a2WHA2cCYSJvFwOKoNsvMrDdQ3OyQxwiSmREkvlgTI/1eU/GvICIiUqqdch2Au/8VwMyGE/zgthN33whsLHxtZm0I8twFUW02AX+K3D8EaFzC508E3jazTHffXNkvkzLy8uDdd+Hss8Es2dGIiFR3FRnXjYlp8x8z6xNp81zkWnfge3cfF3m9xMzuA+6Lea/GdSIiKSCZS7yPAT52dy+lTUPgl5Jumtm+wMkEM0uir18GtKCYmZVRZgN7JXqfFBERqVHKkut25Q/AOuC/FXjvHIIfI3VMdbQ5c2DdOi3vFhGJj0qP60poMx1oaWb9LbAHMBB4I+Z9GteJiKSAZBYo2wIrS7oZ+ZVtCMF0/9h7M8xsK/A18AHBnieF9zoTLCM4z93zS/n8FZHnduUNXEREpIxKzXW7Etmv6/fAU5El2+USmTW5HuW6nWVlBTMnjzsu2ZGIiKSCCo/rotr0A44DHi685u4zgUEES7pzCZaNG3BRzNs1rhMRSQHJLFDWI9g/qwgz60iwp9Y97l7cjJEBwGHAYIIlBX+LvK8O8Dww3N2X7OLzt0TFISIiUhVKzHVldArQBni0En1sQbluZ1lZ0LUrNG2a7EhERFJBZcZ1hXtNPgdc4e6zo64fCPwLuJVgX8uTCVbJPRTThcZ1IiIpIJl7UP4INIm9aGYHANnA8+5+bZF3Ae6+PPLnF2aWBjxqZncSHCRwIDDezMZH2tQKurU8oK+7Z0Wu7x55XhuXbyMiIlJUsbmuHC4FZrj7gkr0sTvKdTusXw+zZsF11yU7EhGRVFHhcZ2Z9SRYsn2Tu8fOsLwOmO3ud0Zef2Zmm4BpZvb3qDGhxnUiIikgmTMo5xIUE7eL/Eo2Bfh/7v5/ZeynFkGhNQ34AegMdIl6PEhwsE4XYEbU+w4GthGcHiciIlIViuS6sjKzVsCpwCMV/fDIflx1gU8q2kfKyc6G/HztPykiEj8VGteZ2W+BN4FR7n5PMU0ygdgtuwpfR59wpnGdiEgKSOYMysnA7WbW1N1/MrODgPcIfmUbY2YtChu6+yoAM7uAYPnAfIJ9SLoC/wReitqb6/PoDzGzNUCOu+90nWAz52k61VRERKrQTrkOth/w1gBoBWSYWZdI2y/cPTfqvb8HNgEvFtdxZPCXAewBNCjsx93nRTU7BvjW3b+O43eq3rKyoEED6NYt2ZGIiKSKiozrehMs/X4AeDaqTb67F86E/B/wiJn9OfIZLYF7gE/c/buoz9e4TkQkBSStQOnu881sNsFJbPcDvwP2JNhfckBM88JfyPIIpvrvF7m2LPLecRUIYRDBYToiIiJVophcB8F+kr2ims2NPLcHlkKwLwnB6d3PljLgeoPgYILYfqJnlQyiEjMwU1JWFhx7LKSnJzsSEZGUUMFx3RCCGZLDI49Cy4gcduPuT5jZbsBfgLsJDn3LBkbE9KlxnYhICkjmEm+AUcAVZpbm7iPd3Yp7FDZ29wnufpi77+buDdz9IHcf4+5bSvqASL8HR18zs1MJlge8VGXfTEREJLA91wG4e+8S8t3Swjd4oL27X1ZSp+7errScaWYHE2xvUuKpqTXON98EDy3vFhGJt/KO64aU0KZddKfufl9kzJfp7i3dfbC7f194X+M6EZHUkdQCpbu/RfArW+sEf3R9YKi75yX4c0VEpIZJYq5rBVzo7usT/LnhlRU5J08FShGRuNK4TkREKiuZe1AC4O7/SsJnFrufl4iISFVIUq7LSvRnhl5WFrRrB/vum+xIRERSjsZ1IiJSGcle4i0iIiJS9bZtg/feC2ZPmu26vYiIiIiIJIwKlCIiIpL6Zs+GX3/V8m4RERERkRBSgVJERERSX1YW1KoVnOAtIiIiIiKhogKliIiIpL6sLDjySGjSJNmRiIiIiIhIDBUoRUREJLX98kuwxFvLu0VEREREQkkFShEREUlt770HBQUqUIqIiIiIhJQKlCIiIpLasrKgYcNgibeIiIiIiISOCpQiIiKSutxh8uTgcJz09GRHIyIiIiIixVCBUkRERFLX4sWwbJmWd4uIiIiIhJgKlCIiIpK6srKCZxUoRURERERCSwVKERERSV1ZWdChA+yzT7IjERERERGREqhAKSIiIqlp27bgBG/NnhQRERERCbUyFSjNrK+ZnR/1ei8zyzaztWb2jJllVl2IIiIiVU+5LgXNmgUbN6pAKSISRflORETCqKwzKG8GWke9HgccALwInALcFOe4REREEk25LtVkZUFaGvTpk+xIRETCRPlORERCp6wFyn2BTwHMrC7QDxjm7pcD1wHnVE14IiIiCaNcl2qysuCoo6Bx42RHIiISJsp3IiISOmUtUNYDNkf+7g5kAG9FXi8EWsU5LhERkURTrkslP/8MH32k5d0iIkUp34mISOiUtUC5DOgW+bs/8Im7/xJ53QzYEO/AREREEky5LpW8+y64q0ApIlKU8p2IiIRO7TK2ewz4h5n1B44Croi6143glzYREZHqTLkulWRlQaNGcMQRyY5ERCRslO9ERCR0ylSgdPe7zOwXgoT1FPBI1O1mkWsiIiLVlnJdCnEPCpTHHQe1y/pbrIhIzaB8JyIiYVTmf2t398cIfm2LvT40rhGJiIgkiXJdivjqK/juO7j++mRHIiISSsp3IiISNmXdg1JERESkesjKCp61/6SIiIiISLVQ4gxKM9sCeBn7cXevH5+QREREEkO5LkVlZcG++0L79smOREQkFJTvREQk7Epb4n0vZU9iIiIi1ZFyXarJzYXsbLjoomRHIiISJsp3IiISaiUWKN392kQGIiIikmjKdSlo5kzYtEnLu0VEoijfiYhI2GkPShEREUkdWVmQlgZ9+iQ7EhERERERKaPS9qAcUY5+3N3vjEM8IiIiCaNcl4KysqB7d2jYMNmRiIiEhvKdiIiEXWl7UN5Wjn4cUBITEZHqRrkulfz4I3z8MYwalexIRETCRvlORERCrbQCZb2ERSEiIpIcynWp5N13wV37T4qIFKV8JyIioVbaITk5iQxEREQk0ZTrUkxWFjRuDF27JjsSEZFQUb4TEZGw0yE5IiIiUv25BwXK448PDskREREREZFqo8wFSjO70MxmmtnPZrY59lGVQYqIiCSCcl019uWX8P33Wt4tIlIGynciIhI2ZSpQmtkg4DHga6Ax8F/gf0Ae8D1wb1UFKCIikgjKddVcVlbwfMIJyY1DRCTklO9ERCSMyjqD8mrgdmBo5PU4dx8A7AtsA5bGPzQREZGEUq6rzrKyYP/9oV27ZEciIhJ2ynciIhI6ZS1Q7g9kAwWAAxkA7r4GuBUYViXRiYiIJI5yXXWVkwNTpmh5t4hI2SjfiYhI6JS1QLkVwN0dWAW0i7q3Hmgd37BEREQSLmG5zsxGmpnHPFZF3bdImxVmtsXMppjZQVH365jZ02b2q5l9ZWbHx/R/hZk9F694Q2/GDNi8GU46KdmRiIhUB8p3IiISOmUtUH5BMOUfYDpwrZkdamadgZuAr+IVkJKYiIgkScJyXcQioGXUo3PUvREES/D+ChwBrAHeNrPdIvcvBQ4HugMPA8+ZmQGYWRuC2S9XxTne8MrKgvR06N072ZGIiFQHynciIhI6tcvY7jF2/LJ2E/AOMCfyejNwVnzDYhHQO+p1ftTfhUlsSKTdTQRJrKO7b2DnJHYKQRJr7u4elcSOjHO8IiJS/SU61+W5+6rYi5GB11XAbe7+38i1iwgGbYOBh4BOwER3X2Bm3wJ3AnsAa4H7gZGRpXo1Q1YW9OgBDRokOxIRkepA+U5EREKnTAVKd3866u8vIzMWjwEygWnuvjLOcSmJiYhIQiUh13Uwsx+AXOBD4Hp3/xZoD7QAsqLi2WJm7wM9CHLdp8AFZlYPOAlYCfxoZucC9d39iTjHGl5r18Inn8A//pHsSEREqgXlOxERCaOyzqDcibuvBybFOZZoSmIiIpJUVZzrPiRYCfAlsCdwAzAjMkhsEWmzOuY9q4G9In8/DhxCsEzvR+BcoBFwG3Cimd0MnEewt9il7v5lFX2P5HvnneBZB+SIiFSI8p2IiIRBmQqUZnY+0Nbdi0xPMLPrgaXuHq99HZXEREQk4RKZ69z9zZj+ZwHfAhcBswqbxYZReM3dtwGXx/TxKMH+XAcAAwi2OxkEPE2wr1dqysqCpk3h0EOTHYmISLWgfCciImFU1kNyrgY2lnBvQ+R+XLj7m+7+ort/5u7vAP0I4rwoulnM23ZKYu5+ubu3d/cj3P0D4C6KJrFnCJKYiIgIJDDXxXL3jcACYD+CH9Bgx49yhfak6A90AJhZL4LcdhdwLPB6ZF/mZ4GuUYcNpBb3oEB5/PGQlpbsaEREqgvlOxERCZ2yFij3BeaXcG8BO06BizslMRERSZCk5Tozq0vwI9pKYAlBvjsh5v4xwIxi3lsH+A/BqoA8gtyeHrmdEXlOzerdggWwYoWWd4uIlI/ynYiIhE5ZC5QFQNMS7jUtRz/lpiQmIiIJkrBcZ2Z3mVkvM2tvZkcBLwH1gSfd3YF7gGvN7CwzOxh4gmC2S3FL7m4EJrv7R5HXHwBnm1kX4Bpggbuvi1fsoZIV2ZL6hBNKbyciItGU70REJHTKmnw+Ai4p4d4lwJz4hKMkJpJKxs4cS+1bajN25thkhyJSFgnLdUBrYAKwCHgZyAG6ufuyyP07gLHA/ZHPbQmcGFkBsF0kDw4g2K+50MuRRzZwGjtvkZJasrKgUydo0ybZkYiIVCfKdyIiEjplPcX7NuCtyGnZjwA/EBxKczFwNHByHGMqTGJ7AGsJNk+OTWL1CJJYE4JDdUpLYl2iLr9MMNsyO/IdlMREqsjYmWO5OivYwqjweVj3YckMSWRXEpbr3H3gLu47MDLyKK3d5wRboERfKwCujDxS19atMHUq/PGPyY5ERKS6Ub4TEZHQKVOB0t3fMbPBwDjgSYIDaQxYAQyKHGYTF0piItVfdHGykIqUEnaJzHUSBx98EBQptf+kiEi5KN+JiEgYlXUGJe7+opm9BHQm2JvkR+DzSNFPRAQovjhZSEVKCbualuvM7FLgUoC99947ydGUU1YWpKdDr17JjkREpNqpaflORETCz4IJiRIrZtB2+LJly3bxDhFxd2rfUpsCSv532zRLI++mvARGJWFjZh+7e9dkxyE769q1q8+ZE89tx6pYly6w++7w3nvJjkREpAjluvDQuE5EpGrEO9dV2enb1Z27P+zuXd29a7NmzZIdjkjoTVk6haMeParU4iTAHSfckaCIRCRlrVoFn36q5d0iIrJLGteJiFQPKlCKSKXMXz2fU587lT5P9mHVxlU8cfoT3Hn8ncW2vfvEu7W8W2ocM2tiZqvNbJ8Ef+5dZvavRH5mwrwT2R5NBUoRERERkZSQ1AKlBm0i1dfy9csZ+tpQfvPgb5ixfAZ3HH8Hi/6yiIu6XMTwo4dz94l379RexUmpwa4H3nD3bwDM7F4zm2NmW81saWxjM6trZk+Y2Wdmts3MppTWuZn1NLM8M/s85tbtwBAz6xCn7xEeWVmwxx7BMm8REUk6jetERKSykj2DsryDtnZm5sU8To5pl2Fmt5jZEjPLMbPvzOyKqCapO2gTqWK/bPmFEW+PYL/79mPC/Alc3f1qvrniG645+hrqpdfb3m5Y92HcfeLdpFmaipNSY5lZJnAx8FjU5VoEp6Y+VcLb0oCtwL+B13fRf5NIP+/G3nP3tUAW8OdyBx5m7kGB8oQToFay/zVGREQi4j6uM7OzzCzLzNaa2QYz+9DMTovpSuM6EZEUUeZTvOMtatDWP+py4aCtM1Dauq2TgU+jXv8cc38C0IZgM+SvgebA9sqJu681s8JB2zUV/AoiNcrWvK38e/a/GTNtDOu2ruPC31zILX1uYe9GJZ/8e2y9YTQYO4zjz0hgoCLh0hcoAKYXXnD3vwKY2XCKyXXuvgn4U6TNIUDjUvp/jCBvGnBOMfcnAmNIpVw3fz6sXq3l3SIiIVGF47pewHvADZHr5wGvmFlvd58GGteJiKSSMk89MLPmZjbGzD4wsy/M7MDI9cvMrCKn9hQ7aHP3+4CvdvHen9x9VdQjNyrOE4Hjgb7u/ra7L3X3D919SkwfE4FBFYhbpEbJL8jnqU+fouO/O3LN29fQrXU35v1pHk+c8USpxUmA88+H9eth8OAEBStSSVWQ644BPnZ3j2+kQUxAC2B0Kc1mA3slesldlcrKCp5POCG5cYiIVGNxzndVMq5z9yvd/TZ3n+3ui919FPAxEPvTt8Z1IiIpoEwzKM3sAOB9IB34COgO1I3c7gj0AM4v52dXZtD2spnVJZgdOc7dX4q6d0YkxmFmdiGwBXgTuN7dN0a1NMvgfAAAIABJREFU2z5oK1yKICI7uDtvLX6La9+9ls9Wf0bXVl154vQn6NO+T5G2W7fCd9/B0qXBY9kymDsXFiwI7i9YAJ99BoccktCvIFIuVZTr2gIr4xVjITPrDNwMdHP3fDMrqemKyHM7IDVyXVYWHHQQ7LVXsiMREamWqiDfVdW4rji7Ab/EXNO4TkQkBZR1ifddwBLgJGAjkBt1bzrwzwp8dkUGbRuB4ZHPzANOA14ws4vc/ZlImw5ATyAHOJtgadx9QCt2Xv6WeoM2kTiZs2IOI94eQfbSbDo06cAT/Z7n8Lq/47uFtfjPWzuKkIXPq1bt/P60tKJbww0eDJ/HHuEhEi5VkevqAasrH9oOZlYHeB4Y7u5LdtF8S1Qc1d+WLfD++3DZZcmORESkOot3vquqcd1OzOxyoDXwdMwtjetERFJAWQuUvYDz3X2dmaXF3FsFtKzAZ5d70ObuPwLRRwPPMbM9gBFAYSKrBTgw2N3XA5jZX4DJZtbc3Qs/M7UGbSJRVq6EgQPhhRegRYtdt//116DYOOurb3jo67/zSe4L1MlrRpuF97F+2qUMWZOxU/v0dGjbNniceiq0axf8Xfi8Zg0cccTOn6FZlFINVEWu+xFoUunIdtYSOBAYb2bjI9dqAWZmeQRbnETWQbN75HltnGNIvPx8uPNOyMmBRo2C12mx/5hERKQM4p3vqmpct52ZnQ3cCQx092UxtzWuExFJAeU5JCe/hOtN2ZEUyiNeg7YPgaFRr1cCPxQWJyMWRp73ZkfyTJ1Bm0iMW2+FDz4Inv/9b/jllx0zHmNnPy5dCuty10KvW6Hrg5CfTtpHN9JmxXD2ad2QdmfuXHxs1y4oepZ2eG7fvsVf1yxKqQbinevmAkMqHE3xfiA4dCDaZcAJwJnA0qjrBwPbgPlxjiGx8vPhpJOC2ZMQFCqnTYPJk1WkFBGpmHjmu6oa1wHbi5NPAxe6+8Ri3qdxnYhICihrgXIOcAEwqZh7ZwOzKvDZ8Rq0dWHnJQXTgd+ZWYOoPSf3jzxH/9qWGoM2kRgrV8Ljj0NBAfznP/Dkk7Bp085t6tcPCo2tO2wi4/ixfNrgTraxmdPbXMzI3jfTeXRLSt7Sbte+KWFxTUnXRUKiKnLdZOB2M2vq7j8BmNm+QAOCrUcyzKxLpO0XhYcDRA4ryAD2ABoUtnH3ee6+Ddip1G9ma4Acd4/9CeAYYJq7b65A7IHcXBg9GqZPh6OPhhtugIyMXb+vPNyDzWy3bg2WcRc+F/6dnR18/rZtQftNm+DDD+HNN6Ffv/jGIiKS+uKd76pqXIeZnUtwGvhFpexPqXGdiEgKKGuB8h/AW2b2P+BZgiXUvzWzPwLnAkVPzdi1cg/azOwiguQzl+CkuP7A5cDfovp9DriRYOnbSII9KO8FXnL3NVHtKj9oEwmhm24K6gmFOnSAIUN2ngHZsHEej899jJFTR7Jq4yrO6nQWY44dQ8c9OsYlhi0VmWcmknxxz3XuPt/MZgMDgfsjlx8lWF5XaG7kuT07Zj++QbCnV2yb8v50MIjgMJ2Kyc0Npkz/EjmP4L33YNw4ePhhyMsrvphY3POu7uXklD+2TZtg3jwVKEVEyi/e+a5KxnVmNpBg5uRw4H0zK9y4KNfdf476fI3rRERSQJkKlO7+TuTXq3uAUyOXxxJsSHyuu08v7wdXYtB2A8GgLR/4Cvh99EbK7r7RzI4nOBjnI4JT3l4Fro0JoXKDNpEQWrECxo8PJiNB8Lx4cbC0ukWL4GTuV798lesmXMeinxbRc++evHzuy3Rv0z25gYuEQFXkuohRwL1m9qC757t77zLE0q48H+DuI4GR0dfM7FSCXLmrE1FLNnr0juJkoY0bg/9RKU5GBtSrB3XrBs/Rf9etC40bl3yvpGt168Inn8CoUTv/+lG/PnTpUnwcIiJSonjnu6oa1wF/Ihiv3hN5FJoK9I56rXGdiEgKKPMelO7+spm9AhwE7An8BMx394JKfH65Bm3u/iTBFP9dxboIOLGk+3EZtImE0FlnBVu10S4bzhgKr44nf0Ufbr0VBv9tOiPeGcGM5TPotEcnXhv4Gv33749VZi23SIqpilzn7m+Z2f0EJ4/GbuxfleoDQ909r8I9TC9hjHrUUfDMM0ULiaVtTlsZxx4Lb78dLOvetCkoTh51FJxyStV8nohIiquCfBf3cV1ZftDTuE5EJHWU55Ac3N2J2feqMqr1oE0kZN54Ixi70y4bBveDjM0wuB+5r9/PM/mv8MD4ibTarRWP9H+EIV2GULtWuf7rL1JjxDvXRfr8Vzz7K+NnvljpTo4+OljWHevEE2HffSvdfZmlpQUH4rz5ZrCsu0uXoDipA3JERCosnvlO4zoREaks88K1oKU1CpYAlCouA6GQ6tq1q8+ZMyfZYYiUaNGiYDLRHkdks6J3P7bk7bwFT2Z6JjcccwNXdruSzPTMJEUpEjCzj929a7LjiKVcV0yui92DEqBJE1i1Kv4H5YiIpJCw5jqo2flO4zoRkfiJd64r6xSq50u4Hl3dTMkkJhJ269fD6aeDt5/M8t+eTm5e0cMm3J1urbupOClSOuW6WBkZQTFy9GiYMQN69KiaU7xFRCSRlO9ERCR0ylqg7FTMtaZAP+Ac4KK4RSQiZbJl2xamfzeLy26fyuLuU/F2U4IzEItrm7eFoa8NZelVSxMZokh1o1xXnIwMuOWWZEchIiLxo3wnIiKhU9ZTvBeVcGuGmeUDfwZmxi0qESliU+4mZn4/k6lLpzJl2RRm/zCb3PxcaGW0Se/CkfuezaSvJ5GTX3QGZWZ6JuNPH5+EqEWqD+U6ERGpCZTvREQkjOJxSkY28HIc+pHqauFYmDcCutwBnYYVfS0VsiFnA9OXT2fq0qlMXTaVj1Z8RF5BHmmWxmEtD+PERlcw6d+9uLB3T554sDFmkL0km34T+rF52449KDPTM5k0aBJ92vdJ4rcRqfaU60REpCZQvhMRkaSIR4GyK7B5l60kNS0cC3OvDv6eezWsegdWvrnjNahIWUbrtq7jg+8+2F6Q/GTlJ+R7PrVr1eaIVkcwvPtwerXrxdFtjuabhbvRowccfRg8ch+YBX30ad+HSYMmbS9SqjgpEjfKdSIiUhMo34mISFKUqUBpZiOKuZwBHAycCTwSz6CkjFZnw8yh0H08NO9T9HUizI35j0ZhcbLQvBE1rkCZvSSboa8NZfzp40stDP685WemLZvG1GVBQXLeqnkUeAEZaRkcudeRXNvzWnq360331t2pn1F/+/vWroUzzoDdd4eXXip6VkVhkbIsMYjIDsp1IiJSEyjfiYhIGJV1BuVtxVzLB34AxgGj4haRlM3qbJjSD/I3B8+db4b5o3a87j2p/EXKgm2Q8xPk/hw85/wEuT+V/vdOh/0V49C7KvwVq6PoJdb9JvTbafbi2k1reX/Z+9sLkvNXz8dx6qTVoXub7tz42xvp1bYX3Vp3o156vWL737YNfvc7WL0apk2DFi2Kj6NP+z46EEek/JTrJNy0pYqIxIfynYiIhE5ZC5TFVUu2uXsJZwZLlZs5NChGQvBcWJwsfD3zIjh+atkLjTk/Qd6Gkj+vVgbUaQoZTYPnhgfs+Hv5K7ChmL22W/WFA66K/3cPqdj9Hzdv28zJz57MyfuczOJfFvPF2i8AqFe7HkfvfTS39LmFXm17ceReR1Kndp0yfcb//R9MnQpPPw1du1bZVxGpqZTrJLy0pYqIxI/ynYiIhM4uC5RmlgGMBF5y94+rPCIpm+7jd8yghB3PhTYvh4kdin9veuOgsFinKdTZExp22rn4WPjI2H3H9dr1d2x0GG3h2OKLkwAr3oAv76kRRcriDqcByM3PZeJXE+naqitjjh1Dr3a96NqqKxlpGSX0VLJHH4X774fhw+H88+MVuYiAcp1UA/O0pYqIVJ7ynYiIhNUuC5TunmtmVwJv7qqtJFDzPjsv6y5ktaHZMbDXqVFFx913FB8zmkCteJyNFBE7YIo1d3iNKFAOfW1okeJktLWb1nLdMddVuP8ZM+Cyy+DEE+G24hbliEilKNdJ6HW5Y8dMyeLUsC1VRKRilO9ERCSsapWx3afAgVUZiJTT6uyixUkAz4OfPoTdD4cOQ6B1f2h2NDQ6AOo2i29xEoIBU7RWfUu/n6LGnz6eerWL3zcyMz2T8aePr3Df338PZ50FbdvC889DWlqFuxKR0inXSXh1GgbNjy3+Xg3bUkVEKk35TkREQqesBcoRwN/M7PiqDEbKIXoPSoC0qOJY/maYOSQxcXQaBofeDZYGh42D3q/veH3o3TVmudnhrQ6nef3mRa5npmfudFBOeW3ZAmeeCZs2wWuvQZMmlY1UREqhXCfhNftPsPq94u8VbqkiIlI2ynciIhI6ZS1QPg40Biab2QYz+9rMvop6lLAJoVSZ7uODg2sgeO48CtIyg9dpmcH9ROk0DAbl7Zi9Ufi6hhQn8wryGPDSAL7f8D13nXAXmenBP4fKFifd4dJLYc4ceOYZOFC/c4tUNeU6CZ+CPPj0Blj8UOnt5g5PTDwikgqU70REJHTKut73Y8CrMhApp6bdIL0RbFsPvd+EFsdC067BzMru44M9KiUhhk0exluL3+Lhfg9zyeGXcFjLwxj62lDGnz6+wsVJgHHjgsLkLbfA6afHMWARKYlynYTLpuUwYxCsnQ5Njwq2cCnUqm8wc7JQDdlSRUTiQvlORERCp0wFSncfWNWBSDl9dR/krIXjsqF57+Ba8z5wxtJkRlXj3D/7fu6bfR/Dug3jksMvAaBP+z4svWpppfp9+2245ho4+2z4+9/jEKiI7JJynYTK9xNh1hAo2AY9noV2g2Hh2OBwukPvClYtFL7uckeNWbUgIpWnfCciImFU4hJvM/vWzH6TyGCkjHJ+ggVjoFW/HcVJSbjJiydz5VtX0n///txxQvxmrixeDAMGwEEHwRNPQK2ybsQgIuWmXCehk58Dc66E90+H+u3h5E+C4iTU+C1VRKTilO9ERCTsSptB2Q6ok6A4pDw+Hw15G6DLbcmOpMZasGYB5750LgfveTDPnf0cabXic7T2hg3Bcm4zePVVaNAgLt2KSMnaoVwnYfHr1zB9IPzyCex/BRx6B6TpP54iEhftUL4TEZEQK+selBIWG7+Fr++HDr+HxgclO5oaac2mNfSb0I/M9Ez+N+h/NMiITxWxoAAuvBAWLYLJk6FDh7h0KyIi1cHS52D2H6FWOvz2NWh9WrIjEhERERFJmF0VKLV5cth8+new9ODUbkm4rXlbOfOFM1m1cRVTh0ylTaM2cev7lluCWZP33gvHHRe3bkVk15TrJHnyNsGcK+Dbx6HZ0dBjAtSPX24REYmifCciIqG1qwLlKDP7sQz9uLtfFI+ApBQ/fQTLnoeDb4TMVsmOpsZxdy6eeDEzls/gxXNe5Mi9joxb3y+/DKNGwZAh8Ne/xq1bESkb5TpJjnXz4YMB8OuXcNDfofNIqKXFLSJSZZTvREQktHb1b8FdgJwy9KNf46qaO8y9BuruCZ2uSXY0NdLo90fz7PxnGd1nNL876Hdx63f+/GBp91FHwX/+E+w/KSIJpVwnieUOix+GT66C9EZwbBa0OD7ZUYlI6lO+ExGR0NpVgfIMd5+dkEikdCtehzVT4YgHIH23ZEdT47zw+QvcNOUmLjjkAq4/5vq49fvTT8GhOA0bBrMo69aNW9ciUnbKdZI4ueth9iXw3f+DFidC96egXvNkRyUiNYPynYiIhJbWEVUHBXkwdwTstj/sc3Gyo6lxPvz+Q4a8NoSj2xzNI/0fweI0xTEvDwYMgB9+gPffh1ZatS8iktp+nB2c0r35O+hyW7AiwmolOyoRERERkaRTgbI6+HY8/LoQjnk5ON1TEmbZumWc/vzptNqtFa8MeIU6tevEre8RI+Ddd+Hxx4Pl3SIikqK8AL4cB/OuhXqt4Php0Kx7sqMSEREREQkNFSjDLm8TfHZTcLJn6zOSHU2NsiFnA/0n9Gdr3layL8qmWf1mcev7ySdh3Di48koYOjRu3YqISNhsXQuzhsCKN6D1mdDtMchokuyoRERERERCpcQCpbtrzVEYLLwbtq4KZk/q9JSEyS/IZ9B/B/HF2i9447w36NSsU9z6nj0b/vhHOPZYuOuuuHUrIhWgXCdVavUUmHEe5PwIXf8N+12mXC4iSaF8JyIiYacZlGG2ZTUsvAPanK2lYAk2PGs4r3/9Og/0fYAT9zkxbv2uXAlnnhnsN/nii1Bb/w0UEUk9Bfnw+a2w4FZosC/0fh2adEl2VCIiIiIioaXySJh9Pgryc+A3/0x2JDXKg3Me5J4P7+HKo67kz0f8OW795uTAWWfB+vUwcyY0bRq3rkVEJCw2/xDMmlwzFdpdAEc8AOkNkh2ViIiIiEioqUAZVuu/hMUPw35/hob7JTuaGuOdb9/hL2/8hb779eXuE++OW7/ucNllMGsWvPQSdO4ct65FRCQsfngDZl0E+Vug25PQ4cJkRyQiIiIiUi2oQBlWn14HaZlw8E3JjqTGWLh2Iee8eA6dmnViwtkTSKuVFre+778/OK37xhvh7LPj1q2IiIRBfi58ej18eTc0PgR6vggNOyY7KhERERGRakObJYfRmg/g+1fhoGuhbvxOjpaS/bj5R/pN6Eed2nWYNGgSDes0jFvf2dlw1VVw2mkwcmTcuhURkURanQ2vtgueo18vfRbe7hkUJ/e7DE76UMVJEREREZFy0gzKsHGHuddAvVbQ8apkR1Mj5OTlcNYLZ/HDrz8wZcgU2jZuG7e+lyyB3/0OOnaEp5+GWvpJQESk+lmdDVP6Qf7m4LnzzTB/VPB6xvmQVh96vgR7a4q8iIiIiEhFqEAZNsv/Cz/NgqMeg9qZyY4m5bk7l066lGnfTWPC2RPo1rpb3PretAnOOAPy8+G116Bh/CZliohIIs0cGhQjIXiePzLYZ7JQRmMVJ0VEREREKkHzucIkPxfmXQeNDob2FyU7mhrhtg9u46lPn2JU71EMPHhgXPpcuRJ69YKBA+Hzz+H552HffePStYikCDO7zsw+MrNfzWytmf3PzA6OafOEmXnMY1ZMm7Fm9rOZLTez82Lu9TezD8zMEvGdUlr38cG+0IWii5NpmdDjmcTHJCIiqWXhWJhQO3gu7rWISIoLXYGyRg/aFj8EGxfDoXdAHA9okeL994v/cv171zO482Bu/O2Ncev31lth2jSYNAnuuANOOiluXYtI6ugNPAD0AI4F8oB3zGz3mHbvAC2jHn0Lb5hZf2AwcCIwAnjUzPaI3NsNGAdc6u5epd+kJtizN7QdRJF/bUrLhM4joXnvxMckIhJyNXpcV14Lx8Lcq8Hzg+fsvju/VpFSRGqA0BUoqamDttz18Pkt0PxYaHlysqNJeXNWzOGCVy6ge+vuPHbaY8Tj32kKCuCNN+CRR4KtRNPSYPDgOAQrIinH3U9y9/Hu/rm7zwcuAJoBR8c0zXH3VVGPn6PudQKmuPscd58A/Aq0j9wbAzzj7l9U9XdJaXmb4OsH4bW28O1jQMHO9wuXe6+ekoTgRERCrzc1cVxXEfNG7Px65Zul3xcRSUGhK1DW2EHbwjsg50c49E6o5j8Aht3y9cvpP6E/zRs059WBr1K3dt0K9/Xrr/Df/8Lvfw+tWsGpp0JeXnAvLQ1Gj45T0CKS6nYjyMm/xFzvaWZrzOwrM3vEzPaMuvcp0NXMmpjZ4UA9YLGZdQP6EOQ7qYhN38Hcv8GrbeCjP8PW1Tvf32m592aYOSSh4YmIVAc1dlxXEV3uKP3+oXclJg4RkSQKXYGyGKk/aNv8PXw5FtqdB7sfluxoUtrG3I2c9vxpbMrdxP8G/Y896++56zfFWLwY7r0XTjgB9tgDzjkHXnkFuneH9PQd7XJzYfx4WLUqjl9ARFLVvcA8YGbUtbeAC4HjgKuBI4H3zKwOgLtPBp4BPgKeAC4CNgIPAX8ChprZQjP72Mx6JOh7VF/usHYGfDAAJnaAL++CFsfDCR9A7zd3FCULl3VHv+4+Pmlhi4hUI6k/rquIjd/ChkVACZNUWvWFA65KaEgiIslQHU7xLmnQ9jKwBGgHjCYYtB3u7jnuPtnMCgdtWyh+0HYVsBn4q7vPSNSXKdZnN4EXwCGableV8gvyOe/l8/hs9We8Pvh1Dt7z4F2/Cdi2DaZPD/aUnDQJFi0Krh94IFx1FfTrBz16wBVXFJ38mp8f7El5//1x/jIikjLMbCzQE+jp7vmF1939+ahm883sY2AZcCpBDsTdRwIjo/q6gSBfrgduAboAnYH/Z2bt3T23Sr9MdZSfC9/9P1h0L/z8EaQ3hgOuhv0vh/p772jXe1Jwmnf3J4I9J5t2jbweD837JCt6EZHqJPXHdeWx/gtY8E9YNgEcIv+nqBVvwJf3qEgpIikv1AXKGjFoWzcfvn0COl0NDdolJYSa4tp3rmXiooncd8p9nLxv6ft8/vgjvPlmUJCcPBnWr4eMDOjdGy6/PFjK3aHDzu+ZOTOYNRktNxdmVJ9/TRKRBDOzccBAoI+7f1taW3dfYWbfA/uV0Nf+wO+BQwkGcO+7+0pgpZllAB2B+fGMv1rbujY4nO7rB2DLSmjYEY54ANpfCLXrF23fvA+csbTk1yIiUqIaMa4rq58/gQX/gOWvQFo96HglfHlv6e+ZO1wFShFJeaEtUNaYQdvcEZDRGA66PikfX1M8+smj3DXzLi4/4nL+cuRfitx3h/nzd8ySnDUruNaiRbCEu18/OP54aNCg5M+YO7cKv4CIpBwzu5cgz/V29y/L0H4PYC9gZTH3jGA2yXB3X29mtYD0qHvpQFocw6++1s0PZksueQYKcqDlSXDU49DyRLDqsPONiEj1UmPGdbuy5oOgMLnyLUhvBAf9PShO1t0D6u0VnNZdqFXfYOZkoV3tUSkikgJCWaCsMYO2Ve8ECerQuyCjSVJCqAneW/Ief379z5y0z0ncc/I9269v2QLvvRcUJF9/HZYvD6537Qo33xwUJQ89FGppvCoicWZm9xMcFnAG8IuZtYjc2ujuG82sAcFskf8S5LZ2wD+BNcArxXT5B2Cdu78cef0BcIuZ9QQOAbYBi6rm21QDXgA/vA6L7oHV7wUzVjoMhY5XQKNOyY5ORCRl1ZhxXUncYdXbQWFyzftQpxn8ZgzsdxlkNNrRrtOw4HneiGBseMBVsHBs8LrLHTvui4iksNAVKGvMoM0LgtmT9dvB/kVn9El8fPXTV5z94tns33R/XjjnBVb+UJvXXw8Kku++GxQpGzQIDrwZORJOOQVatkx21CJSA1wWeX435vooghyXT7Bc7UKgMUG+ywbOdfcN0W8ws+bADUSdiuruc8zsnwR5cQNwgbtvif/XCLltG+Db8bDoX7DxG8hsDV1uh30uhjq7Jzs6EZGUVmPGdcXxAvh+IiwYE+xvXG8vOOwe2PcSqJ1Z/Hs6Ddu5EBn7WkQkxYWuQElNGbQtfQ5+mQs9noW0Ogn/+FS1ciUMHAgvvADpDX/i1GdPhfx0ev8wiV7dGvHpp0G7Dh3gkkuCWZK//S3U0T8CEUkgdy/hqM7t97cAJ5Wxr9UEg7rY6/8kGOjVPBu/hUX3wbePw7ZfYY8ewYyVNmdCrfRkRyciUlPUjHFdtII8+O7F4PCb9Z9Dgw5w5MPB/sYa84mIlCp0BcoaMWjL3wqf/h2aHAZtByYtjFT0p9uyef+woZx6xcP80GEMqzO+gyeyeWhFe3r2hDvvDIqSHTsWPXFbRESKt/679Tza7VEunnUxjfZutOs3JIM7rJkaLOP+fiJYGrQdEOzv1fSIZEcnIlLj1IhxXaH8XFjyFHxxWzBjv9GB0P2ZIA/VCt2QW0QklLS7XjJ89W/Y/B0cemdcNuTPXpJNu3vakb0kOw7BVd847n45m4kN+kHjZXxyQF9W15vK0Wsf5/k7e7B2LUyZAsOHwwEHqDgpIlIe7495n40rNzLtn9OSE8DqbHi1XfAc+zp/K3wzHt48FN7tA2unBwcPnL4Mejyj4qSIiFSdvM3BNiL/2wdmXxIcfnrMy9B3PrQ/T8VJEZFy0P9iJlrOz/D5P4KT2VocW+nuspdk029CPzZv20y/Cf2YNGgSfdr3iUOg1SeO5cvhkjHZTN69H2RsDi6m5VPL07l1RCv6tE9IGCIi1Y6ZXQpcCrD33nuX2G7BCwuC5+cX0O8//RIS23ars2FKP8jfHDx3vhnmjwpev3dSsJfXtvXQuDMc9Si0HQy16yU2RhERqVm2/QpfPQCLxsHWNdDsGDjyUWh5omZCiIhUkLl7smMIpZhB2+HLli2LT8efXB0sPzvlU2h8cKW6ii4KFspMz0x4kTLecRR4Advyt5Gbn0tOfg65+bnbHzl5O17/uC6Hp57L5eVZcyjoOQpq5xbpq17tTF4fnJyirYgUz8w+dveuyY5Ddta1a1efM2fO9te37347W3/ZWmL7uk3q8ref/1b1gb3aDjZH5eBadaEgKq60etBrEjTvo0GhiISGcl14xHVcl/MTLLo32Od42zpoeVIwa3/PY+IUrYhI9RHvXKcCZRnEDtoqbOMSmHQAtDsfuj1Wqa6KKwoWql2rNqftfxptGrWp1GeUxfL1y5n41UTyCvKK3EuzNLq17kajuo12KizGFh5j720r2BbXGNs2asvSq5bGtU8RqTgN2sIpNtfNHDuTrKuzSmx/0riT6HZVt6oPLHoGZay0utD7TWjeu+rjEBEpB+W6cKrwuG7LSvhyLHz9H8jbBK3PhIOuh6b6RywiNVe8c52WeCfSp38PNu0/5JZKdzX0taHFFicB8gryeOXLV2hYp2GlP2dXfs35Faf4Ine+5zP7h9n8psVvyEjLoE5aHTLTM4O/a9chIy1j+/XCv2NfR7dLtzrMmpHB889m8OOqOhxxeAaX/zGDLQ0/47KJ/4enFZ3pY3mZjD99fFX/v0FEpFL48RPkAAAgAElEQVTMrAnwJdDD3b+JU5/9gNHAYe5eUN73dx/Wna3rtvL+re8XuffbG3+bmOIkBDMjO98c5FCP+jEsLRM6j1RxUkREKm/hWJg3ArrcAZ2G7Xh94LWQ+wt88xj4Nmg7CA68DhoflOyIRURSTqgLlGEcsFXYT3Ng2YRgCUDmXpXubvzp40ucQZnIZd6lzeSMVxzu8NZbcO218NlncPjh8MLDcOz2LTx70LFpx+KXmV+o5d0iUi1cD7xRmOvMrLhffv7s7g9G7o8Ebi6hr+buvsbdJ5nZKOA84OmKBLVp7aZyXa8Sq7Phs5t3Lk5CMKNy/sjgEBwVKUVEQi+0Y7uFY2Hu1cHfc6+GVe/AyjeD1wv+AdSCff4AB/4NdtsnHmGLiEgxwn6K9/YBm5kNMTMv4bH9iE4zO8LM3jGzX8xsnZm9a2ZHFt5390lAPsGALTHcYe41UKcZHDgiLl32ad+HR/s/WuR6oveg7NO+D5MGTSIzPbNK4pgzB447Dvr2hY0b4fnnYfbs6OJk8XEkYy9OEZGKMLNM4GIgdu+PS4CWUY8no+7dFXOvJTAVmOLua6LajQeuqGhsnz//OQC169XmuH8eR+16we+aC55fUNEuy2/mkJg9J6PyTf7m4L6IiFQH5R7bAZjZ+WY2z8y2mtmPZvZU4b24jO3mxYzPCouTOyKAox5WcVJEpIqFtkBZzIDtBYoOxp4BlgBzIu9pALwFrAB6AN2BlcBkM9stqvtKDdjKbcUbsGZKsEQtPT7Lrgu8gMfmPkbd2nWpW7sukLyiXFUUB7/5BgYOhCOOgPnz4V//goULYcAAqFXCf2oL42jbqK2KkyJSnfQFCoDpMdfXufuqqMeWwhvuvjH6HpAOHAM8EtPHRKCrme1bkcDS66az/2n7c92v19Hz2p78bd3f2K/fftSum8AFGK1P3/F34bLuwiJlWiZ01zYeIiJhV5GxXeR9VwB3EvwwdzDQB3gtpvvKje263FH6/cPuqnDXIiJSdqE9JMfMzgEeAvbwYoKMJLkVwB3uPiZyrSvwEdDB3ZdErrUHvgWOcPfCQubewDJgP3dfvKtYKnVITkEevPkbKNgGpy6AWukV6yfGAx89wOVvXM5D/R5iv933Y+hrQxl/+vikFuWyl2RXOo61a+HWW+HBByE9HYYNg2uugYZVv52miCSIDg7YmZndC3Ry9xOjrjlBjqtDMFh7DHi4pOVrkSXffwVauXtOzL1VwN/dvdTT2eJ2IFw8bVoOrx8IDQ+ArWuh+xPBcu7V2TBzaFCcbK4fo0QkfJTrdlbBsV1j4AfgDHd/u5S+yzy2KzHXZfctZuYk0Kov9H69tC5FRGqsmnRIzjHAx8UlsIhzgfoEv5gVWgSsBf5gZrdGrl0CfAdsX4/m7t+Z2WqgF7DLAmWlLHkS1n8BPV+KW3Hym5+/4Zq3r+GkfU7iksMuwcxCcUp1n/Z9KhzHpk0wdizceSds3gx/+AOMHAktW8Y1RBGRMGpLMNs/2k1ANrAROA64G9iDYJ+tnZhZLeD3wFOxxcmIFUC7OMabGO4w5y/gBdDzRWjQfse95n3gjKVJC01ERMqtImO7E4E0oLmZfQE0AmYDV7v7t4WNKj22Wzi2+OIkBCvhvrwHDriq3N2KiEj5hHaJN8UP2KJdCkxy9+1t3H0D0BsYAGyOPAYAJ0QvjYuo+gFb3ib47EbYozu0OSsuXRZ4AUNfG0p6rXQePe1RzCwu/SZLXh489BDsuy/cdFOw3+TnnwfXVJwUkRqiHrA1+oK73+ruH7j7PHe/GxgFXFPC+08B2gBFNyYObIl8RvWy/GX4YSIccsvOxUkREamOyj22AzoQjFdvAIYBZxJsaZIdmXEZreJju9g9KGPNHV6hbkVEpHzCXKAsMmArZGYHEewv+UjM9XrA48BMoBtwNDAXeM3M6sd0U/UDti/HwZaVcOidEKdC4r2z7mXad9O49+R7ad2wdVz6TAZ3eOUVOPhg+NOfYJ99YPr04NoBByQ7OhGRhPoRaLKLNh8CDc2seTH3LgVmuHtJJ9fsTrC6oPrIXQcf/xWaHAodr0x2NCIiUnnlHtsRjFXTgSvc/S13n01wGM6eQP+YthUf28XuQdmqb+n3RUSkSoS5QFnagO1SYDnBgTjRBgP7AEPd/SN3nxW5tjfBL27RqnbAtnUNfHE7tD4Tmh0dly4X/biI69+7nv779+fC31wYlz6TYfp06NkTzjorqNu++ipMmwY9eiQ7MhGRpJgLHLiLNl0IBnbroi+aWSvgVIoO6grv1yXIi59UPswEmnctbF0NRz0CtcK8G42IiJRRRcZ2hbMpvyi84O7rCWZL7h3TtuJju07D4NC7wdLgsHHBnpOFrw+9O7gvIiJVLsz/1j8XGBJ7MTLYugD4VzGHBWQCTnAaaqGCyLXtxdiEDNjm3wL5W6DLbXHpLq8gj4tevYjM9Ewe7v9wtVzavXAhXHstTJwYLN9++GEYOhRqh/k/hSIiVW8ycLuZNXX3n8ysP9CCYDXAFoITS28hOCQndo/J3wObgBdL6LsbkEPRE8LDa80HsPghOOBq2P3wZEcjIiLxUZGxXWHu6gh8H2nfgODE72UxfVRubNdp2M6FyNjXIiJS5cI8g3Iy0MnMmsZcP4dgg+THi3nP20BD4AEz6xRZLjAeyAfei2pXtQO2X78KBlf7/hEa7h+XLu+acRcf/vAh9/e9nxYNWsSlz0RZsQIuuSRYzp2dDaNHw9dfB9dUnBSRms7d5xNs+j8wcmkbcBlBgfIz4EqCQ3Oujn6fBb9U/QF41t03l9D9oF3cD5f8HJh9KdRvB4eMSnY0IiISP+Ue27n7V8BrwL1mdrSZHUgwtlsDTIpqWv1+jBMRkSJCWx5y9/lmVjhguz/q1iXAZHf/rpj3fBmZeXIzwcDOgXnAKe7+fVTTqh2wfXodpNWFg2+KS3efr/mcm6fczDkHnsOAgwbEpc9EWL8e7rgDxo0LDsP561/hhhtgjz2SHZmISOiMIhiAPejub1F0mVsRkZNQSzw9xsyaEQz8usYtyqr2xW3w60Lo/SbUjt06WkREqquKjO0iLgDGAf8DDPgAOC5mHFe9fowTEZFihbZAGRE9YMsHcPdepb3B3d8mmElZrCofsK2dEZw8esitUK+4swzKZ1v+Ni585UIa1WnEA30fCPXS7pUrYeBAeOqp4LCb0aPhp59g0KDg7w4dkh2hiEg4uftbZnY/0JqoZWuV1B64zN2XxKm/qrV+ISwYA20HQ6uTkx2NiIjEX0XGdhuAiyOPIqrlj3EiIlKsUBcoq92AzR3mDod6LeGA/4tLl2OmjWHuqrm8fO7LNKvfLC59VpVbbgkOu+ncGTZsgOOOg9tvh8O1hZiIyC65+7/i3N9sgqXj4ecFwdLu2g3g8HHJjkZERKpAtRvbiYhIQoW6QAnVbMD2/Svw40w48pG4LE37ZOUnjJ42mvM6n8eZnWIPIQ+XTz6Bhx4KarQbN8KECTBgQHBKt4iISKm+eRTWfgDdxkPdPZMdjYiIVJFqNbYTEZGECvMhOdVLwTaYdy00Ogg6DKl0dzl5OVz4yoU0y2zGfafcV/n4qtDi/9/encdZWZd9HP9cLII+YiKUS+ReiGGJ2mOYJKFokqSlPbaLe7llAy+0tMS1UhhFzTJNJSvTFhVyYdTEXEjFQEXRLPcEBRcQRNl+zx/3GeYwDsOs5z5n5vP2Na/xnPs+97nmx3C+/K57+zfstVfWnATo3j07ktLmpCRpnZbOhZljYdNhsM1heVcjSZIkKQc2KNvKv38Fbz8DO/8MurT+wNRx08bxxPwnuPKLV9J7/d5tUGD7ePxx2GOP7KjJWsuWwdVXw7x5+dUlSaoQM06CVe/B/17uni1JkiSpk7JB2VJzquG6btn35Ytg5inZ84ueavWm//HyPzj/gfM5ctCRjPjoiFZvr708+GB25OSSJdlRk8VWroSzz86nLklShXh5Mrz0Jxj4Y+i1fd7VSJIkScqJDcqWmFMNM0dDWpl9v20XWLkkWzZzTLa8hZYuX8qom0bRb6N+VO/X8u20t7vuym6Cs8kmsPXWsHz5msuXLYMHHsilNElSJVi+CGYcDxvvBAPG5F2NJEmSpBzZoGyJWWPXfLz4P40vb4bT/nYaT7/+NFd98So26rFRi7fTnm6+GUaMgG22ya41+cQT2fUn63/NnJl3pZKksvXo6fDOf7Mby3Xpvu71JUmSJHVYNihbYufzG18+aHyLNnvvC/dy0T8u4rjdjmPvbfdu0Tba27XXwsEHw6BBcM89sPnmeVckSao4C/4B/7oUPnYC9N0972okSZIk5cwGZUsMqILN92942RYjYIeTm73JxcsWM+rmUWzText+NvxnrSywfVx6KXz729l1J++8Mzu9W5KkZlm1HB48Gjb4MHzy3LyrkSRJklQGbFC2xJxqmHtbw8teuRWeuqjZmzzljlN47s3nuObAa9hwvQ1bWWDbSgnOPRdOPBEOPBBuuQU2LK8SJUmVYs54WDgbdrsMuvfKuxpJkiRJZcAGZUus6xqTM5t3sf+7nr2Ly2ZcxsmfPpkhWw1pRWFtLyUYOxZOPx2++U344x+hZ8+8q5IkVaRFz8DjZ8JHDoF+I/OuRpIkSVKZsEHZEvWvQbnFiMaXN2LRe4s4YvIR9O/Tn3OHldepbitXwjHHwPjxcPzxMGkSdPc+BpKklkgJHj4WuvaE3S7OuxpJkiRJZcQGZUsMqIJBEyC6wi4XwtBb6h4PmpAtb6KqqVW8vOhlJh00ifW7r9+ORTfPsmXwta/BlVdmR09ecgl08bdFktRSz14Dr94Ng86H9b3DmiRJkqQ63fIuoGINqFqzEVn/cRPc+syt/Hrmrzn1M6eye7/yuYvpO+9kd+q+/Xa44AIY07wz1iVJWtO7r8HM0fDBIbDdUXlXI0mSJKnM2KDMyZtL3+ToKUcz8EMDGTd0XN7lrLZwIRxwANx/P1xxBRzlPFKS1FqPnAwrlsD/Xg7h4fiSJEmS1mSDMicn3X4Sry15jSlfm0KPbj3yLgeA+fNhv/1g9mz4wx/g//4v74okSRXvldvghetgp3HwgQF5VyNJkiSpDNmgzMGNc27kt4/9ljP2OoNdNt8l73IAeOklGD4cXnwRbr4Z9t8/74okSRVvxRJ4+Luw0QDY8dS8q5EkSZJUpmxQltj8JfM59q/HMmizQZw25LS8ywHgmWdgn33grbdg6lQYMiTviiRJHcJjP4YlL8A+90LX8jhbQJIkSVL5sUFZQikljrv1OBa+t5C7DrqL7l27510Sjz6anda9ciXcfTfsUh4HdEqSKt0bj8DTF8H2x8KH9sy7GkmSJEllzCvVl9D1T1zPn578E2cOPZOdNt0p73J44AEYOhS6d4d777U5KUlqI6tWwINHQ89NYeef5l2NJEmSpDJng7JE5i2ex/G3Hs/uH96dMXuMybsc7rgju+Zk375w332www55VyRJ6jCevgjenAm7XgLrbZx3NZIkSZLKnA3KEkgpccyUY3hn+TtMOmgS3brke2b9jTfCAQfA9ttnR05utVWu5UiSOpLFz2XXnux3IHzky3lXI0mSJKkC2KAsgd88+hum/GsK5w07j/59++dayzXXwCGHwK67wrRpsNlmuZYjSepIUoKHvgPRFXa7FCLyrkiSJElSBbBB2c5eXvQy37v9ewzZcgjf+/T3cq1l4kQ4/HAYNgxqaqB371zLkSR1NM//HubVwCd/Ahv0y7saSZIkSRXCBmU7Silx5OQjWb5qOVcfeDVdIp/hTgnOOgtOPhm+/GX4619hww1zKUWS1FG9uwD+eTL0+TR89Lt5VyNJkiSpgtigbEdX/PMKav5TwwXDL2C7TbbLpYaUYPRoOOMMGDUKrr8eevTIpRRJUoWqnl5Nt7O6UT29eu0rzRwDy96C3X8FXbqWrjhJkiRJFS/fu7V0YM+/9Tyja0az9zZ7853dvpNLDStWwDHHwNVXw0knwYUXQhdb0pKkZqieXs3omtEAq79XDa5ac6V5d8Fzk+Djp8HGO5W6REmSJEkVznZVO1iVVnH4zYcTBFcdeFUup3a/9x589atZc/KMM+Cii2xOSpKap7g5WWt0zeg1j6RcsRQeOhZ6fRQGnl7iCiVJkiR1BB5B2Q5+/tDPmfb8NK4ceSVbfmDLkr//kiXZtSZrarKjJk8+ueQlSJIqXEPNyVprHEk5+yxY/B/Y+2/QtWcpS5QkSZLUQXhMXRv71+v/4pQ7T2HER0dwxKAjSv7+b70F++4Ld94JV11lc1KS1DJj7xi77uVvPgpzLoBtj4BNP1eiyiRJkiR1NDYoW2nWLNh4Y3jsMVi5aiWjbhpFj249uGLkFURESWqYOxf22gsefxyGDoWHH4YbboDDDy/J20uSOqDzh5/f6PIL9vkpPHg09OgDgy4oUVWSJEmSOiIblK30zW/CwoXw9a9np8NNf3k6l+x/CVv02qJkNZx9Ntx7LwwZAs88A1OmwMEHl+ztJUkdUNXgKjadNQFSvQUJNp01ge9v0h3eeBh2mQg9NsmlRkmSJEkdg9egbIVZs+CJXtXw47E8cX8Vp9ZczEfeOYip479BTYlqeOcduOkmSClrlE6eDPvtV6I3lyR1aAtvr4J5wH6jIcialVMnsNGLh8BjO8Lm+8NWh+ZcpSRJkqRKZ4OyFT5/ZnXdpG3PC1i1/H+IW37J/e+W5tRugAULYOXK7P+7d4fbb4eRI0v29pKkDmzpUoAqqqdn15w8f9/zqTrj+3DPSHg1wacugxJdzkSSJElSx2WDsoVG/6maVz9ZaE5C9r37Er5y9u8Yf3BVSWqYOxe23bbu8fLlcPXV8KMfwWablaQESVInUDW4KrtjN8ALN8Art8Au1bDh1rnWJUmSJKlj8BqULVA9vZrq2UXNyVoBEx4fTfX06pLUcfbZsGrVms+tXJk9L0nqGCLiuIh4LiLejYhHImJI0bLqiHgjIl6KiG/Ue93IiLgvWnPHtjnVcF237DvA7HPg/kNh/X7wsRNbvFlJkiRJKlbRDcq8Jm1j7xj7/ubk6o0XlpfA9OmwbNmazy1bBg88UJK3lyS1s4g4FJgInAcMAh4AbouILSNiJPB1YF9gLHBlRPQtvK4XcCFwTEqp/m1ummZONcwcDWll9v3uEfDYj7JlS1+Gpy9u3Q8nSVJBrjvjJElloWIblHlO2s4ffn6rlreVmTOzm+PU/5o5syRvL0lqf1XANSmlK1JKc1JKJwJzge8CA4BpKaUZKaXrgEXANoXXnQf8NqX0ZIvfeVa9nW1zb2t8uSRJLZDrzjhJUtmo2AYlOU7aqgZXMWHfCQ0um7DvhLrrdEmS1EIRsR6wK1BTb1ENsAfwKLBbRPSOiF2B9YF/R8Sngc+R5V3L7byOnW2Dxrdq85IkFeS3M06SVDYqskGZ+6SNhpuUNiclSW2oL9AVeLXe868Cm6WUpgK/BR4GrgEOAxYDlwPfAQ6PiDmFU+X2aPa7D6iCzfdveNkWI2CHk5u9SUmSipXDvE6SVB4qskFJ3pO2gtomZdfoanNSktRe6p+2FrXPpZTGpZS2TyntlFK6ETgFmA4sBM4ChgE/AP5YmAQ23Zzq95/WXeuVW+Gpi5q1OUmSGlAW8zpJUv665V1AKzU6aQPGrV4QcTprTtp2BnYim7Rtk1Kqd7uZpqkaXGVjUpLUHhYAK4HN6j3/Id4/kSMiPgYcQXb9rsOAv6eU5gJzC83J/sDjTX73dV1jcuYYj6KUJLWV3Od1kqR8VeoRlC2dtJ1CdirA31NKc1NKNUDtpE2SpLJRmGA9Agyvt2g42Q0EVivcvfRyYExKaSFZvncvWtad7AiVpqt/DcotRjS+XJKk5nNeJ0kCKrRBmfukTZKk0qgGRkXEURExICImAlsAv6y33pHAWymlvxQe3wcMi4g9yW4ysBx4ulnvPKAKBk2A6Aq7XAhDb6l7PGhCtlySpFZwXidJqlXJp3hXA9dGxEPA/WTXIGnqpO2swqTtE7Rk0iZJUgmklK6PiD7A6cDmwGxgRErphdp1ImLTwvLPFL1uRkT8BLgReBv4VkppabMLGFC1ZiOy/mNJklrPeZ0kqXIblLlP2iRJKoGU0mXAZY0sfxXYuoHnfwL8pP0qkySp9cppXjf+pvHM+9Y8Nrt2M8YcNKY1m5IkNVPFNijBSZskSZIkVbpymNdVT69m2jnT+NTiTzHtnGl02bSLN0OVpBKqyGtQSpKk1ouIYyJiRkTMmD9/ft7lSJKUi+rp1YyuGc3A2QMBGPjEQEbXjKZ6enXOlUlS52GDUpKkTiql9KuU0m4ppd0++MEP5l2OJEkld+ZGZ/L2Hm8zbtw4er7XE4Ce7/Zk3LhxvL3H25y50Zk5VyhJnYMNyrXwqBJJkiRJqmzrmtdN3WMqiZStS6zxPZGYusfU0hUrSZ2YDcq18KgSSZIkSaps65rXHXLGIdzz2XtWNylXv47EPZ+9h0POOKRUpUpSp2aDUpKkChARvSPi1YjYronrnxARk9u7LkmSKlnV4Cr6vjgcyJqStf8B9H1xuDfKkaQSqfgGpRM2SVIn8UPg1pTSfwAiYsuImBIRSyJiQURcHBHrFa1/BbBbRAzJpVpJkpopr7nd9s93AYIV3VZw5953sqLbCiDY7vmurd20JKmJIqW07rXKWERcAPRNKR1eeLwl8HNgGLAU+D0wJqW0rLC8B/AccGhK6d4mvsd84IVGVukLLGjxD9GxOBYZx6GOY5FxHOr0Tyn1yruIShIRGwD/BUamlO6LiK7ALOB1oAroA0wC/pxSOrHodeOBrVJKX2nCe5h1TedY1HEsMo5DHcciY9a1QHvP7daWdZuy6SdWsGLJ6xu+vphe9ONtXu6zuM+G3ej2P6/y6mNt9gNWFv8u13EsMo5DHcci06ZZ162tNpSHwoTtKGBk4XFX4BayCdsQ6iZsAZwIkFJ6LyJ+D5wENKlBmVJq9CKUETEjpbRbC3+MDsWxyDgOdRyLjONQJyJm5F1DBRoBrALuLzzeF/g4WfPxJYCIGAtcGRGnpZQWFdabDNwRERuklN5p7A3MuqZzLOo4FhnHoY5jkTHrmq8Uc7t1ZV3hff0dxnEo5lhkHIc6jkWmrbOu0k/xXtuE7VsppX+mlO4AxgJHR8RGRa+bDHyxEIKSJJW7IcAjqe60h8HAnNrmZMFUoAewa9FzM8h2Rg4uSZWSJLWccztJ6sQqvUHphE2S1BlsBcwterwZ8Gq9dRYAKwvLACgcNbkQ2Lqd65MkqbWc20lSJ1bpDcpymbD9qo220xE4FhnHoY5jkXEc6jgWzbc+8G6959Z2Een6zy8tvL61/HOr41jUcSwyjkMdxyLjODSfc7vy4jjUcSwyjkMdxyLTpuNQ6Q3KcpiwkVLyl7PAscg4DnUci4zjUMexaJEFQO+ix/MompwV9AW68v7J3CbA/NYW4J9bHceijmORcRzqOBYZx6FFnNuVEcehjmORcRzqOBaZth6HSm9Q5j5hkySpBGYCOxY9ng4MiIh+Rc8NB94DHql9IiK2A3oC/yxFkZIktYJzO0nqxCq9QemETZLUGUwly7c+hcc1wBPAbyJiUETsA1wAXFF0B2/Iruf1bErpmdKWK0lSszm3k6ROrNIblLlP2CLiuIh4LiLejYhHImJIa7dZSSLiBxHxcEQsioj5ETElIgbmXVfeIuKHEZEi4tK8a8lDRGweEZMKvxPvRsSTEbFX3nWVWkR0jYiziz4jnouIcyKiW961tbeI+GxETI6I/xb+LoyqtzwiYlxEvBIRSyNiWkR8PKdyy15K6XHgIeCrhccrgS8A75Dd7fR64C/AmHov/RpwRWvfv7NnHZh3a2PemXdmnVnXhnKd25l1Zt3amHVmnVlXmqyr6AZlGUzYDgUmAucBg4AHgNsiYsvWbruCDAUuA/YAhgErgDsjYpM8i8pTRHwaOBp4LO9a8hARG5P9/Quyv48DgBOB1/KsKyenAMcDJwE7AN8rPP5BnkWVyIbAbLKfeWkDy8cCo8l+Nz5F9vtxR0T0KlmFledM4KSI6AqQUnoxpXRASmmDlFKflNKJKaX3alcuTCh2Bn7Rmjc161Ybinm3BvPOvCsw68y6NpHn3M6sW20oZt0azDqzrsCsK0HWRUpru+5wZYiIz5OFyY6FEFvX+gOBu4CPpZQWtvK9HwQeSykdXfTcM8CfUkqd4Rf1fSJiQ7K76B2UUpqSdz2lFhEfIDu95Gjgx8DslNIJ+VZVWhFxHrBXSukzedeSt4j4K/B6SumwoucmAX1SSgfkV1lpRcRi4ISU0jWFxwG8AlyaUjq38Nz6ZGE2JqV0eV61lruIOAm4OaX0QhPW3Zcs56e28j3NugaYd+adeZcx6zJmXdvIa25n1jXMrDPrzLqMWZdp76yr6CMoAVJKtwM/B/qta92CLYBvt0Fzcj1gV7JTD4rVkO1x6qx6kf1evZl3ITn5Fdk/ZP6WdyE5Ogh4MCKuj4jXImJWRJxQ+PDqbO4DPhcROwBExI5ke6NvzbWq/G1DdtH71Z+fKaWlwN/p3J+f65RSurgpzcnCujVt0Jw069bOvDPvzLuMWdcws64F8pjbmXWNMuvMOrMuY9Y1rE2zrkOcL59SurgZ69YPnpZa2x3kXgX2aaP3qEQTgVlkF7XuVCLiaGB74Ft515KzbYHjgAuBn5KdYnpJYVlnu27Lz8j+YfdkRKwk+8w9N6V0Wb5l5a72jpwNfX5+uMS1qHFm3dqZd+adeZcx6xpm1rVQDnM7s27tzDqzzqzLmHUNa9Os6xANypzVP0c+GniuU4iIamBPYM+mnJLRkUREf7Jr1gxJKS3Lu56cdQFmFJ0OMzMiPkp2jY7OFGIAhwLfBr5OdpH3nYGJEfFcSunXuVZWHvz8rBz+WRUx78y7AvMuY9Y1zs/PyuGfVRGzzqwrMOsyZl3j2uTzs+JP8c7RAmAldQU7E8cAAAfCSURBVB3jWh/i/d3jDi8iLiS7QPWwlNKzedeTg8Fke19nR8SKiFgB7AUcV3jcI9/ySmou8GS95+YAne0i45DdaXJ8SukPKaXHU0rXAtV0jospN2Ze4bufn+XPrKvHvDPviph3GbOuYWZd5TDr6jHrzLoiZl3GrGtYm2adDcoWKuxJeQQYXm/RcLK7vnUaETGRbE/CsJTSU3nXk5ObgJ3I9qTUfs0A/lD4/8605+1+oH+95z4GNOm6eR3MBmT/4C22Ej97nyMLs9WfnxHRExhCJ/v8LHdm3ZrMO8C8K2beZcy6hpl1FcKsW5NZB5h1xcy6jFnXsDbNOk/xbp1q4NqIeIjsL+53yC7U/MtcqyqhiPg52XU5DgLejIjazvnilNLi/CorrZTSW8Bbxc9FxBLgjZTS7Hyqys2FwAMRcRpwPTAIOAn4Ya5V5WMKcGpEPEd2KsAgoAr4Ta5VlUDhro/bFx52AbaMiJ3J/k68GBEXAadFxFPAv4DTgcXA73MpWI3p9FkH5l0t824N5l3GrMuYdZXNrMOsq2XWrcGsy5h1mXbNukip015Wo01ExHHAWGBzYDbw/ZTS3/OtqnQiYm2/QGemlMaVspZyExHTgNkppRPyrqXUIuILZNdt6Q+8SHZ9kktSJ/vAiYhewNnAl8gOc59Ltuf1rJTSu3nW1t4iYihwdwOLJqWURhXu/HcGcCzQG3gQOL4T/qOvInT2rAPzrjHmXefOO7POrOsozDqzrjFmnVmHWVdfm2edDUpJkiRJkiRJuens58tLkiRJkiRJypENSkmSJEmSJEm5sUEpSZIkSZIkKTc2KCVJkiRJkiTlxgalJEmSJEmSpNzYoJQkSZIkSZKUGxuUUhNFxKiISGv52qeZ2zqq8Lp+7VVve4iIboW6Ty967pyIWJFnXZKktmHWmXWS1BmYd+adyk+3vAuQKtBXgJfrPfdkHoVIktROzDpJUmdg3kllwgal1HyzUkr/zrsISZLakVknSeoMzDupTHiKt9SGImL9iJgYEU9ExJKImBsRkyOifxNe+62ImFV43cKIeCwijqq3zuci4m8RsbjwdVtE7NjE2j4XEXdGxKLCezwaEaOKln8jIqZFxPyIeDsi/hkR32z2IGTbqoqIORGxNCLeiIiHI+KLLdmWJKm8mHWrt2XWSVIHZt6t3pZ5p5LwCEqp+bpGRPHfnZRSWln4//ULX2cB84A+wPHA9IjYIaX0WkMbjIi9gEnARcBooCuwI9C7aJ0DgT8Dk4Gvk+1gOBW4NyI+kVL679oKjoiDgRuAvwPHAAuAgcBWRattW1jn38AqYChwTUT0TClduY4xKX6vw4CfAWcC9xfG45OFsZAkVQazrhFmnSR1GOZdI8w7lZINSqn5nqr3+H5gT4CU0htkIQFARHQFpgLzgUOBS9ayzcHAgpRSVdFzNUXbCWAicFdK6ctFz08DngW+D4xpaMMR0YUsHB8G9k4prSosurN4vZTS2fVeMw34MPBdoMkhVvhZZqaUzil67tZmvF6SlD+zrnFmnSR1DOZd48w7lYyneEvN9yXgU0VfRxYvjIivRsRDEbEQWAEsJtvT1NipAA8DH4yI30TEFyLiA/WW70C2R+x3kd1trVthT99i4EHgs41se0egH3BlUYC9T0T0j4jrI+K/wPLC16h11L22n2XXwukQe0fEBs18vSQpf2Zd48w6SeoYzLvGmXcqGRuUUvPNTinNKPp6unZBRHwJuA6YDXwN2J0s6N4Aeq5tgymlu8j2wm0N3AQsiIiaiBhYWOVDhe+TqAuY2q/P0/gh9rXL6t+dbrWI2Ai4A/g4cAowpFD3pMbqXourgBOAPQrbfD0i/hwRWzZzO5Kk/Jh1jTPrJKljMO8aZ96pZDzFW2pbXwWeSikdUftERPQENl7XC1NKNwA3RMSGwDCya33cVvjwf72w2ljg7gZe/l4jm15Q+P7hRtb5DPARYHBK6R9FtXdfV931pZQS8AvgFxGxCbAfMIEs3D/T3O1JksqOWWfWSVJnYN6ZdyohG5RS29qA7ND/Yt+mGUcrp5QWA5MjYnuyD//ewJPAS8COKaULmlnTnMJrj4qIqwoh01DdkO21AyAi+gAjm/leayhct+W6iBgMHNaabUmSyoZZV8Ssk6QOy7wrYt6pvdmglNrW7cClETEeuI3sUPrjgUWNvSgiziU7XP9uYC6wJdmh9DMKQUBEnAD8pbDX7o9ke942Izvc/tmU0sSGtp1SWhURJxdec2dEXE625+3jQO+U0llkF4NeTLZnbBzQC/gR8BrZqQlNFhG/Bt4EppNdQLo/2Z3pahp7nSSpYph1Zp0kdQbmnXmnEvIalFLb+iXwE7IP7Slkh8AfALy9jtc9CGxLdke2OwrbuIuivVwppcnAXsBGwK/J7iD3U7JrmDzY2MZTSn8p1NIVuBqYTHYB6BcKy+eRXSB6PeAvwLmFn+UPTfmh67mPLLx/SRZcPyC73skRjb1IklQxzDqzTpI6A/POvFMJRcNHBEuSJEmSJElS+/MISkmSJEmSJEm5sUEpSZIkSZIkKTc2KCVJkiRJkiTlxgalJEmSJEmSpNzYoJQkSZIkSZKUGxuUkiRJkiRJknJjg1KSJEmSJElSbmxQSpIkSZIkScqNDUpJkiRJkiRJufl/a446vzZMNGEAAAAASUVORK5CYII=\n", "text/plain": [ "<Figure size 1584x792 with 6 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "DEBUG cov: 50\n", "\u001b[31mDEBUG multi-mapping contig (callclass=58, mappings=2), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=45, mappings=2), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=155, mappings=2), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=111, mappings=2), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=91, mappings=2), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=26, mappings=2), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=177, mappings=2), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=313, mappings=2), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=146, mappings=5), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=434, mappings=3), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=621, mappings=3), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=596, mappings=3), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=1553, mappings=3), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=254, mappings=2), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=1546, mappings=3), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=988, mappings=4), FALSE call\u001b[0m\n", "\u001b[32mDEBUG multi-mapping contig (callclass=597, mappings=2), TRUE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=1064, mappings=2), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=900, mappings=2), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=1394, mappings=2), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=1316, mappings=2), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=1381, mappings=2), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=784, mappings=2), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=1385, mappings=5), FALSE call\u001b[0m\n", "\u001b[32mDEBUG multi-mapping contig (callclass=561, mappings=3), TRUE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=1609, mappings=3), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=1499, mappings=2), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=573, mappings=2), FALSE call\u001b[0m\n", "\u001b[32mDEBUG multi-mapping contig (callclass=844, mappings=3), TRUE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=1032, mappings=3), FALSE call\u001b[0m\n", "\u001b[32mDEBUG multi-mapping contig (callclass=1145, mappings=2), TRUE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=1489, mappings=4), FALSE call\u001b[0m\n", "\u001b[32mDEBUG multi-mapping contig (callclass=1157, mappings=3), TRUE call\u001b[0m\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 1584x792 with 6 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "delta = 10\n", "linestyle = '-'\n", "coverage = ('10', '20', '30', '50')\n", "legend_placement = (1, 4, 4, 4)\n", "xmaxes = (725, 180, 120, 110)\n", "#coverage = ['30', '50']\n", "#legend_placement = [4, 4]\n", "#xmaxes = [120, 110]\n", "for cov, legloc, xmax in zip(coverage, legend_placement, xmaxes):\n", " print('DEBUG cov:', cov)\n", " seaborn.set_context({'figure.figsize': (22, 11)})\n", " matplotlib.rcParams['axes.labelsize'] = 16\n", " matplotlib.rcParams['xtick.labelsize'] = 14\n", " matplotlib.rcParams['ytick.labelsize'] = 14\n", " \n", " categories = [\n", " ('SNV', None, None, 'SNVs'),\n", " ('INDEL', 1, 10, 'INDELs 1-10bp'),\n", " ('INDEL', 11, 100, 'INDELs 11-100bp'),\n", " ('INDEL', 101, 200, 'INDELs 101-200bp'),\n", " ('INDEL', 201, 300, 'INDELs 201-300bp'),\n", " ('INDEL', 301, 400, 'INDELs 301-400bp'),\n", " ]\n", " fig, ((ax11, ax12, ax13), (ax21, ax22, ax23)) = plt.subplots(2, 3)\n", " axes = (ax11, ax12, ax13, ax21, ax22, ax23)\n", " seaborn.set_context({'figure.figsize': (24, 12)})\n", " \n", " for i, (category, axis) in enumerate(zip(categories, axes)):\n", " vartype, minlength, maxlength, label = category\n", " with kevlar.open('SimulatedVariants_chr17_hg38_markII.bed', 'r') as instream:\n", " variants = subset_variants_bed(instream, vartype, minlength, maxlength)\n", " index = populate_index_from_bed(variants)\n", " \n", " kevlar_truecalls = roc(\n", " load_kevlar_vcf(\n", " 'kevlar-'+cov+'x-binomscore-nohomopoly-noabundfilt.vcf.gz', index, delta=delta,\n", " vartype=vartype, minlength=minlength, maxlength=maxlength, debug=(i==0),\n", " ),\n", " index, delta=delta, fmt='vcf'\n", " )\n", " scalpel_truecalls = roc(\n", " load_scalpel_vcf(\n", " 'scalpel.'+ cov +'x.denovo.indel.vcf', cov=cov,\n", " vartype=vartype, minlength=minlength, maxlength=maxlength,\n", " ),\n", " index, delta=delta, fmt='vcf'\n", " )\n", " discosnp_truecalls = roc(\n", " load_discosnp_vcf(\n", " 'discosnp.'+ cov +'x.vcf.gz', cov=cov, applyfilters=False,\n", " vartype=vartype, minlength=minlength, maxlength=maxlength,\n", " ),\n", " index, delta=delta, fmt='vcf'\n", " )\n", " gatk_truecalls = roc(\n", " load_gatk_mvf(\n", " 'JointCall-'+ cov +'x-PBT.mvf',\n", " vartype=vartype, minlength=minlength, maxlength=maxlength\n", " ).iterrows(),\n", " index, delta=delta, fmt='mvf'\n", " )\n", " triodenovo_truecalls = roc(\n", " load_triodenovo_vcf(\n", " 'JointCall-'+ cov +'x-TDN.vcf', cov=cov,\n", " vartype=vartype, minlength=minlength, maxlength=maxlength,\n", " ),\n", " index, delta=delta, fmt='vcf'\n", " )\n", " \n", " if i > 2:\n", " xmax = 10\n", " \n", " doplot(axis, gatk_truecalls, 'blue', 'GATK PBT ({}x)'.format(cov), linestyle, '^', 6, xmax)\n", " doplot(axis, triodenovo_truecalls, 'green', 'Triodenovo ({}x)'.format(cov), linestyle, 'D', 5, xmax)\n", " doplot(axis, scalpel_truecalls, 'purple', 'Scalpel ({}x)'.format(cov), linestyle, '*', 9, xmax)\n", " doplot(axis, kevlar_truecalls, 'red', 'Kevlar ({}x)'.format(cov), linestyle, 'o', 5, xmax)\n", " doplot(axis, discosnp_truecalls, 'orange', 'DiscoSnp++ ({}x)'.format(cov), linestyle, 'X', 8, xmax)\n", " \n", " nvariants = len(index.trees['chr17'])\n", " ticknums = [0, math.ceil(nvariants * 0.25), int(nvariants * 0.5), math.ceil(nvariants * 0.75), nvariants]\n", " ticklabels = ['{:d}%\\n({:d})'.format(round(tn / nvariants * 100), tn) for tn in ticknums]\n", " \n", " _ = axis.set_xlabel('False calls', fontsize=16)\n", " if i > 2:\n", " _ = axis.set_xlim(-0.25, 10)\n", " _ = axis.set_xticks(list(range(0, 11, 2)))\n", " else:\n", " _ = axis.set_xlim((-5, xmax))\n", " _ = axis.set_yticks(ticknums)\n", " _ = axis.set_yticklabels(ticklabels)\n", " _ = axis.set_ylabel('True calls', fontsize=16)\n", " _ = axis.set_ylim((-5, nvariants))\n", " _ = axis.set_title(label, fontsize=18)\n", " if i == 0:\n", " _ = axis.legend(fontsize=14, loc=legloc)\n", " \n", " _ = plt.subplots_adjust(hspace=0.3, wspace=0.3)\n", " _ = plt.savefig('five-callers-'+ cov +'x-combined-sep.pdf', dpi=300)\n", " _ = plt.show()" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "DEBUG cov: 10\n", "\u001b[31mDEBUG multi-mapping contig (callclass=10, mappings=2), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=200, mappings=4), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=1328, mappings=3), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=8388, mappings=2), FALSE call\u001b[0m\n", "\u001b[32mDEBUG multi-mapping contig (callclass=1125, mappings=3), TRUE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=1701, mappings=3), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=2934, mappings=3), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=1081, mappings=3), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=10240, mappings=9), FALSE call\u001b[0m\n", "\u001b[32mDEBUG multi-mapping contig (callclass=1800, mappings=3), TRUE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=4744, mappings=8), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=1425, mappings=2), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=638, mappings=2), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=6446, mappings=2), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=227, mappings=2), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=1036, mappings=5), FALSE call\u001b[0m\n", "\u001b[32mDEBUG multi-mapping contig (callclass=156, mappings=2), TRUE call\u001b[0m\n", "\u001b[32mDEBUG multi-mapping contig (callclass=834, mappings=4), TRUE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=1354, mappings=3), FALSE call\u001b[0m\n", "\u001b[32mDEBUG multi-mapping contig (callclass=1423, mappings=3), TRUE call\u001b[0m\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 1584x792 with 6 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "DEBUG cov: 20\n", "\u001b[31mDEBUG multi-mapping contig (callclass=21, mappings=2), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=201, mappings=7), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=73, mappings=3), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=357, mappings=2), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=603, mappings=2), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=1019, mappings=2), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=1142, mappings=5), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=468, mappings=2), FALSE call\u001b[0m\n", "\u001b[32mDEBUG multi-mapping contig (callclass=406, mappings=2), TRUE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=1405, mappings=3), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=1192, mappings=2), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=1907, mappings=2), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=832, mappings=2), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=1204, mappings=2), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=617, mappings=3), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=1543, mappings=2), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=420, mappings=2), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=843, mappings=3), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=653, mappings=2), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=1012, mappings=5), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=1107, mappings=2), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=2188, mappings=2), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=1893, mappings=2), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=1437, mappings=2), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=21, mappings=2), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=75, mappings=2), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=209, mappings=2), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=167, mappings=2), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=228, mappings=4), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=240, mappings=2), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=329, mappings=2), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=337, mappings=3), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=387, mappings=2), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=173, mappings=3), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=576, mappings=3), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=999, mappings=3), FALSE call\u001b[0m\n", "\u001b[32mDEBUG multi-mapping contig (callclass=814, mappings=2), TRUE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=834, mappings=2), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=913, mappings=3), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=1381, mappings=3), FALSE call\u001b[0m\n", "\u001b[32mDEBUG multi-mapping contig (callclass=1416, mappings=2), TRUE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=1832, mappings=2), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=811, mappings=2), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=748, mappings=2), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=1184, mappings=2), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=590, mappings=5), FALSE call\u001b[0m\n", "\u001b[32mDEBUG multi-mapping contig (callclass=371, mappings=3), TRUE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=979, mappings=4), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=388, mappings=2), FALSE call\u001b[0m\n", "\u001b[32mDEBUG multi-mapping contig (callclass=1627, mappings=3), TRUE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=628, mappings=4), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=2059, mappings=2), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=822, mappings=3), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=2335, mappings=2), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=1087, mappings=2), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=1412, mappings=2), FALSE call\u001b[0m\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 1584x792 with 6 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "DEBUG cov: 30\n", "\u001b[31mDEBUG multi-mapping contig (callclass=45, mappings=3), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=8, mappings=2), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=43, mappings=2), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=101, mappings=2), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=5, mappings=2), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=141, mappings=7), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=164, mappings=6), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=165, mappings=5), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=123, mappings=2), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=383, mappings=3), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=306, mappings=2), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=272, mappings=9), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=171, mappings=4), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=390, mappings=2), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=1189, mappings=2), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=1297, mappings=2), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=693, mappings=2), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=1006, mappings=2), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=1376, mappings=2), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=669, mappings=2), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=631, mappings=3), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=774, mappings=3), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=1333, mappings=2), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=1074, mappings=5), FALSE call\u001b[0m\n", "\u001b[32mDEBUG multi-mapping contig (callclass=1087, mappings=3), TRUE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=1677, mappings=8), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=1893, mappings=3), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=45, mappings=3), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=6, mappings=2), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=43, mappings=2), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=101, mappings=2), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=255, mappings=2), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=11, mappings=2), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=105, mappings=2), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=164, mappings=5), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=122, mappings=2), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=305, mappings=2), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=382, mappings=3), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=384, mappings=3), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=317, mappings=3), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=784, mappings=3), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=170, mappings=4), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=389, mappings=2), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=1382, mappings=2), FALSE call\u001b[0m\n", "\u001b[32mDEBUG multi-mapping contig (callclass=855, mappings=2), TRUE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=1187, mappings=2), FALSE call\u001b[0m\n", "\u001b[32mDEBUG multi-mapping contig (callclass=1396, mappings=2), TRUE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=1394, mappings=2), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=1395, mappings=2), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=1055, mappings=2), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=1003, mappings=2), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=1293, mappings=2), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=1142, mappings=3), FALSE call\u001b[0m\n", "\u001b[32mDEBUG multi-mapping contig (callclass=1085, mappings=3), TRUE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=1373, mappings=2), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=692, mappings=5), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=1668, mappings=2), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=668, mappings=2), FALSE call\u001b[0m\n", "\u001b[32mDEBUG multi-mapping contig (callclass=783, mappings=3), TRUE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=630, mappings=4), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=1328, mappings=4), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=1739, mappings=2), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=773, mappings=3), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=1839, mappings=5), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=1652, mappings=4), FALSE call\u001b[0m\n", "\u001b[32mDEBUG multi-mapping contig (callclass=1540, mappings=3), TRUE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=1073, mappings=5), FALSE call\u001b[0m\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 1584x792 with 6 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "DEBUG cov: 50\n", "\u001b[31mDEBUG multi-mapping contig (callclass=58, mappings=2), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=45, mappings=2), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=155, mappings=2), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=26, mappings=2), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=17, mappings=8), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=177, mappings=2), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=314, mappings=2), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=146, mappings=5), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=434, mappings=3), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=254, mappings=2), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=622, mappings=3), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=237, mappings=4), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=1064, mappings=2), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=1548, mappings=3), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=1394, mappings=2), FALSE call\u001b[0m\n", "\u001b[32mDEBUG multi-mapping contig (callclass=598, mappings=2), TRUE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=784, mappings=2), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=988, mappings=4), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=1381, mappings=2), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=594, mappings=10), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=1317, mappings=2), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=1610, mappings=3), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=1385, mappings=2), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=574, mappings=2), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=1032, mappings=2), FALSE call\u001b[0m\n", "\u001b[32mDEBUG multi-mapping contig (callclass=844, mappings=3), TRUE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=1578, mappings=5), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=1500, mappings=2), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=1490, mappings=2), FALSE call\u001b[0m\n", "\u001b[32mDEBUG multi-mapping contig (callclass=1157, mappings=3), TRUE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=58, mappings=2), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=45, mappings=2), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=155, mappings=2), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=111, mappings=2), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=91, mappings=2), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=26, mappings=2), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=177, mappings=2), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=313, mappings=2), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=146, mappings=5), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=434, mappings=3), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=621, mappings=3), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=596, mappings=3), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=1553, mappings=3), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=254, mappings=2), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=1546, mappings=3), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=988, mappings=4), FALSE call\u001b[0m\n", "\u001b[32mDEBUG multi-mapping contig (callclass=597, mappings=2), TRUE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=1064, mappings=2), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=900, mappings=2), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=1394, mappings=2), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=1316, mappings=2), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=1381, mappings=2), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=784, mappings=2), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=1385, mappings=5), FALSE call\u001b[0m\n", "\u001b[32mDEBUG multi-mapping contig (callclass=561, mappings=3), TRUE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=1609, mappings=3), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=1499, mappings=2), FALSE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=573, mappings=2), FALSE call\u001b[0m\n", "\u001b[32mDEBUG multi-mapping contig (callclass=844, mappings=3), TRUE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=1032, mappings=3), FALSE call\u001b[0m\n", "\u001b[32mDEBUG multi-mapping contig (callclass=1145, mappings=2), TRUE call\u001b[0m\n", "\u001b[31mDEBUG multi-mapping contig (callclass=1489, mappings=4), FALSE call\u001b[0m\n", "\u001b[32mDEBUG multi-mapping contig (callclass=1157, mappings=3), TRUE call\u001b[0m\n" ] }, { "data": { "image/png": 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tRtqE7Wvvxw7mfNjZIuNjLDO7GZtwuQboBKwCLjbGvFTLn0WpZKF9naoz7b+Aqv3XsVQt2RW+gelk7JiyJqdR9cHZjWHfVyYkjTE7RORkbGLyD9i6sd8DFxljqqy8wyYvJ2BX8l2C/d/jdiJ2xw7zCPaB3rXYh24/AjcYY/4dpW2JE/uDzvUE+7vgxn2YAapcJm6W3HCeTFxjjHnWeX8Q9mnD8caYL8PavQlsN8b8RkQuB/4NtAw9TXNqlBRhEyiTotzndqCTMeYPIvI1cJuTwT8LuMsY0ze+P6lSSqmmSvs6pVRtid2leBIwyBgz391olKo97euUUvEiIvOB7saY7i6HohpYstWQXAcUEFZkXESaYZ8Qh2qLfIbNzIcXEu8PNGfv+iOhz/fBFlC+1TnkwS7DAbsxSFrDha+UUkrVSPs6pZRSjZ32dUoppaqV8CXbTvHvPOetBzhQRI4GfjLG/CgiDwF/EZHl2JoCE7DFjl8CMMb8ICLvAP8Rkd9hp+r+B5htjFkRcS8B/oudxhuqJ/IxcKWI/ICt7xD3nbeUUko1LdrXKaWUauy0r1NKKbUv3Jgh2Q9bn24xkA381fk+VAPin8ADwGPYreG7AEMjNuO4GLvj2hxs/YZvsDUKIo3DFkoO33XpTmxNkq+AIFoAVSmlVMPTvk4ppVRjp32dUkqpenO1hqRSSimllFJKKaWUUqppSbYakkoppZRSSimllFJKqUYs4TUkU4WIjMMuDaB58+Z9+/Tp43JESqlUUeQtYv2u9XRv3Z0WWS3cDifpLFq0aLsxpoPbcSjt65RSKl60r0se2tcppVR87Gtfp0u2a6Ffv37mq6++cjsMpVQKmLduHsOnDKfUX0pORg6zR89mUI9BbodVa1u2wEUXwbRp0LlzfO4hIouMMf3ic3VVX9rXKaVUw9G+LjlpX6eUUg1nX/s6XbKtlFINJDwZCVDqL2X4lOHMWzfP5chq7+674eOP7atSSimllFJKKRUPri7ZFpE2wHLgJGPMmgTe934g0xhzXaLuqdxT7Ctm9srZTP9+Oh+s/QB/0O92SCqBgkEIVEBFAEwwjjfyBCGtHGTvw6X+Uk57ejhZM2aT9mNyz5Q0BsrK7PeTJsFtt8VvlmRT4mJfdw12N9OzE3VPpZRSTZOO65RSStWV2zUk/wy8ZYxZIyJHAbcApwDtgR+B/wH/MubnNIKICHA9cCXQA/gJeM4Yc4tz/hfA34HeQA6wAfifMeb+sPtOBNaIyEPGmLVx/hlVPfkCPj5Y+wEvf/8ys1fOpshbVK/r+IN+giZIl9wu/OqwX9Eqq1UDR6pqo7QUVq6ClStg82ab/Io3AwQDgMABB0CnTvG717dp/8Un5dFPZpSS9avLGFe6Pn4BNID33oOlS50kbsDOknzsMbejahTq1NeJyJ3AHTGu1ckYU+i0q7Y/BJ4C/iwiA4wxH8XlJ1NKKaWsuvZ13YF1Ua5zpjHmnciDInIKMB9Ybow5POyUjuuUUipFuZaQFJEc4ApghHOoL7ANuATbaR2PHUxlAPeGffRfwHDgJmAp0AroEna+GHjYOVcKnAz8R0RKjTGPAxhjtonIHOAq5zpxFQwGWb16NVu2bKFLly7k5eXh8TTO1fJffQXjxtmkRiQjAcyBH2EOeRnTeybkbKv+YhK0s87KWyKrRsCe/esXVDCTtLVDKNx4MpO0SoFrKirsa69ecPUZkJ2dmPv26AHnnBPfZCTAvHXD9lquHS4nI4eZoycxqEd8Y9gXW7bAo4/aZCSAz6ezJBtCPfu6+4EnIy41FTChZKSj2v7QGOMVkZeA6wBNSCqllIqLfRjXAZwBfBP2/qco128DPA98AHQNP5focZ1SSqmG4+YMyWFAEPgEwBjzTMT5tSJyLHAeTsclIr2Ba4EjjTE/hLVdHPrGGLMIWBR2bp2InAsMAB4PO/6Gc924dlzBYJDJkyeTn5+Pz+cjMzOTrl27MmbMmEaVlPR64a67YOJEm7wYPx7Cf7wNMp+30q9gp6wh3WSTZ86irelV/UWN0NV/Ij08Q0jvnbVvAZ6wbx9X+655cxg+HI44AkRqbp9qBvUYxOzRs6skJVNlY5u77/45GRmisyQbRJ37OmNMMfbhGgAicgC2D7sk7FiN/aHjDeA9EckxxlTNlidI+e5yZo6dyTnPnkOzVs3cCkMppVR81LmvC7PDGFNQw/WfBp7DFsY5P8r5hIzr6kL7PaWUqpmbCckBwCJT/TbfLYGdYe9HAmuBM0TkTeymPB8CN0XMGqkkIscAJwF3Rpz6AugqIj3jWedk9erVbNq0Cb/f1i30+Xzk5+ezevVqevWqISHnCAajL2/dsgVmzIDp0+HTTxOzBLYml10GDzwArVvb98W+Ym55/xZe/PIx8trm8cSgqQzvNZzmmc3dDVSpOIhMSqZKMhLgs8/srMhwPp/93aL2SX36uki/BXYBM8KO1bY//Arb1/fHzixxxYo3VrBi5gpWzlrJkWOOdCsMpZRS8bEvfd2rItIMWAU8aIx5JfykiFwNdAYuAG6Lce2EjOvqQvs9pZSqmZsJyW7AllgnnadoY4GLww4f5HzuIuecwS5tmyUi/SNqTW4COmB/xr8aYyKXv212XrsDceu4tmzZUpmMDPH5fBQUFNSYkCwvh9tvh4ceAn81+7AccQTcdFPilsDGMmAADB7883tjDIOfH8yX+V9y/QnXc+/ge8nJyHEvQKUSIJSUvOz1y5g0clJKJCMBFkfOq1MNpT59Xfh5D3A58Lwxxht2qlb9oTGmVER2Y/s61yx+ZnHlqw7MUse8dfNS7neZUsoV9enrioHx2FmVFcDZwDQR+Y0xZrLzuSOwNZVPNMYEJPYSm4SM6+pC+z2llKqZmwnJbGBrtBPOUrQ3gYeMMeEzQjxAFnCJMWal0/YSYAVwHLAwrO0AIBc4EZgoIuuMMS+EnS8LiyNuunTpQnp6OhWhAnpAZmYmnWsoyvb553a24fLlMGaMrbsXqXlzOOss6N27oaNuGHPXzeWL/C944qwnuLLflW6Ho1TCDOoxiPU3rHc7DJUc6tPXhTsTOAC7GUC4uvSHZcS5r4vm+dOfZ90Hdr8C8dhB5MZPNvJX+SsAPQb34NL3L010WKqW5q2bVznbe/iU4Skz21sp5Yo693XGmO3YWsghX4lIe+D/gMkikoWtnzzeGBNt85twCRnX1WSvfi9N+z2llKqJmwnJ7UCbyIMi0geYB0wN2yk0ZAtQERp8OVZhn6odSNgALKzjWioinbBLtsMTkm2d1xp2Vtk3eXl5tGvXjq1bbR8dqiGZl5cX8zPffAMnnwxdu8KcOTBkSDwjjJ9HvniE9jntGXv0WLdDUUopt9Snrws3DvjUGLMs4nit+0NsfxfXvi6aAX8ZwKbPNuEv9WOCdhVfwBcAICMng1MnnJrokFQthScjAU1KKqVqsq99XchC4DLn+y7AocAkEZnkHPPYy0oFMMwYM8c5npBxXaTIjUtP/vPJP/d7Ae33lFKqJm7uqrIY28lUEpFDgfnAdGPMH6N85hMgXUR6hh07CJtY3VDNvUIzScIdDvixO5PGjcfjoX///gCccMIJnHfeeTVuaPP++7Zu5GefpW4yct3Odbyx4g1+3/f3NEvXQs5KqSarPn1dqN1+wFnYnUkj1ao/dM43A76uZ/z11mNQD86fdj7pzfZ+9pneLJ3zp51P94HdEx2SqoV56+Zx1ktn7bU5F/yclJy3bp5LkSmlkli9+7oIR/Pz0u984AjnWOjrSWC18314leuEjOvCBYNBJr/wAjOmTWP+/PnMmDaNTzZ+zLlTz9V+TymlasnNhOS7wCEi0g5ARA7DPkGbD9wrIp1DX2GfeR87qHpGRI5xNqx5Bvs07SvnOteKyHAROdj5+i22PsnkiPsPAD5KxK6jHTt25JRTTmHgwIH06tkTz1tv2a1rZ8+229hGWLQIDjjAzpBMVY99+Rge8ehSbaVUU1efvi7kcqAEeDnKuRr7Q8cAYK0xZlVD/UB1Ub6rHE+6/VND0gTxCJ50D+W7yt0Ip8nbsgV+8QsoiLGf7ZKCJQx7aRhlFWVRz5f6S7ns9cuinlNKNWl17utE5Dci8msROUREeovIeOAPwCMAxhi/Mea78C+gEPA674vD7p+wcV3I6pUryV+zBl/QbmHgCwbJX7OGDfnrfu73PNrvKaVUdVxLSBpjlmJ3RLvIOXQB0BG4EPtkLPwr9JkgMBzbGS3Adn6bgJFhG9qkAROBJdhB2R+AW4A/R4QwmuizThpcly5dGDx4MM0yMuCXv4QLL4Q77oDRo+37iKTkokXQt28iIouPEl8JTy9+mvMOPY/9W+7vdjhKKeWa+vR1YNejYXfXfjHaAKuW/SEksK+L5uunv8ZX6kM8Qp9z+tDpqE74Sn2Vxf5VYt19N3z8sX0NV+ov5eb3bqbff/uRnZ5Nuid6RZ+cjBwmjZwU9ZxSqumqb18HTMCO1750Pnu5MebBeoSQ8L5uy0cf4YvYZMcnwuYPPsRX6iO9WTr7n7i/9ntKKVUNN2tIAvwV+LeIPGmMuRNb57Faxpgt2E4u1vmHgIequ4aInAUEgFfqEmx9lZWVYYwhZ+5c+PRTKHNmHhQXw8KF8PbbMHw4AHv2wMqVcMkliYis4fgDfnaU7QBgytIp7CrfxXXHX+dyVEoplRTq09cZoEcNbartD0XkcOyytl/VJdiG1KxVM4beN5S5f5lLm4PacP608/n8oc/58aMf3Qoppa1aBTfeaF9rI+jxkj9gJP6cDRjA7wGugic8MOffkO78Fbi9dDvbS7dzxTFX8M8h/2RJwZK9akiCTUZqDUmlVDXq1NcZY54DnqvLDaJdN9HjupAuW7aQ6ffjy/q5Klim389+PxXS577f8cUjX9CmZxtGThqp/Z5SSsXgakLSGPOOiDwG7E/1NSAbWnPgMmNMRY0tG8C8efNYunQpN/t8PycjQ0pKYMmSyoTkYufhWSrNkDTGMGDSABbm/7yHwjGdj+GkA05yMSqllEoOLvZ1+wGXGmN2J/Cee7lopp0s8/4t7yNpgifNw0k3nsRJN2r/UBd+P9x/P/z1r9CsGQwdChETc6Ja1ea/lHR5l/2KRrB7Rzb+EsCAeMA0hyOdvzUyPBlccewVDOw+EIBBPQYxe/TsyqSkJiOVUjVpKuO6kLxjjqHrq6+yrls3ADJ9ProWFDDkhlF4RvTno3s/IrNFpvZ7SilVDbdnSGKMediFe0arxxU3Xq+XZs2agdNh7aV5czj66Mq3XzmVv1IpITlr5SwW5i/kuuOv45AOhwAwuMdgpDajJaWUagJc6uvm1NwqMUzA4Elzs2x1cjHGPoB89VWYORPWr6++fUUFeL1w/vnw8MPQpUvN9yjxlXDQw/cwsMNAXhzyOj17CjglzILA5mz4eC10jla9lJ+Tkpe9fhmTRk7SZKRSqkZNYVwX4hk2jIvGj+fv3brRfd06+i9ZQl6HDniGDQPAV+Qjq2XknqpKKaXCuZ6QbAq8Xi9ZWVmwcaM9kJ0N5eU2GXnCCXDmmZVtQxvadOzoUrB1ZIzhzvl30rNNT/71y3/FrDullFKqaTLGYIKmssh/Ktq9O+oedHViDCxb9nMScsMG8Hjg1FNtOemanuENGgRnnVX7+z288GEKSwqZeeFM7rlHCAb3Ph8I2FqSjz1WzT17DGL9Detrf1OllGoqPB72tGkDIhzdsye9LrjAjunS0qjwVhDwBchqoQlJpZSqjmaPEsDr9ZKVmQmPPw4DBsD//Z9dpn300ZUdV0iqbWgza+UsFhcs5tmRz2oyUimlVBUmYAC7y3aqWb8err7alnpuKFlZMGQI3H47nH02tG/fcNcO2Vm2k39++k9G9BpB/wP6c/Vn4PPt3cbns2WtlVJK1cM337Bzh62f32bsWDjwwMpTviL7CzezRaYbkSmlVMrQDFIClJeX07K01I5s7rvP1ot0akaGS7UNbcJnR1585MVuh6OUUioJBQN2al4yLtkOBuGHH2yNxkhz58Jtt9mZixMmQIcO+36//fazsyFbtNj3a0XaVrKN/KJ8AJ5Z/Ay7yndxz2n3AD/Xp1ZKKdVAXniB9rt3M/SUU+gQ0UF4i7wAOkNSKaVqoAnJBDjxxBM3IJbUAAAgAElEQVTJ/NvfYP/94ZxzYrZLtQ1tdHakUkqpmoRmSCbbku3ly2HcOPjoo9htzjrLLm4Im/iSlN5f+z4jp47ca1fs0YeP5shOR7oYlVJKNVKBALz0Em1OOon+gwdXOR2aIak1JJVSqnqaRUqAozp0gOnTbbGm9Nj/k6fShjYzvp/BVW9eRV7bPJ0dqZRSKqZghZ0hmegl23v22CXJkUuVwZZH+cc/bCnnhx6Kvudc+/Zw8sm1283aTbNWzOL86efTp30f7vjFHXjEgyC6CY1SSsXLBx9AQQEF559Ps127aN269V6nvXvsDEldsq2UUtXThGQCFK5aRYtmzcg+7LBq26XChjbbS7dzzVvXMG3ZNI7tciyTR03W2ZFKKaViiveS7e3bYf58u1cc2ETkm2/C++9HT0aGXHgh/Pvf0KlTXMJKiClLp3DpzEs5pvMxvDPmHdpmt3U7JKWUavzmzoX0dF4vKqLFW2/x61//eq/TumRbKaVqRzNJcVZRUcET8+dz2nHHMaBVq2rbpsKGNle/eTUzl8/k7kF3c/PJN5ORluF2SEoppZJYwB+AXrDWt5a2y1uTt2oVniVL4JhjqmzsFqmiAl55BZx9A/ZSUgLvvAMffkiVHaR79IBrrrFLriMmrgCQmwu9eu3jD+aircVbueHdG5j63VQGHDiA2b+eTcuslm6HpZRSTYPXi8nOZufu3RwQZYq9bmqjlFK1ownJOPN6nSdk5eVQTUIyVTa0Wb59OcMOHsaEUye4HYpSSqkkFwwGmf7GdDgPVpWtYsNLy+m6aRNjnnsOT04OnHACvPtu1KTk11/DFVdUvyHLoYfCrbfCiBHQrp09lpFhaz4m+1Lr+thavJVpy6Zx5/w7KfGX8NeBf+Xmk28mK11n4SilVML4/ZS1aIHX66VNmzZVTlfOkNQakkopVS1NSMZZubOGLMvrhZaxZy988419PfbYRERVfwXFBZx0wEluh6GUUioFrF69mi1bt4AzJvOlpZHfuTOr8/LotXIlLFwIb78Nw4dXfsYYu7v13/9uS5hMmwaDopRDTEuDtk1ghXIgGGDSkkk8u+RZPt34KQbDqd1O5T/D/0Of9n3cDk8ppZoev59dTiIyakJyjy7ZVkqp2tCEZJyFZkg283qrnSG5YoV9PfTQRERVP/6An+2l2+mc29ntUJRSSqWALVu24K/w73XMl5FBQefONiFZUgJLluyVkFy2DP72N7joIrvDdZSxXpPxXeF3/G7W7/h80+cc0fEI7vjFHYw6ZBRHdDwCaYxTQJVSKhX4/ex0nohFS0hWLtnO1SXbSilVHU1Ixlltl2yvWAFZWXZTm2S1rXQbBqMJSaWUUrXSpUsXMtIz9kpKZvr9dC4osG+aN4ejj97rM6+9ZpdbP/BA001GlleUc8+Ce5j4yURaN2vNC6Ne4OIjLtYkpFJKJYOKCg7cvp0LLriAtlGm6nuLvKRnp+NJj89mbkop1VhoQjLO2rVrx3Cvl/Z79tiMYwwrV8LBB1db2991BcV2AKkJSaWUUrWRl5dHp3ad2PTjJsiCzIoKuubnk7dqld1Z5oQT7MY2YWbOhBNPhC5dXAraZR+u/5Bxs8excsdKLjnyEh745QO0z2nvdlhKKaVC/H5aVFRwaIylbb4in9aPVEqpWtDHNnHWsmVL+v70E7kZ1e9GvWJF8u/4qQlJpZRSdeHxeBhx6giYAYe2P5TzBg1izAsv4Dn3XJgypcqGNhs22M1sRo1yMWgX3bPgHgY+NxB/wM+7Y97l+VHPazJSKaWSjd/Pqv33Z/PmzVFP+4p8Wj9SKaVqQROScbZnzx42e70Eq1muXVEBa9ZA794JDKwethZvBTQhqZRSqvZMwMBB0C6nHb1atMBjDIwda+tGRiwLeP11+3rOOYmP023lFeVM/GQiw3sN57urv2Noz6Fuh6SUUioav5+3+/bls88+i3rau8dLZgutH6mUUjXRhGScLVmyhKe6d8dUk5Bcv94mJVNlhmSn5p1cjkQppVSq8Pv9cCLs8u6CXbvswdato7Z97TU47DBbwqSpmbtuLsW+Yq7udzU5GTluh6OUUiqGYEUFu3NyaB2jL/MWeXWGpFJK1YImJOPM6/WSHgiQ1rJlzDahHbZTISHZKqsV2RnZboeilFIqRQT8AQDS0tNg9257MMogbscOWLCgac6OBHjth9dokdmC03qc5nYoSimlqrHH4yHo8UTdYRvskm2dIalU4zBv3Ty6P9SdeevmuR1Ko6QJyTjzer1k+f3V7rC9cqV9TfYl2wUlBbpcWymlVJ1U+CsASEtLq3aG5KxZEAw2zfqRgWCAN1a+wbCDh5GVrrNqlFIqme1Mt/vCxkpIeou8uqmNUo3AvHXzGD5lOBt2b2D4lOGalIwDTUjGmdfrJcvrhRpmSLZtC+3aJTCweigo1oSkUkqpuvH7/UBEQjLKQ7rXXoMDDoBjj01kdMnhs02fUVhSyKg+TTAbq5RSKWZnpp39GDMhqTUklUp5M3+YyVkvnUWpvxSAUn+pJiXjQBOSceb1eskqK6txhmSyz44ETUgqpZSqu0BFAAJhCUmPB3Jz92qzezfMmWOXa4u4FKiLXvvhNTLTMjnz4DPdDkUppVQNDs3P57fffkvLGBNOdJdtpVJXQXEB/Z/uz6iXR1FWUbbXOU1KNjxNSMbZKSefzOlz5tSYkEz2+pGgCUmllFJ11zyjOdwNeV3ybEKydesqWccpU6C8HC65xKUgXWSMYeaKmQzuMZiWWbFXUyillEoOzcrK2N/rxeOpOpQOBoL4S/06Q1KpFOGt8FLqL6XUX8qM72dw+OOHs3DTwpjtS/2lXPb6ZQmMsHHThGScHdimDQetXRszIVlcDPn5yZ+QLPWXsse7RxOSSiml6iQYCAJhm9pEqR/51FNw1FHQr1+io3Pf0sKlrN25lnP6NNHdfJRSKsWsa92axR07Rj3nK/YBaA1JpVLA018/Te7fc2l+b3Oa39uc86efT7dW3Xlq+DM0S8uJ+pmcjBwmjZyU4Egbr3S3A2js1n7/PS3bt6d9jITkqlX2NdmXbG8t3gqgCUmllFJ1UlRSBCNhZ+lOuoVmSIb5+mv79cgjTXO59szlMxGEs3uf7XYoSimlamFp166s3m8/jolyzrvHC6BLtpVKct8Vfsc1b1/DifufyNBuI5g1C776sANffzOGK4IZ0L0b/Ho4ZJZWfibd5DB79GwG9RjkYuSNiyYk4+zluXM56rjjODNGjZHQDtvJPkOyoLgA0ISkUkqpuikpL4FjoLyi3C7ZjnhA99RT0KwZXHyxSwG6bP76+fTdr6/2r0oplSICIqQZE/Wcr8jOkNQl20olrzJ/GRe9chGtslox/sBX+OMVndiwAcb9DvY/N9RqEHc9Pxv/BU5S0peDZ8ZsDrlSk5ENSZdsx5ExBq/fT1Z5ecwl2ytW2BkheXkJDq6ONCGplFKqPioqKgBIT0//uYako6QEXnwRLrgAYmxW2uhtKd5C99bd3Q5DKaVULQWAtBjnvEU6Q1KpZHfd7PEs27aMA756nlFDOiECCxbAk0/ChAn2a/NmkA2D4KXZsKubfV03iLvvdjv6xkVnSMaR3+8HIMvrjZmQXLkSDjwQsrMTGVndaUJSKaVUfexVQzIiITl9OhQVwe9+51Z07issKaRjTvRaZEoppZJPQCRmQjI0Q1JrSCqVOD4fzJljN0mcMwe83qptTHopFQfNpqLPFCoOngmf3sj2lUP5y1/g5pshN3fv9p99Zq/L+kHw0Hp7H+DTT+P90zQtmpCMo/LycgCaVZOQXLEi+Zdrg01IesRDh5wOboeilFIqhVTOkMxMr7KpzVNP2RrKp5ziVnTu8gf8/FT2Ex2ba0JSKaVSRXUJyVANSV2yrVTDCwTsTMapU+Gtt6CszB4vLbXft20LI0ZAm7aGbzIf58usv1OBzcn4pZiAeMkJdqZf4CYm3nE3A/rHrl++eHGCfqgmThOSceR1UvNZXi9EqSFpjJ0heemliY6s7gqKC+iQ04E0T6zuVymllKrKBA2UQYZ47HRIJyHp9dqnzBMmNM3NbAC2lW4DoFNuJ5cjUUopVVtnv/cewfPOi3pOl2yrpmj9epg2za582bw5fvcpLbXPtps3h2HDILTZfUYGDBkCp58OheWbuPz1y5m39j0Gdh/IYR0OAyA7PZthBw/j1G6nak4jiWhCMo5atWrFJZmZdFy/PuoMycJC2LMnRWZIlhTocm2llFJ11jm7M0yEDqOc2SJOf5ifb9/26OFSYEmgsKQQQGdIKqVUCsndvRvSoic0dFMblUzKyuC222DGDDsZKh6CQdi40X5/4olw1lnxe9CclgaDBtl7NG8eFoMJ8tnGz7jx/am88O0L+IN+njjrCX7f9/dIU33qnSI0IRlHmZmZHFRcbH8ThP+LcSxbZl/79ElwYPVQUKwJSaWUUnVXWUOytMgecGZIbtpk3x5wgBtRJYetxVsB6NRcZ0gqpVSqWNynD1lZWRwa5VzlDEmtIalctmgRXHIJ/PADnH12fDcP7NMHLrwwvg+ZP1j7Abd+cCsfbN3FhGf2Prfbu5vCkkKapTdjRK8R/O20v3Fwu4PjF4xqMJqQjKOdO3eyubycg9u1IzNKZn7RIvt67LEJDqweCooLOKT9IW6HoZRSKsUUFBXABeDb/ZM94CQkQ0/Tm3JCUmdIKqVU6vmib19aZmZGT0ju8eLJ8JCepcPsVPDjj3DDDbaMWmOzYgV06gTvvgtDh7odTf2V+cu49YNb+ffCf5PXNo/j9juuSpuMtAyGHDSEs3ufTcusqqXyVPLS35RxtG7dOma1bMkNHToQbdL+okXQrRu0a5fw0OrEGKMzJJVSStVLsa8YDgNK9tgDEQnJ/fd3J65ksLXEzpDUhKRSSqWIYJBAWhrpMZaB+op8Wj8yBRgDkyfDNdfYJcdDhza+etZDhsDtt8d3ZmRDKfYVM3bmWNbuXFvl3NaSrWwu2sw1x13DxCETycnIcSFCFS+akIyjyk1tmjWLev6rr6Bfv0RGVD+7ynfhC/g0IamUUqrOAsEAAJllJfZAWEKydWvIzXUrMvcVlhSSlZalT/OVUipVVFRQkZ5OmscT9bSvyKf1I5NMaSm8+Sa8/PLPD0NLSuC77+CUU+C55+Cgg9yNsam77u3rePWHVxl28DA8sve/rR5tenBl3ysZ0nOIS9GpeNKEZByFEpKZOVWz+Lt2wZo18NvfJjqquisoLgDQhKRSSqk6CwRsQjK9uNgeCKsh2ZSXa4NNSHZs3lELriulVKrw+wmkpcVMSHqLvFo/Mgn4fDBnDkydCq+/DsXF0LkzHHmknQnZujVcfjlcd13M/YlUgkz9biqTlkxiwoAJ3H3a3W6HoxJME5JxVF5eTqbfjyfKDttff21f+/ZNcFD1EEpIatF9pZRSdRUM2k1t0ot22wNOn7hxoyYkt5ZspVOu9q1KKZUyakpI7vHqku04Cwbhk09ssjFa7Udj7Fh7505o2xZGj7Zfp56qycdks27nOn4/+/f0378/dwy8w+1wlAs0IRlHXq+XLJ+vcvAVLrShTSolJHWGpFJKqbryGA/sgvTMIjstoaVdnrxxIxx/vMvBuaywpFD7VqWUSiUVFfzh0UfxTJwY9bSvyEezNtHLdanYjLFLp195xX5fXbulS+0qi+xsOOooiJYbHj4cLroITj8dMnUFfVIoryjnndXv8OoPr7KjbAcAy7cvB+Cl814i3aOpqaYo6f5fF5H1QLcop94yxpwlIncCkenzrcaYyr/oRWQ8cBMgwERjzL/Czh0DTAWONsaUNXD4exk4cCDH33wzDB5c5VyqbGgDmpBUSilVfwemHcjKh1YiV++xyUiPh7Iy2L69aW9oAzYheWSnI90OQyml4qIxjesq+f1kl5fHzHJ5i7y0OrDqZBQV29atMG4cvPEG5OVVVnaJqW9fmDgRRoyAFi0SE6OqH3/Az9x1c5ny3RReW/4ae7x7aJfdjh5tegB2U79HznyE7q27uxuock3SJSSB44DwydRdgEXAy2HHVgADw94HQt+IyJHAXcBwbMc1W0TmGGOWikga8BTwh0R0Wq1atqTVmjUwalSVc199lRqzI8EmJDPTMmndrIbeQSmlVK00ykFaDCZgpzrInt171Y+Epr1k2xhDYUmhlkNRSjVmjWZcV8nv54PBg+kRCBBtHxRfkY/MljolrzY2boRp0+Cf/4Q9e+CBB+D666PPeFSpI2iCfPzjx0z9birTv5/O9tLttMxqyag+oxh9+GgGHzRYZ0OqSkn3X4IxZlv4exH5LbAHmB52uMIYUxDjEn2Ab40xc53Pf+scWwrcAHxnjHm/wQOP4rvFi8k+8EB6RizZTqUNbQAKSgronNtZi+4rpVTDaXyDtBg2+DfAeSC7NSEZbrd3N76Aj47NO7odilJKxUVjGteFGJ+PjwcMIL2iImpCUmtIVm/rVrsse+pU+Phje+yUU+DJJ+Gww9yNTdXP018/zfwN8wEIBAMs2LCA/KJ8stOzGdF7BKMPH80ZeWfQLF1LGaiqki4hGU5sBuy3wGRjTGnYqYNEJB/wAQuBPxtj1jrnlgK9RORA7CCtF/CdiHQHrgH6JSh8PvzoIzr27VslIZlKG9oArN+1nv1bNvF1dUop1YAa4yAtluJgsZ0Lumj3XhvaQNNOSG4t3gqgCUmlVJOQ6uO6kIDPB0BalN1RjDH4in1kttAZkpG+/RbGj4cPPrCb0hx2GNxzD1x4oV2mrVKTt8LL9e9cT2ZaJm2y2wDQd7++3HfYfYzoPYLczFyXI1TJLqkTksAQoAfwv7BjC4GxwHKgIzAB+FREDjPG7DDG/CAifwbec9rf6hx7B/gLMEBE7gI8wARjzMx4BV9eXk6W11tlU5tU2tDGGMOywmWce8i5boeilFKNUmMZpMUSDAYhALJrly2ezM8Jya5dXQzMZYUlhQC6ZFsp1VSk9LguJOD1ApCWXnUY7S/1Y4JGZ0hGWLYMTjsN0tPh1lvtZjOHH+52VKohfPTjR5T4S5h6/lSG9xrudjgqBSV7QvJ3wJfGmCWhA8aYt8MbiMjnwFrgN8ADTpsngSfD2oxxvn0fWAn0x3Zcn4hIL2NMYUMHHgwGKff52Na+PSuDQfKCQTxOQYxU2tBmW+k2dpTt4NAOh7odilJKNVaNYpAWS9DYhCRhS7Y3brR9YE6OW1G5L5SQ1BmSSqkmImXHdeEqqklI+ors7MmslpqQDFm50u7vmpkJCxbobMjG5q1Vb5GVlsWg7oPcDkWlqKRNSIpIR2Ak8Ifq2hljikVkGXBwjOu0A+4GBgEnAquMMT8451YBJwCzGjB0gsEgL7zwAhXBIJv2358Z69fTdfJkxowZg8fjSakNbZYVLgPgsA5a1EMppeKkUQzSYgkShCC2gHJYDcmmvFwbYGuJXbLdKVdnSCqlGrdUHtdFqm7JtrfIJit1yTb4fPDuu3D11XaJ9rx5moxsjN5a9RYDuw+keWZzt0NRKSqZ97AaC3ixu4TGJCLNsHWztsRo8gDwiDFmPfbnzQg7l8nemwo0iNWrV5Ofn2/feDz4AgHy8/NZvXp15YY2/ZJmMV31lm1zEpIdNSGplFINLWyQ9lR17YwxxUBtBmlXEjZIM8YsA0KDNFdkBDKQnVSZIdnUE5KhGZLtc9q7HIlSSsXdWFJ0XBepRVoaE+6+m6M7d65yzrvHJiSb6pLtQMDWiLziCujUCc4+2x57/3045BC3o1MNbc1Pa1ixYwXDDh7mdigqhSXlDEmnntYVwFRjTFHEufuxT75+xC5juw1oDjwX5TqnA4cClzuHvgR6i8gIbCfWG/iioePfsmULfr9/r2M+n4+CggJ+/LEXkDozJL/f9j2tslrRJbeL26EopVRjNJa6DdLmxWhSOUgTkaNxYZAWS/fS7pTO2gLG7LWpzcknuxVRcthavJV22e1I9yTln2JKKdUgUn1cVyWOigrSAgHIqpp0DC3ZbmozJDdvhn/8A15+2e6inZsLo0bZWpGnn26Xa6vG5+3VdjGPJiTVvkjWv4IHYmeBjIlybn9gCtAe2AZ8DpxojNkQ3khEsoHHgIuMMQEAY0y+iFyJXeImwO+NMZsbOvguXbqQmZmJz5nSD5CZmUnnzp2Z7uyfevzxDX3X+Fi2bRmHdTwM+7eEUkqphtLYBmmxmIAh22NnjdC6NSUlsHOnzpAsLC3U5dpKqaZgICk8rou0p7iYj4cNo6/XS+Rv8NCS7aZUQ3LaNLjqKigpgREjYPRoGDYMsrPdjkzF25ur3qRXu17ktdW1+Kr+kjIhaYyZh+1Yop27qJbXKMMOwiKPP0eUAV1DysvLo2vXruSvXYvPGDKzsujatSt5eXksXAi9e1euWktqusO2UkrF1UAa0SAtlrU5a/H+wmPTq61bV+6w3eQTkiWFuqGNUqrRS/VxXaSi0lK+PP548vz+KgnJyk1tmsCS7WXL4G9/gylT7ESbF16AXr3cjkolSqm/lHnr5nFVv6vcDkWluKRMSKY6j8fDmDFjWH3LLRQsXkznxx4jLy8PEQ8LF8IZZ7gdYe3oDttKKRU/jW2QFktJWgmeNs6b1q3ZtMl+29QTkluLt3Jsl2PdDkMppVQdBCoqAEjLyKhyLlRDsjEt2S4thalT7QxIgB07YMYM+O47SE+Hu+6CW2+136umY966eXgDXl2urfaZ/uqIQUTGAeMADjzwwDp/3uPx0GvHDnr98EPl46ING6CwEE5wbWuButEdtpVSSu0rI4b0oB3A0bo1G7+x3zb1hKTOkFRKqcTY13FduICzT0BalMKIlUu2G8kMyYUL4dJLYeXKvY+ffDI8+iicf77dvEY1Lb6Aj6nLppKTkcOp3U51OxyV4jQhGYMx5r/AfwH69etn6nWR3bsrC/iD/aUOKZSQdHbY1hmSSinVODXkIC2WIEHSAwH7plWryiXbXbvG5XYpobyinN3e3ZqQVEqpBGiQcZ0jlJBMj5KQ9BX5QCCjedXZk8muogLeeMMOXwG+/x4efBD22w/mzIFjnQn9mZnQooV7carEWrljJZ/8+AkABsPnmz5nxg8z+KnsJy496lKy0htH8l25RxOS8bR7N7RsWfl24UK7IdsRR7gYUx0sK1xGq6xW7NdiP7dDUUopFQcNOUiLeQ8iZkhuhI4do25Q2mRsK9kGQKfmOrVEKaVSSbCigrSKiuhLtou8ZLXISrnNQFetsjMhP/987+OXXgoPP7zX/BrVhHy1+SsGPz+YPd49lcdyM3MZ2Xskow8fzdCeQ12MTjUWmpCMpz17oE2byrcLF9qnS1EeqCWl77d/rztsK6WU2ifNypvRYqdTfKpVKzZt0uXahSWFADpDUimlUkyvrCwm3HMPXH555TFjDEsmLWHx/xbTpmebaj6dXIyBJ5+E8ePtQ8LJk+GUU+y5rCzo3Nnd+JR7vin4hqEvDKVddjsWjF1A62Z2R96OzTuSnaFbqKuG42pCUkTaAMuBk4wxaxJ43/uBTGPMdXG90e7d0L07AH4/LFoEV14Z1zs2mNAO26P6jHI7FKWUSmmNvq+rwQE/HsDBX74JubmQns7GjXDwwW5G5L6tJVsB6JSrMySVUo1Dk+nrnCXbODMkS3eU8sblb7DijRV0H9idkc+OTEgY+2rzZptTffddGDoUnnmmaZdSaeo2F23my/wvASirKOPat68lNzOXub+ZS/fW3d0NTjVqHpfv/2fgrVCnJSL/FpGvRKRcRNZH+4CI/EpElohIqYhsEJGborT5dVibAhGZLCLhz3gmAmNF5KB4/FCVwmpILl0K5eWpUz+ysKSQHWU7OKyjbmijlFL7qLKvE5GjRGSKiGwUkTIRWSEiN4nIXv2xiBwhIh86bfJF5HYJm64uIvNFxET5WhZ2mcT0dTUwAUOWKYPWrQkG7QZvOkNSZ0gqpRqdBh/XiUgXEXlJRJaLSEBEno1ymYT2detLSph5zjmUOrWRP73/U1bOXsnQB4Zy6QeX0rpb60SEsU9efhkOPxwWLIDHHoN33tFkZFNljOHZJc/S59E+nDPtHM6Zdg6jZ4wmw5PBB5d+oMlIFXeuzZAUkRzgCmBE2GEP8BxwBFClKIGInAm8BFwHvAMcAjwlImXGmEedNicDLwDjgZlAJ+Bx4EVgMIAxZpuIzAGuAqokNBtEIAA//WQrAs+ezRcbzgTSUiYh+f227wHdYVsppfZFlL6uL7ANuAT4ETgeeArIAO51PtMSeA9YABwH9AaeBUqAfznXORcILwCSBSwFXg4dSEhfVwtr89bSfEcXDtm+h5UrobgYjjnGrWiSw9ZiO0NSE5JKqcYgXuM6bN+2HfgHzgZskRLd1233+fjm6KMZ7DwjLN9VTna7bPr/sX+8b90gHn0Urr3WTpJ5/nno1cvtiJRbtpduZ9yscby2/DVO7XYq9552LzkZOQD0bNuTllkta7iCUvvOzSXbw4Ag8EnogDHmWgARGU+Ujgs7gJtljHnceb9WRP4O3CwijxljDNAf2GSMedBps05EHgEeibjWG9jBX8N3XIEADBkCPh988gmMHs2pLU6gU/t36d49rcFvFw+6w7ZSSjWIvfo6Y8wzEefXisixwHk4CUngYiAH+I0xpgz4TkQOAf4kIg8Y66fwi4jIxUBzIPL68evrasEYgzfHS6CZB1q35osv7PHjj3cjmuRRWFJITkYOuZm5boeilFINIS7jOmPMemzCEhE5v5r7J6yvq3BmRqY3awZAsCKIJ93tRYe187//2WTkyJEwfXrlqnPVRI2bNY43V73JfUPu448n/pE0T2rkKVTj4uZvzwHAIieJWFtZQHnEsTJgf6Cb8/4ToIuIjBCrPXAR8FbE574AuopIz7qHXoO336Zy1AVQXEy3goVc1f1tUmV/GN1hWymlGkRt+rqWwM6w9/2Bj5xkZMi7wH5A9xjX+B3wtjFmY8Tx+DhDuB8AACAASURBVPV1tRAMBgHI8HsrE5K5udCnjxvRJI/C0kKdHamUakziNa6rrYT1dQEnIZmWlQWAqTApkZCcPBnGjYMzzoBp0zQZ2dT9uPtHXl/xOuP7j2f8SeM1Galc4+Zvz27Aljp+5l3gHBEZKiIeEekF3Oic6wJgjPkMGI1dou3DLo0T4DcR19rsvHave+g1WLwYSkr2OtTMlHBqyyUNfqt4MMbwzpp36H9Af91hWyml9k21fZ0zO3Is8ETY4c7A1oimW8PORV6jF/AL7NLvSPHr62ohNHDLqvg5IdmvH6Q18b97NxdtpktuF7fDUEqphhKXcV0dJKyvCzgP2tJSZIZkWRn86U9wySUwaBC8+qrdQVs1bf9d9F8AxvWNWglBqYRx87dnNlWfitXkKeBh4HVssvFzYKpzLgAgIoc6be7G1uo6AzuA+0/EtUIzTxp+3/pjjrFTQMKU0py0vkc3+K3iYdGWRazftZ5fHfort0NRSqlUF7OvE5HewJvAQ8aYGRGnI2eZSIzjYGdHbnGuFSl+fV0thBKSmf5yAs1b8s03cNxxbkSSXNb8tIaD2ri615BSSjWkuIzr6iBhfV1aIEB2aSkeZ4phsiUkd+yAZcvs19y59iHggw/CH/4As2ZBtit/Dahk4gv4eOrrpxjeazjdWtd1MrJSDcvN357bgTZ1+YBTS+RmIBf7JK4zdoo+wHrn9VbgC2PMfcaYb40x7wJXA5eISPi+nm2d1231jD+2M8+0lYJzc0GEQHYuCzmB0l+c2eC3iofpy6aT7klnZJ+RboeilFKpLmpfJyJ9gPnAVGPMLRGnC6g6EzK0vnevmZMikoldATDJGFMR5f7x6+tqQURotuP/2bvv8KiqrYHDv51JJgVC6IQqUhWCFBFEVLqClGtDQbBgBbte9bNeUdQriAg2rBcQEBQvl6YiRVBQVEDA0AWBCIFAIAnpmbK/P87MMGkwCTNzJmS9z5NnMqfMWaBk5ayz9t5R1Eg7ztHcWAoKZP7IfHs+SRlJNK9hyih6IYQIhEDd1/kqaLmuu83GU5MmocKM2+hQKkj++CM0aWKsoJ2QAH36QHo6fPedsZhNTIzZEYpQMH/HfI5mH+X+zvebHYoQpi5qswljmFqZaa0dwCEApdRwYJ3W+qhrdwzFn6q533uPP04AbBirkvqXxWL85P/2W9i8mU32Dlz90gDW1gz9MWpaa77c/iV9m/WlZnTNM58ghBDidIrlOlcn//fAl1rrx0o4Zx0wXikVpbV2d5z0wxiStr/IsdcCtYFPS7l+4HKdD6Kioqi7sRZtd27j1+Y3A1KQ3J++H42meU0pSAohzhmBuq/zVfBync1WaALGUClIrlsHAwfCeefB2LGgFISFQe/eUKNMpWJxrnt//fs0r9Gcfs37mR2KEKYWJL/DuOGqpbU+DqCUaoHxlKwBYFVKucc4b9daF7gWqBmK0VUSCYxyve/h9bmLgY+VUmNc16gPTAZ+11oneR13BcaiATkB+dNZLDBoEAwaxJ9zjGXnqlcPyJX8yj1c+4UrXzA7FCGEOBcUynVKqbYYxchVwGtKKU8npNb6iOvbz4EXgelKqVeAVsDTwEslLBhwL7BSa/1XKdcPbK7zgaXAqKn+lVKFevWgceMznHCO25u2F0A6JIUQ55JA3dfhdV41wOl6X6C13u51WNBy3QalODRwIO5xZKFQkNy40RigFx8PK1ZAA1mTVBSRkpWCUzvZc2IPa5LWMLHfRMKU+YV0IUwrSGqtE5VSv2GsgP2ea/MnFE5Cm1yv53OqK+Q24A2Mbsd1QE+ttWdJa631dKVULPAg8CaQgXHj91SREIZj3PAFXEaG8RoXF4yrnR33cO1rL7jW7FCEEKLCKyHXDcUYfn2z68ubcp2ToZTq5zp+A8YK3G8CkwodrFQzoLfrs0sTtFxXkuPHj/N37xPszmjJzoNVueQSo2ujMtt7wlWQlA5JIcQ5IlD3dUXOcxsMHKDwAjZBy3XJYWHsbXrq0mYXJLWG664zGl9WrpRipCjuqeVP8cbPb3jeR4VHcUeHO8wLSAgvZnZIArwETFFKfaC1dmite57uYK11KtDtTB+qtX4HeKe0/UqpgRjDuL8qW7jlU1EKklpr5m2fJ8O1hRDCv7xz3Vhg7JlO0FonAlee4Zi/OM1c0MHOdSWx2WzYYzROi4U9R6pW+uHaYHRIVomoQr0q9cwORQgh/ClQ93WnfYwV7Fzn0BqLa6VtML8gefQo/P03TJlizB8phLfElETeXPcm111wHVc3vxqAC+tcSK2YWiZHJoTB1IKk1nqpUuo9oBHGk65gqQKMKmUBAL/LyDBGcIf6RMIbD29kX/o+nr/yebNDEUKIc0ZlyXUlca+ybXE4yEQKkmAUJJvVaIaq7K2iQohzSmXJdaFWkNy503ht3dq0EESI0lrzyNJHqB5VnU+GfCINRyIkmd0hidb6bROu+WUwr5eRYXRHhvq9x/wd82W4thBCBEBlyHUl8S5IZmEM2a7s9p7YS+vacucohDj3VIZcV2JBMsK8guSuXcarFCRFUf/b+T9W7V/FuwPelWKkCFkyk2kQuAuSoW7bsW1cWPtC+YElhBDCL7wLkjUaVaVmJU8vTu3kr7S/ZEEbIYSooKJtNuJycz3vze6Q3LULoqJkuLYoLM+exz+X/ZOEugnc1/k+s8MRolSmd0hWBhWlIJmcmUzDag3NDkMIIcQ5Ijo6mhp7s6mSnU2zLlXMDsd0yZnJ5DvypSAphBAV1JCDB2HHDs/7UChItmoFYdJmJLy8v/599qfvZ8WtKwgPk5KPCF3yoysIKkpB8tDJQzSoKkuzCSGE8I/4+HhaLj5I7dRULr6yqtnhmE5W2BZCiArOZoOICM/bUChIynBt4U1rzaebPuWyxpfRp1kfs8MR4rSkIBkEFaEgaXfaSclOkQ5JIYQQ/pWXB8CV10hBcm+aqyApHZJCCFEhfd2wISsTEjzvzSxIFhTAvn1SkBSFbTqyie3HtnPrRbeaHYoQZyQFySCoCAXJlKwUnNpJg1jpkBRCCOEf27Zt4/eH25JWowYNWklBcu+JvViUhSZxMtmXEEJURAerVOGo142dmQXJvXvB4ZCCpChs1h+zsFqs3NT2JrNDEeKMpCAZBBWhIJmcmQxAw1jpkBRCCOEfJ08WYI8Jx+lUYLWaHY7p9qbt5bzq5xFhiTjzwUIIIUKOQyksXu/NLEjKCtuiKLvTzueJnzOw5UBZqFZUCFKQDDCt4eTJ0C9IHso8BCAdkkIIIfxm2zZjlW3CwkEpc4MJAXvT9spwbSGEqMAcShVaFVYKkiKUrPhrBSnZKYy8aKTZoQjhEylIBlhWFjidoV+Q9HRIyhySQgghzkbKKljQFFJWsXWrg6bR+6j+8nFjeyW394QUJIUQoiILtQ7J+HioVs2Uy4sQNOuPWdSIqsHAlgPNDkUIn0hBMsAyMozXUC9IHjp5CIuyUCemjtmhCCGEqAi8Co+e9/+Nh1UDIOcAevUg2lk+Y3iDz7HUdMDqQZW6KJmWm0ZaXpqssC2EEBVYrcxM4mw2z3uzC5LSHSncsgqy+N/O/3FT25uIDI80OxwhfBJ+5kPE2agoBcnkrGTqx9bHEmY588FCCCEqt5RVRoHRkWO8tnsREl8y3rsoRw63tPsP1jDXjZsjB9aNgmv3mxOzyWSFbSGEqPhuXbMGmp/6OW52QfKGG0y5tDDR2qS1PLPyGfLt+YW2ZxVkkWPLkdW1RYUiBckAcxckq1c3N44zOXTykMwfKYQQwjfrRp0qPjpyihUj3aKteafeWGKg2/TgxBeC9p4wCpItarYwORIhhBDlZrNBxKmFycwqSB4/bnxJh2Tlciz7GEPnDcWiLFxU76JC+2rH1KZX015c1vgyk6IToux8Kkgqpa4BamqtZ7neNwRmAQnAd8C9WuvidyKi4nRIZibTqlYrs8MQQgjTSK4rg27TTnVIQonFSG+6IAzVZSzU6xnw0EKVu0OyWY1mJkcihKjsJN+V3yd9+tDB6aSz671ZBUlZ0Kby0VozauEo0nLT+PXuX2kf397skIQ4a77+9HwRaOT1/i3gAuBLYADwLz/Hdc6oKAXJQ5nSISmEqPQk1/mqXi9jmLYlpsgO5foqstXqhMSxkLI6CMGFpj0n9hBfNZ4q1ipmhyKEEJLvysHpdHKoTh2yQqBDUgqSlc87v73D139+zRv93pBipDhn+PrTswWwBUApFQUMAh7XWj8APAPcGJjwKr70dOM1lAuSObYc0vPSaRgrK2wLISo1yXW+SllVyjBt7foyFDi9JlV35MC6O4IRXchxOB18t/c7ujTsYnYoQggBku/Kxel0AhAeduoW2syCZEQENG0a9EsLEyzbu4wnlz/JoFaDeLDLg2aHI4Tf+PrTMxpw33V0A6zAUtf7HYC01pWiInRIJmcmA0iHpBCispNc5yvvOSShhE5JyM6P4Zes4dgc4aeO6TYtSAGGljVJa0jOTGZ4wnCzQxFCCJB8Vy4OhwMAi6sgqbU2tSDZogWEy4oQ57QcWw4Pf/swV8+6mmY1mjHtH9NQqvhIFCEqKl9/eh4ALnV9Pxj4XWud5npfB8j0d2DniowMsFggpvi9WsiQgqQQQgCS63zXbdqpIqQlBtqNPfU+LJJ8VY+Bb3zNDj2EBXuuxZ5ZBXouMYZ6V0JzEudQJaIKg1sNNjsUIYQAyXflYrfbAbBYLABopzEiINgFyexs2LJFhmuf6w6kH6DTh51457d3eLjLw2y8dyO1Y2qbHZYQfuXrT89PgZeVUmuBhwDvFodLMZ6kiRJkZBjdkaH8IOPQyUMANKwmQ7aFEJWa5Dpf1etlFBhjzoOeX0ObJ0+97/Utn2Ud4YcdPYmyFnA4vQGHFo6stMXIAkcB87bP4x8X/EPmjxRChArJd+WglKLJ339TzfXeaTeGcAezIPnLL9ChA+zfD9dfH7TLChM8vPRhDmUeYvmty5kyYAoxESHc4SREOfnU5K21nqiUSsNIUJ8BH3vtruPaJkrgLkiGMumQFEIIyXVlVq8XXLu/xPdJSRAWBhc0qkf+Z5tx1upc4kdUBsv2LiMtL41bEm4xOxQhhAAk35VXTEwMo2bNgkceAQJXkJw7F77/vvj2zEz48kto1MjY37OnXy8rQsiyvctYtGsR4/uOp2+zvmaHI0TA+DzrhNb6U4ynaUW3j/JrROeYilCQPJR5iJiIGOIiQzxQIYQIMMl1/pGUBA0bwkV144n+8Uf23nKl2SGZZs7WOdSMrkm/5v3MDkUIITwk35WTzWasJsOpgmT1mE2wYIQxnUm9XsbCb+tGnXpfBg4HPPAA5OdDtWrF948aBW++Gfr3l6L8bA4bjy59lBY1W/BI10fMDkeIgJJpcAOsIhQkkzOTaRDbQCbIFUII4RdJSdCkCdjS0rFaLOjoyjnMKLsgmwU7F3DrRbditVjNDkcIIcRZSD16lC/GjGFARATNMAqSTdvs44Iar0NOHqweBO1ehMSXjIXfVg8q8/zJmzfDiRMwaxaMGBG4P4sIXVM3TGVH6g4WDVtEZHik2eEIEVClFiSVUrmA9vFztNZaJkYqQUYGNG1qdhSndyjzEA1jZf5IIUTlI7kuMJKSoGtX+O/mX+C22+heiQqSablprDu4Dq0165PXk2PL4ZZ2MlxbCGEuyXdnLz8nh9Q6dbC7Vtl22p38494FWFSecYAj51Qx0v1+3ajC05ucwcqVxmvv3n4MXFQYSRlJvLj6Ra5qfhWDWg0yOxwhAu50HZJT8D1piVJUlA7Jrg27mh2GEEKYQXKdnzmd8PffMHQoOGx2rA4HOqaq2WEFxf70/fT5rA9/pf3l2da0elMub3K5iVEJIQQg+e6sOQoKALCEG7fQTruThR9dy8hnvyhclHSzxEC36WW6xooV0LYt1K/vj4hFRaG15vPEz3ngmwdwaAeTr54soxdFpVBqQVJr/XQwAzlXhXpBUmvNoZOHaHiBdEgKISofyXX+l5JiTLHVuDE4MuxYHA6oBB2Su1J30XdmX7IKslg4bCH1qxp3k+dVP48wFbwVWIUQoiSS786eIz8fKFyQ3L/9fI447qVh5CfFi5HtxkK9nj5/fn4+rF0L99zjx6BFyMvMz+SuRXcxb/s8Lmt8GZ9d+xnNazY3OywhgkLmkAwgreHkydAuSKblpdHNms8LaZ9CyjVnPRGzEEKIyi0pyXht0gT+3uzA4nDgjDm3Rv4VOAr4ft/3bD6yGQCndjLl1ylorVl9+2rax7c3OUIhhBD+VlKHZNM2+6hv+QIceUUOzoHEsVDrEp+LkuvWQW4u9Onjx6BFSMux5TBoziB+SvqJf/f5N09e9iSWMIvZYQkRNKebQ/KpMnyO1lq/4Yd4zilZWcbQtVAuSKbv/x9LGkAVR5pfJmIWQoiKRHKd//39t/HapAkc+N1pdEhWPTcKkhuTNzLpl0ks2b2Ek/knC+07v/r5fDPiGy6ofYFJ0QkhROkk3529aIuFFn/+ScyllwKn5pAMU97FyDDAWH3bmEPyDp/nkFyxAiwW6NHDn1GLUJVnz+Mfc//B2qS1fH7959yccLPZIQkRdKfrkHy9DJ+jAUlaRWRkGK/VqwM5h6AgzdR4StIg8Smi3CPJHDnwx4vg9JoDpYwTMQshRAUjuc7PvDsk2+U7qbVjB1xTseeQzCrI4l+r/sWUX6cQFxnHjRfeyHUXXkeP83oQYYkAwGqxytBsIUQok3x3lhrWrMmI2bOhl9Gs4Z5D8tbnviCMPEBB9XaQ+adxH2WJMUac+WjlSrjkktBuZhH+kZSRxJivx7DirxVM/8d0KUaKSut0BcnooEVxjnIXJOtWS4GFTUA7zQ2oBFFFNzi9nvCVYyJmIYSoYCTX+VlSElStajyMuzg9m6pbt7I9puLOIflHyh8MmTOEAxkHGH3xaF7v+zpxUXK3KISocCTfnS2bzXiNMB5EueeQ/Ns6lfPCx0LBcah7BVz8Vpmnv8rIgPXr4WmZ6fOc5XA6+Pj3j5n1xyx++vsnFIoPBn7A7R1uNzs0IUxzukVt8oMZyLnIXZCsU/UIZDjhwiehVhdzgyriq+1fkbv/C0ZWt6KcBad2lGMiZiGEqGgk1/lfUpLRHakU5ORmo6zRhEVGmB1WuWituf/r+8m157Jm1BpZLVsIUWFJvjt7W/fuZfljj3EXUA2jIAmQF9kNBu6BuRFgrWUUIcs4wuyHH8DhgL59/R62CAFO7eTORXfy2ZbPSKibwCu9XmFYwjBZvEZUerKoTQC5C5JxVTIhA4jvB/X7mRqTt9X7V/Pfvav5tLoqXIyEck3ELIQQQiQlGStsA0xrVIsL+/TlAkvFHMr87Z5v+envn5g6cKoUI4UQopLLy83lZFwcymtRG4Cw8DAoOGEcFFm7XJ+9ciVER0O3bn4JVYQQ98PNz7Z8xss9X+aFHi+YHZIQIcPnOwSl1G1KqXVKqRNKqZyiX4EMsqJyFyRjozKNbyJizQvGS3ZBNg998xC9ZvTijWrHiVH61E6L17A690TMQghRSUiuO3vuDkkAhwIcoCzK1JjKw6mdPLvyWZrVaMadHe80OxwhhPAryXdl57DbAQi3WoEiBcn848ZBkbXK/Lm//AL/+Y+xunZkpH9iFaEhx5bDI0sf4cONH/LM5c/w/JXPmx2SECHFpw5JpdRw4FNgDtAVmA1YgQHAEeC/gQqwInMXJKu6C5LhoVGQfGHVC7y7/l0e7vIwtdpdDT8NPTXxcruxRmdkOSZiFkKIikxy3dnLzYVjx7wLkgocrpu1CmbetnlsSdnCrOtmYbVYzQ5HCCH8RvJd+dhdc0haSixIphoHlbFD8vffoX9/qFcPPvzQf7GKwLM5bKw7uI6T+SeL7csqyGLRrkUs2rWIbFs2j3Z9lFd7v4pSFe8BrRCB5OuQ7X8C44EXgZHAW1rr35VSdYFVwP7AhFexuQuSVaxZxjcR5q8yqrXmvzv+y5DWQ5gyYIqxsecS18TL043h2bU6l3kiZiGEOAdIrjtLBw8ar02aGPnGGRaGdijCKtiQbbvTzgurXiChbgLDEoaZHY4QQvib5LtycHdIllyQdHdI+l6Q3LoVrrrKWFV75Upo0MC/8Qr/2X5sOwdPGr/k5NnzWLpnKfO2zyM1J7XUc2pG12REuxEMbzecHuf1kGKkECXwtSDZCvg/wAlojCdoaK2PKqXGAS8B8kyniIwMsFjAGhY6HZJbj24lKSOJF670mrui6MTL5ZiIWQghzgGS685SUpLx2qQJOBwO400ID9lec2ANV8+6mnxH4bUetNZoNAtuXoAlzGJSdEIIETCS78qhdmQkbbZtI+xyY07hkjskfRuyvWvXqSHa338P550XkJCFH6TnpXPxRxeTZ8/zbIsOj2ZI6yHc3PZmGsc1LnaORVlIqJtAhKViLuonRLD4WpDMA9Baa6XUEaAp8ItrXwbQyP+hVXzp6cYTL2UPnTkkF+9eDMDAlgNNjkQIIUKO5Lqz5F2QVErRa+NmwvYdD9kh28v/Wk6+I59nLn8GReGiaXzVeIa0HmJSZEIIEVCS78qhTY0atJk3Dx56CCi6qI3vHZJ790Lv3sb3K1dCc1loOaQt3rWYPHsen137Gc1rNkehaFevHVWt5o9+FKKi87UguR1oAawEfgKeVkrtAuzAv4DdgQmvYsvIMAqS2DIhLBLCzH9Csnj3Yjo36Ez92PpmhyKEEKFGct1ZSkoCpaBhQ7BYLFy6fiM7jtQI2SHbm49spnWt1rzS+xWzQxFCiGCSfFcerjkkiTDu6Yp1SFqiIDymtLMBI0/27g15ebB6NVxwQSADFv4wf+d8GlVrxIiLRhCmQvP3GSEqKl//RX0K1HV9/y+gNrAB2AwkAE/5P7SKz1OQtGeGRHfk0eyj/HrwVwa3Gmx2KEIIEYok152lpCRjYv7ISGPIdnqUlVxrVMgO2d6SsoX28e3NDkMIIYJN8l05fLdvH28//HApBcnjZ+yOdDrhxhuNe8Tly6Fdu4CHLM5SdkE2S/cs5foLrpdipBAB4NO/Kq31TK31ONf3O4G2wD+A4UArrfVyfwWklBqrlNJFvo547VeuY5KVUrlKqdVKqbZe+yOVUjOVUieVUruVUn2LfP7DSqnP/RXv6ZzqkMyCcPNbur/58xs0WgqSQghRAsl1Zy8p6dQK25mZmUy941b+btM4JDsk03LTSMpIokO9DmaHIoQQQSX5rnzy7XZsEREQbgwyLNYhaT39/JEzZsD69fDuu9CpU8DDFX7w7Z5vybPncf2F15sdihDnJF+HbBeitc4Alvg5Fm+7gJ5e7x1e3z+FsTLcHa7j/gUsV0q11lpnAvcCFwPdgAHA50qpeq45UhoDjwNdAhi7R0YGNG1KyHRILt69mIaxDekQLzdfQghxJpLryu7gQWjTxvje4Rra5nRYQnIOyS0pWwCkQ1IIUelJvvON0+Eg3G4vV4fkyZPwzDPQrRuMGBGMaIU/zN8xnzoxdbi8yeVmhyLEOcmnOwSl1Eil1HOl7HtWKXWLf8PCrrU+4vV1zHUtBTwKvK61/q/WeitwOxALuGO4EFiktd4GvAfUwRiGgOv9WK31UT/HW6JCc0iavMJ2vj2fZXuXMajVIIy/RiGEEN4k15291FSo6xoE6MjOBsDpCAvJIdtbjhgFSXlIJ4SobCTflY/D6cTicJQ+h+RpVth+9VVISYG33zbmWhahL9+ez5LdS7j2gmuxhFnMDkeIc5KvLQv/BLJK2Zfp2u9PzZRSh5RS+5RSc5VSzVzbzwfigWXuA7XWucCPwGWuTVuAy5VS0cDVwGEgVSl1E1BFaz3dz7GWKj3dWDmtINf8DsnV+1eTVZAlw7WFEKJ0kuvOgsMBJ05AbddtoiMz07XdEpJDtjenbKZulbrEV403OxQhhAg2yXflYHc4Si9IFpTeIfnnn/DWWzBqFHTuHKxoxdla8dcKMgsyZbi2EAHk6x1CCyCxlH3bXPv95VeMlv0BwD0YSepnpVQt1/cAKUXOSfHa9x+MxLUdeA64CYgDXgfuU0q96Jp/5EelVMDWNdPaaM1PTobjR8zvkFy6ZylR4VH0Pr+3qXEIIUQIk1x3FtLTjQn7a7kaRNwdko5QHbJ9ZAvt68lwbSFEpST5rhyaRUTQZvv24nNIWjTknyi1Q3L8eGOxt9deC2R0wt/m75hPXGSc3D8LEUC+ziHpBErrQa+F74XNM9Jaf+v9Xin1C/AXRvv+L+7Dipym3Nu01jbggSKf8QnwEXABcDPGPCTDgZnAJf6K3duePUZREsCem0WOrSoxgbiQj1YfWE33xt2Jjog2MYqKyel0kpqaSnp6Og6H48wnCFGJWSwWqlevTu3atQkLC70i1BlIrjsLx48br+4OyepKcc2SJew82jrkhmzbHDa2HdvGI10fMTsUIYQwg+S7crgkMhJ++KF4h6Q+aYRbSofkgQPGitrx0pBfYczbNo8vtn3BdRdeh9ViNTscIc5ZvhYk12M80ZpXwr57gA1+i6gIrXWWUmob0BJY4NocD/ztdVhdij9ZA0Ap1QMjSY0GJgBfa60zlVKzgQ+VUrGuCZP9aty4U99Xjcrkt99j6WnSw5UTuSfYcmQLL/d62ZwAKriDBw+ilKJp06ZERETIHJxClEJrjc1mIyUlhYMHD9LEvdxyxSG57iykphqv7oJkVbudSzZsYCcXhtyQ7Z2pOylwFEiHpBCispJ8V57YCwpQUKwgaXGmGQeUssp2WhrUqxeIiIQ/aK1xauO/ZUZ+Bg99+xCfJ35Ol4ZdeLX3qyZHJ8S5zdeC5OvAUqXUj8DHwCGgIXA30B3oH5jwQCkVhfH0axWwDzgC9MNIpO79VwBPlnBuJDAVuF1rbVdKhXHqiZ/7UYffZ6g9fBi+/NL9ThMblclvq2O54Ig5T8bWHFiDRtOzac/gX/wckJ2dTevWrStit5cQQaWUwmq1YhPYagAAIABJREFU0rBhQ3bt2mV2OOUhue4sFC1I5qWnkxYfT35qZMgN2XavsC0L2gghKinJd+XwaWYm0SNGMKJoh6S7IFlKh2R6OrRuHaioRHk4tZM1B9Ywd+tcvtrxFak5qZ594WHhvNzzZZ654hnCw3wtlwghysOnf2Fa6xWu1dbeAmZgtNArIBkYrrVe4a+AlFITgcVAEsbTsReAKsAMrbVWSk0GnlNK7QR2A89jTMr8eQkf9wLwndZ6vev9WuAtpdR0jPlHtmmt0/0Vu9u4caeGa0dF5BFucZCRE8u4cfDee/6+2pmt3r+a6PBoLmkQ8BF75ywpRgrhu4r670Vy3dlxD9l2zyG57+hRvhw9mhpTj4fckO3NRzYTaYmkdW25QxRCVD6S78rHoTVhTmfxIduOE8YBpcwhmZYGNWoEKirhK60165PXM3frXL7Y9gXJmcnERMQwuNVg2tZpCxgP1we2HEjH+h1NjlaIysHnkr/W+kul1FdAO4y5RVKBrVq7+pv9pxEwB6gNHMOYW+RSrfUB1/4JQDTwHlADY6Lkq4q25iulEjDmFPFuf5iP8cRtFcaTwNv9HDsA69ZBQYHxfWy0EVZ6diw//xyIq53Z6gOruazxZUSGR5oTgBBCVBCS68qvaIekIzcXALsjPOSGbG9J2UJC3QTpfBBCVFqS78rOobWxynbRRW3s7oJk8Q5JrY0OyerVAxmZOJ3ElETmbp3L3G1z+SvtL6wWKwNaDODmtjczpPUQqlirmB2iEJVWmX4TdyWoLQGKxX2NYWfYr4Gxrq/THbcVY24S721O4BHXV8Bs2uT1JisLFsF7H1SFZoG8aslk/kghhCgbyXXlk5oKVitUrWq8dxckbY6IkBqyrbVmy5EtDG412OxQhBDCVJLvysZTkLQYo8LdBUllK71DMjMTnE7pkDTLyPkjmZ04G4uy0KdZH5674jmuv/B6qkdJhViIUBA6dwjnKpvr4V5ErCmXl/kjhTizXbt2ER8fT2ZmQNf8KGby5Mlcf/31Qb2mEIFy/LgxXNu97pcjLw8wCpKhNGT7cNZhjuUco328LGgjhBDCdw4g3On0JDqn3YkKU6iC4xBmhfCqxc5Jdw0glw7J4Ft/aD2zE2czpvMYkv+ZzHcjv+POjndKMVKIECIFyVIope5VSm1QSm04duxY+T/IXZAMN6cgKfNHVm4pKSk89thjtGzZkqioKOrWrctll13GO++8Q1ZWVrHjJ02ahMVi4bnnniu0feTIkSilSv0Kdw1dGTlyJNdee22hcxcuXEh0dDQvvvhiiTHa7fZCn1WtWjW6dOnCwoULPcc8//zzhY6pUaMGvXv35rfffgNgxYoVp41PKcWsWbNK/Xt6+umneeihh4iNNf6d7tmzp8TPWLGi8JRKq1atolOnTkRFRdG8eXM+/vjjUq9RktGjR/Pzzz/zs1nzOYhKz2+5DqNDsrbXaDWHa+4SR4gM2T6SdYQPN3zI8P8OB2RBGyGEqCz8les6FhTQ/MABz3un3WmMACg4bnRHquIP39Jc691Ih2TwvbrmVWpE1WB83/HUrVLX7HCEECUw/w4hRGmtP9Jad9Zad65Tp075P8huboekzB8ZOg4fhh494MiR4Fxv//79dOrUiaVLlzJu3Dh+//13vv/+e5544glWrlzJokWLip3z6aef8vTTTzN9+nQcDodn+3vvvcfhw4c9X5GRkbz77rue94cOHSoxhunTpzN06FDeeOMNXnrppdPGO23aNA4fPsxvv/1GmzZtuOGGGzwFR4C2bdt6rrdmzRpq167NgAEDKCgo4MorrywU32233cYVV1xRaNuNN95Y6t/TkiVLuOOOO4rtW7FiRaHPuPLKKz379u7dyzXXXMOVV17Jpk2bePLJJxkzZkyhQuqZREVFMWzYMN555x2fzxHCn/yW6yhekGyWnc2QxUsgX5k+ZHvT4U00mtSI0V+PJjkzmZd6vsTlTS43NSYhhBDB4a9c1yMnh4Q9ezzvPQXJ/NTTrrAN0iEZbIkpiSzctZBHuj5CbKQ59+FCiDOTgmSgeToki7fwB5p7/kgZrh0axo2DtWuN12AYM2YMYWFhbNiwgWHDhtGmTRsSEhK4/vrrWbBgAcOHDy90/Lp160hNTWXs2LFER0fz7bffevbFxcURHx/v+Sq6rV69esWu/+abb3LfffcxY8YMHnzwwTPGW716deLj47ngggv46KOPCA8PZ/HixZ794eHhnuslJCTw7LPPcuLECQ4cOIDVai0UX3R0dLFtUVFRJV537ty5dOzYkYYNGxbbV6tWrUKfYbVaPfvef/99mjZtyuTJk7nwwgsZPXo0I0eOZOLEiYDRnVqvXj1ee+01zzmbNm0iMjKSBQsWeLYNGTKEBQsWkOca3ipEReUesu1W6+RJ2uzeB3ZMH7K9/dh2HNrBspHL2P3gbv7V41+EKfkVSAghhO/y7XYckaeaPE4VJI+XWpCUDklzvLb2NWKtsTzU9SGzQxFCnIYsLxlodtewWBM6JGX+yMB49FHYvLls5+Tnw2+/GZNaf/CBsfCRV23rjDp0gMmTfT/+xIkTfPfdd7z22mtUqVLyynGqyLCSTz75hGHDhhEREcHIkSP55JNPGDRokO8X9fLMM8/w9ttvs3DhQvr371/m8yMiIggPD8dms5W4Py8vj1mzZlG/fn2aNGlSrhjd1qxZQ+fOnUvcN2TIEPLy8mjVqhWPP/54ofke161bx1VXXVXo+Kuvvpo77rgDh8NBvXr1+M9//sN1111H3759SUhI4JZbbuG2224rNKz9kksuIS8vj19//ZUePXqc1Z9FhCalVA1gJ3CZ1npvEK87EbBqrR8OxvWKdkim5+Vx9LymsBVUmLkFyaPZRwG4pOElxX72CSGEOHsm5roHMVblHhLoa02oXZvLunWjj+t9oQ7JuIQSz5EOyeDbfXw3X2z9gqe6P0XN6JpmhyOEOA2f2wOUUvWUUq8ppdYqpbYrpdq4tt+vlCr5bv7Mn1lDKZWilGpenvPLSyk1USn1dlAuZuIckusOriMiLELmjwwBBw6A1sb3WhvvA+nPP/9Ea03r1q0LbW/UqBFVq1alatWqjB492rM9KyuLL7/8kltvvRWA2267jW+++YYj5Rhf/u233/L6668zf/78chUj8/Pzeemll8jOzqZ3796e7YmJiZ7YY2JimD59OnPnziUy8uymIzhw4AD169cvtK1atWpMmjSJL7/8km+++YaePXsydOhQ5s6d6znmyJEjxTpD69WrR0FBASdOGKstDhw4kHvvvZcRI0YwZswYnE4nk4tUlmNjY4mNjWX//v1n9ecQ/hGIXAc8C3zjvkFTSk1xzWWVp5TaX0IMbZRSq1z5MU8p9ZcrJmuR425RSm1WSuUopY4opWYppeK9DhkP3KGUalbOuH3mdMKJE4ULkpuio5lzwyCwFH8AEmxHs48SERZBXGScqXEIIUSoCEC+K2uu66mUWqiUOuzKY38ope4scsx0pZQu4Svb67CPgc5KqSvKEbPPtNY4lcLitU06JENLri2Xr7Z/xe0LbicqPIrHuz1udkhCiDPwqUNSKXUB8CMQAawHugHu8Y+tgcuAkeW4frHEBXQHEoAjWuumReJoA7wHtAHigGRgLjBWa13gOqY+8CbQCWgJzNRa31HkuuOBvUqpyVrrv8oRt+9MnEPyj5Q/uLDOhTJ/pJ+VpVMRjLkjmzUrXJBMS4O5cyE+/vTn+tuaNWtwOBzce++9hYYIz507l0aNGnk6BZs1a8Yll1zCjBkz+L//+78yXeOiiy7i+PHjjB07lksvvZS4ON8KAMOHD8disZCbm0v16tV56623CnUgtm7d2jPv5cmTJ5kzZw5Dhgzhhx9+oH378q+Wm5ubW2w4d926dXnsscc87zt37szRo0eZMGECw4YN82wvWmTRrv/I3tsnTpzIsmXLmD17Nr/88kuJHavR0dHk5uaW+88g/CMQuU4pFQPcDQz22hwGzADaAVeVcFqBa/8mIB1oj3HDFQ485frc7sBM4AlgAVAPeB+YDUbzhtb6mFJqGTAGeLIscZdVerpRlPQesu2w27E4nGApQzt4gBzLOUbdKnVNL4wKIUQo8He+K2euuwxIBCYAh4GrgY+UUnla689dxzwCPF3kvJ9csQOgtc5XSn0OPAys8TXmsnLPrW5x/0KPqyAZwalFbUqQnm6sdVOtWqAiq9wKHAUs37ucudvmsmDnArIKsoivGs/bA96WhWyEqAB8HbI9EdiHkSiyMG6W3H4C/l3WCwfqJg2IBFKB14F7S7p2MG/SsGVCWCRGtgquxKOJ9DhPhoCabdw440bdm8NhbH/vvcBcs0WLFiil2LlzZ6Ht559/PgAxMTGFtn/yySfs2rXLs1o2gNPp5NixY2UuSDZs2JD58+fTq1cv+vXrx7Jly6juwziVt956i759+1KtWjXq1i3+C4TVaqVFixae9506deJ///sfkydPZtq0aWWK0Vvt2rVJcz++Po2uXbsyZ84cz/v4+PhiHaRHjx7FarVSw+sx+F9//cXBgwcB2LdvX7Hh4Vpr0tLSONsFRYRf+D3XAdcATtf5AGitHwJQSj1BCblOa70H2OO16YBSqifg3f3RDTiotX7L9X6fUuodoOgKSYuA1whwrktNNV4LrbJttxPmdKJNnj8SjA7JOlXk35gQQrj4O9+VJ9e9VmTTVKVUL+AG4HPXMRlAhvsA18O4ZsCtRc5dBCxXSsVorXPKGLtP3AXJcK8HW9quia5SANp52g7JatUgTKYtPitO7WTNgTXM3TqX7anbAeN36K1Ht5KWl0aNqBoMazuM4e2G0+O8HljCLGf4RCFEKPC1INkDGKm1TldKFf3XfQSoX8I5ZxKQmzSt9X6MJ2QopUpeVtcQlJs07JkQEfwFbdJy0zh48iAX1bso6NcWha1bBwUFhbcVFMDPPwfumrVq1eKqq67i3Xff5aGHHqJq1dL/H9y2bRu//vory5cv9yxYA0bnYPfu3fnxxx8LrS7ti8aNG/PDDz/Qu3dv+vbty/LlywsV6UoSHx9fqODoC4vFQk7O2f3e2bFjR7Zv337G4zZv3lxoaHe3bt0KLfwDsHz5crp06YLFYvyYLCgoYMSIEdxwww20b9+e++67j27dutGoUSPPObt378Zms9GpU6ez+nMIvwhErrsC2Ki1V0tFGSmlWgD9MfKW20/Aa0qpwcASoBYwDPimyOm/AQ2VUs0DOafX8ePGq3dB0u50YnFqnBbz78KOZh+VTgkhhDjF3/nurHOdSzXg4Gn23wNs01oX/S16A8Z9bTdg5VnGUCJPh6TXNqfdSUw11++hp+mQlOHaZePUTqaun8rCXQvRGP9LbT+2neTMZGIiYri4/sVGwVHBoFaDuLntzfRr3g9rCIzIEEKUTVkWtXGUsr0WUJ6xhoG6SfNVUG7SsGWaMn9k4tFEANrVbRf0a4vCNm0y57rvv/8+3bt35+KLL2bs2LG0b9+e8PBwNm7cyJYtWzzDoT/55BM6duxI3759i31Gnz59+OSTT8pckASjU9JdlOzTpw/Lly+nVq2Sf1nzhd1u93QkZmZm8vnnn7N7925efPHFcn8mGAvRjB49GofD4SkkTps2jejoaDp06IBSioULF/LBBx8wadIkz3ljxozh/fff55///Cf33HMPP/74IzNnzmTevHmeY5599lnS0tJ47733iI2NZenSpdx2222sXLnSM3R0zZo1tGrVytO9Kkzn71x3HsZQtDJTSv2MMf1IJMZogGfd+7TW65RSwzGGaEdj5PPlwO1FPibZ9doUCFiuc3dIFhqy7XAQpjWEh0ZBsmWtlmaHIYQQocSf+a7cuc5NKTUIY8qR7qXsjwOG4pUL3bTWOUqpDIxcFxDh4eH0SE6m4cmTnm1Ou5OYWNdfVSkdkunpsqBNWfyd8TejFo5i5b6VtK3TlrgoY+qnSxtdytA2QxncajBVrCUv2CmEqHh8vUvYQPHWeLcbgF/Kce2zuklTSuUBfwJrKSEx+cD7Ji1w7FmmzB+ZmOIqSNaTgmRl1axZMzZt2kT//v154YUX6NixI506dWLSpEncf//9TJ48mYKCAmbNmsWNN5bcTDx06FC++uorMjIyStx/JvHx8axevRqbzUbv3r1JdVctymHbtm3Ur1+f+vXr06FDB+bPn89HH33ELbfcUu7PBBg8eDAWi4UVK1YU2v7yyy/TqVMnunTpwrx585gxYwYPPfSQZ3+LFi34+uuv+f7772nfvj2vv/4677//vmcF7ZUrVzJlyhRmzZpFtWrVUErx2Wef8ccffzBx4kTP58yZM4d77rnnrP4Mwm8CkeuigbwzHlWymzEKkrdgjCrwzJ/gmlP5bWAccDHGw7l44MMin+G+qYwuZww+KWnIdpfff+eyDftRITJku26MdEgKIYSLv/Pd2eQ691Dsz4GHtda/lXLYSIwGxZml7M8lgLnOarXS8++/aZiV5dlmFCRdHZLWkh+6p6VJh6Qv9pzYw7gfxtFuajt+OfgLHw36iMQxifx050/8dOdP/Pem/zIsYZgUI4U4x/jaIfkqsFQptRijG0MDVyql7gNuAnqV49rRQEo5zgPjJi0WYw7JNzBu0so610lQbtLM7JCsHlWdhrENg35tETri4+OZMmUKU6ZMKfWYY8eOlbrvzjvv5M477yy23XtBHG+zZs0qtq1u3bokJiaWeo3w8HDO1Cj9yiuv8Morr5z2GG8ffPCBz8eGh4fz7LPPMmnSJK6++moARo0axahRo854bu/evdlUSgtsnz59sNlshbY1aNCgUFF2y5YtbNu2jQULFvgcrwioQOS6VKBctyJa679d3253Dan7RCn1htbaDjwD/Ka1fsN1zB+uVUfXKKWe8zq3puu19H/oflDSkO34AwfIikogzOQh2zm2HLJt2TKHpBBCnOLvfFfuXKeUuhxjupF/aa2nnubQe4D/aq1PlLK/JgHMdQ6Hg2yliImM9NxAO+1Ooqu6CpJRpXdItpQG/VJtTN7I6K9HsyF5AwBXNb+KqQOn0qxGM5MjE0IEg08FSa31CqXUTcBkYKBr8ySMLsObtNY/lXpy6QJ1k+aroNykYc8Ea/AfiyUeTaRd3XayoqgQPhg9ejQZGRlkZmYSGxu8BwiHDx9m5syZQb2mKF2Act0m4A4/hBeGkbMtgB2IofhwO/d77x/8CYANYyXTgElNhYgI8ExX63BwqFYtDteOI+yYuQXJY9lGmpc5JIUQwhCAfFeuXKeUuhL4GhirtZ58muO6YDSiPFrK/uYYq4T/XtYYfJWamsoHXbowNDGRNq5tTruT6CrSIVleWmvGfD2GvzP+ZmK/idzU9iYaxzU2OywhRBD5PIek1nq+Uup/QFugLnAcSNRaO09/ZqkCdZPmq6DcpGHLhJjg/mDVWpOYksht7W8L6nWFqKjCw8N57rnngn7d/v37B/2a4vQCkOu+A8YrpWpprY+DZ/7jqkADwKqU6uA6drvWukApdSvG0LdEjJVPO2OMAvhKa53vOnYx8LFSaozrGvUxbix/11oneV3/CmBNoFYddUtNNbojPc/AcnL4oUcPjtWth9pt7oOxo9lHASlICiGENz/nu/Lkup4Yxcj3gdlKKffKig6tddGGkXsxpur6oZTrXwH8pbX+sxyx+8SzyrbXctlGQTIbVDhEVCvxPJlDsnTL/1rO+uT1fDToI+65WKYvEqIyKsuiNrgWoNnqp2sH6iYNr/OqAU7X+wKttfdSukG5STNW2Q5u99OBjANkFmTKgjZCCFEO/sx1WutEpdRvGCtgv+fa/AnGCqdu7nH/5wP7MR6uPQO0xOh2POA69y2vz52ulIoFHgTeBDKAVcBTRUIYDpzdyk8+OH688HBtsrNxWCworUwfsi0FSSGEKJm/8l05c90dGN3+T7i+3A7gNce/K9cNA14+zWKowzEWfwsYu93oe7F4jT5z2p1ExeRAZE2vJ3Kn2GyQnS0dkqV55cdXaFStkTTRCFGJ+VSQdLX0n5bW+suyXDhQN2lFznMbTJHkRpBu0rBlBX0OSVnQRgghyi4Quc7lJWCKUuoDrbVDa93zDNeYA8zxIZZ3gHdK26+UGogxjPursoVbdqmphVfY9i5IKpNX2XYXJOvEyBySQggBAct3Zc11d+DDaDmtdSZGw0qJlFIJQAeMuS8Dxt0habFYPNucdidRUdmlDtdOTzdepUOyuB8P/MiapDW83f9tIsMjzQ5HCGESXzsk55ay3fspVSjdpJ12fFjQbtK0NqVDMvGoUZBMqJsQ1OsKIUQFF5Bcp7VeqpR6D2iE8XAsWKoAo8o4v3K5pKZCgnfKcRcknQpMXmX7WI7MISmEEEX4Pd+ZmOsaALdprTMCeRFPQbLIkO3IaFeHZAnS0oxX6ZAs7tU1r1K3Sl3u7nS32aEIIUzka0HywhK21QIGATcCt5fn4uf8TZojD7QDwkt9qBcQiUcTaVq9KdUiS57LRAghRIkCkusAtNZvl/fcs7hmeR4UlkuxIds5OTgsFgiRIdsxETFUsVYxNQ4hhAghgbq3MyPXLQvGdWrVqkW/TZuoXuVULnHanURGZoO1eYnnSIdkydYfWs+yvcsY33c80RHRZocjhDCRr6ts7ypl189KKQcwBlhXngDO6Zs0e6bxasKQbZk/UgghyiaQue5c5nQaBcmiQ7YHLVnC+ovu4XAIDNmW7kghhDhF8l3Z1ahRg8u2boWuXT3bnHYnkVFZYJUOybJ4f8P7xFpjGdN5jNmhCCFM5o+7hFXAED98zrnH5ipIBnHIdr49n13Hd0lBUggh/EtyXSnS042iZNFFbRokJxOZbUWZPGT7aPZRmT9SCCF8J/muBHl5eaRGR+OIiPBsc9qdWK2lFySlQ7K4HFsOX23/iqFthhIbGdymHSFE6PFHQbIzENiVqisqe5bxGsSC5M7UndiddlnQRggh/EtyXSmOHzdeixYkd1x4IWmxYaYP2T6Wc0w6JIUQwneS70qwY8cO3rvxRjKjok5t1PlERORDZMmL2kiHZHELdy4kqyCLkReNNDsUIUQI8HWV7adK2GwFEoDrgI/9GdQ5wxb8IdvuBW2kQ1KU19KlSxkwYACZmZlUrVr++U9btGjB3XffzdNPP+3H6Mxz7Ngx2rZty8aNG2ncuHHQrvvVV1/xxhtv8Msvv6CUuZ1m5zrJdeWTmmq8Fh2yvWTQIKLSNTEnzB+y3b5ee1NjEEKIUCL5ruw8i9qEn7p9toZnG9+UsqiNdEgWNytxFo2rNaZH0x5mhyKECAG+3iW8XsLXi8ClwFvAkwGJrqLzzCEZvEVtNh/ZTKQlkla1WgXtmsI3q/atounkpqzatyrg11JKnfbrjjvuKPXc3r17c/jwYapUkQUgvL388svccMMNnmJkXl5eiX+306dPL3Tepk2buPzyy4mKiqJRo0b8+9//LtN1b7jhBrKzs/nqq6/89UcRpZNcVw7ugmTRDklbRATabjF1yLbWWuaQFEKI4iTflYHT6eTw4cMA7I+Kwul0AhBhdd3rnWYOychIiJZ1WwBIyUrhuz3fMaLdCMKUuQ8rhRChwddVtkv6MWrTWjv9Gcw5x4Q5JDce3kj7+PZEWCLOfLAImlX7VjFoziBybDkMmjOIJcOX0Ov8XgG7nvuXJoAlS5Zwzz33FNoWXcpvRjabDavVSnx8fMBiq4hOnjzJ9OnTWbWqeDF55syZ9O3b1/M+Li7O8/2JEyfo168f/fv358MPPyQxMZG77rqLatWq8cADD/h0bXcB+e2332bo0KFn/4cRpyO5rhxKGrKts7KwWa1op8XUIdsn809S4CiQOSSFEKIwyXc+cjqdzJo1i6SkJAAWV63KplmzGDlyJJFWV4fkaeaQlO7IU+ZunYtDO7i1/a1mhyKECBFnvEtQSlmBsUCC1jrf60sS1pnYg1uQdGonG5M30rl+56BcT/jGuxgJeIqSgeyUjI+P93xVd/0m5L0tLi6OnTt3opRi3rx59OjRg6ioKGbMmMHSpUtRSpGVleX5vC+++II2bdpgtVo577zzmDBhQqHrHT58mEGDBhEdHc3555/PrFmzisV04sQJ7rrrLurUqUO1atXo3bs3mzdv9uz/4IMPqF27NkuXLqVNmzZUrVqVvn37en4BdHvnnXdo1qwZVquVVq1aMWPGDM++6667jhEjRhQ63m63U69ePaZOnQpAbm4uDz74IHXq1CEqKoru3bvzyy+/nPbvc9GiRcTGxtK5c/F/W9WrVy/0d+td7J0xYwZOp5Np06bRtm1bhg0bxmOPPcbEiRMByM7OpnXr1tx///2ecw4cOED16tV59913PduGDBnC2rVrOXjw4GnjFOUnua78ShqybctxTT9mcofk0eyjANIhKYQQLpLvymbPnj0cOnTIM2TbphSHDh1iz549xoI2UOqQ7bQ0mT/S26zEWXSM70ibOm3MDkUIESLO2CGptS5QSj0CfBuEeM4tNleSCtIckntO7CGzIJOLG1wclOtVVo8ufZTNRzaf+UAgLS+NrUe34izyO16OLYe+M/uSUDeBGlFn/k2lQ3wHJvefXK54z+Tpp5/mzTffpH379kRGRvLHH38U2v/zzz8zfPhwxo0bx9ChQ/n555+5//77qVGjBvfccw8AI0aM4MSJE6xatYqIiAgeeeSRQh2ZDoeD/v3706BBA7799lvi4uL4+OOP6d27N7t27aJOHaN7KTMzk7feeovPPvsMi8XCrbfeykMPPcTChQsBmDNnDk888QRTpkyhd+/eLFmyhDvvvJMGDRrQr18/Ro4cye233052drZnyPny5ctJS0vjpptuAuDRRx/l66+/ZubMmTRp0oQJEybQv39/9uzZQ+1CY05PWbNmTYnFSIDRo0eTn5/P+eefz3333cedd97pmetx3bp19OrViwivFRmvvvpqXn31VQ4fPkz9+vWZPXs23buXcQLEAAAgAElEQVR3p3///gwcOJCRI0dy2WWX8eCDD3rOadmyJdWrV+eHH34oVnAV/iG5rvxSUyEiAmK9Up0tJwesVrApwsLN65A8lnMMkIKkEEK4Sb4rm8OHD1NQUFBoW0FBAUeOHCEySjokT+dw5mHWHVyHUztJy01jQ/IGJl01yeywhBAhxNch21uANsCPAYzl3BPkOSQ3Jm8E4OL6UpAMFbtSdxUrRro5tZNdqbu4tNGlQY6qsMcff5xrr73W875oQfLNN9+kf//+PPfccwC0atWKnTt3Mn78eO655x7++OMPVq1axYYNG7j4YuP/vWnTptGq1al5TL/77jv27NnD2rVrsVqtAEyYMIFFixYxZ84cHn74YcD4Be+jjz7ivPPOA+Cxxx7jkUce8XzOxIkTueuuuxg9erQn9t9++43x48fTr18/Bg0aREREBAsWLPAU7mbPns2AAQOoVasWaWlpfPrpp3z++ef0798fgI8//piVK1fywQcf8Pzzz5f4d3TgwAFPTG4Wi4VXXnmFnj17EhMTw/Lly3nwwQdJS0vjiSeeAODIkSMkJCQUOq9evXqeffXr16dz58689NJL3HXXXQwfPpzdu3cX+2+glKJ+/frs37+/xPiE30iuK4e//walICUF3LM9RGVnc++KFSyMe4CwWPMKktIhKYQQJZJ856P69etjtVoLFSXd0xsdijx9QTItDepUshlD8ux5zNwyk7nb5rJ6/+pC90FR4VEMbzfcxOiEEKHG14LkU8BnSqk9WusVgQzonGLLhDArWKxBudzGwxuJtERKG3yAlaVTsehwbW8xETEBn0vSF6V1/rnt2LGjWFfe5ZdfzoQJE8jLy2PHjh1YrVY6derk2d+yZctC3YYbN24kIyODmjUL/8KWl5fH3r17Pe+rVatWqPDXoEEDsrOzycnJISYmhp07d/L4448Xi+W1114DIDIykhtvvJHZs2czYsQIcnJyWLBgAdOmTQPgzz//xOFw0L17d8/5ERERdO3ale3bt5f6d5Cbm0tUVFShbREREZ4iLUDHjh3Jy8tjwoQJnoIkUGxlbK11se1PPfUUixcv5p133mHRokWeoqW36OhocnNzS41R+IXkunJYtw4KCmDcOHjvPWObJTub+nl5hFnDUNVlyLYQQoQYyXc+atGiBQ0bNuTQwYMU5OdjDQujYcOGtGjRgtSobJw6jLCIaiWem54OLVsGOWAT/X74d279361sP7adljVb8vwVzzOw1UBiImIAqBldk/iqMk+9EOIUXwuS/wGqA98ppXKAI4D22q+11q39HVyFZ8+UBW0quV7n92LJ8CXFipKhUowEzriatta6WFENTq3kXdp+b06nk0aNGrFy5cpi+7wXgfEe2uy+hvv8kgp5RY8DGDlyJH379uXYsWMsX74ci8XC4MGDPX8WXz6jqNq1a5OWllbqfreuXbvy4osvkpGRQVxcHPHx8Rw5cqTQMUePGgUS76JjSkoKu3fvxmKxsGfPnhI/+8SJE56h7SJgJNeV0eHD4G7cnTYNXnjB6JLMzM9nZ0ICtmSbqUO23QXJ2jElT8cghBCVlOQ7H4WFhTFy5Ej2JCZy5OGHiR8wgBYjRxIWFkZUdA52eyzWUn6HrCxzSDqcDv699t+89MNL1K1Sl69v+ZoBLQac8f5ACCF8vUvYCCwFvgSWABtc29xfvwckuorOlhm0+SPdC9rIcO3Q4y5Kup8OhlIx0hdt2rRh7dq1hbatXbuWZs2aERkZSZs2bcjPz2fTpk2e/Xv37iXVvdIF0KlTJ5KTk4mMjKRFixaFvnwtsimluOCCC0qMpU2bU13BV155JQ0aNOCLL75g9uzZ3HjjjZ7uxlatWmGxWAp9hs1m49dffy30GUV17NjxtB2Ubps3b6Zq1apUrWpM09CtWzdWr16NzWbzHLN8+XKaNm1K/fr1AaNIescdd9CuXTtmzpzJM888U2zIdmZmJklJSYW6UEVASK4ro3HjTn3vcJx6nwp806EDtmibqatsH80+SlxkHJHhkabFIIQQIUjyXRmEhYXRqkkTrvzxR1pFRREWZuS1yJgcbI6S7/W0rjxzSP5r1b94YdUL3NjmRhLHJHJNy2ukGCmE8IlPHZJa62GBDuScZM8KWoeke0Gbzg1khe1Q5C5Kjlo4imn/mFZhipEATzzxBN27d+e1117zLGrz9ttvM2XKFAAuuugievbsyd13383UqVOJiIjg0UcfLbTa9DXXXEOnTp0YMmQI48ePp1WrViQnJ/PNN98wePBgunbt6lMsTz75JLfffjsdOnSgV69eLF68mHnz5rF06VLPMUopbrnlFqZOncru3btZtmyZZ1+NGjW4++67+ec//0lcXByNGzdmwoQJZGZmct9995V63auvvpqXX36ZkydPUq2aMSxn/vz5pKen07VrVyIjI1mxYgXjxo3jgQcewGKxAHDbbbfx6quvctddd/F///d/bNu2jUmTJjF+/HjPZ0+ZMoX169fzxx9/0KhRI7799ltGjBjB+vXrPYXUn376ibi4OJ//nkT5SK4rm8OHja5IV+Px/7N353FRV+sDxz9fdoRARQPE3JfcQTFcU8Cl3K5FpbjkSqWZ6S+7mWlZ2rW4mlfNrqUmbqGZZWaKW6hU5u5Vk1wQyF1AQdlnmO/vj4GRYVHEYQbweb9evHC+yzlnMjl8n3mec8jOvpslaQjCZ2PRXbYT0hOkXFsIIQqQ+a4UtFr9d5u7j88OVdLR6op+1ktN1X9QV9kDkr/E/sKcX+cw2ns0y/+x3NLDEUJUMMWmLSiKckFRlDbmHEylY8YMSdnQpvzzr+9P3KS4ChWMBH2WX3h4OGvWrKFFixbMmDGDmTNnGnbYBv3GMR4eHnTr1o3nnnuOMWPGUKtWLcN5a2trduzYQadOnRg5ciRNmjRh8ODBxMbG4uFR8rVkBg8ezNy5c/nkk09o0aIFS5YsYfny5fTs2dPouuHDh3P69GnDmPKbP38+AwYMYNiwYbRt25azZ88SERFR7A7bAO3bt6dFixZs2LDBcMzW1pYFCxbg5+eHt7c3//3vfwkNDTWsZwng5ubGjh07iImJoV27dkyePJlp06bx+uuvA3Dy5EmmTp3KkiVLqF27NgCLFy8mPT2dd955x9BOeHg4L7/8smFDIGE6MteV3qxZoCuwZ1delmR2XkAyC4uXbEtAUgghZL57aHnzWu7yQqqq4lglHY3OtcjLk5P13ytzyXZieiLDfxhOE7cmLHx2oaWHI4SogJS8NdUKnVAUHdBBVdWD5h1S+ePr66sePnz4wW/c7ge2VSFgu+kHVcCUHVP4/ODn3Hn3jqwhaWLR0dE0a9bM0sMQFrZp0yZmzJjBiRMnzFqGcuXKFVq2bGnIoKwo7vXvRlGUI6qqlot0bpnr7nrQuc7HB44fL3zc2xu+frw3mzt1wuU7F+p51+O51c+ZcKQl1+q/rWhUvRE/DPrBIv0LIR5d5WmuA5nv8pT6uS4+HurVg+XLYfRodDk6Uha5keX4FB6vFn7WO3EC2rSBDRvghRceftyWFJccV+QGnVN3TWV7zHb+GPMHPp4+FhiZEMLSHnauK+mmNqI0NHegyhNm6Uo2tBGibA0cOJDY2FiuXLmCl5eX2fqNi4tj6dKlFSoYKR4N+ZaNLeRAT33qpJqlWrRk+0baDTrV7mSx/oUQQlQSBTIkdVodjs4ZpGuL32EbKnaGZEpmCm9GvMnK/60s9pr5vedLMFIIUWr3C0gWnT4pSsZMu2znbWgzrPWwMu9LiEfZ5MmTzd5np04STDEDmetMzPvYMRq1bk1YhrPFSrZ1qo7E9EQp2RZCiLtkviutAmtI6rKzcKiSRc7te5dsV9Q1JCNjIxn540gu377MO53foa1n4Y0Va1SpgX+9irUUlRCifLlfQPJDRVES73MNgKqq6ghTDKhSuB4J+0dBdrJ+Dcm81x1XgLvpf2jnbWgj60cKIUSpyFxnSqqK/a1b2Ds6omoslyF5M+MmOlUnAUkhhLhL5rvSKpghmXETgByKDkjeuqX/XpEyJC+mXGT9n+sJPxXO0atHaVy9Mb+N/g2/2rKpohCibNwvIOkNZJWgHfm0Lc/1SNjTD3Jy19lIPgUxy/Wv9/SD7lseOiipU3V8efhLlhxZgiZHQ5omDUB22BZCiNKRuc6UsrOJqV+fRAcH1BwVK2vLZEjeSLsBQE2nmhbpXwghyiGZ70qr4KY2mUkA5CiVI0Py6NWjdFreiaycLJ7yeorPen3GK+1ewcnOydJDE0JUYvcLSA581Bc+fmD7R90NRgIk/Apq7gSWk64/PzCu1M3HJccxZvMYfon9BT8vP5q6NQXguSefo+XjLR9i4EII8ciSuc6U0tI43bw5Z3Q6dDk6i5VsX0+9DiAZkkIIcZfMd6VVXEDSquiIY16GpGvR8cpyRVVVJm6biKuDK7+O+pXGbo0tPSQhxCNCNrUxtY4rjDMk84KRANZVoGNYqZs+deMUHZZ1wEqx4qt+XzG27Viz7vYrhBBC3FdaGhpbW+ysrEjT6ixWsh2fEg9AXde6FulfCCFEJVJgDUky9SXbOqviMyRdXMDa2hyDezjrTq3jt4u/saz/MglGCiHMSgKSpubuD60+gBMzQZdx97h1FWg1E9y7l7rpTX9tIk2TRszEGBpUa/CwIxVCCCFMLy2NbDs77KytSbVgyXZcchwKCk+4PmGR/oUQQlQiBTMks/QZkrpiMiQTEyvG+pFp2Wn8c9c/aefZjlE+oyw9HCHEI8YyTwmV2fVIOPmhcTAS9BmTJ2fC9T2lbvrI1SM0rt5YgpFCCCHKr9wMSVtbW3Q5lsuQjE2OxcvFCztrO4v0L4QQohIpEJAkW1+TrdoUHXU8dAhatzbHwB5O6G+hXLp9iQXPLMBKkdCAEMK8is2QVFVVfiKVRsE1JK2r3H2dkw77R5Z6DckjV47QpU6Xhx6iEEIIPZnrykBehqStLTptusXWkIxLjqN+1foW6VsIIcobme8eUoGApJJ9E1UHqrVLoUuvXYOzZyEkxJwDLDlNjoadF3ay7tQ61v+5nuCWwXSu09nSwxJCPIJkYjK1jiv0QUi4W6ad/3XHFaVqNiEtgYu3L9LOs51pximECcycOZOWLU2/mZKiKHz33Xf3vEaj0dCkSRP27dtn8v7v5eTJk3h5eZGWlmbWfoWoMNLSGLZmDUE+PhbdZTsuOY56VetZpG8hhBCVTME1JLNvkpHmiFVexmQ+UVH6708/baaxlUCOLofI2Ehe/elVPOZ50Pebvvx09idebv0yC59daOnhCSEeURKQNDV3f+i+BarUhe4/Q/O3873eoj9fCkeuHgGgXS0JSFZUmSmZrHtuHZkpmWbpLyEhgfHjx1OvXj3s7e1xd3cnMDCQnTt3mqX/svbVV1/h5eXF0/l+26tXrx6Kohh9TZ061ei+v//+m/79++Pk5ESNGjWYOHEi2dnZJe63VatWdOjQgc8++8xk70WISiUtDfusLBxd9etqWaJkW5Oj4dLtSxKQFEIIYRoFMyS1t/QBySKqAPbtAycn8PEx5wCLl5adRuslrQlYFcDak2vp3bA3mwdv5vqU6ywdsJQaVWpYeohCiEeUbGpTFtz9jcuyC74uhcNXDgPg41FOZjbxwM5sPsOZTWc4+9NZWg8r+0VlgoKCSE9PZ/ny5TRq1IgbN26wd+9ekpKSyrxvc1i0aBHTp08vdPz9999n3LhxhtfOzs6GP+fk5NC3b1/c3NyIiooiKSmJESNGoKoqixYtKnHfo0aN4pVXXuHdd9/FxkZ+jAphJC2NvU8/jXuSfgdSS5RsX7x9EZ2qk4CkEEII0ygYkNTcIiO1+IBkx453l5u0tB/P/MjphNMsfGYho31G42TnZOkhCSEEIBmSFUbehjauDq6WHooopWNfHzP6XpaSk5OJiorik08+ITAwkLp169K+fXumTJnC4MGDDddlZ2czbdo06tati729PQ0aNGDhQn3ZRk5ODmPGjKF+/fo4OjrSuHFjQkND0el0xfY7cuRI+vXrx+zZs3F3d8fZ2ZlRo0aRkXF3kydVVQkNDaVhw4Y4OjrSqlUr1qxZ80Dv7/Dhw5w9e5Z+/foVOvfYY4/h4eFh+MofkNyxYwd//vknq1evpm3btvTs2ZPQ0FCWLl3K7du3UVWVnj170qNHD1RVBSA1NZXGjRszYcIEQzu9evXi5s2b7Nmz54HGLcQjIT2d3zt35kJiIoBFSrbjkuMAZA1JIYQQplEgIGmVU3SG5K1bcPJk+SrXXn1iNXVc6/D6U69LMFIIUa5Iak8FceTKEVlsuJyImBTBtePXSnTt9f9dJzM5t0Q7t2oxbk8cHyofAuBQ1QH3Nu73bcfD24Nn/vNMicfo7OyMs7MzmzdvpkuXLjg4OBR53YgRI4iKimLBggX4+PgQHx/PxYsXAdDpdHh5efHtt99Ss2ZNDh48yCuvvIKbmxtjxowptu+9e/fi6OjI7t27uXz5MqNHj+add94xBDqnT5/Od999x+LFi2natCn79+8nJCSEatWq0bdv3xK9v6ioKBo1akTVqlULnZs7dy5z5szhiSee4MUXX+Ttt9/Gzk6/y+7+/ftp1qwZTzzxhOH63r17k5WVxZEjR/D392flypW0bt2auXPn8vbbbzNx4kTs7Oz497//bbjHzs4Ob29v9u7dS48ePUo0ZiEeFWpqKtl2dtg6OAKWKdnOC0hKhqQQQgiTKLCGpJKTTEaqC04FApK//QaqWn4CktdSr7EjZgdTO0+VXbSFEOWOBCQrgLwNbXw9fS09FPGAXOu4knU7C1Wngpp7MPe7YqXgWrdsMl5tbGwICwsjJCSEr776Ch8fHzp37syLL76In58fAOfOnWPdunVs27aNZ57RBzsbNGhgaMPW1paPPvrI8LpevXocPXqU8PDwewYkra2tWbFiBc7OzrRs2ZJPP/2UMWPGMGfOHAA+++wzduzYQdeuXQGoX78+Bw8eZPHixSUOSMbHx+Pp6Vno+MSJE/Hx8cHNzY2DBw8ydepUYmNjWbZsGQDXrl3D3d04AFyjRg2sra25dk0fZK5VqxbLli1j0KBB3L59m7Vr13Lw4EEcHR2N7qtVqxZxcXElGq8QjxJtejooCja5AUlLlGzHJcdhpVhR26W22fsWQghRCRXKkEwmM9WdxwrMcfv2gZ0dPPWUuQdYtPCT4ehUHcNaD7P0UIQQohAJSFYAsqFN+fIgmYoAZ7ecZcOLG9Bmag3HbBxseHHDizTp18TUwzMICgqib9++REVFsX//fiIiIpg3bx4ff/wx06ZN49ixY1hZWeHvX/xGS0uWLGHZsmXEx8eTkZGBRqOhbt269+y3devWRmXSHTt2JDs7m5iYGLKyssjMzOSZZ55BUe5mTWk0GurVq1fi95aRkVFk1uf//d//GY3DxcWFQYMG8emnn+Lm5gZg1G9++Y8PHDiQIUOGMHv2bEJDQ2nTpk2h6x0dHY1K0YUQepq0NHBywsYm96HNAiXbscmx1Hapja11OVnASwghRMWWPyCpy8FKvV1kyfa+fdC+PRT4HNti1pxcQzvPdjSr2czSQxFCiEIkb7sCkA1tKrbM5EysbKxQrBRsHG1QrBSsbKzulnKXIQcHB3r27Mn777/P77//zpgxY5g5cybZ2dmGNRKLs379eiZNmsTIkSPZvn07x48fZ/z48Q+0I3VBeetP/vTTTxw/ftzw9eeff7Jjx44St1OjRg1u3bp13+vyskHPnz8PgIeHhyETMk9iYiI5OTlGmZOZmZkcOnQIa2trw70F3bx5k5o1a5Z4zEI8KjS5gfq8YKClSrZl/UghhBAmkz8gqUlBQS20qU1aGhw5Un7KtU8nnObo1aMMbz3c0kMRQogiSUCyApANbSq2o8uPkp2ejXsbdwb/OBj3Nu5kp2ebZXObgpo3b45WqyUzM5O2bdui0+mIjIws8tpff/0VPz8/JkyYQNu2bWnUqBExMTH37ePkyZOkpaUZXv/xxx/Y2dnRsGFDmjdvjr29PfHx8TRq1Mjo636Zl/n5+Phw5syZe26wA3D8+HEAQ3l3x44diY6O5tKlS4Zrdu7cib29Pe3a3c1Afvvtt8nKymLnzp2sWLGCH3/8sVDbp06dom3btiUesxCPCtc7d5geFsaTjZ4ELFeyLetHCiGEMImcHDhxQv/nnTshIwGgUIbkH3/ol5osLwHJ1f9bjbVizeCWg+9/sRBCWICUbFcAsqFNxebg6kCvf/eiw6QOKFYK9QPq88d//uDvqL/LrM+kpCRefPFFRo8eTevWrXnsscc4fPgwoaGhBAYG4uLigouLCy+99BJjx45lwYIFtG3blkuXLhEXF8fw4cNp0qQJYWFhbNu2jUaNGrFu3Tr27t1LtWrV7tm3Vqtl9OjRvP/++1y5coWpU6cSEhKCk5N+V78pU6YwZcoUVFXl6aefJjU1lT/++AMrKyteeeWVEr0/f39/MjMzOXHiBN7e3oB+w5o//vgDf39/XF1dOXToEJMnT2bAgAHUqVMH0O+O3aJFC15++WXmzZtHUlISb7/9NiEhIbi4uAAQERHBl19+SVRUFH5+fsycOZOxY8fi5+eHh4cHAHFxcVy+fJlevXqV6u9HiEotLQ1rR0cUnT4z0twl29k52Vy+fVkCkkIIIR5eTg707g1RUfrXo0dDv2YwgEIZkvv2gZUVdOpkobHmup11mx//+pEVx1fQq2Ev3J3vv4GmEEJYggQky7m8DW3aecr6kRXV4E3Gn0paWVvR6a1OdHqr7H5bcXZ2pkOHDixYsIDz58+TlZWFl5cXQ4YMYfr06YbrVq1axYwZM5g4cSKJiYnUrl2byZMnA/Dqq69y/PhxhgwZgqqqBAUF8dZbb/H111/fs+9u3brRokUL/P39SU9PJygoiNDQUMP5WbNm4e7uzty5cxk3bhwuLi54e3vzz3/+s8Tvz83Njeeff561a9caApL29vasX7+eDz/8kKysLOrWrUtISIhRu9bW1vz888+MHz+ezp074+joyJAhQ5g7dy4ACQkJjBw5kunTpxvKvadOncqOHTsYNWoUW7duRVEUwsPD6dWr1wNldQrxqEjMyeGgnx/Nc5dVMHfJ9t8pf6OiSkBSCCHEw9u2DQ4cgLwli9LSIO4UUDggGR0NjRpB7mfcZpGQlsD0X6YTnxIP6D+U+/3i72TlZFHHtQ4znp5hvsEIIcQDUu63jpy5KYryLvA80BTIAv4A3lVV9VS+a8KAEQVuPaCqaod813wGjATSgKmqqq7Nd64/8A7QVS3BfwBfX1/18OHDpX1LD2XTX5t4bv1z/PLyL/jXL37zEVF2oqOjadZMFoIuiZEjR5KYmMiWLVvKvK8///wTf39/zp8/b8huNIesrCwaN25MeHg4nTtL5nJx7vXvRlGUI6qq+pp5SOI+TDXXnXv+eb5p04YXn3mRDR02MHDVQNoML7wxVFnZdWEXPVf3ZM+IPXSr181s/QohRH4y11WS57pZs+CDDyB/052A12HRWxMY/Mv71GymX1O8Y0dwcoJdu0re/MPYcnYLYzaPITkzGW8PbxQUFEXB19OX4FbBdKjdAStFVmgTQpSdh53rymOGZHfgC+AQoAAfAbsURWmuqurNfNftAvKv0GvYaSN3YhoC9AIaA18rirJdVdVERVEeA+YDA0oyaVnaV0e+wsPZQ0q2hSigRYsWzJ07l9jY2CJ3wS4r8fHxvPfeexKMFA+lPD6kmUq2VguANdaA+Uu245LjACRDUgghLK87Ff25zsdHH2VMTb17rLodkF0oQ/LSJejRo0xGYZCjy2Fv/F6WHV1G+Klw2ri3YdfwXbRyb1W2HQshRBkodwFJVVV753+tKMpwIAXoDPyU71SWqqrG2+Xe1QzYo6rqYeCwoij/AeoDicC/gDWqqp42+eBN7GzSWbad38aH3T/EztrO0sMRotx5+eWXzd5nkyZNaNKkidn7FZVOdyr6Q1oxsnNyALCx0v+KYe6S7bjkOKwVa7xcvMzarxBCCGOV4rnu2WfBz09ftp2Wpg9ONq0JxJKZ7mAISGq1cOUK1K5t2u6XHF7Cvvh9AOSoOeyL38e11Gs42Trxbpd3+aDbB9jb2Ju2UyGEMJNyF5AswmPodwO/VeB4F0VRbgDJwF7gPVVVb+Se+x/wiqIo1YAGgCNwXlGUDoA/UCG2xl18cDG2Vra80q5kG30IYWlhYWGWHoIQFUKleEgrhiY3IGnIkDTzLttxyXE84fqEISAqhBCi3Kh4z3XW1rB9u34tyePHwdsbPHaRc2Ypqs7aMMdduwY6HTzxhOm6/iH6B8b9PI7aLrVxtHEEoGPtjgS3DKZvk75Usa1ius6EEMICKsJv6wuA48D+fMcigO+BWKAeMBv4RVGUdqqqZqmqul1RlDXoM08y0Je8pQJfAq8BoxRFmQSkA2+oqvq7ud5MSd3Ous2K4ysY1HIQHs4elh6OEEKIslXxHtKKodNosFJVrNA/pJm7ZDs2OZb6VeubtU8hhBAlUjGf66ytoV8//RfAgc3kqPoAYV5A8uJF/SlTZUheTLnImM1j8K3ly2+jf5NqOSFEpVSuA5K5a2N1AbqoqpqTd1xV1XX5LjupKMoRIB7oi35CQ1XVmcDMfG1NRz/5paAvjfMGWgEbFEWpr6pqNuXIyuMruZN9h4lPTbT0UIQQQpS9ivmQVoQOv/9Oh3btuKJYrmS7d8Pe979QCCGE2VSq5zptGroCAclLl/SnTJEhmaPLYfgPw8nOyeab57+RYKQQotIqtwFJRVHmA4MBf1VVL9zrWlVVryiKcgn9GlpFtdUEGA34oH9g26eq6lXgqqIodug3FThpyvE/DJ2q4/NDn+Pn5Ud7r/aWHo4QQogyVKke0nJyIDMTnJzQ5egA82ZIZmmzuHLnimxoI4QQ5Uile67TppKjK5sMSVVVmf7LdPbG72XlwJU0dscUGxUAACAASURBVCvyP4MQQlQK5TIgqSjKAvSTVndVVf8qwfU1AC/gahHnFPTZIlNUVU1RFMUKsM13zhZyF7oqJyJjIzmbdJY1z62x9FCEEEKUoUr3kJaeztG2bblhY0NzbW5A0oxrSP6d8jcgO2wLIUR5USmf67SpRZZsOzlB1aqlb/by7cuM3jyaHTE7GOU9iuGth9//JiGEqMDKXUBSUZTF6HcUHQjcUhQlbwHFVFVVUxVFcUafDbIR/URVD5gD3AB+KKLJMUCyqqrf577+FfhIUZQuQGtAA5wpm3dTOuv/XI+znTPPN3ve0kMRQghRRirlQ1paGvF16/K3TkezHP3m3uYs2T5y9QgAzWs2N1ufQgghilZpn+u0qeTkOADGJdu1a4NSyinvx79+ZNSPo8jKyeKLPl/wmu9rKKVtTAghKohyF5AExud+313g+IfoJ6wc9OVnLwNV0U9ekcBLqqreyX+DoijuwHT0u5YCoKrqYUVR5qCf5O4Aw1VVzTD92ygdTY6G76O/Z0DTATjaOlp6OEIIIcpApX1IS0sj284OO2tri5Rs77qwi6oOVfHx8DFbn0IIIYpVOZ/rtGlodTUA4wzJ0q4fefL6SQZ9N4iWj7ckPChcyrSFEI+McheQVFX1nh8F5U4yJVqtXlXV6+gf4goen4P+wa7c2RO3h6SMJF5s/qKlhyJEiYwcOZLExES2bNlSJu2fO3eOLl26cPbsWVxdXcukj6J8/vnn7Nixg82bN5utT/FIqZwPaenpaGxtsbWxQWeBku3dsbvpXq871lblaiUWIYR4JFXa5zptKjk5+sUi82dI9uz54E2la9IJ3hhMVYeqbB26lcedHjflSIUQolwz31OCKJENpzfgbOcsO4SKhzZy5Ej69etndGzLli1UqVKF9957z0KjenDTpk1j/PjxhmBkXFwciqIU+oqIiDC6b+/evbRr1w4HBwcaNGjAkiVLHqjfkJAQDh8+TFRUlMneixB5VFVVivmamXs+Q1XV3qqqPq6qqp2qqnVVVR2pqurFItq6rqpqPVVVLxc4PkdV1ZqqqjZQVTWi4H1lIi9D0tYW1cwl2xduXSAuOY7A+oFm6U8IIcQjSpuKVuuAYq3/HVSrhatXS5ch+db2t/gz4U9WP7dagpFCiEeOBCTLESnXrsRycmDLFpg1S/89J+f+95jY6tWrCQoKYs6cOXz88cdm7/9eNBpNkccvXrzIpk2bGDVqVKFzERERXL161fAVEBBgOBcbG0ufPn3o1KkTx44d49133+WNN95g48aNJR6Tvb09Q4YMYeHChQ/+hoR4VKWlYZedjZOjo9lLtndf0Ceb9mjQwyz9CSGEqBgURXlFUZTDiqIcTkhIePgGNfqAZF525NWroNM9+A7b30d/z5IjS3i709v0bFiK9EohhKjgJCBZDJNPXCUg5dqVVE4O9O4NwcHwwQf67717mzUouWDBAsaOHcuyZct48803jc6tWLGC5s2b4+DgQJMmTZg/fz46nT6QEBwcTFBQkNH1Op2OJ554gvnz5xfZV0REBF27dqVatWpUr16d3r17Ex0dbTifl+EYHh5OQEAAjo6OfPnll0W2tX79elq1akWdOnUKnXNzc8PDw8PwZWdnZzi3ZMkSatWqxaJFi2jWrBkhISGMGDGCuXPnApCQkICnpycfffSR4Z4TJ07g4ODAd999Zzg2YMAANm/eTHp6epHjE6KiM/lcl5bGsDVrCGrf3uwl27tjd1PrsVo0dWtqlv6EEEJUDKqqfqWqqq+qqr41a9Z8uMZ0WtBlodXaG60fCQ+WIRkVH8Ww74fRvlZ7ZgfMfrgxCSFEBVXu1pAsL1RV/Qr4CsDX11c1R5/f/vmtlGtXBJMmwfHjJb8+KQlOn9Z/dAqQmgqRkeDtDW5uJWvD2xv+858HHyswY8YM5s2bx/fff0/fvn2Nzi1dupT333+fRYsW0a5dO06dOkVISAi2trZMmDCBYcOGERQURHJyMlWrVgX0pdBXr14lODi4yP7S0tKYNGkSrVu3JiMjg9mzZ9O/f39Onz5tFDR89913mTt3LsuXL8fW1rbItqKiovD19S3y3PPPP09mZiaNGzdm8uTJvPDCC4Zz+/fvp1evXkbX9+7dm5UrV6LRaKhZsyZhYWH079+fnj174u3tTXBwMMHBwUbt+Pr6otVq2b9/P4GBUgYqKh+Tz3VpafrvTk5mLdnWqTp2x+7m2UbPyq6kQgghyo5WP89p8mVIXrqkP1XSgOSBSwfo800f6laty0/BP2FnbXf/m4QQohKSgGQ5ocnR8MNfP0i5dmWUmno3GJlHp9MfL2lAspR27tzJzz//zJYtWwoFIwFmzZpFaGioIQhXv359pk6dyhdffMGECRPo3bs3Li4ubNy4kTFjxgCwdu1aAgMD8fDwKNQeUCijcsWKFbi4uHDw4EG6dOliOP7GG28YBf+KEh8fj7e3t9ExZ2dn5s6dS+fOnbGxsWHz5s0MGjSIlStXMmzYMACuXbtGjx7GZZvu7u5otVoSExPx9PSkd+/ejB8/nqFDh9KtWzeysrJYtGiR0T1VqlTB1dWVuLi4e45TCJErLY0NL75I4ytXsM1xB8xTsn3y+kkS0xNl/UghhBBlS5uq/6YpnCFZXMn2pduX+O3v3wBI06Tx1o63cHdyZ9fwXbg7u5f5kIUQorwq1wFJRVGqAX8BnVRVjTFRmxOAXqqqDjBFe6agqiof7PmApIwkBrUYZOnhiPt50EzFLVv0ZdqpqXePOTvDokVQYNMZU2vZsiUpKSl8+OGHdO7c2ZDlCPqy5YsXL/Lqq68ybtw4w3GtVouq6jObbGxsGDRoEGvXrmXMmDFkZWWxcePGe66rGBMTw4wZMzhw4AAJCQnodDp0Oh1///230XXFZT7ml5GRgYODg9GxGjVq8NZbbxm1k5iYSGhoqCEgCRTKksp7T/mPf/rpp0RERLBq1Sp+//13nJ2dC43B0dGRjIyy35xYPNoqzXyXlsZfTz5J9fR03HMzJM1Rsr07Vr9+ZGADCUgKIUR5VSnmutyApCZfQPLSJf2v9rn7LxYS8lMIEefv7i1X17Uuu1/ejZeLV5kPVwghyrPyvobkNGBr/glLUZRhiqIcVxQlU1GUREVRVuW/QdGbpCjKX4qiZCmKclVRlE/yXbIU8FUUpauZ3sM9qarKjMgZzPl1DiFtQ+jXpGwDVMICnn0W/Pz0v6koiv67n5/+eBnz9PRk7969pKSk0KNHD27dumU4l7dO5JIlSzh+/Ljh69SpU/z555+G64YNG8bevXu5fPkyP//8M9nZ2Tz33HPF9tm/f38SEhL48ssvOXDgAMeOHcPGxobs7Gyj65ycnO47/ho1ahiNuTh+fn6cO3fO8NrDw4Nr164ZXXPjxg1sbGxwy5eVGhcXx8WLF1EUhQsXLhTZ9s2bN3no9YaEuL/SzHcv5Z5PVxQlXlGUtwu0afb5Lic1FZ21NbZOToY1JM1Rsr07djdN3JpQ2+UBdxQQQghhTg881+W7rrGiKHcURUktcMq8c11eQDLbzihDsnZt/a/5BWVqM9kbt5eR3iM5Pf60/uv109StWtcswxVCiPKs3GZIKopSBRgL9M93bCLwLvA28AfgCDQpcOs8oF/uNScBV8Az76SqqlmKonwDTASiyvAt3FdeMPLjqI8JaRvCkn5LsFLKe4xYPDBra9i+HbZt06896e2tD0ZaW5uley8vL/bs2UNAQACBgYHs3LkTNzc33N3d8fLyIiYmhpdffrnY+/38/GjYsCHh4eHs37+fgQMHFplJCJCUlER0dDSLFy/G398fgKNHj6LVaks1dh8fH06fPn3f644fP46np+GfOR07dmTTpk1G1+zcuRNfX1/DepUajYahQ4cyYMAA/Pz8GDduHJ07dzbaQCcmJobMzEzatm1bqvELURKlme8URXkWyJvLIoBmwFJFUTJUVf0cLDPfZaeng50ddg4OZttlW5OjYW/cXka0GVGm/QghhCi9h3i2Q1EUO2AdsA/olv+c2ee63DUktdkORgHJ4taP3H9xPxnaDJ5/8nma1WxW5sMTQoiKpNwGJIE+gA74DUBRlKrAHGCgqqo78113Mu8PiqI0Bd4AWquqGp3vmmMF2t4M7FQUpYqqqhbbPnfXhV0SjHxUWFvry7PLuES7OJ6enuzZs4fAwEACAgLYvXs3NWrUYObMmbzxxhtUrVqVPn36oNFoOHr0KJcvX+bdd9813D906FCWLVtGXFwcP/zwQ7H9VKtWjRo1arB06VKeeOIJLl++zNtvv42NTel+1PTu3ZtRo0ah1WoNbaxcuRJbW1t8fHywsrLip59+YvHixXz66aeG+1577TU+//xzJk2axKuvvspvv/1GWFgY4eHhhmtmzJjBjRs32LVrF66urkRERDB8+HAiIyOxstL/W4yKiqJBgwY0bty4VOMXooQeeL4DhgM/qar6Re7rC4qizAHeURRlsZq3RoGZ5ztNRgbY2WFrZ2fY1KasS7bXnlxLmiaNXg173f9iIYQQllKauS7Pp8AJYC8FApK5zDfXafIyJI1Ltlu0KPry3bG7sVas6VavqGELIcSjrTxHwLoCR/I9VPUCrAF3RVFOK4pyWVGUHxRFaZDvnn8AF4BnFEW5oChKnKIoKxVFebxA24fRB2M7lvWbuJcFBxbwuNPjLHp2kQQjRZlzd3cnMjISAH9/f27cuMHYsWP5+uuvWb16NW3atKFr16589dVX1K9f3+jeYcOGcebMGVxdXenZs2exfVhZWbF+/XpOnDhBy5Ytef3115k1axb29valGnOfPn1wdHRk+/btRsdnz56Nr68v7du3Z926dXz99ddMnjzZcL5+/fps3bqVffv24e3tzccff8zChQsNG+7s3buXefPmsWrVKqpWrYqiKISFhREdHW0U2AwPDyckJKRUYxfiAZRmvrMHMgu0kwHUBvLXgZl1vtNlZOCWnIyTmUq2E9ISeGvHW3R6ohP9m/a//w1CCCEspTRzHYqi9EVf/TbxHm2bb67LLdnOztKXbGs0cPVq8RmSu2N385TXU7jYu5T50IQQoqIpzxmSdYGr+V43QB9AnQ5MAm4C7wORiqI0y/00rEHufYOBkYAKzAV+UhSlo6qqOgBVVdMVRUkB6pnnrRR2/uZ5tp7byoynZ2BvU7pgjRD3EhYWVuhYzZo1+d///md0LDg4mODg4Hu21bBhQ+7+/njvfgICAjh16pTRsdR8G/rUq1ev2LYKsra25r333uOzzz4z7BI+YsQIRoy4f2lmt27dOHr0aLHnNBqN0TEPDw9u3LhheH3q1CmOHz/Ot99+W6KxCvEQSjPfbQcWKIrSC9gFNALydnvyBOLA/PNd1du3mbBrF8yfz6HIQ0DZlmy/teMt7mTd4at+X8kHe0IIUb498FynKIon+jUin1dV9U7BDQvzmHWuy1tDMssOKxt9MFJVi95hOyUzhYOXDzKty7QyH5YQQlRE5fm3d0eMsz+sAFtgoqqqEaqqHgSGAo9zdy0SK/RZI8NVVd2nqmoU+rK2p4D2BdrPyO3DIhYfXIy1lTWv+b5mqSEIUSGEhIQQGBhISkqKWfu9cuUKq1atwrW4LROFMJ3SzHdLgYXAj0A2+rW31uWeyynQvvnmu7Q0qFIFwFCyXVYZkrsu7GL1idX8s/M/afF4MbVyQgghyovSzHVrgP+qqvpHCdo3z1yXu4ZkXobkxYv6w0VlSO6N34tO1dGjQY8yH5YQQlRE5TlDMhGolu913idqhh0uVFVNURTlClAn3zVaVVXP5rvvHKDNveZAvuPVgQRTD7okUrNT+fr417zY/EU8H/O8/w1CPMKsra2ZNs38nyz36iXr0QmzeeD5Lrfk7R1FUaYBHujns8Dcy+MKtG+2+e6ilRW7u3enX2KioWS7LNaQzNJmMe7ncTSq3oj3ur5n8vaFEEKYXGme7QKAboqifJD7WgGsFEXRAuNVVf0qX3vmmevySrYz7bCy0XHpkv5wURmSuy7swtHGkQ61O5T5sIQQoiIqzxmSx4Dm+V7/lvu9ad4BRVGc0Zemxee7xkZRlIb57muAPvAan+++hoADUHQ9Zxlb9b9V3M66zUS/ey2FIoQQ4hFRmvkOAFVVc1RVvayqajYQDOxXVfVGvvvMOt/dUVXiq1cnJyenTHfZ3hi9kfM3z/Of3v/B0dZixQ5CCCFKrjRzXSvAO9/X++gzIb2BDfnuM99cp00FxQZtttV9MyR3x+7m6bpPy/JcQghRjPIckNwONFMUxQ0gN+vxR/RrZnVWFKU5sAK4AWzJvWcX+onoa0VRfBRF8QG+Rp8ZeThf212BC6qqnjPPW7lLp+pYdHAR7Wu1x8/Lz9zdCyGEKH8eeL5TFKWGoijjFEVppiiKt6IoC4AX0a/DlZ9Z5zuNVguAra1tmZZsLz26lPpV6/Ns42dN3rYQQogy8cBznaqqp/J/AZcBXe7rW/naNt9cp0kFG2d0WpXMbCvCwqBaNXApsGfN1TtXOZ1wmsD6gUU2I4QQohwHJFVVPQkcRL9BTZ7h6NfJ+gn9p2oOQGDuAv/kblrTD/1Etg/9xHcJ+Efehja5gtGvv2V2J6+f5K/Ev3jN9zWKW5hZCCHEo6M0812ul4FDuedbAN1z1+DKz6zzXXZuQNLOzq7MSrbPJZ1jT9wexrYdKxvZCCFEBfEQc11JmG+u06ai2jgRH6vj6HErrl+H1auh4GPd7tjdALJ+pBBC3EN5XkMS4EP0n5otyS1LuwOMzf0qkqqqV9FniRRJUZSW6NP8XzL1YEvir8S/AGhfq+AeO0IIIR5hDzTfqaqaCHS8V4OWmO+y82VIllXJ9vJjy7FWrBnlPcqk7QohhChzD/xsl5+qqmFAWP5jZp/rtGlcT3LmQoyOajWsOXUK3N0LjZM1J9bg5uhGG482ZhmWEEJUROU6tUBV1QhgMVDEMsGlVgt4WVVV827Zm+tM0hkUFBpVb2SJ7oUQQpRDlWK+U1Wq3LyJh05XZiXbmhwNYcfD6Nekn2wKJ4QQFUxlmOuuXUrl0jVnvDx1+LSzKhSMBPji0Bdsj9nOjKdnSCa/EELcQ3nPkERV1YUmbm+HKdt7UGeTzlLHtY4swi+EEMJIhZ/v0tLwOXoUn8GDwcoKnVaHYqWYdHmSLWe3cD3tOiFtQ0zWphBCCPOpqHPd1aswcCDMezYVR0dnatfSFbkkyYnrJ3hrx1v0adxHNjAVQoj7kI9szOxM0hmauDWx9DCEEEII00rJTU5xdQVAl6Mz+YY2S48uxesxL3o36m3SdoUQQoh7+fBDOHgQHG1TebKlE+QUDkima9IJ3hhMVYeqrPjHCtkvQAgh7kMCkmakqipnk85KQFJUKiNHjqRfv36WHsYj59y5c7i7u5OSYt7VJz7//HMGDBhg1j5FBZGSwu6AAMLv3AFAzVFNun5kQloCEecjGOU9Churcl/gIYQQopK4ehWWL9f/2ck+DSt7Z3RafUAy9LdQbGfZYv2RNc7/cuZ0wmlWP7eax50et+yghRCiApCApBndSLvB7azbNHVraumhiEdAUYHCLVu2UKVKFd577z0Ljap4e/bsQVEUnnzySbS5G2PkqVevHnPnzi2zvhMSEhg/fjz16tXD3t4ed3d3AgMD2blzZ5n1+bCmTZvG+PHjcc3NRouLi0NRlEJfERERRvft3buXdu3a4eDgQIMGDViyZMkD9RsSEsLhw4eJiooy2XsRlURKCkk1anAz99+vrojskYfxV+JfqKh0qdPFZG0KIYQQ9zNr1t0/O9mncuykPiCZlpPG9F+m07VOV6Z1mcZ7Xd9j06BN9GzY03KDFUKICkRSDMzoTNIZAMmQfATpdDrOnz/P1atX8fT0pFGjRlhZmffzgNWrVzN27FhCQ0N58803zdr3/Wg0GsOf4+PjWb58Oa+++qrZ+g8KCiI9PZ3ly5fTqFEjbty4wd69e0lKSiqzPrOzs7GzsyvVvRcvXmTTpk3Mmzev0LmIiAjatLm7o2P16tUNf46NjaVPnz6MHj2aNWvW8OuvvzJ+/Hhq1qxJUFBQifq2t7dnyJAhLFy4kK5du5Zq/KKSSklBY2tr+P9apzVtyXbMrRgAGlZvaLI2hRBCiHu5ehVWrIC8z8qdHVI5tt+Z7CwdZxKOYW9jzzdB3+Dh7GHZgQohRAUkGZJmdDbpLCAByUeNTqdjzZo1bNy4kT179rBx40bWrFmDTqcz2xgWLFjA2LFjWbZsWaFg5IoVK2jevDkODg40adKE+fPnG8YWHBxcKFCl0+l44oknmD9/fpF9RURE0LVrV6pVq0b16tXp3bs30dHRhvN5mXzh4eEEBATg6OjIl19+aTg/ceJEZs6cSVpaWrHvJzs7m3feeYfatWvj5ORE+/bt2b59u+G8n58fn376qeH10KFDURSFa9euAZCeno6dnR2//fYbycnJREVF8cknnxAYGEjdunVp3749U6ZMYfDgwUZ9Tps2jbp162Jvb0+DBg1YuPDuuuz79u3Dz88PBwcH3N3dmTx5MtnZ2Ybz3bt3Z9y4cUyZMoWaNWvSuXNnAFJSUnjllVd4/PHHeeyxx+jWrRuHDx8u9r0DrF+/nlatWlGnTp1C59zc3PDw8DB85Q96LlmyhFq1arFo0SKaNWtGSEgII0aMMGSfJiQk4OnpyUcffWS458SJEzg4OPDdd98Zjg0YMIDNmzeTnp5+z3GKR0xyMtl2dtjZ2wOmL9mOuRmDtWJNXde6JmtTCCGEuJdZsyD/r+zO9qncyXAmISmNi6kXmfH0DAlGCiFEKUmGpBmdSTyDvbU9dVwLBxFExRIWFlboWIsWLWjfvj0ajYa1a9cajqenp5OYmIiqqoA+sBUXF8eSJUuoUqWK4TpfX19atmxJSkoKP/zwg1HbI0eOLPVYZ8yYwbx58/j+++/p27ev0bmlS5fy/vvvs2jRItq1a8epU6cICQnB1taWCRMmMGzYMIKCgkhOTqZq1aqAvuT36tWrBAcHF9lfWloakyZNonXr1mRkZDB79mz69+/P6dOnjYJj7777LnPnzmX58uXY2tpy/vx5AN544w3Cw8P57LPPmDFjRpF9jBo1ipiYGL755htq167N1q1b6d+/P4cOHaJNmzZ0796dyMhI3nnnHcOYa9SowZ49exg8eDC//fYbtra2PPXUUyiKgrOzM5s3b6ZLly44ODgU2eeIESOIiopiwYIF+Pj4EB8fz8WLFwG4fPkyzz77LMOHDycsLIyYmBjGjh2LlZWVURbjmjVreOWVV4iKikJVVVRVpW/fvri6urJlyxaqV6/OypUrCQgI4MyZM3h6ehY5lqioKHx9fYs89/zzz5OZmUnjxo2ZPHkyL7zwguHc/v376dWrl9H1vXv3ZuXKlWg0GmrWrElYWBj9+/enZ8+eeHt7ExwcTHBwsFE7vr6+aLVa9u/fT2BgYJHjEI+g3AxJ59x/Q6Yu2Y65FUMd1zrYWtuarE0hhBDiXvbvh+xsoF4kts+NxNZGy+0MB7J0t3BydOJNv/JVdSSEEBWJZEia0dmbZ2lUvRHWVtaWHoowo+zsbEMwMo+qqkbZc2Vl586dzJ49mw0bNhQKRgLMmjWL0NBQXnjhBerXr0///v2ZOnUqX3zxBaAPVrm4uLBx40bDPWvXriUwMBAPj6I/DQ4KCiIoKIjGjRvTunVrVqxYQWxsLAcPHjS67o033jD0W7t2bcNxBwcHZs2axb///W8SEhIKtR8TE0N4eDjffvstTz/9NA0aNGDChAn06dPHkGnZvXt3fv31V7RaLefOnTNkIUZGRgL69So7deqEra0tNjY2hIWFsWbNGqpWrUrHjh2ZMmUKBw4cMPR57tw51q1bx7JlywgKCqJBgwb4+/vz8ssvA/DFF1/g6enJF198QbNmzejXrx+ffPIJn3/+uVEWYf369Zk3bx5PPvkkzZo1IzIykuPHj/Pdd9/x1FNP0ahRI2bNmkWDBg1YvXp1sX+v8fHxhYKVzs7OzJ07l2+//ZatW7cSGBjIoEGDWLNmjeGaa9eu4e7ubnSfu7s7Wq2WxMREQP93Pn78eIYOHcr48ePJyspi0aJFRvdUqVIFV1dX4uLiih2jeASlpFDryhU8vLyAsinZlnJtIYQQ5nTsGPxyIZIqIf1wrvY3APUHfYtCDh3qdsDext7CIxRCiIpLMiTN6GzSWZrXbG7pYQgTuFfGoq2trdH5s2fPsnHjRqMApJ2dHX369KFJk8Ll+66urg+VEZlfXsblhx9+SOfOnQ1ZjqAvz7148SKvvvoq48aNMxzXarWGAKqNjQ2DBg1i7dq1jBkzhqysLDZu3GhUqlxQTEwMM2bM4MCBAyQkJKDT6dDpdPz9999G1xWX4QcwfPhw5s2bx6xZswr1dfToUVRVpXlz439LWVlZBAQEANC1a1eysrI4dOgQp06domvXrvTo0YPXXnsN0Ack+/TpY7g3KCiIvn37EhUVxf79+4mIiGDevHl8/PHHTJs2jWPHjmFlZYW/v3+R442OjqZjx45G64J26dKF7Oxszp8/T+vWrQFo166d0X1HjhwhPT2dmjVrGh3PzMwkJiam2P8+GRkZhTI5a9SowVtvvWV47evrS2JiIqGhoQwbNsxwXFGMA0R5f9f5j3/66adERESwatUqfv/9d5ydnQuNwdHRkYyMjGLHKB5BKSn027YNNm8GyqZk+4XmL9z/QiGEEMJEImMj6Rfej3RNOm65T877r53CSxlIA7cGlh2cEEJUcBKQNBOtTkvMzRgGNh1o6aEIM2vUqBFeXl5cvnzZsJGJl5cXjRo1KvO+PT092bx5MwEBAfTo0YOdO3dSrVo1AMM6kUuWLKFTp07FtjFs2DA6derE5cuXOXDgANnZ2Tz33HPFXt+/f3+8vLz48ssv8fLywsbGhubNmxfKCHVyciq2DSsrKz755BMGDhxYaM1LnU6HoigcObTu6wAAIABJREFUOnQIW1vj0k1HR0dAny3Ytm1bIiMj+fPPP/H396djx47Ex8dz7tw5Dh06RGhoqNG9Dg4O9OzZk549e/L+++8zduxYZs6cyZQpUwpluBakqmqhQF+e/McLvmedToe7u3uRO1a7uLgU21+NGjW4devWPccE+rU0V6xYYXjt4eFhWEczz40bN7CxscHNzc1wLC4ujosXL6IoChcuXMDPz69Q2zdv3iwUSBWPuJQUcHWF3P/n1RzVZBmSKZkpJGUk0bCaZEgKIYQwj/zBSADn3M/YUnWg1Wi5kn7FgqMTQoiKTwKSZhKXHIdGp5ENbR5BVlZWDBs2jPPnz3Pt2jU8PDzMusu2l5cXe/bsISAggMDAQHbu3Imbmxvu7u54eXkRExNjKD0uip+fHw0bNiQ8PJz9+/czcODAIjPmAJKSkoiOjmbx4sWGbMKjR4+izdua8AH06dOHzp0789577xkd9/HxQVVVrl27VmzGImBYRzI6OppJkybh4OCAn58fH3/8sWH9yHtp3rw5Wq2WzMxM2rZti06nIzIykmeeeabIa7/99lt0Op3h7/XXX3/Fzs6Ohg2LD6C0bduW69evY2VlRYMGJf+U3cfHh9OnT9/3uuPHjxuVdnfs2JFNmzYZXbNz5058fX0NwV2NRsPQoUMZMGAAfn5+jBs3js6dOxttoBMTE2P47yJEnuTUVFaMGcOzf/3Fk08+iU5rujUkZYdtIYQQ5jbqx1GGYCSAc+5nbGk6UHQK2+O2M5axFhqdEEJUfLKGpJmcSTwDQNMaTS08EmEJVlZWNGnShKeffpomTZqYLRiZx9PTkz179pCdnU1AQIBhvcCZM2cSGhrK/PnzOXPmDKdOnWLVqlXMmTPH6P6hQ4eybNkyfv75Z6Py34KqVatGjRo1WLp0KefPn2fv3r289tpr2NiU7rOP0NBQNmzYYJTV16RJE4YOHcrIkSP57rvvuHDhAocPH2bu3Ll8//33huvyApJ37twxBM66d+/OmjVrDOtHgj6IGhAQwJo1azhx4gSxsbFs2LCB0NBQAgMDcXFxoXHjxrz00kuMHTuWjRs3EhsbS1RUlGGdx/Hjx3PlyhXGjx9PdHQ0P//8M1OnTmXChAlGGxcV1KNHDzp37sw//vEPtm3bRmxsLPv37+eDDz4oMmsyT+/evfnjjz+MAr0rV67km2++ITo6mjNnzjB37lwWL17MG2+8Ybjmtdde49KlS0yaNIno6GiWLVtGWFgYU6ZMMVwzY8YMbty4wX//+1/efPNNOnTowPDhw412hY+KiqJBgwY0btz4vn+H4tFxJzOT205OWFvr10nW5ehMVrIdczM3ICkZkkIIIcxkxT9WUMX27u9xTnkZkipY6ax4tumzFhqZEEJUDhKQNJOzSWcBJENSWIy7u7thUxd/f39u3LjB2LFj+frrr1m9ejVt2rSha9eufPXVV9SvX9/o3mHDhnHmzBlcXV3p2bNnsX1YWVmxfv16Tpw4QcuWLXn99deZNWsW9valW/C7ffv2vPDCC2RlZRkdX7FiBaNGjeKf//wnTz75JP369WPfvn3UrVvXcE3Xrl1RFIWuXbsaAiT+/v7k5OTQvXt3w3XOzs506NCBBQsW0K1bN1q0aMG0adMYMmQI69evN1y3atUqhgwZwsSJE3nyyScZOXIkKSkpgD4Lddu2bRw7dgxvb29Gjx5NcHAw//rXv+75/hRFYevWrQQEBBASEkLTpk156aWXOHPmDLVq1Sr2vj59+uDo6Mj27duNjs+ePRtfX1/at2/PunXr+Prrr5k8ebLhfP369dm6dSv79u3D29ubjz/+mIULFxIUFATodyOfN28eq1atomrVqiiKQlhYGNHR0Xz66aeGdsLDwwkJCbnnexOPnlSNBsCQQW3Kku28DMkG1WS9LiGEEObhX9+fLcFbDEHJvJJtrZUDdthRr0Y9yw1OCCEqAeV+a6MJ8PX1VQ8fPvxQbYzbMo71f64n6Z9Jxa41J8qn6OhomjVrZulhCGFkyZIlbNiwgd27d5u131OnThEYGMjZs2dxdXUt9rp7/btRFOWIqqrF72okLOJh57rDAwfys48P//d//8djjz3G+ufWczPmJuNOjLv/zfcRsjmEzWc3c33K9YduSwghzEHmuvKpNHNd3lqSA+zTCfeEP9qEsb1lHN0+6Eb3md3LZqBCCFEBPOxcJxmSZnIm6QxNazSVYKQQwiRCQkIIDAw0ZGmay5UrV1i1atU9g5Hi0ZSa+z1vmQKTlmzfipFybSGEEBaRlylZ16k6AE/VCQAw2TrJQgjxqJKfomZyNumslGsLIUzG2tqaadOmmT0w2KtXL3r37m3WPkXF4Hb1Kq2zsgxLJJi6ZFs2tBFCCGEp/vX9+aT7DAB0OAISkBRCiIclP0XNIDU7lct3LtPUTTa0EUIIUQnpdLQ6dIjn8m1gZapdtrO0WVxMuSgZkkIIISxLq68FkICkEEKYhvwUNYNfYn8BwMfDx8IjEUIIIcpAaiqqqkK+jF1TlWzHJcehokpAUgghhGVpU8HKHp1OXwkgAUkhhHg48lPUDMKOh/G40+P0aNDD0kMRQgghTC8lhc/feIOf822UZ6qS7bwdtqVkWwghhEVpUsHWGZ1WB0hAUgghHpb8FC1jCWkJ/HT2J4a1Goatta2lhyOEEEKYnHrrFrddXLC1tzccM1XJdszN3ICkZEgKIYSwJG0qWDtJQFIIIUxEfoqWsfBT4Wh1WkZ4j7D0UIQQQogykX3zJlpbW5wfe8xwzFQl2zG3YnCydeJxp8cfui0hhBCi1LRpkiEphBAmJD9Fy1BkbCRv73ibxtUb09q9taWHI4QQQpSJ1KQkAJzzrSFpypLthtUboiim2bFbCCGEKBVtKthIQFIIIUxFfoqWkcjYSPp804dsXTZxyXFExkZaekhCCCFEmUhNTgbAqXp1wzFdjulKtqVcWwghhMVJQFIIIUxKfoqWgcjYSPqF9yNTmwmARqehX3g/CUqKcqt79+5MmDDB0sOoFG7duoW7uzsxMTFm7XfLli14e3uj0+nM2q8QAFXS02l/8CButWoZjum0pS/Z3n5+O4sPLmbxwcVcuHVBApJCCCEsTwKSQghhUvJT1MTygpHpmnSj4+madAlKPqquR8KmevrvRb0uIyNHjkRRFBRFwdbWlscffxx/f38WL16MRqMxuvb7779nzpw5ZTqe/JYtW4aPjw/Ozs64urrSunVrpk+fbrb+y9K//vUv+vTpQ8OGdwMoeX8P+b+WLFlidN/Jkyfp1q0bjo6OeHl58dFHH6Hm27H4fvr164e1tTVr16412XsRoqRqpqbSZ+tWqtaubThW2pLtbee28czaZ5iwbQITtk0gKyeLp7yeMuVwhRBCiAenTQMb2dRGCCFMRX6KmtioH0cVCkbmSdekM+rHUWYekbCo65Gwpx+kx+u/nw41fl3GQckePXpw9epV4uLi2PH/7N13eFRV/sfx9zeBEHpVqhRBEURERTG6SFlsu2JZO6sSu65tFdeyFpB1XV27u6uuugv29lNX7IhSooIKgoIiNlCkSROEECBwfn+cO+EyTEISpmRmPq/nuc9kzj333nMmmflmzj1l7FgGDx7M8OHD6du3L2vXri3L16xZMxqGFqNIpP/+979ceumlXHDBBcyYMYPJkydzww03UFwc+30TT4WFhYwYMaLS+Tt27MiECRMqnb+4uJhHHnmEs88+e5t9Dz/8MIsWLSrbhg7dstDV6tWrOfTQQ2nZsiUff/wx9913H7fffjt33XVXpa8NcOaZZ3LfffdV6RiReFi/ahWb6tSB/PyytOoM2V67YS0XvnYh3Vp0Y9GwRfx05U+suGoFJ+55YryLLCIiUjXqISkiElf6FI2zUceMol7tejH31atdj1HHjEpyiSSlJp8Jm4KGtk3FMPOmrZ9PTmwDdZ06dWjVqhVt27alV69eXHHFFUyYMIFPPvmEv//972X5oodsv/jii/Ts2ZO6devSrFkz+vXrx5IlS8r2v/baa/Tp04e6devSvHlzBg8eTEmJn6Jg5cqVDB06lKZNm1K3bl0GDRrE559/XnbsmDFj+N3vfsf5559Ply5d6N69OyeeeOJWjW8jRoygR48ePPPMM3Tu3JmGDRty7LHHsmzZsrI8hYWFHHXUUdx88820bNmSBg0acOaZZ7Ju3bqEvJaV8frrr5OTk8PBBx+8zb4mTZrQqlWrsq1u3bpl+5588kmKi4t59NFH6dGjB8cffzxXX301d911F845SkpK6NGjB2eddVbZMQsXLqRFixbccccdZWlHH300U6dO5ZtvvklsRUWivOUc915yCYQWnqnOkO3hE4bz/arveWjwQ7Rq0Iqd6u9E07pN411cERGRqtuoBkkRkXjSp2icDeg0gFdPfXWbRsl6tevx6qmvMqDTgBSVTOJm2h9hXP/KbXmN2epttincCzAH8ppU7jzT/hi34vfo0YMjjjiCF154Ieb+xYsXc8oppzB06FBmz57NpEmTOP3008v2v/nmmxxzzDEceuihTJs2jfHjx9OvX7+yuQsLCwv58MMPefnll/noo4+oV68eRxxxRFlDYatWrfjoo4/47rvvKiznvHnzePbZZ3nppZcYO3Ys06dP57rrrtsqz8SJE/n000955513eOGFFxg7dixXX331jrw8O6SoqIj99tsv5mrAl112GS1atGD//ffnwQcf3Gqux8mTJ9O3b9+tGikPP/xwFi5cyLx588jPz+epp57iqaee4vnnn8c5xxlnnMHee+/NsGHDyo5p3749LVu2ZOLEiYmtqEiUNZs3U3/Dhq3Sqjpke/qi6dwz5R7O3fdcftX+V/EuooiISPU553tI1laDpIhIvNRKdQEyUaRRMjKXpBojs1heU6jfAdZ+D4QXG8mB+h19g2QKdO/enXHjxsXct3DhQjZu3MgJJ5xAhw4dAN+IGfGXv/yFE044gZtvvrksrWfPngB8/fXXjBkzhokTJ3LIIYcA8Pjjj9O+fXuefPJJzjnnHIYPH86nn35K586d6dKlC3369OGwww7j1FNPpXbt2mXnLC0tZfTo0TRu3BiA8847j1Gjtu5hnJuby6hRo2jQoAE9evTgtttu4+yzz+Zvf/sb9evXj8MrVTXff/89rVu33iZ95MiRDBgwgAYNGvDOO+8wbNgwli1bVjZv5uLFi2kXmnsPoGXLlmX7OnXqRM+ePbn11ls577zzmDx5MtOnT+ezzz7bpvGzTZs2zJs3LzEVFCnHWqBBaG5a5xzFy4up07hOzPyr16/m9a9f57WvX2PFuhUAzPppFi3qteC2Qbclo8giIiKVt6kEcJpDUkQkjtQgmSCRRskzXz6TUceMUmNkJtnvnsrnjcwhSfTKx5uhZDEc+B9o2T+Ohasc51zMXnwAe++9N4MGDaJHjx4cdthhDBo0iBNOOIGddtoJgOnTp1NYWBjz2NmzZ5OTk0NBQUFZWuPGjdlrr7344osvAGjdujWTJ09m1qxZTJw4kQ8++IDzzz+fu+++m/fff5969Xzv4g4dOpQ1RoJvaPvpp5+2ul7Pnj1p0KBB2fOCggI2bNjAt99+S8+ePbnlllu45ZZbyvavX78eM9tqmPMbb7xB3759ATjyyCMpKioq21dcXMyRRx5Jbm5uWdqaNWti1h1g3bp1ZQ2JYTfccEPZz7169WLTpk389a9/3Wohn+jfR2RBm3D6ZZddxpgxY7j77rt57rnnaNu27TbXqlu3bkqHrUt2WlOrFjuHFmFat3wd61etp/luzbfKt9lt5pwx5/DkzCfZsGkDO9XbiQ5N/I2PNg3bMLL/SA3RFhGRmqc0+P9PQ7ZFROJGDZIJNKDTAOb9cV6qiyGpFJ5DEiC3XtQckoVw7LykF+uLL75g1113jbkvNzeXsWPHMmXKFMaOHct//vMfrr32WiZOnMjee+9d4XkrWhU6usGtR48e9OjRg4suuoj33nuPvn378txzz5U1doZ7S0aODw9zrowLLriAk046qez51VdfTdu2bbn00kvL0sKNeo888shWjXn9+/fntttuo0+fPpW6XosWLVi5cuV28/Xp04fVq1ezZMkSWrZsSatWrVi8ePFWeSKNr+EGzmXLljF79mxyc3PLnSdyxYoVZY3HIsngnGNNXh71Q+/x5V8vB6BZl2Zb5X1o2kOMmjGKc/Y5h6G9hlLQroDcnFxERERqNDVIiojEnT5FRRKpYJRvhAT/uNeIrZ8XJH+Ro1mzZvHmm29ywgknlJvHzCgoKGD48OF8/PHHtGnThmeffRaAffbZh3feeSfmcd27d2fz5s1Mnjy5LG316tXMnDmT7t27l3u9yL6Keh/GMnPmzK1WC58yZQp5eXl07twZ8KuHd+nSpWxr2LDhNmnheRvbtm271b5atWptk1aRffbZp6wnaEVmzJhBfn4+TZr4IfsFBQUUFRWVLQwE8Pbbb9OmTRs6duxYlnbOOefQuXNnnn32WYYPH860adO2Om9JSQnffvst++6773bLIBIv7373Lvt9MJ6ma7csOrXiGz8Mu9luWxokF/2yiGvGXcPATgN5aPBD/Kr9r9QYKSIi6UENkiIicacekiKJ1HIA9H/V95QsGO2HZzfvHTwf5fcn0Pr161m8eDGbN29m6dKlvPPOO9xyyy3st99+XHnllTGPmTJlCuPGjePwww+nZcuWTJ8+nfnz55c1Gl533XUMHjyYLl26MGTIEJxzjB07lvPPP5/ddtuNY445hvPPP5+HHnqIJk2acN1119GoUSOGDBkCwIUXXkibNm0YOHAg7dq1Y9GiRdx8883Uq1ePww47rEr1Ky0t5ayzzuLGG29k4cKFXHPNNZx77rkpmT8S/EI0V199NcuXL6d5cz9U9ZVXXmHx4sUUFBRQt25dxo8fz4033sh5551HnTp+fr0hQ4Zw0003UVhYyPXXX89XX33FrbfeyvDhw8t6lj744INMmDCBGTNm0KlTJwoLCxkyZAjTp08vG+Y+ZcoU6tSpE3OVb5FEGD93PEc/ezSLJhXzWO9arJ47ngGdBrDi6xVYjtG005bh15e9eRklpSU8+NsHy50yQkREpEbaGGmQ1BySIiLxok9RkURrOcAPy47MFVn2PPHzio4bN47WrVvTvn17fv3rXzNmzBiGDx/OpEmTym20a9y4Me+//z5HHXUUu+22G8OGDeOGG27gtNNOA+A3v/kNL730Em+88Qb77LMP/fr1Y/z48eTk+I+TUaNGccABB3D00UdzwAEHUFxczJtvvlnWE/HQQw/lww8/5KSTTmL33XfnuOOOA3yPwN13371K9evXrx977rknAwYM4LjjjmPgwIH8/e9/r+7LtcP22msvDjjgAJ555pmytNq1a3P//fdTUFBAz549uffeexk5ciR33nlnWZ7GjRvz9ttvs3DhQnr37s1FF13EsGHDuOKKKwCYM2cOw4YN4x//+AedOnUC4J577sHMuPzyy8vO8/TTT/P73/++rIFSJJHGzx3PUU8fxeb1m9hctzHL8jbz26d+yytzXmHxl4tp2L4hazavYVXJKl6a/RLPf/E81x9yPbs13y3VRRcREamaTcGIHPWQFBGJG6tozjfxevfu7aZOnZrqYkiKzJ49m27duqW6GBKlsLCQZcuW8eqrr6a6KFt58803ueyyy/jiiy+2Wgwn0ZYuXcoee+zB1KlTyxotU6mi942ZTXPO9U5ykWQ7qhLrIo2RxRuL6bNhL47MO57VM/7JXb38sO1zHzqXdXXX8cTpT5Qd032n7kw/fzp5uXkJKb+ISE2iWFczVft73fz/QdFxcOR0vng3j+dPeJ4LZ17Izj12jn8hRUTSxI7GOg3ZFhGJoyOOOIKLLrqIH3/8kQ4dOiTtunPnzuX++++vEY2RkvnOfPlMijf6BbqalzaAPFhlwXA2By1WtKDBbxpw12F3AX5e2uO7Ha/GSBERSS9Lxvupljr6qYf4eRZd1l9Fx+6HqYekiMgOUoOkiEichVfxTpYDDjiAAw44IOnXlew06phRHPX0UazbuI62ObtgmzdT2rQ9xtc029iMOiV1GNh3IAcWHJjqooqIiFTPkvEw4SjYVAyz7/BpH55HHus49cqnKCk+DjgmpUUUEUlnaX1bx8z+YGZzzazEzKaZWd/QvrvMbIWZzTez30cdN9jM3jPNqi+StkaPHl3jhmuLJEJNjHUDOg3glZNfYagNpXWdrjgzdml3PENtKI/s8wiw9QrbIiIiFamJsY7JZ/rGSAC30T9uXgdAXp2NNJh7SdwvKSKSTdK2QdLMTgbuBW4B9gE+AN4ws/ZmNhgYAhwGXAU8YmYtguMaAncD5zlNoCkiIjVYTY517Ta1Y9fcXcmxXDCjVk4dds3dlbrL/AJWzbqoQVJERLavxsa6glGQG3uhwA3ra7Ou6wNxv6SISDZJ2wZJ4ApgtHPuYefcbOfcJcAi4EKgGzDBOTfVOfc0sBqITKx2C/CEc+6LlJRa0pLarkUqT++XuKqxsW7RokVlK41GbC7dzKKFi7Aco2mnpom6tIiIZJaaGetaDoC9hm/TKLnJ5TPxhf64Fv0SclkRkWyRlg2SZpYH7AeMjdo1FjgI+BTobWZNzWw/oC7wjZkdCAzABy+RSqlduzbr1q1LdTFE0sa6deuoXbt2qouR9mp6rGvdujV5eVsvUpOXl4db6GjcoTG5eclbZV5ERNJTjY51S8bDzJu2DNsO5FoJ/Y6fQK3V7yXs0iIi2SAtGySBFkAusCQqfQnQyjn3FvAE8DEwGhgKrAH+DVwAnGlms4P5SQ5KWqklLe28884sWLCA4uJi9fwSqYBzjuLiYhYsWMDOO++c6uJkghod67p06ULbtm3JA9i8mbzatWnbti0bZmyg+W7N4305ERHJTDU31oXnkIStekrm1dlInVnnx/VyIiLZJt1X2Y5uHbJImnNuBDCibIfZ9cBkYBUwEugF7AU8b2adnHMbklBeSUONGjUCYOHChWzcuDHFpRGp2WrXrk3Lli3L3jcSFzUy1uXk5HDaaafxzZ//zOKPP6bVAw/QuXNnbr/kdnY5cJd4XUZERLJDzYt1BaO2rLKdWw/2GgEzR8CmYjasr40d9DAaDyIiUn3p2iC5DNgEtIpK35lt765hZrsDZ+EnSR4KTHLOLQIWBcMEugIzE1piSWuNGjVSA4uIJFuNj3U5OTnsvmIFu3/+Oey+O2uXrmX96vVa0EZERCqr5sa6lgOg/6u+p2TBaGjZH5r3puStU3n2jiP4/akD43IZEZFslZZDtoO7XtOAQ6N2HYpfla2MmRm+S/+VzrlV+DrXDu2rjR8mICIiUmOkRazbtAlmz4bSUnj1VVZ8uRSAZrupQVJERLavxse6lgPg2Hm+MTJ4/tH855j3RSdyaqXlV2kRkRojnT9F7wIKzewcM+tmZvcCbYAHo/KdDfzsnHsxeP4eMNDMfoVfuW0jMCcRBSxZVcIzxz1DyaqSRJxeREQyX82NdZs2weGH4yZPxi1fjjvlFJpccDLGZs0hKSIiVVFzY10g/L1uc+lmACzXEnEpEZGska5DtnHOPWtmzYHrgdbALOA3zrnvI3nMrGWw/+DQcVPN7G/AS8AvwOnOuYQsoTxnzBzm/G8OX73yFT1P65mIS4iISAar0bHujTfgww+xTZv887Vrqfv1Z+xmXWjSsUlcLyUiIpmrRse6QPh73ebSzViu4TtliohIdaVtgySAc+5+4P4K9i8BOsZI/xvwt8SVzJv+3+llj2qQFBGR6qixsW76dFi7dquk3I3r6dj0Z3LzNBOKiIhUXo2NdYHI97pJf5mE5Rq5tRXnRER2VFo3SNZEjw16jLnvzAUo+0I2//353GQ3AdDp1504Y9wZKSufiIjIjnps0GPUemc2x1ObOmxZzHQDtZm7sjFfD3pMsU5ERNJa+HtdZHj28q+Wb7VfsU5EpPrMOZfqMtRIZnYecF7wtCuVnI+kDnUaNqNZF2LPz7l5BSu+Wc/6X+JUzJqmBX6lvGyQTXWF7Kqv6pp4HZxzO6XguhJlx2Jd0y7NWZGTx0YMh8PYQG2W02zzClYq1mWObKqv6pqZFOuynL7XVZs+JzKT6pq5UlHfHYp1apBMMDOb6pzrnepyJIPqmrmyqb6qq0jVZdPfUjbVFbKrvqprZsqmukpiZdvfUjbVV3XNTNlUV0jP+qbzKtsiIiIiIiIiIiKSZtK+QdLMmprZEjPrXMn8F5vZmESXS0REJJ4U70REJNMp1omIZI+0b5AE/gy87pz7FsDM2pvZK2a21syWmdl9ZpYXyv8w0NvM+iapfA8l6To1geqaubKpvqqr1FQ1Od5l099SNtUVsqu+qmtmyqa6ZgLFupojm+qrumambKorpGF903oOSTOrBywABjvn3jOzXGAGsBy4AmgOPAq84Jy7JHTcHfjJN09MQbFFRESqRPFOREQynWKdiEh2SfcGyROAfwMtnHPOzI4EXsMHpPlBntOAR4CdnXOrg7RDgLeBps654tSUXkREpHIU70REJNMp1omIZJd0H7LdF5jmtrSqFgCzIwEr8BZQB9gvlDYVqBXkFxERqekU70REJNMp1omIZJF0b5DsACwKPW8FLInKswzYFOwDILhztgromKiCmdkfzGyumZWY2bQkzlmZMGZ2rZl9bGarzWxpMJ9Lj6g8ZmYjzGyhma0zswlmtmeqyhwvZvZnM3Nm9s9QWkbV1cxam9mjwe+2xMy+MLN+of0ZUV8zyzWzv4Ten3PN7GYzqxXKk7Z1NbNDzGyMmS0I/mYLo/Zvt27BhPKPm9mqYHvczJoktSISrUbGO8W69PuMqIhiXebUV7FOsS5N1chYB4p36fg5UZFMj3fZEusgs+NdNsS6dG+QrAuURKWVNwY9On1dcHzcmdnJwL3ALcA+wAfAG2bWPhHXS6L+wP3AQcBAoBQYZ2bNQnmuAoYBlwBIrAiAAAAgAElEQVT7Az8Bb5tZw+QWNX7M7EDgXOCzqF0ZU9fgQ+l9wIDfAt3w9foplC1T6ns1cBFwKbAHcFnw/NpQnnSuawNgFr5e62Lsr0zdngL2BY4Ejgh+fjyBZZbtq3HxTrEubT8jYlKsK5Mp9VWsU6xLRzUu1oHiHen7ORFTpse7LIt1kNnxLvNjnXMubTfgSeC50PORwOdReXbCB6wBUenrgJMTVK4PgYej0r4G/pbq1yzO9WyAv0M5OHhu+Lua14Xy1AV+Ac5PdXmrWcfGwLf4ID0B+Gcm1hX/D9b7FezPmPoCrwKPRqU9CryagXVdAxRW5feI/6fFAQeH8vwqSOua6jpl61YT451iXfp/RoTKr1iXYfVVrFOsS8etJsa64NyKd1vypOXnRKj8GR/vsinWBWXPiniXqbEu3XtITge6h55PBrqZWbtQ2qHAemBaJMHMOgP5wCfxLpCZ5eHnNBkbtWss/u5TJmmI72W7MnjeCT98oqzuzrl1wCTSt+4PAf/nnHs3Kj3T6nos8KGZPWtmP5nZDDO72Mws2J9J9X0PGGBmewCYWXf8PyWvB/szqa7RKlO3AnzA+yB03PvAWtK//umsRsU7xbqM+4xQrPMyqb6KdYp16ahGxbrg3Ip3mfU5kQ3xLptiHWRvvMuIWJfuDZJv4YNU8+D5WOBz4DEz28fMBgG34+9orQ4d1xf4zjn3dQLK1ALIZdv5TpYQmuskQ9wLzMD/swBb6pcRdTezc4EuwA0xdmdUXYFdgT8A3wGH43+3t+K7u0Nm1fc2fDf1L8xsI/4z41Hn3P3B/kyqa7TK1K0VsNQFt9AAgp9/Iv3rn85qWrxTrMuQuivWKdZFHZeOdY2mWJe+alqsA8U7yJC6Z1G8y6ZYB9kb7zIi1qV1g6RzbibwEXBK8HwTfp6EYnzL77PAi8CVUYeeCjyc6OJFPbcYaWnLzO7Cd/c9Pnjdw9K+7mbWFd/d/ffOuQ0VZE37ugZygE+cc9c656Y750YB97ElcEVkQn1PBs4AhuDn0DgD+IOZnR2VLxPqWp7t1S1WPTOp/mmnBse7TH6fKNZtkfZ1DSjWKdYp1tVgNTjWQWa/VxTvtkj7upJdsQ4U79I61qV1g2TgJuBSM8sFcM794Jw7yjlXzznX3Dl3iXNufSSz+ZXDegEPJKg826z8FtiZbVuv05KZ3Y0P/AOdc9+Fdi0OHjOh7gX4O6KzzKzUzEqBfvgPt1JgeZAvE+oKfv6JL6LSZgORyboz6Xd7O3CHc+4Z59xM59zjwF1smfg4k+oarTJ1WwzsHBrWQfDzTqR//dNdTYp3inWZUXfFOsW6TKhrNMW69FaTYh0o3kFm1D2b4l02xTrI3niXEbEu7RsknXNvAv8C2m0vb6ANcIZzblWCyrMBP6fJoVG7DmXrsftpyczuxd99GOic+zJq91z8H/2hofz5+GEU6Vb3/wF74f/BiWxTgWeCn78ic+oK/q5z16i03YHvg58z6XdbD/+PZdgmtnweZlJdo1WmbpPxk5oXhI4rAOqT/vVPazUp3inWZcxnhGKdYl0m1DWaYl0aq0mxLiiP4l1mfE5kU7zLplgH2RvvMiPWJWv1nGza8N2GNwDn4Fc2uhc/mWiHVJdtB+v1L2A1fpLYVqGtQSjP1UGe3wE98B/yC4GGqS5/HOo/gWAltkyrK7A/sBG4Dj+3yonAKuCiTKsvMBr4ET8EqCNwHLAUuDMT6ooPOpF/tIqBG4Of21e2bsAbwEzgQHzQmgm8kuq6aatZm2Jden5GVKL+inUZUF/FOsU6bfHbFO/S83OiEvXPyHiXTbEuqEvGxrtsiHUpL0CmbviJZOexZRW4Q1JdpjjUyZWzjQjlMWAEvqt4CTAR6JHqssep/tFBK6PqGnyIfxrU5SvgUsAyrb74FQTvwd8lXIef8PkWID8T6gr0L+d9OrqydQOaAU8EAW518HOTVNdNW83bFOvS7zOiEvVXrMuA+irWKdZpi++meJd+nxOVqH/GxrtsiXVBXTI23mVDrLOgkCIiIiIiIiIiIiIJl/ZzSIqIiIiIiIiIiEj6UIOkiIiIiIiIiIiIJI0aJEVERERERERERCRp1CApIiIiIiIiIiIiSaMGSREREREREREREUkaNUiKiIiIiIiIiIhI0qhBUqSSzKzQzFw526Aqnuuc4Lh2iSpvIphZraDc14fSbjaz0lSWS0RE4kOxTrFORCQbKN4p3knq1Up1AUTS0InAj1FpX6SiICIiIgmiWCciItlA8U4kRdQgKVJ1M5xz36S6ECIiIgmkWCciItlA8U4kRTRkWySOzKyumd1rZp+b2VozW2RmY8ysayWOPd3MZgTHrTKzz8zsnKg8A8zsXTNbE2xvmFn3SpZtgJmNM7PVwTU+NbPC0P7fm9kEM1tqZr+Y2SdmdlqVXwR/rivMbLaZrTOzFWb2sZkdXZ1ziYhIzaJYV3YuxToRkQymeFd2LsU7SQj1kBSpulwzC793nHNuU/Bz3WAbCSwGmgMXAZPNbA/n3E+xTmhm/YBHgXuAYUAu0B1oGspzDPACMAYYgr+hcA1QZGY9nXMLyiuwmR0PPAdMAs4DlgE9gA6hbLsGeb4BNgP9gdFmlu+ce2Q7r0n4WkOB24CbgPeD12Pv4LUQEZH0oFhXAcU6EZGMoXhXAcU7SSQ1SIpU3ZdRz98HfgXgnFuBDwoAmFku8BawFDgZ+Ec55ywAljnnrgiljQ2dx4B7gXecc78LpU8AvgMuB66MdWIzy8EHw4+BXzvnNge7xoXzOef+EnXMBKAtcCFQ6aAV1GW6c+7mUNrrVTheRERST7GuYop1IiKZQfGuYop3kjAasi1SdccB+4e2s8M7zewUM/vIzFYBpcAa/J2kirr2fwzsZGaPmdlvzaxx1P498He8njS/Glqt4E7eGuBD4JAKzt0daAc8EgpY2zCzrmb2rJktADYGW+F2yl1eXfYLhjf82szqVfF4ERFJPcW6iinWiYhkBsW7iineScKoQVKk6mY556aGtjmRHWZ2HPA0MAs4FeiDD2wrgPzyTuicewd/l60j8D9gmZmNNbMeQZadg8dH2RJQItsRVNxlPrIvevW4MmbWCHgb2BO4GugblPvRispdjv8CFwMHBedcbmYvmFn7Kp5HRERSR7GuYop1IiKZQfGuYop3kjAasi0SX6cAXzrnzookmFk+0GR7BzrnngOeM7MGwED8XB1vBB/2y4NsVwHjYxy+voJTLwse21aQ52BgF6DAOTclVPba2yt3NOecAx4AHjCzZsDhwJ34YH5wVc8nIiI1jmKdYp2ISDZQvFO8kwRSg6RIfNXDd+UPO4Mq9EZ2zq0BxphZF/yHfVPgC2A+0N05d3sVyzQ7OPYcM/tvEFRilRv8XTkAzKw5MLiK19pKMO/K02ZWAAzdkXOJiEiNoVgXolgnIpKxFO9CFO8k3tQgKRJfbwL/NLM7gDfwXeMvAlZXdJCZ/RXf/X48sAhoj+8aPzX44MfMLgZeDO7KPY+/s9YK333+O+fcvbHO7ZzbbGZ/DI4ZZ2b/xt9Z2xNo6pwbiZ+8eQ3+ztcIoCFwA/ATfqhBpZnZf4CVwGT8hM9d8SvHja3oOBERSRuKdYp1IiLZQPFO8U4SSHNIisTXg8Df8B/Sr+C7tB8F/LKd4z4EdsWvmPZ2cI53CN3Fcs6NAfoBjYD/4Fd4uxU/B8mHFZ3cOfdiUJZcYBQwBj9h8/fB/sX4CZ3zgBeBvwZ1eaYylY7yHj5YP4gPVNfi5ys5q6KDREQkbSjWKdaJiGQDxTvFO0kgi93DV0RERERERERERCT+1ENSREREREREREREkkYNkiIiIiIiIiIiIpI0apAUERERERERERGRpFGDpIiIiIiIiIiIiCSNGiRFREREREREREQkadQgKSIiIiIiIiIiIkmjBkkRERERERERERFJGjVIioiIiIiIiIiISNKoQVJERERERERERESSRg2SIiIiIiIiIiIikjRqkBQREREREREREZGkUYOkiIiIiIiIiIiIJI0aJEVERERERERERCRp1CApIiIiIiIiIiIiSaMGSREREREREREREUkaNUiKiIiIiIiIiIhI0qhBUkRERERERERERJJGDZIiIiIiIiIiIiKSNGqQFBERERERERERkaRRg6SIiIiIiIiIiIgkjRokRSStmdk8M5uQ6nKIiIgkiplNMLN5qS6HiIhIIineZRc1SErKmVl/M3NmdmVUugu2J8o5boKZrYlKGxE6zpnZBjNbamZTzOxuM+tZzrlGRx0XvT0SylsYpJ0Qj/qXx8yuNbPnzey74HrztpO/j5mNM7NfzGy1mb1pZr1i5OtqZneY2btm9nNw7hFVLJuZ2Wlm9oyZfWNmxWb2g5mNMbM+5RyTY2aXm9mXZlZiZvPN7E4zq19O/t+Y2QdmttbMVgSvRaeqlFNEpKZQrItZnt3NbGRQ7qVB/JphZtdVEBu6mtn/zGxlEB+KzGxgjHytzeyvQSxcGtRldDXKONTM3jKzH4PYtdTMJgevT245x5xhZtPNbJ2ZLTGzR8xsp3LyVip2i4ikC8W7mOXpamZPmtlsM1sVfHf60szuMrPWFRyTtHgX47xHhl6v3uXkUbyTHVIr1QUQqYQhZnaHc25GFY65EZgL5AJNgV7A2cBlZnYX8CfnnItx3IXAmhjp31SxzPFwC7AC+ARoUlFGMzsQmAAswNcd4GKgyMwOcs7NDGUvAK4AvgWmAdsEtkqoAzwOzACewb/WrYELgMlmdoZzLvqfjbuBS4GXgDuBbsHzfcxskHNuc6g+vwP+D/gU+BPQGPgj8L6Z9XbOLaxGmUVEarJsjHVnARcBY4AngY3AAOBm4CQzO9A5ty6S2cw6Ax8ApcDfgVXAucBbZnakc25c6NxdgT8D84GPgSOrWcZ9gZXAv4CfgAbAb4FRQF/8613GzC4H7gImApcB7fAxt8DMDnDOrQ3lrUrsFhHJFNkY79rhvyu9BPyIj2N7AecBp5hZL+fcT5HMKYp3ZYKbgg/gX7sG5eRRvJMd55zTpi2lG9AfcMCVUekO+AwoAd6KcdwEYE1U2ojguN4x8jcD3gn2Xx21b3SQ3qIS5S0M8p6Q4Ndl19DPs4B5FeT9CFgNtA2ltQ3SxsZ4HZoEP/cO6jKiimWrBfSLkd4SWAYsAXJC6XsCm4EXovJfElx/SCitNj5YfQ80CKX3AjYBD0WdYx4wIdV/x9q0adNW0aZYF/MavYHGMdJvDq59cVT6c0Ec6BVKaxDEizmAhdIbAjsFP7cIzjc6jmV/LYhrrUJpLYC1QUzODaUPDq7/56hzVCV2T6jo/wBt2rRpqymb4l2VXqsTg2tfFZWe0niH70jyI74TyTavv+KdtnhtGrItNd0PwP3AYWb26x05kXNuBf5DfzVwbXnDweLFzJoFQwm+DYZ5LTezaWb2p0qW97tKXqcLsD/wvHNuQej4BcDzwCAzaxVKX+Gc+7lqtdmmbKXOuYkx0pfg75LtHGwRpwIG3BN1yMNAMXBaKK0f0AZ4xDlXdkfT+buoE4CTzax29LXNbF/zw9DXmB/i/aiZ7RyVJzIkY1AwBOR7M1tvZp+Z2SlVeAlEROIpK2Odc26qc25VjF3PBo89QtepDxyNvwFV1qsmiBOPALvjY2Ek/Rfn3NJqVqsyvsfHtcahtGOBesA/nHObQmV5BfiOUKyrauwOHbermb0cDPlbbWYvmdmuUXkiwyULzewSM/sq+N18ZWaXxKX2IiLVk5XxrgLfB49NQ9dJabwLhmdfgh+d9ks52RTvJC7UICnp4K/4buq3mZntyImCwPUS/gvEr2JkaWZmLWJsedW43PP4ruhv4D/UR+LvDvWvXunLFQlIk2Psm4L/wrRfnK9ZkXbABiDc6Lk/vifJR+GMzrkS/LDv/aPyQvn1aYQPxNHXfAcfAK8CXgROB8abWb0Y57kNOAU/FOFGIA942swKK66aiEjCKNZt0S54XBJK64mfLqS82ABbx5K4MrPGwWu0m5ldjB9u/hVbD/vbXvzaw8waVDJvrNhdHxiPj7HXAv8BfoOfzmSbL3P438c1wBNB/p+B+8xseLkVFRFJvKyNd2aWH1y/nZkdBvw72PV6KFvK4p2Z1cJ3GBnrnPu/CrIq3klcaA5JqfGcc8vN7O/44HUyfs7CHfFZ8Lg78FbUvjnlHHMifk7DSjGzxvi5GR9wzl1c5RJWTZvgcUGMfZG0tgkuA+AXogEOAB4PGhsj2gDLnHPrYxy2ADjIzPKccxuofH0+D6V3Bi53zpX1wDSzz/HzmlwK3Bp1nhZAz0jPHDN7EP93cZeZPetCc5aJiCSDYl3ZOXPxN4pKgadCu1Id695hyxcmB4wDLgj3DGH7ZbQgz1eVyAvb1qcFcK9z7o+RBDObhL8JNwI/j3PY7kA359yPQd5/Ae8B15vZfyLpIiLJlOXx7hzgH6Hn84DTnHNFobRUxrth+Dkpf7edfIp3EhfqISnp4h5gIXCzxRiuW0Wrg8dGMfYdDxwaY5tUxWusA9YDfcysY7VKWXmRHoCxGvtKovIkjJnthl/oZgE+mIXVI3b5YNsyVqc+q/G9HcPuD9KPi3GeB8LDBIOfH8QPl+hfTjlFRBJNsc6/BgcCNzrnwl8kUx3r/oB/jc7Az+1Vm9AQu6jrV6aM1a3PVjfYnHMv4b9wHxsj75PhL2HBTb+78R0SBsfILyKSLNka7/4XXP84fA/Ln4HoValTEu+C4dDDgb845+ZuJ7vincRF0hskzewQMxtjZgsi4/2j9pv5ud0Wml8+foKZ7RmVp6mZPR7MJ7Aq+LlJaH9HM5tkZmuDx45Rx79oZuclsJoSZ865YvzdkM5se0ekqiLBanWMfZOcc+NibD/FyFuu4EPwj/j5r+aa2edm9o8dnSulHMXBY50Y+/Kj8lSameWaWauorXE5eTuxZVLpI2PMY1JcTvlilbE69fkuuvdl8Pw7YFe2NTtG2hfBY6z8IlWiWCfVke2xzsz+gh8O95Bz7m9RuxMS64LrRse6ZtF5nHMfBa/R4865U/DDzCaZXwm1OmWsTn1+ds4tjpF/NtDStp0/TbFOEkqxTqorW+Odc+7H4Pr/c84NB4bih65fG8qWqnj3b/xK5ndU4lSKdxIXqegh2QC/YvBl+DsN0a7C9666BD/fwE/A22bWMJTnKWBf/JL2RwQ/Px7afye+l1YvYBGhN5WZHYvvAvxwfKojSfRf4Evghqi/h6rqGTyW14U/LpxzDwIdgXOBT4ATgHFmtqPDEqItDB5jdd2PpMXqIr89u+DfP+Ht3uhMwT+G4/Hv7UOdczPLKWMLM4sViNrih3NvCOUNlz06L2xbH1dOHcqblyZW/h2aw0YkimKdVFdWxjozGwFcD4wi9pfTRMU62DbWvViJYx7F9+goDKVtr4wulEexTjKBYp3siKyMd1Hn/AyYju+FH5H0eGdmxwGD8O+vDmbWxfxiNJEGy3ZBWqT9SPFO4iLpDZLOudedc38OJkndHN5nZoa/83Crc+4F59ws/F2DhsCQIE83fLA6zzn3gXNuMnA+cJSZdQ1O1Q141Dn3NTA6eI6ZNcIHtfOcc+X9kUsNFczTdC2+W/uV1TlHcBfoOPxEyu/Fr3SxOecWOececc6djp+k/2n8KtHxnIj44+CxIMa+A/Ef0NOqcd7FbDu84e/hDGbWAd8Y2RjfGDm9gjLm4OeXDB+fj/8Hc2pUXii/Pqvx85GEdbaoyamDxs9O+F6S0brHSOsWPFZqdXORiijWSXVlY6wzP+n8cOAx4Jxy/m5n4od7lRcbYOtYUhXRsS562pFY6gaP4d4lFcWvPsCcYJXU7eUtL3Y3tdiT+e8B/OScWxuVrlgnCaVYJzsiG+NdOeqydSxJRbzrEDz+F/g6tEVWqn4peB4pp+KdxEVNm0OyE9AKGBtJCBaXmAQcFCQVAGuAD0LHvQ+sDeX5FL98fA5wGFsmur0VGO2c+zJRFZDEcs79D/+7vwLYuSrHBgHreXy3/r8GQwUSwszqWdTqzkHQjfwtbjMcrLqcc9/gg9KJZhaZNJjg5xOBd8vp8r6985bEGN4Q6foeaYycgJ9D6zDnXEWNns/ig80fo9LPxfcweTKUNhF/x+4c27I6G2a2N35+x+edcxujztOIre8sEjxvhJ+rJdqFFhp+Hvx8AX4el4kV1EMkHhTrpELZFOvM7Eb8sL3HgTOdc5tj5Qu+2LwC9A/iQeT4BvhFAr7Gr3ZaZTFi3bTg3LXMrHk5h0W+pE0Jpb2M7yV2sfnFeSJlHIwfllgW63Ygdl8TfhL0aulK7Fj3ezNrF8qbB1wObAJeLadeIvGiWCfblS3xrpzGNcxsAH4YeFksSUW8w8eEE2Nszwf7rw6eR4bFK95JXNS0VbYjb9QlUelL2NKdtxWwNHwnzDnnzOyn0PFX4udAmIf/kDjfzA4C+gIDzOxJfACcgl8hMdZ8E1JzXQ0U4e96RN8diTjSzPbAN7o3BfbB3z1rCNzunLu9nONOMLM1MdJ/cs6NjUo7PrhGtG/x81hMNLOX8ENZVgblvRA/N0dRjOO2Ymans+Vu1U5AnpldHzz/3jkXHs5yGb6nYpGZRVZuuwRf/616egSNb5EvUpGgcEjo3GOC4QMVla1hcL2O+JXiuobuZEe87ZxbAuCcm2l+tbOLzexF4HX863EpvgGwbCVV59xGM7sM34hZZGYP4//RuBxYiu9FE+1bYLiZ9cDfYdsPOAs/DOS+GPmXAR+a2X/x3fnPBNrje+Yk7J8ZkYBinVRGxsc6M7sIuAn4Ab9q9RDfqarMEufc26Hn1wK/Bsaa2d34L0bn4t83v43uJRWKa5EvkT1DaZOcc9tb1KAB8GOofkvw779jgd74uZPD8Wupmd2AH/I2zsyeDso2DB+P7ok6f6Vjd2AZ8LvgS9wEYDf8zbcl+EbdaF/hY92DwC/4Xmn74xcsmL+duovsKMU6qayMj3fAA2bWGngX+B4/d+J+wCn4z+foz/ykxrug0fCb6PTguxX4RsOpofyKdxIfzrmUbfg7YoWh5wfhe1HtEpVvFPBm8POfgW9jnGsucE0518nDd30+CP+meRTfGPsE/gMspa9Dtm/4Xm8OuDIq3QGvlnPMy8H+NVHpI4L0yLYB/4H2EX6lrZ7lnG901HHR23uhvIXbyfsm0Dy43gx8r7t1+A/5e4DWlXxdJlRwjQkx8hfgvxytwX8QvwXsGyNfx+2Uv7ASZdveORzQP+qYXHzAmYMfhrAAuAtoUM41jsL/c1mMD/r/B3SOkW9e8Frtiw/ya4P8jwMto/JGfneD2PIleD3+H4shqX4vaMvMDcU6bU6xrprlmRDjmG7B6/JzEB/eAwaVc/6Kzj2iEuXLww8J/Th4fUuD+PIecBFQu5zjCvG9ukrwc+b9F9i5nLyVjd0T8PFu16D+q4P8LwNdyvlbK8Tf+PsaH+u+Bi5L9XtBW2ZuKNZp2/I7inwGKd5tucZJwGvA/CA2rMM33P0DaF/OMUmLdxWUO/L69y5nfyGKd9p2YLPgF5kSwd2Ki51zo4Pnu+LvQBzgnPs4lO81/KIXQ83sLPzCGo1cUPhgjpJfgEucc6NiXOdGfMPERWb2CXCDc+41M/stMNI5t19iayoiNYH51R9HAQOccxNSWxrJFop1IpJMZtYf3xPlzMjnjkiiKdaJSLIp3qW/mjaH5Fy2LKQBlC160Zctc4tMxg+hCU+KWgDUZ+v5RyLH74GfQPnaICkHqB38nIfvtSUiIpIsinUiIpLpFOtERKRCSZ9DMpiMtUvwNAdob2a9gBXOuR/M7B7gOjP7Ej8XwPX4br1PATjnZpvZm8C/zexc/Pxv/8Z3/54TdS0DHgKGuS3zibwHXGBms/FzPiR8NS4REckuinUiIpLpFOtERGRHpKKHZG9gerDVxc/jNh0YGez/O35euX/hV2NqjV/B95fQOX6Pn6tgLH7ugU+B02Nc6zz8RMnhlZhG4OcZmApsJvakqCIiIjtCsU5ERDKdYp2IiFRbSueQFBERERERERERkexS0+aQFBERERERERERkQyW9Dkk04WZnYcfGkD9+vX322OPPVJcIhGRzDBt2rRlzrmdUl0OUawTEUkUxbqaQ7FORCQxdjTWach2JfTu3dtNnTo11cUQEckIZjbNOdc71eWQrSnWiYjEj2JdzaRYJyISPzsa6zRkW0RERERERERERJImpQ2SZtbUzJaYWeckX/cOM7svmdcUEZHspFgnIiKZLoWx7v/M7IpkXlNEROIj1T0k/wy87pz71sx2MrO3zGyhma03s/lm9i8zaxzJbGb5ZjbazD4zs41mNiH6hGb2OzMba2ZLzewXM/vQzI6OynYbUGhmuya2eiIiIlvFur3N7Okgxq0zszlm9iczK4vHZtbfzF42s0VmVhzEvLOiT2pmQ8xsRpBnsZk9YWatQlkU60REJFmq9L0OwMz2MrOJQTxcYGY3mpnFOrmZnWpmzsxejdp1E3B99LlFRKTmS1mDpJnVA84B/hMkbQZeAgYDuwOFwK+Bh0OH5QIlwD+B18o5dT/gXeC3wD7A68BLZtY3ksE5txQYC1wYn9qIiIhsK0as2w9YCpwO7AkMB24ErgkddhAwEzgB6AE8ADxkZkNC5z0YeBx4NDjPsUB34MlIHsU6ERFJhup8rzOzRsDbwBJgf+BS4E/ANr0dgxtrtwNF0fucczOB74DT4lUfERFJjlSusv0bfLB6H8A5txx4MLT/ezO7H7g2kuCcWwtcAGBmPYEm0Sd1zl0WlXSTmf0W/2UtHMTGALfgA5+IiEgiRMe6/0bt/87M9gWOx8cknHO3ROV5wMwGBHmeCtIKgB+dc3cHz+ea2T+Af0Qdq1i3A0qLiym69VZ+WL2a9o0a0feaa6hVr16qiyUiUtNU+Xsd8HugHjDUObcOmGVm3YArzOwuF6y8ama1gaeB64ABQIsY1x8DnG5cTHoAACAASURBVAr8K661EhGRhErlkO2+wDRXzjLfZtYG+B0wMQ7XagisjEr7CGib7HlOREQkq1QY6wKN2DZGbS/P+0BrMxtsXgvgFPyogDDFumoqLS7mzpEjmZSTw7wmTZiUk8OdI0dSWlyc6qKJiNQ01fleVwAUBY2REW8BbYCOobS/AvOcc49WcP2PgAPMrG41yi4iIimSygbJDsCi6MRgbq1iYAHwC3DmjlzEzC4C2uGHtoUtDB477sj5RUREKhAz1kUEvSML8cOyy8tzFH6o20ORNOfcZHxvkCeBDfhh4AYMjTpcsa6aim69lZL8fDAr20ry8ym69dZUF01EpKapzve6Vvjh2mFLQvsws8OAkwlGyFVgIVAb35gpIiJpIpUNknXx80FGuxzYFz/EelfgnupewMyOx8838nvn3PdRuyN343QnTUREEqW8WIeZdcXPh3yPc+6FcvIcjB+mfalz7qNQenfgPuAv+Hkpj8B/gft31CkU66rph9WrY6bPLyddRCSLVfd7XXSPysiCNi7o+T8aP6R7e6MIFOtERNJQKueQXAY0jU50zi0GFgNfmtlyoMjMbnbOza/KyYPGyMeBM5xzY2JkaRY8Lq1asUVERCotZqwzsz2A8cAzzrlrtjnK5/kVfgj2jc656B6U1wIfOeduD55/ZmZr8THzulDMVKyrpvaNGjEvRvoujRoluygiIjVddb7XLSboCRmyc/C4BL+oW2tgXGjh7RwAMysF9nTOzQnSFetERNJQKntITsevCFqRSPnqVOXEZnYS8ARQ6Jz7v3Ky9QA24lcyFRERSYRtYl3Qu3EC8Lxz7vJYB5nZIcAbwE3OuVgjBeoBm6LSIs8tlKZYV017NAu+3zpXtuWXlND3mpjtxyIi2aw63+smA33NLD+U51D88Ot5wMfAXkCv0DYGv0hpL2Bu6LgewELnXPQQcBERqcFS2UPyLeA2M2vunFsezJHVHJgGrAH2xA+3nuKc+yZyUPBFLg+/wloDM+sF4JybEew/Bd8z8kpgkplF7rxtcM6tCF2/L34iZc1OLyIiiRId6/YE3sX3jrwlFKMiPUkws/74odz3A0+G8mxyzkV6f7wCPGxmFwbXaI0fCveJc+6H0PUV66pj2TKKpk2jdseO9DZj8erV7NKoEX1vvFGrbIuIbKs63+ueAoYDo83sZmB34Br8jTgHrAVmhS9iZj8DtZxzW6XjY92biamaiIgkSsoaJJ1zM83sI/yqoP/CzztyAdANf+dsPvASED17/Ov4iZMjpgePkR4hF+DrdQ9bz1MyEegfen4qPgiKiIgkRIxYdyJ+SNrJwRYWiWOF+B6QVwZbxPcEi9M450abWUPgYuBOYBW+kfOqqHMq1lXDohtuYHaXLhzSrRsDTjop1cUREanRqvO9zjm3yswODfJPBVbi49ldVbl20MPyOODwHa9J8pWWllJUVMQPP/xA+/bt6du3L7VqpbLPkIhI8pi/AZWii5sdAdwLdHfORQ89S+R1f4u/S9fTOVe6vfy9e/d2U6dOTXzBRESygJlNc871TnU5kkWxLs1MmcLcIUMYd9ppnP7nP5Ofn7/9Y0REoijWJe26FwHHOOcOq0z+mhTrSktLufPOOykp2bIeUH5+PsOGDVOjpIikhR2NdamcQxLn3Jv4u2Ltknzp+sCZlfmCJiIisiMU69LIpk3whz/QacMGzr3qKjVGiohUUgpj3UbgkiRfMy6Kioq2aowEKCkpoaioKEUlEhFJrpTfenHO3ZeCaz6X7GuKiEj2UqxLD+7++5m5aRPd7ryT2g0apLo4IiJpJUWx7qFkXzNefvjhh5jp8+fPT25BNmyAm2+G99+Hgw+G66+HvLzklkFEslLKGyRFREREUm7JEr57+GFeOv54NnTuTNaMsxQRkZRo37498+bN2yZ9l112SV4hNmyAVq1g5Ur//N134Z//hMWL1SgpIgmX0iHbkr5KS0sZP348jz76KOPHj6e0VCMCRUQkfbmrruLdggIa16tHr169Ul0cERHJcH379iUvqtEvPz+fvn37Jq8QN9+8pTEyYuVKny4ikmDqISlVVlpayp133FE258m8uXP56MMPGXbllZqAWURE0k9REXM+/JCFp57K0YMGKZaJiEjC1apViz/96U8UFRUxf/58dtlll+Svsv3++7HTP/ggeWUQkayl/7ilyorGj/eNkWZlaSUlJRSNH8+AQw9NYclERESqaONG3B/+wPjDD6d506bsvffeqS6RiIhkgS+//JImTZowYMCA1BXi4IP9MO1oBx2U/LKISNbRkG2psrnvvRczfX456SIiIjXWP/9J8dy55O+yC/0HDiQnR/8aiYhIYm3evJlXX32V91L9/en666F+/a3Tmjb16SIiCab/utPQqh9WcWebO1n1w6qUXL9jOSu/7ZLsFeFERER2xMKFMHw49fv1o/CKK9hzzz1TXSIREckCCxYsYO3atXTt2jW1BcnLg6OOgnr1YOBAuOEGLWgjIkmjBsk0NOmWSaxZtIaivxUl9brr169n5cqVHNKoEfnr1oFzZVv+unX0bdcuqeURERHZIcOGMX+nnfjl1luxnBwsNBWJiIhIonz55Zfk5OSw2267pbYgGzfCW2/BySfDO+/AyJFqjBSRpNEckmlo1tOzAPj8mc856oGjknLNTZs28dxzz7F06VIu+ewzhk2cSNEhhzC/fXt2+eEH+s6cSa2FC5NSFhERkR327ruUPv88L1x/PU0/+YShe+2V6hKJiEiWmDNnDh07diQ/Pz+1BSkqgp9/hqOPTm05RCQrqUEyTdzW7DZKVpZslVbycwk32U0A5DfN5+oVVyfk2s45/ve///Hdd99xTF4etcePh3//mwE//uhXYOvb199Z0900ERFJBxs2wEUXMe3ww1llxtF9+6a6RCIikiV++eUXVq9eTZ8+fVJdFBgzBvLzQQuTikgKqEEyTRxy/SGMHTa23P39buyXsGuPHTuWWbNm8es99qDX6afD8cfDuedutcq2iIhI2rj7bjZ8+y1Fp51Gx3bt6NSpU6pLJCIiWaJhw4b86U9/wjmX2oI45xskBw3admEbEZEk0BySaaLgigIOueGQmPsOueEQDvzjgQm57qeffsqUKVM4YN99OXj4cNh5Z3joITVGiohIepo/H0aO5KPCQtaWljJw4EDNHSkiIklVu3Zt8lI9umzWLJg7V8O1RSRl1EMyjaxdurZK6fHQrVs31q5dS8Hjj2NffQXjxkGzZgm7noiISEJdfjk4xy+DBrH7pk3ssssuqS6RiIhkiV9++YWnnnqKI444gg4dOqS2MGPG+MfBg1NbDhHJWuohmUZmPTOr7Oc+l/ehVl3fnvz5M5/H/Vo//vgj69evJy8vj4OWLcMefBCuvBIGDoz7tURERJLirbfghRfg+us58qSTOPnkk1NdIhERySJz5sxh8eLF1K1bN9VFgZdfhj59oFWrVJdERLKUGiTTSO382jTfvTkAg24ZxNU/X81uR+1Grfz4dnRdsGABjz32GG+88QYsWgRnnw377AN/+UtcryMiIpI0JSVw8cUU9+jB4tNOAyAnR/8GiYhI8syZM4emTZuy0047pbYgCxfCxx9ruLaIpJT+E08jwxYNo83+bWjcvjG18mtRK68WQ14ZwrBFw+J2jeXLl/PUU09Rv359fj1gABQWwtq18OSTUKdO3K4jIiKSVHfcAd98w3uXXspDo0ezevXqVJdIRESyyPr165k7dy5du3ZN/dzFr77qH485JrXlEJGspjkk08yKr1fQbLfEzOG4Zs0annjiCQBOO+00Go4eDWPHwgMPQLduCbmmiIhIws2dC3/9K7+ceiof//QTe+21F40aNUp1qUREJIt88803bNq0iT322CPVRfHDtXfdFbp3T3VJRCSLqYdkmln+9fKENUiOGTOGtWvXMmTIEJovWABXXeUnOT7//IRcT0REJCkuuwxyc5l07LFs3ryZ/v37p7pEIv/P3p2HR1We/x9/3wkJS8KSBCHsIKsIsogiKm51QVxb666tS2urFWulX2v7s9burXVpFb9ttW51t7b9AlLEpQgqyI4skmRCkDUhMMMWQkgyeX5/nAkkIZBJmORMyOd1XbmSOec559xztRe3z32eRURamJSUFIYNG+b/ZmpFRfDhh950bb9HaopIi6YRks1IcbCYkh0lZAzMaJT7T5w4kR07dtAjPR0mTIC0NHjuOSUqERFpvqZPh+nT2fm737E0O5tRo0aRlpbmd1QiItLC9O3bl759+/odBrz/Puzfr+naIuI7jZBsRkKBEEBMR0g651ixYgXOOTp16kS/fv3ggQdg1Sp48UXwe8FlERGRhtq3zxsdOXQo+RMn0rp1a8466yy/oxIRkRZm165d8bN28dSp3sCTM87wOxIRaeE0QrIZCQaCADEbIemcY9asWSxYsIDWrVszePBgmDkTnnzS68BNmBCT54iIiPjit7/11o+cPZsThg9nwJAhJCUl+R2ViIi0MJ9++inLly/n/vvvp1UrH7vg4bC3oc3EiaB8KCI+0wjJZiQUCGEJRtrxsZlqNm/ePBYsWMDYsWMZNGgQFBbCrbfCsGHwu9/F5BkiIiK+yM2F3/8ebriBLYMG4ZxTMVJERJqcc47s7GyOP/54f4uRAPPnQzCo6doiEhdUkGxGQoEQHft0JDE58ajv9fnnn/PBBx9w4oknctFFF2EAt98OO3fCa69BmzZH/QwRERFfOAeTJkHr1hQ88ADPPvssixcv9jsqERFpgfLz89m9e3f87K6dlAQXXeR3JCIimrLdnAQDwZhM1y4uLmbGjBn069ePK6+8EjODP//ZG77/pz/B8OExiFZERMQn//43vPsuPPEEs1etok2bNgxXbhMRER9kZWVhZt6MNL9NmwbnngsdOvgdiYiIRkg2F845QrmhmGxo065dO26++WauvfZab9rAmjVw333empGTJsUgWhEREZ/s3Qv33gsnncSmr36VnJwcTj/9dNpo5L+IiPggJyeH3r17065dO38Dyc6GnBxN1xaRuKERks1E8fZi9u/af1QFyWAwyJYtWxg+fDi9evXyDu7fDzfcAKmp8MILYBajiEVERHzwq1/Bxo3w+uv8d84cUlJSGDt2rN9RiYhIC3XjjTdSXFzsdxjedG2Ayy7zNw4RkQgVJJuJUCAEQPqAhhUki4qKeOWVVygrK2PgwIEHR4o8+CAsX+4N38/MjFW4IiIiTS8rCx57DG65haIRI9i+YAFnnnkmycnJfkcmIiItVPv27Wnfvr3fYXj9vVGjoHJgioiIzzRlu5kIBoIADVpDcv/+/bz66qvs3buX66+//mAx8oMP4NFH4c479aZMRESaN+fge9+DlBT4/e9JTU3lnnvuYcyYMX5HJiIiLdQHH3zAmjVr/A4Dtm2DefM0XVtE4ooKks1EKBDCEo1O/TrV67ry8nLefPNNCgsLueaaa+jRo4d3IhiEb34ThgzxipIiIiLN2VtvwX//C7/+NTuSkgiHw7Rq1cpbK1lERKSJFRcXM2/ePPLz8/0Oxdu81Dm4/HK/IxEROUAFyWYiFAjRqW8nEpMS63VddnY269at4/LLL2fAgAHeQefg29/23pS99hr4vcCyiIjI0dizx9ucbfRo3B138Oabb/Lqq6/6HZWIiLRgOTk5OOcYMmSI36F407V79YKRI/2ORETkAA0baCaCgWCDpmufeOKJZGRkkFl1fcjnn4d//xv+8AdvHREREZHm7OGHIT8f/vUvvsjOZuvWrXz1q1/1OyoREWnBsrOz6dChA926dfM3kH374L334NZbtYGpiMQVjZBsBpxzhAKheu2wvXDhQjZv3gxQvRiZkwP33APnneeNJhEREWnOVq2CP/0JvvUtKk45hdmzZ3PccccxbNgwvyMTEZEWqqysjNzcXAYPHoz5XQT88EMoLtZ0bRGJOypINgN7t+6ltKg06oLk559/zsyZM1m8eHH1E2VlcOON0Lo1vPQSJOh/fhERacYqN7Lp2BF++1tWrFhBMBjk3HPPJUE5TkREfLJ79266dOkSP9O127eHs8/2OxIRkWo0ZbsZqM8O24FAgKlTp9KvXz8uueSS6icffhgWL4a334aePRshUhERkSb0yiswdy488wxkZJDz4Yd069YtPjqAIiLSYmVkZPDtb3/b7zCgogKmT4eLL/YGpYiIxBEVJJuBUCAEUOcIyc2bN/OPf/yDrl27cu2111bfWXTuXPjtb+G22+CqqxozXBERkca3cyf88IcwdizcfjsAV199NcXFxf5PjxMRkRaroqKC8vJykpOT/Q4FFi2CggJN1xaRuKT5TM1AMBAkoVUCnfp0OmK7xYsXk5KSwo033kjrqm/Adu6Em26C/v29dbZERESau4cegm3b4H//l7Jw+EAhMiUlxe/IRESkBdu4cSOPPPII69ev9zsUb7p2YqI3QlJEJM5ohGQzsCN3B2nHp5HQ6sj148suu4yioiJSU1MPHnQOvvtd2LIF5s2DqudERESao2XL4Omn4c47YfRoFs2bx9y5c7nrrrvo0KGD39GJiEgLlpWVBdTYWNQv06bBWWdBevSbo4qINJW4GyFpZl+amavlZ0bk/MO1nCuocY8fmtlWMys0s8k1zo0ys2wza9uU3+toBANB0gfUnkRKSkr497//zZ49e0hISDi0I/bKK/Dmm/Dzn8OppzZBtCIiUhfluqNQUQF33QUZGfCrX7F//34+/fRTevTooWKkiEgcaYm5zjlHdnY2/fr1qz5jzQ95ebBqlaZri0jciscRkqcAiVU+dwOWAG9VOZYNnFPlc7jyDzM7CfgFcClgwDtm9p5zbqWZJQLPAt9zzu1rnPBjyzlHKDdE33P6HnKuvLycN998kw0bNjBixAjat29fvUFenrf76JlnwgMPNE3AIiISDeW6hnrxRfjsM+93WhqfzZlDcXEx5513nt+RiYhIdS0u123bto0dO3Zw+umn+x2KNzoSVJAUkbgVdwVJ59y2qp/N7HZgN/CPKofLnXPV3p5VMQRY4Zz7b+T6FZFjK4F7gVXOuQ9iHngjKcovomxv2YENbSoqKsjNzSU/P5+8vDw2bNjAV7/6VY4//vjqF5aXe+tGmnmjJBMTa7m7iIj4QbmugUIh+NGP4Iwz4Oab2bdvH/Pnz2fw4MH06NHD7+hERKSKlpjrKqdrDx482OdI8AqSw4ZBzX6iiEiciLuCZFXmbZN5O/CKc664yqnjzWwzUAosAH7inMuLnFsJDDKz3nhv0gYBq8ysL3A3MKaJwo+JYCAIQMbADCoqKnjllVfYtGkTZWVlAKSlpTFs2LBDL/z1r2H+fHjtNejTpylDFhGRelCuq4ef/AR27ID//V9ISCAnJ4fS0lLOPfdcvyMTEZEjaCm5bujQoaSkpBw6c62phUIwd673Ek9EJE7F3RqSNVwA9AP+VuXYAuAW4GLg20AmMM/MMgCcc2uAnwDvA+8BP44c+wvw/4DxZrbCzFaZ2ZVN9UUaKhQIAZA+MJ3c3Fw2b958oBgJUFRURG5ubvWL5s2DX/zCGyF5/fVNGa6IiNRfi891UVm0CJ55BiZNgpNOAmDEiBFMmjSJrl27+hyciIjUoUXkus6dO3PyySf7HQbMnAnhsKZri0hci+sRkniJaZFzbnnlAefczKoNzOwzIA/4JvB4pM1f8BJVZZubIn9+AOQA4/CKsZ+a2SDnXGFjfomjEQwESUxOpGPvjqz4ZAWlpaXVzpeVlVFQUMCgQYO8A7t3e4XI3r1hyhQfIhYRkXpq8bmuTuGwt5FN167eJm3Avn37aNu2LWlpaT4HJyIiUTjmc926desoLS1l0KBBeANCfTR1KmRmwimn+BuHiMgRxO0ISTPrAlyBt1jxYTnnioDVwMDD3CcD+CXwXeA0IOCcW+OcWw0EgLGxjDvWQoEQacenkZCYQLdu3UhKSqp2Pjk5mczMzIMHJk2C9eu9dSM7dmziaEVEpD6U66L07LOweDE89hh06MCuXbt44oknWL58ed3XioiIr1pKrvvkk094//33/S9G7t8P774Ll10GCXHb3RcRid+CJN7w/f3AG0dqZGZt8BY3zj9Mk8eBp5xzX+J936oVvWSq7/wWd0KB0IENbQYMGEBGRsaBc8nJyfTo0YMBAwZ4B954A/7+d3jwQW/BfxERiXe3oFx3ZNu2eWtHnnvugWVI5syZQ0VFBf369fM5OBERicItHOO5rqSkhC+//DI+NrOZMwf27NF0bRGJe3E5ZTuy6PG3gDecc3tqnHsUmA5sALoAPwVSgJdquc/5wFDgtsihRcBgM7sML4kNBhY20tc4aq7CEcoNcfyF3s5oCQkJnHTSSRQUFHDGGWfQu3dvBgwYQEJCAmzYAN/9Lpx2Gvz0pz5HLiIidVGui9IDD3gdqylTwIxgMMjy5cs55ZRT6KiZACIica2l5LpAIEBFRQVDhgzxK4SDpk6Fdu3gK1/xOxIRkSOKy4IkcA7eUP2bajnXE3gd6AxsAz4DTnPOra/ayMzaAk8D1znnwgDOuc1m9l28dUgM+I5zbktjfYmjtXvzbspLyskYeHBUZGFhISkpKZx//vkHG4bDcPPN3u9XXoFW8fo/q4iIVHEOynW1C4e9BfmnToXnn4fJk2HoUMAbHdmqVSvGjx/vc5AiIhKFc2gBuS47O5uUlBR69OjhVwge52DaNLjwQmjb1t9YRETqEJeVK+fcbLzEUtu566K8xz68N2U1j79ELW/d4lEo9+AO25UKCgro1q1b9YaPPAJz58KLL0L//k0YoYiINJRy3WGEw3DRRbBgARQVgZm3fmQ4zN6SEtasWcPYsWNJTU31O1IREalDS8h1zjkKCwsZNGiQN3PNT8uXw6ZN8Itf+BuHiEgU4rIgKZ5QwCtIVo6QDIfDFBYW0r9q0XHRInjoIbjmGvjGN/wIU0REJHZmzjxYjARvtMeSJTBzJimXXsrdd99N69at/Y1RREQkwsy48847KSsr8zsUb2aBGVx6qd+RiIjUKZ43tfGVmd1hZovNbPG2bdt8iSEYCJLYOpEOPTsAUFFRwYQJEzjhhBO8BkVFcOONkJkJf/mLl3xERESiFA+57hDLlsHevdWP7d1L6bJlAHTs2JE2bdr4EJiIiDRHTZHrzIzk5ORGuXe9TJsGp58Oxx3ndyQiInVSQfIwnHPPOOfGOOfGHOfTP+ihQIj0/ulYgldoTEpK4pRTTjm4NskPfgC5ufDyy5CW5kuMIiLSfMVDrjvEqFFQcwRkSgpvpabyz3/+05+YRESk2WrMXOec469//StLliyJ6X0bZONG76XeFVf4HYmISFQ0ZTuOhQKhQ9aPTE5MJH3BAnj1VXjjDbj/fjjnHP+CFBERiaWLL4aUFCgp8Ub+p6Tw5YUXsnb3bi6ouYayiIiIjzZv3kxBQQFJSUl+h+KNjgS4/HJ/4xARiZKvBUkzSwOygNOdc2ub8LmPAsnOuXua6pn15SocobUhBkwccODYuzNnEl61itv/8hcoLoaEBG8NyXAYEhN9jFZERA7Hx1x3N3Chc6559Uzy8iAYhOuvh6FDcSNG8N/CQlJ37uSUU07xOzoREalFS+3XZWdnY2YMHDjQj8dXN20aDBoEgw/Z/0dEJC75PWX7J8B/nHNrzWyEmb1uZhvNbJ+ZZZvZ/5jZgRjNbKiZzTazrWZWYmZ5ZvYbM6t1wQ4zO9PMys1sVY1TvwduMbPjG/G7HZVdG3cR3h8+sKGNc46CTZvIzMvzipEAFRVeQXLmTB8jFRGROhzIdQBm9qfIWlYlZvZlzcZm9rCZucP8dIm0aWNmL5rZCjMrM7OPannus8AYMxvfiN8t9qZMgaQkePxxePBBcocMYeOmTZx11lnxMQJFRERqU99+3TlmNtXM8s2sOJLPbqt5UzO7wcyWR9oUmNkrZpZZpYmv/bqsrCz69u1L27Zt/Xj8Qbt3w+zZmq4tIs2KbwVJM2sHfAt4LnLoZGAbcDNwIvAz4CHggSqXlQIvARcCg4F7gduBX9Vy/zTg78CHNc8557YB7wF3xubbxF7lDtuVU7Z37tzJ/ooKMjdurN5w715YvrypwxMRkSjUkuvAy70v4eWo2jwKdKvxMwf4yDlXGGmTCJQAU4AZtd3EObcfeA2I29kAh9i9G154Aa65xtuwDViwYAGdOnVi9OjRPgcnIiK1aWC/7nRgJfB1YBjwZ+AZM7uhyn3PAF7Gy5knAlcCQ4FXK9v42a8LBoNs376dwfEwIvHdd6GsTNO1RaRZ8XPK9kSgAvgUwDn3fI3zeWY2GrgK+E2kTS6QW6XNejM7B6ht9MdzeMnL8BJdTdMi9/2fhn+FxhMMBAEOjJAsKCgAIHPHjuoNU1Jg5MgmjU1ERKJWLdcBOOcmAZjZD/FesFXjnCsCiio/m1kvvDx3c5U2e4HvRs6fBHQ6zPOnAe+bWTvnXPHRfplG99JLsGcPFZMmkZuTQ35+PqNGjSItLY1ELU0iIhKvGtKv+02NNn82s3MjbV6LHBsHbHLOPRH5vM7MngKeqnGtL/26hIQETj75ZIYMGdKUj63dtGnQuTOMG+d3JCIiUfOzIDkeWOKcc0do0wHYcbiTZjYAmICXhKoevwvIBK4GfnqYyxcCPcysf1OucxKtUCBEq7ataN+9PeAVJM2MLqmpXoPIQv+MHettACAiIvEomlxXl9uBnUBDtphejJfrx1HLjIG4UlEBTz1FxdixvJKdzaZNmygrKyM5OZkePXpw0003kZDg90ozIiJSi6Pu11Vps6nK50+B35jZZcA7QAZwHfCfGtf50q9LS0vj0ksvbarHHV5ZGcyYAVdeqX0FRKRZ8bMg2QfIP9zJyFu0W4Abazk3DxgNtMZbI+snVc4Nx5sWcJpzLmxmh3vElsjvvkBcFiTTB6RjCV78Y8aMoXfv3iQVFcHq1fDggzBmjFeMVOIREYlX1/PQwgAAIABJREFUR8x1dYmst3Ub8PfIFOx6cc4Vm9kuvFwX32bNgkCA3L/+lc2bN1NWVgZAaWkpmzdvJjc3l0GDBvkcpIiI1KLB/boqbS4FvgKcUXnMOTffzK7Hm6LdFq/v+j7wzRqXN3m/bt++fYRCIbp3784R+ptN45NPYOdOTdcWkWbHz6EGbfHWvzqEmQ3GWxPrj8652kaEXItXkLwBb4rAjyLXtQbeAH7onFtXx/P3VYkj7gQDwQPTtQHat29P//79vfUiTzoJHn4YLr1UxUgRkfh22FwXpYuBXsDfjuIe+4jTXFfNU09BZib5AwZQWlpa7VRpaemBpUtERCTuHE2/rnKtyNeAe5xzC6scHwo8CfwSb13KCXiz4P5a4xZN3q9bs2YNf/vb3ygsLKy7cWObNg1at4YLLvA7EhGRevGzILkdSKt50MyGAB8BbzjnHqh5HsA5t9E594Vz7nW8xZF/Zmat8Bb+Hwq8ENlduxxvAeUTI5+rrtWVHvm9LWbfKEYqwhXsyNtxYEObkpISPvvsM3bt3AlLl4IW9hcRaS5qzXX1cAcwzzm3+ijukU4c5rpqcnJg5ky480669ex5yHqRycnJZGZmHuZiERHxWYP7dWZ2JjATeMg59+cap38MLHTO/cE5t8I5Nwu4C7g5sr5ypSbv12VnZ9OpUye6dOnSVI+snXMwdSqcfz5ULu0lItJM+FmQXIZXPDwg8hbsI+AfzrkfRHmfBLzh+4nAZmA4MLLKz1/wNsIZCcyrct0woAxvd7e4smvDLirKKkgf4OXWLVu2MGvWLIJr1kAwCCef7HOEIiISpUNyXbTMrDtwCd7SJA1iZv2BNsDSht6jSUyZAklJ8J3v0L9//2prRVauITlgwAAfAxQRkSNoUL/OzM7CK0b+3Dn3x1qatAPCNY5Vfq46T7pJ+3WlpaWsXbuWwYMH+z9de/VqWLdO07VFpFnycw3JWcDvzSzDORc0sxOB/wKz8RYvPjAUwjlXAGBmN+NNB1gJlAJjgN8Cb1dZW2tV1YeYWSGw3zlX7Tje4ssfx+Ouo6FACODACMkDO2xviqzxrBGSIiLNRbVcBwc2ZEsFugPJZjYy0vYL51zVucq3AXuBt2q7caSzlwx0BlIr7+OcW16l2XggzzkXiOF3iq3du+GFF+Daa6FrVwJZWZSVlXH66afTunVrMjMzGTBggDa0ERGJXw3p152DN5X7f4FXq7QJO+cqRzpOB541szsjz+gG/BFY6pzbUOX5TdqvW7t2LeFwOH521wZvKS8RkWbGt4Kkc26lmS3E2yntabwdsbvgrQ95bY3mla+eyvGG7g+MHFsfufaJBoRwPd7mN3EnGAgCHFhDcuvWrXTo0IF2K1Z4a0aedJKf4YmISJRqyXXgrQd5dpVmyyK/+wFfApg35OJ24NUjdLD+g7eRQM37VB2ucT1HMcKySbz4IhQVwT33ADBo0CCuueYaBg8erCKkiEgz0MB+3S14IyB/GPmptJ7IRmzOuRfNrD1wN/AYsAuvyHl/jXs2ab8uJyeHtm3b0rt376Z65OFNmwanngrdu/sdiYhIvfn9X/o/B+4xs0Tn3MPOOavtp7Kxc+5159xo51x751yqc+5E59xvnHP7DveAyH2HVT1mZpfgDfd/u9G+2VEIBUIkpSSR2s1bB6SgoMBbO2vJEhg6FNrG/94EIiJywIFcB+CcO+cw+e7Lygucp59z7q7D3dQ51/dIOdPMhuEtV1JzTa74UVHhbWZz2mlwyikAJCQkcMIJJ6gYKSLSvNS3X3fLYdr0rXpT59xTkT5fO+dcN+fcDc65TZXn/ejXTZw4kW984xv+56n8fFiwQNO1RaTZ8vVfUefcu3hv0Xo28aNTgFudc+VN/NyohAIh0gekY2aEw2GCwSBdu3b1CpKari0i0qz4mOu6A99wzu1q4udGb9YsyM2Fe+7BOcfLL7/M8uXL675ORETiSkvq1yUlJcXHRmvvvOP9VkFSRJopP9eQBMA596QPz6x1Pa54EQwEyRzhJbnExER+9KMfEd60CQoLVZAUEWmGfMp17zX1M+vtySehWze46iqys7PJy8vjJC1LIiLSLLWEft28efMIh8OMHz++KR9bu2nToF8/GDas7rYiInFI86HiTEV5BTvX7TywoQ14b+HarIrsyaMdtkVE5FiQnQ3vvgt33olLSmLu3LmkpaUxfPhwvyMTERE5hHOOhQsXsmnTprobN7a9e+GDD7zRkX7v9C0i0kAqSMaZnV/upKK84kBBcuHChcyZMweWLvWSzYgRPkcoIiISA1OmQHIy3HEHgUCA/Px8xo8f7/+aXCIiIrXYunUru3btYvDgwX6HAu+/DyUlmq4tIs2a71O2pbqaO2yvWrUKM+PspUthyBBITfUzPBERkaO3a5e3u/Z11+G6dGHOtGl06tRJ07VFRCRuZWVlAcRHQXLqVOjUCeJh6riISAOpIBlnQoEQAOkD03HOUVBQwKhRo7wNbc45x9/gREREYuHFF6GoCCZNwsy44IILKCsrIzEx0e/IREREapWdnU3v3r1JSUnxN5Bw2NvQZuJESEryNxYRkaOggmScCeWGSG6fTEqXFEKhEGVlZWS2awebN2tDGxERaf4qKrzp2uPGwZgxAPTt29ffmERERI6gvLycTp060a9fP79Dgc8+g+3bNV1bRJo9FSTjTCgQImNgBmZGQUEBAJnbtnkntaGNiIg0d+++C7m58MtfkpeXR1ZWFueddx5t2rTxOzIREZFatWrVimuvvdbvMDxTp3ojIydM8DsSEZGjopXj40wwECR9gLehzf79+0lJSeG4yHoljBzpY2QiIiIx8OST0L077mtf46OPPiI7O5tWrfR+VERE4tfevXv9DuGgadO8pbw6dvQ7EhGRo6KCZBwJl4XZ+eXOAztsjx49msmTJ9Nq2TIYMEBJR0REmresLJg1C+68ky83b2bjxo2cccYZKkiKiEjc2rdvH48//jgLFizwOxTIzvZ+NF1bRI4BKkjGkZ3rduLC7kBBEsDMvA1tNF1bRESauylTIDkZ7riDOXPm0L59e0ZrfWQREYljOTk5VFRU0LNnT79D8UZHggqSInJMUEEyjgQDQQAyBmZQVFTEn//8Z3KXLoX167WhjYiING+7dnm7a19/PV8WF7N+/XqNjhQRkbiXnZ1N+/bt6d69u9+heAXJkSOhd2+/IxEROWoqSMaRUCAEQPrAdAoKCigsLCQxL887qYKkiIg0Zy+8AHv3wqRJdOzYkTFjxmh0pIiIxLWysjJyc3MZNGiQN3PNT9u2wbx5Gh0pIseMqAqSZjbRzG6q8rmHmc02s21m9oqZtWu8EFuOYCBI646tade53cEdtnNzvZPqtImINCrlukZUUeFN1z79dDj5ZNLS0rjkkktISkryOzIRkRZH+S5669ato6ysjCFDhvgdCsyY4eXTK67wOxIRkZiIdoTkz4Cqi2Y8AQwB3gIuBh6KcVwtUigQImNgBmbG1q1b6dixI22XLYO+fSE9vc7rRUTkqCjXNZaZM2HtWrjnHubMmXPgpZuIiPhC+S5KPXv25LLLLqNv375+h+JN1+7RA0aN8jsSEZGYiLYgOQD4HMDM2gCXAvc5574H/Bj4euOE17KEAqEDG9rk5+eTmZkJS5dqdKSISNNQrmssTz4J3buzaexYPvroI9auXet3RCIiLZnyXZTatWvH6NGj/V/vuKQEZs3ypmv7PXVcRCRGoi1ItgWKI3+PA5KBdyOf1wBxsMJv81a+v5xdG3aRPjAd5xw9e/ZkQI8ekJurHbZFRJqGcl1jWLMG3nsP7rqLOZ9+Srt27TjllFP8jkpEpCVTvovC1q1bWbRoEfv37/c7FPjwQygu1nRtETmmRFuQXA+cFvn7MmCpc25H5PNxwJ5YB9bS7MjbgatwB6ZsX3nllYwJh72TGiEpItIUlOsaw5Qp0Lo1m6+4gtzcXMaNG0dycrLfUYmItGTKd1FYsWIF7777Ls45v0PxpmunpsI55/gdiYhIzEQ79vw54NdmdhkwFrinyrnT8N6kyVEI5R7cYbu8vJzExERs6VLvpAqSIiJNQbku1nbuhJdeguuvZ+7KlbRt21ajI0VE/Kd8VwfnHFlZWfTr1482bdr4G0xFBUyfDhMmQOvW/sYiIhJDURUknXOPmtkOvAT1d+DZKqePixyToxAKRAqSA9KZOXMmX375JZOWLIGePaFLF5+jExE59inXNYIXXoC9e3F3302XnTvp27cvrdWZEhHxlfJd3bZv304oFOK0006ru3FjW7wY8vM1XVtEjjlRr87rnHsO721azeO3xjSiFioYCNImrQ3tMtqxdetWOnTooA1tRESamHJdDIXD3nTtM87ATj6Zr/gdj4iIHKB8d2RZWVkADB482OdI8KZrJybCxIl+RyIiElPRriEpjSwUCJExMIOKigq2bt1K1/R0yM5WQVJERJqnmTMhL49t3/kOOTk58bEGl4iISBR27NhBjx49vEEifps2Dc48E9LT/Y5ERCSmDjtC0sz2AdH2HpxzLiU2IbVMoUCI3uN7EwwGKS8vp1tJCTinHbZFRBqRcl0jevJJ6NGD2UlJ5P3rX9x7773+r8MlItJCKd/Vz+WXX055ebnfYcC6dbByJTz+uN+RiIjE3JGmbP+J6JOWHIXyknJ2bdxF+sB0CgoKAMjcuNE7qRGSIiKNSbmuMXzxBbz/PoW/+hVrsrM566yzVIwUEfGX8l2UnHOYGa1aRb26WeOZNs37ffnl/sYhItIIDvuvrHPugaYMpCULrQ2Bg4yBGXTu3JnTTz+dzq++CpmZ0L273+GJiByzlOsayZQp0Lo1c/v0IXnjxvjYFEBEpAVTvoveW2+9RWpqKpdcconfoXgFyaFDoX9/vyMREYk5rSEZBw7ssD0wnW7dunHBBReQuGSJRkeKiEjzs3MnvPQS2775TVavXcupp55K27Zt/Y5KRESkTvv37ycQCJCUlOR3KLBjB8yZo921ReSYdaQ1JO+vx32cc+4PMYinRQoGggCkD0hny5YtHJeSQtIXX8CVV/ocmYjIsU25rhE8/zwUF7P7a1+jc24u48aN8zsiEZEWT/kuOrm5uYTDYYYMGeJ3KN7mcOGwpmuLyDHrSAtj/K4e93FAi0xasRAKhGjXuR1liWU8++yzTBgyhLEVFdrQRkSk8SnXxVI4DE8/DWeeSf+LLuKuCy/EzPyOSkRElO+ikpWVRbt27ejZs6ffoXjTtbt2hVNP9TsSEZFGcaSCpOZXNZFQIFR9Q5v8fO+EpmyLiDQ25bpY+s9/IC+PvAcfpHd5eXxsCCAiIqB8V6dwOEwgEOCEE04gIcHnlc1KS70RktdcA37HIiLSSI60qc3+pgykJQvlhuh3Xr+DBcnVqyEjA3r18jkyEZFjm3JdjD35JKGhQ3ll40bGf/wx5557rt8RiYgIynfRKC8vZ9y4cfTt29fvULy1I3fv1nRtETmm6XWLz8qKy9i9aTfpA9PZunUraWlptF682JuurWluIiLSXHzxBXzwAR9fdx2JiYmccsopfkckIiIStdatW3P22WfTp08fv0Pxpmu3bQvnn+93JCIijSbqgqSZfcPM5ptZyMyKa/40ZpDHstDayA7bA9LJz88ns0sXWLVK07VFRHygXHcUnnqKHV278rlznHzyyaSmpvodkYiIHIbyXXXOOXJycigrK/M7FHAOpk6FCy/0ipIiIseoqAqSZnY98BwQADoB/wSmA+XAJuBPjRXgsS4UiBQkB6Zz+eWXc3rnzlBeroKkiEgTU647Cjt2wN//zsc330xCQgJnnHGG3xGJiMhhKN8dKj8/n9dff53Vq1f7HQp8/jls3Kjp2iJyzIt2hORk4PfArZHPTzjnrgUGAGXAl7EPrWUIBoIAZAzMoG/fvvTMy/NOaIdtEZGmplzXUM8/T0VJCdt69WL06NG0b9/e74hEROTwlO9qyMrKwswYNGiQ36F4oyPN4NJL/Y5ERKRRRVuQHATMBioAByQDOOcKgV8C9zVKdC1AKBAipUsK2/dsJysrC7d0KXTsCP36+R2aiEhLo1zXEOEwPP00CWecwW2TJnHBBRf4HZGIiByZ8l0N2dnZ9O7dm3bt2vkdird+5Lhx0KWL35GIiDSqaAuSJQDOOQcUAH2rnNsF9IxtWC1HKBAifWA6S5YsYfr06bB0qTddWxvaiIg0NeW6hpgxg71bt1J8112YGUlJSX5HJCIiR6Z8V0UoFKKwsJAhQ4b4HQps2uT1BzVdW0RagGgLkl/gDeEH+BR4wMxGmdlw4CEgpzGCawmCgSAZAzMoKCggs0sXbMUKTdcWEfGHcl1DPPkkcy65hCkbNlBaWup3NCIiUjfluyrWrl0LwODBg32OBG90JMAVV/gbh4hIE2gVZbvnOPjm7CHgA2Bx5HMx8LXYhtUylBaVUpRfRKcBnVhZuJKx/frB/v3a0EZExB/KdfW1ejW7Fy5k6eTJjDjhBJKTk/2OSERE6qZ8V8WYMWPo06cPaWlpfofiFSQHDoR4KI6KiDSyqEZIOudeds79MvJ3FnAicAVwPTDIOfd+rAIys4fNzNX4Kahy3iJttpjZPjP7yMxOrHK+tZm9bGa7zSzHzM6vcf97zOy1WMV7NEK53g7bSb2TCIfDZO7Y4Z1QQVJEpMkp1zXAU0/x6dln4xISGD9+fKM/TkREjp7y3SEx0iUe1mvcvRv++19vuraW7xKRFiDaEZLVOOd2Ae/EOJaqsoFzqnwOV/n7fryd4W6JtHsIeN/MBjvn9gB3ACcD44CLgdfMrKtzzplZL7xFmk9txNijVrnDdnmncgAyAwFITfXeiomIiK+U6+qwYwd7/vlPlt51FyeddBKdOnVq1MeJiEjjaMn57osvviA3N5cJEyb4P8p/1iwoK9N0bRFpMaIaIWlmN5nZ/zvMuZ+Y2Q2xDYty51xBlZ9tkWcZcC/wO+fcP51zq4BvAu2ByhhOAKY551YDTwPHAZ0j554GHo7sIOe7UMAbIXnq2afyve99j4zFi2HUKEiIdmlPERGJFeW6enruOQK9e1ORmKjRkSIizYjy3UErVqxg7dq18bEh27RpkJHh7bAtItICRFv5mgwUHebcnsj5WDrezDab2Toze8PMjo8c7wdkAu9VNnTO7QPmAqdHDn0OnGlmbYGLgHxgu5ldA6Q4516McawNFsoNkdotlTYd2tA5LY2EZcu0oY2IiH+U66IVDsPTTzM6NZV7vv990tPTG/VxIiISU8p3QFlZGWvXrmXIkCGY31Oky8thxgy45BJo1aBJjCIizU60/9oNAFYe5txqDu7SFgsL8IbsZwFdgAeBeZG1RDIjbbbWuGYr0CPy9/PASXi7x20HrgE6Ar8DLjSznwE3AgXAHZF1U3wRCoRIG5DGzJkzGdq2LX327dP6kSIi/lGui9Y771CSn0+bRx+lY8eOMb21iIg0uhaf78rLy/m///s/ysvL2bdvH+Xl5bTysxD4ySewY4ema4tIixLtv7oVQMZhzmUQ/UjLOjnnZlb9bGafAXl4w/c/q2xW4zKrPOacKwO+V+MefwOeAYYA1+KtQ3I98DJwSqxir69gIEify/uwcOFCOnfuTB9oVgXJx+c/zv3v388jFzzCfePu8zscEZGjpVwXpb1//jNPTZ7M+T17MiaWNxYRkabQovNdeXk5jz32GCUlJQCsXLmSQCDA5MmT/StKTpsGyclw4YX+PF9ExAfRJptFwLcPc+7bwOLYhHMo51wR3pu6gXhvvuDg27RKXTj0zRoAZnY2XpJ6FDgPmBFZIPlVYIyZtW+MuOuyf/d+9m7dS6s+XtLLzMuDtm1hyBA/wqm3x+c/zuT3JhN2YSa/N5nH5z/ud0giIkdLuS4aq1Yxv7yc/cnJ9Dn++Lrbi4hIvGnR+e7jjz8+UIysVFJSwscff1yPbxJDznkFya98xdvgVESkhYi2IPk74Dwzm2tmN5vZeZHfc/ASwa8bK0Aza4P39isfWIeXuC6ocX48MK+Wa1sDf8Ybvl+O930rVyyu3EYtsbFiP5JQrrehTTjd22Su65IlMHIkJPoSTr1UFiOrUlFSRI4BynVRKJ4yhUWnnsqwgQM57rjjYnVbERFpOi06323YsKHW4xs3boz2a8TWF1/A2rWari0iLU5UBUnn3Ad4O531B14C3o/87g9cHzkfE2b2qJmdbWb9zGws8DaQArzknHPAH4EHzOxrZjYMeBFvUebXarndT4FZzrlFkc+fAFeZ2Ujgf4DVzrmdsYq9PoKBIADFicVkZGSQvGRJs5iuXVsxspKKkiLSnCnXRSEU4rN16yht3Zrx558fk1uKiEjTaun5rnfv3rUe79WrV/2+XCyEw/DII97fbdp4n0VEWoioF8lwzr1lZm8Dw/HWFtkOrHLOVcQ4pp7A60BnYBve2iKnOefWR84/ArQFngbS8BZKvjAyVP+ASEK7FhhZ5fC/8N64zQY2461d4otQIDJCslWYzKQUKCpqFjts3//+/XWe13qSItJcKdcdWflzz7FkxAiGdu9Oly5dYnVbERFpYi05340fP56FCxdWm7bdpk0bxo8fX+8vd1TCYbjoIpg92/t8993w8sswa1azmDUnInK0zHsxJUcyZswYt3hxbJdS+b9v/h95H+Zx36b7qHjtNRJuvBGWL4cRI2L6nIYqLoY1a2DVKli50vu9ahVs7vU4XDTZW2q6Fo9d+JgKkiJyRGa2xDmnvVDiTJ25LhyG/v3ZPWgQFW+9RadOnZouOBGRZka5Lj5V5rry8nI+/vhjNm7cSK9evRg/fnzTb2jzzjtw7bVex6tSaiq8/jpcemnTxiIi0gBHm+t82kZMgoEgGQO9ze0Sli2D1q1h6NAmj6OsDAKBgwXHyuLj2rXe+spwMLTzzoNhw+7jM4N/761RlHQwebiKkSIix6qKadNIWL+eDo8/DipGiohIM9aqVSvOPfdcf4NYtKh6MRJg715vkIoKkiLSAqggeRhmdgdwBxx+nZGjEQqEOO7G43j99de5eulSWp10EiQl1X1hA1VUwPr1BwuPlT9ZWVBa6rVJSICBA729dW66CYYN837694eqLwyHjvgOXHE/JIS9oqQDZj3Gu2/fx6NXNdpXEBGRGKtPrvt46lS+vOMObpw4Uf/xICIizUZj9+sabNGiQ4+lpHidMRGRFkB9isNwzj0DPAPe0P5Y3rtkZwnF24sp7VxKcEuQVkuWwHXXRX19fr7X/M03ITOzZtxQWFh9mvWqVbB6tbdMZaXevb1i48UXHyw8DhniraVcl9yK/0JiGBZ9B07+G7z3CHx2H2ujuFZEROJHtLlu/9KlfJaZSZ+UFFpFkyhERETiRGP26xrs5Zdh5kyvUxYKeSMjU1Jg7FivgyYi0gKoIOmDUK63oc2+pH1kdugAu3bVa4ftX/4SPvkEHnwQbrvt0OLj9u0H23buDMOHw623ekXH4cO96dcdOzY8/tt+O4NXV6YSnPokyYl/gZ83/F4iIhL/Fr7yCiUdO3LW1Vf7HYqIiEjztnIlfOc7cNZZ8N578P773jTtkSO9YqQ2tBGRFsLXgqSZpQFZwOnOubVN+NxHgWTn3D1N9cyqgoEgJMLu/bsZtj+ScKLcYXvWLHjmGW8K9nPPeT/grX88bBhceeXBEY/Dh0OsN0F1zvFOzjtc1P8ikhOTY3tzEZFjUHPPdfsLCpifnMzAkhK6DxkSo+hERORY0txzXZPZtQuuusobHfLmm95i/ZdeqjUjRaRFSoi2oZl1NbPfmNknZvaFmQ2NHL/LzBq6q85PgP9UJi0z+5OZLTazEjP7spYY+pqZq+VnQo12yWb2CzNbZ2b7zWyDmVVNUr8HbjGz4xsY91EJBUJwHFS4CjLz870FGocNO2z7cBimToWzz4YJE7zP4L08mzgR1q3zctv8+fDss/D978NXvhL7YiTA51s/Z/OezVwy8JLY31xExGdxkuvamNmLZrbCzMrM7KM6Yj7TzMrNbFWNUzHJdUuef559bdty1oQJdTcWEZFmoRHyXcz7dWb2NTN7z8y2mdkeM1tgZpfXuJWv/bp6cc6btpaXB2+9dejaWyIiLUxUBUkzGwKsBO4EioHBQOUiUoOBe+v7YDNrB3wLeK5GPC8Bf6/j8glAtyo//61x/vVImzsi8V0NrKg86ZzbBrwX+T5NLhQI0a5HO3r16kW3Vau8YmTr1oe0KyqCKVNg8GBv5OPatdU3lwmHYfZsb93HhKhLy0fnnZx3AJg4cGLTPFBEpInEUa5LBEqAKcCMOu6fFrnPhzXPxSTXlZcz5rnnuOqLL+h59tkNvo2IiMSPWOe7RuzXnR35fAkwCvgP8G8zG1/ZwO9+Xb08+ij8+9/whz/A+PF1txcROcZFW8Z6FFgH9AMm4u2tXOlTYFwDnj0RqIhcD4BzbpJz7ikgp45rg865gio/pZUnzOxC4HxgonPufefcl865Bc65j2rcYxpwfQPiPmrBQJCuHbty2623kv7pp4dM1960CX70I+jVCyZN8taBfOstbyR/zcJjOOytKdlUZgRmcGqPU+ma2rXpHioi0jTiItc55/Y6574bWYR/Ux33fw6vwzf/MOePLtdNn05yXh7D6rHxmoiIxL1Y57tG6dc5577vnPudc26hcy7XOfdzYAlwZY17+Navi9pHH8EDD8DXvw731vv9pojIMSnaguTZwG+cczuBmjuTFeC9zaqv8cAS51xDdjr7l5kVmtmnZvb1GueuBBYB95nZJjMLmNmTZpZao91CoIeZ9W/A849KKBAibWAabNwIweCBDW0WL4Ybb4R+/bwXaOefD/PmwWefwdVXw4IFUFpa/V6lpV6bplC4t5AFmxZouraIHKviLdcdkZndBWQCvzpCswbnurKyMl6cO5e8006Dyy5raJgiIhJ/Yp3vGqtfV5v2wI4ax3zr10Vlyxa47joYONDbAMCs7mtDbKsaAAAgAElEQVRERFqA+mxqEz7M8QxgXwOe3QfIr+c1RcAP8d6+lQOXA2+a2Tedc69E2hwPnAnsB64COgFPAd2BqkluS+R3X6DJFl7eF9rHvh37WNV9FWkzt3AmMGfPaB46G+bOhfbtvVGR99wDfftWv3bZsqaKsnYzAzNxOC4dpEWXReSYFQ+5rk5mNhz4GXCacy5sh+/cNDjXLZk+nfWdOtHqiiuqrxciIiLHgljmu8bq11VjZt8DegIv1zjlS78uKmVlcO21sGcPfPghdOjgd0QiInEj2h7GYuBm4J1azl0FfNaAZ7cFttbnAufcduCxqnGZWWfgfqAycSXgvem7wTm3C8DM7gZmmVlX51zlMysTbdsGxN5gwUAQOkGpK2Xrp5spJ5EJD4ygax94/HG4/fb4zVMzAjPo3r47ozJH+R2KiEhjiItcVxczaw28AfzQObeujub1znXl5eV89NFHfPb553TcvZvuP/5xQ0MVEZH4FOt811j9ugPM7CrgD8B1zrn1NU770q+LygMPwCefwKuvwokn+h2NiEhciXbK9q+Bq8xsOt4GMQ44y8z+ClwD/KYBz94OpDXgupoWAAOrfM4HNlcWIyPWRH73rnIsPfJ7WwxiiFrugpA3wQ4If5jF+rYn8Pe32pKbCz/4QfwWI0vDpcxaO4uJAyZyhJE4IiLNWTznuqq6AUOBFyK7a5cDDwEnRj5fWKVtvXJdeXk5jz36KJ9+8gnhhAR2dezIY88/T3l5eWy/gYiI+CnW+a6x+nXAgWLky8A3nHPTarnOl35dnd5+2xtxcvfdcMMNfkcjIhJ3oipIOuc+wEtOI4DX8BY+fhxvx7NrnHOfHuHyw1mG16E6WiOpPkXgU6B7jTUjB0V+V32bNgwow9thrtFVrg/5y3uDuExwzrikfB79rzmZq6+O/9lwn2z4hN37d2u6togcs+I811W1GRiOl/8qf/4C5Eb+rrqycL1y3cezZ1NSUnJwfSszSkpK+Hj27JgFLyIi/mqEfNdY/TrM7Bq8EZO3OOfePsx1Tdqvi0p2Ntx6K5x2Gjz2WN3tRURaoKjLYM65f5nZv4ETgS5AEFjpnKto4LNnAb83swznXBDAzAYAqXjrPSab2chI2y+cc6Vm9k28ZLMMbye3y4DvAT+qct/XgJ/ijRx5GG8NyT8BbzvnCqu0Gw987JwrbmD8dQqHYfp078XYxx9760PeMyhEq36tSE9vT0rh5gMb2sS7GTkzaJ3Ymq8c/xW/QxERaTTxkOsibYYCyUBnILWyjXNuuXOuDFhV9SFmVgjsd85VO049c92GTz6pdbH9jZ98AhdcENUXFhGR+BfjfNco/Tozuw5vZOQPgblmFplnRqlzLlTl+Y3er6uXoiL42tegTRv4xz8gOdnviERE4lK0U7YBcJ5Vzrn/Ouc+P4oOGs65lXg7ol1X5fDf8JLSD/CmpC2L/HSv0uZBvHVPFkWuvc0590SV+xYB5wMdI23eAuYAt9UI4Xrg2YbGfyRFRfDUUzB4MHz1q7Bhg1eU3LQJ+rQP0amsIyenRgZwNpOC5DuBdzi337mkJqfCmsfh9Vbebzj0s4hIMxYnue4/kWPXAidXaVNf9cp1vTdurPV4r8McFxGR5itW+a6x+nXAd/EG0PwRb+Rk5c+/aoTQaP26enMO7rgDsrLgjTegZ0+/IxIRiVtRjZCMDJU/IufcWw14/s+BP5nZX5xzYefcOXU84yXgpShiyQYuPNx5M7sEb2e5ww37b5CNG2HKFHjmGdi5E8aNg9/+1itKtmoFzjmCgSDDTx3O2I2LvVEoI0fWfWOf5QRzyAnmMOnUSV7Rcdlk78SyyVDwAeTPPPgZ4IT7/AlUROQoxEuuizynb30e4Jx7GHi46rGG5LrxPXuycN8+Stoe3Begzb59jFeHSkTkmNFI+S7m/bpo8mVj9esa7Omn4fXX4de/hq9oZpmIyJFEO2X7jcMcd1X+rncnzTn3rpk9DfSk+vqOjS0FuNU5F5NV+hcv9kZAvvWW91Lsqqu8DWrGjavebl9wH/tL95N6fCrMXeoNoUxNrf2mtdk6G+bfCuNegK7nHvq5kczImQHAJQMvgXdrrDNdWYystPx+FSRFpLlq8bmu1YMPMrl7dz4ePpyNvXvTa8MGxq9cSastWxoxzDi15nEvp418xMtrNT+LiDRfMc93zSnXNZr58+G+++Cyy7zdtUVE5IiiLUieUMuxDOBS4OvANxsagHPuyYZeexTPbMgIF5Yvh3POgblz4cQTYdo0eOKJg+tDfv/7MGkS9O1b+/XBQBBGwUdFH3Hq6tW0HTs2+odvnQ0fXQrhYu/38J/Byp8f/HzOO7EtSjrn3busiGWBf3BV5vH0K9sEx98Ca587/HWjHo1dDCIiTUu5LjmZVlu2cO6vfgXz5sH48TBrVstb/0qzAUTk2NYo+a7Z5LrGsG0bXH019OoFL70ECfVaGU1EpEWKqiAZmQJdm3lmFgbuBObHLKo4ddNNsGsXXHQRtGsHeXnQp483OvL226FDhyNfHwqEIBNSW7el7dq1cNdd0T98/q1egRC835XFyMrP82+BS1ZB2R4o3wPlRd7fZZG/y6v8XdmmrMbxan8XQWQZmb+3AtoDH5x15Bi7T4Qh90b/nURE4ohyXURyMvziF35H4a/l91f/rNkAInIMUb6LsXAYrr8egkFvlGRamt8RiYg0C1Hvsn0Eszl0YeFjzvLlsHq193dBAYwY4W2aduWV3vqQ0QgGgpAJ3dpERprUZ0ObcS8cHCEJB39XKt4A/6ijIlopsR0ktYdWqZHf7aH1cZB6/MHjrdpDUipLt+fyxOK/8eNzfsXQ7mNh0zuQ86fa77vlP5D1RxUlReRY1CJynUSMfOTgSMjaaDaAiBy7lO/q66GH4MMP4fnnm8X+ACIi8SIWBckxQHGdrZq5m26q/rm8HL7+9frdY3vudhgE3fbs8Q6MGhXdhRVhKNsNbbrC3nXVz1kr6Hy6N127ZpExqbKwWOV4YgokJEYd81NTb2VGWRovjPwRJLSC2ROOfMGyH6ogKSLHohaR6yRiyA/gy9dgx5JDz2k2gIgc25Tv6mP6dPjNb+Bb34Jbb/U7GhGRZiXaXbbvr+VwMjAM+CrwbCyDijfFxbBmTfVjq1fDihVw0knR36ewsBCGQGZeHgwYAB07HvmCsiLIexGy/whFawE7tI0rh9BiOOnn0PWc6IOJQoWrYEbODC4eeDGtEiL/V6k5aqT7RG9kZKWRj8Q0BhGRptLSc51EbJsP82489AVgJc0GEJFmTvkuRvLy4OabvVlvTz3ldzQiIs1OtCMkf1fLsTCwGXgC+HnMIopD6w7TJ7nhBli1Krp7OOfYvWo3fUf1pddnbxx5unbxJsh+CnKfgbKd0HkclO2C/dsPtklsd+gakld+GV0wUVq0eRHbird5u2tXqlwza/n93pS1Ifdq51EROVa06FzX4u3Ohs9/AhujmKmo2QAi0rwp3x2tffvgqqu8zWvefhvatPE7IhGRZifagmTbWo6VORfZ9eQYt39/7cfXro3+HnsL91JWWMaJGd1IzcqCW245tFFwMWQ9ARveAiqg11Uw+Adw3Ljqu2wntoPhD8PKhw9+HvdC/b9YHd7JeYcES2DCgBrTtE+4r3rhseZnEZHmqUXnuhareAus+jmsfQ4S28LwX3jLoaz4ycE2mg0gIscW5bujdffd3iYDM2ZAv35+RyMi0izVWZA0s2TgYeBt51wtiykd+0aPhsWLj+4eoUAIBkK7ivXegZNP9n5XhGHzdMh+Agrnems+Dr4HBk2C1L4Hb9D1XDjnHW+37XH/v707j4+qvvc//vqQECDsIKvIvgi4AGIFFZS6tYCt3WxtXWqt1ms3C17tonWp1S6CtffWettatbVVe396Ww0oYgVxwQUKlVXCvpiwBQOBJGT5/P44EzIZsmcyZyZ5Px+PPJKzzifHMG/PZ875nseD27N7ToxMPxYsj7N52fM456Rz6NGhR9z3LSKSTJR1rdDRfFj3S1g/Nxj+ZMRNcMrt0L53sDytne4GEJEWR3kXB48+GjzA5o47YPr0sKsREUlZdTYk3f2omX0XeDEB9bRY+zbsgy/ApsJdjAE4dURwW/YHDwXjQ3YcBBPmwrDroG0NT8vuM63qbdmx03G06+AuVuSu4GcXVHdHh4hIy6Ksa0XKiiH7t7DmXijeD4O+BKfdC52HVV1PdwOISAukvGui5cvhm9+Eiy6CO+8MuxoRkZRW31u2/w2MAZY0Yy0t2oebPoQMGLJvA9zQBZaMqxwfctzPYMBlwVOsk8S87HkAzBw5M+RKREQSRlnXknk5bH0K3r8dDm+FPhfA+J9DjzPCrkxEJNGUd42Rlwef/zz07g1//SukpYVdkYhISqtvB+xW4E9mttHdX2nOglqq0iNv85mTnmfs0NXQxqHf5XDy9+CESWGXVq152fMY3G0wY3qNCbsUEZFEUda1RO6QuxBW3gYHVkL38fCx30G/i8KuTEQkLMq7hiovD56ovWsXvP46nHBC2BWJiKS8+jYk/wh0AxaY2REgF/Co5e7uo+JdXMqrGB9y/VwuO/N1issy8JfBTpkNX3kg7OpqVFhSyCubX+HacddiZmGXIyKSKMq6liZvOay4DXb/EzoOhrP/EtyibW3CrkxEJEzKu4a67z6YPx8efhjOOivsakREWoT6NiSXUzWkpDYlBbD5sWPjQ3rHQby8/hKySyfwrSfvh/kXhF1hrRZvXcyRkiO6XVtEWhtlXUtxaFNwa/a2p6FdT5jwKxhxY/CgGhERUd41xMKF8OMfw5VXwo03hl2NiEiLUa+GpLt/qbkLaREO74AN/w0bf1dlfMiCtAt4+8pf8+lLDwbrTZgQbp11yNqQRWbbTM4ffH7YpYiIJIyyrgUo2gOr74WNj4C1hbG3w+hbIKNr2JWJiCQN5V0DbN8OV1wBY8fCI4+A7h4TEYmbGu9ZMrPNZnZ6IotJWkW74al0WDc3mF43t+r0/mXw5pfh+SGw/gHodzFcvBQufgsGfp68TQehAIblboQTT4Q+fcL7Xerg7szLnsdFQy+ifXr7sMsREWlWyroWoqQAVt0Dzw+D7Idh6NfgUxvh9J+oGSkigvKuUYqL4QtfgKNH4dlnoWPHsCsSEWlRahtEaTCge5sAjuwEL4MVs2HR9OB7xfQ/hsKCM2FXFoz6Lly6Cc59psrDajav2QzngO3eBGck99M81+xdw7b8bcwYMSPsUkREEmEwyrrUVV4C2b+FF4bDqjuh3yUwYw187BHo0C/s6kREkslglHcNM2sWvPsuPP44jBwZdjXNI/ZCm9hpEZFmVN8xJKVCzotVpw9vgQlzYdh10LZLtZts27UNLoK0n22Cy7+QgCIbL2tDFgDTR0wPuRIREZEauMOOZ+HfP4RD2dBrCkz9e5UPA0VERBrtySeDB9jccgt89rNhV9M81s0NLrCB4HvuK5XnuhXzR88KpzYRaRXqesykBjuuy7gH4OTv1diMBDhw+ABtD5XToago6cePnJc9jwn9JnBilxPDLkVEJFGUdclk9yL4++Dge7XTi+HlSfDGF6BNBpz3Alz4mpqRIiJ1U97Vx6pVcMMNMHUq3H9/2NU0n5W3Vp2OvfAmdrmISJzVdYXk3Wa2rx77cXe/Jh4FJaW2XYCDx8/vPx3GzK5z88Nph+mVXxhMJPEt2/uP7OetHW/xoyk/CrsUEZFEUtYli92LYPFMKDsSfD/1Tlh1dzC9aDp0Ow3y3oXMATDpMRh8FbRJC7tqEZFUobyry8GD8LnPQdeu8MwzkN6Cbygc94vKKyGrM/6BxNUiIq1SXe+w44DieuynZX/aVlJNMxLgw/mw/ldw8s01blpcVExZpzL6b9sXPMymX/KOafXSxpco93JmjpwZdikiIomkrEsWS68Nmo8QfK9oRgKUF0Hee8EJ1MhvQXqH8OoUEUlNyrvauMO118LmzbBoEfTtG3ZFzWvAp2H9g1C48/hl/afXeo4rIhIPdTUkL3P3dxNSSapacUutb9Y7P9gJ5TBo15bgdm2zBBbXMFnZWfTu2JuJ/SeGXYqISCIp65LF5Mcqr5CEyu8Alg5TnoMBl4ZTm4hI6lPe1WbuXHjuOZgzB6ZMCbua5vPRGlhzH2x7ihp7z/W48EZEpKnqGkNSYvWPedjLuF/UunragTTS7ithzHtvJvXt2qXlpby08SVmjJhBG9OfhYiIhKDPtOA27bSYqx/TOsDp96kZKSIizWPJErjtNvj85+F73wu7muaRtxyWfBbmnwK7/lH3+ituaf6aRKRVU+epPjIHgKXBhAfh/Hkwfk4wPX5OnU8e25+9n75lu0kvLUnqB9q8teMtPir6iBkjZoRdioiItFa7F8Gqu6CssOr8ssJg/u7FIRQlIiItWk4OXH45DB8Ojz6a1He0Ncqe12HRJ+CliUHOnnIHfHrb8WNENvDCGxGRplJDsj7a94ErSisvWR89K5iuoxkJ8N6290j/WHkwkcQNyawNWbRt05aLhl0UdikiIhKy/O35zOk/h/zt+Yl94aXXVG1GpmVW/lx2BJZ+NbH1iIhIy1ZSEjQjDx2CZ5+FLl3Crig+3CHnZXjlPHhlKuT9C06/Hy7bBqfdA+16BueyFRfaNOLCGxGRpqpxDEl3V7OyicrLy9mTtofhfcqhZ08YODDskmo0L3se5w0+jy7tWkgIi4jUg7KuekvuW0JBTgGv3/86M3+boAedlRyC9KgMSsuEU++KXDF5JJie/FhiahERaWGUdzGOHoV774XHH4cdO+CJJ2Ds2LCrajovh53Pw5qfQt4y6HAiTPgVDL8e0jOPX3/0rKqNx9hpEZFmpGBqRvv378fbOIP3bk/qB9psPrCZtXvX6nZtEREBYM0za4LvT69JzAuWFMDi6XBoPZxyF2QOCq7UGPOfcH5WZDorGGNSRESkKY4eDZ6g/ZOfBM1IgJtvDuanqvIy2PoUzD8dXv8MHM2Dj/0OPrUJTv5u9c1IEZGQ1fWUbWmCnA9zABiydR1cc3XI1dRs3oZ5AMwcmaCrYEREJOnkrszlbru7yryij4qOzWvfvT235d0W/xcuPQyvzYB9S+Hsv8Kgy+G0OyuX95kGl22N/+uKiEjrdO+9cOBA1XkHDgTz77knnJoaq+wobP0zrPkZFGyELqNh8p9h0JegjU71RSS56V2qGW3fuB1Knd57dif1E7azsrMY1XMUw3sMD7sUEREJSed+nWFnzcvP+/F58X/R0iPw2qWw9w2Y/JegGSkiItKc3nyz+vlvvZXYOpqitBA2/QHW/RKO7IDuE2DKszDgMjDdBCkiqUHvVs2oOL+YLpsPklZenrQPtCk4WsDirYt1u7aISCvXsU9Hpt4xtdplU++YyqSbJ8X3BUsLYcmngydnT/oTDP5SfPcvIiJSnXPOqX7+2Wcnto7GKDkEa38Bzw+G5d+BjgPh/PnwiWVw0mfVjBSRlKJ3rGY06Oggpvz1dbxLFxg6NOxyqvXK5lc4WnZUt2uLiAiH9x5u0PxGKyuCJZdB7j9h0uMw5Cvx3b+IiEhNbr8dunevOq9792B+sirOg/fvgn8MgpW3QbfT4YLFcOHr0P+TSfusAhGR2qgh2Yz2Z++nv+UEt2snaUhkbciiS7sunDvw3LBLERGRkK1+ejUA6R3SueD+C0jvEIzsEteH25QVw5LPQu5COOtRGJq8YyyLiEgLlJEBublwxx1wwQXB99zcYH6yKcyFFbcGjcjVd0OvKXDxO/Dxl6HPeUl7jikiUh8aQ7KZZGdnszxjOeN6lmBJert2uZczP3s+nxj+CdqmtQ27HBERCVnb9m0Z9KlBfPHZL9ImvQ2TZk3ib5/7GznLcuLzAmXF8PrnIOdF+NjvYdi18dmviIhIQ2RkhP8Am92LYOm1MPmx4AFu0dOdhgXjQ276A5QfhYGXw9gfQrdTw61ZRCSO1JBsJjk5OZS0L6HbofykfaDNipwV5BTkaPxIEREBYHbO7CrT6RnpfPmFL8dn52VH4Y3L4cN5cOYjMPzr8dmviIhIqtm9CBbPhLIjwfdT74RVdwfTr14E7sF4kEOuhjHfhy4jwq5YRCTu1JBsJjk5ObTLK6ZdcXHSPtAma0MWhvHJ4Z8MuxQREWnJykvgzS/Brudh4m9gxDfCrkhERCQ8S68Nmo8QfH//TigvCqa9DNI7w4xV0HFQeDWKiDQzjSHZTHJ25dA9J4+ydpkwIjk/0crKzmLSgEn06tgr7FJERKSlKi+BN6+Anf8HZ/waRt4UdkUiIiLhKS2EU++ANlFjVlY0IwHSOsB5z6sZKSItnq6QbAZFRUXkH8pnWO4uSkaNJa1N8vV9cwtyWfbhMu6ddm/YpYiISEtVXgpvXQk7noUJD8Kob4ddkYiISPNzhyM74dAHcDDq69AHcHg74NVvl5YJp94Ffc5PYLEiIuFQQ7IZlJSUMKDdiYzd9gR20SfCLqda87PnAzBz5MyQKxERkRapvBSWXg3b/wbjH4CTbw67IhEREVg3F1beCuN+AaNnHT/dECWH4NCG45uOBzdU3pINkN4ROo+CE86GodcGGbnugapXRkKwzaq7oOeZakqKSIuXdA1JM/sB8FlgFFAMvA38wN1XR63zOHBNzKbvuPukqHXmAl8FDgPfd/e/RC27FLgNmOLuNXw81XidO3dm5Noyhm7fgk85K967j4usDVkM6DKA0/qcFnYpIiKtTkvIulqVl8Hb18K2p2Dcz2D07Lq3ERGRFiUps65oN6yIZNKK2ZD7CuS8WDkNxzcly8vg8NaoZmNU47EwJ+oXbgMdBweNx97nQZdRwc9dRkGH/mBWue7fB8fcpp1ZdUzJpV+Fy7bW+euIiKSypGtIAucDDwPvAQbcA7xiZmPcPS9qvVeAq6Kmj1b8EAmmLwMXAyOAP5rZAnffZ2adgQeBTzXXCdqRI0do8/7KoJaJE5vjJZqkuLSYhZsXcuWpV2LRwSgiIolyPimedTUqL4N3roOtT8LpP4UxtyX05UVEJGmcT7Jl3ZGdVacrmpEVVvwntOtRtel4aCOUH61cJ6N70Gjse3HQbKxoPHYeBmnt61UGkx+rfMp2xW3aq+6qnJ78WP32IyKSwpKuIenul0RPm9lVQD5wDvBC1KJid8+tYTejgcXuvgxYZma/AoYA+4D7gCfdfW3ci4/485//DKelc9bqDNJHjWqul2m0JduWUHC0gBkjZ4RdiohIq9QSsq5aXg7vXg9bnoBT74GxP0zoy4uISPJIyqzLHADsrGWF8uAK/zZtodOwoNnYf2bVxmO7nlWvdmyMPtPg/KzgaduTHw9uz+45MTL9WLBcRKSFS7qGZDU6EzwN/EDM/HPNbA/wEfAa8CN33xNZ9m/gBjPrDgwFOgAbzWwSMA2Y0FzFlpWVsXfvXkbn5lDQZxjd0pPvEGdtyKJ9ens+PuTjYZciIiKBlMq6ank5vPsN2PwYnPLj4AmiIiIilcLPuvZ9oN+px18ZCdDlZBj/y6Dp2GkItGnm87g+06relh07LSLSwiXf45+P9xCwElgaNe8l4GrgAmA28DHgVTNrB+DuC4AnCW4PeJxgXJIC4H+AG4FrzWydmS03s7PjWey+ffsoKytjSO4mSk4+NZ67jgt3Jys7iwuGXEBm28ywyxERkUBKZd1x3OG9b8KmP8DYHwW3nomIiFQVftYV7a6+GQlwcH1we3aXEc3fjBQRkeS+QjIygPG5wLnuXlYx392fjlptlZktB7YBM4DnIuvcBdwVta/bCcIvn2D8knHAqcD/mtkQd48aGKTxcnODuw1OytmJTf9yPHYZVx/s/4DNBzZzy+Rbwi5FRERIzayrwh2WfQs2PgJjvg+n/aTpt7KJiEiLkjRZFzuGZKwVt8DJN9f/FxMRkUZL2iskzexB4Arg4+6+ubZ13f1DgsFARtSwr5HA1wiewDYNWOLuOe7+MpBB8OS3uMjJySGtHHru30/mhefEa7dxk7UhC0DjR4qIJIFUzbqoomD5zZD9MIy+BU6/T81IERGpIqmyLnNA1en+06tOj/tFrZuLiEj8JGVD0sweInia2sfdfX091j8BOBHIqWaZEVzSf4u75xP8zm2jlrUF0uJV++jRozlj9S7KvQ0dpibfE7azNmRxWp/TGNh1YNiliIi0aqmcdUDQjPzXbNjwaxj1veAkTs1IERGJknRZ174PjJ8DlgYTHoTz51VOj58Do2c18DcUEZHGSrpbts3sN8BVwGXAATPrG1lU4O4FZtaJ4JL9ZwmCajBwP7AH+L9qdnkd8JG7PxeZfgO4x8zOBU4DSoAP4lX/oEGDaPfam+R1GEDvjIx47TYuPir6iDe2v8Gt59wadikiIq1aqmcd7rDyVvjgQRj5HZgwR81IERGpImmzbvSsqo3H2GkREUmIpGtIAjdFvv8zZv7dBIFVRjBGyNVAN4LwWgRc7u6Hojcwsz7A7cCxe6fdfZmZ3U8QcoeAq9y9MB6FFxYWsnfPHrof2sGeAWfROx47jaMFGxdQ5mXMHDkz7FJERFq7lM063OHfP4B1D8CIb8IZv1IzUkREqpO6WSciIs0u6RqS7l7rWU0kZC6p5752E3zSFjv/foJP3+Jq27ZtPPPMM1zXuytHR50S7903WVZ2Fj079OSsE88KuxQRkVYtZbPOHd6/A9b+HIbfCBP/S81IERGpVspmnYiIJERSjiGZqiqesN17927anJlc40eWlZfxYvaLTB8xnbQ28R1GTEREWolVd8Gan8Kw6+HM36gZKSIiIiIijaKGZA3M7AYzW2Zmy/bu3VuvbXJzc+leXEpaSRkdzjuzmStsmHd2vcP+wv3MGKGna4uISKBBWbfqHlh9Dwz9GnzsETD9L4SIiCS/xpzXiYhI89PZRA3c/XfuPtHdJ/bq1ate2+Tm5tIrdw976E3PU05s5gobJmtDFmmWxiXD63VXhIiItGPezXIAACAASURBVAL1zro198GqO2HINXDW79WMFBGRlNGY8zoREWl+OqOIk8LCQvLz8+m/eRN72g4gs1dm2CVVkbUhiymDptCtfbewSxERkVSy9ufw7x/B4CvhrEfVjBQRERERkSZL6rMKM+tuZrvNbFgc9znTzFaaxfeMKiMjg6/NmMHpK5dxqO8ILInG1dqev51Ve1bpdm0RkSSVNHm3bi48lR58B1h8Kaz8PnQfD5MeB41BLCIijZQ0WSciIkkh2d+4fwjMd/dNZvZVM/Mavo4N2GhmZ5rZK2Z2wMw+MrN/mtnHKpa7exZQBnwlnoWmpaVx0s6ddMvPp2TUqfHcdZPN2zAPgJkjZ4ZciYiI1OBY3gHUkHU3VqxsZnfVkom9oRF5V7QbVswGLwu+vzAaPswKlh1YAR88FN/fWEREWpsGn9sBmNmVkaZjkZntM7M/VSxrrnM7ERFpfknbkDSzTODrwKORWc8A/WK+ngS2AMsi23QCXgI+BM4GJgM5wAIz6xy1+8eA78Sz3rVr1/LBsuWUY7SZOC6eu26yrOwshnUfxqieo8IuRUREYlSTdxWup2rmPRG17AGOz8TXgMXuvidqvfrn3ZGdVacPra86vfLWeu1GREQkVmPO7SLbfQf4JUHunQJMA/4Rs/u4n9uJiEjzS9qGJDAdKAfeBHD3QnfPrfgCDgKXAn9wd49sczLQA7jT3de5+zrgDqAbEN2Nex6YaGbD41XskiVLeDf/IHvpRbcx/eO12yY7UnKEV7e8ysyRM5PqNnIRETmmSt5F+Sg699y9sGKBuxfEZGJbYArw+5h91D/vMgfUvnz8A3XuQkREpAYNPrczs27A/cDV7v6ku29091Xu/mzMvuN+biciIs0vmRuSU4DlUc3GWJcDHQk+EavwAbAXuM7M2plZO4IrTLYDaypWcvftwG7gvHgUWlpayt69e+m7ZQs59KPniJ7x2G1cvLrlVYpKizR+pIhI8qop7x6K3Jr2npndWMf4WNcBHwFVTtIalHft+0C/T1a/rP90OPnmOnchIiJSg8ac210MpAF9zGytme0ys/8zs6HRG8b73E5ERBIjmRuSgwhut67JDUCWux9bx90PAecDXwSORL6+CFwUfWVJxIfA4HgUunfvXsrLy+m/ZRO59KPH8B7x2G1cZG3IolNGJ6YOmhp2KSIiUr3q8u7HBPl1IfA0MIdg7K3jRBqVXwP+5O7F1axSv7wr2g05L1a/7MP5sP5Xde5CRESkBg0+twOGEpyv3g7MAj5DcEfAosgt4NHidm4nIiKJkcwNyQ5AUXULzGwswfiQv4+Z3wH4I7AUmAScA6wA/mFmHWN2Uxh5jSbLzc0FoG9uLvs6DaZDz7jstsncnXnZ87h42MW0S28XdjkiIlK94/LO3X/i7m+4+0p3nwPcDfxnDdt/EjgJ+EMNy+uXd7FjSMZacUuduxAREalBg8/tCM5V2wLfcfeX3P1dgofX9Ca4vTta3M7tREQkMZK5IbkP6F7DshuAHQQPsIn2ZWAYcK27v+fub0fmDST4RC1aD4Lbu5tsz549tAW6HzhA6cgxSTNW4/u732fnwZ26XVtEJLnVlncV3gG6mFmfapbdALzl7muqWQb1zbvYMST7T686Pe4Xde5CRESkBo05t6u4WnJtxQx3zye4GnJgzLpxO7cTEZHESOaG5ApgTOxMM2sPXAX80d3LYxZnAk4wYHKF8si8Y79rZB/DgH/Fo9CLL76Yb69axUdpvehy8onx2GVcZG3IAmD6iOl1rCkiIiGqNu9ijCO4suSj6Jlm1h+YwfFXlVQsr3/ete8D4+eApcGEB+H8eZXT4+fA6Fn1+FVERESq1Zhzu4qHvY2KWr8TwRO5t8XsI27ndiIikhjJ3JBcAIw2s9gnxHwe6Epwa3ashUAX4GEzGx25/P8xoAx4NWq9SUAxxz/RtFHMjE5vv8PO0j70GJFE40dmZ3Fm/zPp26lv2KWIiEjNquSdmV1qZteb2SlmNszMvg7cA/yumjEivwYcBv5Ww74blnejZ8EVpZUPsKmYVjNSRESapsHndu6+AfgHwUPezjGzMQTndnuArKhV43puJyIiiZG0DUl3XwW8C3wpZtH1wILI09Rit1lPMJ7IqQTjSL4BDAA+6e7Rg2NdAfzF3Y80tc78/Hz+8fTT7D1aHDzQJkkaknsP7+Wdne/odm0RkSRXTd6VADcR5Nj7wHcJHnIzO3o7C8YHuY7a8yxueSciItJYjTm3i7gKeBt4gaDh2B64ICbXlHUiIikoPewC6nA3wSdij7h7GYC7n1fbBu6+kOBKyWqZWS+CT+ImxqPAXbt2sfKDDzizbVty6MfYEbEf+oXjxY0v4jgzR84MuxQREalbdN69xPHjaB3H3R0YUtPyeOediIhIEzXm3O4Q8PXI13GUdSIiqStpr5AEiJyU/YbgKsd4GQLc5O5b4rGz3NxcDOi9Zw859E2aKySzNmTRr1M/xvcbH3YpIiJSh1TIOxERkaZQ1omISLRkv0ISd/91nPf3LsHtAnGxe/duehUVUZx5Am3a9qBD9w7x2nWjlZSVsGDTAi4fczltLKl7ziIiEpHseSciItJUyjoREamgblUT5ebm0nfXLva2O4meSXK79hvb3+Bg8UFmjNT4kSIiIiIiIiIiklzUkGyCkpISMtLS6JudzY6jvZPqdu2MtAwuHHph2KWIiIiIiIiIiIhUoYZkE7Rt25Zvjh3LpLffZmt+D3oMT5KGZHYW0wZPo1NGp7BLERERERERERERqUINyab6178wIId+SXGFZPb+bDbs38CMEbpdW0REREREREREko8akk3w4osv8o8PP+Ror/4UkpkUY0jOy54HoPEjRUREREREREQkKakh2QRbtmzhSEEBh3oPB0iKKySzNmQxptcYhnYfGnYpIiIiIiIiIiIix1FDspFKSkrYt28ffTdtYk/7k8jslUn7ru1Drelg8UFe2/YaM0fMDLUOERERERERERGRmqgh2Uh79uzB3embk8PO4t5Jcbv2wk0LKS0v1e3aIiIiIiIiIiKStNSQbKTc3FwA+ubmsmlv1+S4XTs7i27tu3H2SWeHXYqIiIiIiIiIiEi11JBspA4dOjDi8GG6ZnZk9+7wx48s93LmZ8/nk8M/SXqb9FBrERERERERERERqYk6V400ZswYxmRlUTRiLHxI6LdsL/twGXsO72HGCN2uLSIiIiIiIiIiyUtXSDaCu1Oanw/r1yfNE7azNmTRxtrwieGfCLUOERERERERERGR2qgh2Qj79+/n/oceYt2oUexpfxIAPYaH35A8+6Sz6ZkZ/sN1REREREREREREaqKGZCPk5uZS7k73jz5i59HedOrbiXad24VWz66Du1iRu0K3a4uIiIiIiIiISNJTQ7IRcnNzaeNOL+DDXeFfHTk/ez4AM0fODLUOERERERERERGRuqgh2Qi7d++md34+aePHk7fxQPjjR2ZnMajrIMb2GhtqHSIiIiIiIiIiInVRQ7IRcnJy6Lt1K6VjT6MgtyDUhmRRaRGvbH6FmSNnYmah1SEiIi3H3KVzSb8nnblL54ZdioiIiIiItEBqSDZQeXk5kwcMYOzq1RzqMwKAniPCe5DM4q2LOVJyRONHiohIXMxdOpfZL8+mzMuY/fJsNSVFRERERCTu1JBsoDZt2nDOgQMM37iRvR0GAoR6hWTWhiwy22Yybci00GoQEZGWoaIZGU1NSRERERERiTc1JBsoLy+PgytXQo8e5B7IAMJ7qI27k7UhiwuHXkj79Pah1CAiIi3D7sO7j2tGVlBTUkRERERE4kkNyQZ69dVXeaxrV5gwgbyNB+jcvzMZHTNCqWXN3jVsy9+m27VFRKTJdh7cWevyWxfemqBKRERERESkpVNDsoFyc3Lou2MHnHEGedl5od6uPW/DPAA1JEVEpMkGdBlQ6/JfXPSLBFUiIiIiIiItnRqS9VRaWsrChQvZn5dHYbt2lI4bx/7s/eGOH5mdxfi+4zmxy4mh1SAiIi1Dn4596LNyDnjMAoc+K+cwa/KsUOoSEREREZGWJz3sAlKBuzNnzhyKiooA2DZ4MA9s3kzx/pLQnrC9/8h+3trxFj+a8qNQXl9ERFqe/JdmQS5wyWwwgubkgjnkr1QzUkRERERE4kdXSNZDQUHBsWYkAGYUl5TA1PCesL1g0wLKvVy3a4uISNwUFoIvncWcS+aQZmnMuWQOvnQWhYVhVyYiIiIiIi2JrpCsh6NHj1a/YGA4T9hetGUR179wPV3bdeXME89M+OuLiEjLNmvyLN2iLSIiIiIizUZXSNZDRkY1T9F2YDv0GJbYhuSiLYuY+dRMjpQcoeBoAa9tfS2hry8iIiIiIiIiItIUakjWQ6eOHWlXWAjux77aFRXSZWN72ma2TVgd0c1IgDIvY+ZTM1m0ZVHCahAREREREREREWkK3bJdD0e2b2T2L//NG1OnsmPgQE7avp1zlyxhae+pwG1xeY1yL6ekrISjZUcpLivmaNnR4OfS4Oe3drzFzS/dTFFZUZXtjpQcYeZTM8m6IotpQ6bFpRYREREREREREZHmooZkPZQfOkR6OUxbvPjYPAe6HF7N1MemcmrvU49vJFbTVKxtfkl5SaPrO1JyhGv/cS1bb97a5N9VRERERERERESkOakhWQ+HMoBqnmvzrxPa8/r211m7dy3t0tuRkZZBRloG7dKifk5vR9f2XY+fH7NOxc81LVu/bz33LrmX4rLi4+rIbJvJY59+rPkPhIiIiIiIiIiISBOpIVkPOZ3hQCl0j7pbupD2zD2jA3MunpOwJ5Gec9I5VcaQhKAZqdu1RUREREREREQkVaghWQ8Dug6gz3U7uX0JnLMDNrbrxd713+Caz3RJWDMSYNqQaWRdkXWsKalmpIiIiIiIiIiIpBo9Zbse+nTsw8+nz+Guj8NF18AzfcZSbul877PfS3gtFU3JQV0HqRkpIiIiIiIiIiIpR1dI1lPFlZC3LryVC9tdSMeBHUlvH87hmzZkmh5gIyIiIiIiIiIiKUlXSDbArMmzKP1xKb0P9KbniJ5hlyMiIiIiIiIiIpJyUrohaWY3mdkWMysys+VmNiVq2VwzyzOzHWb2lZjtLjWzN8zMGvO6eRvz6DGiR1PLFxERqVNYWSciIpIoyjoRkdYnZW/ZNrMvAg8BNwFvRL6/aGZjgNOBLwMXAyOAP5rZAnffZ2adgQeBT7m7N/R1j+w/QtGBInoMV0NSRESaV1hZJyIikijKOhGR1imVr5CcBTzu7r9393Xu/m0gB/gPYDSw2N2XuftTwEFgSGS7+4An3X1tY140LzsPQFdIiohIIoSSdSIiIgmkrBMRaYVSsiFpZhnAGcDLMYteBs4G/g1MNLPuZnYG0AHYaGaTgGkE4dUo+7P3A2gMSRERaVZhZp2IiEgiKOtERFqvlGxIAicAacDumPm7gb7uvgB4EngPeBy4BigA/ge4EbjWzNZFxic5uyEvnJedh7Uxug/t3sRfQUREpFahZZ2IiEiCKOtERFqplB1DMiJ2rBCrmOfudwF3HVtgdjuwFMgH7gHGAacC/2tmQ9z9aH1eMC87j66DupKWkdbk4kVEROoh4VknIiKSYMo6EZFWJlUbkvuAMqBvzPzeHP/pGmY2EvgaMJ7gU7Ul7p4D5ERuExgFrKrPC+/P3q/btUVEJBFCyzoREZEEUdaJiLRSKXnLduRTr+XARTGLLgLeip5hZkZwSf8t7p5P8Du3jVrWluA2gfq8LnnZeXqgjYiINLuwsk5ERCRRlHUiIq1Xql4hCTAX+LOZvQu8STCGSH/gkZj1rgM+cvfnItNvAPeY2bnAaUAJ8EF9XjBnZQ7FB4vJPCEzHvWLiIjUJeFZJyIikmChZN0Df3+A3Kty6fvnvtxy2S1N/R1ERKSBUrYh6e7PmFlP4HagH7AamO7u2yrWMbM+keXnRG23zMzuB/4POARc5e6F9XnN1+56DYBd7+6K168hIiJSozCyTkREJJHCyLq5S+ey+N7FnFlwJovvXUybPm2YNXlWHH8rERGpS8o2JAHc/WHg4VqW7wYGVzP/fuD+hr7e5oWbAdj+xvaGbioiItIoic46ERGRREtk1s1dOpfZL8/mttW3AXDKmlOY/fJsADUlRUQSyNxjH2gmsfpbf/8G36h2Wfvu7bkt77YEVyQikrrMbLm7Twy7Dqlq4sSJvmzZsrDLEBFpEZR1yal/Wn//RnlwXuc4hh37DkBnuPPgnSFWKCKSOpqadWpI1sDMbgBuAOhAhzO60a3a9Q5xaEcBBXsSWVuSOYHg6XiiYxFLx6OSjkVVo9y9c9hFSNWsI3gyafTYW/q7raRjUZWORyUdi6p0PCop65JEdNalt00/o1dJrxrXPdjlIIcPHl6eqNqSjP79VqXjUUnHoiodj0pNyjo1JBvIzJbp085KOh6VdCyq0vGopGNRlY5HatB/p0o6FlXpeFTSsahKx6OSjkVq0H+nSjoWVel4VNKxqErHo1JTj0WbeBYjIiIiIiIiIiIiUpuUb0iaWXcz221mw+q5/rfM7PnmrktERCSelHciItLSKetERFqPlG9IAj8E5rv7JgAzG2hmL5jZYTPbZ2a/NrOMqPV/D0w0symNfL3fNbXgFkbHo5KORVU6HpV0LKrS8Wgc5V14dCyq0vGopGNRlY5HJR2LxlHWhUfHoiodj0o6FlXpeFRq0rFI6TEkzSwT2AVc6u5vmFkasBLYD8wCegJPAM+6+7ejtnsAGOTuXwihbBERkQZR3omISEunrBMRaV1SvSH5eeB/gBPc3c3sk8A8gkDaEVnnSuAPQG93PxiZNxVYCHR39yPhVC8iIlI/yjsREWnplHUiIq1Lqt+yPQVY7pVd1cnAuorAilgAtAPOiJq3DEiPrC8iIpLslHciItLSKetERFqRVG9IDgJyoqb7Artj1tkHlEWWARD55CwfGNyQFzOzm8xsi5kVmdnyJoxVkrLM7Adm9p6ZHTSzvZExXU4Ju65kYGY/NDM3s/8Ou5awmFk/M3si8rdRZGZrzey8sOsKg5mlmdlPot4ztpjZvWaWHnZtiWBmU83seTPbFfl38dWY5WZmd5nZh2ZWaGaLzWxsSOWmgoTlnbIuoLyrmfJOeVdBWaesizNlXYIp62qmrFPWVVDWNV/WpXpDsgNQFDOvpnvQY+cXRravFzP7IvAQcB8wHngLeNHMBtZ3Hy3E+cDDwNnAx4FS4BUz6xFmUWEzs0nA9cD7YdcSFjPrBrwJGDADGA18G9gTZl0hug34JvAd4GTgu5HpH4RZVAJ1AlYT/N6F1Sy/FZhN8DdyJsHfyUIz65ywClNLQvJOWVfF+SjvjqO8U97FUNYp6+JJWZd456OsO46yTlkXQ1nXTFmX6h3dfUD3qOlc4JyYdU4A0jj+07UewN4GvNYs4HF3/31k+ttm9gngP2g9f4i4+yXR02Z2FcEnkucAL4RSVMjMrCvwF+A64MchlxOmW4Ecd786at6WsIpJAmcDL7h7xb+LrWb2PHBWiDUljLvPB+YDmNnj0cvMzICbgZ+5+7ORedcQhNeXCcaPkqoSlXfKugjl3fGUd8co7yop65R18aSsSzBl3fGUdcco6yop65op61L9CskVwJio6aXAaDMbEDXvIqAYWF4xw8yGAe2Bf9XnRcwsg2CckpdjFr1M8MfZmnUm+Ds6EHYhIfod8P/c/dWwCwnZZcA7ZvaMme0xs5Vm9q3Im1Rr9AYwzcxOBjCzMQSfPM8PtarkMITgVqtj76nuXggsQe+pNWn2vFPW1Ul5p7yroLyrpKyrmbKu4ZR14VPWKesqKOsqKetq1qSsS/WG5AKCkOoZmX4ZWAP8yczGm9mFwC+B31c8hS1iCrDZ3bPr+To1fRK3m6jxS1qph4CVBP/D0OqY2fXAcOCOsGtJAkOBm4DNwCUEfxs/I7icvTX6OfBnYK2ZlRC8Nz3h7g+HW1ZSqHjf1Htq/SUi75R1tVPeKe8qKO8qKetqpqxrOGVd+JR1yroKyrpKyrqaNSnrUroh6e6rgHeBL0WmywjGNzhCMN7BM8BzwC0xm14B/J6Gix2rxKqZ12qY2VzgXOBzkWPfqpjZKIKxZ77i7kfDricJtAH+5e4/cPcV7v4Y8GtaZ2gBfBG4muBS9QmRn28ys+tCrSq56D21nhKcd/rvEkN5p7yLobyrpKyrm95T60lZFy5lnbIuhrKukrKubo16T031MSQB7gYeMrNH3L3M3bcDM2ta2YKnho0DLm/Aaxz3NLeI3hzfCW4VzOxBgv9ZmObum8OuJySTCT5lXR115XoaMNXMbgQ6untxWMWFIAdYGzNvHcHgt63RL4EH3P3pyPQqMxtEMDbRo+GVlRRyI9/7Ajui5rfa99R6au68U9ZVQ3kHKO9iKe8qKetqpqxrHGVdCJR1gLIulrKukrKuZk3KupS+QhLA3V8CfgMMqGvdiP7A1e6e34DXOEowTslFMYsuIngqW6tiZg8RfDrwcXdfH3Y9Ifo7cCrB/wRVfC0Dno783No+WXsTGBUzbySwLYRakkEmwf/wRiujBbzvxsEWgvA69p5qZu0Jbrlqde+p9dXceaesO57y7hjlXVXKu0rKupop6xpBWZd4yrpjlHVVKesqKetq1qSsawlXSOLuv27AurEDGNfXXODPZvYuwT/OGwkC8JFG7i8lmdlvgKsIBrk9YGYVny4WuHtBeJUlnrt/BHwUPc/MDgN57r46nKpC9SDwlpn9iOCWmvHAd4AfhlpVeF4Avm9mWwjGGRlP8FTHP4VaVYKYWSeCMXggCOuBZjaO4N/HdjP7FfAjM1sPbABuBwqAv4ZScIpIQN4p6yKUd5WUd8dR3lVS1inr4k5ZlzjKukrKuuMo6yop65op68y9VQ+V0SBmdhNwK9APWA18z92XhFtVYplZTX8wd7v7XYmsJRmZ2WJgtbt/K+xawmBmMwjGXhkFbAf+G/gvb4VvNGbWGfgJ8BmCS9ZzCD5hvcfdi8KsLRHM7HxgUTWLnnD3r0ae0Hcn8A2gO/AO8M1W+j98SUVZF1De1U55p7wDZZ2yLnUp6wLKutop65R1oKxrzqxTQ1JEREREREREREQSRve8i4iIiIiIiIiISMKoISkiIiIiIiIiIiIJo4akiIiIiIiIiIiIJIwakiIiIiIiIiIiIpIwakiKiIiIiIiIiIhIwqghKSIiIiIiIiIiIgmjhqRIPZnZV83Ma/i6sIH7+npkuwHNVW9zMLP0SN23R82718xKw6xLRETiQ1mnrBMRaQ2Ud8o7CV962AWIpKAvADtj5q0NoxAREZFmoqwTEZHWQHknEhI1JEUabqW7bwy7CBERkWakrBMRkdZAeScSEt2yLRJHZtbBzB4yszVmdtjMcszseTMbVY9trzKzlZHt8s3sfTP7esw608zsVTMriHy9aGZj6lnbNDN7xcwORl7j32b21ajlXzGzxWa218wOmdm/zOzKBh+EYF+zzGydmRWaWZ6ZvWdmn2rMvkREJLko647tS1knItKCKe+O7Ut5J81CV0iKNFyamUX/23F3L4v83CHydQ+QC/QEvgksNbOT3X1PdTs0s/OAJ4BfAbOBNGAM0D1qnU8DzwLPA18m+EDh+8DrZnaau++qqWAz+xzwN2AJcAOwDzgFGBS12tDIOhuBcuB84HEza+/uf6jjmES/1jXAz4G7gTcjx+P0yLEQEZHUoKyrhbJORKTFUN7VQnknzUkNSZGGWx8z/SZwLoC75xGEAgBmlgYsAPYCXwT+q4Z9Tgb2ufusqHkvR+3HgIeAf7r7Z6PmLwY2A98Dbqlux2bWhiAM3wMucPfyyKJXotdz95/EbLMYOBH4D6DeoRX5XVa4+71R8+Y3YHsREQmfsq52yjoRkZZBeVc75Z00G92yLdJwnwHOjPq6LnqhmX3JzN41s3ygFCgg+CSptkv73wN6mdmfzGyGmXWNWX4ywSdef7HgaWjpkU/yCoB3gKm17HsMMAD4Q1RgHcfMRpnZM2a2CyiJfH21jrpr+l3OiNzecIGZZTZwexERCZ+yrnbKOhGRlkF5VzvlnTQbNSRFGm61uy+L+vqgYoGZfQZ4ClgNXAGcRRBseUD7mnbo7v8k+JRtMPB3YJ+ZvWxmp0RW6R35/gSVgVLx9Qlqv2S+Ylns0+OOMbMuwEJgLHAbMCVS9xO11V2DPwLfAs6O7HO/mT1rZgMbuB8REQmPsq52yjoRkZZBeVc75Z00G92yLRJfXwLWu/vXKmaYWXugW10buvvfgL+ZWSfg4wRjdbwYebPfH1ntVmBRNZsX17LrfZHvJ9ayzjnAScBkd387qva2ddUdy90d+C3wWzPrAVwCzCEI83Mauj8REUk6yjplnYhIa6C8U95JM1JDUiS+Mgku5Y92NQ24GtndC4DnzWw4wZt9d2AtsAMY4+6/bGBN6yLbft3M/hgJlerqhuBTOQDMrCdwaQNfq4rIuCtPmdlk4Jqm7EtERJKGsi6Ksk5EpMVS3kVR3km8qSEpEl8vAf9tZg8ALxJcGv9N4GBtG5nZTwkuv18E5AADCS6NXxZ548fMvgU8F/lU7n8JPlnrS3D5/GZ3f6i6fbt7uZndHNnmFTP7H4JP1sYC3d39HoLBmwsIPvm6C+gM3AHsIbjVoN7M7FHgALCUYMDnUQRPjnu5tu1ERCRlKOuUdSIirYHyTnknzUhjSIrE1yPA/QRv0i8QXNI+EzhUx3bvAEMJnpi2MLKPfxL1KZa7Pw+cB3QBHiV4wtvPCMYgeae2nbv7c5Fa0oDHgOcJBmzeFlmeSzCgcwbwHPDTyO/ydH1+6RhvEIT1IwRB9QOC8Uq+VttGIiKSMpR1yjoRkdZAeae8k2Zk1V/hKyIiIiIiIiIiIhJ/ukJSREREREREREREEkYNSREREREREREREUkYNSRFREREREREREQkYdSQFBERERERERERkYRRKgxnNgAAAEhJREFUQ1JEREREREREREQSRg1JERERERERERERSRg1JEVERERERERERCRh1JAUERERERERERGRhFFDUkRERERERERERBLm/wNa1QMX1nXn/QAAAABJRU5ErkJggg==\n", "text/plain": [ "<Figure size 1584x792 with 6 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "delta = 10\n", "linestyle = '-'\n", "coverage = ('10', '20', '30', '50')\n", "legend_placement = (1, 4, 4, 4)\n", "xmaxes = (725, 180, 120, 110)\n", "#coverage = ['30', '50']\n", "#legend_placement = [4, 4]\n", "#xmaxes = [120, 110]\n", "for cov, legloc, xmax in zip(coverage, legend_placement, xmaxes):\n", " print('DEBUG cov:', cov)\n", " seaborn.set_context({'figure.figsize': (22, 11)})\n", " matplotlib.rcParams['axes.labelsize'] = 16\n", " matplotlib.rcParams['xtick.labelsize'] = 14\n", " matplotlib.rcParams['ytick.labelsize'] = 14\n", " \n", " categories = [\n", " ('SNV', None, None, 'SNVs'),\n", " ('INDEL', 1, 10, 'INDELs 1-10bp'),\n", " ('INDEL', 11, 100, 'INDELs 11-100bp'),\n", " ('INDEL', 101, 200, 'INDELs 101-200bp'),\n", " ('INDEL', 201, 300, 'INDELs 201-300bp'),\n", " ('INDEL', 301, 400, 'INDELs 301-400bp'),\n", " ]\n", " fig, ((ax11, ax12, ax13), (ax21, ax22, ax23)) = plt.subplots(2, 3)\n", " axes = (ax11, ax12, ax13, ax21, ax22, ax23)\n", " seaborn.set_context({'figure.figsize': (24, 12)})\n", " \n", " for i, (category, axis) in enumerate(zip(categories, axes)):\n", " vartype, minlength, maxlength, label = category\n", " with kevlar.open('SimulatedVariants_chr17_hg38_markII.bed', 'r') as instream:\n", " variants = subset_variants_bed(instream, vartype, minlength, maxlength)\n", " index = populate_index_from_bed(variants)\n", " \n", " kevlar_truecalls = roc(\n", " load_kevlar_vcf(\n", " 'kevlar-calls-'+ cov +'x-nohomopoly-ambig-thresh.vcf.gz', index, delta=delta,\n", " vartype=vartype, minlength=minlength, maxlength=maxlength, debug=(i==0),\n", " ),\n", " index, delta=delta, fmt='vcf'\n", " )\n", " kevlar_truecalls_prime = roc(\n", " load_kevlar_vcf(\n", " 'kevlar-'+cov+'x-binomscore-nohomopoly-noabundfilt.vcf.gz', index, delta=delta,\n", " vartype=vartype, minlength=minlength, maxlength=maxlength, debug=(i==0),\n", " ),\n", " index, delta=delta, fmt='vcf'\n", " )\n", " scalpel_truecalls = roc(\n", " load_scalpel_vcf(\n", " 'scalpel.'+ cov +'x.denovo.indel.vcf', cov=cov,\n", " vartype=vartype, minlength=minlength, maxlength=maxlength,\n", " ),\n", " index, delta=delta, fmt='vcf'\n", " )\n", " discosnp_truecalls = roc(\n", " load_discosnp_vcf(\n", " 'discosnp.'+ cov +'x.vcf.gz', cov=cov, applyfilters=False,\n", " vartype=vartype, minlength=minlength, maxlength=maxlength,\n", " ),\n", " index, delta=delta, fmt='vcf'\n", " )\n", " gatk_truecalls = roc(\n", " load_gatk_mvf(\n", " 'JointCall-'+ cov +'x-PBT.mvf',\n", " vartype=vartype, minlength=minlength, maxlength=maxlength\n", " ).iterrows(),\n", " index, delta=delta, fmt='mvf'\n", " )\n", " triodenovo_truecalls = roc(\n", " load_triodenovo_vcf(\n", " 'JointCall-'+ cov +'x-TDN.vcf', cov=cov,\n", " vartype=vartype, minlength=minlength, maxlength=maxlength,\n", " ),\n", " index, delta=delta, fmt='vcf'\n", " )\n", " \n", " if i > 2:\n", " xmax = 10\n", " \n", " doplot(axis, gatk_truecalls, 'blue', 'GATK PBT ({}x)'.format(cov), linestyle, '^', 6, xmax)\n", " doplot(axis, triodenovo_truecalls, 'green', 'Triodenovo ({}x)'.format(cov), linestyle, 'D', 5, xmax)\n", " doplot(axis, scalpel_truecalls, 'purple', 'Scalpel ({}x)'.format(cov), linestyle, '*', 9, xmax)\n", " doplot(axis, kevlar_truecalls, 'red', 'Kevlar ({}x)'.format(cov), linestyle, 'o', 5, xmax)\n", " doplot(axis, kevlar_truecalls_prime, 'grey', 'KevlarNewScore ({}x)'.format(cov), '--', 'o', 5, xmax)\n", " doplot(axis, discosnp_truecalls, 'orange', 'DiscoSnp++ ({}x)'.format(cov), linestyle, 'X', 8, xmax)\n", " \n", " nvariants = len(index.trees['chr17'])\n", " ticknums = [0, math.ceil(nvariants * 0.25), int(nvariants * 0.5), math.ceil(nvariants * 0.75), nvariants]\n", " ticklabels = ['{:d}%\\n({:d})'.format(round(tn / nvariants * 100), tn) for tn in ticknums]\n", " \n", " _ = axis.set_xlabel('False calls', fontsize=16)\n", " if i > 2:\n", " _ = axis.set_xticks(list(range(0, 11, 2)))\n", " else:\n", " _ = axis.set_xlim((-5, xmax))\n", " _ = axis.set_yticks(ticknums)\n", " _ = axis.set_yticklabels(ticklabels)\n", " _ = axis.set_ylabel('True calls', fontsize=16)\n", " _ = axis.set_ylim((0, nvariants))\n", " _ = axis.set_title(label, fontsize=18)\n", " if i == 0:\n", " _ = axis.legend(fontsize=14, loc=legloc)\n", " \n", " _ = plt.subplots_adjust(hspace=0.3, wspace=0.3)\n", " # _ = plt.savefig('five-callers-'+ cov +'x-combined-sep.pdf', dpi=300)\n", " _ = plt.show()" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.7" } }, "nbformat": 4, "nbformat_minor": 2 }

mit

chesters99/ghpages

content/nytaxi.ipynb

1

2372076

gpl-3.0

austinjalexander/sandbox

python/py/edx/lab0_student.ipynb

2

9058

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# + \n", "# **First Notebook: Virtual machine test and assignment submission**\n", "#### This notebook will test that the virtual machine (VM) is functioning properly and will show you how to submit an assignment to the autograder. To move through the notebook just run each of the cells. You will not need to solve any problems to complete this lab. You can run a cell by pressing \"shift-enter\", which will compute the current cell and advance to the next cell, or by clicking in a cell and pressing \"control-enter\", which will compute the current cell and remain in that cell. At the end of the notebook you will export / download the notebook and submit it to the autograder.\n", "#### ** This notebook covers: **\n", "#### *Part 1:* Test Spark functionality\n", "#### *Part 2:* Check class testing library\n", "#### *Part 3:* Check plotting\n", "#### *Part 4:* Check MathJax formulas\n", "#### *Part 5:* Export / download and submit" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### ** Part 1: Test Spark functionality **" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### ** (1a) Parallelize, filter, and reduce **" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from pyspark import SparkContext, SparkConf\n", "sc = SparkContext('local','pyspark')" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "4999950000\n", "714264285\n" ] } ], "source": [ "# Check that Spark is working\n", "largeRange = sc.parallelize(xrange(100000))\n", "reduceTest = largeRange.reduce(lambda a, b: a + b)\n", "filterReduceTest = largeRange.filter(lambda x: x % 7 == 0).sum()\n", "\n", "print reduceTest\n", "print filterReduceTest\n", "\n", "# If the Spark jobs don't work properly these will raise an AssertionError\n", "assert reduceTest == 4999950000\n", "assert filterReduceTest == 714264285" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### ** (1b) Loading a text file **" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Check loading data with sc.textFile\n", "import os.path\n", "baseDir = os.path.join('data')\n", "inputPath = os.path.join('cs100', 'lab1', 'shakespeare.txt')\n", "fileName = os.path.join(baseDir, inputPath)\n", "\n", "rawData = sc.textFile(fileName)\n", "shakespeareCount = rawData.count()\n", "\n", "print shakespeareCount\n", "\n", "# If the text file didn't load properly an AssertionError will be raised\n", "assert shakespeareCount == 122395" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### ** Part 2: Check class testing library **" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### ** (2a) Compare with hash **" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# TEST Compare with hash (2a)\n", "# Check our testing library/package\n", "# This should print '1 test passed.' on two lines\n", "from test_helper import Test\n", "\n", "twelve = 12\n", "Test.assertEquals(twelve, 12, 'twelve should equal 12')\n", "Test.assertEqualsHashed(twelve, '7b52009b64fd0a2a49e6d8a939753077792b0554',\n", " 'twelve, once hashed, should equal the hashed value of 12')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### ** (2b) Compare lists **" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# TEST Compare lists (2b)\n", "# This should print '1 test passed.'\n", "unsortedList = [(5, 'b'), (5, 'a'), (4, 'c'), (3, 'a')]\n", "Test.assertEquals(sorted(unsortedList), [(3, 'a'), (4, 'c'), (5, 'a'), (5, 'b')],\n", " 'unsortedList does not sort properly')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### ** Part 3: Check plotting **" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### ** (3a) Our first plot **\n", "#### After executing the code cell below, you should see a plot with 50 blue circles. The circles should start at the bottom left and end at the top right." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Check matplotlib plotting\n", "import matplotlib.pyplot as plt\n", "import matplotlib.cm as cm\n", "from math import log\n", "\n", "# function for generating plot layout\n", "def preparePlot(xticks, yticks, figsize=(10.5, 6), hideLabels=False, gridColor='#999999', gridWidth=1.0):\n", " plt.close()\n", " fig, ax = plt.subplots(figsize=figsize, facecolor='white', edgecolor='white')\n", " ax.axes.tick_params(labelcolor='#999999', labelsize='10')\n", " for axis, ticks in [(ax.get_xaxis(), xticks), (ax.get_yaxis(), yticks)]:\n", " axis.set_ticks_position('none')\n", " axis.set_ticks(ticks)\n", " axis.label.set_color('#999999')\n", " if hideLabels: axis.set_ticklabels([])\n", " plt.grid(color=gridColor, linewidth=gridWidth, linestyle='-')\n", " map(lambda position: ax.spines[position].set_visible(False), ['bottom', 'top', 'left', 'right'])\n", " return fig, ax\n", "\n", "# generate layout and plot data\n", "x = range(1, 50)\n", "y = [log(x1 ** 2) for x1 in x]\n", "fig, ax = preparePlot(range(5, 60, 10), range(0, 12, 1))\n", "plt.scatter(x, y, s=14**2, c='#d6ebf2', edgecolors='#8cbfd0', alpha=0.75)\n", "ax.set_xlabel(r'$range(1, 50)$'), ax.set_ylabel(r'$\\log_e(x^2)$')\n", "pass" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### ** Part 4: Check MathJax Formulas **" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### ** (4a) Gradient descent formula **\n", "#### You should see a formula on the line below this one: $$ \\scriptsize \\mathbf{w}_{i+1} = \\mathbf{w}_i - \\alpha_i \\sum_j (\\mathbf{w}_i^\\top\\mathbf{x}_j - y_j) \\mathbf{x}_j \\,.$$\n", " \n", "#### This formula is included inline with the text and is $ \\scriptsize (\\mathbf{w}^\\top \\mathbf{x} - y) \\mathbf{x} $." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### ** (4b) Log loss formula **\n", "#### This formula shows log loss for single point. Log loss is defined as: $$ \\begin{align} \\scriptsize \\ell_{log}(p, y) = \\begin{cases} -\\log (p) & \\text{if } y = 1 \\\\\\ -\\log(1-p) & \\text{if } y = 0 \\end{cases} \\end{align} $$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### ** Part 5: Export / download and submit **" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### ** (5a) Time to submit **" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### You have completed the lab. To submit the lab for grading you will need to download it from your IPython Notebook environment. You can do this by clicking on \"File\", then hovering your mouse over \"Download as\", and then clicking on \"Python (.py)\". This will export your IPython Notebook as a .py file to your computer.\n", "#### To upload this file to the course autograder, go to the edX website and find the page for submitting this assignment. Click \"Choose file\", then navigate to and click on the downloaded .py file. Now click the \"Open\" button and then the \"Check\" button. Your submission will be graded shortly and will be available on the page where you submitted. Note that when submission volumes are high, it may take as long as an hour to receive results." ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.10" } }, "nbformat": 4, "nbformat_minor": 0 }

mit

harmsm/pythonic-science

chapters/02_regression/prep-work.ipynb

1

17870

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Simplex\n", "\n", "Simple simplex implementation for teaching about regression." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import matplotlib.pylab as plt\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "class Simplex:\n", " \"\"\"\n", " Simplex minimizer for arbitrary 2D objective functions.\n", " \"\"\"\n", " \n", " def __init__(self,objective_function,guess_min=-10,guess_max=10):\n", " \"\"\"\n", " Create simplex instance. \n", " \n", " arguments:\n", " ----------\n", " objective_function: objective function for the minimizer. must take x and\n", " y as first arguments.\n", " guess_min: minimum initial guess (for both x and y)\n", " guess_max: maximum initial guess (for both x and y)\n", " \"\"\"\n", " \n", " # Record objective function\n", " self._objective_function = objective_function\n", "\n", " # Create initial points as random raws from uniform distribution\n", " self.points = np.random.uniform(guess_min,guess_max,size=(3,2))\n", " \n", " # Record guesses\n", " self._guesses = np.copy(self.points)\n", " \n", " # Create values for points\n", " self._update_values()\n", " self._num_moves = 0\n", " self._plot = False\n", " \n", " \n", " def _update_values(self):\n", " \"\"\"\n", " Calculate values and rank points from min to max.\n", " \"\"\"\n", " \n", " self.values = [0,0,0]\n", " for i in range(3):\n", " self.values[i] = self._objective_function(*self.points[i,:])\n", " \n", " tmp = [(v,i) for i, v in enumerate(self.values)]\n", " tmp.sort()\n", " \n", " # points, from minimum to maximum\n", " self.min_pt = tmp[0][1]\n", " self.mid_pt = tmp[1][1]\n", " self.max_pt = tmp[2][1]\n", " \n", " \n", " def _flip(self):\n", " \"\"\"\n", " Reflect maximum point across the midpoint between the other two points.\n", " \"\"\"\n", "\n", " # midpoint for flipping\n", " mid_flip = self.points[self.min_pt,:] + (self.points[self.mid_pt,:] - self.points[self.min_pt,:])/2\n", " \n", " # move max point so its origin is the mid_flip point\n", " to_flip = self.points[self.max_pt,:] - mid_flip\n", " \n", " # Flip using some basic trig\n", " L = np.sqrt(np.sum(to_flip**2))\n", " thetax = np.arccos(to_flip[0]/L) + np.pi\n", " thetay = np.arcsin(to_flip[1]/L) + np.pi\n", " flipped = L*np.array((np.cos(thetax),np.sin(thetay)))\n", " \n", " # Move the flipped point back to the original coordinates\n", " new_point = flipped + mid_flip\n", " \n", " # Calculate value. If it is no longer the max point, this is a successful move.\n", " new_value = self._objective_function(*new_point)\n", " if new_value < self.values[self.min_pt] or new_value < self.values[self.mid_pt]:\n", " \n", " # Plot if requested\n", " if self._plot:\n", " plt.plot((self.points[self.max_pt,0],new_point[0]),\n", " (self.points[self.max_pt,1],new_point[1]),'y-')\n", " plt.plot((new_point[0]),(new_point[1]),\"yo\",ms=9)\n", " \n", " self.points[self.max_pt,:] = new_point \n", " return True\n", " \n", " return False\n", " \n", " def _small_step(self):\n", " \"\"\"\n", " Move the max point to midway between it and the midpoint between the other\n", " two points. \n", " \"\"\"\n", " \n", " # midpoint for flipping\n", " mid_flip = self.points[self.min_pt,:] + (self.points[self.mid_pt,:] - self.points[self.min_pt,:])/2\n", " \n", " # move max point so its origin is the mid_flip point\n", " to_flip = self.points[self.max_pt,:] - mid_flip\n", " \n", " # Move using some basic trig\n", " L = np.sqrt(np.sum(to_flip**2))\n", " thetax = np.arccos(to_flip[0]/L) + np.pi\n", " thetay = np.arcsin(to_flip[1]/L) + np.pi\n", " flipped = -0.5*L*np.array((np.cos(thetax),np.sin(thetay)))\n", " \n", " # Move the flipped point back to the original coordinates\n", " new_point = flipped + mid_flip\n", " \n", " # Calculate value. If it is no longer the max point, this is a successful move\n", " new_value = self._objective_function(*new_point)\n", " if new_value < self.values[self.min_pt] or new_value < self.values[self.mid_pt]:\n", " \n", " # Plot if requested\n", " if self._plot:\n", " plt.plot((self.points[self.max_pt,0],new_point[0]),\n", " (self.points[self.max_pt,1],new_point[1]),'y-',lw=2)\n", " plt.plot((new_point[0]),(new_point[1]),\"yo\",ms=9)\n", " \n", " self.points[self.max_pt,:] = new_point\n", " return True\n", " \n", " return False\n", "\n", " def _contract(self):\n", " \"\"\"\n", " Contract the simplex towards the two better points.\n", " \"\"\"\n", " \n", " # Plot starting triangle\n", " if self._plot:\n", " plt.plot(self.points[:,0],self.points[:,1],\"ko\",ms=6)\n", " plt.plot((self.points[self.min_pt,0],self.points[self.max_pt,0]),\n", " (self.points[self.min_pt,1],self.points[self.max_pt,1]),\"k-\")\n", " plt.plot((self.points[self.mid_pt,0],self.points[self.max_pt,0]),\n", " (self.points[self.mid_pt,1],self.points[self.max_pt,1]),\"k-\")\n", " plt.plot((self.points[self.min_pt,0],self.points[self.mid_pt,0]),\n", " (self.points[self.min_pt,1],self.points[self.mid_pt,1]),\"r--\")\n", " \n", " # Midpoint between better two points\n", " mid_flip = self.points[self.min_pt,:] + (self.points[self.mid_pt,:] - self.points[self.min_pt,:])/2\n", " \n", " # Midpoint between the max and min points\n", " new_mid = self.points[self.max_pt,:] + (self.points[self.min_pt,:] - self.points[self.max_pt,:])/2\n", " \n", " # Perform the contraction. \n", " self.points[self.max_pt,:] = new_mid\n", " self.points[self.mid_pt,:] = mid_flip\n", " \n", " # Plot final triangle\n", " if self._plot:\n", " plt.plot(self.points[:,0],self.points[:,1],\"yo\",ms=6)\n", " plt.plot((self.points[self.min_pt,0],self.points[self.max_pt,0]),\n", " (self.points[self.min_pt,1],self.points[self.max_pt,1]),\"y-\")\n", " plt.plot((self.points[self.mid_pt,0],self.points[self.max_pt,0]),\n", " (self.points[self.mid_pt,1],self.points[self.max_pt,1]),\"y-\")\n", " plt.plot((self.points[self.min_pt,0],self.points[self.mid_pt,0]),\n", " (self.points[self.min_pt,1],self.points[self.mid_pt,1]),\"y-\")\n", " \n", " \n", " return True\n", " \n", " \n", " def make_move(self,plot=False):\n", " \"\"\"\n", " Make a simplex move. Try flip, then small step, then contract.\n", " \"\"\"\n", " \n", " self._plot = plot\n", " \n", " # Update to current values\n", " self._update_values()\n", " \n", " # Plot initial triangle\n", " if self._plot:\n", " \n", " plt.plot(self.points[:,0],self.points[:,1],\"ko\",ms=6)\n", " plt.plot((self.points[self.min_pt,0],self.points[self.max_pt,0]),\n", " (self.points[self.min_pt,1],self.points[self.max_pt,1]),\"k-\")\n", " plt.plot((self.points[self.mid_pt,0],self.points[self.max_pt,0]),\n", " (self.points[self.mid_pt,1],self.points[self.max_pt,1]),\"k-\")\n", " plt.plot((self.points[self.min_pt,0],self.points[self.mid_pt,0]),\n", " (self.points[self.min_pt,1],self.points[self.mid_pt,1]),\"r--\")\n", " \n", " # If flip fails, small_step. If small_step fails, contract. \n", " if self._flip():\n", " print(\"flip\")\n", " elif self._small_step():\n", " print(\"simple\")\n", " else:\n", " print(\"large\")\n", " self._contract()\n", " \n", " self._num_moves += 1\n", " \n", " \n", " def restore_guesses(self):\n", " \"\"\"\n", " Restore the fitter to the initial guesses.\n", " \"\"\"\n", " \n", " self.points = np.copy(self._guesses)\n", " \n", " @property\n", " def estimate(self):\n", " \"\"\"\n", " Current best estimate of the minimum.\n", " \"\"\"\n", " \n", " self._update_values()\n", " \n", " return self.points[self.min_pt], self.values[self.min_pt]\n", " " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": false }, "outputs": [], "source": [ "\n", "def objective_simple(x,y):\n", " \"\"\"\n", " Single-peaked 2D polynomial in x and y.\n", " \"\"\"\n", " \n", " return 20*(x + 2)**2 + 15*(y - 0.5)**2\n", "\n", "\n", "def objective_doomed(x,y):\n", " \"\"\"\n", " Saddle-shapped 2D polynomial in x and y.\n", " \"\"\"\n", " \n", " return -20*(x + 2)**2 + 15*(y - 0.5)**2\n", "\n", "\n", "def objective_multi(x,y):\n", "\n", " return -(20*(x + 5))**2 - (20*(y + 5))**2 + (20*(x - 5))**2 + (20*(y - 5))**2\n", "\n", "\n", "def run_simplex(objective_function,prefix=\"z\",simplex=None,figsize=(10,10)):\n", "\n", "\n", " if simplex == None:\n", " simplex = Simplex(objective_function)\n", "\n", "\n", " xlist = np.linspace(-10.0, 10.0, 100)\n", " ylist = np.linspace(-10.0, 10.0, 100)\n", " X, Y = np.meshgrid(xlist, ylist)\n", " Z = objective_function(X,Y)\n", "\n", " for i in range(20):\n", "\n", " plt.figure(figsize=figsize)\n", " cp = plt.contourf(X, Y, Z,20,cmap=\"terrain\")\n", "\n", " #plt.plot((-2),(0.5),\"b+\",ms=10)\n", " plt.axis(\"equal\")\n", " plt.axis(\"off\")\n", " plt.xlim((-10,10))\n", " plt.ylim((-10,10))\n", "\n", " simplex.make_move(plot=True)\n", " #simplex.make_move(plot=False)\n", " \n", " name = \"{}\".format(i)\n", " name = name.zfill(5)\n", " plt.savefig(\"{}{}.png\".format(prefix,name),bbox_inches=\"tight\")\n", " plt.show()\n", " \n", " return simplex\n", "\n", "#s.restore_guesses()\n", "x = run_simplex(objective_simple)\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Prep: Create a m/b SSR heat map" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "m_list = np.linspace(-0.5, 0.5, 100)\n", "b_list = np.linspace(-0.5, 0.5, 100)\n", "M, B = np.meshgrid(m_list,b_list)\n", "\n", "def ssr(x_obs,y_obs,m,b):\n", " \n", " out = np.zeros(m.shape) \n", " for i in range(m.shape[0]):\n", " for j in range(m.shape[1]):\n", " out[i,j] = np.sum(((m[i,j]*x_obs + b[i,j]) - y_obs)**2)\n", " \n", " return out\n", "\n", "Z = ssr(d.time,d.obs,M,B)\n", "\n", "plt.figure(figsize=(8,8))\n", "cp = plt.contourf(M, B, Z,20,cmap=\"terrain\")\n", "plt.axis(\"equal\")\n", "plt.xlabel(\"m\")\n", "plt.ylabel(\"b\")\n", "\n", "plt.plot((0.0696240601504),(0.186085714286),\"y+\",ms=20)\n", "\n", "plt.savefig(\"param-space.png\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Regression engine" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "## Models\n", "\n", "def lin(x,a=1,b=1):\n", " return a + b*x\n", "\n", "def hb(x,a=1,b=1):\n", " return a*(b*x)/(1 + b*x)\n", "\n", "def hbc(x,a=1,b=1,c=1):\n", " return a*(b*(x**c))/(1 + b*(x**c))\n", "\n", "def expt(x,a=1,b=1):\n", " return a*(1 - np.exp(-b*x))\n", "\n", "def second(x,a=1,b=1,c=1):\n", " return a + b*x + c*(x**2)\n", "\n", "def third(x,a=1,b=1,c=1,d=1):\n", " return a + b*x + c*(x**2) + d*(x**3)\n", "\n", "def trig(x,a=1,b=1,c=1):\n", " return a*np.sin(b*x + c)\n", "\n", "def trig2(x,a=1,b=1,c=1,d=1):\n", " return a*np.sin(x*b) + c*np.sin(x*d)\n", "\n", "def logd(x,a=1,b=1):\n", " \n", " return a*np.log(x + b)\n", "\n", "def logdc(x,a=1,b=1,c=1):\n", " \n", " return a*np.log(x*b + c)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "### TEST FITTING\n", "\n", "import inspect\n", "import scipy.optimize\n", "import pandas as pd\n", "\n", "def residuals(param,x,y,f):\n", " \"\"\"A generalized residuals function.\"\"\"\n", " return y - f(x,*param)\n", "\n", "def fitter(x,y,f):\n", " \"\"\"\n", " A generalized fitter. Find parameters of `f` that minimize the \n", " residual between `f` and `y` for values of `x`. This function\n", " assumes that `f` has the form:\n", " \n", " f(x,param1,param2,param3...)\n", " \n", " x and y should be numpy arrays of the same length.\n", " \"\"\"\n", " \n", " # Create a list of parameter names and guesses using `inspect`\n", " names = []\n", " guesses = []\n", " s = inspect.signature(f)\n", " for i, p in enumerate(s.parameters):\n", " names.append(s.parameters[p].name)\n", " guesses.append(s.parameters[p].default)\n", " \n", " # Fit the model to the data.\n", " x0 = np.array(guesses[1:])\n", " fit = scipy.optimize.least_squares(residuals,x0,\n", " args=(x,y,f))\n", "\n", " \n", " # Plot hte fit\n", " x_range = np.linspace(np.min(x),np.max(x),100)\n", " plt.plot(x_range,f(x_range,*fit.x),\"-\")\n", " \n", " # Calculate R^2\n", " ss_err = np.sum(residuals(fit.x,x,y,f)**2)\n", " ss_tot = np.sum((y - np.mean(y))**2)\n", " R_sq = 1 - (ss_err/ss_tot)\n", " \n", " print(len(fit.fun))\n", " return len(fit.x), fit.cost #np.sum(residuals(fit.x,x,y,f)**2)\n", " \n", "# Load in dataset\n", "d = pd.read_csv(\"data/dataset_0.csv\")\n", "plt.plot(d.x,d.y,\"ko\")\n", "\n", "# Fit all of those functions to the data\n", "func_list = [lin,hb,hbc,expt,second,third,trig,trig2,logd,logdc]\n", "results = []\n", "for f in func_list:\n", " results.append((str(f).split()[1],fitter(d.x,d.y,f)))\n", " print(results[-1])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Generate data sets to fit" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Generate data\n", " \n", "x = np.linspace(0,10,41)\n", "y = expt(x,4,0.3) + np.random.normal(0,0.3,len(x))\n", "\n", "d = pd.DataFrame({\"x\":x,\"y\":y})\n", "plt.plot(x,y,\"o\"); plt.show()\n", "d.to_csv(\"dataset_0.csv\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "x = np.linspace(0,10,41)\n", "y = hbc(x,4,0.005,4) + np.random.normal(0,0.3,len(x))\n", "\n", "d = pd.DataFrame({\"x\":x,\"y\":y})\n", "plt.plot(x,y,\"o\"); plt.show()\n", "d.to_csv(\"dataset_1.csv\")" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.3" } }, "nbformat": 4, "nbformat_minor": 1 }

unlicense

uweschmitt/python_tutorial

python_kurs_ethz.ipynb

1

246106

{ "metadata": { "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Why are you here ?\n", "\n" ] }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "== Why is Python getting famous ?\n", "\n" ] }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "== What are the benefits of Python ?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Programming is not only about solving problems at all. It is also about:\n", "\n", " * **Effiency** : How long do I need to deliver a bug free implementation ?\n", " * **Extendability**: What happens if I or others want to modify the implementation ?\n", " \n", "\n", "General characteristics of Python:\n", "\n", "* **clean and simple** language: Easy-to-read and intuitive code, easy-to-learn minimalistic syntax\n", "\n", "* **expressive language**: Fewer lines of code, fewer bugs, easier to maintain.\n", "\n", "* **general-purpose language** in contrast to e.g. Matlab (numerics), PHP (websites), R (statstics). No need to mix languages for a full application, which gets data from a data base and provides a web service,\n", " or is integrated in a web service\n", "\n", "Technical details:\n", "\n", "* **dynamically typed**: No need to define the type of variables, function arguments or return types.\n", "\n", "* **automatic memory management**: No need to explicitly allocate and deallocate memory for variables and data arrays. No memory leak bugs.\n", "\n", "* **interpreted**: No need to compile the code. The Python interpreter reads and executes the Python code directly.\n", "\n", "\n", "**The main advantage is ease of programming, minimizing the time required to develop, debug and maintain the code.**\n", "\n", "Other principles:\n", "\n", " * https://en.wikipedia.org/wiki/Kiss_principle\n", " * https://en.wikipedia.org/wiki/Principle_of_least_astonishment\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "More facts:\n", "\n", "* invented in 1991 by Guido van Rossum, a fan of *Monty Pythons Flying Circus*\n", "* Current versions are 2.7 and 3.4 which are not compatible any more. This course is about 2.7. But the next one will be 3.4\n", "* Python ships with many versatile modules, e.g. for math, file system access, web service access, etc. This is often quoted as **Python comes with batteries included**" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Python's fields of application:\n", " \n", " * text processing\n", " * glueing other applications\n", " * web frameworks, eg Django, Pyramid, Flask, Plone\n", " * administrative tasks\n", " * (R replacement) data analyis aka 'big data'\n", " * (matlab replacement) numerics, simulations, ...\n", " * (everything else)" ] }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Installing Python on Windows" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* Depending on your OS versions: download 32 or 64 bit .exe for Python **2.7.5.X** from https://code.google.com/p/winpython/\n", "* This is a large Python distribtution, which includes *many* precompiled scientific packages without version conflicts\n", "* Follow the instructions on https://code.google.com/p/winpython/wiki/Installation **INCLUDING REGISTRATION**\n", "* Start installed application called *Spyder* \n" ] }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Installing Python on Ubuntu/Debian Linux" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Global install of Python interpreter, you need root access as super user:" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "$ sudo apt-get install python2.7 python2.7-dev\n", "$ python -V\n", "Python 2.7.3 # or similar 2.7.X\n", "\n", "$ sudo apt-get install python-qt4\n", "\n", "$ sudo apt-get install python-setuptools\n", "$ easy_install pip\n", "$ easy_install virtualenv" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Create a local isolated version of Python. This keeps your laptop clean and avoids version conflicts !" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "$ cd $HOME\n", "$ virtualenv python_kurs" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Activate this isolated version as follows, **you have to to this after each install before you start using this isolated environment !**" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "$ cd python_kurs\n", "$ . bin/activate\n", "(python_kurs) $ " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we install a local version of needed python packages:" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "(python_kurs) $ pip install pygments spyder\n", "(python_kurs) $ spyder" ] }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Spyder startup" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "1. Open Spyder (Windows: see start menu, others: from command line with \"$ spyder\")\n", "\n", "2. Use Spyders explorer (top right window, may be hidden in tabs) to create\n", " a \"folder python_course_examples\" and select this folder.\n", "\n", "3. Menu: Interpreters -> Open IPython console\n", "\n", "4. Enter \"pwd\" in IPython console and check if the new created\n", " directory is printed. " ] }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Edit / Execute workflow" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "1. create a new Python module within the directory created above (right click in file explorer, \"new module\")\n", "\n", "2. enter \"print 42\" in the code editor\n", "\n", "2. Use F5 to execute the script in the shell, take care to choose\n", " \"open in existing interpreter\".\n", " You should see the output from the print statement." ] }, { "cell_type": "raw", "metadata": {}, "source": [ "Python is not just an interpreter, it has a shell for trying things out and for learning.\n", "\n", "Most examples you will see here can be executed in a shell." ] }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Console Input / Output" ] }, { "cell_type": "code", "collapsed": false, "input": [ "print \"hello word\" # this is a comment" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "hello word\n" ] } ], "prompt_number": 86 }, { "cell_type": "raw", "metadata": {}, "source": [ "commata separate output, comma on end of print statement supresses new line" ] }, { "cell_type": "code", "collapsed": false, "input": [ "print 3, 4,\n", "print 5" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "3 4 5\n" ] } ], "prompt_number": 88 }, { "cell_type": "raw", "metadata": {}, "source": [ "Note: in Python 3.X \"print\" is a function, so you have to put paranthesis around the arguments" ] }, { "cell_type": "code", "collapsed": false, "input": [ "name = raw_input(\"your name: \") # strange name for input function\n", "print \"Hello\", name, \"!!!\"" ], "language": "python", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "stream": "stdout", "text": [ "your name: uwe\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Hello uwe !!!\n" ] } ], "prompt_number": 92 }, { "cell_type": "raw", "metadata": {}, "source": [ "TAKE CARE: Python also has a function \"input\" which is different from \"raw_input\"." ] }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Variables" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "VARIABLES ARE NAMES FOR OBJECTS\n", "\n", "other languages have concepts as pointers, references etc.\n", "\n", "keep this sentence in mind, it will simplify the deeper understanding\n", "how things work in Python." ] }, { "cell_type": "raw", "metadata": {}, "source": [ "Variables in Python are just assigned, no type declaration needed !" ] }, { "cell_type": "code", "collapsed": false, "input": [ "pi = 3.141\n", "my_name = \"uwe schmitt\"" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 93 }, { "cell_type": "raw", "metadata": {}, "source": [ "Nevertheless these variabes have a type:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "type(pi)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 94, "text": [ "float" ] } ], "prompt_number": 94 }, { "cell_type": "heading", "level": 4, "metadata": {}, "source": [ "Valid variable names" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "allowed are: alphabetic characters, digits and underscore \"_\"\n", "\n", "disallowed: digit in first place\n", "\n", "disallowed: names which refer to Python statements as \"print\" " ] }, { "cell_type": "code", "collapsed": false, "input": [ "# examples for valid names\n", "a = 123 \n", "a_123 = 2\n", "_xyz = 2" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 95 }, { "cell_type": "code", "collapsed": false, "input": [ "aBcD = 4 # uppercase matters\n", "abcd = 5\n", "aBcD == abcd" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 96, "text": [ "False" ] } ], "prompt_number": 96 }, { "cell_type": "raw", "metadata": {}, "source": [ "Invalid variable names:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "1abc = 3" ], "language": "python", "metadata": {}, "outputs": [ { "ename": "SyntaxError", "evalue": "invalid syntax (<ipython-input-97-26b7134673f5>, line 1)", "output_type": "pyerr", "traceback": [ "\u001b[1;36m File \u001b[1;32m\"<ipython-input-97-26b7134673f5>\"\u001b[1;36m, line \u001b[1;32m1\u001b[0m\n\u001b[1;33m 1abc = 3\u001b[0m\n\u001b[1;37m ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid syntax\n" ] } ], "prompt_number": 97 }, { "cell_type": "code", "collapsed": false, "input": [ "print = 4" ], "language": "python", "metadata": {}, "outputs": [ { "ename": "SyntaxError", "evalue": "invalid syntax (<ipython-input-98-15218bea882c>, line 1)", "output_type": "pyerr", "traceback": [ "\u001b[1;36m File \u001b[1;32m\"<ipython-input-98-15218bea882c>\"\u001b[1;36m, line \u001b[1;32m1\u001b[0m\n\u001b[1;33m print = 4\u001b[0m\n\u001b[1;37m ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid syntax\n" ] } ], "prompt_number": 98 }, { "cell_type": "raw", "metadata": {}, "source": [ "Note: most Python programmers personally prefer \"_\" separation in long variable names instead of 'CamelCase'.\n", "Check for readability:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "this_is_a_long_variable_name = 3\n", "\n", "thisIsALongVariableName = 4" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 99 }, { "cell_type": "raw", "metadata": {}, "source": [ "For later reading: http://www.python.org/dev/peps/pep-0008/" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# some forbidden variable names:\n", "# id, str, type, input, list, file, ...\n", "# often used solution in python community: add a \"_\"\n", "id_ = 3\n", "type_ = \"int\"\n", "print_ = 0" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 100 }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Calculating with numbers" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "Python has integer numbers, floating point and even complex numbers. \n", "\n", "Basic operations as in other languages:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "print 32 * (41 + 1) / 7" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "192\n" ] } ], "prompt_number": 101 }, { "cell_type": "raw", "metadata": {}, "source": [ "two different types of division:\n", "\n", "floating point division: as you would expect" ] }, { "cell_type": "code", "collapsed": false, "input": [ "print 29.0 / 2 " ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "14.5\n" ] } ], "prompt_number": 102 }, { "cell_type": "raw", "metadata": {}, "source": [ "integer division: rounds down to next smallest integer" ] }, { "cell_type": "code", "collapsed": false, "input": [ "print 31 / 7 " ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "4\n" ] } ], "prompt_number": 103 }, { "cell_type": "raw", "metadata": {}, "source": [ "alternative:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "print 31 // 7" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "4\n" ] } ], "prompt_number": 104 }, { "cell_type": "raw", "metadata": {}, "source": [ "Note: in Python 3.x you have to use \"//\" for integer division !" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "modulo a.k.a. division reminder:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "print 31 % 7" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "3\n" ] } ], "prompt_number": 105 }, { "cell_type": "raw", "metadata": {}, "source": [ "Divion by zero:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "print 7 / 0" ], "language": "python", "metadata": {}, "outputs": [ { "ename": "ZeroDivisionError", "evalue": "integer division or modulo by zero", "output_type": "pyerr", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mZeroDivisionError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m<ipython-input-106-ead4a91e8906>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;32mprint\u001b[0m \u001b[1;36m7\u001b[0m \u001b[1;33m/\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[1;31mZeroDivisionError\u001b[0m: integer division or modulo by zero" ] } ], "prompt_number": 106 }, { "cell_type": "raw", "metadata": {}, "source": [ "Python has classical 32 or 64 bit integers, but switches to \"long integers\" in case of overflow.\n", "Those \"long integers\" in Python may be as large as they fit into memory. No suprise = no astonishment = no incidental bug." ] }, { "cell_type": "code", "collapsed": false, "input": [ "3**117" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 107, "text": [ "66555937033867822607895549241096482953017615834735226163L" ] } ], "prompt_number": 107 }, { "cell_type": "markdown", "metadata": {}, "source": [ "can be used for calculating pi up to many digits: http://www.craig-wood.com/nick/articles/pi-machin/" ] }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "More math than just addition, et al:" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "load special module, ships with Python, is one of the \"included batteries\":" ] }, { "cell_type": "code", "collapsed": false, "input": [ "import math" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 108 }, { "cell_type": "raw", "metadata": {}, "source": [ "methods and constants are \"attached\" to math module:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "print math.pi" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "3.14159265359\n" ] } ], "prompt_number": 109 }, { "cell_type": "code", "collapsed": false, "input": [ "print math.cos(math.pi * 2.0)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "1.0\n" ] } ], "prompt_number": 110 }, { "cell_type": "code", "collapsed": false, "input": [ "print math.sqrt(121.0) " ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "11.0\n" ] } ], "prompt_number": 111 }, { "cell_type": "code", "collapsed": false, "input": [ "print math.pow(2, 4) " ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "16.0\n" ] } ], "prompt_number": 112 }, { "cell_type": "code", "collapsed": false, "input": [ "print 2**4 # alternative to math.pow, preserves type" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "16\n" ] } ], "prompt_number": 113 }, { "cell_type": "raw", "metadata": {}, "source": [ "to explorer content of math module:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "print dir(math)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "['__doc__', '__name__', '__package__', 'acos', 'acosh', 'asin', 'asinh', 'atan', 'atan2', 'atanh', 'ceil', 'copysign', 'cos', 'cosh', 'degrees', 'e', 'erf', 'erfc', 'exp', 'expm1', 'fabs', 'factorial', 'floor', 'fmod', 'frexp', 'fsum', 'gamma', 'hypot', 'isinf', 'isnan', 'ldexp', 'lgamma', 'log', 'log10', 'log1p', 'modf', 'pi', 'pow', 'radians', 'sin', 'sinh', 'sqrt', 'tan', 'tanh', 'trunc']\n" ] } ], "prompt_number": 114 }, { "cell_type": "raw", "metadata": {}, "source": [ "alternative: enter \"math.<TAB>\" in IPython shell\n" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "to show help:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "help(math.log1p)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Help on built-in function log1p in module math:\n", "\n", "log1p(...)\n", " log1p(x)\n", " \n", " Return the natural logarithm of 1+x (base e).\n", " The result is computed in a way which is accurate for x near zero.\n", "\n" ] } ], "prompt_number": 115 }, { "cell_type": "raw", "metadata": {}, "source": [ "alternative kinds of import:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from math import sin\n", "print sin(3)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "0.14112000806\n" ] } ], "prompt_number": 116 }, { "cell_type": "raw", "metadata": {}, "source": [ "should be avoided, clutters namespace, error prone, eg if one\n", "unses math and numpy module in one script:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from math import *" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 117 }, { "cell_type": "code", "collapsed": false, "input": [ "import math as m\n", "print m.sin(0)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "0.0\n" ] } ], "prompt_number": 118 }, { "cell_type": "raw", "metadata": {}, "source": [ "\n", "PRACTICE TIME\n", "\n", "Write a scripts for answering the following questions:\n", "\n", "1. How many rice corns do you have on a checker board if you put on corn on the first field and double\n", " the number of corns from each field to the next ???\n", "\n", " HINT: geometric sum: 1 + a^1 + a^2 + ... a^n = (a^(n+1)-1)/(a-1)\n", "\n", " An average rice corn weights 25 mg, how many kg do you have on the board ?\n", "\n", " The earth has a weight of 5.972E24 kg, put this into relation to the weight of the rice \n", "\n", " Use variables for the intermediate results !\n", "\n", "2. Calculate the area of a circle with diameter 21.0 cm\n", "\n", " Calculate the diameter of a circle with area 1.0 cm^2\n" ] }, { "cell_type": "heading", "level": 4, "metadata": {}, "source": [ "Some special notes" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "floating point math precision is limited:" ] }, { "cell_type": "code", "collapsed": false, "input": [ ".25 - .2" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 119, "text": [ "0.04999999999999999" ] } ], "prompt_number": 119 }, { "cell_type": "raw", "metadata": {}, "source": [ "type coercion: finds common type when calcaulating with different types" ] }, { "cell_type": "code", "collapsed": false, "input": [ "type(1 + 3)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 120, "text": [ "int" ] } ], "prompt_number": 120 }, { "cell_type": "raw", "metadata": {}, "source": [ "the decimal point indicates floating point numbers:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "type(1.0 + 3)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 121, "text": [ "float" ] } ], "prompt_number": 121 }, { "cell_type": "heading", "level": 4, "metadata": {}, "source": [ "complex numbers" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "complex unit is \"j\", not \"i\". \"j\" is often used in electrical engineering" ] }, { "cell_type": "code", "collapsed": false, "input": [ "z = 1 + 0.8j" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 122 }, { "cell_type": "raw", "metadata": {}, "source": [ "common operations:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "print z, abs(z), z.imag, z.real, z.conjugate()" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "(1+0.8j) 1.28062484749 0.8 1.0 (1-0.8j)\n" ] } ], "prompt_number": 123 }, { "cell_type": "raw", "metadata": {}, "source": [ "same algebraic operations as for floats and ints:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "print z + z, z * z, z**3" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "(2+1.6j) (0.36+1.6j) (-0.92+1.888j)\n" ] } ], "prompt_number": 124 }, { "cell_type": "raw", "metadata": {}, "source": [ "POSSIBLE SOURCE OF ERROR: \n", "\n", " pure imaginary numbers must be declared as below to avoid clash with a variable named \"j\"" ] }, { "cell_type": "code", "collapsed": false, "input": [ "z = 1j\n", "print z*z" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "(-1+0j)\n" ] } ], "prompt_number": 125 }, { "cell_type": "raw", "metadata": {}, "source": [ "extra module 'cmath' for functions as sin, cos on complex numbers, math module does not work:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "math.sin(1j)" ], "language": "python", "metadata": {}, "outputs": [ { "ename": "TypeError", "evalue": "can't convert complex to float", "output_type": "pyerr", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m<ipython-input-130-bf9d45ed73bb>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mmath\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m1j\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[1;31mTypeError\u001b[0m: can't convert complex to float" ] } ], "prompt_number": 130 }, { "cell_type": "code", "collapsed": false, "input": [ "import cmath\n", "cmath.sin(1j)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 131, "text": [ "1.1752011936438014j" ] } ], "prompt_number": 131 }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Logical values, Comparison operators" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "logical values represent logical state, that is \"True\" of \"False\"\n", "\n", "\"==\" is used for testing equality, do not confuse with \"=\" which is\n", "assignment:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "print 3 == 4" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "False\n" ] } ], "prompt_number": 132 }, { "cell_type": "code", "collapsed": false, "input": [ "print 3 = 4 # would be valid in C/C++ !!!" ], "language": "python", "metadata": {}, "outputs": [ { "ename": "SyntaxError", "evalue": "invalid syntax (<ipython-input-133-e8f8a9176a62>, line 1)", "output_type": "pyerr", "traceback": [ "\u001b[1;36m File \u001b[1;32m\"<ipython-input-133-e8f8a9176a62>\"\u001b[1;36m, line \u001b[1;32m1\u001b[0m\n\u001b[1;33m print 3 = 4 # would be valid in C/C++ !!!\u001b[0m\n\u001b[1;37m ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid syntax\n" ] } ], "prompt_number": 133 }, { "cell_type": "raw", "metadata": {}, "source": [ "!= is the test for inequality:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "print 3 != 4" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "True\n" ] } ], "prompt_number": 134 }, { "cell_type": "raw", "metadata": {}, "source": [ "tests for different kinds of ordering:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "print 3 >= 4, 3<=4, 3 < 4, 3 > 4" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "False True True False\n" ] } ], "prompt_number": 135 }, { "cell_type": "raw", "metadata": {}, "source": [ "logical operations written in \"plain text\":" ] }, { "cell_type": "code", "collapsed": false, "input": [ "print not not True" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "True\n" ] } ], "prompt_number": 136 }, { "cell_type": "code", "collapsed": false, "input": [ "print 3 < 4 and 3 >= 4" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "False\n" ] } ], "prompt_number": 137 }, { "cell_type": "code", "collapsed": false, "input": [ "print 3 < 4 or 3 >= 4" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "True\n" ] } ], "prompt_number": 138 }, { "cell_type": "raw", "metadata": {}, "source": [ "side note: & and | are bitwise operations:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "print 6 & 12" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "4\n" ] } ], "prompt_number": 139 }, { "cell_type": "code", "collapsed": false, "input": [ "print 6 | 12" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "14\n" ] } ], "prompt_number": 140 }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Strings represent text" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "Python provides four alternatives for declaring a string constant:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "print \"Python\"" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Python\n" ] } ], "prompt_number": 141 }, { "cell_type": "code", "collapsed": false, "input": [ "print 'Python'" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Python\n" ] } ], "prompt_number": 142 }, { "cell_type": "raw", "metadata": {}, "source": [ "two variants which allow strings over muliple lines:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "print \"\"\"Python:\n", "programming done easy\"\"\"" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Python:\n", "programming done easy\n" ] } ], "prompt_number": 143 }, { "cell_type": "code", "collapsed": false, "input": [ "print '''Python\n", "programming done easy'''" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Python\n", "programming done easy\n" ] } ], "prompt_number": 144 }, { "cell_type": "raw", "metadata": {}, "source": [ "advantage: you can use quotes inside a string without using escaping as done in C/C++:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "print \"'abc' is a string\"" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "'abc' is a string\n" ] } ], "prompt_number": 145 }, { "cell_type": "code", "collapsed": false, "input": [ "print '\"abc\" is a string'" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "\"abc\" is a string\n" ] } ], "prompt_number": 146 }, { "cell_type": "code", "collapsed": false, "input": [ "print \"\"\"those are strings: \"abc\" and 'abc'\"\"\"" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "those are strings: \"abc\" and 'abc'\n" ] } ], "prompt_number": 147 }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Comparing strings" ] }, { "cell_type": "code", "collapsed": false, "input": [ "print \"abc\" == \"abc\"" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "True\n" ] } ], "prompt_number": 148 }, { "cell_type": "code", "collapsed": false, "input": [ "print \"abc\" != \"ABC\"" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "True\n" ] } ], "prompt_number": 149 }, { "cell_type": "raw", "metadata": {}, "source": [ "strings are ordered lexycographically (phone book ordering):" ] }, { "cell_type": "code", "collapsed": false, "input": [ "print \"abc\" < \"abe\"" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "True\n" ] } ], "prompt_number": 150 }, { "cell_type": "code", "collapsed": false, "input": [ "print \"abc\" < \"abcd\" " ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "True\n" ] } ], "prompt_number": 151 }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Indexing: accessing single characters in a string" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "indexing is used to access single characters, counting starts with zero\n", "\n", "Matlab users: be prepared !" ] }, { "cell_type": "code", "collapsed": false, "input": [ "name = \"Monty_Python\"\n", "print name[0]" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "M\n" ] } ], "prompt_number": 152 }, { "cell_type": "raw", "metadata": {}, "source": [ "negative indices start from the end. This convenient as you do not need to know the length of the string:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "print name[-1] " ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "n\n" ] } ], "prompt_number": 153 }, { "cell_type": "code", "collapsed": false, "input": [ "print name[-2]" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "o\n" ] } ], "prompt_number": 154 }, { "cell_type": "code", "collapsed": false, "input": [ "print name[-12] == name[0]" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "True\n" ] } ], "prompt_number": 155 }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Slicing: beyond indexing" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "expression like \"string[start:end]\" is called slicing\n", "\n", "end is exclusive !!!\n" ] }, { "cell_type": "code", "collapsed": false, "input": [ "\"01234\"[1:4]" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 160, "text": [ "'123'" ] } ], "prompt_number": 160 }, { "cell_type": "raw", "metadata": {}, "source": [ "extended form : \"string[start:end:stepsize]\"\n", "\n", "stepsize is called \"stride\"\n", "\n", "start:end is the same as start:end:1" ] }, { "cell_type": "code", "collapsed": false, "input": [ "print \"0123456789\"[1:8:2]" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "1357\n" ] } ], "prompt_number": 163 }, { "cell_type": "raw", "metadata": {}, "source": [ "default cases: you can omit values m, n or k, \n", "\n", "default for k is 1,\n", "default for m is 0 (if step size k is positive), \n", "default for n is the length of the string (if step size k is positive)" ] }, { "cell_type": "code", "collapsed": false, "input": [ "name = \"Monty_Python\"\n", "print name[:7] " ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Monty_P\n" ] } ], "prompt_number": 164 }, { "cell_type": "code", "collapsed": false, "input": [ "print name[7:]" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "ython\n" ] } ], "prompt_number": 165 }, { "cell_type": "code", "collapsed": false, "input": [ "print name[3:-2:2]" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "t_yh\n" ] } ], "prompt_number": 169 }, { "cell_type": "code", "collapsed": false, "input": [ "print name[-2:3:-2] " ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "otPy\n" ] } ], "prompt_number": 170 }, { "cell_type": "code", "collapsed": false, "input": [ "print name[:]" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Monty_Python\n" ] } ], "prompt_number": 171 }, { "cell_type": "raw", "metadata": {}, "source": [ "This slicing has two nice properties:\n", "\n", "1. for a slice m:n the legth of the sliced string is just m - n. (see KISS principle)" ] }, { "cell_type": "code", "collapsed": false, "input": [ "len(name[3:7]) == 7 - 3" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 172, "text": [ "True" ] } ], "prompt_number": 172 }, { "cell_type": "raw", "metadata": {}, "source": [ "2. concatenating slices is easy to interpret " ] }, { "cell_type": "code", "collapsed": false, "input": [ "name[3:7] + name[7:11] == name[3:11]" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 173, "text": [ "True" ] } ], "prompt_number": 173 }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "String *methods*" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "methods = functions \"attached\" to \"object\":" ] }, { "cell_type": "code", "collapsed": false, "input": [ "print name.upper()" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "GUIDO\n" ] } ], "prompt_number": 175 }, { "cell_type": "raw", "metadata": {}, "source": [ "Note: strings are never changed inplace, all operations for changing a string return a new string !\n", " aka STRINGS ARE IMMUTABLE" ] }, { "cell_type": "code", "collapsed": false, "input": [ "print name" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Guido\n" ] } ], "prompt_number": 177 }, { "cell_type": "code", "collapsed": false, "input": [ "# first occurence of \"ui\" in \"Guido\":\n", "print \"Guido\".find(\"ui\")" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "1\n" ] } ], "prompt_number": 178 }, { "cell_type": "raw", "metadata": {}, "source": [ "PRACTICE\n", "\n", "1. Use pen and paper to determine the values of the following expressions,\n", " the use the Python shell to validate your results:\n", "\n", " \"abcdefghi\"[2:-2]\n", " \"abcdefghi\"[3:8:2]\n", " \"abcdefghi\"[7:4:-1]\n", " \"abcdefghi\"[::2]\n", " \"abcdefghi\"[::-1] \n", " \"abcdefghi\"[:7:3]\n", " \"abcdefghi\"[2::2]\n", "\n", "2. Look up what the \"replace\" method for strings does, and use this to write a\n", " script which starts with a string \"123\" and then transforms this to\n", " \"one_23\" and then to \"one_two_three_\" and then to \"one_two_three\" !\n", "\n", "3. Look up string \"endswith\" and \"startswith\" methods. Do you see any benefit compared to\n", " string comparison with \"==\" ???\n", "\n", "4. Enter the snippet as a script and try to explain its output:\n", "\n", " number = raw_input(\"please input number: \")\n", " print number, \"times 3 is\", number * 3" ] }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "'Calculating' with strings" ] }, { "cell_type": "code", "collapsed": false, "input": [ "name = \"uwe\"\n", "action = \"says hello\"\n", "print name + \" \" + action + \"!\" # \"+\" concatenates strings" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "uwe says hello!\n" ] } ], "prompt_number": 179 }, { "cell_type": "code", "collapsed": false, "input": [ "# strings can be multplied with an integer\n", "print \"123_\" * 4" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "123_123_123_123_\n" ] } ], "prompt_number": 180 }, { "cell_type": "code", "collapsed": false, "input": [ "# the position of the integer factor does not matter\n", "# addition is something we have seen above\n", "print 2 * \"123_\" + \"abc\"" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "123_123_abc\n" ] } ], "prompt_number": 181 }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "String to number conversion" ] }, { "cell_type": "code", "collapsed": false, "input": [ "number = raw_input(\"please input number: \")\n", "print number, \"times 3 is\", number * 3" ], "language": "python", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "stream": "stdout", "text": [ "please input number: 1\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "1 times 3 is 111\n" ] } ], "prompt_number": 185 }, { "cell_type": "raw", "metadata": {}, "source": [ "float() and int() can be used to convert a string representing a number to the numbers value:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# correct\n", "number = raw_input(\"please input number: \")\n", "print number, \"times 3 is\", float(number) * 3" ], "language": "python", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "stream": "stdout", "text": [ "please input number: 1\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " 1 times 3 is 3.0\n" ] } ], "prompt_number": 189 }, { "cell_type": "code", "collapsed": false, "input": [ "print int(\"123\") * float(\"3.141\")" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "386.343\n" ] } ], "prompt_number": 190 }, { "cell_type": "code", "collapsed": false, "input": [ "print int(\"a3\")" ], "language": "python", "metadata": {}, "outputs": [ { "ename": "ValueError", "evalue": "invalid literal for int() with base 10: 'a3'", "output_type": "pyerr", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m<ipython-input-191-7d7f3234561e>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;32mprint\u001b[0m \u001b[0mint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"a3\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[1;31mValueError\u001b[0m: invalid literal for int() with base 10: 'a3'" ] } ], "prompt_number": 191 }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Variables, interlude" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "We know some Python types already:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "print type(3)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "<type 'int'>\n" ] } ], "prompt_number": 192 }, { "cell_type": "code", "collapsed": false, "input": [ "print type(3.0)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "<type 'float'>\n" ] } ], "prompt_number": 193 }, { "cell_type": "code", "collapsed": false, "input": [ "print type(\"\")" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "<type 'str'>\n" ] } ], "prompt_number": 194 }, { "cell_type": "code", "collapsed": false, "input": [ "print type(True)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "<type 'bool'>\n" ] } ], "prompt_number": 195 }, { "cell_type": "raw", "metadata": {}, "source": [ "Note for C/C++ programmers: variables can be reassigned, in the case below \"hi\" does not have\n", "a name any more and occoupied memory will be deallocated from memory automatically." ] }, { "cell_type": "code", "collapsed": false, "input": [ "a = \"hi\"\n", "a = a + \" there\"\n", "print a" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "hi there\n" ] } ], "prompt_number": 197 }, { "cell_type": "raw", "metadata": {}, "source": [ "We already know that we do not need to declare a variables type,\n", "but beyond that, this type is allowd to change during execution:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "a = 123\n", "print type(a), a\n", "a = \"i am a\"\n", "print type(a), a" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "<type 'int'> 123\n", "<type 'str'> i am a\n" ] } ], "prompt_number": 198 }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "For loops" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "Simple loop counting 0 up to n-1 is done with a range(n), the\n", "upper limit of this range is exclusive, counting starts with zero." ] }, { "cell_type": "code", "collapsed": false, "input": [ "for i in range(3): # aka for (int i=0; i<3; i++) in C/C++\n", " print i" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "0\n", "1\n", "2\n" ] } ], "prompt_number": 199 }, { "cell_type": "raw", "metadata": {}, "source": [ "Code blocks in Python do not need any braces or some \"begin\" and \"end\" statements.\n", "\n", "Lines having the same identation belong to the same block of code.\n", "\n", "Most Python programmers use 4 spaces, no tab." ] }, { "cell_type": "code", "collapsed": false, "input": [ "for i in range(3):\n", " print \"i=\",\n", " print i" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "i= 0\n", "i= 1\n", "i= 2\n" ] } ], "prompt_number": 200 }, { "cell_type": "code", "collapsed": false, "input": [ "for i in range(3):\n", " print \"i=\",\n", " print i" ], "language": "python", "metadata": {}, "outputs": [ { "ename": "IndentationError", "evalue": "unindent does not match any outer indentation level (<ipython-input-201-f44eae0d3110>, line 3)", "output_type": "pyerr", "traceback": [ "\u001b[1;36m File \u001b[1;32m\"<ipython-input-201-f44eae0d3110>\"\u001b[1;36m, line \u001b[1;32m3\u001b[0m\n\u001b[1;33m print i\u001b[0m\n\u001b[1;37m ^\u001b[0m\n\u001b[1;31mIndentationError\u001b[0m\u001b[1;31m:\u001b[0m unindent does not match any outer indentation level\n" ] } ], "prompt_number": 201 }, { "cell_type": "raw", "metadata": {}, "source": [ "The range function is similar to slicing:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "for i in range(1, 5):\n", " print i" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "1\n", "2\n", "3\n", "4\n" ] } ], "prompt_number": 202 }, { "cell_type": "code", "collapsed": false, "input": [ "for i in range(1, 5, 2):\n", " print i" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "1\n", "3\n" ] } ], "prompt_number": 203 }, { "cell_type": "raw", "metadata": {}, "source": [ "Loops can be nested by further identation:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "for i in range(1, 3): \n", " for j in range(2, 4): \n", " print i, \"times\", j, \"is\", i*j " ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "1 times 2 is 2\n", "1 times 3 is 3\n", "2 times 2 is 4\n", "2 times 3 is 6\n" ] } ], "prompt_number": 204 }, { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "Control Flow" ] }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "If / then / else" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "There is no \"else if\" in Python, only \"elif\". Similar to the \"for\" expression lines end with a colon:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "number = int(raw_input(\"input number: \"))\n", "\n", "if number < 0:\n", " print \"number is negative\"\n", "elif number == 0:\n", " print \"number is zero\"\n", "else:\n", " print \"number is positive\"\n" ], "language": "python", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "stream": "stdout", "text": [ "input number: 3\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "number is positive\n" ] } ], "prompt_number": 208 }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "While statement" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "Python only has a \"while\", no \"do until\", or \"do while\" where the\n", "loop continuation is checked at the end of the loop:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "x = 3\n", "while x > 0:\n", " print x\n", " x -= 1" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "3\n", "2\n", "1\n" ] } ], "prompt_number": 209 }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Breaking out of a loop" ] }, { "cell_type": "code", "collapsed": false, "input": [ "for i in range(10):\n", " print i\n", " if i > 1:\n", " print \"stop\"\n", " break" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "0\n", "1\n", "2\n", "stop\n" ] } ], "prompt_number": 210 }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Skipping execution of rest of loop body with \"continue\"" ] }, { "cell_type": "code", "collapsed": false, "input": [ "for i in range(5):\n", " print i,\n", " if i % 2 == 0:\n", " print \"is even\"\n", " continue\n", " print \"is odd\"" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "0 is even\n", "1 is odd\n", "2 is even\n", "3 is odd\n", "4 is even\n" ] } ], "prompt_number": 211 }, { "cell_type": "raw", "metadata": {}, "source": [ "PRACTICE:\n", "\n", "1) Write a script which asks for two numbers a, b and calculates a^2 + a^4 + .. + a^(2b).\n", " You will need to write a for loop. Try to write two different solutions, using different\n", " stepsizes for the exponent.\n", "\n", "2) write a program which asks for two numbers, and let the user choose an operation +, -, *, /\n", " apply this operation to the two numbers and print the result\n", "\n", "3) extension:\n", " after each computation, ask if the user wants to do another calculation, and handle this accordingly\n", "\n", "4) let the user input a number and check if this is a prime number" ] }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Python container types: lists" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "The range function introduced above returns something called \"a list\":" ] }, { "cell_type": "code", "collapsed": false, "input": [ "print type(range(3))" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "<type 'list'>\n" ] } ], "prompt_number": 212 }, { "cell_type": "raw", "metadata": {}, "source": [ "For loops is in most cases not just do counting up to a limit, but \"iterating\" over an \"object you can iterate over\". " ] }, { "cell_type": "code", "collapsed": false, "input": [ "for i in [3, 4, 7, 2]:\n", " print i, \"squared is\", i*i" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "3 squared is 9\n", "4 squared is 16\n", "7 squared is 49\n", "2 squared is 4\n" ] } ], "prompt_number": 213 }, { "cell_type": "raw", "metadata": {}, "source": [ "Side Note: you can iterate over strings too:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "for ci in \"Python\":\n", " print ci, chr(ord(ci)+1) # ord returns ascii code of character, chr is inverse" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "P Q\n", "y z\n", "t u\n", "h i\n", "o p\n", "n o\n" ] } ], "prompt_number": 218 }, { "cell_type": "raw", "metadata": {}, "source": [ "The values in a list may have arbitrary type:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "print [3, \"hello\", 3.12]" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "[3, 'hello', 3.12]\n" ] } ], "prompt_number": 219 }, { "cell_type": "raw", "metadata": {}, "source": [ "To check if an element is in a list, use \"in\"" ] }, { "cell_type": "code", "collapsed": false, "input": [ "print 3 in [1, 2, 4]" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "False\n" ] } ], "prompt_number": 220 }, { "cell_type": "raw", "metadata": {}, "source": [ "Good practice: \"not in\" is more readable than \"if not ... in ...\":" ] }, { "cell_type": "code", "collapsed": false, "input": [ "print 3 not in [1, 2, 4]" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "True\n" ] } ], "prompt_number": 223 }, { "cell_type": "raw", "metadata": {}, "source": [ "the \"append\" method adds an element to a list:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "numbers = [1, 3, 2]\n", "numbers.append(4)\n", "print numbers" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "[1, 3, 2, 4]\n" ] } ], "prompt_number": 224 }, { "cell_type": "raw", "metadata": {}, "source": [ "list support slicing:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "print numbers[1:3]" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "[3, 2]\n" ] } ], "prompt_number": 225 }, { "cell_type": "raw", "metadata": {}, "source": [ "\"len\" returns the lenght of a list:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "print len(numbers)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "4\n" ] } ], "prompt_number": 226 }, { "cell_type": "raw", "metadata": {}, "source": [ "[] is the empty list:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "print type([]), len([])" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "<type 'list'> 0\n" ] } ], "prompt_number": 227 }, { "cell_type": "raw", "metadata": {}, "source": [ "list \"algebra\": adding and multiplication with lists" ] }, { "cell_type": "code", "collapsed": false, "input": [ "print [0, 11] * 3" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "[0, 11, 0, 11, 0, 11]\n" ] } ], "prompt_number": 228 }, { "cell_type": "code", "collapsed": false, "input": [ "print [1, 2, 3] + [5, 6]" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "[1, 2, 3, 5, 6]\n" ] } ], "prompt_number": 229 }, { "cell_type": "raw", "metadata": {}, "source": [ "Note: contrary to strings, lists are mutable, so many list methods change the list in-place." ] }, { "cell_type": "code", "collapsed": false, "input": [ "print numbers" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "[1, 3, 2, 4]\n" ] } ], "prompt_number": 230 }, { "cell_type": "code", "collapsed": false, "input": [ "numbers[0] = 2\n", "print numbers" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "[2, 3, 2, 4]\n" ] } ], "prompt_number": 231 }, { "cell_type": "code", "collapsed": false, "input": [ "numbers.remove(2)\n", "print numbers" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "[3, 2, 4]\n" ] } ], "prompt_number": 232 }, { "cell_type": "raw", "metadata": {}, "source": [ "Example:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "li = [3, 4, 2]\n", "# remove all even numbers:\n", "for i in li:\n", " if i % 2 == 0:\n", " li.remove(i)\n", "print li" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "[3, 2]\n" ] } ], "prompt_number": 233 }, { "cell_type": "code", "collapsed": false, "input": [ "# PROBLEM: list is changed during execution\n", "li = [3, 4, 2]\n", "# remove all even numbers, iterate over a copy of li:\n", "for i in li[:]:\n", " if i % 2 == 0:\n", " li.remove(i)\n", "print li" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "[3]\n" ] } ], "prompt_number": 234 }, { "cell_type": "raw", "metadata": {}, "source": [ "PRACTICE TIME\n", "\n", "1. Create a list and use the IPython shell to find out how to sort the list.\n", " How can you sort in reversed order ?\n", "\n", "2. Let the user input a number > 3. Create a list with the Fibonacci numbers up to this value.\n", " [start with [1, 1], then append iteratively the sum of its two predecessors. That is\n", " you get a sequence 1, 1, 2, 3, 5, 8, 11, 19, ....]\n", "\n", "3. Write a script which takes a number of values and prints those numbers concatonated without spaces.\n", " hint: fist transform [1,3,12] -> [\"1\", \"3\", \"12\", then lookup help(\"\".join)" ] }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Container types: tuples" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "Tuples are defined as lists, but with parantheses instead of square brackets:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "for i in (1, 2, 3):\n", " print i" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "1\n", "2\n", "3\n" ] } ], "prompt_number": 235 }, { "cell_type": "raw", "metadata": {}, "source": [ "Contrary to lists, tuples are immutable:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "tp = (1, 2, 3)\n", "tp.append(4)" ], "language": "python", "metadata": {}, "outputs": [ { "ename": "AttributeError", "evalue": "'tuple' object has no attribute 'append'", "output_type": "pyerr", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mAttributeError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m<ipython-input-236-80b2ba7e971d>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[0mtp\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m2\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m3\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mtp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m4\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[1;31mAttributeError\u001b[0m: 'tuple' object has no attribute 'append'" ] } ], "prompt_number": 236 }, { "cell_type": "raw", "metadata": {}, "source": [ "Although lists can contain values of different types, most Python programmers prefer tuples for\n", "grouping objects of different types." ] }, { "cell_type": "raw", "metadata": {}, "source": [ "Slicing again......" ] }, { "cell_type": "code", "collapsed": false, "input": [ "tp = (1, 2, 3, 4)\n", "print tp[-3:-1]" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "(2, 3)\n" ] } ], "prompt_number": 237 }, { "cell_type": "heading", "level": 4, "metadata": {}, "source": [ "Tuple unpacking" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "Values grouped in a tuple can be \"ungrouped\":" ] }, { "cell_type": "code", "collapsed": false, "input": [ "tp = (1, 2, 3)\n", "a, b, c = tp\n", "print a * b * c" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "6\n" ] } ], "prompt_number": 238 }, { "cell_type": "raw", "metadata": {}, "source": [ "If the Python tries to interprete values separated by commata as tuples, so most tuples can be written without paranthesis:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "a, b, c = 1, 2, 3" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 239 }, { "cell_type": "code", "collapsed": false, "input": [ "x = 1,\n", "print type(x), len(x)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "<type 'tuple'> 1\n" ] } ], "prompt_number": 240 }, { "cell_type": "raw", "metadata": {}, "source": [ "() is the empty tuple:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "print type(()), len(())" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "<type 'tuple'> 0\n" ] } ], "prompt_number": 241 }, { "cell_type": "raw", "metadata": {}, "source": [ "tuple unpacking is convenient for swapping and other permutations of values. No temporary variables are needed:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "a, b = b, a\n", "print a, b" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "2 1\n" ] } ], "prompt_number": 242 }, { "cell_type": "raw", "metadata": {}, "source": [ "This pairing of two lists is often needed. Python provides the zip function:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "a_values = [1, 3 ,5]\n", "b_values = [2, 4, 6]\n", "print zip(a_values, b_values)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "[(1, 2), (3, 4), (5, 6)]\n" ] } ], "prompt_number": 243 }, { "cell_type": "raw", "metadata": {}, "source": [ "tuple upacking works in many places, eg in for loops:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "for a, b in zip(a_values, b_values):\n", " print a + b" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "3\n", "7\n", "11\n" ] } ], "prompt_number": 244 }, { "cell_type": "raw", "metadata": {}, "source": [ "Tuple unpacking can be used when enumerating an \"iterable\":" ] }, { "cell_type": "code", "collapsed": false, "input": [ "words = [\"you\", \"me\", \"and\", \"i\"]\n", "for i, word in enumerate(words):\n", " print \"word\", i, \"is\", word" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "word 0 is you\n", "word 1 is me\n", "word 2 is and\n", "word 3 is i\n" ] } ], "prompt_number": 245 }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Python collections: dictionaries" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "Dictionaries represent a mapping of keys to values. Also known as \"Map\", \"HashMap\", etc in C++ or Java.\n", "Dictionaries are defined like this:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "age_of = { \"jan\": 18, \"alan\": 27, \"universe\": 13.8e9 }" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 246 }, { "cell_type": "markdown", "metadata": {}, "source": [ "This corresponds to the following table:\n", "\n", "key | mapped to\n", "------------- | -------------\n", "jan | 18\n", "alan | 27\n", "universe | 13.8e9" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "To access the value assigned to a key square brackets are used:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "print age_of[\"universe\"]" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "13800000000.0\n" ] } ], "prompt_number": 247 }, { "cell_type": "raw", "metadata": {}, "source": [ "As there is no key \"god\" in age_of, this does not work:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "print age_of[\"god\"]" ], "language": "python", "metadata": {}, "outputs": [ { "ename": "KeyError", "evalue": "'god'", "output_type": "pyerr", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m<ipython-input-248-a6fca65d74ed>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;32mprint\u001b[0m \u001b[0mage_of\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"god\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[1;31mKeyError\u001b[0m: 'god'" ] } ], "prompt_number": 248 }, { "cell_type": "raw", "metadata": {}, "source": [ "But Python has a dictionary method for providing default values for undefined keys:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "print age_of.get(\"god\", \"unknown\")" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "unknown\n" ] } ], "prompt_number": 249 }, { "cell_type": "raw", "metadata": {}, "source": [ "\"in\" tests if a given key exists:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "print \"uwe\" in age_of" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "False\n" ] } ], "prompt_number": 250 }, { "cell_type": "raw", "metadata": {}, "source": [ "keys and values of a dictionary can be extraced as lists ..." ] }, { "cell_type": "code", "collapsed": false, "input": [ "print age_of.keys(), age_of.values()" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "['jan', 'universe', 'alan'] [18, 13800000000.0, 27]\n" ] } ], "prompt_number": 251 }, { "cell_type": "raw", "metadata": {}, "source": [ "... or as (key, value) tuples in a list:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "print age_of.items()" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "[('jan', 18), ('universe', 13800000000.0), ('alan', 27)]\n" ] } ], "prompt_number": 252 }, { "cell_type": "raw", "metadata": {}, "source": [ "tuple unpacking again:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "for who, age in age_of.items():\n", " print who, \"is\", age, \"years old\"" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "jan is 18 years old\n", "universe is 13800000000.0 years old\n", "alan is 27 years old\n" ] } ], "prompt_number": 254 }, { "cell_type": "raw", "metadata": {}, "source": [ "Example: counting digits" ] }, { "cell_type": "code", "collapsed": false, "input": [ "a = \"abcdabcdfdcdcabcabcabcabcabcaaaab\"\n", "counter = dict()\n", "for ai in a:\n", " if ai not in counter:\n", " counter[ai] = 0\n", " counter[ai] += 1\n", "print counter[\"a\"]" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "11\n" ] } ], "prompt_number": 255 }, { "cell_type": "raw", "metadata": {}, "source": [ "shorter:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "a = \"abcdabcdfdcdcabcabcabcabcabcaaaab\"\n", "counter = dict()\n", "for ai in a:\n", " counter[ai] = counter.get(ai, 0) + 1\n", "print counter[\"a\"]" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "11\n" ] } ], "prompt_number": 259 }, { "cell_type": "raw", "metadata": {}, "source": [ "even shorter:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from collections import Counter\n", "a = \"abcdabcdfdcdcabcabcabcabcabcaaaab\"\n", "counter = Counter(a)\n", "print counter[\"a\"]" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "11\n" ] } ], "prompt_number": 260 }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Python collections: sets" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "compared to lists, sets have no order and no duplicate elements. They can be constructed\n", "from a list or a tuple:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "a = set((1, 2, 3))\n", "b = set([2, 3, 4])\n", "print a, b" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "set([1, 2, 3]) set([2, 3, 4])\n" ] } ], "prompt_number": 261 }, { "cell_type": "raw", "metadata": {}, "source": [ "set union:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "print a | b" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "set([1, 2, 3, 4])\n" ] } ], "prompt_number": 262 }, { "cell_type": "code", "collapsed": false, "input": [ "print a.union(b)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "set([1, 2, 3, 4])\n" ] } ], "prompt_number": 264 }, { "cell_type": "raw", "metadata": {}, "source": [ "set intersection" ] }, { "cell_type": "code", "collapsed": false, "input": [ "print a & b" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "set([2, 3])\n" ] } ], "prompt_number": 263 }, { "cell_type": "code", "collapsed": false, "input": [ "print a.intersection(b)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "set([2, 3])\n" ] } ], "prompt_number": 265 }, { "cell_type": "raw", "metadata": {}, "source": [ "set difference:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "print a - b" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "set([1])\n" ] } ], "prompt_number": 266 }, { "cell_type": "code", "collapsed": false, "input": [ "print a.difference(b)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "set([1])\n" ] } ], "prompt_number": 267 }, { "cell_type": "raw", "metadata": {}, "source": [ "set() is the empty set:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "print type(set()), len(set())" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "<type 'set'> 0\n" ] } ], "prompt_number": 268 }, { "cell_type": "raw", "metadata": {}, "source": [ "PRACTICE TIME:\n", "\n", "1. Enter and try to understand:\n", " a = \"abcdabcdfdcdcabcabcabcabcabcaaaab\"\n", " counter = dict()\n", " for ai in a:\n", " print \"ai=\", ai\n", " counter[ai] = counter.get(ai, 0) + 1\n", " print \"counter=\", counter\n", "\n", "2. Write a one line expression which counts the unique elements in a given list [1, 3, 1, 2, 7, 3, 2]\n", "\n", "3. Write a script which transforms a given string by replacing a->x e->y e->z o->j u->q\n", " Use a dictionary. Hint: first create a list then use strings join method !" ] }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Collections in collections" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "As everything in Python is an \"object\" and the proposed collection types group\n", "all kinds of \"objects\", one can deeply nest collections:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "a = [1, [2,3], (3, 4, 5), \"abc\", { 3: 4 }]\n", "for ai in a:\n", " print ai" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "1\n", "[2, 3]\n", "(3, 4, 5)\n", "abc\n", "{3: 4}\n" ] } ], "prompt_number": 269 }, { "cell_type": "code", "collapsed": false, "input": [ "a[1].append(4)\n", "print a" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "[1, [2, 3, 4], (3, 4, 5), 'abc', {3: 4}]\n" ] } ], "prompt_number": 270 }, { "cell_type": "code", "collapsed": false, "input": [ "numbers = { \"even\": [2, 4, 6], \"odd\": [1, 3, 5] }" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 271 }, { "cell_type": "code", "collapsed": false, "input": [ "print numbers[\"even\"][0]" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "2\n" ] } ], "prompt_number": 272 }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Restrictions for collections in collections" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "Keys in dictionaries and items in sets must be \"hashable\". We will not discuss this deeper, but\n", "remenber \"lists are not hashable\". So the following statements fails:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "a = { [1,2] : 2 }" ], "language": "python", "metadata": {}, "outputs": [ { "ename": "TypeError", "evalue": "unhashable type: 'list'", "output_type": "pyerr", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m<ipython-input-273-2d0cf91d660b>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0ma\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m{\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m:\u001b[0m \u001b[1;36m2\u001b[0m \u001b[1;33m}\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[1;31mTypeError\u001b[0m: unhashable type: 'list'" ] } ], "prompt_number": 273 }, { "cell_type": "code", "collapsed": false, "input": [ "a = set()\n", "a.add([1, 2])" ], "language": "python", "metadata": {}, "outputs": [ { "ename": "TypeError", "evalue": "unhashable type: 'list'", "output_type": "pyerr", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m<ipython-input-274-d58d292c1821>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[0ma\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mset\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0ma\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0madd\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m2\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[1;31mTypeError\u001b[0m: unhashable type: 'list'" ] } ], "prompt_number": 274 }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "List comprehensions" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "Python has a powerful construct which is similar to the math notation { f(x) | x \\in A and p(x) }:\n", "\n", "Create a list with values 3*0 ... 3*9:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "values = [3 * a for a in range(10)]\n", "print values" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "[0, 3, 6, 9, 12, 15, 18, 21, 24, 27]\n" ] } ], "prompt_number": 275 }, { "cell_type": "raw", "metadata": {}, "source": [ "Another example:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "print [3*i for i in values if i % 7 == 6]" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "[18, 81]\n" ] } ], "prompt_number": 276 }, { "cell_type": "raw", "metadata": {}, "source": [ "\n", "This replaces 90% of all loops and is very readable ! \n" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "PRACTICE:\n", "\n", "1. for a given list of numbers [ai] write a list comprehension for computing numbers [2*ai+1],\n", " eg [2, 3, 4] -> [5, 7, 9]\n", "\n", "2. Use list a nested comprehension for computing [ [1, 2, 3], [2, 4, 6], [3, 6, 9]]" ] }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "FUNCTIONS" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "In Python \"def\" declares a function:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "def add(a, b, c):\n", " return a + b + c" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 277 }, { "cell_type": "code", "collapsed": false, "input": [ "print add(1, 2, 3)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "6\n" ] } ], "prompt_number": 278 }, { "cell_type": "raw", "metadata": {}, "source": [ "But arguments are not typed. The function works if \"+\" is somehow defined:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "print add(\"a\", \"bc\", \"def\")" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "abcdef\n" ] } ], "prompt_number": 279 }, { "cell_type": "raw", "metadata": {}, "source": [ "This one fails, because 1 + \"a\" + 3 is not defined:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "print add(1, \"a\", 3)" ], "language": "python", "metadata": {}, "outputs": [ { "ename": "TypeError", "evalue": "unsupported operand type(s) for +: 'int' and 'str'", "output_type": "pyerr", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m<ipython-input-280-1120d14149a2>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;32mprint\u001b[0m \u001b[0madd\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"a\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m3\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[1;32m<ipython-input-277-d3a5ee10ec2c>\u001b[0m in \u001b[0;36madd\u001b[1;34m(a, b, c)\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0madd\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mb\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mc\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0ma\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mb\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mc\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[1;31mTypeError\u001b[0m: unsupported operand type(s) for +: 'int' and 'str'" ] } ], "prompt_number": 280 }, { "cell_type": "raw", "metadata": {}, "source": [ "For mixed types:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "def mul(a, b):\n", " return a * b" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 281 }, { "cell_type": "code", "collapsed": false, "input": [ "print mul(3, 4)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "12\n" ] } ], "prompt_number": 282 }, { "cell_type": "code", "collapsed": false, "input": [ "print mul(3, \"abc\")" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "abcabcabc\n" ] } ], "prompt_number": 283 }, { "cell_type": "heading", "level": 4, "metadata": {}, "source": [ "Keyword arguments" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "You can arange the arguments when calling a function in arbirary order if you use \"keyword arguments\":" ] }, { "cell_type": "code", "collapsed": false, "input": [ "add(c=1, b=2, a=3)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 284, "text": [ "6" ] } ], "prompt_number": 284 }, { "cell_type": "raw", "metadata": {}, "source": [ "You can mix this, first argument is positional argument, followed by two keyword arguments:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "add(1, c=2, b=3)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 285, "text": [ "6" ] } ], "prompt_number": 285 }, { "cell_type": "raw", "metadata": {}, "source": [ "But this fails:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "add(a=2, b=3, 3)" ], "language": "python", "metadata": {}, "outputs": [ { "ename": "SyntaxError", "evalue": "non-keyword arg after keyword arg (<ipython-input-286-dd0fb32a1ea6>, line 1)", "output_type": "pyerr", "traceback": [ "\u001b[1;36m File \u001b[1;32m\"<ipython-input-286-dd0fb32a1ea6>\"\u001b[1;36m, line \u001b[1;32m1\u001b[0m\n\u001b[1;33m add(a=2, b=3, 3)\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m non-keyword arg after keyword arg\n" ] } ], "prompt_number": 286 }, { "cell_type": "heading", "level": 4, "metadata": {}, "source": [ "Default arguments" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "Some arguments may have default values, which are used if you omit them when calling a function:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "def greet(name, salute_with=\"hello\"):\n", " print salute_with, name, \"!\"\n", "\n", "greet(\"jan\")" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "hello jan !\n" ] } ], "prompt_number": 287 }, { "cell_type": "code", "collapsed": false, "input": [ "greet(\"urs\", \"gruezi\")" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "gruezi urs !\n" ] } ], "prompt_number": 288 }, { "cell_type": "heading", "level": 4, "metadata": {}, "source": [ "Arbitrary many number of arguments" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "If you want to declare a function which takes an arbitrary number of arguments, you can\n", "declare the function as follows:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# arbitrary many arguments:\n", "def multiply(v0, *values):\n", " print \"got input arguments\", v0, \"and\", values\n", " product = v0\n", " for v in values:\n", " product *= v\n", " return product\n", " \n", "print multiply(2, 1, 13, 1)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "got input arguments 2 and (1, 13, 1)\n", "26\n" ] } ], "prompt_number": 289 }, { "cell_type": "heading", "level": 4, "metadata": {}, "source": [ "Multiple return values" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "Thanks to tuples, Python functions can return more than one value:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "def func(a, b, c):\n", " result1 = a + b * c\n", " result2 = a - b / c\n", " return result1, result2" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 290 }, { "cell_type": "raw", "metadata": {}, "source": [ "Tuple unpacking makes it easy to grab multiple return values:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "r1, r2 = func(1, 2, 3)\n", "print r1, r2" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "7 1\n" ] } ], "prompt_number": 291 }, { "cell_type": "heading", "level": 4, "metadata": {}, "source": [ "Doc strings" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The first string below a function declaration is called a **doc string**. Calling **help()** on this function will return this string." ] }, { "cell_type": "code", "collapsed": false, "input": [ "def magic(a, b, c):\n", " \"\"\" this function is magic !\n", " it take a, b, c and returns the sum \n", " \"\"\"\n", " return a + b + c" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 292 }, { "cell_type": "code", "collapsed": false, "input": [ "help(magic)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Help on function magic in module __main__:\n", "\n", "magic(a, b, c)\n", " this function is magic !\n", " it take a, b, c and returns the sum\n", "\n" ] } ], "prompt_number": 293 }, { "cell_type": "heading", "level": 4, "metadata": {}, "source": [ "Functions are \"objects\" as numbers, lists, ..." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can pass a function to a variable:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "new_name = magic\n", "print new_name(1, 2, 3)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "6\n" ] } ], "prompt_number": 294 }, { "cell_type": "markdown", "metadata": {}, "source": [ "And you can pass a function as an argument to a function:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "def eval_function(fun, *a):\n", " return fun(*a)\n", "\n", "print eval_function(set, (1, 2, 3, 4, 5))" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "set([1, 2, 3, 4, 5])\n" ] } ], "prompt_number": 295 }, { "cell_type": "raw", "metadata": {}, "source": [ "BE CAREFUL: [] as default argument: (One of the few dark cornes of Python)" ] }, { "cell_type": "code", "collapsed": false, "input": [ "def add(a, li= []):\n", " li.append(a)\n", " return li\n", "\n", "li = add(3)\n", "li2 = add(4)\n", "print li" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "[3, 4]\n" ] } ], "prompt_number": 296 }, { "cell_type": "code", "collapsed": false, "input": [ "print li is li2" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "True\n" ] } ], "prompt_number": 297 }, { "cell_type": "raw", "metadata": {}, "source": [ "Solution:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "def add(a, li=None):\n", " if li is None:\n", " li = []\n", " li.append(a)\n", " return li\n", "\n", "li = add(3)\n", "li2 = add(4)\n", "print li" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "[3]\n" ] } ], "prompt_number": 298 }, { "cell_type": "code", "collapsed": false, "input": [ "print li is li2" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "False\n" ] } ], "prompt_number": 299 }, { "cell_type": "raw", "metadata": {}, "source": [ "PRACTICE:\n", "\n", "1. Write a function which takes a radius of a circle and returns the circles area\n", "\n", "2. Write a function which takes two numbers m,n, and returns the sum and the product of 1*1 ... m*n \n", "\n", "3. Modify this function, so that a missing \"m\" is respected as m=1\n", "\n", "4. Write a function with arbitrary arguments a1, .. an\n", " which returns a1 * a2 + a2 * a3 + a3 * a4 + ..." ] }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "CLASSES" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "* Objects group values and functions which we call methods in this context.\n", "\n", "* A class describes how an object is created and defines the methods.\n", "\n", "* An object is also called \"instance of the class\"." ] }, { "cell_type": "code", "collapsed": false, "input": [ "# first version\n", "\n", "class SimpleAddress(object):\n", " \n", " \"\"\"This class represents a person living in a \n", " city.\n", " \"\"\"\n", " \n", " def say_hello(self, formula):\n", " \"\"\" greets the person \"\"\"\n", " print formula, self.name, \"from\", self.city\n", "\n", "# maual setting of attributes: \n", "a = SimpleAddress()\n", "a.name = \"tina turner\"\n", "a.city = \"z\u00fcrich\"\n", "\n", "# when calling say_hello 'self' is replaced by 'a':\n", "a.say_hello(\"gruezi\")\n", "\n", "# TODO: insert print statements" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "gruezi tina turner from z\u00fcrich\n" ] } ], "prompt_number": 307 }, { "cell_type": "code", "collapsed": false, "input": [ "class SimpleAddress(object):\n", " \n", " def __init__(self, name, city):\n", " self.name = name\n", " self.city = city\n", " \n", " def say_hello(self, formula):\n", " print formula, self.name, \"from\", self.city\n", "\n", "# this 'constructs' an instance aa and calls __init__ with self \n", "# replaced by a and following arguments \"tina turner\", \"z\u00fcrich\"\n", "a = SimpleAddress(\"tina turner\", \"z\u00fcrich\")\n", "\n", "# when calling say_hello 'self' is replaced by 'a':\n", "a.say_hello(\"gruezi\")" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "gruezi tina turner from z\u00fcrich\n" ] } ], "prompt_number": 308 }, { "cell_type": "raw", "metadata": {}, "source": [ "* \"self\" is used by convention, you could use \"this\" or anything else\n", "\n", "* You have to declare this parameter when defining a method\n", "\n", "* Normally you don't use it when calling the method on a given object" ] }, { "cell_type": "heading", "level": 4, "metadata": {}, "source": [ "* what is going on ?" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# 1st: methods are attached to the class:\n", "print SimpleAddress.say_hello" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "<unbound method SimpleAddress.say_hello>\n" ] } ], "prompt_number": 309 }, { "cell_type": "code", "collapsed": false, "input": [ "print SimpleAddress.__init__" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "<unbound method SimpleAddress.__init__>\n" ] } ], "prompt_number": 311 }, { "cell_type": "code", "collapsed": false, "input": [ "# 2nd: address.say_hello(\"gruezi\") is handled internally as follows:\n", "SimpleAddress.say_hello(a, \"gruezi\")" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "gruezi tina turner from z\u00fcrich\n" ] } ], "prompt_number": 312 }, { "cell_type": "code", "collapsed": false, "input": [ "# address = Address(\"tina turner\", \"z\u00fcrich\") is handled as follows:\n", "address = SimpleAddress.__new__(Address)\n", "SimpleAddress.__init__(a, \"tina turner\", \"z\u00fcrich\")\n", "print a.name" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "tina turner\n" ] } ], "prompt_number": 314 }, { "cell_type": "raw", "metadata": {}, "source": [ "PRACTICE TIME:\n", "\n", "Write a class which ..." ] }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Overloading operations" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "Some methods have a special meaning. We have seen __init__ above.\n", "\n", "We declare __add__ and __str__ below:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "class Vector3D(object):\n", " \n", " def __init__(self, x, y, z):\n", " self.x = x\n", " self.y = y\n", " self.z = z\n", " \n", " def __add__(self, other):\n", " return Vector3D(self.x + other.x,\n", " self.y + other.y,\n", " self.z + other.z)\n", " \n", " def __str__(self):\n", " return \"Vector(%f, %f, %f)\" % (self.x, self.y, self.z)\n", " \n", "v1 = Vector3D(1.0, 2.0, 3.0)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 315 }, { "cell_type": "raw", "metadata": {}, "source": [ "__str__ defines how an object can be casted / transformed to a string:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "print str(v1)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Vector(1.000000, 2.000000, 3.000000)\n" ] } ], "prompt_number": 316 }, { "cell_type": "raw", "metadata": {}, "source": [ "This transformation is automatically called when printing an object, so\n", "__str__ is a convenient way to return printable information about an object:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "print v1" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Vector(1.000000, 2.000000, 3.000000)\n" ] } ], "prompt_number": 317 }, { "cell_type": "raw", "metadata": {}, "source": [ "Using \"+\" with objects on both sides calls __add__. In the following\n", "example __add__ is called with arguments self=v1 and other=v2:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "v2 = Vector3D(2.0, 0.0, -1.0)\n", "v3 = v1 + v2\n", "print v3" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Vector(3.000000, 2.000000, 2.000000)\n" ] } ], "prompt_number": 318 }, { "cell_type": "raw", "metadata": {}, "source": [ "These were only a few examples, there are may other special methods as __mul__, __len__, __contains__ etc" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "PRACTICE TIME\n", "\n", "1. implement a method Vector.scale which takes a number and scales the vector by this number\n", "\n", "2. implement a method __mul__ which calculates the dot product of two vectors" ] }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "* Inheritance" ] }, { "cell_type": "code", "collapsed": false, "input": [ "class Dog(object):\n", " \n", " def __init__(self, name):\n", " # print \"\\n.. this is Dog.__init__, self is\", self\n", " self.name = name\n", " \n", " def greet(self):\n", " # print \"\\n.. this is Dog.greet, self is\", self\n", " print \"hi\", self.name\n", " \n", " def say(self):\n", " # print \"\\n.. this is Dog.say, self is\", self\n", " print \"barf\"\n", " \n", "d = Dog(\"hasso\")\n", "d.greet()\n", "d.say() " ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "hi hasso\n", "barf\n" ] } ], "prompt_number": 319 }, { "cell_type": "code", "collapsed": false, "input": [ "class SuperDog(Dog):\n", " \n", " def __init__(self):\n", " #print \"\\n.. this is SuperDog.__init__, self is\", self\n", " super(SuperDog, self).__init__(\"fifi\")\n", " \n", " def say(self): \n", " #print \"\\n.. this is SuperDog.say, self is\", self\n", " print \"BARF !!!\"\n", " \n", "sd = SuperDog()\n", "sd.greet()\n", "sd.say()" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "hi fifi\n", "BARF !!!\n" ] } ], "prompt_number": 320 }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "File I/O" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "The following exaple opens a file for writing:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "fp = open(\"text.txt\", \"w\")\n", "print type(fp)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "<type 'file'>\n" ] } ], "prompt_number": 321 }, { "cell_type": "raw", "metadata": {}, "source": [ "So \"fp\" is an instance of class file. There are a two ways to write to this file:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "fp.write(\"line 1\\n\")\n", "print >> fp, \"line 2\"\n", "fp.close()" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 322 }, { "cell_type": "raw", "metadata": {}, "source": [ "Reading the full content of a file is done by calling \"read\" on the file object:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "print open(\"text.txt\", \"r\").read()" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "line 1\n", "line 2\n", "\n" ] } ], "prompt_number": 324 }, { "cell_type": "raw", "metadata": {}, "source": [ "Calling \"readlines\" on the file object returns a list containing the separate lines of the file:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "print open(\"text.txt\", \"r\").readlines()" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "['line 1\\n', 'line 2\\n']\n" ] } ], "prompt_number": 325 }, { "cell_type": "raw", "metadata": {}, "source": [ "Iterating over a file instance works:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "for line in open(\"text.txt\", \"r\"):\n", " print repr(line)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "'line 1\\n'\n", "'line 2\\n'\n" ] } ], "prompt_number": 326 }, { "cell_type": "code", "collapsed": false, "input": [ "# use enumeration\n", "for (i, line) in enumerate(open(\"text.txt\", \"r\")): # tuple unpacking\n", " print \"line\", i, \"is\", repr(line)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "line 0 is 'line 1\\n'\n", "line 1 is 'line 2\\n'\n" ] } ], "prompt_number": 327 }, { "cell_type": "raw", "metadata": {}, "source": [ "PRACTICE TIME\n", "\n", "iterate over all files with extension \"*.py\" in a given directory and\n", "print for each file the number of lines in this file.\n", "provide an overall sum at the end.\n", "HINT: \"import glob\"\n" ] }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ " Exceptions" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "We already know some an exception:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "print 1/0" ], "language": "python", "metadata": {}, "outputs": [ { "ename": "ZeroDivisionError", "evalue": "integer division or modulo by zero", "output_type": "pyerr", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mZeroDivisionError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m<ipython-input-328-e19d6e6ac7e1>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;32mprint\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m/\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[1;31mZeroDivisionError\u001b[0m: integer division or modulo by zero" ] } ], "prompt_number": 328 }, { "cell_type": "raw", "metadata": {}, "source": [ "On can raise this exception in a controlled way:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "raise ZeroDivisionError(\"this is some extra information which is printed below\")" ], "language": "python", "metadata": {}, "outputs": [ { "ename": "ZeroDivisionError", "evalue": "this is some extra information which is printed below", "output_type": "pyerr", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mZeroDivisionError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m<ipython-input-329-0913c8b046b9>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;32mraise\u001b[0m \u001b[0mZeroDivisionError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"this is some extra information which is printed below\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[1;31mZeroDivisionError\u001b[0m: this is some extra information which is printed below" ] } ], "prompt_number": 329 }, { "cell_type": "raw", "metadata": {}, "source": [ "On can catch exceptions and handle them." ] }, { "cell_type": "code", "collapsed": false, "input": [ "int(\"abc\")" ], "language": "python", "metadata": {}, "outputs": [ { "ename": "ValueError", "evalue": "invalid literal for int() with base 10: 'abc'", "output_type": "pyerr", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m<ipython-input-330-908f2fe4faa4>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"abc\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[1;31mValueError\u001b[0m: invalid literal for int() with base 10: 'abc'" ] } ], "prompt_number": 330 }, { "cell_type": "code", "collapsed": false, "input": [ "try:\n", " int(\"abc\")\n", "except ValueError:\n", " print '\"abc\" is not a valid number'\n", " " ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "\"abc\" is not a valid number\n" ] } ], "prompt_number": 331 }, { "cell_type": "raw", "metadata": {}, "source": [ "Exceptions avoid encoding errors in function return values. The user of a function can decide at which level an error is handled..." ] }, { "cell_type": "code", "collapsed": false, "input": [ "def divide(a, b):\n", " return a / b\n", "\n", "def secure_divide(a, b):\n", " try:\n", " result = divide(a, b)\n", " except:\n", " result = None\n", " return result\n", "\n", "print secure_divide(12, 4), secure_divide(3, 0)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "3 None\n" ] } ], "prompt_number": 332 }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Write you own module" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# save the example above in a file \"secure_math.py\"\n", "import secure_math\n", "print secure_math.secure_divide(12, 4)" ], "language": "python", "metadata": {}, "outputs": [ { "ename": "ImportError", "evalue": "No module named secure_math", "output_type": "pyerr", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mImportError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m<ipython-input-333-9168c65284b5>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[1;31m# save the example above in a file \"secure_math.py\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[1;32mimport\u001b[0m \u001b[0msecure_math\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 3\u001b[0m \u001b[1;32mprint\u001b[0m \u001b[0msecure_math\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msecure_divide\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m12\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m4\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mImportError\u001b[0m: No module named secure_math" ] } ], "prompt_number": 333 }, { "cell_type": "raw", "metadata": {}, "source": [ "TOPICS NOT COVERED\n", "\n", " * generators\n", " * decorators\n", " * module packages\n", " * package installation" ] }, { "cell_type": "code", "collapsed": false, "input": [ "!pip install requests" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Requirement already satisfied (use --upgrade to upgrade): requests in /home/uweschmitt/python_kurs/lib/python2.7/site-packages\r\n", "Cleaning up...\r\n" ] } ], "prompt_number": 334 }, { "cell_type": "code", "collapsed": false, "input": [ "import requests\n", "print requests.get(\"http://telize.com/jsonip\").json()" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "{u'ip': u'188.97.232.201'}\n" ] } ], "prompt_number": 337 }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "numpy = container types for vectors, matrices and n-th order tensors" ] }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Vectors" ] }, { "cell_type": "code", "collapsed": false, "input": [ "import numpy as np\n", "x = np.array((1.0, 2, 3))" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 345 }, { "cell_type": "code", "collapsed": false, "input": [ "print x" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "[ 1. 2. 3.]\n" ] } ], "prompt_number": 346 }, { "cell_type": "raw", "metadata": {}, "source": [ "An numpy array has a common type of all elements:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "print x.dtype" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "float64\n" ] } ], "prompt_number": 347 }, { "cell_type": "code", "collapsed": false, "input": [ "# indexing and slicing\n", "print x[0], x[::2]" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "1.0 [ 1. 3.]\n" ] } ], "prompt_number": 348 }, { "cell_type": "code", "collapsed": false, "input": [ "# assignment:\n", "x[::2] += 1\n", "print x" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "[ 2. 2. 4.]\n" ] } ], "prompt_number": 349 }, { "cell_type": "code", "collapsed": false, "input": [ "print 2 * x + x" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "[ 6. 6. 12.]\n" ] } ], "prompt_number": 350 }, { "cell_type": "code", "collapsed": false, "input": [ "print x * x" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "[ 4. 4. 16.]\n" ] } ], "prompt_number": 351 }, { "cell_type": "code", "collapsed": false, "input": [ "print np.dot(x, x)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "24.0\n" ] } ], "prompt_number": 352 }, { "cell_type": "code", "collapsed": false, "input": [ "print np.sin(x)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "[ 0.90929743 0.90929743 -0.7568025 ]\n" ] } ], "prompt_number": 353 }, { "cell_type": "code", "collapsed": false, "input": [ "print x.shape" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "(3,)\n" ] } ], "prompt_number": 354 }, { "cell_type": "heading", "level": 4, "metadata": {}, "source": [ "Special vector creation" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# range with known stepsize 0.3\n", "np.arange(0, 2, 0.3)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 355, "text": [ "array([ 0. , 0.3, 0.6, 0.9, 1.2, 1.5, 1.8])" ] } ], "prompt_number": 355 }, { "cell_type": "code", "collapsed": false, "input": [ "# range with known number of points\n", "np.linspace(0, np.pi, 10)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 356, "text": [ "array([ 0. , 0.34906585, 0.6981317 , 1.04719755, 1.3962634 ,\n", " 1.74532925, 2.0943951 , 2.44346095, 2.7925268 , 3.14159265])" ] } ], "prompt_number": 356 }, { "cell_type": "heading", "level": 4, "metadata": {}, "source": [ "Matrices" ] }, { "cell_type": "code", "collapsed": false, "input": [ "mat = np.array(((1, 2, 3), (2, 3, 4)), dtype=np.float)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 369 }, { "cell_type": "code", "collapsed": false, "input": [ "print mat" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "[[ 1. 2. 3.]\n", " [ 2. 3. 4.]]\n" ] } ], "prompt_number": 370 }, { "cell_type": "code", "collapsed": false, "input": [ "print mat.shape" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "(2, 3)\n" ] } ], "prompt_number": 371 }, { "cell_type": "raw", "metadata": {}, "source": [ "\"*\" is element wise, not \"matrix multiplication\":" ] }, { "cell_type": "code", "collapsed": false, "input": [ " mat * mat" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 372, "text": [ "array([[ 1., 4., 9.],\n", " [ 4., 9., 16.]])" ] } ], "prompt_number": 372 }, { "cell_type": "raw", "metadata": {}, "source": [ "np.dot is matrix x vector multiplication, aka \"inner product\":" ] }, { "cell_type": "code", "collapsed": false, "input": [ "np.dot(mat, x)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 373, "text": [ "array([ 18., 26.])" ] } ], "prompt_number": 373 }, { "cell_type": "raw", "metadata": {}, "source": [ "transpose of mat :" ] }, { "cell_type": "code", "collapsed": false, "input": [ "print mat.T" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "[[ 1. 2.]\n", " [ 2. 3.]\n", " [ 3. 4.]]\n" ] } ], "prompt_number": 374 }, { "cell_type": "code", "collapsed": false, "input": [ "print np.dot(mat, mat.T)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "[[ 14. 20.]\n", " [ 20. 29.]]\n" ] } ], "prompt_number": 375 }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Special matrices" ] }, { "cell_type": "code", "collapsed": false, "input": [ "print np.eye(3)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "[[ 1. 0. 0.]\n", " [ 0. 1. 0.]\n", " [ 0. 0. 1.]]\n" ] } ], "prompt_number": 376 }, { "cell_type": "code", "collapsed": false, "input": [ "print np.zeros((2, 3))" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "[[ 0. 0. 0.]\n", " [ 0. 0. 0.]]\n" ] } ], "prompt_number": 377 }, { "cell_type": "code", "collapsed": false, "input": [ "np.diag(np.arange(1, 4))" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 378, "text": [ "array([[1, 0, 0],\n", " [0, 2, 0],\n", " [0, 0, 3]])" ] } ], "prompt_number": 378 }, { "cell_type": "code", "collapsed": false, "input": [ "# complex matrices" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 379 }, { "cell_type": "code", "collapsed": false, "input": [ "mat = np.array(((1.0, 1+1j), (1-1j, 2.0)), dtype=np.complex)\n", "print mat" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "[[ 1.+0.j 1.+1.j]\n", " [ 1.-1.j 2.+0.j]]\n" ] } ], "prompt_number": 380 }, { "cell_type": "code", "collapsed": false, "input": [ "print np.dot(mat, mat.T.conj())" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "[[ 3.+0.j 3.+3.j]\n", " [ 3.-3.j 6.+0.j]]\n" ] } ], "prompt_number": 381 }, { "cell_type": "heading", "level": 4, "metadata": {}, "source": [ "Changing the shape of an array" ] }, { "cell_type": "code", "collapsed": false, "input": [ "vec = np.arange(3)\n", "print vec" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "[0 1 2]\n" ] } ], "prompt_number": 382 }, { "cell_type": "code", "collapsed": false, "input": [ "# create matrix with artificial column dimensions\n", "vec_as_matrix = vec[:, np.newaxis]\n", "print vec_as_matrix" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "[[0]\n", " [1]\n", " [2]]\n" ] } ], "prompt_number": 384 }, { "cell_type": "code", "collapsed": false, "input": [ "# and back:\n", "vec_back = vec_as_matrix.squeeze()\n", "print vec_back" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "[0 1 2]\n" ] } ], "prompt_number": 385 }, { "cell_type": "code", "collapsed": false, "input": [ "vec = np.arange(24, dtype=np.int)\n", "print vec" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "[ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23]\n" ] } ], "prompt_number": 401 }, { "cell_type": "raw", "metadata": {}, "source": [ "reshape \"reshapes\" an array, one dimension results from the other dimensions, you can use \"-1\" for this:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "mat = vec.reshape(3, -1)\n", "print mat" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "[[ 0 1 2 3 4 5 6 7]\n", " [ 8 9 10 11 12 13 14 15]\n", " [16 17 18 19 20 21 22 23]]\n" ] } ], "prompt_number": 402 }, { "cell_type": "code", "collapsed": false, "input": [ "print mat.reshape(-1, 3)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "[[ 0 1 2]\n", " [ 3 4 5]\n", " [ 6 7 8]\n", " [ 9 10 11]\n", " [12 13 14]\n", " [15 16 17]\n", " [18 19 20]\n", " [21 22 23]]\n" ] } ], "prompt_number": 403 }, { "cell_type": "raw", "metadata": {}, "source": [ "Even tensors:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "print mat.reshape(3, 4, -1)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "[[[ 0 1]\n", " [ 2 3]\n", " [ 4 5]\n", " [ 6 7]]\n", "\n", " [[ 8 9]\n", " [10 11]\n", " [12 13]\n", " [14 15]]\n", "\n", " [[16 17]\n", " [18 19]\n", " [20 21]\n", " [22 23]]]\n" ] } ], "prompt_number": 404 }, { "cell_type": "heading", "level": 4, "metadata": {}, "source": [ "Fast indexing" ] }, { "cell_type": "code", "collapsed": false, "input": [ "print mat" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "[[ 0 1 2 3 4 5 6 7]\n", " [ 8 9 10 11 12 13 14 15]\n", " [16 17 18 19 20 21 22 23]]\n" ] } ], "prompt_number": 405 }, { "cell_type": "code", "collapsed": false, "input": [ "mat % 3 == 0" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 406, "text": [ "array([[ True, False, False, True, False, False, True, False],\n", " [False, True, False, False, True, False, False, True],\n", " [False, False, True, False, False, True, False, False]], dtype=bool)" ] } ], "prompt_number": 406 }, { "cell_type": "code", "collapsed": false, "input": [ "mat[mat % 3 == 0] = 0\n", "print mat" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "[[ 0 1 2 0 4 5 0 7]\n", " [ 8 0 10 11 0 13 14 0]\n", " [16 17 0 19 20 0 22 23]]\n" ] } ], "prompt_number": 407 }, { "cell_type": "heading", "level": 4, "metadata": {}, "source": [ "Type coercion:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "print mat.dtype\n", "mat = mat.astype(np.float)\n", "print mat.dtype\n", "print mat" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "int64\n", "float64\n", "[[ 0. 1. 2. 0. 4. 5. 0. 7.]\n", " [ 8. 0. 10. 11. 0. 13. 14. 0.]\n", " [ 16. 17. 0. 19. 20. 0. 22. 23.]]\n" ] } ], "prompt_number": 408 }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "numpy File I/O" ] }, { "cell_type": "heading", "level": 4, "metadata": {}, "source": [ "Binary files" ] }, { "cell_type": "code", "collapsed": false, "input": [ "mat.tofile(\"matrix.bin\")\n", "print mat.dtype" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "float64\n" ] } ], "prompt_number": 409 }, { "cell_type": "code", "collapsed": false, "input": [ "print np.fromfile(\"matrix.bin\", dtype=np.float64)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "[ 0. 1. 2. 0. 4. 5. 0. 7. 8. 0. 10. 11. 0. 13. 14.\n", " 0. 16. 17. 0. 19. 20. 0. 22. 23.]\n" ] } ], "prompt_number": 410 }, { "cell_type": "code", "collapsed": false, "input": [ "print np.fromfile(\"matrix.bin\", dtype=np.float64).reshape(3, -1)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "[[ 0. 1. 2. 0. 4. 5. 0. 7.]\n", " [ 8. 0. 10. 11. 0. 13. 14. 0.]\n", " [ 16. 17. 0. 19. 20. 0. 22. 23.]]\n" ] } ], "prompt_number": 411 }, { "cell_type": "code", "collapsed": false, "input": [ "np.fromfile(\"matrix.bin\", dtype=complex)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 412, "text": [ "array([ 0. +1.j, 2. +0.j, 4. +5.j, 0. +7.j, 8. +0.j, 10.+11.j,\n", " 0.+13.j, 14. +0.j, 16.+17.j, 0.+19.j, 20. +0.j, 22.+23.j])" ] } ], "prompt_number": 412 }, { "cell_type": "code", "collapsed": false, "input": [ "np.fromfile(\"matrix.bin\", dtype=np.int)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 414, "text": [ "array([ 0, 4607182418800017408, 4611686018427387904,\n", " 0, 4616189618054758400, 4617315517961601024,\n", " 0, 4619567317775286272, 4620693217682128896,\n", " 0, 4621819117588971520, 4622382067542392832,\n", " 0, 4623507967449235456, 4624070917402656768,\n", " 0, 4625196817309499392, 4625478292286210048,\n", " 0, 4626041242239631360, 4626322717216342016,\n", " 0, 4626885667169763328, 4627167142146473984])" ] } ], "prompt_number": 414 }, { "cell_type": "heading", "level": 4, "metadata": {}, "source": [ "Alternative: txt file" ] }, { "cell_type": "code", "collapsed": false, "input": [ "np.savetxt(\"matrix.txt\", mat)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 415 }, { "cell_type": "code", "collapsed": false, "input": [ "np.loadtxt(\"matrix.txt\")" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 416, "text": [ "array([[ 0., 1., 2., 0., 4., 5., 0., 7.],\n", " [ 8., 0., 10., 11., 0., 13., 14., 0.],\n", " [ 16., 17., 0., 19., 20., 0., 22., 23.]])" ] } ], "prompt_number": 416 }, { "cell_type": "code", "collapsed": false, "input": [ "print open(\"matrix.txt\").read()" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "0.000000000000000000e+00 1.000000000000000000e+00 2.000000000000000000e+00 0.000000000000000000e+00 4.000000000000000000e+00 5.000000000000000000e+00 0.000000000000000000e+00 7.000000000000000000e+00\n", "8.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+01 1.100000000000000000e+01 0.000000000000000000e+00 1.300000000000000000e+01 1.400000000000000000e+01 0.000000000000000000e+00\n", "1.600000000000000000e+01 1.700000000000000000e+01 0.000000000000000000e+00 1.900000000000000000e+01 2.000000000000000000e+01 0.000000000000000000e+00 2.200000000000000000e+01 2.300000000000000000e+01\n", "\n" ] } ], "prompt_number": 417 }, { "cell_type": "heading", "level": 4, "metadata": {}, "source": [ "Plotting" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "matplotlib is \"the\" Python library for plotting. pylab uses matplotlib and provides a matlab like interface:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# this is just for this presentation !\n", "%matplotlib inline " ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 422 }, { "cell_type": "code", "collapsed": false, "input": [ "import pylab\n", "x = np.linspace(0, 2 * np.pi, 150)\n", "y = np.sin(x) * np.cos(x*x+1)\n", "\n", "pylab.plot(x, y, label=\"y\")\n", "\n", "y2 = y * np.sin(y)\n", "pylab.plot(x, y2, label=\"y2\")\n", "\n", "pylab.legend()\n", "pylab.show()\n" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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0SkFNSHBs3zAMQ9IrnVg3KqUKaoPabgVLrRYwKlxH9GGKMAwxQwiL7LexUThF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\nTfx+Pzwez5druF5htKHqe3h7e4PL5cL6+jrKeX19WiODwYC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"text": [ "<matplotlib.figure.Figure at 0x3485390>" ] } ], "prompt_number": 423 }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Final example: pca" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# 2 x 1000 matrix with normal distributed entries:\n", "points = np.random.randn(2, 1000)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 426 }, { "cell_type": "code", "collapsed": false, "input": [ "import pylab\n", "pylab.plot(points[0,:], points[1,:], \".\")\n", "pylab.show()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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khcVQh+EySelrotulDVHLAqj3ttYjSkyY1p5cCngWenafw2AXYTVtL7P4bNu6dHi2xXpN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\nAElFTkSuQmCC\n", "text": [ "<matplotlib.figure.Figure at 0x35f01d0>" ] } ], "prompt_number": 427 }, { "cell_type": "code", "collapsed": false, "input": [ "deformation_matrix = np.array(((1.5, 1.0), (0.9, 1.0)))" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 428 }, { "cell_type": "code", "collapsed": false, "input": [ "x = np.dot(deformation_matrix, points)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 429 }, { "cell_type": "code", "collapsed": false, "input": [ "import pylab\n", "pylab.plot(x[0,:], x[1,:], \".\")\n", "pylab.show()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "<matplotlib.figure.Figure at 0x346e1d0>" ] } ], "prompt_number": 430 }, { "cell_type": "code", "collapsed": false, "input": [ "# np.linalg.eigh is eigenvalue decomposition of hermetian matrix\n", "# np.linalg has others like \n", "# np.linalg.solve for solving linear equation systems\n", "# np.linalg.svd for singular value decomposition and others\n", "\n", "# this is pca for centered matrix \n", "eigvals, eigvecs = np.linalg.eigh(np.dot(x, x.T))" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 431 }, { "cell_type": "code", "collapsed": false, "input": [ "# column vectors describe main directions in data:\n", "print eigvecs" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "[[ 0.59546539 -0.80338096]\n", " [-0.80338096 -0.59546539]]\n" ] } ], "prompt_number": 432 }, { "cell_type": "code", "collapsed": false, "input": [ "# plot data again\n", "pylab.plot(x[0,:], x[1,:], \".\")\n", "\n", "# plot lines describing directions of main variance:\n", "pylab.plot([-3*eigvecs[0,0], 3*eigvecs[0,0]], [-3 * eigvecs[0,1], 3 * eigvecs[0,1]], 'g', linewidth=2)\n", "pylab.plot([-3*eigvecs[1,0], 3*eigvecs[1,0]], [-3 * eigvecs[1,1], 3 * eigvecs[1,1]], 'g', linewidth=2)\n", "\n", "pylab.show()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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MmjULQ4YMQefOnfHggw/yBiZTJ7KzqbGUvz/FkNPSqpeMAyTe6pRBdSm8Tkcv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\nYhogjmgne+A+ij2PfP58pRo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"text": [ "<matplotlib.figure.Figure at 0x35de6d0>" ] } ], "prompt_number": 433 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }

gpl-2.0

bicepjai/Deep-Survey-Text-Classification

deep_models/paper_02_cnn_sent_model/models.ipynb

1

46080

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Setup" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "ExecuteTime": { "end_time": "2017-10-26T00:32:16.487931Z", "start_time": "2017-10-26T00:32:15.306107Z" }, "collapsed": true }, "outputs": [], "source": [ "import sys\n", "import os\n", "\n", "import re\n", "import collections\n", "import itertools\n", "import bcolz\n", "import pickle\n", "sys.path.append('../../lib')\n", "\n", "import numpy as np\n", "import pandas as pd\n", "import gc\n", "import random\n", "import smart_open\n", "import h5py\n", "import csv\n", "import json\n", "import functools\n", "import time\n", "import string\n", "\n", "import datetime as dt\n", "from tqdm import tqdm_notebook as tqdm\n", "\n", "import numpy as np\n", "import pandas as pd\n", "\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "\n", "import global_utils\n", "\n", "random_state_number = 967898" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "ExecuteTime": { "end_time": "2017-10-26T00:32:16.985230Z", "start_time": "2017-10-26T00:32:16.489185Z" } }, "outputs": [ { "data": { "text/plain": [ "['/gpu:0', '/gpu:1']" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import tensorflow as tf\n", "from tensorflow.python.client import device_lib\n", "def get_available_gpus():\n", " local_device_protos = device_lib.list_local_devices()\n", " return [x.name for x in local_device_protos if x.device_type == 'GPU']\n", "\n", "config = tf.ConfigProto()\n", "config.gpu_options.allow_growth=True\n", "sess = tf.Session(config=config)\n", "get_available_gpus()" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "ExecuteTime": { "end_time": "2017-10-26T00:32:17.074281Z", "start_time": "2017-10-26T00:32:16.986890Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Using matplotlib backend: TkAgg\n", "Populating the interactive namespace from numpy and matplotlib\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/bicepjai/Programs/anaconda3/envs/dsotc-c3/lib/python3.6/site-packages/IPython/core/magics/pylab.py:160: UserWarning: pylab import has clobbered these variables: ['random']\n", "`%matplotlib` prevents importing * from pylab and numpy\n", " \"\\n`%matplotlib` prevents importing * from pylab and numpy\"\n" ] } ], "source": [ "%pylab\n", "%matplotlib inline\n", "%load_ext line_profiler\n", "%load_ext memory_profiler\n", "%load_ext autoreload" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "ExecuteTime": { "end_time": "2017-10-26T00:32:17.078729Z", "start_time": "2017-10-26T00:32:17.075713Z" }, "collapsed": true }, "outputs": [], "source": [ "pd.options.mode.chained_assignment = None\n", "pd.options.display.max_columns = 999\n", "color = sns.color_palette()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Data" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2017-10-26T21:22:31.466526Z", "start_time": "2017-10-26T21:22:31.455325Z" }, "collapsed": true }, "outputs": [], "source": [ "store = pd.HDFStore('../../data_prep/processed/stage1/data_frames.h5')\n", "train_df = store['train_df']\n", "test_df = store['test_df']" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "ExecuteTime": { "end_time": "2017-10-26T00:32:35.739219Z", "start_time": "2017-10-26T00:32:35.425475Z" } }, "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>ID</th>\n", " <th>Gene</th>\n", " <th>Variation</th>\n", " <th>Class</th>\n", " <th>Sentences</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>0</td>\n", " <td>[fam58a]</td>\n", " <td>[truncating, mutations]</td>\n", " <td>1</td>\n", " <td>[[cyclin-dependent, kinases, , cdks, , regulat...</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>1</td>\n", " <td>[cbl]</td>\n", " <td>[w802*]</td>\n", " <td>2</td>\n", " <td>[[abstract, background, non-small, cell, lung,...</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>2</td>\n", " <td>[cbl]</td>\n", " <td>[q249e]</td>\n", " <td>2</td>\n", " <td>[[abstract, background, non-small, cell, lung,...</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>3</td>\n", " <td>[cbl]</td>\n", " <td>[n454d]</td>\n", " <td>3</td>\n", " <td>[[recent, evidence, has, demonstrated, that, a...</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>4</td>\n", " <td>[cbl]</td>\n", " <td>[l399v]</td>\n", " <td>4</td>\n", " <td>[[oncogenic, mutations, in, the, monomeric, ca...</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " ID Gene Variation Class \\\n", "0 0 [fam58a] [truncating, mutations] 1 \n", "1 1 [cbl] [w802*] 2 \n", "2 2 [cbl] [q249e] 2 \n", "3 3 [cbl] [n454d] 3 \n", "4 4 [cbl] [l399v] 4 \n", "\n", " Sentences \n", "0 [[cyclin-dependent, kinases, , cdks, , regulat... \n", "1 [[abstract, background, non-small, cell, lung,... \n", "2 [[abstract, background, non-small, cell, lung,... \n", "3 [[recent, evidence, has, demonstrated, that, a... \n", "4 [[oncogenic, mutations, in, the, monomeric, ca... " ] }, "metadata": {}, "output_type": "display_data" }, { "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>ID</th>\n", " <th>Gene</th>\n", " <th>Variation</th>\n", " <th>Sentences</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>0</td>\n", " <td>[acsl4]</td>\n", " <td>[r570s]</td>\n", " <td>[[2, this, mutation, resulted, in, a, myelopro...</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>1</td>\n", " <td>[naglu]</td>\n", " <td>[p521l]</td>\n", " <td>[[abstract, the, large, tumor, suppressor, 1, ...</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>2</td>\n", " <td>[pah]</td>\n", " <td>[l333f]</td>\n", " <td>[[vascular, endothelial, growth, factor, recep...</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>3</td>\n", " <td>[ing1]</td>\n", " <td>[a148d]</td>\n", " <td>[[inflammatory, myofibroblastic, tumor, , imt,...</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>4</td>\n", " <td>[tmem216]</td>\n", " <td>[g77a]</td>\n", " <td>[[abstract, retinoblastoma, is, a, pediatric, ...</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " ID Gene Variation Sentences\n", "0 0 [acsl4] [r570s] [[2, this, mutation, resulted, in, a, myelopro...\n", "1 1 [naglu] [p521l] [[abstract, the, large, tumor, suppressor, 1, ...\n", "2 2 [pah] [l333f] [[vascular, endothelial, growth, factor, recep...\n", "3 3 [ing1] [a148d] [[inflammatory, myofibroblastic, tumor, , imt,...\n", "4 4 [tmem216] [g77a] [[abstract, retinoblastoma, is, a, pediatric, ..." ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "display(train_df.head())\n", "display(test_df.head())" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "ExecuteTime": { "end_time": "2017-10-26T00:32:35.843893Z", "start_time": "2017-10-26T00:32:35.740546Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "352220 352220\n" ] } ], "source": [ "corpus_vocab_list, corpus_vocab_wordidx = None, None\n", "with open('../../data_prep/processed/stage1/vocab_words_wordidx.pkl', 'rb') as f:\n", " (corpus_vocab_list, corpus_wordidx) = pickle.load(f)\n", "print(len(corpus_vocab_list), len(corpus_wordidx))" ] }, { "cell_type": "markdown", "metadata": { "ExecuteTime": { "end_time": "2017-08-14T08:20:17.449244Z", "start_time": "2017-08-14T08:20:15.593136Z" }, "collapsed": true }, "source": [ "# Data Prep" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To control the vocabulary pass in updated corpus_wordidx" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "ExecuteTime": { "end_time": "2017-10-26T00:32:35.881731Z", "start_time": "2017-10-26T00:32:35.845026Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(2988, 5)\n", "(333, 5)\n" ] } ], "source": [ "from sklearn.model_selection import train_test_split\n", "x_train_df, x_val_df = train_test_split(train_df,\n", " test_size=0.10, random_state=random_state_number,\n", " stratify=train_df.Class)\n", "\n", "print(x_train_df.shape)\n", "print(x_val_df.shape)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "ExecuteTime": { "end_time": "2017-10-26T00:32:43.169601Z", "start_time": "2017-10-26T00:32:35.883008Z" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Using TensorFlow backend.\n" ] } ], "source": [ "from tensorflow.contrib.keras.python.keras.utils import np_utils\n", "from keras.preprocessing.sequence import pad_sequences\n", "from keras.utils.np_utils import to_categorical" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "ExecuteTime": { "end_time": "2017-10-26T00:32:43.172882Z", "start_time": "2017-10-26T00:32:43.170730Z" }, "collapsed": true }, "outputs": [], "source": [ "vocab_size=len(corpus_vocab_list)" ] }, { "cell_type": "markdown", "metadata": { "heading_collapsed": true }, "source": [ "## T:sent_words" ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "### generate data" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "ExecuteTime": { "end_time": "2017-10-26T00:14:20.659301Z", "start_time": "2017-10-26T00:14:20.653375Z" }, "collapsed": true, "hidden": true }, "outputs": [], "source": [ "custom_unit_dict = {\n", " \"gene_unit\" : \"words\",\n", " \"variation_unit\" : \"words\",\n", " # text transformed to sentences attribute\n", " \"doc_unit\" : \"words\",\n", " \"doc_form\" : \"sentences\",\n", " \"divide_document\": \"multiple_unit\"\n", " }" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "ExecuteTime": { "end_time": "2017-10-26T00:16:51.148007Z", "start_time": "2017-10-26T00:14:22.265259Z" }, "collapsed": true, "hidden": true }, "outputs": [], "source": [ "%autoreload\n", "import global_utils\n", "gen_data = global_utils.GenerateDataset(x_train_df, corpus_wordidx)\n", "x_train_21_T, x_train_21_G, x_train_21_V, x_train_21_C = gen_data.generate_data(custom_unit_dict, \n", " has_class=True,\n", " add_start_end_tag=True)\n", "del gen_data" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "ExecuteTime": { "end_time": "2017-10-26T00:16:55.113872Z", "start_time": "2017-10-26T00:16:51.149180Z" }, "hidden": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Train data\n", "(2622081,) [364606, 113692, 197002, 330024, 326252, 151042, 75648, 1818, 276247, 61043, 228115, 326252, 74974, 301275, 76659, 326252, 361104, 329709, 253643, 205596, 153283, 326252, 80594, 326252, 113692, 18820, 349251, 59442, 123801, 228752, 245229, 307200, 17105, 60555, 69032, 1818, 274163, 151942, 246684, 222367, 253643, 243777, 274163, 50915, 274163, 12413, 1818, 228752, 364603, 232434, 214275, 235155, 163151, 123801, 101614, 101366, 364607]\n", "(2622081, 3) [364606, 97957, 364607]\n", "(2622081,) [364606, 326252, 364607]\n", "(2622081,) 6\n" ] } ], "source": [ "print(\"Train data\")\n", "print(np.array(x_train_21_T).shape, x_train_21_T[0])\n", "print(np.array(x_train_21_G).shape, x_train_21_G[0])\n", "print(np.array(x_train_21_V).shape, x_train_21_V[0])\n", "print(np.array(x_train_21_C).shape, x_train_21_C[0])" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "ExecuteTime": { "end_time": "2017-10-26T00:17:10.385302Z", "start_time": "2017-10-26T00:16:55.114982Z" }, "collapsed": true, "hidden": true }, "outputs": [], "source": [ "gen_data = global_utils.GenerateDataset(x_val_df, corpus_wordidx)\n", "x_val_21_T, x_val_21_G, x_val_21_V, x_val_21_C = gen_data.generate_data(custom_unit_dict, \n", " has_class=True,\n", " add_start_end_tag=True)\n", "del gen_data" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "ExecuteTime": { "end_time": "2017-10-26T00:17:10.853790Z", "start_time": "2017-10-26T00:17:10.386619Z" }, "hidden": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Val data\n", "text (293702,)\n", "gene (293702, 3) [364606, 112978, 364607]\n", "variation (293702,) [364606, 295010, 364607]\n", "classes (293702,) 2\n" ] } ], "source": [ "print(\"Val data\")\n", "print(\"text\",np.array(x_val_21_T).shape)\n", "print(\"gene\",np.array(x_val_21_G).shape, x_val_21_G[0])\n", "print(\"variation\",np.array(x_val_21_V).shape, x_val_21_V[0])\n", "print(\"classes\",np.array(x_val_21_C).shape, x_val_21_C[0])" ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "### format data" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "ExecuteTime": { "end_time": "2017-10-26T00:17:10.857143Z", "start_time": "2017-10-26T00:17:10.854958Z" }, "collapsed": true, "hidden": true }, "outputs": [], "source": [ "word_unknown_tag_idx = corpus_wordidx[\"<UNK>\"]\n", "char_unknown_tag_idx = global_utils.char_unknown_tag_idx" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "ExecuteTime": { "end_time": "2017-10-26T00:17:10.872469Z", "start_time": "2017-10-26T00:17:10.858162Z" }, "collapsed": true, "hidden": true }, "outputs": [], "source": [ "MAX_SENT_LEN = 60" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "ExecuteTime": { "end_time": "2017-10-26T00:17:27.327704Z", "start_time": "2017-10-26T00:17:10.873609Z" }, "hidden": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(2622081, 60) (293702, 60)\n" ] } ], "source": [ "x_train_21_T = pad_sequences(x_train_21_T, maxlen=MAX_SENT_LEN, value=word_unknown_tag_idx,\n", " padding=\"post\",truncating=\"post\")\n", "x_val_21_T = pad_sequences(x_val_21_T, maxlen=MAX_SENT_LEN, value=word_unknown_tag_idx,\n", " padding=\"post\",truncating=\"post\")\n", "print(x_train_21_T.shape, x_val_21_T.shape)" ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "keras np_utils.to_categorical expects zero index categorical variables\n", "\n", "https://github.com/fchollet/keras/issues/570" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "ExecuteTime": { "end_time": "2017-10-26T00:17:27.564842Z", "start_time": "2017-10-26T00:17:27.328915Z" }, "collapsed": true, "hidden": true }, "outputs": [], "source": [ "x_train_21_C = np.array(x_train_21_C) - 1\n", "x_val_21_C = np.array(x_val_21_C) - 1" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "ExecuteTime": { "end_time": "2017-10-26T00:17:27.631996Z", "start_time": "2017-10-26T00:17:27.566066Z" }, "hidden": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(2622081, 9) (293702, 9)\n" ] } ], "source": [ "x_train_21_C = np_utils.to_categorical(np.array(x_train_21_C), 9)\n", "x_val_21_C = np_utils.to_categorical(np.array(x_val_21_C), 9)\n", "print(x_train_21_C.shape, x_val_21_C.shape)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## T:text_words" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### generate data" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "ExecuteTime": { "end_time": "2017-10-26T00:32:43.191953Z", "start_time": "2017-10-26T00:32:43.173899Z" }, "collapsed": true }, "outputs": [], "source": [ "custom_unit_dict = {\n", " \"gene_unit\" : \"words\",\n", " \"variation_unit\" : \"words\",\n", " # text transformed to sentences attribute\n", " \"doc_unit\" : \"words\",\n", " \"doc_form\" : \"text\",\n", " \"divide_document\": \"single_unit\"\n", " }" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "ExecuteTime": { "end_time": "2017-10-26T00:32:53.910517Z", "start_time": "2017-10-26T00:32:43.193444Z" }, "collapsed": true }, "outputs": [], "source": [ "%autoreload\n", "import global_utils\n", "gen_data = global_utils.GenerateDataset(x_train_df, corpus_wordidx)\n", "x_train_22_T, x_train_22_G, x_train_22_V, x_train_22_C = gen_data.generate_data(custom_unit_dict, \n", " has_class=True,\n", " add_start_end_tag=True)\n", "del gen_data" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "ExecuteTime": { "end_time": "2017-10-26T00:32:54.978928Z", "start_time": "2017-10-26T00:32:53.911691Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Train data\n", "text (2988,)\n", "gene (2988, 3) [352216, 164788, 352217]\n", "variation (2988,) [352216, 86196, 352217]\n", "classes (2988,) 4\n" ] } ], "source": [ "print(\"Train data\")\n", "print(\"text\",np.array(x_train_22_T).shape)\n", "print(\"gene\",np.array(x_train_22_G).shape, x_train_22_G[0])\n", "print(\"variation\",np.array(x_train_22_V).shape, x_train_22_V[0])\n", "print(\"classes\",np.array(x_train_22_C).shape, x_train_22_C[0])" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "ExecuteTime": { "end_time": "2017-10-26T00:32:56.258929Z", "start_time": "2017-10-26T00:32:54.980078Z" }, "collapsed": true }, "outputs": [], "source": [ "gen_data = global_utils.GenerateDataset(x_val_df, corpus_wordidx)\n", "x_val_22_T, x_val_22_G, x_val_22_V, x_val_22_C = gen_data.generate_data(custom_unit_dict, \n", " has_class=True,\n", " add_start_end_tag=True)\n", "del gen_data" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "ExecuteTime": { "end_time": "2017-10-26T00:32:56.411716Z", "start_time": "2017-10-26T00:32:56.260210Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Val data\n", "text (333,)\n", "gene (333, 3) [352216, 217983, 352217]\n", "variation (333,) [352216, 41934, 352217]\n", "classes (333,) 4\n" ] } ], "source": [ "print(\"Val data\")\n", "print(\"text\",np.array(x_val_22_T).shape)\n", "print(\"gene\",np.array(x_val_22_G).shape, x_val_22_G[0])\n", "print(\"variation\",np.array(x_val_22_V).shape, x_val_22_V[0])\n", "print(\"classes\",np.array(x_val_22_C).shape, x_val_22_C[0])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### format data" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "ExecuteTime": { "end_time": "2017-10-26T00:32:56.437499Z", "start_time": "2017-10-26T00:32:56.413156Z" }, "collapsed": true }, "outputs": [], "source": [ "word_unknown_tag_idx = corpus_wordidx[\"<UNK>\"]\n", "char_unknown_tag_idx = global_utils.char_unknown_tag_idx" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "ExecuteTime": { "end_time": "2017-10-26T00:32:56.450804Z", "start_time": "2017-10-26T00:32:56.439121Z" }, "collapsed": true }, "outputs": [], "source": [ "MAX_TEXT_LEN = 5000" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "ExecuteTime": { "end_time": "2017-10-26T00:32:57.501666Z", "start_time": "2017-10-26T00:32:56.452013Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(2988, 5000) (333, 5000)\n" ] } ], "source": [ "x_train_22_T = pad_sequences(x_train_22_T, maxlen=MAX_TEXT_LEN, value=word_unknown_tag_idx,\n", " padding=\"post\",truncating=\"post\")\n", "x_val_22_T = pad_sequences(x_val_22_T, maxlen=MAX_TEXT_LEN, value=word_unknown_tag_idx,\n", " padding=\"post\",truncating=\"post\")\n", "print(x_train_22_T.shape, x_val_22_T.shape)" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "ExecuteTime": { "end_time": "2017-10-26T00:32:57.536590Z", "start_time": "2017-10-26T00:32:57.502761Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(2988, 1) (2988, 4)\n", "(333, 1) (333, 4)\n" ] } ], "source": [ "MAX_GENE_LEN = 1\n", "MAX_VAR_LEN = 4\n", "x_train_22_G = pad_sequences(x_train_22_G, maxlen=MAX_GENE_LEN, value=word_unknown_tag_idx)\n", "x_train_22_V = pad_sequences(x_train_22_V, maxlen=MAX_VAR_LEN, value=word_unknown_tag_idx)\n", "\n", "x_val_22_G = pad_sequences(x_val_22_G, maxlen=MAX_GENE_LEN, value=word_unknown_tag_idx)\n", "x_val_22_V = pad_sequences(x_val_22_V, maxlen=MAX_VAR_LEN, value=word_unknown_tag_idx)\n", "\n", "print(x_train_22_G.shape, x_train_22_V.shape)\n", "print(x_val_22_G.shape, x_val_22_V.shape)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "keras np_utils.to_categorical expects zero index categorical variables\n", "\n", "https://github.com/fchollet/keras/issues/570" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "ExecuteTime": { "end_time": "2017-10-26T00:32:57.542037Z", "start_time": "2017-10-26T00:32:57.538132Z" }, "collapsed": true }, "outputs": [], "source": [ "x_train_22_C = np.array(x_train_22_C) - 1\n", "x_val_22_C = np.array(x_val_22_C) - 1" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "ExecuteTime": { "end_time": "2017-10-26T00:32:57.576726Z", "start_time": "2017-10-26T00:32:57.543764Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(2988, 9) (333, 9)\n" ] } ], "source": [ "x_train_22_C = np_utils.to_categorical(np.array(x_train_22_C), 9)\n", "x_val_22_C = np_utils.to_categorical(np.array(x_val_22_C), 9)\n", "print(x_train_22_C.shape, x_val_22_C.shape)" ] }, { "cell_type": "markdown", "metadata": { "heading_collapsed": true }, "source": [ "### test Data setup" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "ExecuteTime": { "end_time": "2017-09-26T03:43:32.887420Z", "start_time": "2017-09-26T03:43:29.372697Z" }, "collapsed": true, "hidden": true }, "outputs": [], "source": [ "gen_data = global_utils.GenerateDataset(test_df, corpus_wordidx)\n", "x_test_22_T, x_test_22_G, x_test_22_V, _ = gen_data.generate_data(custom_unit_dict, \n", " has_class=False,\n", " add_start_end_tag=True)\n", "del gen_data" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "ExecuteTime": { "end_time": "2017-09-26T03:43:33.178763Z", "start_time": "2017-09-26T03:43:32.888877Z" }, "hidden": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Test data\n", "text (986,)\n", "gene (986, 3) [364606, 188717, 364607]\n", "variation (986,) [364606, 317947, 364607]\n" ] } ], "source": [ "print(\"Test data\")\n", "print(\"text\",np.array(x_test_22_T).shape)\n", "print(\"gene\",np.array(x_test_22_G).shape, x_test_22_G[0])\n", "print(\"variation\",np.array(x_test_22_V).shape, x_test_22_V[0])" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "ExecuteTime": { "end_time": "2017-09-26T03:43:33.546461Z", "start_time": "2017-09-26T03:43:33.181689Z" }, "hidden": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(986, 5000)\n" ] } ], "source": [ "x_test_22_T = pad_sequences(x_test_22_T, maxlen=MAX_TEXT_LEN, value=word_unknown_tag_idx,\n", " padding=\"post\",truncating=\"post\")\n", "print(x_test_22_T.shape)" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "ExecuteTime": { "end_time": "2017-09-26T03:43:33.575305Z", "start_time": "2017-09-26T03:43:33.548386Z" }, "hidden": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(986, 1) (986, 4)\n" ] } ], "source": [ "MAX_GENE_LEN = 1\n", "MAX_VAR_LEN = 4\n", "x_test_22_G = pad_sequences(x_test_22_G, maxlen=MAX_GENE_LEN, value=word_unknown_tag_idx)\n", "x_test_22_V = pad_sequences(x_test_22_V, maxlen=MAX_VAR_LEN, value=word_unknown_tag_idx)\n", "\n", "print(x_test_22_G.shape, x_test_22_V.shape)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Embedding layer" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### for words" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "ExecuteTime": { "end_time": "2017-10-26T00:32:57.595494Z", "start_time": "2017-10-26T00:32:57.579320Z" }, "collapsed": true }, "outputs": [], "source": [ "WORD_EMB_SIZE = 200" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "ExecuteTime": { "end_time": "2017-10-26T00:33:20.867764Z", "start_time": "2017-10-26T00:32:57.597200Z" } }, "outputs": [ { "data": { "text/plain": [ "(352220, 200)" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "%autoreload\n", "import global_utils\n", "ft_file_path = \"/home/bicepjai/Projects/Deep-Survey-Text-Classification/data_prep/processed/stage1/pretrained_word_vectors/ft_sg_200d_50e.vec\"\n", "trained_embeddings = global_utils.get_embeddings_from_ft(ft_file_path, WORD_EMB_SIZE, corpus_vocab_list)\n", "trained_embeddings.shape" ] }, { "cell_type": "markdown", "metadata": { "heading_collapsed": true }, "source": [ "### for characters" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "ExecuteTime": { "end_time": "2017-09-25T06:04:28.473180Z", "start_time": "2017-09-25T06:04:28.470711Z" }, "collapsed": true, "hidden": true }, "outputs": [], "source": [ "CHAR_EMB_SIZE = 100" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "ExecuteTime": { "end_time": "2017-09-25T06:04:28.479331Z", "start_time": "2017-09-25T06:04:28.474704Z" }, "hidden": true }, "outputs": [ { "data": { "text/plain": [ "(75, 100)" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "char_embeddings = np.random.randn(global_utils.CHAR_ALPHABETS_LEN, CHAR_EMB_SIZE)\n", "char_embeddings.shape" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Models" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## prep" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "ExecuteTime": { "end_time": "2017-10-26T00:33:20.908977Z", "start_time": "2017-10-26T00:33:20.869343Z" }, "collapsed": true }, "outputs": [], "source": [ "import tensorflow.contrib.keras as keras\n", "import tensorflow as tf\n", "\n", "from keras import backend as K\n", "\n", "from keras.engine import Layer, InputSpec, InputLayer\n", "\n", "from keras.models import Model, Sequential\n", "\n", "from keras.layers import Dropout, Embedding, concatenate\n", "from keras.layers import Conv1D, MaxPool1D, Conv2D, MaxPool2D, ZeroPadding1D\n", "from keras.layers import Dense, Input, Flatten, BatchNormalization\n", "from keras.layers import Concatenate, Dot, Merge, Multiply, RepeatVector\n", "from keras.layers import Bidirectional, TimeDistributed\n", "from keras.layers import SimpleRNN, LSTM, GRU, Lambda, Permute\n", "\n", "from keras.layers.core import Reshape, Activation\n", "from keras.optimizers import Adam\n", "from keras.callbacks import ModelCheckpoint,EarlyStopping,TensorBoard\n", "from keras.constraints import maxnorm\n", "from keras.regularizers import l2\n", "\n", "%autoreload" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### KMaxPooling layer " ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "ExecuteTime": { "end_time": "2017-10-26T00:33:20.918486Z", "start_time": "2017-10-26T00:33:20.910344Z" }, "collapsed": true }, "outputs": [], "source": [ "from utils import KMaxPooling" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Folding Layer" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "ExecuteTime": { "end_time": "2017-10-26T00:33:20.938089Z", "start_time": "2017-10-26T00:33:20.920166Z" }, "collapsed": true }, "outputs": [], "source": [ "from utils import Folding" ] }, { "cell_type": "markdown", "metadata": { "ExecuteTime": { "end_time": "2017-08-24T06:58:17.661183Z", "start_time": "2017-08-24T06:58:17.655020Z" } }, "source": [ "## model_1: paper" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " CNN with Dynamic k-Max Pooling with sentences" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "ExecuteTime": { "end_time": "2017-10-26T00:23:23.190159Z", "start_time": "2017-10-26T00:23:21.997528Z" }, "collapsed": true }, "outputs": [], "source": [ "model_1 = Sequential([\n", " Embedding(vocab_size, WORD_EMB_SIZE, weights=[trained_embeddings],\n", " input_length=MAX_TEXT_LEN,trainable=True),\n", " ZeroPadding1D((49,49)),\n", " Conv1D(64, 50, padding=\"same\"),\n", " KMaxPooling(k=5, axis=1),\n", " Activation(\"relu\"),\n", " ZeroPadding1D((24,24)),\n", " Conv1D(64, 25, padding=\"same\"),\n", " Folding(),\n", " KMaxPooling(k=5, axis=1),\n", " Activation(\"relu\"),\n", " Flatten(),\n", " Dense(9, activation=\"softmax\")\n", "])" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "ExecuteTime": { "end_time": "2017-10-26T00:23:23.219070Z", "start_time": "2017-10-26T00:23:23.191474Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "_________________________________________________________________\n", "Layer (type) Output Shape Param # \n", "=================================================================\n", "embedding_1 (Embedding) (None, 5000, 200) 70444000 \n", "_________________________________________________________________\n", "zero_padding1d_1 (ZeroPaddin (None, 5098, 200) 0 \n", "_________________________________________________________________\n", "conv1d_1 (Conv1D) (None, 5098, 64) 640064 \n", "_________________________________________________________________\n", "k_max_pooling_1 (KMaxPooling (None, 5, 64) 0 \n", "_________________________________________________________________\n", "activation_1 (Activation) (None, 5, 64) 0 \n", "_________________________________________________________________\n", "zero_padding1d_2 (ZeroPaddin (None, 53, 64) 0 \n", "_________________________________________________________________\n", "conv1d_2 (Conv1D) (None, 53, 64) 102464 \n", "_________________________________________________________________\n", "folding_1 (Folding) (None, 53, 32) 0 \n", "_________________________________________________________________\n", "k_max_pooling_2 (KMaxPooling (None, 5, 32) 0 \n", "_________________________________________________________________\n", "activation_2 (Activation) (None, 5, 32) 0 \n", "_________________________________________________________________\n", "flatten_1 (Flatten) (None, 160) 0 \n", "_________________________________________________________________\n", "dense_1 (Dense) (None, 9) 1449 \n", "=================================================================\n", "Total params: 71,187,977\n", "Trainable params: 71,187,977\n", "Non-trainable params: 0\n", "_________________________________________________________________\n" ] } ], "source": [ "model_1.compile(loss='categorical_crossentropy', optimizer=Adam(), metrics=['categorical_accuracy'])\n", "model_1.summary()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### training" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "ExecuteTime": { "end_time": "2017-10-26T00:24:20.079839Z", "start_time": "2017-10-26T00:24:20.074483Z" }, "collapsed": true }, "outputs": [], "source": [ "tb_callback = keras.callbacks.TensorBoard(log_dir='./tb_graphs', histogram_freq=0, write_graph=True, write_images=True)" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "ExecuteTime": { "end_time": "2017-10-26T00:25:18.165488Z", "start_time": "2017-10-26T00:25:18.159937Z" }, "collapsed": true }, "outputs": [], "source": [ "checkpointer = ModelCheckpoint(filepath=\"model_1_weights.hdf5\", \n", " verbose=1,\n", " monitor=\"val_categorical_accuracy\",\n", " save_best_only=True,\n", " mode=\"max\")" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "ExecuteTime": { "end_time": "2017-10-26T00:25:18.556945Z", "start_time": "2017-10-26T00:25:18.553156Z" }, "collapsed": true }, "outputs": [], "source": [ "earlystopping = EarlyStopping(monitor='val_categorical_accuracy', \n", " min_delta=0, patience=5, \n", " verbose=1, mode='auto')" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "ExecuteTime": { "end_time": "2017-10-26T00:27:04.039750Z", "start_time": "2017-10-26T00:25:19.225244Z" }, "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "no checkpoints available !\n", "Train on 2988 samples, validate on 333 samples\n", "Epoch 1/10\n", "2944/2988 [============================>.] - ETA: 0s - loss: 1.8754 - categorical_accuracy: 0.2972Epoch 00000: val_categorical_accuracy improved from -inf to 0.37538, saving model to model_1_weights.hdf5\n", "2988/2988 [==============================] - 12s - loss: 1.8752 - categorical_accuracy: 0.2975 - val_loss: 1.7255 - val_categorical_accuracy: 0.3754\n", "Epoch 2/10\n", "2944/2988 [============================>.] - ETA: 0s - loss: 1.5359 - categorical_accuracy: 0.4708Epoch 00001: val_categorical_accuracy improved from 0.37538 to 0.48949, saving model to model_1_weights.hdf5\n", "2988/2988 [==============================] - 10s - loss: 1.5339 - categorical_accuracy: 0.4726 - val_loss: 1.4433 - val_categorical_accuracy: 0.4895\n", "Epoch 3/10\n", "2944/2988 [============================>.] - ETA: 0s - loss: 1.1139 - categorical_accuracy: 0.6539Epoch 00002: val_categorical_accuracy improved from 0.48949 to 0.62162, saving model to model_1_weights.hdf5\n", "2988/2988 [==============================] - 10s - loss: 1.1129 - categorical_accuracy: 0.6543 - val_loss: 1.2026 - val_categorical_accuracy: 0.6216\n", "Epoch 4/10\n", "2944/2988 [============================>.] - ETA: 0s - loss: 0.8395 - categorical_accuracy: 0.7323Epoch 00003: val_categorical_accuracy improved from 0.62162 to 0.63664, saving model to model_1_weights.hdf5\n", "2988/2988 [==============================] - 10s - loss: 0.8383 - categorical_accuracy: 0.7329 - val_loss: 1.1661 - val_categorical_accuracy: 0.6366\n", "Epoch 5/10\n", "2944/2988 [============================>.] - ETA: 0s - loss: 0.7113 - categorical_accuracy: 0.7660Epoch 00004: val_categorical_accuracy did not improve\n", "2988/2988 [==============================] - 8s - loss: 0.7139 - categorical_accuracy: 0.7644 - val_loss: 1.1597 - val_categorical_accuracy: 0.6186\n", "Epoch 6/10\n", "2944/2988 [============================>.] - ETA: 0s - loss: 0.6362 - categorical_accuracy: 0.7758Epoch 00005: val_categorical_accuracy improved from 0.63664 to 0.64264, saving model to model_1_weights.hdf5\n", "2988/2988 [==============================] - 10s - loss: 0.6350 - categorical_accuracy: 0.7751 - val_loss: 1.2112 - val_categorical_accuracy: 0.6426\n", "Epoch 7/10\n", "2944/2988 [============================>.] - ETA: 0s - loss: 0.5894 - categorical_accuracy: 0.7836Epoch 00006: val_categorical_accuracy improved from 0.64264 to 0.66366, saving model to model_1_weights.hdf5\n", "2988/2988 [==============================] - 10s - loss: 0.5873 - categorical_accuracy: 0.7845 - val_loss: 1.1550 - val_categorical_accuracy: 0.6637\n", "Epoch 8/10\n", "2944/2988 [============================>.] - ETA: 0s - loss: 0.5814 - categorical_accuracy: 0.7802Epoch 00007: val_categorical_accuracy did not improve\n", "2988/2988 [==============================] - 8s - loss: 0.5830 - categorical_accuracy: 0.7805 - val_loss: 1.2309 - val_categorical_accuracy: 0.6186\n", "Epoch 9/10\n", "2944/2988 [============================>.] - ETA: 0s - loss: 0.5473 - categorical_accuracy: 0.7891Epoch 00008: val_categorical_accuracy did not improve\n", "2988/2988 [==============================] - 8s - loss: 0.5453 - categorical_accuracy: 0.7895 - val_loss: 1.1817 - val_categorical_accuracy: 0.6486\n", "Epoch 10/10\n", "2944/2988 [============================>.] - ETA: 0s - loss: 0.5308 - categorical_accuracy: 0.7819Epoch 00009: val_categorical_accuracy did not improve\n", "2988/2988 [==============================] - 9s - loss: 0.5303 - categorical_accuracy: 0.7818 - val_loss: 1.1884 - val_categorical_accuracy: 0.6186\n" ] } ], "source": [ "with tf.Session() as sess:\n", " sess.run(tf.global_variables_initializer())\n", " try:\n", " model_1.load_weights(\"model_1_weights.hdf5\")\n", " except IOError as ioe:\n", " print(\"no checkpoints available !\")\n", " \n", " model_1.fit(x_train_22_T, x_train_22_C, \n", " validation_data=(x_val_22_T, x_val_22_C),\n", " epochs=10, batch_size=128,shuffle=True,\n", " callbacks=[tb_callback,checkpointer])" ] }, { "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": "3.6.3" }, "toc": { "nav_menu": {}, "number_sections": true, "sideBar": true, "skip_h1_title": false, "toc_cell": false, "toc_position": { "height": "913px", "left": "0px", "right": "1192px", "top": "52px", "width": "300px" }, "toc_section_display": "block", "toc_window_display": true } }, "nbformat": 4, "nbformat_minor": 2 }

mit

rsignell-usgs/notebook

gebco_plot.ipynb

1

37563

{ "metadata": { "name": "", "signature": "sha256:0cd6b3dbd31432a3681248c60d8f0389d4526dc5a041ecdc618e8ebc4c882d7f" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "import netCDF4\n", "from mpl_toolkits.basemap import Basemap\n", "\n", "# Load data\n", "dataset = netCDF4.Dataset('/usgs/data1/rsignell/bathy/gebco_08_-30_-45_5_65.nc')\n", "\n", "# Extract variables\n", "x = dataset.variables['x_range']\n", "y = dataset.variables['y_range']\n", "spacing = dataset.variables['spacing']\n", "\n", "# Compute Lat/Lon\n", "nx = (x[-1]-x[0])/spacing[0] # num pts in x-dir\n", "ny = (y[-1]-y[0])/spacing[1] # num pts in y-dir\n", "\n", "lon = np.linspace(x[0],x[-1],nx)\n", "lat = np.linspace(y[0],y[-1],ny)\n", "\n", "# Reshape data\n", "zz = dataset.variables['z']\n", "Z = zz[:].reshape(ny, nx)\n", "\n", "# setup basemap.\n", "m = Basemap(llcrnrlon=-30,llcrnrlat=45.0,urcrnrlon=5.0,urcrnrlat=65.0,\n", " resolution='i',projection='stere',lon_0=-15.0,lat_0=55.0)\n", "\n", "x,y = m(*np.meshgrid(lon,lat))\n", "\n", "m.contourf(x, y, flipud(Z));\n", "m.fillcontinents(color='grey');\n", "m.drawparallels(np.arange(10,70,10), labels=[1,0,0,0]);\n", "m.drawmeridians(np.arange(-80, 5, 10), labels=[0,0,0,1]);" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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kPrwcMTkBd3cPglZD+3d/jWR3UJM9F7e5h+bbHkKy2en680tITie2bXuR3G6k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\nkegnTqnEV/Y00ios4H+yPkEtONnZMqPfeQJupqb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mit

csaladenes/blog

iss/parser.ipynb

2

17444

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "ExecuteTime": { "end_time": "2019-04-17T22:02:40.317480Z", "start_time": "2019-04-17T22:02:33.993819Z" } }, "outputs": [], "source": [ "import pandas as pd, numpy as np" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "ExecuteTime": { "end_time": "2019-04-17T22:02:40.335466Z", "start_time": "2019-04-17T22:02:40.330479Z" } }, "outputs": [], "source": [ "url='https://en.wikipedia.org/wiki/List_of_International_Space_Station_expeditions'" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "ExecuteTime": { "end_time": "2019-04-17T22:15:51.226318Z", "start_time": "2019-04-17T22:15:49.887841Z" } }, "outputs": [], "source": [ "import bs4\n", "import requests\n", "r=requests.get(url)\n", "soup = bs4.BeautifulSoup(r.content)\n", "tables=soup.findAll(\"table\")" ] }, { "cell_type": "code", "execution_count": 65, "metadata": { "ExecuteTime": { "end_time": "2019-04-17T22:27:26.024813Z", "start_time": "2019-04-17T22:27:25.991820Z" } }, "outputs": [], "source": [ "ppls={}\n", "for trs in [tables[0].findAll(\"tr\"),tables[1].findAll(\"tr\")]:\n", " for i,tr in enumerate(trs):\n", " if i>0:\n", " tds=tr.findAll(\"td\")\n", " for j,td in enumerate(tds):\n", " aas=td.findAll(\"a\")\n", " for a in aas:\n", " if a:\n", " txt=a.text\n", " if txt:\n", " if '[' not in txt:\n", " if txt not in ppls: \n", " #if j==0:\n", " ppls[txt]=a['href']" ] }, { "cell_type": "code", "execution_count": 66, "metadata": { "ExecuteTime": { "end_time": "2019-04-17T22:27:26.936512Z", "start_time": "2019-04-17T22:27:26.523696Z" } }, "outputs": [], "source": [ "df=pd.read_html(url)\n", "df=pd.concat(df[:2]).reset_index()" ] }, { "cell_type": "code", "execution_count": 67, "metadata": { "ExecuteTime": { "end_time": "2019-04-17T22:27:27.339310Z", "start_time": "2019-04-17T22:27:27.333310Z" } }, "outputs": [], "source": [ "def find_names(s,ppls,z):\n", " nms=s.split(' ')\n", " l=2\n", " while l<4:\n", " ppl=' '.join(nms[:l])\n", " if ppl in ppls:\n", " z.append(ppl)\n", " rest=' '.join(nms[l:])\n", " find_names(rest,ppls,z)\n", " l=4\n", " l+=1\n", " return z" ] }, { "cell_type": "code", "execution_count": 68, "metadata": { "ExecuteTime": { "end_time": "2019-04-17T22:27:28.976965Z", "start_time": "2019-04-17T22:27:27.870445Z" } }, "outputs": [], "source": [ "dgs=[]\n", "for i in df.index:\n", " crew=df.loc[i]['Crew'].replace('\\n','')\n", " crews=find_names(crew,ppls,[])\n", " for c in crews:\n", " dg=df.loc[[i]][['Expedition','Duration(days)']].copy()\n", " dg['Crew']=c\n", " date=df.loc[i]['Launch date']\n", " date=date.split(' ')\n", " if ',' in date[1]:\n", " date=date[1].replace(',','')+' '+date[0]+' '+date[2][:4]\n", " else:\n", " date=date[0]+' '+date[1]+' '+date[2][:4]\n", " dg['Date']=date\n", " dgs.append(dg)" ] }, { "cell_type": "code", "execution_count": 69, "metadata": { "ExecuteTime": { "end_time": "2019-04-17T22:27:29.180958Z", "start_time": "2019-04-17T22:27:28.979965Z" } }, "outputs": [], "source": [ "dgs=pd.concat(dgs).reset_index()\n", "dhs=dgs.set_index(['Expedition','Crew'])" ] }, { "cell_type": "code", "execution_count": 70, "metadata": { "ExecuteTime": { "end_time": "2019-04-17T22:27:29.190966Z", "start_time": "2019-04-17T22:27:29.182961Z" } }, "outputs": [], "source": [ "def get_duration(duration):\n", " default='160'\n", " duration=str(duration)\n", " if duration=='nan':\n", " return default\n", " if 'ransfer' in duration:\n", " duration=' '.join(duration.split(' ')[-2:])\n", " try:\n", " duration=dhs.loc[duration].loc[crew]['Duration(days)']\n", " except:\n", " print(crew,duration)\n", " return default\n", " return duration" ] }, { "cell_type": "code", "execution_count": 71, "metadata": { "ExecuteTime": { "end_time": "2019-04-17T22:27:30.305468Z", "start_time": "2019-04-17T22:27:29.944464Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Mikhail Korniyenko year mission\n", "Scott J. Kelly year mission\n", "Timothy Peake Expedition 47\n", "Aleksey Ovchinin Expedition 60\n", "Christina Koch Expedition 60\n", "Nick Hague Expedition 60\n" ] } ], "source": [ "data=[]\n", "for i in dgs.index:\n", " crew=dgs.loc[i]['Crew']\n", " date=dgs.loc[i]['Date']\n", " if 'ransfer' not in date:\n", " duration=dgs.loc[i]['Duration(days)']\n", " duration=get_duration(duration)\n", " duration=get_duration(duration)\n", " duration=get_duration(duration)\n", " if '[' in duration:\n", " duration=duration[:duration.find('[')]\n", " duration=int(np.round(float(duration.replace('days','').replace('day','').strip()),0))\n", " data.append({'Crew':crew,'Date':date,'Duration':duration}) " ] }, { "cell_type": "code", "execution_count": 72, "metadata": { "ExecuteTime": { "end_time": "2019-04-17T22:27:30.971307Z", "start_time": "2019-04-17T22:27:30.965301Z" } }, "outputs": [], "source": [ "data=pd.DataFrame(data)" ] }, { "cell_type": "code", "execution_count": 73, "metadata": { "ExecuteTime": { "end_time": "2019-04-17T22:27:31.471385Z", "start_time": "2019-04-17T22:27:31.459377Z" } }, "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>Crew</th>\n", " <th>Date</th>\n", " <th>Duration</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>William M. Shepherd</td>\n", " <td>31 October 2000</td>\n", " <td>141</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>Sergei Krikalev</td>\n", " <td>31 October 2000</td>\n", " <td>141</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>Yuri Gidzenko</td>\n", " <td>31 October 2000</td>\n", " <td>141</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>Yuri Usachev</td>\n", " <td>8 March 2001</td>\n", " <td>167</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>James S. Voss</td>\n", " <td>8 March 2001</td>\n", " <td>167</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Crew Date Duration\n", "0 William M. Shepherd 31 October 2000 141\n", "1 Sergei Krikalev 31 October 2000 141\n", "2 Yuri Gidzenko 31 October 2000 141\n", "3 Yuri Usachev 8 March 2001 167\n", "4 James S. Voss 8 March 2001 167" ] }, "execution_count": 73, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.head()" ] }, { "cell_type": "code", "execution_count": 74, "metadata": { "ExecuteTime": { "end_time": "2019-04-17T22:27:32.016391Z", "start_time": "2019-04-17T22:27:31.952370Z" } }, "outputs": [], "source": [ "links={}\n", "country_map={}\n", "for trs in [tables[0].findAll(\"tr\"),tables[1].findAll(\"tr\")]:\n", " for i,tr in enumerate(trs):\n", " if i>0:\n", " aas=tr.findAll(\"a\")\n", " for j,a in enumerate(aas):\n", " if a:\n", " txt=a.text\n", " if txt:\n", " if '[' not in txt:\n", " links[txt]=a['href']\n", " if txt in data['Crew'].values:\n", " if txt not in country_map:\n", " country=aas[j-1].find('img')['alt']\n", " country_map[txt]=country\n", " else:\n", " if j>1:\n", " country=a.find('img')['alt']\n", " links[country]=a.find('img')['src']" ] }, { "cell_type": "code", "execution_count": 75, "metadata": { "ExecuteTime": { "end_time": "2019-04-17T22:27:32.540364Z", "start_time": "2019-04-17T22:27:32.478390Z" } }, "outputs": [], "source": [ "countries=pd.DataFrame(country_map,index=['Country']).T\n", "links=pd.DataFrame(links,index=['Link']).T" ] }, { "cell_type": "code", "execution_count": 76, "metadata": { "ExecuteTime": { "end_time": "2019-04-17T22:27:32.926778Z", "start_time": "2019-04-17T22:27:32.909778Z" } }, "outputs": [], "source": [ "data=data.join(countries,on='Crew')\n", "data['Crew_link']=data.join(links,on='Crew')['Link']\n", "data['Country_link']=data.join(links,on='Country')['Link']" ] }, { "cell_type": "code", "execution_count": 77, "metadata": { "ExecuteTime": { "end_time": "2019-04-17T22:27:33.235709Z", "start_time": "2019-04-17T22:27:33.215706Z" } }, "outputs": [], "source": [ "data.to_csv('data.csv')" ] }, { "cell_type": "code", "execution_count": 78, "metadata": { "ExecuteTime": { "end_time": "2019-04-17T22:27:34.355260Z", "start_time": "2019-04-17T22:27:33.544257Z" } }, "outputs": [], "source": [ "ndata={}\n", "for i in data.index:\n", " start=pd.to_datetime(data.loc[i]['Date'])\n", " periods=data.loc[i]['Duration']\n", " crew=data.loc[i]['Crew']\n", " country=data.loc[i]['Country']\n", " for idate in pd.date_range(start,periods=periods,freq='1D'):\n", " date=str(idate)[:10]\n", " if date not in ndata: ndata[date]={}\n", " if country not in ndata[date]: ndata[date][country]=0\n", " ndata[date][country]+=1" ] }, { "cell_type": "code", "execution_count": 79, "metadata": { "ExecuteTime": { "end_time": "2019-04-17T22:27:34.960272Z", "start_time": "2019-04-17T22:27:34.358255Z" } }, "outputs": [], "source": [ "pd.DataFrame(ndata).to_csv('ndata.csv')" ] }, { "cell_type": "code", "execution_count": 80, "metadata": { "ExecuteTime": { "end_time": "2019-04-17T22:27:34.968262Z", "start_time": "2019-04-17T22:27:34.963255Z" } }, "outputs": [], "source": [ "import json" ] }, { "cell_type": "code", "execution_count": 81, "metadata": { "ExecuteTime": { "end_time": "2019-04-17T22:27:35.057259Z", "start_time": "2019-04-17T22:27:34.972259Z" } }, "outputs": [ { "data": { "text/plain": [ "385280" ] }, "execution_count": 81, "metadata": {}, "output_type": "execute_result" } ], "source": [ "open('ndata.json','w').write(json.dumps(ndata))" ] }, { "cell_type": "code", "execution_count": 82, "metadata": { "ExecuteTime": { "end_time": "2019-04-17T22:27:36.607266Z", "start_time": "2019-04-17T22:27:35.704753Z" } }, "outputs": [], "source": [ "ndata2=[]\n", "for i in data.index:\n", " start=pd.to_datetime(data.loc[i]['Date'])\n", " periods=data.loc[i]['Duration']\n", " crew=data.loc[i]['Crew']\n", " country=data.loc[i]['Country']\n", " for idate in pd.date_range(start,periods=periods,freq='1D'):\n", " date=str(idate)[:10]\n", " ndata2.append({'Date':date,'Name':crew,'Country':country,'Crew':1})" ] }, { "cell_type": "code", "execution_count": 83, "metadata": { "ExecuteTime": { "end_time": "2019-04-17T22:27:36.792792Z", "start_time": "2019-04-17T22:27:36.610267Z" } }, "outputs": [ { "data": { "text/plain": [ "2411906" ] }, "execution_count": 83, "metadata": {}, "output_type": "execute_result" } ], "source": [ "open('ndata2.json','w').write(json.dumps(ndata2))" ] }, { "cell_type": "code", "execution_count": 84, "metadata": { "ExecuteTime": { "end_time": "2019-04-17T22:27:37.139508Z", "start_time": "2019-04-17T22:27:37.103505Z" } }, "outputs": [ { "data": { "text/plain": [ "86441" ] }, "execution_count": 84, "metadata": {}, "output_type": "execute_result" } ], "source": [ "open('ndata2a.json','w').write(json.dumps(ndata2[:1000]))" ] }, { "cell_type": "code", "execution_count": 85, "metadata": { "ExecuteTime": { "end_time": "2019-04-17T22:27:38.304148Z", "start_time": "2019-04-17T22:27:37.687419Z" } }, "outputs": [], "source": [ "pd.DataFrame(ndata).T.to_csv('ndataT.csv')" ] }, { "cell_type": "code", "execution_count": 86, "metadata": { "ExecuteTime": { "end_time": "2019-04-17T22:27:38.833400Z", "start_time": "2019-04-17T22:27:38.320161Z" } }, "outputs": [], "source": [ "df=pd.DataFrame(ndata).T\n", "df.index.name='Date'\n", "df=df.reset_index()" ] }, { "cell_type": "code", "execution_count": 87, "metadata": { "ExecuteTime": { "end_time": "2019-04-17T22:27:38.942521Z", "start_time": "2019-04-17T22:27:38.932515Z" } }, "outputs": [], "source": [ "edata={}\n", "for c in df.columns:\n", " if c!='Date':\n", " edata[c]=list(df[c].values)\n", " else:\n", " edata[c]=list(df[c].values)" ] }, { "cell_type": "code", "execution_count": 88, "metadata": { "ExecuteTime": { "end_time": "2019-04-17T22:27:39.420685Z", "start_time": "2019-04-17T22:27:39.413684Z" } }, "outputs": [ { "data": { "text/plain": [ "dict_keys(['Date', 'Belgium', 'Canada', 'France', 'Germany', 'Italy', 'Japan', 'Netherlands', 'Russia', 'United Kingdom', 'United States'])" ] }, "execution_count": 88, "metadata": {}, "output_type": "execute_result" } ], "source": [ "edata.keys()" ] }, { "cell_type": "code", "execution_count": 89, "metadata": { "ExecuteTime": { "end_time": "2019-04-17T22:27:40.159924Z", "start_time": "2019-04-17T22:27:39.966911Z" }, "scrolled": true }, "outputs": [], "source": [ "json.dump(edata,open('edata.json','w'))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.6" }, "toc": { "base_numbering": 1, "nav_menu": {}, "number_sections": true, "sideBar": true, "skip_h1_title": false, "title_cell": "Table of Contents", "title_sidebar": "Contents", "toc_cell": false, "toc_position": {}, "toc_section_display": true, "toc_window_display": false } }, "nbformat": 4, "nbformat_minor": 2 }

mit

pbutenee/ml-tutorial

source/1/machine_learning.ipynb

1

97846

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Machine Learning 101++ in Python\n", "\n", "#### Created by:\n", "\n", "- Pieter Buteneers ([@PieterButeneers](https://twitter.com/pieterbuteneers)), Director of Engineering in ML & AI at [sinch.com](https://www.sinch.com/) \n", "- Bart De Vylder, Senior Research Engineer at [Google DeepMind](https://deepmind.com/)\n", "- Jeroen Boeye ([@JeroenBoeye](https://twitter.com/JeroenBoeye)), Senior Machine Learning Engineer at [Faktion](https://www.faktion.com/)\n", "- Joris Boeye ([@JorisBoeye](https://twitter.com/JorisBoeye)), Senior Data Scientist at [ZF Wind Power](https://www.zf.com/products/en/wind/home/wind.html)\n", "\n", "\n", "\n", "## 1. Imports\n", "\n", "Let's first start with importing all the necessary packages. Some imports will be repeated in the exercises but if you want to skip some parts you can just execute the imports below and start with any exercise." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "\n", "import matplotlib.pyplot as plt\n", "plt.rcParams[\"figure.figsize\"] = (13.0, 8.0)\n", "%matplotlib inline\n", "\n", "import pickle\n", "\n", "import sklearn\n", "import sklearn.linear_model\n", "import sklearn.preprocessing\n", "import sklearn.gaussian_process\n", "import sklearn.ensemble" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 2. Linear Regression\n", "\n", "Linear Regression assumes a linear relationship between 2 variables. \n", "\n", "As an example we'll consider the historical page views of a web server and compare it to its CPU usage. We'll try to predict the CPU usage of the server based on the page views of the different pages. \n", "\n", "### 2.1 Data import and inspection\n", "\n", "Let's import the data and take a look at it." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import pickle # Pickle files allow us to easily save and load python objects.\n", "\n", "with open('data/cpu_page_views.pickle', 'rb') as file:\n", " cpu_usage, page_views, page_names, total_page_views = pickle.load(file, encoding='latin1')\n", "\n", "print('Array shapes:')\n", "print('-'*25)\n", "print(f'cpu_usage\\t {cpu_usage.shape}')\n", "print(f'page_views\\t {page_views.shape}')\n", "print(f'page_names\\t {page_names.shape}')\n", "print(f'total_page_views {total_page_views.shape}')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": false }, "outputs": [], "source": [ "plt.figure(figsize=(13, 6))\n", "plt.plot(total_page_views, label='Total page views')\n", "plt.plot(cpu_usage, label='CPU %')\n", "plt.legend()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The orange line on the plot above is the number of page views in blue and the orange line is the CPU load that viewing this pages generates on the server.\n", "\n", "### 2.2 Simple linear regression\n", "\n", "First, we're going to work with the total page views on the server, and compare it to the CPU usage. We can make use of a [PyPlot's scatter plot](http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.scatter) to understand the relation between the total page views and the CPU usage:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "nbgrader": { "grade": false, "grade_id": "scatter", "locked": false, "schema_version": 3, "solution": true } }, "outputs": [], "source": [ "plt.figure(figsize=(13, 6))\n", "plt.xlabel(\"Total page views\")\n", "plt.ylabel(\"CPU usage\")\n", "### BEGIN SOLUTION\n", "plt.scatter(total_page_views, cpu_usage)\n", "### END SOLUTION\n", "# plt.scatter( ? , ? )\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There clearly is a strong correlation between the page views and the CPU usage. Because of this correlation we can build a model to predict the CPU usage from the total page views. If we use a linear model we get a formula like the following:\n", "\n", "$$ \\text{cpu_usage} = c_0 + c_1 \\text{total_page_views} $$\n", "\n", "Since we don't know the exact values for $c_0$ and $c_1$ we will have to compute them. For that we'll make use of the [scikit-learn](http://scikit-learn.org/stable/) machine learning library for Python and use [least-squares linear regression](http://scikit-learn.org/stable/modules/linear_model.html#ordinary-least-squares)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import sklearn.linear_model\n", "simple_lin_model = sklearn.linear_model.LinearRegression()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we need to feed the data to the model to fit it.\n", "```\n", " X = [[x_11, x_12, x_13, ...], y = [y_1,\n", " [x_21, x_22, x_23, ...], y_2, \n", " [x_31, x_32, x_33, ...], y_3,\n", " ...] ...]\n", "\n", "```\n", "\n", "In general, the [model.fit(X,y)](http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LinearRegression.html#sklearn.linear_model.LinearRegression.fit) method takes a matrix X and vector y as arguments and tries to find coefficients that allow to predict the `y_i`'s from the `x_ij`'s. In our case the matrix X will consist of only one column containing the total page views. Our `total_page_views` variable however, is still only a one-dimensional vector, so we need to [`np.reshape()`](https://docs.scipy.org/doc/numpy/reference/generated/numpy.reshape.html) it into a two-dimensional array. Since there is only one feature the second dimension should be `1`. You can leave one dimension unspecified by passing -1, it will be determined from the size of the data.\n", "\n", "Then we fit our model using the the total page views and cpu. The coefficients found are automatically stored in the ```simple_lin_model``` object." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "nbgrader": { "grade": false, "grade_id": "model_fit", "locked": false, "schema_version": 3, "solution": true } }, "outputs": [], "source": [ "### BEGIN SOLUTION\n", "simple_lin_model.fit(total_page_views.reshape((-1, 1)), cpu_usage)\n", "### END SOLUTION\n", "# simple_lin_model.fit( ? , ? ) " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can now inspect the coefficient $c_1$ and constant term (intercept) $c_0$ of the model:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "print(f\"Coefficient = {simple_lin_model.coef_[0]:.2f}\\nConstant term = {simple_lin_model.intercept_:.2f}\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "So this means that each additional page view adds about 0.11% CPU load to the server and all the other processes running on the server consume on average 0.72% CPU.\n", "\n", "Once the model is trained we can use it to [```predict```](http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LinearRegression.html#sklearn.linear_model.LinearRegression.predict) the outcome for a given input (or array of inputs). Note that the predict function requires a 2-dimensional array similar to the ```fit``` function.\n", "\n", "What is the expected CPU usage when we have 880 page views per second?" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "nbgrader": { "grade": false, "grade_id": "predict_100", "locked": false, "schema_version": 3, "solution": true } }, "outputs": [], "source": [ "### BEGIN SOLUTION\n", "simple_lin_model.predict([[880]])\n", "### END SOLUTION\n", "# simple_lin_model.predict( [[ ? ]] )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "What is the expected CPU usage when we have 1000 page views per second? Is this technically possible? Why does the model predict it this way?" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "nbgrader": { "grade": false, "grade_id": "cell-b9ac995304287553", "locked": false, "schema_version": 3, "solution": true, "task": false } }, "outputs": [], "source": [ "### BEGIN SOLUTION\n", "simple_lin_model.predict([[100]])\n", "### END SOLUTION\n", "# simple_lin_model.predict( [[ ? ]] )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we plot the linear model together with our data to verify it captures the relationship correctly (the predict method can accept the entire ```total_page_views``` array at once)." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "plt.figure(figsize=(13, 6))\n", "\n", "plt.scatter(total_page_views, cpu_usage, color='black')\n", "plt.plot(total_page_views, simple_lin_model.predict(total_page_views.reshape((-1, 1))), color='blue', linewidth=3)\n", "\n", "plt.xlabel(\"Total page views\")\n", "plt.ylabel(\"CPU usage\")\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Our model can calculate the R2 [`score`](http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LinearRegression.html#sklearn.linear_model.LinearRegression.score) indicating how well the linear model captures the data. A score of 1 means there is perfect linear correlation and the model can fit the data perfectly, a score of 0 (or lower) means that there is no correlation at all (and it does not make sense to try to model it that way). The score method takes the same arguments as the fit method." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "R2 = simple_lin_model.score(total_page_views.reshape((-1, 1)), cpu_usage)\n", "print(f'R2 = {R2:.3f}')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 2.3 Extrapolation\n", "\n", "Now let's repeat this experiment with similar but different data. We will try to predict what the CPU usage will be if there will be 8 page views (per second)." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "with open('data/cpu_page_views_2.pickle', 'rb') as file:\n", " cpu_usage, total_page_views = pickle.load(file, encoding='latin1')\n", "\n", "print('Array shapes:')\n", "print('-'*25)\n", "print(f'cpu_usage\\t {cpu_usage.shape}')\n", "print(f'total_page_views {total_page_views.shape}')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "nbgrader": { "grade": false, "grade_id": "qwerqwer", "locked": false, "schema_version": 3, "solution": true } }, "outputs": [], "source": [ "simple_lin_model = sklearn.linear_model.LinearRegression()\n", "simple_lin_model.fit(total_page_views, cpu_usage)\n", "### BEGIN SOLUTION\n", "prediction = simple_lin_model.predict([[8]])\n", "### END SOLUTION\n", "# prediction = simple_lin_model.predict(?)\n", "\n", "print(f'The predicted value is: {prediction}')\n", "\n", "assert prediction < 25" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now let's plot what you have done." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "all_page_views = np.concatenate((total_page_views, [[8]]))\n", "\n", "plt.figure(figsize=(13, 6))\n", "\n", "plt.scatter(total_page_views, cpu_usage, color='black')\n", "plt.plot(all_page_views, simple_lin_model.predict(all_page_views), color='blue', linewidth=3)\n", "plt.axvline(8, color='r')\n", "\n", "plt.xlabel(\"Total page views\")\n", "plt.ylabel(\"CPU usage\")\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Is this what you would expect? Can you see what's wrong?\n", "\n", "Let's plot the time series again to get a different view at the data." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "plt.figure(figsize=(16, 5))\n", "plt.plot(total_page_views, label='Total page views')\n", "plt.plot(cpu_usage, label='CPU %')\n", "plt.legend()\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "x = np.array([1, 2, 3])\n", "selection = np.array([True, False, True])\n", "x[selection]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The spikes of CPU usage are actually backups that run at night and they can be ignored. So repeat the exercise again but ignore these data points.\n", "\n", "You can subselect parts of arrays with a second array that holds True / False values like so:\n", "```\n", " x = np.array([1, 2, 3])\n", " selection = np.array([True, False, True])\n", " print(x[selection])\n", " array([1, 3])\n", "```\n", "\n", "Try to create this `selection` array with `True` values where there is no backup going on and `False` when the backup occurs." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "nbgrader": { "grade": false, "grade_id": "qwerqwe", "locked": false, "schema_version": 3, "solution": true } }, "outputs": [], "source": [ "### BEGIN SOLUTION\n", "selection = cpu_usage < 25\n", "### END SOLUTION\n", "# selection = ?\n", "\n", "assert selection.dtype == np.dtype('bool'), 'The selection variable should be an array of True/False values'\n", "assert len(selection) == len(total_page_views)\n", "\n", "simple_lin_model = sklearn.linear_model.LinearRegression()\n", "simple_lin_model.fit(total_page_views[selection], cpu_usage[selection])\n", "prediction = simple_lin_model.predict([[8]])\n", "\n", "print(f'The predicted value is: {prediction}')\n", "\n", "all_page_views = np.concatenate((total_page_views, [[8]]))\n", "\n", "plt.figure(figsize=(13, 6))\n", "\n", "plt.scatter(total_page_views, cpu_usage, c=selection, cmap='RdYlGn')\n", "plt.plot(all_page_views, simple_lin_model.predict(all_page_views), color='blue', linewidth=3)\n", "plt.axvline(8, color='r')\n", "\n", "plt.xlabel(\"Total page views\")\n", "plt.ylabel(\"CPU usage\")\n", "\n", "plt.show()\n", "\n", "assert prediction > 23" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "So what you should have learned from the previous exercise is that you should always look at your data and/or write scripts to inspect your data. Additionally extrapolation does not always work because there are no training examples in that area.\n", "\n", "## 3. Multiple linear regression\n", "\n", "A server can host different pages and each of the page views will generate load on the CPU. This load will however not be the same for each page.\n", "\n", "Now let us consider the separate page views and build a linear model for that. The model we try to fit takes the form:\n", "\n", "$$\\text{cpu_usage} = c_0 + c_1 \\text{page_views}_1 + c_2 \\text{page_views}_2 + \\ldots + c_n \\text{page_views}_n$$\n", "\n", "where the $\\text{page_views}_i$'s correspond the our different pages:\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# load the data\n", "with open('data/cpu_page_views.pickle', 'rb') as file:\n", " cpu_usage, page_views, page_names, total_page_views = pickle.load(file, encoding='latin1')\n", "\n", "print('Array shapes:')\n", "print('-'*25)\n", "print(f'cpu_usage\\t {cpu_usage.shape}')\n", "print(f'page_views\\t {page_views.shape}')\n", "print(f'page_names\\t {page_names.shape}')\n", "print(f'total_page_views {total_page_views.shape}\\n')\n", "\n", "print(page_names)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's have a look at this data." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "plt.figure(figsize=(13, 6))\n", "for i in range(len(page_names)):\n", " plt.plot(page_views[:,i], label=page_names[i])\n", "plt.plot(cpu_usage, label= 'CPU %')\n", "plt.legend()\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "plt.figure(figsize=(13, 6))\n", "\n", "for i in range(len(page_names)):\n", " plt.scatter(page_views[:,i], cpu_usage, label=page_names[i])\n", "\n", "plt.xlabel(\"Page views\")\n", "plt.ylabel(\"CPU usage\")\n", "plt.legend()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We start again by creating a [```LinearRegression```](http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LinearRegression.html#sklearn.linear_model.LinearRegression) model." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "multi_lin_model = sklearn.linear_model.LinearRegression()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next we fit the model on the data, using `multi_lin_model.fit(X,y)`. In contrast to the case above our `page_views` variable already has the correct shape to pass as the X matrix: it has one column per page." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "nbgrader": { "grade": false, "grade_id": "multi_lin_model_fit", "locked": false, "schema_version": 3, "solution": true } }, "outputs": [], "source": [ "### BEGIN SOLUTION\n", "multi_lin_model.fit(page_views, cpu_usage)\n", "### END SOLUTION\n", "# multi_lin_model.fit( ? , ? )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now, given the coefficients calculated by the model, which capture the contribution of each page view to the total CPU usage, we can start to answer some interesting questions. For example, \n", "which page view causes most CPU usage, on a per visit basis? \n", "\n", "For this we can generate a table of page names with their coefficients in descending order:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Some quick and dirty code to print the most consuming pages first\n", "print('Index\\tCPU (%)\\t Page')\n", "print('-'*41)\n", "\n", "indices = np.argsort(-multi_lin_model.coef_)\n", "for i in indices:\n", " print(f\"{i}\\t{ multi_lin_model.coef_[i]:4.2f}\\t {page_names[i]}\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "From this table we see that 'resources/js/basket.js' consumes the most per CPU per view. It generates about 0.30% CPU load for each additional page view. 'products/science.html' on the other hand is much leaner and only consumes about 0.04% CPU per view. Does this seem to be correct if you look at the scatter plot above?\n", "\n", "Now let us investigate the constant term again." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "print(f'The other processes on the server consume {multi_lin_model.intercept_:.2f}%')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As you can see this term is very similar to the result achieved in single linear regression, but it is not entirely the same. This means that these models are not perfect. However, they seem to be able to give a reliable estimate.\n", "\n", "Now let's compute the R2 score." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "R2 = multi_lin_model.score(page_views, cpu_usage)\n", "print(f'R2 = {R2:.3f}')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As you can see from the R2 score, this model performs better. It can explain 91.5% of the variance instead of just 90.5% of the variance. So this gives the impression that this model is more accurate.\n", "\n", "## 4. Non-linear Regression\n", "\n", "Sometimes linear relations don't cut it anymore, so you might want a more complex method. There are 2 approaches to this:\n", "* Use a non-linear method (such as Neural Networks, Support Vector Machines, Random Forests and Gaussian Processes)\n", "* Use non-linear features as pre-processing for a linear method\n", "\n", "Actually both methods are in essence identical and there is not always a clear distinction between the two. We will use the second approach in this section since it is easier to understand what is going on.\n", "\n", "Please note that it is very often not even necessary to use non-linear methods, since the linear methods can be extremely powerful on their own and they are quite often very stable and reliable (in contrast to non-linear methods).\n", "\n", "### 4.1. Fitting a sine function with linear regression\n", "\n", "As an example task, we'll try to fit a sine function. We will use the [`np.sin()`](https://docs.scipy.org/doc/numpy/reference/generated/numpy.sin.html) function to compute the sine of the elements in a numpy array." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "x = np.arange(0,6, 0.01).reshape((-1, 1))\n", "\n", "plt.figure(figsize=(13, 6))\n", "plt.plot(x, np.sin(x))\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For our training set, we will calculate 10 _y_ values from evenly spaced _x_ values using this function." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# helper function to generate the data\n", "def sine_train_data(): \n", " x_train = np.linspace(0, 6, 10).reshape((-1, 1))\n", " y_train = np.sin(x_train)\n", " return x_train, y_train" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "x_train, y_train = sine_train_data()\n", "\n", "plt.figure(figsize=(13, 6))\n", "plt.scatter(x_train, y_train)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now let's try to fit a model to this data with linear regression." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "nbgrader": { "grade": false, "grade_id": "qwerq2", "locked": false, "schema_version": 3, "solution": true } }, "outputs": [], "source": [ "x_train, y_train = sine_train_data()\n", "\n", "### BEGIN SOLUTION\n", "model = sklearn.linear_model.LinearRegression()\n", "model.fit(x_train, y_train)\n", "### END SOLUTION\n", "# model = ?\n", "# model.fit( ? )\n", "\n", "print(f'The R2 score of this model is: {model.score(x_train, y_train):.3}')\n", "\n", "plt.figure(figsize=(13, 6))\n", "plt.scatter(x_train, y_train)\n", "plt.plot(x, model.predict(x))\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As you can see this fit is not optimal.\n", "\n", "### 4.2. Fitting a sine function using polynomial expansion\n", "\n", "One of the easiest ways to make your machine learning technique more *intelligent* is to extract relevant features from the data. These features can be anything that you can find that will make it easier for the method to be able to fit the data. This means that as a machine learning engineer it is best to know and understand your data.\n", "\n", "As some of you might remember from math class, you can create an approximation of any function (including a sine function) using a polynomial function with the [Taylor expansion](https://en.wikipedia.org/wiki/Taylor_series). So we will use that approach to learn a better fit.\n", "\n", "In this case we will create what we call features using a [polynomial expansion](http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.PolynomialFeatures.html). If you set the degree to 3 it will generate data of the 0d, 1st, 2nd and 3rd order (including cross products) as shown in the example below (change `x` and `degree` to see the different expansions of `x` to a certain `degree`)." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import sklearn.preprocessing\n", "\n", "x = [[2]]\n", "pol_exp = sklearn.preprocessing.PolynomialFeatures(degree=3)\n", "pol_exp.fit_transform(x)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As you can see above this function transforms $x$ into [$x^0$, $x^1$, $x^2$, $x^3$] with $x^0=1$ and $x^1 = x$. If you have 2 inputs it will also take the cross products so that [$x_1$, $x_2$] is transformed into: [1, $x_1$, $x_2$, $x_1^2$, $x_1x_2$, $x_2^2$, $x_1^3$, $x_1^2x_2$, $x_1x_2^2$, $x_2^3$] as shown below." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "x = [[2, 3]]\n", "pol_exp = sklearn.preprocessing.PolynomialFeatures(degree=3)\n", "pol_exp.fit_transform(x)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this example we only have 1 input so the number of features is always the `degree + 1`.\n", "\n", "Because of this polynomial features extraction finding of the coefficients of the polynomial becomes a linear problem, so similar to the previous exercise on multiple linear regression you can find the optimal weights as follows:\n", "\n", "$$y = c_0 + c_1 x + c_2 x^2 + c_3 x^3 + \\cdots + c_n x^n$$\n", "\n", "So for multiple values of $x$ and $y$ you can minimize the error of this equation using linear regression. How this is done in practice is shown below." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "x_train, y_train = sine_train_data()\n", "\n", "pol_exp = sklearn.preprocessing.PolynomialFeatures(degree=3)\n", "pol_exp.fit_transform(x_train)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now play with the degree of the polynomial expansion function below to create better features. Search for the optimal degree." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "nbgrader": { "grade": false, "grade_id": "qwerq", "locked": false, "schema_version": 3, "solution": true } }, "outputs": [], "source": [ "x_train, y_train = sine_train_data()\n", "\n", "### BEGIN SOLUTION\n", "pol_exp = sklearn.preprocessing.PolynomialFeatures(degree=9)\n", "### END SOLUTION\n", "# pol_exp = sklearn.preprocessing.PolynomialFeatures(degree= ? )\n", "\n", "model = sklearn.linear_model.LinearRegression()\n", "model.fit(pol_exp.fit_transform(x_train), y_train)\n", "\n", "train_score = model.score(pol_exp.fit_transform(x_train), y_train)\n", "print(f'The R2 score of this model is: {train_score:.6f}')\n", "\n", "\n", "plt.figure(figsize=(13, 6))\n", "plt.scatter(x_train, y_train)\n", "x = np.arange(0,6, 0.01).reshape((-1, 1))\n", "plt.plot(x, model.predict(pol_exp.fit_transform(x)))\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "What do you notice? When does it work better? And when does it work best?\n", "\n", "Now let's test this on new and unseen data." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def sine_test_data():\n", " x_test = 0.5 + np.arange(6).reshape((-1, 1))\n", " y_test = np.sin(x_test)\n", " return x_test, y_test" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "nbgrader": { "grade": false, "grade_id": "qwer", "locked": false, "schema_version": 3, "solution": true } }, "outputs": [], "source": [ "assert train_score > .99999, 'Adjust the degree parameter 2 cells above until the train_score > .99999'\n", "\n", "x_test, y_test = sine_test_data()\n", "\n", "plt.figure(figsize=(13, 6))\n", "plt.scatter(x_train, y_train, label='train')\n", "plt.scatter(x_test, y_test, color='r', label='test')\n", "plt.legend()\n", "x = np.arange(0, 6, 0.01).reshape((-1, 1))\n", "plt.plot(x, model.predict(pol_exp.fit_transform(x)))\n", "plt.show()\n", "\n", "### BEGIN SOLUTION\n", "test_score = model.score(pol_exp.fit_transform(x_test), y_test)\n", "### END SOLUTION\n", "# test_score = model.score( ? )\n", "\n", "print(f'The R2 score of the model on the test set is: {test_score:.3f}')\n", "\n", "assert test_score > 0.99" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If everything is correct your score is very close to 1. Which means that we have built a model that can fit this data (almost) perfectly.\n", "\n", "### 4.3. Add noise to the equation\n", "\n", "Sadly all the data that we measure or gather doesn't have the mathematical precision of the data we used here. Quite often our measurements contain noise.\n", "\n", "So let us repeat this process for data with more noise. Similarly as above, you have to choose the optimal degree of the polynomials." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# a helper function to create the sine train set that can also add noise to the data\n", "def noisy_sine_train_data(noise=None):\n", " x_train = np.linspace(0, 6, 10).reshape((-1, 1))\n", " y_train = np.sin(x_train)\n", " \n", " # If fixed, set the random seed so that the next call of the\n", " # random function always returns the same result\n", " if noise == 'fixed':\n", " np.random.seed(1)\n", " \n", " x_train += np.random.randn(len(x_train)).reshape((-1, 1)) / 5\n", " \n", " return x_train, y_train" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "nbgrader": { "grade": false, "grade_id": "asdqet", "locked": false, "schema_version": 3, "solution": true } }, "outputs": [], "source": [ "x_train, y_train = noisy_sine_train_data(noise='fixed')\n", "\n", "### BEGIN SOLUTION\n", "pol_exp = sklearn.preprocessing.PolynomialFeatures(degree=9)\n", "### END SOLUTION\n", "# pol_exp = sklearn.preprocessing.PolynomialFeatures(degree= ? )\n", "\n", "model = sklearn.linear_model.LinearRegression()\n", "model.fit(pol_exp.fit_transform(x_train), y_train)\n", "\n", "train_score = model.score(pol_exp.fit_transform(x_train), y_train)\n", "print(f'The R2 score of this method on the train set is {train_score:.3f}')\n", "\n", "assert train_score > 0.99" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now let's see what this results to in the test set." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "x_test, y_test = sine_test_data()\n", "print(f'The R2 score of the model on the test set is: {model.score(pol_exp.fit_transform(x_test), y_test):.3f}')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As you can clearly see, this result is not that good. Why do you think this is?\n", "\n", "Now plot the result to see the function you created." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "plt.figure(figsize=(13, 6))\n", "plt.scatter(x_train, y_train)\n", "x = np.arange(0,6, 0.01).reshape((-1, 1))\n", "plt.plot(x, model.predict(pol_exp.fit_transform(x)))\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Is this what you expect?\n", "\n", "Now repeat the process below a couple of times for random noise." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "x_train, y_train = noisy_sine_train_data()\n", "\n", "pol_exp = sklearn.preprocessing.PolynomialFeatures(degree=9)\n", "\n", "model = sklearn.linear_model.LinearRegression()\n", "model.fit(pol_exp.fit_transform(x_train), y_train)\n", "print(f'The R2 score of this method on the train set is {model.score(pol_exp.fit_transform(x_train), y_train):.3f}')\n", "\n", "plt.figure(figsize=(13, 6))\n", "plt.scatter(x_train, y_train)\n", "x = np.arange(x_train[0], x_train[-1], 0.01).reshape((-1, 1))\n", "plt.plot(x, model.predict(pol_exp.fit_transform(x)))\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "What did you observe? And what is the method learning? And how can you avoid this?\n", "\n", "Try to figure out a solution for this problem without changing the noise level." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "nbgrader": { "grade": false, "grade_id": "qwe", "locked": false, "schema_version": 3, "solution": true } }, "outputs": [], "source": [ "x_train, y_train = noisy_sine_train_data(noise='fixed')\n", "x_test, y_test = sine_test_data()\n", "\n", "### BEGIN SOLUTION\n", "pol_exp = sklearn.preprocessing.PolynomialFeatures(degree=3)\n", "model = sklearn.linear_model.LinearRegression()\n", "model.fit(pol_exp.fit_transform(x_train), y_train)\n", "### END SOLUTION\n", "# pol_exp = ?\n", "# model = ?\n", "# model.fit( ? )\n", "\n", "print(f'The score of this method on the train set is: {model.score(pol_exp.fit_transform(x_train), y_train):.3f}')\n", "\n", "plt.figure(figsize=(13, 6))\n", "plt.scatter(x_train, y_train, label='train')\n", "plt.scatter(x_test, y_test, color='r', label='test')\n", "x = np.arange(0,6, 0.01).reshape((-1, 1))\n", "plt.plot(x, model.predict(pol_exp.fit_transform(x)))\n", "plt.legend()\n", "plt.show()\n", "\n", "test_score = model.score(pol_exp.fit_transform(x_test), y_test)\n", "print(f'The score of the model on the test set is: {test_score:.3f}')\n", "\n", "assert test_score > 0.99, 'Adjust the degree parameter until test_score > 0.99'" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 5. Over-fitting and Cross-Validation\n", "\n", "What you have experienced above is called over-fitting and happens when your model learns the noise that is inherent in the data.\n", "\n", "This problem was caused because there were to many parameters in the model. So the model was too advanced so that it became capable of learning the noise in the data by heart. Reducing the number of parameters solves this problem. But how do you know how many parameters is optimal?\n", "\n", "(Another way to solve this problem is to use more data. Because if there are more data points in the data and if there is more noise, your model isn't able to learn all that noise anymore and you get a better result. Since it's often not possible to gather more data we will not take this approach.)\n", "\n", "In the exercise above you had to set the number of polynomial functions to get a better result, but how can you estimate this in a reliable way without manually selection the optimal parameters?\n", "\n", "### 5.1. Validation set\n", "\n", "A common way to solve this problem is through the use of a validation set. This means that you use a subset of the training data to train your model on, and another subset of the training data to validate your parameters. Based on the score of your model on this validation set you can select the optimal parameter.\n", "\n", "So use this approach to select the best number of polynomials for the noisy sine function." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# create the data in case you skipped the previous exercise\n", "\n", "# a helper function to create the sine train set that can also add noise to the data\n", "def noisy_sine_train_data(noise=None):\n", " x_train = np.linspace(0, 6, 10).reshape((-1, 1))\n", " y_train = np.sin(x_train)\n", " \n", " # If fixed, set the random seed so that the next call of the\n", " # random function always returns the same result\n", " if noise == 'fixed':\n", " np.random.seed(1)\n", " \n", " x_train += np.random.randn(len(x_train)).reshape((-1, 1)) / 5\n", " \n", " return x_train, y_train\n", "\n", "def sine_test_data():\n", " x_test = 0.5 + np.arange(6).reshape((-1, 1))\n", " y_test = np.sin(x_test)\n", " return x_test, y_test" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "nbgrader": { "grade": false, "grade_id": "qw", "locked": false, "schema_version": 3, "solution": true } }, "outputs": [], "source": [ "x_train, y_train = noisy_sine_train_data(noise='fixed')\n", "\n", "# we randomly pick 3 data points to get a nice validation set\n", "train_i = [0, 1, 3, 4, 6, 7, 9]\n", "val_i = [2, 5, 8]\n", "\n", "# create the train and validation sets\n", "x_train_i = x_train[train_i, :]\n", "y_train_i = y_train[train_i]\n", "x_val_i = x_train[val_i, :]\n", "y_val_i = y_train[val_i]\n", "\n", "### BEGIN SOLUTION\n", "pol_exp = sklearn.preprocessing.PolynomialFeatures(degree=4)\n", "### END SOLUTION\n", "# pol_exp = sklearn.preprocessing.PolynomialFeatures(degree= ? )\n", "\n", "model = sklearn.linear_model.LinearRegression()\n", "model.fit(pol_exp.fit_transform(x_train_i), y_train_i)\n", "\n", "### BEGIN SOLUTION\n", "train_score = model.score(pol_exp.fit_transform(x_train_i), y_train_i)\n", "validation_score = model.score(pol_exp.fit_transform(x_val_i), y_val_i)\n", "### END SOLUTION\n", "# train_score = model.score( ? )\n", "# validation_score = model.score( ? )\n", "\n", "print(f'The R2 score of this model on the train set is: {train_score:.3f}')\n", "print(f'The R2 score of this model on the validation set is: {validation_score:.3f}')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now let's train on the entire train set (including the validation set) and test this result on the test set with the following code." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "assert pol_exp.degree < 5, 'Select a polynomial degree < 5'\n", "\n", "model = sklearn.linear_model.LinearRegression()\n", "model.fit(pol_exp.fit_transform(x_train), y_train)\n", "\n", "x_test, y_test = sine_test_data()\n", "\n", "plt.figure(figsize=(13, 6))\n", "plt.scatter(x_train, y_train, label='train')\n", "plt.scatter(x_test, y_test, color='r', label='test')\n", "plt.legend()\n", "x = np.arange(0,6, 0.01).reshape((-1, 1))\n", "plt.plot(x, model.predict(pol_exp.fit_transform(x)))\n", "plt.show()\n", "\n", "print(f'The score of the model on the test set is: {model.score(pol_exp.fit_transform(x_test), y_test):.3f}')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As you can see this approach works to select the optimal degree. Usually the test score is lower than the validation score, but in this case it is not because the test data doesn't contain noise.\n", "\n", "### 5.2. Cross-Validation\n", "\n", "To improve this procedure you can repeat the process above for different train and validation sets so that the optimal parameter is less dependent on the way the data was selected.\n", "\n", "One basic strategy for this is **leave-one-out** cross validation, where each data point is left out of the train set once, and the model is then validated on this point. Now let's implement this. First make a 2-dimensional array `results` to store all your results using the [`np.ones()`](https://docs.scipy.org/doc/numpy/reference/generated/numpy.ones.html) function: 1 dimension (row) for each validation set and 1 dimension (column) for each degree of the `PolynomialFeatures()` function. Then you loop over all the validation sets followed by a loop over all the degrees of the `PolynomialFeatures()` function you want to try out. Then set the result for that experiment in the right element of the `results` array.\n", "\n", "We will use the [mean squared error (MSE)](http://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_squared_error.html) instead of R2 because that is more stable. Since the MSE measures the error, smaller values are better.\n", "\n", "Once you have your results, average them over all validation sets (using the [`np.mean()`](https://docs.scipy.org/doc/numpy/reference/generated/numpy.mean.html) function over the correct axis) so that you know the average error for each degree over all validation sets. Now find the degree with the smallest error using the [`np.argmin()`](https://docs.scipy.org/doc/numpy/reference/generated/numpy.argmin.html) function.\n", "\n", "Tip: Python doesnt have `{` and `}` for the beginning and end of for loops. It uses the tab indentation. So make sure your indentation is set right for each of the for loops." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "nbgrader": { "grade": false, "grade_id": "q", "locked": false, "schema_version": 3, "solution": true }, "scrolled": false }, "outputs": [], "source": [ "x_train, y_train = noisy_sine_train_data(noise='fixed')\n", "\n", "### BEGIN SOLUTION\n", "results = np.inf * np.ones((10, 10))\n", "\n", "for i in range(10):\n", "### END SOLUTION\n", "# results = np.inf * np.ones(( ? , ?))\n", "# The results array should have a shape of \"the number of data points\" x \"the number of polynomial degrees to try\"\n", "# The ones are multiplied with a very large number, np.inf, since we are looking for the smallest error\n", "\n", "# for i in range( ? ):\n", " train_i = np.where(np.arange(10) != i)[0]\n", " x_train_i = x_train[train_i, :]\n", " y_train_i = y_train[train_i]\n", " x_val_i = x_train[i:i+1, :]\n", " y_val_i = y_train[i:i+1]\n", "\n", "### BEGIN SOLUTION\n", " for degree in range(10):\n", " pol_exp = sklearn.preprocessing.PolynomialFeatures(degree=degree)\n", "### END SOLUTION\n", " # for degree in range(?):\n", " # pol_exp = sklearn.preprocessing.PolynomialFeatures(degree= ? )\n", "\n", " model = sklearn.linear_model.LinearRegression()\n", " model.fit(pol_exp.fit_transform(x_train_i), y_train_i)\n", " \n", "### BEGIN SOLUTION\n", " results[i, degree] = sklearn.metrics.mean_squared_error(model.predict(pol_exp.fit_transform(x_val_i)), y_val_i)\n", "### END SOLUTION\n", " # Fill out the results for each validation set and each degree in the results matrix\n", " # results[ ? ] = sklearn.metrics.mean_squared_error(model.predict(pol_exp.fit_transform(x_val_i)), y_val_i)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's plot these results in a box plot to get an idea on how well the models performed on average.\n", "\n", "Tip: change the `max_degree` variable if you want to see more details for the lower degrees." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "max_degree = 10\n", "\n", "plt.boxplot(results[:, : max_degree])\n", "plt.xticks(range(1, max_degree + 1), range(max_degree))\n", "\n", "plt.xlabel('Polynomial degree')\n", "plt.ylabel('Mean Squared Error')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next we will compute the best degree." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "nbgrader": { "grade": false, "grade_id": "optimal-solution-cv", "locked": false, "schema_version": 3, "solution": true, "task": false } }, "outputs": [], "source": [ "### BEGIN SOLUTION\n", "average_results = np.mean(results, axis=0)\n", "degree = np.argmin(average_results)\n", "### END SOLUTION\n", "# average the results over all validation sets\n", "# average_results = np.mean(results, axis= ? )\n", "# find the optimal degree\n", "# degree = np.argmin( ? )\n", "\n", "print(f'The optimal degree for the polynomials is: {degree}')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now let's train the model on the entire train set (including the validation set) and have a look at the result." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "assert degree == 3\n", "\n", "pol_exp = sklearn.preprocessing.PolynomialFeatures(degree=degree)\n", "\n", "model = sklearn.linear_model.LinearRegression()\n", "model.fit(pol_exp.fit_transform(x_train), y_train)\n", "print(f'The score of this method on the train set is: {model.score(pol_exp.fit_transform(x_train), y_train):.3f}')\n", "\n", "plt.figure(figsize=(13, 6))\n", "plt.scatter(x_train, y_train, label='train')\n", "plt.scatter(x_test, y_test, color='r', label='test')\n", "plt.legend()\n", "x = np.arange(0,6, 0.01).reshape((-1, 1))\n", "plt.plot(x, model.predict(pol_exp.fit_transform(x)))\n", "plt.show()\n", "\n", "print(f'The score of the model on the test set is: {model.score(pol_exp.fit_transform(x_test), y_test):.3f}')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As you can see this automatic way of selecting the optimal degree has resulted in a good fit for the sine function.\n", "\n", "### 5.3 Regularisation\n", "\n", "When you have too many parameters in your model, there is a risk of over-fitting, i.e. your model learns the noise. To avoid this, techniques have been developed to make an estimation of this noise. \n", "\n", "One of these techniques is Ridge Regression. This linear regression technique has an additional parameter called the regularisation parameter. This parameter basically sets the standard deviation of the noise you want to remove. The effect in practice is that it makes sure the weights of linear regression remain small and thus less over-fitting.\n", "\n", "Since this is an additional parameter that needs to be set, it needs to be set using cross-validation as well. Luckily sklearn developed a method that does this for us in a computational efficient way called [`sklearn.linear_model.RidgeCV()`](http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.RidgeCV.html)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "nbgrader": { "grade": false, "grade_id": "asdfasdf", "locked": false, "schema_version": 3, "solution": true } }, "outputs": [], "source": [ "x_train, y_train = noisy_sine_train_data(noise='fixed')\n", "\n", "pol_exp = sklearn.preprocessing.PolynomialFeatures(degree=9)\n", "### BEGIN SOLUTION\n", "model = sklearn.linear_model.RidgeCV()\n", "### END SOLUTION\n", "# model = sklearn.linear_model. ?\n", "\n", "\n", "model.fit(pol_exp.fit_transform(x_train), y_train)\n", "print(f'The R2 score of this method on the train set is: {model.score(pol_exp.fit_transform(x_train), y_train):.3f}')\n", "\n", "plt.figure(figsize=(13,8))\n", "plt.scatter(x_train, y_train, label='train')\n", "plt.scatter(x_test, y_test, color='r', label='test')\n", "plt.legend()\n", "x = np.arange(0,6, 0.01).reshape((-1, 1))\n", "plt.plot(x, model.predict(pol_exp.fit_transform(x)))\n", "plt.show()\n", "\n", "print(f'The R2 score of the model on the test set is: {model.score(pol_exp.fit_transform(x_test), y_test):.3f}')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As you can see above, the result of Ridge Regression is not as good as reducing the number of features in this example. However it works a lot better than without regularisation (try that). In the example above you will notice that it makes the result a lot smoother and removes the unwanted spikes. It will actually make sure that if you have too many features you still get a reasonable result. So this means that it should be in your standard toolkit.\n", "\n", "The removal of the extra features can be automated using feature selection. A very short introduction to sklearn on the topic can be found [here](http://scikit-learn.org/stable/modules/feature_selection.html).\n", "\n", "Another method that is often used is [`sklearn.linear_model.LassoCV()`](http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LassoCV.html#sklearn.linear_model.LassoCV) which actually combines removal of features and estimation of the noise. It is however very dependent on the dataset which of the two methods performs best.\n", "\n", "Cross-validation should be applied to any parameter you set in your function and that without looking at the test set.\n", "\n", "Over-fitting is one of the biggest issues in machine learning and a lot of the research that is currently being done in machine learning is a search for techniques to avoid over-fitting. As a starting point we list a few of the techniques that you can use to avoid over-fitting:\n", "* Use more data\n", "* Artificially generate more data based on the original data\n", "* Use a smaller model (with less parameters)\n", "* Use less features (and thus less parameters)\n", "* Use a regularisation parameter\n", "* Artificially add noise to your model\n", "* Only use linear models or make sure that the non-linearity in your model is closer to a linear function\n", "* Combine multiple models that each over-fit in their own way into what is called an ensemble\n", "\n", "### 5.4 Extrapolation\n", "\n", "Now let's extend the range of the optimal plot you achieved from -4 to 10. What do you see? Does it look like a sine function?" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "x_train, y_train = noisy_sine_train_data(noise='fixed')\n", "\n", "pol_exp = sklearn.preprocessing.PolynomialFeatures(degree=3)\n", "\n", "model = sklearn.linear_model.RidgeCV()\n", "model.fit(pol_exp.fit_transform(x_train), y_train)\n", "print('The R2 score of this method on the train set is:',\n", " f'{model.score(pol_exp.fit_transform(x_train), y_train):.3f}')\n", "\n", "# Now test outside the area of the training\n", "x_test_extended = np.array([-3,-2,-1,7,8,9]).reshape((-1, 1))\n", "y_test_extended = np.sin(x_test_extended)\n", "\n", "plt.figure(figsize=(13, 8))\n", "plt.scatter(x_train, y_train, label='train')\n", "plt.scatter(x_test_extended, y_test_extended, color='r', label='test')\n", "plt.legend()\n", "x = np.arange(-4,10, 0.01).reshape((-1, 1))\n", "plt.plot(x, model.predict(pol_exp.fit_transform(x)))\n", "plt.show()\n", "\n", "\n", "print('The R2 score of the model on the test set outside the area used for training is:',\n", " f'{model.score(pol_exp.fit_transform(x_test_extended), y_test_extended):.3f}')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As you can see, the extrapolation results for non-linear regression are even worse than for those of linear regression. This is because models only work well in the input space they have been trained in. \n", "\n", "A possible way to be able to extrapolate and to use a non-linear method is to use forecasting techniques. This is handled in part 7, an optional part for those interested and going through the tutorial quite fast. Otherwise continue to the section on classification in exercise 6." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 6. Classification\n", "\n", "In classification the purpose is to separate 2 classes. As an example we will use the double spiral. It is a very common toy example in machine learning and allows you to visually show what is going on.\n", "\n", "As shown in the graph below the purpose is to separate the blue from the red dots." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Some code to generate spirals. You can ignore this for now.\n", "\n", "# To comply with standards in machine learning we use x1 and x2 as opposed to x and y for this graph \n", "# because y is reserved for the output in Machine Learning (= 0 or 1 in this case)\n", "\n", "r = np.arange(0.1, 1.5, 0.0001)\n", "theta = 2 * np.pi * r\n", "x1_0 = r * np.cos(theta)\n", "x2_0 = r * np.sin(theta)\n", "x1_1 = - r * np.cos(theta)\n", "x2_1 = - r * np.sin(theta)\n", "\n", "perm_indices = np.random.permutation(range(len(x1_0)))\n", "x1_0_rand = x1_0[perm_indices[ : 1000]] + np.random.randn(1000) / 5\n", "x2_0_rand = x2_0[perm_indices[ : 1000]] + np.random.randn(1000) / 5\n", "x1_1_rand = x1_1[perm_indices[1000 : 2000]] + np.random.randn(1000) / 5\n", "x2_1_rand = x2_1[perm_indices[1000 : 2000]] + np.random.randn(1000) / 5\n", "\n", "plt.figure(figsize=(8, 8))\n", "plt.scatter(x1_0_rand, x2_0_rand, color = 'b', alpha=0.6, linewidth=0)\n", "plt.scatter(x1_1_rand, x2_1_rand, color = 'r', alpha=0.6, linewidth=0)\n", "\n", "plt.plot(x1_0, x2_0, color = 'b', lw=3)\n", "plt.plot(x1_1, x2_1, color='r', lw=3)\n", "plt.xlim(-2, 2)\n", "plt.ylim(-2, 2)\n", "plt.xlabel('X1')\n", "plt.ylabel('X2')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In a colored image this is easy to do, but when you remove the color it becomes much harder. Can you do the classification in the image below?\n", "\n", "In black the samples from the train set are shown and in yellow the samples from the validation set." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Create a train and validation set\n", "\n", "x_train_0 = np.concatenate((x1_0_rand[ : 800].reshape((-1,1)), x2_0_rand[ : 800].reshape((-1,1))), axis=1)\n", "y_train_0 = np.zeros((len(x_train_0),))\n", "x_train_1 = np.concatenate((x1_1_rand[ : 800].reshape((-1,1)), x2_1_rand[ : 800].reshape((-1,1))), axis=1)\n", "y_train_1 = np.ones((len(x_train_1),))\n", "\n", "x_val_0 = np.concatenate((x1_0_rand[800 : ].reshape((-1,1)), x2_0_rand[800 : ].reshape((-1,1))), axis=1)\n", "y_val_0 = np.zeros((len(x_val_0),))\n", "x_val_1 = np.concatenate((x1_1_rand[800 : ].reshape((-1,1)), x2_1_rand[800 : ].reshape((-1,1))), axis=1)\n", "y_val_1 = np.ones((len(x_val_1),))\n", "\n", "x_train = np.concatenate((x_train_0, x_train_1), axis=0)\n", "y_train = np.concatenate((y_train_0, y_train_1), axis=0)\n", "\n", "x_val = np.concatenate((x_val_0, x_val_1), axis=0)\n", "y_val = np.concatenate((y_val_0, y_val_1), axis=0)\n", "\n", "# Plot the train and test data\n", "\n", "plt.figure(figsize=(8, 8))\n", "plt.scatter(x_train[:, 0], x_train[:, 1], color='k', alpha=0.6, linewidth=0)\n", "plt.scatter(x_val[:, 0], x_val[:, 1], color='y', alpha=0.6, linewidth=0)\n", "plt.xlim(-2, 2)\n", "plt.ylim(-2, 2)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As you can see classifying is very hard to do when you don't get the answer even if you saw the solution earlier. But you will see that machine learning algorithms can solve this quite well if they can learn from examples.\n", "\n", "This figure also illustrates that the train and validation set are from the same distribution, which is why they look very similar on the plot. If you want to put a model trained on this data set in production and the real data is from the same distribution, you can expect similar results in real life as on your validation set.\n", "\n", "### 6.1 Linear classifier\n", "\n", "Let's try to do this with a linear classifier.\n", "\n", "Logistic regression, despite its name, is a linear model for classification rather than regression. Its sklearn implementation is [`sklearn.linear_model.LogisticRegression()`](https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression)." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "nbgrader": { "grade": false, "grade_id": "asdfasd", "locked": false, "schema_version": 3, "solution": true } }, "outputs": [], "source": [ "### BEGIN SOLUTION\n", "model = sklearn.linear_model.LogisticRegression()\n", "model.fit(x_train, y_train)\n", "### END SOLUTION\n", "# model = sklearn.linear_model. ?\n", "# model.fit( ? )\n", "\n", "train_score = sklearn.metrics.accuracy_score(model.predict(x_train), y_train)\n", "print(f'The train accuracy is: {train_score:.3f}')\n", "val_score = sklearn.metrics.accuracy_score(model.predict(x_val), y_val)\n", "print(f'The validation accuracy is: {val_score:.3f}')\n", "\n", "assert val_score > 0.5" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now let's plot the result." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# A quick and dirty helper function to plot the decision boundaries\n", "def plot_decision_boundary(model, pol_exp=None):\n", " n=250\n", " lin_space = np.linspace(-2, 2, num=n).reshape((-1, 1))\n", " x1 = np.dot(lin_space, np.ones((1, n))).reshape((-1, 1))\n", " x2 = np.dot(np.ones((n, 1)), lin_space.T).reshape((-1, 1))\n", " \n", " x = np.concatenate((x1, x2), axis=1)\n", " if pol_exp is None:\n", " y = model.predict(x)\n", " else:\n", " y = model.predict(pol_exp.fit_transform(x)) \n", " i_0 = np.where(y < 0.5)\n", " i_1 = np.where(y > 0.5)\n", " plt.figure(figsize=(8,8))\n", " plt.scatter(x[i_0, 0], x[i_0, 1], color='b', s=2, alpha=0.5, linewidth=0, marker='s')\n", " plt.scatter(x[i_1, 0], x[i_1, 1], color='r',s=2, alpha=0.5, linewidth=0, marker='s')\n", " plt.plot(x1_0, x2_0, color = 'b', lw=3)\n", " plt.plot(x1_1, x2_1, color='r', lw=3)\n", " plt.xlim(-2, 2)\n", " plt.ylim(-2, 2)\n", " \n", "# Call the function\n", "plot_decision_boundary(model)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As you can see a linear classifier returns a linear decision boundary.\n", "\n", "### 6.2 Non-linear classification\n", "\n", "Now let's do this better with a non-linear classifier using polynomials. Play with the degree of the polynomial expansion and look for the effect on the validation set accuracy of the [`LogisticRegressionCV()`](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegressionCV.html) model. This is a more advanced version of the default [`LogisticRegression()`](https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression) that uses cross validation to tune its hyper-parameters. What gives you the best results?\n", "\n", "_If you get a lot of \"failed to converge\" warnings consider increasing the `max_iter` parameter to 1000 or so. Getting some warnings is normal._" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "nbgrader": { "grade": false, "grade_id": "asdfas", "locked": false, "schema_version": 3, "solution": true }, "scrolled": false }, "outputs": [], "source": [ "### BEGIN SOLUTION\n", "model = sklearn.linear_model.LogisticRegressionCV(max_iter=1000)\n", "pol_exp = sklearn.preprocessing.PolynomialFeatures(degree=10)\n", "model.fit(pol_exp.fit_transform(x_train), y_train)\n", "### END SOLUTION\n", "# model = sklearn.linear_model. ?\n", "# pol_exp = sklearn.preprocessing.PolynomialFeatures(degree= ? )\n", "# model.fit( ? )\n", "\n", "train_score = sklearn.metrics.accuracy_score(model.predict(pol_exp.fit_transform(x_train)), y_train)\n", "print(f'The train accuracy is: {train_score:.3f}')\n", "val_score = sklearn.metrics.accuracy_score(model.predict(pol_exp.fit_transform(x_val)), y_val)\n", "print(f'The validation accuracy is: {val_score:.3f}')\n", "\n", "plot_decision_boundary(model, pol_exp=pol_exp)\n", "\n", "assert val_score >= 0.8" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If everything went well you should get a validation/test accuracy very close to 0.8.\n", "\n", "### 6.3 Random Forests\n", "\n", "An often used technique in machine learning are random forests. Basically they are [decision trees](https://en.wikipedia.org/wiki/Decision_tree_learning), or in programmers terms, if-then-else structures, like the one shown below.\n", "\n", "<img src=\"images/tree.png\" width=70%>\n", "\n", "Decision trees are know to over-fit a lot because they just learn the train set by heart and store it. Random forests on the other hand combine multiple different (randomly initialized) decision trees that all over-fit in their own way. But by combining their output using a voting mechanism, they tend to cancel out each other's mistakes. This approach is called an [ensemble](https://en.wikipedia.org/wiki/Ensemble_learning) and can be used for any combination of machine learning techniques. A schema representation of how such a random forest works is shown below.\n", "\n", "<img src=\"images/random_forest.jpg\">\n", "\n", "Now let's try to use a random forest to solve the double spiral problem. (see [`sklearn.ensemble.RandomForestClassifier()`](http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "nbgrader": { "grade": false, "grade_id": "asdfa", "locked": false, "schema_version": 3, "solution": true } }, "outputs": [], "source": [ "import sklearn.ensemble\n", "\n", "### BEGIN SOLUTION\n", "model = sklearn.ensemble.RandomForestClassifier()\n", "model.fit(x_train, y_train)\n", "### END SOLUTION\n", "# model = ?\n", "# model.fit( ? )\n", "\n", "train_score = sklearn.metrics.accuracy_score(model.predict(x_train), y_train)\n", "print(f'The train accuracy is: {train_score:.3f}')\n", "val_score = sklearn.metrics.accuracy_score(model.predict(x_val), y_val)\n", "print(f'The validation accuracy is: {val_score:.3f}')\n", "\n", "plot_decision_boundary(model)\n", "\n", "assert val_score > 0.7" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As you can see they are quite powerful right out of the box without any parameter tuning. But we can get the results even better with some fine tuning.\n", "\n", "Try changing the `min_samples_leaf` parameter for values between 0 and 0.5." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "nbgrader": { "grade": false, "grade_id": "asdf", "locked": false, "schema_version": 3, "solution": true } }, "outputs": [], "source": [ "### BEGIN SOLUTION\n", "model = sklearn.ensemble.RandomForestClassifier(min_samples_leaf=.02)\n", "### END SOLUTION\n", "# model = sklearn.ensemble.RandomForestClassifier(min_samples_leaf= ? )\n", "model.fit(x_train, y_train)\n", "\n", "train_score = sklearn.metrics.accuracy_score(model.predict(x_train), y_train)\n", "print(f'The train accuracy is: {train_score:.3f}')\n", "val_score = sklearn.metrics.accuracy_score(model.predict(x_val), y_val)\n", "print(f'The validation accuracy is: {val_score:.3f}')\n", "\n", "plot_decision_boundary(model)\n", "\n", "assert val_score > 0.5" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The `min_samples_leaf` parameter sets the number of data points that can create a new branch/leaf in the tree. So in practice it limits the depth of the decision tree. The bigger this parameter is, the less deep the tree will be and less likely each tree will over-fit.\n", "\n", "For this parameter you can set integer numbers to set the specific number of samples, or you can use values between 0 and 0.5 to express a percentage of the size of the dataset. Since you might experiment with a smaller dataset to roughly tune your parameters, it is best to use values between 0 and 0.5 so that the value you chose is not as dependant on the size of the dataset you are working with.\n", "\n", "Now that you have found the optimal `min_samples_leaf` run the code again with the same parameter. Do you get the same result? Why not?\n", "\n", "Another parameter to play with is the `n_estimators` parameter. Play with only this parameter to see what happens." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "nbgrader": { "grade": false, "grade_id": "asd", "locked": false, "schema_version": 3, "solution": true } }, "outputs": [], "source": [ "### BEGIN SOLUTION\n", "model = sklearn.ensemble.RandomForestClassifier(n_estimators=100)\n", "### END SOLUTION\n", "# model = sklearn.ensemble.RandomForestClassifier(n_estimators= ? )\n", "model.fit(x_train, y_train)\n", "\n", "train_score = sklearn.metrics.accuracy_score(model.predict(x_train), y_train)\n", "print(f'The train accuracy is: {train_score:.3f}')\n", "val_score = sklearn.metrics.accuracy_score(model.predict(x_val), y_val)\n", "print(f'The validation accuracy is: {val_score:.3f}')\n", "\n", "plot_decision_boundary(model)\n", "\n", "assert val_score > 0.7" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As you can see increasing the number of estimators improves the model and reduces over-fitting. This parameter actually sets the number of trees in the random forest. The more trees there are in the forest the better the result is. But obviously it requires more computing power so that is the limiting factor here.\n", "\n", "This is the basic idea behind ensembles: if you combine more tools you get a good result on average.\n", "\n", "Now try combining the `n_estimators` and `min_samples_leaf` parameter below." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "nbgrader": { "grade": false, "grade_id": "as", "locked": false, "schema_version": 3, "solution": true } }, "outputs": [], "source": [ "### BEGIN SOLUTION\n", "model = sklearn.ensemble.RandomForestClassifier(n_estimators=1000, min_samples_leaf=0.02)\n", "### END SOLUTION\n", "# model = sklearn.ensemble.RandomForestClassifier(n_estimators= ? , min_samples_leaf= ? )\n", "model.fit(x_train, y_train)\n", "\n", "train_score = sklearn.metrics.accuracy_score(model.predict(x_train), y_train)\n", "print(f'The train accuracy is: {train_score:.3f}')\n", "val_score = sklearn.metrics.accuracy_score(model.predict(x_val), y_val)\n", "print(f'The validation accuracy is: {val_score:.3f}')\n", "\n", "plot_decision_boundary(model)\n", "\n", "assert val_score > 0.7" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "As you have noticed by now it seems that random forests are less powerful than linear regression with polynomial feature extraction. This is because these polynomials are ideally suited for this task. This also means that you could get a better result if you would also apply polynomial expansion for random forests. Try that below." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "nbgrader": { "grade": false, "grade_id": "a", "locked": false, "schema_version": 3, "solution": true } }, "outputs": [], "source": [ "### BEGIN SOLUTION\n", "model = sklearn.ensemble.RandomForestClassifier(n_estimators=100, min_samples_leaf=0.01)\n", "pol_exp = sklearn.preprocessing.PolynomialFeatures(degree=15)\n", "model.fit(pol_exp.fit_transform(x_train), y_train)\n", "### END SOLUTION\n", "# model = sklearn.ensemble.RandomForestClassifier(n_estimators= ? , min_samples_leaf= ? )\n", "# pol_exp = sklearn.preprocessing.PolynomialFeatures(degree= ?)\n", "# model.fit( ? )\n", "\n", "train_score = sklearn.metrics.accuracy_score(model.predict(pol_exp.fit_transform(x_train)), y_train)\n", "print(f'The train accuracy is: {train_score:.3f}')\n", "val_score = sklearn.metrics.accuracy_score(model.predict(pol_exp.fit_transform(x_val)), y_val)\n", "print(f'The validation accuracy is: {val_score:.3f}')\n", "\n", "plot_decision_boundary(model, pol_exp=pol_exp)\n", "\n", "assert val_score > 0.7" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As you have may have noticed, it is hard to get results that are better than the ones obtained using logistic regression. This illustrates that linear techniques are very powerful and often underrated. But in some situations they are not powerful enough and you need something stronger like a random forest or even neural networks (check [this](https://playground.tensorflow.org/#dataset=spiral&noise=45) simulator if you want to play with the latter).\n", "\n", "There is one neat trick that can be used for random forests. If you set the `n_jobs` it will use more than 1 core to compute. Set it to -1 to use all the cores (including hyper-threading cores). But don't do that during this tutorial because that would block the machine you are all working on.\n", "\n", "To avoid over-fitting, you can set the `max_depth` parameter for random forests which limits the maximum depth of each tree. Alternatively, you can set the `min_samples_split` parameter which determines how many data points you need at least before you create another split (this is an additional if-else structure) while building the tree. Or the `min_samples_leaf` that sets the minimum amount of data points you have in each leaf. All 3 parameters are dependent on the number of data points in your dataset especially the last 2 so don't forget to adapt them if you have been playing around with a small subset of the data. (A good trick to solve this might be to use a range similar to `[0.0001, 0.001, 0.01, 0.1] * len(x_train)`. Feel free to extend the range in any direction. It is generally good practice to construct them using a log scale like in the example, or better like this: `10.0**np.arange(-5, 0, 0.5) * len(x_train)`.) In our experience `min_samples_split` or `min_samples_leaf` give slightly better results and it usually doesn't make sense to combine more than 1 of these parameters.\n", "\n", "In the previous exercises we have done a lot of the optimizations on the test set. This should of course be avoided. What you should do instead is to optimize and select your model using a validation set and of course you should automate this process as shown in one of the earlier exercises. One thing to take into account here is that you should use multiple initialisation of a random forest because the decision trees is randomly generated." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 7. Forecasting (Optional)\n", "\n", "We are going to forecast page views data, very similar to the data used in the anomaly detection section. The data contains 1 sample per hour. " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "with open('data/train_set_forecasting.pickle', 'rb') as file:\n", " train_set = pickle.load(file, encoding='latin1')\n", "\n", "print(f'Shape of the train set = {train_set.shape}')\n", "\n", "plt.figure(figsize=(20,4))\n", "plt.plot(train_set)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In the graph above you can clearly see that there is a rising trend in the data.\n", "\n", "### 7.1 One-step ahead prediction\n", "\n", "This forecasting section will describe the one-step ahead prediction. In this case, this means that we will only predict the next data point i.e. the number of page views in the next hour.\n", "\n", "Now let's first build a model that tries to predict the next data point from the previous one. \n", "\n", "We will use a technique called teacher forcing where we assume that the output of the previous prediction is correct. This means that we can use the original time series as input. Now we only need to align input and output so that the output corresponds to the next sample in the input time series." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import sklearn\n", "import sklearn.linear_model\n", "import sklearn.gaussian_process\n", "\n", "model = sklearn.linear_model.LinearRegression()\n", "\n", "# the input x_train contains all the data except the last data point\n", "x_train = train_set[ : -1].reshape((-1, 1)) # the reshape is necessary since sklearn requires a 2 dimensional array\n", "\n", "# the output y_train contains all the data except the first data point\n", "y_train = train_set[1 : ]\n", "\n", "# this code fits the model on the train data\n", "model.fit(x_train, y_train)\n", "\n", "# this score gives you how well it fits on the train set\n", "# higher is better and 1.0 is perfect\n", "print(f'The R2 train score of the linear model is {model.score(x_train, y_train):.3f}')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As you can see from the score above, the model is not perfect but it seems to get a relatively high score. Now let's make a prediction into the future and plot this.\n", "\n", "To predict the data point after that we will use the predicted data to make a new prediction. The code below shows how this works for this data set using the linear model you used earlier. Don't forget to fill out the missing code." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "nbgrader": { "grade": false, "grade_id": "nof_predictions", "locked": false, "schema_version": 3, "solution": true } }, "outputs": [], "source": [ "n_predictions = 100\n", "\n", "import copy\n", "# use the last data point as the first input for the predictions\n", "x_test = copy.deepcopy(train_set[-1]) # make a copy to avoid overwriting the training data\n", "\n", "prediction = []\n", "for i in range(n_predictions):\n", " # predict the next data point\n", " y_test = model.predict([[x_test]])[0] # sklearn requires a 2 dimensional array and returns a one-dimensional one\n", " \n", " ### BEGIN SOLUTION\n", " prediction.append(y_test)\n", " x_test = y_test\n", " ### END SOLUTION\n", " # prediction.append( ? )\n", " # x_test = ?\n", "\n", "prediction = np.array(prediction)\n", "\n", "plt.figure(figsize=(20,4))\n", "plt.plot(np.concatenate((train_set, prediction)), 'g')\n", "plt.plot(train_set, 'b')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As you can see from the image above the model doesn't quite seem to fit the data well. Let's see how we can improve this.\n", "\n", "### 7.2 Multiple features\n", "\n", "If your model is not smart enough there is a simple trick in machine learning to make your model more intelligent (but also more complex). This is by adding more features.\n", "\n", "To make our model better we will use more than 1 sample from the past. To make your life easier there is a simple function below that will create a data set for you. The ```width``` parameter sets the number of hours in the past that will be used." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def convert_time_series_to_train_data(ts, width):\n", " x_train, y_train = [], []\n", " for i in range(len(ts) - width - 1):\n", " x_train.append(ts[i : i + width])\n", " y_train.append(ts[i + width])\n", " return np.array(x_train), np.array(y_train)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "width = 5\n", "x_train, y_train = convert_time_series_to_train_data(train_set, width)\n", "\n", "print(x_train.shape, y_train.shape)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As you can see from the print above both `x_train` and `y_train` contains 303 data points. For `x_train` you see that there are now 5 features which contain the page views from the 5 past hours.\n", "\n", "So let's have a look what the increase from 1 to 5 features results to." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "width = 5\n", "x_train, y_train = convert_time_series_to_train_data(train_set, width)\n", "model = sklearn.linear_model.LinearRegression()\n", "model.fit(x_train, y_train)\n", "print(f'The R2 score of the linear model with width={width} is {model.score(x_train, y_train):.3f}')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now change the ```width``` parameter to see if you can get a better score.\n", "\n", "### 7.3 Over-fitting\n", "\n", "\n", "Now execute the code below to see the prediction of this model." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import copy\n", "\n", "# this is a helper function to make the predictions\n", "def predict(model, train_set, width, n_points):\n", " prediction = []\n", " # create the input data set for the first predicted output\n", " # copy the data to make sure the original is not overwritten\n", " x_test = copy.deepcopy(train_set[-width : ]) \n", " for i in range(n_points):\n", " # predict only the next data point\n", " prediction.append(model.predict(x_test.reshape((1, -1))))\n", " # use the newly predicted data point as input for the next prediction\n", " x_test[0 : -1] = x_test[1 : ]\n", " x_test[-1] = prediction[-1]\n", " return np.array(prediction)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "n_predictions = 200\n", "prediction = predict(model, train_set, width, n_predictions)\n", "\n", "plt.figure(figsize=(20,4))\n", "plt.plot(np.concatenate((train_set, prediction[:,0])), 'g')\n", "plt.plot(train_set, 'b')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As you can see in the image above the prediction is not what you would expect from a perfect model. What happened is that the model learned the training data by heart without 'understanding' what the data is really about. This phenomenon is called over-fitting and will always occur if you make your model too complex.\n", "\n", "Now play with the width variable below to see if you can find a more sensible width." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "nbgrader": { "grade": false, "grade_id": "jkl", "locked": false, "schema_version": 3, "solution": true } }, "outputs": [], "source": [ "### BEGIN SOLUTION\n", "width = 22\n", "### END SOLUTION\n", "# width = ?\n", "\n", "x_train, y_train = convert_time_series_to_train_data(train_set, width)\n", "model = sklearn.linear_model.LinearRegression()\n", "model.fit(x_train, y_train)\n", "print(f'The R2 score of the linear model with width={width} is {model.score(x_train, y_train):.3f}')\n", "\n", "prediction = predict(model, train_set, width, 200)\n", "\n", "plt.figure(figsize=(20,4))\n", "plt.plot(np.concatenate((train_set, prediction[:,0])), 'g')\n", "plt.plot(train_set, 'b')\n", "plt.show()\n", "\n", "assert width > 1" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As you will have noticed by now is that it is better to have a non-perfect score which will give you a much better outcome. Now try the same thing for the following models:\n", "* ```sklearn.linear_model.RidgeCV()```\n", "* ```sklearn.linear_model.LassoCV()```\n", "* ```sklearn.ensemble.RandomForestRegressor()```\n", "\n", "The first 2 models also estimate the noise that is present in the data to avoid over-fitting. `RidgeCV()` will keep the weights that are found small, but it won't put them to zero. `LassoCV()` on the other hand will put several weights to 0. Execute ```model.coef_``` to see the actual coefficients that have been found.\n", "\n", "`RandomForestRegressor()` is the regression variant of the `RandomForestClassifier()` and is therefore thus a non-linear method. This makes this method a lot more complex and therefore it will be able to represent more complex shapes than the linear method. This also means that it is much more capable to learn the data by heart (and thus to over-fit). In many cases however this additional complexity allows to better understand the data given the correct parameter settings (try a couple of times `width=25` (since it is random) and see what the results are; set the `n_estimators` parameter to a higher number to get a more stable results). \n", "\n", "### 7.4 Automation\n", "\n", "What we have done up to now is manually selecting the best outcome based on the test result. This can be considered cheating because you have just created a self-fulfilling prophecy. Additionally it is not only cheating it is also hard to find the exact `width` that gives the best result by just visually inspecting it. So we need a more objective approach to solve this.\n", "\n", "To automate this process you can use a validation set. In this case we will use the last 48 hours of the training set to validate the score and select the best parameter value. This means that we will have to use a subset of the training set to fit the model." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "nbgrader": { "grade": false, "grade_id": "find_best_model", "locked": false, "schema_version": 3, "solution": true } }, "outputs": [], "source": [ "model_generators = [sklearn.linear_model.LinearRegression(), \n", " sklearn.linear_model.RidgeCV(cv=3),\n", " sklearn.linear_model.LassoCV(cv=3), \n", " sklearn.ensemble.RandomForestRegressor(n_estimators=10)]\n", "best_score = 0\n", "\n", "### BEGIN SOLUTION \n", "for model_gen in model_generators:\n", " for width in range(1, 50):\n", "### END SOLUTION \n", "# for model_gen in ? :\n", "# for width in range( ? , ? ): \n", " x_train, y_train = convert_time_series_to_train_data(train_set, width)\n", " # train the model on the first 48 hours\n", " x_train_i, y_train_i = x_train[ : -48, :], y_train[ : -48]\n", " # use the last 48 hours for validation\n", " x_val_i, y_val_i = x_train[-48 : ], y_train[-48 : ]\n", " \n", " # there is a try except clause here because some models do not converge for some data\n", " try:\n", " # Constructs a new, untrained, model with the same parameters\n", " model = sklearn.base.clone(model_gen, safe=True)\n", " ### BEGIN SOLUTION \n", " model.fit(x_train_i, y_train_i)\n", " this_score = model.score(x_val_i, y_val_i)\n", " ### END SOLUTION \n", " # model.fit( ? , ? )\n", " # this_score = ?\n", " \n", " if this_score > best_score:\n", " best_score = this_score\n", " # Constructs a new, untrained, model with the same parameters\n", " best_model = sklearn.base.clone(model, safe=True)\n", " best_width = width\n", " except:\n", " pass\n", "\n", "print(f'{best_model.__class__.__name__} was selected as the best model with a width of {best_width}',\n", " f'and a validation R2 score of {best_score:.3f}')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If everything is correct the LassoCV methods was selected.\n", "\n", "Now we are going to train this best model on all the data. In this way we use all the available data to build a model." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "nbgrader": { "grade": false, "grade_id": "best_model_gen_plot", "locked": false, "schema_version": 3, "solution": true } }, "outputs": [], "source": [ "### BEGIN SOLUTION\n", "width = best_width\n", "model = best_model\n", "### END SOLUTION\n", "# width = ?\n", "# model = ?\n", "\n", "x_train, y_train = convert_time_series_to_train_data(train_set, width)\n", "\n", "### BEGIN SOLUTION\n", "model.fit(x_train, y_train) # train on the full data set\n", "### END SOLUTION\n", "# model.fit( ? , ? )\n", "\n", "n_predictions = 200\n", "prediction = predict(model, train_set, width, n_predictions)\n", "\n", "plt.figure(figsize=(20,4))\n", "plt.plot(np.concatenate((train_set, prediction[:,0])), 'g')\n", "plt.plot(train_set, 'b')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Although the optimal result found here might not be the best visually, it is a far better result than the one you selected manually just because there was no cheating involved ;-).\n", "\n", "Some additional info:\n", "* This noise level of `RidgeCV()` and `LassoCV()` is estimated by automatically performing train and validation within the method itself. This will make them much more robust against over-fitting. The actual method used is [Cross-validation](https://en.wikipedia.org/wiki/Cross-validation_(statistics)) which is a better approach of what we do here because it repeats the training and validation multiple times for different training and validation sets. The parameter that is set for these methods is often called the regularization parameter in literature and is well suited to avoid over-fitting." ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "# 8. Main take home messages\n", "\n", "Because we can't cover everything, we listed all the basics of what you should take home before working on your own machine learning project below. \n", "\n", "### 8.1 The basic rules of machine learning\n", "\n", "Any good club has its own set of rules. The rules for machine learning club are the following:\n", "\n", "* First rule of ML is: Over-fitting is a real problem and try anything to avoid it\n", "* Second rule of ML is: You are probably over-fitting. Are you sure you are not fitting on your test data?\n", "* Third rule of ML is: You think over-fitting will not happen to you, but it is happening right now!\n", "* Fourth rule of ML is: Talk about it with your peers because over-fitting is a real issue.\n", "\n", "### 8.2 Our winning strategy\n", "\n", "Although we'd like to claim it as ours, it is a general (non-written) consensus amongst data scientists to use the following approach. Even experts should not skip any of the steps below.\n", "\n", "1. Create a train set and a test set\n", "2. Rescale your train set to zero-mean-unit-variance (most methods assume gaussian distributed data)\n", "3. Don't look at the test set\n", "4. Implement a cross-validation framework\n", "5. Try **linear regression with regularisation** for regression (`RidgeCV` or `LassoCV`) and classification (`LogisticRegressionCV`).\n", "6. Try techniques to avoid over-fitting\n", "7. Check the validation score\n", "8. If the results are not optimal and there is no over-fitting going on try **adding features** else go to step 17\n", "9. Rescale your features to zero-mean-unit-variance (most methods assume gaussian distributed data) or select those features that have this property\n", "10. Try techniques to avoid over-fitting (including removing features for more info see [feature selection techniques](http://scikit-learn.org/stable/modules/feature_selection.html))\n", "11. Check the validation score\n", "12. If the results are not optimal and there is no over-fitting going on try **random forests** else go to step 17\n", "13. Try techniques to avoid over-fitting (such as feature selection, to rank the features you can use [this approach](http://scikit-learn.org/stable/auto_examples/ensemble/plot_forest_importances.html))\n", "14. Check the validation score\n", "15. If the results are not optimal and there is no over-fitting going on try **neural networks** or **deep learning** else go to step 17. You will only gain from using \n", "16. Try techniques to avoid over-fitting\n", "17. Only in the end check the score on the test set and make sure it is similar to the validation score. Otherwise you have been over-fitting and you need to take a couple steps back.\n", "18. Make an ensemble of your best (but significantly different) methods\n", "19. Finally build the model using all the data available and run it in production\n", "\n", "You can try other machine learning techniques, but usually the difference is quite small. So don't waste too much time on getting to know them because they all have their own quirks and specific ways of over-fitting. Besides maybe most important of all, Kaggle competitions are usually won with one of these techniques.\n", "\n", "If you do want to dive into other methods or if you want more details on the methods discussed here, the [sklearn website](http://scikit-learn.org/stable/) is a good starting point.\n", "\n", "### 8.3 How to avoid over-fitting\n", "\n", "As you should know by now over-fitting is one of the biggest issues in machine learning. So pay attention for it. \n", "\n", "Below you can find some of the most common techniques to avoid over-fitting:\n", "\n", "* Use more data\n", "* Artificially generate more data based on the original data\n", "* Use a smaller model (with fewer parameters)\n", "* Use fewer features (and thus fewer parameters)\n", "* Use a regularisation parameter\n", "* Artificially add noise to your model (can be random noise or can be on/of noise in neural networks so that you get dropout)\n", "* Only use linear models (or in neural networks make sure that the non-linearity in your model is closer to a linear function)\n", "* Combine multiple models that each over-fit in their own peculiar way into what is called an ensemble\n", "\n", "\n", "### 8.4 Most common features\n", "\n", "Although there is no general rule to which features you should use, there are a couple of features that come back regularly:\n", "\n", "* Log: Take the log of the data to make it more Gaussian. This works best for data that is exponentially or log-normally distributed\n", "* Polynomials: The square is quite common but higher orders are often used as well\n", "* Differentials: The first and sometimes the second derivative are used (see [`numpy.diff()`](https://docs.scipy.org/doc/numpy-1.10.1/reference/generated/numpy.diff.html))\n", "* Integrals (use [`numpy.sum()`](https://docs.scipy.org/doc/numpy-1.10.1/reference/generated/numpy.sum.html) for example to implement it)\n", "* Mean: Often used to smooth the data\n", "* Median: Same as the mean but this ignores outliers\n", "* Standard deviation or variance\n", "* Skewness and kurtosis: These are rarely used but sometimes they contain valuable information\n", "* Fourier transform: If your data contains a frequency spectrum. Typically used when processing speech and sound. (see [`numpy.fft.fft()`](https://docs.scipy.org/doc/numpy/reference/generated/numpy.fft.fft.html#numpy.fft.fft))\n", "* Frequency filtering: Similar to the fourier transform (see [`scipy.signal.butter()`](https://docs.scipy.org/doc/scipy-0.18.1/reference/generated/scipy.signal.butter.html#scipy.signal.butter))\n", "* Spatial filters: Are often used for images. Edge detectors for example\n", "* Any other feature that seems to make sense regarding your data\n", "\n", "\n", "## Feedback\n", "\n", "If you have any feedback regarding this tutorial, feel free to share it with us. You can mail to <a href=\"mailto:[email protected]\">[email protected]</a>." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "anaconda-cloud": {}, "celltoolbar": "Create Assignment", "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": "3.9.7" }, "pycharm": { "stem_cell": { "cell_type": "raw", "metadata": { "collapsed": false }, "source": [] } } }, "nbformat": 4, "nbformat_minor": 1 }

mit

google/applied-machine-learning-intensive

content/00_prerequisites/01_intermediate_python/01-exceptions.ipynb

1

13842

{ "cells": [ { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "view-in-github" }, "source": [ "<a href=\"https://colab.research.google.com/github/google/applied-machine-learning-intensive/blob/master/content/00_prerequisites/01_intermediate_python/01-exceptions.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "copyright" }, "source": [ "#### Copyright 2019 Google LLC." ] }, { "cell_type": "code", "execution_count": 0, "metadata": { "colab": {}, "colab_type": "code", "id": "h8rAl_sPizbx" }, "outputs": [], "source": [ "# Licensed under the Apache License, Version 2.0 (the \"License\");\n", "# you may not use this file except in compliance with the License.\n", "# You may obtain a copy of the License at\n", "#\n", "# https://www.apache.org/licenses/LICENSE-2.0\n", "#\n", "# Unless required by applicable law or agreed to in writing, software\n", "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", "# See the License for the specific language governing permissions and\n", "# limitations under the License." ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "zXUyqihI0cQ8" }, "source": [ "# Intermediate Python - Exceptions" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "u_ff6_8Yi6yV" }, "source": [ "In this colab, we will move into a more advanced concept called exceptions. You'll learn how to handle pre-built exceptions and how to build your own exceptions." ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "kRCwiJVGO8SW" }, "source": [ "## Exceptions" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "mjefp6Xi7JGY" }, "source": [ "Inevitably in any coding language, things will go wrong. Data might be of the wrong type, memory might run out, an object that you try to iterate on might be non-iterable, the list goes on and on.\n", "\n", "Exceptions are a way to handle these cases, and tell you where you went wrong. Below is an example of an exception when you try to divide by zero." ] }, { "cell_type": "code", "execution_count": 0, "metadata": { "colab": {}, "colab_type": "code", "id": "zNM0AH847Iu1" }, "outputs": [], "source": [ "1 / 0" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "AcPX9OEG7_Hc" }, "source": [ "Dividing by zero is undefined in mathematics. Whenever you try to divide by zero in Python, you will get the `ZeroDivisionError` exception.\n", "\n", "In practice, you'd likely never hard-code a zero as a denominator. However, you might have two computed variables that you want to calculate the ratio of." ] }, { "cell_type": "code", "execution_count": 0, "metadata": { "colab": {}, "colab_type": "code", "id": "3_ivi-549K3T" }, "outputs": [], "source": [ "my_array = [2, 3, 4]\n", "your_array = []\n", "\n", "ratio = len(my_array) / len(your_array)" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "Ax4rAeYt9jQi" }, "source": [ "There are a few ways to handle this scenario. One is defensive programming, where you check if the denominator is zero using an `if` statement. When you change the number of entries in `your_array`, you will see the output of the cell change." ] }, { "cell_type": "code", "execution_count": 0, "metadata": { "colab": {}, "colab_type": "code", "id": "nFw0-hIJ9ohH" }, "outputs": [], "source": [ "my_array = [2, 3, 4]\n", "your_array = []\n", "\n", "ratio = 0\n", "if len(your_array) != 0:\n", " ratio = len(my_array) / len(your_array)\n", "else:\n", " print(\"Couldn't calculate ratio, denominator is zero\")" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "kTYwCr7I956S" }, "source": [ "Another option is to allow an exception to be thrown, but then catch the exception. You can do this using the `try` keyword, which tries to complete any code within the block, unless an exception matching the `except` keyword is thrown." ] }, { "cell_type": "code", "execution_count": 0, "metadata": { "colab": {}, "colab_type": "code", "id": "IFXloMvr95kI" }, "outputs": [], "source": [ "my_array = [2, 3, 4]\n", "your_array = []\n", "\n", "ratio = 0\n", "try:\n", " ratio = len(my_array) / len(your_array)\n", "except ZeroDivisionError:\n", " print(\"Couldn't calculate ratio, denominator is zero\")" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "OzKm4yf9-Zt2" }, "source": [ "In the example above we caught the `ZeroDivisionError`. This code block could have been written to catch any exception by leaving out the error name." ] }, { "cell_type": "code", "execution_count": 0, "metadata": { "colab": {}, "colab_type": "code", "id": "PsPsjywS-qZC" }, "outputs": [], "source": [ "my_array = [2, 3, 4]\n", "your_array = []\n", "\n", "ratio = 0\n", "try:\n", " ratio = len(my_array) / len(your_array)\n", "except:\n", " print(\"Couldn't calculate ratio, some error occurred\")" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "jUyR0kkM-wy0" }, "source": [ "Catching every possible exception in the `except` block is easy, but can be problematic because you can hide bigger problems in your program. Typically it is best to catch and handle specific errors only.\n", "\n", "If an exception is thrown and not handled with an `except`, it terminates your program. In some cases, this is what you want to happen. For instance, if the program is out of memory, there isn't much you can do at the moment to handle the problem.\n", "\n", "There are varying opinions on whether it is better practice to prevent or handle exceptions. In the example above, is it best to check if a value is zero before dividing by it, or is it best to wrap division in a `try`/`except` block?\n", "\n", "In general, using exceptions for control flow is probably not a good idea. As the name suggests, exceptions should be used for \"exceptional\" cases - things that you don't expect.\n", "\n", "Let's look at some other common exceptions you'll see." ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "NDaQ6OMLBMqF" }, "source": [ "You'll get a `KeyError` if you try to access an element in a dictionary with square braces and the key doesn't exist." ] }, { "cell_type": "code", "execution_count": 0, "metadata": { "colab": {}, "colab_type": "code", "id": "Dky7cLlXBEwQ" }, "outputs": [], "source": [ "my_dict = {\n", " \"a\": 1234\n", "}\n", "\n", "my_dict[\"b\"]" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "CH6fqWSYBdkL" }, "source": [ "You'll get an `IndexError` if you try to access an index in a string, list, or tuple and that index doesn't exist." ] }, { "cell_type": "code", "execution_count": 0, "metadata": { "colab": {}, "colab_type": "code", "id": "HVEG7IbCBY-4" }, "outputs": [], "source": [ "my_array = [1, 2, 3, 4]\n", "my_array[56]" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "ZstGPJOjCAVg" }, "source": [ "The comprehensive list of built-in exceptions can be found in the [official Python documentation](https://docs.python.org/3/library/exceptions.html). Built-in in exceptions are core exceptions provided by Python." ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "BDqieWwCCOER" }, "source": [ "#### Creating Your Own Exceptions\n", "\n", "To create your own error, you simply need to create a class that inherits from the built-in `Exception` class and then `raise` an instance of that class." ] }, { "cell_type": "code", "execution_count": 0, "metadata": { "colab": {}, "colab_type": "code", "id": "iFCWl9KfDCEL" }, "outputs": [], "source": [ "class MyVeryOwnError(Exception):\n", " pass\n", "\n", "raise MyVeryOwnError" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "uNmC8ligDnAF" }, "source": [ "You can then use your error just like any system error. The custom exception is raised in `my_func` if the input is zero. When you change the value of the input to `my_func` in the `try` block, it changes whether the exception is thrown." ] }, { "cell_type": "code", "execution_count": 0, "metadata": { "colab": {}, "colab_type": "code", "id": "Y7RVjfO_DrQP" }, "outputs": [], "source": [ "class MyVeryOwnError(Exception):\n", " pass\n", "\n", "def my_func(x):\n", " if x == 0:\n", " raise MyVeryOwnError\n", " else:\n", " return x\n", "\n", "try:\n", " print(my_func(0))\n", "except MyVeryOwnError:\n", " print(\"Handling my custom exception\")" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "GnavZP1tFWEZ" }, "source": [ "# Exercises" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "l6FbU_oSPKwk" }, "source": [ "## Exercise 1" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "0gBLFnUXFZBS" }, "source": [ "What are some reasons that you might want to create your own exception?\n" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "J45CYZQE7l3Q" }, "source": [ "### Student Solution" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "_IyHuI-dHWKM" }, "source": [ "*Your answer here*" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "5_x1oionanus" }, "source": [ "---" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "exercise-1-solution-1" }, "source": [ "**Solution**" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "pBF0zPgPIIus" }, "source": [ "To provide more readable and specific information when the code throws an error, which is helpful for debugging.\n", "\n", "This article has a nice explanation: https://dbader.org/blog/python-custom-exceptions" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "S3qFdLwtPNig" }, "source": [ "## Exercise 2" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "crpuwPeQFm3g" }, "source": [ "Handle the exception in the code block below using `try`/`except`. If the addition can't be done, print \"Unable to add\"." ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "yZgWoufL8ACG" }, "source": [ "### Student Solution" ] }, { "cell_type": "code", "execution_count": 0, "metadata": { "colab": {}, "colab_type": "code", "id": "eo18xFbzFuIT" }, "outputs": [], "source": [ "left = 1\n", "right = \"2\"\n", "\n", "### YOUR CODE HERE ###\n", "\n", "left + right" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "gEKp-R2ga2o_" }, "source": [ "---" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "rBXxC_IEPU5c" }, "source": [ "## Exercise 3" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "A2ypEESJGsDu" }, "source": [ "Using `if`/`else` or some other flow control, prevent the exception in the code below from being thrown." ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "QaJNZW8H8JxL" }, "source": [ "### Student Solution" ] }, { "cell_type": "code", "execution_count": 0, "metadata": { "colab": {}, "colab_type": "code", "id": "Cb2DNg3kG5dh" }, "outputs": [], "source": [ "array_one = [1, 2, 3]\n", "array_two = [4, 5]\n", "\n", "### YOUR CODE HERE ###\n", "\n", "for i in range(len(array_one)):\n", " print(array_one[i] + array_two[i])" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "tOdueeOWa7PE" }, "source": [ "---" ] } ], "metadata": { "colab": { "collapsed_sections": [ "copyright", "exercise-1-key-1", "exercise-2-key-1", "exercise-3-key-1" ], "include_colab_link": true, "name": "Intermediate Python - Exceptions", "private_outputs": true, "provenance": [], "toc_visible": true }, "kernelspec": { "display_name": "Python 3", "name": "python3" } }, "nbformat": 4, "nbformat_minor": 0 }

apache-2.0

twosigma/beakerx

doc/old/DisplayVegaInJupyterLab.ipynb

3

979

{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "// bar-chart.vg.json from https://gist.github.com/hadim/629b8fde56eac74c587136139a0dd5c2#file-bar-chart-vg-json\n", "\n", "import com.twosigma.beakerx.mimetype.MIMEContainer\n", "import groovy.json.JsonSlurper\n", " \n", "fname = \"../resources/bar-chart.vg.json\"\n", "fileContents = new File(fname).text\n", "def jsonSlurper = new JsonSlurper()\n", "def json = jsonSlurper.parseText(fileContents)\n", "new MIMEContainer(\"application/vnd.vega.v2+json\", json)" ] } ], "metadata": { "kernelspec": { "display_name": "Groovy", "language": "groovy", "name": "groovy" }, "language_info": { "codemirror_mode": "groovy", "file_extension": ".groovy", "mimetype": "", "name": "Groovy", "nbconverter_exporter": "", "version": "2.4.3" } }, "nbformat": 4, "nbformat_minor": 2 }

apache-2.0

JoeriHermans/dist-keras

examples/mnist.ipynb

3

25370

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# MNIST using Distributed Keras\n", "\n", "**Joeri Hermans** (Technical Student, IT-DB-SAS, CERN) \n", "*Departement of Knowledge Engineering* \n", "*Maastricht University, The Netherlands*" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "!(date +%d\\ %B\\ %G)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this notebook we will show you how to process the [MNIST](http://yann.lecun.com/exdb/mnist/) dataset using Distributed Keras. As in the [workflow](https://github.com/JoeriHermans/dist-keras/blob/master/examples/workflow.ipynb) notebook, we will guide you through the complete machine learning pipeline.\n", "\n", "## Preparation\n", "\n", "To get started, we first load all the required imports. Please make sure you installed `dist-keras`, and `seaborn`. Furthermore, we assume that you have access to an installation which provides Apache Spark.\n", "\n", "Before you start this notebook, place the MNIST dataset (which is provided in this repository) on HDFS. Or in the case HDFS is not available, place it on the local filesystem. But make sure the path to the file is identical for all computing nodes." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline\n", "\n", "import numpy as np\n", "\n", "import seaborn as sns\n", "\n", "from keras.optimizers import *\n", "from keras.models import Sequential\n", "from keras.layers.core import *\n", "from keras.layers.convolutional import *\n", "\n", "from pyspark import SparkContext\n", "from pyspark import SparkConf\n", "\n", "from matplotlib import pyplot as plt\n", "import matplotlib.patches as mpatches\n", "\n", "from pyspark.ml.feature import StandardScaler\n", "from pyspark.ml.feature import VectorAssembler\n", "from pyspark.ml.feature import OneHotEncoder\n", "from pyspark.ml.feature import MinMaxScaler\n", "from pyspark.ml.feature import StringIndexer\n", "from pyspark.ml.evaluation import MulticlassClassificationEvaluator\n", "\n", "from distkeras.trainers import *\n", "from distkeras.predictors import *\n", "from distkeras.transformers import *\n", "from distkeras.evaluators import *\n", "from distkeras.utils import *" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In the following cell, adapt the parameters to fit your personal requirements." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Modify these variables according to your needs.\n", "application_name = \"Distributed Keras MNIST Notebook\"\n", "using_spark_2 = False\n", "local = False\n", "path_train = \"data/mnist_train.csv\"\n", "path_test = \"data/mnist_test.csv\"\n", "if local:\n", " # Tell master to use local resources.\n", " master = \"local[*]\"\n", " num_processes = 3\n", " num_executors = 1\n", "else:\n", " # Tell master to use YARN.\n", " master = \"yarn-client\"\n", " num_executors = 20\n", " num_processes = 1" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# This variable is derived from the number of cores and executors, and will be used to assign the number of model trainers.\n", "num_workers = num_executors * num_processes\n", "\n", "print(\"Number of desired executors: \" + `num_executors`)\n", "print(\"Number of desired processes / executor: \" + `num_processes`)\n", "print(\"Total number of workers: \" + `num_workers`)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import os\n", "\n", "# Use the DataBricks CSV reader, this has some nice functionality regarding invalid values.\n", "os.environ['PYSPARK_SUBMIT_ARGS'] = '--packages com.databricks:spark-csv_2.10:1.4.0 pyspark-shell'" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "conf = SparkConf()\n", "conf.set(\"spark.app.name\", application_name)\n", "conf.set(\"spark.master\", master)\n", "conf.set(\"spark.executor.cores\", `num_processes`)\n", "conf.set(\"spark.executor.instances\", `num_executors`)\n", "conf.set(\"spark.executor.memory\", \"4g\")\n", "conf.set(\"spark.locality.wait\", \"0\")\n", "conf.set(\"spark.serializer\", \"org.apache.spark.serializer.KryoSerializer\");\n", "\n", "# Check if the user is running Spark 2.0 +\n", "if using_spark_2:\n", " sc = SparkSession.builder.config(conf=conf) \\\n", " .appName(application_name) \\\n", " .getOrCreate()\n", "else:\n", " # Create the Spark context.\n", " sc = SparkContext(conf=conf)\n", " # Add the missing imports\n", " from pyspark import SQLContext\n", " sqlContext = SQLContext(sc)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Check if we are using Spark 2.0\n", "if using_spark_2:\n", " reader = sc\n", "else:\n", " reader = sqlContext\n", "# Read the training dataset.\n", "raw_dataset_train = reader.read.format('com.databricks.spark.csv') \\\n", " .options(header='true', inferSchema='true') \\\n", " .load(path_train)\n", "# Read the testing dataset.\n", "raw_dataset_test = reader.read.format('com.databricks.spark.csv') \\\n", " .options(header='true', inferSchema='true') \\\n", " .load(path_test)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As shown in the output of the cell above, we see that every pixel is associated with a seperate column. In order to ensure compatibility with Apache Spark, we vectorize the columns, and add the resulting vectors as a seperate column. However, in order to achieve this, we first need a list of the required columns. This is shown in the cell below." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# First, we would like to extract the desired features from the raw dataset.\n", "# We do this by constructing a list with all desired columns.\n", "# This is identical for the test set.\n", "features = raw_dataset_train.columns\n", "features.remove('label')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Once we have a list of columns names, we can pass this to Spark's [VectorAssembler](http://spark.apache.org/docs/latest/ml-features.html#vectorassembler). This VectorAssembler will take a list of features, vectorize them, and place them in a column defined in `outputCol`." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Next, we use Spark's VectorAssembler to \"assemble\" (create) a vector of all desired features.\n", "# http://spark.apache.org/docs/latest/ml-features.html#vectorassembler\n", "vector_assembler = VectorAssembler(inputCols=features, outputCol=\"features\")\n", "# This transformer will take all columns specified in features, and create an additional column \"features\" which will contain all the desired features aggregated into a single vector.\n", "dataset_train = vector_assembler.transform(raw_dataset_train)\n", "dataset_test = vector_assembler.transform(raw_dataset_test)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Once we have the inputs for our Neural Network (features column) after applying the VectorAssembler, we should also define the outputs. Since we are dealing with a classification task, the output of our Neural Network should be a one-hot encoded vector with 10 elements. For this, we provide a `OneHotTransformer` which accomplish this exact task." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Define the number of output classes.\n", "nb_classes = 10\n", "encoder = OneHotTransformer(nb_classes, input_col=\"label\", output_col=\"label_encoded\")\n", "dataset_train = encoder.transform(dataset_train)\n", "dataset_test = encoder.transform(dataset_test)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## MNIST\n", "\n", "[MNIST](http://yann.lecun.com/exdb/mnist/) is a dataset of handwritten digits. Every image is a 28 by 28 pixel grayscale image. This means that every pixel has a value between 0 and 255. Some examples of instances within this dataset are shown in the cells below." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def show_instances(column):\n", " global dataset\n", "\n", " num_instances = 6 # Number of instances you would like to draw.\n", " x_dimension = 3 # Number of images to draw on the x-axis.\n", " y_dimension = 2 # Number of images to draw on the y-axis.\n", "\n", " # Fetch 3 different instance from the dataset.\n", " instances = dataset_train.select(column).take(num_instances)\n", " # Process the instances.\n", " for i in range(0, num_instances):\n", " instance = instances[i]\n", " instance = instance[column].toArray().reshape((28, 28))\n", " instances[i] = instance\n", "\n", " # Draw the sampled instances.\n", " fig, axn = plt.subplots(y_dimension, x_dimension, sharex=True, sharey=True)\n", " num_axn = len(axn.flat)\n", " for i in range(0, num_axn):\n", " ax = axn.flat[i]\n", " h = sns.heatmap(instances[i], ax=ax)\n", " h.set_yticks([])\n", " h.set_xticks([])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "show_instances(\"features\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Normalization\n", "\n", "In this Section, we will normalize the feature vectors between the 0 and 1 range." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Clear the dataset in the case you ran this cell before.\n", "dataset_train = dataset_train.select(\"features\", \"label\", \"label_encoded\")\n", "dataset_test = dataset_test.select(\"features\", \"label\", \"label_encoded\")\n", "# Allocate a MinMaxTransformer using Distributed Keras.\n", "# o_min -> original_minimum\n", "# n_min -> new_minimum\n", "transformer = MinMaxTransformer(n_min=0.0, n_max=1.0, \\\n", " o_min=0.0, o_max=250.0, \\\n", " input_col=\"features\", \\\n", " output_col=\"features_normalized\")\n", "# Transform the dataset.\n", "dataset_train = transformer.transform(dataset_train)\n", "dataset_test = transformer.transform(dataset_test)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "show_instances(\"features_normalized\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Convolutions\n", "\n", "In order to make the dense vectors compatible with convolution operations in Keras, we add another column which contains the matrix form of these images. We provide a utility class (MatrixTransformer), which helps you with this." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "reshape_transformer = ReshapeTransformer(\"features_normalized\", \"matrix\", (28, 28, 1))\n", "dataset_train = reshape_transformer.transform(dataset_train)\n", "dataset_test = reshape_transformer.transform(dataset_test)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Model Development" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Multilayer Perceptron" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "mlp = Sequential()\n", "mlp.add(Dense(1000, input_shape=(784,)))\n", "mlp.add(Activation('relu'))\n", "mlp.add(Dense(250))\n", "mlp.add(Activation('relu'))\n", "mlp.add(Dense(10))\n", "mlp.add(Activation('softmax'))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "mlp.summary()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "optimizer_mlp = 'adam'\n", "loss_mlp = 'categorical_crossentropy'" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Convolutional network" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Taken from Keras MNIST example.\n", "\n", "# Declare model parameters.\n", "img_rows, img_cols = 28, 28\n", "# number of convolutional filters to use\n", "nb_filters = 32\n", "# size of pooling area for max pooling\n", "pool_size = (2, 2)\n", "# convolution kernel size\n", "kernel_size = (3, 3)\n", "input_shape = (img_rows, img_cols, 1)\n", "\n", "# Construct the model.\n", "convnet = Sequential()\n", "convnet.add(Convolution2D(nb_filters, kernel_size[0], kernel_size[1],\n", " border_mode='valid',\n", " input_shape=input_shape))\n", "convnet.add(Activation('relu'))\n", "convnet.add(Convolution2D(nb_filters, kernel_size[0], kernel_size[1]))\n", "convnet.add(Activation('relu'))\n", "convnet.add(MaxPooling2D(pool_size=pool_size))\n", "\n", "convnet.add(Flatten())\n", "convnet.add(Dense(225))\n", "convnet.add(Activation('relu'))\n", "convnet.add(Dense(nb_classes))\n", "convnet.add(Activation('softmax'))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "convnet.summary()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "optimizer_convnet = 'adam'\n", "loss_convnet = 'categorical_crossentropy'" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Evaluation\n", "\n", "We define a utility function which will compute the accuracy for us." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def evaluate_accuracy(model, test_set, features=\"features_normalized_dense\"):\n", " evaluator = AccuracyEvaluator(prediction_col=\"prediction_index\", label_col=\"label\")\n", " predictor = ModelPredictor(keras_model=model, features_col=features)\n", " transformer = LabelIndexTransformer(output_dim=nb_classes)\n", " test_set = test_set.select(features, \"label\")\n", " test_set = predictor.predict(test_set)\n", " test_set = transformer.transform(test_set)\n", " score = evaluator.evaluate(test_set)\n", " \n", " return score" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Training" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "dataset_train.printSchema()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "dataset_train = dataset_train.select(\"features_normalized\", \"matrix\",\"label\", \"label_encoded\")\n", "dataset_test = dataset_test.select(\"features_normalized\", \"matrix\",\"label\", \"label_encoded\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "dense_transformer = DenseTransformer(input_col=\"features_normalized\", output_col=\"features_normalized_dense\")\n", "dataset_train = dense_transformer.transform(dataset_train)\n", "dataset_test = dense_transformer.transform(dataset_test)\n", "dataset_train.repartition(num_workers)\n", "dataset_test.repartition(num_workers)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Assing the training and test set.\n", "training_set = dataset_train.repartition(num_workers)\n", "test_set = dataset_test.repartition(num_workers)\n", "# Cache them.\n", "training_set.cache()\n", "test_set.cache()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "print(training_set.count())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### DOWNPOUR (Multilayer Perceptron)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "trainer = DOWNPOUR(keras_model=mlp, worker_optimizer=optimizer_mlp, loss=loss_mlp, num_workers=num_workers,\n", " batch_size=4, communication_window=5, num_epoch=1,\n", " features_col=\"features_normalized_dense\", label_col=\"label_encoded\")\n", "trained_model = trainer.train(training_set)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "print(\"Training time: \" + str(trainer.get_training_time()))\n", "print(\"Accuracy: \" + str(evaluate_accuracy(trained_model, test_set)))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "trainer.parameter_server.num_updates" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### ADAG (MultiLayer Perceptron)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "trainer = ADAG(keras_model=mlp, worker_optimizer=optimizer_mlp, loss=loss_mlp, num_workers=num_workers,\n", " batch_size=4, communication_window=15, num_epoch=1,\n", " features_col=\"features_normalized_dense\", label_col=\"label_encoded\")\n", "trained_model = trainer.train(training_set)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "print(\"Training time: \" + str(trainer.get_training_time()))\n", "print(\"Accuracy: \" + str(evaluate_accuracy(trained_model, test_set)))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [], "source": [ "trainer.parameter_server.num_updates" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### EASGD (MultiLayer Perceptron)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "trainer = AEASGD(keras_model=mlp, worker_optimizer=optimizer_mlp, loss=loss_mlp, num_workers=num_workers,\n", " batch_size=4, communication_window=35, num_epoch=1, features_col=\"features_normalized_dense\",\n", " label_col=\"label_encoded\")\n", "trained_model = trainer.train(training_set)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "print(\"Training time: \" + str(trainer.get_training_time()))\n", "print(\"Accuracy: \" + str(evaluate_accuracy(trained_model, test_set)))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "trainer.parameter_server.num_updates" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### DOWNPOUR (Convolutional network)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "trainer = DOWNPOUR(keras_model=convnet, worker_optimizer=optimizer_convnet, loss=loss_convnet,\n", " num_workers=num_workers, batch_size=4, communication_window=5,\n", " num_epoch=1, features_col=\"matrix\", label_col=\"label_encoded\")\n", "trainer.set_parallelism_factor(1)\n", "trained_model = trainer.train(training_set)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "print(\"Training time: \" + str(trainer.get_training_time()))\n", "print(\"Accuracy: \" + str(evaluate_accuracy(trained_model, test_set, \"matrix\")))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "trainer.parameter_server.num_updates" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### ADAG (Convolutional network)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "trainer = ADAG(keras_model=convnet, worker_optimizer=optimizer_convnet, loss=loss_convnet,\n", " num_workers=num_workers, batch_size=15, communication_window=5, num_epoch=1,\n", " features_col=\"matrix\", label_col=\"label_encoded\")\n", "trainer.set_parallelism_factor(1)\n", "trained_model = trainer.train(training_set)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "print(\"Training time: \" + str(trainer.get_training_time()))\n", "print(\"Accuracy: \" + str(evaluate_accuracy(trained_model, test_set, \"matrix\")))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [], "source": [ "trainer.parameter_server.num_updates" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### EASGD (Convolutional network)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "trainer = AEASGD(keras_model=convnet, worker_optimizer=optimizer_convnet, loss=loss_convnet, \n", " num_workers=num_workers, batch_size=35, communication_window=32, num_epoch=1,\n", " features_col=\"matrix\", label_col=\"label_encoded\")\n", "trained_model = trainer.train(training_set)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "print(\"Training time: \" + str(trainer.get_training_time()))\n", "print(\"Accuracy: \" + str(evaluate_accuracy(trained_model, test_set, \"matrix\")))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "trainer.parameter_server.num_updates" ] } ], "metadata": { "anaconda-cloud": {}, "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 }

gpl-3.0

hetaodie/hetaodie.github.io

assets/media/uda-ml/qinghua/dongtaiguihua/迷你项目：动态规划（第 0 部分和第 1 部分）/Dynamic_Programming_Solution-zh.ipynb

4

29007

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# 迷你项目：动态规划\n", "\n", "在此 notebook 中，你将自己编写很多经典动态规划算法的实现。\n", "\n", "虽然我们提供了一些起始代码，但是你可以删掉这些提示并从头编写代码。\n", "\n", "### 第 0 部分：探索 FrozenLakeEnv\n", "\n", "请使用以下代码单元格创建 [FrozenLake](https://github.com/openai/gym/blob/master/gym/envs/toy_text/frozen_lake.py) 环境的实例。" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from frozenlake import FrozenLakeEnv\n", "\n", "env = FrozenLakeEnv()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "智能体将会在 $4 \\times 4$ 网格世界中移动，状态编号如下所示：" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "[[ 0 1 2 3]\n", " [ 4 5 6 7]\n", " [ 8 9 10 11]\n", " [12 13 14 15]]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "智能体可以执行 4 个潜在动作：" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "LEFT = 0\n", "DOWN = 1\n", "RIGHT = 2\n", "UP = 3" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "因此，$\\mathcal{S}^+ = \\{0, 1, \\ldots, 15\\}$ 以及 $\\mathcal{A} = \\{0, 1, 2, 3\\}$。请通过运行以下代码单元格验证这一点。" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# print the state space and action space\n", "print(env.observation_space)\n", "print(env.action_space)\n", "\n", "# print the total number of states and actions\n", "print(env.nS)\n", "print(env.nA)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "Discrete(16)\n", "Discrete(4)\n", "16\n", "4\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "动态规划假设智能体完全了解 MDP。我们已经修改了 `frozenlake.py` 文件以使智能体能够访问一步动态特性。 \n", "\n", "请执行以下代码单元格以返回特定状态和动作对应的一步动态特性。具体而言，当智能体在网格世界中以状态 1 向左移动时，`env.P[1][0]` 会返回每个潜在奖励的概率和下一个状态。" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "env.P[1][0]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "[(0.3333333333333333, 1, 0.0, False),\n", " (0.3333333333333333, 0, 0.0, False),\n", " (0.3333333333333333, 5, 0.0, True)]\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "每个条目的格式如下所示" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "prob, next_state, reward, done" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "其中：\n", "- `prob` 详细说明了相应的 (`next_state`, `reward`) 对的条件概率，以及\n", "- 如果 `next_state` 是终止状态，则 `done` 是 `True` ，否则是 `False`。\n", "\n", "因此，我们可以按照以下方式解析 `env.P[1][0]`：\n", "$$\n", "\\mathbb{P}(S_{t+1}=s',R_{t+1}=r|S_t=1,A_t=0) = \\begin{cases}\n", " \\frac{1}{3} \\text{ if } s'=1, r=0\\\\\n", " \\frac{1}{3} \\text{ if } s'=0, r=0\\\\\n", " \\frac{1}{3} \\text{ if } s'=5, r=0\\\\\n", " 0 \\text{ else}\n", " \\end{cases}\n", "$$\n", "\n", "你可以随意更改上述代码单元格，以探索在其他（状态、动作）对下环境的行为是怎样的。\n", "\n", "### 第 1 部分：迭代策略评估\n", "\n", "在此部分，你将自己编写迭代策略评估的实现。\n", "\n", "你的算法应该有四个**输入**参数：\n", "- `env`：这是 OpenAI Gym 环境的实例，其中 `env.P` 会返回一步动态特性。\n", "- `policy`：这是一个二维 numpy 数组，其中 `policy.shape[0]` 等于状态数量 (`env.nS`) ， `policy.shape[1]` 等于动作数量 (`env.nA`) 。`policy[s][a]` 返回智能体在状态 `s` 时根据该策略选择动作 `a` 的概率。\n", "- `gamma`：这是折扣率。它必须是在 0 到 1（含）之间的值，默认值为：`1`。\n", "- `theta`：这是一个非常小的正数，用于判断估算值是否足够地收敛于真值函数 (默认值为：`1e-8`）。\n", "\n", "该算法会返回以下**输出结果**：\n", "- `V`：这是一个一维numpy数组，其中 `V.shape[0]` 等于状态数量 (`env.nS`)。`V[s]` 包含状态 `s` 在输入策略下的估算值。\n", "\n", "请完成以下代码单元格中的函数。" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "\n", "def policy_evaluation(env, policy, gamma=1, theta=1e-8):\n", " V = np.zeros(env.nS)\n", " while True:\n", " delta = 0\n", " for s in range(env.nS):\n", " Vs = 0\n", " for a, action_prob in enumerate(policy[s]):\n", " for prob, next_state, reward, done in env.P[s][a]:\n", " Vs += action_prob * prob * (reward + gamma * V[next_state])\n", " delta = max(delta, np.abs(V[s]-Vs))\n", " V[s] = Vs\n", " if delta < theta:\n", " break\n", " return V" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "我们将评估等概率随机策略 $\\pi$，其中对于所有 $s\\in\\mathcal{S}$ 和 $a\\in\\mathcal{A}(s)$ ，$\\pi(a|s) = \\frac{1}{|\\mathcal{A}(s)|}$。 \n", "\n", "请使用以下代码单元格在变量 `random_policy`中指定该策略。" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "random_policy = np.ones([env.nS, env.nA]) / env.nA" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "运行下个代码单元格以评估等概率随机策略并可视化输出结果。状态值函数已调整形状，以匹配网格世界的形状。" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from plot_utils import plot_values\n", "\n", "# evaluate the policy \n", "V = policy_evaluation(env, random_policy)\n", "\n", "plot_values(V)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "\n", "\n", "运行以下代码单元格以测试你的函数。如果代码单元格返回 **PASSED**，则表明你正确地实现了该函数！ \n", "\n", "**注意：**为了确保结果准确，确保你的 `policy_evaluation` 函数满足上文列出的要求（具有四个输入、一个输出，并且没有更改输入参数的默认值）。" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import check_test\n", "\n", "check_test.run_check('policy_evaluation_check', policy_evaluation)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**<span style=\"color: green;\">PASSED</span>**\n", "\n", "\n", "### 第 2 部分：通过 $v_\\pi$ 获取 $q_\\pi$\n", "\n", "在此部分，你将编写一个函数，该函数的输入是状态值函数估值以及一些状态 $s\\in\\mathcal{S}$。它会返回输入状态 $s\\in\\mathcal{S}$ 对应的**动作值函数中的行**。即你的函数应同时接受输入 $v_\\pi$ 和 $s$，并针对所有 $a\\in\\mathcal{A}(s)$ 返回 $q_\\pi(s,a)$。\n", "\n", "你的算法应该有四个**输入**参数：\n", "- `env`：这是 OpenAI Gym 环境的实例，其中 `env.P` 会返回一步动态特性。\n", "- `V`：这是一个一维 numpy 数组，其中 `V.shape[0]` 等于状态数量 (`env.nS`)。`V[s]` 包含状态 `s` 的估值。\n", "- `s`：这是环境中的状态对应的整数。它应该是在 `0` 到 `(env.nS)-1`（含）之间的值。\n", "- `gamma`：这是折扣率。它必须是在 0 到 1（含）之间的值，默认值为：`1`。\n", "\n", "该算法会返回以下**输出结果**：\n", "- `q`：这是一个一维 numpy 数组，其中 `q.shape[0]` 等于动作数量 (`env.nA`)。`q[a]` 包含状态 `s` 和动作 `a` 的（估算）值。\n", "\n", "请完成以下代码单元格中的函数。" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def q_from_v(env, V, s, gamma=1):\n", " q = np.zeros(env.nA)\n", " for a in range(env.nA):\n", " for prob, next_state, reward, done in env.P[s][a]:\n", " q[a] += prob * (reward + gamma * V[next_state])\n", " return q" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "请运行以下代码单元格以输出上述状态值函数对应的动作值函数。" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "Q = np.zeros([env.nS, env.nA])\n", "for s in range(env.nS):\n", " Q[s] = q_from_v(env, V, s)\n", "print(\"Action-Value Function:\")\n", "print(Q)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "Action-Value Function:\n", "[[ 0.0147094 0.01393978 0.01393978 0.01317015]\n", " [ 0.00852356 0.01163091 0.0108613 0.01550788]\n", " [ 0.02444514 0.02095298 0.02406033 0.01435346]\n", " [ 0.01047649 0.01047649 0.00698432 0.01396865]\n", " [ 0.02166487 0.01701828 0.01624865 0.01006281]\n", " [ 0. 0. 0. 0. ]\n", " [ 0.05433538 0.04735105 0.05433538 0.00698432]\n", " [ 0. 0. 0. 0. ]\n", " [ 0.01701828 0.04099204 0.03480619 0.04640826]\n", " [ 0.07020885 0.11755991 0.10595784 0.05895312]\n", " [ 0.18940421 0.17582037 0.16001424 0.04297382]\n", " [ 0. 0. 0. 0. ]\n", " [ 0. 0. 0. 0. ]\n", " [ 0.08799677 0.20503718 0.23442716 0.17582037]\n", " [ 0.25238823 0.53837051 0.52711478 0.43929118]\n", " [ 0. 0. 0. 0. ]]\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "运行以下代码单元格以测试你的函数。如果代码单元格返回 **PASSED**，则表明你正确地实现了该函数！ \n", "\n", "**注意：**为了确保结果准确，确保 `q_from_v` 函数满足上文列出的要求（具有四个输入、一个输出，并且没有更改输入参数的默认值）。" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "check_test.run_check('q_from_v_check', q_from_v)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**<span style=\"color: green;\">PASSED</span>**\n", "\n", "\n", "### 第 3 部分：策略改进\n", "\n", "在此部分，你将自己编写策略改进实现。 \n", "\n", "你的算法应该有三个**输入**参数：\n", "- `env`：这是 OpenAI Gym 环境的实例，其中 `env.P` 会返回一步动态特性。\n", "- `V`：这是一个一维 numpy 数组，其中 `V.shape[0]` 等于状态数量 (`env.nS`)。`V[s]` 包含状态 `s` 的估值。\n", "- `gamma`：这是折扣率。它必须是在 0 到 1（含）之间的值，默认值为：`1`。\n", "\n", "该算法会返回以下**输出结果**：\n", "- `policy`：这是一个二维 numpy 数组，其中 `policy.shape[0]` 等于状态数量 (`env.nS`) ， `policy.shape[1]` 等于动作数量 (`env.nA`) 。`policy[s][a]` 返回智能体在状态 `s` 时根据该策略选择动作 `a` 的概率。\n", "\n", "请完成以下代码单元格中的函数。建议你使用你在上文实现的 `q_from_v` 函数。" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def policy_improvement(env, V, gamma=1):\n", " policy = np.zeros([env.nS, env.nA]) / env.nA\n", " for s in range(env.nS):\n", " q = q_from_v(env, V, s, gamma)\n", " \n", " # OPTION 1: construct a deterministic policy \n", " # policy[s][np.argmax(q)] = 1\n", " \n", " # OPTION 2: construct a stochastic policy that puts equal probability on maximizing actions\n", " best_a = np.argwhere(q==np.max(q)).flatten()\n", " policy[s] = np.sum([np.eye(env.nA)[i] for i in best_a], axis=0)/len(best_a)\n", " \n", " return policy" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "运行以下代码单元格以测试你的函数。如果代码单元格返回 **PASSED**，则表明你正确地实现了该函数！ \n", "\n", "**注意：**为了确保结果准确，确保 `policy_improvement` 函数满足上文列出的要求（具有三个输入、一个输出，并且没有更改输入参数的默认值）。\n", "\n", "在继续转到该 notebook 的下个部分之前，强烈建议你参阅 **Dynamic_Programming_Solution.ipynb** 中的解决方案。该函数有很多正确的实现方式！" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "check_test.run_check('policy_improvement_check', policy_improvement)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**<span style=\"color: green;\">PASSED</span>**\n", "\n", "\n", "### 第 4 部分：策略迭代\n", "\n", "在此部分，你将自己编写策略迭代的实现。该算法会返回最优策略，以及相应的状态值函数。\n", "\n", "你的算法应该有三个**输入**参数：\n", "- `env`：这是 OpenAI Gym 环境的实例，其中 `env.P` 会返回一步动态特性。\n", "- `gamma`：这是折扣率。它必须是在 0 到 1（含）之间的值，默认值为：`1`。\n", "- `theta`：这是一个非常小的正数，用于判断策略评估步骤是否足够地收敛于真值函数 (默认值为：`1e-8`）。\n", "\n", "该算法会返回以下**输出结果**：\n", "- `policy`：这是一个二维 numpy 数组，其中 `policy.shape[0]` 等于状态数量 (`env.nS`) ， `policy.shape[1]` 等于动作数量 (`env.nA`) 。`policy[s][a]` 返回智能体在状态 `s` 时根据该策略选择动作 `a` 的概率。\n", "- `V`：这是一个一维 numpy 数组，其中 `V.shape[0]` 等于状态数量 (`env.nS`)。`V[s]` 包含状态 `s` 的估值。\n", "\n", "请完成以下代码单元格中的函数。强烈建议你使用你在上文实现的 `policy_evaluation` 和 `policy_improvement` 函数。" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import copy\n", "\n", "def policy_iteration(env, gamma=1, theta=1e-8):\n", " policy = np.ones([env.nS, env.nA]) / env.nA\n", " while True:\n", " V = policy_evaluation(env, policy, gamma, theta)\n", " new_policy = policy_improvement(env, V)\n", " \n", " # OPTION 1: stop if the policy is unchanged after an improvement step\n", " if (new_policy == policy).all():\n", " break;\n", " \n", " # OPTION 2: stop if the value function estimates for successive policies has converged\n", " # if np.max(abs(policy_evaluation(env, policy) - policy_evaluation(env, new_policy))) < theta*1e2:\n", " # break;\n", " \n", " policy = copy.copy(new_policy)\n", " return policy, V" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "运行下个代码单元格以解决该 MDP 并可视化输出结果。最优状态值函数已调整形状，以匹配网格世界的形状。\n", "\n", "**将该最优状态值函数与此 notebook 第 1 部分的状态值函数进行比较**。_最优状态值函数一直都大于或等于等概率随机策略的状态值函数吗？_" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# obtain the optimal policy and optimal state-value function\n", "policy_pi, V_pi = policy_iteration(env)\n", "\n", "# print the optimal policy\n", "print(\"\\nOptimal Policy (LEFT = 0, DOWN = 1, RIGHT = 2, UP = 3):\")\n", "print(policy_pi,\"\\n\")\n", "\n", "plot_values(V_pi)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "Optimal Policy (LEFT = 0, DOWN = 1, RIGHT = 2, UP = 3):\n", "[[ 1. 0. 0. 0. ]\n", " [ 0. 0. 0. 1. ]\n", " [ 0. 0. 0. 1. ]\n", " [ 0. 0. 0. 1. ]\n", " [ 1. 0. 0. 0. ]\n", " [ 0.25 0.25 0.25 0.25]\n", " [ 0.5 0. 0.5 0. ]\n", " [ 0.25 0.25 0.25 0.25]\n", " [ 0. 0. 0. 1. ]\n", " [ 0. 1. 0. 0. ]\n", " [ 1. 0. 0. 0. ]\n", " [ 0.25 0.25 0.25 0.25]\n", " [ 0.25 0.25 0.25 0.25]\n", " [ 0. 0. 1. 0. ]\n", " [ 0. 1. 0. 0. ]\n", " [ 0.25 0.25 0.25 0.25]] \n", "\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "\n", "\n", "运行以下代码单元格以测试你的函数。如果代码单元格返回 **PASSED**，则表明你正确地实现了该函数！ \n", "\n", "**注意：**为了确保结果准确，确保 `policy_iteratio` 函数满足上文列出的要求（具有三个输入、两个输出，并且没有更改输入参数的默认值）。" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "check_test.run_check('policy_iteration_check', policy_iteration)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**<span style=\"color: green;\">PASSED</span>**\n", "\n", "\n", "### 第 5 部分：截断策略迭代\n", "\n", "在此部分，你将自己编写截断策略迭代的实现。 \n", "\n", "首先，你将实现截断策略评估。你的算法应该有五个**输入**参数：\n", "- `env`：这是 OpenAI Gym 环境的实例，其中 `env.P` 会返回一步动态特性。\n", "- `policy`：这是一个二维 numpy 数组，其中 `policy.shape[0]` 等于状态数量 (`env.nS`) ， `policy.shape[1]` 等于动作数量 (`env.nA`) 。`policy[s][a]` 返回智能体在状态 `s` 时根据该策略选择动作 `a` 的概率。\n", "- `V`：这是一个一维 numpy 数组，其中 `V.shape[0]` 等于状态数量 (`env.nS`)。`V[s]` 包含状态 `s` 的估值。\n", "- `max_it`：这是一个正整数，对应的是经历状态空间的次数（默认值为：`1`）。\n", "- `gamma`：这是折扣率。它必须是在 0 到 1（含）之间的值，默认值为：`1`。\n", "\n", "该算法会返回以下**输出结果**：\n", "- `V`：这是一个一维 numpy 数组，其中 `V.shape[0]` 等于状态数量 (`env.nS`)。`V[s]` 包含状态 `s` 的估值。\n", "\n", "请完成以下代码单元格中的函数。" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def truncated_policy_evaluation(env, policy, V, max_it=1, gamma=1):\n", " num_it=0\n", " while num_it < max_it:\n", " for s in range(env.nS):\n", " v = 0\n", " q = q_from_v(env, V, s, gamma)\n", " for a, action_prob in enumerate(policy[s]):\n", " v += action_prob * q[a]\n", " V[s] = v\n", " num_it += 1\n", " return V" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "接着，你将实现截断策略迭代。你的算法应该接受五个**输入**参数：\n", "- `env`：这是 OpenAI Gym 环境的实例，其中 `env.P` 会返回一步动态特性。\n", "- `max_it`：这是一个正整数，对应的是经历状态空间的次数（默认值为：`1`）。\n", "- `gamma`：这是折扣率。它必须是在 0 到 1（含）之间的值，默认值为：`1`。\n", "- `theta`：这是一个非常小的正整数，用作停止条件（默认值为：`1e-8`）。\n", "\n", "该算法会返回以下**输出结果**：\n", "- `policy`：这是一个二维 numpy 数组，其中 `policy.shape[0]` 等于状态数量 (`env.nS`) ， `policy.shape[1]` 等于动作数量 (`env.nA`) 。`policy[s][a]` 返回智能体在状态 `s` 时根据该策略选择动作 `a` 的概率。\n", "- `V`：这是一个一维 numpy 数组，其中 `V.shape[0]` 等于状态数量 (`env.nS`)。`V[s]` 包含状态 `s` 的估值。\n", "\n", "请完成以下代码单元格中的函数。" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def truncated_policy_iteration(env, max_it=1, gamma=1, theta=1e-8):\n", " V = np.zeros(env.nS)\n", " policy = np.zeros([env.nS, env.nA]) / env.nA\n", " while True:\n", " policy = policy_improvement(env, V)\n", " old_V = copy.copy(V)\n", " V = truncated_policy_evaluation(env, policy, V, max_it, gamma)\n", " if max(abs(V-old_V)) < theta:\n", " break;\n", " return policy, V" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "运行下个代码单元格以解决该 MDP 并可视化输出结果。状态值函数已调整形状，以匹配网格世界的形状。\n", "\n", "请实验不同的 `max_it` 参数值。始终都能获得最优状态值函数吗？" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "policy_tpi, V_tpi = truncated_policy_iteration(env, max_it=2)\n", "\n", "# print the optimal policy\n", "print(\"\\nOptimal Policy (LEFT = 0, DOWN = 1, RIGHT = 2, UP = 3):\")\n", "print(policy_tpi,\"\\n\")\n", "\n", "# plot the optimal state-value function\n", "plot_values(V_tpi)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "Optimal Policy (LEFT = 0, DOWN = 1, RIGHT = 2, UP = 3):\n", "[[ 1. 0. 0. 0. ]\n", " [ 0. 0. 0. 1. ]\n", " [ 0. 0. 0. 1. ]\n", " [ 0. 0. 0. 1. ]\n", " [ 1. 0. 0. 0. ]\n", " [ 0.25 0.25 0.25 0.25]\n", " [ 0.5 0. 0.5 0. ]\n", " [ 0.25 0.25 0.25 0.25]\n", " [ 0. 0. 0. 1. ]\n", " [ 0. 1. 0. 0. ]\n", " [ 1. 0. 0. 0. ]\n", " [ 0.25 0.25 0.25 0.25]\n", " [ 0.25 0.25 0.25 0.25]\n", " [ 0. 0. 1. 0. ]\n", " [ 0. 1. 0. 0. ]\n", " [ 0.25 0.25 0.25 0.25]] \n", "\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "\n", "\n", "运行以下代码单元格以测试你的函数。如果代码单元格返回 **PASSED**，则表明你正确地实现了该函数！ \n", "\n", "**注意：**为了确保结果准确，确保 `truncated_policy_iteration` 函数满足上文列出的要求（具有四个输入、两个输出，并且没有更改输入参数的默认值）。" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "check_test.run_check('truncated_policy_iteration_check', truncated_policy_iteration)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**<span style=\"color: green;\">PASSED</span>**\n", "\n", "\n", "### 第 6 部分：值迭代\n", "\n", "在此部分，你将自己编写值迭代的实现。\n", "\n", "你的算法应该接受三个输入参数：\n", "- `env`：这是 OpenAI Gym 环境的实例，其中 `env.P` 会返回一步动态特性。\n", "- `gamma`：这是折扣率。它必须是在 0 到 1（含）之间的值，默认值为：`1`。 \n", "- `theta`：这是一个非常小的正整数，用作停止条件（默认值为：`1e-8`）。\n", "\n", "该算法会返回以下**输出结果**：\n", "- `policy`：这是一个二维 numpy 数组，其中 `policy.shape[0]` 等于状态数量 (`env.nS`) ， `policy.shape[1]` 等于动作数量 (`env.nA`) 。`policy[s][a]` 返回智能体在状态 `s` 时根据该策略选择动作 `a` 的概率。\n", "- `V`：这是一个一维 numpy 数组，其中 `V.shape[0]` 等于状态数量 (`env.nS`)。`V[s]` 包含状态 `s` 的估值。" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def value_iteration(env, gamma=1, theta=1e-8):\n", " V = np.zeros(env.nS)\n", " while True:\n", " delta = 0\n", " for s in range(env.nS):\n", " v = V[s]\n", " V[s] = max(q_from_v(env, V, s, gamma))\n", " delta = max(delta,abs(V[s]-v))\n", " if delta < theta:\n", " break\n", " policy = policy_improvement(env, V, gamma)\n", " return policy, V" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "运行下个代码单元格以解决该 MDP 并可视化输出结果。状态值函数已调整形状，以匹配网格世界的形状。" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "policy_vi, V_vi = value_iteration(env)\n", "\n", "# print the optimal policy\n", "print(\"\\nOptimal Policy (LEFT = 0, DOWN = 1, RIGHT = 2, UP = 3):\")\n", "print(policy_vi,\"\\n\")\n", "\n", "# plot the optimal state-value function\n", "plot_values(V_vi)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "Optimal Policy (LEFT = 0, DOWN = 1, RIGHT = 2, UP = 3):\n", "[[ 1. 0. 0. 0. ]\n", " [ 0. 0. 0. 1. ]\n", " [ 0. 0. 0. 1. ]\n", " [ 0. 0. 0. 1. ]\n", " [ 1. 0. 0. 0. ]\n", " [ 0.25 0.25 0.25 0.25]\n", " [ 0.5 0. 0.5 0. ]\n", " [ 0.25 0.25 0.25 0.25]\n", " [ 0. 0. 0. 1. ]\n", " [ 0. 1. 0. 0. ]\n", " [ 1. 0. 0. 0. ]\n", " [ 0.25 0.25 0.25 0.25]\n", " [ 0.25 0.25 0.25 0.25]\n", " [ 0. 0. 1. 0. ]\n", " [ 0. 1. 0. 0. ]\n", " [ 0.25 0.25 0.25 0.25]] \n", "\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "\n", "\n", "运行以下代码单元格以测试你的函数。如果代码单元格返回 **PASSED**，则表明你正确地实现了该函数！ \n", "\n", "**注意：**为了确保结果准确，确保 `truncated_policy_iteration` 函数满足上文列出的要求（具有三个输入、两个输出，并且没有更改输入参数的默认值）。" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "check_test.run_check('value_iteration_check', value_iteration)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**<span style=\"color: green;\">PASSED</span>**" ] } ], "metadata": {}, "nbformat": 4, "nbformat_minor": 2 }

mit

mirjalil/ml-visual-recognition

ipynb/classify_hf3.ipynb

2

30776

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<tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>161</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>163</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>56</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>119</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>138</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "metadata": {}, "output_type": "pyout", "prompt_number": 2, "text": [ " 0\n", "0 161\n", "1 163\n", "2 56\n", "3 119\n", "4 138" ] } ], "prompt_number": 2 }, { "cell_type": "code", "collapsed": false, "input": [ "np.unique(y[0], return_counts=True)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 3, "text": [ "(array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,\n", " 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26,\n", " 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39,\n", " 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52,\n", " 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65,\n", " 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78,\n", " 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91,\n", " 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104,\n", " 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117,\n", " 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130,\n", " 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143,\n", " 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156,\n", " 157, 158, 159, 160, 161, 162, 163, 164]),\n", " array([ 1263, 1261, 1255, 1256, 1252, 1235, 1240, 1264,\n", " 1256, 1281, 1245, 1278, 1278, 1253, 1255, 1255,\n", " 1291, 1277, 1308, 1285, 1322, 1309, 1318, 1322,\n", " 1327, 1339, 1361, 1361, 1335, 1396, 1359, 1393,\n", " 1373, 1356, 1398, 1416, 1386, 1398, 1396, 1404,\n", " 1430, 1398, 1416, 1406, 1420, 1445, 1433, 1445,\n", " 1454, 1451, 1481, 1482, 1477, 1474, 1478, 1486,\n", " 1512, 1492, 1557, 1557, 1548, 1530, 1574, 1582,\n", " 1606, 1611, 1666, 1650, 1704, 1739, 1735, 1743,\n", " 1728, 1796, 1737, 1810, 1822, 1864, 1847, 1838,\n", " 1857, 1913, 1910, 1917, 2006, 1992, 2033, 2063,\n", " 2072, 2063, 2096, 2128, 2134, 2206, 2215, 2212,\n", " 2258, 2279, 2287, 2319, 2356, 2435, 2438, 2491,\n", " 2486, 2485, 2502, 2555, 2594, 2629, 2575, 2587,\n", " 2777, 2875, 2897, 2884, 2978, 3087, 3179, 3368,\n", " 3388, 3421, 3409, 3453, 3536, 3586, 3615, 3696,\n", " 3821, 3802, 3934, 4059, 4069, 4253, 4819, 4939,\n", " 5038, 5259, 5310, 6080, 6487, 6623, 7256, 8279,\n", " 9069, 9221, 9707, 9998, 10557, 10645, 11484, 12382,\n", " 12858, 16548, 18562, 21943, 30679, 34092, 45439, 60513,\n", " 64478, 65211, 92241, 130122]))" ] } ], "prompt_number": 3 }, { "cell_type": "code", "collapsed": false, "input": [ "yuniq,ycount = np.unique(y[0], return_counts=True)\n", "\n", "print(np.sum(ycount[np.where(np.in1d(yuniq, range(162, 164)))[0]]))\n", "print(np.sum(ycount[np.where(np.in1d(yuniq, range(164, 165)))[0]]))" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "157452\n", "130122\n" ] } ], "prompt_number": 4 }, { "cell_type": "code", "collapsed": false, "input": [ "import pickle\n", "\n", "cstat = pickle.load(open( \"../data/sum_features.dat\", \"rb\" ) )" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 5 }, { "cell_type": "code", "collapsed": false, "input": [ "### Calclulate Standardized Mean Difference Between Classes\n", "\n", "def calStandMeanDiff(y, cstat, yneg, ypos):\n", " sx = np.zeros(shape=ndim, dtype=float)\n", " ssx = np.zeros(shape=ndim, dtype=float)\n", "\n", "\n", " n1 = np.sum(np.in1d(y, yneg))\n", " n2 = np.sum(np.in1d(y, ypos))\n", " sys.stderr.write(\"Number of samples in NegClass: %d and PosClass: %d \\n\"%(n1, n2))\n", "\n", " for yi in yneg:\n", " sx += cstat[yi][0]\n", " ssx += cstat[yi][1]\n", " r1_mean = sx / float(n1)\n", " r1_var = (ssx - 2*sx*r1_mean + r1_mean**2) / float(n1)\n", "\n", " sx = np.zeros(shape=ndim, dtype=float)\n", " ssx = np.zeros(shape=ndim, dtype=float)\n", " for yi in ypos:\n", " sx += cstat[yi][0]\n", " ssx += cstat[yi][1]\n", " r2_mean = sx / float(n2)\n", " r2_var = (ssx - 2*sx*r2_mean + r2_mean**2) / float(n2)\n", "\n", " tot_mean = cstat['all'][0] / float(cstat['all'][2])\n", " tot_var = (cstat['all'][1] - 2*cstat['all'][0]*tot_mean + tot_mean**2) / float(cstat['all'][2])\n", "\n", " rdiff = (r1_mean - r2_mean) / np.sqrt(tot_var)\n", "\n", " return (rdiff)\n", "\n", "\n", "## unit test:\n", "mean_test = calStandMeanDiff(y, cstat, np.arange(162,164), np.arange(164, 165)) \n", "print(np.sum(mean_test > 0.1))" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "342\n" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "Number of samples in NegClass: 157452 and PosClass: 130122 \n" ] } ], "prompt_number": 18 }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Classify items belonging to first half (1) Second half (-1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Finding Good Features" ] }, { "cell_type": "code", "collapsed": false, "input": [ "rdiff = calStandMeanDiff(y, cstat, np.arange(162,164), np.arange(164, 165))\n", "\n", "\n", "## Good Features:\n", "goodfeatures = np.where(rdiff > 0.1)[0]\n", "\n", "goodfeatures" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stderr", "text": [ "Number of samples in NegClass: 157452 and PosClass: 130122 \n" ] }, { "metadata": {}, "output_type": "pyout", "prompt_number": 19, "text": [ "array([ 4, 6, 7, 11, 13, 16, 18, 20, 21, 22, 29, 31, 33,\n", " 36, 44, 46, 50, 54, 59, 63, 64, 68, 70, 73, 75, 77,\n", " 78, 81, 83, 86, 90, 95, 98, 101, 104, 106, 107, 108, 109,\n", " 113, 114, 115, 117, 123, 124, 125, 126, 127, 130, 131, 134, 138,\n", " 142, 144, 147, 150, 154, 155, 157, 160, 162, 164, 172, 176, 177,\n", " 182, 183, 187, 189, 190, 194, 201, 203, 205, 207, 210, 211, 212,\n", " 218, 227, 231, 235, 236, 238, 239, 241, 244, 247, 248, 249, 250,\n", " 251, 253, 259, 260, 264, 268, 270, 273, 275, 276, 277, 280, 283,\n", " 284, 292, 297, 298, 299, 303, 304, 305, 307, 309, 313, 317, 319,\n", " 321, 323, 324, 326, 327, 333, 339, 342, 349, 351, 356, 357, 361,\n", " 362, 366, 368, 370, 376, 378, 380, 383, 384, 385, 390, 391, 392,\n", " 393, 394, 403, 406, 409, 410, 411, 415, 419, 420, 424, 426, 427,\n", " 431, 433, 434, 435, 438, 439, 444, 446, 448, 450, 453, 454, 456,\n", " 457, 458, 460, 463, 468, 469, 471, 472, 475, 477, 478, 479, 480,\n", " 483, 486, 488, 489, 491, 494, 499, 503, 505, 506, 512, 513, 514,\n", " 516, 517, 518, 519, 520, 521, 523, 525, 526, 531, 534, 535, 536,\n", " 538, 541, 543, 546, 550, 551, 553, 554, 555, 557, 558, 559, 560,\n", " 562, 565, 572, 573, 574, 575, 580, 581, 583, 586, 587, 591, 592,\n", " 593, 596, 598, 599, 604, 606, 611, 616, 618, 621, 623, 627, 630,\n", " 637, 638, 639, 642, 647, 649, 655, 657, 663, 664, 668, 670, 672,\n", " 675, 677, 678, 682, 684, 685, 687, 688, 691, 693, 694, 695, 696,\n", " 697, 700, 707, 709, 713, 715, 728, 729, 736, 737, 739, 745, 747,\n", " 749, 756, 765, 768, 771, 772, 777, 783, 787, 793, 794, 795, 799,\n", " 804, 807, 808, 809, 810, 813, 814, 817, 819, 820, 823, 826, 829,\n", " 830, 835, 837, 838, 839, 843, 847, 850, 851, 853, 854, 856, 860,\n", " 862, 863, 865, 866, 868, 872, 873, 874, 880, 882, 883, 888, 893,\n", " 895, 897, 898, 899])" ] } ], "prompt_number": 19 }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Read a Random Sample" ] }, { "cell_type": "code", "collapsed": false, "input": [ "def readRandomSample(data_fname, y, size, goodfeat=None, acc_miny=None, acc_maxy=None):\n", " \"\"\" Read a random sample\n", " \"\"\"\n", " if goodfeat is None:\n", " goodfeat = np.arange(ndim)\n", " Xsub = np.empty(shape=(size,goodfeat.shape[0]), dtype=float)\n", " ysub = np.zeros(shape=size, dtype=int)\n", "\n", " if acc_miny is None:\n", " acc_miny = np.min(y)\n", " if acc_maxy is None:\n", " acc_maxy = np.max(y)\n", " \n", " #yuniq, ycount = np.unique(y, return_counts=True)\n", " #tot_acceptable = np.sum(ycount[np.where((yuniq >= acc_miny) & (yuniq <= acc_maxy))[0]])\n", " \n", " acceptable_indx = np.where((y>=acc_miny) & (y<=acc_maxy))[0]\n", " assert(acceptable_indx.shape[0] > size)\n", " choice_indx = np.sort(np.random.choice(acceptable_indx, size, replace=False))\n", " #print(choice_indx.shape)\n", " #sys.stderr.write(\"Total Accetables: --> %d\"%(tot_acceptable))\n", " \n", " #proba = 1.0 - size/float(tot_acceptable)\n", " \n", " \n", " with open(data_fname, 'r') as fp:\n", " n = 0\n", " nf = 0\n", " for line in fp:\n", "# if (y[n] >= acc_miny and y[n]<=acc_maxy):\n", "# if np.random.uniform(low=0, high=1) > proba and nf < size:\n", " if nf < size:\n", " if n == choice_indx[nf]:\n", " line = line.strip().split()\n", " ix = -1\n", " for i,v in enumerate(line):\n", " if np.any(goodfeat == i):\n", " ix += 1\n", " Xsub[nf,ix] = int(v)\n", " ysub[nf] = y[n]\n", "\n", " nf += 1\n", " n += 1\n", " return(Xsub, ysub)\n" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 6 }, { "cell_type": "code", "collapsed": false, "input": [ "## unit testing readRandomSample()\n", "gf_test = np.arange(18,27)\n", "Xsub, ysub = readRandomSample('/home/vahid/Downloads/data/ml/data_train.txt', y[0], \\\n", " size=2000, goodfeat=gf_test, acc_miny=15, acc_maxy=20)\n", "\n", "print(Xsub.shape)\n", "print(np.unique(ysub))\n" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "(2000, 9)\n", "[15 16 17 18 19 20]\n" ] } ], "prompt_number": 20 }, { "cell_type": "code", "collapsed": false, "input": [ "### Performance Evaluation\n", "def evalPerformance(ytrue, ypred):\n", " tp = np.sum(ypred[np.where(ytrue == 1)[0]] == 1)\n", " fp = np.sum(ypred[np.where(ytrue == -1)[0]] == 1)\n", " tn = np.sum(ypred[np.where(ytrue == -1)[0]] == -1)\n", " fn = ytrue.shape[0]-(tp+fp+tn)\n", " #sys.stderr.write('%d %d %d %d\\n'%(tp,fp,tn,fn))\n", " prec = tp / float(tp + fp)\n", " recall = tp / float(tp + fn)\n", " f1score = 2*tp/float(2*tp + fp + fn)\n", "\n", " return (prec, recall, f1score)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 21 }, { "cell_type": "code", "collapsed": false, "input": [ "Xsub, ysub = readRandomSample('/home/vahid/Downloads/data/ml/data_train.txt', y[0], size=200, \\\n", " goodfeat=goodfeatures, acc_miny=162, acc_maxy=164)\n", "\n", "assert(np.sum(ysub < 162) == 0)\n", "ysub[np.where(ysub < 164)[0]] = -1\n", "ysub[np.where(ysub >= 164)[0]] = 1\n", "\n", "print(np.sum(ysub == -1), np.sum(ysub==1))\n", "\n", "#Xsub = Xsub[:, goodfeatures]\n", "features_idx = np.where(np.std(Xsub, axis=0)> 0.001)[0]\n", "print(\"Number of Good Features: %d\"%features_idx.shape[0])\n", "\n", "Xsub = Xsub[:,features_idx]\n", "\n", "Xsub = (Xsub - np.mean(Xsub, axis=0)) / np.std(Xsub, axis=0)\n", "\n", "Xsub.shape" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "(105, 95)\n", "Number of Good Features: 342\n" ] }, { "metadata": {}, "output_type": "pyout", "prompt_number": 23, "text": [ "(200, 342)" ] } ], "prompt_number": 23 }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Grid-Search (coarse)" ] }, { "cell_type": "code", "collapsed": false, "input": [ "import sklearn.svm\n", "\n", "ntot = Xsub.shape[0]\n", "tr_idx = np.random.choice(ntot, size=ntot/2, replace=False)\n", "ts_idx = np.setdiff1d(np.arange(ntot), tr_idx, assume_unique=True)\n", "yts = ysub[ts_idx]\n", "\n", "for c in [0.0001, 0.001, 0.01, 0.1, 1.0]:\n", " for gm in [0.001, 0.01, 0.1, 1.0]:\n", " clf = sklearn.svm.SVC(C=c, kernel='rbf', gamma=gm)\n", " clf.fit(Xsub[tr_idx, :], ysub[tr_idx])\n", " ypred = clf.predict(Xsub[ts_idx, :])\n", " prec, recall, f1score = evalPerformance(yts, ypred)\n", " print (\"C=%.4f Gamma=%.4f ==> Prec:%.3f Recall:%.3f F1Score:%.3f\"%(c, gm, prec, recall, f1score))\n", " " ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "C=0.0001 Gamma=0.0010 ==> Prec:nan Recall:0.000 F1Score:0.000\n", "C=0.0001 Gamma=0.0100 ==> Prec:nan Recall:0.000 F1Score:0.000" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "C=0.0001 Gamma=0.1000 ==> Prec:nan Recall:0.000 F1Score:0.000" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "C=0.0001 Gamma=1.0000 ==> Prec:nan Recall:0.000 F1Score:0.000" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "C=0.0010 Gamma=0.0010 ==> Prec:nan Recall:0.000 F1Score:0.000" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "C=0.0010 Gamma=0.0100 ==> Prec:nan Recall:0.000 F1Score:0.000" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "C=0.0010 Gamma=0.1000 ==> Prec:nan Recall:0.000 F1Score:0.000" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "C=0.0010 Gamma=1.0000 ==> Prec:nan Recall:0.000 F1Score:0.000" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "C=0.0100 Gamma=0.0010 ==> Prec:0.666 Recall:0.512 F1Score:0.579" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "C=0.0100 Gamma=0.0100 ==> Prec:0.648 Recall:0.181 F1Score:0.283" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "C=0.0100 Gamma=0.1000 ==> Prec:nan Recall:0.000 F1Score:0.000" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "C=0.0100 Gamma=1.0000 ==> Prec:nan Recall:0.000 F1Score:0.000" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "C=0.1000 Gamma=0.0010 ==> Prec:0.715 Recall:0.698 F1Score:0.707" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "C=0.1000 Gamma=0.0100 ==> Prec:0.727 Recall:0.445 F1Score:0.552" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "C=0.1000 Gamma=0.1000 ==> Prec:nan Recall:0.000 F1Score:0.000" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "C=0.1000 Gamma=1.0000 ==> Prec:nan Recall:0.000 F1Score:0.000" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "C=1.0000 Gamma=0.0010 ==> Prec:0.730 Recall:0.786 F1Score:0.757" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "C=1.0000 Gamma=0.0100 ==> Prec:0.786 Recall:0.601 F1Score:0.681" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "C=1.0000 Gamma=0.1000 ==> Prec:0.763 Recall:0.010 F1Score:0.020" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "C=1.0000 Gamma=1.0000 ==> Prec:nan Recall:0.000 F1Score:0.000" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n" ] } ], "prompt_number": 13 }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Fine-grid search" ] }, { "cell_type": "code", "collapsed": false, "input": [ "import sklearn.svm\n", "\n", "ntot = Xsub.shape[0]\n", "tr_idx = np.random.choice(ntot, size=ntot/2, replace=False)\n", "ts_idx = np.setdiff1d(np.arange(ntot), tr_idx, assume_unique=True)\n", "yts = ysub[ts_idx]\n", "\n", "for c in [0.2, 0.5, 1, 1.5, 2, 5, 10]:\n", " for gm in [0.0005, 0.0005, 0.001, 0.0015, 0.002, 0.005]:\n", " clf = sklearn.svm.SVC(C=c, kernel='rbf', gamma=gm)\n", " clf.fit(Xsub[tr_idx, :], ysub[tr_idx])\n", " ypred = clf.predict(Xsub[ts_idx, :])\n", " prec, recall, f1score = evalPerformance(yts, ypred)\n", " print (\"C=%.4f Gamma=%.4f ==> Prec:%.3f Recall:%.3f F1Score:%.3f\"%(c, gm, prec, recall, f1score))" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "C=0.2000 Gamma=0.0005 ==> Prec:0.720 Recall:0.697 F1Score:0.708\n", "C=0.2000 Gamma=0.0005 ==> Prec:0.720 Recall:0.697 F1Score:0.708" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "C=0.2000 Gamma=0.0010 ==> Prec:0.728 Recall:0.724 F1Score:0.726" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "C=0.2000 Gamma=0.0015 ==> Prec:0.737 Recall:0.728 F1Score:0.732" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "C=0.2000 Gamma=0.0020 ==> Prec:0.742 Recall:0.725 F1Score:0.733" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "C=0.2000 Gamma=0.0050 ==> Prec:0.752 Recall:0.644 F1Score:0.694" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "C=0.5000 Gamma=0.0005 ==> Prec:0.724 Recall:0.735 F1Score:0.730" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "C=0.5000 Gamma=0.0005 ==> Prec:0.724 Recall:0.735 F1Score:0.730" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "C=0.5000 Gamma=0.0010 ==> Prec:0.738 Recall:0.750 F1Score:0.744" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "C=0.5000 Gamma=0.0015 ==> Prec:0.746 Recall:0.757 F1Score:0.752" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "C=0.5000 Gamma=0.0020 ==> Prec:0.753 Recall:0.754 F1Score:0.754" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "C=0.5000 Gamma=0.0050 ==> Prec:0.770 Recall:0.695 F1Score:0.731" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "C=1.0000 Gamma=0.0005 ==> Prec:0.729 Recall:0.750 F1Score:0.739" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "C=1.0000 Gamma=0.0005 ==> Prec:0.729 Recall:0.750 F1Score:0.739" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "C=1.0000 Gamma=0.0010 ==> Prec:0.740 Recall:0.773 F1Score:0.756" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "C=1.0000 Gamma=0.0015 ==> Prec:0.749 Recall:0.777 F1Score:0.763" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "C=1.0000 Gamma=0.0020 ==> Prec:0.755 Recall:0.776 F1Score:0.765" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "C=1.0000 Gamma=0.0050 ==> Prec:0.774 Recall:0.728 F1Score:0.751" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "C=1.5000 Gamma=0.0005 ==> Prec:0.732 Recall:0.759 F1Score:0.745" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "C=1.5000 Gamma=0.0005 ==> Prec:0.732 Recall:0.759 F1Score:0.745" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "C=1.5000 Gamma=0.0010 ==> Prec:0.743 Recall:0.779 F1Score:0.760" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "C=1.5000 Gamma=0.0015 ==> Prec:0.750 Recall:0.787 F1Score:0.768" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "C=1.5000 Gamma=0.0020 ==> Prec:0.756 Recall:0.788 F1Score:0.772" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "C=1.5000 Gamma=0.0050 ==> Prec:0.778 Recall:0.738 F1Score:0.758" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "C=2.0000 Gamma=0.0005 ==> Prec:0.731 Recall:0.764 F1Score:0.747" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "C=2.0000 Gamma=0.0005 ==> Prec:0.731 Recall:0.764 F1Score:0.747" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "C=2.0000 Gamma=0.0010 ==> Prec:0.745 Recall:0.785 F1Score:0.765" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "C=2.0000 Gamma=0.0015 ==> Prec:0.751 Recall:0.793 F1Score:0.771" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "C=2.0000 Gamma=0.0020 ==> Prec:0.754 Recall:0.792 F1Score:0.773" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "C=2.0000 Gamma=0.0050 ==> Prec:0.780 Recall:0.733 F1Score:0.756" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "C=5.0000 Gamma=0.0005 ==> Prec:0.736 Recall:0.786 F1Score:0.760" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "C=5.0000 Gamma=0.0005 ==> Prec:0.736 Recall:0.786 F1Score:0.760" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "C=5.0000 Gamma=0.0010 ==> Prec:0.749 Recall:0.802 F1Score:0.775" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "C=5.0000 Gamma=0.0015 ==> Prec:0.750 Recall:0.800 F1Score:0.774" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "C=5.0000 Gamma=0.0020 ==> Prec:0.757 Recall:0.791 F1Score:0.773" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "C=5.0000 Gamma=0.0050 ==> Prec:0.793 Recall:0.718 F1Score:0.753" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "C=10.0000 Gamma=0.0005 ==> Prec:0.741 Recall:0.797 F1Score:0.768" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "C=10.0000 Gamma=0.0005 ==> Prec:0.741 Recall:0.797 F1Score:0.768" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "C=10.0000 Gamma=0.0010 ==> Prec:0.746 Recall:0.802 F1Score:0.773" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "C=10.0000 Gamma=0.0015 ==> Prec:0.754 Recall:0.790 F1Score:0.771" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "C=10.0000 Gamma=0.0020 ==> Prec:0.765 Recall:0.776 F1Score:0.770" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "C=10.0000 Gamma=0.0050 ==> Prec:0.794 Recall:0.713 F1Score:0.751" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n" ] } ], "prompt_number": 14 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }

apache-2.0

stuliveshere/PySeis

docs/notebooks/.ipynb_checkpoints/Test Processing Flow-checkpoint.ipynb

1

12779

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " key : min max\n", "=========================================\n", "('tracl', '<i4') 1.000 71284.000\n", "('fldr', '<i4') 231.000 481.000\n", "('tracf', '<i4') -1.000 282.000\n", "('ep', '<i4') 32.000 282.000\n", "('cdpt', '<i4') 1.000 284.000\n", "('nhs', '<i2') 1.000 1.000\n", "('scalel', '<i2') -10000.000 -10000.000\n", "('scalco', '<i2') -10000.000 -10000.000\n", "('counit', '<i2') 3.000 3.000\n", "('ns', '<u2') 1501.000 1501.000\n", "('dt', '<u2') 2000.000 2000.000\n", "('gain', '<i2') 3.000 3.000\n", "('igc', '<i2') 1.000 1.000\n", "('afilf', '<i2') 207.000 207.000\n", "('afils', '<i2') 298.000 298.000\n", "('hcf', '<i2') 207.000 207.000\n", "('hcs', '<i2') 298.000 298.000\n", "('year', '<i2') 1998.000 1998.000\n", "('trace', '<f4', (1501,)) -0.303 1.000\n", "=========================================\n" ] } ], "source": [ "from toolbox.processing import *\n", "#%ls /home/stewart/su/2d_land_data/2D_Land_data_2ms/\n", "file = \"/home/stewart/su/2d_land_data/2D_Land_data_2ms/su/Line_001.su\"\n", "#file = \"/home/sfletcher/Downloads/2d_land_data/2D_Land_data_2ms/Line_001.su\"\n", "#initialise file\n", "data, params = initialise(file)\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#no coordinates in the headers, but we know energy point number and channel.\n", "#%cat /home/stewart/su/2d_land_data/2D_Land_data_2ms/Line_001.TXT\n", "#%cat /home/stewart/su/2d_land_data/2D_Land_data_2ms/Line_001.SPS\n", "#%cat /home/stewart/su/2d_land_data/2D_Land_data_2ms/Line_001.RPS\n", "#%cat /home/stewart/su/2d_land_data/2D_Land_data_2ms/header" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import numpy as np\n", "from matplotlib import collections\n", "dmap = np.memmap(file, dtype=toolbox.typeSU(1501), mode='r')\n", "eps = np.unique(dmap['ep'])\n", "for ep in eps[:1]:\n", " panel = dmap[dmap['ep'] == ep].copy()\n", " panel = toolbox.agc(panel, None, **params)\n", "\n", " trace_centers = np.linspace(1,284, panel.size).reshape(-1,1)\n", " trace_width = 284/(panel.size*0.5)\n", " x = panel['trace'].copy()\n", " x += trace_centers\n", " y = np.meshgrid(np.arange(1501), np.arange(284))[0]\n", " \n", " x = np.split(x.ravel(), 284)\n", " y = np.split(y.ravel(), 284)\n", " \n", " bits = [zip(x[a],y[a]) for a in range(len(x))]\n", " fig = pylab.figure()\n", " ax = fig.add_subplot(111)\n", " \n", " col1 = collections.LineCollection(bits)\n", " col1.set_color('k')\n", " ax.add_collection(col1, autolim=True)\n", " ax.autoscale_view()\n", " pylab.xlim([0,284])\n", " pylab.ylim([0,1500])\n", " ax.set_ylim(ax.get_ylim()[::-1])\n", " pylab.tight_layout()\n", " pylab.show()\n", " \n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import numpy as np\n", "from matplotlib import collections\n", "dmap = np.memmap(file, dtype=toolbox.typeSU(1501), mode='r')\n", "eps = np.unique(dmap['ep'])\n", "for ep in eps[:1]:\n", " panel = dmap[dmap['ep'] == ep].copy()\n", " panel = toolbox.agc(panel, None, **params)\n", "\n", " trace_centers = np.linspace(1,284, panel.size).reshape(-1,1)\n", " scalar = 284/(panel.size*0.5)\n", " panel['trace'][:,-1] = np.nan\n", " x = panel['trace'].ravel()\n", " x[x < 0] = 0\n", " y = np.meshgrid(np.arange(1501), np.arange(284))[0].ravel() \n", " \n", " zero_crossings = np.where(x == 0)[0]+1\n", " zero_crossings = zero_crossings[np.diff(zero_crossings) == 1]\n", " #zero_crossings = np.where(np.diff(np.signbit(x)))[0]+1\n", " \n", " x = ((panel['trace']*scalar)+trace_centers).ravel()\n", "\n", " xverts = np.split(x, zero_crossings)\n", " yverts = np.split(y, zero_crossings)\n", " \n", " \n", " polygons = [zip(xverts[i], yverts[i]) for i in range(0, len(xverts)) if len(xverts[i]) > 2]\n", " \n", " xlines = np.split(x, 284)\n", " ylines = np.split(y, 284)\n", " lines = [zip(xlines[a],ylines[a]) for a in range(len(xlines))] \n", "\n", "\n", " fig = pylab.figure()\n", " ax = fig.add_subplot(111)\n", " col = collections.PolyCollection(polygons)\n", " col.set_color('k')\n", " ax.add_collection(col, autolim=True)\n", " col1 = collections.LineCollection(lines)\n", " col1.set_color('k')\n", " ax.add_collection(col1, autolim=True)\n", " ax.autoscale_view()\n", " pylab.xlim([0,284])\n", " pylab.ylim([0,1500])\n", " ax.set_ylim(ax.get_ylim()[::-1])\n", " pylab.tight_layout()\n", " pylab.show()\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [], "source": [ "import numpy as np\n", "from matplotlib import collections\n", "%pylab tk\n", "def polytrace(data, **kwargs):\n", " segs = []\n", " segl = []\n", " nt = data.shape[-2]\n", " for i in range(nt):\n", " trace = data[i]\n", " \n", " line = list(zip(trace, np.arange(1501)))\n", " segl.append(line) \n", "\n", " xx = trace #np.ma.array(trace, mask=(trace <= 0))\n", " yy = np.arange(1501) #np.ma.array(np.arange(0,1501), mask=(trace <= 0))\n", " \n", " curve = [(0, 0)]\n", " curve.extend(list(zip(xx, yy)))\n", " curve.extend([(0, 1501)])\n", " \n", " segs.append(curve)\n", " #print segs[0] \n", " print ''\n", " return segs, segl\n", "\n", "\n", "#first lets do some checks. does of energy points should equal number of records?\n", "print np.unique(data['ep']).size, np.unique(data['fldr']).size\n", "#no duplicates - that makes it easier.\n", "print 251*284\n", "#284 traces per shot, 2 aux traces . lets have a look\n", "dmap = np.memmap(file, dtype=toolbox.typeSU(1501), mode='r')\n", "eps = np.unique(dmap['ep'])\n", "for ep in eps[:1]:\n", " panel = dmap[dmap['ep'] == ep].copy()\n", " panel = toolbox.agc(panel, None, **params)\n", "\n", " trace_centers = np.linspace(1,284, panel.size).reshape(-1,1)\n", " trace_width = 284/(panel.size*0.5)\n", " buf = panel['trace'].copy()\n", " buf *= trace_width\n", "\n", "\n", " segs, segl = polytrace(buf)\n", " fig = pylab.figure()\n", " ax = fig.add_subplot(111)\n", " offs = (10.0, 0.0)\n", " offs = list(zip(np.arange(238), np.zeros(238)))\n", " col = collections.PolyCollection(segs, offsets=offs)\n", " col.set_color('k')\n", " ax.add_collection(col, autolim=True)\n", " \n", " #col1 = collections.LineCollection(segl, offsets=offs)\n", " #col1.set_color('k')\n", " #ax.add_collection(col1, autolim=True)\n", " ax.autoscale_view()\n", " pylab.xlim([0,284])\n", " pylab.ylim([0,1500])\n", " ax.set_ylim(ax.get_ylim()[::-1])\n", " pylab.tight_layout()\n", " pylab.show()\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import numpy as np\n", "from matplotlib import collections\n", "import matplotlib.pyplot as pylab\n", "\n", "#make some oscillating data\n", "panel = np.meshgrid(np.arange(1501), np.arange(284))[0]\n", "panel = np.sin(panel)\n", "\n", "#generate coordinate vectors.\n", "panel[:,-1] = np.nan #prevent wrapping when flatten 2d array\n", "x = panel.flatten()\n", "y = np.meshgrid(np.arange(1501), np.arange(284))[0].ravel() \n", "\n", "#find indexes of each zero crossing\n", "zero_crossings = np.where(np.diff(np.signbit(x)))[0]+1 \n", "\n", "#calculate scalar used to shift \"traces\" to plot corrdinates\n", "trace_centers = np.linspace(1,284, panel.shape[-2]).reshape(-1,1) \n", "gain = 0.5 #scale traces\n", "\n", "#shift traces to plotting coordinate\n", "x = ((panel*gain)+trace_centers).ravel()\n", "\n", "#split each vector at each zero crossing\n", "xverts = np.split(x, zero_crossings)\n", "yverts = np.split(y, zero_crossings)\n", "\n", "#we only want the vertices which outline positive values\n", "if x[0] > 0:\n", " steps = range(0, len(xverts),2)\n", "else:\n", " steps = range(1, len(xverts),2)\n", "\n", "#turn vectors of coordinates into lists of coordinate pairs\n", "polygons = [zip(xverts[i], yverts[i]) for i in steps if len(xverts[i]) > 2]\n", "\n", "#this is so we can plot the lines as well\n", "xlines = np.split(x, 284)\n", "ylines = np.split(y, 284)\n", "lines = [zip(xlines[a],ylines[a]) for a in range(len(xlines))] \n", "\n", "#and plot\n", "fig = pylab.figure()\n", "ax = fig.add_subplot(111)\n", "col = collections.PolyCollection(polygons)\n", "col.set_color('k')\n", "ax.add_collection(col, autolim=True)\n", "col1 = collections.LineCollection(lines)\n", "col1.set_color('k')\n", "ax.add_collection(col1, autolim=True)\n", "ax.autoscale_view()\n", "pylab.xlim([0,284])\n", "pylab.ylim([0,1500])\n", "ax.set_ylim(ax.get_ylim()[::-1])\n", "pylab.tight_layout()\n", "pylab.show()\n" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/stewart/.virtualenv/PySeis/lib/python2.7/site-packages/ipykernel/__main__.py:19: RuntimeWarning: invalid value encountered in greater\n" ] } ], "source": [ "import numpy as np\n", "from matplotlib import collections\n", "dmap = np.memmap(file, dtype=toolbox.typeSU(1501), mode='r')\n", "eps = np.unique(dmap['ep'])\n", "for ep in eps[:1]:\n", " panel = dmap[dmap['ep'] == ep].copy()\n", " panel = toolbox.agc(panel, None, **params)\n", " panel['trace'][:,-1] = np.nan\n", " trace_centers = np.linspace(1,284, panel.size).reshape(-1,1)\n", " scalar = 284/(panel.size*0.5)\n", " y = np.meshgrid(np.arange(1501), np.arange(284))[0].ravel() \n", " offsets = (np.meshgrid(np.arange(1501), np.arange(284))[1]+1).ravel()\n", " x = ((panel['trace']*scalar)+trace_centers).ravel()\n", " \n", " fig,ax = plt.subplots()\n", " #or i in range(284):\n", " \n", " #ax.plot(x[i],y[i],'k-')\n", " ax.fill_betweenx(y,offsets,x,where=(x>offsets),color='k')\n", "\n", " pylab.xlim([0,284])\n", " pylab.ylim([0,1500])\n", " ax.set_ylim(ax.get_ylim()[::-1])\n", " pylab.tight_layout()\n", " pylab.show()\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": "2.7.8" } }, "nbformat": 4, "nbformat_minor": 0 }

mit

agiovann/Constrained_NMF

demos/notebooks/demo_OnACID_mesoscope.ipynb

1

10699

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Example of online analysis using OnACID\n", "\n", "Complete pipeline for online processing using CaImAn Online (OnACID).\n", "The demo demonstates the analysis of a sequence of files using the CaImAn online\n", "algorithm. The steps include i) motion correction, ii) tracking current \n", "components, iii) detecting new components, iv) updating of spatial footprints.\n", "The script demonstrates how to construct and use the params and online_cnmf\n", "objects required for the analysis, and presents the various parameters that\n", "can be passed as options. A plot of the processing time for the various steps\n", "of the algorithm is also included.\n", "@author: Eftychios Pnevmatikakis @epnev\n", "Special thanks to Andreas Tolias and his lab at Baylor College of Medicine\n", "for sharing the data used in this demo." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "try:\n", " if __IPYTHON__:\n", " # this is used for debugging purposes only. allows to reload classes when changed\n", " get_ipython().magic('load_ext autoreload')\n", " get_ipython().magic('autoreload 2')\n", "except NameError:\n", " pass\n", "\n", "from IPython.display import display, clear_output\n", "import glob\n", "import logging\n", "import numpy as np\n", "import os\n", "import scipy\n", "import cv2\n", "\n", "logging.basicConfig(format=\n", " \"%(relativeCreated)12d [%(filename)s:%(funcName)20s():%(lineno)s] [%(process)d] %(message)s\",\n", " # filename=\"/tmp/caiman.log\",\n", " level=logging.INFO)\n", "\n", "import caiman as cm\n", "from caiman.source_extraction import cnmf as cnmf\n", "from caiman.paths import caiman_datadir\n", "from caiman.utils.utils import download_demo\n", "import matplotlib.pyplot as plt\n", "\n", "import bokeh.plotting as bpl\n", "bpl.output_notebook()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## First download the data\n", "\n", "The function ```download_demo``` will look for the datasets ```Tolias_mesoscope_*.hdf5``` ins your caiman_data folder inside the subfolder specified by the variable ```fld_name``` and will download the files if they do not exist." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "fld_name = 'Mesoscope' # folder inside ./example_movies where files will be saved\n", "fnames = []\n", "fnames.append(download_demo('Tolias_mesoscope_1.hdf5',fld_name))\n", "fnames.append(download_demo('Tolias_mesoscope_2.hdf5',fld_name))\n", "fnames.append(download_demo('Tolias_mesoscope_3.hdf5',fld_name))\n", "\n", "print(fnames) # your list of files should look something like this" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Set up some parameters\n", "\n", "Here we set up some parameters for running OnACID. We use the same `params` object as in batch processing with CNMF." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "fr = 15 # frame rate (Hz)\n", "decay_time = 0.5 # approximate length of transient event in seconds\n", "gSig = (4,4) # expected half size of neurons\n", "p = 1 # order of AR indicator dynamics\n", "min_SNR = 1 # minimum SNR for accepting new components\n", "rval_thr = 0.90 # correlation threshold for new component inclusion\n", "ds_factor = 1 # spatial downsampling factor (increases speed but may lose some fine structure)\n", "gnb = 2 # number of background components\n", "gSig = tuple(np.ceil(np.array(gSig)/ds_factor).astype('int')) # recompute gSig if downsampling is involved\n", "mot_corr = True # flag for online motion correction \n", "pw_rigid = False # flag for pw-rigid motion correction (slower but potentially more accurate)\n", "max_shifts_online = np.ceil(10./ds_factor).astype('int') # maximum allowed shift during motion correction\n", "sniper_mode = True # flag using a CNN to detect new neurons (o/w space correlation is used)\n", "init_batch = 200 # number of frames for initialization (presumably from the first file)\n", "expected_comps = 500 # maximum number of expected components used for memory pre-allocation (exaggerate here)\n", "dist_shape_update = True # flag for updating shapes in a distributed way\n", "min_num_trial = 10 # number of candidate components per frame \n", "K = 2 # initial number of components\n", "epochs = 2 # number of passes over the data\n", "show_movie = False # show the movie with the results as the data gets processed\n", "\n", "params_dict = {'fnames': fnames,\n", " 'fr': fr,\n", " 'decay_time': decay_time,\n", " 'gSig': gSig,\n", " 'p': p,\n", " 'min_SNR': min_SNR,\n", " 'rval_thr': rval_thr,\n", " 'ds_factor': ds_factor,\n", " 'nb': gnb,\n", " 'motion_correct': mot_corr,\n", " 'init_batch': init_batch,\n", " 'init_method': 'bare',\n", " 'normalize': True,\n", " 'expected_comps': expected_comps,\n", " 'sniper_mode': sniper_mode,\n", " 'dist_shape_update' : dist_shape_update,\n", " 'min_num_trial': min_num_trial,\n", " 'K': K,\n", " 'epochs': epochs,\n", " 'max_shifts_online': max_shifts_online,\n", " 'pw_rigid': pw_rigid,\n", " 'show_movie': show_movie}\n", "opts = cnmf.params.CNMFParams(params_dict=params_dict)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Now run the CaImAn online algorithm (OnACID).\n", "\n", "The first ```initbatch``` frames are used for initialization purposes. The initialization method chosen here `bare` will only search for a small number of neurons and is mostly used to initialize the background components. Initialization with the full CNMF can also be used by choosing `cnmf`.\n", "\n", "We first create an `OnACID` object located in the module `online_cnmf` and we pass the parameters similarly to the case of batch processing. We then run the algorithm using the `fit_online` method." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "cnm = cnmf.online_cnmf.OnACID(params=opts)\n", "cnm.fit_online()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Optionally save results and do some plotting" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "logging.info('Number of components: ' + str(cnm.estimates.A.shape[-1]))\n", "Cn = cm.load(fnames[0], subindices=slice(0,500)).local_correlations(swap_dim=False)\n", "cnm.estimates.plot_contours(img=Cn)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## View components\n", "\n", "Now inspect the components extracted by OnACID. Note that if single pass was used then several components would be non-zero only for the part of the time interval indicating that they were detected online by OnACID.\n", "\n", "Note that if you get data rate error you can start Jupyter notebooks using:\n", "'jupyter notebook --NotebookApp.iopub_data_rate_limit=1.0e10'" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "cnm.estimates.nb_view_components(img=Cn, denoised_color='red');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Plot timing\n", "The plot below shows the time spent on each part of the algorithm (motion correction, tracking of current components, detect new components, update shapes) for each frame. Note that if you displayed a movie while processing the data (`show_movie=True`) the time required to generate this movie will be included here." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "T_motion = 1e3*np.array(cnm.t_motion)\n", "T_detect = 1e3*np.array(cnm.t_detect)\n", "T_shapes = 1e3*np.array(cnm.t_shapes)\n", "T_online = 1e3*np.array(cnm.t_online) - T_motion - T_detect - T_shapes\n", "plt.figure()\n", "plt.stackplot(np.arange(len(T_motion)), T_motion, T_online, T_detect, T_shapes)\n", "plt.legend(labels=['motion', 'process', 'detect', 'shapes'], loc=2)\n", "plt.title('Processing time allocation')\n", "plt.xlabel('Frame #')\n", "plt.ylabel('Processing time [ms]')\n", "plt.ylim([0,140])" ] } ], "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": "3.6.6" } }, "nbformat": 4, "nbformat_minor": 4 }

gpl-2.0

mtasende/Machine-Learning-Nanodegree-Capstone

notebooks/prod/.ipynb_checkpoints/n08_simple_q_learner_fast_learner_11_actions-checkpoint.ipynb

1

334493

{ "cells": [ { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "# In this notebook a simple Q learner will be trained and evaluated. The Q learner recommends when to buy or sell shares of one particular stock, and in which quantity (in fact it determines the desired fraction of shares in the total portfolio value). One initial attempt was made to train the Q-learner with multiple processes, but it was unsuccessful." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Populating the interactive namespace from numpy and matplotlib\n" ] } ], "source": [ "# Basic imports\n", "import os\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import datetime as dt\n", "import scipy.optimize as spo\n", "import sys\n", "from time import time\n", "from sklearn.metrics import r2_score, median_absolute_error\n", "from multiprocessing import Pool\n", "\n", "%matplotlib inline\n", "\n", "%pylab inline\n", "pylab.rcParams['figure.figsize'] = (20.0, 10.0)\n", "\n", "%load_ext autoreload\n", "%autoreload 2\n", "\n", "sys.path.append('../../')\n", "\n", "import recommender.simulator as sim\n", "from utils.analysis import value_eval\n", "from recommender.agent import Agent\n", "from functools import partial" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "NUM_THREADS = 1\n", "LOOKBACK = 252*2 + 28\n", "STARTING_DAYS_AHEAD = 20\n", "POSSIBLE_FRACTIONS = np.arange(0.0, 1.1, 0.1).round(decimals=3).tolist()\n", "\n", "# Get the data\n", "SYMBOL = 'SPY'\n", "total_data_train_df = pd.read_pickle('../../data/data_train_val_df.pkl').stack(level='feature')\n", "data_train_df = total_data_train_df[SYMBOL].unstack()\n", "total_data_test_df = pd.read_pickle('../../data/data_test_df.pkl').stack(level='feature')\n", "data_test_df = total_data_test_df[SYMBOL].unstack()\n", "if LOOKBACK == -1:\n", " total_data_in_df = total_data_train_df\n", " data_in_df = data_train_df\n", "else:\n", " data_in_df = data_train_df.iloc[-LOOKBACK:]\n", " total_data_in_df = total_data_train_df.loc[data_in_df.index[0]:]\n", "\n", "# Create many agents\n", "index = np.arange(NUM_THREADS).tolist()\n", "env, num_states, num_actions = sim.initialize_env(total_data_in_df, \n", " SYMBOL, \n", " starting_days_ahead=STARTING_DAYS_AHEAD,\n", " possible_fractions=POSSIBLE_FRACTIONS)\n", "agents = [Agent(num_states=num_states, \n", " num_actions=num_actions, \n", " random_actions_rate=0.98, \n", " random_actions_decrease=0.999,\n", " dyna_iterations=0,\n", " name='Agent_{}'.format(i)) for i in index]" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def show_results(results_list, data_in_df, graph=False):\n", " for values in results_list:\n", " total_value = values.sum(axis=1)\n", " print('Sharpe ratio: {}\\nCum. Ret.: {}\\nAVG_DRET: {}\\nSTD_DRET: {}\\nFinal value: {}'.format(*value_eval(pd.DataFrame(total_value))))\n", " print('-'*100)\n", " initial_date = total_value.index[0]\n", " compare_results = data_in_df.loc[initial_date:, 'Close'].copy()\n", " compare_results.name = SYMBOL\n", " compare_results_df = pd.DataFrame(compare_results)\n", " compare_results_df['portfolio'] = total_value\n", " std_comp_df = compare_results_df / compare_results_df.iloc[0]\n", " if graph:\n", " plt.figure()\n", " std_comp_df.plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Let's show the symbols data, to see how good the recommender has to be." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Sharpe ratio: 1.601691549431671\n", "Cum. Ret.: 0.4244923418116293\n", "AVG_DRET: 0.0007179294312480581\n", "STD_DRET: 0.00711546265440581\n", "Final value: 205.54\n" ] } ], "source": [ "print('Sharpe ratio: {}\\nCum. Ret.: {}\\nAVG_DRET: {}\\nSTD_DRET: {}\\nFinal value: {}'.format(*value_eval(pd.DataFrame(data_in_df['Close'].iloc[STARTING_DAYS_AHEAD:]))))" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Starting simulation for agent: Agent_0. 512 days of simulation to go.\n", "Date 2014-12-31 00:00:00 (simulating until 2014-12-31 00:00:00). Time: 0.3559868335723877s. Value: 11284.81.Epoch: 099.\n", "Elapsed time: 18.198962211608887 seconds.\n", "Random Actions Rate: 0.5877471869078387\n", "Sharpe ratio: 0.884008363182153\n", "Cum. Ret.: 0.12848099999999985\n", "AVG_DRET: 0.0002468314035199208\n", "STD_DRET: 0.0044324547369028406\n", "Final value: 11284.81\n", "----------------------------------------------------------------------------------------------------\n", "Starting simulation for agent: Agent_0. 512 days of simulation to go.\n", "Date 2014-12-31 00:00:00 (simulating until 2014-12-31 00:00:00). Time: 0.3632316589355469s. Value: 11885.740000000002.Epoch: 1\n", "Elapsed time: 18.30073380470276 seconds.\n", "Random Actions Rate: 0.352496689508243\n", "Sharpe ratio: 1.1646905320354186\n", "Cum. Ret.: 0.18857400000000024\n", "AVG_DRET: 0.000350175230196531\n", "STD_DRET: 0.004772821014226167\n", "Final value: 11885.740000000002\n", "----------------------------------------------------------------------------------------------------\n", "Starting simulation for agent: Agent_0. 512 days of simulation to go.\n", "Date 2014-12-31 00:00:00 (simulating until 2014-12-31 00:00:00). Time: 0.3613243103027344s. Value: 13175.480000000007.Epoch: 2\n", "Elapsed time: 18.237686157226562 seconds.\n", "Random Actions Rate: 0.21140707923754626\n", "Sharpe ratio: 2.1027842202820173\n", "Cum. Ret.: 0.3175480000000006\n", "AVG_DRET: 0.000549464258948947\n", "STD_DRET: 0.004148059804164802\n", "Final value: 13175.480000000007\n", "----------------------------------------------------------------------------------------------------\n", "Starting simulation for agent: Agent_0. 512 days of simulation to go.\n", "Date 2014-12-31 00:00:00 (simulating until 2014-12-31 00:00:00). Time: 0.3607292175292969s. Value: 13129.46.Epoch: 302.\n", "Elapsed time: 18.421624898910522 seconds.\n", "Random Actions Rate: 0.12678971032068426\n", "Sharpe ratio: 2.209652650966676\n", "Cum. Ret.: 0.31294599999999995\n", "AVG_DRET: 0.000541566305873438\n", "STD_DRET: 0.003890701363853403\n", "Final value: 13129.46\n", "----------------------------------------------------------------------------------------------------\n" ] } ], "source": [ "# Simulate (with new envs, each time)\n", "n_epochs = 4\n", "\n", "for i in range(n_epochs):\n", " tic = time()\n", " env.reset(STARTING_DAYS_AHEAD)\n", " results_list = sim.simulate_period(total_data_in_df, \n", " SYMBOL,\n", " agents[0],\n", " starting_days_ahead=STARTING_DAYS_AHEAD,\n", " possible_fractions=POSSIBLE_FRACTIONS,\n", " verbose=False,\n", " other_env=env)\n", " toc = time()\n", " print('Epoch: {}'.format(i))\n", " print('Elapsed time: {} seconds.'.format((toc-tic)))\n", " print('Random Actions Rate: {}'.format(agents[0].random_actions_rate))\n", " show_results([results_list], data_in_df)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Starting simulation for agent: Agent_0. 512 days of simulation to go.\n", "Date 2014-12-31 00:00:00 (simulating until 2014-12-31 00:00:00). Time: 0.36817169189453125s. Value: 14589.710000000008.Sharpe ratio: 2.965039725820134\n", "Cum. Ret.: 0.4589710000000009\n", "AVG_DRET: 0.0007489340046246382\n", "STD_DRET: 0.004009713139519721\n", "Final value: 14589.710000000008\n", "----------------------------------------------------------------------------------------------------\n" ] }, { "data": { "text/plain": [ "<matplotlib.figure.Figure at 0x7f6b60bfc4a8>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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QvY9e5wHDUs1T1FbnVbQ639EUCImIiIiIiIhI15TzKnx5P/gGQerZsP0bSBlthi7RvSGq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\nV+UyuO/LsOAlNmR3Zk5DT/6e9wl+3HXXgc6Jw7tz3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"text/plain": [ "<matplotlib.figure.Figure at 0x7f6b60c9e080>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "env.reset(STARTING_DAYS_AHEAD)\n", "results_list = sim.simulate_period(total_data_in_df, \n", " SYMBOL, agents[0], \n", " learn=False, \n", " starting_days_ahead=STARTING_DAYS_AHEAD,\n", " possible_fractions=POSSIBLE_FRACTIONS,\n", " other_env=env)\n", "show_results([results_list], data_in_df, graph=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Let's run the trained agent, with the test set" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### First a non-learning test: this scenario would be worse than what is possible (in fact, the q-learner can learn from past samples in the test set without compromising the causality)." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Starting simulation for agent: Agent_0. 484 days of simulation to go.\n", "Date 2016-12-28 00:00:00 (simulating until 2016-12-30 00:00:00). Time: 0.37982630729675293s. Value: 11027.090000000007.Epoch: 3\n", "Elapsed time: 17.302062273025513 seconds.\n", "Random Actions Rate: 0.12678971032068426\n", "Sharpe ratio: 0.6484623427256084\n", "Cum. Ret.: 0.10018600000000055\n", "AVG_DRET: 0.0002115262393951507\n", "STD_DRET: 0.005178211177401479\n", "Final value: 11001.860000000006\n", "----------------------------------------------------------------------------------------------------\n" ] }, { "data": { "text/plain": [ "<matplotlib.figure.Figure at 0x7f6b60cd9a20>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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iIiIiItKTvG5Y9SAkpMHcn8H4y+DzR6Bof8fu3/VfOLoN5vwf2Jsu3jJNk+ziahIGtB0W\n1XOG1YVFx+yI5vH6+OJAESPjwrHbDGanxpAYFcRfP07n3S9zuHr6UCYPGdDsWdF1wVNRZS0MnQnB\nTtj5Vsc+Ex0Ii0zTXIkVCLV2Pc80zfWA+yuXTgEyTNPcb5pmLfBv4KIOz0xEREREREREpCdtXQKl\nmVZVkWHAOb8Bmz988MvGMfs/hYKM5vf6vLDiPnCOhPELm10uqXJT7fa2uwytnjM0ALCWoQF4fSY/\nfm0r27JL+dZUa7dzu81g0alDycirIMDPxndmDWvxWdEh1rMKK2qtEGv0BbD3gw7NA3q2wXUCcPiY\nn7PqzomIiIiIiIiI9AzThN3vQW1V2+O8blj5Z4ifDCPOsc6Fx8Psn8Ce/0HGMijPhZcWwtu3Nb9/\n+xtQsAfO+BnYmjewzi6pBmjSf6gtDj874YF+FFTU4PWZ/OS1rfx3Sw4/nT+Sq6YPbRh3WVoiIQF2\nrpmRRExdNdJX1VcWNVQpjb3YWorWQX1mNzTDMG4yDGODYRgb8vPze3s6IiIiIiIiItIfZXwM/74S\n1j3R+hi3Cz7+NZQcaqwqqjfjFhiQDO//H3z2MHhrIfNzKNzXOMbrgU/ug9hxMLrlRVSdDYvAWop2\ntKyGO1/fyn82Z/OTeal8f+7wJmOiQgL45M4z+Om5I1t/TugxlUUAQ0+H+X/s8Dx6MizKBhKP+Xlw\n3bkWmab5lGmaaaZppsXExPTgtERERERERETkpLX6Ieu46+2Wr7tK4YnTYc3fYcIV1u5nx/JzwPz7\noGCvtZNY8mwwbLDl5cYxW5dYfY3O+DnYWo5WDhdZlTwd7VkEEBPq4MOduby5KZsfnZPKrWeOaHlc\nmAM/e+uRTrPKIrsfTL+5w/PoybBoPTDCMIxkwzACgCuAVv5LiYiIiIiIiIgcB58PMtfCoc8geoS1\nnX3J4ebj1v8DCtPhyn/DJU82rSqqlzofhp9tfX/uHyDlLCssylgGXzwNK35vLV8beX6r01mdUUBS\ndDBRdf2DOiImzIHPhDvOGsHtZ7UcFHVESIAdh5+NwsraLt3v194AwzCWAHMBp2EYWcA9gD+AaZpP\nGIYRB2wAwgGfYRg/AMaYpllmGMatwAeAHXjWNM0dXZqliIiIiIiIiEhrMtfCcwvA54agAfCtf1rV\nQ7vegRnfbxznroa1j1tB0MjzWn+eYcDFT0DOJogbD1OvhVeugn9dYl0fkAznPdBy0ARU1Xr4fF8h\ni04d0qmPceOsYcxOjWloaN1VhmHgDHU02VmtM9oNi0zTvLKd67lYS8xauvYe8F6XZiYiIiIiIiIi\n0hF737eOc38OSTOtgGfgWNj0ApjexnF5u6EyH07/YfvPDI2B1HOt70ctgJs+BY8LQgdCVMu7kNX7\nPKOQWo+Ps0bFdupjTEyMZGJiZKfuaY0zNKCxZ1EntRsWiYiIiIiIiIj0aZlrIX4SzL2r8dykb8OH\nv4APf9l0bNIsGDqzc883DOv5HbR8Tx4hAXZOSY7q3Hu6UXSog7xyV5fuVVgkIiIiIiIiIv2X2wXZ\nG+HU7zY9f9qtMPU6wGx63j+k1eVj3cE0TVbszuP0EU4C/HpvE/rokAB2HSnr0r29N2sRERERERER\nkfa4q+H9n8ODY6D4UPPrR7ZY29sPmdH8miMUHGFNv1rZvay7FFe5OVLqYlpS71UVgVVZVFhRi2ma\n7Q/+CoVFIiIiIiIiItJ3vfhNawv78iOw9rHm1zPXWMfEU0/svFpR4fIAEBHk36vzcIYGUOv1sXx3\nHm6vr1P3KiwSERERERERkb6ptsoKg07/EUy4HDa9CNXFjdd9Pjj4GUSPgBBn783zGOU1bgDCAns3\nLJo8JJLgADs3PL+BX/5ne6fuVc8iEREREREREembyrKtY8woGHcpbF0Cr98A/kFQtN/68rhgyrW9\nO89jlNdVFoUF9m7kMnVoFJvuPofbl2xm+Z68Tt2rsEhERERERERE+qbSLOsYMRjixllb2Kd/CAOS\nIToFUs60jqMv7N15HqN+GVqoo/cjl0B/OzNSovlw51Hyyjq+M1rvz1xEREREREREpCUNYVGCdbz8\nX2CaPd6k+nhU1PSNyqJ64xIiANiWXdrhe/rGzEVEREREREREvqo0CzAgLN762TB6dNv77lBeFxaF\n9pGwaMygcAwDtmeXdfievhvFiYiIiIiIiMjXW1kWhMWBX0Bvz6TDyl11Da4dvdvgul6Iw49hzpBO\nVRYpLBIRERERERGRvqk0C8ITensWnVLh8uBnMwj07zuRy7iECHbkKCwSERERERERkf6uNMtqbt2P\nVNR4CA30w+hDy+XGJ0RwpLTjDa4VFomIiIiIiIhI32Oa/TMscnn6xE5oxxobH9Gp8QqLRERERERE\nRKTvqSoCj6vfhUVlfTEsSgjnutOSOjxeYZGIiIiIiIiI9D2lh61jPwuLKmrchAf2jebW9cID/fn1\nhWM7PF5hkYiIiIiIiIj0PaVZ1rHfhUVWz6L+TGGRiIiIiIiIiPQ9ZdnWMbyfhUV9cBlaZ/Xv2YuI\niIiIiIiczDy18OrV4KmxtpAPj7e+ooZB8mzoQztudauczbDxefAPgRBnb8+mU8pdHsL6eWVR/569\niIiIiIiIyMksfxfsfd8Kh/L3QEUumD7r2vXvw9AZvTu/nlB2BJ6ZB4GRcMlT/S4QKz8JlqH179mL\niIiIiIiInEx8PtjwDGz9N1y5xAqIAK54GQaOBq8Hig/A36fBwVUnZ1iUtxO8tbDwWUie1duz6ZQa\nj5daj48wLUMTERERERERkW7x5o2w/XXr+/2fWmGRYYeoFOuc3Q+cIyB2LBz6vPG+shwoPghDTzvh\nU+52JZnWMSq5d+fRBZU1XgDC+thuaJ2lBtciIiIiIiIiJ0pBOlQXt3ytthJ2vAlTrgG/QKtvT/5u\nawmaX0DTsUNmwOEvwOuGVQ/CI1Phn+dB3u7GMaYJW16GP6XArnd67jN1t5JDYPODsEG9PZNOK3e5\nAfp9g2uFRSIiIiIiIiIngs9n9eJ576ctX8/dbvUjSj0P4sbDkS1QsBdiRjYfO3QGuCvh0z/Cst9A\n8hywO+CLp6zr1cXw2nXw1vegqgAOru6xj9XtSjIhYjDY7L09k04rd3kA+n3PIoVFIiIiIiIiIidC\n8QGoLoI974G7uvn1I1us46CJMGgS5GyBwn0QM6r52CF1y81WPgDOVLj8RRh3qdXraNe78PhM2P0u\nnPUriB0PhRk997m6W0kmRA7p7Vl0SUWNFRb1955FCotEREREREREToQjW61jbQVkLGv5ekgMhMdD\n/GSrcsj0thwWhQ+CAXU9fc75Ldj94dSbrHteWQT+QXDDRzDrx+AcboVO3c3tgqqi7n9ufw6L6iqL\n1LNIRERERERERNqX+yXY/CFoAOx8q/n1nC1WRZFhWGFRvZjUlp835WoYfxmkzrd+jp8MU6+DU26C\n766EhCnW+agUK4Dxurv14/DW96wKJk9N9z3TXQ0VRyEyqfueeQKV19T1LOrny9D69+xFRERERERE\n+osjW2HgKCsQ2vGW1ezaOcK65q62mlmPPM/62ZkKfkHgcUH0iJafN+vHzc9d8Lfm56JTrAql4kNW\nlVF3yNliNeMG2PY6TF7UPc8tzbKO/byySA2uRURERERERKRtpmmFRYMmwpRrrRDo72nw9Fmw/h9W\nA2rTC/GTrPF2Pxg0wQpNAoKP791RKdaxqBuXoi3/nVUh5UyFtY9bn687lByyjv00LCqv71mkyiIR\nERERERERaVNZDlQVQtxESJwGP9wO216DLUvgf8dUCA2a1Pj9vN9DTdnxvzu6Lizqrr5Fh9ZAxkdw\n9m8gOArevg0OroLk2cf/7OL+HRZVuDz42w0cfv27NkdhkYiIiIiIiEhPq29uPWiidQyLg9Nugxm3\nQu422LoEaiutLePrJU7rnncHR4Mjonsqi0wTlt8LobFWbyTDgI9+BRv+2T1hUUmm1dcpLO74n9UL\nyl0eQh1+GIbR21M5LgqLRERERERERHra0e3WMXZs0/OGYS03GzSh595tGBA9rHsqi/Yth0Ofwfl/\nblweN/4y2PhPa2e04Kjje35JphWY2ezHP9deUO5y9/vm1qCeRSIiIiIiIiI9ryIPAiPBEdo7749K\ngaM74LXrYc1jHbun+KDVyLpefVVRxBCr71K9yVeBtxa2v3H88yzJhAFDj/85vWTP0QqGRoX09jSO\nm8IiERERERERkZ7mKoGgyN57f/RwqMyDnW/BBz+H9I9g3ZPwyR/B520+ftML8NgMePZcKMiwzu1+\nF3I2w9y7wC+gceygCRA3Hja/ePzzLMnslX5FGw4WMf7XH7A7t+s9ospdbvbkljF16IBunFnvUFgk\nIiIiIiIi0tOqS6zKot6Sdj2c9wD8YDvEjIKXFsLSn8Inf4A3bwKvu3HsvuVW0+qEqeAXCP+9xbq+\n/PcQPQImXNH8+ZOvtvoy5W7r+hzd1Vag1Qth0T8/O0i5y8NjK7q+VG9zZgk+E9KSFBaJiIiIiIiI\nSHtcJRAY0XvvD4uDU2+CiAS47AUYdgZ86zlrR7Ptr8O/F1lhDcCGZ62m2Fe9AfPvh8Nr4b5EyN8F\nZ/wc7C305Bn/LbAHwOaXuj7HksPWMfLELkMrrKjhw525hAX68e6XOWQWVnXpORsOFWMzYPIQhUUi\nIiIiIiIi0h5Xae8uQztWTCpc8xaM/Sac/gNY8BCkfwj/Wmg1wd6zFCZeCX4OmHiFdX3aDVawNObi\nlp8ZHAUjz4cvXwFPbdfmVXLIOp7gyqI3NmXh9po8cdVU/Gw2nl61v0vP2XCwiNGDwgl1qMG1iIiI\niIiIiLSnt5ehtSVtMVz6D6uC6MnZ4PM0NrA2DOv6ub+3giVbGzHC5Kuhugj2Lu3aPHohLDJNk3+v\nP0za0AHMHO7km5MTeHXDYQoqajr1HI/Xx5bDJaSdBP2KQGGRiIiIiIiISM8yzd5vcN2e8Qvh8pes\noChpllV91FkpZ0BYPHz8GyhI7/z9JZnWUrbQuM7f20VfHChif34lV5xiBVQ3zRlGrdfHc58d7NRz\nduSUUVXrZWpSVA/M8sTr/7VRIiIiIiIiIn2Zu9raWr6vVhbVGzkfblkHAaFdu99mh4XPwitXWRVK\nYXHg9YDPbTXIDoqE65dC6MCW7y/JhIjEtquXutm/1x8mLNCPb4wfBEBKTCjnjonjhTUHuXluCiEB\ndq56Zh0zhkVz65kjWn3O/7Ydwc9mcPpw5wmaec9SZZGIiIiIiIhIT3KVWMe+XFlUb0AShBxH4DF0\nBtz0iVWplDAVkmbC8LNh9AIoOgCrH2r93pLME7oErbTKzXvbjnDxpASCAuwN52+em0KZy8OSdZls\nzSrls4xClnxxGNM0W3yOx+vjP5uzOWPUQKJCAk7U9HuUKotEREREREREelJ1XVjU1yuLuktkIlz4\nSPPzXg+sfwZOuw3C45tfL8m0mmSfAG6vj/vf302Nx8cVpyQ2uTYpMZIZw6L5x+r9pOeVA5BdUs3u\n3HJGDwpv9qzVGQXkl9dw6ZTBJ2TuJ4Iqi0REREREREQ6qiQTais7d4+r1DoGRnT/fPqTOXeC6YXV\nf21+rbYKKvNPSGVRjcfLFU+tZckXmVw/M4mx8c3/u9w8N4WjZTW8uiGLWSOcGAZ8vPNoi897c1M2\nkcH+nDEqpqenfsIoLBIRERERERHpCJ/P6sXT1lKqlvSnZWg9aUASjDgXMj5ufq0k0zpGDu3xaXy0\n8ygbDxVz3yXjueeCsS2OmT3CyZi6KqLvzUlhUmIkH+9qHhaVu9x8sCOXCybE4/CzN7veXyksEhER\nEREREemI0sNQXQzFhxrP+Xzw6QNQWdj6fV+3ZWhtSZgCRfsafyf1GsKinq8sen1jFoMiArksLbHV\nMYZhcPeCMVyWNpjpw6I5e3QsW7NKOVrmajJu6bZcajw+LpmS0NPTPqEUFomIiIiIiIh0RP128JV5\njefydsKK38GON1u/r6GyaEDPza2/SJhiHXM2Nz1fUhfADejZyqLcUhcr9+Zz6ZTB2G1Gm2NnpETz\np4UTsdkMZo2wmn5vOFjcZMwbm7IY5gxhUuLJFQQqLBIRERERERHpiII91rGyoPFcRd3SpMJ9rd/X\nUFn0Ne/RpBueAAAgAElEQVRZBBA/2TrmbGp6viQT7A4IGdijr//P5mx8Jlw6tXPNqEfGheFvN9iW\nXdpw7nBRFesOFHHJlAQMo+3gqb9RWCQiIiIiIiLSEQV7rWNlfuO5+u+L9rd+n6sEHOFgO3l62nRZ\n0ACIGgbZLYRFkYlg69mY4t0vc5g8JJJkZ0in7nP42RkZF8a27Mblc29tzgbg4skn1xI0UFgkIiIi\nIiIi0jH59WFRgdWrCBori4raqCxylapf0bHip7SwDC2zx/sV5Za62JFTxjljYrt0//iECLZnl2Ga\nJqZp8ubmbKYPi2LwgOBunmnvU1gkIiIiIiIi0hEFe8GwW9u/V9f1rqmo619UfAi8npbvqy7RErRj\nJUyBsmwoP2Z3sRMQFq3YY/23OnNU15a6jUuIoLTazeGiajYfLuFAQSWXTOnccrb+QmGRiIiIiIiI\nSHuqiqCqAOInWT/XN7muryzyua3d0lriKoEgVRY1iK9rcp213jrWVFi/2x4Oi5bvziMhMoiRsWFd\nun98ghX4bcsu5c1NWQT62zhvXFx3TrHPUFgkIiIiIiIi0p76fkVDT7OO9b2KKvLA5m9931rfIlUW\nNZUwBfyD4cCn1s/1IVtkz+2E5nJ7WZ1ewBmjYrrcjLq+yfW7X+bw9pYc5o+NIyzQv5tn2jcoLBIR\nERERERFpT37dTmhDT7eOx4ZF9dVGrYVFqixqys8BSafDvuXWzyWZ1rEHw6J1B4qodnu7vAQNGptc\nL92eS4CfnZtmp3TjDPsWv96egIiIiIiIiEifV7AX/AKtqhiAivqw6CgMnQFHd7RTWaSwqImUMyH9\nQ6vXU0NY1HPL0FbsziPQ38ZpKc7jes71pyWzMbOYn8wbSVRIQDfNru9RWCQiIiIiIiLSnoK9ED0c\ngp1Wk+vKfPC6oboIQuOs7eALW9gRzVMDnmpVFn1VypnWcd9yKDlkBXGhXa/6aYtpmizbfZTTUpwE\n+tuP61mXTh3MpVNPzqbWx1JYJCIiIiIiItKegr1WY2abDUKcVoPr+qVooQMhKhmyNsLaJ6C2HGor\nra+qImuMKouacqZC+GArLDIMiEi0jj1gX34Fh4uqT+plY91NYZGIiIiIiIhIW9wua7nUhCusn0Ni\noLKgcSe00IEwaBLsegfev8s6Zw+AgBAICIXY8TBkeu/Mva8yDBhxNmx5GRzhMGhij71q+W5r57rj\n6Vf0daOwSERERERERE64Go+XG1/YyPRhUXx/7vDeng5FlbVsPFTMoIhARg8Kx247psqlMAMwISbV\n+jkkxqoqqrBCCEJjYeT5MOnb4B8E/iHgd/L2s+k2Z/7K6vWUtb5H+xUt353HqLgwEiKDeuwdJxvt\nhiYiIiIiIiIn3L3v7mTl3nw+2Z3f4Xt8PpNfvrWNTZnF3TqXnJJqLnp0NTe+sIEFj6zmoY/2Nh1Q\nUPez85iwqCKvaWWRYUB4PAQNUFDUUSHRcO07MPunkHZ9tz223OVm3kOfsnZ/IaXVbtYfLOYMVRV1\nisIiEREREREROaHe2pzNv9ZmEhxg50BhZcN50zR54IPd7DpS1uJ9a/cX8q+1mbyzNafb5pJb6uLb\nT6+lpNLNk1dPZVrSAN7emoNpmo2DCvYChtXgGqxwqLKgsbIoREFEl/kHwZm/6NZlaLuOlLP3aAUv\nr8tkVXo+Xp/JWQqLOkVhkYiIiIiIiJwwe3LL+dmb2zglOYqb56SQX15DRY0HgM2HS3h0xT7ufmt7\n07CmzhubsgE4WFDZ7FpX5JVbQVF+eQ3PLT6Fc8fGccmUwWQWVbHz2MCqYK+1TMq/bhlTiBPclVB8\nABwR4B/YLfOR7lH/72PZrqMs3Z5LZLA/k4cM6OVZ9S8Ki0RERERERKTHVNV6eHhZOqVVbspdbr73\nr42EOPz4+5WTSYkJBeBQXXXRe18eAWDDoWJWZxQ0e87S7db1g4VVxz2vgooavv30OnLLXDy3+BSm\nDrXChHljYrEZ8MH23MbB+Xsbl6BBYyXRnqU9tt27dF19tVplrZf/fXmEOakxTXtQSbvU4FpERERE\nRER6zKd78nnwo72s2VdIZLA/h4qqeOk7pzIwPJAkZzAABwuqGDMonKXbczl9uJP9+RX87t1dXDip\nFH+7gZ/NxoGCSqpqvUxLGsDmzBI8Xh9+9q7VPxRV1nLVP9aRVVzFc9efwrSkqIZr0aEOTkmOYun2\nXH40byT4fFCYDsPmND5g5HnWzmg7/gOJ2uWsrzlYUEliVBDlLg8lVW7tgtYFCotERERERESkx6Tn\nVQCwZn8hAP933iimD4sGICk6BICDhZVszSolu6SaH5w9gkB/Oz9+dSsPfLCnybNSYkJYOHUw6w8W\nk1VcTZIzpNPzKamygqIDBZU8e920hrkc65wxcdz77k6OlFYzyHcUPC5wjmgcEBwFlzwJ5z8ANv1Z\n3dccKKhkeEwoA8MCeX1TFnNSY3p7Sv2O/lWLiIiIiIhIj8nIqyAhMojvzEomq7ia784e1nAtxOFH\nTJiDgwWVlFa78bMZzBsTR0SwP98YPwi3z4fHa+L2+nB7TcIC/fgyqxSwlhq1FhZl5JXzz88O8qsL\nxuDwszecN02TG57fQEZeBU9fm8bM4c4W748NdwBQ4fJAWbp10jmy+cDA8K78SqQHmabJocIqZqRE\nc9uZI/hW2mAig7U7XWcpLBIREREREZEek5FXwfCBoVw/M7nF68nRIaTnVZBVXM3ckTFEBPsDYLMZ\nOGx2HF/5q7Vx6VoltJDfAPzp/T18uPMoExMjuSwtseF8QUUtGw8V89P5I9usNqkPmGo8Psivq246\ntmeR9Dl7csv5yWtb+f03x1Ht9pLsDCEqJICokKj2b5Zm1OBaREREREREuuSOf2/mokc/44EPdrNm\nXyE1Hi8+n8mDH+3liqfW4HJ72V9ghUWtSXIGs+VwCQUVNVwxbUi774wJdRASYG91R7T9+RV8tOso\nhgHPrDrQZFe19KPlAExIiGzzHQ4/60/lGo/X2gktOBpCmi9Xk75jw6EitmWXcv/S3UDjEkfpGlUW\niYiIiIiISJd8vPMo/n42tmeX8uiKfQT524mPDGRfvhXk/GdzNi63r82waGjdH/Wx4Q7mjmy/t4xh\nGCQ5QzjQyo5oz6w+gL/dxk/mpfKH93azMr2goYqovn/SiNjW5wPHhEVuHxSkq6qoH8gvrwHg831W\nb6zkLvSzkkaqLBIREREREZFOq6r1UFnr5abZw9jyq3N4+po0Lp+WSHiQP3cvGIPDz8bTK/cDtBkW\n1f9Rf1laYod3N0tyhrRYWVRYUcPrG7O4dEoC152WzMAwB/9ae6jhenpeOeGBfgwMc7T5fIf/McvQ\nCvY0bW4tfVJeXVgEEGC3ER8Z1Iuz6f9UWSQiIiIiInIy89TA/k9g+Dlg6756gYLyWsBaFhYW6M85\nY2I5Z0xsw/XPMgpYvjsPgOExrYdFp6VEc9GkeK6eMbTD706ODmHptiOUudyEB/o3nH9x7SFqPD5u\nOH0YAX42zhkTy3+35ODx+vCz20g/WsGI2DAMw2j+0NoqOLIFAkIIsCUB4KssgKrClptbS5+SX17D\nsJgQSqrcDAj2x25r4b+xdJgqi0RERERERE5mO96Cly+DFb/r1sfmV1iVHM5WqnTOGj0QgOiQAAaE\ntL4bVWRwAH+7YjIDwwI7/O65I2OwGQaLnl5HYd08XG4vL6w5xNmjBzZUMs0c7qSixsPWuh3UMvIq\nGNFSlVNlITxzDvzzPHhyNvFfWL+rgJIM67qWofV5eeU1JEQG8YdvjufH8xTuHS+FRSIiIiIiIiez\nvB3WcdVfYOsrbY/1+WDDs3BgpfV9G+p7xMSEthIWjbKqjFLaWILWVWlJUTx1zVT2Hi3np69/CcAb\nm7IoqqzlxlnDGsbNGGY1pf48o4DCihoKK2ubL4mrLIQXLoTCDLjoMRh9IRHbnyeGEgJL9lljYhQW\n9XUF5TXEhDmYPy6O88cP6u3p9HtahiYiIiIiInIyy9ttLaMKHQhv3wpRyZB4Sstj97wH7/7Q+j4i\nEcZ/CyZeATHNKzUK6ip6YlqpLIqLCOTiSfFMTGx757GuOnNULDfNHsbfV2RwuKiKf6w6wMTBEZyS\n3LhV+oCQAMYMCuezfQVMqzs/Ijas8SFVRfDCRVZQdOUSSDkThkyH3e9yg997hJRHgV+g9buQPss0\nTfLLazpVnSZtU2WRiIiIiIjIySx/F8SNg8tegIjB8O9vQ0lm83GmCasfhAFJcOkzMHA0fPY3ePQU\neGaeFawc+9i6yqKoNpaY/fWKyVw/M7k7P00Tl6VZIc5tSzZzoKCSm2anNOtHNHN4NJsOlbA5swSA\n1Pqd0KqK4PkLoTAdrnjZCooAolPwjLqI6+3vM/zwmxA9Amz2HvsMcvxKq93Uen2tBpfSeQqLRERE\nRERETlY1FVYwFDMagqPgylfAUwsvXwHlubDyATi0xhp7cBVkb4TTbofxC2HRa/Dj3XDOvXB4HWx8\nrsmjC8pd3BD0Kf6r/wJ73ofSbCtwAtj/Kax9ovHn1pRkgqusyx8vMSqY04c72XK4hMSoIM4dG9ts\nzGnDndR6ffzx/d2EBfoRFx5YV1F0IRTstYKi4Wc1ucd35t287zuFzAGnwuk/6PL85MSoDy7b2+VO\nOk7L0ERERERERE5WBXsAeDsnjLNqPITEpMJlz8G/FsJDY8HngaAoWPwBfPBzCBkIkxY13h86EGbe\nDukfwsZ/wswfgGFAdTHnH/g9M833YcUx7wsaAOEJcHS79XPceGvZ2/LfwfTvWxVO1cWw/U3YugSy\n1sOEy+GSp7r8Eb99yhBWpReweGYyfvbm9RCzhju5e8EYTNNkwuBIjOpiKyjK32stPftKUAQQ4BzG\nHe5buX3kCH40Xv2K+rq88raXRErnKSwSERERERE5WeXtBuChL/14172FJ66aii3lTLjgb7DtNZh8\nNbx9GzwxE0wf5hUv83F6KclOT9NG0GnXw+uL4eNfwZevQUUuM4E3wq7i0lvug7ydkLsNcr+Eggw4\n61fw+SOw7gmr58+2V2HHf2DYXMhYBt4aq9opZjQc+vy4PuL8cXE8e10as0fEtHjdz27jhtPrlsL5\nvPCPs+uCouYVRfUMw8DhZ6PG4z2uubXkyqfWcvaY2MY5yXFTZVH3U1gkIiIiIiJyknlp3SFeWpvJ\n/0btwmsEcMiM5cDOo/z14738aN5ImHK19QXgcVlNrS9+gs9tU7nxhXUATEyMZOGUBC6YGE/kqAsg\n2GkFQHHj4bTbuP1TH0bCLC4NDLeaQg+Z3nQSrjL4/GEwfTD1eijaZ1USTb0OJn0bBk2ENY/Ch7+A\n8qMQ1nwJWUcYhsGZozp4b/pHkLMJLn4chp/d5lCHn40ad9s7wnVWrcfH2gOFhDjsCou6UV65C1Bl\nUXdSWCQiIiIiInKSWbE7n51HynCF7yDPbzDjBg9gVFwYDy/PIDUujAUT4hsHT7na6lHkH8QTz6wj\nJszBTbOG8camLO7+7w7ufXcX88bG8pf5D+Ao2Wf1NPJz8NHS9/l2aBt/nE/7jhUWhcTAOb+FwPDm\nYxKmWsecTTDyvPY/mM8Hn/wB8vdYTaeDomDCZc2DKtOEqkKrkijE2dig+osnISze2uWtHQ5/OzWe\n7g2LcktdmCYcKqzq1ud+HbjcXj7Zk8e5Y+OaNTHPL68hyN9OqEMRR3fRb1JEREREROQkMzDrAz4K\neAnH4Tx2mdMZMTCMey8ex778Sn7y2laSokMYlxDReIN/ENuzS1mVXsBP54/kxtnDuHH2MHbklPLs\n6oO8sSmLb586m9MmXAJAZY2Hare37UqOyET4xl8gcmjLQRFY1UWG3Wqs3ZGwaNtrVlPuqBSrd1J5\nLmx7HW5ZC+HxUFNujdnwrLUsDsCZCpc8Df7BsG85nPFLsPu3+6qeWIaWVWKFRJlFVZim2Sz0kNa9\ntC6Te9/dycs3nsppKc4m1/LKa4gJc+j32Y20G5qIiIiIiMhJpMzl5gzXx0QbpWwPn81TrrNIjQ3F\n4WfniaumEhUcwI0vbODLrBJ++vpW3tt2BIBHlqcT6vBj0alDG541Nj6C284cDsCRElfD+foeMc62\nKosA0ha32hcIgIBgiB1jhUW734NXroZXr4XtbzTfSc3tshplx02AWzfAbRvhuyvBWwtvfc9aSveX\nUdbRxKpmOvc+a0e4p+bAo9PA5g9Tr+3Q7zHAz9btlUU5db/DGo+voSmzdMyyXUcB+GB7brNr+XVh\nkXQfVRaJiIiIiIicRHbnlDHVtpePfVP5Q9nNlJhubo21mlXHhDl4+to0Fj6+hgv//hkA72w9QlZx\nFR/sOMpP5qUSEdS06iYuIhCAnJLqhnMFFfVhUcDxTzhhKnz5KhxYBcHR1pKxnW/BJ3+0lpAZNquK\nyFUGpZlw4cNgq6t7iE6xmml/8DPIXAtjL7ECqsFp1j0AE6+ADc9Y4VP8ZGuHtw5w+Nm7vWfRsb/D\nQ4VVxIYHtjiutMrNY59k8KN5qTj87N06h/6ozOXmiwNFGAa8vyOXey4Yi83WWEWUV17DiGMbsstx\nU1gkIiIiIiJyEsnev41TjArKY6ZQkuMGYMTAsIbrY+MjeGzRFP637QhXTEvkhuc38If3djMqLozv\nzklp9rxAfzvO0ABySpuHRd1SzZGQBhufgwHJcONyCIyALS/B9jfB57EaZPt84OeAWT+GlDOa3n/q\nzRCTCvFTIDiq+fODo2D2nZ2eVk8sQ8sursZmgM+EQ4WVnJLcwnyBj3Yd5cmV+zlz1EBOHRbdrXPo\nj1buzcfjM7lq+hD+tTaTzYdLGOYM4b3tR3h7Sw4ZeRXMGuFs/0HSYQqLRERERERETgIHCioxTRPP\nwbUAOEfPghwXQf52EiKDmow9Y9RAzhhlVdj8aeEE7vnvDh5YOBF/e8udSuIjg8huYRlaTHvL0Dpi\nxDkw7Aw49w+NYc+Ua6yvjrDZ2t3ZrCscfjZqu3sZWmk1oweFs+tIGYeLWm9yvT+/AoCiytpufX9/\ntWxXHgOC/fnJvJG8sv4wty/ZzNEyFx6fSUpMCD86J5Wrpg9t/0HSYQqLRERERERE+rmMvAr+/Nij\nGIbBN+wbqDBCSUydBMvWMnxgaJMlO1917tg45o2JbbM5cHxEEBl1AYbPZ/K/bUcIdfgRFdINy9DC\n4uCat47/Od3M4W+nrNrd7PyTn+7j1GHRTEqM7PQzs4utsKi02s2hNsKiAwWVABTWhUW7c8sY5gwl\nwO/r2XZ4VXo+c1JjiAwO4IKJ8azbX8QNpydz4aR4xgwKV2PrHqCwSEREREREpB8rqarlrX/8jsd4\nAp/PoMIXRE7EBMbER2K3GR3q5dLeH9vxkUGsSs/HNE2eWX2AtfuL+OOl4/FrpRLpZOBoocF1da2X\n+5bu5toZQzsdFpmmSXZJNWePiaWkupZDhW1VFllhUVFlLYUVNZz/t1XMSY3hyavTvnaBkdvro6Ci\nlmSn9e/4wcsm9fKMvh6+Xv/KRERERERE+jHTNFnyRSZlLqvixe318fYTv+QntY9TnjCHsqhxRBqV\nuOOnEehv575LxvOdWcOO+73xkYFU1no5UFDJAx/sYd6YWC5LSzzu5/ZlLfUs2ldXXVVR0/leRoWV\ntdR4fMRHBDIkKoTMViqLfD6TA4WNYVFOiQufCSv25PPj17bi9Zkt3teaqloPlz+5hs8zCjo9576g\ntK66KzLYv52R0p0UFomIiIiIiPQT27PL+Nmb27h/6W5Mn48VT93JNWVPkj3oHCKuf42oG/9L0agr\nGXn2YgAuS0tkTHz4cb83vq7n0Rubsqj1+rh5bspJv/Snpd3Q6sOiyhpPp59XvxNafGQQQ6ODKaqs\npdzVfJlbdkl1Q6+kwspajpZZvaIWTBjEO1tz+NV/t2OaHQ+M3tiUzboDRWw4VNzpOfcFJVUKi3qD\nlqGJiIiIiIj0E4eKKomjkP99UcV5uU8y7+iL7HDOZ+x3XgK7H/hFEXXFE93+3vqw6D+bsgl1+DEh\nIaLb39HXOPybL0Pbl1cXFtV2PizKLrbCooQBQfjqwp70vAqmDBnQZNz+un5F/naDwooa8uqaif/i\nG6MZPCCYJz7dR0SQPz+dP6rdd/p8Js99dgCgxWCqPyipsvo2RQZ3Q38s6TCFRSIiIiIiIv1EeeY2\nPnPcjt0w4Sh8GraAWd97Aez2Hn1vfGQgADmlLs4cNfCk7lVUr6VlaBkNy9C6EBbVVRYlRAYRG279\nPj9LL2gWFh2oe8fY+AiK6iqLDAOcoQ7umj+SMpebxz6xAqPvzklpcq/L7SXQv/HfwuqMAvbV9T8q\nd3V+zn1BQ2VRkCqLTqST//9wERERERGRk0Tcof9iGgY7Rt/BCzE/Iu2W57D1cFAE4Axx4G+3lp2d\nlhLd4+/rCwJaaHC9L88KXiq6ELzkldfg8LMREeSPM9TBuIRwVqbnNxu3v6CSMIcfI2PDKKysJa+8\nhuiQAPztNgzD4N6LxrFgwiDuW7qbPbnlANR4vHz/pY3MvH85VcdUPf3zswM4Qx0kRgX137BIPYt6\nhcIiERERERGR/sA0GVv0MdsCJjP28t9yzS33EBJ4Yv6AttkMBkVYS9GmD/t6hEUOPzu1Hl9DfyCP\n19ewpX1XehaVVrmJDPZv6PU0e0QMmzJLGpqV19ufX8mwmBCiQgMorqssigkLbLhutxn84OwRAOw8\nUorPZ3LjCxt5b1suhZW1bMsqrXtOBSv25HPV9CFEhTiavae/0DK03qGwSEREREREpD/IWs9A71F2\nRp/TK6+PjwwkIsifMYOOv2F2f+Co26K+vrrocHE1tV4fYYF+XVqGVlrtJuKYpVSzU2Pw+kw+zyhs\nMu5AQSXJzhCiQwLw+Ewy8iqIDXc0GZMYFYzNgAP5lewvqGTl3ny+c3oyAJsPlwDw/OcHCbDbWHTq\nUMID/fpvZVGVG5sBYQ510TmRFBaJiIiIiIj0ZUUH4ONf4/vgl9SY/hQPObdXpvH9ucP57UVjsdlO\n7l3Q6tWHRbVeKyyqb249YXAEFTWeTu1IBlBSXUtkUGN1zJQhAwgJsDdZiub2+jhSWs2QqGCiQqyx\nmUVVxB5TWWTNzU7CgCAOFFaRftRainbRpASGRgezObOYMpeb1zdmsWDiIGLCHIQH+vffBtfVtUQE\n+X9t/t31Fe1Gc4ZhPAssAPJM0xzXwnUD+BtwPlAFXGea5qa6aweBcsALeEzTTOu+qYuIiIiIiJzk\n0j+GNxZDbSXYHfzbO5e4gQN7ZSqzU2N65b29xVHXKLrG7YPAxubWEwZH8llGIS63j6AAa0xJlRVo\n1C8xa0lptYeEul3lwOqJNCPFycq9+ZimiWEY5Ja68JnWjmnRoY3VRAO/UlkEkOwM5UBBBXuOlmMY\nMHxgKJMTI/l8XyGvrj9MZa2XxTOtaqOwfl5ZpCVoJ15HKoueA+a3cf08YETd103A41+5foZpmpMU\nFImIiIiIiHTCwc9gyeUQMQRu3cDKhVu4x3M9Q6ODe3tmXwuNy9CsHdH25VUQE+Ygvi7wqV+Kllfu\n4pTfL+PTvc2bVR+r7CvL0ADmpDrJKq5u6IXUuGNaMNEhjQHJwPCmlUUAw5whHCyoYu/RcoZEBRMU\nYGfykAHkldfw+Cf7mJY0gHEJEYAVFvXXnkWl1W41t+4F7YZFpmmuBIraGHIR8IJpWQtEGoYxqLsm\nKCIiIiIi8rVTchhevQYGJMP1/4OoZA4XVQEwNEph0Ynw1Z5FGfkVpMSEEOqwqonqw6Lsul5G9VvU\nt6a++uhY9dVaK+uCpqziurBoQFDDMjSAgWHNK4uSooOpqPGwdn8RqbFhAEweEglAYWUt152W3DA2\nLNAfl9uH2+tr9py+rriqlsgghUUnWnf0LEoADh/zc1bdOQAT+NgwjI2GYdzUDe8SERERERE5udVW\nwb+/Dd5auHIJBFrVIYcKqwj0txHTQnAg3c/h17gMzTStRtPDB4YSEmB1c6nfEa2kyqrYKayoafVZ\nbq+PylpvswqZodEhDI0OZmV6AWAFTwCDIgKbhEWxLVQWJceEAlBUWUtqrPX9qLhwHH424iMCOXds\nbMPYsEBrzv1xKZqWofWOnm4nfrppmtmGYQwEPjIMY3ddpVIzdWHSTQBD/p+98w5vq77b961ta3jv\nESex42wSkhCSEAhhhFFmGYW2tIUWKKWlu7T9tW/7vp100VKgdNNCoYMNhbBH9t6J48SOHe9t7a3z\n++NIsmRLtmzLlpN87+viEpGOjr5WHOmc5zzP85k2bYKXJRAIBAKBQCAQCARTEEmCl74A7Qfgo/+C\nvFkcaDbzxX/tod/hZVqOftheHEHy0GkGYmhdNjdWl4+qfCPG4FSukLOo1y6Pdu+xeeLuy+KUBaXB\nziKAC2bl88yuZtw+Py39DgpMOtKCfUkGrQq7xx/TWTQzzxD+/5CzSKtWcv/lc5iWo0etGvCGZKTJ\nr2t1eaNEqPHS7/DQ5/AyI2ItycbsGBrfE0w8yXAWtQDlEX8uC96HJEmh207geWB5vJ1IkvQHSZKW\nSZK0LD//zCpOEwgEAoFAIBAIBAIANj8EB5+Bi78L1fLUs20neqjvslNg0nHlQtH4MVlExtCOByeh\nVRYYMQZdOragS6fPERSL7PHFIvNwYlF1Pk6vn10NfbT0OynNHijBzjHKwk4sN1lJVjraoCA0u8gU\nvv+O1TO4ZF5h1LYT4SySJIl7ntzNTY9tGfVkuETx+gNY3T6yhbNo0kmGWPQS8AmFzArALElSm0Kh\nMCgUChOAQqEwAOuAg0l4PYFAIBAIBAKBQCA4/Tj2Frz5PZh/Paz+SvjuTqsbnVrJa188ny9dUp3C\nBZ5ZhGNovoE+oqoCI4ags8juGSwWxY+h9Q8jFq2szEWtVPD+sS5a+pxRE9NyDDpyDVo0qqGn7iql\ngmm5elRKxYjOHlPQWZTMkusPjnWzpb6Hbps7XNCdbEIimyi4nnxGjKEpFIqngQuBPIVC0Qx8D9AA\nSB8MLccAACAASURBVJL0GPAqcCVwHHAAtwefWgg8H7RIqoGnJElan+T1CwQCgUAgEAgEAsHpwbs/\ngtwquPYRiIiatZtdFGWmifjZJBN2Fnn91HXaMGhVFGWk0WmVRaFQDK0v3FmUgLMohuhh1KlZWpHN\n+0e7aO13cdmCovBj03L0qJXx/97nFmeQplGGha14JNtZFAhIPPBaDRlpaiwuH7sa+5gZ7FBKJqE+\nKCEWTT4jikWSJN06wuMScG+M++uBRWNfmkAgEAgEAoFAIBCcQfTWwcKbQBvtEmm3uCg0DS04Fkws\ng2NolQVGFArFQGdRKIYWjJ/1DhNDG66zCOQo2s9fPwpAWYSz6IfXLsAbiD/B7IfXLcDjG3nCWaiz\nKLSO8fLKgTYOt1n41c2L+P5Lh9h9sp+blpWP/MRRYnbK76noLJp8khFDEwgEAoFAIBAIBALBeHD2\ngcsM2TOGPNRpcVGYKcSiySbk1vH4AtR12agKOmf0WhUKxcA0tFAMzeb24fL6Y+5ruM4igDXVA729\nkZ1FmXoNecb40+8y0zUJTcdLprPI4wvwyzeOMqfIxHWLSzl7Wja7G/vGvd9YDDiLRGfRZCPEIoFA\nIBAIBAKBQCBINb0n5Nvs6VF3S5JEu8VFUcbIgoAguYSmofXaPbSZXVQWyGKRQqHAoFVjc8vCUJ99\nwK0Tr+Q6JHrEE4vmFWeQG5xSVpqlT84PEMFYxSKLy0ub2Rl13792NtHY4+Abl89GqVSwtCKb2k5r\nUvuQQoQiftkihjbpCLFIIBAIBAKBQCAQCFJNX4N8O0gssjh9uLwBCjOEs2iyCcXQjrRZAKiM6OQx\n6FTY3LKQ0efwkBecWtZji11ybXZ6MWhVMYuqAZRKBefPygOinUXJQq1SoteqsI5S0PmfFw5y9W83\n4fbJwpjD4+Oht4+xfHoOa2cXALBkWjaSBHtP9id93f1B11ZWunAWTTZCLBIIBAKBQCAQCASCVBMW\niyqi7m63uACEWJQCQjG0Q62yWFRVMNAlZdSpsbv9SJJEv8MbFpLiOYvMTu+IvTufW1vFd6+aF+5E\nSjamNPWonEU+f4B3ajrptrl5/VAHAH/d1ECX1c39V8wOF64vKs9EpVSwua4n6Ws2O70oFAPOKMHk\nIcQigUAgEAgEAoFAIEg1fQ2gzwOdKerujqBYVCQ6iyYdbdBZdLzLhlqpoCI3WiyyuX3YPX48/gBV\nwYhavIlo/Q4vGSOIRdWFJj69emhnVbIwpWmwuhN3Fu1r7sfi8qFUwD+2NtJn9/DYe3VcMreQpRU5\nUfu9YFYeL+5twR+Qkrrm1n4X+UYdymEmwgkmBiEWCQQCgUAgEAgEAkGq6WsYEkGDAWdRkXAWTToq\npQKNSoE/IFGRq4+KkBmCYlFoEtqAWBQ7hmZJwFk00YzWWfTe0S5USgV3r6lk24lernxoAw6vn69f\nNnvItjcuLafN7GJzXXcyl8yxTivVhaaRNxQkHSEWCQQCgUAgEAgEAkGqiSMWdZhlsSiRiVeC5BOK\nokX2FUEohuYLT0Iry9ajVSvpHSaGlpXikuaMNA0WZ+LOoveOdnF2eRafXj0DvVaFUafm6TtXMLto\nqHhzybwCMtM1/Gdnc9LWGwhIHOuwMavQOPLGgqQjxCKBQCAQCAQCgUAgSCV+L5ib4zqLsvUa0jSq\nyV+XIFxyHXIOhQjF0ELTunIMGvIMWrrjxNAS6SyaaELOov/sbGLLCP1C3TY3B1rMXDg7nzyjjg3f\nWMurXzyf5TNyYm6vU6u4dnEJrx9qH3WJdjya+5w4vX7hLEoRQiwSCAQCgUAgEAgEglRibgbJH9tZ\nZHGLcusUEuotGuwsGhxDy9ZryTXq6LHHjqH1Oz1TQCzS0Nzv5BvP7ufBN2uH3fZgixmAc6bL4lCu\nURd3kluIKxcW4/YF2HQ8OUXXtR1WAKqFsyglCLFIIBgjNe0W3jjUnuplCAQCgUAgEAhOZY6/Dbse\nl/8/pljkEmJRConrLEqLjqHJYpE2ZsG12+fH5Q2kXCzKSFPj8QWQJLm82uMLxN22pd8JQHmOPuH9\nL63IxqRT835t57jXClDbKYtFVQXCWZQKxPw5gWCMPPBaDZuO97D7fy6dsPGWAoFAIBAIBILTGJcF\n/nGT7CpSqiGvesgm7RYX84ozUrA4AUR0FsWIoXn9Eu0WFwoFZKRryDFoOdhi5sW9LfTZPfQ6vPTZ\nPeGJdqkWi0Lj59dU5/N+bRcHW80smZYdc9uWPidqpWJUQqVGpeS8qjzeO9qFJEkoFOObYHasw0ZR\nRlrK37czFXGGKxCMAa8/wPYTvXj8Ad6t6eTqRSWpXpJAIBAIBAKBYKph7YBnPw3Vl8HKz8Pgk+fW\n3bJQdN1jMOMCMBVGPfzm4Q66rG7mlwqxKFXoNEqKMtKGXBw2aGURqbnPSVa6BpVSQVm2nm6bhy/+\ncy8g/3VnpmvI0WtZPiOHlZV5k77+SK4IxsQ+dm4FK37yNrsa+uKKRc19Tooy01CNcmT9mtn5rD/U\nTm2HLWYR9mio7bBSPc59CMaOEIsEgjGwr6kfu8cPwOuH2oVYJBAIBAKBQCCIxtELT1wPnYegYQNY\n2mDdD0EZ0QTSvEO+nX05pEeftFtcXr7zwgHmFJm4dfm0SVy4IJLSrPSYUSxjmux22d3YR7ZeC8Dn\nLqzkwtn5ZKTJLqPMoIg0VajMN/LVdfLY+2k5enY29nInM2Nu29LvpCw7fdSvceHsfADer+0cl1jk\nD0gc77SxcmbumPchGB+is0ggSICmXgcPvX2ME912ADYd70GhgMvnF/He0S7cPn+KVygQCAQCgUAg\nmFJ88HPoroWPPwfn3gNbH4Hn7wJfRKdN8045epY+1N3xk1dr6LK6+dmNZ41YLCyYOB669WwevHnx\nkPsvmVvAkmlZtJld5Jl0AKRpVCyZlk1VgZEcg3ZKCUWDWVaRza7GPn7z1jF+8toRJEmKerylz0lp\nVuJ9RSGKM9OZXWji/dquca2vuc+B2xdglii3ThnCWSQQjIDL6+fuJ3ZxuM3Cg2/VcuvyadS2W5lf\nksFHziln/aF2Nh/vYe2cglQvVSAQCAQCgUAwVeg/CXmzoOpiqLwIjAXw9v/KjqOb/w5agywWVV82\n5Klb6np4evtJ7rpgJmeVZaVg8YIQ8YS6LL2WZ+9ZxfYTvWQbtJO8qvGzdHo2z+1p4cG35Klo84oz\nuHZxKQAeX4AOq4vSMTiLAFZV5fLUtpO4ff5w59NoCRVsl2WPXrASJAchUQsEI/DT12o43Gbh5zee\nxadWTeepbSfZ2djHeZV5rKrKxaBV8eaRjlQvUyAQCAQCgUAwlXD2DziGFAo4/ytwzcNQ/y787Wpo\n2QWObihdGv00j59vPbefilw9X75kaOG1YOqgUCg4d2Yu1YWnXq/OpfMKWTs7n8c+vpTF5Vn878uH\n6bG5AWg3u5AkKMsao1hUmYfbF2DPyf4xry9UCi4mAaYOIRYJBMPwxqF2Ht/cwB3nzeCmZeV87+r5\n/O8189GplaybX4ROrWJVVR4f1HYNsW5ORZp6HRzvtKV6GQKBQCAQCASnP87eofGyJbfBR/4BnYdl\nwQig7JyoTX79Vi0NPQ5+8uGFpGvH5soQCEaiwJTGX29fzuULivjZjWdhc/n43kuHAGjudwCM2Vm0\nfEYOSgVsrusZ8/razbJwVZQpxKJUIcQigSAOrf1Ovv7MfhaUZnD/FbPD939y1XQO/u9lLK2Qv/zX\nVOfT3Oekrssecz9/29zAkTbLpKx5JL7/0iG+9p99qV6GQCAQCAQCwemPsy9mFxFzroTbngeVBrRG\nKJgXfmh/cz9/3FDPrcvLWZXiyVmCM4fqQhP3XVzFK/vbWH+wnZY+OQJWOkZnUWa6hgWlmWwdh1jU\nYXFh1KmHTKETTB5CLBIIYuDzB7jv6T34/AF+e+uSIVnbyOzymupQ4//QEjePL8D3XjrE3U/swu72\nTeyiE6DD6gpbOk9VJEmipd/JS/taeXr7yVPC0SUQCAQCgeAMJJ5YBFCxCu56Hz7xIqgGToZ/+UYt\nuUYd37xi7iQtUiCQuXtNJfOKM/juiwc5HLzQXZw1dlfPyspc9jT10Wv3YHZ66bG56bC4MDu9CT2/\n3eyiMEM35tcXjB8h0wkEMfjN28fY2djHb25ZzIw8w7Dblufoqcw38H5tF59ePSPqsT6HPO3iZK+D\nB9bX8H/XLpiwNSdCn91Lj92DJEkoFFN3OsNgvP4A/9x+kq31vexq7KM9QvBaVpHNrFMwJy4QCAQC\ngeA0xuMAnyu+WASQM0P+L4I2s5Ml07LITNdM8AIFgmg0KiU/u/Esrn1kE3/b3EBhhm7M5dQg9xb9\n/v16lvzgzaj7tWol27518Yil4O0Wl4igpRghFgkEg9hS18PD7x7npqVl4YkAI7GmuoAntzXi9Pij\nsuU9Nlksmpln4O9bGrl3bVVKS9r6HR48vgB2j/+UsXRKksQ3nz3As7ubKc1KZ/mMHJZMyyLHqOO+\np/dwqNUixCKBQCAQCAQAHO+0ka3XkGtMsSPB2Sff6nNG9TS7249RJ4QiQWpYUJrJPWsqefjd42OO\noIVYXZXH/107H4fHj1qpQKNScqLbzuObG2jsdYwoFnVYXKyszB3XGgTj49Q4WxQIJgmzw8tX/72X\n6bkGvn/N/ISfd+HsfP6y6QRbT/SwdnZB+P6Qs+jKhcU8/O5xGrrtKROLQiIRQK/Nc8qIRb995zjP\n7m7my5dU88VLZoXv9/kDfF2t5GCLmevOTkzUEwgEAoFAcPoSCEjc8oetTMtJ59l7VqXWRR0Si4Zz\nFsXA5vZh1IlSa0Hq+MLFVbxX28mi8qxx7UelVPCJldOj7jvYYubxzQ20m11QHv+5/oBEp9VNkZiE\nllJEZ5FAEMEP/3uYDqubX39kMYZRiCnLZ+SQplHy/tHo3qIeuywWLSzLBKA5WBaXCvqdnvD/99jd\nKVvHaHludzPnz8rjvourou5Xq5TMKc7gUOvUKA8XCAQCgUCQWuq7bXTb3Ow+2c9rB9tTuxhnr3w7\nCrFIkiRsbt+ojkEFgmSjU6t48d7V/M9V80beeJSEYmUjdaj22Nz4A5KIoaUYIRYJBBFsqe/h8gVF\no1bS0zQqVszMHVJy3WuTRZmFpbJY1NKfQrHIMVAm12v3xNxGkiQCgalVGN1pdTOrwBTz6uD8kgwO\ntZpFybVAIBAIBAJ2NMhunqKMNH76Wg1unz91iwk7ixKPobl9AfwBSYhFgpSjUiomxJmXo9eiUSmi\n+kdjEXo8lfUdAiEWjYv/7m/jYIs51csQJJF+h5cC09gy7hdW53Oi205jjz18X6/Di0Ihf9Dlm3Th\nMZSpoM8e6SwaKhb1Ozzc9NgWPvP3nZO5rGGxu304PH4K4kxCmF+SgcXlS6ljSyAQCAQCwdRgR0Mv\nuQYt371qHid7HexvTuFxumP0ziJbcHKuKU2IRYLTE6VSQYEpjQ7zCGJR8HERQ0stQiwaI312D1/6\n1x5+8/axVC9FkCQ8vgA2t49s/fBla/FYE+wq+iDCXdRrd5OVrkGlVFCalU5zv2PYffzqzVoefe/4\nmF5/JPqGcRb12T185Pdb2dnYN6UE0E6r7MzKj1NSOb9Edmwdap06axYIBALBacTR9dB5JNWrECTI\nzoY+lk3PZnqeHpCjLCljDAXX9qBYZNAKsUhw+lKYoRvRWRSKqYkYWmoRYlEMdjX2cv8z+8Mf2LF4\nZX8rXr/EkTbRl3K6EOr0ydaPbQLF9Fw903L0vHc0UizykBNs+i/NTh/WWeT2+fnzhnpeOzAxGft+\nx4BANFgs+tPGemo7rayuyqPb5sbnD0zIGkZLV1AsiucsmlNkQqVUiN4igUAgECSfQACeuR3+vA5O\nbkv1agQj0GFxcbLXwTnTc8g1yMcNsZzUk4azD9RpoEl8opTVFRSLRAxNcBpTlJmWUAxNpVSQl+qp\nhmc4QiyKwR8/OMG/djZxzz92441z0vzcnhZALiy2uLwxtxGMnj67B5c3NfnyUKdP1hidRQqFggtn\n57O5rieckY8Ui8qy02ntd8XtBNrZ0Ifd46d7gq6ChZxF2XoNPbaBgyeHx8eTW0+ybl4hly8oIiBB\nVyqvxEXQaZW/SPLjRAPTNCpmFRjZVt87mcsSCAQCwZmA+SR4HeB1whPXw4kPUr2ioXid0H0czC2p\nXknK2RnsK1o2PYdsg3zhry+lYlHvqCeh2UUMTXAGUJiRRqdl+HONdrObfKMOlTKFEw0FQiwajD8g\nsbmum+m5ej6o7eJH/x1qPa7vsrHnZD8rZ+YCUNNmnexlnpb4/AGufGgDX/n33qTve/Pxbm75wxYc\nnvhusdABxVhjaABrqvNxev3hA5YosSgrHY8/MESI2VzXTbfNzbs1nQD02DwJFzZLksRNj23mya2N\nI27b7/CgVSspzU6nN2Ia2jO7mjE7vdx5/sxwLrh9hBzxZBF2FpniW1CvXVzK9oZejraLf4cCgUAg\nSCJdtfLtjX+GrGnwj5vg2FupXVMkjl54dAU8vBQenAfNu5K3b2s7fPAL8J86F0Rf3tdKZrqG+SUZ\n6NQqjDp1ip1F/aMqtwawe4SzSHD6U5SRhs3tC3d0xaLD4qJQRNBSjhCLBrG/uR+Ly8dX1s3mU6um\n8/jmBrafiHYtvLCnBaUCvnnFHAARRUsS2xt6aTO7ePVAOzsaot9zSZLGNdFiR0MfW+t7eXp7U9xt\n+sLOorHF0ABWVuaiVSl576gs/MhikeyKKc2WbcihMmZJkvjZ+ho++sdt3Pbn7bx1pAMAjz+AxRn/\nwzOSk70OdjT0DXm/YtHn8JCt15Bj0IVjaP6AxJ83nmBxeRZLK7IjxllOFWeRG7VSQVZ6/L+TW84p\nR6dW8rctDZO2LoFAIBCcAXTVyLfTz4dP/RfyquHpW+DIyxP/2rYuqH0djr4W/G89NGyE9gPQf1IW\nIl68V3YUrf1/8nO6jybv9Y+8DO/8AA69kLx9TiB1XTZeP9zObSsq0Kjk05scgzbFzqK+UTuLbG75\nWNeoU03EigSCKUHofGO4i9PtFhdFcWooBJOHEIsGsfFYNwoFrK7K4+uXzaY0K51vPrs/LFQEAhLP\n7WnhvKo8zirLJFuvEWJRknj1QBtpGiUFJh0/fvVIlLvmya2NnPfTd8YsGFmDUcE/flCPxxc7Whjq\n9Mk2jN1ZpNeqWT4jh/druwgEJPocXnKCVuiybLlssbnPgccX4Kv/3sej79Vx4ex8atotNPQ4WFSe\nBUCXLTFnT0jIHMnKCbIYlq3XkmvQhq+0vXm4g8YeB3eePxOFQhEeT9kxQo54suiyusk36VAOY0HN\nNmi5dnEJz+9uwew4da6ACgQCgWCK030UDPlyQbEhFz75MpQshn9/Ehq3TMxrum3wr9vgl7PhqZtl\ncerpW+Dpj8DjH4LHVsOvF8IDFXD0VVj3A1j1Bfm5liRG0WzyBSy2/BYSdDunkj9tqEejUvKp86aH\n78uOON5JCY5eSM8a1VNsorNIcAaQyPlGh9klJqFNAc5oscjh8dFpdUV15Gw41s38kgxyDFoMOjXf\nvWoe9d12Ntf1ALCzsY/mPicfXlKKQqFgbnGGEIuSgD8gsf5gBxfNKeCr66rZc7KfVyOKnt880km3\nzcPxTlvcfbi8fm7/63b+uunEkMcsLnmEfbvFxfN7mmM+P7LTZzysqc6ntsPG0Q4r/oA04CzKkp1F\ntR1W7nh8B8/taeGrl1bz10+dw30XzUKlVHDrOeUAdFkTO7gJOYo6rCOLO/0OD1l6DTkGbdhZ9KcN\n9ZRlp3PZ/EIAcg1aNCrFiKVzk0VnUCwaiVuWT8Pp9bPheNeI2woEAoFAkBBdtZA3e+DP6Vlw2/Og\n1sGh50a/v84a2PggvPeALDYNFmECfnj2M1DzCqz6PNy+Hu56T/7vM+/AJ16Em5+Aax6GdT+C638P\n535WLlBOzwZL29h/1sHYZIc0bftgz5Nw6HnwpVB4GYZOq4tnd7Vw09KyqDLc3IjjnZTg7BvVJDQY\n6CwyCrFIcBpTOELthd3tw+r2iRjaFOCM/iT60EMbOdFtB0CrVpKRpqbX7uGuCyrD25w/Kw+lAvY0\n9rF2dgHP72lGr1Vx2fwiAOYWZ/Dk1kZ8/gBq1RmtvY2LHQ29dNvcXLmwmCsWFPOXjQ08sL6GS+cV\nolTArqAocrjVEh6XHokkSXz7+QO8e7QLrVrJ7efNiHrc6vJRmW8kTaPkd+/VcePS8iGFaf1OudMn\nXTM+6++Fs/P50atHeD5Ygp4bdCoZdGqy9RoeebcOtVLBz288i5uWyeLQly6ZxcdXVNAXdDclWnId\n6kbqStBZNKvASI5Bi8PjZ0tdDzsb+/ifq+aFf3eVSgUFpjQ6pkhnUafFRVn2yFNEZheaAGjscUz0\nkgQCgUBwuuP3gVIlO4sW3Bj9mM4E01aMrex6/Teh/l35/9/7MZiKoydl+dyyO+jKX8DyO0e3b1MJ\nWJMsFuVWyXG3lz4v33fzEzDvmuS9RpJ4fFMDvkCAO8+fGXV/jkFLTaou6ErSGGNoQWeR9ow+RROc\n5oQ7UuNcnA7dL5xFqeeM/SRyeHyc6Lazbl4hi8qzsLp8WFxeXF4/ty4vD29n0KmZU5TBrpN9uLx+\nXtnfxuXzi9AHP8TnFmfg9gVo6LFTVWBK1Y9zyrP+YDs6tZK1swtQKRV868o5fOqvO3hiayPLp+dg\n98juryNxysT/sqmB53a3oFYqMDuHRpGsLh+mNDV3nT+Te/6xm1cPtHH1opKobfrtXrL1GhSK8bXu\nVxUYKclM44WgWBQZa6vINeDxWXn040tZU50fvl+hUJBvGmj8T0Qs6rK6qe+2k2fU0m3zYHf7hrUt\n9zu8ZAVjaAA/e70GU5qam88pj9quMEM3ZZxF3TY3Z08b2cJt0KnJM+o4KcQigUAgEIwHezf8ZjGs\n/hK4zJA/e+g2My6At74vCyrGgsT2K0nQugfO/jhc/oDsTDqxARjkLio/d/RCEUBGCVhaR/+8eNg7\n5VLv638P7fvhlS8nd/9Jwury8sTWRq5YUMz0PEPUYznBGJokSeM+ths1Xgf43WMSi/Ra1bDxe4Hg\nVCddqyIjTR03hha6aC3EotRzxopFoZLhD51VzLWLS4fddklFFs/vbuGNwx1YXT4+vKQs/NjcYlkg\nOtxmFWLRGJEkibeOdHD+rLyw2LGmOp/VVXn89p1jfGLldECOcR1uMw95/qbj3fz41SOsm1dIQJLC\nf7eRWF1eMvVaLptfRGW+gUffq+Oqs4qjDh7kAuix9xWFUCgUrJmdHy7Tzo0Qi3558yJUCsWQA5oQ\nWekaVEpFQmLRzqDb6vIFRTy59SSdVjcz4ohFkiTRHy64ltez52Q/d6+ZOcTqXJiRxtGO1E8W8/kD\n9Ng95A8zCS2SaTnpNPbaJ3hVAsHoONRq5oktjfzwugXCfSoQnAq07gGPVS53hthi0fQL5Nu6d+HY\nG1B1CSy+dfj99jeCqx9Kl4LOCEs+If+XLDKK5chYsrB1Qe4sKFsGJUvgv1+TBaQpxj+3N2F1+bjr\ngplDHssxaHH7Aji9/vBF3knDKTu/Rz0Nze0TETTBGUFRZlrcGFroorWIoaWeM/bItalXdiCU5+hH\n3HZpRTZ2j58H36ylMEPHysrc8GNVBUbUSoXoLRoHRzusNPc5uWRuYfg+hUJ2F5mdXh599zgVuXou\nqM7nSJs1qvj6ZI+De5/aTWW+gV99ZDGZ6VoswziLlEoF91xYxZE2C+8dje63kZ034+srCrGmeuBK\nY06EWFSZb4wrFIEcA8s1aOlOoLNof4sZjUrBxXPk961zGDeQze3DF5DkgmujvB61UsGnVk0fsm1h\nxtSIoclXA0moswhk11ZT71ChUCBIJf/c3sQ/dzRxoGWo0C0QCKYgHYfkW1XwuycvhlhUvAi0Jlh/\nPxx8Bnb/feT9tu4JPndxctY5GFMJ2LuSM+pekmRhyBh0QCuVYMgb6DFKEe/UdPDZJ3YRCMjHgR5f\ngD9vPMHKmbnhASGR5AQvAPbYUtBb5AhOqR1twbUQiwRnCCVZ6bSaYx+3ixja1EGIRdkji0VLpskW\n0hPddq47uzSq60anVlFVYBRi0Th485A8ceOiudFW7vklmVx/dim+gMTy6TnMK8nA7PTSGhQy7G4f\ndz2xk0BA4g+3LcOoU5Ol18SMoVlcPjLS5C/faxeXUJqVzsPvHo8SnpLlLAJYVZWLOvh7kjPK6Wp5\nRl1CzqI+u7ze0mCnT4c1/nP6g+XdcsG1fAB89aISijOH9gEVZaZh9/jDE+RSRWjCW0GCYtG0HD2t\nZmdCE/Ne3NvCx/+0jdUPvENbnC8qgSAZhErot9T3pHglAoEgIToPy11C634IZcvBVDR0G5Uapp8n\nu0fU6dC6e+Ty59a9oNRA4fyJWXdGCSCBtX3oYz118PNZA0LYSLit4HOBIeK4zFAgi1Ep5J/bm1h/\nqJ3aTtn9/NK+VtotLu5eM9RVBAPHX8kuuX5gfQ1PbTs5/EaObvnWkD/8doMYqVJAIDhdKM/Wx73I\n22F2YdKpxb+FKcCZKxb1OUnXqMgzjnwiPy1HH97uw2eXDXlcTEQbH28d6WBxeRYFMeJGX1s3mwKT\njnXzi5hXnAHAkVb5vf7lG7XUdlh5+KNLwm6dzHQNdo8frz8QtR+ry0tGmuwa0qiU3L1mJrsa+8Kj\n50EugE6WsygjTcOSimz0WhVpoyzMzjPp6EpALDI7vWSma8JiynDOolBxdpZeS0WOni9ePIuvXRbj\naikDKn5HAqXZE8WTWxt5Ya/c+ZS4s0iPJBEzhhiJzx/gq//ex4luOx0WF4+8e3zc6xUIYtHv8FDT\nLp/UbKk7dcSiui5b3KmRAsFpT8dhKJgH594Fn3kT4nXdLLoVKs6DK38uCyvt+4ffb9teKJwnT1Kb\nCDKCPYyxeoX2/0t2CoXcTSMRchBF9jEZ81PqLAoEJLYFj9k2H+8hEJD4/ft1zCkyRXVARpITxk93\nhAAAIABJREFUPHbvdSRXLHp5Xyvv147wXoTeK0OCnVahp7l9GHTjG7QiEJwKlGWnY3Z6scS4ON1u\ncVEkImhTgjNWLGruc1CWnZ5Q4Z1CoWBNdQHLKrKZXTS0l2husYkOizu14zlPUXpsbvY1m7lkbuwv\n05KsdLb/v0u4dF4hc4pMKBRwOCjM1XZYWVyexQURBwmZ6bLYExlF8/gCuH0BTGkD6vTNy8rJM2r5\n3ft1wECnT1aSnEUAX7iois9fVDXq5+UZtXQP4xIKYXF5yUjXkJmuQatW0jnMc0K/m9l6DUqlgi9f\nWk1pVuwpY4VhsSg1UbROq4vvvHCQP288ASTuLKrIlV2CI5Vcd1rd+AIS966t4uZl5fxrRxPNfWMv\nxj7YYmZvU39C2+5r6ueXbxyNcrQJTl92BKcVzi3OYGdDHx5fYIRnTA2e2NLI1/6zPxz1EAjOGPxe\neQJa4byRt51/Hdz+qtxXBNC0Lf62kiQ7iyYqggayGwrAGkMsOvyifGtuSWxf9hhiUYqdRYfbLGHn\n+Oa6Ht492smxThufXVMZ91g+FEPrTXIMzery4fWP8PkYS3BLAJvbj1GXnAuXAsFUJlQF0xzDXdRu\ncQuxaIpwxopFTb3OhEZyh/jZjWfx1J0rYj42N+R4abOw8Vh3UuM7kiTx3tHOU+6gPdGTotoOG0DM\nrPlgDDo1JZnp1HfJz+l3Do2NhcSiyCha6O/DlDbw5ZumUXHD0jI2HuvG7vZFdPok7wv6/Fn5fO7C\n0YtF+UYd3TbPiIJCyFmkUCgozNANK+6sP9iOVq1kZr5xxNcPfTi3pai36GjQiXHv2kq+ecWcuKLW\nYEJfOo09w5dch2JnxVlp3Lu2CgUKfv3WsTGv9zsvHOR7LyVm7f/xq0f47TvHOdkrpradCWw/0YNW\npeSza2bi9Po50JKYqJhqeuwe/AEJq8uX6qUIBJNLTx34PVAwiqhYRrE8NWw4sShUbl0ygWJR2FnU\nFn1/Zw101QQfS9AxGMsVE3IWpehix9ZglPfC2flsq+/h0ffqKM1K50NnFcd9TshZ1JdEZ5EkSVhd\n3iEO9iHYOkCdBrrRDb+RC66Fs0hw+hM6D2+KccG2w+wKX7wWpJYzVyzqcyRUbh1CpVSgVcd+u0Ji\n0a/fquXjf94WHpmeDPY09fOpv+7gvdpO/AGJOx7fwSODunamGlvqeqj+zmvc/PstbDg2/FWouqDw\nU5mAiAGy66Yv2L/TZ/eSOUjciS0WySc8kc4igNVVefgCEtsbeiM6fZLnLBoreUYdHn8Aywgnaman\nN9zDVGBKC3f8DKbN7OTZ3c18ZFl5Qv1JpVnppGtUHGhOzYltTZssFn169cxhrxgOJt+oQ69VcXKE\nkuvWflkEK85MoyQrndvPm84zu5p5v3b0V0z9AYmadktCTrDaDmvYQr/xePeoX0tw6rG9oY/F5Vmc\nP0t2P54qUbReu/z7nMwTLIHglKAzKPwn4iyKpHwFNG6Gp2+Fh5fD6/8PGjaB3yeLK+/8UN5u2srk\nrjeS9GxZnBjsLDryEqCAjLLYEbVYhBxEg51Ffje4U1O7sLW+h+m5ej68pAyr28euxj4+c/4MNMNM\nmTTp1GhUCnqS6Py3e/wEJPCN5Cyyd8nvWYLHMOGnic4iwRlCqDd4cH2EPyDRZXOLcuspwhkpFpkd\nXqwuX0Ll1omQZ9RRYNKFIwchMSMZhE5Cj7RZaeix805NJz9//Shf/tdeXN6Ri3xTwfYTvSgUUNdp\n4xevHx1227ouG3qtiuIErYZZem34BMbs9JKVHi1+ZAwrFkULS8sqctCqlGw+3h3eZ7IKrsdDqKNn\npJJri9MXFscKM3R0WGM7gf604QQBiZhjZWOhVStZNj2brfW9I288AdS0Wykw6UZdDK5QKJiWo+dk\nb4LOomC595cvraaqwMj9z+zHPMp/uye67bi8Abpt7hEF3Ce3NqJVKckzatkkxKLTHofHx8EWM+fM\nyCbHoGVReRZ/39JIZ5x/p1OJXntQkBdikeBMo/MIKFSxJ6ANR/lyWRyofw9MhbDt9/D4lfCLKvjb\n1XDgP3DRd6Fg7oQsG5BFCVPxUEHo8IswbYXsako0hmbrAIUS9APTf8PCkW1iomgdFhf+OC56f7Cv\naGVlLitmyqPos/QaPnJO+bD7VCgUZOu1SY2hhdzqIzuLOkcdQQOwimlogjOELL0Go04dHjoVotvm\nxh+QKBQxtCnBGSUW+fwB+uyesN2tPCfxGNpILCjNxKBVoVEpsLmTZ90PuUtqO6zheM6Hzy7lhb2t\nfOxP2xKamjXZ1HZYmZaj57yqPPpjTCaLpK7LTmW+MWH3SI5BFou8/gA2t29IbGz4GFr0l2+6VsWS\niiw21/WEnUXJjKGNlTxjUCwaxq0SCEjhziKQnUVdMZxFkiTx7O5mrlxYPCon3YqZuRztsNKTgt+v\nmnYLc4JuvdEyLUdP4widRW1mFwatKuzKStOo+NXNi+iyufn+ywlOigkS6s9y+wLYPfHFW5vbx3O7\nW7jqrGIunF3A5rqeUy5aKhgdh1st+AMSZ5fL0zR/+uGFWFxe7nt6z8gnGSkm5CzqT+KFD4HglKDj\nMORWgmaUJykLboAV98LdG+CTL8M36uGmv0H15fJ0tcUfg/O/OjFrjiSjNDqG1n0cOg7CvGuDjyUq\nFnXKQpEyIg4VmuplT37JdZvZyeoH3uG6RzaxP4areWt9D1aXj/Oq8igwpfHhs0v56rrZ6LVxRBW/\nDwLy52yOQZvUguvQBUjvSN/hYxCLvP4AHl9AiEWCMwKFQkFZdvqQ3tD2YA2GcBZNDc4IsWhrfQ+3\n/GELs7+7nrN/8Cb3PytPrChLkrMI4P+unc+zn1tFll6b1J6HkOhR22Gjps2CUgE//vBCHv3YEg62\nmLnukU3UdliT9nqJEAhIQ1TgSI52WKkuNMUdYx9JXaeNynxDwq+dpdfQZ/dGjYKPJJZYZIkTQwM4\nrzKPw20WTnTbg/tLvbOoIEMWi+q74ztkbB4fkjTw8xZk6LC6fdgHCZV9Dvm9WpxAJ1QkKyvlq4mT\n7S7y+QMc67QxJ0aRfCKUZKWHv2Ti0dbvojgrutz+rLIsPr+2iuf3tLD+YHTfw67GXnY2xH4fIqcg\nDnfl8oU9LdjcPm5bWcHqqjz6Hd6w0CQ4PTnQYgZgYVkmIMeVf3TdQrbW93L9o5P/uZ0okiSFC/HF\n0AbBGUfrHiheNPrn6XPg8h9DXrCnMC1DLsC+/jFZOLru0VHHkd483MGdf9+JbzTickYJmCN6iY4E\ni63nXg2ZpXKEzJXAd4+9C4yF0feFhI8JKLne32zG65c40W3n2kc28b0XD0ZNSHpmVzOmNDWXzJXX\n9KuPLOa2FRXxd/jHC+Hl+4CgWJTEz7LQABXvSN2c9s4BgS1BQsdwIoYmOFMoy9YPiaG1W4RYNJU4\nrcWiXY29fPSPW7nlD1s50W3nrgtmcss55RwKjl4fjdNiJMqy9cwpysCkUyfXWRT8UqrrsnG4zcKM\nPANpGhVXLizm33evxO0LcMOjm0ecAJVM3jzSwQU/fzdq7HwIt8/PiW47swtNZKbLYlE8B4XT46el\n35lwXxHIky1sbh9dQddNZryCa8dQZ1FG2lDX0KqqPCQJfvGGHJfLHWX0aSKoyjdSVWDkiS2NcaNN\noZ8v9DMVmuQP1MGumoZg2fP03NH9ri8MOuW21E9uXKqhx47HFxizWJSt12J1+4Z1brSZnTFjj5+/\nqIqFpZl8+/mD4d8vgG8+e4Af/vdIzH0dbh046O62x3ZhSZLEk1sbWVCaweLyLFZVyULchmMiinY6\nc6DZTIFJF1XQeMPSMh77+FLa+l3c8fiOKdk9J//7kdclYmiCMwpLq9z3U7o01SvhcKuF+57ew5uH\nO4acSA1LwVwwnwRH8Pjs8ItQdg5klsnOIkist8gWQ+gI/dmWfGdRTZsVhQLe+eoaPrlyOn/f2sjF\nv3yfl/e1YnV5ee1gG1cvKiFNk0Dxs7kF2g/AniegeRdl2ekc77SNTnQbhtAFYV9gmP35fWDvHiq4\nJbhv4SwSnCmUZafT1OuIOh4KDewpzExsGrJgYjktxaK9Tf184i/bueF3W6jtsPLdq+bx/tfXcv/l\nc/jpDWfx3avmcdn8wrCwkEyMaWpsSZyGFnLIeHwBNhzrZk7RQDxnUXkWf7t9OVa3j60nJq849Uib\nBUmCX705tI+ovsuOPyBRXSSLRZIkn3xE4vMH2FLXM1BuXZC4WJQdFHNCTqDBsTGtWoleq0qo4Bpg\nUVkma6rzWTEzl5/dcFZ4/6lEqVTwmdUzONxmiVuIG/r5QjG01bPy0KmV/P6DuqjtGoLv0/S8xN1b\nABqVknNm5PBBbTe1HdZJO6mtCUYtZ49VLDLI78dw8ZlWsyumWKRRKfnVzYuwuX18+/kDSJJEn93D\nsU5blHgUyZE2C7OCv789cZxFOxr6qGm3ctuKChQKBQWmNEqz0qlpF86i05n9LWbOCrqKIrl8QRFf\nWVdNc5+ThkkU+RMl0iEnYmiCM4qW3fJtisUirz/A3U/uJBD83j0xwoTPKEqXyLetu6H3BLTtkyNo\nECEWJTARzR4jQqXPAxRDnUU+N3hHIWjFoKbdwvRcAwUZaXz/mvm8eO95FGbo+MLTe7jqtxtxeQPc\ntLQssZ2d3CLfqtNh/Te5sDofs9PLrsa+ca0xRMjxNGzBtaMHkMLvodPjZ9VP3ubxTSeG3bfdExSL\nYhyvCgSnI+U5euwef1Tfb7vZhVqpIM8gxKKpwGklFnn9AT73j11c98gmDjT3860r5vDBN9by6dUz\noq5GfHr1DH5/27IJWYMx2c6iCOHJHcNxMavQiEqpmFRnUUiA2Frfy+ZBRb2haMXsQlNYyLAMiqK9\nsr+NW/+4lZ++Jo9yHY2zKFRAfaJbFpoGF1wDYUdTiNB7GOtKjVql5G93LOePn1jGzSMUJU4m151d\nSp5Ryx831Md8PPQzZaTLP1NhRhp3rJ7Bi3tbORiMvwA09DhQKgbGU46GaxeXcLLXwboHP+CXb9SO\n4acYPTVtVlRKBVWjEBAjCcUI++M4Ijw+uYw6VG49mFmFJr6+bjZvHu7gpX2t4YPLLuvQAutum5tO\nqzs86Spev9MTWxsxpam5ZlFp+L7CDF3c6XWCUx+b20ddl42FpbHjn+fOkN1l2+qn3nS0yG6PZPZ8\nCARTnpZdoFRD0cKULmN/cz9NvU6+dcUcABqHiaQPoeRs+bZlT3AKGjD3Gvk2o0S+jeUskiTorIEd\nf4Jn7pDdOYOdRSq1HLeLdBad+AAenA8vfC7xNcagpt0adXx7VlkWL967mu9fPY8em4c5RabE4/SN\nm0FrhHU/gObtrDE1oVEpeKcmOY6oULWBZzinUqjXKSgWvXG4nVazi99/UD+s81nE0ARnGqHzk8je\nonaLiwKTDqVydNFdwcRwWolFe5v6efVAO3ecN4MN91/E3Wsq45ffTRBGnTqpnUUWp5cZEa6QwY4L\njUpJSVYajcN0CCWbhh4HyyqyKc5M45dv1kadRB9tt6JWKpiRZ4jZHwQDY8M3Hu9GqYDpeYlHpELO\nkRPd8s87uLMIhopFVpcPvVaFepjxqlONNI2Kj51bwbtHu4YUv8GAABfpjvvsmkqy9BoeWF8Tvq+x\nx05JVjo6dQLW7UFcf3YZG76xlpn5Bg62mkd+QhI41GqmMt8wpvXCgNMs3kTCDosLSYKSrPg56DtW\nz2BOkYk/fFDPjkbZyu/xB7A4o/9dh/qKzp+VBxBzNG+n1cX6g23ctLScdO3Az1RgSqNrCpbTC5LD\n4VbZfbmwLHZRe2W+gTyjjq1TUSyKchZF/06LUnbBlCSe87X/JDTvjP/4YFp2QcE80CRv+MlY2BaM\n+F+9qASDVjU6B2JaJuTOkp1Fh1+EkiWQHez2ySgBFEMnon3wC/h5JTx6Lvz3q7LYMv86WPLJofs3\nFMjOokAANvwS/n6t/OfusV9Qcnh8NPTYo5zzACqlgk+dN4ON96/lqTtXJDwIhZNb5Ol0ZfJFYYO7\nm3Nn5PLWkY4xrzESayLOIlvwtQyyWPTc7ha0KiVtZhdvHIq9jk3HuzncJl9wNerGdgwkEJxqTAtW\nwkTWaHRYXGIS2hTi1Dl7ToBQf8hdF8xMWd7XmJbsziIfhRm68OS2uTGmRE3PNXByNDblcdLYY6e6\nyMS9a6vY1djH+7UDluTaDisz8w1o1cqYYpEkSWyp62FReRZpGiXlOfpRCQNDnEUxxKKMIWKRN2YE\nbapz0zLZcv3c7qHTS0LCRaRYlJmu4fNrq9hwrDs8mr2hxxElNo6W8hw9M/OMI5ZGJwNJktjXbB51\nGXckod+PeF0rbaEJC3GcRSAfoH703GkcarXw7K6B977LFv0ehMSixeVZGHXqmDG0f+9owuuX+PiK\naVH3F2To6LRM/RHqgrERmuazoHRoDA3kCSDnzsxh24nelPUWSZLEf/e3DRGjQ0WwxZlp9NkHPke/\n+ex+7vz7zkldo0AwIj43/O48eOt/B+7rqYMX7oWHzoY/XQx/vwa6RhAzAgFo3ZvyCBrAtvpeqguN\n5Bp1TM8zhGP3CVO6BE5skMWvUAQNQKWRO3QiY2iBAGx+CDLL4ZqH4b498JUjcONfIL966L6N+XK8\n7Z+3wtv/B/OukyfB2cfewVfbYUOSYE5x7Ph5ll5LTqgiQJLkv/N4OHrl6XPTVgZjc4Cjh4vnFlDX\nZQ8748dDeBracM4iW/C42FhAp8XFhmNdfPr8GUzL0fP45qFRNJfXzyf+sp3vvnAQEM4iwZnDjDwD\nSgUc67SF72s3u0S59RTitBOLcgxaCjNSl3FMdsG12eklI01DdYEJg1ZFadbQk9xpOfpJcxaZHV76\nHF6m5+q5eVk5ZdnpPBh0F0mSxJE2eRIaxJ5M1tTrpKXfyQ1LSnnolrP5xmVzRvX6ORGdRSqlIqYo\nGMtZZIpRbj3VKcvWs6oyl2d2NQ85oRzcWRTi4ysqKM1K56ev1RAISDR026kYZbn1YEqy0sIiy0TS\n1Ouk1+5h0TjEopB4GC+G1maWexVKRrhice3iUtI0SrptbhYFe2cGx8YOt1oozkwj2yAfyPYMKrj2\n+QM8te0k58/KY+agqGWBSYfF5cPl9Sf+wwlOGQ60mCnOTKPAFP/3bMWMHNrMLpp6x9f1MRb67B4+\n87ed3PvUbj7/1J6oz5eQQ64y3xglutZ12Xi7pnP0J64CwURy+CXoPAQbfwUbfw3P3QUPL4ODz8A5\nn4HLfiKXHf/pEqh7N/Y+nP1w/C1wm1MuFvn8AXY29LJ8Rg4gXwxsHO3FwJIl4AlOW5x3TfRjGSXR\nMbTOw+Ayw4p7YMltkDNz+KlthgL5/T7+Nlzxc1lUyqoAR3fiDq5B1AQvvMwtiu3EjOKt78uxN8+g\nY15JgmNvwabfyH+ethL0ctwXe3d4itrbSYiihZxFw4pFETG0l/a1EpDgxqVlfGJlBTsa+qLqAgCa\n+5z4AxKm4DFtzhSYzCsQTAZpGhUVuQZq2wcmxHZY3FHDQQSp5fQSi9oszCvOSNyqOgHIBde+pF0t\ntri8ZKZr+NzaKn54/YKY+c2KXD39Dm/UBLCJYmC6luweuu+iWexrNvP2kU72NvXT0u9kxUz5CzqW\nWBSarrWqMpd184v40FnFo3r9rIiYUVa6JubfdWyx6NS8SnPj0jJO9jqGTJ6zuLwoFWAcFLNM06j4\nyqXVHGgx89T2k5idXqbnjt1ZBFCUmYbZ6cXhSZ4IGou9QTfGorJkOIti/1sIiV7FMUTXSDLTNVy5\nUP7dvCJ4Ozg2dqTNGnb65Rq1Q5xFm+p6aDW7+Ni5Q8f7hkSEeMXZglObA81mFsZxFYUIfU5OVhTt\naLuV1w+1805NB1c+tIENx7q5cmERe5v6eXl/W3i7XrubNI1SdhZFiEUOjyxsPrOraVLWKxAkxM6/\nQPZ0qFgNb30PjrwMK++FL+6HKx6AlZ+DuzfI08CeuB6evAHe/B7851PwhwvhpxXwQAU8dZO8v2kr\nUvjDwKFWC3aPP9xrNj1PT1Ofc3hhAtkp+N7RTvwBaaDkuugsWfyJJLMUzBHOosbN8u20lYktsGIV\n5M+F21+Dc++ShSVDPgR84OpPbB+DONhqxqBVjdyt2LxTdkHZu6Du7YH7/V546Qvwjxtg069BlylH\n0LR60OjB0UN5jp7qQiNvJyGKFnJ2+4aL5do65YJtrZFDrRZKs9KpzDdy07Jy0jUq/ra5IWrzpqDD\n83cfX8oL955HgThRFpxBzCowUtspi0U2tw+b20eRiKFNGU4bscjrD3C0w8q8kgSuTEwgRp0GX0DC\n7UvOiE6zUxaLllZkc/3ZsSdBTMuRxYDG3om/4hsWi4LRpuuXlFKRq+dXb9byj20n0WtVXLtYLlGM\nJRZtrush36QbVal1JDq1CkOw+yUzRgQt9LpDY2innrMI5MlJRp2aZ3ZFTy8xO+WfKZZ4eN3Zpcwp\nMvHjV+Vx7xXjFItCk8Mm2l20r6kfnVo55kloAHqtCq1KGTeG1m52YdKpE4qp3rOmkkvnFXL1Ivn3\nOVLYcXn91HXZmBcSiww6ugeJSTsbelEpFVxQnTdk3/lB92OnVUTRTjcsLi/13faYk9AiqSowkmvQ\nJnWSZYfFxaqfvM1X/rWX+q4BS7fF5eVjf9rK3U/s4o7Hd6JVK3n2nlX89tYlzC3O4IHXasIut167\nl1yDjhyDlj6HN3zhw+kNiUXN8gmpQJBqOg7Dyc2w7A64+e9w5S/gSwdg3Q/BFDGyPKsc7lgPa74h\nP2fLI9C2H9Jz5AjVpT+Am5+Az++EvFmp+3mAbcHPg3NnDjiL/AGJ5r7hHYivHmjnU3/dIU9QLVoo\n/2yLPzp0w9Klcr9Q6x75zyc3y1PSsqYN3TYW53wa7t0K5ecM3GcIfseNMorW0u/kukc28eTWkyyp\nyEbptUEgjtvWY4cXPw+mYvlnO/yifL+zXxYA9zwBF3wd7tsLX9o/0Dulzwuv66I5hWw/0Rs1OGYs\nJOQssnXKkT2FAqvLF3aBZ6ZruGFpKS/ua40aitEcTAdUFRjHFcUXCE5FqgtNNPY4cPv84doLEUOb\nOpw2YlF9lx2PL8D8VItFQQdLMkquvf4ADo9/SNRoMKGYUeMkTERr6HagUAwUkmlUSr548SwOt1l4\nZlcz1y4uCQszeq0KtVIRFm5CfUUrZuaOy/0VGm+fHcemm5muweHxh7/IT2VnkV6r5kMLi/nvgbbw\nlAwYEBFjoVIquP/yOWEnwIxRFIjHoihDPuia6N6ivU39LCzNRDOOInKFQkGWXkO/PfbBoNnpJcuQ\nmHA4q9DEHz+xjJLMNLQqZZRYdLzThi8ghZ1FeUbtkILrvU39VBeaYpbsF5iCYpGYiHbacahFjlTE\n6ysKoVAoWD4jh231vcNuNxq2BN1sL+9v5ZJfvc+X/rmH451WHn7nOD12D7+5ZTG/vfVsXvnCahaW\nZaJSKvh/V86lpd8ZvtLda3eTbdCQpdfi8QXCIpHL4yfXoKXD4uaDY13DrEIgmCT2/xNUWlj8cTDk\nwvI7B4SLwaRlwNpvw1cOw3c64L7dcNtzcNWv4Lz75LhWioUigA3HupmZbwi7T0MX5hpGiKL9J+j4\na7e4ZKHky4fg3M8O3XDZHXIJ9ge/kKNbjVtkV9F4HPkRca/RsKG2i71N/XxtXTWPXqSGn1XCj0vh\nD2vhxXth6++g/n25kPvpW6D7KFzzEMy9Co6ul3uo/rxOdkdd9xhc9B3ImQHpEWKLIVeOyAGXzC3A\nF5D4oHZ8n18DnUVS/BSBrUPuh2Job+YnV07H4wvwzx0DLs2mPidatTJ8bCAQnEnMKjTiD0jUd9np\nCPZ5ihja1OG0EYsOt8n533kxCqAnk1DeOBm9RbEmXsUiJNycnITeosYeO8UZaaRpBkqpr11cSmW+\nfEDz0eUDkZvQiXtILKrvttNpdbOqMndcawiJRFlx3pdQVC30uhaXj4xTVCwCuHFZGQ6Pn9cOtofv\nszi9ZKTH/5kunJ3PuTNyUCrk7qPxEJocNpHOIq8/wMEW87j6ikJk67VxnUWWYAfYaFAoFOSbdFFi\n0eFgx0LIyZhr1NJn94SnRQUCEvua+uNeIQydCHSKGNppx4EWOYoxUgwN5ChaS7+TpiR9du9t6idd\no2Lj/Rdx5/kzef1QB5c++AF/3niCm5aWce3iUq5eVBLltFw9K4+1s/N5+N3j9No99No95Bh0QyYL\nOrx+1s0vJMeg5T87RRRNMAVo2weF82VBIFEUClBOzUlT/Q4PW+p6WDevKHxfKEY+XDFzu9kVFkDC\nbhWtPrYAlJYJKz4HNa/Att+DrV2Olo0HQ758ax+dCNNqdqFQwN3nT8f05ldlQW/Z7aAzyWLQ+m/K\n5eQPzpMLu697DKoukUu7PVZ4bLW8/tueh8W3xn6RCGfR2dOyydZrePvI+HqLIi8Gx42iOXrCBdtW\nly98bgDyhajVVXk8ubUxfFGzqddBWXa6GBUuOCMJJQpqO6wDziIRQ5synLpn0IM41GJBp1aOa/JT\nMgjFW2xJcBZZgvsYThQAeWpCnlE3+hLEMdDQYx8Sa1IpFfz0hrPYdLybhYOiF5GTyTbXyfbqlTPH\nKRYFnUXDxdBAFovyjLpTOoYGsKwim+m5ep7Z1cSNS+Uo4nDOIpAFjgc/spiDLeYoYW8shNT9dvPE\nFfEebbfi9gVGJxZJkmxZV0X/+8jSa+iP01lkHoNYBMhiUYRl/EibBb1WRUVQqM016PAFJMxOL+la\nFa39TiwuH4vLYwsGuQYtKqVCxNBOcWra5S6KyM+X/c1mSrPSyTWOfIU4FDXZdqKX8pzxibow4M4r\nzEjjW1fO5a4LZvKnjSfYfqKXr102O+7zvn3lXC7/zQZ+81YtPXYPM/ONZIX6v+weSrPScXr8ZKRp\nuG5xKU9sbQiKSqKEVZBCOmug8qJUryJpvHGoA19A4kMLB7oc84xajDo1T28/yfFOG3cZr0HRAAAg\nAElEQVRdMHPIMdjze1oISKBUMMThGpNz74adf4X198t/rjhvfAsPubkco3MWdfaauV2/Cc0rr8ix\nuBv+DAtvHNjA1gkdh+QS7vw5UHWxfP+MNXLRtlYPH/1P7KltkWvrqgHkY9W1swt452gnPn8A9Rhd\nzJExNp9fIuYhlscGOrluweYe6m7/1KrpfObvO3njUAcfOquYpj4H5eO8sCcQnKrMyDOgUio41mEj\nPVg1ImJoU4fTxll0tEOewjXWD/9kEY6hucdfNm1O0FkEMD1XPzkxtB4H02PEms6ZnsOXLhn6hZ2Z\nrgkXb2+t66EkM23c07lygiJRvBhaKLbX7/Dg8QVw+wJRV3VONRQKBTcuLWNrfW/YgSC7pYb/vSjJ\nSmfd/KJht0mENI2KHIOW1gl0Fu0LllsvHq7c2mOHho2w8UH458fgl7PhZzPkUbkRDOsscg0vssVj\niLOo1cLsIlP4KmCuUf5d/PK/93LeT9/h9UNyiebi8uyY+1MqFeQZtSKGdgpjc/u45rebWPfgB2w8\nNnCSdKDFPGJfUYjqAhNZeg3bklBy7fb5OdxqYfG0gX9DuUYd918+h2fvWTXsZLZZhSZuOaecJ7ed\npMPiIscwMKq6zyE75ty+AOlaFTefU4bXL/Hi3pZxr1kgGDPOPtlVUjC6iapTmVcOtFGek86C0gGH\nvEKh4NrFJTg8fp7b3cJVD23klf0D08y8/gD/3HGSZRXZFGemDxm0EJP0bPjCTrjtBbnrabzvYTiG\nNrrPsYr21/kf/yOw72lYeLPcHxWJsQAq18qF5SGhCEClgc9ugM9uGl4oCq0tIh538dxC+h1e9jSN\nrYwbZKdQyLTlDcTpLfI4QGsIbj/0guXaOQVMy9GH479NvU7Kc0Yo+BYITlN0ahXTc/Uc7bDSYXGR\nkaYOi0aC1HPaiEWdFveUsKwl01kUHo+egBNiWo5+xALE8dJtc9Nr91BVkHgBcahsOhCQ2FLfw4rK\n8fUVAeEr3vFiaCG3R12XnebghIlTPft6/ZIyFAp4drdcdD2SsyjZFGWkTWhn0d6T/eQYtPEPlg6/\nJPcZPP4heXRu5xEoXgRuCzRsiNo026CJOw3N4vSN6NSLRaRYJEkSR4KTFwHY8WcWNP8LgPeOdtFj\n9/DLN45i0KqoKohf5F5gShMxtFOYXpsHjz9Ar93DHX/bgdkpT6Rs7HGM2FcUQqlUcO6MnHGVXPsD\nEg3ddmrarHj8gTFPE/zypdWka1R4/RI5Bm1UDC3UW5SuUTGnKIOzyjL5146mpE39FAhGTafsFiF/\nbmrXkST6HR42H+/mQwtLhhwj/ej6hWy8/yLe/MoFVBUa+fxTe/j28wdwef08u6uZxh4Hd6+plKdy\n2hP8TtGZZCFm3rXjX7xaJ08gG2UMrcB2BLciTe6QuuGPo+tNMhWFnTvDYsgDn1O+2ARcUJ2HRqXg\nrTFORfMHJGxuH9PT3Rhw4o03zMZjB40BSZKwunzhC8khVEoFn1hZwfaGXrbW92B2eoWzSHBGU11o\n4kibhdZ+15Q4nxcMcNqIRb0OD7lTwBJvTEFnEciRLItz/G6m4TjaLo81nDOKaVUhsai200qv3TPu\nCBoQvuKdFSeGVpFrQK9VcbjVwqFWuVtmfmlqu6zGS2lWOudV5vHMrmYCwbjTSMXnyaQ4M21CO4v2\nNfezqCwztpB4+EV5zHHRAtly/vV6uZz0lqfksbgNm6I2z9Jr6Xd4Yp7IWlxjjKEZdfQ6PHj9AVqC\nEbO5xRny1cO3vk/Z8X8AUJ6TzucurMQXkMIFwvEoMOmEWHQKExLzb1xahscX4Gi7lYOtcndeos4i\ngHNn5NLU66S1f2xi/6sH2rjwF+/x09fkk+dFcaKPI5Fn1HHPhZWAHJOMjKGFxaLglb6blpVT024N\nf74KBJNOlzzt83RxFsWKoA2mLFvPv+9eyd1rZvLUtpNc98gmfvP2MRaXZ3HJ3AJyDdrEnEUTQUSR\ndCJIksQ0z3G69FWy2DRR6KMntZnSNJw7I/f/s3fe4XGU5/q+Z3tT75IluXcbV2zTezW9Q2ghITkk\nIY305JCc/CDtwCEJCQQIARJCDQQIpoPBFGODe+9VltW12t7m98c3s0XaXa3KSit77uvytfbOaHZW\n3p35vud73uftd26RuhD8Z+lX/Mzw9+SZRbIMQQ+YbPhDEUIROWmTlSvm1WI16rnrVfFZHoxSZA2N\nkcqZUys40O7lva1NI36B/0jjiBCLZFmmPUfyE9TVg4GIRRsbOvnB8+toU2rPMxGL8swGXIFQVld6\ntyhiUV9am6ti0SdqXtEAw62B6Ip3YYoyNL1OYnJlHpsOOdnQ0IlJr2NCH9xQucoV80ZxoN3LB9ub\nCYQiQ+osqiq0ZC2zyOUPsb3JlTqv6O1fiBDT61+EiWfFwkz1RqhdIErT4ii0Ggkpq3/xqN0F+1uG\nJsvQ5g6w+ZD4HkytzhchoX4nJtcBThxfwr1XzuKbZ0xg0dgSzp9ZnfaY5flmmrXMohFLh1dcnxco\nAviWRifrDgixKJNwa5WFys9/ursVfyjMQx/sxBPI/P6hCo6f7Gql1GGmprD/pQy3nDCG204Zx2mT\ny6NifLsngDcQcxYBXDizGpNBx7Na0LVGNtn0Mvz1bHj2Bnj9R/DxH2HDC6L0uGkLmBxQUDvcZzko\nvJqkBC0ZRr2OH507hb/dPJ+mLj+HOn18/+xJSJJEicOc0I59SLGX9clZ1O72M5m9OIumZfGkSJqn\ndNrkcnY0ufqV86nmFdVFDlIttRJI5iwKegEZjLbo/slyMwusRi6bW8P6g+K+oTmLNI5mLpldw5lT\nKwhHZC2vKMc4IsQipzdEKCLnhlikOIu6BlCG9vKaBp75bD/LlPbEmThI7GYDsky0XXo22NropNRh\nojSD4FaVAqsRpy/IRztaqSu2DbgzF8QCrlM5i0BM5Dc3OFl/oJNJlXmYDCP/o37W1EryzAZ+/vJG\nILPPxWBRVWAV5ShZ+HytP9CJLJNcLAp6oW0XTD5f2Oa7M/p4aNqYkFukZll1D7lWnXf9+b2VKe1s\nm7v8bD7kRJIUh90a4SiSQj7+ftUY5o8uxmzQ89StC7l+YX26Q1KWZ6HVHSAUTmFj18hpVGfRpAqR\nO7T5UBfrD3ZQV2xLKWQnY3JlHgVWI8t3trFk/SHuXrKFNzY29v6DCm5FFJ1VW8jpk8sHVOZrMer5\n/jmTKc+3YNTryDMb6IgvQ1OcRQU2I+dMq+Tfqw/iC2bvnqNxlLP1NRF8fHgTfP4YvPlTeP5mePRs\n0QmtbNLAWr7nCB2eAB/taOG8GVUZf39PnVTO6988kUdvmsdx44UgUuIw0eJO7qrtD25/iCXrD2W2\ns620T5lFLfu2kCd5CVfM6OfZZUjUWRQ7tzOmiJb2fXUXrdzTxmGnDwt+7LIbh+RN7iwKKvmhJkd0\nLpCqI++Ni0ZH/65lFmkczUiSxK8vnUFdsW1QOiNrDB4jfwYN0RptNWR2ODEbdBj1Ug9Xw4rdbby+\nIbMJgOrg+WB7CyaDLqNuVqqjyT0I5W+p2NrY1SdXEQixSJbhwx3NHDcIriKAY0YVMq06n8mVqVfg\nplUX0OUPsWJ3W68rdSMFq0nPzy6YilGvw2rUM6li6NxSqsrf6Bx8J8ya/WnCrVu2AbKYFCSj/gTx\nuDdWihbviIgn0+6CyYgXizY1OBldYsfmOQS73ofqOWKnjn19Oma54lZqGa6yAY0BoYqRhTYjkyvz\n2NLoZP3Bzj65ikDkFs0fXcynu1t5dZ2YmG1R3GuZ4A6EMBl0vHjbcfzm8pl9eu3eyLMY6PKFejiL\nAK6cV4vTF+LNTf3L/tDQ6BVfJ5ROEGHMP26AH+yFSx8W94X9y4+YvKI3N/VegpaM8nwLp02uiP67\nxG4iEIrgHqRFnVfXHeK2J1dFG2ukpY9laN59qwCw1M7q7+llhupEjju3uhIbkyvzeCUuKDwSkfnd\nG1vY2exKepjGTh9X/uUTfvjCesolMWZx4E2+2KPkI2GyRcvWHCmarEyoyOOE8aUUWI1D6hbX0MhF\nShxm3v/eKXyhl8VWjaHliBCL1ElhsT2Ldc8ZIkkSDrMhIeC6yxfktic/59evbc7oGGo2UCAUyThf\nJepo6kUsenvT4X4FFUciMtsOu5hU0TfhRXVx+IKRQSlBA1HX/ertJ0Yn8MlQw4dDEZmp1f3L8MhF\nrpxXy1vfOZlN/3M2x44pHrLXrVLC5g5loRRt7f4O6ktsUcdYAr2FmNbMAYM1oRStKNrFKYWzqB+Z\nRXXFNiQJPt/bzuZGJdx67dOADKf8UOzUsbdPx1Q/vy3DVTagMSDiu1VOrsxnY4OT/W1eZvQhr0hl\n4dhi9rR6WLpVuEk3Hco8C8jtD2E36QfcOCAZDosBl7+nswjguHElFNtNfLi9b6G2GhoZ4+sEi/J9\nkiSwFsLMK2HSeeK58iNDLFqy/hCjiqx9Fpq7U6KMgQerFK1NGVtnlK1nLxO5QKm6g3VD17iOgKyn\nsP6YgZxi73TLLFK5fO4oVu/riI63Nx1y8qf3drJkXXIn1bLtzcgy7GhyUUE7AHbJRyCdWGS0RZ1F\nycrQVH53xUwevWl+Vq7hGhojDe17kHscEWKRGuhX3AfrfzYRA+yYaPPA0p20uAIZrfZ0eAI0On1R\n8acgQxdEJl3YunxBvvz3z3hMadXZF/a1efAGw30Kt4bEvKXBCLfOlEmVeajZwtOrjwxnUTxDfTGt\nUnJQstERTYRbp7CcNm8BnQFKxiXfbjDDuNOEcOMVAzg106qjm7Oosw+B8d0pdZg5dVI5T63Yx95W\nD1MqHaIEbfSJMFpxN/VRLFK/s9ksHdXIHp3eIGbF+TmlKi+aXTGzHxM+NbcoFJGZWOGIukszwe0P\nY0+xaj1Q8ixGXP7kziKdTqKu2EZDh5a7pZEl/J1gTnL/PudXUD1bXPtHOJ2eIB/taOH8PpSgpUJ1\n1w+WW1VdYFHzM9NiKwU5DL64lvTte+Hlb0DDmh6729s3sYNaSgqy7JA254He1MP1dOmcUZj0Op5a\nIRzBH+0Q21tTvNdl21vItxjQ6yQqJDHWyMNLKJyuDM1OVzSzKPU1uqrAytz6oj69LQ0NDY2h4ogQ\ni9QbWXEOlKEBOMzG6GrC/jYPj3y4G52UWYmYOkm4Yt4oIPN8FXXime41NjU4kWVo6keobn/CrSHW\n3n5cmZ3yIQwssxj1jCtzoNdJomuVxoBQy9AG0hGtKUkJ22Gnj0OdvtT1yc1boGS8CLNOxak/EivQ\ny+4FSOjiFI8aNNnfrKfrFtRFB5KLjNugfTfMug5MdjFQ7mMZmsUoLr9eLfNlRNLpCUZLHuNLYqf1\nQyyaUpVPnsVATaGVK+fV0tzlz9hx5vKHUpY4DBTVJZvMWQRQXWihoR9uQ28gnLLcQ0MjSryzKJ6i\n0XDrUqiYOsQnNDh4AqHo/enNTY0EwzLn9bEELRlqnuRgOYvUBZaMjmdP4uB5/7ew6gl46BQhGrkU\nF6KriaquDew2jkOXpmPooCBJSfOUiu0mzp5eyYtK7tpHShOW5iTvNRKR+XBHC2dMqeCiY6oTytCC\noST372gZmj3q9s/WNVpDQ0Mj2xwRYpE6gSvJgYBrUDqT+cVN9rdvbEUnCcurJxAmkiwMLw7VEnv9\nwnoMOiljF4Q9gzK0DUqb4/60Vt3a2IUkwcQ+5uQUKJOpwSpB6wvHjy9lXn1RRplPGumxmvQU2oz9\nLkPbcLCTY+9+h41Ka3GVaF5RKrGoaTOU9dIauXIGHHMNfPoXeO5mitY+jCRBozNx0Of0qkGT/ROL\nTplUTrVSjjfl8H9EJ56pF4qNhXW9i0WyDB/eB49fAP6u6OdSCwgemXR4A9Hr88SKPCQJxpTa++Vc\n0+skfnzeFH62eEq0hHZzhqVonkAImyk71ziHxUBXnLPIZkyc8FQXWGno8PY5UPcfy/ey+A8fEtTC\n3TXSkUosGsH86rXNTP3vN5j9y7f403s7oiVoM/tRvtod1VmUzB3j9AX50QvrMnMJRX8mlPJ4PVDF\noj3LoHGDEIbWPwszr4ZFX4M1/4Q/zoGP74cXbkUXCfJe0RUZn8uASJGndN2COjq9QR7/eA8rd4sm\nGcmEsY0NTtrcAU6cWMpPF0/lminiGq+TZML+JB3VVGdRXBlaf8cdGhoaGsPNESF1t7kD2Ez6nBEF\nHBYDTV0+Pt/bzitrG7j9tPHRAGpvMH3JwJbGLgptRsaU2vnCwnrGltkzes28DAKu1Yl6pivWB9o9\n6CSJ6kIra/a3M67M0WNluTdGFdkYW2rngl7aiGeDOy+YyiA1BdFAuIv6W4amOtMaO31Mi8uQWru/\nA4NOYlqyUsGAB9r3wDFX9/4Cp/1U7LtrKfotr3LcmBf5z7oGvn/2pOjKZb/L0Fb+FdzN6E/6Hv91\n6niWfL4D6/ZXYNrFwlUEQixqXJ/6GLIMb/1MtH4GePOnWBfeBWhi0Uil0xuk0ComZ1aTnunVBQMK\n07/m2Dog5pTdcqiLEyeU9fpzLn84ZaedgZKnOIs8ymfUYkpcX6oqtOILRmj3BPvUjbSpy4c3GMbt\nD/Wpc5zGUUQkAv4usBxZzuD1BzqpK7YxvtzBvW9tQwK+eMKYQSktV7+DyQSh19c38tSK/Rw/vpTF\nGY7H+lSGZi8Xj69+RzxWz4FwAE66Q4SUz70JXv8RvPkTAP7P8F9Ehiqg3FYK7p7ZagvGFHPihFJ+\n98ZWQhEZu0mftITvAyWX7YTxZRTbTRRb41yR/iSifryzSHE0O7J0jdbQ0NDINkeEs6jdHejTQDXb\nOMyig8wv/7OJ8jwzXzl5HDZTZt3KtjY6mVSRhyRJ/PzCadwQ11YzHaoA1b0LWzwbD4qbWiZiUSgc\n4dqHP+W2J1cRich8vred+aP7XlPtMBt4945TWDCEeUUqkiRl3+J8FFFdaO13GdqBdrHS1j23a+2B\nDiZX5fUUev0u4SpC7t1ZBFBQA198Dc74OYT9fHGGkQPt3uggD8TKqlEvifKvQ+vgkz/Be3dDKM33\nwd8Fb/4Mlv4Knrme6+eU8dTxTUgBF8z6Qmy/onro3J883DMShlduF0LR/C/Doq/D549R8cGP+Ib+\nBXw+LfNlJNLhCSaUND5160LuvGDagI9bbDdRkW/O2FnkznYZmj+EL0lmEUBNoXDaNXT0zXGo3qfS\n3a80jnICLpAjR5yzqM0dYGJFHvddPYvKfEu/uqClwmzQk2c2JB3jvbNFdC1sySSsWkEt3c5ILKqY\nBuf+Di5/VDh9G1bBhLOEUATi8QvPw3XPw3n/y998J1HmGKKmNJXT4dBacDYkPC1JwtEZlmV0Epw+\npSKps+iNjY3MqCmINVVxxToby+nEIqPohmYz6dFrY1ENDY0RyhEhdbe6AzlTggZiBWFvq4e9rR5+\ne/lM7GZDLFMoTZit2nHssjk1fX/NXsQiXzDMjmYXep1EqytAJCKnFVKWbGhkX5uHfW0ePtnVitMX\nYm790HXf0sg9Kgss0bKxvnKgXUwmPXGfz0hEZt3+Ti6cFbfK6TwE7/4/WPcMRJRuZpmIRSrKwPSk\n4g5K7CaeWrGPUyaJFU+nN0i+xYgUCcOjZ8es4t4OOO+3yY+3/nkIumHeF+Gzv4lV0ZbtUDQG6hbG\n9iusE6uorsOQHzfwDwXgxVth44tw4h3CARXyQ+M6bJuf5btGH6+2LgZSBHhr5CxOb5DCmphYNJiC\nzeTK/Iw7onn8oawGXHsC4Wh5c3exqFoJvm/o8DK9D1lNLr+4D7r9mqvuqCfkB08b5FWKfBkVn1Ky\nfISJRe2eADNHFZBvMfKX6+eyZP2hQSlBUylxmHpEDfhDYT7cLsqw+hJ+rTqLMipDkyRYcKv4+7RL\nYcqFUJWk09mEM/GHwvhfeL3f+YF9Zv6XxOLQiofEglIcU6ryufXEsRzo8DK2zM7LaxsIhSMY9GIt\nfUdTF+sOdPLT8+NcUF2NRAxWdCGvWNjqTkLAdXvacGsNDQ2NXOeIuIK1uQPRWu1cIE8ZuE+tyuey\nOSKoWs2USOcsOtjhxeUPMamy77Zrs0GHQSel7Ia2pbGLcETm2DHFrNjdhtMXTGn/l2WZB5fupMhm\npN0T5HdvbAVgntat4aimKt9CmzuALxhOcAJFIjKSlL5DWzJn0a4WF13+UGK49Zs/gc3/gTk3iC4m\nchhKJ2Z+kiVCLDK27+LyeSfzyLLdHHb6qMi34PSFxOC0bZcYzF3wB2jeCsv/BEYrOMrjDiRB/XGw\n6nEonwrn3wtGG3xyv9h86k8TJzaF9eLxqauFG+nmJaKLz7M3wI634Mz/geO/KfYxWuDGV/Dv/QzL\n305Hp3Rx08g9Or1B8i2GpJ/tDm+wX/lEmVBXbGPtgcyEWZc/hD2LmUUAzV1+THpddAKlUlUgxKK+\nOg5dimNBcxYdhax8BLYsga5G6DoEXpEVwzm/gYVfje13BIpFsizT7g5SpCxuTq8p6JPImgklDjOt\n7kR3zIrdbdF7b6YxBACdSs5fnwOzJQkmn5dycyzHZ4imIEWjYfJiseBz0vdi5eMKPzpPCEF/Xy46\nmra5A9GGLC+sOohOInFRq6uRQOE4LC0bkP1JOlcmBFwHydPyijQ0NEYwR4xYNKHCMdynEUUNdf7p\n4ilR66k9gzbZ/e04BmKi7rAYUopRal7RyRPLWLG7jRaXP6VYtGx7C5sOOfntZTO5961trNnfQanD\nRH2Jrc/npXHkUKmEOx92+qgviQ22rnl4OUU2E3+6bk5Kq3UyZ9Ha/eIzmRBuve9TmLIYFt/bv5O0\nl4K5AFq3c82C6/nL+7t4duV+vnH6BDHxtxpFhzWAqpnCLn9oLXx0X+pjnvMbMfg97Wew/S1o2dYz\nR6lojHhs2Q7I8MwXRAnFwVVwwe9FXkM3THkiEFTva+vfe9XIKm5/iAV3v81Fx9Tw68tmJAhGgVAE\nTyAc7fY42ORZRFaQLMtpRVhZlnEH0ufgDeg8zDGxSO3eF0+J3YTJoOt3GVomHUI1jiDWPgOvfle4\nRYvHCXdmXpW4/rZuT9xXLe8xHzmZRe5AmEA4QnEWc7pK7Cb2tXkSnntncxNmg47qQmufxKI+laH1\nAdWxNKQiyqKvweaXRdD2sV9OukuZsujc4hJiUSQi89KaBk6aWEZ5ntLNN+AGv5NQ3Tho2YAulbNI\n0oPeRJcve2XCGhoaGkPBEXEFa3X7c6oM7er5dUypzOe4caXR5zJxFm1tFIOj/ohFAHaTIWU3tA0H\nnRRYjdGJeXNXgPHlSXflgaU7qcy3cPHsGlbv7+CpFfuYW180KAGMGiMXteTkUGeiWLThYCfuQJi7\nl2zmZ4t7tjIOhSNR50G8s6jRKZ6rK1ZESOchcB6Amq/1/yQlCUrHQ8t2RpfaOX58CU+v3M9tp45X\nytAMilgkQekkMJjgxlcg0G11MOQXg8p9y2PCkNEC1z4t8o4KaxP3Lx0PVz0J1bNh/6fw/M1gsMLV\nT8Lk85Oeqs4ucrwMfs1ZlIs0d/nxBSM889l+8iwGfnL+lOg1MBqWbsvOZMdhMRCKyPiCkbRNBfyh\nCOGInDWxKOoscvmjuXvx6HQSVQUWDvZRLFKdBYPpLLrnza2s2tfOk19a2PvOGkPP4Y2iffroE+H6\nF0Ef991Z/xy4mhL3PwKdRe2K6FKUxfFqeb6ZT3fHFiBkWebdLU0cP76UYDiScRmaLxgmEIqgk0QZ\nWm/CdV+IOousQzgFqV0ANXNh+QMw7xbQJRG/lQwlVVD7dHcbBzu8fP+cSWKH1p0QFtf+cNF4AKTu\nYwcQgpLJDpJEly+klaFpaGiMaEZ8wLU3EMYXjFBsH6KgvAwotps4dXKiEhPLLEo9ON7S2MWoImu/\nVyHy0jiLNjV0Mq06n1LlZtjdpqyydn8Hn+xq5ZYTxmAy6DhNeR/ztLyiox7VWXSoMzYxdPtDuANh\nKvLN/PXD3fzvG1t7tNE+3OUnHBHPeeI+/05vELNBFytpO/iZeBw1b2AnWjJBDOqAa4+t52CHCLp2\n+hRnUdNmkTFkUkQqnU5MSOL/OMrhhG8Jccga53wqHiu6oCVjymIRtD39UrjqH/Clt1MKRQCYHAQw\nYNTEopyk3SMmVTNqCnjkw93c/+6O6LZ+d9bLEHXFvcsfTLufer3PWhma6ixy+lKKVtUFfQ++z0bA\n9cYGJ6v3dfS4/mjkCBv+JcqKr3gsUSgCcb3t3q3qCBSLVIdONp1Fo0vsdHqDUWFqZ7ObfW0eTp1c\nTqnDnLGzSHUVjSqyEQhF0uZt9hX12EPqLJIkWHgbtO2E7W8k3UVddFbHxy+sOoDDbOCsqZWw5yO4\nfx48fQ0AspKPqEsnFgFdPpGVqKGhoTFSGfFikXpRzyVnUTJsahlaXKCnLMsJA9utjV1M7kdekYpd\n6VzTnWA4wubGLkUsUmy2KTpiPPj+TvItBq5ZINo4nzyxjNtPn8Cl/Qjd1jiyqMxXxaLYxLBJ+Rzd\ncdYkrjm2jvvf28GPX1xPKBzrCnYgzhIfH2gbFW+iO34GOiNUzhzYiZaMFw6lgJszp1ZQ6jDxz0/3\n4fSGxOS+eSuUZ7ll75QLRAeWdEgSneRhDvYvNFwju3QogtDPL5zKpbNruOetbTz+8R4AOr1iIpYt\nsUjN8uhKkUGnon6fshdwHXMW9ehYqFBdaO1zGZo7C2Vo7Z4AnkAYZy+/M41homG1uO7aS3tus5cl\ncRYpZWiWwp77j1BUATqbzqLRiut3d6vIzXlX6YJ22uRySh0mWlz+jARVp5JXNLpUHK+tD8HYmR57\nyEWUqRdBfo0Iu05CqdLtrKUrgDcQ5rUNjZw7vRJrxAUvfgVMDpF5CNF8RH0wRRmaUSxGubLYrVJD\nQ0NjKBjxYlHbENh6BwN15TfeWXTdI59y95LNgOhWsavFzeR+lqCB0uY4yUB5Z7OLQCjCtOoCimwm\n9DopqRV5V7OL1zc2cv2i+ujNzWTQ8Z0zJ0btuRpHL3azgXyLgcZ4sUgpJasqsGxEEgYAACAASURB\nVHL3JdP5xmnjeWrFfm57chW+oJjIqnlFJoMObzDeWRRKDLg8+DlUzhDlXgOhVNjDaduFyaDj8rm1\nvLuliXZPgAKzJLIxyiYN7DUGCacuH4smFuUknR4hFhXZTPzm8pmcMaWCO1/eyIurD0SdRaly3waK\nev3tTSxSFweyNRlRxaJgWI6WUnenutDCYacvQSBOhyzLWcks6lD+vxr76HLSGAJkWYhF1bOTb0/r\nLDpyMotUsag4m2KRIu7saRFi0Tubm5hcmUdNoZUShxlfMDOXkHqNG6NkVbakcKP3hy7FWTSkZWgg\nHG0LvgJ7loly8m7kde3Grg/T4vbz5qZGXP4Ql8ypgXf+B5wNonxy1nWgNyOVjMUvG9ElE4sCnqhz\nWStD09DQGOmMeLFIbemZzZvvYKDmPcQPjrcddrF6n5go7mhyEY7I/c4rAkUsSjL43nBQrNBNr8lH\np5MotpuSWpEfXrYLo17HTceN6fc5aBzZVBdakzqLyvPNSJLEd8+axM8vmMqbmw5z46MrcPqCUbFo\nXJkjtbMoEhaTiYGWoEF0xU+ETcPV82sJR2TCEZlaGkWL+7IsO4syxKXLxxrsHO7T0EiCOrErtJkw\n6nXcf+1sFo0t4Y7n1vHpLpEJku0ytFTdLVXUsk5btjKLzLH3Z03jLIrIotw0E/yhCMGwcDa4/INX\n2qL+f8WXyWrkCB17wdueWiyyl4lA62Cc0OfrEO6M7iVrw8DOZle0dGogtLnFMbJZhlZXbEMnCbGo\n0xvks73tnD5FxAmoMQSpnOXxqO93TDacRcNRhqYy50Yw2mH5n7ud1CGkBxbxRetSWroCvLj6INUF\nFhZW6UV+4axrxfjkwvvh9tWYbAV0YcWQ1FnkBqOdUFg0QtC6oWloaIxkRrRYtO5AB3e+tBGTQTe4\nnbp2fwDP3SQGN4OEyaDDqJcSVnScviD7lZbiW5VOaAN2FiURizY2dGI16hlTKjrGldhNPZxFTU4f\n//r8IFfMHUVZnuYi0khOZYElYTIWFYviPjM3HT+G3189i1X72rnqL8tZe6CD8jwzRTZjj8yiqA29\neQsEXFAzCGJR8ViQdKLtvaeN0aV2ThgvSh9GBUVr3FxxFrn1BdjCmliUi6hOFdX9ZjHqeeALczDo\nJP6htFjOZjc0iK3ApyLmLMpSZlHciniqMrRollmGpWjx96jBchaFI3LUCdHX/CSNIaBhtXhM5ywC\ncMeVovk6cyav6OqHlnP9X1cQzNA9l4p2dwC9Tsqq08Rk0FFTZGV3q4cPtjUTjsjR7MloDEEGuUVq\nx7JoGdogdkTr8oXQSdnLWkuLtRBmfwHWPw9djbHnt78BkRDT9QfY0uhk2fYWLp5dg27dM6KsbP6X\nxH46HRTUYNBLuGQrhpC752somUXq4pjmLNLQ0BjJjGix6EuPf0YoHOGpLy+MrpgMCp/8GTa+CE9e\nKS76g4TdbIi2Dlc7TRx2+vGHwmxt7MKk10VvzP09vjvJSu3Gg06mVOVF25qX5fUMOfzrR7sJRSLc\netLYfr++xpFPVYGlRxmayaDr4bC4aFYNf71xPntb3by7pYlRRVZsJkM3Z1Eo5iw6sFI8DoazyGQT\n7e73fAQPHA+7P+BaJYOryieCr3NFLPLo83FoYlFO0uEJkG8xYNDHbpOFNhPnTK+Miv75WRKLMi1D\ny3Zmkc2oR22AlKoMrUJpKd2UobPInQWxyOkNosawaGJRDtKwRuTRlffslgmAXRGLXHGlaH4nmIe/\nBM3tD9Hc5Wft/g7+761tAzpWmydAkc2ITpfdzrKjS+zsaRH33iKbkVm1RUCcsygTsUi59oxVFhlb\n04hFv1qymXv78LtxeoPkWYzD12F34VchEkp0F20Todf1NLCxwUk4InPp7GpY+QiMmg/VsxIOYdBJ\nuFI5i5QyNNVB5dDEIg0NjRHMiBWL/KEwTV1+rl1Qx9z6osE7cMgvnEWVM0V3ppe+nnrft+6ETS9l\nfGi7yRC13cdPAg62e9nS2MW4cgdGff//SxwW4SyKRGLhhZGIzKZDTqZVx1bounfEcPqC/HP5Ps6b\nUZXQEl1DozuV+VZaXAH8IfE5buryU+YwJx30nTSxjH9+eSFFNiMTK/Kwm/VJnEXKIOrAZ2AtEq6g\nwWDBraIbmckGj1/IOYce5JFz7Yzf9QTUHRftVDLc+IyFOGQXRAa2Yq0x+HR4g0kzia6aVwuI1WJ9\nliZ9+dFuaL2IRQG1G1p2JiM6nRQVrlKVoVXkiwmoml/WG/H3vsHqhqaWoAE0amVouUfDaqiYBoYU\ni3qOMvGYg84i1Uk7qsjKA+/v5OMdLf0+Vrs7QFEWS9BUxpTa2d3iZunWJk6dVJ6wUAjQnEFJmeos\nKs83YzHqaEuRWRSOyPzz030s3dqUdHsyunyhoc8riqd4LBxztQi6blwPQS/sWgpAVbgBgJmjChjv\nXiUyDlVXURySJOHBgjGZs0gpQ1OvdfmaWKShoTGCGbFikVoiMOgBo/uWiwv9KT+CU38MG18QLqPu\n7HwPProPlj+Q8aFtpthkOb7+/UC7V+mElqQELegT4XpqZ5A05Kkd14Ix98beNg8uf4jpNbEVuu4d\nMZ5cvo8uf4ivnjwu4/eicXRSVai4CJxi4NjU5aM8P7Wrb1ZtIe9//1R+cdE0bCZ91JEhy3JiZtHB\nz6FmLgzmSmP1LPjKBzD3RnQf38cZSy9F0hnh0ocG7zUGiM9YiJ6IyOfQyCk6PEGKbD2dQwvHllBb\nbM1aXhGAXSkr660MTXXmZMtZBLH7ijWFs6jIZsKgkzLOLEooQwsMllgU+z1pzqIcIxyEQ2tSl6BB\nnLMo98Sigx3i83TXJTMYU2rnW8+s6XdJVps7MCTNWEaX2HH5Q7R7gpym5BVBLNuzNcMyNLNBh8Wo\np8Ru5sMdrXzjqdU9AuS3NDrp8odo7UOmkdMXJM88zDk+Z98N1mL493/BumdFqVn9CRSEWrHj5ZLZ\nNcJVZC2GqRcnPYRbsmEKp3YWRcV8rRuahobGCGbEikXRFqSZiEX+Llj3HLz/W1h2L3QeTL3vzneE\nXXrMiXD8t8UA5z/fScwvikTg7Z+Lvx/8PDGUMQ02syE6WVZXbQDWH+yk0elLLhbtXw7L7oFN/+71\n+OoNKT4UdWODKHGJdxapHTE8gTD+UJhHP9rNiRNKmV4z/AMzjdymSsknUVtlNzn90TKUVORbjJgN\nemym+DJMEXKbbzGK72fTZmH1HmxMdrjg93DVP0QJxBV/g8LawX+dfhIwKa7IQcxH0xgcOjwBCpLc\nX3Q6iV9eNJ1vnj4ha69t0OuwmfS9BlzHxKLsZX+oJRSpxCKdTqI8z8zhDJ1F6nsqshkHLeC6QxkP\ndC+T1cgBPv2LEH4mn596H3sOO4uUe934cgd/vGY2HZ4g339+bUL7+R1NrowEpHZPIKvh1ipqKLVB\nJ3HihLLo80a9jiKbMcMytNhiTlWBhc2HnLyytoEPuzmrVuwWYf+tbn/C7yTtsb3D7CwCsBWLscHh\njfDK7SJMfe6NAEw1N3PRWGDLEphzfcoOrR7JhimczFnkAZMdjzLet2XJ+amhoaExFIzYK1i7W21r\nnGJ1IuiF7W/BhudFLXIobgD50e/htJ+CvRT0ZjA7xKTS5BA/U7cQzIpwc/av4G/nCIvqtEvEcxtf\nECtlUy8SZWgNq6D+uF7P2WHWRyfLzrhJwDubDwMk74TmETdi9nwIc25If3xlUB+/crvhoBOjXmJC\nhSP6nDrh39vqocMToLnLz68umdHr+WtoqJ+dRmVi2NTlZ9G4kox+1m7S4wmGiUTkqLMu32pQwk/l\nwQm3TsWUC8SfHCNkKRR/8bRCiebsyyU6vMGUGXKnTCpP+vxgkmcx9J5ZFAhj0EmYBlC+3Bu9laEB\nlOdbaO6js6gi3zJomUWqs2hKVX508qqRA3QdhqW/hvFnwvgzUu9ntIC5IDGzyOcEy/BnFjV0eNFJ\nUJFnpqbQyo/Om8wvXtnEE5/s5cbjRgNw46MrOGNKOb+4aHraY7W5g8ytz76jRr1uzRtd1MMBWeIw\n09KVSRlaKFo+de+VszjU6eWqh5b3EJpW7hHfN3UBMhMXjdMXpK54EJvS9JfJ58E3PhfjeEelKJUE\nnri4BOuWp0COwNybU/64N5lYFIkIschoj473synma2hoaGSbESsWdcS1NU5g9zJY8yRs/g8EusSK\n1ezrYfplwrnQvgf+dQssuSP1wc/4Rezvo+aBwSrK06ZdAqEAvPtLqJgB598rbjJ7P85ILLKZDLS6\nRPcz1Vmkk2D1flGCMrkyycDIGycWyXLaMh21I068WLSxoZMJ5XmYDbGb1axaMUFds78jWoc+f0xx\nr+evoVFZYAVEqYcvGKbTG0zohJYOm9mALIMvFI5+/vMtxli4dc2crJxzLhNSnUUebYKba3R4glnr\ndpYJDrOBLn/vZWh2syGrQbEOJT8pnVhUkW9md0tmzSDixaJth7sGfoLExgNTqvJ4d0sTXb6g1q46\nF/j4D2Kh7pxf915i7CgDl1g4EzeK3HAWNXT6qMi3RIPubzpuNMu2t3DXks0cP76UcWV2Gp2+XgPe\nZVmm3TM0mUWjiqzUFdu4bM6oHtvUGILeiHcW1ZXYqC22YjHqaIl7n7Iss2J3GyaDjkAoQps7kJFY\n1OUL5c73s3gsnPBt8fegcJFZ27eLbqoTzoTiMSl/1CPZMIc9iU8GlX+bbNFKgmxlymloaGgMBTl9\nBVt3oIP6YjsFSdxD6kpikT1uW9MWePwC0UFj2kVCIBp9Eujj3mbpePjSO9C2EyJhCPtFxzO/S7Tu\nDgcTHQh6oxCM9n0i/v35Y0Jwuu5fwplUNkWIRRlgN+mjtlTVWTGuzMH2JhcFVmM0KDQBtTzFeRDa\nd6cNAHYoNeDqaq0sy2xscHLGlMRV8LpiGyV2E6v2tdPuDjC+3JHV/A2NIweH2UCe2UBjpy/qJCjv\npQxNRW2T6wmE45xFRtj0ORSPE7bwo4yITbiyZE8Lw9QXRiMJYcX9lqwMbajIsxh7dRa5/KGo8yd7\n55G+DA3ENWD5rswET1Usqsy3sGrf4JRftntES/KJFcKd29jpy53J6NFM02aonC7GXb3hqAC34iwK\neiESzA2xqMMbddSCCDb+5cXTOf7X77JsezPl+WbCEZlOb3ph1+kLEY7I0dygbGLU6/jg+6cm3Vbq\nMLPhYO8dOJ3eYEK+kiRJPZqj7G5x0+IKcObUCt7adJgWl5/aDBxDQojKwemH0QoFtfDZo+BpSRps\nHY9PZ8MYCohFZIPyu1LFIqMtmlGa7tqpoaGhkevk4NVaIMsyVz+0nEtm13BXkhKppJlF298AZLjt\nEyioSX1wvaFvrbPrFsGy/wXnIXj/NzD6RBh/uthWv0jkIUXCoEt/Q7CZDbGAa694nFadz/YmF5Mq\n85KvDnviBtN7PkwrFsVCUcWxG50+2tyBhLwiEDf92XWFrNrXTocnyOmTs19SkZJwUMmR2hd7rv54\nmHFlosg33LiaRD5V3ULRtesopqrQQkOHN7qSWpYm4DoetW7f4w9HP//5Zr3oOjj2lGycas4jK5+l\nsLs1dy/GRyFqK/aUZc5DQEZlaP5Q1ksc8jIoQ6vIN9PpDeILhrGk2Q9EZpFeJ1GaZ8LtDyHLclpn\nVCQis2xHCyeOL03ZcrxdcYFVxTkfJ1QkKevWGFqcDZmX19rLRH4MgF9p6DGMYtFHO1qYN7qIhg5v\njzzHynwLOkl87jqVhcvexKJ2dx9yNrNIdaGVNzcdJhKRU36fQLyf7t1xhVgUK2FTSz7PnV7JW5sO\nZ5TbFInIuPw55CzqTvFY2P0+FNalL50E/DpFGAu4wKAsdgUUh6XJgadTcxZpaGiMfHI24LrLH8IT\nCPPelqakoXkdngAWoy5xYLrjHRFim04o6g91C0Xt8r9uEasNZ/4iZqmuP16UuzWu6/UwDrMBtz/m\nLBJZQmJAmzTcGkQZWn6NGEhtfR0OrRWrGElQu0uoK7cbDooBV3wnNJXZdUXsanbT5g4wu24YxY/P\nH4Old4v/u53viff47/+CPy+EDS8Mf0txdwu8+TO4byY8dTX8bjw8cTEsfxDWPiP+rH8+o251RwqV\nBVYanT6au0RuUaZlaOqk1h0IRQfWxeFmUXqQjXDrEYDOUkBQ1hPqah3uU9GIoz1a5jzcYlH6Cagn\nEM56eKqjl25oIDKLgIxyi1z+EHaTHrvZQEQWWSfp+HR3Gzc+uoLnPt+fcp8OT4BCmzGWqaaFXOcG\nzgYxfskER3ks4NqpNCExD09m0c5mF9c98imPfbSHhk4f1YXWhO16nUShzUS7OxC9Vjh7+a62KfsN\nhbMoHfUlNgKhSDR3MBXOJO3tuzuLVuxpo8RuYv5oIZRk0hHNFQghyzncTr5EccHN+2KvC8A+vSKm\n+ePGf1GxyIbHH0KSwGLM2amWhoaGRq/k6NUa2pSbTkOnjx1Nrh6rhO2eYOIKTcAtSsWOvXXwT2bU\nfJB0sPcj0UKzZm5s2+gTxOOupelbwwI2kx5vMCxKHLxB8i1GRhWJQUjScGsQWSa2YiidJMK6t74q\nVj7OugsmnZuQAxCdjCti0caGTiQpeRbSnDiBaHZdYW+/gcHF1QQf3ifKBJf+WghuN70q3ossw+ZX\n4L274PmbofJeOPUnUBnnLsurBl2Wb76eNpG38OlDEPIKp9PMK4S7a/Mr8PoPEvevWwQ3viLKFo9w\nqvJFZ5TDzr6VoVlVZ1EgFB1YF7UrImv8d+oowmoy0IEDu7ul9501howORczskYk3hOSZjQn5c8kY\nijK03rqhQUww3tHs4rvPreXH502JZuN1R80rUc/b5Q+lPXarkqv34Pu7uHxuLfokboh2d5BCm4ly\nxeWYaWc2jSzi7wJ/J+RXZ7a/vVzkFH32KLz3K9FwZJhy7DY2iMn/P1fsIxCKUF3Q8x5XZDPS5g7Q\noTqLPBk6i4ZZLBqtuIX2tLp7iGBuf4i3Nx/mpTUNtLl75iuV5ZlYsz/mdl+5p435o4spcYj9WjNw\nFiXkFeYio+bDhn+JrNNe8OtUsSguey1ahmbHHQhjN2U3U05DQ0Mj2+SuWOSJ3XSWbm3uIRaJlcS4\nG9meDyEc6NU22i8s+aJLwuFNcPp/J27LqxRupl1LYyF5KbDHTZbFgNnA7NoiqgssLBqboqOUtw2s\nxXDe72DG5WIwteweePoaUbpz9t3RDg7qoF69GW846GRsqT1p4ODMUQXoJLAY9dGchyFj2T3w6YOw\n/E/i32c+GxO9JAmmXija7K5/XriOnro68eeP+wac9f+yc27edvjkT7D8ASFATr8MTv4BlE0U28ef\nAaffKVY+Q8oK255l8Mo34Z1fZO+8BouQX7jkjNbe901BZYGFFpef97Y2UZZnpiTDwa+aWeT2xwKu\nbU2rREfCivRdZI5ULEYdbXIeNi3gOqdQJ37DGXCdaRlaps6+/pJJN7QKxVn07Mr9rNjdxtr9HSnF\nIrcicKn3Q7c/RFma96C6EHe3uHltwyEWz+wpPrR7AowqsmI26HGYDQnjB41hwtkgHjN1Fqm5Rv/5\ntlgQu/GVtGX32WTLISEW7W0VE/+qwp73y2K7ibY4Z1GXP5S2tEt13WR6v8wW9SWidGpvq4fjlArB\njQ2dPPj+Lt7edBhvMEx1gYWvnDSWGxaNTvjZUoeZNneAcESmqcvH/jYvNx83BpvJgNWopzWT4Gy1\nBD0XM4sAjrlajPsMvf8/BfRKGVr73tiCZryzKJBeCNfQ0NAYCeTo1Tq2CmPS61i6rYkvn5Q4aBDO\noriB/I53RNeyukXZOaHTfiZKkpLV3489FVY+IkIZ00zCbebEgN98q5G6Ehsf/+j01K/rbYeCUcJd\nNOlc8dz0y5TVt7vhwRNEa89zfo3ZYKK+xMaKPW34Q2E+3dXKOdMrkx7WbjYwc1Qh+VZj0pXarOHt\ngFV/h8mLhe3cWgyjkrhKdHo45iqYfilsfQ18omMcH/1elOJli2euF+LP1IvhlB9C+ZSe+0iS+D9R\nKRkHjevh4z9C7YKcbNEOiN/9Y+eLYPYbXur3YaoLLciyEHG/cvLYtLkH8dgSnEUhLEYdhoZVUHVM\nRgOzIxGrUU8HDmo9WhlaLtGeqtvmEOKwGPAEhBO1+zVanai5/eGs52HkZ9QNTYhFb20S3ay8wXDK\nfV3+EA6LIbqI0Zt7Sp1c1hZb+dWSLdQX25kxKjFDpsMTjObKFNmN0fGDxjCilpJlGgsw9WL41nqR\nY1hQO6z3hC2NXRTajFHXUE0SsajIZmJfmycqZsqycM0la8gC0KI45FQXznBRVWDFpNexpzXWvfD2\np1bT3OXn0jk1XDSrhnn1RUnv66UOMxEZ2tyBaF7RsUonXVU86w21tDZnM4skKePP3gHzOJy6AvKf\nvQEWfFWMGaPd0Ox4AuHoIpmGhobGSCVnC2nVm85pk8tZubu9R3ZDjxak+5dD3QIwZlYS02cmng2z\nr0u+bdypoqua2jEtBeqg3uUPRcvQesWjOIvi0RthwVfg9tVCKPrsr7D5ZQDOnV7FxztbeWl1A13+\nEIuPSW0Bf+iGufzflcf0fg6DyarHIeiGk78Pi/8PTv9Z+v31RuE0mnOD+FM9G9r2JN/38CZ49Bzh\nDOoPsgwHV8H8L8OVjycXilJx9t3i3P59G7Tt6t/rZ5OgD56+Fg5vEL+nAVBZEBs4XzG3Z2veVMTK\nJIWzqMgswaE1otvgUYrFqGe/XI6pfRuE00+aNYYOdZI4KAHXkUisq2UfUCdTriTuonve2MZlD3zM\noU5vRq2qB0JdiQ2DTooKQskoshkx6iVCEZEv6A2kFou6FGeR6lhy9yIWdXqDmPQ6/njNHCKyzKUP\nfMQjy3YRicSyDMV4QPy+im0m2nopCdIYJHa8LUrKk+RK0qmIRZmWoUmSCBUuGTfsiwdbDjk5eWIZ\nEyscAD3KtSDOWeSOfdbShVy3uQJYjfqsZ4z1hl4nUVtsZW+LEDU6PAF2Nrv56injuOuSGRw7pjjl\nAlCpQzgAW1x+Vuxuw2E2MKUqX9lmoiWJWOTrJhw7letZzpah9QGPsYhvlT4Mc66H5X+GP84VWZsg\nytD82c+U09DQ0Mg2OSsWqSu7NxxXTyAc4bGP9iRs7/AEY+GjkQg0b4PyaUN8lgr1x4HOKEqX/vWl\nlJNxdVDv8YeThgf2IBIRjppULcVtxXDub8BggYbVAJw3o5JwROb/vbqJYruJ48alKG9DZM2UOLJb\nwpBAOAif/kV0k6vqp0hVNAacBxJDviMR8bt/6BQh2G38d/+O7WkVQlam3VviMZjhisdFttWzNwiX\nWS7xwW9F5tao+SJENNj/TA81RHZWbSHjyzMvYbR1yyw6xtwAId9Rm1cEQix6NzwLg78D9n863Kej\nodDhDSJJg7T6vfoJuGcKtO7s04+pXciSBeduOdyFXicRkWPlx9li4dgSPv/pmVQmyW1RkSQpIbss\nrbPIFxRlaHGB9+lQXbizagtZcvuJnDqpnP/36mZufmwlzV1+vIEw/lAk6gIrsps0Z9FQ8elf4O07\nRWl5d9QytLyqoT2nAdLpCdLQ6WNyZT5Xza+jrtiWVDQuspto98TK0CB9yHWrOzDsriKV0SX2qLNo\nzX7h2k5VNhpPqXL+LS4/K/e0Mbe+KOp6FOJZYhnaRztamPLfr/O959bSpDTEUBd+c7YMrQ8Y9Tra\nccAFv4cvvwuFtSJbFKJlaNnuVqmhoaGRbXJWLGpzi9XERWNLOHNqBQ99sCs6AIxEZDrinUUde0UI\ncdmk4TlZk110TNv+Jqx/DtY9nXS3aGZLIENnka9D5Muka9WuN4paaUUsmlFTwKgiK05fiHOnV2LU\n59B/8aaXhDV90df7f4yi0eJ30ql0xulqhCcvh9d/KBxeM64Q7Xf700Wtfa94LKzv57nVw6UPiZK0\nJd/r3zGyQfM2+OgPcMw1MP9L4rnOA/0+XG2RjVKHiZuPH92nn4tNDsM4vSFm6ZTJ81HsLLKa9Lwf\nOYaIzghbXh3u09FQ6PAEKBisEt1tb4r707t9yzPLsyQv05JlmV1NLq6eX8vPL5jaJ3dff0lVWhNP\neb4Zk0GH3aRP6yxSQ7lj7y/1viAy+NSJZZHdxF+un8svL57O8l2tnPv7ZbyyVogS6ngg03IYjUGg\nfQ9Ienj3l7DmqcRtzoMitNowhAtSg8CWRpFXNLkqjy8eP5r3v3dK0oDiYpuJYFjmQHtsYSids6jF\n5R/axbk01JfY2dvqQZZl1uzvQJJg5qgMxCIlW2z7YRfbDruiJWgAJQ5zj25o6w50Isvw7zUHOfV3\nS3lg6c5oN7WcLUPrA0a9jmBYGWvWzIFb3oYL/yg6qTkqcAfC0cYeGhoaGiOVHFISEml3ByiyG5Ek\niTvOmoQrEOLB98XksssXIiLHtTVu3ioe+1I2NNhc+Af4wgtQMUOIBUmwmROdFfm9haeqpQvdy9C6\nUz1b5PhEIkiSxHkzxErehWlK0IYcWYZP7hdtSSec1f/jFI0Wj+17YPcy+PMi2PsxnH8vXPM0jDlJ\nuIM69vT92OrPFPVTLAJRrnjid2H132H1P0Rp0Qf/Cy99Dd66c3BLjboOw1v/DX9aAK/ekei2iuf1\nH4DJBmf+UmRBAHTu6/fLWk16Vv7kDC6alWEWhYLFoEeSYpld1bp2MdFQz+koxGLU4cZKS9ki0ekw\nWTmHxpDT4QkOTrh1JCIcfUY7bHwhKupngjqZ6h5y3dzlp8sfYmJFHjcdP4axZY6Bn+cgsHhmNbee\nOJZCmwlPGrHI7Q8nZBZlUoYWv7AiSRLXL6zn5a+fQLHdyPf/JToqxpehtWsB19knEhELLMfeKu67\nL38dtr8V2+48mHkJWg6xpVF0tppalY8kSSk7WaldzXa3uDAZxFA6nVjU6gpQOszh1iqjS214g2Ga\nu/ys3tfBxPK8jLoqqmVor29oBEgUi+wmWt0B5Lh72IF2D0U2I29++2QWvd6jRQAAIABJREFUjSvl\nN69v4Tevi/F6XpYdkUOBQScRCsfds3U6EZew+P9Ap8cbCGmZRRoaGiOeXsUiSZIelSSpSZKkDSm2\nS5Ik/UGSpB2SJK2TJGlO3LZzJEnaqmz7YV9OrC3OOTSpMo9LZtXw2Md7aOz0RQeCUWdR8xbxWDqx\nLy8xuBSPhfGnQ/UxcGhdz0lfwE1euBMQbX59wQj5vd0sVbEoVRmaStUsCLigdQcAt540ll9cOI35\no3v5uaFk3ydiorTwtoG1vY8Xi975HzA74CsfwPxbROaB0hmOxm4f14OfC0ElXYvygTqLVE75sSi1\ne/W7Iifo3V/Ctjfgo/tg99KBHTueT+4XjiFzPqx8GJ64sOf7czbAzndFBzlHmbBJA3TsH9BL96cV\nrE4nYTXq8SiZXaV0iLBt3dE7mFJDgw+Unyo+002bh/eENADhfhmUle/DG4RD9MxfCNH/P9/OuATU\nEXXeJE5AdzS5ABhfnhsikcotJ4zhjrMnYTHqeuSUqEQictRZFA247qXjm9MXoiCJcDepMo+XvnYC\n1y2oQyfBOOX3UWQXYlWqc9AYJLoaRFZj2US46klx7332Bti/Umx3NmTeCS2H2NLopMhm7LXLYLFd\nfCb3tXmoKxZdsdJmFuVQGVp9iWj5vrvFzdoDqTsXdiffYsCk17Fybxsmg46ZcUHzJQ4TgVAkwQl5\noN3LqCIbY0rtPHLjPJ744rGMLbVTW2zNLdd7PzEa4pxFSdAyizQ0NI4EMrlaPwack2b7ucAE5c+t\nwAMAkiTpgT8p26cC10iSNDXTE2t3ByiOW4X59pkTicgyf3h3e0wssqvOoi2iLt6a2Q0vq1QeA54W\n6DoUe87VBA+dQu1rNwDQ6BSThV6dRWo77UycRRBdtS51mLnxuNEZd6kaEj75kyinO+aagR0nr0q0\nWj+8ERpWwfTLY23tAcqmiNygwxtjz3nbRZezlQ/DX06CA58lP3bHXrCVCAFqIOgNcPmjYCmE7W/A\n6f8N39oAprz+5yklw3VYBIJ+6S247K/i//+vZyZmo2x7XTxOVjq05VULN0/nwMSi/mIzGUQZmi9E\nsdwuOuIdxVgUsWhfidLFcd/Hw3g2GioufwjbYKwI71kmHiedBxfdL76jr34ntQswDnXlvbuzaGez\nEIvG5YijqDs2kyFlZpGaT+QwGxIaPqRDlKElv1daTXruumQGm/7nHCZWiPw0ddyguYuyTPse8Vg0\nGiz5cN3z4KiAf14h3N4j1Fm0q9nN+HJHrwsi6mJlMCwzWmlH70whFsmyTKvbT7E9N8rQ1PN9ZV0D\nHZ4gs+oyGztLkkSpw4Qsi4wjsyF2jVTfW3wJ6IF2D6OKYuHgJ00s4/VvncRb3z55MN7GsGPUSQTD\nqd3AWmaRhobGkUCvYpEsyx8AbWl2uQh4QhYsBwolSaoCjgV2yLK8S5blAPC0sm9GdLld3OJ+GPYt\nB6C22MY1x9bx7Mr90UC+wnhnUdnkTA+dXapmisdDwhqPqxkevwBatmFwC+vu7hYRLNhrZpFX+bX3\n5iwqnQhGW59KHAZMOodOd9p2iTyWebeIcqiBoNOJMrGNL0AkBKNPSNxuskHxOLGir/LKt4SwcuH9\noDOIjmkrHu7p/mrfO3BXkYqjXLSnv+JxUZZmtMCkc2HLf0TQ92DgbhbOHIAZl8MNL4O3Ax45A/av\nEM9vfU0M5tU8L71BDOAH6CzqL3azHrfiLCoId4jJxVGMKha1SUoumbdjGM9GQ0UM8gdhRXjPh8J1\nWlADk8+Hk38Aa56EX9UIATvkT/mjqljk7CEWuXGYDVTk58bEsztWox5PitBq9b3kWQzoFadhb2Vo\nTm+Qgl7CcNXvEcQm8VpuUZZp2y0ei8aIR0c5XP+iaPbxxMXg6xSf+xFGqztAWS+uIiBhMbOqwIpB\nJ6V0Fjl9IYJhORoQPdzUFFopsBr5x3JRjp6pswhiuUULxiSOS1XX1Cc7W1m7vwNZlhVnUWInOb1O\nSvi+jmQMeh2hNM4iTyCMVStD09DQGOEMhg+0BoifeR5Qnkv1fK8Eupr5sevXnN7xvCg1Uvj6aeMx\n6nXc9/Z2QBkURiJiFStXxKKKaYAEjevA3QpPXCREiNqF6AJuJlfm8cZGIRql7AbRsQ8+vj/OWZQm\n4BqEAFA5c+jEonXPwe/Gw6aXU+8TL8Qsf1CINMd+eXBev2i0cAvpDFC7oOf2imkxsahjH2z6txBs\n5lwPX3kfxp0GS+6AF26FgDv2cx17B5ZX1J3yyTDt4ti/p10sznv3B+BzCofT6n+I3KH37ob3fwuP\nLU6fPxSPuwVspbF/1y2AL70NlgIhUK59Gna9DxPPFSV6KgW14vcyDNhMBrY0OglFZPJCbSIA9ShG\nLUPzRgzCMed3DvMZaYDoWDlgsSjoFXlF8YL2yT8UJTtzb4LNL4sssxQ5VXlmsZjQvUxrR5OLcWX2\nfpWCDgUWkx5vMPkESm1Soea92M2GtN3QZFnukVnUG+okXhOLsowabl0QF7BePAa+8C/wi9yfkViG\n1uryU5KBAyheLCqyGSmwGlOKRa1KqHOulKEZ9Dreu+MU7r92NnddMp3JlZl3NVVzi7rHHJQov48f\nvrCeqx76hIZOH/5QhFFFA1wgzGGMeh2BFM6iUDiCPxSJOig1NDQ0Rio5cxWTJOlWRBkbc6t0nEwH\nDXkzqN77kRBbiuopz7Nw8/Gj+fNSUWZTZDOKcpqgZ/g6oXXHnCdWknd/IMSUtp1w7TOw9xPYv5zz\nj63gnrdFtlDKAfAHv4NVT8DYUwFJTP57o2IqbHxx8N5HOj5/DJDhpa9D5XTxfuPZ+R48eYUoCzTn\nC2FixhWQVzk4r6/mFlXPSV4yVjFdCET+rpjDZtJ54tFaJIKwP7wH3r1LfH5ufk10WOvYD1MuHJxz\nTMa400Up2tPXie5IKjqjcEkhQ+kkUbrSuh2u/qfotJcKT6sQCeMpGScEo6euhhe/Ip6b1K2KtLBW\nhIIPA3aTns/2OjHowB5sPerL0Ix6CZ2E6B5lyRciosaw4x6MYNK3/lu4K2ZeFXtOp4Mpi8UfR7no\nkFY0Bk77SY8ftxh1mPS6HhPQnc0uFo0tGdi5ZRGbUU9jpzfptu55g1aTDl8KYQnAGwwTisi9l2zH\noWbJaGJRlmnfLe4l+m7/N1Uz4Zp/CkevWiI/jMiynLGwGgpHaPcEMxJ1HGYDRr0oQyq0mdKKRepn\nMRMRaqgotptYPLPvZYJlDjN6ncSc+sRFzEmVeVy7oI5AKMLznx/gtfUiiqG22JrsMEcERr1EKEXn\nXY9Sijso5cwaGhoaw8hgOIsOAvHtjEYpz6V6PimyLD8ky/I8WZbnHTLWclPg+3w86zdi47pno/t9\n5aRx5FsM6CTIP7wS3r5TbBjOTmjdqZopJvwt28SEf+wpUVFj8ZT86G5JB8BBH2x8Sfx913tCcMkk\nANhRKVwrvTlSOvaLFe/+0rEP9n4oOj5IwHM39QxsXXaPyP6ZdB5Uz4Lpl8HJ3+//a3ZHFYtGH598\ne+V08diwRrh3jDYhIKnodHDS9+C834ng7d3vi4ypSHBwnUXdMVpE0O3Ui+D0O+Hqp+Abq+Cnh+Fn\nLfDD/fD1FXDRn2HXUvF7TIUsC2eRPcmk0V4KN74CUy8WE9G64xK3F9SK8NHB7MyWIWpHwCunOZAi\nwaO+DE2SRCmONxgWwqrmLMoJPAMNJt3+Fqx4SAT6dy+VVTnxDpj9Bfjgt7D6yR6bJUkiv9sE1OUP\ncajTFw1zzkWsJn3KzKJ2j3gvqqBjMejTBlGr7z1ZwHUq1OyUdk0syi5tu2P34u6MOQluXwWlE4bk\nVJ74ZA8bDnYmPLer2cVNf1vBCb95j6VbmzI6TpsiZmbS4l6SpKjoWWgzkpdGLGpRWsoX50g3tIFw\n43Gj+fWlM3p0TzMb9Nx9yQx+cI5w+b+8tgHgiHcWhVI4izx+VSzKmTV5DQ0NjX4xGGLRy8ANSle0\nhUCnLMuHgJXABEmSxkiSZAKuVvbtldaAkaWRWZhKx0D98bD2KdElKOSnwGbkp+dP5Yf129A9fj5s\nexOmXSI6guUKtQtBb4Kr/iE6pIFwHAFjHBGmVgnBKKmzaPub4O8U4g/0Hm6tojo03M2p9/F1ijbr\nH9+f2TGTse4Z8XjiHXDxg3BoLbwZtyp+aJ0QyhbdBhf+QQQ9X/oXYU8fLEqUAeiYk5Jvrz9elPRs\nXQIHVggHkj7JDXv29aIM6uM/Dl4ntN6Yf4v4fZz4HZh8nnAC6fTi/CyKkDj7OiGwLX9AhKMnw98l\nOtHEl6HFY7TClY8LMcrQbYBaWAtyWHSzGWJUt8aNM5UB5FHuLAKRt+ILas6iXEGWZeEs6m8wqbtF\nlJeVTxWicCokCRbfB2NOhlduFyWj3Si0Gen0xkSPXTkebg3i8+wNpC9DU/MGo5/9FDi9QtDuSxla\ngdWIJEGbZ5Cy4TQE4RA44xp3tO+J5RUNI/5QmDtf3sh/Pfk5nkAITyDEb1/fwjn3LePzPe2YDDpu\n+ttKXlh1oNdjtSqiTqYt7lXxp0hxFjl9IVpdfho6EhfkWt2iDK00AxEq15lanc8V82pTbi/LM1Nf\nYmPdASHe1RQeuc4ig14ikCKzSM1t0wKuNTQ0Rjq9ikWSJD0FfAJMkiTpgCRJt0iS9FVJkr6q7LIE\n2AXsAB4GbgOQZTkEfB14A9gMPCvL8sYeL5AEVacvtplg3hdFKdefF8JdlfD7WVy59dvc2nw3jJoP\n39sOVzwmXBu5wrFfhu9uhYlnxZ5TxCL8XVw2dxQOs4FCW5IB8LpnhNviDGWS0Vu4tUpULEqzgrZl\nCQTd0Lg2s2MmY92zQowpqhdix3HfgJWPwIZ/ie3L/wxGO8y5sf+v0RvjzxBBmmNPTb7dki9Euo0v\nCvFq1Lzk+xktsOBW2PG2KP2D1CulQ80pPxbht8vuTb7dowSM21OIRSq6JF/xAmWg1z3k2t8F658X\nobxZ4sQJZVw5bxSTHMpgWhOLxORacxblDP5QhIjczxVhWYaXbxcuz0sf7v2+pDfClU9AyXgReP3B\n/4ryXkUkLrQa6YgTPdROaOPL05SnDjM2kx5vihwitQytUHEKmQ06/KHUZWj9cRbpdRKFVqPmLBps\nVj4Cf5glmnb4OkUDjsFcBOonDR0+ZBn2t3m5/anVnHHP+/x56U4Wz6zinTtO5rVvnkh5npkPt/fe\nlEMVizJxFkGsnLJAySxyeoN88+k1nHHv+3y6q7XHcY8EZ1EmzK0TJWrFdtPgNArIUYy61AHXnoDm\nLNLQ0Dgy6PUqJsty2l7nsizLwNdSbFuCEJP6RZHdCBMuFyVmhzeJsq7W7dCyHeoWik5T6TJdhgud\nvqfIY1ZcI/4uvnj8JC6dXdOzI4SnTTiL5n8ZJi8G/bf64CxSynlSOVFAdBAD8fvrD+17xf/BvFti\nz51+p8gFevl2aNwgXGALbxPlc9lCpxMh1emYcqFwFgHUHpt6v3m3CAfPgZVihT/bzqJMKR0Ps66F\nzx6Fk+7oKQq5lYGovazvx1bf498vESHhKiGfcBw5KuA7W5ILTQPk2gV1XLugTohScNSXoYEo2/EH\nI0LkbMmsXEIje6it3Pu1IrzqCdj6Kpx1V6wctjeshXDdc/Dw6fDuL8VzeVVw2k8otBlp6IiV+e5s\ncmPQSdSX5OB9T0Etq0yWFdPuDpBvMWDQi2uLJU3nNIi1Ik/ZDCIFRXZTtKRIY5DY+a64R2x9NZaV\nlwOLKwfaPQBMr8nn7c1NTK7M4/fXzE4IYK4vsXGgo/fye9UBlGkQdaKzyMBhp499bR5kWeamv63k\n0Zvms2hcCW3K595kGPx7ai4yp76IF1Yf7NEJ7UjDqNcRkSEckdHrEq91apfHAWffaWhoaAwzOS15\nR1dhKqYpXcZGMFFnkVPUuidbYdr0EoQDMPNKMXE8527IH9Vzv2SookEqscjbLgZ7ehO07hSW8mSl\nWenYrZRJjD0l9pzeKErNHjwRPrwXJp0PZ/y8b8fNBpPOUYKjg8KBlgpbMXx3mxD4cq270HG3w+q/\nw2d/g5O/l7hNdRbZ+hF0WzIOzvwluA4nPm+wiKDtj+6Dw+uh6pj+nXcmqK+tOYuwGHXCWWQv0JxF\nOUCfsiZad4prasEoaNsFr/9QiM4Lb+vbixbWwW2fQDgoStjWPAmn/JACq4nNh7qiu+1oclFXYsOo\nz91Jp9WkJyJDIBzBbEicKLV7ggnuCrNBR7tncJ1FIFzJmrNoEIlEYP9y8ffNr0DTFrHQMCrNQswQ\ncaBdiEB/vGYO+9o8HD+uJCpGqtQUWvlsb3uvx2qJlqFl6CxSsreKbEbyLcaom+TBL8zhnje3cfNj\nK3j0xvn8f/beO0yu+jz7/5zpbWf7rtruqgshIUASvTcDrx0HcME2GOMEY8clhjdO7Dhx/P4SxyV2\nnNf52bEJDnYS94Yb2JgAohmMEAhR1Muqa3uZ3s77x/ec6TNbp+zyfK5L19mZc2bmuzA7M+ee+76f\ngUB00m6l+cAmo/x6votFNqv6zBhPprDmdYuazwW3iEWCIMxx6lIschhv9KbFd16QFUNL07dLTTrz\nL1SXd/xITcMyT9LPuWPy92+edOcLACa7HlBCwDl3KDv5SK8SDabCgceVEyR/8lzjEjVh7MBjakR9\n/nSUWuBuVnG1wX0TCxJTFc2qRftqWHkNbL0XLvpobveQ2U01UQytGJoGF/158X2BPiUW7X24wmJR\nnzrJdlXQgTZHcNutMg2tjjBHufsmchZFAyoenYyBza1ceXYP3PD16bnyzL/lje9WgwMOPEaTZxEj\nWQ6Z/f2Buu4rAvV8BjXhr1AsiqX7imASnUURw1k0hc4iUM6iI0OhKd1GKEP/ThU9a+xWnwN6n1G9\neuZnlxpyZCiEzaLR3eJhWVtxx92iJjcnd5wo6gDJZjAQxWbRJu1k627x0OCy0eCypwXNJo+da05f\nwOalLdxy7x9477e30uJ1zOvunnxWdzawsNHFukWTmOQ7h7EbYlEiVVhybYpF8zmGJwjC64O6/HrS\n47DidVgLY1pzmWJi0Y9ug1/fpX4e7oXDv1euouk4XOxucDaWdha9er+yjJ/xdnV5qlE0XYeDT6hS\n6WLr6z4PLv9EfQhFJjd+Hd4zqU71+uX8P1MC4Kv3514fNJ1F0xCLyuHrUGXx+/5ndu83n0CfKhev\nNzdXDXDZrUQSRmdRbBxSpU+ehcpjxqImdBaNnzScoDer4vpz3ge3/gwaF89sAWv+l4ofv/DfNLnt\nBGNJYokUiWSKQ4NBVtbxJDTIfJNebCLacChW4Cwq11lkFlw3uKZ2wtXicaT7kYRZoPf3anv1p5Vb\nNx6ECz5c2zUZHB0Os6jJXVYEWtzsJpHSOTUWKXkMqG6hVp+jID5ZivdcuJTf3X0pVouWFosuW92O\n1aLR5nPyvfedx7I2LydGI5OOts0HrBaNR/7iMj5w2RS/kJxjmA7PeJHXsGD6fWQenccIgvC6pC7F\nog6/i39+ex1NN5sNiolF4ydUmXAyDi//WF13xtum/xi+9uIF16EhNYp93Y2ZUbYDe6Z23/271H2X\nmkBWj7ibwb+o1quYGSuuVG6zZ7+mBDuT0KAqEndUYCztyqtVD1V4ZPbuc/gQ3HOp2oISwCSCBqiR\nw2lnEeS+RghVJxg1vxGe4EO+6e7bcDNc+48qNrxk08wXYHOq94Hdv6HNoZw1o+E4h4dCxJN63TuL\nzJMj85v1bIaD8ZzBDk67lUi8fAzN57QVxIomosXnYDgYR9eLj7UWpsjhZ1WP1rqb1ICE5ZfDwg21\nXhWgOosmijuZrp78KWX5DAajtE4yggbqtXtho7pvUyy68rTM+1qrz8n33nc+5y1r4fzl04iMz2E8\nDltZAW8+YL4uxVOFr2GhdGeROIsEQZjb1KVY5LRZuG79glovY3Zx5IlFiZjqJ4kF4Ng2FUHrvlBN\nGZsuvs7izqKdv1IRtHU3qo4eT9vUxSJzrPOyy6a/PmHqaBqc/wE48ZL6wG4SHABvhT58rrpGRWpm\n01300g/V77Dz1+pysE/KrQ2cNgvxZCqrBF+iaLVk0s4iU5ivhOi59k2QjLJy/DkARsMx9vcHAVjR\nXr/l1kDaERwuJhaFYmrKafpYC9EJYmj+KbqKAFq9DmLJVLrzSJgBug6Hn1FDRSwW+JOH1ATaOuHo\ncHjSYtGxCcSiAcNZNB0uWtXGh69YybXrcj+7tngd/PD9F/Dei2o/OU6YXRzpzqIiMbS4dBYJgjA/\nqEuxaF5itak+C/NEMJQZq8ozX4WB3SqCNhO87cXFolfvh5blmQkmbatVl08+sRAcewFe/A789pPw\nXzfAU/+i9r3yE+VwmYmYJUyPDe9Q3T7P/lvmumD/7EfQTJacowp3n79v9u5z56/U9tBTahvoU044\nAbtVUx82TWeR9BbVlLSzaEKxyOwNq8DzuPtCcDXR3fcYACOhOPv7AwCsmK0Ymq7DoaczkxVnCbOz\nKL+LKBJPEoolc4Y7OG3WsjG00XAc/xTLrQFOW6D+ll4+Njrl2wp5jByGsWPqOQkqZuluru2aDCLx\nJH3jUZY0l3fYLjbEJLMMuxSDwSht0yyi9rvsfOzaNfOrPkEoi83opkskizmLklgtGs7XyQQ8QRDm\nL+KPrCbOBuUkglyxaOevVNnvuhtmdv++Ttj/WO51wQHVNXTx3Zl+mLZVsMtweIwdh4c/rdxNwwdB\nN970bC71gfDQk9C6Uo2Wv/azM1ufMD0cHtj8Xnj6K6rbqrlHTUPzVch9Z7HCue+H3/0NHN8OiyaI\nhA7sUyJo5/rcEm6ToQNquprDp7ov4hF1oi3OIgAcNguxhDiL6oV018REMbSAIRZVQrS12mD1dbTu\n/i1W3sJIKM6+vgAdDc4plz0XoOuw5yHY8jk4sV393f7JQ+CcHRGqVAxtJKRcPs15zqJYMlWyeHhs\nmmLRmV2NWDTY1jvMJatElJ4RpqO154LarqMIZqxsImeRx2Gj2WOfOIYWiNFabFKtIBTBVsZZFIwl\n8Disk+6/EgRBqFdE8q4mDl8mhmaOPl+0UW1XvWHm39b52iE6qk7GTXb+UkWK1t2Yua5ttRKrfvNx\nuPdKNSmtYy1c+pfwtv+EDz8PnzwOd25R43F/8qdgdcKZ75zZ+oTpc877AA2e+3d1OTg4vUlok2Xj\nu9Xz9YkvKiExXuJDdjIB37oO7r0CPrcYvn4x/PQOeOJL6nk1uB9e+4U69uK71fPz4U8pUbLrvMqt\nfw5htxoxNHEW1QVTcha5Wyo3TfG0N2KLjnCOZTcjYeUsmna5dSoFP3s//PNa+P83wvdvhvCwml7Z\n9xrc/351zCyQjqHlOYvMwunm7M4iY1parIS7aDQcn5Y41uCys2aBn22TGJcuTMDh3yshu+P0Wq+k\ngCPDplg0cXffoiZ32RhaKJYgFEu+rkbcCzPDnNwcL+EsknJrQRDmA+IsqibOhiyxyHAWnfE2OP4C\nnPmOmd+/6dQI9qkYEagIWusq6FyXOe6sd6kOgq3fVKWVdzycu9+kYQGcc4eKyW24WfUdCbWhcbFy\nnr3w32rqXGgAPBUszHQ1wsbbVPRt16+heRm87Vuw6Ozc4w4+rk6aL7pLCUB9O9U30WZhu8mCDXDW\nLfDoPyjBa+FZqkhbyHIWGWOGxVlUU0KxBBZNuV7KEuyrbEn7yqvQNQsXWF5jJBRjX1+AG84qMmnt\n4JPwo3fD1f8HNr6n+ITBJ78EO34Aq69XXx5c/L/Ve47VrpxRD/01bPksXPm3M1622dGRH0MbDiqx\nqCnPWWQeW6zbYzQcZ92i6TmpNvU08fMXj084Ll2YgN5nlLBvqb8T36PDIWBiZxGo3qKDA8GS+wcD\n6vn5eppaJswMs+A6UaKzSMqtBUGYD8grWTXJFovMnogz3qrGzpsOo5lgikUBQywK9KmOmEs+lnsC\n4WmBd3wXIqNgcxePDplcfLf65vmij858fcLMOP+D8MpPYcvnIRGprLMI4Kq/U4630CA8/HfwzWvg\nDZ+B896feT698jP1rfPlfw12V+a20XHo363Eo4Hd6n78C1WkcXAfXPbx4ie1r0McVhXFyTiLpGel\nkhwfCXN0OMzJsQh9YxFOjkZo8Tl4/6UrsFo0glH1IX/C+EBwoDJ9RSYOL3ha6Rgb5qW+AOORRPFy\n6yN/UC6hX31UdcxZiogrg/uU4H/jPYV/d+f/GfS9qlyEHWth/VtmtOxSMbRhI4bWktdZBBTtLdJ1\nncFAjLaG6Z28b+pp5jvPHmbPqXHWLvRP6z5e9wQH1ev3mTfXeiVFOTocxm7V6PS7Jjx2cbObp/cN\noOt60b/tQUPMbBOxSJgkdiOGFivqLEpMHGUWBEGYA4hYVE2cfhjpVT+bziJ3y+x9O22euAROqe1r\nv1Buj/U3FT/e1TiJ+2yDd98/O+sTZsaSzbD0EuX0AuUKqyR2N6y4Qv284kr4+Z/Bbz+ueqz++Kuq\nsH3Xr+C0N+YKRaCE0SWb1b9sznyHch6tub6ya59DOGxKLNKdDWggzqIK8tzBId5+zzM51zltFqKJ\nFLFEiruuXk0oNskP+cF+WHBGhVaq0LwdLAyOc58Rp1rZ0VB40PgJ5Uq7/ONw9Pnid7TyarjqU8UF\nWk2DN35ZdY/9/IPKRbh4+l9euEtMQxsqEkPLdhblMxZJEEumaJ9mLGhTt3LCbusdFrFouhwx+oq6\n66+vCODwUIjFTe5JOccWN7kJxpIcHQ7T1eJB13WOj0bYeXyM106M8cx+9ZmsxSsxNGFy2K2lC65V\nZ5GcYgmCMPeRV7Jq4mzImoY2oDqKZrPvIttZBPDyT6D9NPVtsTA/uOXHqkMocArW/K/qPa6nBd75\nAxVLe/jT8I1LVHQxMgrrSoiRxbj0Lyu3xjmK3WpB1yFpcWKzOqSzqIJsP6JEl2/etpnuVg+dfhd+\nl42/+PFLfOWRvWzuaSEYm2R8INAP3grG0AB87XQMHGdvnzkJrYhJBAmNAAAgAElEQVSzaOwE+BfB\nBR+a/uPYnHDzd1T32A/epUTd9rXTcpSU6iwaKRJDK+csGgxEgenHgrpa3LT5nLzQO8yt58sUz2lx\n+Bk1fGM2nM8VoHcwSHdrkb+JIpy7rAWbReP6rzzJ6Yv87D45zmg4nt6/tNXDTWcv5rQFRQRZQSiC\nzVK64DoUS+a4KAVBEOYqIhZVk/zOotnunPG2g2aFkzvUFKsjz6rYkDB/sLth6cW1eWxNUyekXefD\ngx+DU69Bz0Ww/PLarGee4DBG68aSKWxOvziLKsjeUwHaG5xcfXruJL5/vOEMnto7wPefO0wknpzY\nWZSIqrL2SsbQALwdtLJT/eiwsqBY3Gb8uIp4zhRfO7zz+/C9m9XkRTQVSZviFxpOmwWLVtxZ5HPa\n0s93KO8sGgiYsaDpOT00TWNTTxPbDkvJ9bTpfUYJRfnO0TpA13V6B0Ns7J7cYJANS5p46O5L+fLv\n9nB8NMwbNyxk7UI/py9sYM0CPz6nfBwWpkazIQYNBqMF+0KxJF3N8pwSBGHuI69k1cQUi3Rd9V3M\n9shlmwPOvgVe+C/o36OmWW28bXYfQxCWbII7H6v1KuYNppU9ntBVb5E4iyrG3r4Aq4pMFHM7rKxZ\n0MCR4RBuu3Xi+ECwX219FRaLfB006SOAzooOX/EepbET0FFkQMF0WHAG/O/X4Plvwa/vgsBJaFwy\npbvQNA233VroLArFafbm9imZLqTiYpE6AZuuWASwuaeFh149Rf94lPYGiRdNiVgQTmyHCz9S65UU\nZTgUZzySoLtl4kloJivafXztlvp0SQlzD/O5d3gwVLAvFE3INDRBEOYFE4x7EWYVZwOkEqqcODRU\nmWlWV35KlVb3PgVnv3tyvUSCINQM02kRTSZVr5k4iyqCruvsKyEWgRq/fXQ4TCiWxDvRh3xTLKq0\ns8jXgVOP4iXCivYi604m1FS22XAWZWMKRKPHpnVzt8OaU3CdSKbY1jtMV96Ic6f53K9ADA1gY49y\nnbwg7qKpc2yb+rzSfWGtV1KU3kE12WzpJGNogjDbuOxWOv1ODhURi4KxJF5xqwmCMA8QsaiaOI0s\nfHRcdRZ5KyAW+Trgik+CzaWmVgmCUNc4rFm9B+IsqhgnRiMEoglWdhbvJFnS7GYoGKN/PIrHaVNR\ns+++XZU+7/yVclqYBEyxqMKdRcb9t2mjxSehBfvUEIPZLrv3L1bb0SPTurnbYc1xCz3w8gkOD4V4\nz4VLc44r5yzqD8TQNGjxTF8sWr/Yj8NqYVuviEVTpvcZQIOuc2u2hHse38/Gf3iYex7fX/AcOTyk\nTtB7WifvLBKE2aanxcvhoWDB9aFYArc4iwRBmAeI7F1NnOZo7LHKdBaZnP9nKo4mriJBqHvSnUWJ\nlHqNCB6o8YrmJ2ZJ9OoSzqIuI1JwciyinEVHt8Leh8DqhO3fVQL88sth7ZtBN05cvbMcJc7HiLm1\nMcrKYuseO6G2/kWz+7ims2hsms4iuzXdWZRK6fzbY/tZ1eHjmrW5XVETOYtaPA5s1ul/p+W0WTlj\nSaOIRdPh8DNqiIG7qWZL2H1ynOFQjM/9Zhff/v0h7r56NTdtXIzNauHQgBKLuqYQQxOE2aa71cMT\ne/pzroslUsST+sQOVUEQhDmAOIuqieksGjum7N2z3VlkomkiFAnCHCHdWZRMqb9bcRZVhL2n1HCB\nVSWcRV3N7vTPXqcNDj0FaKrD57ZfwqbbVan7Lz4IT3xJHeirjrOoXRstHkMbP662s+0scvmVcDnt\nGJqNkOEEeXRXH7tPjfPBK1ZgyRtxPlFn0UwiaCabepp5+ego0UThYwhZvPwT+OwS+N3fqs7Do1uh\n+4KaLikQTbCms4Hvv+98Ovwu/uqnO7juK0/y3MEheoeCLGx0pZ9DglALlrZ66BuP5hT6mz9P2H0n\nCIIwBxCxqJqYYtHwIbWtlLNIEIQ5g8Oa5yySzqKKsPdUgFavo+Q442yHgtdhiEULNyj30PLL4Pov\nwF07lLNopBfsHnBUuC/FEKNuWe+qrrMIlLto9Oi0buq2W4jEkui6zlcf28eSZjd/tKFwjeWcRQOB\n2IzKrU02djcTS6Z45Zj8XZXlxHaIBeCZr8HXzlE/d59f0yUFYwm8ThsXrGjl5x+8kG/cupFoIsmH\nv/cCe08FplRuLQiVoNvozDJjkaCetwDeiaZqCoIgzAFELKomTuPD/vBBta10hEEQhLrHbsbQkinl\n6IiOQ6rw5FmYGXv7xosLLvEIJKK0eh24DZdCgy0BR56DpZfkHqtp8MYvK6G/0q4iMNynGpcs1ItP\nQhs/DhZ7ZVyq/sUwNl2xyEoonuCZA4NsPzLC+y9bUTRO5izjLBoMRGdHLOpRMaoXJIpWnuCg+n/+\nkRfgTf8XLroL1lxf0yUFosn0RClN07hu/UK+cNMG+sajvHxsVMqthZrTYwiWZuE6kC73d4uzSBCE\neYC8klUTs7Mo7SxqqdlSBEGoD5xmDM10FqFDbFyipLPM4aEwN660wA9uUVHg0KA6QY4HwdWEdstP\n6Gpxs+dUgJ7wTkhGYenFhXfka4d3/RjCQ5VftNWmhKlAX/H9YyegYQFYKvC9T+NiOP5i5vKxF9Tv\nvPLqCW/qcdgIx0J8fct+2nxO3rZpSdHjXPbyzqLZiKF1NLjobvGwrXeY98343uYxwX71BVbLMvWv\nDghFEyxqdOVcd8GKVjb3NPN87zDdUm4t1BizYL03ayJayHQWSWeRIAjzAHEWVRMzhnbkObWtVGeR\nIAhzhgJnEUhvUQUYj8TZHH4adv0aXE1qJPim2+HKT4G7Gb5zExd7lJOma2wbaJbSnS1LNsGqa6qz\ncF+HOpEvxvjx2e8rMmlcoqZ2xsPq8q8+Ct95C/zqrsx1JXDZrRwZCvPk3gHed8mykr0yDqsFTYNo\nnrMoEk8SiCZmxVkEsLmnmW2Hh9F1fVbub14S7Adve61XkUMwmigYP65pGh+9ehUAq0v0jwlCtWjy\nOPC7bPRmTUQLRqWzSBCE+YOIRdXE0worr4HAKbB7qxNjEAShrnFkF1yb7sN67S2KjMJz98KzX4e+\nnbVezaSJJVJEEym6w7vUCfG774eb7oHrPguXfgxu/zVY7bwl/BMAOkZ3QEdtJ0Gl8bYXOotSKYgF\nM86iSuA3J6IdV4916lVoXQXbvgXfvAYG9pW8qdthIZZM4XfZuOX8npLHaZqG02YhkucsGghEAWib\nBWcRwMaeZvrHoxwZKi9yva4JDtSdWBSIJvA5C0+4L1nVzu/uvpSrTpPPUELt6Wn1FnUWecRZJAjC\nPEBk72piscKtP4FYSH34trsnvo0gCPMae3bBtbvOnUU7fgQPfkz9vPJquPWntV3PJAlG1Yf3hcHX\nYPEm1T2UTeMS6DqPhUd3A9Awvg9WXlbtZRbH16EmU4ESiV77OTz2WRjcq65beVVlHrdxsdqOHoHx\nk6An4Q2fUY6r+++Ef78M/ugrcMZbC25qfqN++4VLi57sZ+O0WQucRQOBGMCsOYs29TQDsO3wkESX\niqHrhrOofoZu6LpOMJYsWRIsriKhXuhp9fDysdH0ZbOzSAquBUGYD4izqBY4PKr3QhCE1z2OdAxN\nB6fRUxQZLXOLGjJ8CGwuWPUGJSDMEQLRBA2EaAwehMWbix/Uvoam8GGaGMcVOgntp1V3kaXwdkCg\nH3b/Fu65FH7yXrDY4PK/hrNvhTPfUZnHbTScRaPHMmLVks2w+g3wgaegcx389E9h638U3HRRo4tG\nt53bL5q4+8ZltxCJ5zqLBtPOotkRi1Z3NuBz2tgmJdfFiQVUR1cdOYuiiRTJlF4QQxOEeqOn1cOx\n4bByB5PtLJLnriAIcx95JRMEQaghjmxnkavOY2ijR5WI4F+sCo/nCIFogjMsB9DQYfHG4ge1n4Yl\nFefbFw3BNupHLPK1qxLu798Mzcvgpnth/VuUU7WS+E1n0VE4uQNalmcmeDYugdsfgG9cAjt/Cef8\nac5Nb7tgKW/d3DWhqwgMZ1Ei31mkxKLZKLgGsFo0zu5uYlvvyKzc37zD7MSqI7EoYLgBJ/McEoRa\n0tPiJZHSOT4SpqfVm+4s8opYJAjCPECcRYIgCDXEdBbldBbVq7PIFIsaFqhpYsl4rVc0KQLRBGdp\n+9WFkmLRGgDOGtuSc7nmLL0UFp2tIl8f3gob3l55oQjA5oTO9fDif8ORP8CSc3L3W+3QdS4c365i\nTFlYLNqkT/KLOYsOD4WwaLPnLALY2N3M7pNjjEfmxnO2qgQH1LaOxCIzOiruDKHe6c6biGY6i9zS\nWSQIwjxAxCJBEIQaYreq/pw55SzydQB65iSzTtB1na2Hhvirn7zEfzx1MH19IJLgTMt+Iv7lavJZ\nMdpWq+3+R1XUrnlp5Rc8GZZsgju3qMltVnt1H/tN/wJjx5TzJF8sAiViRUZUPHGauOyFzqLH9/Sz\nqae55BS16bCpp5mUDi8dqVMhtpaknUX1M6E14yySE26hvukxxaIhUyxKYrdq6S+CBEEQ5jLySiYI\nglBDcpxFNhdY7PVZcJ2IQuAkNHaBr1NdF6iP3qLBQJR/f2I/V335cd72jWf40fNH+c/fH0rvH48m\nWGc5RLxzQ+k7cXihqQdScWhbVR33Tr3TdS5c+BHj5/MK9y86S22Pvzjth3Dacp1FfWMRXjk2xhWz\nPOnqrO4mNA229Q6j63o66iaQEX099SMWZUqCxVkk1DedDS6cNguHB4OAeu6KI04QhPmCiEWCIAg1\nxJyGFk2k1JQul78+nUVjx9W2cQn4jHHt+SPdq0w8meJzv9nJ+Z97hM8+uItmj4N/eusGbr9wKcdH\nwiRTKh4VjCZoYxTN7OEphdlTVC99RfXAVZ+GOx6FhUWEto7TweqAE9unffcuu5VIlrPosd3qOXXl\nLItFfpedNZ0NPN87xL1PHuCizz8qkTSTOnYWiVgk1DsWi0Z3iycdQwtGE3glgiYIwjxB3oUFQRBq\niFlwbU5SwemvT2fR6BG1TcfQgMCpmi0nnkzxrnufZeuhYd66aQl3Xro8PU77e384TCKlc3IswuIm\nN+FgAJcWJ+orEUEzaV8Dex8SsSgbi1VF4YphcyrBaIbOosFAxln0yM4+FjW6WFOB0eibepr5xfbj\n7DwxRjSRYiAQo8FV5WhfPRIcAEcD2N21XkmaoBRcC3OInlZPVmdRUvqKBEGYN4izSBAEoYZYLBo2\ni6Y6i6B+nUWjR9W2sSsjFo3XTiw60B9k66Fh/uq6NXzpbWemhSKArhZ10nvE6JCIB9XIdLu3pfyd\nirNo6iw6G46/VFByPVmcWc6iaCLJU/sGuHJtB5qmzeYqASUWBaIJBgIxAMbC4iwClLOojlxFkBGL\nxFkkzAW6W7wcHgqh6zqhWEKet4IgzBtELBIEQagxdqtlDjiLDLHIv1g5StzNNXUWmZ0zZ3cVuoW6\nmlXh6NHhMACpkBKLLJ6m8ne66g2w4WZYdsksrnSes+gsiI7C0IFp3dxpsxA1OoueOzhEKJac9Qia\nyaYe9VxpdCs30ZjE0BShgboTiwLG+HGfdL8Ic4CeVg/heJL+8SjBWBKPOIsEQZgnyLuwIAhCjXHY\nLFnOosZpn3hXlNEj4O0Au0td9nXWViwaD/MR688489Gvwi+P5Ozr0XX+xnYmR4ZWAaCHlVhUchKa\nia8dbvr3Six3/rLgDLU99Sq0rpjyzbOnoT2ysw+nzcIFyysjXHS3ePjwFStZ0eHl7h++xKg4ixTB\nAWjqrvUqcjCdRR6ZhibMAbqzJqKFYgk6G1w1XpEgCMLsIM4iQRCEGmO3WogljRhPPTuLGpdkLvs6\nattZdHIXf2H/Cc7IIPRcBMsuTf/T7G7ebHuWI8MqhmaJjKgbuSZwFglTp30toEHfa5O/TSwIp9Tx\nLpuVaDyFrus8truPC1e0VqzvQ9M0PnbtmrQYNRZOVORx5hx1GkNz2CzpAQCCUM8sbfUC0DsYks4i\nQRDmFeIsEgRBqDHOHGdRHXcWZXf5+BbA0edqt57hQwBYbvgadJ2Tu+/xL9L52GfoGzLiZ9FRdb1b\nxKJZx+GBlmXKWTRZnrsXtnwOPnEYp91CJJHkwECQ3sEQd1yyvHJrNfC71UcfiaEBqZRyFnnba72S\nHIKxhJRbC3OGxU1uLBr0DgYJRZN4JT4pCMI8Qb6yEQRBqDF2q5bbWRQdVydxpUjGIZUsvX+20XXD\nWdSVuc7XAYG+aRcbzxTb2GEAtOalhTtbDcFh8KA6NmaKRRPE0ITp0XF6xll06lVIxMofP9ILiQgE\n+3HZrMSTOg+/plxq0+4revyL8LXz4Oi2CQ91263YrZrE0E7sgB+9G/Rk7t92ldB1nVePj/L1LfvT\nZfQmwWgSr0TQhDmCw2ZhUZOb3sEQwVhC4pOCIMwbRPoWBEGoMY58ZxE6xMZVf1E28Qg89S/w7Ndh\n9bXwlnurs8DgAMRDub0mvk51XXTcWHN1cQePEsGJq1h8pkV15zSEeoklUtjjY6TQsDgbC48VZk7H\n6bD7QTi2De69Cm68B868ufTxgb701mlX/09+88pJ1nQ2sLhpmuPbj26F/l1w37Vw609h+WUlD9U0\nDb/L/vqdhnbiJdjyBdj9ADgb4fK/hrNuqcpD67rOK8fGeODlE/zmlRPpceOBaJy/vDbjXAxEE+LO\nEOYUPa0eo7NICq4FQZg/iLNIEAShxhRMQ4PivUUv/Bc8/nloWAAv/wgOPVWdBQ4rhw4tyzLXNSxQ\nW/PEv8r4I8cZsC+EYiPWjaLlpZzkxGgYV3yUiMULFnnLqwidp4Oegkc/A+iqA6cc5v5gPy6b+n/y\n0pERrpjJFLTQICzerCb17XpgwsP9bjtjkddZZ9GJl+D774J7LlWvHZd/Eu7aAZd/AmyOqizh7h9u\n54+++hTffPIAPa1ePn/TGTR77AwFc4W7YFRiaMLcorvFy/6+AMmUjkeETkEQ5gnyaiYIglBjHDYL\nsWS2s4jivUWBk6BZ4c4t8LVz4TefgPc/DpYKf4tp9AORHfnyGSf2j/69chmZeNvh0r8sLuLMIm2J\nE4x5Fxff6Wwg5mpjaeIkR4bCuFPjRB1+PBVd0euYjnVqu/9RtY0Fyx9vFqMH+3Ha16avvmrtDMSi\n8BAs2gjo0L9zwsP9bvvrK4a26wH4wbsMJ9En4bz3V73DKxRL8MDLJ7jhrEX8nzevo8mjBKpvPnWQ\nkVBudDEYTaT3C8JcYGmrh4Axxc8rziJBEOYJIhYJgiDUGLs1K4ZWzlkUC4LDp0qF3/AP8OPbYdu3\n4Zw/rewCh5SzaNS5iMdePMYfn7UIrWOdEo8OPpE5LhmHWABOvwHaV1dsOXoqxcJUH7u855U+pmU5\nS0Mn2TcUZFEyQNRe/ajc64aW5WB1QjKqLscC5Y8PGM6iQB8un3IWNbrtnN01A/EiNAieVrC5YO/v\nJjzc77K9vmJox7YpofmuHTUren+hd4R4UueGsxfnCEFNbjvDeWJRIJpgcfM0I4mCUAN6WjNfR3jE\nFScIwjxBPPmCIAg1xpnjLDJ6dYo5i2JBJRSBEmR6LlbRn/BwZRc4fAgaFvGLVwe564fb2XpoGHzt\n8NGX4OOHMv/e9UN1/Pjxii5nbLgPnxYm4S9dyuvoWMVy7SQvHx2lUQuQcEhfUcWw2qB9Ddg94Ggo\n7yyKBiBu7DcKrgEuW92Obbpj0pMJiIwqsah9DQT7IDRU9iYqhvY6EotMobmGEwGfPTCI1aKxeWlL\nzvVNHgcjofwYmkyUEuYW3S3e9M/SWSQIwnxBxCJBEIQaM+nOolgQHMYHUk2D6z8PkRHY8vnKLnD4\nEDQv5dRYBICfbz9W/LiGhWo7dqKiyxk7sQ8Arbmn5DFa6wratRF29h6jkSBJKbeuLBffBdd+FjzN\n5cWiYFbHVaAPl12dVE17ChpkxFJPC3QYsbb+XWVv8roruM4WmmvEswcGWb+4saCLqNljLyIWJfCK\nO0OYQ3RnOYtE6BQEYb4gYpEgCEKNcVjzp6EB0dHCA+Mh5d4wWXAGbLodnrsX+sqfHM+I4YPQsoz+\ncRUzemDHCaKJZOFx/kVqO1ZCTJolQn0qFudoX176IGMiWmLgAH4tiJ4/WU6YXda/BTa/V7lXysXQ\nAlnl18E+zl/eyl9eu4br1i+Y/mOHBtXW0wLtxkStvvK9RY1uO2PhBLquT/9x5xKxYO5rR5UJx5K8\ndHSE85e3FOxr9jpyYmi6rhOMScG1MLfwOW20+VS8UpxFgiDMF+SdWBAEocbYbRbiSeOkdTKdRdlc\n8bfwyk/ht5+Ad98/+8XS8TCMn4DmpfQfiGK3aoyG42zZ3c+16/JO8O1ucDWp42fC899Sv1M+F/45\nrH4DyUElFvk6V5S+D2Mi2jJO0EiQoLt5ZmsSJofDOzlnUVM3BAdwO6x86IqVM3tMUyxyt0DjEvU3\n0r+77E38bhuxZIpoIpV2N81r4qGMK7EGvHB4mHhS5/zlrQX7mjx2ookU4VgSt8NKJJ4ipSPOImHO\n0d3iYSAQk+euIAjzBnEWCYIg1JgcZ5HdDRZbic6iQOEJn7dVTTc68Bjse2T2Fzfcq7bNy+gbj3LB\nijbafA4efLmEIORfPPMY2vP3Qd9rahy7+e/Uq/C4ittZRg8zrPtoaSk88UzTuoqUxc4myx4cWhKL\niEXVYSKxyJyE1rkeAn2lj5sKYaOfyNOqxNL2NRNORPO77ACvn4lo2RHWGrD10BCaBpt7Cv8Om42y\na9NdZE6U8jlfByKeMK9Y2qr+xtziLBIEYZ4gYpEgCEKNcdi0TMG1pil3UVFnUah478jGd6vtqZdn\nf3HDh9S2eSn941EW+l30tHoZDMSKH+9fOPMY2tgxWPtmeO+DmX8X360mOg3up3H4FQ7rnemTzKLY\nXSTaTudyy3YArF4Ri6qCwzeBWGTE0DrWKkdQMjHzx0zH0AzxsH3thLHMRrcSi143vUU1Fot2HB1l\nRbuPBkOky6bZo64zxaKgIRZ5pPdFmGOYvUXSWSQIwnxBxCJBEIQak+MsAtVbVHIamq/wepsxYjoe\nntlCAn3wxJfgxe/Aoadh7DgMHQAg2dTDYDBGe4MTt91KOF6kswhUyfVMYmjxiDr5b1yce/36m9T2\nFx9iYXAnD9quwmIpH7mz95zLMotysjh8hV0pQgVweMt3FgX7lKjTsBDQM0LPTMjuLAIlRAX7Mq64\nIvhNsej1MhEtv++siui6zo6jI2xYUrw3rMkQfc2S6//Zqf5mW7xlxGBBqEPetGEh7zqvm/YGZ62X\nIgiCMCuI9C0IglBjcqahQWlnUbxESa3FAjbXzMWi578FWz5beL3DxzB+kimd9gYnLrslp5A2B/8i\nJTol42AtdBFMiOlK8ueJRY1LoOci6H2aE7YunvK9ccK70pacA1vvVb9CQ5nImjB7TOgs6gNvB3jb\n1eVgHzR0zuwxQ0Pq78JuiKanvxl+97dK9Lzyb4rexO9SH3/mZAwtPKx+59YynV35lBKaq8Dx0QgD\ngRhndTUV3Z8dQ/veHw7zmQd2cvXaTi5e1VbNZQrCjFnZ0cBnbzyj1ssQBEGYNUQsEgRBqDEOW76z\nqLGMs8jLWCSO02bBacvqRbC5IBGZ2UKObYO2NfDO76v42fBBtW1dSb8RO1Ni0QTOInTVTdO4ZOpr\nGDuutvliEcCGt0Pv09znfg9t/km4JJZsTv/o9otYVBUm7CzqA187+DrU5WB/6WMnS2goE0EDVZ69\n8iolFl32cbAWftRJO4vCsxCDqybfeSvse1j9/KGt0L56creLBYtHWKvAjiMjAGxYUlwsakrH0OJ8\n88kDbOpp5t9u2YjdKuZ3QRAEQagl8k4sCIJQY+xWC4mUTiqVNREt31mUjEMyxiP7A2z+h//h0794\nNe9O3DNzFum6EouWnKMcCyuvgnPugDd8BjbdTv94FMiIRdF4qvj9mCKPKfpMlVLOIoCz3w3ve5Rf\nRs+m0z8Jm3/LcuJO1VXk8EoMrSo4fJAIQ6qEmBjsA1+nchdBpsOoHMGB8vtDg5BfYL7xPTB+HPb9\nT9GbNM7FGFo8on6fxZvU5VOvTP62sRKuxCrw0tFR7FaNtQsbiu43xaKTo2EOD4W4ZFUbDpt8PBUE\nQRCEWiPvxoIgCDXGPDGKpwwBplhnkeHW+P2RCG0+Bw+9epKkKS7BzJ1FI4chNACLNxbdnRaLfBN0\nFvkXqu2MxaJFhfssVpILN9I/HqXT75r4vjQNe5fhLnIXdzUIs4xZolzKXRToV0KRLyuGVo79j8EX\nV8Cj/6gEzWKE85xFAGuuV4+z7dtFb9JgxNDmVMH1SC+gw9m3qstD+yd3u2QCktGaxdB2HB3htAX+\nXCdkFk6bFY/DyrbeYXQdVnUUF5UEQRAEQaguIhYJgiDUGIcRt0hH0Yo5i+IhAEK4+Pj1pzEcirPj\n6Ehmv90zM2fRsW1qa7oW8ugPZDuLLERKxtAMkWe6Jdejx5RLpERkZjAQJaVDx2TEIoBV16honENO\nQKtCObEoGlC9W7529Ry3OlQsrRzbv6e2T/yT6iFKFhF3QoOFYpHVDmffAnsfKipcOm1WXHbL3Oos\nGjqotp3r1XN68MDkbhc3/l/UIIaWSum8fHS0ZLm1SbPHwYuH1evZqs7aiFqCIAiCIOQiYpEgCEKN\nSTuLkoZzwnQWpbKiXsbJd9zq5tJV7Vg02LI7K8Jjn2HB9bFtYHVC57qiu/vHo3gdVrxOW9pZpBdz\nenha1P1M21l0HPylu45OjSnRqnOy02bOvRPuelmVgAuVx3SvFJuIZrqIfJ2gaarkulzELBaCXQ/A\nxtvgnPfBM1+F+66FwTxHTWgwMwktm423gZ6CF79b9O4b3fa51Vk0bIhFLcuhZUV6UuGExJTQnBby\nqsiR4RDj0QTrF5cXi5o8dqKJFDaLxtLW6q9TEARBEIRC5NOzIAhCjbEXcxah555wG2JRwuah2evg\nzK4mtuzJEots7pnF0I69AAvPLDnBrH88mh4H7LRb0XWIJcicyr4AACAASURBVIv0FmmaiqId3QpH\nny8dHSrF2LHiETSDU2Pqd5xUDM1cz3SmsgnTI+0sKiIWmf1EZl9RqSJ3kz2/Ua6YM94Gb/wSvO3b\nSij6xsXw/H3quZVMQGS00FkESlRZdim88F+5wquB32WfY86iA8oh52mF1uWTj6EZrkTs1Rdhdp4Y\nB2DtQn/Z48yJaEvbvNJXJAiCIAh1grwjC4Ig1JiMsyirswhyT6QNsShpVePBL1/dwY6jIwwa8bAZ\nOYtSSTixvWRfEeSKRW676h6JxEqUXHedB4efgW9eBT+7UxXzTpYJxKI+ozupYzIF10L1KRdDC5xS\nW7OvyFmkmyubl38KvgXQc5G6vO5G+OAz6vn167vh+++Agd1qXzGxCGDT7TB6GA48WrDL7SjTvVWP\nDB2ElmVKAG1ZoSbJ5cdVi2EKdzWIoe08MYamwZrO8jFQs+R6VYdE0ARBEAShXhCxSBAEocbYrRoA\n0RxnEbkngqZYZFMnfJevaUfX4cm9RoxnJtPQRg4r90GJCBqoziJTLHKZYlGixIn2jffA3a/C5X8N\nL/8I7rkEfvMJGO4tv454REWKGotMQjM4NRZB06DNJ2JRXZKOoRURi7JjaKBE0VJih65D79Ow+lqw\nZBUj+xfBrT+D674AB7bAN69R1+dPQzM57U3gboFt/1mwy2WzEi31HK4V2/4THv0MvPQD5cwLD2f2\nDR1QYhEo1xRMzl1UwxjarpNjLGv14nYUL7c2MZ1FqyYQlQRBEARBqB4iFgmCINQYs+C6rLPIKKlN\nGlGSMxY30up1sGW3cQJuc6uR5dNhcJ/atq4qeUjfWIR2nykWqfWGYyVOtDUNGpfA5Z+Am78LnjZ4\n7h549t/KryM9Ca1QLHrw5RPs7w/QNx6h1etMR/eEOmPCGJqmng9Q3lkUHIDICLSfVrjPYoHzPwB3\nPq7iWFD0OQOAzQlnvQt2P1hQpu20W4jES7jjasVDfwNPfBHuf79y5n1hKXxpDfQ+o0RdUyRqXaG2\n+f1NxTCFuxrE0HadHJ8wggbQLM4iQRAEQag75NO2IAhCjTFjaJnOIqMMtoizCLtyFlksGpeubufx\nPf0kU7oRQ5tmZ9HAXrVtKy4WxZMpxiIJWn15MbTJuDLWvgn+5DfKtTRRIa9Zip134p9M6dz1g+18\n+eE9nBqL0ikRtPplohiapwWsamx9WWfRoPmcXF36sTpOgzsehdsfhO7zSx+38T2QSsD23KJrl91a\neqpfrYiH4IIPw4e2wju+D2/4DKDDLz8CqTg0G84iczuZkuv0NLTqikWBaILewRCnLZjYLdSUdhaJ\nWCQIgiAI9YKt1gsQBEF4vWOfjLMoVlhSe/madu5/8Rg7jo5w9oycRXvB1VSy98UsAW50q2//zRha\nSWdRMVqWw6lXyx+z/xHj2GU5Vx8fCRNLpvjDgSE6GpwsaJxkubVQfcqJRcH+TAQNyjuL0gLmyvKP\nZ3PA0ovKH9O+GrovVEXXF92lnG+A02bJCLT1QDIOelIVf7evVv9ATXR7+O/Uz6azyOFRouqpV5Tj\nSE8Z//Ssn1PK4Wf+v6hyZ9Huk5Mrtwa4bv0CxiJxVndIDE0QBEEQ6gURiwRBEGpMobPI7CwazRyU\nLqnNiEWXrGpH02DL7n7Ozu8s6t8ND/wF3HSvmk5WjIF96mR8YK9yFRkn0fmUEoumFOFpWQ67HlRl\n2pYi/SWD++GZr8GGd0BTd86uw0NKKBsIRBkJxTizq/wYbqGGpDuLisXQ+sDbnrnsbIBkTDni7HkC\n4MAesLmgsWt21rXpPSradehJNSGNOnQWmdMMbXn/LTbdDo9/EWLjuUJq2yp47RfqXyl6LlLF4JD5\nf1Mldp5QQuBpCycWgBY1ubnr6jIuMkEQBEEQqo6IRYIgCDXGdBbFynYWhUhiwerInEi2eB2cuaSJ\nLXv6uXudW514p5Lq30/vgJM74MRLxcWi3mfgW9fBu36kOouWX1FyfYVikVrvlE60m5epGM3oUWju\nKdz/0CfB6oRr/r+CXYcGMy6VREqno0GcRXWLzQFWR+kYWte5mcsuQ/SLjhWKRYP71MSvYsLidDj9\nj+E3fwXbvq2eg898jQUtnyaSKC6Q1gQzRmp3517vaoRz3wcvfgcasiYFvvHLqgRcsxT5p8EL/62c\nR3HTlVhdZ9GeU+M0uGwsbnJPfLAgCIIgCHWHiEWCIAg1xpnvLLJ7QLMWdBZFcOJy5L5sX76mna88\nspfgWhteUO6EJ7+shCJQJcHF2Ps7td32bRg/UTbuY4pFfkMsMicbTUksSk9vOlAoFuk67HsEzrkD\nGhYU3LR3MITDZsHvsjMQiNIhnUX1jcM7+RgaqOe5ryP32IE9sGDD7K3J7lautefvU06cVIJ3jX+K\n78b/ZvYeY6aYMdJ8ZxHAlZ+Ci+9W5d4mrSsyRdfFGNgLBx6DsPEaUGWxaCQUp83nRCvhWBQEQRAE\nob6RgmtBEIQak+ks0tUVmqbcRdFcsSiEKx0BM7liTQe6DvuHDeEmHoGXfwxLzlGXs6Ns2Rx4TG13\nP6i2ZSahjeU7i2xGZ9F0xaJ8ElHlOvK1F+4DDg0E6WnxcN7yFgA6xVlU3zh8hWJRNKAcLtkxtLSD\nLu85mojCcG/JwvVps+k9mZLoG+9hUWgXd+o/nt3HmAnxEjE0UCKRa+LunxwaDEfh0H4lFFmq+5Ev\nEk+mhXBBEARBEOYe8i4uCIJQY9KdRcks8cXpL3AWBXVXWqgxOWNxI61eB6/1x9QVibASiDrXq8vh\nIs6i0BAc3w5d52WuK3Ninh9DyziLptBZ1LBQxcyKiUWmKOYsfjLcOxiip9XD+cuUWCQF13WOw1vY\nWRQ4pbbZDiLz/3d0PPfYoYOq6LncJLTp0LkO3vkDeM8v4cx30Oc7jdPozRTL1xrTWZQfyZsufiOy\nNri/6q4igEgihdM+SzFCQRAEQRCqjohFgiAINcZuVTGNeELPXFnEWRTUHem+IBOLRePS1e3sOBU3\njgupk29PCzgaisfQDj4O6HDVp43eGC3j/CnCaGgWnEUWiyrnHTpYuM8UC4qIRbqu0zsUpKfVy1s3\ndfFPb93AukVTdFgI1aVYDC3Yr7bZYpErK4aWzaAxCa11gklo02HN9WkRJWHz4NZiROtlIloiqra2\nWer4MZ1Fg/tzivGrRSSexCXOIkEQBEGYs8i7uCAIQo1xGuJLNJHtLGrMOYlOxYIEceEu8k395Wva\nGYwaL+fhIeXKcPqVEFTMWXRgi7r/rvPgjLfDwjPBVroHaDQcx223ph1QLsc0Cq5BCVLDxcQi01lU\nODWpbzxKJJ5iaasHt8PK2zd3SQdKvVNMLAr0qa23mLMoTyw6sQPQZj+Gloduc+MiWj8T0eIVchYl\nwjURi6KJVEFsVhAEQRCEuYOIRYIgCDWmwaVKq8ejicyVec4iPRogrDuLnnxdsKKVCA51wYz7uPzg\nbireWbT/MVJLL+a/tx4jds1n4U8fLru+0XA87SoCcFgtaNo0xaKhg5DKc3KYoliRTpZDA0p06Gmt\n/smuME0cvsnF0Eo5i3b+CnouLCoezia6zY2bWP2IRYkynUXTwd2sop9QG7FIOosEQRAEYU4j7+KC\nIAg1xmmzYLdqjIWzxKK8ziI9FiKIqyCGBioeFtENsWj8VOb2rqbCGNrQARjp5WDDOXzq56/w9MER\nNe68DGORXLFI0zTcduvUT7KblyqXQ+Bk7vXpGFqhONA7qMZ+LxWxaO5QMoamgactc10xZ1HfLujf\nCaffUPFlKmdRbGrdW5UkXmYa2nTQNPAbUbRadBbFk+IsEgRBEIQ5jIhFgiAINUbTNBpcdsYj8cyV\nLn/ulKhYgBCuooWxDquFcIGzqFE5i/JjaPvVFLRXPRuBzKSzcuQ7iwBcduvUOoug9ES0MjG0Q4NB\nbBaNRU1Saj1nKBVD87SC1Za5zmJVLqRsZ9FrPwc0OP3NlV+n3W10FtWLs8joLLLPUmcRQIMRRatZ\nDE0+ZgqCIAjCXMU28SGCIAhCpfG7bIxH8pxF0XHQdeUQiIcI6U6aiohFmqaRtBpiSiDbWdRY6Cw6\n8Bg0dvFqpB0ITFIsSrA4T6xRzqIpOjLSYtFBWHpx5vq0s6ix4Ca9gyGWNLuxWeWkc87g8CmR8tF/\nhL7X1L+hg7BgfeGxzoZcUfTV+1UErWFBxZep2c3OojpxFiVm2VkEGWdRjQqunTZxFgmCIAjCXEU+\nfQuCINQBDS47Y/nOIj2V7n6xxIOEShRcA6TMbhKzSNhlxNCynUWpJBx8ApZfzpFhdWI6li1QlWAs\nHMef5yxy2i1TdxY1doHFNiVnkTkJTZhDNCyAZBSe/GcY2AMLzoDLPwF//LXCY7Pjln07oX9XVSJo\nAJrDg5sY0XrpLIobnUWz6iyqZQxNnEWCIAiCMJcRZ5EgCEId0FDMWQTqRNruwZKMEtKdJU++dJsL\nkmT6gMwYWjwIyThY7XB8uyq8Xn45h7eoLqAcgaoERWNoNuvUT7KtNmjqLhSLImPKTZHXnaTrOr0D\nITZ1N0/tcYTact4HYNW16v/1RJO9sovcX61iBA0lFtm1JLFYtCqPNyFpZ1HpyYRTxpyI5vDN3n1O\nAl3XiSSks0gQBEEQ5jLylY8gCEId4C/WWQTqRNpwF4UoPg0NIGU13Aims8iMoUFmItqBR9V2+eUc\nGVInpuN5zqJ4MjeSk0imCEQTBWKR2zGNziIwJqLlO4vGi7qKhoIxxqMJcRbNNax2aF89uRHwZtwS\nVF9RlSJoAFanctvEI8EJjqwSZmeRrQLOIkd1nUXxpI6uI9PQBEEQBGEOI+/igiAIdUCDy5Y7Dc3T\nqrbjJ2FgHwDH9DZcJTpAdLPnJNAHmlV1lLia1HVmFG3/FliwgVGtkVGjqyi7s+iBHSfY+PcPc3Ag\nc/JsxtQKC64t0+t6aVkOw4dUF5NJdKxEubUxCa2t+hEaoUq4jBiaGUFbd2PVHtrqVCJkPFonYlE8\nDFYHWGbxo5m/NgXXEaM0XJxFgiAIgjB3EbFIEAShDvC785xFC89U26PPw7FtAGxPrcTtKP6ybbE5\nSaGBnlTCi6apGBqokutYEI78AVZcwZHhUPp22c6irYeGGI8m+PQvX0U3xBxTVCpwFtmthGPTcBY1\nL1PiUGgwc110PBO7y6J3UJ3Ed7eIs2je4jRiaGYEbW11ImgAVsNtk4yGJjiySiQis+sqAvAvVtsi\nYmwliRiuw2LTGwVBEARBmBuIWCQIglAHNLhsBGNJEmYMzN0M7WuVwHPsecLOdk7QUnK6kMNuJa4Z\nXSdmhC3bWdT7e0jFVV/RkDo5bvU6cjqLdp8cx2bReGJPP799RXUflRKLnHZr2j0wJdIT0bKiaCVi\naL2DITQNulpm+QRaqB9MZ9Gr90PPRdDQWbWHtrmUCFk3YlE8PLno3lRo6oK33gfr3zK79zsBUcN1\n6JIYmiAIgiDMWeRdXBAEoQ5ocCkxJhDNiqJ1nQtHn4OjWxloXA9oJWMdTpuFmGYURJsj6NOdRSOw\n/zGwOqH7grRYdPoif46zaG/fOG8+axGnL/Tz979+jWA0UdZZFM2LoZ0cjfCvj+wlldIpSTGxKDKW\nWWsWvYNBFjW6Zfz2fMbZqIqdB3bDuupMQTOxu5SzKBWrE7EoEZ3dcmuT9W9R4nMViSbEWSQIgiAI\ncx0RiwRBEOoAv0sNp8wpnO46T5VTDx3ghO90gJLT0Bw2CxHynEXZMbQDj0HPBWB3c2QoRLPHzqJG\nd7qzaCgYYyAQY+0CP/9ww3pOGMJPKbHIZbcUFFz/8+928+WH96TFqKI09wAaDB3MXFfCWXRoMCR9\nRfMd87la5QgagN3oLErVi7MoEZ79GFqNiIizSBAEQRDmPPIuLgiCUAeYzqLRrMJpus5L/3jUsw4o\nXRjrsFmIpp1FeTG0/j3Q9xosvwKAw0Mhuls8+N22tDi155SaSLWq08emnmZu3tzFfzx1kG2HhoDi\nzqJIlljUPx7lF9uPA+RE2wqwOaFxSV4MbbREDC0ok9DmO+ZztcoRNMgUXOvxOhGL4pHZj6HVCPO1\nQQquBUEQBGHuImKRIAhCHVDUWdS6AtwtABx2rcFm0bBbi79sO20WorohFpluDbsLbC7Y+Ut1eYUS\ni44Mhehq8dDgshOOJ4knU+w1xKI1C5Ro8/HrT8PnsvFfz/aq9RU4i6yE48l0EfZ3nu0lZvQt5Qhe\nxWhZlhGLdL1owfVoKM5wKE5PiziL5jXmc7XKETQA7MrFUzdi0TxyFkUT6rXAKc4iQRAEQZizyLu4\nIAhCHWCKMTkT0TQNll8GCzYwpnvKfkvvsFmJkOcsAtUFNH4CPK3QeQbJlM7R4bByFmUJVHtOBWhw\n2ljgV86GFq+Dv7r2NHRdnfDlP7bLbkXXIZZMEYkn+c6zvfS0KmFnLJygLC3LM2JRPAR6qsBZ1Duk\nJqGJs2ie030BbHovnPG26j+2IRYRj1T/sYshziJBEARBEOoIEYsEQRDqgAZDuBmL5Aktf/Sv8O6f\nE44nS/YVATisFsLkOYsgE0VbdhlYLJwYDZNI6XQbziKAsXCc3afGWdXpQ9O09E3fcU4XZ3U10d5Q\nWLprngRGYil+uf04g8EYf37lKuN3mMhZtBzCQ2pKW2SscM2oSWiAdBbNdzwt8Ef/N9OvVU3s6rml\nJcLVf+xiJCLKCTgPSHcWiVgkCIIgCHMWEYsEQRDqAL+riLMIlIjibSUaT5Y98XLaLYR1IyqWPVnM\nPAlPR9DUiXFXiyftZhqLxNl7apzVnbnuHotF477bz+G+288peDy3sZZwPMl9Tx/ktAUNXLt+gbq/\niWJozcvUdvigiqBBQQytd1A5i7olhiZUCsNZpMVFLJpt0tPQJIYmCIIgCHMWeRcXBEGoA3yms6hE\nhCuSKC8WOawWwnqxGJohFi3P9BUBhrNIPWbvYIjhUJyVHb6C+23xOgpEJMhMZXt0Vx+7To7zJxcv\nw+uwYrVok3MWgYqilRCLDg2G6PQ78Ths5e9LEKaL4SyyJutELIpHMtG4OY44iwRBEARh7iNikSAI\nQh1gt1pw262FziKDSDxVNobmtFsIpUxnkZ9ANMFXH91LomUFLDobmroANQnNatFY2OhKu5m2HxkB\nYEV7oVhUCtNZ9I3H99Pmc/DmMxehaRp+l21yBddgiEWjxi+Q11k0GKSnRfqKhApitZPEgiVRJ51F\nifC8cRZlOovkY6YgCIIgzFXkXVwQBKFOyB5ln08knsRlKxNDs2aJRc5G/vWRvXzpd3t4ZsVd8Ce/\nSx93eCjE4iY3NqsFv1u5dl4yxKLl7ZMXZ0zHwOGhELee35O+7HfbJy64dnjB1wlDh7KcRbli0aHB\nULowWxAqRVRz1Y+zKBGdU2LR3lPjPL1voOi+SEIKrgVBEARhriNikSAIQp3Q4LKXjHCFJ+gsctgs\nhFFF1KdiDr799CEAAtEU2Bzp4w4PhdI9QGbB9cvHRnFYLSxpnrw44zQcAw6bhVvP70lf3+gu/Tvk\nYE5EK1JwHYol6B+PsrRNnEVCZYlrTqzJaK2XoYiH59Q0tK88spc7/vN5gtFCcThqxNAcVvmYKQiC\nIAhzFXkXFwRBqBMaXOWcRanyBdc2KxGU+PMfzw+SSKmTtfG8E7kjQyG6WlQvSoPThqZBNJFiaZsH\nq0VjspgxtBvOWkSbLzMtze+yT1xwDRmxqIizyJyEJs4iodLELS5sqTqIoaWSkIqDbe50Fg2HYoTj\nSR5+7VTBvkgiicNmwTKF1xRBEARBEOoLEYsEQRDqBL/LXrKzSE1DK/2S7bBZiBgF1384nuDiVe0A\nOd/6B6MJBoMxugxnkcWi4TMKpJe3Tb6vCGB1ZwPXr1/Ah69Ylfs7uG2MlRC8cmhZBoGT6h/kFFyb\nk9CWtoqzSKgscasLez2IReZEtjnkLDK7ye5/8VjBvmg8hUsmoQmCIAjCnEbeyQVBEOqEBldpoSUy\niRjacb2NlKOBkzE3i5vUSWcg6/6ODGcmoZn43cqNNJW+IgCv08bXb91Ed577Z9LOomaj5HrHj8Hd\nDJbM73bIcBbl37cgzDYJS52IRQkjCjeHnEUjIfV3/uTefvrHc6N8kXgSp/QVCYIgCMKcRsQiQRCE\nOsHvLu0sCk/gLHLaLNyfupijtz3LcNyK323HYbMQiGXEosODhWJRg8twFk1hElo5/G77xNPQAFpX\nqm0iDDd8PWdX72CQFq8jPa1NECpF0urCoddBZ1HCcBbZnOWPqyNGQ3EuXtlGSocHdhzP2RdNlJ/e\nKAiCIAhC/SPv5IIgCHVCeWdRKt0TVAyHzUIKCwGLn1gihddho8Fpy4mhHR4q4ixyTc9ZVIpGt51o\nIpUenV2ShWfCW/4DPvgsrLk+Z9eRoXA6KicIlSRpdeGsB7Eobrib7HPDWZRIphiPJtjU08yyNi9b\n9vTn7J9oeqMgCIIgCPXPpMQiTdOu0zRtt6Zp+zRN+0SR/c2apt2vadoOTdOe0zRtfda+Q5qmvaxp\n2nZN056fzcULgiDMJ/wuO7EiQouu60QS5WNoTuPEbCQUA8DjsOJ12nJjaEMhGlw2Gt0Zx47pLFox\nxc6i0r+Dur9SRd1pNA3OeCs0LCjYNRKO0eIRV5FQeVI2Fw49hq7rtV1I2lk0NzqLTFG7yWPnstXt\nPHtgMOd1S8XQ5PtIQRAEQZjLTPhOrmmaFfgacD1wOvBOTdNOzzvsk8B2Xdc3ALcBX8nbf4Wu62fp\nur55FtYsCIIwLykltMSSKXSdCTuLAIYMscjrtCmxKJo5gTsyHKar2YOmZSYUtXgdtDc4aZwlccbs\nQBorEaebDIFIggaJoAlVIGVz4yZKNJGq7ULMzqI6dRY9tquPN3/1KeJJ9d/JFKWbPHYuXd1GJJ5i\n66Gh9PHRREqcRYIgCIIwx5nM1z7nAvt0XT+g63oM+AHwx3nHnA48CqDr+i5gqaZpnbO6UkEQhHmO\nKZDkCy2RuDpBc5aZLuSwqn3DRumsx2GlwWkjEM3c1+GhUE4EDeCjV6/im7fNno5vxtomVXJdgvFI\nAp8hnAlCJdFtbtxarHxsMhqo/ELi9e0senjnKXYcHWU4qEQis5es0W3n/OWtOKwWnsiKok1UyC8I\ngiAIQv0zGbFoMXAk6/JR47psXgJuAtA07VygB1hi7NOB/9E0bZumaXfObLmCIAjzF7+7hLMoMbFY\nZEY+RoyTOa/DhtdpJWg4i1IpnSNDoYIJY0uaPZzZ1TQ7vwCZ32FSJdclGI8k0vE4Qagodjeucs6i\noYPwhR44srWy60gYnUV1KhbtOTkOZP6uR9JikQOPw8Y5y5p5PEcskoJrQRAEQZjrzNY7+eeBJk3T\ntgMfAV4EzK/pLtZ1/SxUjO1DmqZdWuwONE27U9O05zVNe76/v7/YIYIgCPMa01mUPxEtkVInsnbr\nxM4iM4bmcVrxuewEjILr/oA6Ia50cXTaWTRRZ1EJookksWSKBqeIRUIVsHtwU8ZZ1LcTUgkYPljZ\ndZjOInv9iUW6rrP7lBKLTNfjqOFgbDLiq5euamfPqQAnRtXvEUkk0z1qgiAIgiDMTSYjFh0DurIu\nLzGuS6Pr+piu6+81RKHbgHbggLHvmLHtA+5HxdoK0HX933Vd36zr+ub29vYp/yKCIAhzHdNNMxbO\nFVriCVW+aysjFpmuoxHjJM7rsOFzWtNiUbFJaJXALM+ebgzNdFVJZ5FQDTS7B7uWJBIpMRFt9Kja\nRkYruxCzs8hWf51Fx0cj6b9L01lkbpuMv/fL1qjPbU/uGQAgGk9JwbUgCIIgzHEm806+FViladoy\nTdMcwDuAX2YfoGlak7EP4A7gCV3XxzRN82qa1mAc4wXeALwye8sXBEGYP/hLOItiSdNZpBXcxsT8\nFn/IjKE5rXgdNoKGWHTEEIu6mit7MjrTgutAWiwSZ5FQeTSH+nuIl+olGjPEoqhy1jB6NCPszCaJ\n+nUWmRE0yIqhGaK0+fe+prOBTr8zHUWLTjC9URAEQRCE+mdCsUjX9QTwYeAhYCfwI13XX9U07QOa\npn3AOGwt8IqmabtRcbOPGtd3Ak9pmvYS8BzwgK7rv53tX0IQBGE+0FBiGtqkYmhpZ5ERQ3PY8Lls\nhGJJkimdw0MhNA0WV1gsctosOKyWAnfUZDF/d5/E0IQqYHEop108Ei5+gOksio5BKgVfvxCe/PLs\nLyRudhbVn7NoV7ZYFDI7i2L4nLb0a5KmaVzy/9q78/jI7urO+99Te5WkknqTevPS2N12u+22AWMw\nMWPM7heJgUnIEBgIDNmGJ5CHyRCSeJgJQ2aSPGEWksAQP9nIvJI4CQmBEGcIAR7AJsELxjZeum3c\nXnqTetMu1fp7/rj3lkp7SSrVvbr6vF8vvVqquiVd9VGVfvfonPPbv0N3P3VWtbrTdKW+5Iw1AAAQ\nfS2txp1zd0m6a85tn256/58kHVjgcU9LunaN5wgAm0JXJiWz+VU5QRvaUsmi4MLswqw2NO8lfqJc\n1XPnJ7WrmFv3OSJmpmI+teoB12P+7m20oaETEtkgWbRIZdGI33VfGpPKY1472rP3tP4FKtPSlz8i\n3fRBqbh78eOCyqJUtvXP3SFHTo9qR09WZ8ZKGpmaaUcLWk4DNx/Yoc8+cFwPHR+msggAgBjgzz4A\nEBGJhKknm5pXWVSpL9+GFlQWBVtb5zNJdfnJovHpqp4/P7nuw60DxVx61W1oY7ShoYOyuS5J0vTk\nYsmiYGbR6MzcopMPSvVFBmLP9dy3pHvvkB66c+njgsqidPQqi44MjuvQ7qK6MsmZmUWTlcZw68BN\nl2+XmfS1J4ZUqTnlGHANAMCGRrIIACKkJ5eeNxy6Um29DW2sVFUmmVAmlZipLCp5lUXrPdw6sL07\nqyOnx1SvuxU/lplF6KRCV48kaXJibP6dtao0dtJ7vzQmTQ1775fHpTNHWvsCJx/0/n32W/PvO37/\nzO1T56VEWkpEK8FSqdX1/aFxXTHQo978TBJ4eIHKlhHicQAAIABJREFUoi1dGV27t09ffmxQkhhw\nDQDABsdvcgCIkGI+PW/b+Wp9+Ta0VMKU8AuPClnvgjNIFp0dL2twtNSxyqJ3vOxiPTU0ri88dHLF\njw2Ge9OGhk7o6vaSRVMLJYvGT0vOS9SqNCZND8/cd+KB1r7Aie94/z73z/Orkb50u/SZH5K+8ZvS\n/X8gXf5qffPJM/rsA8f1wLPndW68JOdWnnBtp2fOTqhcq+uKnT0q5tOzdkObW1kkSf/iwI7GjKMc\nM4sAANjQ+NMtAERITy616G5oqSXa0MxMmVRC05W6ujLeS3u3X51z5PSoJHWssuiHDu/WHd94Wh//\nhyO69ZqdK5qTxIBrdFKm0CtJqo2fnX+n34JWVkaaGFYmaEOTpBP3Sy9658zH42ekI38nff+r0ovf\nI112i3f7yQelbNEbkH36EWn3dU2POS3Vq9JXf1Xacqn05v+ln/3N+2fN+yrmUrqsv1u/+SOHdXl/\nT7u+7ZYdGfQSP3OTRcOTFfXmM/OOv/nADv3WV56UJGYWAQCwwbEaB4AIKeZSOjE8Peu2oA0ts0Rl\nUXD/dKWuQsa7SAuSRo+f8i74OlVZlEiYPvT6K/TuP7xPX3p0ULddu8Rg3znGS1VvRzWqEtAJ/Vdp\nSlkNXFigUshPFh2t79a+qRFlgja0bZd7lUUXnpWe+KL0+N96lUPyq4CmLnjJorFBafSEdOPPSv/0\nO17L2axk0Rnp8NukbI90w09Kha2aKFX1o9fv1Ruu3qljZyd15PSo/uL+47r32IVwkkWnx5RMmC7b\n0a3efFrPn5+Uc04jU+V5bWiSdO3eXhVzKY1OV0kWAQCwwbEaB4AIKebS8yqLgja0pSqLJCnrX5wF\ng62D6pzHTnW2skiSXrF/h7qzKX376XMretzodJV5ReicVEaPpq/RZWP3z7/PTxYdcXuVqjS1oV32\naq9K6BOHpS/9steidvMvSD/9TekVPy89c7c0cVY66begXfmDXuVQ8y5qpXGpMiH1Xym98ePSjitU\nrdVVrTvt3VLQq64c0Htv2qeP/OBVkqTJ8uzW1E554vSYLt1WUC6dVK9fWTRVqalScwu2oaWSCd20\nf7ukmR0aAQDAxsSKHAAixGtDm7MbWm35AdfSTOVRVzCzKGhDGxxTPp3U9u75bSPrJZkwvfiSLbr3\n2PkVPW68VGVeETrqaOFFun7kDmnkhNS7Z+aOkeOaTvXodHWrUpVxb8C1JaTr3yMNPydd8nLp4A9K\nW18w+xN+879JT/ydl2yyhLTrsHTJD0hH/l5yTjKTJoa8Y7v6Gw+b9isIc02DoQuZYEh9i7uvtdnR\nwTFdvdtr1evNe8P3hye9ZHbfApVFkteKdtcjp6ksAgBgg+PPPgAQIT1+ZVHzYNtKzXt/uTa0YPeh\n4AIzSBqVq3VdvLUgs6Urk9rthn1b9eTQuM6Nl1p+zNh0hXlF6Kjnt9zgvXPs6zM3VsvSuac0nBrQ\nuCso6arejKFsUeo/KL39TukHPjA/UbTzGmnLPuk7fyw99nlpx0Ep0+UllqbOz+yiNn7G+7e7KVlU\n8RJCzUmWZMKUTSVCqSyaLHu7KB4Y8NrfevNpTZRrOjdelqQFK4sk6dZrduntL71YL7p4S8fOFQAA\ntB8rcgCIkGI+pbqTJsq1RtKk0sKAa6mpssifWZRNJZVOmio1p4u25tfxrBf20n1bJUn3PXNBb7h6\nZ0uPGacNDR02veVKnXumqG3f/VPp3FPe/KETD0jVaZ3o+hcak//cGX5eyvct/cnMpKtuk+75hJdY\nesunvdsvebn377P3eK1njcqiHTPnESSL5gyE78qmNBFCsujo4Lic84ZbS948NUn6/plxSdKWwsKV\nisVcWv/1Ldd05iQBAMC6obIIACIkaMEabdoRqdpiG1owI6TQVJkTJJw6Ndy62TV7e5VNJXTfM623\noo2RLEKH9XZl9Y3aNdIz35Tu/p9SZUq6/t9IP/rH+s3cBzTm/GTRyPNSbplkkSS97H3STR+U3vfP\n0pVv9G7bsk/q2eUNuZakcT9Z1D3QeNh0xXueZ9Ozn+eFTFKT5c63oR097Q3Gv9JPFvX6lUTf9ltL\ng4ojAAAQT6zIASBCin6yqHluUdlvQ0snlksWBbugzVQmdGVTujBZ6ehw6+bzeeHFffraE0P68Rsv\n1cXblj+H8VJV3VlmFqFztnZl9NHKu3TL2z+svsuu99rGfM/8zVdUkP9zO3J8pkJoKT07pdf8yuzb\nzLzHPvstb27RhN+G1rW9cchCbWiSt6vh5DrPLHLOqVStz/raT5weUy6daCSag93P/vnpc9pZzGlL\nV+dmoAEAgM6jsggAIiSoqmneEa0x4Dq1TBtaavbMImmmsiiMZJEkve0lF+u585O6+eNf012PnFr2\n+NHpCpVF6Ki+QkbD6tGZrS+alSiq1Z3OjJc0HlQW1cpSrnf1X+jiG6Wxk9KFZ6TxQSm/VUrOJEZL\n1YWTRYVsct3b0D77wHG9/Ne/qqmmCqYjg6M6MNCjZMJ73QmSRcfOTujgLqqKAACIO5JFABAhQaJk\ndHp+G1pqmcqiIFkUDLaWwk8WvfmFe3T3h1+l3nxa33zyzJLH1uvO3w2NZBE6Z6s/e+fCZGXW7efG\nS6rVncbVNO+rlTa0xVzyA96/z37La0NrGm4tzbSh5eZsOd+VSa17G9oz5yZ0fqKsx06NNG47cnpM\nVzS1mvU27X52cFdxXc8HAACEj2QRAERIMb9EG9oyA66zC1QWdfnJor1bwkkWSdLO3px29+Y1OLr0\nrmiTlZqcE8kidFSwq9f5ifKs20+PTkuSRtX03FluwPVSdlwp5bd4yaKJMwskixapLMokNVFa38qi\nCb/N7eHjXrLo7HhJZ8fLjeHW0sxrk0SyCACAzYBkEQBEyExl0czFYbVWVzppMmutDa25sqiYT2tH\nT1b5THKxh3XEQDGrobHpJY8JWu+CId9AJwSzd4Yn5ySLRryf10YbmrS2NrREQrr45d6OaONDUtci\nlUULJIvWu7IoSE4HyaJguPWsZFGOZBEAAJsJySIAiJDiAruhVWr1ZVvQJCmTnF9Z9L5XXqaPv/Xa\nNp/lyg0Uc8tWFo37F6zdWSqL0DlBG9r5OcmiQb+yKNfdVE20ljY0yRtyfeGYt7PaopVFc3ZDy6Y0\nuc4zi4LKpYePD0vyhltLs5NFuXRS2VRCuXRC+7Z3zf8kAAAgVliRA0CEZFMJZZKJWW1olZpbtgVN\nmtlyu6spWXRwV1EHd7X/PFeqvyers+MlVWt1pZILJ76Caira0NBJ+YyXBBmeM7NocLSkZMI0sKVH\npbNZZV1pbW1o0sxuavXq/GTRIgOuuzLJRpvYegkGaD99dkJj0xUdHRzT1q6MdnRnZx3Xm09rV2+u\nMfQaAADEF5VFABAhZqaeXGrWgOtKra70IgmWZpmkd5FZyIbbcraQ/mJOzknn5syFaTZeIlmEcGzt\nyiw4s2hHd1aFdFKT5s8tWmtl0c7DUqbbe3+xNrTU3Da0lKYqNdXrbm1fewlj01Vlkgk5J33vxKie\nOD2mAwPd81pfX3vVgG67bs+6nQcAAIgOkkUAEDHFfHpOZVFryaKFKouior/Hq1AIWnsWEsyMYWYR\nOq2vkJk1s2iyXNX3z4xroDenXDqhCbUpWZRMSRe91Ht/kTa07Jw2tGAG2VRl/aqLJkpVXXex9719\n9/lhHR0c05U7588l+i9vuUbvvWnfup0HAACIjuhdUQDAJteTSzWGPUtSteaUaqENbWZmUfQqiwaK\nOUlacm7Rt54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"text/plain": [ "<matplotlib.figure.Figure at 0x7f6b60ce2c18>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "TEST_DAYS_AHEAD = 20\n", "\n", "env.set_test_data(total_data_test_df, TEST_DAYS_AHEAD)\n", "tic = time()\n", "results_list = sim.simulate_period(total_data_test_df, \n", " SYMBOL,\n", " agents[0],\n", " learn=False,\n", " starting_days_ahead=TEST_DAYS_AHEAD,\n", " possible_fractions=POSSIBLE_FRACTIONS,\n", " verbose=False,\n", " other_env=env)\n", "toc = time()\n", "print('Epoch: {}'.format(i))\n", "print('Elapsed time: {} seconds.'.format((toc-tic)))\n", "print('Random Actions Rate: {}'.format(agents[0].random_actions_rate))\n", "show_results([results_list], data_test_df, graph=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### And now a \"realistic\" test, in which the learner continues to learn from past samples in the test set (it even makes some random moves, though very few)." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Starting simulation for agent: Agent_0. 484 days of simulation to go.\n", "Date 2016-12-28 00:00:00 (simulating until 2016-12-30 00:00:00). Time: 0.45553112030029297s. Value: 10498.319999999994.Epoch: 3\n", "Elapsed time: 20.0622079372406 seconds.\n", "Random Actions Rate: 0.07820145305925467\n", "Sharpe ratio: 0.3392076357526945\n", "Cum. Ret.: 0.04739599999999933\n", "AVG_DRET: 0.00010915858102368484\n", "STD_DRET: 0.005108489817155958\n", "Final value: 10473.959999999994\n", "----------------------------------------------------------------------------------------------------\n" ] }, { "data": { "text/plain": [ "<matplotlib.figure.Figure at 0x7f6b60d78588>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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tXDYnjyvmjot0OVFPYZGIiIiIiIgMWX2ooye7j86ivNQ4rlkwnsvn5I3I5184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dv6T0ARIW0DxCJt5re/s+jyu99jTnUBP/jsbALhCLs6PJw2p98MRjQqQuHs5cnvV1gD598JR18L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\nWZSIxzSSvcD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"text/plain": [ "<matplotlib.figure.Figure at 0x7f6b60d975f8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "env.set_test_data(total_data_test_df, TEST_DAYS_AHEAD)\n", "tic = time()\n", "results_list = sim.simulate_period(total_data_test_df, \n", " SYMBOL,\n", " agents[0],\n", " learn=True,\n", " starting_days_ahead=TEST_DAYS_AHEAD,\n", " possible_fractions=POSSIBLE_FRACTIONS,\n", " verbose=False,\n", " other_env=env)\n", "toc = time()\n", "print('Epoch: {}'.format(i))\n", "print('Elapsed time: {} seconds.'.format((toc-tic)))\n", "print('Random Actions Rate: {}'.format(agents[0].random_actions_rate))\n", "show_results([results_list], data_test_df, graph=True)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import pickle\n", "with open('../../data/simple_q_learner_fast_learner_10_actions.pkl', 'wb') as best_agent:\n", " pickle.dump(agents[0], best_agent)" ] }, { "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 }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "cap_env", "language": "python", "name": "cap_env" }, "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

lisenkovkv/math

matan.ipynb

2

33022

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "1 Найдите интеграл $\\int_{-\\frac{\\Pi}{2}}^\\frac{\\Pi}{2} \\frac{\\sin^{2014}x}{\\sin^{2014}x + \\cos^{2014}x}dx$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Решение\n", "\n", "$\\int_a^b f(x) dx = \\int_a^b f(a+b-x) dx$\n", "\n", "$I = \\int_{-\\frac{\\Pi}{2}}^\\frac{\\Pi}{2} \\frac{\\sin^{2014}x}{\\sin^{2014}x + \\cos^{2014}x} dx = $\n", "\n", "$= \\int_{-\\frac{\\Pi}{2}}^\\frac{\\Pi}{2} \\frac{\\sin^{2014}(\\frac{\\pi}{2}-x)}{\\sin^{2014}(\\frac{\\pi}{2}-x) + \\cos^{2014}(\\frac{\\pi}{2}-x)} dx = $\n", "\n", "$= \\int_{-\\frac{\\Pi}{2}}^\\frac{\\Pi}{2} \\frac{\\cos^{2014}x}{\\sin^{2014}x + \\cos^{2014}x} dx$\n", "\n", "Тогда, $I + I = \\int_{-\\frac{\\Pi}{2}}^\\frac{\\Pi}{2} dx = \\pi$ и $I = \\frac{\\pi}{2}$.\n", "\n", "Предполагаем, что $\\sin^{2014}x + \\cos^{2014}x \\neq 0$ т.е. $0 \\leq x \\leq \\frac{\\pi}{2}$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "2 Пусть функция $f$ непрерывна и ограничена на промежутке $(x_0,+\\infty)$. Докажите, что для любого числа $T$ существует последовательность $\\{ x_n \\}$, стремящаяся к $+\\infty$ и такая, что $f(x_n+T)-f(x_n) \\to 0$, при $n \\to \\infty$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "3 Найти предел $\\lim_{\\lambda \\to 0+} \\frac{1}{\\ln \\lambda} \\int_{\\lambda}^{a} \\frac{\\cos x}{x} dx$" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "4 Найдите минимум и максимум функции $f(x,y) = x^2+y^2-12x+16y$ на круге $x^2 + y^2 \\leq 25$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "5 Найдите сумму ряда $\\sum_{n=1}^{\\infty} \\frac{2^n}{n!}$ " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "6 Найдите предел последовательности $\\{ c_n \\}$, определяемой рекурентным соотношением $c_{n+1} = (1-\\frac{1}{n})c_n + \\beta_n$, где $\\beta_n$ - любая последовательность со свойством $n^2 \\beta_n \\to 0$ при $n \\to \\infty$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "7 Вычислите интеграл $\\int e^{e^x + 2014x} dx$" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "8 Исследуйте на сходимость и абсолютную сходимость ряд: $\\sum_{k=1}^{\\infty} \\sin(\\pi\\sqrt{n^2+1})$" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "9 Найдите предел последовательности $a_n$, для которой $a_0 = - \\frac{1}{2}$, $a_{n+1} = \\frac{a_n^2(a_n-3)}{4}$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "10 Вычислите интеграл $\\int_{\\frac{1}{3}}^3 \\frac{arctg x}{x^2-x+1} dx$" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "11 Решите уравнение $\\lim_{n \\to \\infty} \\cos nx =1$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "12 Пусть $f : R^2 \\to R$ - ограниченная гладкая функция, причем ее среднее значение на каждой окружности радиуса 1 равно значению в центре этой окружности. Докажите, что f постоянна." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "13 Докажите, что из последовательности из $mn+1$ различных действительных чисел всегда можно выделить возрастающую подпоследовательность из $n+1$ числа или убывающую подпоследовательность из $m+1$ числа." ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "14 Исследуйте на сходимость ряд $\\sum_{n=3}^{\\infty} (\\ln{\\ln{n}})^{-\\ln{n}}$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "15 Существует ли непрерывная функция $f(x)$, для которой $f(f(x)) = 1 - x^3$?" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "16 Вычислите $\\int_{0}^{2\\pi} (\\sin x)^8 dx$." ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "17 Найдите $\\Pi_{k=1}^{\\infty} \\cos{(x2^{-k})}$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "18 Найдите сумму ряда $\\sum_{n=1}^{\\infty} \\frac{f(n)}{n(n+1)}$, где $f(n)$ - количество единиц в двоичном представлении числа n." ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "19 Вычислите сумму интегралов: $\\int_{\\sqrt{\\pi / 6}}^{\\sqrt{\\pi / 3}} \\sin{x^2} dx + \\int_{1/2}^{\\sqrt{3}/2} \\sqrt{\\arcsin x} dx$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Решение\n", "\n", "Пусть $x = \\sqrt{\\arcsin{t}}$, тогда $\\int_{\\sqrt{\\pi / 6}}^{\\sqrt{\\pi / 3}} \\sin{x^2} dx = \\int_{1/2}^{\\sqrt{3}/2} t\\ d\\sqrt{\\arcsin t}$\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "20 Пусть а - действительное число. Для каждого целого $n \\geq 0$ обозначим через $a_n$ расстояние от a до ближайшего рационального числа вида $\\frac{m}{2^n}$, где m - целое. Найдите наибольшую возможную сумму ряда $\\sum_{n=0}^{\\infty} a_n$" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "22 Ниже приведен фрагмент графика гладкой функции $f(x)$. В скольких точках на отрезке $[0,4]$ выполняется равенство $f'(x) = 0$?\n", "![title](f.jpg \"ShowMyImage\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "23 Посчитайте площадь, ограниченую кривой\n", "\n", "$x = 12(t - \\sin{t}),\\ y=12(1-\\cos{t}),\\ 0\\leq t \\leq 2\\pi$\n", "\n", "и прямой\n", "\n", "$y=0$.\n", "\n", "Число $\\pi$ стоит брать с 15 знаками после запятой." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "24 Какие из этих рядов сходятся условно?\n", "\n", "$\\sum_{n=1}^{\\infty} \\frac{(-1)^n}{n^3+2}$\n", "\n", "$\\sum_{n=1}^{\\infty} \\frac{(-1)^n}{3n-1}$\n", "\n", "$\\sum_{n=1}^{\\infty} \\frac{n(-1)^{n-1}}{\\sqrt{n+1}}$\n", "\n", "$\\sum_{n=1}^{\\infty} \\frac{(-1)^{n}(n^2+1)}{\\ln{(n+1)}}$" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "25 Найти минимальный положительный корень уравнения $\\sin{3x} = a,\\ a>0$, если известно, что какие-то два из корней различаются на $\\pi/4$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Имеем $\\sin{3x} = a, \\sin{3(x-\\frac{\\pi}{4})} = a, \\sin{3x} \\in (0,1], \\sin3(x-\\frac{\\pi}{4}) \\in (0,1]$.\n", "\n", "Применив формулу разности синусов, получим $\\cos(3x+\\frac{3\\pi}{8}) = 0$ или $x = \\pi \\frac{(8n-3)}{24}, n\\in Z$, получим $x = \\frac{5\\pi}{24}$\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "26 Вещественные числа x и y удовлетворяют равенствами $x^3 + 3x^2 + 5x + 1 = 0$, $y^3 + 3y^2 + 5y + 5 = 0$. Найдите $x+y$." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Populating the interactive namespace from numpy and matplotlib\n" ] } ], "source": [ "#РЕШЕНИЕ\n", "import matplotlib.pyplot as plt\n", "%pylab inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "x = arange(-10,10,0.1)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def fx(x):\n", " return x**3+3*x**2+5*x+1\n", "\n", "def fy(y):\n", " return y**3 + 3*y**2+5*y+5" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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SCrLzszl3ZC+yMiJ49exPubCzukL2R0FERESkhDnn+Nv4u9iS/Su93Y8MvKux1yWFLAUR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\nEqnfqoiIyGHRW2YxOOe46b0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"text/plain": [ "<matplotlib.figure.Figure at 0x7faa6a0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(x,fx(x))\n", "plt.plot(x,fy(x))\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "1. Каждое уравнение имеет один действительный корень\n", "2. Сделаем замену $x+1 = t, y+1 = z$ и просуммировав уравнения получим $(t+z)(t^2 + tz + z^2 +2) = 0$ или $t+z = 0$ или $x+y=2$." ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "27 Последовательность $\\{ a_n \\}_{n=0}^{\\infty}$ определена рекурсивно. $a_0=1$,$a_{n+1} = \\frac{a_n}{1+na_n}$. Найдите формулу общего члена последовательности. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Решение.\n", "\n", "Пусть $b_n = \\frac{1}{a_n}$, тогда $b_{n+1} = \\frac{1+na_n}{a_n} = b_n + n$, $b_0 = \\frac{1}{a_0} = 1$. Получаем $b_n = b_0 + 1 + 2 + \\cdots + n-1 = 1 + \\frac{n(n-1)}{2} = \\frac{n(n-1)+2}{2}$. Получаем $a_n = \\frac{2}{n(n-1)+2}$." ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "28 $I_m = \\int_{0}^{2\\pi} \\cos{(x)} \\cos{(2x)} \\cdots \\cos{(mx)} dx$. Для каких $m \\in [0,10],\\ I_m \\neq 0$?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Решение\n", "\n", "Многократно используем формулу $\\cos{(lx)} \\cos{(nx)} = \\frac{1}{2} (\\cos{((l+n)x} + \\cos{((l-n)x)})$:\n", "\n", "$\\cos{(x)} \\cos{(2x)} \\cdots \\cos{(mx)} = \\frac{1}{2^{m-1}} (\\cos{(\\alpha_1 x)} + \\cdots + \\cos(\\alpha_{2^m}x)$, где $\\alpha_i=1 \\pm 2 \\pm 3 \\cdots \\pm m \\in Z$.\n", "\n", "Несложно убедиться, что четности всех чисел $\\alpha_i$ будут одинаковы. Более того, в случаях $m = 4k,\\ m=4k+3$ все $\\alpha_i$ четны. Если $\\alpha_i \\neq 0$, то $\\int_0^{2\\pi} \\cos{(\\alpha_i)} x dx = 0$.\n", "\n", "Значить при $m = 4k+1,\\ m=4k+2 \\ I_m=0$. Если же $m=4k,\\ m=4k+3$, то среди $\\alpha_i$ обязательно есть ноль, так как между числами $1,2,\\cdots,m$ можно так расставить знаки \"+\" и \"-\", чтобы получисля ноль. Действительно,\n", "\n", "(1-2-3+4)+(5-6-7+8)+...+((4k-3)-(4k-2)-(4k-1)+4k) = 0,\n", "\n", "(1-2-3)+(4-5-6-7+8)+...+((4k-3)-(4k-2)-(4k-1)+4k) = 0.\n", "\n", "Таким образом, $m = \\{4k,4k+3\\}\\ I_m = 0$.\n", "\n", "Ответ: при m=0,3,4,7,8" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "29 Рассмотрим бесконечный двумерный массив $\\forall (m,n):\\ a_{mn} \\leq mn$, состоящий из натуральных чисел, причем каждое число встречается ровно 8 раз. Докажите, что $\\exists (m,n): a_{mn} > mn$." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Решение\n", "\n", "Пусть, $\\forall (m,n): a_{mn} \\leq mn$. Выберем $k \\in N$ и рассмотрим кривую на плоскости $y = k/x$. Если $i,j \\in N$ и точка $(i,j)$ лежит под кривой $y = k/x$, то $a_{ij} \\leq ij \\leq i \\frac{k}{i} = k$. Таким образом, количество целых точек под кривой $y = k/x$ должно быть не больше 8k. С другой стороны, количество целых точек под этой кривой не меньше чем $\\int_2^{k} \\frac{k}{x} dx = k \\ln(x) |_{2}^{k} = k (\\ln k - ln 2)$. Притдостаточно большом k это число больше 8k. Таким образом, мы получаем противоречие. Следовательно, найдется пара $(m,n)$ такая, что $a_{mn} > mn$." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [conda root]", "language": "python", "name": "conda-root-py" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.12" } }, "nbformat": 4, "nbformat_minor": 1 }

mit

barjacks/foundations-homework

10/Forecasts_API_homework.ipynb

2

12012

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Forecasts_API_Homework" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import requests\n", "import datetime\n", "import time\n", "\n", "#human readable time\n", "localtime = time.asctime(time.localtime(time.time()))\n", "#UNIX-Time\n", "current_UNIX_Time = int(time.time())\n", "current_UNIX_Time = str(current_UNIX_Time)" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "x = (\"https://api.forecast.io/forecast/e554f37a8164ce189acd210d00a452e0/47.407875,9.464714,\" + current_UNIX_Time)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#What I need:\n", "#Temperature x\n", "#Summary\n", "#High-Temp Feeling in words for the day\n", "#Time of High Temperature\n", "#High-Temp for the day\n", "#Low-Temp for the day\n", "#Rain-Warning" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [], "source": [ "response = requests.get(x)\n", "weather_forecast_trogen = response.json()" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#Temperature:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def celsius(x):\n", " return round((x -32) /1.8)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "dict_keys(['humidity', 'windSpeed', 'time', 'summary', 'temperature', 'ozone', 'dewPoint', 'apparentTemperature', 'precipIntensity', 'cloudCover', 'windBearing', 'pressure', 'precipProbability', 'icon', 'visibility'])" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "weather_forecast_trogen['currently'].keys()" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "16" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Current_Temperature = celsius(weather_forecast_trogen['currently']['apparentTemperature'])\n", "Current_Temperature" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#Summary" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [], "source": [ "Summary = weather_forecast_trogen['currently']['summary']" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#weather_forecast_trogen['daily']['data']" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#High-Temp Feeling in words for the day" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [], "source": [ "for TemperatureMax in weather_forecast_trogen['daily']['data']:\n", " apparentTemperatureMax_tomorrow = TemperatureMax['apparentTemperatureMax']\n", " break\n", "\n", "apparentTemperatureMax_tomorrow = celsius(apparentTemperatureMax_tomorrow)\n", "\n", "if apparentTemperatureMax_tomorrow > 30:\n", " apparentTemperatureMax_tomorrow = \"hot\"\n", "elif apparentTemperatureMax_tomorrow > 25:\n", " apparentTemperatureMax_tomorrow = \"warm\"\n", "elif apparentTemperatureMax_tomorrow > 18:\n", " apparentTemperatureMax_tomorrow = \"warmish\"\n", "elif apparentTemperatureMax_tomorrow > 8:\n", " apparentTemperatureMax_tomorrow = \"cool\"\n", "elif apparentTemperatureMax_tomorrow > 0:\n", " apparentTemperatureMax_tomorrow = \"nearly freezing\"\n", "else: \n", " apparentTemperatureMax_tomorrow = \"icey cold\"" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#High-Temp for the day" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "29" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "for TemperatureMax in weather_forecast_trogen['daily']['data']:\n", " TemperatureMax_tomorrow = TemperatureMax['apparentTemperatureMax']\n", " break\n", "\n", "celsius(TemperatureMax_tomorrow)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#Time of High Temperature" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [], "source": [ "for TemperatureMaxTime in weather_forecast_trogen['daily']['data']:\n", " TemperatureMaxtime_tomorrow = TemperatureMaxTime['apparentTemperatureMaxTime']\n", " break\n", "\n", "import datetime\n", "TemperatureMaxTime = (\n", " datetime.datetime.fromtimestamp(\n", " int(TemperatureMaxtime_tomorrow)\n", " ).strftime('%H:%M')\n", ")" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#Min-Temp of the day" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "16" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "for TemperatureMin in weather_forecast_trogen['daily']['data']:\n", " TemperatureMin_tomorrow = TemperatureMin['apparentTemperatureMin']\n", " break\n", "\n", "celsius(TemperatureMin_tomorrow)" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#Time of Min of the day" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [], "source": [ "for TemperatureMinTime in weather_forecast_trogen['daily']['data']:\n", " TemperatureMintime_tomorrow = TemperatureMinTime['apparentTemperatureMinTime']\n", " break\n", "\n", "import datetime\n", "TemperatureMinTime = (\n", " datetime.datetime.fromtimestamp(\n", " int(TemperatureMintime_tomorrow)\n", " ).strftime('%H:%M')\n", ")" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#Rain-Warning" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [], "source": [ "icon = weather_forecast_trogen['currently']['icon']" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "clear\n" ] } ], "source": [ "if icon == 'clear-day':\n", " icon = \"clear\"\n", "elif icon == 'clear-night':\n", " icon = \"clear\"\n", "elif icon == 'rain':\n", " icon = \"rainy\"\n", "elif icon == 'snow':\n", " icon = \"snowy\"\n", "elif icon == 'sleet':\n", " icon = \"sleety\"\n", "elif icon == 'wind':\n", " icon = \"windy\"\n", "elif icon == 'foggy':\n", " icon = \"foggy\"\n", "elif icon == 'cloudy':\n", " icon = 'cloudy'\n", "elif icon == 'partly-cloudy day.':\n", " icon = 'partly-cloudy'\n", "elif icon == 'partly-cloudy-night':\n", " icon = 'partly-cloudy'\n", "\n", "print(icon) " ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The current temperature is 16 °C. It is clear. Tomorrow, a warm day awaits you, with a maximum temperature of 29 °C, peaking at 09:00. The coldest hour of day is 20:00 reaching a low of 16 °C. All in all it will be a clear day.\n" ] } ], "source": [ "print(\"The current temperature is \" + str(Current_Temperature) + \" °C. It is \" + Summary.lower() \\\n", " + \". Tomorrow, a \" + str(apparentTemperatureMax_tomorrow) + \" day awaits you, with a maximum temperature of \" + str(celsius(TemperatureMax_tomorrow)) + \" °C, peaking at \" + \\\n", " str(TemperatureMaxTime) + \". The coldest hour of day is \" + str(TemperatureMinTime) + \" reaching a low of \" \\\n", " + str(celsius(TemperatureMin_tomorrow)) + \" °C. All in all it will be a \" + icon + \" day.\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Mailing" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Status: 400\n", "Body: {\n", " \"message\": \"Sandbox subdomains are for test purposes only. Please add your own domain or add the address to authorized recipients in domain settings.\"\n", "}\n" ] } ], "source": [ "key = 'key-Xxxxxxxxxxxxxx'\n", "sandbox = 'sandboxXxxxxxxxxxxxxx.mailgun.org'\n", "recipient = 'barnaby.skinner@sonntagszeitung'\n", "\n", "request_url = 'https://api.mailgun.net/v2/{0}/messages'.format(sandbox)\n", "request = requests.post(request_url,\n", " auth=('api', key),\n", " data={\n", " 'from': '[email protected]',\n", " 'to': recipient, \n", " 'subject': 'Weather Report For Tomorrow',\n", " 'text': (\"The current temperature is \" + str(Current_Temperature) + \" °C. It is \" + Summary.lower() \\\n", " + \". Tomorrow, a \" + str(apparentTemperatureMax_tomorrow) + \" day awaits you, with a maximum temperature of \" + str(celsius(TemperatureMax_tomorrow)) + \" °C, peaking at \" + \\\n", " str(TemperatureMaxTime) + \". The coldest hour of day is \" + str(TemperatureMinTime) + \" reaching a low of \" \\\n", " + str(celsius(TemperatureMin_tomorrow)) + \" °C. All in all it will be a \" + icon + \" day.\")\n", "})\n", "\n", "print('Status: {0}'.format(request.status_code))\n", "print('Body: {0}'.format(request.text))" ] }, { "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

ioam/holoviews

examples/reference/containers/plotly/NdOverlay.ipynb

1

4575

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "<div class=\"contentcontainer med left\" style=\"margin-left: -50px;\">\n", "<dl class=\"dl-horizontal\">\n", " <dt>Title</dt> <dd>NdOverlay Container</dd>\n", " <dt>Dependencies</dt> <dd>Plotly</dd>\n", " <dt>Backends</dt> <dd><a href='./NdOverlay.ipynb'>Bokeh</a></dd> <dd><a href='../matplotlib/NdOverlay.ipynb'>Matplotlib</a></dd> <dd><a href='../plotly/NdOverlay.ipynb'>Plotly</a></dd>\n", "</dl>\n", "</div>" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import holoviews as hv\n", "hv.extension('plotly')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "An ``NdOverlay`` is a multi-dimensional dictionary of HoloViews elements presented overlayed in the same space. An ``NdOverlay`` can be considered as a special-case of ``HoloMap`` that can only hold a single type of element at a time. Unlike a regular ``Overlay`` that can be built with the ``*`` operator, the items in an ``NdOverlay`` container have corresponding keys and must all have the same type. See the [Building Composite Objects](../../../user_guide/06-Building_Composite_Objects.ipynb) user guide for details on how to compose containers." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### ``NdOverlay`` holds dictionaries" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Using the ``sine_curve`` function below, we can declare a dictionary of ``Curve`` elements, where the keys correspond to the frequency values:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "frequencies = [0.5, 0.75, 1.0, 1.25]\n", "\n", "def sine_curve(phase, freq):\n", " xvals = [0.1* i for i in range(100)]\n", " return hv.Curve((xvals, [np.sin(phase+freq*x) for x in xvals]))\n", "\n", "curve_dict = {f:sine_curve(0,f) for f in frequencies}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We now have a dictionary where the frequency is the key and the corresponding curve element is the value. We can now turn this dictionary into an ``NdOverlay`` by declaring the keys as corresponding to the frequency key dimension:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "ndoverlay = hv.NdOverlay(curve_dict, kdims='frequency')\n", "ndoverlay" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that the ``NdOverlay`` is displayed with a legend using colors defined by the ``Curve`` color cycle. For more information on using ``Cycle`` to define color cycling, see the [User Guide](../../../user_guide/04-Style_Mapping.ipynb#Cycles-and-Palettes)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### ``NdOverlay`` is multi-dimensional" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "By using tuple keys and making sure each position in the tuple is assigned a corresponding ``kdim``, ``NdOverlays`` allow visualization of a multi-dimensional space:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "curve_dict_2D = {(p,f):sine_curve(p,f) for p in [0, np.pi/2] for f in [0.5, 0.75]}\n", "ndoverlay = hv.NdOverlay(curve_dict_2D, kdims=['phase', 'frequency'])\n", "ndoverlay" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### ``NdOverlay`` is similar to ``HoloMap``" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Other than the difference in the visual semantics, whereby ``NdOverlay`` displays its contents overlaid, ``NdOverlay`` are very similar to ``HoloMap`` (see the [``HoloMap``](./HoloMap.ipynb) notebook for more information).\n", "\n", "One way to demonstrate the similarity of these two containers is to cast our ``ndoverlay`` object to ``HoloMap`` and back to an ``NdOverlay``:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "hmap = hv.HoloMap(ndoverlay)\n", "hmap + hv.NdOverlay(hmap)" ] } ], "metadata": { "language_info": { "name": "python", "pygments_lexer": "ipython3" } }, "nbformat": 4, "nbformat_minor": 2 }

bsd-3-clause

dnxbjyj/python-basic

useful-func/func-handout.ipynb

1

11907

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Python常用内置函数典型用法" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Python中有许多功能丰富的内置函数，本文基于Python 2.7，就常用的一些函数的典型用法做一些积累，不断更新中。" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# sorted函数的三种用法" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```python\n", "# coding:utf-8\n", "# sorted函数的三种用法\n", "from operator import itemgetter\n", "\n", "data1 = [{'aa':22,'bb':11},{'aa':12,'cc':23},{'aa':67,'dd':103}]\n", "data2 = [{'age':18,'name':'Tom'},{'age':10,'name':'Tim'},{'age':30,'name':'John'},{'age':18,'name':'Amy'}]\n", "\n", "def sort1():\n", " # 对data1依据'aa'字段值的大小从小打到排序\n", " ret = sorted(data1,key = lambda item:item['aa']) # 注：如果这里的key写'bb'或'cc'，会报KeyError，因为这两个属性并不是每个元素都有的\n", " print ret\n", " # 输出：\n", " '''\n", " [{'aa': 12, 'cc': 23}, {'aa': 22, 'bb': 11}, {'aa': 67, 'dd': 103}]\n", " '''\n", "\n", "def sort2():\n", " # 对data1依据'aa'字段值的大小从小打到排序\n", " ret = sorted(data1,cmp = lambda x,y:cmp(x['aa'],y['aa']))\n", " print ret\n", " # 输出：\n", " '''\n", " [{'aa': 12, 'cc': 23}, {'aa': 22, 'bb': 11}, {'aa': 67, 'dd': 103}]\n", " '''\n", "\n", "def sort3():\n", " # 使用itemgetter对data1依据'aa'字段值的大小从小打到排序\n", " ret = sorted(data1,key = itemgetter('aa'))\n", " print ret\n", " # 输出：\n", " '''\n", " [{'aa': 12, 'cc': 23}, {'aa': 22, 'bb': 11}, {'aa': 67, 'dd': 103}]\n", " '''\n", "\n", "def sort4():\n", " # 对data2进行排序，先按照'age'从小到大排序，'age'相同的情况下，再按照'name'排序\n", " ret = sorted(data2,key = itemgetter('age','name'))\n", " print ret\n", " # 输出：\n", " '''\n", " [{'age': 10, 'name': 'Tim'}, {'age': 18, 'name': 'Amy'}, {'age': 18, 'name': 'Tom'}, {'age': 30, 'name': 'John'}]\n", " '''\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 执行命令行命令的三种方式" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```python\n", "# coding:utf-8\n", "# 执行命令行命令的三种方式\n", "import os\n", "import commands\n", "\n", "command = 'ls -al /root'\n", "\n", "def method1():\n", " '''\n", " 方式1\n", " '''\n", " os.system(command)\n", " # 执行结果：返回执行状态码\n", "\n", "def method2():\n", " '''\n", " 方式2\n", " '''\n", " out1 = os.popen(command)\n", " print out1.read()\n", " # 输出：执行结果字符串\n", "\n", "def method3():\n", " '''\n", " 方式3\n", " '''\n", " (status,out) = commands.getstatusoutput(command)\n", " # 输出：status是执行状态码，out是执行结果字符串\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# zip函数的用法" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```\n", "Docstring:\n", "zip(seq1 [, seq2 [...]]) -> [(seq1[0], seq2[0] ...), (...)]\n", "Return a list of tuples, where each tuple contains the i-th element\n", "from each of the argument sequences. The returned list is truncated\n", "in length to the length of the shortest argument sequence.\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "先来看看zip函数的文档，从文档中可以看出，zip函数接收1个或多个序列作为参数，返回一个由元组组成的列表。" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "结果列表的第i个元素是seq1~seqn的第i个元素组成的元组。" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "结果列表的长度等于seq1~seqn中最短的序列的长度。" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "一段测试代码如下：" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```python\n", "# coding:utf-8\n", "\n", "def main():\n", " a = '1234'\n", " b = [4,6,7]\n", "\n", " print zip()\n", " # 输出：[]\n", "\n", " print zip(a)\n", " # 输出：[('1',), ('2',), ('3',), ('4',)]\n", "\n", " print zip(a,a)\n", " # 输出：[('1', '1'), ('2', '2'), ('3', '3'), ('4', '4')]\n", "\n", " print zip(a,[])\n", " # 输出：[]\n", "\n", " print zip(a,b)\n", " # 输出：[('1', 4), ('2', 6), ('3', 7)]\n", "\n", "if __name__ == '__main__':\n", " main()\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# map函数的用法" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "map函数是一个高阶函数，支持传入一个函数作为参数。先来看它的文档是怎么说的：" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```\n", "Docstring:\n", "map(function, sequence[, sequence, ...]) -> list\n", "Return a list of the results of applying the function to the items of\n", "the argument sequence(s). If more than one sequence is given, the\n", "function is called with an argument list consisting of the corresponding\n", "item of each sequence, substituting None for missing values when not all\n", "sequences have the same length. If the function is None, return a list of\n", "the items of the sequence (or a list of tuples if more than one sequence).\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "从map函数的文档中可以看出，该函数的第一个参数为一个函数对象，后面可以跟一个或多个序列，函数的返回值是一个list." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "对比zip函数的用法，可以发现其实map函数就是一个增强版的zip函数，与zip函数不同的是，map函数支持传入一个函数参数来处理序列。" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "如果第一个函数参数不为None，那么返回的结果list的第i个元素，是将该函数作用于每个序列的第i个元素的结果。如果传入的序列的长度不都是相同的，那么结果list的某些元素将会是None." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "如果第一个函数参数为None，那么返回的的结果list的第i个元素，是每个序列第i个元素组成的n元组（n为序列的个数），如果每个序列的长度不都是相同的，那么结果list的某些元素将是None." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "下面通过一段程序来看map函数的实际用法：" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```python\n", "# coding:utf-8\n", "\n", "def main():\n", " a = [1,2,3,4]\n", " b = [3,5,9]\n", " c = [8,2,3]\n", " print map(None,a,b,c)\n", " # 输出：[(1, 3, 8), (2, 5, 2), (3, 9, 3), (4, None, None)]\n", "\n", " print map(lambda x : x ** 2,a)\n", " # 输出：[1, 4, 9, 16]\n", "\n", " # print map(lambda x,y : x + y,a)\n", " # 输出：TypeError <lambda>() takes exactly 2 arguments (1 given)\n", "\n", " print map(lambda x,y : x + y,b,c)\n", " # 输出：[11, 7, 12]\n", "\n", " # print map(lambda x,y,z : x + y + z,a,b,c)\n", " # 输出：TypeError: unsupported operand type(s) for +: 'int' and 'NoneType'\n", "\n", " print map(lambda x,y : x + y if x is not None and y is not None else None,a,b)\n", " # 输出：[4, 7, 12, None]\n", "\n", "if __name__ == '__main__':\n", " main()\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# reduce函数的用法" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "先看函数文档：" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```\n", "Docstring:\n", "reduce(function, sequence[, initial]) -> value\n", "Apply a function of two arguments cumulatively to the items of a sequence,\n", "from left to right, so as to reduce the sequence to a single value.\n", "For example, reduce(lambda x, y: x+y, [1, 2, 3, 4, 5]) calculates\n", "((((1+2)+3)+4)+5). If initial is present, it is placed before the items\n", "of the sequence in the calculation, and serves as a default when the\n", "sequence is empty.\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "reduce函数接收三个参数：function，seq，init，其中前两个是必选参数，最后一个为可选参数。" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "reduce函数做了这样一件事情：从左到右遍历seq，将seq[0]和seq[1]传入函数function进行运算（function为一个接收两个参数的函数），得到一个结果值，然后将这个结果值再和seq[2]传入fucntion进行运算再得到一个新的结果值...以此类推。最终得到一个值，就是该函数的返回值。" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "如果传入了init，那么init和seq[0]会作为第一次传入funciton的参数，如果seq为空，init也会作为reduce的返回值返回。" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "用法示例如下：" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```python\n", "# coding:utf-8\n", "\n", "def main():\n", " lst = [1,2,3]\n", " f = lambda x,y:x*y\n", " print reduce(f,lst)\n", " # 输出：6\n", "\n", " print reduce(f,lst,-1)\n", " # 输出：-6\n", "\n", " print reduce(f,[],-2)\n", " # 输出：-2\n", "\n", "if __name__ == '__main__':\n", " main()\n", "\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# base64编解码" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```python\n", "# coding:utf-8\n", "# 测试base64编解码\n", "import base64\n", "\n", "def main():\n", " s = '123abc'\n", "\n", " # 编码\n", " print base64.b64encode(s)\n", " # 输出：MTIzYWJj\n", "\n", " # 解码\n", " print base64.b64decode('MTIzYWJj')\n", " # 输出：123abc\n", "\n", "if __name__ == '__main__':\n", " main()\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": "2.7.13" } }, "nbformat": 4, "nbformat_minor": 2 }

mit

gileno/curso-scrapy-aulas

modulo-01/requests-beautifulsoup.ipynb

1

21095

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import requests\n", "from bs4 import BeautifulSoup" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "response = requests.get('http://www.gilenofilho.com.br/')" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "200" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "response.status_code" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'<!DOCTYPE html>\\n<html xmlns=\"http://www.w3.org/1999/xhtml\"\\n xmlns:og=\"http://ogp.me/ns#\"\\n xmlns:fb=\"https://www.facebook.com/2008/fbml\"\\n itemscope itemtype=\"http://schema.org/Blog\"\\n lang=\"pt\">\\n <head>\\n <meta http-equiv=\"Content-Type\" content=\"text/html; charset=UTF-8\"/>\\n <meta name=\"viewport\" content=\"width=device-width, initial-scale=1, maximum-scale=1.0\"/>\\n <meta http-equiv=\"content-language\" content=\"pt-br\">\\n <meta name=\"robots\" content=\"index, follow\">\\n <meta name=\"description\" content=\"Website Gileno Filho\"/>\\n <meta name=\"keywords\" content=\"programação, python, recife, desenvolvimento, data science\"/>\\n <meta name=\"author\" content=\"Gileno Filho\">\\n <meta name=\"generator\" content=\"Pelican\">\\n <title>Gileno Filho</title>\\n\\n <!-- Feeds -->\\n <link href=\"http://www.gilenofilho.com.br/feeds.atom\" type=\"application/atom+xml\" rel=\"alternate\" title=\"Gileno Filho ATOM Feed\"/>\\n <link href=\"http://www.gilenofilho.com.br/feeds.rss\" type=\"application/rss+xml\" rel=\"alternate\" title=\"Gileno Filho RSS Feed\"/>\\n\\n <!-- CSS -->\\n <link rel=\"stylesheet\" href=\"/theme/css/font-awesome.min.css\">\\n <link href=\"/theme/css/bootstrap.css\" type=\"text/css\" rel=\"stylesheet\" />\\n <link href=\"/theme/css/style.css\" type=\"text/css\" rel=\"stylesheet\" />\\n <!-- HTML5 shim and Respond.js IE8 support of HTML5 elements and media queries -->\\n <!--[if lt IE 9]>\\n <script src=\"https://oss.maxcdn.com/libs/html5shiv/3.7.0/html5shiv.js\"></script>\\n <script src=\"https://oss.maxcdn.com/libs/respond.js/1.4.2/respond.min.js\"></script>\\n <![endif]-->\\n <!-- Metadata -->\\n <meta property=\"og:type\" content=\"website\"/>\\n\\n <!-- Google Analytics -->\\n <script>\\n (function(i,s,o,g,r,a,m){i[\\'GoogleAnalyticsObject\\']=r;i[r]=i[r]||function(){\\n (i[r].q=i[r].q||[]).push(arguments)},i[r].l=1*new Date();a=s.createElement(o),\\n m=s.getElementsByTagName(o)[0];a.async=1;a.src=g;m.parentNode.insertBefore(a,m)\\n })(window,document,\\'script\\',\\'//www.google-analytics.com/analytics.js\\',\\'ga\\');\\n ga(\\'create\\', \\'UA-15015773-2\\', \\'auto\\');\\n ga(\\'send\\', \\'pageview\\');\\n </script>\\n\\n </head>\\n\\n <body>\\n <header>\\n<!-- Fixed navbar -->\\n<div class=\"navbar navbar-default navbar-fixed-top\" role=\"navigation\">\\n <div class=\"container\">\\n <div class=\"navbar-header\">\\n <button type=\"button\" class=\"navbar-toggle\" data-toggle=\"collapse\" data-target=\".navbar-collapse\">\\n <span class=\"sr-only\">Toggle navigation</span>\\n <span class=\"icon-bar\"></span>\\n <span class=\"icon-bar\"></span>\\n <span class=\"icon-bar\"></span>\\n </button>\\n <a class=\"navbar-brand\" href=\"/\">Gileno Filho</a>\\n </div>\\n <div class=\"navbar-collapse collapse navbar-right\">\\n <ul class=\"nav navbar-nav\">\\n <li><a href=\"/\">Início</a></li>\\n <li ><a href=\"/sobre\">Sobre</a></li>\\n <li ><a href=\"/categorias/cursos/\">Cursos</a></li>\\n <li ><a href=\"/categorias/palestras/\">Palestas</a></li>\\n <li ><a href=\"/categorias/tutoriais/\">Tutoriais</a></li>\\n <li ><a href=\"/categorias/utils/\">Utils</a></li>\\n </ul>\\n </div><!--/.nav-collapse -->\\n </div>\\n</div> <div id=\"blue\">\\n <div class=\"container\">\\n <div class=\"row\">\\n <h3>\\n programador, cientista, pythonista e minimalista. Recife, Brasil\\n</h3>\\n </div><!-- /row -->\\n </div> <!-- /container -->\\n </div><!-- /blue -->\\n </header>\\n <main>\\n <!-- Content -->\\n <div class=\"container mtb\">\\n <div class=\"col-md-8\">\\n<ol class=\"breadcrumb\">\\n <li>\\n <a title=\"Gileno Filho Home\" href=\"/\"><i class=\"fa fa-home pr-10\"></i> Início</a>\\n </li>\\n</ol><article>\\n <a href=\"/python-datasets\"><h3 class=\"ctitle\">Bibliotecas Python para carregar Dataset\\'s</h3></a>\\n <p>\\n Publicado em: <time datetime=\"2017-01-02T09:00:00-03:00\">Seg 02 Janeiro 2017</time>.\\n |\\n Por: <a href=\"/autores/gileno-filho\">Gileno Filho</a>\\n |\\n Arquivado em: <a href=\"/categorias/tutoriais\">tutoriais</a>\\n </p>\\n <p>\\n <p>Desde que a biblioteca <a href=\"http://pandas.pydata.org/\">Pandas</a> se tornou bastante popular em análise de dados com Python, várias outras libs surgiram para auxiliar a importação de dados para objetos do tipo DataFrame (utilizados pelo Pandas).</p>\\n<p>Neste artigo irei comentar sobre duas libs:</p>\\n<ul>\\n<li><a href=\"https://github.com/yhat/db.py\">db.py</a>: Facilita a importação de bancos de dados para …</li></ul>\\n </p>\\n <p><a href=\"/python-datasets\">[Leia mais]</a></p>\\n <div class=\"hline\"></div>\\n <div class=\"spacing\"></div>\\n</article>\\n<article>\\n <a href=\"/como-funciona-o-orm-do-django\"><h3 class=\"ctitle\">Como funciona o ORM do Django</h3></a>\\n <p>\\n Publicado em: <time datetime=\"2016-07-15T22:00:00-03:00\">Sex 15 Julho 2016</time>.\\n |\\n Por: <a href=\"/autores/gileno-filho\">Gileno Filho</a>\\n |\\n Arquivado em: <a href=\"/categorias/tutoriais\">tutoriais</a>\\n </p>\\n <p>\\n <p>Uma das coisas mais interessantes do framework <a href=\"https://djangoproject.com/\">Django</a> é sem dúvidas o seu <a href=\"https://pt.wikipedia.org/wiki/Mapeamento_objeto-relacional\">ORM</a>. E o que o torna interessante é a sua simplicidade e objetividade quando se utiliza os <a href=\"https://docs.djangoproject.com/en/1.9/ref/models/lookups/\">Lookups</a> para realizar consultas simples e até as complexas que envolvem join\\'s.</p>\\n<p>Neste artigo irei explorar algumas coisas básicas para …</p>\\n </p>\\n <p><a href=\"/como-funciona-o-orm-do-django\">[Leia mais]</a></p>\\n <div class=\"hline\"></div>\\n <div class=\"spacing\"></div>\\n</article>\\n<article>\\n <a href=\"/curso-gratuito-de-django-com-python-3\"><h3 class=\"ctitle\">Curso Gratuito de Django com Python 3</h3></a>\\n <p>\\n Publicado em: <time datetime=\"2016-03-23T13:40:00-03:00\">Qua 23 Março 2016</time>.\\n |\\n Por: <a href=\"/autores/gileno-filho\">Gileno Filho</a>\\n |\\n Arquivado em: <a href=\"/categorias/cursos\">cursos</a>\\n </p>\\n <p>\\n <p>Olá pessoal, a algum tempo eu fechei as inscrições do meu curso de Django com Python 3 que comecei em 2014. Neste curso foi desenvolvido um projeto chamado SimpleMOOC, uma simples plataforma para ensino a distância que tinha fins didáticos.</p>\\n<p>Recentemente eu resolvi disponibilizar as vídeo-aulas desse curso pois irei …</p>\\n </p>\\n <p><a href=\"/curso-gratuito-de-django-com-python-3\">[Leia mais]</a></p>\\n <div class=\"hline\"></div>\\n <div class=\"spacing\"></div>\\n</article>\\n<article>\\n <a href=\"/programacao-felicidade-mercado-e-outras-coisas\"><h3 class=\"ctitle\">Programação, felicidade, mercado e outras coisas...</h3></a>\\n <p>\\n Publicado em: <time datetime=\"2015-11-10T17:00:00-03:00\">Ter 10 Novembro 2015</time>.\\n |\\n Por: <a href=\"/autores/gileno-filho\">Gileno Filho</a>\\n |\\n Arquivado em: <a href=\"/categorias/utils\">utils</a>\\n </p>\\n <p>\\n <p>Obs: No final do texto tem alguns links e palestras interessantes mas vou contextualizar minha história antes.</p>\\n<p>Nos últimos 2 anos eu tenho estudado bastante coisas relacionadas a Lifestyle Business, e isso começou quando eu percebi que o buraco negro corporativo e nem o conto de fadas das startups eram …</p>\\n </p>\\n <p><a href=\"/programacao-felicidade-mercado-e-outras-coisas\">[Leia mais]</a></p>\\n <div class=\"hline\"></div>\\n <div class=\"spacing\"></div>\\n</article>\\n<article>\\n <a href=\"/usando-o-scrapy-e-o-rethinkdb-para-capturar-e-armazenar-dados-imobiliarios-parte-iii\"><h3 class=\"ctitle\">Usando o Scrapy e o Rethinkdb para capturar e armazenar dados imobiliários - Parte III</h3></a>\\n <p>\\n Publicado em: <time datetime=\"2015-09-08T13:33:00-03:00\">Ter 08 Setembro 2015</time>.\\n |\\n Por: <a href=\"/autores/gileno-filho\">Gileno Filho</a>\\n |\\n Arquivado em: <a href=\"/categorias/tutoriais\">tutoriais</a>\\n </p>\\n <p>\\n <h3>Introdução</h3>\\n<p>Olá pessoal, esta é a parte III da série sobre o Scrapy, abaixo os links para todos os artigos da série:</p>\\n<ul>\\n<li><a href=\"http://gilenofilho.com.br/usando-o-scrapy-e-o-rethinkdb-para-capturar-e-armazenar-dados-imobiliarios-parte-i/\">Parte I - Configurando e rodando o Scrapy</a></li>\\n<li><a href=\"http://gilenofilho.com.br/usando-o-scrapy-e-o-rethinkdb-para-capturar-e-armazenar-dados-imobiliarios-parte-ii/\">Parte II - Instalando, configurando e armazenando os dados no Rethinkdb</a></li>\\n<li><a href=\"http://gilenofilho.com.br/usando-o-scrapy-e-o-rethinkdb-para-capturar-e-armazenar-dados-imobiliarios-parte-iii/\">Parte III - Deploy do projeto Scrapy</a></li>\\n</ul>\\n<p>Nos artigos anteriores mostrei como …</p>\\n </p>\\n <p><a href=\"/usando-o-scrapy-e-o-rethinkdb-para-capturar-e-armazenar-dados-imobiliarios-parte-iii\">[Leia mais]</a></p>\\n <div class=\"hline\"></div>\\n <div class=\"spacing\"></div>\\n</article>\\n<nav>\\n <ul class=\"pagination\">\\n <li class=\"disabled\">\\n <a href=\"\" aria-label=\"Previous\"><span aria-hidden=\"true\">&laquo;</span></a>\\n </li>\\n <li 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action=\"https://www.google.com/search\" method=\"get\">\\n <input name=\"q\" type=\"text\" class=\"form-control\" placeholder=\"Pesquisar...\" />\\n <input type=\"hidden\" name=\"sitesearch\" value=\"http://www.gilenofilho.com.br\">\\n </form>\\n\\t\\t \\t\\t\\t</p>\\n \\t\\t \\t\\t<div class=\"spacing\"></div>\\n\\n \\t\\t \\t\\t<h4>Categorias</h4>\\n \\t\\t \\t\\t<div class=\"hline\"></div>\\n <p><a href=\"/categorias/cursos\"><i class=\"fa fa-angle-right\"></i> cursos</a> <span class=\"badge badge-theme pull-right\">6</span></p>\\n <p><a href=\"/categorias/palestras\"><i class=\"fa fa-angle-right\"></i> palestras</a> <span class=\"badge badge-theme pull-right\">6</span></p>\\n <p><a href=\"/categorias/tutoriais\"><i class=\"fa fa-angle-right\"></i> tutoriais</a> <span class=\"badge badge-theme pull-right\">12</span></p>\\n <p><a href=\"/categorias/utils\"><i class=\"fa fa-angle-right\"></i> utils</a> <span class=\"badge badge-theme pull-right\">4</span></p>\\n\\n \\t\\t \\t\\t<div class=\"spacing\"></div>\\n\\n \\t\\t \\t\\t<h4>Artigos Recentes</h4>\\n \\t\\t \\t\\t<div class=\"hline\"></div>\\n\\t\\t\\t\\t\\t<ul class=\"popular-posts\">\\n\\t\\t <li>\\n\\t\\t <p><a href=\"/python-datasets\">Bibliotecas Python para carregar Dataset\\'s</a></p>\\n\\t\\t <em>Publicado em Seg 02 Janeiro 2017</em>\\n\\t\\t </li>\\n\\t\\t <li>\\n\\t\\t <p><a href=\"/como-funciona-o-orm-do-django\">Como funciona o ORM do Django</a></p>\\n\\t\\t <em>Publicado em Sex 15 Julho 2016</em>\\n\\t\\t </li>\\n\\t\\t <li>\\n\\t\\t <p><a href=\"/curso-gratuito-de-django-com-python-3\">Curso Gratuito de Django com Python 3</a></p>\\n\\t\\t <em>Publicado em Qua 23 Março 2016</em>\\n\\t\\t </li>\\n\\t\\t <li>\\n\\t\\t <p><a href=\"/programacao-felicidade-mercado-e-outras-coisas\">Programação, felicidade, mercado e outras coisas...</a></p>\\n\\t\\t <em>Publicado em Ter 10 Novembro 2015</em>\\n\\t\\t </li>\\n\\t\\t </ul>\\n\\n \\t\\t \\t\\t<div class=\"spacing\"></div>\\n\\n \\t\\t \\t\\t<h4>Tags</h4>\\n \\t\\t \\t\\t<div class=\"hline\"></div>\\n\\t\\t \\t\\t\\t<p>\\n\\t\\t \\t<a class=\"btn btn-theme\" href=\"/tags/python\" role=\"button\">python</a>\\n\\t\\t \\t<a class=\"btn btn-theme\" href=\"/tags/evento\" role=\"button\">evento</a>\\n\\t\\t \\t<a class=\"btn btn-theme\" href=\"/tags/engenharia-de-avaliacoes\" role=\"button\">engenharia-de-avaliações</a>\\n\\t\\t \\t<a class=\"btn btn-theme\" href=\"/tags/database\" role=\"button\">database</a>\\n\\t\\t \\t<a class=\"btn btn-theme\" href=\"/tags/comunidade\" role=\"button\">comunidade</a>\\n\\t\\t \\t<a class=\"btn btn-theme\" href=\"/tags/lifestyle\" role=\"button\">lifestyle</a>\\n\\t\\t \\t<a class=\"btn btn-theme\" href=\"/tags/inteligencia-artificial\" role=\"button\">inteligência-artificial</a>\\n\\t\\t \\t<a class=\"btn btn-theme\" href=\"/tags/design\" role=\"button\">design</a>\\n\\t\\t \\t<a class=\"btn btn-theme\" href=\"/tags/pythonbrasil\" role=\"button\">pythonbrasil</a>\\n\\t\\t \\t<a class=\"btn btn-theme\" href=\"/tags/universidade\" role=\"button\">universidade</a>\\n\\t\\t \\t<a class=\"btn btn-theme\" href=\"/tags/django\" role=\"button\">django</a>\\n\\t\\t \\t<a class=\"btn btn-theme\" href=\"/tags/redes-complexas\" role=\"button\">redes-complexas</a>\\n\\t\\t \\t<a class=\"btn btn-theme\" href=\"/tags/video-aula\" role=\"button\">video-aula</a>\\n\\t\\t \\t<a class=\"btn btn-theme\" href=\"/tags/startup\" role=\"button\">startup</a>\\n\\t\\t \\t<a class=\"btn btn-theme\" href=\"/tags/deploy\" role=\"button\">deploy</a>\\n\\t\\t \\t<a class=\"btn btn-theme\" href=\"/tags/dados\" role=\"button\">dados</a>\\n\\t\\t \\t\\t\\t</p>\\n </div>\\n </div>\\n\\n <!-- Footer -->\\n<div id=\"footerwrap\">\\n <div class=\"container\">\\n <div class=\"row\">\\n <div class=\"col-md-6\">\\n <h4>Sobre</h4>\\n <div class=\"hline-w\"></div>\\n <p>\\n Website e Blog de Gileno Filho, escrevo sobre: Desenvolvimento,\\n Python, Django, Ciência de Dados, Engenharia de Avaliações,\\n Inteligência Artificial e Design Minimalista.\\n</p>\\n </div>\\n <div class=\"col-md-6\">\\n <h4>Social</h4>\\n <div class=\"hline-w\"></div>\\n <p>\\n <a class=\"white-text\" href=\"https://github.com/gileno\" title=\"GitHub\">\\n <i class=\"fa fa-github\"></i>\\n </a>\\n <a class=\"white-text\" href=\"https://twitter.com/gilenofilho\" title=\"Twitter\">\\n <i class=\"fa fa-twitter\"></i>\\n </a>\\n <a class=\"white-text\" href=\"https://www.facebook.com/gilenofilho\" title=\"Facebook\">\\n <i class=\"fa fa-facebook\"></i>\\n </a>\\n <a class=\"white-text\" href=\"mailto:[email protected]\" title=\"E-mail\">\\n <i class=\"fa fa-envelope\"></i>\\n </a>\\n </p>\\n </div>\\n </div>\\n </div>\\n</div>\\n <!-- Scripts -->\\n <script src=\"https://code.jquery.com/jquery-2.2.4.min.js\" integrity=\"sha256-BbhdlvQf/xTY9gja0Dq3HiwQF8LaCRTXxZKRutelT44=\" crossorigin=\"anonymous\"></script>\\n <script src=\"/theme/js/bootstrap.min.js\"></script>\\n </main>\\n </body>\\n</html>'" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "response.text" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "soup = BeautifulSoup(response.text, 'html.parser')" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<title>Gileno Filho</title>" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "soup.find('title')" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true }, "outputs": [], "source": [ "link = soup.find('h3', attrs={'class': 'ctitle'})" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<h3 class=\"ctitle\">Bibliotecas Python para carregar Dataset's</h3>" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "link" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": true }, "outputs": [], "source": [ "links = soup.find_all('h3', attrs={'class': 'ctitle'})" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "<h3 class=\"ctitle\">Bibliotecas Python para carregar Dataset's</h3>\n", "<h3 class=\"ctitle\">Como funciona o ORM do Django</h3>\n", "<h3 class=\"ctitle\">Curso Gratuito de Django com Python 3</h3>\n", "<h3 class=\"ctitle\">Programação, felicidade, mercado e outras coisas...</h3>\n", "<h3 class=\"ctitle\">Usando o Scrapy e o Rethinkdb para capturar e armazenar dados imobiliários - Parte III</h3>\n" ] } ], "source": [ "for l in links:\n", " print(l)" ] }, { "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": 2 }

gpl-3.0

bjshaw/phys202-2015-work

assignments/midterm/ProjectEuler52.ipynb

1

4018

{ "cells": [ { "cell_type": "markdown", "metadata": { "nbgrader": {} }, "source": [ "# Project Euler: Problem 52" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": {} }, "source": [ "https://projecteuler.net/problem=52\n", "\n", "It can be seen that the number, $125874$, and its double, $251748$, contain exactly the same digits, but in a different order.\n", "\n", "Find the smallest positive integer, $x$, such that $2x$, $3x$, $4x$, $5x$, and $6x$, contain the same digits." ] }, { "cell_type": "markdown", "metadata": { "nbgrader": {} }, "source": [ "First, write a function `same_digits(x,y)` that returns `True` if two integers `x` and `y` have the exact same set of digits and multiplicities and `False` if they have different digits." ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "collapsed": false, "nbgrader": { "checksum": "aad5ed41801af39fc06f00c8d275a010", "solution": true } }, "outputs": [], "source": [ "def same_digits(x, y):\n", " \"\"\"Do the integers x and y have the same digits, regardless of order.\"\"\"\n", " i = 0\n", " if len(x) == len(y):\n", " i += 1\n", " j = 0\n", " for m in x:\n", " for n in y:\n", " if m == n:\n", " j += 1\n", " if i == 1 and j == len(x):\n", " return True\n", " else:\n", " return False" ] }, { "cell_type": "code", "execution_count": 42, "metadata": { "collapsed": false, "deletable": false, "nbgrader": { "checksum": "dd0aff5d565bc794cee175aaa6c0cb3d", "grade": true, "grade_id": "projecteuler52a", "points": 4 } }, "outputs": [], "source": [ "assert same_digits('132', '321')\n", "assert not same_digits('123', '3')\n", "assert not same_digits('456', '0987654321')" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": {} }, "source": [ "Now use the `same_digits` function to solve this Euler problem. As you work on this problem, be careful to debug and test your code on small integers before trying it on the full search." ] }, { "cell_type": "code", "execution_count": 122, "metadata": { "collapsed": false }, "outputs": [], "source": [ "lst = []\n", "x = range(1,5000)\n", "for m in x:\n", " if same_digits(str(2*m),str(3*m)) == True and same_digits(str(3*m),str(4*m)) == True and same_digits(str(4*m),str(5*m)) == True and same_digits(str(5*m),str(6*m)) == True and same_digits(str(m),str(6*m)) == True:\n", " lst.append(m)" ] }, { "cell_type": "code", "execution_count": 123, "metadata": { "collapsed": false, "deletable": false, "nbgrader": { "checksum": "6cff4e8e53b15273846c3aecaea84a3d", "solution": true } }, "outputs": [ { "data": { "text/plain": [ "1001" ] }, "execution_count": 123, "metadata": {}, "output_type": "execute_result" } ], "source": [ "min(lst)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "deletable": false, "nbgrader": { "checksum": "dafbda681e8fb50925790dc1d0600750", "grade": true, "grade_id": "projecteuler52b", "points": 6 } }, "outputs": [], "source": [ "assert True # leave this cell to grade the solution" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.4.0" } }, "nbformat": 4, "nbformat_minor": 0 }

mit

nkmk/python-snippets

notebook/sklearn_train_test_split_usage.ipynb

1

15760

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "from sklearn.model_selection import train_test_split" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[0 1 2 3 4 5 6 7 8 9]\n" ] } ], "source": [ "a = np.arange(10)\n", "print(a)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[array([3, 9, 6, 1, 5, 0, 7]), array([2, 8, 4])]\n" ] } ], "source": [ "print(train_test_split(a))" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "<class 'list'>\n" ] } ], "source": [ "print(type(train_test_split(a)))" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2\n" ] } ], "source": [ "print(len(train_test_split(a)))" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "a_train, a_test = train_test_split(a)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[3 4 0 5 7 8 2]\n" ] } ], "source": [ "print(a_train)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[6 1 9]\n" ] } ], "source": [ "print(a_test)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "a_train, a_test = train_test_split(a, test_size=0.6)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[9 1 2 6]\n" ] } ], "source": [ "print(a_train)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[5 7 4 3 0 8]\n" ] } ], "source": [ "print(a_test)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "a_train, a_test = train_test_split(a, test_size=6)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[4 2 1 0]\n" ] } ], "source": [ "print(a_train)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[7 6 3 9 8 5]\n" ] } ], "source": [ "print(a_test)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "a_train, a_test = train_test_split(a, train_size=0.6)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[2 9 6 0 4 3]\n" ] } ], "source": [ "print(a_train)" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[7 8 5 1]\n" ] } ], "source": [ "print(a_test)" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [], "source": [ "a_train, a_test = train_test_split(a, train_size=6)" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[9 3 0 8 7 1]\n" ] } ], "source": [ "print(a_train)" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[5 6 4 2]\n" ] } ], "source": [ "print(a_test)" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [], "source": [ "a_train, a_test = train_test_split(a, train_size=0.25)" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[1 2]\n" ] } ], "source": [ "print(a_train)" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[0 8 4 7 5 6 3 9]\n" ] } ], "source": [ "print(a_test)" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [], "source": [ "a_train, a_test = train_test_split(a, test_size=0.3, train_size=0.4)" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[3 0 4 9]\n" ] } ], "source": [ "print(a_train)" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[7 2 8]\n" ] } ], "source": [ "print(a_test)" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [], "source": [ "a_train, a_test = train_test_split(a, test_size=3, train_size=4)" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[9 7 0 4]\n" ] } ], "source": [ "print(a_train)" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[3 8 5]\n" ] } ], "source": [ "print(a_test)" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [], "source": [ "# a_train, a_test = train_test_split(a, test_size=0.8, train_size=0.7)\n", "# ValueError: The sum of test_size and train_size = 1.500000, should be smaller than 1.0. Reduce test_size and/or train_size." ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [], "source": [ "# a_train, a_test = train_test_split(a, test_size=8, train_size=7)\n", "# ValueError: The sum of train_size and test_size = 15, should be smaller than the number of samples 10. Reduce test_size and/or train_size." ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [], "source": [ "a_train, a_test = train_test_split(a, shuffle=False)" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[0 1 2 3 4 5 6]\n" ] } ], "source": [ "print(a_train)" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[7 8 9]\n" ] } ], "source": [ "print(a_test)" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [], "source": [ "a_train, a_test = train_test_split(a, random_state=0)" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[9 1 6 7 3 0 5]\n" ] } ], "source": [ "print(a_train)" ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[2 8 4]\n" ] } ], "source": [ "print(a_test)" ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[ 0 10]\n", " [ 1 11]\n", " [ 2 12]\n", " [ 3 13]\n", " [ 4 14]\n", " [ 5 15]\n", " [ 6 16]\n", " [ 7 17]\n", " [ 8 18]\n", " [ 9 19]]\n" ] } ], "source": [ "X = np.arange(20).reshape(2, 10).T\n", "print(X)" ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[0 1 2 3 4 5 6 7 8 9]\n" ] } ], "source": [ "y = np.arange(10)\n", "print(y)" ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [], "source": [ "X_train, X_test, y_train, y_test = train_test_split(X, y)" ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[ 7 17]\n", " [ 3 13]\n", " [ 0 10]\n", " [ 8 18]\n", " [ 6 16]\n", " [ 4 14]\n", " [ 2 12]]\n" ] } ], "source": [ "print(X_train)" ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[ 5 15]\n", " [ 1 11]\n", " [ 9 19]]\n" ] } ], "source": [ "print(X_test)" ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[7 3 0 8 6 4 2]\n" ] } ], "source": [ "print(y_train)" ] }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[5 1 9]\n" ] } ], "source": [ "print(y_test)" ] }, { "cell_type": "code", "execution_count": 45, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ 0 10 20 30 40 50 60 70 80 90]\n" ] } ], "source": [ "z = np.arange(10) * 10\n", "print(z)" ] }, { "cell_type": "code", "execution_count": 46, "metadata": {}, "outputs": [], "source": [ "X_train, X_test, y_train, y_test, z_train, z_test = train_test_split(X, y, z)" ] }, { "cell_type": "code", "execution_count": 47, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[ 6 16]\n", " [ 9 19]\n", " [ 1 11]\n", " [ 2 12]\n", " [ 7 17]\n", " [ 0 10]\n", " [ 3 13]]\n" ] } ], "source": [ "print(X_train)" ] }, { "cell_type": "code", "execution_count": 48, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[ 8 18]\n", " [ 4 14]\n", " [ 5 15]]\n" ] } ], "source": [ "print(X_test)" ] }, { "cell_type": "code", "execution_count": 49, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[6 9 1 2 7 0 3]\n" ] } ], "source": [ "print(y_train)" ] }, { "cell_type": "code", "execution_count": 50, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[8 4 5]\n" ] } ], "source": [ "print(y_test)" ] }, { "cell_type": "code", "execution_count": 51, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[60 90 10 20 70 0 30]\n" ] } ], "source": [ "print(z_train)" ] }, { "cell_type": "code", "execution_count": 52, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[80 40 50]\n" ] } ], "source": [ "print(z_test)" ] }, { "cell_type": "code", "execution_count": 53, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[0 1 2 3 4 5 6 7]\n" ] } ], "source": [ "y_mismatch = np.arange(8)\n", "print(y_mismatch)" ] }, { "cell_type": "code", "execution_count": 54, "metadata": {}, "outputs": [], "source": [ "# X_train, X_test, y_train, y_test = train_test_split(X, y_mismatch)\n", "# ValueError: Found input variables with inconsistent numbers of samples: [10, 8]" ] }, { "cell_type": "code", "execution_count": 55, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[0 0 0 0 0 1 1 1 1 1]\n" ] } ], "source": [ "y = np.array([0] * 5 + [1] * 5)\n", "print(y)" ] }, { "cell_type": "code", "execution_count": 56, "metadata": {}, "outputs": [], "source": [ "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=100)" ] }, { "cell_type": "code", "execution_count": 57, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[0 1 0 0 0 0 1 1]\n" ] } ], "source": [ "print(y_train)" ] }, { "cell_type": "code", "execution_count": 58, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[1 1]\n" ] } ], "source": [ "print(y_test)" ] }, { "cell_type": "code", "execution_count": 59, "metadata": {}, "outputs": [], "source": [ "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=100,\n", " stratify=y)" ] }, { "cell_type": "code", "execution_count": 60, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[1 1 0 0 0 1 1 0]\n" ] } ], "source": [ "print(y_train)" ] }, { "cell_type": "code", "execution_count": 61, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[1 0]\n" ] } ], "source": [ "print(y_test)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.7" } }, "nbformat": 4, "nbformat_minor": 2 }

mit

dk14/machine-learning-exercises

MNIST.ipynb

1

20507

{ "cells": [ { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/plain": [ "\u001b[32mimport \u001b[39m\u001b[36m$ivy.$ \n", "\u001b[39m\n", "\u001b[32mimport \u001b[39m\u001b[36m$ivy.$ \n", "\n", "\u001b[39m\n", "\u001b[32mimport \u001b[39m\u001b[36morg.nd4j.linalg.activations.Activation\n", "\u001b[39m\n", "\u001b[32mimport \u001b[39m\u001b[36morg.nd4j.linalg.dataset.api.iterator.DataSetIterator\n", "\u001b[39m\n", "\u001b[32mimport \u001b[39m\u001b[36morg.deeplearning4j.datasets.iterator.impl.MnistDataSetIterator\n", "\u001b[39m\n", "\u001b[32mimport \u001b[39m\u001b[36morg.deeplearning4j.eval.Evaluation\n", "\u001b[39m\n", "\u001b[32mimport \u001b[39m\u001b[36morg.deeplearning4j.nn.api.OptimizationAlgorithm\n", "\u001b[39m\n", "\u001b[32mimport \u001b[39m\u001b[36morg.deeplearning4j.nn.conf.MultiLayerConfiguration\n", "\u001b[39m\n", "\u001b[32mimport \u001b[39m\u001b[36morg.deeplearning4j.nn.conf.NeuralNetConfiguration\n", "\u001b[39m\n", "\u001b[32mimport \u001b[39m\u001b[36morg.deeplearning4j.nn.conf.Updater\n", "\u001b[39m\n", "\u001b[32mimport \u001b[39m\u001b[36morg.deeplearning4j.nn.conf.layers.DenseLayer\n", "\u001b[39m\n", "\u001b[32mimport \u001b[39m\u001b[36morg.deeplearning4j.nn.conf.layers.OutputLayer\n", "\u001b[39m\n", "\u001b[32mimport \u001b[39m\u001b[36morg.deeplearning4j.nn.multilayer.MultiLayerNetwork\n", "\u001b[39m\n", "\u001b[32mimport \u001b[39m\u001b[36morg.deeplearning4j.nn.weights.WeightInit\n", "\u001b[39m\n", "\u001b[32mimport \u001b[39m\u001b[36morg.deeplearning4j.optimize.listeners.ScoreIterationListener\n", "\u001b[39m\n", "\u001b[32mimport \u001b[39m\u001b[36morg.nd4j.linalg.api.ndarray.INDArray\n", "\u001b[39m\n", "\u001b[32mimport \u001b[39m\u001b[36morg.nd4j.linalg.dataset.DataSet\n", "\u001b[39m\n", "\u001b[32mimport \u001b[39m\u001b[36morg.nd4j.linalg.lossfunctions.LossFunctions.LossFunction\n", "\u001b[39m\n", "\u001b[32mimport \u001b[39m\u001b[36morg.slf4j.Logger\n", "\u001b[39m\n", "\u001b[32mimport \u001b[39m\u001b[36morg.slf4j.LoggerFactory\u001b[39m" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import $ivy.`org.deeplearning4j:deeplearning4j-core:0.7.2`\n", "import $ivy.`org.nd4j:nd4j-native-platform:0.7.2`\n", "\n", "import org.nd4j.linalg.activations.Activation\n", "import org.nd4j.linalg.dataset.api.iterator.DataSetIterator\n", "import org.deeplearning4j.datasets.iterator.impl.MnistDataSetIterator\n", "import org.deeplearning4j.eval.Evaluation\n", "import org.deeplearning4j.nn.api.OptimizationAlgorithm\n", "import org.deeplearning4j.nn.conf.MultiLayerConfiguration\n", "import org.deeplearning4j.nn.conf.NeuralNetConfiguration\n", "import org.deeplearning4j.nn.conf.Updater\n", "import org.deeplearning4j.nn.conf.layers.DenseLayer\n", "import org.deeplearning4j.nn.conf.layers.OutputLayer\n", "import org.deeplearning4j.nn.multilayer.MultiLayerNetwork\n", "import org.deeplearning4j.nn.weights.WeightInit\n", "import org.deeplearning4j.optimize.listeners.ScoreIterationListener\n", "import org.nd4j.linalg.api.ndarray.INDArray\n", "import org.nd4j.linalg.dataset.DataSet\n", "import org.nd4j.linalg.lossfunctions.LossFunctions.LossFunction\n", "import org.slf4j.Logger\n", "import org.slf4j.LoggerFactory" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "SLF4J: Failed to load class \"org.slf4j.impl.StaticLoggerBinder\".\n", "SLF4J: Defaulting to no-operation (NOP) logger implementation\n", "SLF4J: See http://www.slf4j.org/codes.html#StaticLoggerBinder for further details.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "\n" ] }, { "data": { "text/plain": [ "\u001b[36mnumRows\u001b[39m: \u001b[32mInt\u001b[39m = \u001b[32m28\u001b[39m\n", "\u001b[36mnumColumns\u001b[39m: \u001b[32mInt\u001b[39m = \u001b[32m28\u001b[39m\n", "\u001b[36moutputNum\u001b[39m: \u001b[32mInt\u001b[39m = \u001b[32m10\u001b[39m\n", "\u001b[36mbatchSize\u001b[39m: \u001b[32mInt\u001b[39m = \u001b[32m64\u001b[39m\n", "\u001b[36mrngSeed\u001b[39m: \u001b[32mInt\u001b[39m = \u001b[32m123\u001b[39m\n", "\u001b[36mnumEpochs\u001b[39m: \u001b[32mInt\u001b[39m = \u001b[32m15\u001b[39m\n", "\u001b[36mrate\u001b[39m: \u001b[32mDouble\u001b[39m = \u001b[32m0.0015\u001b[39m\n", "\u001b[36mmnistTrain\u001b[39m: \u001b[32mMnistDataSetIterator\u001b[39m = org.deeplearning4j.datasets.iterator.impl.MnistDataSetIterator@6328fce2\n", "\u001b[36mmnistTest\u001b[39m: \u001b[32mMnistDataSetIterator\u001b[39m = org.deeplearning4j.datasets.iterator.impl.MnistDataSetIterator@69fa20b6" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ " //number of rows and columns in the input pictures\n", "val numRows = 28;\n", "val numColumns = 28;\n", "val outputNum = 10; // number of output classes\n", "val batchSize = 64; // batch size for each epoch\n", "val rngSeed = 123; // random number seed for reproducibility\n", "val numEpochs = 15; // number of epochs to perform\n", "val rate = 0.0015; // learning rate\n", "\n", " //Get the DataSetIterators:\n", "val mnistTrain = new MnistDataSetIterator(batchSize, true, rngSeed);\n", "val mnistTest = new MnistDataSetIterator(batchSize, false, rngSeed);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Create MLP model:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "\u001b[36mbuilder\u001b[39m: \u001b[32mNeuralNetConfiguration\u001b[39m.\u001b[32mBuilder\u001b[39m = NeuralNetConfiguration.Builder(activationFn=relu, weightInit=XAVIER, biasInit=0.0, dist=null, learningRate=0.0015, biasLearningRate=NaN, learningRateSchedule=null, lrScoreBasedDecay=0.0, l1=NaN, l2=7.5E-6, dropOut=0.0, updater=NESTEROVS, momentum=0.98, momentumSchedule=null, epsilon=NaN, rho=NaN, rmsDecay=NaN, adamMeanDecay=NaN, adamVarDecay=NaN, layer=null, leakyreluAlpha=0.01, miniBatch=true, numIterations=1, maxNumLineSearchIterations=5, seed=123, useRegularization=true, optimizationAlgo=STOCHASTIC_GRADIENT_DESCENT, stepFunction=null, useDropConnect=false, minimize=true, gradientNormalization=None, gradientNormalizationThreshold=1.0, learningRatePolicy=None, lrPolicyDecayRate=NaN, lrPolicySteps=NaN, lrPolicyPower=NaN, pretrain=false, convolutionMode=Truncate)\n", "\u001b[36mres2_1\u001b[39m: \u001b[32mNeuralNetConfiguration\u001b[39m.\u001b[32mBuilder\u001b[39m = NeuralNetConfiguration.Builder(activationFn=relu, weightInit=XAVIER, biasInit=0.0, dist=null, learningRate=0.0015, biasLearningRate=NaN, learningRateSchedule=null, lrScoreBasedDecay=0.0, l1=NaN, l2=7.5E-6, dropOut=0.0, updater=NESTEROVS, momentum=0.98, momentumSchedule=null, epsilon=NaN, rho=NaN, rmsDecay=NaN, adamMeanDecay=NaN, adamVarDecay=NaN, layer=null, leakyreluAlpha=0.01, miniBatch=true, numIterations=1, maxNumLineSearchIterations=5, seed=123, useRegularization=true, optimizationAlgo=STOCHASTIC_GRADIENT_DESCENT, stepFunction=null, useDropConnect=false, minimize=true, gradientNormalization=None, gradientNormalizationThreshold=1.0, learningRatePolicy=None, lrPolicyDecayRate=NaN, lrPolicySteps=NaN, lrPolicyPower=NaN, pretrain=false, convolutionMode=Truncate)\n", "\u001b[36mlstBuilder\u001b[39m: \u001b[32mNeuralNetConfiguration\u001b[39m.\u001b[32mListBuilder\u001b[39m = MultiLayerConfiguration.Builder(confs=[], dampingFactor=100.0, inputPreProcessors={}, pretrain=false, backprop=true, backpropType=Standard, tbpttFwdLength=20, tbpttBackLength=20, inputType=null, cnnInputSize=null)\n", "\u001b[36mres2_3\u001b[39m: \u001b[32mNeuralNetConfiguration\u001b[39m.\u001b[32mListBuilder\u001b[39m = MultiLayerConfiguration.Builder(confs=[], dampingFactor=100.0, inputPreProcessors={}, pretrain=false, backprop=true, backpropType=Standard, tbpttFwdLength=20, tbpttBackLength=20, inputType=null, cnnInputSize=null)\n", "\u001b[36mres2_4\u001b[39m: \u001b[32mNeuralNetConfiguration\u001b[39m.\u001b[32mListBuilder\u001b[39m = MultiLayerConfiguration.Builder(confs=[], dampingFactor=100.0, inputPreProcessors={}, pretrain=false, backprop=true, backpropType=Standard, tbpttFwdLength=20, tbpttBackLength=20, inputType=null, cnnInputSize=null)\n", "\u001b[36mres2_5\u001b[39m: \u001b[32mNeuralNetConfiguration\u001b[39m.\u001b[32mListBuilder\u001b[39m = MultiLayerConfiguration.Builder(confs=[], dampingFactor=100.0, inputPreProcessors={}, pretrain=false, backprop=true, backpropType=Standard, tbpttFwdLength=20, tbpttBackLength=20, inputType=null, cnnInputSize=null)\n", "\u001b[36mres2_6\u001b[39m: \u001b[32mNeuralNetConfiguration\u001b[39m.\u001b[32mListBuilder\u001b[39m = MultiLayerConfiguration.Builder(confs=[], dampingFactor=100.0, inputPreProcessors={}, pretrain=false, backprop=true, backpropType=Standard, tbpttFwdLength=20, tbpttBackLength=20, inputType=null, cnnInputSize=null)\n", "\u001b[36mres2_7\u001b[39m: \u001b[32mNeuralNetConfiguration\u001b[39m.\u001b[32mListBuilder\u001b[39m = MultiLayerConfiguration.Builder(confs=[], dampingFactor=100.0, inputPreProcessors={}, pretrain=false, backprop=true, backpropType=Standard, tbpttFwdLength=20, tbpttBackLength=20, inputType=null, cnnInputSize=null)\n", "\u001b[36mconf\u001b[39m: \u001b[32mMultiLayerConfiguration\u001b[39m = {\n", " \"backprop\" : true,\n", " \"backpropType\" : \"Standard\",\n", " \"confs\" : [ {\n", " \"iterationCount\" : 0,\n", " \"l1ByParam\" : {\n", " \"b\" : 0.0,\n", " \"W\" : 0.0\n", " },\n", " \"l2ByParam\" : {\n", " \"b\" : 0.0,\n", " \"W\" : 7.5E-6\n", "\u001b[33m...\u001b[39m\n", "\u001b[36mmodel\u001b[39m: \u001b[32mMultiLayerNetwork\u001b[39m = org.deeplearning4j.nn.multilayer.MultiLayerNetwork@4829e0b4" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "val builder = new NeuralNetConfiguration.Builder()\n", "\n", "builder\n", " .seed(rngSeed) //include a random seed for reproducibility\n", " .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT) // use stochastic gradient descent as an optimization algorithm\n", " .iterations(1)\n", " .activation(Activation.RELU)\n", " .weightInit(WeightInit.XAVIER)\n", " .learningRate(rate) //specify the learning rate\n", " .updater(Updater.NESTEROVS).momentum(0.98) //specify the rate of change of the learning rate.\n", " .regularization(true).l2(rate * 0.005) // regularize learning model\n", "\n", "val lstBuilder = builder.list()\n", "\n", "lstBuilder.layer(0, new DenseLayer.Builder().nIn(numRows * numColumns).nOut(500).build())\n", "lstBuilder.layer(1, new DenseLayer.Builder().nIn(500).nOut(100).build())\n", "lstBuilder.layer(2, new OutputLayer.Builder(LossFunction.NEGATIVELOGLIKELIHOOD) \n", " .activation(Activation.SOFTMAX)\n", " .nIn(100)\n", " .nOut(outputNum)\n", " .build())\n", "\n", "lstBuilder.pretrain(false)\n", "lstBuilder.backprop(true)\n", "\n", "val conf = lstBuilder.build()\n", "\n", "val model = new MultiLayerNetwork(conf)\n", "model.init()\n", "model.setListeners(new ScoreIterationListener(5)) //print the score with every iteration" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Train model:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Train model....\n", "Epoch 0\n", "Epoch 1\n", "Epoch 2\n", "Epoch 3\n", "Epoch 4\n", "Epoch 5\n", "Epoch 6\n", "Epoch 7\n", "Epoch 8\n", "Epoch 9\n", "Epoch 10\n", "Epoch 11\n", "Epoch 12\n", "Epoch 13\n", "Epoch 14\n", "Epoch 15\n" ] } ], "source": [ "println(\"Train model....\");\n", "(0 to numEpochs).foreach{i =>\n", " println(\"Epoch \" + i)\n", " model.fit(mnistTrain)\n", "}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Evaluate model:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Examples labeled as 0 classified by model as 0: 971 times\n", "Examples labeled as 0 classified by model as 2: 1 times\n", "Examples labeled as 0 classified by model as 3: 1 times\n", "Examples labeled as 0 classified by model as 6: 1 times\n", "Examples labeled as 0 classified by model as 7: 1 times\n", "Examples labeled as 0 classified by model as 8: 4 times\n", "Examples labeled as 0 classified by model as 9: 1 times\n", "Examples labeled as 1 classified by model as 1: 1128 times\n", "Examples labeled as 1 classified by model as 2: 2 times\n", "Examples labeled as 1 classified by model as 3: 1 times\n", "Examples labeled as 1 classified by model as 5: 1 times\n", "Examples labeled as 1 classified by model as 6: 2 times\n", "Examples labeled as 1 classified by model as 8: 1 times\n", "Examples labeled as 2 classified by model as 0: 2 times\n", "Examples labeled as 2 classified by model as 1: 2 times\n", "Examples labeled as 2 classified by model as 2: 1015 times\n", "Examples labeled as 2 classified by model as 3: 1 times\n", "Examples labeled as 2 classified by model as 4: 3 times\n", "Examples labeled as 2 classified by model as 6: 3 times\n", "Examples labeled as 2 classified by model as 7: 2 times\n", "Examples labeled as 2 classified by model as 8: 4 times\n", "Examples labeled as 3 classified by model as 0: 1 times\n", "Examples labeled as 3 classified by model as 2: 4 times\n", "Examples labeled as 3 classified by model as 3: 990 times\n", "Examples labeled as 3 classified by model as 5: 3 times\n", "Examples labeled as 3 classified by model as 7: 4 times\n", "Examples labeled as 3 classified by model as 8: 3 times\n", "Examples labeled as 3 classified by model as 9: 5 times\n", "Examples labeled as 4 classified by model as 2: 3 times\n", "Examples labeled as 4 classified by model as 4: 968 times\n", "Examples labeled as 4 classified by model as 5: 1 times\n", "Examples labeled as 4 classified by model as 6: 2 times\n", "Examples labeled as 4 classified by model as 9: 8 times\n", "Examples labeled as 5 classified by model as 0: 2 times\n", "Examples labeled as 5 classified by model as 3: 5 times\n", "Examples labeled as 5 classified by model as 4: 2 times\n", "Examples labeled as 5 classified by model as 5: 875 times\n", "Examples labeled as 5 classified by model as 6: 4 times\n", "Examples labeled as 5 classified by model as 7: 1 times\n", "Examples labeled as 5 classified by model as 8: 2 times\n", "Examples labeled as 5 classified by model as 9: 1 times\n", "Examples labeled as 6 classified by model as 0: 3 times\n", "Examples labeled as 6 classified by model as 1: 3 times\n", "Examples labeled as 6 classified by model as 2: 2 times\n", "Examples labeled as 6 classified by model as 3: 1 times\n", "Examples labeled as 6 classified by model as 4: 9 times\n", "Examples labeled as 6 classified by model as 5: 4 times\n", "Examples labeled as 6 classified by model as 6: 936 times\n", "Examples labeled as 7 classified by model as 0: 1 times\n", "Examples labeled as 7 classified by model as 1: 6 times\n", "Examples labeled as 7 classified by model as 2: 7 times\n", "Examples labeled as 7 classified by model as 3: 1 times\n", "Examples labeled as 7 classified by model as 4: 3 times\n", "Examples labeled as 7 classified by model as 7: 1000 times\n", "Examples labeled as 7 classified by model as 8: 6 times\n", "Examples labeled as 7 classified by model as 9: 4 times\n", "Examples labeled as 8 classified by model as 0: 4 times\n", "Examples labeled as 8 classified by model as 2: 4 times\n", "Examples labeled as 8 classified by model as 3: 3 times\n", "Examples labeled as 8 classified by model as 4: 3 times\n", "Examples labeled as 8 classified by model as 5: 5 times\n", "Examples labeled as 8 classified by model as 6: 2 times\n", "Examples labeled as 8 classified by model as 7: 3 times\n", "Examples labeled as 8 classified by model as 8: 948 times\n", "Examples labeled as 8 classified by model as 9: 2 times\n", "Examples labeled as 9 classified by model as 0: 3 times\n", "Examples labeled as 9 classified by model as 1: 2 times\n", "Examples labeled as 9 classified by model as 3: 8 times\n", "Examples labeled as 9 classified by model as 4: 10 times\n", "Examples labeled as 9 classified by model as 5: 4 times\n", "Examples labeled as 9 classified by model as 7: 3 times\n", "Examples labeled as 9 classified by model as 9: 979 times\n", "\n", "\n", "==========================Scores========================================\n", " Accuracy: 0.981\n", " Precision: 0.9809\n", " Recall: 0.9808\n", " F1 Score: 0.9809\n", "========================================================================\n", "****************Example finished********************\n" ] }, { "data": { "text/plain": [ "\u001b[36meval\u001b[39m: \u001b[32mEvaluation\u001b[39m = org.deeplearning4j.eval.Evaluation@6c85a02d" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "val eval = new Evaluation(outputNum) //create an evaluation object with 10 possible classes\n", "\n", "while(mnistTest.hasNext()){\n", " val next = mnistTest.next()\n", " val output = model.output(next.getFeatureMatrix()) //get the networks prediction\n", " eval.eval(next.getLabels(), output) //check the prediction against the true class\n", "}\n", "\n", "println(eval.stats());\n", "println(\"****************Example finished********************\");" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Scala", "language": "scala", "name": "scala" }, "language_info": { "codemirror_mode": "text/x-scala", "file_extension": ".scala", "mimetype": "text/x-scala", "name": "scala211", "nbconvert_exporter": "scala", "pygments_lexer": "scala", "version": "2.11.8" }, "latex_envs": { "LaTeX_envs_menu_present": true, "bibliofile": "biblio.bib", "cite_by": "apalike", "current_citInitial": 1, "eqLabelWithNumbers": true, "eqNumInitial": 1, "labels_anchors": false, "latex_user_defs": false, "report_style_numbering": false, "user_envs_cfg": false } }, "nbformat": 4, "nbformat_minor": 2 }

mit

deeplycloudy/MetPy

examples/notebooks/Advanced_Sounding.ipynb

1

152884

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "import numpy as np\n", "\n", "from metpy.cbook import get_test_data\n", "from metpy.calc import get_wind_components, lcl, dry_lapse, parcel_profile\n", "from metpy.plots import SkewT\n", "from metpy.units import units, concatenate\n", "\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Parse the data\n", "p, T, Td, direc, spd = np.loadtxt(get_test_data('may3_sounding.txt'),\n", " usecols=(0, 2, 3, 6, 7), skiprows=4, unpack=True)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Add units to the data arrays\n", "p = p * units.mbar\n", "T = T * units.degC\n", "Td = Td * units.degC\n", "spd = spd * units.knot\n", "direc = direc * units.deg" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Convert wind speed and direction to components\n", "u, v = get_wind_components(spd, direc)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "data": { "image/png": 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qPElqbflShRclau4u4IxEbZUm6cWjnMXfZgvwAPzc6yQdPvCAISfpWEmTJqY9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CVzdfMoq0CXAScHGaNkUH8tzXmTCzWcUEDjxnAS+mBA6E04tpCCvcc4DHLWFToUZrqByBgw1x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"text/plain": [ "<matplotlib.figure.Figure at 0x108b36f28>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Create a new figure. The dimensions here give a good aspect ratio\n", "fig = plt.figure(figsize=(9, 9))\n", "skew = SkewT(fig, rotation=45)\n", "\n", "# Plot the data using normal plotting functions, in this case using\n", "# log scaling in Y, as dictated by the typical meteorological plot\n", "skew.plot(p, T, 'r')\n", "skew.plot(p, Td, 'g')\n", "skew.plot_barbs(p, u, v)\n", "skew.ax.set_ylim(1000, 100)\n", "skew.ax.set_xlim(-40, 60)\n", "\n", "# Calculate LCL height and plot as black dot\n", "l = lcl(p[0], T[0], Td[0])\n", "lcl_temp = dry_lapse(concatenate((p[0], l)), T[0])[-1].to('degC')\n", "skew.plot(l, lcl_temp, 'ko', markerfacecolor='black')\n", "\n", "# Calculate full parcel profile and add to plot as black line\n", "prof = parcel_profile(p, T[0], Td[0]).to('degC')\n", "skew.plot(p, prof, 'k', linewidth=2)\n", "\n", "# Example of coloring area between profiles\n", "skew.ax.fill_betweenx(p, T, prof, where=T>=prof, facecolor='blue', alpha=0.4)\n", "skew.ax.fill_betweenx(p, T, prof, where=T<prof, facecolor='red', alpha=0.4)\n", "\n", "# An example of a slanted line at constant T -- in this case the 0\n", "# isotherm\n", "l = skew.ax.axvline(0, color='c', linestyle='--', linewidth=2)\n", "\n", "# Add the relevant special lines\n", "skew.plot_dry_adiabats()\n", "skew.plot_moist_adiabats()\n", "skew.plot_mixing_lines()\n", "\n", "# Show the plot\n", "plt.show()" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.4.3" } }, "nbformat": 4, "nbformat_minor": 0 }

bsd-3-clause

james-prior/cohpy

20160923-dojo-list-loading-speed.ipynb

1

2020

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "n = 10**7\n", "filename = 'bigdata'" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import random" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "with open(filename, 'w') as f:\n", " for _ in range(n):\n", " print(random.randint(1, n), file=f)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def foo(filename):\n", " return [int(line.strip()) for line in open(filename)]" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1 loop, best of 3: 6.46 s per loop\n" ] } ], "source": [ "%timeit foo(filename)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(10000000, [2937736, 8865497, 9244595], [6419711, 7310417, 7464754])" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "a = foo(filename)\n", "len(a), a[:3], a[-3:]" ] } ], "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

google/starthinker

colabs/sdf_to_bigquery.ipynb

1

9318

{ "license": "Licensed under the Apache License, Version 2.0", "copyright": "Copyright 2020 Google LLC", "nbformat": 4, "nbformat_minor": 0, "metadata": { "colab": { "name": "DV360 SDF To BigQuery", "provenance": [], "collapsed_sections": [], "toc_visible": true }, "kernelspec": { "name": "python3", "display_name": "Python 3" } }, "cells": [ { "cell_type": "markdown", "metadata": { "id": "351157ff-001" }, "source": [ "#DV360 SDF To BigQuery\n", "Download SDF reports into a BigQuery table.\n" ] }, { "cell_type": "markdown", "metadata": { "id": "351157ff-002" }, "source": [ "#License\n", "\n", "Copyright 2020 Google LLC,\n", "\n", "Licensed under the Apache License, Version 2.0 (the \"License\");\n", "you may not use this file except in compliance with the License.\n", "You may obtain a copy of the License at\n", "\n", " https://www.apache.org/licenses/LICENSE-2.0\n", "\n", "Unless required by applicable law or agreed to in writing, software\n", "distributed under the License is distributed on an \"AS IS\" BASIS,\n", "WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", "See the License for the specific language governing permissions and\n", "limitations under the License.\n", "\n" ] }, { "cell_type": "markdown", "metadata": { "id": "351157ff-003" }, "source": [ "#Disclaimer\n", "This is not an officially supported Google product. It is a reference implementation. There is absolutely NO WARRANTY provided for using this code. The code is Apache Licensed and CAN BE fully modified, white labeled, and disassembled by your team.\n", "\n", "This code generated (see starthinker/scripts for possible source):\n", " - **Command**: \"python starthinker_ui/manage.py colab\"\n", " - **Command**: \"python starthinker/tools/colab.py [JSON RECIPE]\"\n", "\n" ] }, { "cell_type": "markdown", "metadata": { "id": "351157ff-004" }, "source": [ "#1. Install Dependencies\n", "First install the libraries needed to execute recipes, this only needs to be done once, then click play.\n" ] }, { "cell_type": "code", "metadata": { "id": "351157ff-005" }, "source": [ "!pip install git+https://github.com/google/starthinker\n" ] }, { "cell_type": "markdown", "metadata": { "id": "351157ff-006" }, "source": [ "#2. Set Configuration\n", "\n", "This code is required to initialize the project. Fill in required fields and press play.\n", "\n", "1. If the recipe uses a Google Cloud Project:\n", " - Set the configuration **project** value to the project identifier from [these instructions](https://github.com/google/starthinker/blob/master/tutorials/cloud_project.md).\n", "\n", "1. If the recipe has **auth** set to **user**:\n", " - If you have user credentials:\n", " - Set the configuration **user** value to your user credentials JSON.\n", " - If you DO NOT have user credentials:\n", " - Set the configuration **client** value to [downloaded client credentials](https://github.com/google/starthinker/blob/master/tutorials/cloud_client_installed.md).\n", "\n", "1. If the recipe has **auth** set to **service**:\n", " - Set the configuration **service** value to [downloaded service credentials](https://github.com/google/starthinker/blob/master/tutorials/cloud_service.md).\n", "\n" ] }, { "cell_type": "code", "metadata": { "id": "351157ff-007" }, "source": [ "from starthinker.util.configuration import Configuration\n", "\n", "\n", "CONFIG = Configuration(\n", " project=\"\",\n", " client={},\n", " service={},\n", " user=\"/content/user.json\",\n", " verbose=True\n", ")\n", "\n" ] }, { "cell_type": "markdown", "metadata": { "id": "351157ff-008" }, "source": [ "#3. Enter DV360 SDF To BigQuery Recipe Parameters\n", " 1. Select your filter types and the filter ideas.\n", " 1. Enter the [file types](https://developers.google.com/bid-manager/v1.1/sdf/download) using commas.\n", " 1. SDF_ will be prefixed to all tables and date appended to daily tables.\n", " 1. File types take the following format: FILE_TYPE_CAMPAIGN, FILE_TYPE_AD_GROUP,...\n", "Modify the values below for your use case, can be done multiple times, then click play.\n" ] }, { "cell_type": "code", "metadata": { "id": "351157ff-009" }, "source": [ "FIELDS = {\n", " 'auth_write':'service', # Credentials used for writing data.\n", " 'partner_id':'', # The sdf file types.\n", " 'file_types':[], # The sdf file types.\n", " 'filter_type':'', # The filter type for the filter ids.\n", " 'filter_ids':[], # Comma separated list of filter ids for the request.\n", " 'dataset':'', # Dataset to be written to in BigQuery.\n", " 'version':'5', # The sdf version to be returned.\n", " 'table_suffix':'', # Optional: Suffix string to put at the end of the table name (Must contain alphanumeric or underscores)\n", " 'time_partitioned_table':False, # Is the end table a time partitioned\n", " 'create_single_day_table':False, # Would you like a separate table for each day? This will result in an extra table each day and the end table with the most up to date SDF.\n", "}\n", "\n", "print(\"Parameters Set To: %s\" % FIELDS)\n" ] }, { "cell_type": "markdown", "metadata": { "id": "351157ff-010" }, "source": [ "#4. Execute DV360 SDF To BigQuery\n", "This does NOT need to be modified unless you are changing the recipe, click play.\n" ] }, { "cell_type": "code", "metadata": { "id": "351157ff-011" }, "source": [ "from starthinker.util.configuration import execute\n", "from starthinker.util.recipe import json_set_fields\n", "\n", "TASKS = [\n", " {\n", " 'dataset':{\n", " 'auth':{'field':{'name':'auth_write','kind':'authentication','order':1,'default':'service','description':'Credentials used for writing data.'}},\n", " 'dataset':{'field':{'name':'dataset','kind':'string','order':6,'default':'','description':'Dataset to be written to in BigQuery.'}}\n", " }\n", " },\n", " {\n", " 'sdf':{\n", " 'auth':'user',\n", " 'version':{'field':{'name':'version','kind':'choice','order':6,'default':'5','description':'The sdf version to be returned.','choices':['SDF_VERSION_5','SDF_VERSION_5_1']}},\n", " 'partner_id':{'field':{'name':'partner_id','kind':'integer','order':1,'description':'The sdf file types.'}},\n", " 'file_types':{'field':{'name':'file_types','kind':'string_list','order':2,'default':[],'description':'The sdf file types.'}},\n", " 'filter_type':{'field':{'name':'filter_type','kind':'choice','order':3,'default':'','description':'The filter type for the filter ids.','choices':['FILTER_TYPE_ADVERTISER_ID','FILTER_TYPE_CAMPAIGN_ID','FILTER_TYPE_INSERTION_ORDER_ID','FILTER_TYPE_MEDIA_PRODUCT_ID','FILTER_TYPE_LINE_ITEM_ID']}},\n", " 'read':{\n", " 'filter_ids':{\n", " 'single_cell':True,\n", " 'values':{'field':{'name':'filter_ids','kind':'integer_list','order':4,'default':[],'description':'Comma separated list of filter ids for the request.'}}\n", " }\n", " },\n", " 'time_partitioned_table':{'field':{'name':'time_partitioned_table','kind':'boolean','order':7,'default':False,'description':'Is the end table a time partitioned'}},\n", " 'create_single_day_table':{'field':{'name':'create_single_day_table','kind':'boolean','order':8,'default':False,'description':'Would you like a separate table for each day? This will result in an extra table each day and the end table with the most up to date SDF.'}},\n", " 'dataset':{'field':{'name':'dataset','kind':'string','order':6,'default':'','description':'Dataset to be written to in BigQuery.'}},\n", " 'table_suffix':{'field':{'name':'table_suffix','kind':'string','order':6,'default':'','description':'Optional: Suffix string to put at the end of the table name (Must contain alphanumeric or underscores)'}}\n", " }\n", " }\n", "]\n", "\n", "json_set_fields(TASKS, FIELDS)\n", "\n", "execute(CONFIG, TASKS, force=True)\n" ] } ] }

apache-2.0

kvn219/elevate

process_data.ipynb

1

8531

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "ExecuteTime": { "end_time": "2016-08-21T19:04:27.650050", "start_time": "2016-08-21T19:04:27.273098" }, "collapsed": false }, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "pd.set_option('precision', 2)\n", "pd.options.mode.chained_assignment = None\n", "pd.set_option('display.max_rows', 500)\n", "pd.set_option('display.max_columns', 500)\n", "pd.set_option('display.width', 1000)\n", "pd.options.display.max_colwidth = 100" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "ExecuteTime": { "end_time": "2016-08-21T19:04:27.656321", "start_time": "2016-08-21T19:04:27.652317" }, "collapsed": true }, "outputs": [], "source": [ "# helper function to clean variables\n", "def strip_spaces(x):\n", " try:\n", " clean_x = x.strip()\n", " return clean_x\n", " except:\n", " return x" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "ExecuteTime": { "end_time": "2016-08-21T19:04:27.705466", "start_time": "2016-08-21T19:04:27.658086" }, "collapsed": false }, "outputs": [], "source": [ "# load data via pandas \n", "df = pd.read_excel(\"./tab9-6.xlsx\" , header=2)\n", "# clean up column names\n", "df.columns = [col.strip() for col in df.columns]\n", "# keep only the data we need for the visualization\n", "df = df.iloc[6:47,:]" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "ExecuteTime": { "end_time": "2016-08-21T19:04:27.713094", "start_time": "2016-08-21T19:04:27.707173" }, "collapsed": false }, "outputs": [], "source": [ "# strip white spaces\n", "df['Highest degree and occupation'] = df['Highest degree and occupation'].apply(lambda x: strip_spaces(x))\n", "\n", "# create some lists to make the data preprocessing easier\n", "rows_to_clean = df[df['Highest degree and occupation'] == 'Postsecondary teacher']['Highest degree and occupation'].index.tolist()\n", "science_categories = ['Biological/life scientist', 'Computer and information scientist', 'Mathematical scientist', 'Physical scientist', 'Psychologist', 'Social scientist', 'Engineering occupations']" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "ExecuteTime": { "end_time": "2016-08-21T19:04:27.720977", "start_time": "2016-08-21T19:04:27.714811" }, "collapsed": true }, "outputs": [], "source": [ "# rename some of the variables with names that arent visualization friendly \n", "for row, replacement_val in zip(rows_to_clean, science_categories):\n", " df.loc[row, 'Highest degree and occupation'] = replacement_val + ' professor'" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "ExecuteTime": { "end_time": "2016-08-21T19:04:27.729858", "start_time": "2016-08-21T19:04:27.722658" }, "collapsed": false }, "outputs": [], "source": [ "# clean up dataframe by dropping rows with missing data\n", "df = df.dropna(subset=['Highest degree and occupation'])\n", "df = df.dropna(axis=1,how='all')\n", "df = df.dropna(axis=0,thresh=5)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "ExecuteTime": { "end_time": "2016-08-21T19:04:27.733202", "start_time": "2016-08-21T19:04:27.731249" }, "collapsed": false, "scrolled": false }, "outputs": [], "source": [ "# breakdown = ['S&E-related occupations', 'Non-S&E occupations']" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "ExecuteTime": { "end_time": "2016-08-21T19:04:27.740212", "start_time": "2016-08-21T19:04:27.734933" }, "collapsed": false }, "outputs": [], "source": [ "# create occupation categories from the data\n", "occupations = []\n", "label = 'HEADER'\n", "for row in df['Highest degree and occupation']:\n", " if row in science_categories:\n", " label = row\n", " occupations.append(label)\n", " else:\n", " occupations.append(label)\n", " \n", "df['occupation'] = occupations" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "ExecuteTime": { "end_time": "2016-08-21T19:04:27.754229", "start_time": "2016-08-21T19:04:27.741609" }, "collapsed": false, "scrolled": false }, "outputs": [], "source": [ "# refacoting notes and assumptions\n", "# * = estimate < 500.\n", "# D = suppressed for data confidentiality reasons. \n", "# S = suppressed for reliability; coefficient of variation exceeds publication standards.\n", "\n", "# refactor * to 500\n", "df = df.replace('*', 500)\n", "# refactor supressed data to 100\n", "df = df.replace('D', 100)\n", "# refactor supressed data to np.nan\n", "df = df.replace('S', np.nan)\n", "# fill missing values as 0\n", "df = df.fillna(0)\n", "# add sequential count to keep track of headers\n", "df[\"sequential_count\"] = df.groupby(\"occupation\").cumcount() + 1" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "ExecuteTime": { "end_time": "2016-08-21T19:04:27.759264", "start_time": "2016-08-21T19:04:27.755567" }, "collapsed": false }, "outputs": [], "source": [ "# rename columns\n", "df.columns = ['Highest degree and occupation',\n", " 'All',\n", " 'Hispanic or Latino',\n", " 'American Indian or Alaska Native',\n", " 'Asian',\n", " 'Black or African American',\n", " 'Native Hawaiian or Other Pacific Islander',\n", " 'White',\n", " 'More than one race',\n", " 'occupation',\n", " 'sequential_count']" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "ExecuteTime": { "end_time": "2016-08-21T19:04:27.770321", "start_time": "2016-08-21T19:04:27.760639" }, "collapsed": false }, "outputs": [], "source": [ "# remove subset header -- it contains aggerated infomation per variable\n", "viz_df = df[df['sequential_count'] != 1].drop(['sequential_count'], axis=1)\n", "# restructure the data into long format for visualization\n", "viz_df = pd.melt(viz_df, id_vars=['Highest degree and occupation', 'occupation'], value_name='total')\n", "# save data to tsv\n", "viz_df.to_csv('./temp/data.tsv', sep='\\t', index=False)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "ExecuteTime": { "end_time": "2016-08-21T19:04:27.783095", "start_time": "2016-08-21T19:04:27.771772" }, "collapsed": false }, "outputs": [], "source": [ "# open data data to convert thousands seperator into ints\n", "viz_df = pd.read_csv(\"./temp/data.tsv\", sep='\\t', thousands=\",\")\n", "# rename columns for visualization\n", "viz_df.columns = ['Occupation', 'Category', 'Race', 'Total']\n", "# remove whitespace from Occupation column\n", "viz_df['Occupation'] = viz_df['Occupation'].apply(lambda x: x.strip())\n", "# rename variables for visualization\n", "viz_df['Occupation'] = viz_df['Occupation'].replace('Psychologist', 'Psychologists')\n", "viz_df['Occupation'] = viz_df['Occupation'].replace('Mathematical scientist', 'Mathematical scientists')\n", "# save data into project folder\n", "viz_df.to_csv(\"./project/data.tsv\", sep='\\t', index=False, encoding='utf-8')" ] } ], "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" }, "latex_envs": { "bibliofile": "biblio.bib", "cite_by": "apalike", "current_citInitial": 1, "eqLabelWithNumbers": true, "eqNumInitial": 0 } }, "nbformat": 4, "nbformat_minor": 0 }

mit

guozheng/data-science-ml-basics

viz-intro.ipynb

1

629412

{ "cells": [ { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "from mpl_toolkits import mplot3d\n", "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Matplotlib 3D Example" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<mpl_toolkits.mplot3d.art3d.Path3DCollection at 0x118fae4e0>" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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+9C8sWrSYj370wwCceeYLeOc731PzGPOSdEsxnb1iMwX3xZFt8wsasqgkzVKe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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x119497320>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = plt.figure()\n", "ax = plt.axes(projection='3d')\n", "data_z = 15 * np.random.random(200)\n", "data_x = np.sin(data_z) + 0.1 * np.random.randn(200)\n", "data_y = np.cos(data_z) + 0.1 * np.random.randn(200)\n", "ax.scatter3D(data_x, data_y, data_z, c=data_z, cmap=\"Reds\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Seaborn Examples" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<matplotlib.legend.Legend at 0x1195ba390>" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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Rq2bkc06nszz10E5WXzKHBYurTvm8rT6541DT3DLKL6C/92x8t6ck1F0uVwTA6XRakYX7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\ns8rhROVwIOoNhA4dnFGT4itqitkx6uP5fhdlOvUEHm/xnA8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JXwjrBtmDV5jNVH/7ewCI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9r2w4VGI6HTalg+N5nnbj3Bn02T4HPqRRWdK7iVPDVzJiPFRohRx8olgf/BmjGWBTPQlMbHABPw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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x1195d20f0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "rng = np.random.RandomState(0)\n", "x = np.linspace(0, 10, 500)\n", "y = np.cumsum(rng.randn(500, 6), 0)\n", "plt.plot(x, y)\n", "plt.legend('123456', ncol=2, loc='upper left')" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x119727208>" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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zLpfp8NZqqrRloFLSPntGs2Qai2gy5hRmkpPl5eyldh3ETsWEJgOVkvaeqMdlWSnXRTTM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\nTQZKKaXQZKCUUgpNBkoppYD/DyH/MgekvFfBAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x118f4d550>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "data = np.random.multivariate_normal([0, 0], [[5, 2], [2, 2]], size=2000)\n", "df = pd.DataFrame(data, columns=['x', 'y'])\n", "sns.distplot(df['x'])" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<seaborn.axisgrid.JointGrid at 0x118fbc8d0>" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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8ylUmhEg0nlkXa6+2YUOPj8eXPrEA2w+exUGnC15/ZlUtMtFQawOgfdg23Q7C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\nYacLHi8PexmHhQ7pwrwX20HVGwiZcAtsc7WtdCo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"text/plain": [ "<matplotlib.figure.Figure at 0x1196a6a20>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.jointplot('x', 'y', df)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<seaborn.axisgrid.PairGrid at 0x119d3afd0>" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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PreetCkLK0MpT+kXvKR5svF9T8x5tAVp45Ajjx46FrGej21B+YwN93/rnO+3S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p2S+nqK7Q14Rx2sTExgPnaM8nPn2fC4WezuYcUxRkn9IYoPN7K2sIjloRO4rlzb1H/G+N0dqPHk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IkTFSwRBCCCGEEEJkjFQwhBBCCCGEEBkjFQwhhBBCCCFExkgFQwghhBBCCJExUsEQQgghhBBC\nZIxUMIQQQgghhBAZIxUMIYQQQgghRMZIBUMIIYQQQgiRMVLBEEIIIYQQQmSMVDCEEEIIIYQQGSMV\nDCGEEEIIIUTGSAVDCCGEEEIIkTFSwRBCCCGEEEJkjFQwhBBCCCGEEBljXu8LSJffHwi53Z71voyc\n4XDYkO/jnmTfh9NZZFiHywFgbGwmlM52m+m3lM+yehshzWbDRkg7G+EaYe2vM9fSrN7n/+SXX1nW\ncb/+3LHVXViGbJQ0l45c+izrmWa3ig3Tg2E2m9b7EnKKfB+xNvL3sZGvPZ58FrFSG+H73gjXCBvn\nOrNlM31++Sxio9owFQwhhBBCCCFE7pMKhhBCCCGEECJjpIIhhBBCCCGEyBipYAghhBBCCCEyRioY\nQgghhBBCiIyRCsYmZzSuLBLbSvfL9DHE1mE2r644kvQmtpql0rzkCSHEesnaOhiKoliArwNNQB7w\nB6qq/ijq/f8N+BQwdvelZ1RVVbN1PVtNoK+H6RMnmL2uYm9RKD56FFNDc9b2y/QxxNbhv3yeqa4u\nPH392BrqKensxNx2IO39Jb2JrSZZmg/09TB98iR3jBCYmcXT349daZU8IYRYc9lcaO+3gAlVVZ9W\nFKUUOA/8KOr9g8DHVFU9m8Vr2JICfT3c+vKXCXq9AMz39jFx/Djbn3su5U1mpftl+hhi6/BfPs/t\nP//KvfTS34/79Bmanv1MWpUMSW9iq0mW5pue/Qy3//wrODoO4j5z9t77ff2SJ4QQay6bQ6T+AfjC\n3b8NgD/u/YPA84qivKkoyvNZvI4tZ/rkSe3mEhH0epk+dTIr+2X6GGLrmOrq0k0vU11dae0v6U1s\nNcnS/FRXF0arleDiouQJIcS6y1oPhqqqswCKohQB3wX+U9wmfwf8OTANfF9RlA+qqvpPqY7pdBZl\n41I3rGTfR796Tff1OVWlJcV3uNL9Mn2Mlcq19OFw2NJeuTTXrn01lvNZ+vv6dV/39PXTmsZxsp3e\nNtPvko7lpNls2Ajf93pfY7I07+nrx9bUyMLomO77NAdDJQAAIABJREFUa1EGr4dkaXa1v9N6/87R\nculaVmszfRaRWjaHSKEoSj3wfeArqqp+O+p1A/AnqqpO3f33T4D7gZQVjLGxmSxebe4xGg0EgyHd\n95zOoqTfh71FYb63D6PVirXUgXfSTdDrpVBRUn6Hkf3iLbVfpo+xEsm+j/UszNxuT1rbpfotc0Wq\ntBhN77Ok2tfWUM98f2Ilw9ZQrx0n1f7ZTG/r9btshDSbDRshH2TzGtPNY3alVTfN2xrqmbrwLvaW\nXbp5KptlcK6l2Uz8TrmSFjdCvkhXLn0WqehkXzYneVcCLwKfVVX15bi3i4FLiqLsBuaAY4QnhAtW\nP2m1+OhRQvMe/B4Pi2PjFO9tw2yzUXz4yJL7TRw/HtO9brRal9wv08cQuWM1aTGdfUs6O3GfPpOQ\nXko6O9PaX9Kb2OjSzWORYAgmuz08FEonz7hPn8GUn6/7vuQJIcRaymYPxucBB/AFRVEiczG+ChSq\nqvo/FEX5PPAqsAi8rKrqC1m8lg0jU5NWJ7tOx0ycNVqtlBx7z5L7lXYeIjA/z8LoGPkVTkwFBcu6\nflNDM9ufe47pUyeZVVXsikLx4SMyuXADWk1aTHdfc9sBmp79TEIUKUNRcVr7S3oTG1m6+SQmGILR\nSNmRwwS9iyyMjWNvbdXS/PbnnmO66xTOY4/hn5nF43LFvC+EEGslm3MwPgd8LsX73wS+ma3zb1Sp\nJq060rxBrPQY0ydPMv7mW9rQqqmLlwh6vRgKC9M+N4Qf+hwNzZSl2eUvctNq0uJy9jW3HaCs7QCV\nZiN+fxAA999/O+39Jb2JjSrdfBITDCEYZOLtExitVip+7j0Uf+RXte0ieSEyFMUpeUIIsU5kob0c\nYjQamE0ygW9WVdNaNGmlx4jeL+j1sjA8ot3Q0j13PLmxbVyrSYsr3TdSuVjp/pLexEaSbjo3m414\ndIIhBL1ept69lHKBSskTQoj1IhWMHBIMhrC3KLrv2RUlrZvFSo+RiXOLzWM16WG1aUnSotgK0k3n\nfn8QW0O97na2hnqtYi6EELlEKhg5pvjoUYxWa8xry52g53j0Ucx2u+4xUrU8R85ttFrJr6rEaLVi\ntttxPPyItk2q1rJsWOvzbTXppIeY7dNMi3ppKXpfqzV1KNSl9hdiM4hP52a7nYL6OoqPPqDlTaPR\nQMmRI7p5seRwZ+xrcfl5JT3PmdxfCLF1GUKhDdMaGMqV8GbZFujrWXLSql64t5hoJDt2kFdZwcSp\nLuy7dlG0Zw8zl68wq15LK1KJp6+fwqZG8muqmeg6jb2xEWt5GZOnz2Krq6WkszOtlZZXKvo6IhN/\nU50vRZjadbtDjo3NpJW51iN0X7qRa9JJi6D/e2GAqVNRrx3uBK+XqbPn8LgGwunoYDvmA50Jx0t2\nzGymuXjrGKY259NsNuRSCMtksnGN/svnmTp9GlNhIYHpaTwDA9gaGihoqGO+tx9Pfz92pZUiZRdT\n5y/g6e3D1lCPfdcuRt94A/uOnRS17WHm0mUtPzsO7MN94dKS5X0yy40el2tpVu93+uSXX1nWcb/+\n3LHVXViGbIR8ka5c+izrmWa3Cqlg5LDlrIMRH40Ewi1czc8/Twi49aUvJbwXH6kk2TEcHQe1SYXR\nfzc9+5msPPDFREyJuo5U55MKRvqS/c6pokOlSovJfq/SzkOMv/mW9lr5Qw/GRDeLbNf0259KqGSs\n5BozTSoYayuXHj6SyfQ1RtK5o+Mg7jNntfRe9sDRmH/DvfRvsRfS8+U/xDs5mXLbSFkdvW86eWcl\neS/X0qxUMHJTLn0WqWBkn4w/yWHLGWueLBrJ1MkTzJw+nTRSSTrHCC4uanHVo/+e6upa5idKT0zE\nlOjPkqXzbTWpItckkyotJvu9AvPz2rAOo9VKYH5e/3c9905GrlGIjWb6ZDg9BxcXtfRutFpj/h0R\nSf+Trx3XKhepto2U1dH7pntNkveEEKslFYxNYKloJIsjQ0nfix7nm+wYC6NjWEsdCX97+vozPkci\nWcSUbJ1vq8lEpLJoqX6v6LRiLXWwMDqmu52n3xUzJyPT1yhELoqk8/i8kSqvxJfnqbaNzn+RfZfK\nO5L3hBCZIk9rm8BS0UjyKqoAYibMAhRFRSpJdYz8CifeSXfC39mIYCIRU7JrpRGaUoWWTfZ7RacV\n76SbPGe57na2+jq83sCqrzFd8pAkckEkncfnjVR5xa4o2Hfs0srwVNtG57/IvunkHYngJoTIBKlg\nbBKpIv4UHTpE+UMPUry3DYPFSvHeNsofehCMBtzf+RaBvp6UxzDm5RH0ehP+LunUn5y7WiWdnfoR\nU7J0vq1mOdGhAn09uL/zLXq/+IWYtBIt2e9lKijQhloEvV7MNpv+79p+/6quMV3pfBYh1lLx0aMA\nmPLzY4YzRf87wmi1kudwMHnyBMX79lL2wFGCfn/SbSNldeTfqfJOdN7IKyvNeN4TQmw9Msl7g0oa\nRUon4k/SyduHOph46+2YCXwxx9ixg7wKZzgSVWMj1rLSNYkiFejrYfHKJeYHBrVoQwW1NeTt2Ztq\nkqFM8l6GdKJDpTvZU//3qsXaUH83ctm9c4Qmx5k69w6efhe2+jpK2u9PGkUq3QhW6X7eFUxclUne\nayiXJoAmk41rDPT1MN11CgMhAjOzeFwubA31FNTXMd/Xj6ffhX3nTkKLi4y/fQKCdxektFopf/wx\nfBMT2Joa8dy6zcLoGPaWXTjaD+C+eJnZa9eWzDsJecNopPyBoxjy8pi9eTOtvJdraVYmeeemXPos\nMsk7+8zrfQEic0wNzTgamimLi/iTdPL2woI2YXv61EkcDc26x7D/3Ae0v0s++GTWhylNnzzJ2Isv\nYrbbsTU1MnXxEhMnTuJ8YhbHGkUQ2uySpZVoqSZ7Rv8OyX+v9+H45d+IPUdDM2UHOqm2mmKGRa30\nGtOV7mcRYq1F0nkkUpvz7v+NRgO2u/+e/O53GHs59gE56PXic08yc03FfeYsRqsVa6kDQ34+zoce\nBGV/WnknIW8Eg4y/+RYVv/ABGn/v92VYlBBiRWSI1CYUfUNId/J2/AS+6GNE/53tykX09fpnZ5m+\ndBn/7KzuNYrVSzXnIp3Jnun8XnrnWKpykc41pksmroqNIHo+XPT/AWYuX9LdZ2FoBLO9MLy918vC\n8Agzly8nHDOZVHkj+jhCCLFcUsHY5NKdvJ0rE/iyPcFXpCfd32Ej/F4b4RqFSCbdMjzCruhvu9xj\nS94QQqyGVDC2gHQmb8dP4Itu1U32d7xMtAQbjYasTPAVy5fu7xDZzmy3U7y3DbPdHrNduukimz0J\nkqbERmU0Gih+4AHd9GspL0t4bblpWvKGECIbZA7GFmBqaKbp2c8w1dWFp68fW0Md9pYWRt94E+cT\n74uZwBfo62H6xAlmr0cmeVcwcfo0ZYcOsTg6Gp7016JQfPSo/j5x76Ur5hhKK03PfoaZK1cyMsFX\nrExiuqmnpLMz4XcwNTTT9K//FVPnL+BxDVCyby8l94cDALi/860l00Um0k86n2X7c89lbNK4ENmm\n5Qv1Grb6eup/89eZvd59rwzf1cLo66/jONSBqchOYHYOk72Q6ZMnKSiwgrMuvXOcPEn5sce0Ceb2\n1lbJG5vE9U99Ylnbt3ztb7JyHWJrkgrGFhDo6+H2n38FCC/M5D59FvfpszQ//zzG+u0x20VHE5nv\n7cNotVL9ix9k8Hvfj3l94vhxtj/3HEDCPpH30r1B6Z134rXX2P7885T96m9KN/06SUw3Z3CfPpPw\n2/rPd3H7r/763u/X34/BZGKy6/SS6UL3t19m+klXJieNC5FNCfmirx/36TOUdh4i5PNqZbij46AW\nCbC08xCj//wiQLj8XCIPxZ/DaLWSV1lB8ZGjmKLuC0IIsRIyRGoLiEQJiUwCjPw9dfKE7nbxFgYG\nkkbgmTlzJul7y72+hGOcPCEPgusoWbqJ/22nzr2TEP41MD+fVrpIFd0pWyRNiVyXLF8E5ufxTrq1\nvBhcXNQiAQbm52PW0lgqD8WfI+j1Mt/vYjruviCEECshFYxNbiXRgKJZSx14XINJ918cGVry2Jm4\nPrG20v1drFYTnn5XzPvWUgcLo2NL7iu/vRCJ0o38F//v+PdS5SHJe0KIbJMKxjqLL8gzXbAnixJi\ntFopPXRoyWhA3kk3troa3WPbFYW8iqqk76XTUpwqiklRW1vS/eQGuHzL+c7SjS7j9Qaw1dWGj2+1\nkl9ViX92jjxnecp9I+Fr1yOCjaQdkQuiK9rR/wco2qNf9sVHjYr+d/x7S+Wh+HNE8m9RW5v08gkh\nVi1rczAURbEAXweagDzgD1RV/VHU+x8Cfg/wA19XVfWr2bqWXBQ/sbVob9vdVY+vZXyia/HRo0wc\nPx7uDjcaKTtymMDiIpOnTrEwOKityh2zXZT82lqtGz4iOsrI+MsvJ31v2dcH2kqyofl5er/4hZjv\nYy0mBG82K/3O9NKD3m9b0nEQg8mE3+Nhcez/Z+/Nw9u6zjv/D7GRBEFSIMV9k0iJRxK1WQslObbj\n2EndLHacpKlnPHUbp844rjPTdpzJOJ1pm87yS9JpmjZt0jZxnGbSdOKsTZykTTp24lUStViy1qNd\nJMV9EUmQIEEA/P0BAsJyAYILCIB8P8+jR8Q9557z3nvf91wc3Pt+zwCO5mocjesZPXsO/+RkxL6F\nW7ZEJH4Xbm1Jqo+lQHxHyASik7fNDge+iQny62pwX+9goqMDx4YNrL3jLTErdweV/6I/G5UZxVC0\niMfaO97CwKHDlLbuxTc5yVT/ADNuN772KxIbgiAsipyZmdT8UqGUehTYobX+PaVUCXBCa10/W2YF\nzgF7gXHgNeA9WuveBE3OZMoS84slOrkOAjcE557dDL5+MPQ5UZJeWVkh8zkfvvYrjB4+RE4ODLz4\ny5i+1z35O1hadobquXRQRaqMwSNHKd27h6m+/oCKVJQCT8Q+C1TnCW+j9MB+ur73A0Mbr33xSzHb\n1z/9NJW7dxiej7KywrT9XN3fP5ZUcM33Ws6HeL6WTBK19+wJRtuO4HO7mezrJ6+8DHN+PkWte7Fs\n2TlnH9FKYIVbtnDtb/42ctKRl8e6Jz6acsWwhZyHVF6XRGSDz6aCdJ3v+bBYG+P5YdUD76H7Rz+O\n2V79gfcx+PpB7HW15NfX4W7vDE1AcsvLGDzchmPjRgq3bGHs3Dlc58/jUIqKu+/CE6UiFa/vun/3\nb+n45v9d0BgRJNN81ug6ffgzL0ZXS8izT9+zOMOWiMX4XKapSGVSjKfTZ1cLqVSR+g7w3dm/cwg8\nqQiyGbiktR4GUEq9Ctw1u8+KJ14CX3jCXjBJz7lEX7SCCjqDX3/GsO+RtjZKW3YaKu043vEu/P4Z\n7GCowLMU6jzhbQx+65uGyeYjbW1xE4Ird+9YUL8rnURJ1HP51sjhtpBCja3Eycip0/g9HmZmZigN\nm2DE62Ps7FmcH3w45BfD3/7HiMkFgH9yMqZeKljMeRCEpWK+QhpTQ8M0/PH/CL1WaPfPUGYwNgM4\nW3aGYqjY4ItcvBhwXbgYY4/EhiAIiyVlEwyttQtAKVVIYKLx38KKi4CRsM9jQPFcbZaVFS6liWmj\nY44EvsmewIOcca1pTnDMCzkfHdeuG26faO9gU4ac3+vxks3bOwzrj2sNZJ5/OJ12LBZzUnVTZXs8\nX5vLtwA6Zs93UEUqSLSvJNvHYmxZLAvtO9N8KtXMx2dTQTac78XYaOSHiYQ0xrWmudSxoL6i7YwX\nAxPtHRH3nYi+s+B6xPPZxfpSJvniQm25sEz9ZFofQmaQ0nUwlFJ1wA+AL2mt/zGsaBQI97JC4OZc\n7WXKo7XF4mhWuK+3x2zPKy9j5NRpYPaVqb174x5zso8aTVG/Ctsb6nF3xH5Rt9fXLdv5jbYpGqPz\n4xkaxrl3j6HtBSqQKBznFalFWrtwhocnkqqXysfGwXMZfAoRlLgsUCpunzabOZC8XV+Hu6MjZt9o\nX4nnz9F9JFsvFSyk7zS+IrXsfQZJ1mdTQSa9PhGPxdoY7Ycmm40cqxV7bU3csW0h/QXtDB9r48WA\nvb6O4SNHF9V3pvnsUvhSpvjicsZFqvvJpBiXiU7qSWWSdwXwc+BjWusXoorPARtnczNcBF6P+rNU\n2ZJpxEueNeXm4vd6Kb39gGES9nyIl0RuLigwTNgubm1dsuNL1qZ4Sbbxks2LW1sZPnI0xvZUJASv\nFIoOHGDGPRFKwC7a2oLFbjc8Z94TbYwcO85E5w3stTUU79hOjsmEd3w8ct/WvTF9JJOonWy9VJDO\nvgUhSMgPvd6A2MZsYnVedZXhuJzrdC4o4Xrk7DmGX3wpYqyNFwPBcTUciQ1BEBZLKpO8/xJ4CAh/\nLvsVoEBr/eUwFSkTARWpL87R5IpJ8obYxOhgkl4OMwmTsIMk+iUgYRL5ocOU7t+H3zPFZF8/9rq6\nBU1g5st8k2zjJY7H2x7vfGRa8qERmZDk7T3RxrUvPxNTr6R1LwOvvpZw32A/o4cPMa41BQkStZdC\nEGChzLdvSfJeXjLp1814LIWNvvYrTF3UkUIWQeU8v5+Jjk7sdYEE7cFDhzFZLPNKuE4U88C8xtVk\nyTSflSTvAE+++Il51f/iPX+6oH6SJZNiXJK8U08qczB+F/jdBOXPA8+nqv9Mxygx2tmyc84k7GRI\nmERusTD4+sGAcsl7H6DgvvcszQEt0KZ4iYTxEseXIqF8NZHseY9ejTtYL7g6cLAs3jULXpfmOW4g\n6bx+4jtCJmCub2QqOi79fgZefY2Sfa3ADDM+HzffOAF+/7wTrhPG/AcflnFVEIRlQRbaSzPhg7nF\nYmIiQRK2xTL35Up2FVi/x8PQkSPLsujYYlaNjXezk5vg3CxmNe4g0asDR++7UNJ5/cR3hHSSKC7d\nXd3MTE/j7upOelXuZNsOb0PGVUEQUo1MMDIIr9ePvb7OsMxeX4fX65+zjUSrI893pdelIl0rNq92\nFrIadzTRPhO9ryAI8yOZMXqhY7WMtYIgZAoywcgwiltbMdlsEdtMNhvF+/fHfYIR/ctW8e23G7Zh\nzs/HVuLEZLNhcThw3nlXXDuW4slGeBvxbJJEwtRSdOBAUue9ePeuuD4TLzE6mSdqYOxLye4rCCsN\nm80cE5cmm438ulrMBQWBz9Grch+4PRQzRvEUvi1RzC/HE2tBEARIsUytMH8sLTtZ9+TvMNLWxkR7\nB/aGegq3b6X3eBu+73yHvMYG7JVVdBw+gmP9enLLyxk8fBjHho0hpSjXxQtUP/gA7u4eJq5dx15X\nS/H2bYycPkOO1UbJvlbyqqvo+OozODZsjFBzSlbpKRERbTQ1BWw8coTq97+PqX7j1cCF1GCub4z0\np/pAUn/0ebfsbGXdb/sYOflmKMm0eOcOyMtjZmYmYt8Z1yiDX/vyrW37WsHtZuTESTqCClQ7d5BT\nVhHjSy6vC9fBw/gut2NuqsdxYB/FjdvTdHYEYflwn2rDdeQ4k503sNfWUv2B9+Hu6MDscOAbGWXi\nxg1mpqdZ99uPMnbtOvnrGnBs3kxhUyM3//XngXirrSF/fQNTgzcp2rcP4FaMqU0Utmxh7MxZ1t5z\nN74xFxOdnTg2baJwyxZGDx3C9fW/X/C4LgiCMB9SpiKVAlaUilQyWCwmhi6epO9//5WxItRssrbR\n3wAWh4O1d92B1zXO0KHDCdsIKozMR+nJiIQKVq8fxOJw0Pj001BZu6hzE42oSBkTvB5AaC0LIOaa\nGtUrad3LUNuRmH2jlaXWPfYo7f/nmzHXvPqB99D53e9HbDNSpSr7+McycpIhKlLLSyYpzMRjoTa6\nT7Vx429iVdqq33s/XT983nDMta5rwnPqDa598Usx5VUPvIepnl6G2o6EykpvP8Dw0WMRTz5yK8qp\n+vVf59pf/fWixvW5yDSfFRWpAKIiFR9RkUo98p5CBuP1+hk7eDi+ItSsuo/R3wBelwvP4BC+ifE5\n2xg9fIixo0fjqo8kS0IFK5sNr8vF8Csvz+c0CIsgeD2Cq3EH/46+ptH1AHxut+G+QWUpgLzKSsbO\nnje85u4bXVgcjoht4fsGt7kOtaXq8AUhI3AdjVVpA3B3dsYdc/3+GUba2gzLJ290MTMzEzGZ8E9N\nRdT1ezy4OzoZORQ7fs93XBcEQZgvMsHIYCwWE75LxqpS4eo+8f4G8Hk8TPb2z9mGS2umersN6y2F\ngkl0X/IucOpJVlHGqJ6txMlk39x+49y7O64C1UTnDezrGuLuG8R3+brkZAgrFpvNzGR7bIzYSpxM\ndHYZ7uPSOqDu1h67ujcEYsuUmxvRVrx4nWjviIm5YB8yDguCkCqSvqsrpTYrpe5USt0V/JdKwzKJ\ndA3CXq8fc1O9YVm4yki8vwHMNhu55WVztuFQitzySsN6S6FgEt2XqJmknmQVZYzqeYaGyS1ba7hv\n+LUcPnIMe221YT17bU2M7LKRKpW5qSEphTRByCaC9w2Px0deXaxKm2doOG7sOJQKqLvFUxWsrcE3\nORnRVrx4tdfXxcRcsA8ZhwVBSBVJTTCUUl8Gfg78D+BPZv99KnVmZQa+9isMP/dNrv/JHzL83Dfx\ntV9ZdhscB/YZKoIEVUbi/R2sl2M2Y87NnbONon37Kdy7d9FKT/EUTKL7EpaHZFWkouv5PR4sdvuc\nylKTPT0UtmwxrJdfU43X5Yq7b3CbY3/r4g5SEDIIo/uGY2+sShtAXk1NwviMpyqYV1ONyWQKlfk9\nHsx5ecYKhK2x8SXjsCAIqSZZFal7gSatdexLpCuU6GRl9/V2Bl96aUkT45KhuHE7fPxjuA614bt8\nnfzG9eRXVHKz7Qhl995DbnkZg4fbKLvvVyjcsoWxc+cC6iPr12MrLWHoyDHsdbWs+/ePMXbxEi4d\nVHa6tV9QzanT3UnHb9xF3aWbWK/1Mr2ugo4Na8grs5FsSra5vpH1Tz/N6OFDCfsSlo+S1r343G4m\n+/rJKy/DnJ8fUyfmus2qfBXfc2/MthnXaISylK+okOoP/Qbu0+dCClT5WzczvXYNZff9SsS+Lq+L\nAlsOvsvXMTc14NjfmpEJ3oKwEBLdN2qeeIzxo2/g7ujEXluLubCQid4eSj/0bxg8fxbrtV7sqpmS\nA28JjZExqoJ1teSvq2dqeITie+6NiE9TURHrnvwdxs6eZVxrCsKU+oxiW8ZhQRBSSbITjHYgH1g1\nE4x4ycqjhw/hXOaBubhxO8WN27FYTKFXSdb/24dCagyOd7wr9Kjb2bKTUlNO6HPxex4M7ePc2RpR\nFr4fQFvPcV6YfBNbgxWnKmZ4shvPZDtv7ymmdn3yqk/m+kac9Y0J+xKWh9FDhxh49TVMNhu2Eicj\np07j93jIKSiI8WOj6wYYbivdspOKWX/8/tUf8ULPq1RsXcued9zO0e5T9Lp+zNvL7uR9H3w40h8h\nxpcFYaWQ8L7xwYfJ39aKzWbmJ1f/lYtDl7k+0oXLdSk05u6sLOWBaAnplp2UttyKN5MpB3uC+HS2\n7KQ5Sq0nXmwLgiCkioQTDKXU14CZ2XonlVIvA95gudb6w6k1Lz3MlRybrkE63heyaFvCP0fvE14W\n/rfJlMPFoasAeHzT9I4PhMouDF3B1DT/Y47Xl7A8hPtxuDoUJPbjZLcFv+wE/abXNcBPLv4iVJ7I\nb2RyIaw0kr1veL1+jvecon3kRqg8OOaeG7jIg+sTx0yy8WmEjMOCICwXcz3B+OXs/y8ZlK3YkSqY\n9Oq+3h5TtlIT4/z+GTY610fc9II0lzSuyGNe6SyHH4vfCEKAZONNYkYQhNVAwiRvrfXXtdZfB6qD\nf4dt27Q8JqaHZJNjM535KGC1Vu3CZrZGbLOZreytvC3lfQupYSF+PN/rFvQbm9lKRcHa0N/z9Zt4\n/YofCdlCongL9+PosdZmtlJbVEVr1a4F9SsxIghCpjHXK1KfAcqBB5RSG6P22w/8QQptSyuZmhh3\nfuw8R7tP0HG0m7qiKvZU7WRTYexcr9PdSVv3cS4OX2Wjcz2tVbuozU+cR1GbX8tTrU9wpOcNLgxd\nobmkkb2Vt825XzS+9iuMHjyI64LG0awoOnAg7edttTIfP16Iz0DAbz5V9xCuQ4fxXWrHvGEdjv37\nKE7Sb+L1u1B7BCFdmOsbKfv4xxg71Ib/8nVMTQ3ktd7G896z6CPPR/jxU61PcLTnBOTM4PKM0zna\nw+GuY7RWzSTt5zLWCoKQqcz1itT3gBYCKlLhr0l5CUjWrmgyLTHu/Nh5/u74N/D4pgHoHO3mWPcp\nHt/1SMQko9Pdyefa/iZUr33kBq90HOap1ieSmmTUrq9dUM4FZI76lnCLZPx4MT7ja79C/5/99a3k\n1vYO3K8ewZHENY/X7+O7Honw9fnYIwjpotPdyec6noMacDYVMzx5FW5cZVfVNtpHbsT6cSUR/t8x\n2jWvuIs31lK2I+XHKgiCkIiEEwyt9RHgiFLq+1rr0WWyKePIhMkFwNHuE6EbURCPb5qjPScjJhht\nPccN6x3peSNpNaiFHnMmqW8JkSS6povxmcVcc6N+Ib6vz8eHBWG5CffncKGMKd8UNrMVj286wo9T\nFXeVu2WCIQhCepnrFSk/s8ncSimAacAP5AKjWmtnqg0UAlgsJjpGuw3LOka6QrKf4ao+0SxUDSpZ\nMlV9S0jMYnxmMdc8Xr/OvOK4vp5qHxaEhZIojvrHh3DmFYcmHReGrmDZaEpZ3AmCIKSbuZK8TVpr\nM/Bl4LeAfK21Hfh14LvLYN+KYbEJrF6vn7qiKoCIZFqAuuLqCAnD3ZXbsJmtOGx2Wso24rDZgYBC\nSbJ9L8TeoIqKEStVfWs5sVgShuucxLt2QVUbI4KqNkZ9m2YnD3Nd80T7GvU7PDkS8vV49ghCJhAe\nU4niqKqwHOus+AHA5rWBlMbNpRuoKFiLw2aPGNPn8vO54k4QBCHdJLvQ3j6t9RPBD1rr7yml/nCu\nnZRS+4DPaq3vjtr++8BjQP/spse1Xpk/uywHdYziAAAgAElEQVRFAmuwbn1xDTUDXuouDWO71odn\nXSUdG5xUVe6IqHfl5jX+Q9k7sL5xgZnL7dBYi2v7OgbyC/js0b9kb/VO+iYGuDrcEdP3Yu0tOnCA\nwZdeinh0n43qW5nEyJU3cR08jO9yO+amehwH9s1r9etkrl1r1S5e6Tgc8bpGniWXO33V3PjmVyL6\nHqsqiWjv3r3bMBlcc99tin+48C06Rm8JEjgsjoh9t5Q1x/QLsKdqJ8e6T0VsX4yimSAsJfFiKjqO\nTDkm9tfuwjfjA2BLWTPrnXUMuIZ49dB32HPpJneZa/GMjTDV2cXUuio6NqxhYxJ+LmOtIAiZTM7M\nzNy/BiqlXgGeBb5N4KnHI8AHtNb3JtjnE7P1xrXW+6PK/gH4vNb62DxsnQlfmTQbiE5ghcCXpOgE\n1uB2o8S+8DaeXHsvtr/9XswNpezjH2OsqiRU74m195D3t9+PqTf10fdzPO8mxw2+uD3VGpg/LtZe\nmFU2WWb1rbKolWvDtqdNv7G/fyypn9rj2Q6ByUVEAjW3rnkyk4x4PhjP18IVxO70VXPzz/8upu+O\n37iL706+Gdr2lrq9VPVPUXfpJtZrvUyvq8CzcyNfvfkqk96piH7f3XwvPzj3L6FteZZcPnLbv+Pc\nwMUY5bJoexaiaLYYEl2XFPeb8T6bCtJ1vudDWVkhb7SfixtTA1N9nOm/hNvrpn98iNuqWvjZpZdi\n6v62807y/vb7OPfsZvjosZgYS1YUI95Yu9znMtN81uj4P/yZF+fV7rNP37M4w5aIxVzLJ1/8xLzq\nf/GeP11QP8mSSTGeTp9dLST7BOM3gL8GvkAgJ+NfCUweEnEZeD/wDYOy3cAnlVKVwE+01p9O0o6s\nIl4C39GeEzF14yX2Bdtw2Ow4Tl1j0iCpz3WojZN31obqFZ26bliv8PR1cvauMbTpeN+beP3eRdsL\nmae+lc24DrUZJnK6DrUlNcGYTxJptILYjX98xrDvuks3sTUEElZtZisT3gm+O3kGW4MVpypmfHqA\njdbCiMlFsN8bYz04bHZcngkAJr1TnBu4yPvW3x/zzvliFc0EIRXEi6lzQxfpGe/hUOcb2MxWyu1r\n6RztNhQxKDp1HQ/gn5palCiGjLWCIGQqSU0wtNbXgfvn0/Dsa1Tr4hR/C/giMAr8QCn1Hq31j+dq\ns6yscD4mpJ2Lx4wT+DpGuiMS/kL1h65Q1hp5jME2Gopr4HLsCrEAvsvXce9fO2c9Lndi219mWNQ/\nPsCAe3jR9qaTTPMPp9OOxWJOqm4829svXTfc7rt8PanjjeeDyVy7eH1br/XiVAF/cOYV0z8+BAS+\nZPWOD1BRsDa0LZqu0V4aims4039xXraki0zzqVQzH59NBdlwvuMlZjtseXR29wCBWJj2TxvGgTOv\nODAWlziZ7OuPKQcY15rmRZ6LbDiXS0E8n13s8WfS+VsuW5ajn0w6r0JqmUtF6sda6/copa4yqyYV\njtZ63u+9KKVygL/QWo/Mfv4JcBsw5wQjUx6tJctG53raR27EbK8rquJY96nY+iWNMccYbOP6yA1o\nrIX2jpj9zE0N5FvyABLWo6mWKW/sr2kAZQVrceatWbS96SLBK1JpsCbA8PBEUvUSPTY2N9XHvebJ\nnPt4PpjMtYvX9/S6CoYnAypPw5MjbClrpjNM9cloW5DqogrO9l2Yty3pII2vSC17n0GS9dlUkEmv\nT8SjrKwwbky5PJPUFFWE/D5eHAxPjkBjPZ7Xj1G0tQV3R2yMFSi1qHORhleklq2vaIx8dimOP1N8\ncTmvZar7yaQYl4lO6pnrCcZHZv+/ewn7LAJOK6U2A+PAPQTyO7IeU9RjaqPEWZvZGkpgDSdeAmuw\nDZdngtHtDeS9Hvm+rsXhoPRtb+W2kgL+39VX4tYz2WyMbm0Abob02IM4bHb2V+/G4502tLe1ehen\n+s4bJtxGH7OwtDgO7MP92pGYa+nY35rU/vF8MOhrRtcvpA4Vp++ODWvwTAaeknl809it+SH1G2de\nMcOTI6Ft0f3WFFbS1nkiYpskbguZTnicxIupzSUbWVtQzBvdZ0LrXThsdmqLqugbH4io79q+Dtvr\nxzDn5WGy2eaVqC1jrpBJXHjsQ8nXBZqf+ftUmSJkGMkmeZ8CfkLgKcNrWuukRrfZV6S+pbXer5R6\nGHBorb+slHoE+I/AFPCC1vqPk2guY5O8E6n0xEtUnU8Ca7DulZvX+LX82zAfP4//agdr9u1lsqeH\nySvXyWmqw7ZvJ4fM/Vy6eeVWvcvXMTc1kNt6G6+Ze7gwfJm91Tvpnxjk2s2OCEWpZmcjTaUNvNl7\njo7RLhrW1FLpKONY15s0rKml3L6WI10n2OBcz+a1GznXf5ELw1fmVMFaDlZqkjfMqkgdasM3ey0d\n+1vnrSIV7WtV/R5GDx7EdUHjaFYUHThAd5ktxo/zbvQy2XYc/+V2TE315LXuYqCigKPdJyLUody+\nCU71nefGaC81RRVsK9+EJcfCid6zdI52U1tUxY6KLdhMtph9wxeJzCQkyXt5yaRfN4NEj+1vbdxH\nKRWGMeXyujjefYqa4go6RroosNkZ84zTNdpLdVE5RbZCXJ5xHLYCXJ4JWj2l2E9dw2G14xtzMdHZ\niWPTpriiGL72KzExGy8RXJK8JckblifJez4TDMicCYYkeaeeZCcYlcCvAu8EdgGHgee11s+l1rwI\nMnKCkaxKT7xfnebza1R43dFrJ+n70y/G/PK15qnHKd+4J1QvuACfURudk5187nCs7a01Oymw2Xmt\n/UgoGTd0XPueIIcc/uzwl5JWlVoOVvIEI0j0tZwvwWvva7/C1c98Zk51qNvr9nC06yRw68kEwJ7q\nHRztOhna9u7me/nJhRdi/CG4PdG+QFr9JhEywVheMm2CkczYHoyp82PnI5T27qxv5fCNN2KfXlfv\n4PWOo6HPH9/3O9TZa/H7ZxLeC+LFbDy1KZlgyAQDZIKRCJlgpJ6kVu7SWvcAXwf+N/AMgVemvpA6\ns7KHRCo94cS7ccznUXd43dHXjdWFJg8fj6gX/YU0vKyt29j2KZ+HvvGBiMlFsOxI9xsc6z2R1DEL\nS8tiJhdw69qPHjoUXx1q9lUnm9mK2+sOverRO/uKh8c3jdvrBqB3fACb2cqNsR5Df7gx1oPNbI27\nb3Cb+I2QiSQztgdj6mj3rTHRZrbimh433NftdYdizOObpq371nid6F4QL2ZHDx9a4NEJgiCklqQm\nGEqpnxKQnf2vwCTwLq11RSoNywZMppy4iiIXhq4kvUr3fLHZzPgvGStF+S+3Y7PNrQKTyHaPz1j9\nBALH1Tc+GLcsVccsLA0mUw4ufd6wzHqtN6BwAxHqUNH0jw+F6jUU19A12mtYL6gYFW/fIOI3QqYx\nn7HdYjHREZbInWzsGLUVz5Z4MevSWmJHEISMJKkJBvAG0AmUAhVApVIqP2VWZQl+/wwbnesNy5pL\nGuP+ImWx3Drt0ZOBZCYHHo+PnKY6wzJTU33MK1ER5bOfE9luM1sps5cYljWXNFJuL41bJsmHy0u4\nL81FKHm7WRmWT6+rYHx6goqCtYxPT7B21gdsZisVBWtDv7yWFZRgNVt598a3MTE9SXWh8W8N1UUV\nAVWzMMoKSkKvRgURvxEyhUTjo8Nmp6VsI7sqt4fqBV9bXO+so6JgLQ6bHasp/vgZ7f/J+H6imHUo\nJbEjCEJGkuw6GP8VQCnlAD5AYA2LeiA3daZlB3Op9IRzfuw8R7tPcGOshz3V2+lx9dM+0kVdURVb\nyps53ae5MdpDTVEFOyu2sr04fiJvwYE9TL12NOad3LFtDXz6yF+w3lkXSspuWrOOLWXNMUnZ8Ww3\n55gxW8yGKkDB4/rF9deTOmYhNQR9KZlk6ehE1Xv3bsP00ksxvuPZuZENlkIGJobYULieRmc9BbZ8\nxj1uBiaG2FLWjMNmp7m0kTN9mpO956kprKClQnG6TzMx7Q61F08xKt+SL34jZBxGQh3B8dE/M8N7\nmu+le6yPotwCuse7+Z+H/jyUuO2wFTAzM4PVbGXLmmaKch3YLfmc6b8Q4+u55tyIV6mS9f2iAwcY\nNIjZRGpTgiAI6SSpCYZS6j7g3tl/ZuC7BFSlVj21+bU81frEnIpQ4UmA+2t38eOwpNg91dv5xsnv\nhT53jnbzRvcZHt1J3EnGTGMNno9+AMfpa3C5E8uGdeiGXL4/8CL+GT/tIzewma3sqtrGC9de5ZWO\nw+yq2kb7yA3aR27wSsdhHt/1CHuqd+D2uukfH6KsoIR1a+q46R7l6s123qvuo39ikCvD7THHlcwx\nC6khOqG0c7SbY92neHzXIzGTjOhE1faRG7xmOcIfffxj+I+dxqU1DqUw7d7Kf+/8Tmj17c7RbuzW\nfI52nYzox2a24p+Z4fCNE6Ftb/Sc4YMt7+Fs/4WQH+Vb8ql1VPP29XdG+AiA3ZIvfiNkDEYx8krH\nYZ5qfYKnWp+gY7yTb595nl1V23jp+q0fZDpGu2ISt4MxsrtqG3uqd+Cf8XNjtJvqokrqi2voHOmm\nvria5pKmefm+ub6R9U8/zejhQ6GYjac2JQiCkAkkNcEAniQwofiC1rozvEAptUtrfXzJLcsiavNr\nqV1fi6kpvgpIMAnQZrYy5ZsK3aQcNjtdrl7DhMCTvWfjTjBevX6Enw+8jKPeTtOOJmwWK0dunIxp\nY8o3FXoSEf63xzfN0Z4THOsKrMfhzCvmTN8F3ug+w31Nd/Nf9vxu6FiM1E2SOWYhNYQnlAYJXM+T\nMRMMo0TVSe8UL+Zc530ffJjS2Wv7/as/Ck0uIDLJO7qfYKJqsMzjm+bi0FWu3+zEarJypi/wy63d\nks/71t8f4yPiN0ImkSiZ+33r7+cXHa8CRIzb4fWM4mHSN8WZvgvsqNzCDHCmT3NtuIPd1Tv4zU3/\nZkG+b65vxFnfGIpZQRCETCZZFakHtNZ/Fz25mOWZJbYpa0mUcxFMAoxOAEyUJNs52m2Yk2Ey5XB+\n4DIALs8EPa4+usf6DNsITyqMTjDsGOnGmVccoRIEcG7gYlLHNVeZsPREJ5SG0zHSFZGTkUyialAe\nM7refBJVIZDQXeUoj/Cj8D6iEb8RMoG5YiQvz0LnaPe84yG4rXusj2nfNC7PBL3jA5zuM07Wng8S\nO4IgZAPJZ4jGRyQs5sDr9VNXVAXA8ORIKHkW4PrIjbhJsrVFVXg8vpjtfv8Mm9Y2hT5HtxlOeFJh\ndIJhXVFVTMItSNJtJhPuS9HUFVdHJPgnK0JgVC9ZnwpilNAtfiRkOnPFyOSkl5qiinnHQ3DbQpK6\nBUEQVgJLMcGQ0TIJ9lTtDKnwlOQXh/52eSaoLqwIfQ5iM1u5rXJr3PbuaNiLzWzFZrbizCvGbs03\nbCOYVGiUYLinamdMu5J0m/mE+1IQm9nKnsodMXVbq3YZ1g1e4+ATj+h6Ht90XJ8yStSuKazE45sO\nqU2JHwnZwlwxclvlNoB5xUO+JSCyGD7+1hZV0Vq1K5WHIgiCkDEkm4MhLJJNhZt4dOdDnOg9w6Wh\na7xr4z0MTAxx7WYn/a4hHtnxAc70XaBztJvaoirU2kZ+dvlFThScNlQI2lS2gcd3PRJSEmJmhkd3\nPsTloetcGLpCo7OeMnspR7pO8Pb1d7J57UbOD1yivrgmIrlWkrWzj02FmwLXvuckHSNd1BVXs6dy\nh6GKVLxr7PK6+IcL34pQoQr3p7qiKnZXbqelrJlTfefoHO2htqiS7eWbmcGPf8Yf8tWt5c3km+3s\nrtpGx2h3IMG1aqf4kZAVJBoHO92dXB64zoOb7qN95AZ3NezD5ZkIqf0V2hw4bAXcWd/KtZud1BRV\n4LAV4PJM8NDW+7k61Mk7Gu/E5Rmnc7SHw13HaK2akdgQBGHFIxOMZaLT3cnXTjwXplTShcNm5z/v\ne4JJ7zSfa/sbbGYrDcU1nO47z/HuU+yq2sbBzuOGCkHn+y/FKAkd7X6Tj+/7HT7Q9EDoMfw91W8N\n/b2pcFNswq0ka2clmwo3salwU0iHPxHR1zieCtW7m+/lWPcpnHnFHOs+xbHuU+yp3sHx7tM484o5\n3n2a492near1CR5p3kV+vhW3ezpGhSfY3lOtT8gXKSErMBoHg369q2obL50/GPLvO+r3MsMMp/s0\nBVY7w5Mj2MxW7qzfxwtXXw3VOz47bofHWsdoV0ihSmJDEISVjORgLBNGSiUuzwSvdx0Llbk8E5zp\nv4jLMxGj+nS0J1Ih6tXrRwwVTdq6j0dMFKInDfEmETK5yE7mmlyEE7zG8VSoboz1YDNbQ4naQYUc\nIGLbkZ43AHC7A20kUuERhGwifBxs6wmII4arR9nMVsanJ+gc7Q4lbgfH7p7xSKGNoFJfNBIbgiCs\nBhI+wVBK3ZWoXGv9MoGF94QEJFIq6RsfYNA9bFgWVCLpHR8IKQR5vf4IFaloLgxdkacRQlwSqVB1\njfbSUFzDmf5bKmLhPhgk3MfmVKoSXxSykKBfR6tHJaMmFR4rQaW+8G0gsSEIwspnricYf5Lg36cA\ntNZXUmhfRmMyJffwJpFSSXnB2rhl4Qok4QpB0SpS4YhKiQDGvhmcoMZToTJSgjJSyJlLgcqoniCk\ng2TH6GiCfh2tHjVfNSlR6hMEYbWS8AmG1vpty2VINtHp7qSt+zgXh6+y0bme1qpdc75P21q1i1c6\nDseojfjxU2JfE7FQU7AsXIEkWiHojoa9/PLawZh9RLlndWPkmy6vKyJ5e2v5Jt7oOROzsF5NYSVt\nnScithkp5ET7WDzfFl8U0sVCxujo/UvsawDIs+RGLFAa/jlIvFjZU7WTY92nItqW2BAEYTWQVJK3\nUuoO4D8DDgI5F2agQWu9LnWmZSbRCa3tIzeSTtrbU70Dt9dN//gQZQUl5FvycU2N88KVV9lfG5BK\nvDLczro1tZTanRzrepMDdbsNFYI2lW0QBSghAiPfdPsmOdp1MiYB+7d2fJDT/TpChcphcfD29eMR\n/gRgt+Qn9LFwFZ6LQ1fYKL4opJHFjNHh+3v9PlprduLxeSLUo/IteTy+6xHODVxMKlZknBYEYTWS\nrIrUM8BngQ8BXwDeCRxPkU0ZTaKE1tr18W8abT3Heb3jaGjdijN9F/D4prmtqgWLyczrHUe5r+lu\nPrn390KPzn+17u0Jk3hFAUoIJ9o3bWYrbq/b0F9P92t+Y+NDMSpURv6UjI8FfbGstZD+/rElPCpB\nmB8LHaON9j/UeTw0ZjeWNDDDDDazLaTilkysyDgtCMJqJFkVKbfW+mvAL4Fh4CPAW1NlVKYyZ0Jr\nnPd9w/fz+KZDyiNwKzEQ4NzAxYj9klUIkpuWYOSbiRJSw0UDojHyJ/ExIRtY6BidaP/gmN0x0sW0\nb5pzAxdD7cwnViSGBEFYTSQ7wZhUSpUAGtivtZ4BClJnVmYRfjPZWNJoWCdR0l54IqzNbA2tdgyR\niYGb12407FcQwNgfInwzysfGpyfiJqQGRQMStSkI2UayogPBFeyDBD/7/TNsLt0QMUYHCY7VzXHu\nAYIgCMItkn1F6s+B54D3A0eUUv8OODrXTkqpfcBntdZ3R22/H/gjwAs8q7X+ynyMXi6CiYJXR67z\nlrq9XBy6hsNmN0zwmytpr7VqF27fJBPTbgYmhthS1ozdms/MzAxev4/b6/Yw6Zvk00f+gvXOOsrt\naznSdYKmNesiEhRDyYvHFpa8KGQOyV5Lo4RVIGZbtI9tKFzPemcdZ/svxPjr9vLNfP/Kj+ZsU3xL\nyDYSiQ6cHzsfJnhQzdZyxZk+TftoF/VF1Wyr2Mykfwqr2cqWsmbyLLm03TiBxWQm15wLQGGunf+v\n7fM0lzRKjAiCIMQh2QnG/wO+q7WeUUrtBpqBm4l2UEp9AngEGI/abgU+D+ydLXtNKfUjrXXvfI1P\nJeGJgg9uuo/nzjyPxzeNKcdEa81OpnxTDE4M01zSlHTSXnSyrc1s5a3rDvCBze/ih/pnEUmJNrOV\nXVXbeOHaq6EERWBRyYtC5pBsImoyidvBfR/d+VCMj50fuBST0L29fDNfP/mdkIpUcP891Tt4veNo\nQnsEIdOJl1jt8roMVrB/k11V2+gc7aa2qIqvnXguZox+r3oHY55xxj1u9lTv4If6X/HP+GVVbkEQ\nhATMtdBeHQHVqJ8C71RKBd+dGAH+GdgUb1/gMoEnHt+I2r4ZuKS1Hp7t41XgLuA787Y+hQQT/Rw2\nO12u3tBNxz/jDyX+3bP+Ldxf/655tReOxzfNjH+G0akxw7LwlbyP9LyB2WRaVPKikDkkm4g6n8Tt\nk71nY/qZ9E7FJHR//+qPIiRqg/u7ve6Ip3PiW0K2YpRY/Q8XvhV3nHXY7BErdoeX944P0j3Wy9qC\nEo50nowplxgRBEGIZa4nGH8CvA2oBl4O2+4FfpxoR63195RS6wyKighMUIKMAcVzWgqUlRUmU21J\nuHgskOjXUFxD12jswxWPb5rTfZoP735oXu3FbB+6Qkm+07AsfGXYRPUuDl2hrHX5zk2mspz+kQxO\npx2LxWxYlsgfwq9ldL1Eidudo8arBneMdEWcm3h9G61EvBDfyrTrsBhW0rEkQyKfXQ5Seb47jhqv\nYN8/PkRDcU3cuGofuUFxroPusT7D8kwdf1eL78bz2cUefyadv+WyZSH9XFiGPoTsZK6F9j4MoJT6\nL1rrzy5Rn6NAuIcVMsfrVkGWU/5yo3M97SM3uD5ygy1lzXSOxt6c6oqrk7Yp2F7M9pJGzDnGufZl\nBSWc6bswZ72NJY2rXhq0rMxYHjWdg9nw8ETcskT+EH4c0fWGJ0fi+mNtURXHoxb1glg/jdd3uL/F\ns2cu4l2HbCRdx5KpPptqUn2+64qqDOOmrKCEi4NX2VCy3nicL6riVN/5uOWZOP4ut+9mms8uxfFn\nyjVdzmu5HP1k0nkVUkuyKlJ/oZT6A6XU15VSRUqpP1JK2RbY5zlgo1KqZLaNu4CDC2wrZbRWBRa+\nc3kmqC6siFEUMVpdO5n2otvYW3kbuyp2GJaFr+SdqJ6sCpt9JPKHRPU8vmns1nzDfXdUbInpx8hP\n4/WdzKrdgpCt7KnaGXecdXkmQit0R5fvqdoZsYJ3dLnEiCAIQizJJnn/NdAP7CbwetQG4KsEkriT\nQin1MODQWn9ZKfWfgJ8RmOA8q7WO/Tk1zYQnCr7Ze5aHWu7n0tC1gPrI7MrH0atrJ9ue0Yqu4WWN\nznrK7KUc6TrB29ffaVhPVkzObpJd/Tqe37y19nZDX3p8l42jPScjVuiO9tN4bcLcq3YLQrayqXAT\nj+96JCI+tpYpzvRfoLaoClOOiUd3PsTloeuGq3Ef7TnBWxv245oOrOgtMSIIghCfnJmZuRf/UUod\n11rvUkq9obW+bTbZ+5TWemvqTQwxk65HayZTToR+erIL4CXTXqKyRPVW0qsoS0GCV6TStqhDf/9Y\nUitrJXstjfwhno8k66fzaTMZVpJfpvEVqYz32VSwnOc7Oj6iP8eLgaCNi4mR5SANr0hllM8aHf+H\nP/PivNp99ul7FmfYErGYa/nki5+YV/0v3vOn8+7jwmMfmlf95mf+ft59pIJ0+uxqIdlXpGaiXola\nC2Tu6LrEhN9IFju5iG4vUVkm38CE5Wc+qwYvZhV48TthpRMdH9Gf54oBiRFBEITEJJ2DQWAtjAql\n1F8QWGTv8ymzShAEQRAEQRCErCTZCcZzwL8AZcB/AP4M+FqqjBIEQRAEQRAEITtJNsn7K0AegYXz\nTMBvAk3A76XILkEQBEEQBGEVk605HkLyE4x9WuuQFI1S6nngdGpMEgRBEARBEAQhW0n2FakOpdSG\nsM8VQMZJywqCIAiCIAiCkF6SfYJhBU4qpV4msA7GHUC3UupFAK11Zui5CYIgCIIgCIKQVpKdYPxx\n1Oc/W2pDBEEQBEEQBEHIfpKaYGitX0q1IYIgCIIgCIIgZD/J5mAIgiAIgiAIgiDMiUwwBEEQBEEQ\nBEFYMmSCIQiCIAiCIAjCkiETDEEQBEEQBEEQlgyZYMwTkykn3SYIwqpH4jD7kGsmCIKwekhWpnbV\n097n4uCZHs5fv8mmhjUcaKmkvtyRbrMEYVUhcZh9yDUTBEFYfcgEIwna+1x8+hvHmJr2AXC9Z5Rf\nHr/BJx/ZLTdKQVgmJA6zD7lmgiAIqxN5RSoJDp7pCd0gg0xN+zh4pjdNFgnC6kPiMPuQayYIgrA6\nkQnGHJhMOZy/ftOwTLcPy3vFgrAMSBxmH3LNBEEQVi8ywZgDv3+GTQ1rDMtUvRO/f2aZLRKE1YfE\nYfYh10wQBGH1krIcDKWUCfgSsAOYAh7TWl8KK/994DGgf3bT41prnSp7FsOBlkp+efxGxKP+XKuZ\nA1sr0miVIKwsTKachF8648Zhi8RhpiLXTBAEYXWSyiTvB4E8rfUBpdR+4HPAe8PKdwO/qbU+lkIb\nloT6cgeffGQ3B8/0otuH2VBTTHlJPl//qaa5vlhUUQRhESSrMhQdh6reyYGWCom9DCb8mp2/Pkxd\nhQNHvo1DZ3sAGTcFQRBWKqmcYNwB/AuA1vqQUmpPVPlu4JNKqUrgJ1rrT6fQlkVTX+6gvtxBz7Cb\nT3/jKGMT0wBc7R4RVRRBWCDzVRkKxuFcTzuEzKG+3EFODpy7NsSRs72ha/2LYzJuCoIgrFRSOcEo\nAkbCPvuUUhattXf287eALwKjwA+UUu/RWv84hfYsCS+duBGaXAQJqqLIjVIQ5kcilaFE8SSTi+zi\n9dM9tPeORWyTcVMQVh8XHvtQuk0QlolUTjBGgcKwz6bg5EIplQP8hdZ6ZPbzT4DbgIQTjLKywkTF\ny8L59viqKMttXyacj0wi086H02nHYjEnVTfTbF8M8zmWTIonIzLBhuVkPj47H5K9ztlwvrPBRsge\nOxdLPJ9d7PFn0vlbLlsW0s+FFNixGDLpuq12UjnBeA24H/j2bA7GqbCyIuC0UmozMA7cAzw7V4P9\n/WNzVUk5rZvL6RkYB8BZlMvw6BRT09xZQjcAAB5DSURBVD5UvTOhfTabGY/HF7c8SLKvfpSVFWbE\n+cgU4p2PdA42w8MTSdVbSddyvseyqX4N17tHY7aHx5PFYsLr9S/IHqN4Mtpm1MdSX5f5xHa6SNZn\n42F0jDabmZ0bSpjyeEPjZZCW9SUMDrrw+2eyIg6ywUZYfjszzWeX4vgz5Tov57XMlGNeDMkeg0xE\nUk8qJxg/AN6hlHodyAEeVUo9DDi01l9WSv0B8AsCClMvaK1/mkJbFk0wEVW33+Rdb1lH79AEnb0u\ntjaVUpBniauKcvzyIMd1H529LmorHOxS5exqKo3b/lyJroKwkkikMnT6+jCHz/TS0TtGXUUh+1oq\n2NrgTKpdo3gCYraNuac5tMA+kmU1xLbRMQ6MTUWMfTs3ljE44sZmtXD4bA+3b63E7fHxx88eYVPD\nGu7ZW0+Zw5buQxEEAD78mRfnvc+zT9+TAktu8eSLn0hp+4KwlKRsgqG19gMfjdp8Pqz8G8A3UtX/\nUhKeiPqW7dX89LVroS9E7b1j5FrN7NkcO8E4fnmQr/zT6Yi6x8718ZEHt0ZMMuab6CoIK4V4ylCj\n7mm++N03I2Ln6Llenvy17XNOAIziyT3l5fCZ3pgY29dSwWtvds27j2RZDbFtdIzFjlx++PIVw3Hy\n6LleHv6VZp7714sr+rwIgiCsZmShvSQIJqLmWs1MeryGSaltZ3tj9juu+wzrHtd9hu1H1zt4JrZN\nQVhp1Jc7eOhtTXzq0b089LYm6ssdtIWpDQWJF2fRRMdTrtXM+KRx3I5Pesm1miO2JdNHsqyG2I4+\nxkK7lc4+l+FxT3q82KwmLnWMrPjzIgiCsJqRCcYcmEw5nL8eSFB0FuXSP+w2rNfeM4bFcut02mxm\nOntdhnU7e13YbOaY9qPR7cOYTDmLMV8Qsobgu/sWi4n2HuP3aKPjLBqjeEoUt/3DbpxFufPqI1lW\nQ2wbHeO6qiI6+4zHvv5hd8LylXJeBEEQVjupzMHIOMITEBMlXIaX+f0zbGpYw/WeUcbd09SvK4yR\nWwSoryyMSBL1eHzUVjgM69ZWOEIJ3+Ht51rNMYnjIscpJGIlrgfh9fqpq0gcZ/ESv8PjKcjw6BRb\nm0oN2ytz5nP68qBhH4vFyJYgKyW2w4+x0G5lXVUR7kkPLU2l9A5NxDylKHPmc6F9OO71WCnnRRCS\nQXIqhJXMqphghCcgNtUWU1mSz8FTvTGrcMdLxjzQUol7ysv4pJeSojxyreaYVzBat8TmYOxS5Rw7\n1xdTd5cqj6gX3n7/sHvOxHFByLTE4ZA97TfZVL94e/a1BN7VD4+d/FwL2zes5as/PZ8wKTs6cXxq\n2kdBnsUwbgvttqRieaEkSmJfKRxoqaTYkUtn/xhF9lxGJzycuTLI1g2l5FktHDzdjd8/Q67VTJ7N\ngmfaz8a6NRw/37+iz4sgCMJqZsVPMIwSEIPJhj873B5KLATiJmMCoQRRkymHA1urmPJ46bvpprbM\nQW25w/Bds11NpXzkwa1JqUiFJ6AGEyLv2VWbgjMiZDuZljgcY0/34u0pyrfy3rsa6exz0dnnorbc\nwZbGUr76ozNzJn4bJY4316+BnBzG3dP0D7spc+ZTkGehZX0JXp+f9p4x6isLad2ytCpS8ZLYV1Ii\n86h7mh++fIU9myv4xbHOW9enJzCO/UprPaMTHorsNsbcHvZsruAff36B27dWYrOYuXRjBFXv5J69\ndaIiJQiCsEJY8ROMeEmWkx5v6BfNI+f78Pr8cZMOLWZCZX7/DK+92UWu1czdu2s4eq6X1091c8eO\narYYfDHZ1VTKrqbShOtgLHQ1Y2F1kmn+kgp7Dp7p4WeH20Ov3VztuhlqN7qftrO9MZOC+nIH9eWO\n0Ctkz/7zeV492RV6DfH05UGmpn34Z+DD79y0qLU25iLalpVGMCk+ngDGyLiHa103mSGHdZVFvHGh\nD6/Xz8snunj37ev41KN7s2YdDEEQBCE5VvQEI1GSZTC5s2dwgt6hCfpvThrW0+3DlBbnxWyfmvZx\n7uow1tkVRIOJofG+pMSbXCSTCLoSv5QICyPT/CUV9oS3OTYxzanLg2xrKo2bGJwo9vz+mYik8alp\nHz2DE0ntu9SsxDgOnttEifQ3+lzMkEPP4AQ2izk07gKcuTrEB+9uWk6TBUGIw4XHPpRuE4QVxIpW\nkQomIBpR5sxneHQKgIoSe9x6qt5JZUn+nG0sNDE0kY2S8ChEk2n+kgp7jNq81j1KTXmBYf25Yi+Y\nNL6QfYXEBM/t8OgUZc65x8nwv0HGOEEQhJXKip5gALxtVy2FdmvEtkK7lfVVRdispkBS5+Zy3rKt\nKkIPH24lHe7bUkmu1Uyu1UxlqX02OTSyjdu3VUVIW0ZLLYaXRUtgHmipNO57qyQ8CrHE9ZdlTpAN\n+vhc9gQlmcPJz7fGbAuPmWCbG2oKefzBbVQ486grLzTsJ5iUbSQtG9y2r6UiJoaXKqF7Ncqqhh/z\nvtnrHEykL7Rb2dZUSqHdSqHdyo7mtaj6It5/dxO1ZXacRbmh83/71opVef4EQRBWOjkzM1nz69HM\nfN7PPX55MJRc3VBVSE2Zg6Nn+9i9uZyuARfXu8eorXCwfUMZZ64McKN3nLt213CxfZiO2YTsYFl3\n3wR3763l7NXBUHuVpQUcOdNLdXlBqF5Hj4s9WyoYHHFz5cYomxrWsLHeyYkL/XT1jXPXrhoudtw0\nVMAJqPAEEkE31BRTHkfpKoi8rxxJvPNRVlaYtm8v/f1jSQXXQq5luL8sd+KwkYJV38gkJy72h8QM\nbttYhtmUw9EwgYO9qpxp3wwnLt2qt3NDGeVr8gwVsdouDPBmVF0gYv8dG8ooyDVz8ExvKK72t1Qw\nQ0A4ITzWpr1+jiUhuJCsIlaqlLwy2WfDj3nzujVsqHNy+mo/qraEs9cH2Vjj5Hz7cGicrFpbQNvp\nwDgZTPJuWVfKhc4htjaWcbF9OHT+7tlbn/FJ3tky7i63nZnms0bH/+HPvJhyW559+p551c80mdrf\n/ce+uStlOM3P/H1S9dLps6uFFTnBOH55kK/80+kYCcTHHmjhmTAVmuD2PZsDv8AdPRdIVnQW5aLq\nSzh0upupaR+/ds9Gnn/liuF+wYTv++9spHtgPEZaM1ivam2BYRvRCjg9w24+/Y2jjE1MR9SLVuTJ\nlhvdcrHaJhhBljvnIloxCuDX793ID1++AhBaxwUCv2y/fKIrVO9D797C//25jtj3rp3VEQpqEPD3\nR+9v4WvPx8bqvpYKDp/pjejnvXc18u0XLs7ZZrQ9RnFldHyLqbcQMtVno4/5LdurOXqul3/7K4r/\n+3PN/Xc2zjlO7tkckB/+rXdv5us/OZeS85dKsmXclQmGTDAWgkwwhKVkRb4idVz3xaiZALxxsd9Q\n5WTK48U3qyI1Ne1jeHQKl9vD1LSPQruVrn7XnEpUfUMTeKaNVVQ80176DBadCirghPPSiRsRk4tg\nvYNnIusJAix/4nC0YlSh3UpnnysUOz2DE6G/xye9oVeaSotzudAxHPOFcnzSOGbevNQf03ewTSCi\nn84+V+g1yERthtsT3BYdV4kUsRZSbyURfsy5VjOTHi8Ou4ULHcPYrKakxslJT+D6nb4ySEmRLabu\nSj5/giAIq4kVN8Gw2cx09saqzTiLcg23A/QNu/GEJXqGK6KsqyqKq14TVKICcLmn6R0yVlHpHXLj\nck8blgVVbCA5RR5BSBdG/plsfGxtXBsTf4mUhzp7XaF947UZqtvnYl1V0ZxtGu0bHlfJxt9qjNPo\nYw6e5+B1TdYPgn939rrYu6Uqpu5KPX+CIAirjRUnU+v1+qmtcNDeG/l4dHh0it2by2O2A5Q787GY\nTRF1tzaV0t47RteAi00NpYb7lTnzOX15EABHvhWrxWRYr6Ikn7xc41MdrmITVM+53jMaU0/UVoR0\nY+Sf17pHQ7ESTXh8nL4ygGooiagXHmfR1FY4OHYu9nF9eJuhuuUOdPsQlaV2xt3T1NTHxn+8fcPj\nKpn4C76SttriNPqYg9cueF1PXRpIyg+Cf+/eXM6Rs90xdVfq+ROEbOAvHy6f9z4r4bUqITWsmCcY\n7X0unvvFJf742SOoemeM2gzA9g1lhio0uTYLZrMpVDY17SM/18JdO6tZV1VMod1quF+ezcLUtI9c\nq5l11YU4C3MN69msFipKChIq4ATJFIUgQTAi2j/HJqapqygy9NmCPEvolZnBkSma65wxrygFlYei\n990+m9CdqE2A/FwLzfVO1lUVY7OYaa530lRTPKc9wW3RcWUUf/m5FloaS0Ljy3O/uERLY+mqi9Pw\nczM17SPPZsE14aW5zoln2k9NmbHKV/g4mWcL/NCytbGUoVFPTN2VfP4EQRBWEysiyTs6+dBiMfH+\ntzZxo98VUoQKKsaEq0vVhSlFtfe62LO5nN4hN9e7R2ltqeD5V64yNe3DZMrhwNYqpjxe+m66WV9d\nRNkaO0fP9lJTXkBNmYPvv3QZv38mVK//5iSbGpxsrF/DyYv9AZWqXTVc6rxJe88Y9ZWFtG6piFmB\nOHg8cykEZUuy4XKxWpO800G0f6r6NfQMTdDZ56Kzz0VtuYPaCgdrCnI5fWUwtK2h2sGagnxOGqpI\nxfq7kYqUxRypTLVzYxlfjRJuyM+18NsPtHDiQn9ErBXlW5NS3oo+vpbGEr743Tdj+nji/ds4c2Vo\nyZW8Mtln2/tcvPpmNxc6blJRkk9L41ou3xhmY62Ti53D1JUXca1nNPDaVHURlSV22s72Ul0WUJFy\ne7yoOiddg2M015VEnL979taJitQSIUnekuS9XGTaEwxJ8s4cVsQrUtEJl16vn2+/cJH33rmeJx7c\nGrGK9q6mUnY1lWKzmUPb9zav5Z/bOvjRK1ewWU1srFvD9e7RUJt+/0xIBeW+Aw08ePs6AB68Yx3f\n+cXFCAWbYL0H7mzkna11oT6DqwXfta1yzpWD68sd1Jc7ZBVvISOJ9s9n//k8r57sotBuZV1VEacu\nD/D6qW5u31bF1a6brF1jD227b18Dj71rM/n5VtxheUlG/t7avJbW5rXY7TYmJm792r0zLH6f+8Wl\nmMRi95SXi+03+fA7N8XEWjJxFTy+4JeUeH2cuTLEQ29rWlVxWl/uINdmwuP18ealQY6c66PQbmXc\n7aWkKI9TlwcYuDnB2jV23tCBLx6/eqCBttPdXPHO0LK+lNtbKvD7A69ibG1whs5ftnx5FwRBEOYm\n61+RSpRwefLSYNwv8uGTDpMph7azAVnLsYlpugbGDRO2p6Z9nND9oaRsr9fPiYtDhvWOnOuNSFYM\ntyPZlYNXy5cWITvx+2ewWEy09wS+FI5NTHPq8mBIBa2zzzU7ubi1LZjE6zYQPYjn7+GTiyAejy+p\nZGujWJtPXCXTx2qKU5Mph9NXhkMqXkBozLzQfpNTlwfpHnSHrvnYxDSHT/cy5Z2hZ3CCix03Y5K4\nV9P5EwRBWC1k/QQjmHxoRLIJg9FtDI9OUebMN6xrlJS9mL4FIZvxev3UVRQaltWWO7jWHZkIvZRx\nsRzxJzEeSbzzMTw6RW2F8ethZc780Jol4eOnIAiCsHLJ+gkGLE1itFECoyRlC8Lc7GupMIyB2nJH\nzIKRSx0XyxF/EuORGJ0PgF2qfM4k7+jxUxAEQViZpCwHQyllAr4E7ACmgMe01pfCyu8H/gjwAs9q\nrb+y0L7qyx188pHdSSVwJttGod3KRx7cyskL/VxPkJS9FH0LQjaztcHJk7+2nbazvTFJ1ffta0hp\nXCxH/EmMR5LofIT7wfrqIipK7bSd7uWOHdVxRS0EQRCElUcqk7wfBPK01geUUvuBzwHvBVBKWYHP\nA3uBceA1pdSPtNYLXsZ1KRKjjdoIT9BOZd+CkM1sbXCytcEZk6i7HHGxHPEnMR5JvPMR9IPwMfM9\n+xvktShBEIRVRipfkboD+BcArfUhYE9Y2WbgktZ6WGvtAV4F7lqKTpfqvetwJClbEBbOcsXFcvQj\nMR5JvPOxEFELQRAEYeWQyicYRcBI2GefUsqitfYalI0BxXM1WFZmnEy6WpHzEUmmnQ+n047FEvuu\nuhGZZvtikGPJXubjs6kgG853NtgI2WPnYonns+k4/tVyzjMZuQaZQyonGKNA+JU2zU4ujMoKAWMt\nyDBEI/0WohkfSYKF9tJgTYDh4Ymk6q2kaynHsjT9potkfTYVZIPvZIONkJaF9patr2iMfDZd1+n+\np344r/r5rSkyZBn5y4fL51U/1QvzJXvdZSKSelL5itRrwLsAZnMwToWVnQM2KqVKlFI2Aq9HHUyh\nLYIgCIIgCIIgLAOpfILxA+AdSqnXgRzgUaXUw4BDa/1lpdR/An5GYJLzrNb6RgptEQRBEARBEARh\nGUjZBENr7Qc+GrX5fFj588DzqepfEARBEARBEITlJ2dmRlRRBEEQBEEQBEFYGlbESt6CIAiCIAiC\nIGQGMsEQBEEQBEEQBGHJkAmGIAiCIAiCIAhLhkwwBEEQBEEQBEFYMmSCIQiCIAiCIAjCkiETDEEQ\nBEEQBEEQloxULrS3ZCilyoFjwDu01ufnqr/SUUp9EngAsAFf0lp/Nc0mpQWllBX4OrAO8AEfySb/\nmLX/WQL25wL/U2v9o7QatUCUUmbgK4ACZoCPaq1Pp9eqhSNjzvKQbTGQ6X6x2u8NSikT8CVgBzAF\nPKa1vpReqxaOUmof8Fmt9d3ptmWhZFuMC0tHxj/BmHXOvwPc6bYlE1BK3Q3cDrwFeCtQl1aD0su7\nAIvW+nbgvwP/K832zJffAAa11ncCvwr8dZrtWQz3A2it3wL8N7LvWoSQMWdZyZoYyHS/kHsDAA8C\neVrrA8DTwOfSbM+CUUp9AngGyEu3LYska2JcWFoyfoIB/Bnwt0BXug3JEO4DTgE/ILAS+o/Ta05a\nuQBYZn+1KgKm02zPfPkO8Iezf+cA3jTasii01v8E/PvZjw3AzTSas1hkzFk+sikGMt0v5N4AdwD/\nAqC1PgTsSa85i+Iy8P50G7EEZFOMC0tIRk8wlFIfAvq11j9Lty0ZxFoCg+YHgY8C31RK5aTXpLTh\nIvDY9TyB13O+kFZr5onW2qW1HlNKFQLfJfDLf9aitfYqpb4O/BXwzXTbsxBkzFlesiUGssQv5N4Q\n+KFpJOyzTymVFa+CR6O1/h7Z96NZDNkS48LSk9ETDODDwDuUUr8EdgL/RylVmV6T0s4g8DOttUdr\nrYFJoCzNNqWL3ydwLpoJvHP7daVUVj1OVkrVAb8AvqG1/sd027NYtNa/BTQDX1FKFaTbngUgY84y\nkyUxkA1+IfcGGAUKwz6btNbyi3mayZIYF5aYjJ7Za63vCv49O7B/VGvdkz6LMoJXgd9VSv05UAUU\nELixrEaGufULzxBgBczpM2d+KKUqgJ8DH9Nav5BuexaDUuoRoFZr/WlgAvDP/ssqZMxZXrIlBrLE\nL+TeAK8RyAf7tlJqP4FXxoQ0ki0xLiw9GT3BEGLRWv9YKXUX0EbgCdSTWmtfms1KF58HnlVKvUJA\nNeUPtNbjabZpPvwB4AT+UCkVfEf1nVrrjEwinYPvA19TSr1MYKL3e1l6HMLyspJiIK3IvQEI5J+8\nQyn1OoH3/R9Nsz2CxPiqJWdmZibdNgiCIAiCIAiCsELI9BwMQRAEQRAEQRCyCJlgCIIgCIIgCIKw\nZMgEQxAEQRAEQRCEJUMmGIIgCIIgCIIgLBkywRAEQRAEQRAEYcmQCUYWoJT6lFLqU3PUuaaUWrfE\n/X5NKdWQqvaFlU8yvptEGz9VSlUbbP+lUupupVSxUuqfZretU0pdW0x/wsojfCxLUOeXSqm7E5Qv\nuW+J7wpzsRS+m0Qf1Uqpn8Ypm5n9v1Up9dnZvz+klPr7hfYnrA5kgiEk4m0EtMQFIW1ord+lte5K\nUMVJYHVlQYhHpo5l4rvCXKTcd7XWXVrrd81RbQtQkUo7hJWFLLS3RCilaoFvElg91Q/8R8BHYDE4\nOzAAPK61vjq7Euw5YB+QR2BRsp8rpbYCfwU4gHLgc1rrL8zTDjPwv4G7Caxq/fda68/P/rrxBwRW\nWd5MYIXTh7XWHqXUfwT+A3ATOA9cBiaBauCnSqk7Z5v/I6XUbbPH85ta68PzOklCRpJO31VKPQWU\na63/i1LqHQQW7HNqrb1KqbMEbq6HCfhzN/AMsAe4BqydbeYLQLVS6gfA7wP5SqlvAVsJrPb+oNZ6\nta1ovKKZHc/+BJgG6ggsLvcY8BDwewR+PDsGPDn7OXwsuwd4Csif/feY1vrlefZfAfzdbN9+4JNa\n6/83+7SuBtgINADPaK3/l1LKyv/f3v3HWl3XcRx/AhP/IVFXbebGMhpv6AcSgjVGdgf4AzETWJOY\n1pYxnRP/MHQ6G5FCY23UWk6pu2pEZbFJKfeOmqBoUISOkF/6mtONShrMOQVbWyOPf7w/Z3w73Av3\nHg7cyz2vxz/3fL/n8z3nc9j7fL6f9+fHAVYD04E3gBrwMHAPjt22MhCxGxEbgEclbYyIFcBkSbMj\n4hLgaeAGYIukj5aVCr8k2/Lt5foLgYeAURHxIBnDHy/3gzHAZkmLTvOfxoYYz2C0zm1Al6QpwH3A\nVWRnaKGkycAqoLNS/vxyfiGwJiJGko3McklTyY7ViibqsQigvPaVwJcqCcI04C4ywRgDXBsRE8mG\n7Arg8+SNEUkrgYPA9ZUb3H5JnyE7kkuaqJsNTgMZu93AzPJ4JpkAT46Iy4Cjkg5Vyi4GkDSBTILG\nlvN3AwclzS3HHwK+L+lTwCFgQR/rYueWK8m2azyZ7C4h279pkiYBh4El1baM7LTfAdwg6XJgJXBv\nE+/9Q+Bnkq4AbgR+HBEfKM9NBK4hk/D7S+fsDjKBH0/+79JTS1nHbns627FbbWevAiaUwcjrgMal\nUY+QA5OTgG0Akt4GlgJPSaq37WOAeWR/YnZEfLLvH9/agWcwWmcTsL6M8HeTX9qlwFMRUS9zQaV8\nJ4CkXRHxL/Km9E3guoh4oByPaqIes4BJETGjHI8CPg3sB/ZK+idARLwMXEwmFF2SjpTzj5PT9j35\nffm7D5jfRN1scBqw2JX0SlmHfhGZ4D4CfAH4d6lLVQc5aoykVyPiz7287EFJO8rjfRyf6bCh5XlJ\nAoiIteTs15vA9hK3I4Gd1QskvRcRc4EvRhbqIGfr+msWMD4iHirH53E84X1W0n+BwxHxFjAauBro\nlFQDDkTE5l5e17HbHs527HaT7Xk9CX4JmAzMJtvcqg7gK+Xxr4CfnuQzvFU+w2s4Vq2BE4wWkbQt\nIj5BTjXeTI7ovl5GAepLl6rrF49VHg8vx+vIUYoNwG9obvRqBHCfpPXlfT9IdtY+Sy57qquR6zr/\nR99nsup1rl9rQ8AgiN0/AHPJuOoil47UgG83lKvx/7F6jJ5VzztWh67GOBwBrJN0N0BEjKLhHlfO\nvQCsBZ4HdpOzuv01AphR6WB9hJxxuInTa2cdu+3hrMaupH9ExHByYHAbGaszyZUL28ilWnXVdrZG\nLgE81WdwrNoJvESqRSLie8CtktaQX/pJwMWV5UlfB35duWRBuW4KOWOwhxzlWirpSXIUt965649n\ngEURcV5pkLaSyUVvNgPXR8QFZanLfLKxgGxAnIQOcYMgdrvJ/UFbgV3kZsJxknY2lNsELIyI4eVX\nVaaV847T9jQ9Ii4tHaevkuvV50bEhyNiGPBYOQfHY2Qc2WH6LtlWziY7d/31DHAnQEnOd5P7lXrz\nNLAgIoaVZKSDbGcdu+1pIGJ3I/AtYEu5fjHwV0mNsyCbgFvK43nA+Q31MOsTJxit8yNgfkTsAn4H\n3A58GVgVEbuBr5Fr3es+FhE7gZ8AN5cv+TJgazl/LbmR9bJ+1mM18CrwN+BF4OeStvRWWNJecpPs\nX4A/AUeB/5Snu8jNZf2tg51bBjp2twCXkJsMa2TsPtdDuUeBI+Qm805gbzl/CPh7RDzbx/ezoeEg\n8Aty+ecb5FKP75Cdp33k/W1lKdtFLv17h0xiXyGXoLxLbsbur8XA58r347dkgn70JOU7ybZ1D7AG\nOEC2s47d9jQQsdtdym8lE+KR5bUb3UXeD3aTez/qcb2DjPmVPVxjdoJhtVrt1KWspcovLyw7Wcf/\nbImIccAcST8ox0+Sv3yyYWBrZoPRYIpda1/ll3iWSeoY4Kr0SUTMAYZJ6oqI0WQSPaW+xMrax7kW\nu2bN8nTXOaSMcvW0AXu1pNVNvuwBYGpE7CWn7P9Iz6MaZk07Q7Fr1lIRMRZ4openvyHpxSZfej+w\nNiKWl+OlTi6slc5g7Jo1xTMYZmZmZmbWMt6DYWZmZmZmLeMEw8zMzMzMWsYJhpmZmZmZtYwTDDMz\nMzMzaxknGGZmZmZm1jJOMMzMzMzMrGXeB2vS0TFEK/bIAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x119cdce48>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "iris = sns.load_dataset('iris')\n", "sns.pairplot(iris, hue='species', size=2.5)" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " total_bill tip sex smoker day time size\n0 16.99 1.01 Female No Sun Dinner 2\n1 10.34 1.66 Male No Sun Dinner 3\n2 21.01 3.50 Male No Sun Dinner 3\n3 23.68 3.31 Male No Sun Dinner 2\n4 24.59 3.61 Female No Sun Dinner 4\n" ] }, { "data": { "image/png": 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kSW3Qb7B9BngxQEQcDHxpYBVJkrQE/d55ZCPwmxHxWWACOG5wJUmS1L++jrFJ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\nrkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x11bb95e10>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "tips = sns.load_dataset('tips')\n", "print(tips.head())\n", "tips['tip_pct'] = 100 * tips['tip'] / tips['total_bill']\n", "grid = sns.FacetGrid(tips, row='sex', col='time', margin_titles=True)\n", "grid.map(plt.hist, 'tip_pct', bins=np.linspace(0, 50, 20));" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<seaborn.axisgrid.JointGrid at 0x11bc983c8>" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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sK2NjUzXHzvYCpD4LkU9zHVZLMptUzCYzLsfM506kaToxTSMe14nHx3w9+r8XiWmEwrFU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\nWbyuD/Dky2fzppv8PpWQ38NELIWqKJiWZfdQUsCrqlhYrG+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</tr>\n", " <tr>\n", " <th>1</th>\n", " <td>32</td>\n", " <td>M</td>\n", " <td>01:06:26</td>\n", " <td>02:09:28</td>\n", " <td>3986.0</td>\n", " <td>7768.0</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>31</td>\n", " <td>M</td>\n", " <td>01:06:49</td>\n", " <td>02:10:42</td>\n", " <td>4009.0</td>\n", " <td>7842.0</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>38</td>\n", " <td>M</td>\n", " <td>01:06:16</td>\n", " <td>02:13:45</td>\n", " <td>3976.0</td>\n", " <td>8025.0</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>31</td>\n", " <td>M</td>\n", " <td>01:06:32</td>\n", " <td>02:13:59</td>\n", " <td>3992.0</td>\n", " <td>8039.0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ "<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>age</th>\n", " <th>gender</th>\n", " <th>split</th>\n", " <th>final</th>\n", " <th>split_secs</th>\n", " <th>final_secs</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>33</td>\n", " <td>M</td>\n", " <td>01:05:38</td>\n", " <td>02:08:51</td>\n", " <td>3938.0</td>\n", " <td>7731.0</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>32</td>\n", " <td>M</td>\n", " <td>01:06:26</td>\n", " <td>02:09:28</td>\n", " <td>3986.0</td>\n", " <td>7768.0</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>31</td>\n", " <td>M</td>\n", " <td>01:06:49</td>\n", " <td>02:10:42</td>\n", " <td>4009.0</td>\n", " <td>7842.0</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>38</td>\n", " <td>M</td>\n", " <td>01:06:16</td>\n", " <td>02:13:45</td>\n", " <td>3976.0</td>\n", " <td>8025.0</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>31</td>\n", " <td>M</td>\n", " <td>01:06:32</td>\n", " <td>02:13:59</td>\n", " <td>3992.0</td>\n", " <td>8039.0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ] }, "execution_count": 45, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = pd.read_csv('marathon-data.csv')\n", "df['split_secs'] = df['split'].apply(pd.to_timedelta).astype(int) / 1E9\n", "df['final_secs'] = df['final'].apply(pd.to_timedelta).astype(int) / 1E9\n", "df.head()" ] }, { "cell_type": "code", "execution_count": 47, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x11bcccd30>]" ] }, "execution_count": 47, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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n+k7W0bzxTjNv7mnOqHJZJQUWehxuDKRuB+4hQa20JRjUwNW4FF/XZGqXj71I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\n640c1GxWI9d8dP7QY5LCL4TIQBLY0mR4tmOk7Vq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"text/plain": [ "<matplotlib.figure.Figure at 0x11bbb8f98>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "g = sns.jointplot('split_secs', 'final_secs', df)\n", "g.ax_joint.plot(np.linspace(4000, 16000), np.linspace(8000, 32000)) # reference if the speed is steady" ] }, { "cell_type": "code", "execution_count": 50, "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>age</th>\n", " <th>gender</th>\n", " <th>split</th>\n", " <th>final</th>\n", " <th>split_secs</th>\n", " <th>final_secs</th>\n", " <th>split_frac</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>33</td>\n", " <td>M</td>\n", " <td>01:05:38</td>\n", " <td>02:08:51</td>\n", " <td>3938.0</td>\n", " <td>7731.0</td>\n", " <td>-0.018756</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>32</td>\n", " <td>M</td>\n", " <td>01:06:26</td>\n", " <td>02:09:28</td>\n", " <td>3986.0</td>\n", " <td>7768.0</td>\n", " <td>-0.026262</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>31</td>\n", " <td>M</td>\n", " <td>01:06:49</td>\n", " <td>02:10:42</td>\n", " <td>4009.0</td>\n", " <td>7842.0</td>\n", " <td>-0.022443</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>38</td>\n", " <td>M</td>\n", " <td>01:06:16</td>\n", " <td>02:13:45</td>\n", " <td>3976.0</td>\n", " <td>8025.0</td>\n", " <td>0.009097</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>31</td>\n", " <td>M</td>\n", " <td>01:06:32</td>\n", " <td>02:13:59</td>\n", " <td>3992.0</td>\n", " <td>8039.0</td>\n", " <td>0.006842</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ "<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>age</th>\n", " <th>gender</th>\n", " <th>split</th>\n", " <th>final</th>\n", " <th>split_secs</th>\n", " <th>final_secs</th>\n", " <th>split_frac</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>33</td>\n", " <td>M</td>\n", " <td>01:05:38</td>\n", " <td>02:08:51</td>\n", " <td>3938.0</td>\n", " <td>7731.0</td>\n", " <td>-0.018756</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>32</td>\n", " <td>M</td>\n", " <td>01:06:26</td>\n", " <td>02:09:28</td>\n", " <td>3986.0</td>\n", " <td>7768.0</td>\n", " <td>-0.026262</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>31</td>\n", " <td>M</td>\n", " <td>01:06:49</td>\n", " <td>02:10:42</td>\n", " <td>4009.0</td>\n", " <td>7842.0</td>\n", " <td>-0.022443</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>38</td>\n", " <td>M</td>\n", " <td>01:06:16</td>\n", " <td>02:13:45</td>\n", " <td>3976.0</td>\n", " <td>8025.0</td>\n", " <td>0.009097</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>31</td>\n", " <td>M</td>\n", " <td>01:06:32</td>\n", " <td>02:13:59</td>\n", " <td>3992.0</td>\n", " <td>8039.0</td>\n", " <td>0.006842</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ] }, "execution_count": 50, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df['split_frac'] = 1 - 2 * df['split_secs'] / df['final_secs'] # split_frac indicates if a person reduces the speed (>0) or increases the speed (<0)\n", "df.head()" ] }, { "cell_type": "code", "execution_count": 51, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<matplotlib.text.Text at 0x11d8fd390>" ] }, "execution_count": 51, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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cGqTd3UnIufZDa60o6AgBkHeaPWgJaVFLEtJi3lpvGhaMAk9MPoyGxuHQddUo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\nufC5nfjdbnxuJwGPG7/bhct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"text/plain": [ "<matplotlib.figure.Figure at 0x11d8eceb8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.kdeplot(df.split_frac[df.gender=='M'], label='male', shade=True)\n", "sns.kdeplot(df.split_frac[df.gender=='W'], label='female', shade=True)\n", "plt.xlabel('split_frac')" ] }, { "cell_type": "code", "execution_count": 53, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x11f7c31d0>" ] }, "execution_count": 53, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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Pk1NJQgivJ30M7iXBIIQQwoUEgxDC60kfg3tJMAghhHDh1s5npVQA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\nwSCEEMLF/w/oLig9OB2lBQAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x11f8e5f28>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.violinplot('gender', 'split_frac', data=df, palette=['lightblue', 'pink'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Seaborn Regression Examples" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "anscombe = sns.load_dataset('anscombe')" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<seaborn.axisgrid.FacetGrid at 0x116435b38>" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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Cqms4xuNLKqlraAUgIz2V+2ZN5XzL63BlItKbAjjBrDMHeW7VplBLcXFRsKW4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\nrL8Ebie4mtzlwG+A84wxEen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"text/plain": [ "<matplotlib.figure.Figure at 0x107c2b588>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.lmplot(x='x', y='y', data=anscombe.query(\"dataset=='I'\"), ci=None, scatter_kws={'s': 50})" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<seaborn.axisgrid.FacetGrid at 0x116e11080>" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x116e11438>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.lmplot(x='x', y='y', data=anscombe.query(\"dataset=='II'\"), ci=None, scatter_kws={'s': 50}, order=2)" ] }, { "cell_type": "code", "execution_count": 13, "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>total_bill</th>\n", " <th>tip</th>\n", " <th>sex</th>\n", " <th>smoker</th>\n", " <th>day</th>\n", " <th>time</th>\n", " <th>size</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>16.99</td>\n", " <td>1.01</td>\n", " <td>Female</td>\n", " <td>No</td>\n", " <td>Sun</td>\n", " <td>Dinner</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>10.34</td>\n", " <td>1.66</td>\n", " <td>Male</td>\n", " <td>No</td>\n", " <td>Sun</td>\n", " <td>Dinner</td>\n", " <td>3</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>21.01</td>\n", " <td>3.50</td>\n", " <td>Male</td>\n", " <td>No</td>\n", " <td>Sun</td>\n", " <td>Dinner</td>\n", " <td>3</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>23.68</td>\n", " <td>3.31</td>\n", " <td>Male</td>\n", " <td>No</td>\n", " <td>Sun</td>\n", " <td>Dinner</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>24.59</td>\n", " <td>3.61</td>\n", " <td>Female</td>\n", " <td>No</td>\n", " <td>Sun</td>\n", " <td>Dinner</td>\n", " <td>4</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ "<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>total_bill</th>\n", " <th>tip</th>\n", " <th>sex</th>\n", " <th>smoker</th>\n", " <th>day</th>\n", " <th>time</th>\n", " <th>size</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>16.99</td>\n", " <td>1.01</td>\n", " <td>Female</td>\n", " <td>No</td>\n", " <td>Sun</td>\n", " <td>Dinner</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>10.34</td>\n", " <td>1.66</td>\n", " <td>Male</td>\n", " <td>No</td>\n", " <td>Sun</td>\n", " <td>Dinner</td>\n", " <td>3</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>21.01</td>\n", " <td>3.50</td>\n", " <td>Male</td>\n", " <td>No</td>\n", " <td>Sun</td>\n", " <td>Dinner</td>\n", " <td>3</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>23.68</td>\n", " <td>3.31</td>\n", " <td>Male</td>\n", " <td>No</td>\n", " <td>Sun</td>\n", " <td>Dinner</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>24.59</td>\n", " <td>3.61</td>\n", " <td>Female</td>\n", " <td>No</td>\n", " <td>Sun</td>\n", " <td>Dinner</td>\n", " <td>4</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tips = sns.load_dataset('tips')\n", "tips.head()" ] }, { "cell_type": "code", "execution_count": 14, "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>total_bill</th>\n", " <th>tip</th>\n", " <th>sex</th>\n", " <th>smoker</th>\n", " <th>day</th>\n", " <th>time</th>\n", " <th>size</th>\n", " <th>tip_pct</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>16.99</td>\n", " <td>1.01</td>\n", " <td>Female</td>\n", " <td>No</td>\n", " <td>Sun</td>\n", " <td>Dinner</td>\n", " <td>2</td>\n", " <td>5.944673</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>10.34</td>\n", " <td>1.66</td>\n", " <td>Male</td>\n", " <td>No</td>\n", " <td>Sun</td>\n", " <td>Dinner</td>\n", " <td>3</td>\n", " <td>16.054159</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>21.01</td>\n", " <td>3.50</td>\n", " <td>Male</td>\n", " <td>No</td>\n", " <td>Sun</td>\n", " <td>Dinner</td>\n", " <td>3</td>\n", " <td>16.658734</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>23.68</td>\n", " <td>3.31</td>\n", " <td>Male</td>\n", " <td>No</td>\n", " <td>Sun</td>\n", " <td>Dinner</td>\n", " <td>2</td>\n", " <td>13.978041</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>24.59</td>\n", " <td>3.61</td>\n", " <td>Female</td>\n", " <td>No</td>\n", " <td>Sun</td>\n", " <td>Dinner</td>\n", " <td>4</td>\n", " <td>14.680765</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ "<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>total_bill</th>\n", " <th>tip</th>\n", " <th>sex</th>\n", " <th>smoker</th>\n", " <th>day</th>\n", " <th>time</th>\n", " <th>size</th>\n", " <th>tip_pct</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>16.99</td>\n", " <td>1.01</td>\n", " <td>Female</td>\n", " <td>No</td>\n", " <td>Sun</td>\n", " <td>Dinner</td>\n", " <td>2</td>\n", " <td>5.944673</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>10.34</td>\n", " <td>1.66</td>\n", " <td>Male</td>\n", " <td>No</td>\n", " <td>Sun</td>\n", " <td>Dinner</td>\n", " <td>3</td>\n", " <td>16.054159</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>21.01</td>\n", " <td>3.50</td>\n", " <td>Male</td>\n", " <td>No</td>\n", " <td>Sun</td>\n", " <td>Dinner</td>\n", " <td>3</td>\n", " <td>16.658734</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>23.68</td>\n", " <td>3.31</td>\n", " <td>Male</td>\n", " <td>No</td>\n", " <td>Sun</td>\n", " <td>Dinner</td>\n", " <td>2</td>\n", " <td>13.978041</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>24.59</td>\n", " <td>3.61</td>\n", " <td>Female</td>\n", " <td>No</td>\n", " <td>Sun</td>\n", " <td>Dinner</td>\n", " <td>4</td>\n", " <td>14.680765</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tips['tip_pct'] = tips['tip'] / tips['total_bill'] * 100\n", "tips.head()" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<seaborn.axisgrid.FacetGrid at 0x1170164a8>" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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Dj7+2qcWvzVqwW1F3QhCE9SMCvEk2s0I/OjbD/d8/S9mw+5JuNIpis64QCfUShHAQAQ6R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\nAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x117016f60>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.lmplot(x='total_bill', y='tip_pct', data=tips)" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<seaborn.axisgrid.FacetGrid at 0x116dfb6d8>" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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8iRyx2g5g9S2O+eNopwsX+HHxNKnqIWJ6x2U//lovxroS0j3YP/La90cvw+gf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\n1zpGV62Rl2B/EGI0RMIo5VKxyqcfPdP2+9bo4vT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crBIEwI8vr/Pcy8tdoyhFkVEUudFo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\nIBAIBCNHiJFAIBAIRs7/B/UNnXTZunJAAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x116e020f0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.lmplot(x='total_bill', y='tip_pct', hue='sex', data=tips)" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<seaborn.axisgrid.FacetGrid at 0x1172f9a58>" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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GmBRwHHjgMu5bRERERES2qe3BQ9z6UHd3u/cnIiIiIiLtoUniRERERESkJQoeRERERESk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\nRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x11733ec50>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.lmplot(x='total_bill', y='tip_pct', hue='sex', col='time', data=tips)" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x118bdf3c8>" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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FGcZ1gdlEly4h0b3ShjJeKd0R8djWM9FHrhhFxcMPcfaXP6Pbfone6mULdjgr\nHJ3BzZ+cR25+avu+QCDI9i3HeG3zEQqKopQKi9Dea+pmGHe8SUu/G15fgB/+dRflNf1/Ip+SbJOk\n3+ZE/SlOu0uZmDmOEVEmWB2sPszv3vpT1AXRrWZj/9MN+nzUvPD8gLyXbcQIUmfPGZD3GuoC/iCV\n5xu67N+zo2vXT7sI35ejIiy5mCiM/ZvTg3dOVA9IwodQ2YZDp2uYOMqYtWIu2Hj8BZ46/iwAZpOZ\nW6d+kMvyui4us/n0y9ETvsnCquIrYhpnogv6fH2ru2O3Q2vX+SKZ16wha81aQ/fnd+bz+Ynyz65X\nbDYLk2YWcNnC0QMX1ACT7p1u1Azwoid/eupAryZzDVdev5fnTr7Yvh0IBth4YlPEc03dlK344KSb\nmJQ1IepxI6h79eXoByN9dhESftKkSZiTkvCWlw1gZEObp8mL3XHpbWGv18/p49VdZpYnEkn63bAM\n8EibirpmyiPU6DGKAEF8wfDWqdfvjXjuylHLsJoirzuQbJOhr1VPPNZlnzkrm8IvfInkSb1beKb5\n4EGqHnuUUz/8PvWvvTrQIQ5JWzZqWlv6NwGwtqqJk0eqByiigSdJvxtTSrIYyLyf7LCQ5TJuwnJY\n7CwqmBu2b/nIxRHPnZA5jv9c8BWuGLUUS6fFUnKSs5mUNTGmcSa6YDBI0Nv1yzJQXYXnyBHyP3Yr\ntpGj+vKGVD+zYQAjHLoqzvVuERRzD6P1EnlotvTpdyMnI5mPr5nMfU8fiPSsJkxmqp2abmrspCRZ\n+fDVCofd2Ktm3aJuYFz6GE43nGVS5gSm5URvleY5c3nvhGtZVDCPHed2kWR1MDV7ctQ7AKMwmUxk\nXLGSmgiJuuaZDaQtWMior36NY1/+fByiG9oKR2dw6lhHKz2/KI2svFRKT9aERukEYURhGpm5zqhr\n5dodFkaPS9wHuTI5qwcv7z3HfRsOdHvOuiXFOB02/vnikYjHkx0WfnTbItJSep7uLSI7UHWIB/b/\ngwZvI3nJOdw246MUpMRvxmO8BYNBal98noqH/tFlyKZtxAgCLa34a2uivLqr/E/cRtqiyHddRtLo\nbmHrM4c4d6aOEUVpXH71RFxtM+lPH6/m7V1n21vxkRZZMZtNrH3fDIpK4jtgQyZn9UNBds8rXdU3\nelkyrSBq0ve0+DlRVs+McVJ4LRpvwMfjRzewt+IdcpNzuHHCOnKdOTR6G0mzu3jw4L9o8Ibqw5z3\nVPLIoSfOKjXMAAAP3UlEQVT4wmWfinPU8WMymchcuYqgz0/lv/4ZdsxbHnnth2ic02dKwm+T4nKw\n5qauo8kqytw8/fDe9u9XU4TuG7vDwlXXTYl7wu+JJP0eFGSnMGFkOkfO1EXt4imvbiIv00lhtpPS\nqq4Pak3AiExZJrE7G49vYvPp0IiUquYafvXmHwgEAjT5PYxMLaS2JbxmzLnGviW24SrzqqvxHDlM\n45u7L/1NIqxKJsDT1MrbO8/S2NBCwB8Iu6EKBoK4MpJwd5p529riZ8+O0wlfYlke5PbgFw/v4XBb\nwjcRcb2K9tIKORnJXY6ZTXD98rGyNm4PDlSH1y1q8DbS5A/NkTjTUNrl/AyHlLOAUCmGotu/SPrK\nqy75PZLHG3v4aySBQJDH/76HXa+e5ODeMg69c77LOZOn53fZVxuh0ZdoJOl341S5m2OlHbW2g0De\nRYk9yW7huqVjAFg+M7zcrcNm4YefXsTaxSWxDnXIK0ot6NP52cmJ+6AsHrqb1xDxfJsNk9VK2qIl\nZF6zOkZRDU0tzV727TpDTWV4Ak92dpRIz8xO5vVtJ7q8tnh8YrfyQbp3upUUYZJG8QgXy2YUsn1/\nOZkuBzcsH4vNGhpNMntiLreumcy2vaWkJttYt6Sky5eEiGzd2Gs431TB0boTuGypYDLhbu0YPmcx\nWfB3GuM/Xerqh0lbuIjazS90zNK12bAkJeN3R1ggxGSi8PYvkDJNViC72MkjVTz72D78vq6duWMm\n5jBj7ki8Xj/r//pml+N2h4Ukp41gMNjnL+HBJEm/G3kZySyfWchLb4W6F5IdVlYvLGb0CBerFxZH\nfM3SGQUsndG3VqtRPHPiRTad2ooJuKp4RVgphXSHi6/M+RwN3kaSLUlUeqpYf3QD55sqmZEzhclZ\nE9l4YhMN3kYWFcxjvsHXyL1YUskYRt35deq2vQRmM+7tr0ZO+ADBIN7qxJ08FE8vbzocMeGbLSaC\ngM1h4eyp2ogllltb/Ox+9RRJSTZmzu/DPIlBFtMhm0qpBcCPtdYrlFLjgQcI9ZLsA27XWndb5SIR\nhmwCHDlbR2Wdh2ljsmUVrEt0sPowv9lzb9i+L132aSZmxq/m+FDnq6+n6vFHaTlzhpRp08la/W5M\nVisNe/dQ+utfdv9is5msd6/DNW8+jsKiwQl4CPjDT7dGXfgcQkMye6qpX1ScwbXvnzXQofVJd0M2\nY9anr5T6D+D/gAtTUH8B3KW1Xkbomeh1sbr2QBtflM7CKfl9Tvg17hb++eJh/vjkO+w7VhWj6IaG\nY3Unuuw7Xndy8AMZRs79/rfUbd1C89EjVD2+nsrHHgXAMaoYLD1MYAsEqH7ycU5+51u4d+0chGiH\nhuJx3ffJ92YRlaQEbxjG8kHuUeCGTttzgK1tP28ErozhtQdNU7MvYhE1nz/A//xtF8++fprt75Tz\nvw+/xdsGTvzjM8Z02Tcuwj7RO776ejyHLxrxtDu02IwtM5P8j96KxeUCsxnXgkU4p0yN/EaBANUb\nn451uEPGVddOIX9kR918+yXMoM/sxdyeeIpZn77W+t9KqZJOu0xa6wvZ0Q1EWZGgQ2amE6s1Mafc\nV9R4+PFf30CfrCEvM5kv3zKb6eM7Jl+9daiCik5jeIPArkOVvGtByeAHmwBycy+jOnAjTxx8Dkwm\n3jNpFYsmyIPESxXISOKUy4XP3fGw20yQ4Ns7yb18ObnXXcPYdasI+v2YbTb8LS28863v4NZdl/S0\nmoLk5roGM/yEdtv/W059nYfmJi9+f4AH/7AdT1PkwoCRTJ5emNCf52A+yO3cf+8Cant6QU1N4o55\n/d36t9EnQ9Pcz9d4+OmDO/npZxe3T9EOeLtW6rNbTFRU9K6g03C0MHsBC5csaN828mcxEHLf/yHK\n/nxf+6LoLefPc/hXv+XUY09RePsXsLhcmG12INT4yP/qN8g4fozqDU/TuKdjMlfqiivl7yICk9WE\n1Wrhg59dSPnZOvbtKuX44a6lF4qKMyg7W4/JBDPnjyI1wxH3z7O7L53BTPpvKqVWaK23AKuBzYN4\n7QF3qjz8L7XG3YK7qZX01NBiFCPzUlk+s4CX3joHQHaag6vnJ+7CCmLocc1fgHP6DE7/6Pu0lnZM\nYGs5eYLj/3EHZqeTvA9+uH3tW5PJRPLYcRR+7vO4d75O65kzOKdNxzlRxet/YUiw2SyMLMmi2ePr\nkvTzCl2svnF6e9VNiyXxpz4NZtK/A7hXKWUHDgCPDOK1B1x+ljOs+6YoJ6U94V/wsdWTedfskdQ3\ntaJGZWKzJv4/CDF0BJo9nH/wz7SeOxf5eFMTZff+gdotm8n/+Cex5+UBoVm8afMXwvzBjHZoa2ps\nZdtzh9u3zWYTl18zkRSXg9LTtYwsyRwSCR+kymavBINBatwtpKfasZjNnCir5/sP7GyvxWMywVdv\nmcXkYpklKgbP+X/+g9rnn+3dyRYLo//zv0gaHZpfEmhpwXNYY8vJxZ4v80p68vbOM7y8Kbygois9\nCXddqOGXmu7gfR+fhyMpMaY+SZXNfjhX1chvH32bc1VNpKfa+dTaKRw8VRNWfC0YhNLKJkn6YlBd\nPHqnW34/Nc8/S8EnbqOl9Cxnfvrj9slbWWvWknPDe2MU5fBgizCK50LCB2ioa+Gvd7/GtNlFzF1S\njNWWmANQQGrv9Ojvmw5zrq2IUl1DK/dtOEB2WtfVr3LSjbsiloiP5PHjw3dYrdiKRpI8eUrEdXKD\nbevkVj/1ZNhs3epnNuDrQ+19Ixo3KY/s3JT27eQIa2N4W/28uf1UlzuCRCNJvwdnKxrCtqvrW5g1\nPodpYzpa9QunjGB6D5M6hBho2dfdQOrceWCxYM8vYOSX76D4m98K1d+5uNvWbCbjipUA+OrDy1QT\nCOBvCP93LsLZ7BZu/Ogcrr5+KldeO5mi4ugjzo/pikGMrO+ke6cHM8Zlt4/AARhXlEZ6qoOv3DyL\n0spGrBYTeVIrX8SBJTmZws/cHravdusWPId02L6U2XPIXntte39++uKleA52rAbnGF2Moy9r6hpI\na4uP+tpmsnKdWKxmxqpc9uw4xZH90RN7emZiF1mUpN+DW1ZOwGIxs/9EDaPzUrn5XR231IU5Kd28\nUojB4Tl2lEBTI85JU/DVdJ31bcvJpfnIYUwWK46iItIWL8Fkt9Ow6w2sOblkrbomDlEnviMHzrN5\nw0F83gApLgfvvmk62XmplJ3tWsjObIZAIFR+ecmV4yO8W+KQ0TtCDGGl9/yOhp1vAGDLzSXvwx/n\n7K9+3lFi2WLp+NlkIv+Tt7WP2xfR+f0B/vLbV2n2dEyyHDUmk7U3z+St10/z6otH2/dbLCZuuW0+\nrc0+MnNSEmLoZlwKrgkhYstz5HB7wgfwVlTgObifkV+5k9S583DNWwDmTqNIgkGqn3wiDpEOPa0t\nvrCED1BXE1rJbdqcIqbNLsRmt5CemcxV100lLT2ZnBGuhEj4PZHuHSGGKL+761R/f4Mbp5qEU00i\n4PXS8IXPhg0vDvh6X0PGyJKddvJHplN2puOh91iVC4Rm3S5bNZFlqybGK7x+SfyvJSFERM4pU7Fm\ndpobYjaTtmhpx6bNRvrlV4S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"text/plain": [ "<matplotlib.figure.Figure at 0x118c3a358>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.stripplot(x='day', y='tip_pct', data=tips, jitter=True)" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x118f2d550>" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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7dgKHhw+gUtG6Zz/OI9Ftjsqz9De2kHz3PTR951tEfNGgZJg1m4TMbPwdHSQs\nXoJu3UY6O/qp/85/EOjqAqD/TDnVP/k5rvPxn31PfQNF//kjHPv3EXI5MK9Yhb3XhausnOQHHsQw\nYwb63Hz62tto+K+folCpMS1dRiR4+bozEY120j+/i1bm0drUR3BwjeulawooH5UN8Lj97H+3Jhbw\nARov9HLbPbM4e6oNh91LkZRK3YjyDBVlLXjcPlQqBaERrzOZdSxenc+sBZkEgyESkwzY+yb2iv1y\nHcMJvZErSVIu0Z78T2VZ/r0kSd8ZsdsM9E3k+YdsXJpLSY6FA+Vt9Hv8eHxBVs5OjwVTgOVz0lBf\n5Aapw+Mfs62yfjjgAzR0ONlxuCEW8Ic0dbpGvxSFQkG2zcTpEdO/VUoF6xZmY9Jr2HOqBaNOzf1r\nC7GadVjNOuYVpVzT+55qMkxplKbOprw7mhdWKVSsz1lDTd95QiOCpEal4Senf4Vsj+ZdXzv/Fn+z\n5PNkmKb+GsTqpCRSH3yY7ldehlAIjS2NkNtN2BMNDJpUGwZJon/3u3Gvi0QitPzX93FXlKPLySH9\nk0+jLygk7PejTrVhWrgIT0U5yoQEbI98lM7f/Tbu9a6TJ0CtjgV8AG91FXnf+Acchw7St/tdXCdP\nkLzl3ljAjz3vwnmMc+biPlUW22acPQe12Uzy5rsBcJ48QdvPfhwdkgaYSudje+wJGv/tn2M3j/sP\nHcA4Zy6es8NXc8oEM2HXcJCPfrFNLp8vSHaelXA4zMIVeeQUWPENBCg7PBwPCktSUV9k8ZNEi57Z\n8zPQ6tRk5ibx/A8Pxu1vb3Fwy8ZiDrxbSzAQJjUtgaW3FFBztoNDu88z4AkglWZwy8ZiVKrJGZAx\nkTdy04G3gb+QZXnoE14mSdJ6WZb3AJuB3RN1/tE67B7eORG9VD1W1YmUm8QnNpVQ1dhHVoqRO5fn\n0ufyc+Z8d2yUzvpF2ayel8G7J5qHPusoFQpslrFDJwcCIVKT9HSPuLSbV5RMSpKe3741PNzrruV5\nbFqWS2OHi4oLvRh0Ku5dXcCu40102r3cs6qAVRdJ9XxYfHrek5R1nqHL24tOpaPN3c4t2at4tzGa\n9VOgYHHaAt6sezv2Gm/Qy57mg3xU+shkNfuqJN+5mcRVawj22dHl5BJy9OM4fAiFWk3iytUoDQb6\nimYwcOE8AKqkJALdXbjLo2lAX1MTrc/9hJxnv0rzd79FsLcXgIRlK8j67J8DYH9rOz5PY+yc6pQU\nFOGxYyK1fKXfAAAgAElEQVT6Dx2k/91dAPhdLtqf/xWatHQCncMTBo0ls7BuvhuFWo23tgZD4QyS\nbrudrpf+D6XBQNKt6+l7751YwAdwl59BbUuLGy0UdrkwLViEaV4pvrZWEuYvxDhvHv3vv0+go52k\n9behy5rcNWPra7t5608Vscf2Hg9PfHYFy9YWEg5H6O5wkZGdxKKVedh73NTVdMeuCLLzrezcWonX\nHY0P6dlmEi0GHH3D6bvUjASy860Uz0nH6/GzYHkuCuC9bdWx+TlnT7VhSTGy4CqHbn9QJrKn/3XA\nCvydJEl/N7jti8APJUnSAlUM5/wn3J6y+Ms3uamfh9bP5GhVJ69Xd7K7rIWPb5L49udWcbbOjtPr\npyAjkfwMM59/YB47jzWhBO5akY+UZ2HbwYa4sfsr5qRz5/I8/vheLe29HhYV27hvTSEatZLctASq\nG+wUZCaSm5bArmNNpFuMbHw0l1l5Fr714knqB+t0nDzXhS8YYv3CqT/J41qolCqWZiziv8/8Jpbf\n1yjVfGzWw4QiYUosRfT7HWNeFwpP7XK1o6kTE2M3WtUWK8l33R23P/crX8N1uozwwAAJixbT+M/f\njNsf7O6md/u2WMAHcB07wsCdm9EXFGB7/GO0/uSHhN1ulHo9aU98AqVej/PkiVhqyVBcQnDU/QX8\nfpLvupv+g/vxNTVhKJEYaG6i4e//FlViIumffBpNcgqN//ZPRAZz8o4D+9CMrsSpUKBKGFtBUp2U\nhHnpsrht1tvvGPfPbaLVVHbEPXY5fNTXdHPyUCPdHS4UimihNI1WhTlJz/ylOfR2uymYmUKf3UvL\niHLqHS1OVt8+k8qTLfTbvaRlmVm5roitL5zEO9hxbKjtYcX6ojETMjtbx37Gb5SJzOl/kWiQH23d\nRJ3zckaXTFAoYM/JFuSmaIbJ6Qnw6+1V/POnlrN1/wVaBm/0LJFsfP6BeSQYNLx+oJ43Dtbh8GTz\n1Y8tZvuhBrr7veRnmMm1JZCUoOOvH1sYO8fOo43sPNqIUqlgy6oCpFwLf/+ro7T3RvN5u8ta+NSW\n2bGAP+RQRfuHNugDtLk74m7oBsJBzvae48nZj3GmqwJ/OEB2QiYtrugoC7VSzS3ZKyeruRNCoVZj\nXjIcHA0zS+LSLhpbWizojuRrbqTrpf9loL4eQ0kJltvuwFhcjOvMaZyHDpC45hZUBiOa9HQSV67G\nvnMH7jPDgxMUajWmRYtIujX6Z9j28+fwlEf3hxwO2v/nl5iXrYg7d6Cri8Q1a/HK1bHtSWtvxXrH\nJlzHj+Jvjc5X0c8sHjOUdKoxmnRjtjXW9cbq5UcicOpIE4XFqbz9WiVuZ/RKpqPVQUFx6pjXJibp\nePwzywkGQmi0amqrOmMBf+h49m4PSpUi7v5ARs5lb2delr3HjUqlJPEiGYfxmBaTswC2rCrgbIMd\n3+CqOLctyqGhIz7Y+vwhdh5tjAV8iJZcOCF38sttVbHRPnVt1fzlw/NJSdJzqLKdmuZ+3j/Vyl89\nugApL3qTtrKul/97b3gs8As7ZUKhcCzgQ3TU0Ona7rgROgBJCWM/mB8m4cjYNEQoHOS7x39Eqzt6\ng9Oqs3B/0Wb84QBL0xfcFPn862F77HHCAwO4K86gzc4h/cmnCHs8OI8ciqVVNDYb9t3v4W+oB8BT\nfgaFWk2wt5fOF56PHUs/Yya2Rx4j5PWiSUvHOLcUT1UlqsREzMuW0/rjH6JQKrHedTe+5qa4doRd\nrrg0TuyYhUUU/Ou3cZefQZuWjmHWbBQKBfl//0+4z1aiUKsxzpodGwL6Qepsc1B5shWFUkHp0mxS\nbNdeo37Bilzqa7tx9EXTsPMWZzEwMPYq8lxlRyzgA3jdAbQaFWq1kuBgHEiyGsjKs7J/Vy3n5U7M\nSfqLzrRNtOjZeN8cDu+5gNfjRyq9ttW1HP1e9rwp09IY7ahKpRlsuFtCobhkFeWLmjZBvygrkX//\n7Coq63qxWQ3MzE7itf111Lb0x55jNesu9nmnqqEvFvCHnJA7OTziUtEfDPP6gXq+PBj0h64gRuqw\ne8dsSzRpue+WQl59/wIRINGo4f41Bdf2Jm8S2QmZzLIWx6ptqhQqMk0ZnOkernFi9/WhVCq5p2DT\nZDXzhlIlJJD1zBfGbM/5m6/iOHQAVYKZpFvXU//1r8Tt956TCTniUwUD52txlZ+m/Ve/iAZxIHHd\nehJXr6X52/8SC+re87UkrlwdG6oJRMfob96C69RJQn3Rz7ChRMI4ew4KpRLLug1x51Ko1STMX3D9\nP4BLsPd4ePV3p2KTm85VdPDYp5aSlGwkEonQ1tyPUqkgI3t8PecEc7Rn3t7swGDSYE0xUXeui9qz\nw2kwg1GDNXXsOPqEJD0b7pkVncmbYmL2gkzOHG+mYnBQh9cdoL/XS0FxCvU10YEalhQjcxdlYTBq\nKZLGVwmg3+4lHApjTTUB4BsIsHNrJS0N8TFFLm+neE4auYXjn2UN0yjoQzTAjrxJumVVPgP+ICfk\nLmwWA4/dNpNAKMyeslbCg38YCQYNy2elsXvUPYHUJENc7xyiJRd6+gdo6XaTYzONOf+yWWnYnT5O\nnuuKtWfjslxsFgMr5qTT1eelODsJ7SSN372RPrfgzzjecQr7gJ2FtlLqHY1jnhO8yfL4H7RwIIBR\nmoVRmhXbps3Owd8yXA5YXzgDhWbUn7FKhePA/ljAB3C8vxelSh3fiw+H0WZmYrl9I+4zp9BmZpF8\n7wO4T5eRtHYdKpMJjS0NU+n8CenBj8f56s642ayhUJjtL5fz8FNLef0Pp+hsi16tZ+db2PLo/LgR\nMS6nj4A/hHXURCilUklW3nA9o8ISG3fcNxu5vB2DUcviVXmYzDrKj0dz9RCttnlB7qKtKdpJzMpN\nonRpNq2jlkz1DQRZvCqfJavz8ftCZOUloVQqCYfD9HZ5SLTo0Q6mmnu73dRUdqA3aJg1OCLo3Teq\nqBn8AsottHLXQ/M4eahxTMAf0nC+RwT9q6FWKXnstmIeu62YQDBMdaOdRKOWLz++kL2nW9Fr1dy5\nLJf0ZCP3rM5nx+FoMbWFM1PZvCKPhnZnrOAaQEaKka8+d4hwJIJOq2L1vAxOnOtCqVCwZVU+JbkW\ninOSkBv7cHj8lBalxO41pFkMpF1jju5mpFGqWZW5NPbYokvizbpd9Pmif1RGtYHlGYsnq3nXJRwI\n4Dx6hGBPNwmLlqDLvbpRGt7aGtr/51cEOtoxzJpN5v/7bKzoWuanP0v7r36Br6kRQ3EJ6Z94kpDb\njfecTNjtBoWClHvuY6Auftw9kQiqpLG9YV1OLsmbt8DjHyMc8NP4j/+Avz16L0VpMJD3jX+46oAf\ndDrwVJ1Fm5GJPi//ql47msE4tixHX6+XU0caYwEfoKWhj/qabmYMlijZv6uGipMtRCLRAH33I6W4\nXX4Ovncee7eb/BkprFxfhFqjork+mtMvmZdB8Zy0WLrk4aeWcL66K1bye++Oc7HztTb1U1vVhS3D\nTGvTcLZArVFiTTGi1ihRDv7cejpdvPlSOW6nD41WxfrNEklWA1tfLIt9oVWdaWPFrYWxgA/RBdjP\nVXZg7x47v2WIKeHqy5Z8KIN+ZX0vrd1uSotSxrVKVa9jgG+9eIIeR3SM8+p5GXzm3rlxz3nw1hnc\nuTwPfyCM1RzNuX/u/rnsOdVKW4+beYXJ/Hp7VewKwecP0dnn5ad/dStA7IMUCIaparBzvrWf1m43\nW1blo/kAZh3f7IwaA19d9pccbj1OMBJkRcZSkvWXrk3S5+snQWNCrZx6H+HWn/wQT0U5AD3bXifn\nr/4G4+zxlZaIhMO0/fw5gr3R9IC3uoqu//s9mZ/9PN7aGvztbWR94YuoLdZYMNakpFL07/+B55yM\nNj0dbXoGzpMn4m7g6vLysd51N/72NpyHD4FCgXnFKlwVZ+jf/z6JK1cRCUdiAR8g7PXSv+99bA8/\nOu737q2pofkH34vNF0hccwvB3l6CdjvmFStJ3nLvVX2JlMxN58ieC/hGzYG52OLjHnc0B9/e0h83\n2aq1qZ+Kk63I5e3YB1fFKj/RglKpICnZwPs7hxf1aWvqY91dEr1d0eGa5iQ9M2bZKD8ef6UP4HH5\nWLQqj54uN8319mgp5tX5vPG/p+lsc2LLMHP7vbM4tPs87sFZvAF/iH1v11BYkhp3BWPv9tBUZx9z\nDmffAHlFKTSc7x2zT6VSUHINw7un3l/Mdfr9rnOx8fh/VNbyFw+WMn9GCkfOdnCuqY/CrETWlGai\nHHHz4+1jTbGAD3Cwop2NS3NRqRSU1XSTZjGwdJYNk16DaUStLK1GxS2lmfQ4BrAkaBkYVZfD4fbj\n9ASoarCTlWoiNy2B37wlc6gyerPybL2dXoePp7fMjnudZyDA7rIW+px+ls9Jozjn8qV1j1d3sr+8\nDbNBw92r8slMGZtammx1/Y2c6a4k1ZDM8owlsXLKIyVqzWwq2HCRVw/r8fby8/Lf0uxqJUFj4uOz\nH5lStXp8ra2xgA/AYC2epPUbSPvox664cEnI4YgF/CED9XV0vfS/2He+BUTz6NlfehbjrNlEwmHC\nA15URlNcbj1h0WKyvvQsruNH0SSnYLntDoLd3VjWbSD1oUdRKKDp378VG6/vOnEc6+YtY9pztb38\nnm2vxU0QcxzYP7zvta2oTCYst41/CKdGq2LzI/N44w+nY7NcS+alM2dRNpWn2mKBU6NVUThYPXfo\nJm1cuzpdsYA/pLGul8ioC6LqM+3kz0hh59bK4VWxqjq55Y6ZHNtfFxuzr1Ir0enU/N8vj+H1BEhN\nT+CuB+fyzhtVsSuQrnYnu7fLeN3xEzwHvAEudu81K89CdXl77D0pFFAk2UhNT8DlHOCC3I1Gq0Kj\nVWFK0LJgeS6maxj0cVMH/VA4WixtqBft8gbicu+hcITthxu40OrgjYP1AOw51UpTh4snNg5XxnNe\nZNZteV0Pr+2ri+Xtj8s2nvlIadxzDpS38cLbMv5AmNQkPVK+heoRubeSHAtf+dnB2E3gB24p5Fh1\n/Djho9UdcUE/HInwnT+U0Tg4hOy9smb++tGFzL1E3u5UbTc/fXV4skl5XS///rlV6KbQfYHy7rP8\n95nfxC6TT3dV8vkFT1/Tsbae306zKzpE0BVw82LVS/zLmr+96JfIZAj2do/dGInQv/s9tJlZJK1Z\ni+PIIUJOJ+ZlK9AOFizz1tQQtPdimDsXTUYGgRFlGvRFM7G/s2v4cMEgPdteB6WS9l//gmB3N7qC\nQrI+93k0qTbsb79Fz7bXiYRCWG/fSMp9D9D5h9/RNzhJS5ORQeqDj8RN0IJoQTZdXj6+xmgRQVWC\nmaR16+Oe42tpJtjXh6FEii3G4mttwVdfj764mLD38uUF3BXlVxX0ATJzLDzx2RU0nO8l0aInp8CK\nQqHggY8tpOJkKx6XD0uKEa/bT4JZR26hFY1WRcA/3AkrnptOU52dgRFza1JspjFfBEqlgoqy1rhx\n9Q21PazaMINN98/h9LFmdHoNpUuzeetPFfgGR/50d7g4sreOztb4EYGdrQ4WLM/l1JHhUVKZOUks\nXJHHBbmLgcHlGbPyLMyYZSPBrOPU0SbCoTDzluRgyzDHpaoKilPYdP9cVBeZLTxeU+Mv5SqFwmH+\n7YUT1LU5USqiM2c/vkkiHI6MmQQRDIXZcyr+0mzv6VY+ekdxrLe/ujSTw5UdscX6UpP0VDfa427U\nnpC76OzzxvLuPn+I3+06h3/wm7+7f4BsWwIPrC2kqdPFvMJkDle2x4362XaoAatZR9eInkiyWc+B\n8jbq253MzreSZNLGAj5E77vtPdVyyaB/rCr+D9fh9lPdYGfBzLFjiifLnqYDcUshVvZU0+npJs14\n5Tb6Qn6Ot5fhCrhZkr6ANld73H5XwI3T77xsKuhG0qRc+j0N1NbiOHggtmh575tvkPe1b2B/dxeO\n/fsAUBpNpD/1Kfre2YmvuRnTvFKS77k3OnRzhIjPR/uvfk6wJ3pV4Kuvo/MPvyN5y710/fF/Y8/r\n3b4NZWJiLOBDtKCa+/SpMe1TWyxkfuZzuE4cI2C3EwkEcB49SuKq1agtFjpe+A39e6OT6NUpKeR+\n5eu4T5fR+fsXowdQKkm6dR0DFy6MOfYQ5UWqiI5HQqKeuYviZ/OmZSai0XTQVGenqc5O+fEWNt4/\nh5mz09jyaCnH9tUD0UVQ8meksGGLxJ7tcqxnvnL9DDrbHOx67Wzs/vaC5bn0dI0tn1JX083RvReI\nRKK9/PwZybGAP6Sny01WXvw6uVl5FpbfWohGo6Kprpdkm4llawsxmrQ8/pkV1J3rRm/QkD8zGYUi\nmm5Kz0ok4A+RaNHT2tgXl6qqr+mhurx9zM/iatyUQf83O6qpG7yECkfgvZMtLJqZSmFWEivmpscN\npdy4NJet+y7gHDFhQq9VsetYEzsOR3s0m1fm8+xHF3Kwop1EY3REzfM7qsecd+QVmdPjZ8Afn87p\n6R/giw8Pr/Y0esRPMBTmI2uL+M1OGZ8/hF6rIt1q4FdvRmvRvHuimU0XmZqtv8RavHDRIdUkXcPN\nnYk0Ou+uQIEv6ONg6zEStQnMSZFQKpQEw0Eqe6oJhkOUps5BrVTxg5PP0eiMpuveaniPRbZS2j3D\nN7syTelTJuADaDOzMJbOx1N+Zsw+VWIivqPDhdMifj89b27DdfxobFvY48ZddiLaO//9C7jKToBS\ngWnBwrhAnbhqDZ2/fyHu+L6mxlhph5EGamvGbIv4fVju2Ejf4BWEOjmF5M13o9RqMZUuoP6b34gN\n2bTveovMzz4TC/gAwZ4eet/aHiv6Fm18GE91FVl/8UVcp8tQ6nSx44887wfFNxDk7Kn4hYxOHWlE\nq1Pzzutn8Q0EMZm1sUlM+TNSeOBjC1Gpo7NtAcxJekxmHR0tDmyZZrJyLbQ29tF4oTc2maqwJJXT\nR5tif2uhYJjTR5tISjbQ3zs8DDuvKJnSJdnseesc7c39ZGQnsu4uCZVKydJbCpBKM9AbNWg0KiKR\nCKeONFFZ1oJGq8bnK2TmLBuv/PZkLD11+lgTi1bkjXnffb3XV6xN9c1vfvO6DjCRPB7/Ny+2/Y+7\na3GP+pY9fLaDt440kmszsWl5LlmpJh5eP4MFM1MxGzWxYZIKYP3CbF47UI8vEMYXCFNZ18va+Znk\n2BI4fLaDA2faKMgw09DujP2il81KY92IWbJGvYZTNd30j8jXbVicTUmuhdO1PZTVdpGaaODciPH6\npUXJDARCeLwBirIT+fwD8/jj7lpGXpz0uXwsmJlK8+AEMZNezZyCZMpqulAqFaRZDPS7/fz3axU8\nv6OabscAgVFzCJaU2EizTl697tGsegsnOk7FJmXNT53L9vp3ONl5muMdp2h2tbIgdS7/cfKn7G7a\nT1lXOSc7T5Ost/Je077YcUKREPmJuRRbinAHPcy0FPHxWY9i0kyd9wqQsHgxYe8A4UAAUKBQKkla\nfxumhYtwHoyvKKrNzMTfFh+41MnJ2N9+K1qCIRzG39yMacECEleuQaHTkrBwCUnr1+OprCTU3z/i\nvEsxL11G//t74o6X8sBDDNTWEh4YUSPmwYex3rEJ87LlRMJhfE2N0aAeiRDo7MB1bPiLKOLzodBq\n8NXXj2pnCoHWlmjN/EEKlZr0jz+JQqEk5PHgleM7T7qCAsyLl/JBCAXDnDrSFNfxMZq0XKjuxDNY\nHyfgD9Hb5SY9K5FXXyzj+IEGzp3tIDUtAWOClre3VnJ4zwXamvuxphjJyE7CnKSnSEolwaxn7qJM\nFq3I4/j++rjzKBQK7v3oApx9A0QiEaR5GSxfV4jeoEVv0GBK0FE0y0ZaZiIel4/X/3CaQ7svUH6i\nBaNJi73Hw8H3zhMKRQj4Q9TXdGMwaePmC4TDEZKsBuzd7rhzr1hXeMXZuCaT7h8vte+mDPp1bY5Y\nUBwpAjR1uVlYnIqUZ+XNQw28e7KZHFsCj902k/wMMw+tn0FX/wDVo8bXJhq1/P6dGrr6vPS7/Vxo\ndfDEHcVIeRYMOjW1Lf0cqmgnJUlP+mBAXTAzFa8viFatYsPibLasKuC3O2X+uLuWs/V2apv7uXtV\nPhnJRlbNzcDh9nOwoh2nN0BHr5ee/gH6XD6CI6ZnWxJ0fOXxxczKszC3MBnPQJB9p9u40OrgUEU7\nNoueXceaKKvpJhSOjAn4AHcszY2NMJoKkvVWlqUvJs2YyvqcNfR4e6lzDK8u1unpQqPUcKxjuMqj\nJ+jFpDbQ4GyOO1ZhYh6PlNzP+pw1LElfMOUCPkDrcz/FeegAIYcjWvveloanspxAby+qhIRYD1qp\n15P+qc8wUHeBUP9g50ChwLLhdtxlJ0YdVQGRMM5DB/HWnKN/717Snvg4YY+HsN+HYcZMNBmZaCxW\n9DOL8TU1odBoMC9fiXnRYhLXriUSDKAymdAVFEIkEq0A2t9H5+9eIBLwEwkE8FSdRZOeMeaKwbxi\nFYHODsKe4V6m7ZFHUer0sXQVgPWuzdjffIPe7duiAV+lil2OKnQ60p98alxr/o6HWq3E4/bTNWLo\n5soNhcjl8SnPYDBMd4eTrvZo2iYYCNPa1Ec4HKayLDpaKRyO0FRnp3hOGmWHG9m74xztLf0kp5rI\nKbDS3+ulZ0TMmbckmyLJhkIZLe1QWJxKosXAyUMN7N4u09bUT83ZThQKqK/toX6wqm44FKG5zo5G\nq4qVfhhiSTaMuSeQPyOFJWvycbv8GBO0rFxfdNFyEKN96IL+/BkpnKrpjluzdiSLSctLe2pp7nLT\n6/BxqqabWflWVsxJx2zUEg6FOVgRnxsuybWMmUWbkqgnzWpkx+FGBvwh+t1+TshdrFuQhU6rort/\ngMOVHXT0ejAbteSmJfDr7cPLyUWI9gi+8NB8ZmYn8evt1XH3CTr7vHxkbRGVgzlApULBE3eUEA5H\nSLMaSDbreWGnHNemPpefpi533KIwIy2VbGxaNvaScLIZNQbyE3OxGVM52XkmVm5hSJ45hwsjvggA\n5qXMJhQJYR8cu69X6Xl81kOYtdc+DX+i+dvb6BrKcQNEItGVpsJhgl1d6HJySbn/IxhKJNKe+AS6\n9HTMy5ZF6+WkpceWQuzb/V5c/RvjvHk49r0/fNjBq4isz38BwmH69+5moOYcjoMHMBSXkPrgIzgP\nH8QrV9P33jsoNBqSN98dXdS8vg6vXI3z8CGURiPec/GfMUOJRMjhiI77B9SpqaR/7EnMq9YAoElN\nJfWhR0mYvxDTvFI0ycmoTCasm7egy8ml57UR6yJFIhjnL8By+0bSnvg4uswPtspmXlEyqelmkm0m\nVq4vIn9GKh0t/XEjeIrnptPR4oi7sev3hdDp1WNu5Go0Sk4daYreHwxFaGnoIyffwtxF2Wi0KnR6\nNfMWZ7NoZR57tldzbF89bU39VJ9pJ8lq4Ni++liZBmCwiJsC14jRgeFwhMKSVFob4+PN+s0S3R2u\n2PBOY4KWdXeWkJpuRpqXwez5meMuQXG5oH9T5vQ1ahX/9KkV+AIhevu9/P2vj8UF0wSjBu+o4ZNl\n57qIRGDXsUYUCgVrSjM5VRNN+dy1Io95hSlsPxwfdLJSTVSNuiIIBMPUtvSzsDiVH/3pTKyU8r6h\nJdhG5dgjI67LMlKMNIworpaRbOSuFfnMzk+mocOJzaLnhZ3naO/1oFYpuG9N4Zi6PDqNihlZiZTV\njJgUlmzg4fUzMRs1VxzeORXckr2SE52nY+meXHM2t+ev40jHCZz+aO9Hr9KxPHMJG/PXU9ZVjivg\nZpGtFKt+ir+/i91kGWHgwnlsDz1Kz5uv4zpVRtKaW0hctQbr5i24T53EU12FQqki68+foeN3vyXQ\n1YV5yTISV6+JC/pAbKSM/a0dcdt739qOv62NoH34s2vfuQOFShU3SzfkdMSlfIYYZ88h5Z576d/3\nPpFQiKR1G1AZDIQHb+6GPJ7YlYm/rRX72zvxt7XiPX8ey0WWWdRYrFivcsTOeCkUCgpLUiksGe79\n3nHfHI7svUBXu5OsPCvL1hagUERLGg/JzEmiYGYqF+ThvyO1RjlmLVsgtm6ux+XH7fJH/+/0cW5U\nxc7y480oRg2qUSohf2YKbc0j0nCJOhatzCXgD1FZ1opGq2L52kJSbAnc/7GFNNT24PcFKSxJRafX\nXO+PaIybMugP0WlUZKYm8MxHSnnjYB3+QJjbluRQnJ3EtoPxAVyjUfLTreWxmFzb0s8/fWo5aVYD\nVfV2XN4A964uYMeRRkKhMHMKk0m3GvAHwxw5O/zLVSggLz2B7v6BuNr5ABdaHaycmxEbh69QEOt1\ne31BNizK5tV9F+hz+UkyaZmRlcQ3/+coGclGHlw3g20H6mMF2YKhCK8fqOPWhVmxBVvUKiX3rMon\nI8XEgD9EdYOd3PQEnr57NnnpN8fKUr6Qn2S9ha8s/QLHO06RpDWzKms5BrWery79Sw60HiUUCbEq\ncymphuiIpZtpZq42M2vMTdeR9AWFNH3v27FetLe6CqVej6e6OjbCpveN10h/6mkK/+XbsddFIhF0\nBYXDqRSFgqRb11+yHcH+UdP2I5GLrmqly8rB9ujj9O7YRiQSIXnTXRjnzKXzdy/Ecvze87Vkfvpz\ntPznd2MVNT0V5UTCERwH9sXuSQQ62ul/fw/arKzY8xRq9WXbORH0Bg3r7pLitq2+fSYqtZLmeju2\ndDMrNxRhStDh8fipPtOOwaBh+a2FhEJhzoyaiJWVZ+HdbdU0DKZo2pv7cTt9KBSKuE6dQqlgyap8\n9r8zXGhx8ap8SpfmEAqGOS93kZikZ8X6IlQqFas2zGDVhhlx51KplOOu0XOtbuqgP2RhcSoLR+W5\nRpZNkHItGLTquE54KBzh5LkujlV3xoZIFmaa+e6fr+L1A/XsLmuhsq4Xq1nLouJUTtf+/+3dd3Rb\n153g8S86CAIsAAH2Xh4pSpRE9WrJkmVJVixbttxjx44Tl53MZteTnOxMTjIn2bObMvFmshmPk0mx\nnSYgVQwAABv6SURBVHgniS3LVcVykdWtLlntUSRFUSIp9gKQBAEC2D8eBPKJlCxbYr+fc3yO8Pge\ncPlM/N599937+zUTZdKxck4GCbFR9AaCxFgMqiGmzCQbT6wuoigznnOXOlgwJZmc5BiOnm3iN++c\npMcXwGLS8Y01RdS3dvP27ioAqus9XGjwEHPFkvPeQIjFJSnMm5REbXjVrz1GmXXwnQenEwqFvnCG\nvZG0u/ZTNpx9h56Aj+yYDJ4q+ZpqqCbeHMeanLGfYC3lmb/DfegA/qYmTGnptLz3Nt5z54iSCrHN\nmqOqLAXg3r9fmaXTT+vWLZizsql/5Y/0XLiAZVIxSd98is6DB+muqEBjMhLs7lbm4q9cRdPrf4sc\na1+5Gn28XbVIzJiSEk6kdgR/vdIpMSanYJs9W1nJGx52Maak0n3mNO3bP4oc23nkMC2b34sE8r52\n7xuQpdNXc5HcX/6a9l07CHg8xMydhyk17QbO5s1hMOhYuDx/wPbpczKIiTVz+lgdJ4/WUjo3g/nL\ncjl+4CIaYPLMNOITLJGAf1lVRTPF01Mi0yk1GqUMY3Z+As5kG5cutpOYGktyOIXyzIVZzFyYNdS/\n5nUZF0F/MOsW57J8Zjrenl5c8Rb2nbo0YJ82d49qTvy5Ojd7TtarCq60un2U5Bp5ck0Rf9oqs+GT\nSvadrOdb95bwjTuLeXnzGZravRRlxrN+aR57T17i1W1l9PgDnKpq5b+tL+HVbXIkpXNXT4Ct+y+g\n0aqDdV1zFzMkp+q5QmJ8FOmJVrQaDXmD5N8eSwHf4+vkb2VvRZKoneuoZtO5D7hfuovuXi+BUACr\nQb2SuMvfRQgiD2sr2qp4q2IT7T43s5NKWZW1DO2V99OjgEavJ2bOvMhr69S+Ggs9NRcH7K9PGOTB\nnFZL3W/+PdKL7jx2FK3JTPSUKXQeUx54e/Z/StfpkyQ98Q2MKam493+KpaiI2AWLlHbodLgPfIrB\n7iB+5Sr0Vitpz32X5rc2otFpcdy1Dn9TE7W//lVkBk732TLiV60e0JyAxzOg9q/B7kAbFaUuszip\nGF10NPbbV32RUzZiqiubef/NvuyuFypbePjpubjbvJw4XMPejyqor2kn2mZUpVqOiTWzYHkemXkO\nWho7ycixR7JiJqXGXnfWz5EwboM+KDNyLveeZxW6OF7erAzVaGBRSTJxg8xwaXV7rxyWp9Xdw6vb\nyugOB+6apk5e317Bk3cUsXxGGhcbPZRKLkwGLX8OB3yA+pYuXt9eQYtbPTe5qd1LSa5DNb5vMupY\nNSeD2GgTB8404Iw1s3ZhtipdxFjW5G0ekDXzUlcDb5Zv4uMLOwmEgsxKms4jhevRarS8dvYtdtYo\nc8DnJs3krrzVvHDsD3gDypDapnPbsBmsLE6bN+CzRjNTahrxK1fT+v4WCAYx5+QoVbVCIVq3bFJ2\n0miIW3orDX9W18HtPluGr17deenYuwfb/IVc+v1vCXZ24t63B39jI44776LnYjU9Fy4Q6Oykt7UN\nNFou/O8fR8b6u2SZmLnzVFMuCYUI+XvR6PV9w0EaDTGz52Kw22nauAGCQfQOB/avrEUbZabRaKT7\n7FnMOTm4HnxkyM7dUKg4o64X3OPt5fjBC6oFUZVyE5NLU5FPXMLvC2C2GFiwPA9QKm81NXjQ6bXE\n2qMiSdZGs3Ed9PvTabV8885i1i/NUxItRRtpauvm3b3nI73wKJOO22dl8FllC/X9FkBMybZzvEJ9\ne1fX3MmLb52MZNnc9dkl1i3OibzXZQ1t3cwocHJQ7vvjml3kYvXcTKobPNQ2dWI26nhkRQFRJgPL\nZqSxbMbI3w7fbGnWFGKNMapSiEkWF9uqt0de7790GCk+D5vRyicX+wpO76nbT4zJFgn4l51uKRtz\nQR/Aee99xC9fQaCrE1NKamSbpWgSPReqsUwqxpSeQeu29yNDMQDm3FzVw1kAtFratm6JPCMAaNn8\nHmg1tLz7DgD+hnpq/vV54pbfpjreX3+JXvfAsn1RuXlYp06jZfN7hPx+4m5dTlR+PlH5+djmzqe3\npRlzVnYkj1DyN5+5aedmuF1epNXfYA9z9QYta+4vobqihdwiJw6nlX3bKzmyT0kJXnainubGTm65\nvWDAsaPNmJyyeSOiTHrMRuWP1WI2MD3fiVajITclhsdWFuKKtzCjwEkopBRVuXtRDgtLUjgoN6hW\n9c4qdLH3iqf3XV4/VotBtd/S0lTuXZJLMBRCr1VmDa1bnIvNYmTp9FTmFSexdmE22cmj93bwZtBq\ntBTZC2jraUOv0XNL2gLiTXGcbFEv3nFaHPgCvkiBlcsybGmcd19UpXModU2lIF79IGys0JrN6G0x\nqm1Gl4uovHz0sXFoNBqi8vPxVlYScHdgKZ5M4qN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"text/plain": [ "<matplotlib.figure.Figure at 0x118c447b8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.swarmplot(x='day', y='tip_pct', data=tips)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2.0 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.6" } }, "nbformat": 4, "nbformat_minor": 0 }

mit

caganze/wisps

notebooks/lsstdsf_pca.ipynb

1

93855

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import splat\n", "import wisps\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from wisps import Annotator as an\n", "from wisps import datasets\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "spex= an.reformat_table(wisps.datasets['spex'])\n", "data=an.reformat_table(wisps.datasets['stars'])\n", "\n", "data['spt']=data.spt.apply(wisps.make_spt_number)\n", "spex['spt']=spex.spt.apply(wisps.make_spt_number)\n", "\n", "data=data[data.snr1>3.]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Create a training set, a test set and a set to predict for " ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "features=wisps.INDEX_NAMES" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Inspect the features, I know these features (at leasr spectral indices) are correlated but also have high variance, I could pick my favorite features and use those instead " ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "#remove infinities and nans\n", "def remove_infinities_and_nans(array):\n", " array=np.log10(array)\n", " infinbools=np.isinf(array)\n", " nanbools=np.isnan(array)\n", " mask=np.logical_or(infinbools, nanbools)\n", " array[mask]=-99\n", " return array" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "spex[features]=spex[features].apply(remove_infinities_and_nans, axis=0)\n", "data[features]=data[features].apply(remove_infinities_and_nans, axis=0)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "from sklearn.decomposition import PCA\n", "pca = PCA(n_components=3, svd_solver='full')\n", "pca.fit(spex[features].values)\n", "spex_pcaed=pca.transform(spex[features].values)\n", "proj_sample=pca.transform(data[features].values)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "colors=an.color_from_spts(spex.spt.values, cmap='viridis')" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(-1.0, 1.0)" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig, ax=plt.subplots()\n", "plt.scatter(proj_sample[:,0], proj_sample[:,1], alpha=0.006, color='k')\n", "plt.scatter(spex_pcaed[:,0],spex_pcaed[:, 1], color=colors, s=5.)\n", "\n", "plt.xlabel('axis-1', fontsize=18)\n", "plt.ylabel('axis-2', fontsize=18)\n", "\n", "#ax.set_yscale('log')\n", "#ax.set_xscale('log')\n", "plt.xlim([-1., .4])\n", "plt.ylim([-1., 1.])" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "import pandas as pd" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "train_df=spex\n", "train_df['axis1']=spex_pcaed[:,0]\n", "train_df['axis2']=spex_pcaed[:,1]\n", "train_df['spt']=spex.spt" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "pred_df=data\n", "pred_df['axis1']=proj_sample[:,0]\n", "pred_df['axis2']=proj_sample[:,1]\n", "pred_df['spt']=data.spt" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "from sklearn.preprocessing import MinMaxScaler, StandardScaler\n", "from sklearn.model_selection import train_test_split\n", "from sklearn.ensemble import RandomForestClassifier\n", "from sklearn.metrics import confusion_matrix,accuracy_score" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "\n", "def add_labels(spt):\n", " label=0.0\n", " if 17<=spt<20:\n", " label=1.0\n", " if 20<=spt<30:\n", " label=2.0\n", " if 30<=spt<39.:\n", " label=3.0\n", " return label\n", "\n", "\n", "\n", "def compute_accuracy_score(features=features, split_size=0.5):\n", " \n", " scaler = MinMaxScaler(feature_range=(0, 1))\n", " #train_set=train_df[features]\n", " X_train, X_test, y_train, y_test = train_test_split(train_df[features].values, train_df['label'].values, test_size=split_size, \n", " random_state=123456) ###grammar \n", " scaler.fit(X_train)\n", " X_train = scaler.transform(X_train)\n", " X_test = scaler.transform(X_test)\n", " \n", " rf = RandomForestClassifier( oob_score=True, verbose=0.)\n", " rf.fit(X_train, y_train)\n", " \n", " pred_labels = rf.predict(X_test)\n", " model_accuracy = accuracy_score(y_test, pred_labels)\n", " \n", " #pred_probs=rf.predict_proba(X_test)\n", " #decispth=rf.decision_path(X_test)\n", " \n", " return model_accuracy, rf, scaler" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "rf_features=['axis1', 'axis2', 'snr1', 'snr2', 'f_test','line_chi', 'spex_chi']" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "train_df['label']=train_df.spt.apply(add_labels)\n", "pred_df['label']=pred_df.spt.apply(add_labels)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [], "source": [ "train_df[rf_features]=train_df[rf_features].apply(remove_infinities_and_nans, axis=0)\n", "pred_df[rf_features]=pred_df[rf_features].apply(remove_infinities_and_nans, axis=0)" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [], "source": [ "acc, model, scaler=compute_accuracy_score(features=rf_features, split_size=0.5)" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [], "source": [ "#apply model\n", "pred_set=scaler.transform(pred_df[rf_features].values)\n", "pred_labels=model.predict(pred_set)" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(array([56205., 0., 0., 6988., 0., 0., 2314., 0.,\n", " 0., 1245.]),\n", " array([0. , 0.3, 0.6, 0.9, 1.2, 1.5, 1.8, 2.1, 2.4, 2.7, 3. ]),\n", " <a list of 10 Patch objects>)" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.hist(pred_labels)" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "66752" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(pred_labels>0)" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x1c8dc122b0>]" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.plot(pca.explained_variance_ratio_)" ] }, { "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": "3.7.6" } }, "nbformat": 4, "nbformat_minor": 4 }

mit

somma/ipython_notebook

python_async_stuffs/Coroutine_lecture.ipynb

1

23791

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# [Part I] Introduction to Generators and Coroutines" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## generator" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def countdown(n):\n", " print '> counting down from {}'.format(n)\n", " while n > 0:\n", " yield n\n", " n -= 1\n", " print ''\n", " print '< countdown'" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "> counting down from 10\n", "10 9 8 7 6 5 4 3 2 1 \n", "< countdown\n" ] } ], "source": [ "for n in countdown(10):\n", " print n," ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "+ `generator` 함수를 호출하는것은 `generator` 객체를 생성하는것이지 함수를 실행하는 것이 아님\n", "+ `generator`.next() 를 호출하면 함수가 실행되고, \n", "+ `yield` 를 통해서 값을 **생성**하고, 함수의 실행을 잠시 중단하고, \n", "+ `.next()` 호출을 통해 실행을 재개한다. \n", "+ `generator` 가 리턴하면, iteration 은 멈춘다." ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "<generator object countdown at 0x035BD8F0>\n", "> counting down from 3\n", "3\n", "2\n", "1\n", "\n", "< countdown\n" ] }, { "ename": "StopIteration", "evalue": "", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mStopIteration\u001b[0m Traceback (most recent call last)", "\u001b[1;32m<ipython-input-26-b93f85de60e0>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 7\u001b[0m \u001b[1;32mprint\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnext\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 8\u001b[0m \u001b[1;32mprint\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnext\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 9\u001b[1;33m \u001b[1;32mprint\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnext\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 10\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mStopIteration\u001b[0m: " ] } ], "source": [ "# calling generator fucntion creates the generator object not start the function\n", "x = countdown(3)\n", "print x\n", "\n", "# call `.next()` starts generator object.\n", "print x.next()\n", "print x.next()\n", "print x.next()\n", "print x.next()\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## tail -f (python version)" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "d:\\work.python\\ipython_notebook\\python_async_stuffs\n" ] } ], "source": [ "import os\n", "print os.getcwd()" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "collapsed": false }, "outputs": [ { "ename": "KeyboardInterrupt", "evalue": "", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", "\u001b[1;32m<ipython-input-37-857ca5c48920>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 28\u001b[0m \u001b[1;31m#logfile = open(\"run/foo/access-log\",\"r\")\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 29\u001b[0m \u001b[0mlogfile\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34mr'd:\\work.python\\python_async_stuffs\\coroutine_www.dabeaz.com\\run\\foo\\access-log'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 30\u001b[1;33m \u001b[1;32mfor\u001b[0m \u001b[0mline\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mfollow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlogfile\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 31\u001b[0m \u001b[1;32mprint\u001b[0m \u001b[0mline\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m<ipython-input-37-857ca5c48920>\u001b[0m in \u001b[0;36mfollow\u001b[1;34m(thefile)\u001b[0m\n\u001b[0;32m 13\u001b[0m \u001b[0mline\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mthefile\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreadline\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 14\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mline\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 15\u001b[1;33m \u001b[0mtime\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msleep\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0.1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 16\u001b[0m \u001b[1;32mcontinue\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 17\u001b[0m \u001b[1;32myield\u001b[0m \u001b[0mline\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mKeyboardInterrupt\u001b[0m: " ] } ], "source": [ "\"\"\"\n", "generator 강의자료에 run/foo/xxx 등이 있는데 거기에 있는걸 복사해서 실행환경을 만들면 됨\n", "\"\"\"\n", "# follow.py\n", "#\n", "# Follow a file like tail -f.\n", "\n", "import time\n", "\n", "def follow(thefile):\n", " thefile.seek(0,2)\n", " while True:\n", " line = thefile.readline()\n", " if not line:\n", " time.sleep(0.1)\n", " continue\n", " yield line\n", "\n", "# Example use\n", "# Note : This example requires the use of an apache log simulator.\n", "# \n", "# Go to the directory run/foo and run the program 'logsim.py' from\n", "# that directory. Run this program as a background process and\n", "# leave it running in a separate window. We'll write program\n", "# that read the output file being generated\n", "# \n", "\n", "#logfile = open(\"run/foo/access-log\",\"r\")\n", "logfile = open(r'd:\\work.python\\python_async_stuffs\\coroutine_www.dabeaz.com\\run\\foo\\access-log')\n", "for line in follow(logfile):\n", " print line," ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Coroutine\n", "+ yield 를 표현식으로, \n", "+ next() 또는 send(None) 메소드로 함수를 시작하고\n", "+ yield 를 통해 값을 대기하고, send() 메소드로 값을 전달" ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def grep(pattern):\n", " print 'looking for %s' % pattern\n", " while True:\n", " line = (yield)\n", " if pattern in line:\n", " print line," ] }, { "cell_type": "code", "execution_count": 47, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "looking for python\n", "wow python rocks!\n" ] } ], "source": [ "g = grep(\"python\")\n", "g.next() # prime it!\n", "g.send(\"hey!!\")\n", "g.send(\"welcome to koread\")\n", "g.send(\"wow python rocks!\")\n", "g.send(\"really?\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "+ next() 는 까먹기 쉬우니까 decorator 를 이용하자.\n", "+ 종료시에는 close() 를 호출하자 (가비지 컬렉터가 알아서 close() 를 호출하지만)\n", "+ close() 를 호출하면 `GeneratorExit` 예외가 발생하니까 coroutine 에서 잡아준다. " ] }, { "cell_type": "code", "execution_count": 55, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def coroutine(func):\n", " \"\"\"\n", " \n", " \"\"\"\n", " def start_coroutine(*args, **kwargs):\n", " crtn = func(*args, **kwargs);\n", " crtn.next()\n", " return crtn\n", " return start_coroutine" ] }, { "cell_type": "code", "execution_count": 77, "metadata": { "collapsed": false }, "outputs": [], "source": [ "@coroutine\n", "def grep(pattern):\n", " print '[*] looking for %s' % pattern\n", " try:\n", " while True:\n", " line = (yield)\n", " if pattern in line:\n", " print line,\n", " except GeneratorExit as e:\n", " print '\\n[*] Going away, bye (gc or U called close())'\n", " " ] }, { "cell_type": "code", "execution_count": 79, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[*] looking for python\n", "wow python rocks! \n", "[*] Going away, bye (gc or U called close())\n" ] } ], "source": [ "g = grep('python')\n", "#g.next() # no need to call next()\n", "g.send(\"hey!!\")\n", "g.send(\"welcome to koread\")\n", "g.send(\"wow python rocks!\")\n", "g.close()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 예외 던지기" ] }, { "cell_type": "code", "execution_count": 83, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[*] looking for python\n", "\n", "[*] Going away, bye (gc or U called close())\n", "wow! python is rock! wow! python is rock! wow! python is rock!" ] }, { "ename": "RuntimeError", "evalue": "exception thrown", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mRuntimeError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m<ipython-input-83-573c8937e6d6>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 3\u001b[0m \u001b[0mg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'wow! python is rock!'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[0mg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'wow! python is rock!'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 5\u001b[1;33m \u001b[0mg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mthrow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mRuntimeError\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"exception thrown\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[1;32m<ipython-input-77-849e2bda9321>\u001b[0m in \u001b[0;36mgrep\u001b[1;34m(pattern)\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[1;32mwhile\u001b[0m \u001b[0mTrue\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 6\u001b[1;33m \u001b[0mline\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;32myield\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 7\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mpattern\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mline\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 8\u001b[0m \u001b[1;32mprint\u001b[0m \u001b[0mline\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mRuntimeError\u001b[0m: exception thrown" ] } ], "source": [ "g = grep('python')\n", "g.send('wow! python is rock!')\n", "g.send('wow! python is rock!')\n", "g.send('wow! python is rock!')\n", "g.throw(RuntimeError, \"exception thrown\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## bogus sample" ] }, { "cell_type": "code", "execution_count": 85, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Counting down from 5\n", "5\n", "2\n", "1\n", "0\n" ] } ], "source": [ "# bogus.py\n", "#\n", "# Bogus example of a generator that produces and receives values\n", "\n", "def countdown(n):\n", " print \"Counting down from\", n\n", " while n >= 0:\n", " newvalue = (yield n)\n", " # If a new value got sent in, reset n with it\n", " if newvalue is not None:\n", " n = newvalue\n", " else:\n", " n -= 1\n", "\n", "# The holy grail countdown\n", "c = countdown(5)\n", "for x in c:\n", " print x\n", " if x == 5:\n", " c.send(3)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 정리\n", "+ **generator 는 iteration 을 위해 데이터를 생성한다. (producer)**\n", "+ **coroutine 은 데이터의 사용자이다. (consumer)**\n", "+ **coroutine 은 iteration 과 아무 상관없다. **" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "# [Part II] Coroutines, Pipelines, and Dataflow" ] }, { "cell_type": "markdown", "metadata": { "collapsed": false }, "source": [ "+ coroutine 은 파이프를 설치하는데 사용할 수 있음\n", "+ send() -> [coroutine] -> send() -> [coroutine] ->" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## example" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "67.195.44.107 - - [24/Feb/2008:06:00:26 -0600] \"GET /robots.txt HTTP/1.0\" 200 71\n", "\n", "86.157.119.197 - - [24/Feb/2008:06:00:44 -0600] \"GET /favicon.ico HTTP/1.1\" 404 133\n", "\n" ] }, { "ename": "KeyboardInterrupt", "evalue": "", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", "\u001b[1;32m<ipython-input-13-03233fc77ce1>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 35\u001b[0m \u001b[0mf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34mr'd:\\work.python\\python_async_stuffs\\coroutine_www.dabeaz.com\\run\\foo\\access-log'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 36\u001b[0m \u001b[1;31m#follow(f, printer) # 요거는 오류 남, 'AttributeError: 'function' object has no attribute 'send'\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 37\u001b[1;33m \u001b[0mfollow\u001b[0m\u001b[1;33m(\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mprinter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 38\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m<ipython-input-13-03233fc77ce1>\u001b[0m in \u001b[0;36mfollow\u001b[1;34m(thefile, target)\u001b[0m\n\u001b[0;32m 20\u001b[0m \u001b[0mline\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mthefile\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreadline\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 21\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mline\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 22\u001b[1;33m \u001b[0mtime\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msleep\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0.1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 23\u001b[0m \u001b[1;32mcontinue\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 24\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mKeyboardInterrupt\u001b[0m: " ] } ], "source": [ "import time\n", "\n", "def coroutine(func):\n", " \"\"\"A decorator function that takes care of starting a coroutine\n", " automatically on call.\n", "\n", " \"\"\"\n", " def start(*args,**kwargs):\n", " cr = func(*args,**kwargs)\n", " cr.next()\n", " #print 'coroutine started...'\n", " return cr\n", " \n", " return start\n", "\n", "# data source\n", "def follow(thefile, target):\n", " thefile.seek(0, 2) # goto end of the file\n", " while True:\n", " line = thefile.readline()\n", " if not line:\n", " time.sleep(0.1)\n", " continue\n", " \n", " target.send(line) # 최초로 호출되는 시점에 객체(target)가 생성됨 \n", " \n", "# sink - a coroutine that receives data\n", "@coroutine\n", "def printer():\n", " while True:\n", " line = (yield)\n", " print line\n", " \n", "# useage\n", "f = open(r'd:\\work.python\\python_async_stuffs\\coroutine_www.dabeaz.com\\run\\foo\\access-log')\n", "#follow(f, printer) # 요거는 오류 남, 'AttributeError: 'function' object has no attribute 'send'\n", "follow( f, printer() ) \n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Filter example" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "no 128.143.38.123 - - [24/Feb/2008:10:31:14 -0600] \"GET /favicon.ico HTTP/1.1\" 404 133\n", "\n", "no 71.206.180.32 - - [24/Feb/2008:10:31:38 -0600] \"GET /ply/ply.html HTTP/1.1\" 200 97238\n", "\n", "no 71.206.180.32 - - [24/Feb/2008:10:31:40 -0600] \"GET /favicon.ico HTTP/1.1\" 404 133\n", "\n", "no 71.206.180.32 - - [24/Feb/2008:10:31:40 -0600] \"GET /favicon.ico HTTP/1.1\" 404 133\n", "\n", "no 74.6.8.73 - - [24/Feb/2008:10:34:02 -0600] \"GET /ply/ply-1.3.1.tar.gz HTTP/1.0\" 304 -\n", "\n" ] }, { "ename": "KeyboardInterrupt", "evalue": "", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", "\u001b[1;32m<ipython-input-16-012153cf2e24>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 28\u001b[0m \u001b[1;31m# useage\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 29\u001b[0m \u001b[0mf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34mr'd:\\work.python\\python_async_stuffs\\coroutine_www.dabeaz.com\\run\\foo\\access-log'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 30\u001b[1;33m \u001b[0mfollow\u001b[0m\u001b[1;33m(\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mgrep\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'python'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mprinter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[1;32m<ipython-input-16-012153cf2e24>\u001b[0m in \u001b[0;36mfollow\u001b[1;34m(thefile, target)\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[0mline\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mthefile\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreadline\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 6\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mline\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 7\u001b[1;33m \u001b[0mtime\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msleep\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0.1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 8\u001b[0m \u001b[1;32mcontinue\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 9\u001b[0m \u001b[0mtarget\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mline\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mKeyboardInterrupt\u001b[0m: " ] } ], "source": [ "# data source\n", "def follow(thefile, target):\n", " thefile.seek(0, 2)\n", " while True:\n", " line = thefile.readline()\n", " if not line:\n", " time.sleep(0.1)\n", " continue \n", " target.send(line)\n", " \n", "# filter \n", "@coroutine\n", "def grep(pattern, target):\n", " while True:\n", " line = (yield) # receive a line\n", " if pattern in line:\n", " target.send(line) # send to next stage\n", " \n", "# sink - a coroutine that receives data\n", "@coroutine\n", "def printer():\n", " while True:\n", " line = (yield)\n", " print line,\n", "\n", "# useage\n", "f = open(r'd:\\work.python\\python_async_stuffs\\coroutine_www.dabeaz.com\\run\\foo\\access-log')\n", "follow( f, grep('python', printer()) ) \n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Interlude" ] }, { "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

khalido/nd101

nd101_wk1.ipynb

1

134364

{ "cells": [ { "cell_type": "code", "execution_count": 172, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#libraries used\n", "import pandas as pd\n", "import numpy as np\n", "from sklearn import linear_model\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Week 1: Types of machine learning, when to use machine learning, neural network architecture\n", "\n", "## Linear regression demo\n", "\n", "`brain_body.txt` is dataset of brain and body sizes." ] }, { "cell_type": "code", "execution_count": 72, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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04xGRzJXqfVYubUWfKcCUFs5vByYFH831+QAY39x5EckcO3f6UFJV5ds/+AH8\n4Q/RjklEMpseZCgiaTN7Nnz724n22283vvNHRKQpepChiKTchx/65/nEg8qvfuXXqyioiEhrqLIi\nIik1fTpMDq0gq62Fzp2jG4+IZB+FFRFJiY0bG2+L/+CDMHFidOMRkeyly0Ai0uZ+/ONEUOneHT75\nREFFRPadKisi0maqq6F370S7vBzOPrv5/iIiraHKiojsN+fg/PMTQeXLX4b6egUVEWkbqqyIyH5Z\nuhQGDUq0ly8HPW5LRNqSKisisk/q6+HUUxNBZfx4X2FRUBGRtqbKiojstblz4ZxzEu0334SePSMb\njojkOFVWRKTVPv4YCgsTQeXnP/fVFAUVEUklVVZEpFUGDIC//S3R3rQJDjssuvGISP5QZUVEWlRd\nDWaJoHLBBb6aoqAiIumiyoqINMuscVvVFBGJgiorIrKbZ59tHFQuu0zVFBGJjiorItJIcjVl+3Zo\n3z6asYiIgCorIhL4058aB5Vp03w1RUFFRKKmyopInquvhwOS/iVoaNi9wiIiEhVVVkTy2HXXNQ4q\nc+f6aoqCiohkElVWRPLQ1q3QqVPjY85FMxYRkT1RZUUkz5x9duOg8sorCioiktlUWRHJE+vWwec+\nl2gfdxysWRPdeEREWkthRSQPFBbCli2J9nvvQffu0Y1HRGRv6DKQSA5butQvlo0HlfhW+QoqIpJN\nVFkRyVHJd/Rs2wYHHxzNWERE9ocqKyI5ZtasxkHl5pt9NUVBRUSylSorIjmioQEKChofq6+HdvqR\nRESynP4ZE8kBN9/cOKjMmuWrKQoqIpILUvpPmZldb2bLzGyLmW00s8fNrHcT/W40s/Vmts3MnjKz\n4qTzHcxshpltMrOtZjbbzLom9Skys0fMrM7Mas3sPjPrmMr5iURt2zZ/yeeGGxLHnINvfzu6MYmI\ntLVU/9w1GLgTGAgMAw4EnjSzT6+em9l1wNXAZcAA4COg3MzCj0+bBowBzgeGAEcCjyW916NAX2Bo\n0HcIcE/bT0kkM3z729AxFMeXLtXmbiKSm1K6ZsU5NzrcNrOLgPeBEuD54PBk4Cbn3Nygz0RgI3Ae\nMMvMOgGXABc65xYFfS4GVprZAOfcMjPrC4wASpxzK4I+k4B5Znatc25DKucpkk4bNsARRyTaRUVQ\nUxPdeEREUi3dV7Q7Aw6oATCzY4HuwNPxDs65LcBS4LTgUH98qAr3WQWsC/UZBNTGg0qgInivgamY\niEgUevYMIiYEAAAZwUlEQVRsHFTWrlVQEZHcl7awYmaGv5zzvHPuteBwd3yg2JjUfWNwDqAbsCMI\nMc316Y6v2HzKOVePD0Xa/kqy3iuv+LUpa9f69ogR/pJPjx7RjktEJB3Seevy3cDngdPT+J57VFZW\nRmFhYaNjpaWllJaWRjQikcaSN3fbsgU++9loxiIi+ScWixGLxRodq6urS+sY0hJWzOwuYDQw2Dn3\nXujUBsDw1ZNwdaUbsCLUp72ZdUqqrnQLzsX7JN8dVAAcGurTpKlTp9KvX7+9m5BIGsydC+eck2j/\n5Cdwyy3RjUdE8lNTP8BXVlZSUlKStjGkPKwEQeXrwJnOuXXhc865N81sA/4Onn8E/Tvh15nMCLot\nB3YFfR4P+vQBegAvBX1eAjqb2amhdStD8UFoaYqmJpISTe2PsmvX7hu+iYjki1Tvs3I3MA4YC3xk\nZt2Cj4NC3aYBPzezc8zsi8BDwDvAHPh0we39wB1mdpaZlQB/BF5wzi0L+rwOlAP3mtmXzOx0/C3T\nMd0JJNlk2rTGQeWBB3x4UVARkXyW6srK5fgFtM8mHb8YH0pwzt1qZofg90TpDDwHjHLO7Qj1LwPq\ngdlAB2AhcFXSa44F7sLfBdQQ9J3chnMRSZkdO6BDh8bHtGeKiIiX6n1WWlW5cc5NAaa0cH47MCn4\naK7PB8D4vRuhSPS+/324775E+9ln4cwzIxuOiEjG0YMMRSKyeTN06dL4mKopIiK702PORCJQUtI4\nqFRXK6iIiDRHYUUkjVat8vumVFb69sCBPqQUF7f8dSIi+UyXgUTSJHlzt5oa/1wfERFpmSorIin2\nf//XOKhccYWvpiioiIi0jiorIimUXE3ZsQMOPDCasYiIZCtVVkRS4L77GgeVO+/01RQFFRGRvafK\nikgb2rVr90DS0LB7hUVERFpPlRWRNvKjHzUOKgsW+GqKgoqIyP5RZUVkP23ZAoWFjY9pzxQRkbaj\nyorIfhg6tHFQefVVBRURkbamyorIPnjrLTj22ES7Tx94/fXIhiMiktMUVkT2UseOsG1bor1xI3Tt\nGt14RERynS4DibTSSy/5xbLxoDJ2rL/ko6AiIpJaqqyItELyHT0ffwwHHRTNWERE8o0qKyItiMUa\nB5Vf/9pXUxRURETSR5UVkSY0NEBBwe7HtGeKiEj6qbKSR6qqqliwYAHV1dVRDyWjTZnSOKg89pg2\ndxMRiZIqK3mgpqaGsWMnUF4+/9NjI0aMJhabSZEe/fupbdv8nT5h2jNFRCR6qqzkgbFjJ1BRsQSY\nCawDZlJRsYTS0vERjyxzfPObjYPK3/6moCIikilUWclxVVVVQUVlJjAuODqO+npHefkEqqur6dWr\nV4QjjNb69XDUUYn24YfD++9HNx4REdmdKis5bs2aNcFnQ5LOnAnA6tWr0zqeTHLMMY2DyttvK6iI\niGQihZUcd/zxxwefLU46swiA4uLitI4nE7z8sl8s+847vv21r/lLPkcfHe24RESkaboMlON69+7N\niBGjqai4hvp6h6+oLKKgYDLDho3Ou0tAyXf0bN0Kn/lMNGMREZHWUWUlD8RiMxk2bBAwAegBTGDY\nsEHEYjMjHln6PPFE46Dys5/5aoqCiohI5lNlJQ8UFRWxcOE8qqurWb16NcXFxXlTUXEO2iVF8l27\ndt/wTUREMpcqK3mkV69ejBo1Km+Cym9/2zioPPSQDy8KKiIi2UWVFck527fv/uwe7ZkiIpK9UlpZ\nMbPBZvaEmb1rZg1mdm4TfW40s/Vmts3MnjKz4qTzHcxshpltMrOtZjbbzLom9Skys0fMrM7Mas3s\nPjNL2otU8sHFFzcOKs89p6AiIpLtUn0ZqCPwMnAlsNt/GWZ2HXA1cBkwAPgIKDez9qFu04AxwPn4\nzUKOBB5LeqlHgb7A0KDvEOCetpyIZLZNm/wC2gce8O2CAh9Szjgj0mGJiEgbSOllIOfcQmAhgFmT\nj4GbDNzknJsb9JkIbATOA2aZWSfgEuBC59yioM/FwEozG+CcW2ZmfYERQIlzbkXQZxIwz8yudc5t\nSOUcJXonnQT//GeivXo1fLq9jIiIZL3IFtia2bFAd+Dp+DHn3BZgKXBacKg/PlCF+6zCP+Am3mcQ\nUBsPKoEKfCVnYKrGL9FbudJXU+JB5fTTfTVFQUVEJLdEucC2Oz5QbEw6vjE4B9AN2BGEmOb6dAca\nbZLunKs3s5pQH8kxyXW62lro3DmasYiISGrl/d1AZWVlFBYWNjpWWlpKaWlpRCOSllRUwPDhifak\nSTB9enTjERHJdbFYjFgs1uhYXV1dWscQZVjZABi+ehKurnQDVoT6tDezTknVlW7BuXif5LuDCoBD\nQ32aNXXqVPr167dPE5D0aWpztx074MADoxmPiEi+aOoH+MrKSkpKStI2hsjWrDjn3sSHiaHxY8GC\n2oHAi8Gh5cCupD598HvGvxQcegnobGanhl5+KD4ILU3V+CV97rmncVD5/e99eFFQERHJDymtrAR7\nnRTjgwPAcWZ2MlDjnHsbf1vyz81sNfAWcBPwDjAH/IJbM7sfuMPMaoGtwHTgBefcsqDP62ZWDtxr\nZlcA7YE7gZjuBMpuO3dC+/aNjzU07L5eRUREcluqKyv98Zd0luMX0/4WqAR+CeCcuxUfLO7BV0EO\nBkY553aEXqMMmAvMBp4F1uP3XAkbC7yOvwtoLrAY+EEqJiTp8cMfNg4qTz7pqykKKiIi+SfV+6ws\nYg+ByDk3BZjSwvntwKTgo7k+HwDj92mQklHq6na/q0c70IqI5Dc9yFAyxpAhjYPKv/6loCIiIrp1\nWTLAG2803sjtC1+AV1+NbjwiIpJZFFYkZaqqqlizZg3FxcX06tWryT7t2/uFtHHvvw+HH56mAYqI\nSFbQZSBpczU1NQwefBZ9+vRh9OjR9O7dm5Ejx1BbW/tpnxde8Itl40Fl4kR/yUdBRUREkqmyIm2q\npqaG3r0/z+bNnwAz8Q/AXkxFxTWUlo5n4cJ5u93R88kn0KFDBIMVEZGsoMqKtKmvf/0bbN68EZgB\njAOOAcZRX/87ysuLGgWVW2/11RQFFRERaYkqK9JmqqqqeP75xUFrSNLZ8YTvLtfmbiIi0lqqrEib\nWbNmTai1uMk+M2as1+ZuIiKyVxRWpM0c/+n9x6cAN+x2fvDgs7jyyiPTOiYREcl+CivSZnr37s2I\nEaPxT1h4I3TmVA47rDtz5jwe0chERCSbKaxIk6qqqliwYAHV1dWt/pply6C8fF7SUeOMMwqprl5J\nUVFR2w5SRETyghbYSiM1NTWMHTuB8vL5nx4bMWI0sdjMFsNG8hqU2bPXccgh/6K4uKrZDeFERERa\nQ5WVPLKnaklVVRXDh4+komIJfo+UdcBMKiqWUFra9HMib7pp96BSVVXN+ef3YNSoUQoqIiKy3xRW\n8kBNTQ0jR45pdkfZ8PnKyr9RXz+d3fdImb9byDGD//qv8JEjAWtyx1oREZF9pbCSB8aOndBitWTs\n2Ak89dSLwHHBVyTvkXImAKtXrwZg4MDdqykFBYcBtzX5+iIiIvtDa1ZyXFVVVbD+ZCa+WgK+WuIo\nL5/Ak08+GZw/BXgzOL841Bd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"text/plain": [ "<matplotlib.figure.Figure at 0x118bd69b0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#read data\n", "df = pd.read_fwf('linear_regression_demo/brain_body.txt')\n", "x_values = df[['Brain']]\n", "y_values = df[['Body']]\n", "\n", "#train model on data\n", "body_reg = linear_model.LinearRegression()\n", "body_reg.fit(x_values, y_values)\n", "\n", "#visualize results\n", "plt.scatter(x_values, y_values)\n", "plt.plot(x_values, body_reg.predict(x_values))\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Siraj's Week 1 challange\n", "\n", "The weekly challange is to make a prediction of life expectancy from BMI at birth.\n", "\n", "The challenge for this video is to use scikit-learn to create a line of best fit for the included 'challenge_dataset'. Then, make a prediction for an existing data point and see how close it matches up to the actual value. Print out the error you get." ] }, { "cell_type": "code", "execution_count": 109, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x1154b2668>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#read data\n", "df = pd.read_csv('linear_regression_demo/challenge_dataset.txt', names=['Data','Outcome'])\n", "x_values = df[['Data']]\n", "y_values = df[['Outcome']]\n", "\n", "#train model on data\n", "reg = linear_model.LinearRegression()\n", "reg.fit(x_values, y_values)\n", "\n", "#visualize results\n", "plt.scatter(x_values, y_values)\n", "plt.plot(x_values, reg.predict(x_values))\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "So now we have simple trained dataset. now to make a prediction." ] }, { "cell_type": "code", "execution_count": 115, "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>Data</th>\n", " <th>Outcome</th>\n", " <th>Predictions</th>\n", " <th>Pred_Error</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>6.1101</td>\n", " <td>17.5920</td>\n", " <td>3.393774</td>\n", " <td>-14.198226</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>5.5277</td>\n", " <td>9.1302</td>\n", " <td>2.698951</td>\n", " <td>-6.431249</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>8.5186</td>\n", " <td>13.6620</td>\n", " <td>6.267196</td>\n", " <td>-7.394804</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>7.0032</td>\n", " <td>11.8540</td>\n", " <td>4.459272</td>\n", " <td>-7.394728</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>5.8598</td>\n", " <td>6.8233</td>\n", " <td>3.095158</td>\n", " <td>-3.728142</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Data Outcome Predictions Pred_Error\n", "0 6.1101 17.5920 3.393774 -14.198226\n", "1 5.5277 9.1302 2.698951 -6.431249\n", "2 8.5186 13.6620 6.267196 -7.394804\n", "3 7.0032 11.8540 4.459272 -7.394728\n", "4 5.8598 6.8233 3.095158 -3.728142" ] }, "execution_count": 115, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df['Predictions'] = reg.predict(x_values)\n", "df[\"Pred_Error\"] = df['Predictions'] - df['Outcome']\n", "df.head()" ] }, { "cell_type": "code", "execution_count": 114, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "2.194245398827007" ] }, "execution_count": 114, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# calculating the avg error\n", "e = 0\n", "for i in df['Pred_Error']:\n", " e += abs(i)\n", "e / len(df['Pred_Error'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Linear Regression Quiz\n" ] }, { "cell_type": "code", "execution_count": 124, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(163, 3)\n" ] }, { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Country</th>\n", " <th>Life expectancy</th>\n", " <th>BMI</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>Afghanistan</td>\n", " <td>52.8</td>\n", " <td>20.62058</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>Albania</td>\n", " <td>76.8</td>\n", " <td>26.44657</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>Algeria</td>\n", " <td>75.5</td>\n", " <td>24.59620</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>Andorra</td>\n", " <td>84.6</td>\n", " <td>27.63048</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>Angola</td>\n", " <td>56.7</td>\n", " <td>22.25083</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Country Life expectancy BMI\n", "0 Afghanistan 52.8 20.62058\n", "1 Albania 76.8 26.44657\n", "2 Algeria 75.5 24.59620\n", "3 Andorra 84.6 27.63048\n", "4 Angola 56.7 22.25083" ] }, "execution_count": 124, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pandas as pd\n", "from sklearn.linear_model import LinearRegression\n", "\n", "# Assign the dataframe to this variable.\n", "# TODO: Load the data\n", "bmi_life_data = pd.read_csv('bmi_and_life_expectancy.csv') \n", "print(bmi_life_data.shape)\n", "bmi_life_data.head()" ] }, { "cell_type": "code", "execution_count": 136, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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cuXPt6aefnve46aabwgqumlLSIthqxF3NMWfOPNvW1hFoGrCau8p6Mjph3rV+\n6UvemYFyvL7L7NnzIsgUVD/7YS3b9yqjBiiSOM+zN910U9F1cu7cuYFmCmoNCA4BdgOn5SxT9UHM\nGqlBVBKqOcL6466msV89dZJhNCY8/nj/wUCl7zJ16rSSwUu1waBXAASTLBwdyG+magGpRZKOm0S0\nKQAuBZ4C2gqWl2po+MEy21JQEJBGyhQkoaFO2EFUqbvKoH6nIO5avQKBU0+t/v3VTECUG7zMn3+K\nnT//lKoDGq8AaOrUabatbXIgJ2TNWyC1SNJxE3tQABjgCeBfPV77ArAdOB04EqdL4ijqkhiZJEWw\npSQleImrHEnI6HgFA1dc4X871XyX3OCl1mAwdxthnJBVLSC1SMJxk4Sg4BSctgOegxK5WYTM4EXD\npdbLWV9BQYCSFMGWkoSLYkYcQVRcwcjEhHcw8L//W/s2/XyXoL93Ek7IInGLPSgI+qGgIBxJPmEm\nJVNgbXxBVJTBSKkxBp58MpjtV/tdkhQMijQLBQVSl7hb+2ckrZoj6iAqimDkySe9g4FXXgnsI6y1\n1X+XJAWDIs1CQYHkqfYin4TW/rkaoZojCmEEI3ff7R0MhK2a75K0YFCk0SkoEGut/4t8Elr7e0ly\nNUejuf76eIIBPxQMigQr6KDAWOfCHBtjTBcwMjIyQldXV6xlaSQLFixizZp1jI9/A2dkrbtobz+f\n7u4TGBpambduKpVi5syZOKMcLsl5pR9YRiqVCn0627ilUinGxsaYPn16033Xj34Uvv/9/GVz58La\ntfGUpxqjo6Ns3ry56t+jmX8/kXps3LiRWbNmAcyy1m6sd3v1DHMsMUmlUgwPD7oBwRIqDfXa9MNy\nlpFOp1mwYBEzZ86kt7eXGTNm1DWMcZK8/e3OUMS5AcEllzj5gSQEBOWGcO7s7GThwoUVL/DN/PuJ\nJJGCggbk9yIf1Xj5SdSMc0EY4zz+8Ifssv/+bycYWLEivnJlBHkhb8bfTyTRgqiDqOeB2hT4Vksr\n7lZs4NVMrd1LjTHw29/GXbJiQbVfaabfTyQsSZolUWIyY8YMenp6aW8/H+cO6kmgn/b2C+jp6fVM\nybbiLG/NUG3y4otOVqCt4C91+3bn8njEEfGUqxS/VVvlNMPvJ9JoFBQ0KL8X+Y6ODoaGVhZNd9vR\n0RFlsX3xO61woUauNnniCScY2H///OWvveYEA1OmxFIsT7m/U5AX8lp+v3qPGZGWF0S6oZ4Hqj6o\nSzN26Qvl9IF7AAAeXElEQVRyTIVGqza5447kdyvM8J4yeW6gKf9qf7+kjcMhEhWNUyBNL8gxFRql\nX/w11zROMJBRy5TJflX7+yV1HA6RsCkokKYWVuOypGZU+vqKA4GenrhLVVml32nOnHmBBmLlfj81\nSJRWFnRQsFdo9RIiNaimTrqWwWs6OzsTNejNQQfBtm35y1ascMYZaASVfqeLL76I7373Bl8DFJVT\n7vcL4pjR4EgiDgUFkij5jctyR19MfuPAahhTvOzWW+H006MvSz2q+Z2iCsTqOWbS6TSLFy9jeHhw\nz7Kenl4GBvoT3QhXJCzqfSCJUkt3y6SbmMgOOJTr0UedJHejBQSQrN+pnrJocCSRAkHUQdTzQG0K\nIpeU6ZNLaZTGgZWMjKQ8Gw/u2BF3yYKRpN+plrKoLYI0A7UpkJo1Sqo0M6aC30lzkuLXv36OY4+d\nDOSXedu2HRx4YHL2c72S9DvVUpaw2q+INDJVH7SQOFOltQwqU+2kOUmxapVTReAEBLl+SHv7VJYu\nbc6UdJJ+Jz9laeTBrUTCoqCgRQQ5/KwfUc5yF9dodldd5QQDPT25S3fn/D/8/Sz+JaldhEhSKCho\nEXGNIx9FdiKu6XXPOssJBi68MLvshBOeAQzwp4K1NV5/ErXinCAi5SgoaBFxpEqjyk5EXS2y//5O\nMHDLLdllV1zhNFH7wQ92ukuUkm4EjTgnSCPSnBQNJIjWivU8UO+DyEQ9D8Dg4KDbKnZLQevuLRaw\ng4ODdX9GlC3IvXoSDA0Vr1dpPye994dIUDQnRfg0dbLULOpUaRTZibCrRUqNMZBKOWFBfjsCR6n9\n/O1vfzOWag7J0h1rtDQORAMKIrKo54EyBZErNY58GHewYWcnwsoUPPecd2bg+eer30bhfm6USXua\nMZOhO9boaRyIaGhCJAlcmCfMKAa4CTLwePRR72BgfLy+MjbCCbKZL5yNEpA1kyiqD0VBgYQgihNm\nmLMUBhF4/PKX3sFAUBrhBNmsF85GCMiakfZ7NBQUSKCa6Q+3lsDjK18pDgSmTg2+bEnfz0kvXz0a\nISBrVlE3bm5FamgogYpr/IIgFDYa8xrNrlTDsoULncaDuVMVn3OOc6l49tngy5r0gXIa+TioRCMX\nxkfjQDSgICKLeh4oUxCrRrxDrKbuu9Q6xkwUZQa+/vVoyp2kCYQKNeJx4IfuWOMVZvVhq1P1gQSu\n0U6Y1dR9F69TXE1w++3xlD+pJ8hGOw78SHJAJlIPBQUSuEY6YVZzR5td54clgoHH4v4aidRIx0Gt\nkhqQidRKUydL4JIyBW4qlWJsbKzs51dT9/3CC3vj/I0U+iNwMK+8MggcGkyhm0hSjoMwdXZ2lv1O\n1RyDIs1MQYHsUemEGZZ0Os3ixcsYHh7cs6ynp5eBgf6iMejzG40tyXllLfBuensXlvmkOwE1LKsk\nruMgTn6OQZFmpt4HEjs/Q6F6t+JfCywFHs5bt719Kkls6S/Jo+F4RRwKCiRWtcykmO3m9Ji7/rw9\nrx18sNNyIJ3eoa5QUpWoZvMUaQSqPpBYVdNGoPDO/qyzOli7dmXeso98BL7//ezzVqgfl2DUcgyK\nNCsFBRKr8m0E8uv/C2cqBLjuOvj4x0tvvxXrx8UfP8egSLNT9YHEqpqR/rymLr7rLqeaoFxAIFKN\npI82KRIlBQUSO6+hUOfPn83w8MqiYGDLFicYmDMnhoJK09JwvCIOVR9I3ert251b/79+/RaWLj2Z\n1avz19m1C17/+oAKLFJAbVBEHAoKpGZB9u1etw5OPLETyD8RT0x4tyUIkgaskQy1QZFWp+oDqVkQ\nfbu/8x3non/iifnLM4MShxkQpNNpFixYxMyZM+nt7WXGjBksWLCIHTt21LS9UjMyiog0CgUFUpN6\n+3Z/+MPOBT+3oWB7ezYYiEJQA9YEHVx4UcAhIlFQUCA1qaZvt5e/+isnGLjxxuyyM890AoHdu4Mv\nZylBDlgT5mh4UQQcIiIZCgqkJvl9u3N59+3OdCt89tnssq99zQkGfv7z8MpZSq1BTaGwR8PT8Lsi\nEiUFBVKTavt2e40xcMcdTjCwfHnUpc7yG9SUElRw4UXD74pI1BQUSM1K9e3+z//s9wwGMmMMnHRS\nDIUtENSANX6DCz9tA8IMOEREvCgokJpl+nanUikGBwe5667HGB5eyZvfnN8d8ZZbhkmlRjn44JgK\nWkIQA9ZUG1zU0jYgqGyGiEjVrLWxPoAuwI6MjFhpTGvXZvoM5D9OPbXXAnsePT29Np1Ox13cIqlU\nyg4ODtpUKlXT+9PptO3pKf9de3p6bXv7FAv9FrZY6Lft7VNsT09v2W1n33ej+74bq3qfiLSGkZGR\nzHmnywZxTQ5iI3UVQEFBw7rmGu9gwNraL4KNrFRwsWnTJvePtr9gX91ogbLBSDUBh4i0rqCDAo1o\nKL598IPw05/mLzvgANi50/l/poGck07PzDq3hPFxy/DwMkZHR0MdNS6uEQpLjYZXz9S8Gn5XRKLk\nu02BMeYtxpgbjTHPGmN2GWMeNMZ0FazzZWPMVvf11cYYVX42gf32cxoP5gYEfX3OPW8mIID4Gsgl\ntU9/EG0DOjs7WbhwoQICEQmVr6DAGDMZuBd4BegBDgc+B+zIWeci4DPAucBxwIvAsDFmn4DK3LLC\nGNWumm1mehLs2pVd9q1vOcHATTcVrx9XA7mk9unX1Lwi0jD81DUA/wasrbDOVmB5zvMDgJeAD5VY\nX20KKti+fXvg9cqVtjkx4d1e4O67q9u+3wZymzZtqquxXz319lFQ2wARCUOsDQ2B3wH/DvwYeAbY\nCPyfnNcPBSaA9xS8707g6hLbVFBQQRiN9kpts7v7TM9g4Kmn/G2/2otgUAHP4OCg+/4tBWXfYgE7\nODjo7wuEpN6eDiIiueIOCl4CdgErgKOAj7nPl7mvnwiMA9MK3nczMFBimwoKygjjDrj0NosfL79c\nX/krXQSDCHi2b99uZ8+em+hMgYhIGOLufdAGrLfW/ov7/EFjzBHAJ4AbS7+tsuXLlzNp0qS8ZX19\nffT19dWz2YZXT8t1/9vMcuI1f7xa/Zebnz6oXgqLFy/jV796GDgaOB/n72MesJb29gvo7la9vYg0\nvoGBAQYGBvKW7cxt5R0EPxEE8ATwnYJlnwCetKo+CEV4mYLPlcgO+N9mrVUAQaT88/dP2kJ+OebM\nmVdzvX297RxERMIWdKbAb5fEe4GZBctmAn9wA4zHgaeBkzMvGmMOAI4H7vP5WULwLddPOw1mzpwB\nXJWzdNzd5tSatunV6n/16l/x/vefVfZ9QfRSyM96dAArgRTwAwAuvvgiOjqcYZer7b2R1K6NIiKh\n8xNBAMfgdEe8GDgMWAy8AJyTs84XgO3A6cCRwC3AKLBPiW0qU1BBEC3XvbICb33rcN0N/CplMmbP\nLn+nXu8wvtVkUvxmMlpxNEYRaUyxD3MM9AIP4TQw/B3wUY91LsXpmrgLGAaml9megoIq1dJyfd99\ni4OB//iP+raZq1IVQFvb/mUvpul02s6ePa+u4KRSYOHnIp/0ro0iIrliDwqCfigoCN7EhLXnnVcc\nDNx/f/CfVekiCleWvJh63cFXyix4KZdJ8XuRb5SujVFS2wqR5Iq7TYEk2O7d8JGPQFsbfPOb2eU7\ndjiXteOOC/4zM20e2trOI7fNA1yAk1Q6Gyge2jiVSnHKKT2sXn0vuW0RfvWr3/oegbBwCudUKsXQ\n0Eo6Ojp8D7ms6Yqz1LZCpAUFEVnU80CZgrq9+KK1J52UnxW44AJrx8ej+XyvKgCnF0C66I7cKzuQ\nXTf4NH0t1QGartihthUiyafqA9njz3+29h3vyA8GrroqvvLMnj3PtrXt71YZeF9MvS40MMUNDMJJ\n0/u9yGtIYrWtEGkUcQ9eJAmweTO8850wPp5d9qMfwdlnh/u5mcGJ2tvbGR8fL5rG99Zbf05f31KG\nhy8ELgSgu7uXgYH+Pe/3GqzIOZ6X4XRSuR8INk0/MNDvlmvZnmW55Sqk6YrDGTRLRJJPQUEDuf9+\nOOGE/GV33gnz5oX7uel0msWLl7kX9Iw2YIKeHufi2tHRUfFiWulCA9+hvf17gY9AWOtFvtxojM0u\nv23FkpxXWq9thUgrUUPDBvA//+NMXZwbEDz8sJPMDTsgAO/BiWAycLTn1MSdnZ0sXLiw6IJaqREf\nXEV39wkl7+DrVapcUkzTPYu0JgUFMalmdL3rrnOCgTPOcJ5PmwZ//KMTDLz73dGVc3h4kPHxb+Dc\nMR7s/nsN8BvGxy9meHiw4iiBUPpC09b2GQ47rJNVq1bt6TUg8RsY6Ke7+wScqp1DgGWhBm0iEj8F\nBRGr1M3LWvjnf3aCgU9+0nnPscfCc8/B00/DW98abXkrp/wPAoq79pVSfKH5eyYmdjI2Nsqpp56q\nLm8JUq6rp4g0JwUFEfNKxa9Zs46zz/4wS5c6Ywx89avOun/3d/DKK7B+PRRMIBmZyin/PwPV1zHn\nXmi6uo6lvX0yhfvC7zgFpVQ714GUp2oXkRYSRBeGeh60UJfE0t28ns57/o//6IxKmBReXfqcboRH\n19xvPcwub7XO2igi0mg0omEDK52KnwbAuec+grXw7//uVB8khVfdMjwH/KbmOma/Iw36USobE1QG\nQkSkWalLYoRKd/O6C5jH5z+fir5QVSjs0rfXXnuxe/fuuvrv19PlLTNegtfnlxoLYXzcMjy8jNHR\nUaXBRURKUFAQoUzr+zVrzmd83OLcFa+lvf2CwPvmhyHIfvu17Auv8RJyx0mAxhh0p1xQIyISJ1Uf\nREzdvLL87otqqgWSPKGRJhgSkaQz1mnsF18BjOkCRkZGRujq6oq1LFFq5SF0C1WzL1KpFDNnziS/\nWgD3+TJSqdSe9y5YsIg1a9YxPn4N+RmIExgaWhnulykjW65v4GQy7qK9/fzYyyUijWvjxo3MmjUL\nYJa1dmO921P1QUxaeQjdQtXsi0rVAgMDA/T19dHZ2el7roMoqK2DiDQCVR9IQ/CuFkgDznCPX/rS\nl/ak44HEDboTZm8LEZGgKCiQhuA9RPLJwOOUamOQpEF3ktzWQUQkQ0GBNIzihom/Ab5F7pwM4+PX\nVD0XQ5Q0wZCINAIFBdIwcodIvuyyy9yl5dPxSRrqWD1PRCTpFBRIw+ns7OScc85xn3mn4w888MDE\ndf/TBEMiknQKCqQhVUrH/8u/XJrYoY6T1NZBRCSXggJpWKXS8StWXMrw8KA7HkDy2xuIiCSFggJp\nWKXS8c8++6y7hrr/iYj4ocGLpOEVDn5Uz2RLIiKtTJkCaTrq/iciUhsFBRKopHQBVPc/ERH/VH0g\ngahmWuMoZdobaOIpEZHqKVMggahmWuNKwsgyqPufiEj1FBRI3TIzANbaBTCdTiduoCERkVakoEDq\nVmkGwDvvvLNsBiCILIOXpLRvEBFpFAoKpG6lZwBcCbRx7rnnlswA1Jtl8KLMg9RLAaW0KgUFUrdS\nXQDhsxhzAOUyAJWyDLUMNBRW5kGanwJKaXUKCiQQXl0A4RWsvZZyGYDSWYbsQEN+7trCyDxI61BA\nKa1OQYEEonDI4RtuuMF9pXwGoNxAQ/Pnn8J5533W111bGJkHaQ0KKEUUFDS1OOpFM10A587NXJRL\nZwAySg00BPi+a6sm8yDiRQGliAYvakpJGEgokwFYs+Z8xsctzol1Le3tF9DdnT/UcO5AQ3feeSfG\nGA455BB6enpwAoLM/AVLGB+3DA8vY3R01HPsAT+fK5JLc2aIANbaWB9AF2BHRkasBKOnp9e2t0+x\n0G9hi4V+294+xfb09EZajnQ6bXt6ei2w59HT02vT6XTRutu3by9aF9osPGTB5jy2WMAODg4G8rki\nubJ/Oze6x9qNsfztiFRrZGQkc57rsgFck411LsyxMcZ0ASMjIyN0dXXFWpZmkEqlmDlzJvl32LjP\nl5FKpSK/W65mqOEFCxaxZs06tz53Ls7d2qeBtwK/y1mz+u+hIY7Frx07dtDXtzQxw3WLVLJx40Zm\nzZoFMMtau7He7an6oMlUUy8a9QWycGrjQpkGXoVVBU7wuwx4F3Az8KCvaoBKnytSSHNmSKtTUNBk\nGrFetFIgA38AjgYm6O7u1UyHEjoFlNKq1PugyZTr4tfTk8yGdpV6DMBlwASrVq1iaGil0rgiIiFR\nUNCESnXxS+oddiaQaWs7j/wRES8AeoGzAdi9e3dsZRQRaQWqPmhCjVgvOjDQzxlnnMU99yzLWdqL\nExysBJJZ9SEi0kwUFDSxRqoX7ejo4O6772TOnPdx330jTEx8CSdDsFJjDIiIRETVB5Iot976c045\nZS5wIY1Q9SEi0kyUKWgBqVSKsbGxhqhGaMSqDxGRZqGgoIklYbjjWjVS1YeISLNQ9UET0zSwIiLi\nhzIFTarUKIGVJhQSEZHWpUxBk9I0sCIi4pevoMAY8yVjzETB45GCdb5sjNlqjNlljFltjFHn8hhU\nGiVQff5FRKRQLZmCh4FpwJvcx+zMC8aYi4DPAOcCxwEvAsPGmH3qL6r40YjDHYuISLxqCQp2W2u3\nWWv/7D7SOa9dAKyw1v7SWvsw8GHgLcCZQRRW/Gm04Y5FRCRetTQ07DTGPAW8DPwKuNha+6Qx5lCc\nzMHtmRWttc8bY+4HTgR+HESBpXrq8y8iIn74DQrWAR8BNgFvBi4F7jLGHIETEFjgmYL3POO+JjFR\nn38REamGr6DAWjuc8/RhY8x6nMnuPwT8vp6CLF++nEmTJuUt6+vro6+vr57NioiINIWBgQEGBgby\nlu3cuTPQzzDW2vo24AQGq4H/AMaAo621D+W8fifwgLV2eYn3dwEjIyMjdHV11VUWERGRVrJx40Zm\nzZoFMMtau7He7dU1ToExZn9gOrDVWvs48DRwcs7rBwDHA/fV8zkiIiISPl/VB8aYK4H/wakyeCtw\nGfAa8CN3la8DlxhjNgNPACuAPwK/CKi8IiIiEhK/DQ3fBtwETAW2AfcAJ1hrtwNYa68wxrwBuB6Y\nDNwNLLTWvhpckUVERCQMfhsaVmz1Z629FKdXgoiIiDQQzX0gIiIigIICERERcSkoEBEREUBBgYiI\niLgUFIiIiAigoEBERERcCgpEREQEqG3qZJHQpVIpxsbGNN2ziEiElCmQREmn0yxYsIiZM2fS29vL\njBkzWLBgETt27Ii7aCIiTU9BgSTK4sXLWLNmHdAPbAH6WbNmHX19S2MumYhI81P1gSRGKpVieHgQ\nJyBY4i5dwvi4ZXh4GaOjo6pKEBEJkTIFkhhjY2Pu/+YWvDIPgM2bN0daHhGRVqOgQBLjsMMOc/93\nV8ErawGYPn16pOUREWk1CgokMWbMmEFPTy/t7efjVCE8CfTT3n4BPT29qjoQEQmZggJJlIGBfrq7\nTwCWAYcAy+juPoGBgf6YSyYi0vzU0FASpaOjg6GhlYyOjrJ582aNUyAiEiEFBZJInZ2dCgZERCKm\n6gMREREBFBSIiIiIS0GBiIiIAAoKRERExKWGhiI+aPZGEWlmyhSIVEGzN4pIK1BQIFIFzd4oIq1A\n1QciFWj2RhFpFcoUSCKkUiluu+02RkdH4y5KEc3eKCKtQkGBxKoR6uo1e6OItAoFBRKrRqir1+yN\nItIqFBRIbDJ19ePj38Cpqz8Yp67+GoaHBxNVlaDZG0WkFaihocSmmrr6pNyFa/ZGEWkFCgokNvl1\n9UtyXkluXb1mbxSRZqbqA4mN6upFRJJFQYHESnX1IiLJoeoDiZXq6kVEkkNBgSSC6upFROKn6gMR\nEREBFBSIiIiIS0GBiIiIAAoKRERExKWgQERERAAFBSIiIuJSUCAiIiKAggIRERFxKSgQERERQEGB\niIiIuBQUiIiICKCgQERERFwKCkRERARQUCAiIiIuBQUiIiICKChInIGBgbiLkAjaD1naFw7tB4f2\nQ5b2RfDqCgqMMf9kjJkwxnytYPmXjTFbjTG7jDGrjTHT6ytm69BB7tB+yNK+cGg/OLQfsrQvgldz\nUGCMORY4F3iwYPlFwGfc144DXgSGjTH71FFOERERCVlNQYExZn+gH/g/wHMFL18ArLDW/tJa+zDw\nYeAtwJn1FFRERETCVWum4FvA/1hr78hdaIw5FHgTcHtmmbX2eeB+4MRaCykiIiLh28vvG4wx5wBH\nA8d4vPwmwALPFCx/xn3Ny74Ajz76qN+iNKWdO3eycePGuIsRO+2HLO0Lh/aDQ/shS/si79q5bxDb\nM9ba6lc25m3Ar4Fut2oAY8z/Ag9Ya//RGHMicA/wFmvtMznvuxmYsNb2eWxzMfDD+r6GiIhIS1ti\nrb2p3o34zRTMAv4K2GiMMe6ydmCuMeYzwDsBA0wjP1swDXigxDaHgSXAE8DLPssjIiLSyvYF3o5z\nLa2b30zBfsBfFyz+T+BR4N+stY8aY7YCV1prr3bfcwBOgPBha+1Pgii0iIiIBM9XpsBa+yLwSO4y\nY8yLwHZrbaZi4+vAJcaYzTh3/yuAPwK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"text/plain": [ "<matplotlib.figure.Figure at 0x117f30ac8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Make and fit the linear regression model\n", "#TODO: Fit the model and Assign it to bmi_life_model\n", "x_vals = bmi_life_data[['BMI']]\n", "y_vals = bmi_life_data[['Life expectancy']]\n", "\n", "bmi_life_model = LinearRegression()\n", "bmi_life_model.fit(x_vals, y_vals)\n", "\n", "plt.scatter(x_vals, y_vals)\n", "plt.plot(x_vals, bmi_life_model.predict(x_vals))\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 138, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[ 60.31564716]])" ] }, "execution_count": 138, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Mak a prediction using the model\n", "# TODO: Predict life expectancy for a BMI value of 21.07931\n", "laos_life_exp = bmi_life_model.predict(21.07931)\n", "laos_life_exp" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Programming Quiz: Multiple Linear Regression\n", "\n", "> In this quiz, you'll be using the Boston house-prices dataset. The dataset consists of 13 features of 506 houses and their median value in $1000's. You'll fit a model on the 13 features to predict on the value of houses." ] }, { "cell_type": "code", "execution_count": 157, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ 6.32000000e-03 1.80000000e+01 2.31000000e+00 0.00000000e+00\n", " 5.38000000e-01 6.57500000e+00 6.52000000e+01 4.09000000e+00\n", " 1.00000000e+00 2.96000000e+02 1.53000000e+01 3.96900000e+02\n", " 4.98000000e+00] 24.0\n" ] } ], "source": [ "from sklearn.linear_model import LinearRegression\n", "from sklearn.datasets import load_boston\n", "\n", "# Load the data from the the boston house-prices dataset \n", "boston_data = load_boston()\n", "print(boston_data.data[0], boston_data.target[0])" ] }, { "cell_type": "code", "execution_count": 162, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ 23.68420569]\n" ] } ], "source": [ "x = boston_data['data']\n", "y = boston_data['target']\n", "\n", "# Make and fit the linear regression model\n", "# TODO: Fit the model and Assign it to the model variable\n", "model = LinearRegression()\n", "model.fit(x,y)\n", "\n", "# Make a prediction using the model\n", "sample_house = [[2.29690000e-01, 0.00000000e+00, 1.05900000e+01, 0.00000000e+00, 4.89000000e-01,\n", " 6.32600000e+00, 5.25000000e+01, 4.35490000e+00, 4.00000000e+00, 2.77000000e+02,\n", " 1.86000000e+01, 3.94870000e+02, 1.09700000e+01]]\n", "# TODO: Predict housing price for the sample_house\n", "prediction = model.predict(sample_house)\n", "print(prediction)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Siraj's Linear Regression live course\n", "\n", "[Siraj's code is here](https://github.com/llSourcell/linear_regression_live).\n", "\n", "> dataset of student test scores and the amount of hours they studied. Intuitively, there must be a relationship right? The more you study, the better your test scores should be. We're going to use linear regression to prove this relationship." ] }, { "cell_type": "code", "execution_count": 181, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>0</th>\n", " <th>1</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>32.502345</td>\n", " <td>31.707006</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>53.426804</td>\n", " <td>68.777596</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>61.530358</td>\n", " <td>62.562382</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>47.475640</td>\n", " <td>71.546632</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>59.813208</td>\n", " <td>87.230925</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " 0 1\n", "0 32.502345 31.707006\n", "1 53.426804 68.777596\n", "2 61.530358 62.562382\n", "3 47.475640 71.546632\n", "4 59.813208 87.230925" ] }, "execution_count": 181, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Step 1 - collect our data\n", "df = pd.read_csv('linear_regression_live/data.csv', header=None)\n", "df.head()" ] }, { "cell_type": "code", "execution_count": 192, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[ 32.50234527, 31.70700585],\n", " [ 53.42680403, 68.77759598],\n", " [ 61.53035803, 62.5623823 ],\n", " [ 47.47563963, 71.54663223],\n", " [ 59.81320787, 87.23092513]])" ] }, "execution_count": 192, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#collect data using numpy\n", "points = np.genfromtxt('linear_regression_live/data.csv', delimiter=',')\n", "points[:5]" ] }, { "cell_type": "code", "execution_count": 235, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x11cfd6d68>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# lets see the data\n", "plt.scatter(df[0], df[1])\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Step 2 - define our hyperparameters for the eq y = mx + b (slope formula)\n", "how fast should our model converge?" ] }, { "cell_type": "code", "execution_count": 185, "metadata": { "collapsed": true }, "outputs": [], "source": [ "learning_rate = 0.0001\n", "initial_b = 0\n", "initial_m = 0\n", "num_iterations = 1000" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Step 3: Train the model" ] }, { "cell_type": "code", "execution_count": 230, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def compute_error_for_line_given_points(b, m , points):\n", " totalError = 0 #initialize error at 0\n", " for i in range(0, len(points)): #for every point\n", " x = points[i, 0] #get x val\n", " y = points[i, 1] #get y val\n", " totalError += (y - (m*x + b)) **2\n", " return totalError / float(len(points))\n", "\n", "def gradient_descent_runner(points, starting_b, starting_m, learning_rate, num_iterations):\n", " b = starting_b\n", " m = starting_m\n", " \n", " #gradient descent\n", " for i in range(num_iterations):\n", " #update b & m with new more accurate b and m\n", " b, m = step_gradient(b, m, np.array(points), learning_rate)\n", " return [b,m]\n", "\n", "def step_gradient(b_current, m_current, points, learningRate):\n", " b_gradient = 0\n", " m_gradient = 0\n", " N = float(len(points))\n", " for i in range(0, len(points)):\n", " x = points[i, 0]\n", " y = points[i, 1]\n", " #direction with respect to b and m\n", " #computing partial deriavitives of our error function\n", " b_gradient += -(2/N) * (y - ((m_current * x) + b_current))\n", " m_gradient += -(2/N) * x * (y - ((m_current * x) + b_current))\n", " #update b and m values using partial derivates\n", " new_b = b_current - (learningRate * b_gradient)\n", " new_m = m_current - (learningRate * m_gradient)\n", " return [new_b, new_m]" ] }, { "cell_type": "code", "execution_count": 233, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "starting gradient descent at b = 0, m = 0, error = 5565.107834483211\n", "ending point after 1000 iterations at b = 0.08893651993741346, m = 1.4777440851894448, error = 112.61481011613473\n" ] } ], "source": [ "print('starting gradient descent at b = {0}, m = {1}, error = {2}'.format(initial_b, initial_m, compute_error_for_line_given_points(initial_b, initial_m, points)))\n", "[b, m] = gradient_descent_runner(points, initial_b, initial_m, learning_rate, num_iterations)\n", "print('ending point after {0} iterations at b = {1}, m = {2}, error = {3}'.format(num_iterations, b, m, compute_error_for_line_given_points(b, m, points)))" ] }, { "cell_type": "code", "execution_count": 232, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(0.088936519937413458, 1.4777440851894448)" ] }, "execution_count": 232, "metadata": {}, "output_type": "execute_result" } ], "source": [ "b,m" ] }, { "cell_type": "code", "execution_count": 245, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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YSONQ4CCRon8Ao8svYHAuvNUBk3//ZzmYGiDALActgX83yh3VKBXwRHkJZRgJ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\nwBQ4iIiISGAKHERERCQwBQ4iIiIS2P8HEQ0MTqMAteUAAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x1200da4e0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "x_vals = df[[0]]\n", "y_vals = df[[1]]\n", "\n", "live_model = LinearRegression()\n", "live_model.fit(x_vals, y_vals)\n", "\n", "plt.scatter(x_vals, y_vals)\n", "plt.plot(x_vals, live_model.predict(x_vals))\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 246, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " 0 1\n", "0 32.502345 31.707006\n", "1 53.426804 68.777596\n", "2 61.530358 62.562382\n", "3 47.475640 71.546632\n", "4 59.813208 87.230925\n" ] }, { "data": { "image/png": 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HFVazxZVaVf65FYHM0jVf+tKXMjf3ucrRq4A/KmzZ5zw1UlisH4uQWaBLhZTH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\ngE8Cr0spHc21MZIkKVctTY6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"text/plain": [ "<matplotlib.figure.Figure at 0x120378ef0>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "starting gradient descent at b = 0, m = 0, error = 5565.107834483211\n", "ending point at b = 0.08893651993741346, m = 1.4777440851894448, error = 112.61481011613473\n" ] } ], "source": [ "run gradient_descent.py" ] }, { "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 }

gpl-3.0

mvdwoord/PyGNS3

tools/api_gen/ast_explore.ipynb

1

34390

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# AST - Abstract Syntax Tree\n", "\n", "For reasons, I want to parse a python source code file and extract certain elements. The case in point involves looking for all functions with a given decorator applied and return certain attributes of the function declaration.\n", "\n", "When assuming a certain coding style, this could probably be done with a handful of lines or even a regex. This becomes problematic if youu want to be able to properly parse any and all (valid) python code. You'll soon find yourself reinventing the (lexer-)wheel which is already available in Python itsself.\n", "\n", "Thanks to others, there is a built-in ast module which parses Python source code into an AST. The AST can then be inspected and modified, and even recompiled into source code. In our case we are only interested in inspection." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import ast\n", "example_module = '''\n", "@my_decorator\n", "def my_function(my_argument):\n", " \"\"\"My Docstring\"\"\"\n", " my_value = 420\n", " return my_value\n", " \n", "def foo():\n", " pass\n", " \n", "@Some_decorator\n", "@Another_decorator\n", "def bar():\n", " pass\n", " \n", "@MyClass.subpackage.my_deco_function \n", "def baz():\n", " pass'''" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The [ast module](https://docs.python.org/3/library/ast.html) \"helps Python applications to process trees of the Python abstract syntax grammar. The abstract syntax itself might change with each Python release; this module helps to find out programmatically what the current grammar looks like.\"\n", "\n", "The tree of objects all inherit from ast.AST and the actual types and their properties can be found in the so called ASDL. The actual grammar of python as defined in the Zephyr Abstract Syntax Definition Language. The grammar file can be found in the Python sources at [Parser/python.asdl](https://github.com/python/cpython/blob/master/Parser/Python.asdl)." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "<_ast.Module object at 0x10ec9d668>\n" ] } ], "source": [ "tree = ast.parse(example_module)\n", "print(tree) # the object" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Module(body=[FunctionDef(name='my_function', args=arguments(args=[arg(arg='my_argument', annotation=None)], vararg=None, kwonlyargs=[], kw_defaults=[], kwarg=None, defaults=[]), body=[Expr(value=Str(s='My Docstring')), Assign(targets=[Name(id='my_value', ctx=Store())], value=Num(n=420)), Return(value=Name(id='my_value', ctx=Load()))], decorator_list=[Name(id='my_decorator', ctx=Load())], returns=None), FunctionDef(name='foo', args=arguments(args=[], vararg=None, kwonlyargs=[], kw_defaults=[], kwarg=None, defaults=[]), body=[Pass()], decorator_list=[], returns=None), FunctionDef(name='bar', args=arguments(args=[], vararg=None, kwonlyargs=[], kw_defaults=[], kwarg=None, defaults=[]), body=[Pass()], decorator_list=[Name(id='Some_decorator', ctx=Load()), Name(id='Another_decorator', ctx=Load())], returns=None), FunctionDef(name='baz', args=arguments(args=[], vararg=None, kwonlyargs=[], kw_defaults=[], kwarg=None, defaults=[]), body=[Pass()], decorator_list=[Attribute(value=Attribute(value=Name(id='MyClass', ctx=Load()), attr='subpackage', ctx=Load()), attr='my_deco_function', ctx=Load())], returns=None)])\n" ] } ], "source": [ "# Built in dump method shows the actual content of the entire tree\n", "print(ast.dump(ast.parse(example_module)))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The [astunparse]() module helps in pretty printing the tree, which we rely heavy upon during exploration." ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Module(body=[\n", " FunctionDef(\n", " name='my_function',\n", " args=arguments(\n", " args=[arg(\n", " arg='my_argument',\n", " annotation=None)],\n", " vararg=None,\n", " kwonlyargs=[],\n", " kw_defaults=[],\n", " kwarg=None,\n", " defaults=[]),\n", " body=[\n", " Expr(value=Str(s='My Docstring')),\n", " Assign(\n", " targets=[Name(\n", " id='my_value',\n", " ctx=Store())],\n", " value=Num(n=420)),\n", " Return(value=Name(\n", " id='my_value',\n", " ctx=Load()))],\n", " decorator_list=[Name(\n", " id='my_decorator',\n", " ctx=Load())],\n", " returns=None),\n", " FunctionDef(\n", " name='foo',\n", " args=arguments(\n", " args=[],\n", " vararg=None,\n", " kwonlyargs=[],\n", " kw_defaults=[],\n", " kwarg=None,\n", " defaults=[]),\n", " body=[Pass()],\n", " decorator_list=[],\n", " returns=None),\n", " FunctionDef(\n", " name='bar',\n", " args=arguments(\n", " args=[],\n", " vararg=None,\n", " kwonlyargs=[],\n", " kw_defaults=[],\n", " kwarg=None,\n", " defaults=[]),\n", " body=[Pass()],\n", " decorator_list=[\n", " Name(\n", " id='Some_decorator',\n", " ctx=Load()),\n", " Name(\n", " id='Another_decorator',\n", " ctx=Load())],\n", " returns=None),\n", " FunctionDef(\n", " name='baz',\n", " args=arguments(\n", " args=[],\n", " vararg=None,\n", " kwonlyargs=[],\n", " kw_defaults=[],\n", " kwarg=None,\n", " defaults=[]),\n", " body=[Pass()],\n", " decorator_list=[Attribute(\n", " value=Attribute(\n", " value=Name(\n", " id='MyClass',\n", " ctx=Load()),\n", " attr='subpackage',\n", " ctx=Load()),\n", " attr='my_deco_function',\n", " ctx=Load())],\n", " returns=None)])\n" ] } ], "source": [ "import astunparse\n", "print(astunparse.dump(tree))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We want to look at function definitions which are aptly named FunctionDef in the ASDL and represented as FunctionDef objects in the tree. Looking at the ASDL we see the following deifnition for FunctionDef (reformatted):\n", "\n", " FunctionDef(identifier name,\n", " arguments args,\n", " stmt* body,\n", " expr* decorator_list,\n", " expr? returns,\n", " string? docstring)\n", " \n", "Which seems to correspond to the structure of the object in the AST as shown in the astunparse dump above. There is some documentation at a place called [Green Tree Snakes](https://greentreesnakes.readthedocs.io/en/latest/nodes.html#function-and-class-definitions) which explains the components of the FunctionDef object." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Traversing and inspecting the tree\n", "\n", "There are two ways to work with the tree. The easiest is `ast.walk()` which \"Recursively yield all descendant nodes in the tree starting at node (including node itself), in no specified order.\" and apparently does so breadth first. Alternatively you can subclass the `ast.NodeVisitor` class. This class provides a `visit()` method which does a depth first traversal. You can override `visit_<Class_Name>` which are called whenever the traversal hits a node of that class." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Using NodeVisitor (depth first):\n", "Nodetype: Module <_ast.Module object at 0x10ec9d668>\n", "Nodetype: FunctionDef <_ast.FunctionDef object at 0x10ec9d6a0>\n", "Nodetype: arguments <_ast.arguments object at 0x10ec9d6d8>\n", "Nodetype: arg <_ast.arg object at 0x10ec9d710>\n", "Nodetype: Expr <_ast.Expr object at 0x10ec9d748>\n", "Nodetype: Str <_ast.Str object at 0x10ec9d780>\n", "Nodetype: Assign <_ast.Assign object at 0x10ec9d7b8>\n", "Nodetype: Name <_ast.Name object at 0x10ec9d7f0>\n", "Nodetype: Store <_ast.Store object at 0x10d24e780>\n", "Nodetype: Num <_ast.Num object at 0x10ec9d828>\n", "Nodetype: Return <_ast.Return object at 0x10ec9d860>\n", "Nodetype: Name <_ast.Name object at 0x10ec9d898>\n", "Nodetype: Load <_ast.Load object at 0x10d24e668>\n", "Nodetype: Name <_ast.Name object at 0x10ec9d8d0>\n", "Nodetype: Load <_ast.Load object at 0x10d24e668>\n", "Nodetype: FunctionDef <_ast.FunctionDef object at 0x10ec9d908>\n", "Nodetype: arguments <_ast.arguments object at 0x10ec9d940>\n", "Nodetype: Pass <_ast.Pass object at 0x10ec9d978>\n", "Nodetype: FunctionDef <_ast.FunctionDef object at 0x10ec9d9b0>\n", "Nodetype: arguments <_ast.arguments object at 0x10ec9d9e8>\n", "Nodetype: Pass <_ast.Pass object at 0x10ec9da20>\n", "Nodetype: Name <_ast.Name object at 0x10ec9da58>\n", "Nodetype: Load <_ast.Load object at 0x10d24e668>\n", "Nodetype: Name <_ast.Name object at 0x10ec9da90>\n", "Nodetype: Load <_ast.Load object at 0x10d24e668>\n", "Nodetype: FunctionDef <_ast.FunctionDef object at 0x10ec9dac8>\n", "Nodetype: arguments <_ast.arguments object at 0x10ec9db00>\n", "Nodetype: Pass <_ast.Pass object at 0x10ec9db38>\n", "Nodetype: Attribute <_ast.Attribute object at 0x10ec9db70>\n", "Nodetype: Attribute <_ast.Attribute object at 0x10ec9dba8>\n", "Nodetype: Name <_ast.Name object at 0x10ec9dbe0>\n", "Nodetype: Load <_ast.Load object at 0x10d24e668>\n", "Nodetype: Load <_ast.Load object at 0x10d24e668>\n", "Nodetype: Load <_ast.Load object at 0x10d24e668>\n", "\n", "Walk()ing the tree breadth first:\n", "Nodetype: Module <_ast.Module object at 0x10ec9d668>\n", "Nodetype: FunctionDef <_ast.FunctionDef object at 0x10ec9d6a0>\n", "Nodetype: FunctionDef <_ast.FunctionDef object at 0x10ec9d908>\n", "Nodetype: FunctionDef <_ast.FunctionDef object at 0x10ec9d9b0>\n", "Nodetype: FunctionDef <_ast.FunctionDef object at 0x10ec9dac8>\n", "Nodetype: arguments <_ast.arguments object at 0x10ec9d6d8>\n", "Nodetype: Expr <_ast.Expr object at 0x10ec9d748>\n", "Nodetype: Assign <_ast.Assign object at 0x10ec9d7b8>\n", "Nodetype: Return <_ast.Return object at 0x10ec9d860>\n", "Nodetype: Name <_ast.Name object at 0x10ec9d8d0>\n", "Nodetype: arguments <_ast.arguments object at 0x10ec9d940>\n", "Nodetype: Pass <_ast.Pass object at 0x10ec9d978>\n", "Nodetype: arguments <_ast.arguments object at 0x10ec9d9e8>\n", "Nodetype: Pass <_ast.Pass object at 0x10ec9da20>\n", "Nodetype: Name <_ast.Name object at 0x10ec9da58>\n", "Nodetype: Name <_ast.Name object at 0x10ec9da90>\n", "Nodetype: arguments <_ast.arguments object at 0x10ec9db00>\n", "Nodetype: Pass <_ast.Pass object at 0x10ec9db38>\n", "Nodetype: Attribute <_ast.Attribute object at 0x10ec9db70>\n", "Nodetype: arg <_ast.arg object at 0x10ec9d710>\n", "Nodetype: Str <_ast.Str object at 0x10ec9d780>\n", "Nodetype: Name <_ast.Name object at 0x10ec9d7f0>\n", "Nodetype: Num <_ast.Num object at 0x10ec9d828>\n", "Nodetype: Name <_ast.Name object at 0x10ec9d898>\n", "Nodetype: Load <_ast.Load object at 0x10d24e668>\n", "Nodetype: Load <_ast.Load object at 0x10d24e668>\n", "Nodetype: Load <_ast.Load object at 0x10d24e668>\n", "Nodetype: Attribute <_ast.Attribute object at 0x10ec9dba8>\n", "Nodetype: Load <_ast.Load object at 0x10d24e668>\n", "Nodetype: Store <_ast.Store object at 0x10d24e780>\n", "Nodetype: Load <_ast.Load object at 0x10d24e668>\n", "Nodetype: Name <_ast.Name object at 0x10ec9dbe0>\n", "Nodetype: Load <_ast.Load object at 0x10d24e668>\n", "Nodetype: Load <_ast.Load object at 0x10d24e668>\n" ] } ], "source": [ "class MyVisitor(ast.NodeVisitor):\n", " def generic_visit(self, node):\n", " print(f'Nodetype: {type(node).__name__:{16}} {node}')\n", " ast.NodeVisitor.generic_visit(self, node)\n", " \n", "\n", "v = MyVisitor()\n", "print('Using NodeVisitor (depth first):')\n", "v.visit(tree)\n", "\n", "print('\\nWalk()ing the tree breadth first:')\n", "for node in ast.walk(tree):\n", " print(f'Nodetype: {type(node).__name__:{16}} {node}')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For our purposes we should be able to use the walk method, I find it simpler to use for now. Let;s see what happens if we grab those `FunctionDef` objects and inspect them in the same way. Using the `unparse()` methof of astunparse we can transform it back into source code for extra fun." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Nodetype: FunctionDef <_ast.FunctionDef object at 0x10ec9d6a0>\n", "\n", "\n", "@my_decorator\n", "def my_function(my_argument):\n", " 'My Docstring'\n", " my_value = 420\n", " return my_value\n", "\n", "Nodetype: FunctionDef <_ast.FunctionDef object at 0x10ec9d908>\n", "\n", "\n", "def foo():\n", " pass\n", "\n", "Nodetype: FunctionDef <_ast.FunctionDef object at 0x10ec9d9b0>\n", "\n", "\n", "@Some_decorator\n", "@Another_decorator\n", "def bar():\n", " pass\n", "\n", "Nodetype: FunctionDef <_ast.FunctionDef object at 0x10ec9dac8>\n", "\n", "\n", "@MyClass.subpackage.my_deco_function\n", "def baz():\n", " pass\n", "\n" ] } ], "source": [ "for node in ast.walk(tree):\n", " if isinstance(node, ast.FunctionDef):\n", " print(f'Nodetype: {type(node).__name__:{16}} {node}')\n", " print(astunparse.unparse(node))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We wanted to only grab functions who have a certain decorator, so we need to inspect the `decorator_list` attribute of the `FunctionDef` class." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "my_function ['my_decorator']\n", "foo []\n", "bar ['Some_decorator', 'Another_decorator']\n" ] }, { "ename": "AttributeError", "evalue": "'Attribute' object has no attribute 'id'", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-7-666517aaaecf>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mnode\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mast\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwalk\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtree\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mast\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mFunctionDef\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[0mdecorators\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0md\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mid\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0md\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mnode\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdecorator_list\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 4\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnode\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdecorators\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m<ipython-input-7-666517aaaecf>\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mnode\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mast\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwalk\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtree\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mast\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mFunctionDef\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[0mdecorators\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0md\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mid\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0md\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mnode\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdecorator_list\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 4\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnode\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdecorators\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mAttributeError\u001b[0m: 'Attribute' object has no attribute 'id'" ], "output_type": "error" } ], "source": [ "for node in ast.walk(tree):\n", " if isinstance(node, ast.FunctionDef):\n", " decorators = [d.id for d in node.decorator_list]\n", " print(node.name, decorators)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "So looking more closely there is a different representation in the AST for a single keyword (`@function`) decorator as there is for a compound (`@Class.method`).\n", "Compare the decorator in `my_function`:\n", "\n", " decorator_list=[Name(\n", " id='my_decorator',\n", " ctx=Load())]\n", "\n", "against the compound decorator in `baz`:\n", "\n", " decorator_list=[Attribute(\n", " value=Attribute(\n", " value=Name(\n", " id='MyClass',\n", " ctx=Load()),\n", " attr='subpackage',\n", " ctx=Load()),\n", " attr='my_deco_function',\n", " ctx=Load())]\n", " \n", "So we need to modify our treewalk to acomodate for this. When the top level element in the decorator_liist is of type `Name`, we grab the id and be done with it. If it is of type `Attribute` we need to do some more extra work. From the ASDL we can see that Attribute is a nested element:\n", "\n", " Attribute(expr value, identifier attr, expr_context ctx)\n", " \n", "Assuming it's nested `ast.Attribute`s with a `ast.Name` at the root we can define a flattening function." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def flatten_attr(node):\n", " if isinstance(node, ast.Attribute):\n", " return str(flatten_attr(node.value)) + '.' + node.attr\n", " elif isinstance(node, ast.Name):\n", " return str(node.id)\n", " else:\n", " pass" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "my_function ['my_decorator']\n", "foo []\n", "bar ['Some_decorator', 'Another_decorator']\n", "baz ['MyClass.subpackage.my_deco_function']\n" ] } ], "source": [ "for node in ast.walk(tree):\n", " if isinstance(node, ast.FunctionDef):\n", " found_decorators = []\n", " for decorator in node.decorator_list:\n", " if isinstance(decorator, ast.Name):\n", " found_decorators.append(decorator.id)\n", " elif isinstance(decorator, ast.Attribute):\n", " found_decorators.append(flatten_attr(decorator))\n", " \n", " \n", " print(node.name, found_decorators)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The actual sources I want to parse have an additional complication, the decorator functions have arguments passed into them. And I want to know what's in them as well. So let's switch to some actual source code and see how to do that. I have removed the body of the function as we are only interested in the decorator now." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Module(body=[FunctionDef(\n", " name='list',\n", " args=arguments(\n", " args=[\n", " arg(\n", " arg='request',\n", " annotation=None),\n", " arg(\n", " arg='response',\n", " annotation=None)],\n", " vararg=None,\n", " kwonlyargs=[],\n", " kw_defaults=[],\n", " kwarg=None,\n", " defaults=[]),\n", " body=[Pass()],\n", " decorator_list=[Call(\n", " func=Attribute(\n", " value=Name(\n", " id='Route',\n", " ctx=Load()),\n", " attr='get',\n", " ctx=Load()),\n", " args=[Str(s='/projects/{project_id}/snapshots')],\n", " keywords=[\n", " keyword(\n", " arg='description',\n", " value=Str(s='List snapshots of a project')),\n", " keyword(\n", " arg='parameters',\n", " value=Dict(\n", " keys=[Str(s='project_id')],\n", " values=[Str(s='Project UUID')])),\n", " keyword(\n", " arg='status_codes',\n", " value=Dict(\n", " keys=[\n", " Num(n=200),\n", " Num(n=404)],\n", " values=[\n", " Str(s='Snasphot list returned'),\n", " Str(s=\"The project doesn't exist\")]))])],\n", " returns=None)])\n" ] } ], "source": [ "source = \"\"\"\n", "@Route.get(\n", " r\"/projects/{project_id}/snapshots\",\n", " description=\"List snapshots of a project\",\n", " parameters={\n", " \"project_id\": \"Project UUID\",\n", " },\n", " status_codes={\n", " 200: \"Snasphot list returned\",\n", " 404: \"The project doesn't exist\"\n", " })\n", "def list(request, response):\n", " pass\"\"\"\n", "\n", "print(astunparse.dump(ast.parse(source)))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We find the decorator_list to contain a `ast.Call` object rather than a Name or Attribute. This corresponds to the signature of the called decorator function. I am interested in the first positional argument as well as the keyword arguments. Let's grab the [0] element of the decorator list to simplify." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Call(\n", " func=Attribute(\n", " value=Name(\n", " id='Route',\n", " ctx=Load()),\n", " attr='get',\n", " ctx=Load()),\n", " args=[Str(s='/projects/{project_id}/snapshots')],\n", " keywords=[\n", " keyword(\n", " arg='description',\n", " value=Str(s='List snapshots of a project')),\n", " keyword(\n", " arg='parameters',\n", " value=Dict(\n", " keys=[Str(s='project_id')],\n", " values=[Str(s='Project UUID')])),\n", " keyword(\n", " arg='status_codes',\n", " value=Dict(\n", " keys=[\n", " Num(n=200),\n", " Num(n=404)],\n", " values=[\n", " Str(s='Snasphot list returned'),\n", " Str(s=\"The project doesn't exist\")]))])\n" ] } ], "source": [ "complex_decorator = ast.parse(source).body[0].decorator_list[0]\n", "print(astunparse.dump(complex_decorator))" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Route.get /projects/{project_id}/snapshots\n", "Parameters:\n", " project_id: Project UUID\n", "Status Codes:\n", " 200: Snasphot list returned\n", " 404: The project doesn't exist\n" ] } ], "source": [ "decorator_name = flatten_attr(complex_decorator.func)\n", "decorator_path = complex_decorator.args[0].s\n", "for kw in complex_decorator.keywords:\n", " if kw.arg == 'description':\n", " decorator_description = kw.value.s\n", " if kw.arg == 'parameters':\n", " decorator_parameters = ast.literal_eval(astunparse.unparse(kw.value))\n", " if kw.arg == 'status_codes':\n", " decorator_statuscodes = ast.literal_eval(astunparse.unparse(kw.value))\n", "\n", "print(decorator_name, decorator_path)\n", "print('Parameters:')\n", "for p in decorator_parameters.keys():\n", " print(' ' + str(p) + ': ' + decorator_parameters[p]) \n", "print('Status Codes:')\n", "for sc in decorator_statuscodes.keys():\n", " print(' ' + str(sc) + ': ' + decorator_statuscodes[sc])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Time to bring it all together and write a function that takes a filename and a decorator as argument and spits out a list of tuples which hold the:\n", "\n", "- Function name (str)\n", "- description for the given decorator (str)\n", "- parameters for the decorator (dict)\n", "- status codes for the decorator (dict)\n", "\n", "for every function in the sourcefile which is decorated with that decorator." ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "controller/compute_handler.py create /computes \n", "controller/compute_handler.py post_forward /computes/{compute_id}/{emulator}/{action:.+}\n", "controller/drawing_handler.py create /projects/{project_id}/drawings \n", "controller/link_handler.py create /projects/{project_id}/links \n", "controller/link_handler.py start_capture /projects/{project_id}/links/{link_id}/start_capture\n", "controller/link_handler.py stop_capture /projects/{project_id}/links/{link_id}/stop_capture\n", "controller/node_handler.py create /projects/{project_id}/nodes \n", "controller/node_handler.py start_all /projects/{project_id}/nodes/start \n", "controller/node_handler.py stop_all /projects/{project_id}/nodes/stop \n", "controller/node_handler.py suspend_all /projects/{project_id}/nodes/suspend \n", "controller/node_handler.py reload_all /projects/{project_id}/nodes/reload \n", "controller/node_handler.py start /projects/{project_id}/nodes/{node_id}/start\n", "controller/node_handler.py stop /projects/{project_id}/nodes/{node_id}/stop\n", "controller/node_handler.py suspend /projects/{project_id}/nodes/{node_id}/suspend\n", "controller/node_handler.py reload /projects/{project_id}/nodes/{node_id}/reload\n", "controller/node_handler.py post_file /projects/{project_id}/nodes/{node_id}/files/{path:.+}\n", "controller/project_handler.py create_project /projects \n", "controller/project_handler.py close /projects/{project_id}/close \n", "controller/project_handler.py open /projects/{project_id}/open \n", "controller/project_handler.py load /projects/load \n", "controller/project_handler.py import_project /projects/{project_id}/import \n", "controller/project_handler.py duplicate /projects/{project_id}/duplicate \n", "controller/project_handler.py write_file /projects/{project_id}/files/{path:.+} \n", "controller/server_handler.py shutdown /shutdown \n", "controller/server_handler.py check_version /version \n", "controller/server_handler.py write_settings /settings \n", "controller/server_handler.py debug /debug \n", "controller/symbol_handler.py upload /symbols/{symbol_id:.+}/raw \n" ] } ], "source": [ "import collections\n", "\n", "Route = collections.namedtuple('Route', 'filename function_name path description parameters status_codes')\n", "\n", "def extract_routes(file, decorator_name):\n", " routes = []\n", " filename = file\n", " with open(file) as f:\n", " try:\n", " tree = ast.parse(f.read())\n", " except:\n", " return routes\n", " \n", " for node in ast.walk(tree):\n", " if isinstance(node, ast.FunctionDef):\n", " funcname = node.name\n", " for d in node.decorator_list:\n", " if isinstance(d, ast.Call):\n", " if flatten_attr(d.func) == decorator_name:\n", " route_path = d.args[0].s\n", " description = None\n", " parameters = None\n", " statuscodes = None\n", " for kw in d.keywords:\n", " if kw.arg == 'description':\n", " description = kw.value.s\n", " if kw.arg == 'parameters':\n", " parameters = ast.literal_eval(astunparse.unparse(kw.value))\n", " if kw.arg == 'status_codes':\n", " statuscodes = ast.literal_eval(astunparse.unparse(kw.value))\n", " r = Route(filename, funcname, route_path, description, parameters, statuscodes)\n", " routes.append(r)\n", " \n", " return routes\n", "\n", "get_routes = []\n", "from pathlib import Path\n", "\n", "pathlist = Path('./controller').glob('*.py')\n", "for path in pathlist:\n", " # because path is object not string\n", " filename = str(path)\n", " get_routes += extract_routes(filename, 'Route.post')\n", "\n", "for route in get_routes:\n", " print(f'{route.filename} {route.function_name:{20}} {route.path:{40}}')" ] }, { "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 }

mit

GoogleCloudPlatform/nvidia-merlin-on-vertex-ai

04-e2e-pipeline.ipynb

1

15841

{ "cells": [ { "cell_type": "markdown", "id": "f7086876-72ed-4ac8-aeba-f05dbc641798", "metadata": { "id": "f7086876-72ed-4ac8-aeba-f05dbc641798" }, "source": [ "# End-to-end Recommender System with NVIDIA Merlin and Vertex AI.\n", "\n", "This notebook shows how to deploy and execute an end-to-end recommender system on Vertex Pipelines using NVIDIA Merlin.\n", "The notebook covers the following:\n", "\n", "1. Training pipeline overview.\n", "2. Set pipeline configurations.\n", "3. Build pipeline container images.\n", "4. Configure pipeline parameters.\n", "5. Compile KFP pipeline.\n", "6. Submit pipeline to Vertex AI.\n" ] }, { "cell_type": "markdown", "id": "13889502-7856-4d1f-bbf1-064a7978e225", "metadata": { "id": "13889502-7856-4d1f-bbf1-064a7978e225" }, "source": [ "## 1. Training Pipeline Overview\n", "\n", "The following diagram shows the end-to-end pipeline for preprocessing, training, and serving `NVIDIA Merlin` Recommender System using `Vertex AI`.\n", "The pipeline is defined in [src/training_pipelines.py](src/training_pipelines.py) module. \n", "\n", "The `training_bq` pipeline function reads the criteo data from `Cloud Storage` and perform the following steps:\n", "\n", "1. Preprocess the data using `NVTabular`, as described in the [01-dataset-preprocessing.ipynb](01-dataset-preprocessing.ipynb) notebook:\n", " 1. Convert CSV data to Parquet and write to `Cloud Storage`.\n", " 2. Transform the data using an `NVTabular` workflow.\n", " 3. Write the transformed data as parquet files and the workflow object to `Cloud Storage`.\n", "2. Train a DeepFM model using `HugeCTR`. This step is submits a [Custom Training Job](https://cloud.google.com/vertex-ai/docs/training/create-custom-job) to `Vertex AI` training, as described in [02-model-training-hugectr.ipynb](02-model-training-hugectr.ipynb).\n", "3. Export the model as a `Triton` Ensemble to be served using `Triton` server. The ensemble consists of of the `NVTabular` preprocessing workflow and a `HugeCTR` model. \n", "4. The exported `Triton` ensemble model is uploaded to `Vertex AI` model resources.\n", "\n", "Once the model is uploaded to `Vertex AI`, a long with a reference to its serving `Triton` container, it can be deployed to `Vertex AI` Prediction, as described in [03-model-inference-hugectr.ipynb](03-model-inference-hugectr.ipynb). \n", "\n", "All the components of the pipelines are defined in the [src/pipelines/components.py](src/pipelines/components.py) module.\n", "\n", "<img src=\"images/merlin-vertex-e2e.png\" alt=\"Pipeline\"/>" ] }, { "cell_type": "markdown", "id": "54eee537-5fc7-480f-a011-ff56e8e623ab", "metadata": { "id": "54eee537-5fc7-480f-a011-ff56e8e623ab" }, "source": [ "## Setup\n", "\n", "In this section of the notebook you configure your environment settings, including a GCP project, a GCP compute region, a Vertex AI service account and a Vertex AI staging bucket. \n", "\n", "Make sure to update the below cells with the values reflecting your environment.\n", "\n", "First import all the necessary python packages." ] }, { "cell_type": "code", "execution_count": null, "id": "ec7e9c16-0c12-4ada-a71e-125b08b2b589", "metadata": { "id": "ec7e9c16-0c12-4ada-a71e-125b08b2b589" }, "outputs": [], "source": [ "import os\n", "import json\n", "from datetime import datetime\n", "from google.cloud import aiplatform as vertex_ai\n", "from kfp.v2 import compiler" ] }, { "cell_type": "markdown", "id": "9335743c", "metadata": {}, "source": [ "Change the following variables according to your definitions." ] }, { "cell_type": "code", "execution_count": null, "id": "c0cb5545", "metadata": {}, "outputs": [], "source": [ "# Project definitions\n", "PROJECT_ID = '<YOUR PROJECT ID>' # Change to your project.\n", "REGION = '<LOCATION OF RESOURCES>' # Change to your region.\n", "\n", "# Service Account address\n", "VERTEX_SA = f'vertex-sa@{PROJECT_ID}.iam.gserviceaccount.com' # Change to your service account with Vertex AI Admin permitions.\n", "\n", "# Bucket definitions\n", "BUCKET = '<YOUR BUCKET NAME>' # Change to your bucket. All the files will be stored here." ] }, { "cell_type": "markdown", "id": "0f72f557", "metadata": {}, "source": [ "Change the following variables ONLY if necessary. \n", "You can leave the default variables." ] }, { "cell_type": "code", "execution_count": null, "id": "fa917976-d352-4f1b-aeb2-798f7cbbef83", "metadata": { "id": "fa917976-d352-4f1b-aeb2-798f7cbbef83" }, "outputs": [], "source": [ "# Bucket definitions\n", "MODEL_NAME = 'deepfm'\n", "MODEL_VERSION = 'v01'\n", "MODEL_DISPLAY_NAME = f'criteo-hugectr-{MODEL_NAME}-{MODEL_VERSION}'\n", "WORKSPACE = f'gs://{BUCKET}/{MODEL_DISPLAY_NAME}'\n", "TRAINING_PIPELINE_NAME = f'merlin-training-pipeline'\n", "\n", "# Docker definitions for data preprocessing\n", "NVT_IMAGE_NAME = 'nvt-preprocessing'\n", "NVT_IMAGE_URI = f'gcr.io/{PROJECT_ID}/{NVT_IMAGE_NAME}'\n", "NVT_DOCKERNAME = 'nvtabular'\n", "\n", "# Docker definitions for model training\n", "HUGECTR_IMAGE_NAME = 'hugectr-training'\n", "HUGECTR_IMAGE_URI = f'gcr.io/{PROJECT_ID}/{HUGECTR_IMAGE_NAME}'\n", "HUGECTR_DOCKERNAME = 'hugectr'\n", "\n", "# Docker definitions for model serving\n", "TRITON_IMAGE_NAME = f'triton-serving'\n", "TRITON_IMAGE_URI = f'gcr.io/{PROJECT_ID}/{TRITON_IMAGE_NAME}'\n", "TRITON_DOCKERNAME = 'triton'" ] }, { "cell_type": "markdown", "id": "d9843d31-6716-40db-a88b-3eb2a051b2ea", "metadata": { "id": "d9843d31-6716-40db-a88b-3eb2a051b2ea" }, "source": [ "## 2. Set Pipeline Configurations" ] }, { "cell_type": "code", "execution_count": null, "id": "f153b818-0d36-4101-a269-ada711ff76e6", "metadata": { "id": "f153b818-0d36-4101-a269-ada711ff76e6" }, "outputs": [], "source": [ "os.environ['PROJECT_ID'] = PROJECT_ID\n", "os.environ['REGION'] = REGION\n", "os.environ['BUCKET'] = BUCKET\n", "os.environ['WORKSPACE'] = WORKSPACE\n", "\n", "os.environ['TRAINING_PIPELINE_NAME'] = TRAINING_PIPELINE_NAME\n", "os.environ['MODEL_NAME'] = MODEL_NAME\n", "os.environ['MODEL_VERSION'] = MODEL_VERSION\n", "os.environ['MODEL_DISPLAY_NAME'] = MODEL_DISPLAY_NAME\n", "\n", "os.environ['MEMORY_LIMIT'] = '680'\n", "os.environ['CPU_LIMIT'] = '96'\n", "os.environ['GPU_LIMIT'] = '8'\n", "os.environ['GPU_TYPE'] = 'NVIDIA_TESLA_A100'\n", "\n", "os.environ['MACHINE_TYPE'] = 'a2-highgpu-1g'\n", "os.environ['ACCELERATOR_TYPE'] = 'NVIDIA_TESLA_A100'\n", "os.environ['ACCELERATOR_NUM'] = '1'\n", "os.environ['NUM_WORKERS'] = '12'\n", "\n", "os.environ['NUM_SLOTS'] = '26'\n", "os.environ['MAX_NNZ'] = '2'\n", "os.environ['EMBEDDING_VECTOR_SIZE'] = '11'\n", "os.environ['MAX_BATCH_SIZE'] = '64'\n", "os.environ['MODEL_REPOSITORY_PATH'] = '/model'\n", "\n", "os.environ['NVT_IMAGE_URI'] = NVT_IMAGE_URI\n", "os.environ['HUGECTR_IMAGE_URI'] = HUGECTR_IMAGE_URI\n", "os.environ['TRITON_IMAGE_URI'] = TRITON_IMAGE_URI" ] }, { "cell_type": "markdown", "id": "fbcbe88a-30cc-478d-a040-5045f9aaabb9", "metadata": { "id": "fbcbe88a-30cc-478d-a040-5045f9aaabb9" }, "source": [ "The following cell lists the configuration values in `config.py`" ] }, { "cell_type": "code", "execution_count": null, "id": "502059d5-0218-43c7-b64f-1634bc884716", "metadata": { "id": "502059d5-0218-43c7-b64f-1634bc884716" }, "outputs": [], "source": [ "from src.pipelines import config\n", "import importlib\n", "importlib.reload(config)\n", "\n", "for key, value in config.__dict__.items():\n", " if key.isupper(): print(f'{key}: {value}')" ] }, { "cell_type": "markdown", "id": "b5d7a468-618b-40e3-b0a5-546349801ff8", "metadata": { "id": "b5d7a468-618b-40e3-b0a5-546349801ff8" }, "source": [ "## 3. Build Pipeline Container Images\n", "\n", "The following three commands build the NVTabular preprocessing, HugeCTR training, and Triton serving container images using Cloud Build, and store the container images in Container Registry." ] }, { "cell_type": "markdown", "id": "86f0ed33-51ea-4593-9a3c-0bba29dfbbd4", "metadata": { "id": "86f0ed33-51ea-4593-9a3c-0bba29dfbbd4" }, "source": [ "### Build NVTabular preprocessing container image" ] }, { "cell_type": "code", "execution_count": null, "id": "ead5075a-f081-4589-8eae-dae8d62edd0a", "metadata": { "id": "ead5075a-f081-4589-8eae-dae8d62edd0a", "tags": [] }, "outputs": [], "source": [ "FILE_LOCATION = './src'\n", "! gcloud builds submit --config src/cloudbuild.yaml --substitutions _DOCKERNAME=$NVT_DOCKERNAME,_IMAGE_URI=$NVT_IMAGE_URI,_FILE_LOCATION=$FILE_LOCATION --timeout=2h --machine-type=e2-highcpu-8" ] }, { "cell_type": "markdown", "id": "c2545dfd-0e4c-4113-8c3b-ccb6487596f5", "metadata": { "id": "c2545dfd-0e4c-4113-8c3b-ccb6487596f5" }, "source": [ "### Build HugeCTR training container image" ] }, { "cell_type": "code", "execution_count": null, "id": "7565ad2c-f145-481c-ae09-c9055ba043b7", "metadata": { "id": "7565ad2c-f145-481c-ae09-c9055ba043b7", "tags": [] }, "outputs": [], "source": [ "FILE_LOCATION = './src'\n", "! gcloud builds submit --config src/cloudbuild.yaml --substitutions _DOCKERNAME=$HUGECTR_DOCKERNAME,_IMAGE_URI=$HUGECTR_IMAGE_URI,_FILE_LOCATION=$FILE_LOCATION --timeout=2h --machine-type=e2-highcpu-8" ] }, { "cell_type": "markdown", "id": "966fbbb8-4f01-44d7-8877-3378c6a7af7c", "metadata": { "id": "966fbbb8-4f01-44d7-8877-3378c6a7af7c" }, "source": [ "### Build Triton serving container image" ] }, { "cell_type": "code", "execution_count": null, "id": "e34c940b-77b1-40fd-b483-9f31dec06184", "metadata": { "id": "e34c940b-77b1-40fd-b483-9f31dec06184" }, "outputs": [], "source": [ "FILE_LOCATION = './src'\n", "! gcloud builds submit --config src/cloudbuild.yaml --substitutions _DOCKERNAME=$TRITON_DOCKERNAME,_IMAGE_URI=$TRITON_IMAGE_URI,_FILE_LOCATION=$FILE_LOCATION --timeout=24h --machine-type=e2-highcpu-8" ] }, { "cell_type": "markdown", "id": "ee19e322-1ddf-4127-a31e-6992a582c797", "metadata": { "id": "ee19e322-1ddf-4127-a31e-6992a582c797", "tags": [] }, "source": [ "## 4. Configure pipeline parameters" ] }, { "cell_type": "markdown", "id": "5e869715", "metadata": {}, "source": [ "Change the following variables according to your definitions." ] }, { "cell_type": "code", "execution_count": null, "id": "80959a0a", "metadata": {}, "outputs": [], "source": [ "# List of path(s) to criteo file(s) or folder(s) in GCS.\n", "# Training files\n", "TRAIN_PATHS = ['gs://renatoleite-criteo-full/'] # Training CSV file to be preprocessed.\n", "# Validation files\n", "VALID_PATHS = ['gs://renatoleite-criteo-full/day_0'] # Validation CSV file to be preprocessed." ] }, { "cell_type": "code", "execution_count": null, "id": "50a3fb0f-d012-4f31-81da-e10bf99d0647", "metadata": { "id": "50a3fb0f-d012-4f31-81da-e10bf99d0647" }, "outputs": [], "source": [ "# Data preprocessing parameters\n", "num_output_files_train = 24 # Number of output files after converting CSV to Parquet\n", "num_output_files_valid = 1 # Number of output files after converting CSV to Parquet\n", "\n", "# Training parameters\n", "NUM_EPOCHS = 0\n", "MAX_ITERATIONS = 25000\n", "EVAL_INTERVAL = 1000\n", "EVAL_BATCHES = 500\n", "EVAL_BATCHES_FINAL = 2500\n", "DISPLAY_INTERVAL = 200\n", "SNAPSHOT_INTERVAL = 0\n", "PER_GPU_BATCHSIZE = 2048\n", "LR = 0.001\n", "DROPOUT_RATE = 0.5" ] }, { "cell_type": "code", "execution_count": null, "id": "c282810b-c760-4de7-a00e-8cf72c33f829", "metadata": { "id": "c282810b-c760-4de7-a00e-8cf72c33f829" }, "outputs": [], "source": [ "parameter_values = {\n", " 'train_paths': TRAIN_PATHS,\n", " 'valid_paths': VALID_PATHS,\n", " 'shuffle': json.dumps(None), # select PER_PARTITION, PER_WORKER, FULL, or None.\n", " 'num_output_files_train': num_output_files_train,\n", " 'num_output_files_valid': num_output_files_valid,\n", " 'per_gpu_batch_size': PER_GPU_BATCHSIZE,\n", " 'max_iter': MAX_ITERATIONS,\n", " 'max_eval_batches': EVAL_BATCHES ,\n", " 'eval_batches': EVAL_BATCHES_FINAL ,\n", " 'dropout_rate': DROPOUT_RATE,\n", " 'lr': LR ,\n", " 'num_epochs': NUM_EPOCHS,\n", " 'eval_interval': EVAL_INTERVAL,\n", " 'snapshot': SNAPSHOT_INTERVAL,\n", " 'display_interval': DISPLAY_INTERVAL\n", "}" ] }, { "cell_type": "markdown", "id": "93628fa4-41e0-4ac2-9162-8c971383fb74", "metadata": { "id": "93628fa4-41e0-4ac2-9162-8c971383fb74" }, "source": [ "## 5. Compile KFP pipeline" ] }, { "cell_type": "code", "execution_count": null, "id": "0dc54b32-c48b-4d66-882f-6bd0e29678e1", "metadata": { "id": "0dc54b32-c48b-4d66-882f-6bd0e29678e1" }, "outputs": [], "source": [ "from src.pipelines import training_pipelines\n", "\n", "compiled_pipeline_path = 'merlin_training_pipeline.json'\n", "compiler.Compiler().compile(\n", " pipeline_func=training_pipelines.training_pipeline,\n", " package_path=compiled_pipeline_path\n", ")" ] }, { "cell_type": "markdown", "id": "41e4e255-f4a6-474c-aed4-d225cefdfbd5", "metadata": { "id": "41e4e255-f4a6-474c-aed4-d225cefdfbd5" }, "source": [ "## 6. Submit pipeline to Vertex AI" ] }, { "cell_type": "code", "execution_count": null, "id": "f8ecf7a1-9b79-4386-baa3-e5ff74347ac0", "metadata": { "id": "f8ecf7a1-9b79-4386-baa3-e5ff74347ac0" }, "outputs": [], "source": [ "job_name = f'merlin_training_{datetime.now().strftime(\"%Y%m%d%H%M%S\")}'\n", "\n", "pipeline_job = vertex_ai.PipelineJob(\n", " display_name=job_name,\n", " template_path=compiled_pipeline_path,\n", " enable_caching=False,\n", " parameter_values=parameter_values,\n", ")\n", "\n", "pipeline_job.submit(service_account=VERTEX_SA)" ] } ], "metadata": { "colab": { "collapsed_sections": [], "name": "04-e2e-pipeline.ipynb", "provenance": [] }, "environment": { "kernel": "python3", "name": "managed-notebooks.m87", "type": "gcloud", "uri": "gcr.io/merlin-on-gcp/merlin-vertex-dev@sha256:5af46d488ff0ff373c348d87010c0e3ca22fa075db37c57e7c12a473e5954e4f" }, "interpreter": { "hash": "d4d1e4263499bec80672ea0156c357c1ee493ec2b1c70f0acce89fc37c4a6abe" }, "kernelspec": { "display_name": "Custom [merlin-vertex-dev] (Local)", "language": "python", "name": "local-gcr.io_renatoleite-dev_merlin-vertex-dev_latest__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": "3.8.10" } }, "nbformat": 4, "nbformat_minor": 5 }

apache-2.0

jentjr/enviropy

docs/tutorial/Untitled1.ipynb

1

22816

{ "cells": [ { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "from scipy import stats" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [], "source": [ "data = pd.read_csv('~/Dropbox/gw_data.csv')" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [], "source": [ "mw1_arsenic = data[(data['location_id'].isin(['MW-1']) & data['param_name'].isin(['Arsenic, dissolved']))]" ] }, { "cell_type": "code", "execution_count": 21, "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>location_id</th>\n", " <th>sample_date</th>\n", " <th>analysis_result</th>\n", " <th>lt_measure</th>\n", " <th>default_unit</th>\n", " <th>param_name</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>35</th>\n", " <td>MW-1</td>\n", " <td>2008-10-28</td>\n", " <td>1.90</td>\n", " <td></td>\n", " <td>ug/L</td>\n", " <td>Arsenic, dissolved</td>\n", " </tr>\n", " <tr>\n", " <th>406</th>\n", " <td>MW-1</td>\n", " <td>2007-12-20</td>\n", " <td>10.00</td>\n", " <td>&lt;</td>\n", " <td>ug/L</td>\n", " <td>Arsenic, dissolved</td>\n", " </tr>\n", " <tr>\n", " <th>625</th>\n", " <td>MW-1</td>\n", " <td>2012-08-22</td>\n", " <td>2.63</td>\n", " <td></td>\n", " <td>ug/L</td>\n", " <td>Arsenic, dissolved</td>\n", " </tr>\n", " <tr>\n", " <th>677</th>\n", " <td>MW-1</td>\n", " <td>2008-12-16</td>\n", " <td>1.80</td>\n", " <td></td>\n", " <td>ug/L</td>\n", " <td>Arsenic, dissolved</td>\n", " </tr>\n", " <tr>\n", " <th>1065</th>\n", " <td>MW-1</td>\n", " <td>2010-04-13</td>\n", " <td>1.70</td>\n", " <td></td>\n", " <td>ug/L</td>\n", " <td>Arsenic, dissolved</td>\n", " </tr>\n", " <tr>\n", " <th>1316</th>\n", " <td>MW-1</td>\n", " <td>2008-08-26</td>\n", " <td>1.70</td>\n", " <td></td>\n", " <td>ug/L</td>\n", " <td>Arsenic, dissolved</td>\n", " </tr>\n", " <tr>\n", " <th>1479</th>\n", " <td>MW-1</td>\n", " <td>2008-06-18</td>\n", " <td>10.00</td>\n", " <td>&lt;</td>\n", " <td>ug/L</td>\n", " <td>Arsenic, dissolved</td>\n", " </tr>\n", " <tr>\n", " <th>1826</th>\n", " <td>MW-1</td>\n", " <td>2009-04-29</td>\n", " <td>1.30</td>\n", " <td></td>\n", " <td>ug/L</td>\n", " <td>Arsenic, dissolved</td>\n", " </tr>\n", " <tr>\n", " <th>2130</th>\n", " <td>MW-1</td>\n", " <td>2008-04-24</td>\n", " <td>10.00</td>\n", " <td>&lt;</td>\n", " <td>ug/L</td>\n", " <td>Arsenic, dissolved</td>\n", " </tr>\n", " <tr>\n", " <th>2255</th>\n", " <td>MW-1</td>\n", " <td>2012-04-03</td>\n", " <td>1.93</td>\n", " <td></td>\n", " <td>ug/L</td>\n", " <td>Arsenic, dissolved</td>\n", " </tr>\n", " <tr>\n", " <th>2539</th>\n", " <td>MW-1</td>\n", " <td>2009-02-25</td>\n", " <td>1.70</td>\n", " <td></td>\n", " <td>ug/L</td>\n", " <td>Arsenic, dissolved</td>\n", " </tr>\n", " <tr>\n", " <th>2899</th>\n", " <td>MW-1</td>\n", " <td>2008-02-20</td>\n", " <td>10.00</td>\n", " <td>&lt;</td>\n", " <td>ug/L</td>\n", " <td>Arsenic, dissolved</td>\n", " </tr>\n", " <tr>\n", " <th>3247</th>\n", " <td>MW-1</td>\n", " <td>2009-10-21</td>\n", " <td>1.50</td>\n", " <td></td>\n", " <td>ug/L</td>\n", " <td>Arsenic, dissolved</td>\n", " </tr>\n", " <tr>\n", " <th>3568</th>\n", " <td>MW-1</td>\n", " <td>2012-06-20</td>\n", " <td>2.40</td>\n", " <td></td>\n", " <td>ug/L</td>\n", " <td>Arsenic, dissolved</td>\n", " </tr>\n", " <tr>\n", " <th>3706</th>\n", " <td>MW-1</td>\n", " <td>2012-10-03</td>\n", " <td>2.27</td>\n", " <td></td>\n", " <td>ug/L</td>\n", " <td>Arsenic, dissolved</td>\n", " </tr>\n", " <tr>\n", " <th>4192</th>\n", " <td>MW-1</td>\n", " <td>2011-05-18</td>\n", " <td>1.10</td>\n", " <td></td>\n", " <td>ug/L</td>\n", " <td>Arsenic, dissolved</td>\n", " </tr>\n", " <tr>\n", " <th>4400</th>\n", " <td>MW-1</td>\n", " <td>2010-11-02</td>\n", " <td>1.60</td>\n", " <td></td>\n", " <td>ug/L</td>\n", " <td>Arsenic, dissolved</td>\n", " </tr>\n", " <tr>\n", " <th>5391</th>\n", " <td>MW-1</td>\n", " <td>2011-10-05</td>\n", " <td>1.50</td>\n", " <td></td>\n", " <td>ug/L</td>\n", " <td>Arsenic, dissolved</td>\n", " </tr>\n", " <tr>\n", " <th>6384</th>\n", " <td>MW-1</td>\n", " <td>2012-11-14</td>\n", " <td>2.48</td>\n", " <td></td>\n", " <td>ug/L</td>\n", " <td>Arsenic, dissolved</td>\n", " </tr>\n", " <tr>\n", " <th>6546</th>\n", " <td>MW-1</td>\n", " <td>2013-04-10</td>\n", " <td>5.35</td>\n", " <td></td>\n", " <td>ug/L</td>\n", " <td>Arsenic, dissolved</td>\n", " </tr>\n", " <tr>\n", " <th>7528</th>\n", " <td>MW-1</td>\n", " <td>2013-10-02</td>\n", " <td>4.65</td>\n", " <td></td>\n", " <td>ug/L</td>\n", " <td>Arsenic, dissolved</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " location_id sample_date analysis_result lt_measure default_unit \\\n", "35 MW-1 2008-10-28 1.90 ug/L \n", "406 MW-1 2007-12-20 10.00 < ug/L \n", "625 MW-1 2012-08-22 2.63 ug/L \n", "677 MW-1 2008-12-16 1.80 ug/L \n", "1065 MW-1 2010-04-13 1.70 ug/L \n", "1316 MW-1 2008-08-26 1.70 ug/L \n", "1479 MW-1 2008-06-18 10.00 < ug/L \n", "1826 MW-1 2009-04-29 1.30 ug/L \n", "2130 MW-1 2008-04-24 10.00 < ug/L \n", "2255 MW-1 2012-04-03 1.93 ug/L \n", "2539 MW-1 2009-02-25 1.70 ug/L \n", "2899 MW-1 2008-02-20 10.00 < ug/L \n", "3247 MW-1 2009-10-21 1.50 ug/L \n", "3568 MW-1 2012-06-20 2.40 ug/L \n", "3706 MW-1 2012-10-03 2.27 ug/L \n", "4192 MW-1 2011-05-18 1.10 ug/L \n", "4400 MW-1 2010-11-02 1.60 ug/L \n", "5391 MW-1 2011-10-05 1.50 ug/L \n", "6384 MW-1 2012-11-14 2.48 ug/L \n", "6546 MW-1 2013-04-10 5.35 ug/L \n", "7528 MW-1 2013-10-02 4.65 ug/L \n", "\n", " param_name \n", "35 Arsenic, dissolved \n", "406 Arsenic, dissolved \n", "625 Arsenic, dissolved \n", "677 Arsenic, dissolved \n", "1065 Arsenic, dissolved \n", "1316 Arsenic, dissolved \n", "1479 Arsenic, dissolved \n", "1826 Arsenic, dissolved \n", "2130 Arsenic, dissolved \n", "2255 Arsenic, dissolved \n", "2539 Arsenic, dissolved \n", "2899 Arsenic, dissolved \n", "3247 Arsenic, dissolved \n", "3568 Arsenic, dissolved \n", "3706 Arsenic, dissolved \n", "4192 Arsenic, dissolved \n", "4400 Arsenic, dissolved \n", "5391 Arsenic, dissolved \n", "6384 Arsenic, dissolved \n", "6546 Arsenic, dissolved \n", "7528 Arsenic, dissolved " ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mw1_arsenic" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "KstestResult(statistic=0.1424598588650321, pvalue=0.77956103342550875)" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stats.kstest(mw1_arsenic['analysis_result'], 'lognorm', stats.lognorm.fit(mw1_arsenic['analysis_result']))" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(1.2632192051513607, 1.0334867609792653, 1.2701669010844197)" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stats.lognorm.fit(mw1_arsenic['analysis_result'])" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/jentjr/anaconda3/envs/enviropy/lib/python3.6/site-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n", " \"\"\"Entry point for launching an IPython kernel.\n" ] } ], "source": [ "mw1_arsenic['analysis_result'] = mw1_arsenic['analysis_result'].transform(np.log)" ] }, { "cell_type": "code", "execution_count": 31, "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>location_id</th>\n", " <th>sample_date</th>\n", " <th>analysis_result</th>\n", " <th>lt_measure</th>\n", " <th>default_unit</th>\n", " <th>param_name</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>35</th>\n", " <td>MW-1</td>\n", " <td>2008-10-28</td>\n", " <td>0.641854</td>\n", " <td></td>\n", " <td>ug/L</td>\n", " <td>Arsenic, dissolved</td>\n", " </tr>\n", " <tr>\n", " <th>406</th>\n", " <td>MW-1</td>\n", " <td>2007-12-20</td>\n", " <td>2.302585</td>\n", " <td>&lt;</td>\n", " <td>ug/L</td>\n", " <td>Arsenic, dissolved</td>\n", " </tr>\n", " <tr>\n", " <th>625</th>\n", " <td>MW-1</td>\n", " <td>2012-08-22</td>\n", " <td>0.966984</td>\n", " <td></td>\n", " <td>ug/L</td>\n", " <td>Arsenic, dissolved</td>\n", " </tr>\n", " <tr>\n", " <th>677</th>\n", " <td>MW-1</td>\n", " <td>2008-12-16</td>\n", " <td>0.587787</td>\n", " <td></td>\n", " <td>ug/L</td>\n", " <td>Arsenic, dissolved</td>\n", " </tr>\n", " <tr>\n", " <th>1065</th>\n", " <td>MW-1</td>\n", " <td>2010-04-13</td>\n", " <td>0.530628</td>\n", " <td></td>\n", " <td>ug/L</td>\n", " <td>Arsenic, dissolved</td>\n", " </tr>\n", " <tr>\n", " <th>1316</th>\n", " <td>MW-1</td>\n", " <td>2008-08-26</td>\n", " <td>0.530628</td>\n", " <td></td>\n", " <td>ug/L</td>\n", " <td>Arsenic, dissolved</td>\n", " </tr>\n", " <tr>\n", " <th>1479</th>\n", " <td>MW-1</td>\n", " <td>2008-06-18</td>\n", " <td>2.302585</td>\n", " <td>&lt;</td>\n", " <td>ug/L</td>\n", " <td>Arsenic, dissolved</td>\n", " </tr>\n", " <tr>\n", " <th>1826</th>\n", " <td>MW-1</td>\n", " <td>2009-04-29</td>\n", " <td>0.262364</td>\n", " <td></td>\n", " <td>ug/L</td>\n", " <td>Arsenic, dissolved</td>\n", " </tr>\n", " <tr>\n", " <th>2130</th>\n", " <td>MW-1</td>\n", " <td>2008-04-24</td>\n", " <td>2.302585</td>\n", " <td>&lt;</td>\n", " <td>ug/L</td>\n", " <td>Arsenic, dissolved</td>\n", " </tr>\n", " <tr>\n", " <th>2255</th>\n", " <td>MW-1</td>\n", " <td>2012-04-03</td>\n", " <td>0.657520</td>\n", " <td></td>\n", " <td>ug/L</td>\n", " <td>Arsenic, dissolved</td>\n", " </tr>\n", " <tr>\n", " <th>2539</th>\n", " <td>MW-1</td>\n", " <td>2009-02-25</td>\n", " <td>0.530628</td>\n", " <td></td>\n", " <td>ug/L</td>\n", " <td>Arsenic, dissolved</td>\n", " </tr>\n", " <tr>\n", " <th>2899</th>\n", " <td>MW-1</td>\n", " <td>2008-02-20</td>\n", " <td>2.302585</td>\n", " <td>&lt;</td>\n", " <td>ug/L</td>\n", " <td>Arsenic, dissolved</td>\n", " </tr>\n", " <tr>\n", " <th>3247</th>\n", " <td>MW-1</td>\n", " <td>2009-10-21</td>\n", " <td>0.405465</td>\n", " <td></td>\n", " <td>ug/L</td>\n", " <td>Arsenic, dissolved</td>\n", " </tr>\n", " <tr>\n", " <th>3568</th>\n", " <td>MW-1</td>\n", " <td>2012-06-20</td>\n", " <td>0.875469</td>\n", " <td></td>\n", " <td>ug/L</td>\n", " <td>Arsenic, dissolved</td>\n", " </tr>\n", " <tr>\n", " <th>3706</th>\n", " <td>MW-1</td>\n", " <td>2012-10-03</td>\n", " <td>0.819780</td>\n", " <td></td>\n", " <td>ug/L</td>\n", " <td>Arsenic, dissolved</td>\n", " </tr>\n", " <tr>\n", " <th>4192</th>\n", " <td>MW-1</td>\n", " <td>2011-05-18</td>\n", " <td>0.095310</td>\n", " <td></td>\n", " <td>ug/L</td>\n", " <td>Arsenic, dissolved</td>\n", " </tr>\n", " <tr>\n", " <th>4400</th>\n", " <td>MW-1</td>\n", " <td>2010-11-02</td>\n", " <td>0.470004</td>\n", " <td></td>\n", " <td>ug/L</td>\n", " <td>Arsenic, dissolved</td>\n", " </tr>\n", " <tr>\n", " <th>5391</th>\n", " <td>MW-1</td>\n", " <td>2011-10-05</td>\n", " <td>0.405465</td>\n", " <td></td>\n", " <td>ug/L</td>\n", " <td>Arsenic, dissolved</td>\n", " </tr>\n", " <tr>\n", " <th>6384</th>\n", " <td>MW-1</td>\n", " <td>2012-11-14</td>\n", " <td>0.908259</td>\n", " <td></td>\n", " <td>ug/L</td>\n", " <td>Arsenic, dissolved</td>\n", " </tr>\n", " <tr>\n", " <th>6546</th>\n", " <td>MW-1</td>\n", " <td>2013-04-10</td>\n", " <td>1.677097</td>\n", " <td></td>\n", " <td>ug/L</td>\n", " <td>Arsenic, dissolved</td>\n", " </tr>\n", " <tr>\n", " <th>7528</th>\n", " <td>MW-1</td>\n", " <td>2013-10-02</td>\n", " <td>1.536867</td>\n", " <td></td>\n", " <td>ug/L</td>\n", " <td>Arsenic, dissolved</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " location_id sample_date analysis_result lt_measure default_unit \\\n", "35 MW-1 2008-10-28 0.641854 ug/L \n", "406 MW-1 2007-12-20 2.302585 < ug/L \n", "625 MW-1 2012-08-22 0.966984 ug/L \n", "677 MW-1 2008-12-16 0.587787 ug/L \n", "1065 MW-1 2010-04-13 0.530628 ug/L \n", "1316 MW-1 2008-08-26 0.530628 ug/L \n", "1479 MW-1 2008-06-18 2.302585 < ug/L \n", "1826 MW-1 2009-04-29 0.262364 ug/L \n", "2130 MW-1 2008-04-24 2.302585 < ug/L \n", "2255 MW-1 2012-04-03 0.657520 ug/L \n", "2539 MW-1 2009-02-25 0.530628 ug/L \n", "2899 MW-1 2008-02-20 2.302585 < ug/L \n", "3247 MW-1 2009-10-21 0.405465 ug/L \n", "3568 MW-1 2012-06-20 0.875469 ug/L \n", "3706 MW-1 2012-10-03 0.819780 ug/L \n", "4192 MW-1 2011-05-18 0.095310 ug/L \n", "4400 MW-1 2010-11-02 0.470004 ug/L \n", "5391 MW-1 2011-10-05 0.405465 ug/L \n", "6384 MW-1 2012-11-14 0.908259 ug/L \n", "6546 MW-1 2013-04-10 1.677097 ug/L \n", "7528 MW-1 2013-10-02 1.536867 ug/L \n", "\n", " param_name \n", "35 Arsenic, dissolved \n", "406 Arsenic, dissolved \n", "625 Arsenic, dissolved \n", "677 Arsenic, dissolved \n", "1065 Arsenic, dissolved \n", "1316 Arsenic, dissolved \n", "1479 Arsenic, dissolved \n", "1826 Arsenic, dissolved \n", "2130 Arsenic, dissolved \n", "2255 Arsenic, dissolved \n", "2539 Arsenic, dissolved \n", "2899 Arsenic, dissolved \n", "3247 Arsenic, dissolved \n", "3568 Arsenic, dissolved \n", "3706 Arsenic, dissolved \n", "4192 Arsenic, dissolved \n", "4400 Arsenic, dissolved \n", "5391 Arsenic, dissolved \n", "6384 Arsenic, dissolved \n", "6546 Arsenic, dissolved \n", "7528 Arsenic, dissolved " ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mw1_arsenic" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def dunnettk(n, df=n-1, k, m, method, tail_type, conf_level):\n", " alpha = 1-conf_level" ] } ], "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

idekerlab/ci-service-template

sample-notebooks/sender.ipynb

1

4665

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import time\n", "import zmq\n", "\n", "def producer():\n", " context = zmq.Context()\n", " zmq_socket = context.socket(zmq.PUSH)\n", " print('-----------------------------')\n", " zmq_socket.bind(\"tcp://*:8888\")\n", "\n", " # Start your result manager and workers before you start your producers\n", " print('###########')\n", " for num in xrange(200):\n", " print('....')\n", " work_message = { 'num' : num }\n", " zmq_socket.send_json(work_message)\n", "\n" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "-----------------------------\n", "###########\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n", "....\n" ] } ], "source": [ "producer()" ] } ], "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

ApproxFun/DiskFun.jl

examples/Disk PDEs.ipynb

3

632496

{ "metadata": { "language": "Julia", "name": "", "signature": "sha256:48fddb258628c3bde2bbfaf1b54ef034c58303f64e9a2486c2977618b50eda19" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "# Requires PyPlot\n", "\n", "using ApproxFun" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 1 }, { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "Laplace Equation $u_{xx} + u_{yy} = 0$" ] }, { "cell_type": "code", "collapsed": false, "input": [ "d=Disk()\n", "u=[dirichlet(d),lap(d)]\\Fun(z->real(exp(z)),Circle())\n", "\n", "ApproxFun.plot(pad(u,50,50)) # we pad to get a nice plot;" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stderr", "text": [ "INFO: Loading help data...\n" ] }, { "metadata": {}, "output_type": "display_data", "png": 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", "text": [ "Figure(PyObject <matplotlib.figure.Figure object at 0x10a5fac10>)" ] } ], "prompt_number": 2 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Check the error:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "u[.1,.2]-real(exp(.1+.2im))" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 3, "text": [ "0.0 + 1.3877787807814457e-17im" ] } ], "prompt_number": 3 }, { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "Poisson equation $u_{xx} + u_{yy} = f(x,y)$" ] }, { "cell_type": "code", "collapsed": false, "input": [ "d=Disk()\n", "f=Fun((x,y)->exp(-10(x+.2)^2-20(y-.1)^2),d) \n", "u=[dirichlet(d),lap(d)]\\[0.,f]\n", "ApproxFun.plot(u);" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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ToRQwQSQgUZ3f+otAfJ0fz/Ow2+0b7DTW4/P5AAAul2vL++bzyejq8qCjw4OLFz24etVnWlAVFPDYt88Kl4uDJGnQNB59fUFENpZ/ZZWSEh7z85n5/Wm1MnA4WDgc7IPO4NVmCaMDN16wSZIGSeIhywwURYOi6J24sqzXyem3+r8lSdtywwnD4EFHst4lbNwWFFggy/p+WyyAIKw2dnCc/jy9TlCL1ekZ+xaNqvD5tFjtZDqUlnJbjtiawe3WhaHbzYJhAL9fw8SEjKUlDW1tFrS3r2182bOHxZkzYuzv5EkhqcdhokaTrRpYJyLeq3B9+ni9KCRbGuJJgQQgQcRhts4vFAohEomAZVnY7XYIgmDqYuDz+aBpGtxut+l9mpqKoKNjBZ2dHnR2enD9esCUl11pqYDKSivsdhbhsIrJyQimptYqvba2QrS3Zz7CZwa32wavd+NxWCx6I0NeHgeHg4UoMjERF43qRs9er94V7PerkFI31G7AbhcRDGbgINJEEABJEjOyLosFKChg4XLpncIWi9E9rYtW/bXSO4RXVlQcOSKguzv3kdeSEhYHD4oQBCAY1HD/vozFxY3vuygCdXXCA0Go3x44kPxHwnoDa6M8wyAdA+tk6zdu4wUneRUSTwokAAniAWaMnI0GDwCwWq1p1/n5/X6oqppUAGqahqGhIDo6POjs1EXfvXub1+kBurXH7t0WWCwMAgEF4+NhzM+nVkaNjW709GxBQW0BhtE9AEtKeLhcHPLzLfD79ShbKKTB69WnfCQShZmmqMiKxcXcp2LLynjMzDycOc9tbVbcu6eisJCB3a6LRUXREAxqWFpSMTurIJqF3pTGRhE9PWuFZ3k5g8pKHoKgdzrfuSMnjEKXlLBobRXxlrdY8Ja3WLB//+ZR40QG1om8CrdjGUNehcSTAglAYsdj1AJt1uBh+PmpqgqLxQKbzWbKw2w9iQTg6GgQP//5PIaHg/jBDxaxuLi5IOM4oLrajpISAeGwirt3g1v243M4OEQiYsambxQWcigpEeB2c7BY9OaBYFDF8rKMmRlpzUW+udmNixcfViewFZOTuReANTUiRkZyvlkAwIEDIkZHk0cAdYHOoriYhculp6X1Hz0alpdVTEwoW4q2njhhQX//5h8wQQAOHuRQVKQbdE9P6w0m69m/n8Nb36qLwd/4DUtKn0IgcwbWqbZBXoXE4wYJQGLHYqbOT5ZlBIPBmIGzzWZLWee3GYFAAMFgFP39Mn7+83m89to8hocDAIAjR5wYGtp40bPZWNTU2OF2c/D5ZNy6FUQgkDnxcvx4Pq5dM18EWFDAoaJChNvNQdP06RVer4qZGQmBgPnTSVuba0NdWK44eNCGu3dznw5taLDi6tXcC8+CAhYrK0xaVj/r4XndNLqkRJ++IkkaVlZ0YZjsfd+3j8PYGLa0XbebwaFDPKxWXRCuF68sC5w8KeCtb7XgzW+2oKlJhChmZqpJIgPr7YpC8iokHjVIABI7DuPkb3T/AYnr/ILBYGz8md1uhyhuvXbr/v0QXnttDj/96Qx+/evlpJ2oVVX5WFxUcOiQDTYbi8VFCSMjQchy9r6m588X4sKFjXWAhYW60HO5OCiKAr9fxdSUjOXlzAiYkyft6Ot7OB2xR47YMTSUG+PpeJqb7bh4MffbbWy0oKcnO9tlGKCsTJ9N7HDoItPjUTA5qeDYMQs6OzMj8ktLWRw4wIFhNNy7J2NmZu13wm5n0NIi4u1vt+Ad77Ciujq9H2pmpprEi7VsiUKypSFyBdnAEDuGeOuHZA0eRp1fKBQCwzCw2+2wWCxpn4SjURXt7Uv4+c/n8bOfzWNoyJ90WUFgcPSoE3l5PGw2AXfueHNWlwcAgYCEEyfscLl4aJpeizc5GcXSkoKlpdT1h1tlbk4C8HAMmc1GijINxz2c7Sbrrs0EmgZMT6uYnl4r5m02BlNTGhoaBNjtDPx+DWNjelfwVpibUzE3t7qNfft4VFSwiEQ03Lolw+vV8PrrEYTDwH/4D34cO8bjfe+z4AMfsOLYMSHl+pNNNVnfbBKNK5RcLwoz6VWoKApYloXFYiFRSGQFigASOwIzRs6Gn5+qqrEGj3Tq/BRFwy9/uYCXXprCP/zDLDye5BGX0lIRhw7ZIUkahoZWbVxKSkQsLXGb+shth7w8HgcO6DYwkYiGiYkwZmclOJ0OeDy5jcYxDGCzWR9KN25DgwNXr+Y+/dza6kRHR+7rHg8ftmB4OLeRx+ZmS8JoZ3k5g/JyBnY7B69Xxb1722/84Tjg0CEOlZUsZmc1XLu29gtUXc3h/e+34n3vs+LkydRicDMSeRXGTzbJ1FSTYDAYSxMb95FXIZFJSAASTzSGkbMh/OK9vQyMBg9FUSAIAux2+5ooQKr1d3Yu4+WXp/GjH01jbi7xxZ3jjCgfi5mZCO7cSW69cvp0Ea5c2b4qcjg4HDxoRX6+gGhUxdRUBOPjiWv9zp4tRHd39qJ9yaiuduLWrdynRPVxbLkXgA9juw4Hg3CYzdqPimQcPixieDj1RhkGqKxkUV7OgeeBlRUVIyPyFhtOBPT3y9i1i0VVFQefT8PgoLzGy3H/fg7ve58F73ufFWfOmLNvSkUiUbjeqzAdA2tArxcWBCFWekJehUSmIQFIPJGYNXIOhUKIRqPgOC7m52eGvj4PXnppGi+/PIXx8cTCqaBAwJEjDqiqhqEh/6YRwXjOni1IW4yxrNEZLEJRNMzORjE6GjJdfN/Skv9QZvM2Njo2WIRkE5uNQX4+h9paG2ZmNPD8qgkzy+rG0cZHRFX1v1WTaA3RqO6vp6q6155hMK3/e/U+WdZNnI3HdUNnDbW1Nty6pTwwqjZMoPXpIjyv/1AwDKEN82iWxRpDa2Nd0ai+P8GghkBAhderJRxzd+KEiP7+3Kq/w4d5DA9v/dJiswE1NTxcLgaLi7ogTCVgz54V0N298TtWWMjg8GEe4bCG69fXCsuKChbvfa8VH/6wFY2NmfFnjCcdA+v1gs3v90MUxU1rj8mrkNgOJACJJwozRs6apiEUCiEcDsfq/ERRTHlSHBnx4wc/mMLLL09jZCSQcBmnk8Px426EwwoGBrymDJvXI4oM7HYHVlaSX/F4nkF1tR3FxQKCQQW3bgXh9W79Il9RIWBycnupsa3Q0uJEZ+fWIoA8D+Tnc8jLY2G3s7Ba2dj0D0D3F4xENASD6gMzZAUPLBzR0uLY8na3w4ED1k2tWLaLKAIuFwunk4HNps/x3b2bh8ejPy5Juu+fx6NgYUHL2lzi5mYrLl7MXKTT6WRQXa03mczP64Iw/srlcDBwOlnMzm5exuByMTh6VK91HRyU15Qf1NXx+MQnbHjqKZspe5mtYsbAmuO42IShrdQgk1chYQYSgMQTg9k6v2AwCE3TYLVaYwPpkxGNqvjhDyfxwgujuH9fwuTkxhSq1cqivt4NTdMwMOBBJLL9r1RbWzHa21dFpiAwqKmxo7BQgN8vY2Qks1YwAFBR4cbkZG5F0ZkzDly+vFEQsSxQVMSisFC/6OtNGwwkCfD7VSwtKVhYUKBpW7totbU5H4oFTUGBJWNd1GY5ftyKa9cSv69uN4OiIt33z27Xo4/GxBW/X8XsrJr2/rpcDBSFQzCYvUuL282gpkb3mpyZUVBezqX9ftpsQG2tAE3T0Ne3GmG0WoH3v9+Kp5+2o7U181HBRGR7qomxDfIqJOIhAUg89hh1frIsJxR+wNo6P1EUYbPZNq3zm5gI4YUXRvGtb93H3Jwu+lpbi9DRoc/yFQQG9fVuCAKD69e98PszG9Wpq3OBYazIz+fh9coYGQlkLVpj0NJShM7O3IyF4zhg924BBw5YIcsCWFaPTgUCuuCYnZUgy9m7+LS1OdDenluxy3GAqgrb8uJLF54HBIFHaBtvq9vNoLycRX4++2CUnT6LeHpagcez8WBaWiw5ja7u388iGtVr+4JBPbKXbv1gSQmLmhoOY2MKxsdXBW91NYdPfMKOj3/chuLi3E1w0TQtVgPIMMymU00yaWBNXoU7CxKAxGOL2Tq/YDAISZJM1fn9v/83j2984y5+/OOZDelbq5XFiRNF4HkWN254sbyc2Yvcrl0WHDpkRzSq4uZNP0pKnBgdzV1d3rlzebh0KbNRseJiHmVl+ug3jgNCIQ2LixImJiTIsp7uVhQLFCW3F5XWVgc6OnIrAEtKOMzP53YM3JEjAoaGsneKz8tjUF7OIS9Pr2GUZQ08z6K7W9pSE8dWMBo/DNxu4MgRHpoG3Lwpw+83f/wMo6eC7XYGfX2rk2sEAXjPeyz4nd+x461vTV0usl0MAWi1WtcYzz8qBtZkS/NkQAKQeOwwToLRaBRerxdWq3VDnYyqqgiFQohEImBZFjabLWmdn98v47vfHcM3vjGKmzd9Gx7Py+Nx/HgexsdD2LvXjgsXPBk5DpbVp38UFYmYnY1sqCs8f74YFy4krjXMBkVFAhYXeQDpncxFkcHevSIKCjhYLCwkSa+3m56W4PWmTh9WVjoxPp7bJoWWFjs6O3O7zepqAbdu5XSTaG215lToHjnCY2hIhSDoU0CKixmwLAOfT8X9+5mf89zYKGxqcC0IGmpredhsDG7fVjA/bz6d7XYzqKvjMTen4vZt/bPS1CRgeVnFs8868eEPW8Hz2RE+hhH9egGYCDMG1tkQhca6E6WPSRQ+HpAAJB4r4uv8AGBlZQV2ux1WqxWAfqKKRCIIhULQNA02mw1WqzXpyeiFF0bxxS8OwuvdeBGprnaitFTE1asrCIX0E6rdzsFqtWBpaWvhjbw8HtXV+kl9ZCSw6XrKyy2YntZr33JFdXU+bt1KHnUsKOBQWamPgZMkDfPzUYyNSduaJXz6tBtXruS2Hu/MGQsuX87pJh/MxM1t/V9jozVrE0ASoXv/JX8vKypY7NoFWCxAJMJiclLG7OzWLkFWqz6tZmrK7Guq4ehRHkVFLG7fljEzY/69qKnRPQYvX5ZjInb/fg7/5t848M//uS3jRtuGAExVqpKM9QbWqaaaGH9b2cb6mkLyKnx8oEkgxGNBsgYPhmFiJx+jwUNVVYiiCLvdnvKkdvJk/hrxJ4osGhry4fNJGBz0bYjYBIMKTp924MKFFdP7vnu3BVVVDiwvS7hxw4eenuRTQeKZno6gvr4QAwO5qcsDgLIyDrdu6amwPXsElJbysNv1TuPJyQhmZmQsL2d2fx5o95ySa088QC8hAHIrALPZcbwel4tBf//mQn5yUsXkpPE//bUoLGSxb5/e7LO0pOLWLXM1fKdPC2lGNxncvKkAUMAwGk6cEGCxAP39q6neZCwvq1he1iAIQGurgIEBGffuKXjuOS/+23/z4/d/34Hf/V0bnM7MpPi3G5eJt4GJX6eZqSZmDayTTTUBVs/XkiSRV+EjDEUAiUea9XV+xgnNOGEsLy9DFMXYbF+e52G321OmTeL5+Me70dm5hEOHHLh504eFhc3r7txuHprGx6Z3JKKkRMThw04sL0cxOGhO8CWiubkQFy9mXwBWVlqwZ4/edDI2Bty7F0YgkJtTQ1OTE11duVVkJ09a0deX21Nfa6sdHR25i3Tu3au/l7mipUXMSPOHzQYcOsTD7Wbg9+vp1/Wfxd27GSwvY1vNLQZut173t7CgYmRk4+dQFIGqKu6BeFx9Tn29gJs3ZSwu6vtWUMDgX/5LBz79afu2bWQMj9KtRgDNko6B9Xammhi35FX4aEECkHgkMU4WxtzeRA0eqqpiZUWPxLEsG2vwSPfEcfeuH6dO/SIt+5bz50s2RAELCgQcPepEIKDg2jXvmukDW8Xp5KAolox2ANvtLA4dsiMvj0copODevRAWFvQLt8PBIRKxbCulmy5Hj1px82ZuT/a1tRYMDuZ0kzkXgLoXX+7eyJoaIaGA2i48rwuw4mIW4bCGu3dlHDrE4/LlzB/boUMcdu1icfOmFJtZ3NwsJE1rW616JPLuXSU2C9nlYvDJT9rx7LOOLQtBQwCayWJkmnhRmMzAOt2pJptth7wKHx4kAIlHCrNGzuEFtmwYAAAgAElEQVRwGOFwGJqmged5uFyubZ0cPvvZfvyv/zVqevniYhF+P8DzLOrq3IhGdePnbAinc+eKcenS1ptB9uyxYM8eC3iewfy8hNu3g5umQGtr8zE4mLvu47w8Dh5PbvzWDGpqRIyM5PZi0tRkR1dXLgWgLaNmzJtRU8NhZCQ3l5LjxzlEIgxKSvQpIcPDSsatdQRBw8mTAgoKgJ//PPVryPN6Q8r0tN7sAugTSL78ZReeftoGlk3vsybLMsLh8EMRgMlIZ6oJeRU+HlANIPHIYMbI2fDzU1UVFoslZu+yNjKo4ZvfvIuPfWwfHA5zH/EvfvEIvvvdcfj9qRWcXh9nQ0WFHb/4xSK6upbTP9g0kNNUlVVVNpSXi4hENIyOhjAxEcHERIoipzgKC3N7wfF4FBQWslhayl19nB7tze2FI1WdWaZJp8lhuxQXcxgZyX60kWGAUEjv6B0ZMbbN4tAhDrKsYWhIScv2JRmSxCAY1NDXp+DQIR6FhQyuXJGS/nCSZaCrSwLD6CPpxsYUzMyo+Lf/1otvfSuIP/szN5qa0v+R8yiJm0SNIutrCg0v1vXPMSvYktUVGn/RaJS8CjMIRQCJh45h5GwIv0QnAVmWEQwGIcsyBEGA3W4Hx3Hwer1gWRZOpxMAMDCwgs98pgc9PUt47rnD+MpXTpjejz/+45v4yleGkz5eWCigttaNe/f8GB8PoaLCipkZLesNBTzPID/fgYWFxBuqqBCxb58Nqqrh9u0QFha2F/Wpr3diYCC3zQq1tS4MDuYuXVlWxmNmZuu1VSwLWK36qDWbTZ9Uos/31f3wWFZ9MMuXAcNoYBjAbucRChmf69XPuHGrabrA0b8DiLuPAaDF/q/PAtbnASuKMadYv41E9JnFHMdgdpbJSSrfagVEkcu4xUsiWlqETesMBUHDsWM8HA4GY2MKJia29jkuLWWgacD8/OoxVVQw2L+fw9WrUsraQ5sNaGgQcOWKFBs/+NRTVvzn/+xCWVnqz50kSYhEInA4HI+dmElUU5htA+tIJLJBCHIcB7/fD0VRUFZWlunDfCIgAUg8NMwYORt2CNFoFBzHxfz8DAwByHFW/Jf/MoivfW0Esqx/pAWBRXf3O3D0aJ6p/fH5JBw79o+Yn1+b/qyrc8Ph4NDbu4xodO0FpampBF1d3q2+BKZpaytBe7veTFJUxOPAARE8z2FsLIKpqcyma3VLC1tOI1bNzXm4eDF3aef8fBYrKwIYBnC7WbjdxvxcFqKoj0RjGF1oSRIQDqsPZuiq8Pu1Lb02ZWWWnEXlTp0S0denwuVi4HYzsdnAoogHwlSPlK/OS9bg92tYXFTTNnA+d07EpUvZV5puty6wjaYLM+zbx6KyksX4uIL798299oKgTwC5cSPxD67CQuDYMR7Xr8tYWdl8XyoqWOzezeHyZf1FdbkYfP7zTnzmM3YIQnLR8zgLwESkMrDerij0+/0QRRE8z68Rhi+88AIGBgbw3e9+NxuH9dhDKWAi5ySq80uU7g2HwwiFQmAYBna7PeFQdIZh8MYb8/j8529gdHS1Ts5qZXHmTBGee+4qXn31Lab2y+US8Pzzh/Hv//01uFw8TpxwY3o6hOvXk1u+zM8HoUdnsneS1kWJiqYmB2ZmJNy7F8XiYvYuuJGI9sCzLncKkOMy/zvUZmNQUsIjP5+F3c6C5xkoii7mAgEVLMthZUWFxwN4PMbFKHvh3JWV3EVVHQ4WmqbC69XSiswxDIuSEgbFxbogFkU94hiJaPB6VczPqxtET666xevrhbTn/d6/r8aE3+HDHIqKWNy8KWN5Ofk+nzkjoLMz+XaWloD2dhkOB9DaKuLOneSegrrtjYoTJ3j4fBru3lXwpS/58OKLQXzta3loa0ucFo7vlH0SiLeAMSYxJRKF8eUuZg2s41+r9SnqQCAQyw4RGyEBSOQUM3V+0WgUoVAIqqrCarXCarUmLIReWIjg2Wf78fLLU2vuP3UqHwsLEVy4MAcA+N737uFjH9tvav+eeeYAfvWrefziFzNob19IufydOwE0Nhajp2fjBJHtYIyFC4Vk3Ljhwy9/OY/KyjyMj+cmTep25/bCEwqlL7wKCzmUl/NwOlkIAgNV1cWdz6difl7ByoqKsTE5qRUKy3JQ1dwcp9PJwL91N6C08WxxWI2R9pyfT/Z+MLDbGZSW6rOBCwuBaJRFQwOD2Vld7GSDPXv08XLbYXhYBaCC5zWcPs2DYYCBARlxVnhoatpc/MUTCAAdHRJ4XkNrq4hr16SkYru/XwbH6Sns69dlFBayeP/7l/Cv/pUDX/6yM2sTRR5lUolCQxiuF4XJagoTiUOfzwe3252bA3oMIQFI5ASjgSNe+K0XdUaDh6IoEAQBLpcrqQfW3//9BP71v76ChYXVKFVJiQUHDzpw6dLimmW/8IV+vOc9u5GXl7oIWxRZvO995fjxj6dSLmsQCm0/dckwwOHDTpSUiJibC2N42I/Z2bWFRvv3izkTgEtLuUvHAsD8vARg43vtdLLYvZtHfj4HQWAgSXoadnpawtKSuq39tFr1Qv9ckJ/P5UwAchxw5072PifBIHDvni6m2tpEtLevbstmY7FnD4fCQgaCoM9+nptTMDmpbssWqayMw8REZo5JlhlcuaILXLebQWMjj5UVPR2eysg62fo6OiQUFOgC79KlxM0iigJ0dkp485sF+P16acH/+B8BdHZG8Td/k4+9e9d+/p+U6F86xItCAzMG1oB+/TAwri+BQAB79uzJ3QE8Zjwa/eXEE4vR4BGJRGK/5NZH/RRFgd/vh8+nR9FcLldS8aeqGr70pQF89KOdMfHHMEBrazEiEXmD+AOA2dkw/viPr5ve549/fC+OHnWZXn5w0IvaWofp5Q3sdg6NjXlobS1ASQmPoSEvLlxYwPBwYqUwNpa7ENLISAB2e24uQAyj/50+bUFrqx3NzTbU1VlQXMzB79cwMiKhuzuMjo4QurvDGB6OZqThwGrN3QXW5crdqfbAAQ7BYPa3w7LArVtrlU4opN936ZKM9nYZV64oGB/X63GrqnicPSuiuZlHXR0Lh8Pc619Xx2dtnJ3XC3R2ypiYUOBysTh1StjyZJrlZX1d+/fzqK9PHFtpaRHwq19J6OmR0NoqQBSB7m4JbW0L+MlPwrHlqDR/FSPqJwgCLBYLbDYbHA5HbASocZ1QFAXhcBg//OEPUVVVhQ984AOYmprC3bt3MTo6mvI1jUQieP7557F7927Y7XY0NTXh9ddfN7WPKysreOaZZ1BSUgKn04m3vvWt6O3tTbhsZ2cn2tra4HA4UF5ejs9+9rMIBHI38z0eagIhskK8kfN65/f4ZUKhEMLhcKzOTxTFpL98PZ4ofud3LuFnP5uO3XfwoB2iyGBoaHNxxHEMurreiePH803t///5P5N46qlLppYFgMbGAvT0pL7q2u0cjh2zAWBw7ZoPkUh6YZGjR4tw82Y49YIZ4MSJvC1FRDbDbmexb5+IggL9ArmyomBsLAK/X8O+fc6Yh1ou2L3bgamp3Gzv9GkLrlzJTQ1gU5OArq7sn9ZPneLR27v1Y2JZ4MABFqWlLBgGmJtTcPeusiFSePgwj+Hh7L5PjY2rItNo8oif8rHVdc7MrHYit7YKG4zADx/mEAxqGB/Xl/n0p+34r//VBU2LQpZlOBzp/7DcaRim2Uap0I0bN/CjH/0I/f39uHr1KhYX9aBAQUEBGhoa0NDQgHe9611429vetmY9H/3oR/HDH/4Qzz77LKqrq/Gtb30Lly9fxhtvvIHW1tak21dVFefPn8fAwAA+//nPo6ioCH/1V3+F8fFxXLlyBYcOHYot29fXh+bmZtTW1uKZZ57B+Pg4vvrVr+Itb3kLfvrTn2bnBdoEEoBERjHC9cYEDyCx8ItEIgiFQtA0DVarFTabbdOUx9CQFx/6UAdu3VqttWtpKUZPzwKiUXMf4dbWEvziF281fSznz7+By5fNefwxDLB/fx5GRzeKM6uVRX2960HNkQeh0NYvmm1txWhvz81s4JaWPNP1UIkoLxewe7cAm41FJKJhZkbCxEQUmpb4fW5ocOPq1dwZJR844MToaG5S6ropc262dfasFd3d2d/WmTNixqdx2O3AwYMc8vMZRCIabDbg17/O7rG0tSVuLrFYNJw+LWBqSsW9e1sToKKo4exZARwH/OpXiT/bTidQW6unjgFdWH/jGzbs26f/KCY2ZzPT7Pe+97342Mc+hsrKSly9ejX291u/9Vv4+te/Hluuu7sbTU1N+OpXv4rnnnsOgB4RrKurQ2lpKTo6OpJu/6WXXsJHPvIRvPLKK/jt3/5tAMDCwgJqamrw7ne/e00H8m/+5m9iYGAAQ0NDseaUb37zm/jUpz6F1157De94xzsy9rqYgWoAiYyRqsEDWFvnJ4qiqVmXP/nJJP7Fv7gEn0+/EIgiizNnCtHRMW963yor7QiH5bQaQv7oj2rxnve0m1pW03TBYwhAUWRQX+8GzzO4ft2D7u7MmEUPD3vBskJGxsylYnExCrPdzSUlPPbuFWG1svD5FNy7F8X0tIzp6fUX7+Try/W17kHdeU7guNylm7PViBFPfj6D/v7MC7NgELh+XRdbFgtQUMDiwAEe5eUMQiENQ0NyRub/GtTVcUmnpUQizAPPQQ2nTvGQZeDatfSOORplADC4e1d5MC1lo5D0+4FLlyQ0Nwvo65PQ2yvjne/04/vft6OtbQsHtUNJ1gRSWVmJd7/73Xj3u98du19ZV6T5yiuvgOd5PPPMM7H7LBYLPvnJT+IP/uAPMDk5iYqKioTbfeWVV1BWVhYTfwBQXFyMD3/4w/jOd74DSZIgCAK8Xi9ef/11PPfcc2s6kz/xiU/g2WefxUsvvZRzAUg1gMS2MSJ+0aietjBqNtbX+fl8vlidn9vthtPpTCn+vvKVQXzoQx0x8bdrlxXV1Q7T4s9u53D+fAlmZ4O4cmUJX/xiH3w+c1Gmt72tFL/xG8WmlgWA3t5lNDfnoaUlH1Yrg56eZXR1LcHvz1z6an4+ivp6W8bWtxl37oQT1mlZLAyOHrWitdWJxkYbdu3iMT+v4MqVEDo6AhgYCMPrTV+EKEpuzadFMXeiLFd5luJiNicCsLZWWNM9mw3OnBEwM6NhdFRFZ6eC3l4Vsszg+HEBbW0Camo4bKdPoqgImJtTTRi5M+jtVXDtmoIjRzjU1ZmPm7S26tHF8XEVo6MKWluFpPt88aKEsjIOFRUsPB4NTz0VQFdXbpuxHkeSJTE1TYPf70de3kYf2PXXnd7eXtTU1GywjDlz5gwAPXWbjN7eXjQ0NGy4/8yZMwgGgxh5MLLm2rVrkGUZjY2Na5YTBAEnT55MWjOYTUgAElsmvsHDqPUzhJ8h/lRVRSAQgMfjgaIocDgccLvd4PnUJ9Hnn+/FG2/Mxi6edXV5UFUVg4PmjJfPni1EXh6PCxfmYmni6ekw/uRPBk0f43/6T7Upl6mosOH8+SLk5XHgeRWdnUvwerOXtrLbc6MmZFlDdTWHPXtEnDvnREuLE0eOWKGqwM2bEXR0BNDTE8bsbGYErs+Xu/o/ILcRwFxM5ACAfftyk9TJ1HuejIICBv39G7chSQyuXVPQ3q5gZERDQQGLs2cFNDcLKCoyrwb1cY485ubS+y4NDam4fl1BY6OAysrNL59tbWtr/iSJQUeHjJMn+aT7OjqqQFE07N3LwOcD/sk/WcaFCzmeIfiYsZlnot/vN+UDOD09jfLy8g33G/dNTSV3hTD73Onp6TX3x1NWVrbpNrIFpYCJtDFr5Bxf52ez2WC1Wk1bG/zpn97A1742Ap5nUFubh4ICAV1dC7EpH5uxf78DBQUCurs3dgQDwNe/PoKnnz6ImprU/lDNzUV417t24bXXZtfcL4osTp3KQySioK9vBZOTehdXMCjD6RQzGvVbz+CgB4JgSXtig1kOHLBi924LIhEZdjvQ1ydhYiL7tXmzsxKA3KmyFMHnjKLPHs4+VisLILsRwAMHWNy+nd3jOXaMR0dHatW8tAR0d+vfNZYFTpwQYLcDN27I8HiS72NLy8aGjHTo6ZHB8xpaWgQMDm7cVrK6QgDo7VVQXMzgxAkuYRp9ZkZDURGwfz+Le/dUfPCDy/je9wrwtrdZtry/OxWzPoChUAgWy8bX1/qgJTy0Sd1BOBw29VzjNtmym20jW1AEkEgLo7M3Go1CUZQN6V7DyNnj8SAYDEIUReTn56ds8ojnhRdu4w//8BoAPQpVXm7BzZuelOJPFFmcP1+CyckAenuT19xJkornnrtq8oiBP/qjY7G0TVWVA21thXA4GFy6tIi+vrVTQrxeGSdPZtd53uORceJE5grmDhyworU1D+fOuVFcrNcxdnR40NMTwMxM7lJQ8/MyXK7cpWVzWZeXq2kZ3uxPJcTu3dmNG1RWsltqYlFVPWp48aKCQEBvpmhqEjZ8purrk9f9pYMs6zWCmqaneo2IspH23YyFBQ39/TJaWwUkSoYsLgJer4pDhziEQsBHPrKMn/0sN93/jxvG/PhE9wcCAVMC0GazIZJgvmP4wSBnmy152Y3Z5xq3yZZ9GA0/FAEkTGGkew0vv0Rjd2RZRjAYhCzL4HkeTqfTVKo3npdeGsNnP7sqzs6fL8brr8+goaEAS0vRpLVUe/bY4HTysekfqXj99Rn83/87ife8J3FhbzynThXgM585iDfemMONG17cubP51I/RUR84DiZqi7YOz2995fv3W1FRYYEsa7h7N4TR0XDC7mUAuHs3DKfTCb8/NwJm924Ow8O5yZfmMgKYCd/CVLBsdg2gjW2s9/7LNGVl3LYNz2WZiVnUiCLQ2CiAZYGpKRlTU9szpV6P1wt0dMiorORQV8fh1VfN/mjSU8INDTwGB+UN86WXlgBZ1usOh4YU/LN/toIf/CAfb3vbFo0Kn2ASCcBAIABVVU2lgMvLyxOmYI207e7du7f9XCP1a9y/ftnNtpEtKAJIbMr6Oj9go62LUefn9XpjXziXy5W2+HvttWl88pOXoKr6xfL8+RJcuKA3e1y9uoy2tsQNGadOueH1RjE0lF744/Of70U0au5i9slPHjC9/snJEBoasuvfNTDggd1u7utbVCSgqSkPTU1ulJQIuHdPj/BduuR9MIEjObIM1NSknqCSKfLzn8zpB8vL2W/MOHiQz7oBdH19+nVz6XDkCJtxa5lolEFPj4LLlxWUlfGoqeFx4EDm1f++fRxefVVCS4uATQJGG7h6VcGRI4mf4/UCk5MKTp3icfIkh6efXsb9+zkqKH1MSNYE4vf74XA4TF2HTp06hZGRkViTosGlS7oX7MmTJ5M+9+TJk7h69eqG/bh06RIcDgdqamoAAHV1deB5HpcvX16zXDQaRV9f36bbyBYkAImEGKN3otHomgaP9eneUCgEj8eDaDQKu92OvLy8Tc2ck3H16hI++tEOSJJ+oWxrK94QzevsXEB9/aqRsyAwaG0tQm+vZ0tNF3fu+PGXfzliatljx/LwT/+p+ZFCKyvZTdcEgwqOH08cCeB5BnV1Dpw/n48jR+xYXJTQ1eVBV1dqwZeIXHrR5rIxg2FyE9V0uZisd8wCQGlp9kOaopjdS0Y2o7KtrTx6ehR0dioYHdVw4oSAxkYeCcaMb2ndRtq3s1NGaSmHmhrzB9PfL+PgQT5hCURhIYtoVMX4uAKPR8Pv/u6yqVronUKyFLDP54PT6TR1LfrgBz8IRVHw13/917H7IpEIvvWtb6GpqSlmATMzM4OhoaE184k/+MEPYnZ2Fj/60Y9i9y0sLODll1/Ge9/73tic47y8PLz97W/Hd77zHfjj5kJ++9vfRiAQwIc+9KH0D36bkBE0sYF4Pz8gsZGz4eenqmpsPM/6lLBZJElFU9PPMTioT7Bvbi7CxYsLCZctLbVAVfXRUgUFIm7c2OLU+we4XDyuXXsPyspS/2QfHvbi1Kmfm04f1dcXYWAge+Pb9OkjurIoKxNx8KANkqRheDgArzdzabraWgcGzTdOb4tz55y4dCk33cCtra5tNQKkwulkUFDAorychyRx4HkGHKenUfXvFKCqq3/RqAZJ0m/DYUBRtNhnzVhG0/RxiPrt2sdOnRIxOKjAamUgCAwsFoDn9R9KHKf/W98+A4bRwLKIrUeSgHBYQyCgz1r2eLQNJQxuN4NolEE4S79tTp7k0NeXnUjpsWO6B1+ibuyKCgYHDrC4fl3Gykr6l8NEEz4AgOc1nDsn4OJFyfQ54/BhFrOzamw/Ght5DA/L8Pm0B8cgQ5aBf/fvnPjDP0xd27YTCIVCYBgm1nRhcOXKFXzqU5/CyMiIKRH41FNP4e///u/x7LPPoqqqCn/3d3+Hnp4e/OIXv0DbA0PGp59+Gi+++CLu3buHvXv3AtCvl21tbbh+/To+97nPxSaBTExM4PLly6iuro5to7e3Fy0tLTh27Bg+9alPYWJiAn/+53+ON73pTXj11Vcz+KqYg2oAiRiGsNvMyDm+zk8QhKQze9Phv//3oZj4O326MGn3LgDMzUXw9rfvwtWrS7hxY/tdUz6fjL/4iyH86Z+eSrns4cNuPPXUXnz/+2Om1i2K2fttxXEMVFXFW96Sh/v3o7h7N5S1ho2RkSBsNgdCoez/VlxZkWHWfHr7pH88xcUsCgpYOBwsLBYGPK/vq6Lonb7BoC6glpcV+P0M/H4NDgeDoaHsp4CnpjSsrAD6cW3nvdKPye1mkJfHwukE7HYGZWUcVlY0yDIQCKhYXNRM+uiZw5+l30puN2L7nYjJSQ2TkwqsVqC5WcDMjILRUXPvV0sLn/RHhCzrNX562lzFzEzqdQ4Pq6iq4sDzMmpq9GYVI0Rz44aCtjYR7e1R/MVf+PGmN1nw5jdTZ3CyCKBhAWM2G/Xiiy/iS1/6Er797W9jeXkZJ06cwE9+8pOY+AM2BkMA3QHjpz/9KT73uc/hL//yLxEKhXD27Fm8+OKLa8QfoKeaX3/9dTz//PN47rnn4Ha78Xu/93v4kz/5ky0c+fahCCCxpsHD+DIlqvMLBoOIRqPgOA52uz0W2t4Ot2750Nj4M0QiKgoKRLCs9mACRWJOnMjHyIgHjY3FsfrArVJV5UReHo/e3kW0t/9/OH26KOVz7tzxo77+NShK6q8NwwCVlXkYG8tMyMTh4FBb6wLL6qPxVlZkNDfvwsWLWS78AnDyZCH6+rLvR+Z2c/B6c1Nz2NLifDDpYZXiYhbFxRzcbhaCAGgag2BQw/KyitlZZUvRr4YGC65eze5p1uViEAiwWZ0QU1cn4Pr1tRvgOA27drEoKmLgcOgRRkMgTk+rpmfpnjvHZy3y29jIoafH/LpZVsO5czxGR5VNRdu5czwuX5ZNveZuN7B/P4eBgdSlKm43UF8PXL6sbmgMYRgN9fUC+vsllJWx6OwsQXFxDruZHkGCwSA4jttgr/LjH/8Y3/jGN/DrX//6Ie3Zow9FAHcwhp+fJEnw+XxgGGbDLyZN0xAOh2NhdrvdDovFknaNX7Ltf+YzlxGJ6GfQw4dd6OpKnPoFgPp6XfyFQio6O+dRV5eH69fTTwHv2mVFVZUDXV3zsYaTz33uCn75y3emfO7Bgw585CN78N3vjqdcVtOAvXvFbQnA0lIR1dVOhEIyrl/3ort7ac3jkUhubFpcrpxsBl6vgoICNmtNEyyr25iUlvIoKGDR0mJFMKhhZUXFzIyChQVgYUEBkDkxYrGwGV1fIg4c4DEwkD31V17OYnBw4/oVhcHUlIapqURCj0VREVBRwcLl0lPNi4sqxsbWCmlBACYmsiOQz51j0haWqsrg4kUFFouG1lYRg4PShtRwYyOPnh5z4g/QmzmGh2WcPs3jypXkIvDgQRaSpKK9XUVzM79hfrSmMZiaUlBUxGJmRsVzz3nw4ouFaR3fk8ZmTSBmOoB3MiQAdyCapsXSvYaRsyHo1vv5BYNBaJoGq9UKq9W65Tq/RPzt347GoninTxdsKv6OH8/H7dtehEL6/iqKhsXFCPLzBaysmKvjcjh4NDTko6dnAZ2dgTWPdXbO45VX7uODH9yX9PmKoiAQCOD3f38PfvCDCVOF2L29S8jLs8LjMd+kUlVlx+7dViwsRHDzpg9zc8kF5LVrK8jLc8PjyW6KcWUld36AZWXbF4A8D1RUCCgp4WC1sohG9Sje+LiEiQkVExNRnDnDZ7zjNBEZ/Mokxe3OrgF0VRWP6en0Rezioi764mEYFnv3sti1S69TdLmYLfn+pWL/fhb9/Vt/TSIRPYXrdjNoa+Nx5YqEUEj3FxwYkNNOfUciDPr6ZJw7J+DSpY3nrMZGHjdvSjHPyIsXZZw5I+Dy5bXLzs/rs4kXF1X8wz+Ecfu2jEOHdu6lfLMUsBkPwJ0MdQHvMIyIXyQS2WDkbPySkiQJXq8XgUAAPM8jLy8Pdrs9o+IvGJTxN39zBwDgdPKYmkqexqyry8Pdu14Eg2vPuNPTIVRVpf6FxzBAa2sxbDbgwoVZhEKJz9xf/GIvwuGNj8WPs1NVFbW1JfjYx/am3C4ABAIK6utTt9FWVztw/nwh9u2z4M4dPy5cWMDNm5v7DQKAJGk4diz7vmAjIwGIOXKDyctLL7pcWcnjzBkbWlvtOHPGhoMHRTAMi/v3FfT0RNHeHkZ3dwS3bklrIk+5Gs+WiWh5KrI9RGByMnMROk1jMDam4fJlFVeuqLh8WcPiIot9+3g0NwtoauJTjllLhSDozS+ZaFjxeoH2dgVOJ4d3vlPE0JC85a5uRWHQ3a1bxayiPRCY0Q2G4cPDEvbs2fha9PbKOHqUh6oC//N/Zq/R7FFnszFwRhcwkZyd+7Nhh2HU+RkNHsnq/Px+f6zOz+VyZaTOLxE//vEUbt8O4OBBB8rLbejoSFzPd+SIG6OjPgQCiUXblStLOHeuAJcurSR8fPduG4qLBXR0zCZ8PJ779wP42tdu4vnn6wBsPqL4rgwAACAASURBVM7u+eeP4nvfGzMVBbx1y5vQGPrQIQfKyy0YHw/i1i0fbt1KLfgSEQ5nvzYvEtFw7JiAGzeyPxJuM9uuykoe5eUCRJGBz6didFTC+LiK8fH0XwNZzn5jhr6d7G9jfDx7Kebqag63bmXntTp9mkd7u77v9+9ruH/f+D4xKCnhsH+/Xou5sKDizh3FdNTt3Dke7e2ZfeELCoDOTgmHDvFYWNBrHLeCpukTRNraBFy9KqG2lkd7e2JF6fUC5eUMBAEbRj+6XLow/P73Q/jSl9woLKR4Tjw+nw+uXNWuPKbQJ+YJZ72RcyLxZ3j+GdFBh8MBt9udNfEHAP/7f9/H8nIUbreI8fFAwmVsNg7BoJxU/Bn09npw6NDGKFtzcxF8vjAGBpKPhVvPn/3ZIKanQ7EoaDAYhCAIG8bZVVU58ZGPmIsCzsyEceZM3oPnOdDSkoe9e0Xcvu3DhQsLuHdve00c1655kJ+f/a9yYWFuunMjEf39rqzkcfasDW1tdpw4YYXbzWF8XEV3dwTt7WH090e3NWEjW7OU15NtD8CyMjar5szZ8hd0uxlcu5ZcRM3P640QnZ0qRkYAp5PDuXMCzpxJ7JdncOIEZ2qOcDrs2wfMzanw+4Fr1xT4fCrOndve63LnjoLW1o0p3vUMDys4d27jubinJ4qKChbBoIZvfjPxOfRJZ7MIIKWAU0MC8AnFrJFzOBzGyspKzPMvPz8/Y00eyVhYiOAf/3EGgO7DpygMyso2pjEbGwswNpb6xBaNqpBlFTab/nEuKBBx9mwBLl6cg8+X3oXA75fxH/9jT8wR3u12w+l0Jkx/f+ELR03Nk62stCM/n8H+/VbcueNDZ+dyxjqDAX1eci7SwIFA9kJZNhuL+nobWludsNvZjIu9RBim49kmGMxuB/CePdnrAmUYZC36V1/PweMx/9p4PMClS3rKOBxmcOKEgJYWAbt3r343CwqA6Wk16cjIrbB7t55KXolLMvj9DC5dUnHypN7oki7HjnGIRlX88pcR1NamTsRdviyhqGjtuUZVGezfrz/3hRcCiEbJ0CMev99PEcAUkAB8AjEiedFodEOdn8H6CJch+nJRr/SjH41DlrUHc0X9mJwMwWYTUFi4WmR2/Hh+WjYv9+4FcepUIU6fLgDPq+juTt5Qkorvf38M9+7JcLvdm44Rqqpy4qmnKhM+lp8voLW1CLW1LoyP+/Gzn03Dbs/eaxsMZj8NfPt2MGPTMwoLWZw6ZUFTkxXV1SKiURUDA2F0dARw9WowJ7Nzc3XBXF/XlWms1uydxrM1+q2ggEFf39bT1pLEoL9fjw5OTTGoquLR1ibg5Ekho/tbXKyXJMzOJl5nX58GVWXQ2GhehDc18bhzR8LiogZFYbC4qKaMrkciwNGjG89FfX0S3G4GTieDN97I7vShR5FUNYAkADeHBOAThNHZG4lEIMsyGIYBx3FrvhyKosDn88VsX+IjXLmyhDSMlI8fz8fMjH7SGh0NoLTUBqeTg8PBYXk5fUGjTzvQMD+/PTGkqsCXv3zdlBiOjwIKAoMzZwpw5kw+gkEJHR3zMYNrYLVmJxtcv+5BYWF2/cB8PgWHDm2tE6SyUkRTkwNNTQ7s3StiaUlDb28UXV16c4airL7WPp+KgoJM7XVyDPuhbJNOlGsrZFMsm503nS7HjnEZNX6+c0cXU2+8oaKmhkdLi7BpmtgMbjeQn89ibGzzz8nyMtDTo6KtLVXJjIbmZgZdXdE1/n4zMxr27uWR6nRz44aMdcMuIEkazp8XcP++jKtXc1TT8AiSLAVMAnBzSAA+Aayv8wOwIeIX38mqKAocDgdcLlcswhWfFs4my8vRmN2L3b5WsAwN+XDwoBsNDQWYmDBfF8cwQEtLIS5cmMHwsCdhOjldXn99Gv/4j1Mpl6uuduHTnz6I1tYiOJ0sLl9exOXLS4hGN140rl5dxK5d2WmllWUNR49mfypASYm5i2pZmYCmJgfOnnWguJjH+LiErq4gurqCGBtLfaHatSv75rbhcPZ/8PB8dgUaANy7lx0hK4rA4GDmm0tKS4ErVzK73ooKBgMD+jpHRjR0dqqQZQZNTQLq6tL/LImihn37WNy+bX4/29tltLQICW1/rFYNjY0cLl5MvL6+PhnNzZungpeWNDQ0rJ4/jh7lUFHBYnFRhSQBN2/uPAG42fWKagBTQwLwMcao84tv8EhW5+fxeBCJRGCz2ZCXl7ehzi8XqV8AmJ0NQ9P0tFUiE+fp6TAiERUsa25/OI7BmTP56OzU08UrKxJKS60pf02b4Qtf6I0ZRW/GM89UobNzHsvLm5+AJUlDTU3qmcNbJRdpYElKXAfocrE4fdqOlhYn9u4VMTMjo6sriO7u4ANj5fRI1wpmK+RCAGa7OWfvXi5rArO+XoDXm/n1HjiQ2XnCDKO/zoF15cKhEIOuLhXXrwN79+opYjONTAyj4cQJHteupf+57eyU0dDAI34oRXExg/37WfT0bF5D29Ul49ChzfdvclKG3Q60tvIYHpYwOqpgaUl//2/cyJGv0SNEshSwpmkkAE1AAvAxJb7Oz+jsXS/8otEoPB4PgsEgRFHc0MkaT64igAsLukipqnIlbNA4csSF7u4lNDUVp1yXxcKioSF/w+zggYFltLWVbHtfr19fwd/+7Z2Uyx054sYHPrDH1Dpv3vRAFLMjbq5f9zwwBM4eo6O64ZwoMqirs6G11YHDh0UEAiquXAmhszNgKsKXis2sYDJFNKpl3aTZ7c5uJLOsLHvrN/sjLB127WLQ25vZdba2cikjlWNjGtrbVYTDDFpbhQ0NFfE0N2/PILynR0FNDQ+3m0FVFQtB0DA0lFpMqiqDgoLNP/gOB3DsGIuOjmhsCsnYmAyWBe7elakRJA7yAUwNCcDHDFVVEY1G19T5rU/3yrIMn88Hv98PlmXhdrvhcDj+f/bOPDyOvD7znzr6vnRZ1mnZkiXLlg/Jh2xZ0hwMC4EwG2DCkM0SwrEhAySB4ZiQ3TBMIMkGwmQC7MySBPJASNgHMkl2lzw8TyDLDGMdlmzLsi1bkuXbliXrlvpUd1fV/lFuHVZ3q1tdrbEHvc+jxzPdVb/+dXVV/d76Hu+bVMh5vQjg5KROAO9N/8YQE4Tu6JigtbUw4TiyLLBzp4sTJ+I3exw/PkFtbeZPf1/60tmUul9///d3pjTexMQ8DQ2rC0OvBZGIRl1d9rqBy8os1NTYaG21I8sCfX0h2tsDDA5GUFVjyUIkkl3rtBis1uxGGh2O7I5vMmVnfIdDWFMEbDVUVoqGyuJs3y7S3Z36PAMBgfZ2lUBAJ4L3ljQ0N8sr/KHXgnPnFPbvl/B609MLPHUqQmnpyvu0zabR1CTT36+seDgKhaCkRCQahf7++XWr5b4fsJoMjMfjWe8pPVDYIIAPCGJ1fuFwOCHxi9X5zc3NoaoqTqdzWZ3f/YBYBNBkWnnq1da6uHx5sTL82LHxhCSwqamA3t6puO+BLvERCkUXpGHWAo/HxPbtTl588cKq29bX5/JLv1Sc0rhraXBJFZGIcaurySRQV2elpcXN1q0Wbt0K0dExRzSqZF3aZHZ2fQigJctlk9ns0AVWeNQahd27ZcPdRYqLBU6dMq5e0WzW/bbXQiiDQZ0Ier0CR4+aKCwUOHpUpr3dmDq6piZdiHrr1vQitKoqsGXL8n3q6iQKCiQ6OyNoGnEf5GNE9tq1IH6/n0AgQCgUIhKJLIj/v1ERj/zFUsAbTSDJsUEA73MsrfOLpXtjxG9p1C4YDDIzM0M4HMZut+PxeDCbzSnX9q1fBFC/W8fTzysoWLkat7WNr0gH19fncuzY6Kqfde2an717048CFhRYaG3dhKpGOHZshK985RxjY6uvhp/7XGpRwIsXfdTWZqcW8Ny5WZzOtV/WBQUmmpo8HD7sxmaD8+eDtLXNcu3a0qKt7JOz0dH1KWjPtr1dtiJ0wN20X3Z+i0jE+Hlv3SoZGv1rbJS5fDkzQhkKCXR0qGzdKiOKxpwPzc0ynZ0RolHo7lZobk5v0DNnIrjdeiNKc7OJ/v7oMqcXr3flPTqWUSkrs2GxWBBFEVVVF5yM3qikMNF3iH3PjRrA5NgggPcplgo5LyV+ier8gsEgFosFj8ezYFeWDtaLAFqt0t3aopWfc+XKSis0TYNTp6apr9d1QQoKLAwPp26Z1tU1zf79qWmKlJTYaGkpwOcLcezYyEKNos8X5U//9Myq+x85UsDDDydOWy+FIztZYObnVerq0gtr1dTYaW31sHOnnYmJMJ2dM3R1zTI3F39xHRnJvt7Y7KyC2539RpBsR+iy2Vy1bZuUFQ9gj8f49K8e/TNuzJ07RUNStQC1tSLnzim0tamUlprYtWttdZWyrHHkyMooYldXhF27Us/CBAIChw6JlJZKy2r9YhgeXnkcY3aDZWUmTCYTVqsVu92Ow+HAZnvjksJY/fu98Pl8WK1WzOtlYP6A4v7JDW5gAaqqLvPtvTfVC3qdXyAQIBqNYjKZcLlcSNLaC8LXiwBu2eKgoSFvxU0NFtPD9yISUbl0yUdtrRu7XaSnZzLudolw61aA3Fwz09Pxww+bN1upqnLQ3T3O7dvxyeW3vz3E7/7uLqqqkj9Rfu5zO/n5z8dWndPZs7MUFjoYGzM+0hWJJCdosixQV+fA5ZK4fDnIxYt+Ll5Mffzr10Pk5Jizln6MobhYZG4uu9HGbDXkxJDNy2nTJomrVxUsFj3SaDIt/1eW9WYaSRKQJH0uqqqhKLrWpaLE/vTXolH9v2trZdrbjZWW2bpVYmTEmN/SYoFAgLj3kHRRWiowPq4uEOmrV1VA4+hRE2fPRvH5UvsBXS6orJQ5fnzl9RyNCkxNaeTnC0xOJh9PFHUSOTmp3J3LSuhj6fIvMQQCKrIMmzYtf6CJacFKkrRg7alpGqqqLth/xtabxTmISJK0EHCIt/7c7/D5fBsNIClggwDeR4jV+cUuxlid31KoqkogECAcDiNJEi6XK6uevUajoSGHU6dmeOtbN7N5s5U7d3Sy4nTK+HyJ80M+X5SCAgtXr6avSzE2FqKxsWBFt7DJJHLkSD6nT4/T0ZFclTYSUfnCF07z93//cNLtHn10M0eO5HP8eHKSGolo7NjhYGxsJul2a0F/vw+bzUkwuLjYOBwidXVORFF//8yZ1KOo90LT9MUu28Kz6yEFk+1LR0mR84iivnh7PAI2m4DZLGAyCQtyRoqi17qFQrqziM+nIUkSqioQDJJGJDDeMV3+WnGxhKrqQsgul4DDIWC16g0zsqzLrgiCThZDIY2ZGY3RUTXhHEpKBE6eNI7IHzqk19dlCo9HJ8grSZmeFi4qkqipgZ6e5J9VUCCQmytw5kzi7UZHNQ4fNi2UwMRDWZmI2w0dHRE2b04+93sDWzMzGkVFUkqd20tJYQz3kkJFURY0ZeH+JYWJIoBzc3M4nc77Yo73MzYI4H2AWLo3Go2iqupCfd/SkzdW5xcKhRAEAbvdbqhn73pFACsqHOTnm5mdjSAIIlVVTi5f9pGTY0pKAAFmZ8OYTFLSaF4idHdPcOTIpgUR6vr6XGZm9FRvqvinf7rG00/XceBAcoma3//9nbzrXW2rjnfx4iySlDpJSBXBoEpjo5UrV6LU1NiZn1fo6/PR3b1Sd3GtsNmynypaj+xNNmv0YFFrMDdXoKBAxO0WsVh0YheJCPj9GtPTGnfuqNy5I3DnTmxPjXhlEksxlbgHas0oKBA4f14FBObmuKsDGJtHovkIgERuLhQWCrhcYLHoZR7z85CTI/DznxuTrt21y5jUr9msUVaWXD5mdFRjdBRaWky0tcV/2CkvF1FVjaGh1S/ikycjFBeLcbuCm5pkzpyJcOuWfozv3AGnU4gbgRQEjfHx5a9PTKjs3r325TwVUhiNRpetD/cLKUxkA7cRAVwdGwTwdUTsAosRP1jp4BGr8wsEAmiahtVqxWq1JpV0WQvWiwAC1NfncPWqn9HREG63zN69OUQiyfM5W7c6OHdOj5bV1XkIBKJpW3mdPz/Dnj052Gwi3d2rp2nvhabBf/2vp/i3f3tr0u3e9rYSdu/2xBW6Xoo7d0I0NhYaSsxKS61s22bH4dAbbjo6DKy6X4KZmTDZLiGORrNv1ZZB1cQK5OSIlJRIuFwCgqARDOrpV6tVYnpaYHoaEhO79BZOSSJhijAT1NRIdHSsbdzpaZieXk4Wi4oEzp7V0DSRykqRggINUVQJhURu31bT8u21WsHvNyL1q1FfL9PdnRqRbGtTOXLERE9PZFkTy44dEhMTyqpp3RgURaCyUmZkZHGQnBzYvl2is3PldVpeLtHfv3KOBQUi4+OLB8Hj0aOxLS3GPjFlSgpjNqTZJIXJagDdbvdGBHAVbBDA1wlL6/yAuBdKJBIhEAigKAomkwm73Z5Rnd9qEARhXQhgQ0MOr7wyhsMhMTcXZWDAx2OPbaK/P3E6tLzcxrVretry/PlZ9u/3cPr0dFo1Vjt2uBFFdU3kL4af/3yUn/xkmLe8pTTpdp/6VC0f+lDXquOFQpkTtG3b7JSV6en0ixd9DA8HcLlkZNlOJEtZ2kuXApjNTkO7Ou+Fz6eQLjFKF+kqJIminiItLJRwOEQURbd6u31bZXpau1sXuXhSlpYa63oRw7ZtEpcuGT+ud+2VAXFRWSkyOqpHFK9c0bhyBRYfHERycqC8XMDj0VPbly4pCedw8KAxqd+WlvTHOX5cZc8eEzduRJmd1di3T+LSJQW/P737ZW9vFI9HWBhjZETh5Mn4F2kiF5n8/EUCKMsaBw6YOXMmzJvelD0N0BgSkcJ76wnXixQmIoAbEcDUsEEA1xmapi3ruIoXNlcUhUAgQCQSWfc6v/UggK2tm/jqVy+ybZuDvr45wmGVixd9tLZu5tixOyu2t1hE+vqml73W0zNLS8tm2tpWbn8vBAFaWgoX0r2p7pcIf/iHp/gP/6Ek6U3sySfL+aM/6uP6dX/CbQDOnp2hsjKHK1fSa+esrrazebOVW7cCXL3q4+rV5TWMXm+UhgYrp09np2N3fl5j1y4TFy5krw5wfDwCZDcPnCyQLsuwdavMpk36ojU1pXHtmsLwMAwPq8DqoahsWKmBHgW6dMnYCOBi+te48Xp6ko83M7Ncy1AURXbsECgoEAiFNIaGVObmNOrqjEn9NjVJayaR586pbN0qUV+v1+mt5eHK74fWVhlN02hvDyd9gE30rB+TeTp4UGZsTCEU0nC5BB56KPte4PEgCMIKrdnXkxSCTgA3NABXxwYBXCfEq/OLl+5dWufncDjS0vLLFOv1OW95y2Z27XKTm7u4uOsp4WBcErhli52hoZUraVvbOC0thbS1JY7oud0mamqcy2r9enom2LLFwY0byclZIpw9O80Pf3iV9763MuE2sizyyU/W8PTTq/telZSYVyWAoqjLteTmyty4EWJoyMfQUPLGFbs9u2Q+Ly+rwzM2FsViMTOfRYvjGAE0mXSyV1CwSPauXo1y6RJcurS2Ik1Zjq/ZZgSykQjIJP0bDzt2SLS3p3fsVFVgcBAGBxcdHnbuFNm8WXe6GBxc+/z27Fndj3c1lJSI3LypYLEIRCLp/7bl5SKCAMeOrR46T2Tr5vHA7t0SJ0+GsVphZkbh4x+/v9Kdq5HC2Fq4lBQurSdMlRQmSwFvEMDVsaEDmGUsFXKORCIJ9fxCoRAzMzOEQiGsVis5OTmGNnmkgvVKAQuCwGc/u2OhAxj0up7KSifHjunNGkudQmy2xMego2Myoc5fWZlOmE6eXG4XFwgouN1mMjm0X/rSmVVr1D7wgW1s2rT6U3lv7xRO58oVXZYF9u51cfiwm9xciYEBH52dMwwPpxbVu3hxDkHI3u8ZCmXXfF7T9HRrNlBUJHH4sI3cXJEdOywIgsTQEHR2KnR0RBkYUJifz+zaS5TCMwJLJUCMgpHRSo9HoLc38+4mTRPIz5f42c9UBgcFSkslWlpkamvTO7bl5QI3b6oZlUS0tEh0dES4ckVlxw457fvH4cMS09N6yjeVfe99eCgqEjh8WGJsTKGvT/8i+/aZ8Pk03ve+LAmLGogYKTSbzdhsNhwOBw6HY5leXzQaZX5+nkAggN/vJxgMLtieqqq6bH1azQZugwCujg0CmCXEimWXCjnHi/pFIhHm5uYIBAKYTCZycnKw2+2vWzfVegmBPvlkOZGIxoEDi+QtJ0e/CRw/PsXOnZ4FdXubLXH6W1U1BgZ8bN++/AbodpswmeD69fhRsr6+aVpaVtFaSIJLl+b47neTF2HZbDIf/ej2Vcfy+aLs26fXq1gsIgcP5tDUlIPTKXD27CxdXTNMTqa/co2Pz7NrV3YcRwCuXg1kbewY8vKMuQ4qKmSammwcPmynpMTM6KhAV1eE2VmBwUGFcNj4683lys41LIpw7ZqxY27aJHDhgnGkcvduEf/aAuzLsH27wPHji0RyeBja2jQGBgSKiyWam03s2JF8GXO79WOWiW5lS4u0rBP41CmF5ubUynJ0H1+Jrq4IPp9GIKBrXK6GGMm32fS6xdlZhdu3F8kfQCCg8fDDVsrLH8xkXqqkMBQKxSWFEL9saYMApoYNApgFqKpKJBJhfn4eRVHiEj9FUfB6vXi9XgRBwO1243Q6De/uTQfrSQAlSeBTn6ohGFy8uS/t6j17do7ycid5eea4vsFLEQgozMwoFBXZFsauqnJw9WryivYTJybYtm3thcJ/+qdnCIWSRzk++tFqnM7kN2eHQ8JqhcOHczGZNE6enKKzc4qZmcwjbNlM005ORikry15TEuiLX7oQRaiuNtPcbOfQITv5+WauX4fOzghdXWFu3148z7J5utvt2bmWt26VDG8sqa6WDBFWBl0U+dy5zAcTRf1ajia4DEZGoL1djwzu3CnT2CivaOqRJI1t20SuX1/bfARB4+hRMa4MTFtblIMHk1/bVVUiRUUinZ3L9y8qSn7d6PqEKo2NMh6PRlvbPMGgxpYt8sI5W1Ym0dcX4amn3lhEJx4ptNvtcSOFAOFwmGAwyNWrV/mXf/kXrl+/ztzcXMYEcGZmho985CNs2rQJp9PJm970Jk6fXr2kJ4bh4WGefPJJcnNz8Xg8vPOd7+Tq1asrtvvBD37A+973PqqrqxFFkUcffTSjeaeDB/Ox4T5FTMg51uART89PVdWFJxhRFNe9zu9+wvvfX8F//+/91Nfn0Ns7w+XLXsxmYaH2ZXDQx9atDszm1RfSiYkw27e7cDoj1Nfn0ta2uldwKKTctaZbm7TE8HCAv/qrAT7xibqE2+Tmmvnwhyv52teWW2243TK7drlRFIVz56b5f/9vmLq6grudr8ZBj4Bm7zIvLZW4dSt7bh2pPpBUVMiUlZkIBmFoKMLQkMrQUCotytljgFZrdq7pTZtErlwxNgVsZPp33z6ZtrbMz4mmptRrCPv79d9x82aJ6mqBCxcUpqY0Dh+W19w8IkkaBw9KSfYXuH5dw2Ihbp1qU5NMb284rkC23Z783KitFdA0ge7uxYElCS5eXCSSW7ZIHDli5m1vy16U/35BrGxqKRRFIRgMIst6U82rr77KJz7xCQBycnI4deoUPp+PgwcPcuDAAcrKylJeZ1VV5Zd/+Zc5e/YszzzzDPn5+bz00ks88sgjnDp1iu3bk2d2fD4fjz76KF6vl//23/4bsizzwgsv8PDDD9Pb20vekifzb37zm/T09HDo0CGmpqbWlQtsRAANQKyz1+/3L0v3LiV/sTq/2dlZ5ufnsdlseDyeda/zS4b1jAACWCwSX/jCTmLdlJOTEQ4ezF+2zbVrflRVZOvW1SN1ly55aW4upKMj9Q7f/v5ZmpvXngr+8z8/h9ebPD37e79Xg9kskpdnprk5n4YGF4FAiOPH73DixMRCFNFmM55I3bgRoLIye520kpRdq7ZEEVaXS+TgQStHj9opLdUjfO3tEXp6ImlJmWTzdM+WzZwsGztufj6GpX9tNujvz3ys0lLo6Un/3LpzR08R+/0Cb3+7ac0NPGazRn29RFdXcvI4Pq5x4MDyVLDVqqd8Ozvjkz9IfN55PHD4sO4Ic/788vtKQ4N5Qf5lzx4TV65E+OpXU/M5fyPDZDJhs9n44Ac/SH9/P9///vfZt28fJpOJb33rW7zrXe9iy5YtFBUV8eUvfzmlMV9++WU6Ozv57ne/y+c//3k+9rGP8eqrryJJEl/4whdW3f+ll17i0qVL/Ou//iuf+cxn+OQnP8lPfvITRkZGeP7555dt+73vfY/Z2Vn+/d//neLi4jUdg7VigwBmgFiDRywEHUvn3tvgEQ6HmZ2dJRAIYDabycnJwWaz3TfEL4b1JICaphEIBHj88Rz2789h3z7dY/fKFf+KhXN0NMTcnEJtrSfpmIIAQ0NzaRO67u7xNaeCJybm+frXLyTdprTUzsc/XsXMTID29lFOn54iGl15nM+dmyU31/hoXWlp9tK04+NZbNEFpqZitoiwY4ee1t2920YgIHDyZJSOjvBdSZa1IZvneyq2XGuB0R7MRqZ/9++XUhZGToaCAjENe7uVqK0V+bd/U/D7BVpaZKxpSORZrRq7domcOpVa5PD6dXWhK3vLFpHS0pUp33txbwexnmo2IQgaJ05E4hLXWGbE4YCpKYW//Ms88vKyW4LxIGDpOlpaWso73vEOwuEwzz33HCMjI9y8eZP//b//Nx/5yEeorq5OacyXX36ZoqIi3v3udy+8VlBQwJNPPsn/+T//Z5lNXqL9GxsbOXDgwMJrO3bs4LHHHuOHP/zhsm3LyspSmlM2sEEAM0AkEiEcDqMoykJ4eumCEo1G8Xq9+Hw+RFHE7XbjcDhe1zq/ZFgPAqhpGvPz88zOzi50PH/lKw14PDYefriQ0VHdt3cpLl3yEQxGGR4OsWdP4ife+vpcrlzxcuzYWFokcH5exWZLv6svhq997TyTk8mLsn7jN7ahqsmP44jCiwAAIABJREFU7fy8yu7dxnfzjY1lr1njypXgmur0UoHLJVJcbLrbrWticFClvT1MX18ERTGGXCnK+kW8jcJa69kSIRg05lhKEly5kvnxPHJE4syZtX/H0lK4cUNDUcDvF2hr08jPlzh0aPWHK5tNo6ZGTKuDeXhY48ABmcZGiakphcuXV993qcXbjh3SXQmeeWZmNPbulbhzZ/n3P3LEzNmzsc5fMy0tFt7+dnvKc3wjItFapWnaMieQsrIyfuVXfoUvfelLywhdMpw+fZr9+/eveP3QoUMEAgEuXrwYZy8dqqpy9uxZDh48GHf/y5cv4zeiQ8oA3J9M5AFBLNUb0yyKvaaqKn6/n7m5OVRVxel04nK5Vugi/aIhRoj9fj+SJOHxeLDb7TidJr7+9XoiETh0KJ/R0dBdL9HYfhrbt7vxeqNcuuTnwIH8BOMv3ni7uyfZuzf1DogLF2bWnAqem4vwta8ljwLu2pXDW96yenj/5k2DrRiAwUEvJSXZERJXFKisNC4KUVgoc/Sog3377IRC0NkZ5Nq1KFNT2SFqRvswLx/b+AhgebmIL7n8Y1rQU43GEMqDB0VGRjL7nfLzYWBg7T+K3a5htQpLLOl0DA/DiRMae/bIFBXF/10cDo2qKpGzZ9P7fFnWyM0V6O6OxPXujYfxcRW3G44elRkaijA4uBhtvJfXlJSIC+ngfftkrl6N8pWvZFmE8wFANmVgRkZG4qZjY6/dvn074b5TU1OEw+E177+e2CCAGWAp8Yv9Oz8/z8zMDOFwGLvdjsfjeWCaPLIVAbyXELtcLlwu1zI7odpaF089VYnVKuP3azQ3b1o2RkwiJhhUOHt2lsOHl0cJi4stnDu36BYSiajcvBmgvDz1iNqpUxNpbW+zSRw+XEBDQw4vvXSO0dHEkTZVVXnqqcTC0TFcu+Zj717jLYwqK7PnJON2Z3bOVFSYaW52UltrY2xMoaMjyJkzoQXNtoKC7N2m4qXijUI2bPiKiow9Fjt3Sgm7bNNFOr6+ibB9u8RMYkfIVVFXJ3H5cmJCe+6cRjgssnfv8ocWlwsqKkT6+tIjfwUFerq5qyt1T0SzWSeaJhN0dISXpd+LikTOnVs+h4ICCa9Xw+UCp1Pgb/4mj9zcjaU7GWIRQFisv0/lL4ZQKITFslLD1Xq3liCYpD4h9t5a919PbJxFBiDWBAJ6S7rFYsHj8WC1Wh8I4heD0QRwaePLUkKcyNbuySfL2LPHQ0mJla6uWR57bDEid+1aAFnWT9dIROPkyRmamhZJ4lJXkRimp8OYTBJud2qR12BQiTvOUggC7N2by9GjBciyQlfXCKdPj+PzRXj++bNJj0FTk4c9e5LXMUJ2HDz8/uxYwgEEg+kxCEGA2lorLS1Otm61cP16lPb2AAMD88Tz/s2mnFc2I4ChkPG/o8Vi7C3bKJJaXy8RR+EiLezbJ9LVtfYfpKVF5MSJ1fefmoK+Po2WFv0+5Hbrns0XLqT32Tt3ioiiRl9flJmZ1DQry8tFHn7YxPHj4bhi3hUVy+sxjx7VU7/790vs3Svze7/n5qGH3vhdv6kgWQRwqRXcz3/+c+x2e0p/sdSuzWZbkJlZihhJtCWpe4m9t9b91xO/2DlJAxCNRgkEAguilDab7b75cdPF0jR2psQ1EokQCARQFAWLxYLNZkup9vHP/mw3H/zgSUZHQ1y8GODo0UL6+6e5eTNAS0s+bW3jgF67dfz4FM3NhbS3j2GzxR/7yhUf+/fn0ds7uWoNHug2b7Exl8LjMbF3by5Xrsxw9mx867m/+Zt+PvWpvRQX67U50WgUv9+PoiiYzWbsdjtPP13Hhz7UkXQOPT2T5Oc7mZw0zmnj/Pk5XC43Xq/xDhI3boSA1euRdu60kpcnc/lymIGBMJBa1ESWsxml04hHOo1AIAull0aWDjkcGJb+zZRIm0yZNbfs3y/S0ZH6JFRVoK1NpaXFxNRUNG3yd/SobsUWXnIKl5ZKC01L90KWNQ4fNnHq1Dz5+fGX3cZG07JIYnm5iKZp1NQImM0CH/uYm7e97Re77m8pEq1TkUiEUCiEx6M/bO/cuZPvfOc7KY1ZVFQE6KnaeGnakRHdUrSkpCThGHl5eVgsloVt091/PbFBADNAMBjE5/MhSRIul2uhC/hBRyYEUFEUAoEAkUgEWZZxu91p1T6azSLf+c5B/uAP+vje964TDFqw2y0UF9sYHPRht0sEAsrdeUJ7+yQtLYVEo4nJRE/P1F2P4dW1AQHOnJmiuNjGyEiQ8nIHFRV2Tp8e59ix4aT7hUIKX/lKL88/f2RB6zF2bsSink8+WcHnP9/L8HBidhAOq+za5eDYsdmU5psKwmGVhgYLXV3Gpx6mphTKyuLrAVZXWygsNHHtWpj+/tRJ31JEIsaT1hjCYRXITidlqvVg6eDmTeOORV2dRHd35nOsrRUzFn4+eBA6O9c2l4oKgaEhJe1OZrdbY2xMISdHRBCUlCSBzGaNAwckOjpWnseJnF927ZIIhRTa2+cpKxM5c2Zl2LWwUFjQ+BNFjcZGE9PTKsePhzhyxMJHPuLm8cfvf7u3+wE+nw9ZlhfSrZs3b+b9739/WmPU19dz7NixFWthV1cXDoeDmpqahPuKosiePXs4ceLEive6urqoqqrC4bg/fsuNFHAGiEV13G43JpNp3XX0jEYm5DUm6zI7O0s0GsXhcKy58cVkEvnzP9/Dxz++nZGRIJWVDoaGguzY4YnbANLWNonDkfxzjh0b48iRwpQ+3+eLsnNnDocO5XLr1ixtbbfx+1PLlf3t3w7Q3z+ykPKOnRsxyLLI7/zOjlXHuX59DqNFikUxe/nOkpLFc6eqykJLi5PycjNDQxHa2wMMD689mun3Z89z+F45DiMxO2vs2AUFAhMTRpZoGHP7z9TyrqwM0jBYWAanU0MQtLS0H0EnfyUlcPGiSnd3arZuhYUClZVCQomXe9PpDofe5NHfH+bKFf3a27pVikM0NUpKJLxelSNHTGzfDpOTChcvRmhqsvCBD7h44on7gzDcT0gUqJibm8PpdGa0nv3qr/4qd+7c4Z//+Z8XXpuYmOAf//Efefzxx5fd02/cuMHAwMCK/U+cOMGpU6cWXhscHOSVV17hPe95z5rnZTQE7UFmLK8zFEVZSP2Cbh0TI4UPIiKRCF6vF4/Hs6xBIxliOofBYBBVVbFarYZqHD7//BB//ddXKC62cvz4FEeO5DI0NM3k5PIn8N27XeTkyLS1JRaBtloltm61MzCQOLKWm2tm1y4PHR0jNDZuoqsrfro3GT74we28+OJDCVPec3MRqqr+Ga83ObHZt6+QM2eMa/nMzTUxO2szTPNtKR59NJdw2Mrt2xGuXjW2+6GoSGZ0NA0htzRQWCgxNmZ8g4zNBsGgsQLcDQ0yp08bc7s2m/UHrUxTylu2CNy6tTYnnRgaGkROn17bAAcPCpw8md6+TqdGeblAf//yByJdvDn+ubtjh8j0tMLYWOLPqq4WGRrSx9y3T2J8PLrMdtBq1f/uTXU3N5tQFI1btyLMzSls3iwxNKRw9KiF//SfnPzmb76xrN6MQigUQtO0FSVX586d49d+7de4fv36mtchVVVpaWmhr6+Pz372swtOILdu3eLEiRPL9AQfeeQRXnvtNdQlF4HP56OhoQGv18tnPvMZZFnmL/7iL9A0jd7eXvLzFwMZr732Gq+99hoA3/jGN3A4HHzoQx8C4OGHH6a1tXVN3yEVbEQAM8C9J9cbJQKY6ndIJOtiZBr805+u5tOfrsHvj7Jvn4fjx6epqMihomI5yR4a8tPdPU1ra2KplVBIYXY2SkHByu4sURRobt6EpkVob7+NpmlcujS70H2cDv7hH65w61biFK/bbeLXf7181XGsVmPPpenpCHV1xhGpggITR4+6qa62cOtWiPb2gOHkD2BsLIo5S2Ym2WjUgMyjYvHgcBh3u969WzaknrC0NDMR6UOH1k7+jhzR0iZ/drtGRcVK8gdw4kSU8vKVx7ixUeL69UhS8gcwOqpLu+g6hss9pwEOHjQvI3+iqHH0qMyNG2GOHw8xO6tQUiIzNKTwyCMWfuu33BvkLwkSrVOZSsCAnsb98Y9/zHvf+16+/vWv88wzz1BYWMjPfvazFWLS99q9AjidTl599VUeeugh/viP/5hnn32WhoYGfv7zny8jfwCvvPIKzz77LM8++ywTExNcv36dZ599li984Qu88sorGX2P1bARAcwAqqouUwSfm5tDFEWcTuNlPNYDiqIwOzu7rGYtHuL5GSfb3gj84Ac3+cY3LiHLIsePT9Hamk8oFObEicmFbXbvdtHXN0tr6yaOHVtZgLu4XQ4DAzNEo/oN2uUyUVlp58yZiRXbNjVtprMzdWu5GD784VpefLEl4ft9fbc5cuTVpDIkZrOIw+Fketq4FGhrayHHjq29I9jhENmzx0kwqHDunH9h8ZdlAVl2E8pSs/GWLW5u3DA+dGmxCMzPr3wgyBSVlSJXrhhbYn3okDmlLtcYzGZd58/pFHA4BCwWAZMJJEnA4xGZm9PraGMrgKbpkbyl/0oSiKL+JwjcFUvXdxBFjWhUwOfT8Ho1pqY0ZtMoW3U6weEQuHMn/SWork7k4kUlrS5mm01j+3ZhhczKUhw5InL8eOx602hpkWlrW71uNS8Ptm4VuXUrGpco7tkjc/585O71otf4mUwa586FmZvTcDphyxYTt25FaGkx89xz+dTWGn9evpEQCAQQRXGh1i+Gn/70p3z5y1+mq6vrdZrZg4ONJpAM8EaLAK6GmItHTMPIbrevm5fxe99bzpYtdr74xX5aW/M5dmyS1tZ8Wls3094+hqpqeDw6CT12bJzW1qKETR99fTO0tBTS1jZKQYGFvDw5LvkD6Oy8w969eZw9O5XWfP/u7y7y2c/uY+vW+E+ipaU23vnOMl5++WbCMcJhlcOHjW0GuXHDR7qXvckksHevE0FQ6esLcPz43IptolGNHTtkzp/PTr1eXp7AjRvGjzs/n53r1W43/poYHV2cq8cjUFioE7mYaHo4LDA3B7OzMDenEQgIjI/D+PjycUQRcnIEptI7pZdA/7zmZoGOjuXHz2TSyM8X8Hh0UmO1gqpqzM5q3LqlMrfk1Nm3T6K9Pf261Px8XUg5HfJnsWhUVWmrNqt0dyuUl4tMTans3CmmRP7cbjh0SOLf/i2+LaLHA+PjepPK/v0yc3MKc3MKt29HmZvTcLuhvNxEJKLxnvc4+IM/sFJQkN0H6jcy5ubmMo4A/qJggwAaCEEQltUBPGhIlgJeq6yLkWhqyueHPzzMH//xAKIo0NY2cddHOJ/Ll2c4e3aOggIzExNhjh2bSEoC29rGeOyxEoaGJrl4Mbny7OxsGItFZH4+9d82ElH5sz87zTe/+VDc9wVB4Ld/uzIpAQQYGTHQ9gG4fj3A1q0FXLuWfGETBKirc+BwwOBgiFOnVq+yd7uzd+5nQxsxBosF4kh2ZYR7/azXApdLoKxMJCdHxGQS8PlETCaJsTGYnRVWibYl/vydO0XOn89sbhYL9PfHu08IjI7C6OjSz4/9t0R+vkpxMRQVCfj9GqWlAsPDqf+2oqhRWipy9mzq55rZrLFzp0Bv7+r7qKrAtm0iZrPKyZOrP8zs2ydx506UmzcTE9maGhN+v0J+vkhPzzy1tfIC+aupkQkENHJzRZ54wsH736/rz70R1CSyjURNIEakgH9RsEEADcSDHgGMRwAzlXUxGi6XiS9/eQ8/+tEIeXlmXnlljKIiC263BZMJiottTEzojRurkcDZ2RAOx+pP2tev++JqA66Gv//7IZ55pp7KSnfc9w8cyOPw4QK6uuJHHwEuXfKyc+cm+vuNE4ArL5e4di3+e5WVNkpLLVy5EqCvL73PjESiZKusWBSzSQAFwyOBspz6Am6zwZYtEnl5IpIk4PPp0b7RUejv17fZtUvkwgVjSEFurkCmHeYHDqyM/qWCyUmRybtVG319ABJFRbBli66VNzKice2amlCSpakpvaihKGrs3SumROYAamrgzp3oqn6+NptGQ4NMZ2eYmhqJCxfij9/SYiIYVLhwQX/gWkr+jh61cP58mKNHrXzmMzkcOmRZVlK0gdWRiAA+qGVY640NApgB3qgpYE3T0DSNYDBIKBRCEAQcDsd9ZWn3+OPF1Nd7+Iu/uMSPf3ybvDwLly/7KSjQqKvzcP68Hh5JRAIPHMjj5Mkxiott5OdbmJxMHgLq7h5n2zYnV6+mHpGLRjW++tUzvPTSyi6u2LnyO7+zIykBBMjLM5ZUzcwsL9TbvNlMTY2dO3fCXLwY4MqVtWkFjoyEgex06+rR1+yce1arwNycsdetJMWfqyxDVZVEfr6Iogjcvq1x86bG4GBs+/jzyMkRE76XLoxIpcfRuE0ZBw9qnDy5+P96xBD031egqEikqgqmplT6+xejdumKPYPG4cMinZ2pkb+DB0XOn48SDGo4HIlFt3fuFPH51AUtQP23WY6CAoEDB2RefTW4EF3evdvEjRsRolGNw4ct3LwZ4Z3vtPPcc3nk5y9XXbhf7rP3MxKttUtdQDaQHBsE0EA86AQwdtOJRqOEQqGsyLoYifJyO88/v4ddu9z84AfX2LvXw/HjUzz22CbOn1/U0YtHAicm9C7dkZEg+/blMT09kdQpJBLRsNlMCMJKs/Zk+Pu/H+IP/qCB8vL4T6TvetcWSktPJxWGPnNmErvdSiBgTIr1woU5ysry2bLFht+vcPasjzt30hdovhfDw/Pk59vjWlxlirm5KJCduqhs9C/FzpGiIpHychGzWWR6Gq5cUeOQvdWvLaNuKxUVAtevZzZYfb1Ab+/axrBaNW4mr3pYQghFSktFtm2DUEjl4sXUxJpjaG4WaW9Pjfw1N+sSMHoFj0B5ucTAwHKyKcsahw4JdHVFFpqfSkoEenoWrx2bDfbvN6GqKj/7WfBunaJGc7OVrq4Q5eUydrtex/j00/m8/e3L9f0e5PVjPbGaDVzMB3gDybEhA5Mhlp6ADzoBjGkahsPhrMm6GA1RFPit39rKc89Vs327k8ceK+RnP5vgoYeKsdkWn6pjJBB0rb/r1xcjeWfOTNHcnFg+JoYLF6Zpbi5Ka37hsBrXIxj0m5gsizz1VGJVedCFqevrM09pOBwShw/nsn+/m/JykY6OWc6c8RlGLgAqKrJzSxkby54YtBH1ejFUVEg0NZlxOiU2bzYxOipx4oRAe7vGhQsaodDaPiud7tpkiCdzsp5oaNC4k0ZT/fAwdHVpeL0iFRUy9fWp6ZO2tKRG/gRBo7lZor09skzOxu1efpzKy0W2bxfp7Fx0HJEkvYEjEtHHaWwUcDpVwuEoJ07ME4nozTb795tpbw9x6JCZHTtE3vteO9/+9uYV5G8DxsDv929EAFPEBgE0EOnq6N0vUFUVv9/P3N0WPZPJhMvlSlkM+n7Avn1unn9+B7/5m9t44okyenqmaW4upqhoMSV57NgELS1F5OevlFc4duwOBw8WrPo5Z85MsmlTemnO73xnkJGR5RG+paT6wx/evoysxsPc3Nr0Vex2nfQdOpSDoih0dU1w4sQUmpadWqNsNWtMTytkS189kwhgQYFIY6OZI0esFBVZuH5dorNTr3VLh+ishkyjdjGsvfNXR2Ula47+lZZCT0/6+x0+LDI4qHHuHPT2CtTUyBw6JJGoB625WaStbfXz22bT2L9fJ3/3Ym5ukQ0eOaJ7/A4MLCeUTU1mBgZU6uokqqtFurujbNsm0dMTIRrVLfJsNg1RVGlulti+XeS553L5xCdyE2o6GuHD/ouA1SKAGwQwNWwQQAPxoBFATdMIhULMzs4uWJeJorju3b1GQRAE3vOeMl56qYE/+qM9DA7OUleXT22tZ2Gb9vYJtm+Pnx4YGPBSXp6cZXi9Eaqq0ksvhEIKL7wQPwoIkJdn4dd/fVvSMfr6Zti6NTXiabNJNDbmcvhwDpoWI32ThEKLKa2BgdmsNFZ4vdkrYi9MzckvbZhMqS+4DodAQ4OJ5mYLlZUWJiZkurvh+HF1mUyLkV3FZWWZO3aAnnYcGMjsN9+8ee3kpLhYYH4+vf0bGgTa25eXFFy8CCdOCFRXy2zbtvxedeSISEfH6udgQQFs2SJy6lT8KOHt27qoc2OjxPHjYfz+5cetoUHm8uUojY0S58+HuXgxytGjZk6ejKAoGq2tZgoLBWpqRDwe+MAH7Hz5y062bFHw+/0EAgHm5+eJRCKoqvrArBn3GzZSwJnhwVzp7yPcmwKGB4MARiIR5ubmCAQCmM1mPB4PVqv1gU1jL523y2Xi4x+v4kc/akUURQoKrBw+rEf3NA16emY4fHglm/D5onet/JJH444fH2Pv3ry05vetbw0wPr7YXHHvcf6d36lddYzy8sR2GHl5Zpqa8mhszEEQFLq7J+jqmiQYjF80PzMTYccO44vfrl4NYLSHcQy5udm5Xa3W1F5SInL0qIW9e63Mz8ucPq2ndK9cSfw9g2vro4mLoiJjvndNTWauHXl5rNmKbt++9C3bCgvhxo3EHcGDgzA8LNDcLCNJemfyyZORVUsayssFLBYYHIx/beTn6ylfp1Oju3tlbezmzbq238xMlO7uMFarHg3s6Jhn0yaBRx4xEw4rhMPw8MNO/u7vSnjve/NxOBxYrVbMZjOiKBKNRpmfnycQCOD3+wkGgyiKPqcHWU5sPZBsjdpIAaeODQJoIB4EAqgoCl6vF6/XiyAIuN1uHA7HQtTvjZR+qK318H//bzOPP16OJEkLNYBjY/P4/RoNDStJ3OXLXvbu3bTq2F5vJC2pj0Agyte+di7h+zt3enjsseT1hQMD0yzNyldVOWhtzaeuzsn0dJDOznG6uycJBFLrlMzPN/7yn5tTqKjITm+ZzZadczNepUNVlURzs4Xqagu3b8t0dGicPasSjaY2ByMidjHEhJ4zRTjDPp9du8Q1Ob1IkpZ2l7UgaBQVCQuSMYkQDgu0t0Nrq8TwsEJ0lbK/6mqRYFBleDg+wRIEjf37RQYGVlq5ARw4IOF0wquvhggGNSorJUpKJO7cifDmN5twu/Xu/6YmG9/8ZiGf+UwuLtfivVWWZcxmM1arFYfDsYwUAgvRwKWkMBwOE41G7+t1Zb2xkQI2BhsEMEM8KBHA2E1ldnYWRVFwOBy4XK4Vmn5vhAjgva9/4hPVfPvbjbhcFurr85AkAadT4sIFP/v25a/Y5/jx8VWbPa5e9dLUtDmtOf7VX/UzNRVKON/f/d3kUUCfL8pjj+XR0pJHaamZy5fnOHZsjPPnZ9fUyDE6aiBLWYLi4uzcVgQhO1ERSdL/du/WU7v6sZVob9cYGlrbtTC30ixlzTAinWyzwYULa7+uzWYYGFirZ6/I1avpffbRo6mLPVdWQk+PisMhkZeXmCzv2SMyOqowMRF/Lnl5eqTy1q0oyj3PUKWlIg89JDM2FuXyZZ1lNjWZcbmgqkpElvVI5bve5eCv/qqQL34xn8rK1Q2sl5JCm82GJEkL9maxe3M4HCYUCuH3+/H7/YRCIcLhMIqiPJD36mxC07SNFHAa2CCABuJ+JIAx+7aZmRlCoRBWqxWPx5PQwu2NRgBj2LrVwfe/v5/m5lyKiz1cvDhPTo6FS5cC1NXlrtj+5MlJamo8cUZaxKlTE5SUpN6Z4PVG+B//I7EFw1vfWkJ19eKTq9kssmdPDq2tm9izx4WihJma8tHWNs7wcOY5xkuXfBQVGR+tE8X07b1SQThsLAEURd2jNScHPB4zfX16and4OLNxzWYM9UTORHMvhl27xIzS0g0NAhPJ5SrjIi9vdfu1e1FXJ3D8eGr7bN4MgYBuMXf5MuTlSXeFrpfj4EGRixejeL3x7xF1dSImk4qiaPT3L4YRTSbdD9jp1BgcDHPzpoLbDW95ixlRVLDZNKJRlV//dRf/838W8od/mE95eWalFTFSGHNccjgcC7absiyjqirhcJhgMLhQTxgKhYhEIr8wpDBZBHAjBZw6NgiggbjfCGA0GsXr9eL3+5FlOSVZlweVACbD0nrHL36xju9//xAFBTYUxYrfL3DjxjzV1cvJ3vy8Siik4XIlJkiBQJSSkvSkHF566Txzc/FzcYIg8PTTu2ht3UR9vRtRjHLu3DjHjt3m3LkpwmGV3t4JQ31Cq6qMJ4CriWqvFbOzxkjBVFebaG62UVBg5tw5gbk5KePu2KVwu41LVTudcPt25tdjplI3a5Wh2bFDTCsa6vFoTE5qKyJw8eByabhc2rLmm0uXYNu25ef00aMSp09HE0RSNZqbRQYHw9y5oy7rLN63T6KkRGB8PMr4uMKdOyr798tUVAiEQgoul8Djjzv49rc386lP5WXlYQr0+4IoiphMJiwWC3a7HYfDgc1mw2KxIIoiqqou+LTf22Tyi0IKY/D5fBsRwBSxQQAzRDwy9XpfbEtlXTRNw+VyPXCyLukiHnFVVRWfz4fXq/vYxuodGxsLOH36P9DaWkBengOv18TwsEBZ2XKtvRs3/OzalVwa5uTJ8ZTkY2KYmQnz0kvnExLt97yngt7ecXp7l3ftxhCNatTW2lL+vNUwP5+5APS9uHo1mBVx5fHxtRPA0lKJ5mYbW7daGBqC9vYoY2P68ZdlY2+DTqexuoJG3E4uX177IDt2rK17eNs26O5OL/pXXS0uI3SJIMsa27bBpUsrx+/p0di7V7/XtbRIdHRE4hJKj0d3F2lvDxONwqFDMmfORPB44MgRmTNnwuTni4yMKGzbJtLYKGI2a2zbJvOOdzj467/ezO/+bi4FBcYRv1RlYARBQJIkTCYTVqt1GSm8t8kkHil80DuPE0UAFUXZIIBpYMMJxEAIgvC6RtBi6d7g3VxPLG2QTmPHGyECmMpxkCSR//W/DvODH9zgk5/sZXo6QjRqxuHw4Pd7AX1h6eoap7m5mPb2xHm4kZEgVqtIKJSosBzR8pkRAAAgAElEQVS2bHFSVGTDbBb4yU+u8eEPV2KOUyLkcpn4z/+5im9+cyDh5925k7od3Wro75/BbPYQDhv3m4fDGjt2yAwOGivePD2tJLXouhc5OSI7d5qZm4Pz56MMDyeaj7Hnu91uHAH0eDK3gKuuFhkaWvv+OTlr8w7OyRHSqv07elSgoyMVwqhx4IBAV1fiMGEwmFwPsLZWZG4uSk+P/nmVlSIXLkRobJS5fDlCb+88R4+aCQYVGhslIhGorDTR0mLjHe9wYrPdf7GTGClc+qCvaRqKoqCqKoqirGgmidUcxv59UCTAEhFln8+3YF26gdWxQQANxutFoCKRCH6/H1VVF2pH1nIxP6gEMDbvSCRCIBBAUZSUjsN737uFhx8u5C1veZWLFwMIgoQkeVCUCBACopw8OUFNjYeLF+PnwYaH/cus5srLHZSW2jGZBKamgly7Nsv165Ncv764z/e+d5EPfag67nhPPVWblAAODc1RXb2JoaHM6wD9foWGBgunTxtYtIYupZENFBaKXL2amCQIAuzbZ8ZkkujtjdDZGSMJ69fdHo/Yv57YvFlYczNLbu7ahJ/37RM4fTr16F9FBSlv39ws0N6emPwJgkZBgcj16/HJX1OTSE9PeCEl7HaDy6XLDHV3z1NdLbFrl4zDoZKTo3LwoJ13vMNBbe1KAflswEglhlg94VKoqrrwFyOFEd2zbiHdvJQY3q/KEIkIoNPpfGCI7OuNDQKYIe49CdebQCmKQiAQIBKJIMsyTqdzxQWfDh5UAhibs9frRZIk3G53ysehqMjK6dNv4ZFHfsaJE7NIkowgyGiaG4gyPx/B6wWXS8brXRlFqqpyIYoajzxSSG/vGDdvTnHzZvKishdfPM9v/EZV3Pdqa3N45JFiXn01cdSxqEjOKKqzFE6n8d21mpadRhA9GrUShYUS1dVmrl1T7hKWKKmSPqPXNyPT30ZYwK0mpZIMu3aJK4SYV4MgaAQSW1uvgCRp2GxCSk0qR48KSS3eRFHj0CGRzs4oBw+K3Lq1OHdZ1mhslOjoWFr2oHHkiExn5zxer8ojj1iQpCgVFRIPPWTj7W+3Y7Wu3zK5HvfeeyN9mqahadoCIYw1mcQQiywuJYavNylMdJxiEjCv9/weFGwQwCxgPS5iTdMIBoOEQiFEUcThcGA2mw078R8USyIj0t6gp4Rfe+0xnnjiGD/+8RgOhwm/X0EnEVZGRjQKCjbh9c4AISornZSUWLl8eZrLlye4fBkOHNjEzExqDRDDw37+6Z+u8tRT8QWln3qqNikBvHBhClk2rap7lgquXfMCxkY3xsbmAeNDYbYl5Y+iCHv3WpAkkd7eyJr9go2+Xk0mCaPSyjdvZjZObi4MDq5tDFEkabQ1ERobRbq6Ut+vqUmkrW317Q8cEDh+PPFvLMsaDQ0CXV36Nlbr4j2goACKioRl5K+kRGDLFoGf/CREaalIU5PMvn0Sv/IrbvbuNa7O9n5HrHRJFMWFh+YYKbw3fRxDjEQuTR2v93qRSAPQ6czcN/0XBRtxUoOR7YsgXVmXdPEgkL4Ylnb3xm5cmZBgQRD4539+iI9+tAK/P4LZLKLXAurRrImJCB5PLm53IVeuqLS1TTAysliQdurUOAcOrC4iHcOLL15ISD4ef7ycsrLEdSyTk/Ps2WOMOe7NmwEqKozt2rh2LbiMrBkFQVApLNQbOoqKLPT2qpw6FUVR1n7e3q8B76IiIWM9wUzcPxoaBG7fTm8fq1VLy7e4tjY1yZeaGoELF6IJv4vJpLFvn8CJE4uR59i2O3boOn19fYsEprlZpqBA48wZH29+s8BHP2rjW9/axOc/X/C6kr/75cE7087jbDeZJBrb5/NtRADTwAYBzBDrmQJei6xLurjfpGziIV53r9Wamk9uKnjhhUN88Ys7iUYjiKKMHgWMYrWKeL3KXT06O5ALFAIeQCdQ4+OhlL1lh4bm+NGPrsZ9T5JE/st/qUm6vywb9xtt2WJsh7iirJTjyBR791pxuWQmJvQ0YDynhrVAVe/Pc31zejrjcZHJZbyW6PLBg6l18QLYbBrBoLbq52zaBLOzSsIUscWisXu3wKlTy8sOZmdVjhwRuX49zOiofq4UFgo0NQlcuuQlNzfKn/95Hv/0T6X83u/lkZOThdb1NxDS6TyOOZlkixQmawLZ0ABMHRsE0GBkgwCup6zL/UwANU0jFAoxMzNDJBLB4XAs1PoZPe9nnqnjG99oQBDCWCwiYCIUUlDVIIoislg9YQJcQD6wiRs3RGprS1P+nK9+tSfhex/6UM3dKGR89PZOkpdnDMnyeo1tAgE9/ZgpbDaBpiYHVVVWzp6NcPVqFFU19uk+Fc25dGBEWh4y7yYWxbWnfysq4OzZ9PYtKNDSavxoaBBXjRZaLBr5+Rp37sTfzmbT2LFD4PTp5T+iKKrk5WkcPx6+K8qtUlWlEI36mZgI8md/lsePflTG+9+fe99Ei5KJG9+viJHCRPZ2giDE9Tyen58nGo1m5Hm8kQLOHBsE0GAYSQBjhGd2dpZwOIzdbsftdmPKhshanM++n7A03WuxWAxNeyfChz+8ne9+9zDz8/N4PKDXdZmJRCJ4PAogsbzOzQI4OX9ewWwuBQoAB8kus+7uO7z66q247xUW2njnO7ck3DcSUdm1yxi5g/7+WcO9dqMZMKHiYpnmZgdms0xnZ4jLl/UuxYkJ40WmM1iD4iISv/k0bWhaZg94u3aJa24iKS0V044eVleLKUv01NenJvlSXy8ktKBzOjWqqgTOnl1O/lwujTe/WeLYsSAQxmQKkJMTJBiM8IlP5HLq1FaeeCK5y88G1o577e1iTiZLPY+j0SihUGjNnserNYFsIDVsNIFkiGylgMPhMIFAIGNZl3Rxvz19qqpKIBAgHA4jy3LC7t5sRS5/9VcrcLlk3v3uNhwOCb8/ApiZnVUxmTQiET86EbSgE70wqqqhqjI6YbSgp4rD6LIyAfQO1UV89as9PPJI2bLXYt3d73tfGT/84bWE8xsfN0YTcH5eZf9+Cz09xkUCR0fnSbe5ZPduKzabxKlTQUZGVs5lclLBajXWak1RNIyUiTFqbpl074Iub7IWOBykbd+2dWvqos8ej5ZSbaEu9xL/IcLl0igrg76+5eRv06YogjDPsWNhQMXpFJEkePObXXz96/k4nW9cMfz7GdnoPN5IAWeODQJoAJaSvkwJoNGyLunifkkBx6KfwWBwQdgzWYNHNuf91reW8pOfPMp//I+vYTKxoJkViZjQL6EoeqPIPDoZtBGNgtksEQ5H0MmFGT1d7L67bQgIAiH+/d9v0ts7Tn39phXf++GHy2loyOP06fiyMoODs2zfXsilS2nobiSA3W5sKOzGjRBut5W5ueS/idkssH+/nclJhb6+CBA7ZvGxebPE9evG5W2NJoDpSKAkgihm3gE8MpLe98rNhbw8gaoqkakpDUkCSRIQhEWpHE3T/3vpZaZp4HZr5OVp+HwaMzMak5OJU+s1NQInTiT/bocOCXR0xCd/DodGeTlcuBBr0JoHFJzOKJOTCi6XTDCokZMjUVJi4pvf3ER9/fpo+GWCBzEFvFZk2nkc2/ZebEQA08MGATQYMQKYbjfXvbIuTqcTk8n0urXWv54EMF0x5/VAc3Mhr7zyGG960/8jGo3cTc9FsdslAoEYCRTQL6kQoBGNWu/+v4YeAdQAK3pUTEZvJAEI87GP9fCXf3mQ7dslYHnU97d/eydPPdWecG7FxRKXLmX+HW/d8mGkdIum6VZm587FX8hzciR277YxMBDm+PHUw2Y5OcIyUe1MEYkYe677/ZmPV1YmcuPG2vcvLha4enX5vaOwUGPzZr22UBAEwmGRQEBgdlZgclJgelpgeloneOmcT3V1Gh0dy7+zKGoUFkJxsa4zGYlojI2pFBZqdHcnH6+mRqCvLxo3BW02RykoiHLhQgid/OlSTTabQCCg2/rNz0NursznPpfDRz+6kep9ULCUFMYQixIujRTGSGHM53hiYoJXX32VgwcP4vV6KS8vz2geMzMzPPPMM/zLv/wLwWCQxsZGnn/+eRoaGlLaf3h4mKeffpqf/vSnqKrKo48+ygsvvMC2bdsWtpmamuLb3/42P/rRjxgYGCASiVBbW8vTTz/Nk08+mdH804Ggvd6hnjcAwuHwAmGan5/H7/eTm5tacbGmaQvpXk3TsFqt2Gy21+0pUNM0pqencTgcWCzr+9R8b7rXbrenHP1UVZWZmRmcTudCnUk2MDQ0y0MP/TvT07FCLzOSBIoSZpHsWdHJoIrTKeDz6WljPUWsoJPBWCOJGT3ipVBXl4fTKbN7t5s9e9zU17vYv9+NoqhUVf0jU1Px698KC61MTMiG1LKVlm5KYpmWPlpa8mlrWz7eli0mysrMnD4dSkn89140NeXR2WncHKuqTFy+bNyzsMViWnCZWCsOHpQ5eXJt+xYWCjQ0SPj9ApGITuxu3xbw+Va/p+zYoTI4mN7D1u7dKn19q2+3aZOGyaRQWqqLRQ8MqCtkbgoKQJbVJZ3EKvr1EUYQItjtyj11huLdbmIVk0lClqG11c7f/m0BbveDle6N3f9sNtsb2rc9U8SOU2xt+PGPf8wHPvABQNeBLS4u5l3veheHDh2isbGRioqKlNdTVVVpbW3l7NmzPPPMM+Tn5/PSSy9x8+ZNTp06xfbt25Pu7/P52L9/P16vl09/+tPIsswLL7yApmn09vaSl6frvv7rv/4rTzzxBL/8y7/Mo48+iizLvPzyy7zyyis8++yzPPfcc2s+PulggwAagFiLO+hk0OfzkZOTs2rUKhqN4vf7URRloWD29b7wXw8CeG/a0263p63nt14EEOD2bT8PPfRTbt2aR1+gYsdJQ1+suPva0mad2Ht6I4m+Xyw1PA9odwmvxPbtDux2iVBIxWQSqKlxcPXqFK+9dhOdPK5EQ0Mxp097M/5uuu+xcQV2hw65OXFC/x1377ZisUj09ATRtLU/4LS05NLWZlwKeMsWmRs3jGms0usTMx+rpcVEW9vqt2aHQ/exdbtF5ucFbt4UuXNH4MABgVOn0v/cxkaF7u7U70H19VrKVnEHDqjL5iTLGvX14PUqDA5qWCwaW7aoDA2F0K+VKIv1shoul4jXq6KXWaiAhtstL5QYbN9u5k//NJdf+iVj9DHXG4qiEAwG74t14H5GjABardYFEjg9PU1PTw8vvvgiU1NTjI+Pc/PmTQAKCgp45pln+OxnP7vq2D/84Q/5tV/7NV5++WXe/e53AzAxMUFNTQ1ve9vb+Id/+Iek+3/lK1/hc5/7HCdOnODAgQMADA4Osnv3bp555hn+5E/+BIBr164hSdKKaOWb3/xm2tvbmZycxG7P/nm8kQI2GKmkUJdGuiRJwuVyrUtnbypY7xSwUene9Zx3SYmDY8fewmOP/YQrV4LAPIJgvktqYuQzVstmRY/2LX1PQ48ExizL9FRxIAClpdLdtLJCf7+PaFRjbi56147q/7P35dFy1GXaT+1dvdx7s5EEGQwkJEQMWYAEPgITgo4bizoQdZyPM8M4+J3xOMiB4MyB+RwV9egcQPHAKPohIrhABnVGMwPOsAqShBCIOCSsEwjZCElu711dy/fHr9/uX1dXVVfv3Tf1nHPPTfpWVf9q+9VTz/u+z0tZ/SbqH5AWNK07+XulkjfBbBf79pWwcuV0TE7aofL7wsA0u+vb0s0QcColdqUIxE9BnDEDmD9fgiyL2LePhXl///v64ynLwM6drX9nKgU8/3wrxMMOne+4cmVj6Nc0HTz9dAmAgZkzDUhSGS+9RPcFDwmJBOraMIqiiFhMQDrtIBaTcOmlCdxyyzRI0ugbWxwNOYCdwCtXctq0aTj//PPxrW99C9deey0+9rGPYf/+/diyZQu2bNmCRYsWhdr2hg0bMGfOnCr5AxiBXLduHe6++26Uy+XAZ/WGDRuwcuXKKvkDgEWLFuH888/HvffeWyWA8+bN81z/4osvxkMPPYTXXnsNp5xySqgxd4KIAHYZQUSElK5i5QnRbtuyXqMf/YDDVve2in4R17lz43j44ffhrLN+jT17SnAcA4qicRYgtC9FAAJUVYFhSGDkhyeDtAwAKHjzTeDNNxkJUxRg8eIUxsdlZLMmJEmpdLwQwVQQAexhKeDpp03Isl7Jj7HAFJLW8cILk5DlVMdedmNjEpYsSeLVVwt48UUDR45077xks2TB0x0YRvfGlkwKeOutzrdz4AD7reusk0UiIWL/fhEvv8zy9YKweDHw+9+3/p1LlgBPPhl++RUrbDzzTHPCNTHh4OWXbTD1mn7oOmU5fMUikM06kKRy5RonA3YLgIlcTqp8ZkGSBMiygHxewDHHiPiXf5mFpUuHv8ijGaJgXOfIZDIYq5S/z549GxdccAEuuOCC0Otv27YNK1asaPj8jDPOwO23344XX3zRl5jZto3t27fjU5/6lOf6Dz74IHK5HBIJf+uuffv2AWCksx+ICGAXwBM4PwI4KFuXdtBLAthqdW9YDIJEz56t46mnPoSlS/8Vhw+XUS7nEYvpKBb5sdBDC6jlMxEBpOWIDJoQxSKWLp2JeFzGnj0lbN/Oh3WFyvpE7iiP0IRp2pWKW14dIyJocT/ByOVMnHqqhu3b20tiO+44Dccfr+G557J44glmQrd0qYgjR7qn2rH8y+EkgJ2aNwPAggUi5syRMD4uYMcOEc8+29o2x8fbG0MrxFUUHRw65Dd/2WBpDSaYD18Zb71lVj6na5f6JVuQJAfZLCN8lhWDppkolWwAMlIptaL82QBEaJoK22a2RWefHcfPfz4dsVgULj2a4Fct7TgOstlslQC2g71792LNmjUNn8+dOxcAsGfPHl8CeOjQIRiGUV3Wb/2TTjrJd/3vf//7OPfcczG7G22AQiAigF2GmwAO2talXfSCAJbLZeRyuZ6R4H4ol24cc4yOZ565EKec8kvk8w4MowhZlmCavMInoVCwEY87yOeJ+FlgZE4CyxVkqodtO5iczGPnTiCft3DMMSpOOCEOURTw6qt57N/vHkGN2DF7GsoxpOMgoj4XkSeFNuofygzjbRROLl4cQyIhY+vWbCU3soZUqrvnZP/+MkQx1jUD52Kxe+PTtNbJlyAAJ58sYvp0Cbt2MXX3t79t/74g9bAVLFwIvPhi+OVXrkSlhy8L4VKhBil6lKOnaQ7eekvkPqMXIPaZKAqwLFoHAASUSgpEsQzbtpDJ0N9USJKDUsmBLIv4+7+fjr//+/Yf9MOMYYsIDSv8fACpE4jjOCiFrMaiVqLFYtEz953+XgioWqO/tbO+bdv45Cc/iXQ6jW9/+9uhxtwNDD8TGTHQRUkhzkHburSDbo/RHe4dFRIcFnPnxvHrX6/Fe9/7G5imWCkIyoERLyJ3QD4vgIV7yT+QlAt6MAKAgldfLWLuXA0nnZTCc89lcOBALS9vbExAOl2sbFOq2w6bW8hyBqjlHtaHi9n3ESmkz2tkcOfOPMIobLIsYMWKJCYny3jhBf/Et1Kpu4qdaTqYPVvE/v3dYYDlcqO3XbtgfaCbb0iSgHe9S8LYmIRXXhHxwgu1e27lyvbvvxkzgJdeam89f5BvJcvhFIRSpfUaI3k18KSVXYfs+UtKNV0DZTDiCKiqhmKRXlhq6jYj95TqwL7HsoCZMxXcd98MnHba6Id83YhCwOEQdJwymQzGK2+wjz76KNauXRtqmzt27MDChQuh67onaaS0LV3XfbdBf2tn/c9+9rN44IEH8KMf/QhLliwJNeZuYOo8hQcInjDRxZmvZEfruo5YLDYSxI/QLSWtV+FePwxCASScddZs3HTTabjyyqcrHoESaqTORs0GhoWE2Q89xETUd8wwMD6uo1w2sHr1OJ55Jot8nql8rOKRX5blSDEIkGUFpilxf+NzqXjCRCoMjY+IooQDBwBJEisGyYCb0EybJuPkk3W89loRmzc3rzxmHT2607KOMGNG9wggAGhadzp4NHuvefe7GenbsUNsKN4gMBLZHhYsELBpU2vrJBI2nn2WhWtrhUW1AqPaCwLA0hQslEpE6MTK30zUXjAUACKSSQ3ZLG2DXkoEUNccXbdRKPDXJbsGUykJmYzD3c+sC8Ty5XH8+tfTkUgMZ+pMhP4gbAh48eLFuPPOO0Ntc86cOQBYqHaPR5uavXv3AgCOPfZY321Mnz4dmqZVlw27/he/+EX88z//M77+9a/jk5/8ZKjxdgsRAewiyNYFACRJQjKZHMly/m4QqV6He/0wyLfoK65YjKefPoC77nodRKYYCdTAHnJMAWUETQDrBEKegTwU7NjBXiDGxwtYunQMkiRj164i3njDzVJE8AUlrHiDr6SkB7mKRhXOcS1LpJDIX/2xTCRYiPrwYQu/+10W9SQSruVr/969u4SxsUSD51sn6LbZv6oKXQkFs4dS/XbmzhVw4okydu0S8fzzze+BTj0EG0HpBnzlOP3YkCSbM68mhZheUhTUFGSmEFsWvTAAbF/pWqdtMrKXzdL2tLr12TVTRqFABVFEGhmRzGRkADYch52XclnEunUa/vmfJzCkadNdwdHUCaQXoBx7CgHPnj0bl112WUvbWLZsGR5//PGGRg6bNm1CIpHAwoULfdcVRRFLlizBli1bGv62adMmzJ8/v6EA5NZbb8UXv/hFXHXVVaFsarqNKXw79Q+2bSObzSJdecIJggBFUUaS/BHaJVJ0LDKZDERRxNjYGBKJxJTvY0z5Jl//+qk49dQ4agUXEsjnD1BgmgIkyQZ1C2G//QskJidN/O53h/Db3x7AvHka5sxRsGBBrLJNyvlzg0JnpNAIYA9lWoeS6qnakkLVcmV5B5WUFUiSgFRKhKqKyOXMimID1IeNAZ481n8/G4MklSr7yueM8WHE1q43We4u0e+WdSTdNrEYsGqVjFNP1bBvn4YnnpCxe3e4e6B5MQYR9xJYb+k0gMMA3sb27QcA7AXwJoDXAfxP5fdeAG9VlksDyFR+csjn6ToSXNu3ue9grHR8XANTc6kgiXpcF0HdcARBg6rGUMtFpcpf0htMsOuC2iOy8K+u08uMDUliSqllSfjyl5P45jfjKBaLyOfzyOVyKBaLMAwDlmVFodOjDH5dtjKZTEvNA7xwySWXYP/+/bj//vurnx08eBD33XcfLrzwwjoLmNdffx07duxoWH/Lli3Yyhle7ty5Ew8//DAuvfTSumV/9rOf4corr8Sf//mf48Ybb2x7zJ0gMoLuAvL5PNLpNHRdh6ZpSKfTkGU5sNx7mJHNZmHbdkvVVN0wc+4U6XS6mm/ZT5imiXw+D9M0oaoqJEnFwoUb8NZbvF0JGdi6Jyd60KqoD+16Y9GiJIpFAbt2kRJIag0LldVCvn6g/D8+969RAfSH33RB6wkeyzpIJuWKIuS3HYH77ffv2mdLl6bw3HP0d/f3to5jj41jz57Op8I/+ZMY0mkRf/iDiEzG7zgS4bVRT6RtiKINxxHgOKTA8Wocf66B+mMoVEyoSYmjnDq6JvjzSt/JV5QTZG55oKYeCpXCG8o/JZBCCJDK3XguNND1KYoSRJF6vNbOaSymolgkYigilXJQLku4997pWLNGq7bXpJZg1BaMwPeJpX+PmpJWKpVgmubIPjf6hVKpBMuyGoySX3rpJXzwgx/E3r172z73tm1j9erVeP7557F+/fpqJ5Ddu3djy5YtdRW8a9aswWOPPVZ3HWazWSxfvhyZTAbXXHMNZFnGTTfdVO0EMqOSbLt582acc845mJiYwNe//vUG0nr22WfXtY7rFaIQcBegaRrGx8erKtcgc9G6gVbH7w73xuPxkZt824G7fzNv6P3EExdhyZJ/QanEkyp6ePNVuaSSUeEILavAi4jt3JlFLCbiuOM07N5topGwOZBli/PxcxMkFn5rTMznwYiGrisoFPyIWsPRQE3ZCSqE8FP7SIHilSj+++pJz86dZW4ZUpF4wsiv6/V5/f8LBdqee2zN7wNRBObOFVEsCnjuOarUduBN9ILVTlkGDMMdVqd1eSsfytmsEe9YTKqEselao7Cr29ybFFoNqZSATAao5el52QXFAZShqjaKRR31Vex8tw4aV3VvULM7YoqvbVuVAg9aToEoAuUyqdEWJiYkiKKEJ56YiXnz2COK7xNLD8ugPrEAqoSQSCFtI8Jow+/ZlMlkOn75F0URGzduxPr163HLLbdUewHfddddDfYtXtdTMpnEI488gquuugo33HBDXS/gGVyl1QsvvIByuYyDBw/i8ssvb9juD37wg74QwEgB7AKony8hw2ZUpLqdqNQnUMXuxMRE4HJui5tO5fdO0c/jzvs6+hX6/Od/7saFF/4GkqRxhIxPlPeCCUCv/nvOHB3JpI6XX855LGuhVlwSBJ6MAPUESIE/qWsXREzCwEvl8yJhQdMU+SpSWNuLBNK/3QSTIRZrvYOHLDPfv3xeqJxfEfG4WOmO4aCRBLqrZnkwIsfGwZN6d5U2EW1+O6TClbltEbnnjymv+gWdc7o++ZeTMB1iyPCcTzsIug40SBJTO4kU6rqMk09W8B//Mb0SEm4NXiohn1vHq4SSJA0VIfRTtiLUg6xU3BW1jz32GK677jo8++yzgxjWSCJSAHsAQRDqZOFRQzMFsN/VvcMEnvQqioJUKuWb6/me9xyHL395Ba6/fis0TUepxKtcBuq7gRBksHwq1kt43z4TQAazZqk48cQEZFnE/v1GhRDSQ98rtMzDTYoYJMmBZRVRUwj5HL5OQOHK+u2IIisiaSwWaf0dlF2j7rGGmc681EmnQkB4guy1HkMsJkCSBORyqBS21NarqVvu73ETQncOJft3MAkllYxIf60IRxQd2DYpbRT+LXtvprIvrNKbV5pJZTTBCKA73Ou1HVqfkcSaqTap016gdaxK1w8AkCFJwAUX6Pj+98fbnksEQaiqfgS3Ssi/rKBCr7YAACAASURBVPPLDzp0HGkxnSGdTndkAn00IiKAPcCoh4CDwId7Y7EYdF0fGuLXS+LtJr1hfR2vuWYpnn32bfzrv75RUUeAGjkowTvvTwIjiGQSDbz1loG33uL9AGUcc4yGl1+eRKOtTDjUWm7xBSFAfU6a2qbhMhGcmgrlfW7cCqBfCLb+c3Z/ucOVMdSHI8PDMMJ3KvEmaYyM13eBCQJfLFML0yeTQiVXkieKRMqI2BESYJXkQsA5ovNKqmxNCWTVvO6SYwXsOJbQSP7oRYMPAdeuSVHUYdtU3FMPVv1O+0kvCDUVWpIcXHfdGK65pvv5u0Ts+NCx4zhVQmhZlmfomFcJh2WOi8DOn1dRIW8CHSEcIgLYBbgnh1EngF7jH4WOJr067uVyGfl8HpZltZXj+KMfnYcVK+7Fq6/mUVM/GPGZmHBw5IjXtvgHZSOxS6dNpNMmarewA0HIIxZTIUkiymWhkn8YBkQMSqipS2yC7YxPE3nhQ5Fey7R3zhipALdt1j4sXEi7/jt1XazkOzaum0yKMAxw5N0LTHESRYFTE5srfm7UF8oEIQGm0LFwbyIhIZfjizyINFLY108N5BFHzQuwRvYSCRu5HN+71w0ZmiajVOILOwAinpJUhmmSXYw7/KxgbEzEzTeP4dJL/U12uwk+n5DAh44pl5CfS3hC2KvQsV91a4RG+HUBGdW0q0FhuJ7gUwijTgCB2j4creFevoMJs6UYa4v0CoKAhx66GCeeeDdiMQrzMduVI0dMjI2JSKe9wsjsATU2Zvn8ve5b4DgSCgUDlL8lCDYch/LG+BCvX4iT8ra8C1DaQ604hBUp0Gedg1dtaoghHNmpR0CHp5CkTAdQ7lp7unorHYH7ISJFxJoRvFyu9X2uQQIjfwB72SAVj5G9nFf6aRUxABZKJSo64RVC9sPCzESA6/dvzhwZ3/nOGNauHWxnD6/QsVslLJfLlXaLteV5tfBomBOHAX7P1nQ6HRHAFhERwC6BV5+mggIIsEKHQqEwlOFeL3SzgwkVeQBAPB6Hpmkd7fu0aTH82799EO9976+gqiIMg7wAgWxWgSSVYVlkmFuPdNqCrpsoFMI8JCkv0Ibj8BYdBL4ilScWfE5ZGf4Vwu3CgmG4981d/OF1fN15dM1QRK0gpJ/wImBu0u3+AWr7ZEOWHZgmb/nCtwgkUNi3HdQsXhTFQbnMvxy0uk3qXuNAVdVK7h9VBdcgCGrF0oY/t4zYzp4t4V/+ZQJLlvgVRA0WgiDUvfCFCR13akUzzPPrsMBPKY0UwNYREcAegFfQRvGGplytXC43tOHeXsHt6RePx7tmYr169bH4x388DV/4wlbUikBY3iI7vFnUzHHrv7NQELBwoYZdu8oolZqRGwn+OYY84ePhDklSIYDssWx7sG3apjuvr90QsMDllQH1RNJ9vfKEi78n2f/HxhjZzeXAFSbAtVz9OrXP2PFmRTVuuxe/sGkjPAXNOlDYN2hOcVcAkwJbb9nChKwY/ELRwdsn43AWYmYvM3CNS4SiyBWSWftcEBQ4joBjjpHwX/81DccfPzrzSlDo2M+Kxq0SBs0lo/q8GBZks1nMmjVr0MMYKYzO3TfkcCuAowi+0AEYvT7GnSiAQZ5+3cTnP38aHntsD/7rv/aCETTWcoM9M/jiD7LrqHkCvvhiBu961zj27DFx5EizkB8jeomEiVwubHWsl1pYDlEIEv76UBS74vnWOVhemTu0CLBj15oK2FmbOuaTZ4WvI2kDOhj5o9y8Wig/kXCQy/E5f2EsW8KQSYAvRKqFnfnevl6QIctChfwxKIoMyxJg2wLe+U4Fjz02DdOmjX4jKj50TPOFl0rIh46DrGhGZa4dFIKesdlsFieeeGK/hzTSiAhgDzCKCiDva8dCOkaoKtdhQrsEMIynXzfxr//6ISxYcDf27qXWaLxKJ4E9mGOor8plhFCWx3DCCSreekvC7t3NjetyOacSXg5bHMGDLR+P28hm2+kO0ri9crlZUUi3QNYkowKxUkjhDheTsTPZqjQSvOAcvUYIQhyO41XlC5Dqy2xl6CWEKoV1ULs3f6gAeCNyBYLA8hZtG1i0SMJvfzsNmjb65M8P7tAxEGxFQ8qgbdsQRXGknhuDgl8ruMgGpjVEBLAHcBdRDDPc1b2UQ2EYxkiMvxO04unXTUiSiMcf/yiWLPkpCgV6wPIkUESNBBIRYLfq9u2HAQBz58bwnvfMQrFoo1Cw8cILaeTz3oSHKVO1kHOryGapKrTTY+NAUQSUy3xRCsGdE0f/bvcaLKG7OYxBIHUHcBy+/7L7hw8du7t72CiVvFQ1UoC7IS8KSCR05HL0PRTiB9ydPeoVX1IBm8WoNQAmBEGqHAfW+UaWJZTLwKmnKnjgAW1Kkz8/eFnRuEkhwFJQTNOMrGh8EPRMinIAW0dEALsELxl/mAlUkJmzVYllDfP4vRBWeW3X06+bOO64JL773bPwl3/5hIugEUSwxHxqvVWPvXuLyGb34fjjx/GHP2Qqn/LdIohg8T5zVNzRzgOYCEhnpIqRPyI9fD5gGHgVkUiuv/FEq36siYQAyxI8ffxYJbaXDyG4z/jf9FPrg8tul/A5f+EQQ7iQbhCIRFIoX0XN7qUZqaNrMmifBDDyR51A6BhIkGUR5TKweLGKBx7QEHEYBq/QcTabhSzLVTXQy4pmWLuY9AvNQsARAWwNEQHsAYadAPIhT6/q3qk8sfCefoOubH7f+47Dpz+9CLfdthO1MK+7T7A/CcxkynjllcNYvnwGtm2bhHcXD0YKFUWqkC8DjW3CwqI7JLCGVnP1vEyig9atz+EMCpW2nwOooh3bmXBIoNGoOQhU5UuKI6l6AKnKNbubMOdeQ62nsBcE8F09AIHzSpQhCCy3dd48FY89Ng7HmfpRhU4hiiJUtfYiSPmEQV1MjkYrGvd+Oo4ThYDbQEQAe4BhJYBe4V6vkOewjr8ZghTAbnn6dROCIOBLXzoNzz13GE88cQC1hy0/rmASWCxa+P3vD2LlylnYvPmI17cAkCpVn7Q+dZUgA2i+BRpvCeOFbpNAUj57ka9XhqrqMIz2uoOEQ68euCq8yR/f1YPCzvXh23pIlW21egz0yjbr909VRcRiCizLhuMwNZWFL/nlmLLrOAKOPVbBE0+MQ9Na77V8NMKrqYAsy1EXkwqiEHB3ERHALmGYQ8Dt9u4dlvF3AsdxUCqVqpXN3fD06zY2brwAJ510Dw4cKKH20OUJVjAJNE0bmzfvx5lnzsZTT3mRQDdIHQL8pwCWv0XhVHa4FC7M6QSs2yooNN39680wep1v1k3iyps/q+A9AmudPepJXvAt2h75i8fjyOcN1K41tbItE4ZRrqhQ3upgPK4gn2fXzMyZCh5/fAKplFgZ6+jPJ71C2GMTxoomqItJMyuaUYFfCDhSAFtDRAB7gGEigM3CvV6gSWYYxt8K3Me9l55+3QAdY02T8ctffgCrV/8ClsVy9QRBhOO4zXP9SSAAPPXUfsyZo2PfvjCkpBkJrD9O7JDy2yWzYlrXKz8vLDop9mgGv2rXbqEZuRJdP24PQsrbNLl/awDyHY6L8v5aI3+ynKgQOL0yPgP1reQE1Houu4+rVilEEjA+ruChh8Yxa9bw3G9TFZ1Y0Yxa6DhSALuLiAD2CIMmUGHDvVMJNInZto1SqdRzT79uYvnyY/CP/3ga/uEfngYgwHGKEATdpfA0J4H79lGSV5iuIUQCHbjz5cKB7/XrlZ/nDdYJhSdD9Jv/zG97rd5TJmr2JWHBV/HyJtNe463139V1VPoJk2Ln7v8bBirYOe7kgUxFGWH3WQMgI5USkMmUUTOP9oJ3aJhtg+3n3LkK7r9/DPPmTe35ppvotoesXxcTIoSWZXla0XTSxaQf8DtOxWIRhmFECmCLiAhgl+CVuzEIAsiHe0VRbLvCddAEth3QeLPZLBzHGTkj6/XrT8P997+CbduOABAhSWWYppuYEQmMN26gDl5dQLxAla/uApSwoPyv8EoP6xzhR4piAX9rBJ3aerU0mKjV//ZC2O+vr9AN6iccHtTftxMQSfPbPtkBkQVRGYBUIX/Ntuut/NGYVVXBnXemcMop3o+WUbkXpxr40HGQFY1XPiGvEg7D+fML/2qaBk0bbE/pUUNEAHuEQRCodsK9fhg1AmhZVjXPj4jvsCuegiBU/b8I99//QSxadA8MQ4Bp2hgb86pQlcBChN0igUBnJJBCyd0I91GVcjjULtGgnsGd2qj4gapfu4U4Oh1rKpVAJlPv8zc2JiCd5lvC8dUY1NM3SC30VhRZAUqtgEeSFNx6axJnneV9DUUGx83Rz+PjFzp2E8KwXUz6Ab9nUiaTQTKZ7OtYpgKiBI0eoZ8EyrIsZDIZZLNZiKKI8fFxxOPxjm7OUSGA1MJtcnKy6l+o6/rQkz8/zJ2bxOc/vwz0UE2nS5Akr4czKYHN0IqNCJHAdkAhz07Rm2rg3phCd/Phx+dktgZRFBGLaRCEcWQyCpgySd1DikinC2Ckz63ehSN/uk4FH9ynAuA4GuicK4qCa65J4GMfUxs3EaEphmWuJVKoqipisRgSiQTi8ThisVhVOTQMA8ViEblcDrlcrhp+tSyrL/vh9VxLp9NIpVLRC0aLiBTALmEQIeBuhXuDtj/McHv6aZqGycnJQQ8rNPyukeuuW4Wf/vRFvPRSHkDNmLvxdhUhSSVYVjOVr19KoImaD10nMNBqKLg5qDNFN9HN8cXRnKxTRw4BySS7P/N5E7ZtolhUwFThsMdeBDvGQYRfQCymoVBwkz/y/7MBSNB1Ce99bwzXXReF36YivLqYDMqKxu+ZlM1mIwWwDUQEsEfwCu91E90M93phmN+keE8/WZarnn40OQw7cQ2DH//4/TjrrA0wTVKuSL2pV7IsCzjxRA2vvtqMPLRCAr1MqcPCbHM9N1oLBTdHL7wAu3N/M5Nuqlau9/mrt4Cp9eXNZvktUC5iK+RPQzPyB2goFt2kmXX7KJXYdZhKSZg3T8Hdd+uhvnmY55VBottFIL1EkBUNHzruhRWNXxoBVQCPwvEbJkQEsEfolQLYr+reXhPYdjAKnn6twu8aWbJkFj72sRNxzz2vota1g6oveQNnB7t2TeKP//g4vP56AabpwDRtlMsODh0quXq6tlIYQtYk7UwR7ZJHHlRc0q17qIR6b71OQSHbVkgXefxRRTE7f6oqV/KseK+/sGi1owvZuDQnf42KqYSxMQ3pNGs3mEox9e+RR8KRvwhTF3w+IaGZFU27XUy8lotyANtDRAC7hF6HgCnXjaxNet2/dthyAMN4+g2T/2IY+J07Otff+Mbp+Pd//x8cOmQDECHLgGnmUaviZOtbloXHHnsdy5fPwTPPHPbaIvfbgigqsG0ikUH+fdQxpJ2wbrvkkUcJ3Q0Fy+he2zZqgcYsYxRFRLlMxxSoWcBQSJ0qn93fLyOXI3W3nXu51V7BzQpN3ORPApl0y7JYIX+ApkmwLAm/+10CihJOzRmV+3KQGOWXWTf8rGiCQsfNrGj8FMCoDVx7iAhgF8GTpm4RKMdxqrluvQr3Bn33oMETX0mSRsLTrxOUy2XkcjnYtg1d1/HDH74PF130KziOCNOk81GGW8lzHAcvvPAWFi2ajp073WXD9bYotm2iRiLdcJ9zvpcs+7umSSiVwhg/k0egN2SZ3ye/a60Vlc0Nt3cfhVjR8LmioELg3CCl1eZ+s9w3RnIZUSq3yStFUYNtt1v5m0J9RW8zJNCc/MUACBgbkytkj1RJDabJyJ8oinAcEb/5TSIyeu4ShmGu7TXCdDFxW9G4VULbtj0jXlEOYHuICGCP0A0CyId7FUXpq5nzoN9E3cQ3rKffsCmXQeDH6s5rpHP93vfOw3nnvQMPPfQ6arcrdWior7gsFEwcPJjGccfFsXt3UCcJG4kEkMuFGiUYCVRBalep1GrnDm+SYJrNijLou2vVpp5bF8GFutuzg2mdwHXj/pBh2+12KomjNcPoIPJHxSXkQWhXlb6aIkj/Zw/jO++MY8mS0ay0jzA8aLWLCcCiQTR3FgoFTExMRF1A2kT0+tYjdBKOZNV9+aq1STKZ7Luv3SCJlGVZyGazyGazkCQJ4+PjoVXPUSKAQC2vcXJyEuVyGfF4vIHo33ffh5BMug2CSZmpx9tvFyEIBqZPD7LjEJDLFSEIYY+TAEYe2gnFdlJ8QUTT4P7d+GPb/P+DYKB7U143CKDW5naoojnsuu4KYwmyHIMgxMBX83pb+dSTP0WRcP31Oi64oD0VftAvlsOKUSoC6TUodKyqKnRdr7OiIRiGga1bt+Kd73wnTjvtNDz99NN45ZVXsHXr1jqy2AqOHDmCK664ArNmzUIymcTatWuxbdu20Ou/+eabWLduHaZNm4bx8XF8+MMfxmuvvdaw3FVXXYUVK1ZgxowZiMfjWLRoEa699lq8/fbbbY27EwjOKD0thxzlcrlaOGEYBrLZLCYmJkJXPA0y3OtGsVhEPp/H9OnT+/adblubeDzecp7jkSNHqjmCw458Po9ikYXwmvUq/uEPn8enP/0g6kO3DtgDunGdk0+ehl27jAYLDx5MOfMLBfuh1eX59TqBju7YuCTRHWPoODrLJ6SikHbQLJTLgzwBWR5frZqYRxzeRJ0nf+yhfMklMm69VW2rXVg2m4WqqlDVyCvQDcMwYBhGFMYMgOM4yOVy0DQNsizj4MGD+Pd//3c8/fTTePLJJ/Hyyy/Dtm1omoYVK1Zg5cqVuPjii3Heeec13bZt2zjnnHOwfft2XHvttZgxYwZuu+02vPHGG9i6dSsWLFgQuH42m8WKFSuQyWRw9dVXQ5Zl3HzzzXAcB88++2zdc/Scc87B6aefjgULFiAWi+GZZ57BHXfcgfnz52Pbtm19TXGKCGAXwRPAcrmMTCaD8fHxUMqdO9wbj8cHamZcKpWQy+Uwbdq0vhBQPvetE+I7OTkJWZaRSCR6MMrugCe6AJBMJkM9FFetugvPPfc26smUA8rbcmPFimPw7LOZikIWhH6QQPKOaxcCulPFm0B3CkFi6ISQplJJZDKtmHQzqGoShuG3HjtGoijBcag9XjPV049Y1x9rUVSwbJmEjRvFOncAd7uwoJfdiAD6wzAMlMvloZ63Bg2eALpJ0l/+5V/i7LPPxplnnonNmzdj06ZN2Lx5M9atW4evfvWrTbd977334uMf/zg2bNiAj370owCAgwcPYuHChfjABz6Ae+65J3D9b3zjG/i7v/s7bNmyBaeddhoAYOfOnXj3u9+Na6+9Fl/5ylcC17/ppptwzTXX4KGHHsKaNWuajrdbiHIAe4SwIeB+V/cOG9y5b8lksq5ybKrBNE3kcjlYlgVZlmGaZug3vp///CNYtOj7KJf54goBtWrZejzzzAH8r/91LJ588lCTLVM+YdhrrtXlAapAbl/1oorlTglgt6qAO1MjM5nWQ+OSpHLkT0bNM5COrQGgDNsug5E+qlT2QwzevX2pvZtQ/f8xx4h44IE4NE2s83zrpr3H0YxIh2mOoDB5NpvFzJkzcdZZZ+Gss86qfh7WymzDhg2YM2dOlfwBwMyZM7Fu3TrcfffdVWEmaP2VK1dWyR8ALFq0COeffz7uvffepgTwne98JwD0/dkX5QB2EfyF2YwAOo4DwzAwOTmJYrGIWCyG8fFxqKo6FBNmry1VSAWj3LdEIoFUKtXxDTCsOYD09pquNPYdGxtruXH5scem8JnPLIMglNF4mLxDgk8+uQfnnDMzxNbLaI1ctUOkOg3hFtH5lNWKYbIfOh1DAuGPhQqm0iWQSMTASBvlgxpghSBF1DwiCc3sc8jqpRn5U6DrwK9/rUPT2H7z7cLcOVqyLMO2bRiGgUKhgFwuh3w+j1KpploO4/05DBiGeX9UkclkPItAwqZfbdu2DStWrGj4/IwzzkA+n8eLL77ou65t29i+fTtOP/10z/VfeeUV5FwVd5Zl4eDBg9izZw8efPBBXH/99Vi9ejVWr14darzdQkQAewyvyc6ryKHT3r3dRi8JoGmaSKfTyOfzUBQF4+PjI2/oHAQi+qVSCbquVzuXtIOvfW0NZs3SPfoDe3nMMTz++G6cdFKz0BL51YU93w5SqXbOV6f5d924Hjt9y+40NcO9DwqI5LGfOGph/jKI+KbTJdQKYoLQLD/RTfL4cfCfyxAEAbfeGsNJJwXvM7UK0zQN8XgciUQCuq5D0zSIoli19jAMA7lcDoVCAYZhNHSMOFoRHYPm8FMAHcfpuAp47969mDt3bsPn9NmePXt81z106BAMw2hp/S1btuCYY47Bcccdh/e///2YN28eNm7c2Pb428XUjbUNGF5kZpTCvb0ggFTdXCqVeubpN0wKoG3byOVyvjY+/DFuxQX/rrsuwPvffx8mJjQcOcIrSSb8igteffUQJEmBZQW981EoMdy0kMkUwZSmVo93JybR3TaHbget9BUm70EZtXPDWvopilUJnfp1/6ipcGGJcyqlI5PxVzlZ2gEa/i4IUuW+YZ+LogzbFvDpT2u45JLWc/bc9h6kgMuyXO0yZBi1fQpjAhwhAuAfAiYCSM4KYUCVxcVi0TMiQ3+nfG0v0N9aWf+UU07Bf/7nfyKXy+GJJ57ALbfcgj/90z/Fxo0b+5r7HxHALsIvBDxM1b2toptm1rlcDo7jhPb0awfD0MLO3bIukUh0NbS/Zs3xWLv2j/DQQ69XigmIjLjbxdVgWQ5U1YRlyQ1/qweFBcNOQtRirRWQqXK7x6PTa7LT60MEO05i9Scel5DPsxzFRMJBoeBUDLctsHNCilzNvJmlzYU5BhrCEUC1klvoR/7ESm/p+uPHupjUPiej71WrFHzjG62lKTSD2++NPN/CmAC32z92FNDKS+DRiqBnERVcAsCjjz6KtWvXhtrmjh07sHDhQui67kkayaVB1/3bHdLfWlk/lUpVx3jhhRdi6dKl+OQnP4mf/exn+LM/+7NQY+8GIgLYI9DNTOHeQZg5d4JuTUbDVt3ca/At6zRNg67rPXlw/exnF+Md77gN2WwOghBD/dxowKsoxDAob6yZ/xyRiDDjZtYigiCjtXcFMpduB52qgAb8Ca4EvsVb/XGiTiD0GXUFAfKc77a/wTZV5baCZq3bCCJEUQis+JYkrcF8WxCAcpksYgBFEVAqiZg5U8KvftV4DXUTfGcInhRGBSYRvNCsCIQUwMWLF+POO+8Mtc05c+YAYKFarzDv3r17AQDHHnus7zamT58OTdOqy7a6PgB85CMfgSiK2Lx5c0QARxX8hUkX6yiEe73QaQjY7ekX1uqkUwwqBOwO74cJb3dyjFMpDV/5yjlYv/5RJJMOsln3ddXYKaTxb0HXYhnNq0gJFhyHQputgL6jHQQdMyKvPInjW79Rf2O+zRuRSQvNw7vtXset+PcBbPxhSW6sUv3rDV2Po1BoDDPruoZ8vvYdpilDlkX8x3/Uij66iTCdfCh0DNRUQjcpJPBkUJKkKqkcRYzquAeNcrmMQqFQ7QU8e/ZsXHbZZS1tY9myZXj88ccblNhNmzYhkUhg4cKFvuuKooglS5Zgy5YtDX/btGkT5s+f39Tep1QqwbbtvqvcU1dTHxD46l4A1SKPYanuDYtOyEm5XMbk5CQKhUJddfNUBe1vsViErusYHx/vi5nnZz97Ok44YQzZbAmi2FgUoqp+544KPpqhlaKQdqpriXy5QblyMhhBVMBIF/9jo2aDIkGWeQJKuYzlyrhKYGHXImpVs3QMvIyRw4y7H0gg3NiaeRvqnuQvkagnf/G4CscR8J3vxLBwYXdV+nZfykgh9CowUVW1WmBSKpWQz+eRz+dHssBkVMY5SPgpgNlsFrIsB4Zpm+GSSy7B/v37cf/991c/O3jwIO677z5ceOGFdfP566+/jh07djSsv2XLFmzdurX62c6dO/Hwww/j0ksvrX5GrhdufP/73wfATKL7icgIuosg4kfebpZlQVGUkTT3dBwHhw8fbmjBEwS3p188Hu+7rxF9/8TERM+/y72/iUSipfA2VUN3UhX8wgsHsWLFDwEAjkP9ehlEEZiYGMOhQ36qExUnBEGAIKgthHf5ohDB9Zvg1P3WdQ2FQqv9hQFWOdtuGLiTjiDtdAGhB0hYkkxdOJot3+xFgwydg+1ekkkV2Szw0Y9quPPO7ub9AbV7haxiug13LqFl1VTcUSgwyefzEEUx9Fx7NMLPLHvXrl0455xz8Pbbb7etoNm2jdWrV+P555/H+vXrq51Adu/ejS1btuCkk06qLrtmzRo89thjdbnm2WwWy5cvRyaTwTXXXANZlnHTTTdVO4HMmDEDAPCLX/wCf/u3f4tLL70UCxYsgGEYePzxx/Hzn/8ca9euxW9+85u2xt8uohBwF0EFCBTuTKfTI/tm18oE2euih1bQjxAwqbz5SuLXIPd38eKZ+MQnTsaPf7wDglCukEA2DtsGisU8VFWt5P+5YUKWJZhm0LgdOI4BRjR4ha32O5VSkclQYUcRrXbsKBSKYISnVdB67Zzv9gtBZNmG2bKPcwz1/XibIWzlb1BFsl9Yvt7uRZIUZLMOjjtOwQ9+MJpKPfWPJXLZSoEJhY4HiagIpDn85vVMJoNkMtnR8RNFERs3bsT69etxyy23oFAoYOXKlbjrrrvqyB8AzzSDZDKJRx55BFdddRVuuOEG2LaN8847DzfffHOV/AHAqaeeirVr1+KXv/xlNT9w4cKF+OpXv4qrr7667fG3i0gB7CKICNHFkclkAKAjf6JB4vDhw9WKZT/wnS2a9bPtBygPb9q0aT3ZvmVZyOVyME2z4/21LAuTk5Md2+FYlo3Zs7+NbJZsYOof4suXz8G2bUd81vbvJ1wPvjK42URLodnw0LQESqXWu2MwNa4dg2me0LazbqtopZexDu8OHW4E5RQK8G5XR0UuBGbLoigSnnpKx4IFvSnQIgVQCYy5tgAAIABJREFU1/WBFYERISSF0LbturDiIAtMyCKnVXP4ownFYhG2bTf0eX/qqadw5ZVX4g9/+ENEoltElAPYZbitYEaZXweN393ZIpVKIZlMDtyqoVfHnIo8JicnqyrvMOwvAEiSiO997/2V/9lwP/S3bduHs86a5bM2Vaa6jxlth35MhLduCUNe6lEq5dBeQKL1froM7s4ZYdEOeWm1bzAVrzTbZtC+exFO5kkYj0ugvEpVFSFJIr74xVjPyN+wgFRC6mASj8cRj8ehaVpgBxPq8d7ruTwiL+2BuoBEx691RCHgLsJ9AU5FAthPT79O0M2QCnk4WpY1tB6OH/nIIpx22mZs3XoAjNzUk4hnntmDBQtm4OWX0z5bKKJmVuyHMBYy/PZazdFrRwG0UGtr1ipktJ7LJ6P18LEK8v5rjmYFHQA7R0FknBREqeLxR8uzquJ8HhAEC4qiwjCAlStVfOYzvX0UDOM8yNvQEPiKYwob8yqhV9VxNzCMx2fY4DenUwg4QuuICGAPMeoE0I1R8PTrJjGzbRuFQqHauaSTYg0vdLvbyv33fwTz598O06QK11oouFSyUCwWMDamIJ3mCQZV4hKhaKZohrGQIbSao1cCK85olZS1rjgytKsAtkoAwxJbIeS2dfChX+bBSKHderubcpkU3PqiEsdh5C+VknHffdFjgOC2oQHYPMCTQr8OJp3a0AzbS+UwopkHYITWMPj41RTDVAwB8+FPy7KQTCaH1tC6G6SKt/IplUqIx+NdJ3+9wOzZSVx5JTU0t+EmHrt3Z+p6Ak9MUAEBHSsTzVOQWukZ7DSMoTlyaH1aCjJ2DkI710irY4sh/DFIwF/JFMFIXBJsX2OgPEbHoT7QdF7qq4cFQXFtl+UwiqKIm29WMG1a9BgIAm9Do+t6Q59j27arNjTU57hUKoW2oRnlZ0Q/4XecIgLYPob7iTbimAoEkAoVRq2FXbvop8rZi37LN9zwx/jRj/4bBw4UwAhBfSXo1q37cPbZx6FctrB581sN65dKBchyvKIi+oHyA8NMH9T3N+w+tnss2lmvnbBxq2QpbPiX2supqJ0zB6JoV1rK0TEnI2sCfy82kk1VFcEEK1qOXcuSBJx3nop16/r7EjcV5g53n2OgscAk6mDSfQSFgCMC2B4iAthl8KSPf8CP2g3PhzxkWUYymRx6BQxon1RRBXc+n4cgCCPXuYUgCALuuusCfOAD98FxqMCjXtazrCKy2aCWYQZMs1mlK1UchyFEreYDFsDUsFbUwyIEQWvxvBNB7iXc+yAhFpNRLPLE3AJT5Upgx6gWAq9vax2Df3jcW2k0DLJ8AfhK7okJBT/5Sf+Uv1F+EQ4DKjAh8DY0PCkkeFnQjNpcMwhEBLC7GP4n+ghjFAkg7+lH457qFVa8lU0v+/f2C2vWHI8//uPj8Mgju1EL2TJCNzGh4ve/P4hEQsX06RoOHWqsJGV2LKxiNBitFoW04jHXapWuA0GQ4DitkEYiXu17AjZCqGyTTLbJloc6k9goFt3WLSK3rh9i8O+24leY4q4+Zv6MmibhBz9QetLqLQJD2AITviuEYRiwbTtSCX3g9xKRy+Uwa5afy0GEIEQzQA/RixBfL0GdKfJ5Zh5MrZZGaSJq5Zg7joN8Pl9nZZNIJPpG/np5ffzsZxcjkSACV2t3FosJyOVMHDiQx/HHB3UdCJvn52Uh44VWO32Q+XR4sFBpaxDFdhRACYz4xgEkkEolwBROBTXCXagsW6j8BB2neMDfAP+OKgQvoq6hXhFk5C8eF3HxxQrWrImm/n6DQsGqqiIWiyGRSCAej9e1yXTb0BSLxb7Z0Awz/NrAAah2U4rQOqJZoMtwF4EAw08AvTz9iAgN+9j90GzcVORB/XvHxsb60r+3Xxgb03D11VQQIoAI3f79ueoyzz67H6tXzw7YSpjctVYKPQy0NuUU0JoKWIIktRbUYD2E3RDACFMcrOiCCB61T7PB9qUAII9MJg92rNzVyGHuHQHNj18cfiqlKDaG1gVBRi1fEIjFWA6mJAkYH5fxve8NrnhrlF4m+wEKBQOApmkNBSaWZTUUmIxan+NeIyoCaR8RAewhhp0AUrj3yJEjKJVKDURoFItYmj1gbNtGNptFNpuFKIoYHx8faGFLL44xKbn/5/+cghNPpDdjpky5v+vJJ1/H3Lnxhm3UEIYEktVIGBQRntSFLTSpIRYLR26YyqvCMAQwcheDrmuoFawYAPIAsmCVyc1UPDckhDOpTiA4BO3fEi6R0BpUT0kS4Dg1OxhFEVEsCgAE6LqM+++XIxI2xCCVUFGUqkqYSCQQi8WqSqFhGCgWi8jlcsjlclWV0LKskZuvwyJIAczlchEBbBNRDmAPMcwEMGy16zCOPQh+x3yY+vf2CmTXUywWq76Fd911Ac4998eVYgIL7qpg23ZgWUEkrz6H0B+N5tP+27NDLEfIgeWyhcvTK5XyqC/soLZ0lGhvQxRNWBapeDJIgSsUEAJh8wWDCjZ4NFP/FM9lZFlCLgc0Hsd6U2zHYVP82JiMyy+Xccopg3nnH7V5pJ8IIjf0ebsFJuRTOFXgVwQShYDbQ0QAu4xhDwE7joNisYhCoQBRFJFMJutyUHiMYhGLF7rZv7fb6JYCSN1ZbNuu685y+ulzcfHFC/Dzn79cWdKEuw/ugQN5TJ+ewKFDfioe6yoRxiSaVeI2G60BRpDC5g6GufYkABLicRnptApGvkzup7I1B7DqdrPVYpOwSmeY66tZ1484/Aiiabpz/IBUSkMmUxtfMqkimwVkWcSsWSK+9KXh8+2M0DqaFZhMRRuaoJakEQFsHxEB7AOGhQDyJCGMp9+oTRJAPWl1k91UKjWl8vyA+m4lsix7GnTfcccH8cADtyGfJ7LRGFo9dCgHluPmR1xKYKHSIDiVKtww00oJ4buE5FFvC8O3rCPDaxaGTqcpzzBsXmK5hXG08tIQRv3zVxNZRbM32Rwb05FOu/dPrZA/UjoVZLMAhX7/7d+iqX7Y0cl826yDiZ9KyLe0G+b5vlkIOGoF1x6iWaGHoJtq0ATQtm3k83kYhtGSp98oK4BHg4G1YRjVnszU1N5rH3Vdwde+di6uvPIh1AyFvTz8mtm6FNCcBJpIpWLIZJoRMIcbRxAk1PLyqHNJs23n0FoLurA9gcNOl2FC1v7qHgA4jl8IWamQP/4cCVAUoFyuN3sGgHhcxnXXSTjuuOFRvCPUo1fPBwr/0lxPKiEfNua/mw8bd7PPca+RyWQwPj4+6GGMJIZjVphCcN80gySApIBNTk6iXC4jkUgglUqFNnQexhB2M9BYi8UiRFHE2NgY4vH40E5m7Vwftm0jk8kgm81ClmVMTExUQ75++PSnl+Md76A2cKzyVJK8lm9W6OBdkMAjk8ki3NRCeYP1EAQNjERRNWsJTAUMO105aM1zMOx2wy7Xmcoci/kXfiiKjEaCHkO5XDtnrDewAFGUsXixhL/5m8GHfkdpDpmqGOUCEz8F0LIsZLPZKATcJiIFsMcYFAGcaubGzeAu8lAUBclkcmiJXzvotJDl5pvXYt26X3Dbo9y+um9BLU/QC1Tx24xUUIFFM5CqSC3ObDiOX/VsGJWOIR63UTlMIRD2GpEQrgik2TLN+gMT8XUjUWcczKChPo+RuqGISCREbNgwde/5qYJmRSC9RFCBCW9YTXDnEg76mZLL5SAIAhKJRPOFIzQgIoB9QD8JIJkbl0olSJLUUd7bqCiA7ormcrkMWR4Nu4uwLwjdKGS56KKFOPnk6dix4zAAwLYtzJ2bwt69bqZEBM9v+wbChIJrxsheoLZkfDVuM5TAfPmaE8F8vpXq4bDXd5jjLcK/YwdBhp/CJ0kxFIte5M8rTE3Hj75Lqf5fUSR85SsSZsyICGCE8OALTPg+x4MuMPEjydlsFslkcuBEdFQREcAuY1AhYF4dchynrhK0XQw7AfSraD5y5MjQjrlV8D2Km1Vth8FNN52PD37wPhBp2L9/EqlUHJmMm1x0Ix+QevryhIaUw1p3EgYV4chaWBXQARt/GG+XsAUjYe6lZuoe+QN6b0uSBFeVMoGUPh4KaseWt+BRsHKlhL/4i8GHfnmMwgvZoDDMx2YYCkz88tDJBHqYj98wIyKAPUY/LkxeHQry9GsVw0wA3SHuYc7zaxe92Me1a0/A8uXHYNu2AwAE2LaDOXNiHgQQqPn/+X2ngea5dnnUQryAPzkqgJGcZgivAoYndu6iik4gNfleHf55lEkYhtff4mjcX931PaS2SkgkgDvuMFAsmtUH97BXeR7NGMb5tRn8Ckz4sDGv2nmRwm4gnU5HFcAdICKAPYYgCLDtbjabr6EVT7+pAneIe2xsrKGoZRgqr8PCa6y9tq/55jffizVr7qn69b300ts4/fTj8PTTB1xLUsWw3zQRJh9QrGyn2flo5XyFVQGzYOSpmXdfGeGKRsI8tIKsXSjv0gt+1jVehFKBO+8PcCDLAixLxM03C5g2TfTM3xpUleeo3I+DwqiTcy+V0J1LyL/cEIHkcwmDjkGQAjjVcr37iYgAdhn9CgG36unXDoZNAaQQt9vs2AvDMuZWwat+vTqvq1a9A8cfn8KuXRkQiXjxxQOYPj2GQ4fcXUFMeFvGEPzzARMJDblcCYyAhSFixcq2mp27VlRAFeHCwLWOIO1DQlD+XyIRRzbr9x0JeLeOc9vBCK7ty6C8P9OUsWaNhI9/vD6pn+8Y4fcQjlTCwWFU56pmoAITXiXspMAkKAQcoT1EBLAH4Elftwmg29PPy/i3WxgWAsjvs6IoTfd5lB5ipBCHUTa7iS996Vz8xV/8Co7DjlU6beD002d6EECg3XxARv4IeYQjgQbC2KjIchlmKL4WltQ1C90CzYlpcPs31nfYCwq8yZ9X6LeWY8jmFgmAA12XIYoifvrTxodmq1Weo+gFF2H44Vdg4m5p51Vg4hdFS6fTEQHsAFHpTI/RLQLo5+nXK/JHGDYfw2Qy2XSfRykEDDCCOzk5iVKpBF3Xe07+AOBjHzsFCxdOA09qnn56D848c47PGmUEc4Hm/oDhlql1swiCaVInkWbIIdw0F4boNLumgr5H9cnvAxipc4NMr3nUF4I4Dgv9apqIQkHAd78rIR4P3lf+ARyLxRCPxwO94PL5fNULjl5U2kVEJv1xtB4bekFRVRW6riMej1dN7WVZroaOqeiErscNGzbgtddeQyaT6ZgAHjlyBFdccQVmzZqFZDKJtWvXYtu2baHXf/PNN7Fu3TpMmzYN4+Pj+PCHP4zXXnstcJ1XXnkFsVgMoihi69atHY2/EwjOKD0pRwSGYdQZEufzeUybNq3tm3yQnn6HDx+uFiD0E6ZpIp/PwzTNlvc5k8kAwNC/Gdq2jXQ6Ddu2IcsyEolEzwk9j+3b92PVqh/AcWoVpKkUs5jZv7/RRE/XdRQKQdOFiub+gDrCVPvKchym2Wy5MAQQACbQPAycgrcKxyOJYBLr391DlpMwTa91ifyJUFWhohIKqFVFO5BlG6YpgCeY8bha9TmUZQVnnilj48buXDteKiGvwLSjEhaLRdi23fd5ZBRQKLBrU9ebVdUfnaDoCL28vPbaa1i1ahUAYObMmRgbG8Nf//VfY9WqVTjjjDNaKgqxbRvnnHMOtm/fjmuvvRYzZszAbbfdhjfeeANbt27FggULAtfPZrNYsWIFMpkMrr76asiyjJtvvhmO4+DZZ5/F9OnTPde76KKL8PDDDyOfz2PLli1YsWJF+APSRUQKYA/AT4idvNnZto1cLod0Og0AGBsbQyKR6KvnUb/fTOlmJ2KUSqVa3udhVwDJsoda1QmC0Bc1141TT52NtWvngVe2MhkDs2d7E6tCoQBVDRpjmHBrAc1JIvMobI4SwhVvdMO8udkyEhpz9RQwgheHrkuQ5TgYAY6hVl1NOXwmDKOMWg5hofLvMkyTCm3YNnVdRT7PzlkqpUAURfz4x927T4NUQgrd9UolPFpxtCqAYUDHRpIkxGIxLF68GK+88gp+8pOf4Nxzz4VpmvjqV7+KtWvXYnx8HEuXLsU3v/nNUNvesGEDfve73+GHP/wh/uEf/gF/8zd/g0ceeQSSJOELX/hC0/Vvu+02vPzyy/jVr36Fa665Bp/73Ofw4IMPYu/evbjxxhs913nggQfw4IMP4qqrrhr4vRIRwB6jnTw68n6jsGA8Hu9LWNAL/SRT5XIZk5OTKBaLiMViGB8fb7v6ddA3lh9s20Y2m622cVNVtWfmqWFw990XQ1FE8CRw+/b9WL16rufytl2Ed7iUSF0zFY2WCd5f2y5BVcMofGFIZy7EMmEIZyMBVFUFTPlLghVyELmjbRpIJkVkMkWYZgGs0KVUGbeXJQx1ROFfImOoVVyXKuSYtXrLZIDrr5cwMdHbqZxCdaTGJxIJ6LoOTdMgiiIsy6r6VebzeRQKBRiG0dBvNkIjouMTDvwcOWvWLHzoQx/CzJkzcfnll+Pw4cN4/vnncfvtt2PVqlWhnxsbNmzAnDlz8NGPfrT62cyZM7Fu3Tr88pe/9Oi807j+ypUrcdppp1U/W7RoEc4//3zce++9DcuXy2VceeWV+NznPof58+eHGmMvERHAHqNVAmhZFjKZDHK5XOg+r71EPwggkaJMJgNRFDE+Pt6R590wvk3z+YymaSKZTFYd7Af5AJg2Tcf//t9L4BYfN2/ejRNOaOyvydQoN1lihQjcUk2+NQzZAhwnF0L5LaB50YgF7zw7Hs0qisXK98QrPxoAEYZhgZE6VH4bcOcxZrN+2/S6TuPgiaaqSnXWMZqmoVRyAFhIJgXMny/jc5/r/zQe1FeWXlR5ldA0Tdi2HamEEdqC3/VCOYCSJOGUU07BX/3VX+H222/HZz7zmVDb3bZtm2f49YwzzkA+n8eLL77ou65t29i+fTtOP/10z/VfeeUV5HL1L5/f/OY3MTk5ieuvv34o7oGIAPYAXiHgZifbcRwUCoVqWDCZTCKVSg28xU0vCSCvdJbLZcTj8a6EQoctBEykPp/PQ1EUjI+Pt9TDt9f41rfei3hcBk/iDMOCqjqQJK8xuq1O3MqYheYFE0U0CwWXyyZsO0yItxvn2l18ooIpekkw8iiCkcRi5cdNGP1IrQbvvEHdYxtKw7KiyJNbFaUSI4OSpKBYFHDnncNxDQHBKiFd614qoWVZQ3W/DgLDMhcMI4J6JWezWYyNNb6ohsXevXsxd25jtIM+27Nnj++6hw4dgmEYodfft28fbrjhBnz5y18eGvPqiAD2GGEIIIU+C4VCNfQ51Q2deaWTSNEglc5ewE3qU6lUQ9/KYSCriiLhyitXggk3tbHs3Pk2zjxzts9aBdRy2PxIYjPQNoIwieYKXw7N8wqbFYGoAHSoagI1T8ACmH2N0WT7AvwVRBXe++j1Wf1+qmoMxSKpfwIEgULDKhzHwUc+ImPp0uGdwnmVkApGvFTCQqFQzSUslUpHnUp4tOxnL8BXAVOUJcwPoVgsQtMaU01iMRYxoAIdL9Dfwq7/+c9/HvPnz8enPvWpNva0N4h8AHuAsApgPz392kW3O5n0o3vJMJCqUWtVd911Z+Pb394CxxGQTtfIzJNPvoHp01NN/AH90KxVnINwHULCnEsRwaFlA6zSl4ipCKbsCWA5eWUAdoBVS32eZD00+BeIhFX/3J/JKJf5ULoKx6FiEBsTEzHcdtvwXk9+8DIH5vvK+rUQo6rjYb6HOsFU3a9uoJkCSATw0Ucfxdq1a0Ntc8eOHVi4cCF0XUep1Ji3TCQxqDKb/hZm/aeeegp33303HnrooVDj6xciAthjeBFACn3S20EikRiqkCCPbpKpfnS5GDRI9SsWi30xdO4WBEHAP/3T+bjiio3QdRWFAiNTjuNAVf2IVTP1ieWqBatnJSQSKeRyQYphFszKxYuEEnJghK5ZpW68Mq4iGlXBoGsxiGDK8CZ6XkbOjd8jil4vWTKX+6dx3y0hkRDxta8JUJTRv3f4FmJuc2C/FmJuC5pRn0MG/bI6KnCfZ8dx6kLAixcvxp133hlqW3PmML/TuXPneoZ59+7dCwA49thjfbcxffp0aJpWXTZo/WuvvRbnnHMO5s2bh//5n/8BABw8eBAACxPPnDkTxx9/fKixdxPD/2SaIqCbfJCefu2gGwSw310uBqUA8u35mrWqIwyDWkm47LJT8aUv/Ra7d6fBk7Z9+3IYG4sjnXYXd1DoM0gBM9EsPJvLZcEUsCAFr1m42IE/EUtUfhsB42yGoO/2+5vXfd1IClU1jmKRHzevBvKG0BpE0cappyr4xCeGK1IQBmGJWqQSRuARND/yCuDs2bNx2WWXtbTtZcuW4fHHH2/oNbxp0yYkEgksXLjQd11RFLFkyRJs2bKl4W+bNm3C/PnzkUiwueeNN97Arl27cMIJJzQse9FFF2FiYgKHDh1qaezdwPAyjxGGOwRMYdRBe/q1i04IimEYOHLkSF/tbPrdws5xHORyOWQyGQiCgLGxsZFVN2+//YOVf9Ufu3S60Ri6tkwzW5cSYrEgwkJKYRBKaF7Jm6uOQxAksJCvDEYeyYIlaBxB5ND/PpUkL5VPRrBiWdmqqKBY5ENIMtyhYACVe8bC7NkqfvKT0buuOu0g4q44jsfj1VxC8tX0yyUcZgSFNyMwBB2jTCbTURHIJZdcgv379+P++++vfnbw4EHcd999uPDCC+vsZF5//XXs2LGjYf0tW7bUdfPYuXMnHn74YVx66aXVz26//Xb84he/qPv57Gc/CwC48cYbcc8997S9D50g6gTSA5DdAcAu3iNHjlQvYmpzMyo3PIUzp02b1tJ6RHjL5TIURUE8Hu9bfmOpVEIul+uo+0pYGIaBfD5f7XLQ6rnt51jD4owz7sDvf38AgiCBnx0EQYTjEHmnAhCCiCByJUkSHEeBbQdNN0kE27GIaK4Ujld++5HScfgXhAR1KfHuFKJpCkolr+9JeizfqP6x7iq8slrr9Uv/liQBliUjkZBw/fUKPvOZ0VP/8vk8RFGsJsd3G6QS8n1leeLAh42HSSWkl0dN09r2PJ3qME0TxWIRiUSi7rzZto0ZM2Zg//79mDFjRlvbtm0bq1evxvPPP4/169dXO4Hs3r0bW7ZswUknnVRdds2aNXjsscfqXiqy2SyWL1+OTCaDa665BrIs46abbqp2Agka15133onLL78cTz/99MA6gUQh4B7Csizkcjk4jgNRFDE2NjYSih+PVkOUlN9IrXuSySQURenrhMsrgL363lEo4GkHjuPge9/7E5x55t1c0QH9zUY8LiGf9yJgNoIIoGVZUFW90u3CD1kwkuRH8Og7vP6uVH6o04jfeQ9ShEz4K33e2yuVYvA2v/Yao/s+cpM/PvSrVP4tQJIU2DawYIE0kuSvH+BzCQl82JhCxwR32HjQ8/KwENJhhN/zp1AowLKsjlp+iqKIjRs3Yv369bjllltQKBSwcuVK3HXXXXXkD6hF83gkk0k88sgjuOqqq3DDDTfAtm2cd955uPnmm0OR0kGf90gB7AFs2652tKCJRZblofH+aQWtKFTDkt9oGAay2SwmJia6/v0UbspXGrHG4/GOCniGSQE0TRPZbBa2beMTn/gPPPro61BVBYbhJk0x6LqCQqGRTCWTCWSzQQUdfCWuF/xsU3iMcduQwHIQ+Q4lQT17ZQSTPL+q5TF4h3S9FEXq3MHDrf6RsTQdw9q7OHvpYr6MiYSKXM7BrFkyHn5YwfHHj9YLJCGfz0OSJE/LjH5hGFVCepHkrXEi1KNcLqNUKjUogPv27cOyZcuqDQQitI7oiusBSLKmStdsNjs0if7tIkhNc1e+plKpgYYzepUDyIe1VVVFPB7veOLph1rZDF6Vy3ff/WG8853fhmGUkUrFkMnw5KWEQkGEF1HLZvNgJMpPaSOVz69biAFWtBHUTYTGEq8s7w73Bq1rwp8gOlAUCeVyuE4l/t+loJEANqp/9WMg70HAcTRQ8Uw+b2NsTMZll8kjS/6GBaOuEh6t8MsBzGazIymqDBMiAtgDKIpSpz5120uvn2hGStqpfO0XukUAvcLaU8Wo2+/8zZwp4eMffxd+/OM/VNqd8aCiDa/pg/7mdw04YOSGet56IYfgUHAZzBbGL5eviGCS6R9GlWU5NAEURRG27e6KAjSSS901FgX1pLUW+k0mFWSzLPSraSwHc/ZsEV/4wmiHfof1BZiInbvimFRC0zSr+dy9UAmjIpDm8Hs5JhPo6Ni1j+iVpgcgawL+/8M6ATaDn5rm1b93WCpfuzkGvo2bpmld79LS74plAl+57D5/9BC85Zb3QFUllEplLFgw4TV6n60H5dIBjPw0e/f0I286GIHLNfmOoGvAPwQtSd7b9ErvjMU0j++p7+XrPRaF+4yv+hXBWocKAFjPX1mW8KMfRe/p/QKRPFVVPSuOyZfQXXFMvY4j9AZ+BDCZTA7FM2dUERHAHsFtBTNVCKC7f28ikRi6AohukCqvNm6jYtvTDNR6sFQqQdf16vnj1Q9m1q3gc587AwDw2muHMT7uzt8iJc8LJQRbrmQR3CXEQCNJTIE6drDvDlo/F3BNFuA39fmdXq/PvXO26r+TVb3y4XP3/2VuWaVyzTLLF0DCJz4h4V3vGv1rbpRBCiF19KEex5T7Syk/+XweuVwOxWKx5R7HEYnxh98x5NvARWgP0atlnzAVCCBVNZum2bUcuGFEPzuW9FMB5A253ZXLvNkugGp46//+39X4zne2IZ0uYfbsGCYnWcWrLAtg6VIW/N8jS2BqV1Co16tggv87yweMxVIoFt1m0Bk0hldrkCQZluW3bQ1eIWS/81Aue7VydH9CbeVqsCy/qmWgvuqX+v4KUBQRoghMmybippuG58WqU0wVkuOXS8gXl/jlElL3EsKoPhf6Cb8QcJQD2Dmm3tN7CDHKCiCBVD9Sw5LJ5NCSv3ZJFREk3qxvcpopAAAgAElEQVR72Hv4hgWv+sXjcV/Vj9IXaJ8ty8KNN54LAHjxxUNYtuwYAMDs2dQjM+gYN+vja6O5ATTrN8x6a7rPg+PxGbemkQ/4u/e4HMcvjNf4eankJp7UZo5BEDSUyzwhTID2V1Ul1MhfLQycSCgwTQflsoQf/lCeEtceMPWJjiiKUBSlqUqYy+WqKiFvVD1VznM/0akJdISIAPYM7hAwMJqTICkohmEgFothfHx8ShqWEkEqFovQdX1kevg2g1euHxV68OSPvCr5a5XynC66aAFOOWUWAGD37iOIxxXMmsWHXy3out81UUbzfMCgUC6fL+eFXMD6FhQl7vM3r84mqFNu6uEmgBLKZfey9fupabwfIU/4ANvmjxdbLplkli+6LuGCCyScdVY0PY8q+FxCXdcRj8erRvGUS1gqlap9jguFQjWXcBSfE71EUBFIpAB2htF/wo0ARvHtzrbt6qQEsI4Fuq43WWs40ArhDgqL9gO9fDngK3z5LiWO49SFfN3VjKZpolAowLZtxGIxqKqKe+/9MN797u/j4MECzj77j/D22/WhzkKhCEXRPEgRUGvj5qf2ZeDtD6hV1rGhaTpKJT8T6SBzab9KYW87GMvyK2BxE0CyaiGIqPcJVCv9fem4xqrfpWkxTj1kYeB4XEI2ayMel2HbAu64IyJ/UwlkIsxHTchTtFwuV1VCd8Uxb0Ezis+RbsEvBBzlAHaGaJbpA0ZJAaRJiS8SAEaTxDY73vx+8mHRUQf5FXai+pHdDZHGefMm8Od/fgoA4Ikn3kAi0fjuKAhB4dwigqebHOoLKEg9Y2MrlYyA9f37BJfLRTCS5YXGc00P4Ea4ryX3um6lkVcuVdRyA3nlUAVgQBQF5PMSABHFIvC1r8lQ1WhqnurgCWGQSkj35NGqEvrtazabjULAHSJSAHuEUQwBW5aFfD5f7d9LVa+FQmHox86jGVnl27gpijJQ4tfta8OvN3EY1S+fz8NxnKrq5z6Ot976Ptx//4vI5QwcPJjF2JiCdLrMfTdV5voRQWZu7J1/Z6NeZXMrhhZiMb2SC+iFHOo7a/AIGo8XZNffvK4N9z7w/5dR7/PH77MK26awOMthTCQUZDIOYjEJf/RHMv7qr0b/JYRH5HXnD/6+91MJ3UbVR5tKGBQCPuGEEwYwoqmD6DWzDxh2Aug4DorFIiYnJ2GaJpLJJFKpVJ2R9ajBq/DGy8ImmUxOGdUvm80im81CkqQG1Y8eIEGqnyiKdaqfG5Ik4lvfeg8AYNeuNFatmu0xEi/7FoKF4KKQIhiB9A4XF4tZ+Kt5FhgB9IKf5YxfkYh7/F7XB08QifARYtx2+Spl3gJGBeBA0xj5oxZ1/+//RVPy0Yag+TVsLuHRqBJGIeDOEc02fcAwE0DTNJFOp6tGxxMTEw1Gx1OhitmyLGSzWeRyOSiKgvHxcV+iM2qgULab1Lq7GrgrfE3TRCaTqRb4JBKJpmT4E584BSefzJqc5/MFLF0602tE8J9aymjuDxgUmMgH/D0T8Dc/Fc+rN617Wfe+uJVGnvBJqJFBgVuOt4OJATAhSRIMwwEgQZYdrF0rY9myaEo+mtDqvEr3sLvimFR7gKUx+FUcj9o8HqQeRwSwc0Qh4B5h2EPAXv1f/apeR5EA8mHPYW7j1sm14Q5l80bVpPp5ET9SfA3DgCRJ0HW9JRX03ns/jGXL7sBbb+Vw8CCg6zIKBV4Rc+DfCxhgSp9fUUgcjCTKPn+34E3amiEDrx7FqZSATKbZuqJrPRW16mYFjNyRMikDcKCqFgxDQ63IhK2jaayziiAIsCwJogg4DpBMyvje9wbbEzrCaEIQhLq5m+Y9Uv2ppR2BDxu7fQmHFX4EMMoB7AwRAewDho0A8nliYfv3DsvYw4I9YFkbN9M0q2/LozDZhYFhGMixvmFIJBLVnD0+z88d7gXC5fo1w/z503HppYtx773/jVhsHCecMIb//u9DrqWIqPnl2ZVc1bAEypez4d8vOA9gDN5WLnmwiuKS63MbjHTWr5PJ+LeFq4FUOQ1sypQhSRZXMZyujFUEEUDHkbh1ahYwzBjahuNoEAQbti1jbEzEddeZEAQT+bxQZxw8lfK6psp+dBO9IPx8LiFZdvEvhJZlVe1ngFo/ZN6oeljOld9zx3GcqBNIFxARwD5gWAggrxi1YnkyLJNBWPChT1EUkUqlhta7sNVrY1Cqnxvf/e778atfvYx58xL4wx8OY8aMON5+203ISvAngU5FleBJns792wJTzfwIZAaNbdUIVHHsPqZehLEApjry38OrfXEwlc9EzeYlUSF/QH1HkTiIeMqyWknWp5Z4OjRNqBBeViijaQpKJRGnnSbhiitkWJZcPX/uB7Q72X+UMOh5L8LoqoRRCLi3iAhgj+C+YAcZRqUwaKHAHlS8YhQGoxQCpjZupH6Nj4+PHIH1wyBVPzcURcI3vnEevvjFJwAAhw9Tmzb3dUJGz43hXMsqQ1WTMAxS69xt44z/z96ZxzlR3///NbnPhV0OWUAUWRZRqhyC3Cy0XkXbbxX5VlAQtBS1RZDDkx/1qtYLtIpXK5Riq7IetOq3nrtcwrIgiBdQBUVZbvZIdrPJJDO/P8Jn9pPJTI7dZGaS/Twfj32gSSaZJJOZ1+d9vN6g/fNiSZRm5qEcBQwB6Ih4ISgXqRG0+BIGTu0Xvf+06CTbcdTtdgQCxAOQpIF5WCwWBIPRCGA0bS6iuNiM117jpAs0uUjL6zfbY/dne0GP7y4fooR+vx8dOnTQdR9yHSYANUIvEZWJ+b0cx0kji4yKvKZRae6mkUl0bBBfP7k9D9ku1ahfpmc3X3/9eXjqqW1oaAijuTkCu92iYtZM7F/ij6FQyI+o2GqGcrq3GVERpfS8QaingknHcGwE0ekMIxDnDU32ywSTyQ1BEE9tT6CPIRu1n/R+uUEEp8nEQRA4RCOCIQBmmM3CqYYPCwAOwSBgMlnxwgsRWCwmCEKs/Ue682aNErFhpIeRFtZGjBImigCyFHDbYQJQI7QWgLQgamsa1OgRQHriBalp9Pv9ht5nGrUTJzHlbmqKChy6gUWPqJ8S//zn/+DnP38dhw+T2bh0tyuB1POp1fT5ABQmeBViLaPUFNKA6EQPuT+g8ncfCPgBdEDshBBSN8hDEEgkUy09Tps626jnIRdGFwSB7G9UHJImmUgkmpa2WCwIh0249FITRo4UpHGLQLwXHC0K5enfZBEb+uKca2ljhv4kihKqlSpkK0oofx4yRo81gbQNJgCzhJ4pYFoQORwOOJ3OvIwI0OPqlGoac0UAKkFH/eSRWz2jfnL69StCWVlPvPLKd6duiSC+a5bcLk/x0ohoqbVTui8CdQFJ0rTybdUaQuQRPQf1GuT15GbQStvS0T9ifUO2sQLg4fXa4fOF4fXa4PPxADywWAC324yXXzbBZrNJYp4W9XTEnXy/cpNgpYgN2ZZECOkIilwQan1OyMdzUFshi7dcQa1UQW5WTWhrlDDRFBCbzQa7vTWOAAwCE4AaoYUAlAsij8ejau2SDkaMAJJ6OFEUYyZeEIy4z2rQ+9qWqB/P89LUlmxG/eQsX34RPvjgbzhxgtS9qXXw8lBvCjEhKsCUIog49ZzyWjwCEZfxr8lxAXCcRVbCUI9oepY0kZBUMi0U1QRg9HnsdjvVwUye2wkgCI/HCb+fh8djPyX6oiIw+noCQiEbnn++GdHrpAVmsxkWi0X6rmgRSE9waXlPLaJfLW2sFLFRujjLIzbZIFd+h4z0SXbMqTU00YIw0XGnlgJm6d/MwARgllCKAGarjk4uGpQEUVswkphKVA9HY6R9TpVcifrJsdstuPbafnjuua8QDJJInVq0LnFncPRPKYIItAhItc5fL2JTu4AohsFxHsTWCYqIpo0b0RLRkz+nWfZ4QvRiZjIRMUrqDG2n9sEKv5+HzWaB30+PxrMB4OD1WjFunBlXXGFFOBxGKBSSjlNyUbRYLNIFlZw35OI/1bRxouaSRKPFjFLo3x7It884WZRQHpluje1RQ0MDPB5P3n12WsMEoEZkS5DQ83tb2+SRDCOIKbnITbeT2eiEw2HU19cDyI2on5z77x+Jt976Ft9/T4RWdMKFcsRO3hlMH6/Cqe3URGAQJpPjVJ2dHB+UmkIEoQ5mcwdEIqS+rxAtE0XIPgRk+0R/hnTTR3SfWppdyL5bAAjguOhzhkItNjQOhwXNzdEIoMdjxurVHKxWO+x2e4wgC4fDiEQicYKMCEISJSQLSTLajxwbdHQvUdqYbi5JtdA/3zwJjYLe51UtaEuUUC1o4vf74fF4NNn/fCZ3ig9ykGymJEmTR319PSKRCDweDzweT1YjP3qdrIihczpj3IwgWlOBvuiS90aLP3KSBBAX9WtqakJTU5PU5KPnaDuz2YRJk/rirLPotAypB1SCp+6TP4Zsp/xeBKEZNpvaTGAfnE5H3K0WC7mQFCIqQAXEzxVWqycixxFponJAECKIpnSDp54nBMAFUQzDZCJCMdrx29wcTV97PBY8+ywHqzX2vEBmvbpcLni9XhQUFMDlckn1gWSsV0NDA/x+P4LBICKRiLQdEf42m026cJJjh0QZeZ6XjjUausjf4XDEjBYjF+tQKCTNmm1qapJmzaab0WDiUZn2+LmQKKHdbofT6YTb7YbT6YTdbofJZEI4HJaOMwAIBAIIhUL47LPP0NTUJE0BactnV1dXh1mzZqFLly7weDyYMGECduzYkfL2Bw8exOTJk1FYWIgOHTrgf/7nf7B///64x5155plSgwz9d9NNN7V63zMFJ+bCVTJHodM7pCO3sDBRt2NqEK+7SCSiyYSLYDCIxsZGFBYWanqyose4mUwm6aKYCsQsuWPHjlney9ZBvzcAsFgsUkdbrkT95IiiiJKSlyAIZhw9SlKxIqxWM3heKRJoRlTkWRFNycqJNlIoQ2xW4jxd0DKeTZ5m7oHY6KAJsVFKD/V8dCSRWLwQuxry/x5EhZ8NJGLZoYMD9fVhkGihzcYhFDLBZLLiqqtcWLky/e9JqY5PLW1MjhU6bUz/SyDRQaUoodLryy1o5M0liTwJw+EwmpubNS9NyAVIM4ORxlMaBRLkEAQBZrMZwWAQ/fr1A8/z6N+/P3iex5IlSzBixAj06tUrrXOgIAgYM2YMdu3ahUWLFqFTp05Yvnw5fvjhB2zfvh0lJSUJt/f7/Rg8eDB8Ph/mz58Pi8WCpUuXQhRF7Ny5E0VFRdJje/fujaKiIsyfPz/mOUpLS3HBBRek96FkGJYC1ohMRKRI1CcYDCad35tJ6KiTVkKjrSLXyBFA2pvRbrcjHA6nXOsXCATA87wutX7J4DgOL754MebOXU8JQA48Hz2B03VrUUjzRqIooZoIFBEVaEoikId8ikhBQREaGpoQFWak2UNAVMQRzz/6eeh9FWT/kudvPrV9ECaTBWazBfX1PFrqAs0wmaJNMWedZcOLL6q8zSQo1VSRlG+iOj6yDd1cIk8bp9ptrORJmKjzk67rYjBaAzkWyeQim82G//znP6iqqsLHH3+MjRs34pprrgEAFBcXY8SIEbj88ssxY8aMpM9dXl6OzZs3o7y8HFdeeSUAYPLkySgtLcWSJUvw8ssvJ9x++fLl+Oabb1BdXY0hQ4YAAC677DIMGDAAjz/+OB588EHpsaIookePHpgyZUprP4qsYZyrRx4iTwEDrU+jhkIh1NfXIxgMwul0aib+AG1H2RGR09DQAFEU4fV64Xa70xaeRhSAJJ1XX18PQRBi3hudshMEIS6awvM8fD4feJ6Hw+FQbX7RmwkTTsell56B4cOLY263WtVSumoRPvp+tehIVAQ6nW6F+xoRjdQBgBMNDWFEBRzpUCb40dLwQdK6QPxoOBKp7IKiok4AOiMaJQyB41wABPA8B5OJTC6xwGSKpn9dLjuefNIck/ptC0Sg2Ww2OJ3OuLQx0BK1J2njQCDQqrSxWqrXZDJJKTySNiYXaSB6vJLUdTAYFdzkuYz2u9SLRCbHjBbI52M2mzFo0CDMnj0bY8aMwVVXXYUjR45g7dq1mD59Ok6cOIEtW7ak9Jzl5eXo1q2bJP4AoHPnzpg8eTLWrl0rLagSbT9s2DBJ/AFAv3798NOf/hSvvfZa3ONFUZTs2YwEiwBqRGujaPKu11Tn92aDbJ+46ahfvvkXyqN+8ogmKYSWF9vTUT+LxQKn02lI4Ufzpz+NwqRJ/0GfPh3w7bfRxpbm5jAsFivCYaXmjURdw0BLmlV5JFzU3Jl09dLUIzr6zYqW6B1J5ZLHyqOA5LONTvCIRhiLEa0djP7uTj8dOHkyKnQ4rhkWy2HwvB12OzHDtgMQwXHR2r+rrrKjrCy73xnHcbBarTFF9qS2lDSWkEJ7ElEkUUKlbuPWeBKqNZeQfQiFQgiFQq3u/GS0P9Sul2QOcNeuXfGLX/wCv/jFL9J63h07dmDw4MFxtw8dOhQvvPAC9u7di3PPPVdxW0EQsGvXLtx4442K27///vtobGyE292yMP3444/hcrkQiURwxhlnYN68eZgzZ05a+5wNmADMIm2JAJIasdbO780k2X7NbKS29UhbKyGvY6QnspCLrMlkkgrt6c5Psi0AOJ1O6UJtdDiOwwsvjMfkyR/gwAEfeD4qIMLhRCndaNcsx4kqv5EQYtO3NGR0m5IINKElXUvwIVYE+tHSdUxEphXAWQCKII9cOhzk/0VceKEVW7acBuA4IhEy+zeaPrbbrejY0YRnntF+waaWtlXqNgYQIwgz5UlI3xeJROByuWLSxkqdn7k2wjETtKf32hqUPp+2+gAeOnQIZWVlcbcXF0czFzU1NaoC8OTJkwiFQtJj1bbv27cvAOD888/HmDFj0K9fPxw/fhwrV67E3LlzUVNTg4cffrjV7yETMAGoEekIQDLGi0SL9I76ZDMFrDTGLV9OiImifnStn90etQQhERtSNE8wm83SffTF2ch06uTAwoUD8fjjIj755CB1D/HGo6N55Lji4HBYEAjwUI4GBsFxdgAhhWNRSQSSrt0AonV5tAhspPaDjgIGcdpp3XDkyFlQOz2SiYqjRwvYuNGMaNrYArvdhHCYiD8LmprCWLu2A8xmY3xfpKxAbsVBBGEwGJQWHCTFS4tCAK3yJKRTncyTsAWWCk9Ookkg3bt3lx5DjttkOBzREo/m5mbFKSLk/kD8wHAJcl+q269duzbmMTNmzMBll12GJ554Ar///e/Ro0ePlPY9GzABqBGpiCiS7svE/N5Mkg0BKAiC1KmrNMatregZASS1foFAQDXqp9ThSy6WJDJCTjAkPUx3ftIXZ6OmhC+99HS88cZ+1NT48d139dQ96indQCCCFn+++NozUSQeggLiu3zlIrAALUIyAI+nAH6/79T/k+fmqO1MAIrRp88ZOHJE/dTIcRxGj46cEn/R5+A4Kxobg4imic2IRMKYMsWFkSON+d0AiQUZWYiopY0TeRLK08bkuCXdnPTrq6WN6f0gtHWsGCP3SJQCJq4J69atw4QJE1J6vt27d6O0tBROp1NRNJKFt9OpZjPVcl9rtweAefPm4b333sO6det0bQ5hAjCLpJMCNnIkLJMCUF4Mm+3Uttar7ERRP/rimKjDV6nWj+78THZxNlJN1Z/+dCEuvfQ4bDY/QiHaZDmElnSwXOgRM2gOykbSZOScA7FRPaBFzNHiL4rf33DqdiICm0/9v//Ua54BoMupzl11XC4R770Xe+q0WnmEQmYAFlitYXTsaMXy5bll7UELMtLMkShtrGRSTbaRT34AID1HKmljglwQJhorZtSFkBqsCSQxiT4fOgXcv39/rFy5MqXn7NatG4Boqrampibu/kOHDgGAFF1UoqioCHa7XXpsutsDQM+ePQFE08l6wgSgxsgFSbYjYUYi1TFumcBoUT8SIUnm66dW60cujrRnWKo1XXpGSwoL7bj33qFYvHgrvvrqKHUPh6j4o6dx0AiwWKI+fcqNIyKAZhQUeNHQ4FO4j0NL2pf+zclFYAOi9YAFALpQ2ytzwQUijh+X/z7Dp0Rj1FNQEEQ895wzY12/eqKWNqa7hZUi02azWYpmcxwnNXTRUUaCUtqYvo+uB6ajjEpjxeSCkImr/IQ0gQDAaaedhmnTpqW1/cCBA7Fhw4a4CGNVVRXcbjdKS0tVtzWZTPjJT36C6urquPuqqqrQp0+fmAYQJfbt2wcA6NKlS8LHZZvcWjLlMPITEalbqK+vB8/zcLvdhhV/bY0A0vYn4XAYHo8HXq83qyt2La1rwuEwGhoaEAgE4HA40KFDB1it1jgDXQAxYoye5mE2m+H1etOKhpILs9PphMfjQUFBAdxut5Q6VrIC4Xk+azOp1bj00tMxcGAX9OwpN0HnYDIJcDqV16HhsIBwWID6hA6cEn9uxK9lrYj6BJrQYu0ibYVY42kzgJ7S/6kdM+edJ+Lzz02oq5N/P0FwHGliieDii5249FLj/Y4zARFkdrtdOmd5PB44nU6YzWaEw2EEAgH4/X40NzdLixYiDskCxm63S2IRQEx0OxQKSYsapcklZKQYsUNyuVxwOBywWCxSuQSZXBIIBKSJEkatuWMiNTHJUsCtYdKkSThy5AjeeOMN6bbjx49jzZo1uOKKK2JKrw4cOIDdu3fHbV9dXY3t27dLt+3ZswcVFRW4+uqrpdtqa2vj/E95nsfDDz8Mu92O8ePHt/o9ZAI2CSSLCIIQE5U5efIkXC4XrFarlCbM1vzeTFNbWytZs6QDnRLV8r0SUZZNv0R51M/j8cTUU8knJqQb9cvE/tFWIPIJEvL0XTYvRLW1QQwZ8gbq63k0N8s7dc0AukJ59i/Bivh0r/x+IvosiEb06PfjBMeFIYp03WDUxBn4CWiROXy4BVu2xKZv+/UTcfCgCX4/B4/HDL+fLu84AUCAKPI44wwHdu1ynIpetj+IlRMAqZOdvgDSUWk6bUs3lJBIudLkEhKRTIS8uYT2HiTbG6G5hE1ISQzJjhFxTxBFEYMHD8Y//vEPDBs2rNXPPXr0aHzxxRdYuHChNAnkxx9/RHV1tdTBCwBlZWVYv359zGLE7/dj0KBB8Pl8WLBgASwWC5544glpEkinTp0AACtXrsQDDzyAq6++GmeeeSZOnjyJf/zjH/jyyy/x0EMP4fbbb2/lp5MZWApYY0KhkGQJ4vF4cmYEULrGyolSolqQ7QigmmchbZVB1/oRtPT1UyuypwWhVnWEhYV2PP30KFx99QeIr92LIJp2pceyyYnO2o129Cp9p/ypPyL85PsegChysFi8CIdJ+tcPoBfkEUb5wJLevUUcOxYVfx06iKivp587CJdLQGNjGB06WPD3v7dP8UcyGsTKiRY1ydLGtChU8iQkF95MeBLSs7fJY/X2JGQRQGVSrQFsDSaTCe+++y4WLlyIp556CoFAAMOGDcOqVatixB95ffk+eDweVFZWYt68eXjggQcgCALGjx+PpUuXSuIPAM477zyce+65WL16NY4dOwabzYZBgwZhzZo1uOqqq1q9/5mCRQCzCB0BJBEpADlpclxXVyfV7SVD61nFSkQiEdTX12dcZNPC1mw2w+12Gyrqly7yOkI6WpONOsIbb1yHf/7zG0RTs7QI7IyoECO1gWqYEF23qts0RKOJxPtP6fTmOPU8IQCDIV8HDxxowc6d0WPm9NNFhEImHDkSfe99+4r473/JQkYEUAeHg0dzcwR33+3BXXfp37WvNSRSQ37vdrs94bGitBAhwo72wZR7Aip5EsqjhErNJWr7QAtC+riXC8JsLdB4nkcwGGzVpKP2QCQSQSAQiIuQiqKI4uJi7N27V1cLlXyARQCzCIkIEZNjALBarXC5XDrvWfqkcoKibWy0nFWsRDZOqKlE/ejIBsHI0zzS8YajZ8y2tuvy0UeH46OPanD0qHwuL4+WWj0rohYvSuKNmDWTqJ2aMXQALY0gguxxRHj2hNIpkOejr9utm4hIpEX8AUCHDvQjm2G3h9HcHMaIEc52Kf54nkdTUxM4jotZDCWCbmgiyAWh3AdT3nEMtM6TkL5PzQKHRAiVPAnlIxoZ2UMtNkV8cjvE/hgZrYAJwCwSDodRV1cHURThcrkQDAZz9sSRLAVsNBubTFvXqAnbXIz6JULtwqg0UoxO36VaR1hYaMczz4zC1Vd/CCD6u2hqakK8J6AZUSGnlhImAtF56r/pqKFI/UsihWYAdtjtEcq/q6PiMzc3i+jcWYTNZsKBA7Hvx24n/y8AiHa0n3aaA2+/rd6oko/IFzVtjfJzXOqj7ORp43Q8CVNNGwOIEYTZ9CQ08vlAT9RSwD6fD2azWTJdZrQeJgCzCD143Ww2g+d5w3aiJUNNANIRTr2jftmAjvrRwlZe6yePCgiCgObmZkNG/dJB7cJIR2voSEkqdYQ//3kv/PrXffDKK9+iqSkMp9N1yjlfQIsxQXQ7r9cJn08t3Uv8BEV4vd5TjyMG0vLXjQBoQlT72RGNNnqghCiKKCgwYd+++H1veTsNsFgEmExmvPOOAw5H7n23rSUSiaCpqUla7GVjUZPOcQdkfpQdgLj0bzqehKkshnL1WqA3fr8fHo8nJ8+nRiN/rtQGxGQyxdTM0avUXER+wiINLYIgwOVyJa390ZJMWNekGvVrja9fLmMymRKm71LxI3z00eHYsuUovvvOd2r6hw3RKB4dSePg84VhNjsRiQSh3iXMwedrQlT0dUTLPF616CGZJhJ/ASkqAtxuEz7/XPn7CgY5RKOKIQgCj+ee64D+/dvHhYj4+pFJRR6PR1PbKqXjLlm5QiZG2dH3ZdqTMJ/OC5kmUQTQ6/Wyzy4DMAGYZejIWbqdtEaC3vd8N68Oh8Pw+/1x6Wy6m1DJ0Dlfon7popS+k3d9yi/MXq8F7713KYYP/zdqa5thMnEQhDCUPP8ikTBa5vommvkZNYeOpnuBqCC0oGWqSILlPmoAACAASURBVHTsm8tlQlNTfB1u585AQYEZn38OmM0iIpH4C8zx4xEAtQBE/O53XlxzTfs4hQqCgEAgINk56V3iASQvV2jtKDuyoCGkkjZWOvblaWO6uYQYZTMSo+YB2JYOYEYL7ePsZRByXQAKgoBgMHiqZiv7Y9zaSmusa1jUr+3QF2a73a5az+X1Au++Ox5jxnxw6kIZRHQqh1Kkj9xmR1TQhaE0SzieMOQzg6OHb2z6t3NnwOs145RBPzp2BE6ciH0muz1yaqaxBZdcYsNDD+WGhVNbIcc2AMnH1IgkquNLNC1HaZSd3JMw1bRxouYSpQg5EP189fYkNCJq526fzwePx8M+qwzABKCG5LIABFpGueWKeXU6n7daE0t7qfXLJonqufr2tWHFilGYNm0jRDGCqFizI3EXsFwMEg9AQD1NrPQ8UYqLAbvdjP37W+71euUCUIDZfBKiGEZpqROvv57/TR/E8ohE+nPx2E7W5a42ys5isSh6EtKCjkCEW7qehGQqT0tTUmzqur13G4uiqPj+iQBktB0mALNMrqeAaYNXADllXp0K8iaWDh06xJysWdQvO9D1XFde2QeiaMX06R/Dam1EKORCtD6PCEI15GLQBJtNQDTrZz31HCaQ1G/L48PguABEETjrLA6NjSZ8913sM3s8ZJ4wTu1HLZqawuja1Y4tW/Lft41u9HA4HIaO9KdDoggdPYqOPFap2xhoiSzKhSEhUdqY3EZEpNPpjPEkVGouybYnYS7R1jFwjBaYANQQujEhF06m9Bg3s9mMSCSSU+KvtdY1LOqnPVdd1QtNTeNwyy3rYTKFIAg2RE9PJkRFYLLIXlTchUJkokgi4QiYzTz69OFx+LAD9fXx97c4TIQBHAdgQteuNuzc6aWsYPIPURSlhY0ejR5aQ0foyLlNnjaWexKqCcJkaWMi/JQEoZIopfejPXoSsghg9mECUENyRQDS0y7IGDcSETD6vqcCHfWTN7GwqJ9+XHddbwQCEdxzz3Y0NhYiGoEzIRrNE2AyhSEIySLoHOx2C4JBpfpAE6L2LyLC4RC++eYIIpEzFJ/FZhMRnStcC0BAYaEDW7Z40aFD/n7XtLef1WrNuWlFmUItbdzaUXZqnoT0iLtkzSW0IFTyJJTPN84XWBNIdmECMMvQB3AmzYmzhdq0i1y0r1GKANJRP9q6hkX9jMGsWSXw+8P405++hd9Pos0cADMEwQSvN2oN0zI/OB6bLYyWsioOLRNGQiCNIxaLDeGwB0AjojOG6QtNCHv21J26TUBBgQ3btnXAaafl7/dNpisQ03qjNnrogVpTk1JjR7K0Mfkj4+9MJlPcXOJEnoRqzSU8z0v7QB5LC8JcFIWJIoAsBZwZmADUECMLwGRj3HIleimHfNaZiPoBYFG/LCOKIm65pTfq6oJ48smjCMdkcjn4fECLtQup7yNE08A+XxBmsxeRCJn3y0vbR8V+J4TDhXC7a9HYKCAqJAtPPYYHYMWJE2EALhQV2bFtW8e8FX90ja/ZbM6J5i69UWpqSnWUHRF8ZCFJC8p0PQnVmkvkUULyHLnWXKJ2rWlsbETnzp112KP8gwlADTGqAExnjJvR9j0R5D2wqF9uQHvN3XlnKbp29eLFFw/im2+aFR5NGjvkx2P0+7HZgEAgNg1st3sQDHZDtK7vIKJm0CFELWFqEY0EtjR/nH22DevXe+F2G/9i2RqIn2ckEoHdbjeUkXuukcgLUz7KDkBMdJBE6do6yo7cprYPas0luZY2bmhoYCngDMEEYJYxcgo4UVRMTi6dIGjC4TB8Ph+L+hkcJa+53/3uLPA88Pbbx7Bliw9qKV/l52sJHTocNoRC3RAMugGcAOADYEFjYwhmsxuRSAiAG9G0sgtAGOPHF2DtWg/M5vz8zkOhEAKBADiOg9vtzqvxjUZA3tgRDAbR3Nws3U43l9DRvEyOskvU8UzS0ErNJUZJG6tFAFkXcOZgv3oNMZIADIVCaGxslGp+kq3+jbTvqRAKhaSTG4v6GRe68UDp85437yyYTGY0NJjx1Vf1UI76xRMOB1BQ0BFABzQ0dEI0tfvDqW3NiIo9OyIRHtFIYASACSaTiNtu82D+fBOamwMxZsF6XxAzAWv00BbaS1H+eacyyo6uJQRiR9kl8iRMN21MdxwT6JSx1se/2hg4IJoCZhHAzMAEYJZROoD1FFH0GDer1QqXy5WSzUOuCED6/ZGToeOUpwd9sqNXzgQW9dMWuvEg0ed9661noLTUi9mzf8Dx442IzuIFLBbAbnegsZF0C0eL3885x4vGRhsOHQJKSwuwa1cDolYutMG0FSTd63BY0dwcQZ8+brzyShFKSznJEy4VC5Bcgfb2Y8d39kn2eatF6OQTc+jHZnqUnfw2uSBUShvT/+oBSwFnDiYANYT84PQQUaIYHeTe1jFuRhaAJKoJRN8fz/Nxvlws6qc/dFQk1caDyy4rwr59HfHII4exenUt6uqCsFqDOPNMBw4fdqJHDxdMJjO+/TaML74g498EfP75EVitzeB5B6L1fmQ+MHk9O0wmDg8+2BVz57akleQjANUsQOiLslGPGfLbb25ubhfefkaA53k0NTWl9XkrNZekMspOKW3cGk9Cch9dDqDkSUhH5+SCMFMLCrUIoCiKLAWcQTjRyFf0PEAQhJgfbV1dnTRKTSvISpTn+VaPcRMEAXV1dYacBCKParrdbphMJuk2j8cTc0JRi/o5HA4WFckymZow8cEHPixZchhffRWEy2VCJMLB7ycdvQRyjIfhdlvQ2EisY6wwm03o29eJOXOKMG1a6pM95JMjSLQEiI/SGKHbkm6ssdlsCRu8GG0nUco3U89PH3vySJ/SgkTuRUhuo1GLEioh9ySknytTnoTknO5wOOIE6dlnn433338fAwYMaNVzM1pgEcAsI/8BaBkBJBYP9Eq0teLNqClgedSPCAqyn+QCaLVa41bJLOqnHbTdSCaiUBdd5MVFF3lRUxPCggWH8N57fvTqZYHfz+HkyRBix8QBbrcZp59eALvdiuuuc+PGG12wWtP/vukojdrkCDVPOK3rqJQaaxjZg8xKz+b4vESNHUqj7BJFCVvbXJLMk5BuLmmtJ2GiGkAWAcwcLAKYZUj6hVBfXw+z2Zz1UTb0GDe73Q6Xy9Wmk5EoiqitrYXL5ZJq6vRELeoHxK6SQ6FQTLcvOQmRlTOrhco+kUgEgUBAE7sRURSxePExPPPMMdjtJlx8cQHuvrsz+vWzZ+X11PZBLUqjRR0hHYViixttoFO+qdZVZwv5goRuEkk2yk4uCgmJmkvkyD0J6chjqp6E5Jwhz1ZFIhEUFRXh5MmT6NixY9s+KAYTgNlGLgAbGhrAcVzWiljlY9zcbnfGVv61tbXSdBA9oaN+LpcrJuqnVOtHbiNF1fQhnyt1XLmIvPbM6XRqZjdiJMNyeXE/XUeV6eMvUyl2RmrIxXZbF9rZQF7HKre/oiPU5Jyp1lxCk07aWC4IaVEqF4TELLu5uTlOADY0NOD0009HMBhkEe0MwFLAWUbLFLDaGLd8gaRYiIWFUtRPzdeP7mgjdSVKaRP5bE8j1HHlInrXnhnpO1Oy30g1bZfq8SeKopTyZY0e2kAbaRtZbNNpY/koO6XjL5VRdtn2JKRLjujFnN/vZ8d2BmECUGOyIQCJt5faGLdMYcQO5lQ6fIkQkafD1Oq45CcjoxX2Gx1We5aYdOoIgdhuT6U6Qr3FdnuEpHw5LveMtBMdf2qj7OgooZInYVtH2dH7QAQhAOk88uc//xmCIKBPnz7o1KlTq4/vuro6LFq0CG+++SYCgQCGDRuGxx9/HIMGDUpp+4MHD2LevHn44IMPIAgCxo8fj6VLl6J3795xj/X5fLj//vuxZs0aHDp0CJ07d8aIESOwatUq3bNohNw5anMYWjhlWkSlM8atreghAOmon7yDOVHUj0REyIksmRAhqQd6jBKdsjNKYb+RSWbqzFBH6fhLxSQYABPbGpILKd/WoHb8qdkfKUUJ2+JJSO8DED3PhkIhOBwOCIKA/fv34//+7/9QX18PjuMwZMgQjBw5EqNGjcLIkSPRq1evpN+DIAiYOHEidu3ahUWLFqFTp05Yvnw5ysrKsH37dpSUlCTc3u/3Y/z48fD5fLj77rthsViwdOlSjBs3Djt37kRRUZH02Pr6eowbNw41NTWYNWsWSkpKcPToUWzcuBHBYNAwApDVAGoA/eMhjQttLWAlq34yxs3tdmc9LN7Q0CCll7KNWtSP3EefYOSdZXRExGq1wuFwtFmI0BdkclEm0F127VUQpmrqzGgdieoIAUhd7uRizD77zJMrKd9sQM659PFHN3aoLYqVPAnlzSVKaWMyycntdku3RSIRlJeX45FHHsGoUaPwySefYO/evQCAYcOGoaqqKuF7eO211/DrX/8a5eXluPLKKwEAx48fR2lpKS677DK8/PLLCbd/5JFHcMcdd6C6uhpDhgwBAOzZswcDBgzAokWL8OCDD0qPvfnmm/Hqq6/i008/xRlnnJH089ULJgA1gOd56cdCUrWFhYWtfj66CcLpdGo2xD3bDSyEdKJ+8noROupHhEg2kK+Qte70NAqtMXVmtA3aboQcX0b3I8x15GUNuZTyzRZyQShfFMtrqQHlUXZK3cbk+WgBCAD//ve/8fzzz2PdunUAgGPHjmHz5s3w+XyYOnVqwv2dPHkyNm7ciJqampjbZ8+ejdWrV6O2tjbh9WLYsGEwmUzYsmVLzO2XXnopvv32W/z3v/8FEE0zFxcXY+7cuXjooYekAJDdrp0TQaqwo1hj6Lq1dE/KiZogtCDbFxF51I/2LdQj6pcItcJqcuJSmhghPxnmOqzjVHtCoRACgYBi7Vlb6ggZyjBLHXU4joPVao1JG6uNsqM7fZU8Cem0Md0lHAqFYtLGPp8vJgDRpUsX/OIXv0hpf3fs2IHBgwfH3T506FC88MIL2Lt3L84991zFbQVBwK5du3DjjTcqbv/++++jsbERbrdbSvP26dMHkyZNwtq1ayEIAkaMGIFnnnkG559/fkr7qwVMAGpMa068iZogtISu88g0iaaVyF3sE0X99KqDSqfTk66hycWUXaZNnRnJoesr1SZMtLaOMJ8WJZmkPad8W4NaYwftR5hslB0pJQEQ0+hHzv1ff/01GhoapIa/dDh06BDKysribi8uLgYA1NTUqArAkydPIhQKSY9V275v375SJPDOO+9ESUkJ/v73v6Ourg733nsvJkyYgC+//BLdunVLa9+zBROAGkCfNGjhksrJhDZ0bu0Yt0yRrQ5mMq2E4zjVqF+yDl8ton7poNZpp9ZYkispOy1NnRlRwuEwAoGA1OiVan2lmvVGoghNri5KMg2d8s21Ll8jYTKZYqZPJVqUkOsLx3ExaXaS9Vm9ejX+8pe/4JprrgGAmE7lRJDBBc3NzYppWHI/+b6VIPelsr3f75fe+0cffSSNfR00aJAUBbz//vtT2vdsw45qjaEFYCJoYdTWMW6ZJJMCUD6thE6vyKN+benwNQryk2EqI8SMIgjlps7soph96M+cTA9qS6Q1WYQmVxclmYSObrOUb+ZRWpREIhE0NzdLaV9RFNHQ0ICf/exnOOecczBs2DDs3LkTGzduxIcffojhw4ejsrISEyZMSOk1d+/ejdLSUjidTkls0tA142qQ+1LZnvx7xRVXSOIPAC688EL07t0bmzdvTmm/tYCdwTUmFQFIGzpnYoxbpsjUPiQSt7kc9UuXRCk7uReXnjVczGdOe+j0YzYjrUoRGiVBCOR/HSFL+WoPqbGMRCJwOp2w2WwQBAF1dXX46U9/ii1btuDtt9+WOoLvuecejBo1Cueccw6effbZlOxUSLq1uLg4rgEEiKaGAaB79+6qz1FUVAS73S49NtH25N/TTjst7rFdunRBbW1t0n3WCiYANUAtBSyHNnQ2mUzwer2GimxlIgWcaEZxPkb90kFtdaxWw6VFpzEzddYePdOPSoX9cj+4fKwjZClf7aHr/ejP3GQyobCwEL/61a/wxhtv4KabbsKvfvUrVFVVYdOmTXjmmWdw4sQJ/OY3v8ELL7yQ8usNHDgQGzZsiCu/qqqqgtvtRmlpqeq2JpMJP/nJT1BdXR13X1VVFfr06SN1LF9wwQUAgB9//DHusTU1NTjnnHNS3udsw45yjVETgLkwxq0tAlAe9aPFbXuK+qVDohouuTlrpi/GzNRZe4zYcZqs212tjjBXxiiylK/2yEsb5HXtgiDghRdewP33349nnnkG11xzDTiOkxo4RFHE3r170y6HmDRpEsrLy/HGG2/gqquuAhD1AVyzZg2uuOKKmMXtgQMH0NTUhLPPPjtm+zvuuAPbt2+P8QGsqKjAwoULpceVlpbi/PPPx9q1a3HixAl06tQJAPD+++/jxx9/xK233preB5ZFmA+gBpDVMxA9eGtra+F2u6UTalNTE4LBIMxms6FXn8FgEI2NjSgsLEzrxJ5O1E8vX79cRKnTWG49Q9dwpQozddaeXLbUkdcRpmIQbAS0SrMzWqAXlkrlJE1NTZg7dy62bNmC8vJyDBw4MGOvLQgCRo8ejS+++AILFy6UJoH8+OOPqK6uRt++faXHlpWVYf369TGuF36/H4MGDYLP58OCBQtgsVjwxBNPQBRF7Ny5UxJ6AFBZWYmLLroIJSUlmDVrFurr6/HEE0+gR48e2L59e0xtoJ4wAagBgiBItTREALpcLpjNZs3GuGWCdAWgPOrndrtZ1C/LJLoYy70I5d8hM3XWHnlzDTkv5DLJDIKNMDWHTj8yY2dtoBc5SuUk3333HaZMmYLu3btj9erVMaPVMkVdXR0WLlyIt956S5oF/Nhjj8X5A44fPx7r16+POXaBllnA77//fsws4LPOOivutT766CMsXrwYO3fuhNvtxsSJE/HII4+ga9euGX9frYUJQA2gBSAQ9RQym82IRCKajXHLBKFQCH6/Hx07dkwqDNoa9SNmtyzq1zbkncbkhCbvNCYr81yMQOUq7aW5Rl5HSE/y0bqOkKV89YHneSkQIF/kiKKIjz76CDfccANmz56NP/zhDzlxPcwH2LJHQ4i4ASCNucmlC20qHcwkihQIBFitnwFQ6jSmozNyLy2bzcZOvhogj0Dl8yJHrY6Q7jTWoo6QpXy1Ry645Y4WgiBg6dKlWLp0KV566SX88pe/ZN+JhjABqAFkggYZ4wZEL7RGnA2YiGQCMFEjSyodviTql+8XRD2huzzplAwRfaFQSLoYGyVdl0/QF8T2mmZP1yQ9E3WEah2njOxB6ttJFkguuH0+H2bPno09e/bgk08+iWm4YGgD+xVoAM/zqK+vBxCdb5uqg7lRkQtAFvXLLRKZOrcX2w89YFNU1EnmR0ifM9OxQGKCWx/I4lIURcUF/Z49ezBlyhSce+652LJlCwoKCnTa0/YNE4AaYLFY4HA4YLfbYTKZEAwGMz5STQuUTrTJon6RSASCIIDjOBb1MwDJ6s7Stf1obadxeyMUCkkLJBaBSk4qfoTJOt7pY50Jbu2gj3WPxxNzXhBFEe+88w5mz56NhQsX4vbbb2fnDR1hZyENMJlMMY7lJCWca9DijY76mc1mFBQUxESRyAlbFEUW9TMIrTF1lo8PI98tsZ0hF2MgN33gsg1te2G1Wg3p75kLJKsjlB+HJpNJst4iEyYY2YV2EVA61iORCB588EH89a9/xSuvvIKLLrqI/RZ0hglAjaBNlDMxUUMPyI+VDKdnUb/cIJOmzqRj22azxdRv0Rfi9j5PliD3U2QiJHMkqiMMhUKS+AOis1rJsW9EP8J8INkYvdraWsycORPHjh3D1q1b0bt3bx33lkFgAlAHclUAkn0mDu7yqB+p80sl6sciIdqghalzsk7jTBf0Gx1Wd6YfPM9LNZZkrqzSbG2tRim2B5I12Hz++ee45pprMHr0aLz55puGMUFmMAGoC7koAEmtHwBYrVZ4PB4W9TMwepo6q9VvJboQ50unMbMa0Qc1Wx1SHwi0ro6QoU6ykW6iKOK1117Dbbfdhvvuuw+33HIL+1wNBhOAGiEXfbkiAEn6kPzIAUgrZhb1MyZGGyumNNOYvhCTaBmQ25EZusaSNXpoQzIRQpNOHWF7L19IRrKRbjzP45577sHrr7+OtWvXYsyYMezzMyDsDKUDdHTMyD+KcDgMv98fM6quvr4+psmDRf2MA516JB14RjR1TtZprBSZMbL1DB1tZdMltKOtXb5qdYT0xJL2Vr6QCsTTllwX5LWtR48exbRp0xCJRFBdXY0ePXrotKeMZDABqANGP3HIo350rR/QUmzNon7GIZc95pQ6jRN1eNJpY3qMoB4YLdraXsjWJBWlelZaELb3OkI6yq20wKyursa1116Lyy+/HMuWLcu5YQftDSYANULutQYYMwLI83zM6o4O7ZP9DYVC0hxjcgIk27Kon7YkMnXOVdKdFKFHqk7+uRs12ppvpJPyzQRK5Qt0Y0l7qSNMNkNZFEX87W9/w913343HHnsMM2fONNy1jRFPbl8pcpRUZupqDRnbQzoXO3ToEBeREQQBdrtdqt+ia7dITSDretSOZKbO+YR8UkQqqbpsCcL29LkbCXq0mF6fO704aS91hMlGujU3N2PhwoX48MMP8d5772HYsGGa7FdjYyMeeeQRVFVVYevWrairq8OKFSswffr0lLavq6vDokWL8OabbyIQCGDYsGF4/PHHMWjQoCzvuXFgAlAHjCYAk0X96Fo/i8UiRfYEQUAwGIxZBUciEfh8vrzr7jQarTF1zicSperkncb0Rbitx2K2Uo+MxBj1c0+njhDI7LGoBXSJg9Ln/uOPP+Laa6+F1+vFtm3b0KVLF8327dixY7j//vtxxhlnYODAgaisrEz58xQEARMnTsSuXbuwaNEidOrUCcuXL0dZWRm2b9+OkpKSLO+9MWACUCPUUsB6Qkf9LBYLvF6vYtQv1Vo/AEm7O3PlxGdUMmnqnE+odRqTi3BbO41Zo4c+aJ3yzQTJ6gjlx6JR52vzPI+mpibFEgdRFLFx40ZMnz4d1113HR566CHNS0+6d++Ow4cPo2vXrti+fTuGDh2a8rbl5eXYvHkzysvLceWVVwIAJk+ejNLSUixZsgQvv/xytnbbUDABqANGEIB01M/lcsWE9dvS4avW3UlSI/lg96EXWpg65wvp1G4luwjTDTas0UM7jJDyzQSJjsVk87X1aHKi6/2UGvkEQcDy5cvx0EMP4bnnnsPkyZN1+V5sNhu6du0q7XM6lJeXo1u3bpL4A4DOnTtj8uTJWL16tTS6Md9hAlAjjBIBzHTUL9EPX61eRu0izAShMnqaOucLyWq31C7CpMwhXxpscgV6sWOklG8mSDRfmzapJo/Vso6QPscrLXYaGxsxZ84cbN++HevWrcN5552XtX3JJjt27MDgwYPjbh86dCheeOEF7N27F+eee64Oe6Yt7GymA3oJwGxF/VIlkSCU+7/RUZn2LAiZzUh2SKXTmFyEAUhiMBKJ5Gwxfy6QiynftkLP1yboUUcYiUSkaU9Ki519+/ZhypQp6NWrF6qqqlBYWJiR19WDQ4cOoaysLO724uJiAEBNTQ0TgIzsoeU4OLKq0yLqlw7J/N+UojJGrJXJBrli6pxPkE5j2maGXJRJGhjQptO4PZJsukR7Qus6wlAohEAgoDrS7YMPPsCNN96I3/3ud1i8eHHOn4uam5sVPQodDgcASL/1fIcJQI2Qn8i0EoChUCimiyudqB9ZiWvl6yePyrRXQZjLps65DC261S6EdIQwW53G7ZFk3abtnXTrCFM1S6fLS9Tq/R599FE8/fTTWLlyJS6//PK8OLadTqckoGnIb5o0NeY7TADqRLYFIBlKT7oW0436kYH2ek7zSJSmU5oQkesmrPlo6pwr0Me8mujmOA5WqzXlqAzrek8OM9RuHfLsCZC+WTp9zCuNdGtoaMBvfvMb7N+/H5s3b0Zpaamm7zGbFBcXo6amJu72Q4cOAYh2GLcH2NVFJ7IpAEOhUEwtB103ZrSoX7qQNF0qglCepjMyzFxYP4jdBcdxaYluNesZuoYwF+w+9IKlfDOL3CxdHrGm6whNJhMEQQCg7Kv49ddfY8qUKRg4cCA2b94Mr9erzZvQiIEDB2LDhg1x07iqqqrgdrvzSuwmgglAjdAiBUxH/axWK9xut3ShSSfql0snYzVBKD/pGVkQtndTZ72Qeyq6XK42HfO0IFRqckpk92Gk41EL6JSvUvSJ0XaUItZkYRIOh6XHNTU1Yd68eSgsLMSoUaOkyR533303brvttpw/Ng8fPoy6ujqUlJRIi7VJkyahvLwcb7zxBq666ioAwPHjx7FmzRpcccUV7eYczASgTpAQfKZQi/oRiwEi/pJF/XI97ZhoZJhSWoSOyOjht8VMnfVBLkCy4amYyO4jUcRar+NRC2hHAZby1R6e56UsA1mkkHPi22+/jeeeew4A0LNnT3z11Vf429/+htGjR6OkpMSQx+PTTz+Nuro6KZ37r3/9CwcOHAAAzJkzBwUFBbjjjjuwatUqfPfdd+jVqxeAqAAcPnw4ZsyYga+++kqaBCKKIu69917d3o/WcKLe4yjaEbTNSWNjI8LhMDp06NCm50wW9YtEItJryouBczXq1xbkgpCIcK0FITN11gd5zZnL5dJVgCQ7HnN9jiwNS/nqR7KI64kTJzBz5kzwPI+rrroKe/bswcaNG7Fr1y6IoogLL7wQW7Zs0Wnv1enduze+//57ALH2ahzHYf/+/ejVqxdmzJiBVatWSf9PqKurw8KFC/HWW29Js4Afe+wxRX/AfIUJQA2hBSARbR07dmzT85Gon8vlShr1IyjV+uVy1K8t0BEZUr8FZO8CzEyd9SMX6izldVv08UhHCHOtsYSlfPWDHummtOD57LPPMGXKFJSVlWH58uUx+MBuRgAAIABJREFUHbD19fXYvHkz/H4/Jk2apPWuM7IME4AawvO8tMIPBAJobm5ulZmmIAhobGyUxtWwqF/mSOUC3FpByEyd9YOusyQR11yA7jSmj0cgdzqNicecESKu7Qna1kipxlUURbzyyitYsGABHnzwQdx0002GPYYY2aF9hn0MQGuaQEjkrqmpCUBqtX7ybfOl1i9bqBVOy73f0hGEzNRZP+iIay7WWSbrNFaynjHKOEU65aunnVR7hJ6jrGRrFAqFcNddd2Ht2rV4++23MXLkSPbdtEOYAtAJeb1CMuion81mi0kdyqN++dLhawQSeb8pmQHLU3TM1Fk/8jHimqzTmC4z0dMsnaV89YN89mpzlA8fPoxp06aB4zhUV1e3G887RjxMAGoIffFJVQDKo34ej0c6mbKon/aoRWSUBCHx2jKqp2K+0p46TZONU0zkjZloQkRboFO++fzZGxH5Zy+/HmzduhXXXnstrrzySjz++ONMmLdzmBrQCVoAqtGWqB8deWJRv+yhJAh5nkcwGJTqPUk6ho0Lyz7tPe2YaHpOqhMiWkt7/+z1JNlIN1EU8de//hVLlizBsmXLpAggo33DBKBOJBKA6UT95CdtFvXTFzoKSLqrk9VsMUGYGWhrHRZxbSGRNyYxqAba1ujEUr76QZf4KJU6NDc347bbbsO6devw4YcfYsiQITruLcNIMGWgIWopYBpyImVRv9wikamzvGaLCcLMQjfZMGud5JhMJphMppTrWpNFrVnKVz/IogeA4mL/wIEDuPbaa1FYWIjq6mp07txZj91kGBRmA6Mh5CQLRFdtdXV1UnSPXMTomaTyqB8Rf8mifu3Z108P2mLqTBfxk4gM+UkaravTiNDRD9ZkkxloQUifs4DYY9JsNqO5uZmlfHWAPucrLXpEUcS6detw/fXXY+bMmXjggQfYNYERBzsidIKOAEYiEWkyCIv65Q6ZMHWma7bkXZ2kgJ8WhPIi/vYMiTyxUofMolTXqnZMAi2iMFVHA0bbSDZRRRAE/PnPf8ajjz6K559/HpMmTWLfC0MRFgHUEEEQpHobURRRW1sLq9UKnuclk9TW1PqZTCY4nU52AdQQrSxG5DYf4XC43QtC1mygL8FgUMo2mM3muAWqvLGEkTlIY6BaraXf78ctt9yCXbt24fXXX8eAAQN02lNGLsAUg06QDlGe52G322Nc2lnUz7hobeqczOaD5/kYmw+9fN+0Qt5swGYoa0eiTlP5IoUck1rP2M5n6Ho/pfPON998g6lTp6JPnz7YunVrm+fMM/IfFgHUEEEQEAqFpFo/ALDZbPB4PABY1M/oGNHUmRwzJD1HRwjzSRDKj302Ukxb0o14y70IyYI3EyMV2xv0sa80zUYURfznP//BrFmzMHfuXNx99905/VtnaAcTgBoSDodRW1srjecJhUJS9C/ZDF8W9dOPXBPe8qYScvHN1fScIAgIBAJSjSw79rWF53k0NTW1SXirzdgGUus0bq8kG+kWiUTwpz/9Cc899xxWrVqFyy67jH1+jJQx7lUsTxFFEV6vF1arVVoZ03/Jon6s2F1bclF8EN83NSNgtckQRhSEZKIHAObtpzHJzIXTIdFIxUgkwuyQFKCjrkrHfl1dHWbNmoUffvgBW7ZsQUlJiU57yshVmJLQELPZjIKCAumExnGcdHEmY9xY1M845Iv4UDICTjQZwgiCkBYfSmkvRnZJZi7cVtRGKtKLFFoQ0hHC9nAc0FFXpXq/r776Ctdccw2GDh2KTZs2SWVEDEY65P8vyUCQtC6p8+M4DuFwGH6/H4FAAKFQSErZBYNB+P1+iKIIt9vNOh01hKRdmpqaYDab4fF4clb8KUEEodPphNfrhdfrlQQuWXT4fD40NDSgqalJOi61qhaJRCLw+/0IhUJwOBzM2FljeJ6Hz+eDIAjweDya1LoSQWi32+F2u+H1euHxeOBwOGAymSRB5PP54PP5pPMlKW/IF8jCh4yOlIs/URTxxhtv4KKLLsLNN9+M1atXayb+gsEgbr/9dnTv3h0ulwvDhw/Hhx9+mHS7lStXSubj8r+jR49qsOcMNVgEUAeIyLPb7dJFV+7AD0RXvg6HgxW7a0hbTJ1zFflkCHkBf7Zmx8ohc5TZVAl9kEddaWcCrVHqfpc3OymVMuRypzFdbqIUdQ2Hw1iyZAn++c9/4o033kBZWZmm7/P666/H66+/jnnz5qFv375YsWIFfv7zn6OiogKjRo1Kuv3999+P3r17x9zGOpX1hTWBaMiOHTvw8ssvY/To0RgxYgQ8Ho/0Aw6HwygvL8cll1wiXZDpzrlsXngZmTF1zlfoCy9J0wGZPS5zsdYyn8h2yjcbJOo0zrXzJRkGAEBxktPx48cxffp0NDU1Yc2aNejVq5em+7d161YMHz4cjz32GG677TYA0YjggAED0LVrV2zatEl125UrV2LmzJnYtm0bBg8erNUuM1KARQA1xOVyged5/OEPf8Du3bsxaNAgjBkzBv369cOzzz6LTz/9FG+//TbGjRsnpYqT1Wrl8orXKGhl6pyrKBXwqx2XrbH4oP3NcrnWMlch6dVcm6iiNNOYXqgoHZckqmik3zeZaKO28Pz0008xdepUXHzxxXjqqafgdDo138fy8nJYLBbMmjVLus1ut+OGG27AXXfdhYMHD6JHjx4Jn0MURfh8PmbhZCBy45eeJ/Tr1w9PPvkkRFHE8ePH8fHHH+Opp57CY489hm7dumHixInYuHEjTCYThg0bJqWI1S68pEGBCcLWobWpc76QTBCSUoZkgpD+/FnUVXuMlPLNBIk6jeUlNkboNE7WZS2KIlavXo077rgDDz/8MGbNmqXb97Njxw6UlpbG1RsOHToUALBz586kAnD8+PHw+/2w2Wy45JJL8Pjjj7POZZ1hAlAHOI7DiRMnsGzZMlRVVWHu3Ln4/e9/j61bt6KiogLz5s3D999/j6FDh2Ls2LEYO3YsLrjgAthsNsULL7n4KglCvbs5jYoRTZ1zlXQuvPQx2dzcLNXCss9fW3Ix5ZsuyTqNlaxnyL/Z/izoz1+p1jgYDOKOO+7Au+++i3fffRcjRozI6v4k49ChQyguLo67ndxWU1Ojuq3b7caMGTMwfvx4FBQUYNu2bXjiiScwcuRIfPrpp+jZs2fW9puRGFYDqAOiKGLQoEEIBAJYsWIFRo4cGXf/oUOHUFFRgcrKSqxbtw41NTW48MILMXbsWIwZMwZDhgyJO1HJi6TlNTFMEOaeqXM+IBeEtAmwESIx7Q25vVF7Pf4TzdnO5hQdecmD/POvqanBtGnTYLVa8eqrr6Jbt24Ze+3W0qdPH/Tv3x9vv/12zO379u1DSUkJli1bhjlz5qT8fJs2bcLYsWMxa9YsPPvss5neXUaKtM9fvs5wHIfy8nL06NFDsZ6D4zh0794dU6dOxdSpUyGKIn788UdUVFSgoqICq1atwtGjRzFixAgpQjhw4EBYLJaYSIxaN2cuGABnA9ZooA90UT45JsnxJzcBZlMhsgedcmfeisnnbCcyTZdPakoFevGpVPIgiiI2b96M6667Dr/+9a/xyCOPGKYe1ul0Sr9RGhLdT7cucdSoUbjwwgtTspFhZA8mAHUindoHjuNw+umnY9q0aZg2bRpEUcT3338vCcK//OUvqKurw8iRIzFmzBiMHTsW5513nrRyTUUQ5uKIsHTIF1PnXIXUrAqCEJfySiU1xwRh22gPKd+2QgtCtSk6rbVEEkURgUAAPM8rLj4FQcCLL76I++67D08//TSmTJliqO+nuLhYMc176NAhAED37t3Tfs6ePXti7969bd43RuthAjAH4TgOZ555JmbMmIEZM2ZAFEXs27cPH3/8MSorK/HMM8+gsbERo0ePxpgxYzBu3Dicc845ioJQbURYttIfWkOfeFnUQ3vkUQ+lRhu6RMFut8ek5pSmQjBBmB704ieXunyNgNIUHXqEXSod8LTLgNPpjHk+AAgEArj11luxefNmVFRUYODAgdq9wRQZNGgQKisr4fP54PV6pdurqqoAoFX7vG/fPnTp0iVj+8hIH1YDmIcIgoC9e/eisrISFRUVWL9+PXiex6hRozB27FiMGzcOZ599dpwQkgtCUkOYq4KwPZo6Gwk66tSWRg+5IKSnkmhdvJ9LsJRv9pF3wNP1reR45HlestiRL36+//57TJkyBd26dcPLL7+MoqIird9CShAfwEcffRTz588H0OID2KVLF3zyyScAgMOHD6Ourg4lJSXSQuPYsWNxQu/dd9/F5ZdfjltvvRVLly7V9s0wJJgAbAcIgoCvv/5aaipZv349OI7D6NGjpRrCvn37qgpCcmJTEoRGvOgyU2f9yWajQaLifXpurBGPTa3IlPhmpAfd8MTzfMyounA4jCeffBIjR47EiBEjsG3bNsycORO//e1vce+99xregup///d/8eabb2LevHno06cP/va3v2Hbtm346KOPMHr0aADRaSGrVq3Cd999J5lV9+3bF4MHD8aQIUPQoUMHfPrpp3jppZfQo0cPVFdXsyigjjAB2A4RBAFffPFFjCC02+0xgvCss85KKAjVOub0vugyU2d9kXvLaRF10qub06iwLl99IbPESbMZGff59ddf45e//CWOHz8uHZeXXHIJrr/+eowZM8bwQigYDGLx4sVYvXo1amtrcf755+P+++/HRRddJD1mxowZWLVqFfbv3y8JwMWLF+Odd97B/v370dTUhO7du2PixIlYsmSJ4d9zvsMEIAPhcBi7du2SBOHGjRvh8XikhpKxY8fijDPOiBNSaoJQjyiM3NSZuc1rj1HEt1I3Z3sQhCzlqz/0b0Cp2ay+vh7z5s3DkSNHUFBQgM8++wzff/89AKB///6YOXMmFixYoMeuM9ohTAAy4uB5Hjt37pQE4aZNm1BUVBTTVNKzZ884D0K1i26267SYqbO+yL0VjSa+RVGM88iUC8Jc74BnKV/9ISPd1H4De/fuxZQpU9C/f3+sWLECBQUFAIADBw5gw4YNWL9+Pfr374+5c+fqsfuMdggTgIyEiKIInuexfft2VFRUYN26dfjkk0/QtWtXyZR67NixKC4uVhSEanVamejkZKbO+pOr3orJ6ltzSRAmMxZmZJdURrq98847mD17NhYsWIA77rgjJ44rRv7DBCAjLYjoqq6uliKEW7ZsQY8ePWIEYdeuXVUFoVonZzqCMFeFRz5BCw/SZZ2rJOqAN6ppOpulrD/J/BUjkQj++Mc/4i9/+QtWr16Niy++mJ2nGIaBCUBGmyAXoS1btkiCsLq6GmeeeSbGjBkj/XXu3DljgpAucs914ZGLtAfhIS9nUBur2JqJEJnaP7IAYilffUgWea2trcUNN9yAo0ePory8HGeddZYeu8lgqMIEICOjEONlYmpaWVmJ7du3o6SkRIoOjho1CkVFRQkFYTgclu6jawhDoRDC4TArcteJ9lpvmYogJE0l2f48WMpXX0hZTCAQUF0AffHFF7jmmmswcuRIPPfcc3C73TrtLYOhDhOAjKxCLBE2btyIyspKrFu3Dp9++in69+8vNZWMHj0aHTp0SFkQkmkmbBqEttBF7u293pJuKiFj7ID0R4Sl+5r5Hnk1OslGuomiiDVr1mDevHm47777cMstt7DviGFYmABkaIooivD7/diwYYPUVLJr1y6ce+65MRFCj8cDjuPQ1NSEe++9F1OnTkXv3r1hsVgkYUigIzBMEGYe+qKnVOTOSCwI1UaEpQNL+eoPXe+nNNKN53ncc889KC8vx6uvvooxY8aw74hhaJgAZOiKKIqor6+PEYRffvklzj//fPTv3x+bNm3CDz/8gGeffRaTJ0+WTqi04z75l0DXaGmRkstn5OP05Bc9hjJqI8JaIwjzqdkmV6G/A6WRbkePHsX06dPB8zxee+019OzZU4/dZDDSgglAhqEQRRFHjx7FbbfdhldeeQWnnXYa3G43unTpInkQXnjhhYqpF7pGSz6TkwnC9GDpxszSmuOTtjli34E+0N+BWt3xtm3bMHXqVFx++eVYtmwZ7Ha7TnvLYKQHE4AMQ/HNN9/guuuuw9atW3HnnXdi8eLFaGhowLp166QI4bfffosLLrhAShkPGzYsLiWmdsHNVEoun2GmwtknmSA0mUxSKpl9B/pAj3RT+g5EUcSqVatw11134dFHH8UNN9zAviNGTsEEIMNQ3HfffVi1ahX+/ve/Y8SIEXH3i6KII0eOoLKyUvo7cOAAhg0bJo2tGzJkSJwfFxOEqcHmyOoDfXzyPC91GQOIaSphNa7akGykWzAYxMKFC/HBBx9gzZo1GDZsmE57ymC0HiYAM8Thw4exbNkyVFVVYdu2bWhsbERFRQXGjRuX8nMcPHgQ8+bNwwcffABBEDB+/HgsXboUvXv3zuKeGwue5xEMBuHxeFJ6vCiKOHTokGQ5s27dOtTU1ODCCy+UBOHgwYPjRtAlEoTZ6uI0MvQ0A2axow/yyTYOh0OKAirZIjFBmB14nkdTU5PqSLeDBw9i6tSp8Hg8eOWVV9C1a1ed9pTBaBtMAGaIyspKTJgwAaWlpejUqRM2b96MyspKjB07NqXt/X4/Bg8eDJ/Ph/nz58NisWDp0qUQRRE7d+5EUVFRlt9BfiCKIn744QdUVFSgoqICGzZswLFjxzBixAhpUsnAgQPjIluJiva19nnTGjraoTTNgJF9kk22IQsWetFCYIIwM9B1rxaLBS6XK+472LhxI6ZPn45rr70WDz/8MIuQM3IaJgAzhN/vRzgcRseOHVFeXo7JkyenJQAfeeQR3HHHHaiursaQIUMAAHv27MGAAQOwaNEiPPjgg9nc/bxFFEV89913kiBcv3496uvrMXLkSKmp5Cc/+UncKj+Zz5sRR4OlizzipBTtYGSf1nT5pjJJh/zLBGFyaAGutAgSBAHPPvss/vjHP+K5556LcSRgMHIVJgCzQGsE4LBhw2AymbBly5aY2y+99FJ8++23+O9//5uNXW13iKKIb7/9Fh9//DHWrVuHdevWoampCaNGjZJSxuecc05CQZhoNFiuCEI2S1l/MtnlywRh64lEImhsbASgXPfa2NiIOXPmYPv27SgvL8d5552nx24yGBmHxa8NgCAI2LVrF2688ca4+4YOHYr3338fjY2NbJxQBuA4DiUlJSgpKcGsWbMgCAL27t0rRQgff/xxhMNhjB49WhKEZ599tjR9hERn5KPBeJ4HEJ1SIm8qMRryUWLMV0576A7TTAhw0sxkNptht9tjBGEkEkEoFIoRhPQx2p4FIZluoybA9+3bhylTpqBXr16oqqpCYWGhTnvKYGQeJgANwMmTJxEKhVBcXBx3H7mtpqYGffv21XrX8h6TyYSzzz4bZ599Nm666SYIgoCvvvpKaip56KGHYDabpbF1Y8eORd++fWEymSRRCCQWhPKmEr1gjR7GgDbXzpYApwUh0BIhpDuNQ6EQgJZjlK5zzXfo34LSdBtRFPHBBx/gxhtvxO9+9zssXryYlUcw8o78/6W3AnJySOUvExDbDSUDUYfDEfMYRnYxmUwYMGAAfv/73+P111/HkSNH8N5772H06NGoqKjAT3/6U/Tr1w8zZszAihUr8O2330IQBEkMOp1OeL1eeL1eKZ0UDocRCATg8/ng8/kQCAQQCoVirD6yDUlzhUIhOBwOZiqsA6TJoLGxESaTCV6vV7PoKxGENpsNLpcLXq8XHo8HTqcTZrNZ6nylj1G5HU2+IAhCzG9BLv4EQZB8/V566SUsWbJEU/EXDAZx++23o3v37nC5XBg+fDg+/PDDlLatq6vDrFmz0KVLF3g8HkyYMAE7duzI8h4zchUWAVRg3bp1mDBhQkqP3b17N0pLS9v0ek6nE0D0hy+HiEzyGIa2mEwmnH/++Tj//PMxd+5chMNhfPbZZ6isrMR//vMf3HPPPfB6vRgzZowUITzjjDMUI4R0l7FW0RdRFCVvP5PJBLfbzToXdSDTKd+2QkcIyXg/uoZQ6Rg1QhS7rchHusl/Cw0NDZg1axb27duHzZs3t/nc3hquv/56vP7665g3bx769u2LFStW4Oc//zkqKiowatQo1e0EQcDEiROxa9cuLFq0CJ06dcLy5ctRVlaG7du3o6SkRMN3wcgF2JVAgf79+2PlypUpPbZbt25tfr2ioiLY7XYcOnQo7j5yW/fu3dv8Ooy2Y7FYMGTIEAwZMgTz588Hz/PYsWMHKioqsHbtWunESwThuHHj0KNHD5hMppg5uskutpmozxJFUYrkKKW5GNqQzFTYKJBjNBVBaPQ6VzmpNNzs3r0bU6ZMwXnnnYfNmzfD6/Vqvp9bt27Fq6++isceewy33XYbAOC6666T3CA2bdqkum15eTk2b96M8vJyXHnllQCAyZMno7S0FEuWLMHLL7+syXtg5A6sCzgLtLYLmOM4VFVVxdx+8cUXY//+/awLOAcg0bbt27dLNYSbN2/GaaedFiMIu3XrFifE5Bdb8rNsrSCk68ycTmeM+GRoQ77N8pVHsUl62OiCkF4IqXks/utf/8Itt9yCO++8E/Pnz9ftPSxatAjLli3DyZMnY8zwH374Ydx111344Ycf0KNHD8VtJ0+ejI0bN6Kmpibm9tmzZ2P16tWora017OKDoQ/G+qW2Ew4cOIDdu3fH3DZp0iRUV1dj+/bt0m179uxBRUUFrr76aq13kdEKOI6DzWbDiBEjcNddd+G9997D8ePHsWLFCvTu3RurV6/Geeedh8GDB2POnDkoLy/HkSNHIIqiFH1xuVwoKCiA1+uNq89qaGiA3++XLmZKazdSv0rXmTHxpz1EdDQ3N8Nms8HtdhtOGKULOUaV6lwjkYhU59rQ0ICmpiaEQqEYOxo9EAQBfr8fPM/D6XTGRcHD4TD+8Ic/4NZbb8Wrr76KBQsW6Po97dixA6WlpXGTkIYOHQoA2LlzZ8JtBw8eHHf70KFD0dTUhL1792Z2Zxk5D0sBZ5AHHngAAPDll18CAFatWoX169cDAO655x7pcdOmTcP69etjCqxvvvlmvPjii5g4cSIWLFgAi8WCJ554At26dcP8+fM1fBeMTMFxHOx2uxT9W7x4MZqbm1FVVYWKigr89a9/xW9/+1v07t1bqh8cPXo0OnfuHJOOS9TBSXu8mUwmBAIBRCIRxeH1DG3IlZRvW0m1E16vaTr0SDePxxPXyHHixAnMnDkTtbW12Lp1K84888ys71MyDh06lNQNItG2ZWVlCbc999xzM7OjjLyACcAM8v/+3/8Dx3EQRREcx+Gll14CED0B0gKQ47i4E6DH40FlZSXmzZuHBx54IGYWcKdOnTR9H4zswHEcnE4nysrKUFZWJkWJPvnkE1RUVGD58uW44YYbUFpaKgnCUaNGobCwMKZgXy4IaY83ALBarazRQwfkk1WUREc+IxeE8mk6xMkg2/O2k410A4DPPvsMU6ZMQVlZGdauXQuXy5Wx128LgUCg1W4Qzc3NzEmCkRbsKpFBUrVMqKioULy9R48eeO211zK5SwwDw3EcXC4Xfvazn+FnP/sZRFFEY2MjNm3ahIqKCixbtgzTp09H//79JWPq0aNHo6CgQBKEoVAIq1atwrXXXitFV+joC5sTqw3J6szaIxzHxZiny+dt0xFCeQ1haz87uttaKQouiiJeeeUVLFiwAA888ABuvvlmQ31PTqez1W4QbdmW0T5hApDBMAgcx8Hj8eCSSy7BJZdcAlEU4fP5sGHDBlRUVODRRx+VOgLHjBmDs846C0899RQOHjyIcePGYcCAAVIEmm4qCYVC0oWBCcLMQ6d8WcONOskEIREqrRWEyVLvoVAId999N9566y38+9//xqhRowx3/BcXFyumeVNxg2jLtoz2CROADIZB4TgOBQUFmDhxIiZOnAhRFFFXV4f169fjiSeewJNPPolevXph1KhRWLNmDY4cOYIRI0bA5XKpjgULh8NSegxggrAtyD0W21vKt60oCUK6hpA22qePUaXjlIx0U/seDh8+jOnTpwMAqqurDSuGBg0ahMrKSvh8vhgbGuIOMXDgQNVtBw4ciA0bNkglSPS2brdbF09DhrHJ7bY0BqMdwXEcQqEQnn/+eaxfvx5z5szBJ598gptuugmhUAiLFy9Gr169cPHFF+O+++5DZWWlVPdDxKDb7UZBQQE8Ho+UpiTTKRoaGtDY2Ijm5uYYKxpGPCTlGwgEYLVamfjLAKQ20OFwwOPxoKCgAG63W1rEkO52cpwGg0Gp0UPtexBFEVVVVRg7diwGDhyIjz76yLDiD4i6QUQiEbzwwgvSbcFgECtWrMDw4cMlC5jDhw9j9+7d/7+9ew9q8kr/AP5NuAQIN0ER0eIFwUVxDQoICgh23Kpd21oRUKoFdenKrM5SF2Hrz3vd2a6ona2Cq7sKKFMFKs7Oqr2oCUFULgKisqhVEBQQUnQNt0DI+/vDyTuEBAEhIcDzmWE6vsmBEzXm23Pe5zyQy+UqY58/f45z586x1yQSCdLT07Fs2bJhW4xE3h6dA0jIEPKb3/wGt2/fRlJSEpYsWaLyGMMwkEgkEIlEEAqFyMrKwuPHj+Hp6ckWlXh6emq8L6qjo0Nl9UWpp5WXkYi2fAdH5xVC5X+VlMUnlZWVcHR0hImJCRiGwYkTJ7Bjxw4cOnQIn3766ZD4+xsSEoLMzExER0fDyckJycnJKCgowJUrV+Dr6wvgdbeQlJQUVFRUwNHREcDre9B9fX1x9+5dxMTEsJ1Anj59ivz8fOolT9RQACQ68fLlS2zduhWZmZloaWmBl5cXDhw4AHd39x7H7tq1C3v27FG7zuPxRlxl28OHD2FpaYmxY8f2+FyGYfD8+XM2DIpEIlRVVWHu3LlsUYmHhweMjIw0BkJNH7SdD6XW1XEe+qTzVqNyq53onlwuR1NTE4DXVe/K+wl9fX1RXV3N/r3+73//i4yG0QPgAAAapElEQVSMjDe2UNM3MpkM27dvZw9vnjVrFvbu3YtFixaxz4mIiEBKSgrKy8vZAAi8/nc2JiYG58+fZ/+djY+P13g+ICEUAInWKRQK+Pn5qfWorKqq6lWPSmUAPHr0qMoBqQYGBggJCdH29IcNhmFQXV0NoVAIoVAIsViMmpoaeHt7w9/fH/7+/nB3d1frONL13qyOjg72sZESCKmtnv5QhvCu3VUYhkF+fj5++ukn/PTTTygpKYFMJgOPx8PcuXOxYMECLFiwAH5+frRqSwgoABIdSEtLQ2hoqEqPSolEAhcXFyxZsqTHHpXKACiRSGBjY6OLKY8IDMOgqqpKJRBKJBL4+PiwgXDWrFlqZwp2FwgH8jgPfUJbvvqhNy3dxGIxwsPDERERgd27d6O0tBRZWVnIysqCWCzG//73PzQ0NMDS0nIQXwkh+oECING6/vaoVAbAuro6GBsbw8LCYlgEC33DMAwqKipw9epViEQiZGVlQSqVYt68eWwfYzc3N7Vtz+EcCGnLVz8oFAo0Nzejo6NDYwhXKBQ4fPgwvvrqKxw7dgxBQUEa+20/fvy4xx0HQkYKqgImWjdQPSqnTJkCa2trWFpaYs2aNairqxvoqY5oHA4HkydPxvr163Hq1ClUVFQgPz8fH374IUpKShAUFISJEyciJCQER44cwd27d6FQKLqt3lR2LWltbUVjYyOkUimam5shk8kGvUdsT5QHClOV7+CTy+VobGyEQqGAubm5WvhramrCunXrcPLkSWRnZ2PlypUa/0eDy+VS+COkEzoHkGhdf3tU2tjYYNOmTfDx8QGPx4NYLMaRI0eQl5eHgoIClfOyyMBRfmBOnToVkZGRUCgUuH//PrtlHB8fz1YeKreMp02bBi6Xy676AW/uADEYPWJ70nXLt2uRDNGNzq31DA0NYWpqyt7vp/To0SOsXr0aU6ZMQW5uLqytrQdptoQMPRQASZ8o+2z2hrIHZX97VG7evFnl18uXL4eXlxfCwsKQkJCA2NjYXs2H9A+Xy4WrqytcXV0RFRUFhUKB0tJSCIVCiEQi/OUvf4GBgYFKIJw6dSp7REdve8QqvzT1zNa2ng4UJrrR+X6/7lq6ff/994iMjMQf//hHbNu2TS0cEkLejO4BJH0iEomwcOHCXj23rKwMLi4usLCwQGhoKI4fP67y+MWLF/Hb3/4WP/zwg8oRB73l4OAANzc3/Pjjj30eSwaeQqFASUkJGwizs7NhamoKPz8/9hzCyZMnq31Qdw6Ecrmc7andNRBq8wNeuVXd1tZGVb6DrKeWbh0dHfjb3/6GxMREJCcnY+nSpfRnRchboBVA0ieurq5ISkrq1XPt7e0BaK9H5YQJE9DQ0PBWY8nA43K5EAgEEAgEiI6Ohlwux+3btyEUCnHp0iVs27YNlpaWbCBcsGABHB0du+0Rq/zStGU8kIGQtnz1h7KzR3crsC9fvkRkZCSqqqpw8+ZNuqePkH6gFUCidcHBwcjOzkZ1dbXKB2tkZCS+/fZbNDQ09LlNEcMwGDt2LObMmYNLly4N9JSJFrS3t6OwsJA9mDonJwe2trbw9/eHr68vFixYgPHjx2us3uxcZaxcIeRyuWpVxm8zJ2XgoCrfwaO8tUQmk8HQ0BBmZmZqfw9KS0uxatUqeHp64tixYypnghJC+o5umiBa15celZWVlSgrK1MZX19fr/Y9ExMTIZFIsHjxYu1NnAwoIyMjzJ07F3Fxcbh48SIkEglSU1Ph4uKCtLQ0zJ49G7NmzcIf/vAHnD17FjU1NWAYhr2H0NTUFBYWFrCwsICZmRkMDQ3ZewilUimkUilaWlrQ1tbGhsTuKO8xa25upirfQaY84kV5aHPX8McwDDIzM7Fo0SJERUXh9OnTFP4IGQC0Aki0ri89KgMCAiAWi1U+wM3MzBAaGgo3NzeYmJjg2rVrOHv2LAQCAXJycthiEjJ0KSs+c3NzIRKJIBKJkJubi3feeYctKvHz84OdnZ3GFcLOVcadVwi7VhkDqlu+JiYmMDY2pi3fQdLR0cG2dFNuv3cml8uxa9cupKam4ttvv0VgYCD9WREyQCgAEp3obY/KwMBAiMVilXZjkZGRuH79OqqqqtDa2opJkyZhxYoV2LZtG/h8vq5fCtEBZVHGzZs32aKS/Px8TJkyhS0o8fX1ha2tbbeBUBkKOwdCLpcLuVwODocDPp9Pq36DqHPFNZ/PV9vCl0gkCA8PR1NTE9LT01V63hJC+o8CICFE7ym3bHNyctgVwlu3bsHFxYUNhPPnz8eoUaM0BkKZTKayOgiorhB27X9MtKc3FddFRUUICwvDokWL8Pe//x2mpqaDNFtChi8KgISQIYdhGDQ1NeHatWtsUUlxcTFcXV1VAqGlpSXKy8uxZs0abNy4EStXrmTvHVSuEir/CTQwMFApKqFAOPA6t3TTtP3OMAxSU1MRGxuLv/71r4iMjKQ/B0K0hAIgIWTIYxgGr169UgmEd+7cwTvvvIO6ujpYWVnh+PHj8PX1VQscXY+doUCoHXK5HM3NzQDAFvF01tbWhri4OFy4cAFpaWnw8fEZjGkSMmJQACSEDDsymQzR0dFITEzEjBkzYG5ujuLiYggEAvYcQh8fH40Vp12PnekcCJUFJRQIe69zSzcDAwOYmZmp3e9XU1ODNWvWwMjICGfOnGHbRBJCtIcCICFkWHny5AlCQkJQWFiI/fv3s60EGxoakJWVxa4QPnjwAHPmzGEPpfby8oKJiYnGQNi5yrhrIFSGQgqE6jq3dDM2Ntb4+3vz5k2sWbMGwcHB2L9/f5/PBCWEvB0KgISQYSU/Px+ffPIJTp06BS8vL43PYRgG9fX1EIlEbCCsqKiAh4cHe+SMl5eXxnvUulYZUyDUTKFQoKmpie2wYmxsrPb4P//5T+zevRvffPMNwsLCRvzvGSG6RAdBE6IjMpkMsbGxcHBwgJmZGby9vXH58uVejVW2wBozZgzMzc2xcOFCFBUVaXnGQ5OnpydKS0u7DX/A67ZydnZ2CA4ORmJiIu7du4dHjx5h48aNqK+vx+bNmzFhwgQsW7YMX331FW7cuIG2tjYAr4Mej8cDn8+HhYUFzM3N2ZWttrY2NDU14dWrV2hsbERra6vKquFI0d7ejsbGRgCAubm5WvhraWnBxo0b8c033+Dq1av45JNP9CL89ed9tmvXLvaooc5fVMFM9BWtABKiI6tWrcJ3332H6OhoODs74+TJk8jPz4dQKMT8+fO7HadQKODn54eSkhJs3bqVPUi7qqoKt27don6oWsAwDJ49ewahUAihUIjs7GzU1tbC29ubrTJ2d3dXuxew6wqhXC5nH+t8/+BwXSHsTUu3J0+eICwsDHZ2dkhNTYWtre0gzVZVf99nu3btwp49e3D06FGVTiUGBgYICQnR9vQJ6TMKgIToQF5eHry9vREfH4/PP/8cwOsVQTc3N9jZ2SEnJ6fbsWlpaQgNDUVGRgY+/vhjAK8PyXVxccGSJUuQmpqqk9cwkjEMg8rKSjYQisViNDQ0wMfHB/7+/vD398evf/1rtcrWngKhMgwOh0DIMAyam5shl8vB4/HA4/HUwrFQKMS6devwu9/9Dnv27NGrg7j7+z5TBkCJRAIbGxtdTJmQfjHs+SmEkP7KyMiAoaEhIiMj2Ws8Hg/r16/HF198gWfPnmH8+PHdjrW3t2c/lABg9OjRCA4OxunTp9He3k43zmsZh8PBxIkTER4ejvDwcDAMg/Lycly9ehVZWVk4evQopFIp5s2bxxaVuLm5seFOuW3MMAw6OjrYgpLW1lb2ZwzlQNi5vZ6ZmZna30eFQoGvv/4aBw8exL/+9S989NFHevf6Bup9plAo8OrVK1hYWOjdaySkM7oHkBAdKCoqgouLi1oTe09PTwBAcXHxG8d2bZmnHNvc3IwHDx4M7GRJjzgcDqZMmYINGzbg1KlTqKioQF5eHj744APcvn0bK1aswKRJkxAaGoqEhATcu3cPCoUCHA4HhoaG7D2ElpaW4PP54PF4AIDW1lb2HsKmpibIZDKVQhN91PV+v64hSSqVYu3atUhNTUVOTg6WL1+ul8FooN5nU6ZMgbW1NSwtLbFmzRrU1dUN9FQJGRC0AkiIDtTU1Gg820x5rbq6+o1jAwIC3jh2xowZAzNR8la4XC6cnZ3h7OyMzz77DAqFAmVlZWyV8f79+6FQKODr68tuGU+bNk2lHR0AdoVQuV2sXCHkcDgqVcZcLnfQQ1RvWro9fPgQq1evxrRp05CbmwtLS8tBmm3P+vs+s7GxwaZNm+Dj4wMejwexWIwjR44gLy8PBQUFsLCw0NbUCXkrFAAJ0YGWlhZ2laczExMT9vHutLa2vvVYMji4XC6mT5+O6dOnIyoqCgqFAvfu3YNQKIRIJMK+fftgZGSkEgidnJyGTCDsTUu3ixcv4ve//z22bNmCuLg4tcOftUlZjNIbyvdRf99nyvMmlZYvXw4vLy+EhYUhISEBsbGxvZoPIbpCW8CE6ICpqanGDyTlB/qbjoroz1iiH7hcLmbOnInNmzfj3LlzeP78OS5evIh58+bh8uXLCAwMxK9+9SusW7cOSUlJePz4MRiGYbeMTUxMYG5uzm4ZGxsbsytwjY2NkEqlaG5u1smWsVwuR2NjIxQKBbt93Tn8dXR0YN++fYiKikJqair+/Oc/6zT8AUBWVhbMzMx69aXc2tXG+2zVqlWwt7fHlStX+veCCNECWgEkRAfGjRuncZu3pqYGAODg4KCVsUQ/GRgYwN3dHe7u7oiOjoZcLkdxcTGEQiEuXLiAL774AlZWVmzbOn9/fzg6OrKBsPMKYecuJe3t7QCg8jwDA4MBWyFsa2tDS0tLty3dXrx4gQ0bNqC2thY3b96Ek5NTv3/m23B1dUVSUlKvnmtvbw9Ae++zCRMmoKGh4a3GEqJNFAAJ0QF3d3eIRCJIpVKVe4Fyc3MBAAKBoNuxAoEA2dnZ7IpQ57F8Ph8uLi7amzjRCUNDQ3h4eMDDwwMxMTFob29HYWEhhEIhMjMzERMTA1tbW7ZLyYIFC+Dg4AAOhwMjIyO28KJrIFRuW3YOhMqzC/sSCHtq6QYAd+/exerVq+Hj44OMjAzw+fyB+w3qo7Fjx2Lt2rV9GqON9xnDMKioqMCcOXP6PJYQbaMtYEJ0ICgoCB0dHTh27Bh7TSaT4eTJk/D29maPgKmtrUVZWZnKeXFBQUF4/vw5zp07x16TSCRIT0/HsmXL6AiYYcjIyAhz585FXFwcLl26BIlEgtOnT8PZ2RlnzpyBu7s7BAIBNm3ahLNnz6K2tpYNLkZGRipbxspjWRQKBVpaWiCVStkt47a2NigUijfORdnSrb29HaampmrFHgzDID09He+99x42b96M5OTkQQ1/b6sv77PKykqUlZWpjK+vr1f7nomJiZBIJFi8eLH2Jk7IW6KDoAnRkZCQEGRmZiI6OhpOTk5ITk5GQUEBrly5Al9fXwBAeHg4UlJSUFFRAUdHRwBgq0fv3r3LrgQlJCTg6dOnyM/Ph7Oz82C+LKJjygKHvLw8tqgkNzcXjo6O7Haxn58fxowZo7ZKp1whVH4pw1/XFULl1q5cLkdzczMAwMzMTO2g6/b2duzYsQNpaWk4c+YM/P39B706+W315X0WEBAAsVisEp7NzMwQGhoKNzc3mJiY4Nq1azh79iwEAgFycnLYYhJC9AUFQEJ0RCaTYfv27Th9+jRevHiBWbNmYe/evVi0aBH7nIiICKSkpKC8vJwNgMDrHqUxMTE4f/48Wlpa4OXlhfj4eI3nlpGRRVkMcuPGDTYQFhQUwMnJib2H0NfXF7a2tmrhTKFQqFQZdw6EXC4XHR0d4HK54PP5avf71dXV4dNPP0VbWxvS09MxYcIEnb1mbent+ywwMBBisRgdHR3stcjISFy/fh1VVVVobW3FpEmTsGLFCmzbtm1IroiS4Y8CICGEDCPKlmzXr19nA2FhYSGmTZvGrhDOnz8f1tbWGgOhTCZTCYPK67t27YKPjw8CAgLw7NkzhIWFYenSpfj6669pdYuQIYgCICGEDGMMw6CxsRHXrl1j+xgXFxdj+vTp7DmEvr6+sLCwwP379xEWFoaYmBisWLECBgYGkMvlqKysRFBQEB4+fAjg9dl43t7eiIqKQkBAAMaMGTPIr5IQ0lcUAAkhZARhGAavXr1CdnY2hEIhsrKycPfuXUycOBHV1dWws7NDUlISZs+erbJCKJPJsHXrVjx8+BCjRo3CvXv32EDo5uaGd999F4cOHRqy9wASMtJQACSEkBFMLpcjLi4OBw4cwPTp08Hn81FSUgKBQMAeOTNx4kRs2LABZmZmOHv2LOzs7AAAT58+hUgkgkgkwi+//ILMzMxBfjWEkN6iAEgIISNUQ0MDwsLC8MMPP2Dfvn2IjY0Fh8PBL7/8gqysLPYewtLSUqxbtw6JiYl07BAhwwSdA0gI0RsymQyxsbFwcHCAmZkZvL29cfny5R7HJSUlgcvlavyqq6vTwcyHpsrKSty5cwfff/8927KNw+Fg9OjRWLFiBQ4fPow7d+7g9u3b+Mc//kHhj5BhhDqBEEL0Rnh4OL777jtER0fD2dkZJ0+exNKlSyEUCjF//vwex+/duxeTJ09WuWZlZaWt6Q55AoEAjx49Ao/H6/Y5HA4HM2fO1OGsCCG6QFvAhBC9kJeXB29vb8THx+Pzzz8H8HpF0M3NDXZ2dsjJyel2bFJSEtatW4eCggI6G5EQQnqBtoAJIXohIyMDhoaGiIyMZK/xeDysX78eN27cwLNnz3r8HgzDQCqVqhzQSwghRB0FQEKIXigqKoKLiwvMzc1Vrnt6egIAiouLe/wegYGBsLKyAp/Px4cffoiff/5ZK3MlhJChju4BJITohZqaGowbN07tuvJadXV1t2P5fD4iIiIQGBgIS0tLFBQU4ODBg5g3bx4KCwuHRZsyQggZSBQACSF6oaWlRWMxgrLNWEtLS7djV65ciZUrV7K//uCDD/Dee+/B398f+/btQ2Ji4sBPmBBChjDaAiaE6AVTU1PIZDK1662trezjfTF//nzMnTu3V8fIEELISEMBkBCiF8aNG6dxm7empgYA4ODg0OfvOWHCBLx48aLfcyOEkOGGAiAhRC+4u7vjwYMHkEqlKtdzc3MBvD6zrq8eP36MMWPGDMj8CCFkOKEASAjRC0FBQejo6MCxY8fYazKZDCdPnoS3tzfGjx8PAKitrUVZWRnkcjn7vPr6erXvd/HiRRQWFmLx4sXanzwhhAwxdBA0IURvhISEIDMzE9HR0XByckJycjIKCgpw5coV+Pr6AnjdLSQlJQUVFRVwdHQEADg7O2P27NmYM2cOrKysUFhYiBMnTmD8+PHIz8+nVUBCCOmCqoAJIXojJSUF27dvx6lTp/DixQvMmjUL//nPf9jwB7xuTcbhcFTGhYaG4sKFC/jxxx/R3NwMBwcHfPbZZ9i5cyeFP0II0YBWAAkhhBBCRhi6B5AQQgghZIShAEgIIUSramtrERcXh8DAQFhYWIDL5SIrK6tP3+PZs2cIDg7GqFGjYGVlhY8++gjl5eVamjEhwx9tARNCCNEqkUiEhQsXwsXFBba2trhx4wZEIhH8/f17Nb6xsRGzZ8+GVCrFli1bYGhoiEOHDoFhGBQXF8PGxkbLr4CQ4YeKQAghhGiVh4cHGhoaYG1tjYyMDNy4caNP4xMSEvDzzz8jPz8fc+bMAQAsWbIEbm5uOHDgAPbt26eNaRMyrNEWMCGEEK0yNzeHtbX1W4/PyMiAl5cXG/4AYNq0aXj33XeRlpY2EFMkZMShAEgIIURvKRQKlJSUwMPDQ+0xT09PPHr0CE1NTYMwM0KGNgqAhBAyBDU1NWHnzp1YvHgxbGxswOVykZyc3OvxL1++RGRkJMaMGQNzc3MsXLgQRUVFWpzx22loaEBbWxvGjRun9pjymqYe0oSQN6N7AAkhZAiqr6/H3r17MXHiRAgEAohEIrUDsrujUCjw/vvvo6SkBFu3boWtrS0SEhIQEBCAW7duYerUqd2OZRgGMpmsVz/HxMSkV897k5aWFgAAj8fr9vsrn0MI6T0KgIQQMgQ5ODigtrYWdnZ2uHXrFjw9PXs9VlmIkZGRgY8//hgAEBwcDBcXF+zcuROpqandjs3KysLChQt79XPKysrg4uLS63lpYmpqCgAaQ2dra6vKcwghvUcBkBBChiBjY2PY2dkBeL0q1xcZGRmwt7dnwx8AjB49GsHBwTh9+jTa29thZGSkcayrqyuSkpJ69XPs7e37NC9NbGxswOPxUFNTo/aY8pqDg0O/fw4hIw0FQEIIGWGKioowe/Zsteuenp44duwYHjx4gBkzZmgcO3bsWKxdu1bbU2RxuVzMnDkT+fn5ao/l5ubCyckJfD5fZ/MhZLigIhBCCBlhampq9LaoorKyEmVlZSrXgoKCkJ+fj1u3brHX7t+/D6FQiJUrV+p6ioQMC7QCSAghI0xra6vOiyq+/PJLAMC9e/cAACkpKRCLxQCA//u//2Oft3btWojFYigUCvZaVFQUjh8/jvfffx9/+tOfYGhoiIMHD8Le3h5btmwZ8LkSMhJQACSEkBHG1NRU50UVO3bsAIfDAcMw4HA4OHHiBACAw+GoBEAOh6NWzWxubg6RSITo6Gh8+eWXUCgUCAwMxKFDh2BrazvgcyVkJKAASAghI8y4ceM0bvNqs6ii84remwiFQo3Xx48fT10/CBlAdA8gIYSMMAKBAIWFhWrVw7m5ueDz+f0+uoUQov8oABJCyDBWW1uLsrIyyOVy9lpQUBCeP3+Oc+fOsdckEgnS09OxbNmybo+AIYQMH7QFTAghQ9Thw4fx8uVLdjv33//+NyorKwEAmzdvhqWlJeLi4pCSkoKKigo4OjoCeB0Avb29ERERgdLSUrYTCMMw2L1796C9HkKI7nCYvp4gSgghRC9MnjwZT548AQC2cEJZZFFeXg5HR0dEREQgJSWF/bXSy5cvERMTg/Pnz6OlpQVeXl6Ij4/XeD4gIWT4oQBICCGEEDLC0D2AhBBCCCEjDAVAQgghhJARhgIgIYQQQsgIQwGQEEIIIWSEoQBICCGEEDLC/D/YmqJJkcNc0AAAAABJRU5ErkJggg==", "text": [ "Figure(PyObject <matplotlib.figure.Figure object at 0x117f64190>)" ] } ], "prompt_number": 9 }, { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "Helmholtz $u_{xx} + u_{yy} + 100u=0$" ] }, { "cell_type": "code", "collapsed": false, "input": [ "d=Disk()\n", "\n", "u=[dirichlet(d),lap(d)+100I]\\1.0\n", "ApproxFun.plot(u);" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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", "text": [ "Figure(PyObject <matplotlib.figure.Figure object at 0x116f9a050>)" ] } ], "prompt_number": 5 }, { "cell_type": "code", "collapsed": false, "input": [ "d=Disk()\n", "\n", "u=[neumann(d),lap(d)+100I]\\1.0\n", "ApproxFun.plot(u);" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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", "text": [ "Figure(PyObject <matplotlib.figure.Figure object at 0x1177f8150>)" ] } ], "prompt_number": 6 }, { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "Screened Poisson $u_{xx} + u_{yy} - 100u = 0, \\partial u(\\partial d) = 1$" ] }, { "cell_type": "code", "collapsed": false, "input": [ "d=Disk()\n", "u=[neumann(d),lap(d)-100.0I]\\1.0\n", "ApproxFun.plot(u);" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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D2Jxevke1vv9Aa1NNyKvQHZAAJByDGSPnarHD7F38eBKotnWxeoaxWY8/Pyzo9URho+7LUqlUMfElDzWiHZz0fWl3qkm7BtbGbZBXoT2QACR6DqsNYcKv1tWnoigQBAGKoiAUClXETjabdZUAsSIFzFImXrB1sYNCQUM2qyGbVU/+Hv/J5cq3axoQCHAIBIy/AZ4fv43nUfXv6vuXf3Pc+L95ngPP65DlEkKhAHheBs9rhuflEYnw6OsLY/r0AEIhf168EI1xyzmNReWMVNcU2m1gLcsy8vn8JCEYCASQz+ehqioGBgasfeEegQQg0TNqGTnXavAwNjUkk0mEw+Ee7nXv8Lqti6bpVYKttoAbG1OrBF35vmNjGvJ5DYa1xpEsWZLEjh0JcBzQ1wdMmwZMm8ahv59Dfz+P6dN59PfzE24r/+YQiXjjsya8Sz1bmkYG1p2KQnYxzPP8BK/Cu+++G8899xzuvfdee16syyEBSHSdVur8mjU1mE1TOoV2I4DVo+ycaHFTj8OHJezdK2LPniL27Sti714RJ04kcezYRAGXz7vnc+yE8XonYGSk/PP3v+sAdABaw8emUsC0aUZxOFEkstunTSv/P5VypmD0ykWLXbjVfqkeRlFYy8C6k6kmxveq+pxYKBSQTCZtfGXuhgQg0VXM1PlJkgRRFKFpGqLRKKLRqGvEjtXYaevSDI7jKgK9EYWCir1789i7t2j4EbFvXxH79xchCJOfY8oUHiMjduy18+nkq5zLAbmchr17zd0/GsUkUXjKKTwWLmQ/ga6LxF6MQSScRzNRaNbA2thlXE0ul0M6ne7OC3IhJACJrsDC8kbhVy3qjFEuY51fI8yKFLfRK1uXWqiqjgMHROzalcW+fUXs21fCnj1iJZp37FjrOVefZvFP0r3PsFgEBgd1DA7Wjy7Ons1VxODChTzOPLP8e9Ysf150OQU/imQzU01qGVgDmGBTw9aXQqGAuXPndu8FuAwSgISt1Krzq476qaoKURTbinJ5LQXcK1uX48cl7NkjYs8e8WS6VsTu3eWo3uCgBEWx9j32swB02sJ+6JCOQ4dUbNigTrg9nQYWLGCisCwQzzyTx2mn8QgGnfUavIabzml208zAWlGUSlZJVVX853/+J1avXo3FixcjFotBkiTs2bMHr3vd6xoee6VSCV/60pdwzz33YHR0FOeddx5uv/12XHbZZQ33b3h4GHfeeSc2b96Mbdu2oVAo4PHHH8ell1464X6iKOLuu+/GQw89hBdeeAH5fB5nnHEGVq1ahVWrVvUky8Xp9E0jbMBo5Gys0ahO94qiiGKxWIlyhcPhlhbIQqEARVGQyWQsfw12oGkaRkdHazazqKpaeT3BYBCJRMJSWxcAKJU07NiRxdatY9i6NYudO8siL5vtbufE6afPwmuvdXWTjmH58in429/cq4BDIeC008ZFYavpZEEQEAgEEIlEurC37oRZCCUSiV7viuNhAQRWKvTSSy/hd7/7HZ599lk8/fTTOH78OABgypQpuOCCC3DBBRfgne98J972trdNeJ5rrrkGv/3tb3HDDTdgwYIFWLNmDbZu3YrHH38cK1asqLv9DRs24K1vfSsWLlyIadOmYdOmTdiwYQPe9KY3TbjfCy+8gMWLF+Oyyy7DO97xDqTTafzhD3/Agw8+iI997GP4+c9/bvl70wwSgISlGFvzWWq2lvArlUoQRRG6riMajSIWi7UVGWH1cX19fZa9BjvRdR0jIyNIJBKVBVDTNIiiWJloEo/HLbN12b1bwJYtY9iyZQxbt47huedykKTeH/JnnjkLr7zS673oDW9841Rs2tSdOs5uYyadXCgUEAwGSQA2oFgsQtM0xOPxXu+K41EUBcVisWZj3Pve9z585CMfwSmnnIKnn3668nP55ZfjBz/4QeV+W7ZswfLly/Htb38bn/vc5wCURfg555yDGTNmYOPGjXW3n8/noSgK+vr68MADD+DDH/5wTQF4/PhxHDlyBGefffaE26+77jqsWbMGr776KubPn9/p29ESlAImLKNZgwcwuZs1FotZHuVyC9VCuFNbl9FRGVu3MrGXxbZtYzh2TG7+wB7g048cQNk30KvUSydfdlnZq3LlSh0XXshjyZIe7SDhWeo1gZxyyil497vfjXe/+92V21V14vfzgQceQDAYxKpVqyq3RSIRXHfddbj11lsxODiIOXPm1Nyu2S7jadOmYdq0aZNu/4d/+AesWbMGO3fuJAFIuA+jkbOmaTUHhhunVgQCAaTT6UkGou3gthpABrtqbdfWRVE0PP98fkJ079VXBbjlrbDgo3cxHlaAdRAEDk89xeG//osDkEQ6reONbwQuuUTHihU6zj/f3xcFtXBarahTaVRPnc/na5YHVQcdduzYgYULF04Sc0uXLgUAPPPMM3UFYKcMDw8DAPr7+215/kb4+jRMdIaZBo/q9GYikWi5zs/MfrgFtq+lUqmlhpf9+0Vs3Vqu3du8eQzPPJOFKLq3+9nPi70f13VVnfiis1kOf/wj8Mc/lm9Pp3UsXw6sXKlj5UodS5b4+zvipnNar2nkmZjP501F6IaGhjBr1qxJt7PbDh061OFe1kaSJNx55504/fTTK2Kzm5AAJFrGrJGzlenNerjlKtlo6wIA4XAYiUSizklLwbZtrFGj/DM0JE26n5sJBnX4MRLmV2S58WedzXJ47DHgscfK90uldLzhDeMRwgsu8F/U2C3nNidj1gdQFMWaNanRaLTydzv49Kc/jZdffhmPPvpoT7qAfXZIEZ1ixsi5mzYmRlsVJ54wq9+PaDSKYrE4wdVe13Vs2XIcv//9QfztbyVs2ZKDqno7AuBTX28A/lzYi8XW7p/LcVi/Hli/vvxeJZM6li0rC8KVK8uCsEt+6D3BqeczJ1LvvdJ1HYVCwZQAjMViKJVKk24vnvzixmKxzne0im9961v42c9+httvvx3vete7LH9+M5AAJEzB6vyYK3utsTuKokAQhIqNSTKZtKTOz60YbV2MxtalUgmqquMvfzmC3//+AP7f/xvE4GD5CvO00wY8L/4AgOf9GwH0Y3av0wBKPs/hz38G/vzn8ncmkSgLwpUrdVxyiY4LL/S2ICQaU0sAFgoFaJpmKgU8a9asmmneoaEhAMDs2bM730kDP//5z7F69Wr867/+K2699VZLn7sV/Ls6E6aoVedX3eBRXeeXTCYtszFphhMjgPXeD1nWsH79EH7969344x+P4tixyWndRMIfoTGHfFQ9wn8vXhSt/V4XChwefxx4/PHyexmPjwvCt79d90SXsVPOZ06nXr1kPp9HIpEwFYRYsmQJNmzYgFwuh1QqVbl98+bNAIDzzz/fmp0F8NBDD+H666/Hhz70IfzoRz+y7HnbwR+rDdEyTPhJklQxc66evciMnMfGxiBJEuLxODKZjOVNHm6B1fmNjY2hVCohFoshHE7gv/7rCD7+8c045ZSHcMUVT+Deewdrij8AsCHT4Eh8+PXwNYWCvc8vCBw2bOBw++08Lr00gGXLeHznOxwGB+3drl1QE4h56l3853I5JJNJU2vRlVdeCVVVcdddd1VuK5VKWLNmDZYvX17pAB4eHsbOnTsnzCduhSeeeAJXX3013vzmN+Pee+9t6zmshCKAxCSMdX5AbSPnXowrq0Wz0WrdwuhvWCrx+MtfxvDQQy/jsceGUSiYP1mEQv5QRhxHC5xf4Dggn+/uNnfu5PCVr3D42td0XHIJcPXVOq64QodJyzZH4MeL6HapJwCN0bxGLFu2DFdddRVuueUWHDlyBPPnz8fatWuxf/9+rFmzpnK/1atXY926ddi7dy/mzZtXuf32228HALz44osAgHXr1uGJJ54AAHzxi18EAOzbtw/vf//7wfM8PvShD+H++++fsA+LFy/Gueee28Kr7hwSgEQFJuwaGTkb6/yMdW29otcCUNM0CIKAw4cL+O//PoY//OEYHn/8CEql9ixaAgF/CKNeC/be4q+FPZXikM325jVrGoe//AX4y184fP7zOi6/XMc11+h485ud3Yjk7+OjNepFAJkFjFkhvW7dOtx222245557MDIygsWLF+ORRx7BypUrK/epDoYwvvSlL1U8aTmOw9133125PxOAe/bsQTabBcdx+NSnPjXh8RzH4ctf/nLXBSCNgiMmNHiwL3CtOj82di0QCFTGlfUaRVGQzWYtM5Y2i67r2LdvFA8+uB+PPnoEmzaNQFE6P5QuuWQW/vrXyd1oXmPlyn48+WTvvz+94OKLZ+Cpp/wjAmfPDuDQIWfN6p49W8dVV+n4yEd0VE3mcgT5fB7hcHjSvHBiMvVmSz/88MP4j//4j0okjpgMRQB9DPPzk2UZuVwOHMdNumIy+tdxHId4PI5IJOKY9ES3I4B79+bxu9/tw+9/fxDbto1Cs9iLWbP6CR0KXXf6h3jcGecKI4cOcfje9zh873vA4sU6rr5ax4c/rGP69F7vWWNjY2IyjZpAzI5p8yskAH2IruuVdC8THOxkYxRUkiRBEATouo5oNIpoNNqTOj+n8NJLI7jggsds3YYsq83v5AE0zb8CUNf9tbBHo84+Zzz7LIdnn+Vw22063vpW4JprdLz3vTpOegATDqdRCtiMB6CfIQHoM+oZOXMcVxGDxoaGUCiEeDze0zq/RnQjAshsXUIh+1OzxaIKP9SI+VsA9noPuks47GwByFCU8Wkk6bSOK67QcM01Olas4HrStU4RwOY0ipayLmCiPiQAfQKr82PCr16dXz6fr9T5mZ1T20vsFIDV4+xmz04jGOQsqfWrR7lj2NnvuRWQAPQPbuxsz2Y53HNPAPfcA8ybp+HKKxVcfbWGM87gJkzxsQMqj7CGVrqA/Yo7Ls2ItmHCr1QqVfz8qsUf8/xj9YCJRALpdNrx4s9OZFlGNpuFIAgIhULo6+tDIhHH9OmT50VaSS4n2/r8TkFR/FHrWAu/re+BgLuXmf37eXznO2EsWxbFtddGsW1bCYVCAcViEZIkVZrniO7TKAJIKeDmuPvIJOpi1si5WCxidHS04vnX19fnqCYPs1h1AlZVFfl8HrlcDgCQTqeRTCYrtY8zZthbGDQ6Wtsg2mvIsn8FoE/6fCrwvLvOJfUIBHQ8/HAUb397Pz772T4cOABIkoRisYhCoWCZKKQmEGvI5/MUAWwCCUAPwiJ5kiRBVdXK3F7jCaU6wsVEn9tOOlbtr3GqiTEKWm0tM3OmvQJQkjRfjIPzS7NLLfwXLHLXOaUe/f0cdJ2DpnG4//4oVqzoxx13TIUklZ0RgsEgNE2rKwpZ+Q1hHc1qAEkANsb7K42PYJ29pVIJiqKA4ybXq6iqilwuV7F9MUa43HpyYgac7cC6ncfGxiCKIqLRaMMoqN0CEAAyGe8flqrqDVHQDn6LACqKNz7rvr6Jr6NY5PD970dw/vlp/OQncQARxONxJBKJil2WURSKotiSKHTbxXgvqZcCJgHYGGoC8QAs3asoCjRNqxnxY52spVIJPM8jkUhMmNlrTAu78cTTjgBsZ6pJNwRgMul9AejvFDAHwJ0XW+3gFQGYSNR+HaOjHL74xSh++tMwvvCFEq6+WgHPcxMss5jnKvth52sGu1j3s81WOzQ671MNYHPo2+ZimPAzNnjUq/MbGxtDqVRCLBZDJpOZFOFyo+hjtLrvmqahUCggm81C0zQkk0nTI+26IQDjce8flpLkXwHo0kB725RK7j23GGnmC3jgAI9/+ZcYVq6M47HHJp5LmMBj5TYsUhiLxSZFCiWpXAcsiiKlj5tQLwWs6zoJQBNQBNClGP38gMkzClk6WBAEaJqGSCSCWCxW9wrTzRHAeingV18dwYIFUyr/r7Z1icViiEajLb3emTNjluxzIyL2Nho7glLJv4uZ6rPyR1Hs9R5Yg9lmlhdeCODKK+N405sUfO1rJVxwQe2LHSYKjReerCRFlmUEAoHKeX58H/jKD4sYuu183S3IB7A53g81eAx2lWis86s+CSiKglwuh3w+D57nkU6nkUgkGqYXuj1SzU6uJwG0AAAgAElEQVR0Xceddz6N5ct/WXk9tWxdYrFYyyfPGTPsV2d+cN/xcwTQbzWAhYI3BIqmtbZcPvFEEG95SxzXXhvFa6+Zew/Y+RxAzUghz/OTagoFQUCxWIQsy76LFDazgclknDWD2mlQBNAlVNf5GSd4MKrr/JLJJEKhkOevEI0RwCNHBHziE4/hj3/cBwAYGsojneYq5ta1OntbYWDA/gggx3n/BF4s+kwFGfCbADzpqOR6Sm0MAtJ1Dr/7XQgPPxzExz8uY/VqCf395o5vY302ixQyb1ZjTSHzcK0VKWRRQi9HCmu9LpYCpiaQxpAAdDjsQGdze2tF/FidnyiK4Diu0oHWygHvhQjgn/60H9dd90cMDwuV23btGsbixf2Tml7apRs1gLrufYWgKDp4Xj/ZEOEv/JQC5nkgn/fGZ1wotJ8wk2UOd90Vxi9/GcL//t8SPv1pCYlE7fuaOQeTKCxT771iEVGqAWwMCUCHwg5mFvEDUFP4tVLn1wg3C0BV1fH1r2/BD3/4/KQC++PHFfT19Vl2opsyJYxwmLc1hemX9GgsBhQKvd6L7uOnCGA6zWN0tNd7YQ2jo52fQ3I5Dv/2bxH85jdB/OY3Ik47zToLGD+Kwno16/l8HtFoFOFwuAd75R5IADoQY4MH6+yt/pK3Y2HSCLcKwD17xvA//sd/Yvv2ozX/fuyYZPkJbcaMKA4eFJrfsU2KRQVeMc9tRDjM+VIA+ikCmEp5RQDqOH7cumNy164A3va2OH71KxHLlk28IrDyHNxIFDJBWEsUGgWh20QhUBaA1ADSHBKADoLN7WUHo7EgmKFpGgRBqNS0pVIp387s/fWvX8GnP/1nZLP1x6cNDlqvMGbOjNgqAAsFGYD3r1z90O1cCz8JQK9YGk2dCpw4Ya0IOnaMx+WXx3HXXUX8wz8ozR9gEfW6j42iUFVVyPL4XHKnisJ6EcBsNotkMumIfXQyJAAdQC0j51q2LswXqt06v0a4KQJYKMi44YYNWLfupab3HRzMW779ciPIiOXPyxgb84cA9Gt2xk8CMBr1xgI8dSqHEyesf95ikcM//VMUX/taCZ/5zLjg6rZwMSMKq+cbO0UU1hsDRxHA5pAA7CFm6/wkSYIgCNB1HdFoFNFo1HLHeLcIwGefPYqPfey/8Mor5gTYoUPWC0C7G0FGRiQAcXg9DezTwDVU1dnHmJVEIt6IANo5nUfXOdx2WxR79vD49rdLjjkHdyoK2RhSO0VhoxrAdDpNEcAmkADsEc2MnAFUGjxUVUUoFEI8Hu+ozq8ZnczU7QY/+tEzuPXWJ1EqmQ+h2BEBnDXLXisYVdXR1xfE6Ki3Q0V+FYCGzJrnCQa9sQA3mwJiBXffHcb+/TzuuqtUd+xcr6knCqvrCbslCusJQIoAmoMEYJdhnbuNGjxUVYUgCBU3+G7W+TlRAB47JmLVqsfw6KN7W36sHRFAuwUgAPT18SQAPYqfuoC9Mtu2W0J2/fogLr88g3vvHcP8+V3ZZMdwHDfJW7WXohAoC0DyAGwOCcAuUavOr1a611jnZ5V3nVmcGC4/dCiPN73pfhw82J6Qy+dljI2VkMlY13Ewe7b9AjCRCADwdqgoENDh9TR3LfxUA+iVz7ebfpUvvRTCu989Bb/5TRGLF7vzaqGZKGRroVEUGusJzYrCRilgEoDNIQFoM0YjZ/ZlryX8jDNqo9FoW2PKOsVpKeBiUcJHPvJI2+KPMTiYt1QADgzYnw+Kx23fRM/xSnqwVZTuNXz2HK8YfUtSd1/H8HAA7353HHffLeJd7/LGFUMnotA497i6Xp0EYPt4Iz7vQJjwkyQJkiRVrlSqxV+tGbXxeLxn3VROEICapqFQKODWW5/A3/52uOPns7oOsBsp4FCo95+D3ZQjgP7DTwJQlr0hAPP57i+V+TyHa66J4a67vFsrwURhOBxGLBZDIpFAIpGYYOKsKAqKxSIEQUChUKiMO2V2abXWLBKA5iABaAMs4lcqlaCqak3hp6oqcrkccrkcOI5DOp1GMpnsac1MrwUgG2k3NjaGRx7ZjZ/85EVLntdqAThzZhSBgL0Lm6Z5XyX4YeZxLfwkAEslbwhAK02gW0FVOXz+81HcckvEN7WjtURhPB6fJApLJ4czS5IEURSxZ88ePPjgg9i3bx+y2axpAVgqlXDzzTdj9uzZiMfjWL58OdavX9/0ccPDw1i9ejXe8pa3IJVKged5/OUvf6l7/6eeegorV65EIpHArFmz8JnPfAaFHjvhUwrYQpiRM2vwqOXnp2la5QqG5/mu1/k5FWPH8+HDEj772acmjXVrF6sbQXiew8yZURw6JFr6vEYCAR6AN1I/9fBIf0BL8Ly/mkBOrtGuJhCwdgpIO/zoR2GcdpqGVau8XRdcD1YbaERVVYiiiGAwCF3XsWHDBnzmM58BAPT19WH79u3I5/O46KKLcOGFF2Lu3Lk119lrr70Wv/3tb3HDDTdgwYIFWLNmDd7znvfg8ccfx4oVK+ru086dO/HNb34TCxcuxHnnnYdNmzbVXcefeeYZvO1tb8OiRYvw3e9+FwcOHMC3v/1tvPrqq3j00Uc7eGc6w4enYOthnb2FQmFCure6XoFFt0qlEmKxGDKZjKVmzp3SiwhgdSQ0Ekng+usfx8iIdSuHPVYw9tYBapr3o2M87/3XWI3f6h4LBfe/3unTOUfUMn7722EMD/d+P5xGKBRCLBbDxz/+cbz88su47777sHjxYoRCIfzsZz/DBz7wAcybNw8DAwP4xje+MeGxW7Zswf3334877rgD3/jGN3D99dfjz3/+M0499VTcdNNNDbd70UUX4cSJE9i5cyduuOGGhve99dZbMW3aNGzYsAGrVq3C17/+dfzwhz/EH/7wB/z3f/93x+9Bu1AEsAOMRs6lUgmFQgGZTGbClQoTh4IgQNM0RCIRxGIxR9ojdFMA1ut4/uxnN2D79iOWbsuN00DK84C9TaffNY4rz5pNJDjE4zxiMR7hMBAKcQgEuEqEUdN0yDIgyzpKJR3Fog5JKkfjAoHyb55noxfZb33CbcD4fTiuvG12O/s/+xs7LwQCfOU2Bs9z0DQeilKOjgkCkMvpGBnRIdoXUO4ZuVyv96Bz+vo4DA/3dh/SaR3Dwzy++tUIfvKTYm93xmEYAyhz5szBnDlz8IMf/ABf+cpXcMUVV2BwcBDbt2/Htm3bsGDBggmPfeCBBxAMBrFq1arKbZFIBNdddx1uvfVWDA4OYs6cOTW3a9ZnMJvNYv369fjc5z434TEf+9jHcMMNN+DXv/413v72t7fyki2DBGAHVPv5ARMXNUVRIAgCFEVBMBhEMpmc1AXlJLohANlkE1EUoWnahI7n++9/BT/96XOWb/PAAetXIbsbQURRgdcPz0AAmDkzcFK8cQiHyz/BoFG8lW1TyuJNQ7GoQxB05PMacjkN2ayObFYH4I68ajodqDu7Oh4HpkzhkUrxiMdReS90HRXBWCiMC0Y3pFe9IABTqd5H3WbM0JDNBnDffUF84hM8LrjAHd93O6m3Vum6PmESyNy5czF37lxcccUVk+67Y8cOLFy4cJKYW7p0KYBy6raeADTL888/D0VRcNFFF024PRQK4fzzz8eOHTs6ev5O8PYKYzPGzl72ZWRX/8Y6v2QyiVAo5JhUb68wCuJQKIRUKlVxlH/llRP41Kf+ZMt29+93nwDMZmV44fDs7w9hxoww0ukggkEesgzkciqOHFGgqgEcPqyhffHmvuMpFKq/z4IACIL59yOV4pDJcBMEYyDAVURzsVgWjGNjOkZH9a5PIEmnOWSz7vuMqgmHe/8aMhm2vnBYvTqCxx7zYLi4RaywgRkaGsKsWbMm3c5uO3ToUId7Wd6G8TmNDAwM4Mknn+x4G+3i/hWmhwQCgQmj3IByR5EkSeA4DvF43FE1fs2wKwJYLYirJ5sIgoxrrnkU+bw9K1QuJ2F0tIS+Puu8AO2uASzPA7bfbqZTQiEOM2eGMG1aGIlEEBzHoVTSMTKiYWhIwrFjOo4dUwBMTmkvWEA1gJ2Qy+nI5cxHP6dP5zBrVgDJJAeAQy4HDA7qOHHCns8hmeSRzdry1F2m9+dv4yi6v/0tiAceCOLKK71fJtIuLALYDFEUEYlMXheiJ99w0YK6DPYc9bZjxTbahQSgBbA6P6Dcku7kOr9GWC0AjQbXAOoK4v/1vx7HSy8dt2y7tThwIIu+vumWPZ/d00BEUUUsxkEUey+SUqkAZs2KIpMJIhzmoOscCgUNR4+qGB6WcPAgh4MHZbQ6ucQPjS7V9LIC5OhRHUePThYNU6ZwmDMngFSq3LRWKABDQzqOHOns84k5//rFFIrSewFYzZe/HMF736t45j1uh0YRQLOj4GKxWMVOxkixWKz8vVPYc9TbTryHrv8kADvEmNYEyh+2FV+aXmDsWO40amm0dWkkiNeseQH33vtyR9syw4EDOZx7rnUCsNwEYi9TpgRO1gLaz4wZYcycGUYqFUIgwEOWdYyNqRgakjA6qiGXqx3F6yQ6oqr+q2PieeeJiZERHSMjkz/bdJrD7NkB9PWVU8uCABw+rGNoSDdl0RSPB2zY2+7jhE7m6kkkBw7w+P73w7j55tr1pH6g3jolyzKKxSIymUzT55g1a1bNNC9L286ePbvj/WSpX/ac1duxYhvtQgKwA0RRRD6fRyAQQCqVqliZuJ1OBKCqqhAEAbIsIxgMIp1ON2x8+e1vX2l3N1vC6kaQbkwDSaXsiSCHwxzOOCOBadPCkCRg374ShocVHDnSehSvExTFjxFA92QFyg02k4VhPA7MnRvAlCk8gkEOxWI5ujg4qE+YcxyNuue1NmJsrPevo1Yq/c47w/joR2XMnu2/46gR+XwewWCwksZtxJIlS7Bhw4ZJEcPNmzcDAM4///yO9+ecc85BMBjE1q1bceWVV1ZulyQJzzzzDK6++uqOt9Euvf9mu5hwOIx4PI50Ol1p8nDCKLV26US86roOQRAwNjYGRVGQSCSQSqUair+xsSIkqTuCw+pGkBkzIrZ7uiUS1hyeAwNhLFuWwSWXTMWiRX0AwnjpJRl//WsBmzcXMDzcm1oifwpA918gCgKwa5eKzZtlbNwoYft2Cfv3ywgEFMyfr2PpUg4rVvDo7+cwbZr7P+OjR3v/mR05MvlcUChw+PKXratrdhv1AhXZbBbJZNLUenbllVdCVVXcddddldtKpRLWrFmD5cuXVzqAh4eHsXPnzkqmrxUymQwuu+wy/OIXv0A+P25Jds8996BQKOCqq65q+TmtgiKAHVB9leEVAdjKa2hk69KM7duHu5YGtFoAclx5GsjgoH0FvJFI6wtPKMThjDPi6O+PQJaBffskDA3JGB52nneYLPsxBdzrPbAPSQJ271axe3c5DLhyZRDHj5dw+ukcBgYCKJV4vPqquzqDMxkdY2O93d94XMfISO19+PWvg1i1isfSpf47loDG9X9m1qBly5bhqquuwi233IIjR45g/vz5WLt2Lfbv3481a9ZU7rd69WqsW7cOe/fuxbx58yq333777QCAF18sjy1dt24dnnjiCQDAF7/4xcr9/u3f/g0XX3wxLr30UnziE5/AwYMH8Z3vfAfvfOc78Y53vKO9F28BJAA7oPoL5jcB2MjWxQzbtg2hUOiOmZkdXoCzZ8dsFYDBYPPPYfr0MF73uhii0QBOnNDw978X8fLL9er1nIUkufdYaRe7Z0g7CV0vq93XXtPx2mvl7yPH6ViwgMOMGUEUCmVB6IQau3pMn85hbKy3+zBzpoY9e2qfV3Wdw803R/GnPwnwQPVRS9Rbp8xawDDWrVuH2267Dffccw9GRkawePFiPPLII1i5cmXlPtUjXRlf+tKXKus+x3G4++67K/c3CsAlS5Zg/fr1uPnmm/G5z30O6XQa119/Pf793//d9H7aAQlAC/GLAGxm62KW7duHcOJEd1rgrRaAuq5j+vSwpc9ZYysT/hcMsuheGLLM4eBBCYODMo4edV50zwylkv+iFn4SgGqNUda6zuHVV4FXXy0LwkAAOPtsHlOn8sjleOzaxaFYdM57ZFcdbis062XYti2A0VFgypTu7I9TMA5gMJLP502ngIGyPcs3v/lNfPOb36x7nzVr1kyICDK0FgZ7r1ixoqeef7UgAdgBXosANsOsrYtZtm0bwtGjAjgubKqrsBOGhwtQFM2SInxFUVAoFDB9ur2HD8fpuOiiDGKxIEZHVfz970Xs3OmO6J4ZikX/CUAvp4CrkeXm5wVVBV5+edz8OhwGzj2XRzodwOgoh127OFPPYxcm+ghsJx5vfnIsdwl7d+1phWw221IE0M+QALQQjuNauiJwGo0igGZtXcxy6FAOg4PlqNzAQAzDw/ZGAlVVx8GDebzudc3NQethjHwGAgHMm9f+c9Vj6tQwzj67D/k8IIpBbNvmzuieGfwpAJ0T3bIbUWz9tUoS8Pzz44IwHgcWLeKRSARw7BiHV1/loGndew8Dgd4rdjOn2aJ3TxN1qdcE0moK2M+QALQQt0cAawnAVm1dzLJ9+7gnUn9/1HYBCJTTwO0IwHqRzzlzEpbsl1H0Pf+8gI0bBQDA2Wd7w0etHiQAvY2h4bFtBAF45plxQZhOAwsWBBCN8hge5vHaa+W0sl200fRpOZIJq79SyZ8RwHoCsHq2L1EbEoAd4NUUsK7r0HUdoiiiWCyC4zgkEgmEw2HLfA63bh0XgMlkd76G7dQBsnRvrchnJ9NApkwJ4cwzMygWA3juuUJF9Bkpj4Ozu86wd2haOeVnZoHzCn5KAdsxBi6bBbZvVwGUCwynTi0LQlEM4PnnOcvFoDD5sOw6uVzz1+TXCGAtzE4BIUgAWorbBSATd4qioFgstmzr0grbto0LwFCoO1GRAwfMr0iapkEQBEiShEAgUDPyOTDQWoHQ1KlhnHVWHwSBw/PPF/C3vzU+a584IQEIwQmzSO0iGtUnTTnwMl4wijcDz9sjAKs5cQLYvLksCGfPBk4/PYg9ewIYHLTmfS4Ueh+FP3Kk+WupMWXM0zQbA2dmDjBBArBjjKLP7QKQmVxKktSWrYtZdF3H00+PC8BueQGaiQC20uhiZhoIS+8WCmXR99RT5kMKkqSjry+A0VHvpkqjUa4rQsEp+ET/IZPhMTLS3W0eOgQcOlRukjr3XB6JRBDPPsu1VYvIOHastyHbRELH8ePN96GcAiYAoFAoYObMmb3eDVdAAtBCrJyl202MzQ0AKuLPLv7+9xGMjo5fsubzzvACbLXRpb8/gnCYhyRNFGjVoq9WetcsU6cGMTrq3Rxp2ezavRdNreKXGsBeCEAj5UYSCckk8MY3BpDNBvDii62JuWi09ybQAwMadu9ufhHutxRwswggpYDNQQLQQtwmAGtFu4rFYkfdvWaoXgSPH++OF2C9aSBm0r21YNNADhwQMGVKWfSx9G4nos+IE3zI7CTis0lWLjgtWEI87ozvbT4PbNpUThHPm8dh3rwgXn2Vx+HDzT+I6dM5HDhg/z42YsoUcxdHfo0AUgq4M0gAdkh1ChhobZRar6gX7SqVSrbv//z5U3DeeTPw3HNHAADDw3kEAjGoqr3bPXhwYluiFb6Gb3rTTLz4otByetcsTvAhs5M2/MNdjRsuDK0gFnPe69y/X8f+/TJ4Hjj/fB7hcDlFXE88ZTK9F4BmL5D8WgNYi0KhQBFAkzjjMs0juEEAqqqKXC6HXC4HjuOQTqeRSCQqUb9uLVAf/OBZhn3SMTAQt32buZyEkZFyrkSWZWSzWQiCgHA4jEwmg2g02vLrLxaDeOaZQs2pB1YQ9m4TMAD/CUC/EA47d2nRtLK1zJYtEqLREi6+WMGZZ04+ZycSvRexZuujKQU8DqWAzePco9QlGL+AThaAuq5DEASMjY1BVVUkEgmkUqlJqc5uNbJ86ENnTfh/f393coH79o0hn88jlyung6sFcKuccordITrnfZesxG8C0C8RQLfUOo6NAU89peCVV0qYP7+EFStUTJtWPua65U7QCEEwtw9+TQFXo+s6pYBbgASghThRALI05+joKIrFIqLRKDKZTN1UZ7cE4IIFU3HOOdMr/08kulON8MorhyHLMuLxuCWm1qec0r4XoBlk2bsdwEB5Fqy/8MtC7b6lZfduHRs3yhgbK+Gii2QkrPF574jRUXMHyIkTEmRZhqqqjlp/7KJRBJBSwOZx31HqYJwmABVFQS6XQ6FQQDAYRCaTQTwebxiF6KaVzQc+cGbl390SAkNDxbbTvbWwWwCKok25ZYfgJ2NkP+HiiZhQFGDbNhXHjklYvFjE/Pm9OQY5TsfwsJlzlAqe1yr1zIVCAYIgoFQq+UoUMvL5PEUATUKn3w6pJSJ6fbBpmoZCoYBsNgtd15FKpWzz9OsEYxq4VOrOzKUjRyRLu5znzbNXAObzsq3P32t43j8Lk5/wgrn32BiHZ5/VsGePiDe+sYj+/u6q2oEBMybpOgAZF18cRCKRQCwWQzgcBs/zUBSlrijUNK3n61Qn1IsAqqpKArAFqAvYQjiO66kZtBVdrd3c/7PO6sfrX9+Pl146htHR7lQxtzMOrhF2RwBPnJABOEu4W4lPSuJ8Ryfmy07hSNmkAJrGYdMmFYmEgJUrQ9i+PdyV1zd9uo6hoWb3Ks9JvuCCsvVYIBCYcKGv6zpUVYWmaVBVFYqiTDi/BwIB8Dxf+W23BZhV1LNay+fzldGlRHNIAFpMrwSgLMsoFArQNM2UiXE9ur3/H/jAmXjppWMYGsqhG0LHagE4bVoYiUQAhYI9aaLRURnBYNQRQ+ntQYd/6uIAFwddWqJQ6PUedEY6DWSzE7+XhQKHJ59UMDCg4LzzwtiyJWj57GEjiUSzL0s5+hcMcnWbqTiOm1TnrGla5YeJQlmWK/c3CsJAIODYxqV6AjCZTLpGyPYaepc6pPpL2G0BZbR14Xm+467Wbu8/SwPnchKmTbO/E9hqAQgAc+fa1wms60B/v3cjgF7vcq7GLwJwbKzXe9AZM2fWFz3Dw8DmzRIWLhSxaJF9V2bNK3ZUABpa7XfgeR7BYBDhcBixWAzxeBzxeBzRaLQiFiVJQrFYRKFQQKFQQLFYhCRJjqknrLcPzALGqaLVaVAE0Aa6cYDoug5RFCuTOxKJBMLhsGVf/G5NM3n966fjrLOmYefO4xgYiOP4cXsdTYeGCpBlFaGQdaLqlFNieOUV+0IefX0BDA97sxnECYtJNyn7unn7upvn4fr5zul083PfK6/oAModw4cPR3DggLWfa2Nz53L0DwCmT290v+aw0iUmDIHycVkrfcxg6WJj6rjboqueB2AymezqfrgZb5+JeoDdB0Grti6t0osrJ9YNnErZawrHccCKFfNw4IC1EzvsbgRJJLx7mGqavwSgH065mQzv6i5gAIhGzX9O27ZpGB4WsGJFCem0dd/n6hT0RFSw6Pm8eZZtsgIThKFQCJFIBPF4vNJkEolEwPM8NK1+57HdTSb1njufz1MEsAUoAtgh3UwBK4oCQRCgKApCoRDi8bjlnb29mGd84YWzANhrCjxlShSnnz4Tf/1rFrt353D66db5RNndCBKJePdkZvf4P+fBwetp73Saw8hIr/eiM1o9hcsyh40bFfT1ybj44jC2bAlBUTo7boeH64nQ8egfACxY0J3vk7HJJHTyZK3reiVCqGnapCaTWvWEVqwruq7XLHNiApAwBwlAi+E4DprFl7+apkEURZRKJQQCAaRSqcoBaDW98DKcP38KAKBYtMfyZNGifhw/HsD27eW81J497uoEDgS8Kxj8JgA1zfsCMJFwf81qu5M1Rkc5PPWUjFNPldHfH8b27e2dp9NpHWNj9fZhPPoHAOed17vvUy87jykF3DkkAC3GygigFbYunWy7W5x11jQAwMsvH0W5E9i613fJJfOwaVMeijJeQ/faa+4SgJLkXS9ARfG2GKrGDyWPsZj7I9b1xZc59u0D9u2TcN55Co4ejWBoqDWRMzCgIZutJaQnRv8AYNkyZ+Xbu9F53KwJhDAHCcAOsSsFLEkSBEHo2NalVXpRO8FxHCKRwEnTYw1ACJ2KwEwmjIULZ+Gvf51cjb57t7sEoJfHwXn5tdXC7bVxZgiH3V/nyDwAO+W55zTMnCli/vwodu82HxnNZOqtIQqqI8hnnNH+/nWL6kgfazIxpo8lSar8nUUWjcKw1lpbDaWAW4MEoAUYRV+nAlBVVQiCAFmWEQwGkUwmO55X2wq9GmeXTkdw9KiAcnqDQ1kEtsfZZ09DNhvG1q21WxGtjwBGwfP2Le6lknfDRpLkA0VkwA8CkOfdHQGs5QHYCYcPA6Io4txzo3j+eXPn8khNRywN1dG/aJRzpZl6p53H7L7VUASwNdx/qeYwmABsVUDpug5BEDA2NgZVVZFMJpFKpboq/oDeCcCBAaNzu3Lyp3VWrJiLv/9dx+Bg/ckie/bk23rueoRCPAYG7PMwzGa9mwKWJO+K21r4QQC6fVlp5AHYLtksh1deKWLpUnPHsjrJ9UkFMPmclsl0vGuOoZXOY6A8/EAQBOzfvx/r1q3DSy+9hFwuZ3oMXKlUws0334zZs2cjHo9j+fLlWL9+vanHjo6OYtWqVZg+fTqSySTe+ta3YseOHZPuVywWcccdd2DRokVIJpMYGBjAe97zHmzatMn8G2MjFAG0mFZTqLquV9K9uq4jGo0iFov1rI29VwLw1FMzeP75o4Zb2InS3Fc0nQ7jjDOmY+PG5n58gqDg0CEBs2fHW9/ROpxySgyHDtnjYXj8uIxOIqJOplTyhSKq4N2JLuO4XeSa8QBsB0nisG1bCStW6Ni4MdzwvoJg3AcVQO1zy8CAdfvnRGp1HmuaBkEQKsGRbdu24dOf/jSAcp38rFmzkMvlsHTpUixbtgynnnpqzfX02muvxW9/+1vccMMNWLBgAdasWYP3vOc9ePzxx7FixYq6++jpUOwAACAASURBVKRpGt773vfiueeew0033YRp06bhxz/+Md785jdj+/btOMOQk//nf/5n/OpXv8JHP/pR3HDDDRgZGcFPf/pTXHrppdi4cSOWLl1q5dvVMu6+VHMIxi9XKwJKURRks1kUCgWEQiFkMhnE43HfeRjpuo7582tdysoon/was3DhFGQy0/D00+b9/dzUCCIIKpJJbx6qouhytdAikyM73kOS3H3+asUDsFV0ncPGjTJWriyB4+qvEUePsvdQQT3xBwCve52lu+cqgsEgotEoPvjBD2Lfvn148MEHcfHFFyOTyeD+++/HP/7jP+K0007DjBkz8K1vfWvCY7ds2YL7778fd9xxB77xjW/g+uuvx5///GeceuqpuOmmmxpu94EHHsCmTZuwdu1a3HbbbfjkJz+JDRs2IBAI4Mtf/nLlfsViEffffz+uuuoqrF27Ftdffz1uvPFGrF+/Hoqi4L777rPlfWkFb64qPcSMANQ0Dfl8HtmTdvmpVArJZNJyT7926HYEUJZlZLNZnHVWX517SCjXvtRm5cpTsHcvhwMH6qd8a+EmAQgAU6f2/rthB3bNUHYqfhCAouhuAdiNU9+TTypYtqyEUGjyxoJBHcPDQPkCWJr0dyNnneWvEgoAE+rtGVOmTMHb3vY2KIqCm266Cfv378fw8DAefvhhfPKTn8SZZ5454TkeeOABBINBrFq1qnJbJBLBddddh02bNmFwcLDu9h944AEMDAzggx/8YOW2/v5+fPjDH8ZDDz1U6W5m4/ZmzJgx4fHTp08Hz/OIxexdM8xAAtBiGgkoNr5tbGwMsiwjHo8jnU7b5unXLt2YB8xEcC6XA8dxeMtb5je4dwnVIjAWC+INb3gdnnwy31YdmdWdwHZPA0mnvXmoahrggPNg15C9W85ZoWDfVMSu0K4HYKts3qzi9a8vIpGYeP6aOVOFpimobvioxdKl/oqgN8NYAzhz5kxcfvnl+OpXv4r3v//9E+63Y8cOLFy4cJJnIEvJPvPMM3W3sWPHDlxwwQWTbl+6dCkEQcCuXbsAlAXgjTfeiJ///Oe47777sH//fjz33HO49tprMXXq1Anis1dQDaAFmEkB98rWpR3sFIC6rqNYLEIURXAcV5lhnE6Xu9nqb1YCEAGzh1myZC6eeqr9ifNuiwAmEu6OqjQikeAgiv6IZPjB93B0VIeVXp7dplMPwFZ49lkNCxeKOHEiimPHeAA6CgUFjbIeRlas8P73qZpaEUB2ez6fN9UEMjQ0hFmzZk26nd126NChho9985vf3PCxixYtAgB87WtfQzgcxkc/+tHKfp9++unYuHEjXueA/L0zFYiLqRaAqqoil8shn8+D53mk02kkEgnHij+GHQJQlmWMjY1BFEVEIpFJM4yj0UbXI+XB64CON7xhdkfiD7BjGkjU0uerJhRy74LajKi9b52j8HoTCM8DOWsPra5jlQegWXbt0hGJiJg7t1zvNzpqTvwFgxwSieb38yr1fADNTAJha1A10ZMnIzZ8oRbFYtH0Y7/73e/iK1/5Cv7lX/4FDz74IH784x9DURRcccUVOH78eNP9tBuKAFoM+1KyTqVisQie55FMJhEKhVzR4GH1PrL3QpKkht6GU6ZEIYqNLFp0hEIyXnghj/LEkPZxmxl0o4Jxt1OeHOHd12fE6yngvj4eJ044/xxXD6s9AM0yOAgEAiW0Ejn1q91do+BELpdDxoQ3TiwWq9jJGCkWi5W/d/rYQ4cOYfXq1fjUpz6F733ve5X7XXbZZVi0aBG+9a1v4Y477mi6r3bi7DCUSzAKJvblZOIvFoshk8kgHA67QvwB1qWAWc3j6OgoZFlGIpFo6G04a1bzKzdZVlEoHAYwBrNpklqMjEgYGbHOtmXq1DCSSfsaNVTVu7U+kYg/xB8AyLK3X+uUKe5eUuzwADSLqra27RoZTF9gRQp41qxZNdO8Q0NDAIDZs2d3/NgtW7ZAluVJ9YdnnHEGzj77bDz11FNN99Nu3H20OgxFUZA7mf8IBALIZDI99fRrFysEYLN0by1OO61eJ3AtCgCOopFFQjPcVAdYLHq3fbTLXuc9xesRQDsvgrqBXR6AzWl9u6edZsNuuBhWY28mBbxkyRLs2rWrsl4zNm/eDAA4//zz6z72/PPPx9NPPz1pjdy8eTMSiQQWLlwIAJVuYLVG678kSROmnPQKEoAWUG3rwnEcQqGQI2xd2qVdAWjs7m215vHss/tb3JoK4DiAEbQTDbQyDazrOubMsW8aSD7f+5OFXYQbe+J6Cqmxq4fricXcvaTY6QE4GXaObU90nnOOt6PJ9dB1vWYgIZfLIR6Pm5qedeWVV0JVVdx1112V20qlEtasWYPly5djzpw5AIDh4WHs3Llzgli78sorcfjwYfzud7+r3Hbs2DH85je/wfve976KqwfrFP7lL385YdtPP/00du3ahSVLlrTwqu3BR9fe9iHLcsXWJRKJIJvNdn2ShpW0EwGs193bSvTzggvatbUXUY4EZgBEYfaEalUEUFEUCIKAgQH7BP/ICOuC9h4uvk5qCTvnRTuFYNBd2Y5qunPaVlE+Z4XRyTG9bJnHv0wNqNcAkkqlTK05y5Ytw1VXXYVbbrkFR44cwfz587F27Vrs378fa9asqdxv9erVWLduHfbu3Yt58+YBKAvA5cuX4+Mf/zheeumlyiQQXdfx1a9+tfLY+fPn4+qrr8batWuRzWbx9re/HUNDQ/jBD36AeDyOz372sxa8E51BAtACWIqTRbm64aNnJ63uvyzLKBQKFYubdqeZXHzx3JYfM46GciQwAqAPZppEOhWArMaRNfqcfnoagD0thMeOSQiFIp5MIXq5wcVIJMKhQXOhJ9A0d0cA7fUA1FCe58vKVjprf7/4Yn8cN9XUW5tyuZyp9C9j3bp1uO2223DPPfdgZGQEixcvxiOPPIKVK1dW7sNx3KS1jOd5PProo7jxxhvx/e9/H6IoYtmyZVi3bh0WLFgw4b5r167FwoUL8Ytf/AIPP/wwkskkLrnkEnz961+fdN9ewOluVioOgc3zZbC6gpRL27RYx25fX+OaPFVVIQgCZFlGMBg0HX5vRCLxDahqp19JDkAaQByNooErVszAn/70rra2YPR1jMViiEajuO++QVx33bNtPZ8Z5s6dioMHvacAV66chief7PVe2E86HUA227xD0c0sW5bCli3ujQLOnx/G7t1W7z+zsCpiYrd7Bu26GQSDHE6c8G5ZSCOYzUp1p+4TTzyBL3zhCw1NnImJUATQBjiOg+biXE+zCKAV6d56pFJhjI522p2ro9wlLKAcDaw9aaUdL0Cj6A2FQkilUpVaT/vHwfE4eNDWTfQIDX4oRw6H3SuMzCII7n6N1noA6ihP8xBRu0a5/e+8iUZX35HNZk11ABPjkAC0AbengBthTPdGo1HLu5xnzkxaIAAZMsqdwikASVRHA4eGRIiiglis+WFQLXpr+TraPQ7Oq9NAOo/4ugO318eZ4WQfnCtJp3ULPQCZ8KvXvc+jk2kpA+2WS3sAXddrNhWaNYEmxvH+ZXcXqBZAbheAtfafTTQxdve2W+vXiNasYMySQ1kITmzB1HVzdYCyLCObzVYsbfr6+mpGPOfOjcLOAS9etUtxgh1CN/CDADxxotd70D4zZlhx8KoA8id/Glk3dbat00/v6OGup1ETCGEeEoA24XYBCJRfA2t0GBsbg6IoTc2cO2XRolatYMyiADiGcmp4/MTcSAAaLW0AVCxt6oneYJDHrFn2zTXzqhl0DZssT+LlcX4AEI8D+UaDfBxOX18nn4+GsjdpFuXoXzM6W3r9agED1F9bs9ksCcAW8WhMofsYo2ZeiAAC5UYHURRtS/fWYulSu+3tCyd/hwBE8Nprk1cs1tQjCAIAVOx9zLz2U06JYnCwaOH+jiMI3oyU+UUABoPevt6eOjWAk4eMK4lE2vl8dJSbO1o95jv7Lixd6s2LQTPU8wGkCGDrePuM1COMETQ3whpYCoWCreneWqxYMc/2bYx35WXxwx9uwaOPvlaJrrFpLoVCAaFQCJlMBtFo1PRrP/XUuG17PTrqvQ5gAJAkfyxmXvc7zGTc/QJbO12zc8gYWhd/QKdLr18tYBpBArB1SABahFEguG30G4Ole1nkKxaL2ZrurcX06XFEInZvb/zkeeDAGD74wYfx+tevxb//+1PYs+cINE1DKpVCMpk0NcHEyBlnJKze2QpHjlg3u9hJFIv+EIBerwGMx929nJjzAGSdvVmUXQbaFWLtv1fBIIeEfacZR1NvDjBAArAd3H3EOhQ3RgAlSarM7g2fnM1V3eXaLVIp++roykzOOe7bl8VXv7oVF130O/yf/7MZO3Ycb+vzs1MACoKKqVPdHWWphV8EIM97WwCGw+5eTkZHG30+OspNZKzBo9PvLFnAdEK9UXBkA9Ma7j5iHYqbBCDr7s3n8+B5HplMpmKw2av9t3/aRf2is1JJxb337sSb3nQ/Vqz4FdaufRGiaL72bsECey/Np0/3XtmuIPijCNDrApDj3L2cHD5cfQuL9hUAjJ78bVUdbvvvld8tYOpBEcDWcfcR6yBqpYCdLADrdfcGDIVKvdj/XK6EbNbuVKc5wfH000fwP//nepx++s+wevVfsXv3aNPH2C0A02nviQhB8EcE0OspYDe7+cycCeTz7PNRUU7vjqEc7ZPqPq49OHSy9J52mu7otcVOKAVsLSQAbcDpAtCY7o1Go+jr65vQ5drLGsZHHtnTpffN/GscGSnhzjufxqJFa/H+9/9+QtNINZlMCDNmhK3ayUlEIt4TEbKsI2zfW+YYvB4BtHeOrr1MnQqUmzmyJ39KaL++rxmdLbtnnSWhUCigUCigWCxCkiSoqurY9cYOqtcoXdcpBdwG3ssnOQCnCsDq2b3VET9GL/f/scf2ovP6GjNwaOcE/9hj+/DYY/tw6qlprFp1Lv7pnxahv3/iBJAzzkjgyBGrowZlOM5Z3ymrSCQ4SJI3XxvDpb1hpsnl3PYCdZSjfQpefrmbUejOBOCKFQFEIhFomgZVVScYqfM8j0AgUPnNcZxrmxJrQSlga6EIoEU4OQVsJt1b73HdZseOIXRHAHbGvn1ZfOELGzF//v+H6657DFu3Dlf+ZmcauFRycZ6tATF7p+g5Aq9HAEdGer0HZmCir4TyqDYJ3T/fdLbsvvGN5Qa9SCSCeDyORCKBWCyGSCQCnuehKApKpRIEQUChUIAoiiiVSlAUxdUz6o3USwFTBLA1KAJoA04SgMzQuBUzZ3bV2Iv937+f1dm1F6HrNuWmkZdx770v48ILZ+Dqq89CJsP23foFP5eTbXneXuMHAejFz43B804XgBrKDRwqen9esdYChuM4BAIBBAIBhEIhAOW1R1XVCVFC+WR3HcdxlQghixa6JUpIEUBrIQFoE72eBmI23es0RJG1APMw26zRHtZ/Ntu3H8H27UdO/i8IIH7yJwbAmvf+6FEJQMSS53ISfqgB7L3wsI/+fh5HjjS/X3fRURZ9Cpz13rd/LugzOSqd47gJ/q1srCcThKqqQpLGy1R4np+QPnaqKKzXBMJqISkC2BokAC2i+gvZKwGo6zqKxSJEUQTP80gmk235+fVq/+fMyeDgwRHYHy2x+7UpGC8oB8qijQnCKNp9fcePSwiHI5DsKTHsGeGwOyK+neHd19fXF3CIAByv63NuKUn7EcDZs9t7HMvq8DxfEYZMEDJRqGlazXpCY5TQCaKwXvo3EokgEvHexbGdkAC0iV4IqHbSvfXolQD8wAfOwg9+sKkLW9LQ3RLY0smfkZPbjWFcEIZMP4uuAzNnBnHggLdqAcNh74ojBs87PwLfLslkr8rJmUFz8eRvN4zIaP+9mj/fuuOkXuq4WhDWSh0bm0y6Sb01KZfLIZlMdnVfvAAJQJvopoCyI93bKwH4+c9fdFIA2r1tFb3rgdJQNpUtnPx/CBPTxY33K5PhcOCAnfvXfQIB7wtAL9cARiLdOpbYDN6i4Yd9d4JwvgDszAPwnHPsPU6MopBRHSU0po7Z/Y3C0G5RWOv5s9ksUqmUIyKUboIEoEX0IgVsVbq30fN3m5kzExgY6MPwcLb5nTtCRSuRN3uRUTadHTv5f2N0MIxq4RCPe+8k1+LIZVfi5bWp1ZnZ5tEwUexJqH9x6IYIayfvk44LL1TR7QsJVhNoTB3XajIx3t8uK5p6a1I+n6cIYBuQALQJjuNsbbm3Mt1bi15eSb3vfQvwf//vVpu34uSIk3jy5zjKi1rc8BPoYrSle/C8PV3TTsIjDhw10XWrPjsVkwWfWdxwXLQvUjlOxyWXWLgrbe/HeD0ho1bq2CjWqqOE7V4w6LrecAoIRQBbgwSgTdgVAexWd6/dArYRN9649KQAtLsxwA2NByqA3MkfAIjgtdcElFNdCXhFNFl1qHAckExySCZ5JBI8olEOkQiHYJBDIDAehdO08g/Hjf+wxxue7aTx9ritz/gCpFf22fhb11nKTAfHBU7erkPTgHQaWLCAx+iojpER3dWj06qRpHa/hzLKQo+ldTsZBO4GAdjuPupIJhWEQs6MctZKHTezomk3dVzrflQD2B4kAC3C7hQwM3MuFou2pHur6aWNzbx5afT3p3DsmPD/s3fmcVLUd97/VPV9zTAwHAOKUWAUATl0cAywErw1akKQsHlWo6LEuFkNJoKaYBLXqPGIrtmwhk0WAX3iMZqQ9TFPnjVq4oGD4iAximgiHpyOMn131/n8Ufxqqqvv7qquX3XX+/WaF0NPdfevrt/vU98T5paCsYMA1JPFnj17D/8eARCG4sr2QHEXe2APV5geRWAxjNIVhAi4QEAr4IggA0QREAQZ2ayMTEZGMikhkZAQj8uHf0SYe+1UQq6Y6e314t13h1VfezuLjg4WkYgLgQALj0exrIiiIqhSKRnRqCIW0+lGj706kslK5iEZw4KP/Bh5jppbAI4caa+5qlgpmlKu43KlaIpZAJ02cLXhCEAD0YomowSULMvged5Ud2+p77aKc86ZjE2b/mLZ99sDAcPZxVpcyBeFHtCwQLrdDMaP92LUKA+CQRdkmUEqJcHrdaGtDUgk5MM/NAg4Y9G7SaNRCdEoKVBcmlCIwYgRLNraXAgGWfh8LFiWgSQBPM8gnZYRjwNDQ4pobDSxgiG72gxd8mOmV8H667s8tYxREc6TJ9vb2l/KdVysFI3eSihJUkGPlxMDWBuOADQJIwSg1t3r8XgaWszZ6liK66/vwUMPvWmYa7Aw9nqizqeYcCDiKaN73Y1cUeg9/Jqx59rjYdDVpYi8QMAFQLFmDQ6K2LePx4cfMvjwQ1KgV+HEEz1FRETzINahZ5NJGcmkiD17yn+IxwOMHOlCR4cLI0a44HKxSCSAvXtlfPKJGde8jIMHs1DOJ6/5KZWwYQbNLAAlzJnTfMt1tV1MAEAQBHV9TafTGDFihNMFpEaa74qiBG07uGrFVKPdvYWw0gUsiiK6ujwYMyaIAwei5d9QM80qAEttL0BJMNGitxR6oVgRi19vHo9iyRs50o1AwAVRlJHJMBgclLB/f2GRp1D4M7PZRtdlbDyi2JjrjeeBAwdEHDiQLxbb21kceaQbbW2K8I/FgI8/ljA0lP85uZACyyIUK56o+ZHB1xO6Zxh2sJBVe40Ttzlw6ql22L/60buOgeFSNJmM8lDLcRx27NiBc845B5MnT8ZRRx0Fj8eDbdu24YQTTlDFpJ5sNoubb74ZmzZtwtDQEE444QTceuutOP3008uOa2hoCKtWrcJvfvMbpNNpzJ07F/fccw9mz56dty3Hcbj77ruxceNGfPDBB2hvb8dJJ52EX/ziF5gwYUINR8UcHAFoIHoXMFCdALTS3avHCgGoL2vzhS9MxCOPmOkGFmHPeDmCNkGhHojFRgsDhvEiFPKjvd2Pjo4ggsEQOI7FJ58olrwPPmDwwQeFXLXVX6/pdPMLQBqSPhS3Mwd9dm1nJ4uuLjdcLiCVEnHokPIjCETw2SGFmfbrh0VtAlA59vPmGT0e+6CNB/R6vXC73ZgyZQp+9rOf4bXXXsPLL7+M9957D7///e/h8/kwZ84czJ07FxdeeCG+8IUvqJ9z6aWX4oknnsDKlSsxZcoUrF+/Hueeey6ee+45zCtxgCVJwnnnnYcdO3Zg1apVGDVqFNauXYuFCxdi27ZtmDx5srotz/M477zzsGXLFqxYsQInnHACPvvsM2zduhWxWIwqAcjIVgZ6NRk8z6uZszzPIx6Po729vSK3rd7dGwwGLe3dm81mkUwm0dHR0RAByvM8kslkjvB9441P0Nv7XyZ+Kwv799XtRGMXZxeUY+aDYi10H37NjWF3cvXXy9ixbhw4YPdzUZqpUyN4+22rVaCMYVFR6Hc7Mxp0i0A3gGoSFUgMpYRAgMGBA7TULbUGWZaRTCbh8/nyLHyXXXYZ5s2bh97eXmzduhX9/f3YunUrli5dittuuw0AsHXrVvT29uLuu+/GddddB0BZ56ZPn44xY8bgpZdeKvrdjz32GJYtW4a+vj4sXrwYADA4OIju7m6cc845ePjhh9Vt77zzTqxZswYvvfQSTjrpJKMPg6E4FkCT0FoAS0GDu9dKJElCKpUCx3Fwu90Ih8Oq+X/WrDHwetvBcWa5ge1g1ShHo68TEUDq8E8hGAyLQhdyBaL+XxZk/LFYcyV8FILnGyGwiKsWUI4tEXfNIvJKQfucWbv1b9w42vfNfPTeNS2JRAKdnZ045ZRTcMopp6iva0uZ9fX1we12Y8WKFeprPp8Py5cvx0033YQ9e/YUtc719fVh3LhxqvgDgM7OTixduhQPPfSQariRJAn/9m//hsWLF+Okk06CIAjgOA7BYLDu/TcDmh+XbIf2wiwnAGVZBsdxiEajyGQy8Pv9aG9vh9frpUL8VSpga4W4e6PRKHieRygUQiQSyYv9UG4cM9s7WX+s64O28WtLfSShdDf5DMBBAPsAfARgN4D3AOwC8DcAu5FOfwSG2Q1gD4ADh98TgxKvyKMZhEtlcXJEwJFjmAKQgHIcD0EpDv4JgP0A9gL4GMCHAN6HciyV4wkMQckO56HEYRIB2KyUjlmlg2qW2+HYPwCYMoX2fbOWeDxeMAlEm3E8MDCA7u7uvGzhnp4eAMD27duLfv7AwADmzJmT93pPTw9SqRR27doFAHjrrbewb98+zJgxAytWrEAoFEI4HMbMmTPx/PPP17JrpuJYAE2mkICyMru3UswUgIIgIJlMQhRFeL1eBIPBopXhjzwyjKEhDl4vwHHJgtvUhx1rATYTJFEkC1kud36Jm9mj+zeM4cVfu1Ba8bvWrSrl/P7ZZxyGxRhJpJB0/zfqWmw1wWAHW0Y1c/yw9Q8A5sxptfOZTzELoCzLFWUB79u3D11dXXmvk9f27t2b9zftexcuXFjyvdOmTcO7774LALj33nsxatQo/Od//idkWcZtt92Gs88+G6+++ipmzJhRcpyNxBGAJlHIimcnd68ZAlCWZaRSKWSzWbhcLkQikaLZWoTjj+/AX/7yGTjOB4aRIMuUV8R1MBEiFvXlbbpgB3d+PF5+G4daoesBujCVitRc6x8ALFpE3xphFaVawZUinU7D58uPM/b7/erfi5HJZCp6byKRUP/dvn276lJetGgRJk+ejDvvvBObNm0qOc5GYofHJttQzAVMu7u3FEYVs+Y4DkNDQ8hmswgEAmhraysr/gCgt3fs4d8YyHIASokSI7G39S8SscPC59B4Wm1qt8P+VjLG4cQPLSefbMZ47EWptYgkXJYiEAggm9UXzYdaWiYQCNT9XvLvvHnzcuIJjzzySMyfPx8vv/xyyTE2GscCaBJE2ImiiEQiQbW7txBGCdN6s5vPOutI7aigxANW1j2hMuwtAJWWZw4OrU4zCMDC4i8UYqg3FDSCckkg5SyAXV1dBd28+/btAwCMHz++7veSf8eOHZu37ejRo0vGGVqBHe4a26C9MMnFmslkIIoiwuEwwuGwLcQfUL8LmLi7o9Gouv+1iN9jjmlDbnggCyXmy6hLl37XYWnsPn4jcBbHfFrtmNC+lA1nvBdGRrFWeU4GcGl4nkc6nS7bC3j27NnYtWsX4rpYjP7+fgDArFmzir531qxZeP311/PWw/7+foRCIXR3dwMAZsyYAY/Hgz179uR9xt69ezF69OiK9qlR0H7X2A6tuxdQehnaxd2rpR4ByPM8otEo0ul0jru7Vtra9O91QamnZcTxtLouW704AtChEPaZa4yB9qWs1IMvEX+F6e5utXNZmGIWwEQiAbfbXdKFCwBLliyBKIpYt26d+lo2m8X69evR29urumz379+PnTt35vQkXrJkCQ4cOIAnn3xSfW1wcBCPP/44zj//fDWcKRKJ4Nxzz8VLL72Ed955R9327bffxssvv4wzzjijxr03B8cFbCCCICAej0MQBPWCcLvdthJ+eqoRgKVq+tWDkgn8me5VF4AIlFIh9WB3F6rdu5k4OBgB7QKw2PgkKOV6inPiifZdP4ym0Foaj8cRDofLrrNz587FRRddhBtvvBEHDx7EpEmTsGHDBnz44YdYv369ut0NN9yAjRs3Yvfu3Zg4cSIARQD29vbisssuw1tvvaV2ApFlGT/60Y9yvue2227DH//4RyxatAjXXHMNZFnG/fffj87OTtx0000GHAXjcASggTAMA0mSEA6H4fV6EYvFLOunWy/ViFZZlpHNZtVMqFAoZKjFk2QC5+OBIgLrTa+0cykYAY4AdMin1UQD7ftbSACWF38AcNpptO9bYyi2llYqAAFg48aNWLNmDTZt2oRDhw5h5syZeOqppzB//nx1G4bJj7lkWRZPP/00rr/+etx///1qL+CNGzdiypQpOdtOnToVf/rTn7B69WrceuutYFkWp512Gu66666CZWisxGkFZyBECJGLh8QalAtOpZVDXu1AvAAAIABJREFUhw6pbdmKUU1Nv1r5j//4K1auLJU9RYoO1wpJLLEjPphbKNsOjIf9LblGMxat9GDgcnVCFGne3xC0bSf9fgmZTHnxBwDRKJ2lwhpNJpOBJEl5XTVeeeUVXHvttfjrX//qHKcqcSyABqMvBaNtRWM3GIYp2cmk2pp+tXLGGUeU2YJMrGYUiqYdDo4AdGh1RJH2hX/4oZhhRGQyXEXvCoedDOBykC4gznGqHkcAGoj+AiwloOxAofHLsgye55FMJiHLMgKBAPx+v6k33+TJ7WBZoLiWZgD4obiEE7B/Ykc1yLC3C9vBHFppMWRAfwwgsU4KkOWKegICALq6Wuk8lkaW5ZIxgA7V4whAE7G7ANRTb02/emhr82JoqNxTM8kOzqI6a6DdzxELxwXqkEsrCQfaxR9z+EeAvsNHOY49tpXOY3lqrQHoUBja7xzboXcB21kAkvEbVdOvHo48stInPGINHAHFIlgJ9nXTKzi3sYOeVhIOtF//LGoRfwDQ09NK57E0xdZSRwDWDu13jq1pBgEoiqKhNf1q5fjjO6p8BykTU4lwtLvL2FkkHLS02rRO8/6SAtC1zTFOBvAwpVzAjgCsDZrvHFtSrB+w3ZAkCaIoQhRFsCyLtrY2BINBywJth3sCVwMDJUGkA6WtgY771N7Y7/4yl1YTDTTuLxlTFrVenwwDzJhh2ICaAkcAGosTA2giWgFolwwlbU0/Mm4aMqzKZwKXgoViDeSgxAbqJ2S7Cwi7j79e7HFvNY5WOx60lX9hkVvfrzY7i5MBnEsxQ0oymaSuxZpdcASgidjNAqit6efz+SDLMkRRpGISKp8JXA5iDXQDSEERg1pY2DUW0OtlwFVWVaJJscf91ThazbFDy/6yUOYV/fVY2/i6upTady6XCy6Xq2CB4lahWBs4AIjFYmX7ADsUxhGABmNHF3Cxmn6pVCqnH6LVVJYJXA4XlLjAYtZA++H1Si0uAB1yaTWRYLUALJfkUdv5OPZYpfKCdg4mYpBlWVUUtjpOEkjtOALQRGgXgLIsg+M4pFKpgjX9aEtiKdwTuBbKWQPtBcPY03LpYBatJgqscgETr0Gpjh4kCaR6entdCIVCqieGxGVzmqc9hmFyRCHLsk0pCktZAJPJpCMAa8QRgCZCswCstKYfTWMv3hO4VrTWQHr2s1pEUYD1VhArse+5M4fmEwClafS1r03wKEft4vT004cfxN3u4aWalObSikKtlZBYB7WisFkolgTiuIBrwxGABkO7C1iWZWQyGaTTabAsi3A4XLSsC21JLL29Y/Doo38z+FOJNZCBkg1ce9aeVfA8D22f0dbDXufLfKy/VxtLIwVOsTi/UttXD8MAU6cWPo8kFlAr7GRZzqncIAjC4Xlh2EpIhKEdrYSlWpI6ArB2HAHYAGgRgKSFmyRJ8Pv9CAQCJScC2iaJM844EsAWkz6dARAA4EcgwCCdTqGyJ3zrcQSgQy7NY/EpT+0u1uqQ4fWK4Lhq5/LazkVbW3X7pHUFEyRJyhOF6qg0YtAOCSblXMBOK7jacASgiZCbymoBKEkSUqkUOI6D2+1GOBzOcSkUgzYLYP2ZwKUgH8ognQaAIIAAjjrKjb17PwPP0xxnV32HAYdmxvp7tXGYLXZlKJ4BucZEq9rGN2FC/eeQuH/JXE+shFq3sXZtsmuCSTweR3t7u9XDsCWOADQY/U1jpQDU1vQDgFAoBK/XW/GNTaML25hM4GIwyHXtMPjgAxGjRnXi+OMDePvtgxgcTJv03fUgg2VZSOYoYxsgo7VETzla6ViYta8ylIfCeu+p2sZ3/PHG75fWSujxKIXx7ZJgUswCKIoiEomE4wKuEUcAmoxVAlBf0y8QCDRFMLBxmcCFYFGoK8inn/J44QUeLpcfJ588BvF4Am+99alJY6gNt9sFjnMEoAPQWsfCyDlNxrDFzyhqOxdnnNGYc1gqwYQIQ30ZGq372Oo1JZlMgmEYhEIhS8dhVxwB2AAaKQCL1fSrBRotgMZnAmspPemKItDfHwMAHHfcERgxQsa2bfuocA+73axTC9DhMK0kAOstAUNEnwTjk4lcqPVcXHCBNedQm2CitRJanWBSzAKYSCQQDoctF6J2xRGABmOVC7hcTb9aoFEAmpMJTKh8P3fuTAIAOjs7cdxxAezcaa17mKJTZAEtvfMFaCUBWOvCb5SLtxS1idNwmEEoRM85pCHBpFgcOikCbZd4RdpwBKDJNOLCFEURyWQSgiCUrOlXLTQKQHMzgatfDAYHebz4Ig+3O4CTTx6LaDSGnTvNslAWRxDoOUeNp5X3vRCttBhWIwDNcPGWojZxOmkS/dasYgkmWrex1mpXSBQaQSwWczKA68ARgCbDMIxpwfnV1PRrFiZNajM5E7i2iUkQZPT3RwEAU6ceifZ2qaHuYRrc0A604AjAYRRLXygEJJONvkdqE3K9vQYPowEUshLqYwm1CSZEQFaaYFLKAhgOhx0LYI04AtBgGuUCrramXy3QZgEkLu5IxINo1IzSJzyA+gX0228nAAy7hwcH43jnnc9MdtPScY6soZX3vdUp5umQND8MksnGjWiY2gTghRfSbwGsBJJgorUS1pNgUsoF7FAbjgA0Aa3oM1oA6mv6RSIRQ9y9haBFAGr32ePx4Mgjw4hGD5nwTSSb1Jj9Je5hAOjoGIPu7gAYRsDAwEFks0YL2Fa2ADoCMJdWsoZoRQKJ6xMxfAysPBbVfzfDAPPmmTAUCiiWYKJvaVcowaSYFy0WizkCsA6a41GDYowSgMTdG41GwfM8QqGQqeKPYHUdQ/0+h8NhTJ8+0sRvNeeWOHSIR39/DK+8kkI2GwIwEsAIKN1HjKBRcU004gjAXFplWmcw3MKRByCgnjAOI1GenasfR2en/dq01QOxEnq9XgQCAQSDQQSDQfh8PrjdbtV1TJJOUqkUMpkM+vr68P777yMej1ckALPZLFavXo3x48cjGAyit7cXzzzzTEVjHBoawooVKzB69GiEw2EsWrQIAwMDZd8zZswYsCyLJ554oqLvsQLHAmgyREDV003D6pp+VtUxTKVSEAQhb5/PPPNIPPKIWZnAjYBYGRkA/sM/DJRFLIXaxJyI1ln49TgCsLUg7d9YKPcMfYJJlukpAG0nivU5TqVS6uvvv/8+Lr/8cgBKmE1bWxvuuOMOnHzyyejp6SmYFHLppZfiiSeewMqVKzFlyhSsX78e5557Lp577jnMK2FylSQJ5513Hnbs2IFVq1Zh1KhRWLt2LRYuXIht27Zh8uTJBd938803I51OU99ir1VXDFPRnvB6Tr4kSUgmk4jFlNpzbW1tCIVCDRV/jb54yc0ei8UgSRIikUjePl944ecaOqbGIEN5HougNuugUH6TpsURgLnQu+DUBgsl1o94O4ibl+YC4LWNa+FCWvfHOsga5HK54Pf7MXXqVPztb3/Dr3/9a/zDP/wDBEHAbbfdhkWLFqG9vR0zZ87Efffdp75/69atePTRR3HHHXfgJz/5Ca644go8++yzOOqoo7Bq1aqS393X14ctW7Zgw4YNWLNmDa6++mo8//zzcLlc+MEPflDwPW+++SYeeOABrF692vLwqXI4AtBkaomjIy3cotEostksgsEg2traKurfazSNdAHzPI9oNIpMJgO/34/29vaCRaxDIQ/CYbOOhdWxdHrrYAcUQRhBaYO9IwAdmoVigk9vGad5+aotNOeiixwBWAytMWL06NE477zz0NnZicsvvxyHDh3Cm2++iXXr1uHkk0/OWTf6+vrgdruxYsUK9TWfz4fly5djy5Yt2LNnT9Hv7Ovrw7hx47B48WL1tc7OTixduhSbN29W4xW1XHvttVi8eDEWLFhQ7y6bjuMCNplqBaC+pl+jLX56GiEAa0lsmTSpHW+8YUY7NjpiiHIh1sEwlEVPApAFoC08bUZWtF1wBGAutF2/5WAP/5AafZU+hNEsAKsfm8fDYOJEu527xlBsDSIxgC6XC9OmTcO0adOwfPnynG0GBgbQ3d2d5xru6ekBAGzfvh0TJkwo+PkDAwOYM2dO3us9PT1Yt24ddu3ahWnTpqmvP/7449iyZQt27tyJv//971XtoxXQfAfZlkIu4HIiSpZlpNNpRKNRSJKEcDiMSCRieYsbMwWg1tLJ8zyCwWDFiS2nnDLWlDHRLaSKWQfboAjEVl08HAGYC+3XAQvleiVzmwTFgl1t7CvNy1f1FsAjjqD9vFlDsTZwgFIGpq2treT79+3bh66urrzXyWt79+415L3pdBrf/e53cd1112HixIklx0QLNN9BTUElApC4PtPptOr6bPaCzqIoIh6PI5lMwuPxoL29varWdV/60tEmjYzmuKJCyFAWmzCAELzeyOHfA2gdA78jAIchmbE0Qdy5esFXb7gFzctX9QJwzhzazhv9VJIFnE6n4fP58l73+/3q34uRyWQqfu8dd9wBURRx0003VTR2GmiVFaKhVGoBbGRNv1oxupOJUd1LTj21CwxjVg9cFvYrq6JkRXIcOVfaGCpyPZKFl0NziaZm2pd6oUFEkCxdbV0+s76HVqo/D2efTcO5o49yFsByAjAQCCCbzea9nslk1L/X+97du3fj7rvvxtq1axEMBkuOhyYcAWgyhQQgcX2Sp4dQKASv10tluriRLmBtOZt6u5cwDINRo/wYHMwYMjbdp5vwmVZCzh8DwINc9xuJuxJAt/u7FI4AHKaR1y4RegQi+BqVSEWrAKxtXBdc0GzzjrHo1wpZlityAXd1dRV08+7btw8AMH78+Lrfe/PNN2PChAk49dRTsXv3bgDA/v37AQAHDx7E7t27cdRRR1G3xjsCsEEQEWV1Tb9qMUIAktIu2WwWLpfLsIzmqVNH4IUX9tf9Ofk0u6DQdzsh1kIvct10IhRrodWZ0eWgfXyNxOi5ZLijRiTCIh4nDwyAtced1AGkkeoX+XCYQSBAlzighVLrTyUWwNmzZ+P555/Pcxf39/cDAGbNmlX0vbNmzcILL7yQV8e3v78foVAI3d3dAICPPvoI7733Ho455pi8z7j66qsBKMWhy4nVRkPrHWRr9C5g4ka1uqZfrdQjADmOw9DQkCnlbL7whcKZW/VjV0FRr3AlwpDEQbqhxBKGoZShiQAIwoh+ycbS7IK9GmoVEURQFbLqKZa9eLyWRA2zoFksVR/GM2mSCcNoEkq5gOPxeFlRtWTJEoiiiHXr1qmvZbNZrF+/Hr29vWoG8P79+7Fz586c/sRLlizBgQMH8OSTT6qvDQ4O4vHHH8f555+vlpu59dZb8dvf/jbn51//9V8BAKtXr8Zvf/tbKl3DjgXQZMjFS2IGSJsb2kzBxah1nETw8jwPj8eDYDBoeHzjRRcdg1tu2WboZyrQWAqmEswQQuQ4kM9moQhAD3LdyJLmp1DNNjNxBOAwpa5bfYKI9kGHCD27QPODc/Vj6+01YRhNRq0u4Llz5+Kiiy7CjTfeiIMHD2LSpEnYsGEDPvzwQ6xfv17d7oYbbsDGjRuxe/duNYt3yZIl6O3txWWXXYa33npL7QQiyzJ+9KMfqe8t1E2EjKunpwcXXHBBzfttJo4ANBFS00+WZbAsi7a2NltY/LRU6wIm8Y2kdU84HIbH4zFF8E6ZMgJuNwNBMHrhEqAIHLshotYCtNWjdyMT6xGBWBH11iTAeLFoV4utkZDjr4/v1P9rJ5FXCprn0eqX1QsvNGEYTUKx9SedTkMUxYp6AW/cuBFr1qzBpk2bcOjQIcycORNPPfUU5s+fr25TqG0by7J4+umncf311+P+++9HOp3G3LlzsXHjRkyZMqXs99Ju6GFk2nuV2BBJktSOFkTwud3ugj0KaSebzSKZTKKjo6PsxWxFfGN396/x4YcJEz7ZD/stlj7Q556thHyxGA4zSCSAfKFYqOMJA2CM6aM0F+KC1f6r/dEj637INn4AIbMHSwF+KOEINFJdnBfDAIODTMGuRw5KmbRsNotQKJSzBu3fvx+zZs1CPB63nWGFFhwLoAkIgqC2MwsEAkgkEtT3BCyHPghW/7d0Oo1MJgOXy4VIJNKwyWz27E6TBKAdS8HYbbwEvZsZSCQKWRc9AGS4XCzcbhdcLgZKVIEMIABZVsoCSZIMUZQhCMrvxSn0t0peK/V/vXBjEAq5kEzKYFlGM2ZlrMo4i1kwK7XYMUV+b2ZoXfCrP/6dnfRbiqykWAxgIpGwpVGFJhwBaAIejwcjRoxQn0qMrqXXSMpNTDzPI5lMQpIkBAKBqoo5G8GZZx6BzZt3m/DJdpyQW6EfMANRlCGK+n0tdn+Vi4kzn2RSGZskyZAkoED7UAOx43VbC80jAKdOdQRgKYoZH0hWr3PsaofWu8jWMAyTY5JuRD9dsyhWyFqSJCQSCdX83t7eXlddv1pZvNjMjiB2xJkMW5m2tlaZ0mndz+pjcBcssKvlvnEUE4DhcNgRgHXgWABNQiv6mkkAyrIMjuOQSqUAWF/EuqPDj5Ej/fjsM6MLQtvTYpufnOHQSsRirSImmkcAfvnLoiNiSlBs7aykDZxDaRwB2CCaQQCSrGZBEOD1ehEMBqkIvh03rt0kAWjHSdmOYzYCR/gqtML5p7kIdHVLqtcLlGhE4YDiLmAnBrB+HAHYAOxsASRks1lwHAeWZRua5FEJn3xiRjs4u5aCcWhtWkUA0kp1wvSII5R9cSyA1VNJEWiH0jgC0CT0LmCgdCYtrYii4lLiOK7u/r1mIIoSBgdTJnwysQDaTbjbbbwOxkLPvWketFr/qh/XzJlKqEk6nYbL5VJ/aJpjrYbU0dVDYgAdascRgA3AjjezJElIp9PIZrMAgEAggEAgYPGo8vnrX4cgyzzMEWt2LAXjCMDWxn5zTfXQKQAZxoVqHT1nnTVsARQEAfzhFHGGYVQxyLIsWJa15TpiFMVcwE4MYH04ArAB2MkCKMuyWtpFlmUEAgGk02lqxz1jRgdcLjdEkQFgdH0NOve5NHaNXXQwhlY493QKQK/XjcPPyxXBMMAXv6j8HggEIMuyGmstSRJEUczpS0vEYKtZCYuFTyUSCXR2djZ4NM2FIwBNQntzFiulQhuiKCKVSqn9e0OhEFiWRTqdpnbsDMNg+vTReOONT034dDr3uTQSGtcOzoE+WkEU0CkAOa66+27UKMDvBzhO+T9pRaZ1d8qyrIpBIghbzUpYqg7g0UebVQasNXAEYAOgXQAW6t/r9Q63FKN9Urn44sl4441BEz7Zbu5foLH9gB3og+571RjoFICyXN24Jk9W/i01v2pFnvIdjpWQ4LiA64fOO6nJoFkACoKAWCyGVCoFn8+HESNG5Ig/gP4s5m98YwpY1ozx2a+zhtttR9HqYBzNu+APQ9+y5XZXP6aTT65+TSAWQo/HA5/Ph2AwiFAoBL/fr87bPM8jk8kgmUwimUwik8mA53lIkkT1PF6IYm3gAEcAGoFjATQJ2l3A+v69bW1tcLsLXw60C0CPx4VJkzrw7rufwtgCziJI/1m7oLUGOLQijgC0Ar/fhURVLcklXHmlG0Z4GRiGyZm7tVZCYinUWwm1lkI7WAmLCUCnDEx90HcnNSG0CUCO4xCNRpHJZBAIBEqKPwItYy/GV786Cea4Pu14i9hxzA4OlUBnEWhRrM6WMnYsj4kT3aYkBmqthH6/v6CVkOM41UqYSqWotRIWG4ssy04nEAOg705qQmgRgKR/byKRqKp/rx2eEK+99jg1TsaB/vPlYAYMmv/c07lkpdPVzD0STjyxsa0miZXQ6/UiEAggFAohGAzC5/OBZVlIkqTGgSeTSaTTaXAcB0EQLF23HBewuTguYJPQX7BWulFJkkc6nQZQff9e2l3AABCJeHDMMW14912ju4LQvd+FaXYR4NC60Hdtu92AIFQjTDO44oqg+j8rHrC1Gcekq5M+uYQj6cmAmmWsTS6x2jCQSCTQ3t5u6RjsjiMAG4RVIsqI/r0Mw0CSGvvEWi2yLOOMM8bi3XcPGvzJAmi1Ojg45EKfODIe+u7FQIBFPF7p1hLcbh6nn664Yml6sKYxlrCUBdBxAdcPfXdTk9JoASjLMlKpFKLRKCRJQiQSQTgcrlr8AfRbAHmeRzQaxZVXTgTDGP1MY3RxaQdzoPf6bBzNLwA9HvrCPHi+mjk1i2nT7GF3KRVLSKyGjYol1AtA0pveSQKpD3tciTbEShcw6eQhSRKV/XuNQtuuzu12o7t7LLq6wti7d8jgb2JhbHax2dhprA7G0Xz3uB6ep+/azmQqXUYlAFksXTrcv7ZYn1taIVZCYikkhar1xaoJ9VoJS3UB8Xq98Pl8te+MgyMAG0UjBKBeEIXD4bLZvZVAowWQ4zi1XR0JZmYYBmeddRTWr291AUjXuXJoFM0vAOkscl7pHKvE1F1xhd+8oTQYbaFqfSwhcRvrYwn1grCUKCzmAnbcv8bgCECTKGQBNCuOTpZlcByHVCoFADmCyAhoEoCSJCGZTOa1qyNcffVxWL/+7wAqDsqpADr2vXJEONEdrUgrCEAar+tKxiQByGDCBBcCgdztm807U85KqM0sJm5mraWwkuMRi8UQDoeb7tg1GkcANgizRJS2f2+tSR7loEEA6kVusUzmGTPaEAi0IZ0WAKQN+na7dddoRQEoozUEUClaYf9pW7IqPeaKFezMM3O7LFk9rzaCeqyExYwmiUQC4XC44N8cKqfVVomGou8GYuTNTjp5RKNRiKKIcDhcc5JHNd9pBaIoIh6PI5lMwuPxoL29vaSFs6enA0A7AG/Bv1eP3bprtGLiSvMvpOVpBQFI25JViUtaBqCUp/rWtwJ5f21FKxaxEvp8PrUuYSAQUOsSCoKAbDarxhOSuoRvvPEGUqmU2gWk3LHLZrNYvXo1xo8fj2AwiN7eXjzzzDMVjXFoaAgrVqzA6NGjEQ6HsWjRIgwMDORsk06n8fOf/xxnnnkmxo8fj7a2NsyZMwcPPPAA9ZUzAPrupqbFSAFI+vem02n4fD60t7fn9e81EqsmKFmWkclk1EzmSkXupZceCWUx7IAxMUMi7LW42s1i6WAMdrpGa4HG/Ss3v8gAFK9FOMxgypRcC2YrWAArgVgJScZxKBTKC+9JpVI488wzccQRR+CWW27BoUOH8Mgjj+CDDz4oehwvvfRS3Hvvvbj44otx//33w+Vy4dxzz8VLL71UcjySJOG8887Dr3/9a1xzzTW48847cfDgQSxcuBDvvfeeut3f/vY3XHPNNWAYBt/5zndwzz334Oijj8bVV1+Nyy+/3JiDYyKM7FyBpsFxnHphZjIZpFIpjBw5subPI6VdstksXC4XQqGQIUke5eA4DolEAiNGjGhYxpogCEgmkxBFUW16XqkQFUUJkcjTUB7ABACDqN9CFIS9hFU77JW4Ui/toDNBoJEEAeRbmJoHF5TzTBMhFL/uiPhTLPKLFnnx29+OyNmCZLOa+QBvZ0jzgkAgAFEUsWPHDvT39+PZZ5/Fiy++iMThBsxdXV045ZRT8MUvfhGXXXYZAGDr1q3o7e3F3Xffjeuuuw6AYhGcPn06xowZU1IEPvbYY1i2bBn6+vqwePFiAMDg4CC6u7txzjnn4OGHHwYAfPrppzh48CCmTp2a8/7ly5dj/fr1ePfddzFp0iRjD4qBOBZAE9G7gIHan/hI/95sNltx/16jaGQrO+LajsVikGUZkUgEoVCoKiuky8XimGNIpX03gNpFt32h0VpiJs5zbPOfcxoFfinxl4Q2HOPyy3PFeakixw7DkOPjcrkwe/ZsXHXVVViwYAG+8pWv4MCBA9i8eTO+/vWv49NPP8Urr7yivq+vrw9utxsrVqxQX/P5fFi+fDm2bNmCPXv2FP3Ovr4+jBs3ThV/ANDZ2YmlS5di8+bN4HnlvI4aNSpP/AHAl770JQDAzp0769t5k3EEYIOoVURJkoR4PI5EIgGXy1Vx/14zMFsAal3bfr8f7e3tatBwtXzxi2M0//OifsuB3QSGs6i0Hs1+zmkTgMWONxF/2np4wPnnO1a+ailVBzASiWDMmDG44IILcPvtt+P555/HL37xC3WbgYEBdHd35yWL9PT0AAC2b99e9HsHBgYwZ86cvNd7enqQSqWwa9eukuPev38/AEU00owjAE2kHgugNv5NEASEQiGEw2G4XI2fBM0Wm7IsI5lMIhaLAQDa2tqqcvkW4p//eaLulSCAerLG7JYI4uDQbNAmAAuNJ1/8AcCxx3qKzmeOBbA0tbaB27dvH7q6uvJeJ6/t3bvXlPdyHIf77rsPxxxzjCo2aYW2nPqmpRoBKAgCUqkUBEFQs6SsrBZvpgtY27UkEAjA7/cbMiGOGeNDR4cbhw5pJ+IwPB4JPJ+qZaQA7FR1vpXi/wD7WWjNoNmFBG3LlV4ASgASKHTvLV6cP3c44fflKWUBHD9+fMn3kiRJPX6/X/17MTKZTM3v/da3voW3334bTz/9NPVdXugeXRNRiYgiSR6xWEzt36vPhLICMwSgJElIJBKIx+NgWdZw1zbDMPj85/V9IhnwfJt6E1ePnW4XRwC2Hs0uAGm7/7SCtLj4A4BvfKN5un80ElmWC64JpAxMKQKBALLZbN7rmUxG/bvR773rrrvwy1/+ErfeeivOPvvskuOjAdruqKaiGhcwz/OIRqPIZDIIBAJ1xb8ZjZECkBR0jkaj4HkeoVAIkUjEFNf25Zfnm/AVEdiO2qwJdrpdWstlHQpZPQIaaGYBSOO+kTmrtPgbO9aF9vb8ucNJAilNqeNTiQu4q6uroKt23759AFDSgljLex988EHccMMN+OY3v4mbbrqp5NhowU4rWlOgF1FmW8JoguxrIpGA2+0uW9C5HhiGwSmntMPrzb/ERZEF0InqYwLtZGXiym/SRCSTrWbxLETzzRkEK2KfS0OOtQSl7WTx62/RIif5w2hIEkgpZs+ejV27diEez20L2t/fDwCYNWtW0ffOmjUvIiLuAAAgAElEQVQLr7/+et563d/fj1AohO7u7pzXN2/ejCuuuAJf+cpX8POf/7yaXbEURwA2CL3IkWUZ2Wy2IZaweqnXAqhPaAmHw4hEIqa6tsmYTzih2CTBAIgAGIXKg8vtVAfQTmM1AjuJc7NoXgEYCtEW/8diWPyVvvYKdf/Q0owP+0ZSqwt4yZIlEEUR69atU1/LZrNYv349ent7MWHCBABKxu7OnTvVriPkvQcOHMCTTz6pvjY4OIjHH38c559/fo537s9//jOWLVuGhQsXqvUB7QJtd1VTUcwFLIoikskkBEEwrX+v0dTaycTqfV22bDxeey1aYgsvFGtgAkr2Xil4AHS45SvDhdYRgo4AbGYBmEjQtm8sKhF/wSCDGTMKL7NOEkhpih0fWZYrsgDOnTsXF110EW688UYcPHgQkyZNwoYNG/Dhhx9i/fr16nY33HADNm7ciN27d2PiRKV6xJIlS9Db24vLLrsMb731FkaNGoW1a9dClmX86Ec/Ut/7wQcf4IILLgDLsvjKV76CRx99NGcMM2fOxIwZM2o9BKbjCMAGw3EcUqkUWJZFOBy2TQX4agUgsfql02mwLItIJNLQmEYiuC+9dAKuv/5tlB46C6ANSpbvEIq7cyQMP/nbAdoWTTNxFtNmPt+SRNNSJUHp7Vv+eM+ZU37OcyyAhak3BhAANm7ciDVr1mDTpk04dOgQZs6ciaeeegrz589Xt2EYJu87WJbF008/jeuvvx73338/0uk05s6di40bN2LKlCnqdu+//z5isRgYhsE///M/53wGwzD4wQ9+QLUAdFrBmYgkSWrFcFLkGFBSye0W5zc0NASPx4NQBdH29bRxMwpRFBGNRhEOhzF9+gv48MPiafu5SABiAIptH4F94us60DrJIB4o56ZVYdDcHW9GgI6IJQ7Kw0ZlD7Pr1rVh2bLCGcA8zyObzVbd6ahVEEUR6XQ6z2skyzK6urqwa9cu1Y3rUBs03FFNC7GaaYscezweSwRRvVQyXm0ZG0Ap6GzV5Kb9zrPPHlNiSz0slMWmA4VvDzudt1Z6tmulfS1Es0/l1u2f4rgQoDwUihWPhWWBiy6yh4eHRorZpkid3PZ22vpC249mnzUsRRAEDA0NIZvNIhgMwuVy2U74Ecq5gPVlbBrZq7gQ2pjLa645uoZP8APohNutD+C2i/sXsNdY66XVBaA955XKsGqZkhEISOD5NLQ9fSs91pMmueFylR+7XdcEsynmAo7H43C5XHXUc3Ug0BRY0XS4XC54vV74/X64XC7wPG/bwN9iApBY/bLZLFwul+XCrxCTJ4cRibgRj1frDnVBENqhtJE7BEVQ8aXfQhUC6GufZRatJHYL0cwiwgoBKADgUbjhQ2XH+oILSncOsutaYDWJRALhcJj6xEk74BxBE2FZFqFQSC3tUmsmLS3ox04KOhMLJ03iT1+65pRTOmr9JCiZwqMBBADkV4enl1aJ/wMcC6AjAI1BgnKPF3vQY1HJsWYY4KqrGHAcB1EUi877jvWvOKUsgJFIxDl2BuAIQJPRl4KxqwDU7oe2oDMpXm1UD1+zuOKKo+r8BBZAO5RAe7uUgrFLsopD/dB779VPI6zYMpT7JYPS1uTKxjJ/vgeRiPKQnE6nkUwmVU+JIAiQZdm2a0EjKVYDsJIMYIfy0GGuaRHsLgAlSUI2m0UqlQIAhEIheL1eaoWf9niff/5YuFwMRLGe489AKRUTgrJQFG//RA92KlvjUDt03oPGYPYypbh7FYw5jvfdF0Ew6IYsy5AkCaIoQhRFCIKgVoYg8DyvxofTOpdaQbG1Mh6PIxwOO8fKABwB2EDsLAABxfKXTCZtWbyaYRgcd1wYf/1rvMy7KvpkKEkiXiiLRwL0ultbSQAyaF1XcDMvhmZZAEUowq/Sa4ZBJcf5uOPcmDJFWVoZhoHL5VLDgIjlTxRF8DyvPlQTyLYulwssy7a0yJFluWgNwHC42jaeDoVwBKDJ6EWI3QQgaVlHJik7Fa/Ws2RJl0ECkCRXsFBEYAeUxSQFxTJID6GQC8kkreLUwTjofhirHQbG7xtx94qoRji3tbGIxcpvf+utPgiCoFr0tA/K2tdEUenSEwgEVAuhJEnguOHQDZZlcwQh7Q/djaCSNnAOleFcTQ2k3p66jUYURcTjcaRSKfUJ1k7iTy+4r776cwZ9sozcW4eB8izVBqW3cNCg76mfZLJVrH+tTrNaiozcLwmK8Esf/r26z06lys/bY8eyWLTIp1r5BEEAx3HgeR6CIECScu9HIgjdbjd8Ph8CgQBCoRACgQB8Ph9YloUgCMhkMkilUkgmk8hkMuB5vmRySTPgWADNx7EANhCtAKTZtF+ojZsoikilUtSPvRTt7V6MHevDgQNGZPIWOwYuAGEoIjALpb+wlSKsVXoBtzr2vCfLU6+NQoZisRcx7Oqt5VjJEITyrujVq8Pw+XyQJEmN/yP/kh8C+V2SpDwrIbH6kfaZ5L3aWEICsQ6S99h1fi6EkwRiLo4ANBl9FjBAtwVQ28ZN27JO/+RqBwq53M88sxObNu0x4NPLHQ8WStkYH5Q4oySsiRO033lzqIXmWfRzqVUAihgWfkYdm9JjCQYZLFumzJ9utxsulwtut1ud97UikFgDiYUPgOoa1ruNAajuX1JmS59cwvO8mlxCttUKQjuKwlIWQMcFbAyOAGwgNAtAWZaRTqeRyWQKFnS2i/VSDznWpGD1jTdOwEMP7UH9p6DSIsssFBHohbIYJdHIWoJutwBBaI3b3O9nkcm0qsXTPvdkdVRz7RJrn/ZBy6jjUv5zLr3UD5/Pp7psCUSIud1uVfDxPK+6fbWWQhIXCCAvhrCYlRDITS7RWwnJZ9gtuaTYWpNMJtHZ2WnBiJqP1lgZKIFWAcjzPJLJJCRJQiAQKFnTj7axl4Lsg3b/xo9vw4IFo/DnP39a56eTOMBKLWzaOEEJShxSqs4xlMfvF5BItMZtnsm0srWT/gW9Nip5yDLD2lfdONxu4NZb2+DzKSJNL8Z4ns9J7tBaB4mVjnhZSGxfIbcxEW+lkkuI21g/hmLJJXZzG8diMccFbBCtsTJYCM0uYG0bN7fbjUgkoj5R6rHTBKFFEATE4/Gc/fvZz2Zg5sznLRoRg/w4wQTMKl+SSPAIh8NIJJxM4ObGnvdneYoJLwnDoo9g1jHQJ33lc+65AVX8AVATO4gXJZvNIpPJqK+LoqhaCbXWPPKeQm5jYuXTCsJSbmP9GLRuY+KGJm5j7RhocRsXswA6WcDG4QjABkKTAOQ4DslkErIsIxgMwufzlbzhaRp7JZDMOwA5+yfLMqZMCeK448LYuTNR57fUa3EicYJ+KAsZyVA01o05dqy3JQRgKMQimWxVF3AzFnTQl4CRodwb5KeRAqX08b333sIWKZJQx3EcPB6PGlNN/kZctaIo5pTb0gpC8i+AgsklRriNyefok0u0CSaNFITF2sABigvYsQAagyMATabQBWyliJIkCalUSp2QgsFgUaufFrsIQO3+kcnQ7/cDQM5kd8cdx+JLX9pW57cJMOYWIu5hNxRBqNQp6+gQcOhQ/W7i9vbWuM15vlVdwM1p/QsGWShNhxpp7StEafHX0+PFuHH5cyipnEBCazweT55HqJCFjghCrduYbKu3EhZyG8uynCPkKnEbE/SCsJDbWPuvFTguYONojZWBEsgNZ4WIkmUZHMfV3caNZgFIrJqAsn/aWlnamBqWZXHGGWMMKgljdKcN0m3Aj0OHAI8njJkzvWCYLP7610NIpaq35Pn9zWgdyofj6L02zaUZBaCMVEoGw6QNSNiql9IPyIWsfzzPI5VKgWVZhMPhih+ytRY6ADmWOSIKCYUEIXkPEXLF3MZE+BVzGxO08yYZh9Y6pxeERlkJi1kAZVl2XMAG4gjABmOFACRPojzP19zGzep4kFLorZqhUEittE8mTnLMtZPUDTdMwsqVb1k59LLwPPDaaxwABm73SMyc6UM4LGL37iHs2VOZdZBlLV9BHUwkGHQdtpTZmcLXqPXiT0YpAfi5z7kxe/ZwcfxSLt9aICJNn9hRyG1MysQQUUiK9utrEZLXKk0uoa0moVMH0DgcAWgy+hugkQKQtHHTPonW2smDVhew3upHrJpknJIkIZ1Ow+Px5D0l/6//1YnvfY9FKlWPBa9xbkdBAN54g1gsQzj66A5MmMBgaCiJt94agiQVPjcc1/zxfwoymtMaVppKOlTQRyVjNqsHcDWUvp7+9V+HhQjplS5JEvx+f00elrKjKZHYIQiC2nlEu63RySXlahJqk0tqrUlYKgbQsQAahyMALaARIkoURSSTSQiCAJ/Ph2AwaMhkRIsALGb1A4afkskkpZ2QyCREnlovvfQIrF37YR0jMSoOsFoYvP8+j/ffBwAP2trGYupUDwAOO3d+hmh02FWUSHDFPsShKbCDi7+WeYOG/So+ho4OFosXK/HFtbh8jUBrodNa/Iq5jfWJJYWSS4yuSUjiGsm21dQk1P9NkiRHABqIIwAbjNkWwEJt3Ijpvh6sjF/UU8rqp53E3G43PB6P+hoJqtbuw+rVR2Dduo8gCPXsl9FxgNUTi0no71esgwwzAtOn+zBihIw9e6IYHEyjNW711rQA0rnPRswTVgvA0u7ff/mXUI7L1+12G/agXQ/F3Mba3sTakBgiCLW1Cc2sSShJUtGahGTsxdYZ7bzvUD+tsCpYSiNdwMXauDULxMXC83xRq1+hWD8AORltfr9frcXl8Qg466yR+D//p97C0PQgywzefJNMsEF0dbkwb14YgIR4PIsPPkgiGm1Gt7D1DyfWQMc9zrIoGoZQPXTsUzER6vMx+O53AznzrRkuXyPQuo21nUeKuY31VkIrahJqQ4609QATiURDLazNjtWPWC2HGRMEKegci8UAAG1tbaY8iVqZwZzNZhGNRiEIAkKhEMLhsPqkqJ1ItJMQMOwqTqfTcLlcCIeVZu3EZRIMBvGzn52A+g4V3eVH9u0TcfAgg5de4rBjB4NoNIRx4zpx4oljMX/+WJx00kgccUTA6mE61IxVokNGMAj4fMrvxok/gI6lqfhxXbLEh0QiAUmSEAqFytZRpQki8sj8F4lEEIlEEAwG4fV6VatmMplELBZDIpFAJpNRw2q8Xi98Ph+8Xi+8Xq/a4g5AjpWR53m157G+l7x2DH6/H6FQSK3X6na71XUmnU4jmUzi9ttvx49//GP8+c9/xqhRo8oe62w2i9WrV2P8+PEIBoPo7e3FM888U9HxGRoawooVKzB69GiEw2EsWrQIAwMDBbd9+eWXMX/+fIRCIXR1deHaa69VrZR2wLEANgCtcDJaRFXTxq1erBCAWqufPoO5lNVPlmXwPK9W2w8Gg0Vd4WPH+tHTMwJbtw7VOEqr4gArZ9QoN959l/yPwf79Avbv11oBfQiH/fjc5zxob2chSQIGBzP4+9+TEEW7WNbsMk6jMVt4yPB6Gfh8DJJJ6bDQU77TvAQUGiw8hcfgcsm4+WZQ4/I1gmrdxoWshPXUJNSOAVDWGo7j4Pf7IUkS3n//ffz+979HNBoFwzA48cQT8fnPfx7z5s3D5z//eUycODHnPFx66aV44oknsHLlSkyZMgXr16/Hueeei+eeew7z5s0rehwkScJ5552HHTt2YNWqVRg1ahTWrl2LhQsXYtu2bZg8ebK67fbt23Haaadh2rRpuPfee/HRRx/h7rvvxrvvvounn366zjPSGBiZhqCuJkd785DEhREjRtT1mSS7lbRxC4VCppvFY7GYGuRsNsXqFpK/aScYfWYZOTaCIMDj8cDv95cte7N9exTz579cR9kJL2i2BM6b14mXXopX/T6WlTFxoheCIGL//uzhyZwHnfsaAVB/vKv9CAPw1fkZcpHfAWssjAGLvpcgQzmm+WO44ALgwQdHUuvyNQMy55LEEmLZA/LdxtryL4VqEmolRzG3MbEgamP9RFFEX18f7rzzTsybNw8vv/wydu3aBQCYO3cu+vv7AQBbt25Fb28v7r77blx33XUAFIvg9OnTMWbMGLz00ktF9/Oxxx7DsmXL0NfXh8WLFwMABgcH0d3djXPOOQcPP/ywuu25556LHTt2YOfOneqa+Ktf/QpXXnkl/vCHP+CMM86o/YA3CLrNFk2C0RZAbRJEJW3cjKQRzwvVWP308SKVWv30zJrVjkmTgnjvPdsXVCtIOl1bzJ8kMdi9m2QReg7/+A//X4YiBEUootDauMJQiIGNvC8GUs29L+v+LfQ5NIgaq8dACrLn4vHIuO++UfD56hXc9oLMs9oyYnpBSOZdIL+VXaGahKVa2eldxuQzg8Egurq6sH79egDAJ598gi1btiAeH3647evrg9vtxooVK9TXfD4fli9fjptuugl79uzBhAkTCu5nX18fxo0bp4o/AOjs7MTSpUvx0EMPqfHnsVgMzzzzDK677rocg8gll1yClStX4rHHHnMEoEM+2mzVakVbqSSIRmC2yNRb/bR1C82w+um55ZZj8bWvFY71KE+je5NWx/79aQM/TSsUWCjTiA+KqCCikPxLfkSYbTVU+gC34pSmv+4qsebRe61aPzYZxSzJCxe6MXZsa4m/YjAMA4/Hk+M2LtbKTpvpW6gmoT7bmIhCjuNy3MbxeDynCPTo0aNxwQUX5IxrYGAA3d3deZ6qnp4eAIrrtpgAHBgYwJw5c/Je7+npwbp167Br1y5MmzYNf/nLXyAIAk466aSc7TweD2bNmlU0ZpA2WnG2tJRaRJRRbdzqpdiTmRGU6lair2JvlNVPz4UXjsOECQHs2VOLWBKhLBp0RlTs3ZuF3x9AJmOmCCNWk2LCWy9MlJ9wGMhmRfC8COU4igXfXR46j705MLof/Xm1g9ArBp3xfwwj44EHRlowFntQrJUdEYSVtLITBEFd57Tlvcjc//bbbyMWi6ktPQuxb98+dHV15b1OXtu7d2/Rfdi3bx8WLlxY8r3Tpk3Dvn37cl7XMm7cOLz44otFv4MmHAHYAPQNwAFUbAHUFnSutY2bUZiRBKLtVsIwTFGrH7nhjbb66bn66on43vfeqem9oZAbySRffkOL0HhoLIIp+HsiAeRORYXclHqroqj5Xf8+u6AVcDh8/bsQj0uavyv/KmsqczghRz9vFHZX2herBWDhZXHBAjfGjXOWzGogWcOEUq3syPrCMAyCwWBekeqHHnoIv/zlL/GP//iPJdfOdDpd0EXv9/vVvxcjk8lU9F7yb7FtS30HTThXc4OptKWakW3cjMRIAajvVhIIBIpa/WrN8K2Wa689Grfd9veahFwySXdtvUiE1YgLmilkvSpnVSRFe/X3CPk7A48HEEUZxY3YlVzbyjbhMItEotAHacdeSKgV+v3wJ8uAEsqUL4CGw6QKLXzNJP4Aa0vAFCv+LOM//qOj0YNpOgrVAxRFUS0zQ16LxWI4/fTTcfzxx2Pu3LnYvn07XnzxRTzzzDPo7e0t+R2BQEAVlVrIehEIFC95Vel7yb/Ftg0GgyXHSAuOAGwwlQhAbUFnI9u41YtRYyglbq2w+mlhGAZXXfU53HPPu+U3zkMEHfXLChOP1+papRl9PGLx488baJxVrJZWW6qI1cT6ucFYrNwftuD39/a68LnPtWKGubmQmoOiKCIQCMDr9UKSJAwNDeG0007DK6+8gqeeekrNCP7+97+PefPmYd68eejt7S3YEq6rq6ugm5e4bcePH190PJW+l7h+yev6bUt9B03Qu1o1EcVcwHq0BZ1lWUYkEkEoFKJC/AHGuIBFUUQ8HkcqlYLP50N7e3uO+NMGBOutfhzHIZFIQBRFBINB09zht9wyGSecMKqGd0qg2xpjB+tfPTT7/uXTfEW8rC79UsgmImPtWif2z2gEQcgppk3WAZZl0dHRgS9/+cvYu3cvvvnNb+K5557DmjVrEAwG8fOf/xxnnXUWvvvd7xb83NmzZ2PXrl05mcEA1DIxs2bNKjqmWbNm4fXXX89b5/r7+xEKhdDd3Q0AmD59OtxuN1599dWc7TiOw/bt20t+B004ArDBFBOAgiAgFoshk8nA7/ejvb3dMLemUdQjAMmTXjQahSRJOeJWK/xEUSzZzcPtdiMcDpt+bP7wh9loa6ulOwbNApBuF3X9NJ0aqgCar7dasNKqWjiBafZsFsceS9dcbGeIByiZTKoeIOISBpT5/he/+AUuuugi3HPPPbjvvvuwcOFCrF69Gr/73e/wySefYOfOnVi1alXBz1+yZAlEUcS6devU17LZLNavX4/e3l41A3j//v3YuXNnTqHqJUuW4MCBA3jyySfV1wYHB/H444/j/PPPV9ed9vZ2nH766XjooYeQUNwBAIBNmzYhmUzioosuMuZgmYxTCLoBkKBXQLn4Dx06pLYPIla/bDYLl8uFUCiUczPQBLlpOzo6qrJK6mP9tC7tajJ8A4FAQ0Xxiy9Gcc45WyFJ1QgnBjS4BovT7CUs7BF7YxwuNNc59cMauwQp/aK/d2Vs2TIKJ5zQTMfYOmRZRjqdVqs96DtXpVIpfPvb38Yrr7yCvr6+mi1pX/3qV/Gb3/wGK1euxKRJk7Bhwwa89tpr+OMf/4j58+cDULqFbNy4Ebt378bEiRMBKOJz/vz5ePPNN3H99dernUA+/vhjvPrqq5gyZYr6HQMDA/j85z+P448/HldeeSU+/vhj/PSnP8Wpp56K3//+93UcpcbhWAAbQCGxRMRNNBpFNptFIBBAW1sbteKvFuxm9dMzf347Vq/uRvWFdmm+rWgem4ODlddnvvi78cagI/4MQhRFJBIJ8DyPYDCIQCCQszbu3r0bp59+Oj777DNs3bq1Ljfqxo0b8e1vfxubNm3CtddeC1EU8dRTT6niD8g1NhBYlsXTTz+Nr371q7j//vuxatUqjBkzBs8++2yO+AMUV/MzzzyDQCCA6667Dr/85S9xxRVXoK+vr+ZxNxrHAtgAJEnKqX/02WefweVyQRTFhrVxMwISgzdixIiysXf1Wv3S6TQYhmm41a8QZ5zxBl5+uXjtqHxoFvEBNHesXGtZAF0uD0SxOdyTLAtIklXnzwV9KaKbbw5h9ep2i8bTXPA8ryb9BYPBnPVOlmX88Y9/xPLly3HVVVfhhz/8oS3Ww2aA5pWq6SDiBlAEklUFnWulkgxmYvVLp9NgWRaRSCSnUryVGb618rvfTcfUqSl88slQhe8QQG9RaBrH5FArYhMldkuSVfe6vvSLjFtuCeE733HEX72QeD/Ss15f0UKSJNx7772499578V//9V+48MILbbMeNgOOAGwApIMGaeMGKFXO7dZPspwA1Jav8fv9OSb+Sur6EaufkXX9jCAQcOE3v5mBL3zhNfB8pQU+GdAptmjPVHaojmY6l1YJQG3pFxl33BHGv/xLfnkRh+og8e3EC6TvWR+Px3HVVVfhnXfewcsvv4zjjjvOwtG2JtabV1oAEusnCEJexpMd0QtAEtirLV9DnvSqifXzeDyWxPpVwuzZYaxZMwX5hYaLQWtHkObOBPZ6m0kQVUIz7a8V86K29IuMn/404og/AyDxfqRklz7Z45133sHChQvhcrnwyiuvOOLPIhwB2ADcbrda2kXb39BuFDLNk/I16XQ6r3yNtu1Psbp+8Xjc9Lp+RvGd70zAqaeOQ2VZvjJCIfqEbLNbAF2u5t23wjTT/jb+3o9ESPFwGfffH8E3vhFp+BiaDRIrDiDvgV6WZTz11FM47bTTcMkll+Cxxx4rWMzZoTHY2xRlE1iWzWk/Q1zCdkMr3rSxfi6XKyeDmfy9UEFngN5Yv0rYvHkqurtTOHjwM5RLppBlWgO0aHVP1086bb/7qj6aSQA2GhnxuBuAjAceaMPFF4etHpCtIWsCx3HweDx5Wb6iKOLHP/4xfvWrX+GRRx7BGWec4cT7WYwjABuE1upndwugIAhIp9NFY/1EUYQkSXnuXtpj/SrB42Hxf//vNPT0vAFRTAHgim6bSnFQXMatJkqswz79jo2iWRZQax4AGYbFf/5nG/7xHx3xVw8klIesCfrkxkOHDuHyyy/HJ598gq1bt+Loo4+2cLQOBHuYXZoMuwpAMmZSmLmtrS0n1o+4e0mGb6lYP212sN049tggfvrTYwCED/+UWoRpXKDtd+1VyowZfquH0EBovLZqpfFlP9xuF371qzCWLQs1/LubCX1LN32yx1/+8hcsWLAAY8eOxYsvvuiIP4pwBKAF2FEACoKg9lb0eDx5Ll+S5AHkZ/gWivWzu+n/iivG4uKLO6FY+NpR3Jhe3EJoFYGAva69arBJJIFB2PseyqWxApBlZTzwQABnny0jFoshHo8jnU6D4zhbhudYQbmWbrIs49FHH8XZZ5+NlStX4sEHH0Qw2Fp1OmnHKQTdIHieVyeWdDqNdDqNkSPpbzBOMnwzmYxavDoQCCAQCOSUdqkk1k8fE9IMbN78GS6//D1kMhKALIBUga38oCv7lkHl2cz2oq0thFisNaa0SMSDeNyeVvRcGCgFyhuFiJ/9bBQuu6xdrVAgCIIaugIoD+lutxsulwtutztvbmt1yrV043ke3//+9/HEE0/gkUcewYIFC5zjRyGOAGwQWgGYyWSQSqWq7qnbaLSm/UAgAL/fj2g0Cq/Xi0AgkBPrR3s3DzOJxwWcd95ObNuWhCL0kgC0CSA0htr60ZyuYKt6yVpBs/QBdqMxDyQSAB6nnx7E5s1HFt5CJwiJV4NhGFUMulwuuFwuquduMyE1bcm64PXmnruDBw/ikksugSiKeOyxxzBhwgSLRupQjlaZKamC9omDFPCMxWJgGAZtbW051jtJkloi1q9SIhE3/vzn6fjJTybC5XIDaIMiRAgC6LvV6L4GHSrB/udQ6fhl9gOSDOUe5DBqFIsnnywuSFiWVb0V4XAYbW1tascmQHl4TyaTiMViSCQSyGQy4HnediE9tcLzPBKJBGRZRtLHTdcAACAASURBVDgczhN/r776KhYsWIBp06bh2WefdcQf5TgWwAZBniiB6nrqNhqe53Oe7rSmfVlW4mVEUVSfhskTMXlvq1j9ivHhhxmcffbb+OADDkox6CQUy4MPuVZBq2lOC6DL5YMotkofUc/hH/sSibgQj5tpxRRBirKzLPDaa0fj2GNr/z4S9iIIgjqnkyWUZdk8t3GzoG/pFggEcvZPlmVs2LAB3/ve93D33Xfj8ssvp97Q4UCfWaIlqKSnbqORZRnJZBLxeBwMw6C9vV21+mkzfLUtfUgAsDaI2uVyUdvNoxFMnOjHW2/NxtVXjwXDeAC0IRIJgL5kEJrEqHGIIj33lPnYf4GVJLPEugSPhzyEKdx559i6xB8w7Ar2+XwIhUKIRCIIh8MIBAJwuVxqiax4PI5YLIZUKoVsNpsjFO0G8Qhls1n4fL68gv2ZTAbXXHMNfvKTn+APf/gDli9f3hDxl0wm8YMf/ABnn302Ro4cCZZlsWHDhorfPzQ0hBUrVmD06NEIh8NYtGgRBgYGTBwxfTgWwAahtQCS7hnaTForKWf1KxbrJ0kSstksOC5f3OgthK34NLhtWwIXXrgThw4RKwQPupJBmiF+TI/9rWKV44MV5VOMQ4aS/GGkHUKGcp/lZvIuWhTCf/934bg/oykWRwggx0Joh3lRFEWkUilIklSwbuvHH3+Mf/qnf0IkEsEjjzyC0aNHN2xsu3fvxjHHHIOjjjoKRx99NJ5//nk8+OCDuOSSS8q+V5IkLFiwADt27MCqVaswatQorF27Fh999BG2bduGyZMnN2APrMcRgA2CTAjk92g0anl8nPbJzu12IxQKqe7cWjJ8AagTHnGREFpVEIqihH/6p/fw3/99CLIsQRGAWdDhfg2g+YpUNyqhgAaaIeHFyLIgAgo9YI0c6cLu3ZPgcllzrLQeFO06AEBNKCHzIk1uY57nkUqlwLIsgsGgujYAyj69+OKL+PrXv46LL74Yt99+e8ONGRzHYWhoCGPGjMG2bdvQ09NTsQB87LHHsGzZMvT19WHx4sUAgMHBQXR3d+Occ87Bww8/bPbwqcB681MLQoMLWGv1CwaDOcU76+nmQUSez+fLi5fhOA7ZbBYAciY9t9vdtILQ5WLx619346679uKHP/wIoVAQyaQXipUiC2sFGA0i1Fi8XgYFDNJNir3vmXDYg8MtY+tEye4tdD2zLPD//t+Rlok/YLikjLZuKpkXRVEEz/OqF0UfR6j1uDQKbbxfofJdkiRh7dq1uP322/HAAw9g6dKllszfXq8XY8aMUcdcDX19fRg3bpwq/gCgs7MTS5cuxUMPPQSe51sijMkRgA1Ce4NYKQD1Vr9IJFKX1a/UjU/iZUjMTCFBSI5BswvC668fj9dfT+F3v/sULMtCkrwAPGAYEbKcgTUxeTyabQrguGazaJbCzveIjGSyXvc1cfeKKHYsbr99DKZOpas7jHZeBIZ7p2sthEQQNroeoXaOL9TSLZlM4pprrsG2bdvwpz/9CSeccIJpYzGTgYEBzJkzJ+/1np4erFu3Drt27cK0adMsGFljaa7Z3yZYJQDNsvpVSilBKIpiniDUuo2bQRD+7/89CSeemMY776SgLFgMZNkNIIRgUGmkLkl8mU8xEhFKvFwzWQJbRQDa/X5gIMu1WuVIKAURfoWPxamnBvGtb9FfbJ9Y+bQlVfRxhDw/PC+YFUcoiiKSySQAIBQK5bl0//73v+NrX/saJk6ciP7+fnR0dBjyvVawb98+LFy4MO/1rq4uAMDevXsdAehgHo1sB0ee6hph9auGQk/C2omvkGuExliZSmEYBi+9dDwmTdqOaFRr8WOQSjEAgvB6Jfh8POLxTKNGheYSgM20L6WwtwAMhdw4rDUqRIYi+EQMn+Pix2DECBc2bz6i5vFZDamvSh609XGExEULGBNHyHGcWsVBn+UryzL+53/+B1dccQW+9a1vYc2aNTnxgHYkk8nA58tPgvP7FWtxOp1u9JAswRGADUIvmholADmOy8niqsbqx3EcMplMXVa/atAKQq/X25SCMBBw4YUXpuHEE/8Cns8//xzHguN8UCxz3OEf864Tl0uC2JwVYZocOwtAGalUpUuP3tpXHpYF/vCHI+Hx2GNOqIRq4wj13pNiD+2yrHgeOI4rGu9311134d///d/x4IMP4otf/GJTeGMCgYAqoLVkMhn1762AIwAtwmwBSDpycBxXk9UvlUpBFEVLe/jqBSEZG4kh1MbK2KkI66RJfjz66BQsXvwOii9qLJQsTx+UOKcMzBCCosihdbJmmwl6r+9yBAIs0ulS46/O2qelrY3Fww9PwPTpdMX9GY3eewIgRxCSB2aybaE4Qu08X6ilWywWw5VXXon3338fW7ZsQXd3d0P30Uy6urqwd+/evNf37dsHABg/fnyjh2QJjgC0CDMFIMdxObEc2kBe2qx+1cKyLLxeb0WCUPsUTJsgPOusEbjllom4+eaPymzJQBFoHgyXkDHSZCdDEROtEjvXLNB1PVeDLBdzH1Zv7dPy5S9HsGFDl6UZv1ZC5kaCPrFEG0eoJKIp93yhef7tt9/G1772NcyaNQtbtmxBJBJpzE40iFmzZuGFF16ALMs5xo3+/n6EQqGmErulcARgg2iEC1hr9fN4PAiFQqrwqcbq5/V6c4pB00wxQaif9GgUhN/5Thd27Eihr+/TCrZmoIhAN5QFUult2joxbw650H9v/v/2zjssijv/4+9ZFhbYBRUVETyMDUNCFAsIUkRzxiRerkSjxl5yJHqnCfZojD2/3Gmil0vUmDsL0ScW1CSXmGJ0FxQRQSynBjtiBFREpOyybeb3BzeTbfTtfF7Psw86O9/d7+y093xqXdTUGN52mm/t42nb1gP79oUgLs6aNQVdH4Zh4OnpaRRHyD8kG9YiVCqVSElJQbt27RAXF4eamhosWLAAS5cuxdy5c53iWtkSSkpKUF5ejp49ewou9NGjRyMtLQ0HDx7EqFGjANTWAdy/fz9eeuklpzN82AoSgA6CN8Fbi7qsfnyJAV78NWT1s5T95UqYPgUbxhBacosYxhA6ot7W5s2d8fPPVbh0qbFijkHtaStGrXuYt5rw5TCagw6ubFEyxdubQU2NuwtjV95fvMW5+dY+AGAYYOzYNvjXv4Jc4mHVGdBqtdDpdPDy8hIqMfDXxG+++QZbtmwBAHTp0gWXL1/Gzp07ER8fj549ezrlb/zxxx+jvLxccOd+/fXXKCwsBADMmTMH/v7+WLx4MVJTU1FQUIDQ0FAAtQIwJiYG06ZNw+XLl4VOIBzHYeXKlQ7bHntDnUDsiGGZk+rqauh0OrRp06ZFn9mQ1c+wB6VpMLCrWv1agqkg5EW4vQWhTqeDUqkEx3Hw9vbGokXF+Ne/7qNlZ2OtJYVhdOC4ppaTcae2cO5uCWJQ28XF1eBQO3cPtNRyHRjogf37u2DgQFf8HeyPYUs3S/F+Dx8+xPTp06HVajFq1ChcuXIFJ06cwIULF8BxHAYNGoRTp045aPZ1061bN9y+fRuAcXk1hmFw69YthIaGYtq0aUhNTRX+z1NeXo4FCxbgyy+/hEqlQnR0NNavX2+xPqC7QgLQjhgKQF60tW3btkWfx1v9fH19G7T68ViK9XNlq19L4N0ipn07bVWA1TDrzrTkwtat9zB37u0WikDhm+Dlxf4vJtJylwRj3KktnA9c2UXaMHyCkLNTe8z5+4tQUcFbp0VoifXSwwOYMqUtPvqok9s/rFqL+lq6AcD58+cxfvx4JCUlYdOmTUYZsI8fP0ZWVhaqqqowevRoe0+dsDEkAO2IVqsVLE4qlQo1NTXNKqbJsiyqq6uFdjVk9bMepoHThoLQNIawqb+b4VO4pSr7APDtt2V4440ClJWZ9zRtPhx+dblpYFnoecNd4gn9/aWoqHCPbbGMs/Y75gz+Worna5n4Cw31wN69v0GfPq4gfh2PYUs3sVgMX19fo+sNx3HYs2cP5s+fj7Vr12LmzJl0L2hlkAC0I4YCsKamBkqlEgEBja9Uz1vulEolALL62QNrCELDC3FdT+GGaLUspky5ga+/fmQla6DRbP734uMGeaHpAfcJCZagdnvcFc//vZwBDnULPlM8GnjfMl5ewMKF7fH22x2bPr1WCt/yU6fTQSKRGNV/BWq9R0uWLMFXX32Fffv2YfDgwST+WiEkAO2IoQBUq9Worq5Gu3btGnXiGVr9vLy8jFyHZPWzH4YV+Q0FIQCjGEK+RZNer4dKpYJer7d4Ia6Po0cfY/r0GygttaY10BQWtbGDWlgjNss58IL7iFlLOFLgcmAYwNMT0Ou5JhQRb471j0Nioi/27AlBmzbuvD+tC+9p4DgOPj4+ZhmtJSUlmDx5MhiGwd69e1tNzTvCHBKAdsRQMGg0GlRVVaFt27b1ptmbWv34DF/+PbL6OZb6BCFfa4thGIsX4sag17OYPv0m0tLKrDntOhDhV3cx/3LFNiHOZCGzBd6wXxawqVu36Q+OIhHAsk277nTsKMLOnSEYMkTa5O9rzfAt3UQikVFoEFB7rTp9+jQmTpyIl19+GR988IFZMgjRuiABaEcMBYJWq0VlZSXatGlTpzuwJVY/Q8sTWf3sB19WQa1Wm5X5aUkT9xMnKjB+/HU8fGhLayBgblkydPGxEIlYeHiw0GqdOWHEA+6V1WyKLbOcG4rjaw6Nt/55eABvvdUOK1cG0vWqCTTU0o3jOPz73//G8uXLsXHjRsECSLRuSADaEUMBqNPpUFFRAX9/fzOrXFOsfqaxZ2T1cyxarVZoJO7j4wOxWCxkF/NWQh7DGMLGCEKW5fDaa7ewf38prFhC0oTGuhZNLYX8yxlw1TIpjcEa28ZZ+Le1xJ4pfNmXhuAQHe2Nfft+g44d6XrVFAxDfCwll9XU1GDu3LlIT09HWloaBgwY4MDZEs4ECUA7wosA/t+PHz+Gn5+fkWuQj98gq59rwXEcVCoVtFotxGIxfHx8LLr2+X3YEkF4754G8+bdwXffPUJNjXVFl0gEMIxHE2K7DPnVUvir+9hRotA9awF6eYmh0TTWbWcq7gwv9fa6LjSc+NG2LYNt24IxYoR7tRuzB3w9UQAWH/YLCwsxceJEtGvXDl988QU6dOjgiGkSTgoJQDtiKABZlkV5eTlkMhm8vLyETFGlUil05DC1+vHij6x+zoVhUWc+1q+xwpu35hrGEPKnpKG7WCwWm32mSqXHkiW/IDX1gZWFoDUTDHhBaOmvLS897ikAJRJPqNWm8Y11CT1HP/zV7/oViYDXX2+Ldeuopl9TMbzmm9YT5d9PT0/H1KlTMX36dKxZs4buCYQZJADtiKEA5DgOjx49Elqv8Z1ByOrnOtRX1Lkln1mfIDS0EhpWvl+/vgQbNxajvNxaMYL2yDI1LSHCGb1EIg4eHhx0OrYZ5XDcUQDybQD5fdO8pAz7YVlweHkBzz0nxYYNnRAcTEkITcXQ22Dpms+yLP75z39i3bp1+PTTTzF69Gi6JxAWIQFoR1iWFfou8gLQ09MTWq1WqA/XnFg/kUgkxJsR9qExRZ2tgaEg5B8g6hOEX3zxECtW/IJfflG36Hv9/T1RUeEsMX38ww8HiYRBbcQEB41GD7Xa0JpoOF9nFoC/HicMw0AiYSAWi8AwgFYLC9Zc6xVTth+m8+QQGirG7NkBmDkzgARJM+ETA+tq6VZVVYW//OUvuHDhAg4cOICIiAgHzZRwBUgA2hFDAcjHAAKARCIxqtJOVj/npalFnW3x/ab9jPnjRCQSCWLw5EklFi78BRcvVjf7uxjGwwaFqG1JbY06hgFYVoRa4VTXBtSeL97eDGpqLMXINZdfv9PTUwSt1tpxd67wkPer+PP0BIYOlWD16o4ID/exeY9td8Yw3k8qlZpdd65fv44JEyagR48e2LlzZ4v7zBPuDwlAO8Kytb1Z+Vg/APDy8oJMJgNAVj9npyVFnW0Ff8zwYtBUEBYU6DF/fjEyMyubIeZ4EeWKuGMnkMZm1DoKfn4cunQR4/XX2yA5WQqOY4WSSNZoqdjaMLzuW0ow4zgO33//PZKTk/HWW29h6dKlLQ5FIVoHJADtiE6nw6NHj4T2PBqNRrD+kdXPeXE14W0aQ8iyLMrKdFiypBQ//FD5P7dpY3FmwVEfrixe68KZ3b8iiMUiJCX54r33AvH008b9eutqqQi0rD6mu9NQSze9Xo+//e1v2LJlC1JTU/HCCy/Q70c0GhKAdkSn06GsrAy+vr7w9PTE48ePheQBsvo5JyzLQqVSCQk6rii8TWMIMzOr8Nln5cjKUuLx44bqvbiqkGLgvGKpuTinAAwO9sLrrwcgJSUAHh6Nm59hBx3D5Dig6fUx3RXDOGP+nmFIeXk5kpOTcefOHRw4cAA9e/Z00EwJV4UEoB3h48f4C1pFRQUACKKCrH7OhWlR5+a0cnNGDAXh9etKbN5chh9/rEJxsbaOEa4qplzVelkXzvPg5+3NIClJiuXLO6FPn5YX3Tatj1lX9ruHh0ercG9qtVoolco644wvX76MV199FVFRUdi6dasQRkQQTYEEoJ1Rq9VC3Bbf5g2A8MTLZ3NqtVqy+jmIxhZ1dhf4pJJHj9TYurUUBw8+xrVrGrCsaQKDq/0G7iQAHRv/J5Ew6NbNCwkJvpg6NQCRkbbttFJf9juf7MSLQnc6Nw2TzMRisVFyIP/+oUOHMHv2bLz77rt488037bb9arUa7777Lj7//HOUl5ejT58+WLNmDX7729/WO27Hjh2YPn26xfdKSkoQGBhoi+kSjYBUhQPg47IkEgk8PT2FC1xNTY3Reh4eHvD29rZrlmlrpyVFnV0VkUgEkUiEwEBPvPOODEuW1N549+x5iJ07y3DunOp/pUn0cC9R5UrY/xgMDPRAbKwvJk5shxde8LPrecAni/DXPkvJThqNBkDt8WtqIXTFc9Yw3MRSaSmdTofly5fjiy++wMGDB5GUlGTX7Zw6dSoOHDiAlJQU9OrVC9u3b8eLL74IuVyOuLi4BsevXr0a3bp1M1pGmcqOhSyAduTs2bPYvXs34uPjERsbC5lMJpzAOp0OaWlpGDFihHBDNsycM33idcULnDNji6LO7gLHccjMLMfHHz/A8eOVePwYLlIexp3EasMt1VqKnx+DZ57xwqhR/pg8uQN8fZ379zMth+TK10u9Xo/q6tqSTZY6OZWWlmLKlClQKpXYv38/QkND7Tq/06dPIyYmBuvXr8fcuXMB1FoEIyIiEBgYiMzMzDrH8hbA3Nxc9O/f315TJhoBWQDtiK+vL7RaLVasWIH8/Hz069cPCQkJ6N27NzZv3oy8vDx88803GDJkCBiGMcuc493F/AXO1Z94nQV7FXV2VRiGQXx8O8THtwMAFBaq8emn93D6dDVu3dKgtFQHbV3hg4QVYGAL8SeRMHjySS+88IIMr77qhcBAT5dqI8k/KPOxuYYWQr1eb3S9NLQQOltiiUajgUqlqvPBMy8vDxMmTMBzzz2Hjz76CD4+tnW/WyItLQ1isRjJycnCMolEghkzZmDJkiW4e/cuQkJC6v0MjuNQWVlp99qpRN24xpnuJvTu3Rv/+Mc/wHEcSktLcezYMXz00UdYv349goKCMHLkSJw4cQIikQjR0dGCi9j0AscLQj5BgQRh8zAt6iyTyejC1AhCQyVYu/ZXCwTLssjLq8Q33zxGVpYS166pUVqqg76hBGOb4+yt0hqGYQAPDxF0Le7wx0EsZhAa6oVnn5Xhtdfa4emnvQWrt6V4M1eDYRiz66WhhdAwxMYZMo0NvQ6enp7w8fExi/fbtWsXFi9ejPfffx/JyckO2z9nz55FWFiYWbJJVFQUAODcuXMNCsChQ4eiqqoKXl5eGDFiBD744APKXHYwJAAdAMMwePjwITZu3Ijs7Gy89dZbmD17Nk6fPg25XI6UlBTcvn0bUVFRSExMRGJiIgYOHAgvLy+LgpAXhZYEobsFSVsLZyzq7KqIRCIMHNgGAwfWxvPwN97r15X46qtynDypRH6+Bvfu6Uw6Y9ga1xeAHMdAp2vaNkgkDDp08EC3bl545hlvDB7si6FDZWjX7tfLPd9STK/Xu63V2/A6CJhnGvMPfwCMLISGfbZtBcuyUCqV0Ov1FmON1Wo1Fi9ejMOHD+Pw4cOIjY216Xwaori4GJ07dzZbzi8rKiqqc6xUKsW0adMwdOhQ+Pv7Izc3Fx9++CEGDx6MvLw8dOnSxWbzJuqHYgAdAMdx6NevH1QqFbZv347BgwebvV9cXAy5XA6FQoH09HQUFRVh0KBBSExMREJCAgYMGGB2oTINkjaNiSFBSHUVHYGhJaaoqAbffluBEydUuHRJg+JirUErNusjFntYwXrmSOqO/ZNKGXTqJEaPHhL06+eNhAQp4uKkkEjqP78Nyxu5ksvX2jQm09gWpWcMW7pZ+v2LioowefJkeHp6Yu/evQgKCrLadzeXHj16IDw8HN98843R8ps3b6Jnz57YuHEj5syZ0+jPy8zMRGJiIpKTk7F582ZrT5doJCQAHcT169cREhLSqHgOjuPwyy+/QC6XQy6XIyMjA/fv30dsbKxgIYyMjDS7kNQVJG2YNdeaBKE7FHV2ZQzL6/DHX235GQ2OHKnGuXNqFBXpUVKix8OHOjx+zEKp1EOr5VqQdOKqhawBQASGEcHfX4TgYDF695Zg4EBfJCVJERnp0+Rj17TEiLuXN2oqpn22+WoNgPk107Rma2M/n3/4tBTvx3EcsrKyMGnSJIwbNw5///vfnab2aEREBDp37owjR44YLb98+TIiIiLw6aef4s9//nOTPnPw4MF48OABrl27Zs2pEk2ABKALwnEcbt++bSQIy8vLMXjwYCQkJCAxMRF9+vQxi2erTxC6a10tHnct6uwq8DGrLMuaubxMXXOWukJUVgI//6zFlSs1uHFDjcJCDYqKtCgt1aK8XA+lkoVGw9YhFJ0nrrM2po+BlxcDHx8Gvr4e8PcXoV07D3Tq5ImAAA906CBG164SxMXJ0L27l1W+19Dl6K4uX1tgaiFsbqax4cOPpYdPlmXx2WefYdWqVfj4448xfvx4p9o/w4cPR1FRES5dumS0/OjRoxg+fDj+85//YOTIkU36zDFjxuDYsWMoLS215lSJJtA6bf8uDsMweOKJJzBt2jRMmzYNHMfh5s2bOHbsGBQKBT755BNUV1cjPj4eCQkJGDJkCJ566inBlcGLH9OLm2FdLVu5P+xNayvq7GyYWj0sJdoYhihIJBIj1xx/XHp5cejbF+jf3wdisV+dwfuPH+tw6VINfv5ZhRs31CgoUKOigoNeD2g0LLRaQK+v/T/L1v7V6zmw7K9/+eUsy///139zXO2/a/8CHh6Aj48IUqkI/v4eaNvWA+3bi9GxoxidOnkiJESMLl28EBrqidBQL3h62v/YM3z4kUqlrdbl2xxEIhG8vH4V4aYWQkuZxqaC0LDKgI+Pj9HnAYBKpcKbb76JrKwsyOVyREZG2m8DG0m/fv2gUChQWVkJPz8/YXl2djYANGvON2/eRMeOHa02R6LpkAXQDWFZFlevXoVCoRAshFqtFnFxcUhMTMSQIUPw5JNPmgmhup52XVUQtsaizs6EodWpJYk2poLQtE2YPYP3XQly+doe08oMeoPUd8OuTgzDQCqVmj383L59G+PHj0dQUBB2796NgIAAe29Co+DrAK5btw7z5s0D8GsdwI4dO+LkyZMAajt7lJeXo2fPnsKDxoMHD8yE3uHDh/G73/0Ob775JjZs2GDfjSEESAC2AliWxc8//ywklWRkZPyvtlu8EEPYq1evOgWhpXgY09Z1zgQVdXY8tkw0qC9437BvrDMem/bCWuKbaBqGCU9arVa4ZgK1D6T/+Mc/MHjwYMTGxiI3NxfTp0/H66+/jpUrVzp9CaqxY8fi0KFDSElJQY8ePbBz507k5ubi6NGjiI+PB1DbLSQ1NRUFBQVCsepevXqhf//+GDBgANq0aYO8vDxs27YNISEhyMnJISugAyEB2AphWRYXL140EoQSicRIEHbv3r1eQVhXxpyjb7pU1NmxGIpve1mdHJXN6axQlq9j4TgOSqVSSDbj233+/PPP+MMf/oDS0lLhuBwxYgSmTp2KhIQEpxdCarUay5Ytw65du/Do0SP07dsXq1evxvDhw4V1pk2bhtTUVNy6dUsQgMuWLcO3336LW7duQalUIjg4GCNHjsTy5cudfpvdHRKABHQ6HS5cuCAIwhMnTkAmkwkJJYmJiejatauZkKpLEDrCCmNa1JmqzdsfZxHfptmcrUUQksvX8RieA76+vmbJZo8fP0ZKSgru3bsHf39/nD9/Hrdv3wYAhIeHY/r06Zg/f74jpk60QkgAEmZotVqcO3dOEISZmZkICAgwSirp0qWLWQ3Cum66to7ToqLOjsW0tqKziW+O48xqZJoKQlfPgCeXr+PhW7rVdQ5cvXoV48ePR3h4OLZv3w5/f38AQGFhIY4fP46MjAyEh4fjrbfecsT0iVYICUCiXjiOg1arxZkzZyCXy5Geno6TJ08iMDBQKEqdmJiIzp07WxSEdcVpWaMNExV1djyuWluxofhWVxKEDRUWJmxLY1q6ffvtt3jjjTcwf/58LF682CWOK8L9IQFINAledOXk5AgWwlOnTiEkJMRIEAYGBtYpCOvK5GyKIHRV4eFOGAoPV6+tWF8GvLMWTTd0+VKyk2NoqL6iXq/He++9h3/961/YtWsXnnvuObpOEU4DCUCiRfA3oVOnTgmCMCcnB0888QQSEhKEV4cOHawmCKmos2NpDcKjrqLppm0Vm9MRwlrz4x+AyOXrGBqyvD569AgzZszA/fv3kZaWhu7duztimgRRJyQACavCF17mi5oqFAqcOXMGPXv2FKyDcXFxCAgIqFcQWuoG4eHhAY1GA51OR0HuDqK1xls2RhDySSW2WB4jzAAAIABJREFU/j3I5etY+LAYlUpV5wPQxYsX8eqrr2Lw4MHYsmULpFKpg2ZLEHVDApCwKXxJhBMnTkChUCA9PR15eXkIDw8Xkkri4+PRpk2bRgtCvptJS2MIiaZhGOTe2uMtDZNK+DZ2QNNbhDX1O93d8ursNNTSjeM47N+/HykpKVi1ahX+8pe/0D4inBYSgIRd4TgOVVVVOH78uJBUcuHCBTz99NNGFkKZTAaGYaBUKrFy5UpMmDAB3bp1g1gsFoQhj6EFhgSh9TG86VkKcifqF4R1tQhrCuTydTyG8X6WWrpptVq88847SEtLw969e5GQkED7iHBqSAASDoXjODx+/NhIEF66dAl9+/ZFeHg4MjMzcefOHWzevBljxowRLqiGFff5vzyGMVr2cMm5M6bt9ExveoRl6moR1hxB6E7JNq6K4T6w1NLt/v37mDJlCrRaLfbt24cuXbo4YpoE0SRIABJOBcdxuH//PubOnYs9e/agU6dOkEql6Nixo1CDcNCgQRZdL4YxWqY9OUkQNg1yN1qX5hyfhmWOaB84BsN9UFfccW5uLiZMmIDf/e532LhxIyQSiYNmSxBNgwQg4VRcv34dkyZNwunTp/H2229j2bJlqKioQHp6umAhvHHjBgYOHCi4jKOjo81cYnXdcK3lknNnqKiw7WlIEIpEIsGVTPvAMRi2dLO0DziOQ2pqKpYsWYJ169ZhxowZtI8Il4IEIOFUrFq1Cqmpqfj8888RGxtr9j7Hcbh37x4UCoXwKiwsRHR0tNC2bsCAAWb1uEgQNg7qI+sYDI9PrVYrZBkDMEoqoRhX+9BQSze1Wo0FCxbgyJEj2L9/P6Kjox00U4JoPiQArURJSQk2btyI7Oxs5Obmorq6GnK5HEOGDGn0Z9y9excpKSk4cuQIWJbF0KFDsWHDBnTr1s2GM3cutFot1Go1ZDJZo9bnOA7FxcVCyZn09HQUFRVh0KBBgiDs37+/WQu6+gShrbI4nRnDbgZUYscxmHa28fb2FqyAlsoikSC0DVqtFkqlss6Wbnfv3sWECRMgk8mwZ88eBAYGOmimBNEySABaCYVCgWHDhiEsLAzt27dHVlYWFAoFEhMTGzW+qqoK/fv3R2VlJebNmwexWIwNGzaA4zicO3cOAQEBNt4C94DjONy5cwdyuRxyuRzHjx/HgwcPEBsbK3QqiYyMNLNs1Re0b+86b/bG0NphqZsBYXsa6mzDP7AYPrTwkCC0DoZxr2KxGL6+vmb74MSJE5gyZQomTpyI999/nyzkhEtDAtBKVFVVQafToW3btkhLS8OYMWOaJAD//ve/Y/HixcjJycGAAQMAAFeuXEFERAQWLlyItWvX2nL6bgvHcSgoKBAEYUZGBh4/fozBgwcLSSXPPPOM2VN+Q3XenLE1WFMxtThZsnYQtqc5Wb6N6aTD/yVB2DCGAtzSQxDLsti8eTPee+89bNmyxagiAUG4KiQAbUBzBGB0dDREIhFOnTpltPz555/HjRs3cO3aNVtMtdXBcRxu3LiBY8eOIT09Henp6VAqlYiLixNcxk899VS9grC+1mCuIgipl7LjsWaWLwnC5qPX61FdXQ3ActxrdXU15syZgzNnziAtLQ19+vRxxDQJwuqQ/doJYFkWFy5cwGuvvWb2XlRUFH788UdUV1dTOyErwDAMevbsiZ49eyI5ORksy+Lq1auChfCDDz6ATqdDfHy8IAiffPJJofsIb50xbQ2m1WoB1HYpMU0qcTZMW4lRXTn7Y5hhag0BziczeXh4QCKRGAlCvV4PjUZjJAgNj9HWLAj57jZ1CfCbN29i/PjxCA0NRXZ2Ntq1a+egmRKE9SEB6ASUlZVBo9Ggc+fOZu/xy4qKitCrVy97T83tEYlEePLJJ/Hkk09i5syZYFkWly9fFpJK/u///g8eHh5C27rExET06tULIpFIEIVA/YLQNKnEUVCih3NgWFzbVgLcUBACv1oIDTONNRoNgF+PUcM4V3fH8Fyw1N2G4zgcOXIEr732Gv76179i2bJlFB5BuB3uf6Y3A/7i0JiXNeDLblgqIOrt7W20DmFbRCIRIiIiMHv2bBw4cAD37t3DDz/8gPj4eMjlcjz77LPo3bs3pk2bhu3bt+PGjRtgWVYQgz4+PvDz84Ofn5/gTtLpdFCpVKisrERlZSVUKhU0Go1RqQ9bw7u5NBoNvL29qaiwA+CTDKqrqyESieDn52c36ysvCL28vODr6ws/Pz/IZDL4+PjAw8NDyHw1PEZNy9G4CyzLGp0LpuKPZVmhrt+2bduwfPlyu4o/tVqNRYsWITg4GL6+voiJicFPP/3UqLHl5eVITk5Gx44dIZPJMGzYMJw9e9bGMyZcFbIAWiA9PR3Dhg1r1Lr5+fkICwtr0ff5+PgAqD3xTeFFJr8OYV9EIhH69u2Lvn374q233oJOp8P58+ehUCjw/fff45133oGfnx8SEhIEC2HXrl0tWggNs4ztZX3hOE6o7ScSiSCVSilz0QFY2+XbUgwthHx7P8MYQkvHqDNYsVuKaUs303OhoqICycnJuHnzJrKyslp8bW8OU6dOxYEDB5CSkoJevXph+/btePHFFyGXyxEXF1fnOJZlMXLkSFy4cAELFy5E+/btsWnTJiQlJeHMmTPo2bOnHbeCcAXoTmCB8PBw7Nixo1HrBgUFtfj7AgICIJFIUFxcbPYevyw4OLjF30O0HLFYjAEDBmDAgAGYN28etFotzp49C7lcjq+++kq48PKCcMiQIQgJCYFIJDLqo9vQzdYa8VkcxwmWHEtuLsI+NFRU2Fngj9HGCEJnj3M1pTEJN/n5+Rg/fjz69OmDrKws+Pn52X2ep0+fxt69e7F+/XrMnTsXADBp0iShGkRmZmadY9PS0pCVlYW0tDS8/PLLAIAxY8YgLCwMy5cvx+7du+2yDYTrQFnANqC5WcAMwyA7O9to+XPPPYdbt25RFrALwFvbzpw5I8QQZmVloVOnTkaCMCgoyEyImd5s+dOyuYLQMM7Mx8fHSHwS9sHdevmaWrF597CzC0LDB6G6aix+/fXX+Mtf/oK3334b8+bNc9g2LFy4EBs3bkRZWZlRMfz3338fS5YswZ07dxASEmJx7JgxY3DixAkUFRUZLX/jjTewa9cuPHr0yGkfPgjH4FxnaiuhsLAQ+fn5RstGjx6NnJwcnDlzRlh25coVyOVyvPLKK/aeItEMGIaBl5cXYmNjsWTJEvzwww8oLS3F9u3b0a1bN+zatQt9+vRB//79MWfOHKSlpeHevXvgOE6wvvj6+sLf3x9+fn5m8VkVFRWoqqoSbmaWnt34+FXDODMSf/aHFx01NTXw8vKCVCp1OmHUVPhj1FKcq16vF+JcKyoqoFQqodFojMrROAKWZVFVVQWtVgsfHx8zK7hOp8OKFSvw5ptvYu/evZg/f75D99PZs2cRFhZm1gkpKioKAHDu3Ll6x/bv399seVRUFJRKJa5evWrdyRIuD7mArciaNWsAAJcuXQIApKamIiMjAwDwzjvvCOtNnjwZGRkZRgHWs2bNwmeffYaRI0di/vz5EIvF+PDDDxEUFIR58+bZcSsIa8EwDCQSiWD9W7ZsGWpqapCdnQ25XI5///vfeP3119GtWzchfjA+Ph4dOnQwcsfVl8FpWONNJBJBpVJBr9dbbF5P2AdXcfm2lMZmwjuqm45hSzeZTGaWyPHw4UNMnz4djx49wunTp/HEE0/YfE4NUVxc3GA1iPrGJiUl1Tv26aefts5ECbeABKAVeffdd8EwDDiOA8Mw2LZtG4DaC6ChAGQYxuwCKJPJoFAokJKSgjVr1hj1Am7fvr1dt4OwDQzDwMfHB0lJSUhKShKsRCdPnoRcLsemTZswY8YMhIWFCYIwLi4O7dq1MwrYNxWEhjXeAMDT05MSPRyAaWcVS6LDnTEVhKbddPhKBrbut91QSzcAOH/+PMaPH4+kpCR89dVX8PX1tdr3twSVStXsahA1NTVUSYJoEnSXsCKNLZkgl8stLg8JCcG+ffusOSXCiWEYBr6+vvjtb3+L3/72t+A4DtXV1cjMzIRcLsfGjRsxZcoUhIeHC4Wp4+Pj4e/vLwhCjUaD1NRUTJw4UbCuGFpfqE+sfWgozqw1wjCMUfF0037bhhZC0xjC5v52htnWlqzgHMdhz549mD9/PtasWYNZs2Y51X7y8fFpdjWIlowlWickAAnCSWAYBjKZDCNGjMCIESPAcRwqKytx/PhxyOVyrFu3TsgITEhIQPfu3fHRRx/h7t27GDJkCCIiIgQLtGFSiUajEW4MJAitj6HLlxJu6qYhQcgLleYKwoZc7xqNBkuXLsWXX36J//znP4iLi3O6479z584W3byNqQbRkrFE64QEIEE4KQzDwN/fHyNHjsTIkSPBcRzKy8uRkZGBDz/8EP/4xz8QGhqKuLg47N+/H/fu3UNsbCx8fX3rbAum0+kE9xhAgrAlmNZYbG0u35ZiSRAaxhAaFto3PEYtHad8S7e69kNJSQmmTJkCAMjJyXFaMdSvXz8oFApUVlYalaHhq0NERkbWOTYyMhLHjx8XQpAMx0qlUofUNCScG9dOSyOIVgTDMNBoNPj000+RkZGBOXPm4OTJk5g5cyY0Gg2WLVuG0NBQPPfcc1i1ahUUCoUQ98OLQalUCn9/f8hkMsFNyXenqKioQHV1NWpqaoxK0RDm8C5flUoFT09PEn9WgI8N9Pb2hkwmg7+/P6RSqfAQw2e388epWq0WEj3q2g8cxyE7OxuJiYmIjIzE0aNHnVb8AbXVIPR6PbZu3SosU6vV2L59O2JiYoQSMCUlJcjPz4dOpzMae+/ePRw8eFBYVlpaiv379+Oll15y22QkovlQHUCCcCGee+45nD9/Hjt27MALL7xg9B7HcSgtLYVCoYBcLkd6ejpu3ryJqKgoIakkKirKYlyUXq83sr7wNGR5aY2Qy9cxGFoI+b88fPJJYWEhQkND4e3tDY7jsG3bNrz77rvYsGEDpkyZ4hLH79ixY3Ho0CGkpKSgR48e2LlzJ3Jzc3H06FHEx8cDqO0WkpqaioKCAoSGhgKojUGPj4/HxYsXsWDBAqETyC+//IKcnBzqJU+YQQKQsAvl5eVYuHAhDh06BJVKhejoaHzwwQfo169fg2NXrFiBVatWmS2XSCStLrPt2rVr8Pf3R6dOnRpcl+M43Lt3TxCDCoUCd+7cwaBBg4SkkoEDB8LT09OiILR0ozUsSm2vch7OhKGrkXe1E/ZHp9OhuroaQG3WOx9PGB8fj6KiIuG4/vnnn5GWllZvCzVnQ61WY9myZULx5r59+2L16tUYPny4sM60adOQmpqKW7duCQIQqL3OLliwAF9++aVwnV2/fr3F+oAEQQKQsDksyyIhIcGsR+WdO3ca1aOSF4BbtmwxKpDq4eGBsWPH2nr6bgPHcSgqKoJcLodcLkdGRgaKi4sRExODxMREJCYmol+/fmYdR0xjs/R6vfBeaxGE1FbPeeBFuGl3FY7jkJOTgyNHjuDIkSO4cOEC1Go1JBIJBg0ahCFDhmDIkCFISEggqy1BgAQgYQf27duHcePGGfWoLC0tRVhYGF544YUGe1TyArC0tBQBAQH2mHKrgOM43Llzx0gQlpaWIjY2VhCEffv2NaspWJcgtGY5D2eCXL7OQWNaumVkZGDq1KmYNm0aVq5cicuXLyM9PR3p6enIyMjA48ePUVZWBn9/fwduCUE4ByQACZvT0h6VvAC8f/8+vLy84Ofn5xbCwtngOA4FBQU4duwYFAoF0tPTUVlZicGDBwt9jCMiIszcnu4sCMnl6xywLAulUgm9Xm9RhLMsi48//hh/+9vfsHXrVowePdpiv+2bN2826HEgiNYCZQETNsdaPSq7d++Otm3bwt/fH5MmTcL9+/etPdVWDcMw6NatG2bMmIHPP/8cBQUFyMnJwR/+8AdcuHABo0ePRteuXTF27Fh88sknuHjxIliWrTN7k+9aUlNTg6qqKlRWVkKpVEKtVju8R2xD8AWFKcvX8eh0OlRVVYFlWchkMjPxV11djenTp2P79u04fvw4XnnlFYsPGiKRiMQfQRhAdQAJm9PSHpUBAQGYPXs2YmNjIZFIkJGRgU8++QSnT59Gbm6uUb0swnrwN8yePXsiOTkZLMviypUrgst4/fr1QuYh7zLu3bs3RCKRYPUD6u8A4YgesQ1h6vI1TZIh7INhaz2xWAwfHx8h3o/nxo0bGD9+PLp3747s7Gy0bdvWQbMlCNeDBCDRJPg+m42B70HZ0h6Vc+bMMfr/n/70J0RHR2PChAnYtGkTFi1a1Kj5EC1DJBIhPDwc4eHhmDVrFliWxeXLlyGXy6FQKPDee+/Bw8PDSBD27NlTKNHR2B6x/MtSz2xb01BBYcI+GMb71dXS7fvvv0dycjLeeustLF261EwcEgRRPxQDSDQJhUKBYcOGNWrd/Px8hIWFwc/PD+PGjcNnn31m9P7hw4fxu9/9Dj/88INRiYPGEhwcjIiICPz4449NHktYH5ZlceHCBUEQHj9+HD4+PkhISBDqEHbr1s3sRm0oCHU6ndBT21QQ2vIGz7uqNRoNZfk6mIZauun1evz973/H5s2bsXPnTrz44ou0rwiiGZAFkGgS4eHh2LFjR6PWDQoKAmC7HpVdunRBWVlZs8YS1kckEiEyMhKRkZFISUmBTqfD+fPnIZfL8d1332Hp0qXw9/cXBOGQIUMQGhpaZ49Y/mXJZWxNQUguX+eB7+xRlwW2vLwcycnJuHPnDk6dOkUxfQTRAsgCSNicMWPG4Pjx4ygqKjK6sSYnJ+OLL75AWVlZk9sUcRyHTp06YcCAAfjuu++sPWXCBmi1WuTl5QmFqTMzM9G+fXskJiYiPj4eQ4YMQUhIiMXsTcMsY95CKBKJzLKMmzMnXnBQlq/j4ENL1Go1xGIxfH19zY6Dy5cv49VXX0VUVBS2bt1qVBOUIIimQ0EThM1pSo/KwsJC5OfnG41/8OCB2Wdu3rwZpaWleP755203ccKqeHp6YtCgQVi8eDEOHz6M0tJS7N69G2FhYdi3bx/69++Pvn374q9//Sv27t2L4uJicBwnxBD6+PjAz88Pfn5+8PX1hVgsFmIIKysrUVlZCZVKBY1GI4jEuuBjzJRKJWX5Ohi+xAtftNlU/HEch0OHDmH48OGYNWsWdu3aReKPIKwAWQAJm9OUHpVJSUnIyMgwuoH7+vpi3LhxiIiIgLe3N06cOIG9e/ciMjISmZmZQjIJ4brwGZ/Z2dlQKBRQKBTIzs7Gb37zGyGpJCEhAYGBgRYthIZZxoYWQtMsY8DY5evt7Q0vLy9y+ToIvV4vtHTj3e+G6HQ6rFixArt378YXX3yBoUOH0r4iCCtBApCwC43tUTl06FBkZGQYtRtLTk7GyZMncefOHdTU1OCJJ57AqFGjsHTpUkilUntvCmEH+KSMU6dOCUklOTk56N69u5BQEh8fj/bt29cpCHlRaCgIRSIRdDodGIaBVColq58DMcy4lkqlZi780tJSTJ06FdXV1di/f79Rz1uCIFoOCUCCIJwe3mWbmZkpWAjPnDmDsLAwQRDGxcWhXbt2FgWhWq02sg4CxhZC0/7HhO1oTMb12bNnMWHCBAwfPhwfffQRfHx8HDRbgnBfSAASBOFycByH6upqnDhxQkgqOXfuHMLDw40Eob+/P27duoVJkyZh5syZeOWVV4TYQd5KyF8CPTw8jJJKSBBaH8OWbpbc7xzHYffu3Vi0aBHef/99JCcn034gCBtBApAgCJeH4zhUVFQYCcL//ve/+M1vfoP79++jTZs2+OyzzxAfH28mOEzLzpAgtA06nQ5KpRIAhCQeQzQaDRYvXoxvv/0W+/btQ2xsrCOmSRCtBhKABEG4HWq1GikpKdi8eTOefvppyGQynDt3DpGRkUIdwtjYWIsZp6ZlZwwFIZ9QQoKw8Ri2dPPw8ICvr69ZvF9xcTEmTZoET09P7NmzR2gTSRCE7SABSBCEW3H79m2MHTsWeXl5WLdundBKsKysDOnp6YKF8OrVqxgwYIBQlDo6Ohre3t4WBaFhlrGpIORFIQlCcwxbunl5eVn8fU+dOoVJkyZhzJgxWLduXZNrghIE0TxIABIE4Vbk5ORg4sSJ+PzzzxEdHW1xHY7j8ODBAygUCkEQFhQUYODAgULJmejoaIsxaqZZxiQILcOyLKqrq4UOK15eXmbv/+tf/8LKlSvxz3/+ExMmTGj1vxlB2BMqBE0QdkKtVmPRokUIDg6Gr68vYmJi8NNPPzVqLN8Cq2PHjpDJZBg2bBjOnj1r4xm7JlFRUbh8+XKd4g+obSsXGBiIMWPGYPPmzbh06RJu3LiBmTNn4sGDB5gzZw66dOmCl156CX/729+QlZUFjUYDoFboSSQSSKVS+Pn5QSaTCZYtjUaD6upqVFRUoKqqCjU1NUZWw9aCVqtFVVUVAEAmk5mJP5VKhZkzZ+Kf//wnjh07hokTJzqF+GvJebZixQqh1JDhizKYCWeFLIAEYSdeffVVHDhwACkpKejVqxe2b9+OnJwcyOVyxMXF1TmOZVkkJCTgwoULWLhwoVBI+86dOzhz5gz1Q7UBHMfh7t27kMvlkMvlOH78OEpKShATEyNkGffr188sFtDUQqjT6YT3DOMH3dVC2JiWbrdv38aECRMQGBiI3bt3o3379g6arTEtPc9WrFiBVatWYcuWLUadSjw8PDB27FhbT58gmgwJQIKwA6dPn0ZMTAzWr1+PuXPnAqi1CEZERCAwMBCZmZl1jt23bx/GjRuHtLQ0vPzyywBqi+SGhYXhhRdewO7du+2yDa0ZjuNQWFgoCMKMjAyUlZUhNjYWiYmJSExMRJ8+fcwyWxsShLwYdAdByHEclEoldDodJBIJJBKJmTiWy+WYPn06/vznP2PVqlVOVYi7pecZLwBLS0sREBBgjykTRIsQN7wKQRAtJS0tDWKxGMnJycIyiUSCGTNmYMmSJbh79y5CQkLqHBsUFCTclACgQ4cOGDNmDHbt2gWtVkuB8zaGYRh07doVU6dOxdSpU8FxHG7duoVjx44hPT0dW7ZsQWVlJQYPHiwklURERAjijncbcxwHvV4vJJTU1NQI3+HKgtCwvZ6vr6/Z8ciyLDZu3IgPP/wQ//73v/HHP/7R6bbPWucZy7KoqKiAn5+f020jQRhCMYAEYQfOnj2LsLAwsyb2UVFRAIBz587VO9a0ZR4/VqlU4urVq9adLNEgDMOge/fueO211/D555+joKAAp0+fxu9//3ucP38eo0aNwhNPPIFx48Zh06ZNuHTpEliWBcMwEIvFQgyhv78/pFIpJBIJAKCmpkaIIayuroZarTZKNHFGTOP9TEVSZWUlJk+ejN27dyMzMxN/+tOfnFIYWes86969O9q2bQt/f39MmjQJ9+/ft/ZUCcIqkAWQIOxAcXGxxdpm/LKioqJ6xyYlJdU79umnn7bORIlmIRKJ0KtXL/Tq1Quvv/46WJZFfn6+kGW8bt06sCyL+Ph4wWXcu3dvo3Z0AAQLIe8u5i2EDMMYZRmLRCKHi6jGtHS7du0axo8fj969eyM7Oxv+/v4Omm3DtPQ8CwgIwOzZsxEbGwuJRIKMjAx88sknOH36NHJzc+Hn52erqRNEsyABSBB2QKVSCVYeQ7y9vYX366KmpqbZYwnHIBKJ8NRTT+Gpp57CrFmzwLIsLl26BLlcDoVCgbVr18LT09NIEPbo0cNlBGFjWrodPnwYb7zxBubNm4fFixebFX+2JXwySmPgz6OWnmd8vUmeP/3pT4iOjsaECROwadMmLFq0qFHzIQh7QS5ggrADPj4+Fm9I/A29vlIRLRlLOAcikQjPPPMM5syZg4MHD+LevXs4fPgwBg8ejJ9++glDhw7Fk08+ienTp2PHjh24efMmOI4TXMbe3t6QyWSCy9jLy0uwwFVVVaGyshJKpdIuLmOdToeqqiqwLCu4rw3Fn16vx9q1azFr1izs3r0bb7/9tl3FHwCkp6fD19e3US/etWuL8+zVV19FUFAQjh492rINIggbQBZAgrADnTt3tujmLS4uBgAEBwfbZCzhnHh4eKBfv37o168fUlJSoNPpcO7cOcjlcnz77bdYsmQJ2rRpI7StS0xMRGhoqCAIDS2Ehl1KtFotABit5+HhYTULoUajgUqlqrOl26NHj/Daa6+hpKQEp06dQo8ePVr8nc0hPDwcO3bsaNS6QUFBAGx3nnXp0gVlZWXNGksQtoQEIEHYgX79+kGhUKCystIoFig7OxsAEBkZWefYyMhIHD9+XLAIGY6VSqUICwuz3cQJuyAWizFw4EAMHDgQCxYsgFarRV5eHuRyOQ4dOoQFCxagffv2QpeSIUOGIDg4GAzDwNPTU0i8MBWEvNvSUBDytQubIggbaukGABcvXsT48eMRGxuLtLQ0SKVS6/1ATaRTp06YPHlyk8bY4jzjOA4FBQUYMGBAk8cShK0hFzBB2IHRo0dDr9dj69atwjK1Wo3t27cjJiZGKAFTUlKC/Px8o3pxo0ePxr1793Dw4EFhWWlpKfbv34+XXnqJSsC4IZ6enhg0aBAWL16M7777DqWlpdi1axd69eqFPXv2oF+/foiMjMTs2bOxd+9elJSUCMLF09PTyGXMl2VhWRYqlQqVlZWCy1ij0YBl2Xrnwrd002q18PHxMUv24DgO+/fvx4gRIzBnzhzs3LnToeKvuTTlPCssLER+fr7R+AcPHph95ubNm1FaWornn3/edhMniGZChaAJwk6MHTsWhw4dQkpKCnr06IGdO3ciNzcXR48eRXx8PABg6tSpSE1NRUFBAUJDQwFAyB69ePGiYAnatGkTfvnlF+Tk5KBXr16O3CzCzvAJDqdPnxaSSrKzsxEaGiq4ixMSEtCxY0czKx1vIeRfvPgztRDyrl2dTgelUgkA8PX1NSt0rdVq8e4dhfDqAAAKdklEQVS772Lfvn3Ys2cPEhMTHZ6d3Fyacp4lJSUhIyPDSDz7+vpi3LhxiIiIgLe3N06cOIG9e/ciMjISmZmZQjIJQTgLJAAJwk6o1WosW7YMu3btwqNHj9C3b1+sXr0aw4cPF9aZNm0aUlNTcevWLUEAArU9ShcsWIAvv/wSKpUK0dHRWL9+vcW6ZUTrgk8GycrKEgRhbm4uevToIcQQxsfHo3379mbijGVZoyxjQ0EoEomg1+shEokglUrN4v3u37+PKVOmQKPRYP/+/ejSpYvdttlWNPY8Gzp0KDIyMqDX64VlycnJOHnyJO7cuYOamho88cQTGDVqFJYuXeqSFlHC/SEBSBAE4UbwLdlOnjwpCMK8vDz07t1bsBDGxcWhbdu2FgWhWq02EoP88hUrViA2NhZJSUm4e/cuJkyYgBdffBEbN24k6xZBuCAkAAmCINwYjuNQVVWFEydOCH2Mz507h6eeekqoQxgfHw8/Pz9cuXIFEyZMwIIFCzBq1Ch4eHhAp9OhsLAQo0ePxrVr1wDU1saLiYnBrFmzkJSUhI4dOzp4KwmCaCokAAmCIFoRHMehoqICx48fh1wuR3p6Oi5evIiuXbuiqKgIgYGB2LFjB/r3729kIVSr1Vi4cCGuXbuGdu3a4dKlS4IgjIiIwLPPPosNGza4bAwgQbQ2SAASBEG0YnQ6HRYvXowPPvgATz31FKRSKS5cuIDIyEih5EzXrl3x2muvwdfXF3v37kVgYCAA4JdffoFCoYBCocDDhw9x6NAhB28NQRCNhQQgQRBEK6WsrAwTJkzADz/8gLVr12LRokVgGAYPHz5Eenq6EEN4+fJlTJ8+HZs3b6ayQwThJlAdQIIgnAa1Wo1FixYhODgYvr6+iImJwU8//dTguB07dkAkEll83b9/3w4zd00KCwvx3//+F99//73Qso1hGHTo0AGjRo3Cxx9/jP/+9784f/48Pv30UxJ/BOFGUCcQgiCchqlTp+LAgQNISUlBr169sH37drz44ouQy+WIi4trcPzq1avRrVs3o2Vt2rSx1XRdnsjISNy4cQMSiaTOdRiGwTPPPGPHWREEYQ/IBUwQhFNw+vRpxMTEYP369Zg7dy6AWotgREQEAgMDkZmZWefYHTt2YPr06cjNzaXaiARBEI2AXMAEQTgFaWlpEIvFSE5OFpZJJBLMmDEDWVlZuHv3boOfwXEcKisrjQr0EgRBEOaQACQIwik4e/YswsLCIJPJjJZHRUUBAM6dO9fgZwwdOhRt2rSBVCrFH/7wB1y/ft0mcyUIgnB1KAaQIAinoLi4GJ07dzZbzi8rKiqqc6xUKsW0adMwdOhQ+Pv7Izc3Fx9++CEGDx6MvLw8t2hTRhAEYU1IABIE4RSoVCqLyQh8mzGVSlXn2FdeeQWvvPKK8P/f//73GDFiBBITE7F27Vps3rzZ+hMmCIJwYcgFTBCEU+Dj4wO1Wm22vKamRni/KcTFxWHQoEGNKiNDEATR2iABSBCEU9C5c2eLbt7i4mIAQHBwcJM/s0uXLnj06FGL50YQBOFukAAkCMIp6NevH65evYrKykqj5dnZ2QBqa9Y1lZs3b6Jjx45WmR9BEIQ7QQKQIAinYPTo0dDr9di6dauwTK1WY/v27YiJiUFISAgAoKSkBPn5+dDpdMJ6Dx48MPu8w4cPIy8vD88//7ztJ08QBOFiUCFogiCchrFjx+LQoUNISUlBjx49sHPnTuTm5uLo0aOIj48HUNstJDU1FQUFBQgNDQUA9OrVC/3798eAAQPQpk0b5OXlYdu2bQgJCUFOTg5ZAQmCIEygLGCCIJyG1NRULFu2DJ9//jkePXqEvn374ptvvhHEH1DbmoxhGKNx48aNw7fffosff/wRSqUSwcHBeP3117F8+XISfwRBEBYgCyBBEARBEEQrg2IACYIgCIIgWhkkAAmCIAibUlJSgsWLF2Po0KHw8/ODSCRCenp6kz7j7t27GDNmDNq1a4c2bdrgj3/8I27dumWjGROE+0MuYIIgCMKmKBQKDBs2DGFhYWjfvj2ysrKgUCiQmJjYqPFVVVXo378/KisrMW/ePIjFYmzYsAEcx+HcuXMICAiw8RYQhPtBSSAEQRCETRk4cCDKysrQtm1bpKWlISsrq0njN23ahOvXryMnJwcDBgwAALzwwguIiIjABx98gLVr19pi2gTh1pALmCAIgrApMpkMbdu2bfb4tLQ0REdHC+IPAHr37o1nn30W+/bts8YUCaLVQQKQIAiCcFpYlsWFCxcwcOBAs/eioqJw48YNVFdXO2BmBOHakAAkCIJwQaqrq7F8+XI8//zzCAgIgEgkws6dOxs9vry8HMnJyejYsSNkMhmGDRuGs2fP2nDGzaOsrAwajQadO3c2e49fZqmHNEEQ9UMxgARBEC7IgwcPsHr1anTt2hWRkZFQKBRmBbLrgmVZjBw5EhcuXMDChQvRvn17bNq0CUlJSThz5gx69uxZ51iO46BWqxv1Pd7e3o1arz5UKhUAQCKR1Pn5/DoEQTQeEoAEQRAuSHBwMEpKShAYGIgzZ84gKiqq0WP5RIy0tDS8/PLLAIAxY8YgLCwMy5cvx+7du+scm56ejmHDhjXqe/Lz8xEWFtboeVnCx8cHACyKzpqaGqN1CIJoPCQACYIgXBAvLy8EBgYCqLXKNYW0tDQEBQUJ4g8AOnTogDFjxmDXrl3QarXw9PS0ODY8PBw7duxo1PcEBQU1aV6WCAgIgEQiQXFxsdl7/LLg4OAWfw9BtDZIABIEQbQyzp49i/79+5stj4qKwtatW3H16lU8/fTTFsd26tQJkydPtvUUBUQiEZ555hnk5OSYvZednY0ePXpAKpXabT4E4S5QEghBEEQro7i42GmTKgoLC5Gfn2+0bPTo0cjJycGZM2eEZVeuXIFcLscrr7xi7ykShFtAFkCCIIhWRk1Njd2TKtasWQMAuHTpEgAgNTUVGRkZAIB33nlHWG/y5MnIyMgAy7LCslmzZuGzzz7DyJEjMX/+fIjFYnz44YcICgrCvHnzrD5XgmgNkAAkCIJoZfj4+Ng9qeLdd98FwzDgOA4Mw2Dbtm0AAIZhjAQgwzBm2cwymQwKhQIpKSlYs2YNWJbF0KFDsWHDBrRv397qcyWI1gAJQIIgiFZG586dLbp5bZlUYWjRqw+5XG5xeUhICHX9IAgrQjGABEEQrYzIyEjk5eWZZQ9nZ2dDKpW2uHQLQRDODwlAgiAIN6akpAT5+fnQ6XTCstGjR+PevXs4ePCgsKy0tBT79+/HSy+9VGcJGIIg3AdyARMEQbgoH3/8McrLywV37tdff43CwkIAwJw5c+Dv74/FixcjNTUVBQUFCA0NBVArAGNiYjBt2jRcvnxZ6ATCcRxWrlzpsO0hCMJ+MFxTK4gSBEEQTkG3bt1w+/ZtABASJ/gki1u3biE0NBTTpk1Damqq8H+e8vJyLFiwAF9++SVUKhWio6Oxfv16i/UBCYJwP0gAEgRBEARBtDIoBpAgCIIgCKKVQQKQIAiCIAiilUECkCAIgiAIopVBApAgCIIgCKKVQQKQIAiCIAiilfH/b9DjfN/BjOoAAAAASUVORK5CYII=", "text": [ "Figure(PyObject <matplotlib.figure.Figure object at 0x11787b8d0>)" ] } ], "prompt_number": 7 }, { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "Laplacian squared $\\Delta^2 u = 0$" ] }, { "cell_type": "code", "collapsed": false, "input": [ "d=Disk()\n", "\n", "u=[dirichlet(d),neumann(d),lap(d)^2]\\Fun(z->real(exp(z)),Circle())\n", "ApproxFun.plot(pad(u,50,50)) # we pad to get a nice plot;" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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", "text": [ "Figure(PyObject <matplotlib.figure.Figure object at 0x112c8d690>)" ] } ], "prompt_number": 8 } ], "metadata": {} } ] }

mit

zaqwes8811/micro-apps

self_driving/deps/Kalman_and_Bayesian_Filters_in_Python_master/animations/multivariate_animations.ipynb

1

56666

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from __future__ import division, print_function\n", "%matplotlib inline\n", "import sys\n", "sys.path.insert(0,'..') # allow us to format the book\n", "sys.path.insert(0,'../code') # allow us to format the book\n", "sys.path.insert(0,'./code') # allow us to format the book\n", "\n", "# use same formatting as rest of book so that the plots are\n", "# consistant with that look and feel.\n", "#import book_format\n", "#book_format.load_style('..')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This notebook creates the animations for the Multivariate Kalman Filter chapter. It is not really intended to be a readable part of the book, but of course you are free to look at the source code, and even modify it. However, if you are interested in running your own animations, I'll point you to the examples subdirectory of the book, which contains a number of python scripts that you can run and modify from an IDE or the command line. This module saves the animations to GIF files, which is quite slow and not very interactive. " ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import filterpy.stats as stats\n", "import numpy as np\n", "from matplotlib.patches import Ellipse\n", "import matplotlib.pyplot as plt\n", "from matplotlib import cm\n", "from mpl_toolkits.mplot3d import Axes3D" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Anaconda3\\lib\\site-packages\\matplotlib\\collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n", " if self._edgecolors == str('face'):\n" ] }, { "data": { "text/plain": [ "<matplotlib.figure.Figure at 0x7f9e208>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f9e320>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import numpy as np\n", "#import mkf_internal\n", "import matplotlib.pyplot as plt\n", "from gif_animate import animate\n", "\n", "def ellipse_animate(frame):\n", " \n", " plt.cla()\n", " cov = frame/15.\n", " P = np.array([[2,cov],[cov,2]])\n", " stats.plot_covariance_ellipse((2,7), cov=P, facecolor='g', alpha=0.2, \n", " title='|2.0 {:.1f}|\\n|{:.1f} 2.0|'.format(cov, cov))\n", "fig = plt.figure()\n", "animate('multivariate_ellipse.gif', ellipse_animate, 30, 125, figsize=(4, 4))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<img src='multivariate_ellipse.gif'>" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[ 0.0025, 0.005 ],\n", " [ 0.005 , 0.01 ]])" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "f.Q" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Anaconda3\\lib\\site-packages\\matplotlib\\collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n", " if self._edgecolors == str('face'):\n" ] }, { "data": { "image/png": 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l6be/JjE2tMkkcM4vPmDUTa/xwY5NvPvNt6zPONZ0vb/5jXvIsUYDen3TZYRo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\nVRSE0E+xFEmSJJ25Mx4s7u7uRtM0KiryT6rl5eV0dnaOesz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LgSRJ0gT3/wFBopKIIzDV\nWQAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x842bda0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import numpy as np\n", "from matplotlib.patches import Ellipse\n", "import matplotlib.pyplot as plt\n", "from matplotlib import cm\n", "from mpl_toolkits.mplot3d import Axes3D\n", "from gif_animate import animate\n", "import numpy.random as random\n", "from DogSimulation import DogSimulation\n", "import filterpy.stats\n", "from filterpy.common import Q_discrete_white_noise\n", "from filterpy.kalman import KalmanFilter\n", "import book_format\n", "book_format.load_style('..')\n", "\n", "def dog_tracking_filter(R, Q=0, cov=1.):\n", " dog_filter = KalmanFilter(dim_x=2, dim_z=1)\n", " dog_filter.x = np.array([0, 0]) # initial state (location and velocity)\n", " dog_filter.F = np.array([[1.,1],\n", " [0,1]]) # state transition matrix\n", " dog_filter.H = np.array([[1.,0]]) # Measurement function\n", " dog_filter.R *= R # measurement uncertainty\n", " dog_filter.P *= cov # covariance matrix \n", " if np.isscalar(Q):\n", " dog_filter.Q = Q_discrete_white_noise(2, var=Q)\n", " else:\n", " dog_filter.Q = Q\n", " return dog_filter\n", "\n", "R = 5.\n", "Q = .01\n", "noise = 2.\n", "P = 20.\n", "dog = DogSimulation(measurement_var=R, process_var=Q)\n", "f = dog_tracking_filter(R=R, Q=Q, cov=P)\n", "random.seed(200)\n", "zs = []\n", "xs = []\n", "def animate_track(frame):\n", " if frame > 30: return\n", " \n", " if frame == 0:\n", " stats.plot_covariance_ellipse((0, f.x[0]), cov=f.P, axis_equal=True, \n", " facecolor='g', edgecolor=None, alpha=0.2)\n", " xs.append(f.x[0])\n", " \n", " z = dog.move_and_sense()[1]\n", "\n", " zs.append(z)\n", " f.update(z)\n", " xs.append(f.x[0])\n", " \n", " stats.plot_covariance_ellipse((frame+1, f.x[0]), cov=f.P, axis_equal=True, \n", " facecolor='g', edgecolor=None, alpha=0.5,\n", " xlim=(-5,40), ylim=(-5,40))\n", " \n", " plt.plot(zs, color='r', linestyle='dashed')\n", " plt.plot(xs, color='b')\n", " f.predict()\n", "\n", "animate('multivariate_track1.gif', animate_track, 37, 200, figsize=(5.5, 5.5))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<img src='multivariate_track1.gif'>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\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": "3.5.0" } }, "nbformat": 4, "nbformat_minor": 0 }

mit

miguelfrde/stanford-cs231n

assignment3/NetworkVisualization-PyTorch.ipynb

1

27082

{ "nbformat_minor": 2, "nbformat": 4, "cells": [ { "source": [ "# Network Visualization (PyTorch)\n", "\n", "In this notebook we will explore the use of *image gradients* for generating new images.\n", "\n", "When training a model, we define a loss function which measures our current unhappiness with the model's performance; we then use backpropagation to compute the gradient of the loss with respect to the model parameters, and perform gradient descent on the model parameters to minimize the loss.\n", "\n", "Here we will do something slightly different. We will start from a convolutional neural network model which has been pretrained to perform image classification on the ImageNet dataset. We will use this model to define a loss function which quantifies our current unhappiness with our image, then use backpropagation to compute the gradient of this loss with respect to the pixels of the image. We will then keep the model fixed, and perform gradient descent *on the image* to synthesize a new image which minimizes the loss.\n", "\n", "In this notebook we will explore three techniques for image generation:\n", "\n", "1. **Saliency Maps**: Saliency maps are a quick way to tell which part of the image influenced the classification decision made by the network.\n", "2. **Fooling Images**: We can perturb an input image so that it appears the same to humans, but will be misclassified by the pretrained network.\n", "3. **Class Visualization**: We can synthesize an image to maximize the classification score of a particular class; this can give us some sense of what the network is looking for when it classifies images of that class.\n", "\n", "This notebook uses **PyTorch**; we have provided another notebook which explores the same concepts in TensorFlow. You only need to complete one of these two notebooks." ], "cell_type": "markdown", "metadata": { "collapsed": true } }, { "execution_count": null, "cell_type": "code", "source": [ "import torch\n", "from torch.autograd import Variable\n", "import torchvision\n", "import torchvision.transforms as T\n", "import random\n", "\n", "import numpy as np\n", "from scipy.ndimage.filters import gaussian_filter1d\n", "import matplotlib.pyplot as plt\n", "from cs231n.image_utils import SQUEEZENET_MEAN, SQUEEZENET_STD\n", "from PIL import Image\n", "\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'" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### Helper Functions\n", "\n", "Our pretrained model was trained on images that had been preprocessed by subtracting the per-color mean and dividing by the per-color standard deviation. We define a few helper functions for performing and undoing this preprocessing. You don't need to do anything in this cell." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "def preprocess(img, size=224):\n", " transform = T.Compose([\n", " T.Scale(size),\n", " T.ToTensor(),\n", " T.Normalize(mean=SQUEEZENET_MEAN.tolist(),\n", " std=SQUEEZENET_STD.tolist()),\n", " T.Lambda(lambda x: x[None]),\n", " ])\n", " return transform(img)\n", "\n", "def deprocess(img, should_rescale=True):\n", " transform = T.Compose([\n", " T.Lambda(lambda x: x[0]),\n", " T.Normalize(mean=[0, 0, 0], std=(1.0 / SQUEEZENET_STD).tolist()),\n", " T.Normalize(mean=(-SQUEEZENET_MEAN).tolist(), std=[1, 1, 1]),\n", " T.Lambda(rescale) if should_rescale else T.Lambda(lambda x: x),\n", " T.ToPILImage(),\n", " ])\n", " return transform(img)\n", "\n", "def rescale(x):\n", " low, high = x.min(), x.max()\n", " x_rescaled = (x - low) / (high - low)\n", " return x_rescaled\n", " \n", "def blur_image(X, sigma=1):\n", " X_np = X.cpu().clone().numpy()\n", " X_np = gaussian_filter1d(X_np, sigma, axis=2)\n", " X_np = gaussian_filter1d(X_np, sigma, axis=3)\n", " X.copy_(torch.Tensor(X_np).type_as(X))\n", " return X" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# Pretrained Model\n", "\n", "For all of our image generation experiments, we will start with a convolutional neural network which was pretrained to perform image classification on ImageNet. We can use any model here, but for the purposes of this assignment we will use SqueezeNet [1], which achieves accuracies comparable to AlexNet but with a significantly reduced parameter count and computational complexity.\n", "\n", "Using SqueezeNet rather than AlexNet or VGG or ResNet means that we can easily perform all image generation experiments on CPU.\n", "\n", "[1] Iandola et al, \"SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and < 0.5MB model size\", arXiv 2016" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# Download and load the pretrained SqueezeNet model.\n", "model = torchvision.models.squeezenet1_1(pretrained=True)\n", "\n", "# We don't want to train the model, so tell PyTorch not to compute gradients\n", "# with respect to model parameters.\n", "for param in model.parameters():\n", " param.requires_grad = False" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "## Load some ImageNet images\n", "We have provided a few example images from the validation set of the ImageNet ILSVRC 2012 Classification dataset. To download these images, change to `cs231n/datasets/` and run `get_imagenet_val.sh`.\n", "\n", "Since they come from the validation set, our pretrained model did not see these images during training.\n", "\n", "Run the following cell to visualize some of these images, along with their ground-truth labels." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "from cs231n.data_utils import load_imagenet_val\n", "X, y, class_names = load_imagenet_val(num=5)\n", "\n", "plt.figure(figsize=(12, 6))\n", "for i in range(5):\n", " plt.subplot(1, 5, i + 1)\n", " plt.imshow(X[i])\n", " plt.title(class_names[y[i]])\n", " plt.axis('off')\n", "plt.gcf().tight_layout()" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# Saliency Maps\n", "Using this pretrained model, we will compute class saliency maps as described in Section 3.1 of [2].\n", "\n", "A **saliency map** tells us the degree to which each pixel in the image affects the classification score for that image. To compute it, we compute the gradient of the unnormalized score corresponding to the correct class (which is a scalar) with respect to the pixels of the image. If the image has shape `(3, H, W)` then this gradient will also have shape `(3, H, W)`; for each pixel in the image, this gradient tells us the amount by which the classification score will change if the pixel changes by a small amount. To compute the saliency map, we take the absolute value of this gradient, then take the maximum value over the 3 input channels; the final saliency map thus has shape `(H, W)` and all entries are nonnegative.\n", "\n", "[2] Karen Simonyan, Andrea Vedaldi, and Andrew Zisserman. \"Deep Inside Convolutional Networks: Visualising\n", "Image Classification Models and Saliency Maps\", ICLR Workshop 2014." ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### Hint: PyTorch `gather` method\n", "Recall in Assignment 1 you needed to select one element from each row of a matrix; if `s` is an numpy array of shape `(N, C)` and `y` is a numpy array of shape `(N,`) containing integers `0 <= y[i] < C`, then `s[np.arange(N), y]` is a numpy array of shape `(N,)` which selects one element from each element in `s` using the indices in `y`.\n", "\n", "In PyTorch you can perform the same operation using the `gather()` method. If `s` is a PyTorch Tensor or Variable of shape `(N, C)` and `y` is a PyTorch Tensor or Variable of shape `(N,)` containing longs in the range `0 <= y[i] < C`, then\n", "\n", "`s.gather(1, y.view(-1, 1)).squeeze()`\n", "\n", "will be a PyTorch Tensor (or Variable) of shape `(N,)` containing one entry from each row of `s`, selected according to the indices in `y`.\n", "\n", "run the following cell to see an example.\n", "\n", "You can also read the documentation for [the gather method](http://pytorch.org/docs/torch.html#torch.gather)\n", "and [the squeeze method](http://pytorch.org/docs/torch.html#torch.squeeze)." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# Example of using gather to select one entry from each row in PyTorch\n", "def gather_example():\n", " N, C = 4, 5\n", " s = torch.randn(N, C)\n", " y = torch.LongTensor([1, 2, 1, 3])\n", " print(s)\n", " print(y)\n", " print(s.gather(1, y.view(-1, 1)).squeeze())\n", "gather_example()" ], "outputs": [], "metadata": { "collapsed": true } }, { "execution_count": null, "cell_type": "code", "source": [ "def compute_saliency_maps(X, y, model):\n", " \"\"\"\n", " Compute a class saliency map using the model for images X and labels y.\n", "\n", " Input:\n", " - X: Input images; Tensor of shape (N, 3, H, W)\n", " - y: Labels for X; LongTensor of shape (N,)\n", " - model: A pretrained CNN that will be used to compute the saliency map.\n", "\n", " Returns:\n", " - saliency: A Tensor of shape (N, H, W) giving the saliency maps for the input\n", " images.\n", " \"\"\"\n", " # Make sure the model is in \"test\" mode\n", " model.eval()\n", " \n", " # Wrap the input tensors in Variables\n", " X_var = Variable(X, requires_grad=True)\n", " y_var = Variable(y)\n", " saliency = None\n", " ##############################################################################\n", " # TODO: Implement this function. Perform a forward and backward pass through #\n", " # the model to compute the gradient of the correct class score with respect #\n", " # to each input image. You first want to compute the loss over the correct #\n", " # scores, and then compute the gradients with a backward pass. #\n", " ##############################################################################\n", " pass\n", " ##############################################################################\n", " # END OF YOUR CODE #\n", " ##############################################################################\n", " return saliency" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "Once you have completed the implementation in the cell above, run the following to visualize some class saliency maps on our example images from the ImageNet validation set:" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "def show_saliency_maps(X, y):\n", " # Convert X and y from numpy arrays to Torch Tensors\n", " X_tensor = torch.cat([preprocess(Image.fromarray(x)) for x in X], dim=0)\n", " y_tensor = torch.LongTensor(y)\n", "\n", " # Compute saliency maps for images in X\n", " saliency = compute_saliency_maps(X_tensor, y_tensor, model)\n", "\n", " # Convert the saliency map from Torch Tensor to numpy array and show images\n", " # and saliency maps together.\n", " saliency = saliency.numpy()\n", " N = X.shape[0]\n", " for i in range(N):\n", " plt.subplot(2, N, i + 1)\n", " plt.imshow(X[i])\n", " plt.axis('off')\n", " plt.title(class_names[y[i]])\n", " plt.subplot(2, N, N + i + 1)\n", " plt.imshow(saliency[i], cmap=plt.cm.hot)\n", " plt.axis('off')\n", " plt.gcf().set_size_inches(12, 5)\n", " plt.show()\n", "\n", "show_saliency_maps(X, y)" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# Fooling Images\n", "We can also use image gradients to generate \"fooling images\" as discussed in [3]. Given an image and a target class, we can perform gradient **ascent** over the image to maximize the target class, stopping when the network classifies the image as the target class. Implement the following function to generate fooling images.\n", "\n", "[3] Szegedy et al, \"Intriguing properties of neural networks\", ICLR 2014" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "def make_fooling_image(X, target_y, model):\n", " \"\"\"\n", " Generate a fooling image that is close to X, but that the model classifies\n", " as target_y.\n", "\n", " Inputs:\n", " - X: Input image; Tensor of shape (1, 3, 224, 224)\n", " - target_y: An integer in the range [0, 1000)\n", " - model: A pretrained CNN\n", "\n", " Returns:\n", " - X_fooling: An image that is close to X, but that is classifed as target_y\n", " by the model.\n", " \"\"\"\n", " # Initialize our fooling image to the input image, and wrap it in a Variable.\n", " X_fooling = X.clone()\n", " X_fooling_var = Variable(X_fooling, requires_grad=True)\n", " \n", " learning_rate = 1\n", " ##############################################################################\n", " # TODO: Generate a fooling image X_fooling that the model will classify as #\n", " # the class target_y. You should perform gradient ascent on the score of the #\n", " # target class, stopping when the model is fooled. #\n", " # When computing an update step, first normalize the gradient: #\n", " # dX = learning_rate * g / ||g||_2 #\n", " # #\n", " # You should write a training loop. #\n", " # #\n", " # HINT: For most examples, you should be able to generate a fooling image #\n", " # in fewer than 100 iterations of gradient ascent. #\n", " # You can print your progress over iterations to check your algorithm. #\n", " ##############################################################################\n", " pass\n", " ##############################################################################\n", " # END OF YOUR CODE #\n", " ##############################################################################\n", " return X_fooling" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "Run the following cell to generate a fooling image:" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "idx = 0\n", "target_y = 6\n", "\n", "X_tensor = torch.cat([preprocess(Image.fromarray(x)) for x in X], dim=0)\n", "X_fooling = make_fooling_image(X_tensor[idx:idx+1], target_y, model)\n", "\n", "scores = model(Variable(X_fooling))\n", "assert target_y == scores.data.max(1)[1][0, 0], 'The model is not fooled!'" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "After generating a fooling image, run the following cell to visualize the original image, the fooling image, as well as the difference between them." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "X_fooling_np = deprocess(X_fooling.clone())\n", "X_fooling_np = np.asarray(X_fooling_np).astype(np.uint8)\n", "\n", "plt.subplot(1, 4, 1)\n", "plt.imshow(X[idx])\n", "plt.title(class_names[y[idx]])\n", "plt.axis('off')\n", "\n", "plt.subplot(1, 4, 2)\n", "plt.imshow(X_fooling_np)\n", "plt.title(class_names[target_y])\n", "plt.axis('off')\n", "\n", "plt.subplot(1, 4, 3)\n", "X_pre = preprocess(Image.fromarray(X[idx]))\n", "diff = np.asarray(deprocess(X_fooling - X_pre, should_rescale=False))\n", "plt.imshow(diff)\n", "plt.title('Difference')\n", "plt.axis('off')\n", "\n", "plt.subplot(1, 4, 4)\n", "diff = np.asarray(deprocess(10 * (X_fooling - X_pre), should_rescale=False))\n", "plt.imshow(diff)\n", "plt.title('Magnified difference (10x)')\n", "plt.axis('off')\n", "\n", "plt.gcf().set_size_inches(12, 5)\n", "plt.show()" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# Class visualization\n", "By starting with a random noise image and performing gradient ascent on a target class, we can generate an image that the network will recognize as the target class. This idea was first presented in [2]; [3] extended this idea by suggesting several regularization techniques that can improve the quality of the generated image.\n", "\n", "Concretely, let $I$ be an image and let $y$ be a target class. Let $s_y(I)$ be the score that a convolutional network assigns to the image $I$ for class $y$; note that these are raw unnormalized scores, not class probabilities. We wish to generate an image $I^*$ that achieves a high score for the class $y$ by solving the problem\n", "\n", "$$\n", "I^* = \\arg\\max_I s_y(I) - R(I)\n", "$$\n", "\n", "where $R$ is a (possibly implicit) regularizer (note the sign of $R(I)$ in the argmax: we want to minimize this regularization term). We can solve this optimization problem using gradient ascent, computing gradients with respect to the generated image. We will use (explicit) L2 regularization of the form\n", "\n", "$$\n", "R(I) = \\lambda \\|I\\|_2^2\n", "$$\n", "\n", "**and** implicit regularization as suggested by [3] by periodically blurring the generated image. We can solve this problem using gradient ascent on the generated image.\n", "\n", "In the cell below, complete the implementation of the `create_class_visualization` function.\n", "\n", "[2] Karen Simonyan, Andrea Vedaldi, and Andrew Zisserman. \"Deep Inside Convolutional Networks: Visualising\n", "Image Classification Models and Saliency Maps\", ICLR Workshop 2014.\n", "\n", "[3] Yosinski et al, \"Understanding Neural Networks Through Deep Visualization\", ICML 2015 Deep Learning Workshop" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "def jitter(X, ox, oy):\n", " \"\"\"\n", " Helper function to randomly jitter an image.\n", " \n", " Inputs\n", " - X: PyTorch Tensor of shape (N, C, H, W)\n", " - ox, oy: Integers giving number of pixels to jitter along W and H axes\n", " \n", " Returns: A new PyTorch Tensor of shape (N, C, H, W)\n", " \"\"\"\n", " if ox != 0:\n", " left = X[:, :, :, :-ox]\n", " right = X[:, :, :, -ox:]\n", " X = torch.cat([right, left], dim=3)\n", " if oy != 0:\n", " top = X[:, :, :-oy]\n", " bottom = X[:, :, -oy:]\n", " X = torch.cat([bottom, top], dim=2)\n", " return X" ], "outputs": [], "metadata": { "collapsed": true } }, { "execution_count": null, "cell_type": "code", "source": [ "def create_class_visualization(target_y, model, dtype, **kwargs):\n", " \"\"\"\n", " Generate an image to maximize the score of target_y under a pretrained model.\n", " \n", " Inputs:\n", " - target_y: Integer in the range [0, 1000) giving the index of the class\n", " - model: A pretrained CNN that will be used to generate the image\n", " - dtype: Torch datatype to use for computations\n", " \n", " Keyword arguments:\n", " - l2_reg: Strength of L2 regularization on the image\n", " - learning_rate: How big of a step to take\n", " - num_iterations: How many iterations to use\n", " - blur_every: How often to blur the image as an implicit regularizer\n", " - max_jitter: How much to gjitter the image as an implicit regularizer\n", " - show_every: How often to show the intermediate result\n", " \"\"\"\n", " model.type(dtype)\n", " l2_reg = kwargs.pop('l2_reg', 1e-3)\n", " learning_rate = kwargs.pop('learning_rate', 25)\n", " num_iterations = kwargs.pop('num_iterations', 100)\n", " blur_every = kwargs.pop('blur_every', 10)\n", " max_jitter = kwargs.pop('max_jitter', 16)\n", " show_every = kwargs.pop('show_every', 25)\n", "\n", " # Randomly initialize the image as a PyTorch Tensor, and also wrap it in\n", " # a PyTorch Variable.\n", " img = torch.randn(1, 3, 224, 224).mul_(1.0).type(dtype)\n", " img_var = Variable(img, requires_grad=True)\n", "\n", " for t in range(num_iterations):\n", " # Randomly jitter the image a bit; this gives slightly nicer results\n", " ox, oy = random.randint(0, max_jitter), random.randint(0, max_jitter)\n", " img.copy_(jitter(img, ox, oy))\n", "\n", " ########################################################################\n", " # TODO: Use the model to compute the gradient of the score for the #\n", " # class target_y with respect to the pixels of the image, and make a #\n", " # gradient step on the image using the learning rate. Don't forget the #\n", " # L2 regularization term! #\n", " # Be very careful about the signs of elements in your code. #\n", " ########################################################################\n", " pass\n", " ########################################################################\n", " # END OF YOUR CODE #\n", " ########################################################################\n", " \n", " # Undo the random jitter\n", " img.copy_(jitter(img, -ox, -oy))\n", "\n", " # As regularizer, clamp and periodically blur the image\n", " for c in range(3):\n", " lo = float(-SQUEEZENET_MEAN[c] / SQUEEZENET_STD[c])\n", " hi = float((1.0 - SQUEEZENET_MEAN[c]) / SQUEEZENET_STD[c])\n", " img[:, c].clamp_(min=lo, max=hi)\n", " if t % blur_every == 0:\n", " blur_image(img, sigma=0.5)\n", " \n", " # Periodically show the image\n", " if t == 0 or (t + 1) % show_every == 0 or t == num_iterations - 1:\n", " plt.imshow(deprocess(img.clone().cpu()))\n", " class_name = class_names[target_y]\n", " plt.title('%s\\nIteration %d / %d' % (class_name, t + 1, num_iterations))\n", " plt.gcf().set_size_inches(4, 4)\n", " plt.axis('off')\n", " plt.show()\n", "\n", " return deprocess(img.cpu())" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "Once you have completed the implementation in the cell above, run the following cell to generate an image of a Tarantula:" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "dtype = torch.FloatTensor\n", "# dtype = torch.cuda.FloatTensor # Uncomment this to use GPU\n", "model.type(dtype)\n", "\n", "target_y = 76 # Tarantula\n", "# target_y = 78 # Tick\n", "# target_y = 187 # Yorkshire Terrier\n", "# target_y = 683 # Oboe\n", "# target_y = 366 # Gorilla\n", "# target_y = 604 # Hourglass\n", "out = create_class_visualization(target_y, model, dtype)" ], "outputs": [], "metadata": { "scrolled": false, "collapsed": true } }, { "source": [ "Try out your class visualization on other classes! You should also feel free to play with various hyperparameters to try and improve the quality of the generated image, but this is not required." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# target_y = 78 # Tick\n", "# target_y = 187 # Yorkshire Terrier\n", "# target_y = 683 # Oboe\n", "# target_y = 366 # Gorilla\n", "# target_y = 604 # Hourglass\n", "target_y = np.random.randint(1000)\n", "print(class_names[target_y])\n", "X = create_class_visualization(target_y, model, dtype)" ], "outputs": [], "metadata": { "collapsed": true } } ], "metadata": { "kernelspec": { "display_name": "Python 3", "name": "python3", "language": "python" }, "language_info": { "mimetype": "text/x-python", "nbconvert_exporter": "python", "name": "python", "file_extension": ".py", "version": "3.5.2", "pygments_lexer": "ipython3", "codemirror_mode": { "version": 3, "name": "ipython" } } } }

mit

aneesh297/Slack-Log-Extractor

Extractor.ipynb

1

37464

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import json" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Enter file address of logs\n", "C:\\Users\\lenovo\\Documents\\IEEE WebDev\\IEEE SP Web Dev Slack export May 15 2017\n" ] } ], "source": [ "from os import walk\n", "#mypath = r'C:\\Users\\lenovo\\Documents\\IEEE WebDev\\IEEE SP Web Dev Slack export May 15 2017'\n", "mypath = input(\"Enter file address of logs\\n\") " ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import html.parser\n", "html_parser = html.parser.HTMLParser()" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [], "source": [ "with open(mypath + '\\\\' + 'users.json') as data_file: \n", " users = json.load(data_file)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[{'name': 'abhishek', 'id': 'U57BNJK7G'}, {'name': 'aneesh297', 'id': 'U52KA1B41'}, {'name': 'anmolh', 'id': 'U5479Q8KV'}, {'name': 'arvind111', 'id': 'U549B06V8'}, {'name': 'briny_peddler', 'id': 'U5AT2AH6U'}, {'name': 'gj', 'id': 'U57AF2MQU'}, {'name': 'mahim23', 'id': 'U52GBB6TA'}, {'name': 'manas1998', 'id': 'U5866PWNB'}, {'name': 'mehakarora', 'id': 'U5B8B4CBA'}, {'name': 'mehnaz18', 'id': 'U53NLR7B3'}, {'name': 'mirthunraj', 'id': 'U55GZPZPV'}, {'name': 'mishal23', 'id': 'U536M4S7L'}, {'name': 'nishantkr', 'id': 'U548ZLVEW'}, {'name': 'omkar_p31', 'id': 'U52GL5UQ0'}, {'name': 'pachianil', 'id': 'U54M2QJPK'}, {'name': 'pratyush82', 'id': 'U585PDFSM'}, {'name': 'pvgupta24', 'id': 'U53MW3E0G'}, {'name': 'rahul', 'id': 'U59FV35C7'}, {'name': 'sagarb', 'id': 'U552Q5RH6'}, {'name': 'samarthb', 'id': 'U5CT7AE79'}, {'name': 'sanjanak', 'id': 'U58E0QC15'}, {'name': 'shivanimishra123', 'id': 'U5A1N7TGU'}, {'name': 'srahulsrihari36', 'id': 'U5C5SLR29'}, {'name': 'suyashghuge', 'id': 'U57DKAK8F'}, {'name': 'uday_aditya', 'id': 'U586AT5S8'}, {'name': 'vanshika', 'id': 'U54UM5GLU'}, {'name': 'vidhikothari', 'id': 'U554ESJEB'}, {'name': 'viggi', 'id': 'U52HJQQMR'}, {'name': 'visha_1', 'id': 'U56HKB9PS'}, {'name': 'vkgvvinay', 'id': 'U597AC90S'}]\n" ] } ], "source": [ "names = []\n", "for user in users:\n", " temp = {}\n", " temp['name'] = user['name']\n", " temp['id'] = user['id']\n", " names.append(temp)\n", "print (names)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [], "source": [ "names_list = {}\n", "for i in names:\n", " names_list[i['id']] = i['name']" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{'U57BNJK7G': 'abhishek', 'U52KA1B41': 'aneesh297', 'U5479Q8KV': 'anmolh', 'U549B06V8': 'arvind111', 'U5AT2AH6U': 'briny_peddler', 'U57AF2MQU': 'gj', 'U52GBB6TA': 'mahim23', 'U5866PWNB': 'manas1998', 'U5B8B4CBA': 'mehakarora', 'U53NLR7B3': 'mehnaz18', 'U55GZPZPV': 'mirthunraj', 'U536M4S7L': 'mishal23', 'U548ZLVEW': 'nishantkr', 'U52GL5UQ0': 'omkar_p31', 'U54M2QJPK': 'pachianil', 'U585PDFSM': 'pratyush82', 'U53MW3E0G': 'pvgupta24', 'U59FV35C7': 'rahul', 'U552Q5RH6': 'sagarb', 'U5CT7AE79': 'samarthb', 'U58E0QC15': 'sanjanak', 'U5A1N7TGU': 'shivanimishra123', 'U5C5SLR29': 'srahulsrihari36', 'U57DKAK8F': 'suyashghuge', 'U586AT5S8': 'uday_aditya', 'U54UM5GLU': 'vanshika', 'U554ESJEB': 'vidhikothari', 'U52HJQQMR': 'viggi', 'U56HKB9PS': 'visha_1', 'U597AC90S': 'vkgvvinay'}\n" ] } ], "source": [ "print(names_list)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [], "source": [ "with open(mypath + '\\\\' + 'channels.json') as data_file: \n", " channels_json = json.load(data_file)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['bookshelf', 'doubts', 'general', 'random']\n" ] } ], "source": [ "channels = []\n", "for channel in channels_json:\n", " channels.append(channel['name'])\n", "print (channels)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\lenovo\\Anaconda3\\lib\\site-packages\\ipykernel\\__main__.py:23: DeprecationWarning: The unescape method is deprecated and will be removed in 3.5, use html.unescape() instead.\n" ] } ], "source": [ "final_log = []\n", "for channel in channels:\n", " file = open(channel + '.txt','w')\n", " path = mypath + '\\\\'+ channel\n", " f = []\n", " for (dirpath, dirnames, filenames) in walk(path):\n", " f.extend(filenames)\n", " break\n", " log = []\n", " for session in f:\n", " with open(path+'\\\\'+session) as data_file: \n", " data = json.load(data_file)\n", " for mesg in data:\n", " sub = 1\n", " for x in mesg:\n", " if x == 'subtype':\n", " sub = 0\n", " break\n", " if mesg['type'] == 'message' and sub:\n", " temp = {}\n", " temp['name'] = names_list[mesg['user']]\n", " temp['text'] = mesg['text']\n", " temp['text'] = html_parser.unescape(temp['text'])\n", " file.write(temp['name'] + ': ')\n", " file.write(temp['text'] + '\\n')\n", " log.append(temp)\n", " final_log.append(log)\n", " file.close()" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[], [{'name': 'sagarb', 'text': 'Discuss doubts here!'}, {'name': 'sagarb', 'text': '<@U59FV35C7> right click in the page and click on Inspect. A pane should open on the right! Send me screenshot'}, {'name': 'aneesh297', 'text': 'and expand the <head> tag before taking a screenshot'}, {'name': 'sagarb', 'text': \"I'll be back after dinner :stuck_out_tongue:\"}, {'name': 'rahul', 'text': 'ok'}, {'name': 'rahul', 'text': 'how to expand the tag?'}, {'name': 'sagarb', 'text': 'Click on the small arrow on the left of the tag!'}, {'name': 'sagarb', 'text': 'Also, open sources tab and send screenshot'}, {'name': 'rahul', 'text': 'sorry for the delay'}, {'name': 'mahim23', 'text': 'You havent saved the sample.html after editing it'}, {'name': 'rahul', 'text': 'ya ya got it'}, {'name': 'rahul', 'text': 'i just did that only'}, {'name': 'rahul', 'text': 'now its good'}, {'name': 'rahul', 'text': 'thanks and sorry for the trouble'}], [{'name': 'viggi', 'text': 'is this visible to everyone? :stuck_out_tongue:'}, {'name': 'mahim23', 'text': 'Yes it is'}, {'name': 'mishal23', 'text': 'Yes'}, {'name': 'viggi', 'text': 'cool.'}, {'name': 'aneesh297', 'text': 'How many of you are online?'}, {'name': 'mishal23', 'text': 'Me'}, {'name': 'mehakarora', 'text': 'Me'}, {'name': 'mahim23', 'text': 'Me'}, {'name': 'mehnaz18', 'text': 'Me'}, {'name': 'nishantkr', 'text': 'Me'}, {'name': 'pratyush82', 'text': 'me'}, {'name': 'anmolh', 'text': 'Me'}, {'name': 'omkar_p31', 'text': 'Me'}, {'name': 'nishantkr', 'text': 'anmolh: hi Horo Sahab :grin::grin::grin:'}, {'name': 'srahulsrihari36', 'text': 'me'}, {'name': 'aneesh297', 'text': \"Since there's a good number of you right now, let's start\"}, {'name': 'aneesh297', 'text': \"So, lets start with what HTML is first. It's essential to know these basics to do any kind of web development.\"}, {'name': 'aneesh297', 'text': 'HTML is the standard markup language for creating Web pages.'}, {'name': 'anmolh', 'text': 'Kyu re yaha bhi suru ho gaya\\n'}, {'name': 'aneesh297', 'text': 'It stands for Hypertext Markup Language .'}, {'name': 'aneesh297', 'text': \"So what's a markup language and how is it different from a programming language?\"}, {'name': 'aneesh297', 'text': 'Markup languages are designed for the processing, definition and presentation of text.\\nThey are designed not for general purpose programming, but for processing or presentation of text.'}, {'name': 'aneesh297', 'text': 'A simple HTML doc would look like this:'}, {'name': 'aneesh297', 'text': \"You can create and edit HTML documents using a simple text editor like Notepad. Just don't forget to save it as a .html file.\"}, {'name': 'aneesh297', 'text': 'Any doubts so far?'}, {'name': 'aneesh297', 'text': \"I'll clarify what each of those parts of the sample code mean in a moment\"}, {'name': 'aneesh297', 'text': 'Uh is anybody online?'}, {'name': 'mishal23', 'text': 'aneesh297: No'}, {'name': 'mahim23', 'text': 'No doubts'}, {'name': 'manas1998', 'text': 'No'}, {'name': 'aneesh297', 'text': 'Okay cool'}, {'name': 'pratyush82', 'text': 'yes......no doubts so far'}, {'name': 'aneesh297', 'text': 'So'}, {'name': 'aneesh297', 'text': 'The HTML document itself begins with <html> and ends with </html>.'}, {'name': 'aneesh297', 'text': 'The visible part of the HTML document is between <body> and </body>.'}, {'name': 'aneesh297', 'text': 'These things are called elements.'}, {'name': 'aneesh297', 'text': 'An HTML element usually consists of a start tag and end tag, with the content inserted in between:\\n<tagname>Content goes here...</tagname>'}, {'name': 'aneesh297', 'text': 'For example, the paragraph tag:\\n<p>Paragraph content</p>'}, {'name': 'aneesh297', 'text': 'In the body, if we want to have headings, we use any of the <h1> to <h6> tags.'}, {'name': 'aneesh297', 'text': 'Where h1 indicates the most important heading and has the largest font size.'}, {'name': 'aneesh297', 'text': '<h1> Heading </h1>'}, {'name': 'samarthb', 'text': 'h2 will have smaller size is it?'}, {'name': 'aneesh297', 'text': 'yes'}, {'name': 'samarthb', 'text': ':+1:'}, {'name': 'aneesh297', 'text': 'Now coming to attributes.'}, {'name': 'aneesh297', 'text': 'Attributes provide additional information about HTML elements.'}, {'name': 'srahulsrihari36', 'text': 'what does it mean by the visible part?? the tag title wont b visible? its out of the body'}, {'name': 'samarthb', 'text': 'Visible on the browser after running i suppose'}, {'name': 'aneesh297', 'text': 'No not the tag. The contents of the tag is visible on the browser.'}, {'name': 'pratyush82', 'text': 'is it more like the webpage name??'}, {'name': 'aneesh297', 'text': \"In other words, you'd put the any of the parts that are actually going to appear on the webpage in the body section\"}, {'name': 'gj', 'text': 'No. Whatever you see in the browser was coded within the body part.'}, {'name': 'srahulsrihari36', 'text': 'okay!! thnx'}, {'name': 'aneesh297', 'text': 'gj: yes exactly'}, {'name': 'pratyush82', 'text': ':+1:'}, {'name': 'aneesh297', 'text': 'okay . about attributes. All HTML elements can have attributes.'}, {'name': 'aneesh297', 'text': 'for example, if you want to display an image, you use the <img></img> tag.'}, {'name': 'aneesh297', 'text': '*<img> tag'}, {'name': 'samarthb', 'text': 'What do u have inside it? the image name?'}, {'name': 'samarthb', 'text': '<img> name </img>?'}, {'name': 'aneesh297', 'text': 'So to mention which file you want to use, you mention the image name as an attribute'}, {'name': 'aneesh297', 'text': '<img src=\"pic_mountain.jpg\" alt=\"Mountain View\" style=\"width:304px;height:228px;\">'}, {'name': 'aneesh297', 'text': 'So src=\"pic_mountain.jpg\" is an attribute.'}, {'name': 'suyashghuge', 'text': 'src? alt?'}, {'name': 'aneesh297', 'text': 'Alternate text.'}, {'name': 'gj', 'text': 'Source.'}, {'name': 'aneesh297', 'text': 'src is the name of the image file.'}, {'name': 'manas1998', 'text': 'Is it necessary to give width and height ??'}, {'name': 'aneesh297', 'text': 'The alt attribute provides an alternate text for an image, if the user for some reason cannot view it (because of slow connection, an error in the src attribute, or if the user uses a screen reader). (Not that important)'}, {'name': 'briny_peddler', 'text': 'if we place mouse on image, text shown is given by alt right?'}, {'name': 'aneesh297', 'text': '\"Is it necessary to give width and height ?? \"No not necessarily, but you can reformat it to your specification using the width and height attributes'}, {'name': 'aneesh297', 'text': 'Coming to the style attribute.'}, {'name': 'aneesh297', 'text': 'Setting the style of an HTML element, can be done with the style attribute.'}, {'name': 'aneesh297', 'text': 'You can use this attribute for most elements.'}, {'name': 'aneesh297', 'text': 'Usually, we avoid explicitly mentioning the style for each element and try to use an external document called a StyleSheet (CSS) to determine the style of our elements.'}, {'name': 'aneesh297', 'text': '<tagname style=\"property:value;\">'}, {'name': 'aneesh297', 'text': 'The property is a CSS property. The value is a CSS value.'}, {'name': 'aneesh297', 'text': 'CSS = cascading style sheets. Sagar will cover this in a while'}, {'name': 'aneesh297', 'text': 'So lets take some style examples.'}, {'name': 'aneesh297', 'text': 'Lets say you want a different background color other than the plain borin white.'}, {'name': 'aneesh297', 'text': 'You can use <body style=\"background-color:powderblue;\">'}, {'name': 'aneesh297', 'text': 'Or for header text: <h1 style=\"color:blue;\">This is a heading</h1>'}, {'name': 'aneesh297', 'text': 'paragraph text: <p style=\"color:red;\">This is a paragraph.</p>'}, {'name': 'aneesh297', 'text': 'Suppose you want to change text alignment of the heading: <h1 style=\"text-align:center;\">Centered Heading</h1>'}, {'name': 'aneesh297', 'text': \"There are a lot of style attributes like these. I can't cover them all obviously. You can easily look it up online. We'll share a few resources later\"}, {'name': 'mahim23', 'text': 'What if we to change multiple style properties?'}, {'name': 'aneesh297', 'text': 'You use semicolons (;) to separate them in style attribute.'}, {'name': 'aneesh297', 'text': 'For example, <h1 style=\"text-align:center; color: red;\">Centered Heading</h1>'}, {'name': 'rahul', 'text': 'Are there many kinds of tags?'}, {'name': 'aneesh297', 'text': 'yes'}, {'name': 'rahul', 'text': 'I mean like for attaching images or posting sound clips like that'}, {'name': 'aneesh297', 'text': \"There's just one type of <img> element, but it can have many attributes.\"}, {'name': 'rahul', 'text': 'okay'}, {'name': 'sagarb', 'text': 'rahul: Yes there are many kinds of tags. Not for the things you mentioned but for making lists, text boxes, buttons, images, check boxes, radio buttons etc'}, {'name': 'aneesh297', 'text': 'Now, you may notice that \"Are there many kinds of tags?\" is highlighted as a link. This can be done using the <a> or the link tag.'}, {'name': 'aneesh297', 'text': 'HTML links are defined with the <a> tag:\\n<a href=\"https://www.google.com\">This is a link</a>'}, {'name': 'aneesh297', 'text': 'href is the link address attribute'}, {'name': 'rahul', 'text': 'Okay'}, {'name': 'aneesh297', 'text': 'And \"This is a link\" is what you would view on the screen'}, {'name': 'sagarb', 'text': \"But it's not feasible or even required to memorise all these tags all at once. Just remember the basic ones. Search online when need arises.\\n\\nEach tag also has it's own set of attributes. So it's impossible to remember everything!\"}, {'name': 'aneesh297', 'text': 'To help with styling, we have something known as a class attribute.'}, {'name': 'aneesh297', 'text': 'The HTML class attribute makes it possible to define equal styles for elements with the same class name.'}, {'name': 'manas1998', 'text': 'Can we also post sound clips?'}, {'name': 'aneesh297', 'text': 'So for example we could define the style for a <h1> and <p> tag at once by assigning them to the same class.'}, {'name': 'aneesh297', 'text': 'yes you can.'}, {'name': 'rahul', 'text': 'Can you please clear the term attribute'}, {'name': 'sagarb', 'text': \"Fine I'll post on main thread\"}, {'name': 'rahul', 'text': 'Thanks'}, {'name': 'aneesh297', 'text': '<div class=\"cities\">\\n<h2>Paris</h2>\\n<p>Paris is the capital and most populous city of France.</p>\\n</div>\\n\\n<div class=\"cities\">\\n<h2>Tokyo</h2>\\n<p>Tokyo is the capital of Japan, the center of the Greater Tokyo Area,\\nand the most populous metropolitan area in the world.</p>\\n</div>'}, {'name': 'suyashghuge', 'text': 'div stands for?'}, {'name': 'aneesh297', 'text': '<div> is used to separate the code into blocks. Nothing more than that'}, {'name': 'sagarb', 'text': 'Okay so Rahul\\'s doubt : What exactly are attributes?\\n\\nAttributes are extra pieces of information about html tags. They define some properties of tags.\\n\\neg. in `<h1 align=\"centre\">Sample Page</h1>`\\n\\nh1 is the HTML tag.\\nalign is an attribute(or property) of that tag.\\ncentre is the value of the attribute align'}, {'name': 'mahim23', 'text': 'And how do we style the components of a class?'}, {'name': 'sagarb', 'text': \"mahim23: This would be done in CSS. We'll come to that in a few miuntes\"}, {'name': 'aneesh297', 'text': '\"And how do we style the components of a class?\" You could do that by mentioning it either in an external CSS file'}, {'name': 'suyashghuge', 'text': 'so can we directly write <class = \"cities\">?'}, {'name': 'aneesh297', 'text': '<style>\\ndiv.cities {\\n background-color: black;\\n color: white;\\n margin: 20px 0 20px 0;\\n padding: 20px;\\n} \\n</style>'}, {'name': 'sagarb', 'text': \"suyashghuge: div stands for division! It just separates various blocks of html document. It's mainly used to apply different styles/formatting to different blocks.\\n\\nWe'll come to that in a while\"}, {'name': 'aneesh297', 'text': 'No. class is an attribute. Your definition would make it an element.'}, {'name': 'sagarb', 'text': \"suyashghuge: No you can't. `class` is an attribute and not a tag.\"}, {'name': 'rahul', 'text': 'sagarb: ok got it'}, {'name': 'suyashghuge', 'text': ':+1:'}, {'name': 'pratyush82', 'text': 'so will the style tag be within the body tag??'}, {'name': 'aneesh297', 'text': \"Okay. Sagar will cover more about styling soon. I'll finish up the rest now.\"}, {'name': 'manas1998', 'text': 'What is padding?'}, {'name': 'aneesh297', 'text': '*mentioned'}, {'name': 'pratyush82', 'text': ':+1:'}, {'name': 'aneesh297', 'text': 'We can print lists using several different tags.'}, {'name': 'aneesh297', 'text': 'An unordered list starts with the <ul> tag. Each list item starts with the <li> tag.\\nThe list items will be marked with bullets (small black circles) by default:'}, {'name': 'aneesh297', 'text': 'For example:'}, {'name': 'aneesh297', 'text': '<ul>\\n <li>Coffee</li>\\n <li>Tea</li>\\n <li>Milk</li>\\n</ul>'}, {'name': 'aneesh297', 'text': 'here again you use styles. ex: <ul style=\"color:blue;\">'}, {'name': 'aneesh297', 'text': '*you can use'}, {'name': 'aneesh297', 'text': 'You can change the type of bullet by using list-style-type style property'}, {'name': 'aneesh297', 'text': 'An ordered list starts with the <ol> tag. Each list item starts with the <li> tag.\\nThe list items will be marked with numbers by default:'}, {'name': 'aneesh297', 'text': 'Ex:'}, {'name': 'aneesh297', 'text': '<ol>\\n <li>Coffee</li>\\n <li>Tea</li>\\n <li>Milk</li>\\n</ol>'}, {'name': 'aneesh297', 'text': 'The type attribute of the <ol> tag, defines the type of the list item marker:'}, {'name': 'aneesh297', 'text': '<ol type=\"A\"> \\tThe list items will be numbered with uppercase letters'}, {'name': 'aneesh297', 'text': 'Lists can also be nested:'}, {'name': 'aneesh297', 'text': '<ul>\\n <li>Coffee</li>\\n <li>Tea\\n <ul>\\n <li>Black tea</li>\\n <li>Green tea</li>\\n </ul>\\n </li>\\n <li>Milk</li>\\n</ul>'}, {'name': 'aneesh297', 'text': 'Any doubts so far?'}, {'name': 'pratyush82', 'text': 'nope'}, {'name': 'mahim23', 'text': 'No'}, {'name': 'srahulsrihari36', 'text': 'no'}, {'name': 'aneesh297', 'text': 'Okay'}, {'name': 'aneesh297', 'text': 'Moving on to forms.'}, {'name': 'aneesh297', 'text': 'After this sagar will teach you guys about CSS.'}, {'name': 'aneesh297', 'text': 'The HTML <form> element defines a form that is used to collect user input:'}, {'name': 'aneesh297', 'text': 'An HTML form contains form elements.'}, {'name': 'aneesh297', 'text': 'Form elements are different types of input elements, like text fields, checkboxes, radio buttons, submit buttons, and more.'}, {'name': 'aneesh297', 'text': 'The <input> element is the most important form element.'}, {'name': 'aneesh297', 'text': 'The <input> element can be displayed in several ways, depending on the type attribute. Ex: radio-button, text submit'}, {'name': 'aneesh297', 'text': '<input type=\"text\"> defines a one-line input field for text input:'}, {'name': 'aneesh297', 'text': 'So an example form might look like this: \\n<form action=\"/action_page.php\">\\n First name:<br>\\n <input type=\"text\" name=\"firstname\" value=\"Mickey\"><br>\\n Last name:<br>\\n <input type=\"text\" name=\"lastname\" value=\"Mouse\"><br><br>\\n <input type=\"submit\" value=\"Submit\">\\n</form>'}, {'name': 'aneesh297', 'text': 'Where action is the action taken by the browser after clicking submit'}, {'name': 'aneesh297', 'text': 'Sagar will be taking over now.'}, {'name': 'suyashghuge', 'text': '<form action=\"/action_page.php\"> what is this?'}, {'name': 'pratyush82', 'text': 'what is <br>?'}, {'name': 'sagarb', 'text': 'suyashghuge: Okay so when we obtain some data from the user in the form, the data will have to be sent to the backend. So the url to which data is to be sent in mentioned in action'}, {'name': 'sagarb', 'text': 'pratyush82: Line break'}, {'name': 'pratyush82', 'text': ':+1:'}, {'name': 'sagarb', 'text': \"Okay so I'll be taking over now!\"}, {'name': 'sagarb', 'text': \"There are many more HTML tags but we'll won't cover them, We'll use them when needed\"}, {'name': 'sagarb', 'text': 'Now onto CSS'}, {'name': 'sagarb', 'text': 'CSS stands for Cascading style Sheet.'}, {'name': 'sagarb', 'text': 'So as developers, we should always strive to separate different components of our project.'}, {'name': 'sagarb', 'text': 'In developing front end, there are two main things we should cocentrate on :\\n*Content* and *Formatting* of that content.'}, {'name': 'sagarb', 'text': 'We use *html* to *structure* the content of our web page.'}, {'name': 'sagarb', 'text': '*CSS* to *format* them.'}, {'name': 'sagarb', 'text': 'Open a text editor (eg. Notepad) and paste it there!'}, {'name': 'rahul', 'text': 'Style attributes have a separate tag?with the name \"head\"?'}, {'name': 'sagarb', 'text': 'Save the file as `sample.html`. When saving make sure the `file type` is `All files` and not `Text Document`'}, {'name': 'sagarb', 'text': 'Everybody Done?'}, {'name': 'briny_peddler', 'text': 'Yea'}, {'name': 'pratyush82', 'text': 'yes'}, {'name': 'mehnaz18', 'text': 'yeah'}, {'name': 'srahulsrihari36', 'text': 'yes'}, {'name': 'mehakarora', 'text': 'yes'}, {'name': 'pvgupta24', 'text': 'yes'}, {'name': 'sagarb', 'text': 'Now, open the file. It should open in a web browser by defualt.'}, {'name': 'sagarb', 'text': \"I'll tell everything in a while!\"}, {'name': 'sagarb', 'text': 'Is everybody getting this?'}, {'name': 'pratyush82', 'text': 'yes'}, {'name': 'mehnaz18', 'text': 'yes'}, {'name': 'sagarb', 'text': 'Okay fine'}, {'name': 'omkar_p31', 'text': 'yes'}, {'name': 'sagarb', 'text': 'Now, we move on to CSS and see how it can format this web page.'}, {'name': 'srahulsrihari36', 'text': 'yeh fine'}, {'name': 'rahul', 'text': '\"The type attribute of the <ol> tag, defines the type of the list item marker:\\n<ol type=\"A\"> The list items will be numbered with uppercase letters\"...What does this mean?'}, {'name': 'suyashghuge', 'text': 'i didnt understand line 4 and use of id in the code you had sent'}, {'name': 'sagarb', 'text': \"suyashghuge: yes I'll be explaining everything everything in a while\"}, {'name': 'sagarb', 'text': 'All of you downloaded and created CSS file?'}, {'name': 'aneesh297', 'text': \"rahul: Means that your list will be ordered using uppercase letters. That's all. Ex:\\nA. Hi\"}, {'name': 'mahim23', 'text': 'Yup'}, {'name': 'aneesh297', 'text': 'B. Hello\\nC. hey\\nD. howdy'}, {'name': 'pratyush82', 'text': 'yes'}, {'name': 'sagarb', 'text': 'Save the css file in the same folder as the html'}, {'name': 'sagarb', 'text': 'Shall I proceed?'}, {'name': 'briny_peddler', 'text': 'What s hover?'}, {'name': 'rahul', 'text': 'oh ok....so we just need to write the first letter of the whole series...like it was written <ol type=A\" right'}, {'name': 'sagarb', 'text': \"Okay I'll be explaining everything. But for now don't worry about it! Create those two fles\"}, {'name': 'sagarb', 'text': '*files'}, {'name': 'sagarb', 'text': 'Shall I proceed?'}, {'name': 'mahim23', 'text': 'Yes'}, {'name': 'aneesh297', 'text': 'yep'}, {'name': 'pratyush82', 'text': 'yes'}, {'name': 'rahul', 'text': 'okk'}, {'name': 'sagarb', 'text': 'Others??'}, {'name': 'mehnaz18', 'text': 'yeah'}, {'name': 'srahulsrihari36', 'text': 'yeah'}, {'name': 'rahul', 'text': 'Answer please?'}, {'name': 'sagarb', 'text': 'Okay now, open the html file in text editor again!'}, {'name': 'sagarb', 'text': 'Line 4 : \\n`<!-- <link rel=\"stylesheet\" href=\"styles.css\" /> -->`\\n\\n\\nChange line 4 to : \\n`<link rel=\"stylesheet\" href=\"styles.css\" />`\\n\\nComments in HTML are written as `<!-- Some Comment -->`. (I hope you all know what comments are)\\n\\nWhat we are doing here is uncommenting that line. That\\'s all'}, {'name': 'sagarb', 'text': 'Done??'}, {'name': 'pvgupta24', 'text': 'yeah'}, {'name': 'mehnaz18', 'text': 'yes'}, {'name': 'pratyush82', 'text': 'yes'}, {'name': 'mahim23', 'text': 'Yes'}, {'name': 'mahim23', 'text': 'But nothing changed'}, {'name': 'srahulsrihari36', 'text': 'yes'}, {'name': 'sagarb', 'text': 'You have to reload the page!'}, {'name': 'mahim23', 'text': 'No sorry I named the stylesheet as \"style\" instead of \"styles\"'}, {'name': 'mahim23', 'text': 'sagarb: I KNOW'}, {'name': 'sagarb', 'text': 'Okay so all done??'}, {'name': 'srahulsrihari36', 'text': 'yah done'}, {'name': 'mehakarora', 'text': 'Yes'}, {'name': 'pvgupta24', 'text': 'yes'}, {'name': 'anmolh', 'text': 'Yes'}, {'name': 'pratyush82', 'text': 'yes'}, {'name': 'mahim23', 'text': 'Yes'}, {'name': 'sagarb', 'text': 'Okay so this is the power of CSS. It can format a simple HTML page into a good looking page!'}, {'name': 'rahul', 'text': 'aneesh297: So an example form might look like this: \\n<form action=\"/action_page.php\">\\n First name:<br>\\n <input type=\"text\" name=\"firstname\" value=\"Mickey\"><br>\\n Last name:<br>\\n <input type=\"text\" name=\"lastname\" value=\"Mouse\"><br><br>\\n <input type=\"submit\" value=\"Submit\">\\n</form>'}, {'name': 'rahul', 'text': 'I didnt get it'}, {'name': 'sagarb', 'text': \"Now let me tell you exactly what CSS is, how we use it and I'll give a brief run through the code snippet I posted!\"}, {'name': 'aneesh297', 'text': 'what did you not get?'}, {'name': 'rahul', 'text': '<br> what is this? and in the last second line why type=submit?'}, {'name': 'sagarb', 'text': 'The basic syntax of CSS include : \\n1. Selector\\n2. property\\n3. Value'}, {'name': 'rahul', 'text': 'it should be type=text only right?'}, {'name': 'aneesh297', 'text': '<br> is a line break. Think of it as a simple ENTER'}, {'name': 'sagarb', 'text': '*Selector* : Selects HTML elements to be formatted.'}, {'name': 'rahul', 'text': 'its a tag or what?'}, {'name': 'sagarb', 'text': '*Property* : The properties of the selected element to be altered'}, {'name': 'aneesh297', 'text': 'Yep'}, {'name': 'sagarb', 'text': '*Value* : The value of the property'}, {'name': 'aneesh297', 'text': 'Anything enclosed within a <> is a tage'}, {'name': 'aneesh297', 'text': '*tag'}, {'name': 'aneesh297', 'text': 'A tag can have several attributes though.'}, {'name': 'sagarb', 'text': 'Example : \\n```\\nbody{\\n font-family: tahoma;\\n color: #FFFFFF;\\n background: #0d1521;\\n}\\n```'}, {'name': 'rahul', 'text': 'so the body should end with </br> right'}, {'name': 'sagarb', 'text': '(Taken from the CSS file I posted)'}, {'name': 'sagarb', 'text': 'Here, \\n`body` is *selector*\\n`font-family`, `color`, `background` are *properties*\\n`tahoma`, `#FFFFFF`, `#0d1521` are *values*'}, {'name': 'aneesh297', 'text': 'The <br> tag is an empty tag which means that it has no end tag. ie. No need for </br>'}, {'name': 'rahul', 'text': 'oh ok....and the last second line..'}, {'name': 'sagarb', 'text': 'That is in this part of CSS, we are formatting the body element of HTML document.'}, {'name': 'aneesh297', 'text': 'Submit?'}, {'name': 'aneesh297', 'text': 'what about it?'}, {'name': 'sagarb', 'text': \"First we'll learn about the syntax to write *selectors*\"}, {'name': 'rahul', 'text': 'that \"type=submit\" thing....it should be type=text only right'}, {'name': 'rahul', 'text': 'because \"submit\" is \"text\" only right'}, {'name': 'sagarb', 'text': '```'}, {'name': 'sagarb', 'text': '#choice{\\n position: relative;\\n transform: translateY(30%);\\n width: 95%;\\n background: #090d13;\\n margin: 0 auto;\\n padding: 20px;\\n box-sizing: border-box;\\n}'}, {'name': 'sagarb', 'text': '```'}, {'name': 'aneesh297', 'text': 'No. It creates a button called submit. Not a form field.'}, {'name': 'aneesh297', 'text': '<https://www.w3schools.com/html/tryit.asp?filename=tryhtml_form_submit>'}, {'name': 'aneesh297', 'text': 'The source code is in this link. You can visualize it better now'}, {'name': 'sagarb', 'text': \"See the above code. We've used `#choice` as selector. Now what does `#` mean?\"}, {'name': 'sagarb', 'text': 'Here `#choice` selects all elements whose `id` attribute has value `choice`'}, {'name': 'aneesh297', 'text': 'I think sagar must have answered this the css intro'}, {'name': 'rahul', 'text': 'ok got it....and style attributes are written in tag named \"head\"?'}, {'name': 'sagarb', 'text': \"If you go back to out HTML document, you'll notice the `div` element has `id` set to `choice`. So that element will be selected and formatted\"}, {'name': 'aneesh297', 'text': 'yep'}, {'name': 'sagarb', 'text': 'Similary, you can use `.` to select an element with a specific `class` attribute.\\n\\nExample : \\n```\\n.hello{\\n}\\n```\\n\\nWill select all elements with attribute class'}, {'name': 'rahul', 'text': 'ok'}, {'name': 'aneesh297', 'text': '*in the css intro'}, {'name': 'rahul', 'text': 'uhh.....Can you please slow down the pace of the course.....its taking time to read and understand everything'}, {'name': 'sagarb', 'text': \"Now in that CSS file you've downloaded, you'll find some special selectors.\\n```\\n#lichoice:hover{\\n text-align: center;\\n background: rgb(0,0,0);\\n}\\n```\\nSo here, `#lichoice` selects all elements with `id` set to `lichoice`. What does `:hover` do?\"}, {'name': 'sagarb', 'text': '`#lichoice:hover` is called a *psudo-selector*. It formats elements when they are in a particular _state_.'}, {'name': 'suyashghuge', 'text': 'but li already comes under the id choice..so shouldnt it follow the #choice properties?'}, {'name': 'sagarb', 'text': \"Example : In the HTML doc I sent, you'll notice the list elements change color when you move your mouse over them.\\n\\nThis is because, we have specified different formatting for `hover state` (ie. when mouse is over that element) and normal state(ie. when mouse is not over the element)\"}, {'name': 'sagarb', 'text': \"suyashghuge: Yes it is formatted using #choice properties also. In addition we are specifying some properties in #lichoice.\\n\\nIt's like inheritance. Child elements inherit properties of parents plus they have their own properties\"}, {'name': 'mahim23', 'text': \"suyashghuge: We're overriding those properties\"}, {'name': 'suyashghuge', 'text': ':+1:'}, {'name': 'sagarb', 'text': 'Yes overriding happens if there is a conflict!'}, {'name': 'sagarb', 'text': 'Is everyone with me now?'}, {'name': 'suyashghuge', 'text': ':+1:'}, {'name': 'pratyush82', 'text': 'yes'}, {'name': 'srahulsrihari36', 'text': 'yes'}, {'name': 'abhishek', 'text': 'yes'}, {'name': 'mehakarora', 'text': 'yes'}, {'name': 'sagarb', 'text': \"There are many different types of selectors. We'll be sending links. You can read about them online. After this I hope atleast selectors used in that document are clear\"}, {'name': 'sagarb', 'text': 'Okay now moving onto *properties* and *values*'}, {'name': 'sagarb', 'text': \"*Properties* and *Values* are pretty self explanatory and need no explanation I think! Things like `color` is used to set color property is pretty obvious. Also each property can take many types of values.\\n\\nThe thing is you can always search the Internet for a property and the type of values it takes. So you not memorise them. Remember just the basic ones.\\n\\nBut selectors are more important. You have to write selectors so they suit your custom needs. That is, so they are in accordance with the HTML file you've written. So you should know different techniques used to select elements\"}, {'name': 'sagarb', 'text': \"I'll just go through some basic properties.\"}, {'name': 'sagarb', 'text': '`color` : color takes different types of values. I mostly use hexadecimal.\\nFor example black can be assigned as follows : \\n\\n`color : black;`\\n`color : #000000;`'}, {'name': 'sagarb', 'text': '`color : rgb(0,0,0);`'}, {'name': 'sagarb', 'text': \"I use hexadecimal because you can easily get hexadecimal values of any color using Google's color picker.\\n\\n<https://www.google.co.in/search?q=%23ffffff&oq=%23ffffff&gs_l=serp.3..35i39k1.2341.2737.0.3362.3.3.0.0.0.0.129.376.0j3.3.0....0...1.1.64.serp..0.3.376...0.M1iWv9_Zzjc>\"}, {'name': 'sagarb', 'text': 'Some other common properties are `margin` and `padding`. Learn about the differences between them. Both are used to space different elements properly on screen'}, {'name': 'sagarb', 'text': \"It's already 9:30\"}, {'name': 'sagarb', 'text': 'So this is it for today! If you have any doubts you can always PM any of us!'}, {'name': 'sagarb', 'text': 'Is everything clear today?'}, {'name': 'mahim23', 'text': 'Yup'}, {'name': 'omkar_p31', 'text': 'yes'}, {'name': 'pratyush82', 'text': 'yes'}, {'name': 'briny_peddler', 'text': 'Yea'}, {'name': 'mishal23', 'text': 'Yes..thanks for the session:grinning:'}, {'name': 'mehakarora', 'text': 'Yes'}, {'name': 'sagarb', 'text': 'Thank you for attending the session people! Web development gets very interesting and intense when we get to the backend part! Make sure you attend all those sessions with the same enthusiasm'}, {'name': 'sagarb', 'text': 'rahul shift to doubts channel'}, {'name': 'viggi', 'text': 'srahulsrihari36: there is something called metadata. It says information about the webpage. Notice the tab name once you open the web page:relaxed:'}, {'name': 'viggi', 'text': 'aneesh297: one more thing: such alt tags are supposed to kinda describe the image, as the search engine considers alt tags too, to get search results.'}, {'name': 'viggi', 'text': '<@U5866PWNB> padding:20px would give 20px of blank space inside the border in all 4 sides. '}, {'name': 'manas1998', 'text': 'Ok'}, {'name': 'aneesh297', 'text': 'Guys join the <#C5CM4ACHX|bookshelf> channel. We will be sharing resources there. '}], [{'name': 'aneesh297', 'text': \"btw while this is somewhat boring, it's still essential. Wait till Javascript. That's when the fun begins xD\"}, {'name': 'aneesh297', 'text': 'hey, do you guys think the pace is a bit too fast?'}, {'name': 'mahim23', 'text': 'No its fine'}, {'name': 'mahim23', 'text': '(My opinion)'}, {'name': 'pratyush82', 'text': 'no...it seems perfect'}]]\n" ] } ], "source": [ "print(final_log)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.0" } }, "nbformat": 4, "nbformat_minor": 2 }

mit

luwei0917/awsemmd_script

notebook/Optimization/real_contact_cbd_cbd_exclude_volume_term_parameters.ipynb

1

5001633

mit

jayoshih/ricecooker

docs/examples/languages.ipynb

1

19046

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Languages\n", "\n", "This tutorial will explain how to set the `language` property for various nodes and file objects when using the `ricecooker` framework." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Explore language objects and language codes\n", "\n", "First we must import the `le-utils` pacakge. The languages supported by Kolibri and the Content Curation Server are provided in `le_utils.constants.languages`.\n" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Language(native_name='English', primary_code='en', subcode=None, name='English', ka_name=None)" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from le_utils.constants import languages\n", "\n", "\n", "# can lookup language using language code\n", "language_obj = languages.getlang('en')\n", "language_obj" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Language(native_name='English', primary_code='en', subcode=None, name='English', ka_name=None)" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# can lookup language using language name (the new le_utils version has not shipped yet)\n", "language_obj = languages.getlang_by_name('English')\n", "language_obj" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'en'" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# all `language` attributed (channel, nodes, and files) need to use language code\n", "language_obj.code" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Language(native_name='Français, langue française', primary_code='fr', subcode=None, name='French', ka_name='francais')\n", "fr\n" ] } ], "source": [ "from le_utils.constants.languages import getlang_by_native_name\n", "\n", "lang_obj = getlang_by_native_name('français')\n", "print(lang_obj)\n", "print(lang_obj.code)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The above language code is an internal representaiton that uses two-letter codes, and sometimes has locale information, e.g., `pt-BR` for Brazilian Portuiguese. Sometimes the internal code representaiton for a language is the three-letter vesion, e.g., `zul` for Zulu." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Create chef class\n", "\n", "We now create subclass of `ricecooker.chefs.SushiChef` and defined its `get_channel` and `construct_channel` methods.\n", "\n", "For the purpose of this example, we'll create three topic nodes in different languages that contain one document in each." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from ricecooker.chefs import SushiChef\n", "from ricecooker.classes.nodes import ChannelNode, TopicNode, DocumentNode\n", "from ricecooker.classes.files import DocumentFile\n", "from le_utils.constants import licenses\n", "\n", "from le_utils.constants.languages import getlang\n", "\n", "\n", "\n", "class MultipleLanguagesChef(SushiChef):\n", " \"\"\"\n", " A sushi chef that creates a channel with content in EN, FR, and SP.\n", " \"\"\"\n", " channel_info = {\n", " 'CHANNEL_TITLE': 'Languages test channel',\n", " 'CHANNEL_SOURCE_DOMAIN': '<yourdomain.org>', # where you got the content\n", " 'CHANNEL_SOURCE_ID': '<unique id for channel>', # channel's unique id CHANGE ME!!\n", " 'CHANNEL_LANGUAGE': getlang('mul').code, # set global language for channel\n", " 'CHANNEL_DESCRIPTION': 'This channel contains nodes in multiple languages',\n", " 'CHANNEL_THUMBNAIL': None, # (optional)\n", " }\n", "\n", " def construct_channel(self, **kwargs):\n", " # create channel\n", " channel = self.get_channel(**kwargs)\n", "\n", " # create the English topic, add a DocumentNode to it\n", " topic = TopicNode(\n", " source_id=\"<en_topic_id>\",\n", " title=\"New Topic in English\",\n", " language=getlang('en').code,\n", " )\n", " doc_node = DocumentNode(\n", " source_id=\"<en_doc_id>\",\n", " title='Some doc in English',\n", " description='This is a sample document node in English',\n", " files=[DocumentFile(path='samplefiles/documents/doc_EN.pdf')],\n", " license=licenses.PUBLIC_DOMAIN,\n", " language=getlang('en').code,\n", " )\n", " topic.add_child(doc_node)\n", " channel.add_child(topic)\n", "\n", " # create the Spanish topic, add a DocumentNode to it\n", " topic = TopicNode(\n", " source_id=\"<es_topic_id>\",\n", " title=\"Topic in Spanish\",\n", " language=getlang('es-MX').code,\n", " )\n", " doc_node = DocumentNode(\n", " source_id=\"<es_doc_id>\",\n", " title='Some doc in Spanish',\n", " description='This is a sample document node in Spanish',\n", " files=[DocumentFile(path='samplefiles/documents/doc_ES.pdf')],\n", " license=licenses.PUBLIC_DOMAIN,\n", " language=getlang('es-MX').code,\n", " )\n", " topic.add_child(doc_node)\n", " channel.add_child(topic)\n", "\n", " # create the French topic, add a DocumentNode to it\n", " topic = TopicNode(\n", " source_id=\"<fr_topic_id>\",\n", " title=\"Topic in French\",\n", " language=languages.getlang('fr').code,\n", " )\n", " doc_node = DocumentNode(\n", " source_id=\"<fr_doc_id>\",\n", " title='Some doc in French',\n", " description='This is a sample document node in French',\n", " files=[DocumentFile(path='samplefiles/documents/doc_FR.pdf')],\n", " license=licenses.PUBLIC_DOMAIN,\n", " language=getlang('fr').code,\n", " )\n", " topic.add_child(doc_node)\n", " channel.add_child(topic)\n", "\n", " return channel\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Run of you chef by creating an instance of the chef class and calling it's `run` method:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "\u001b[32mINFO \u001b[0m \u001b[34mIn SushiChef.run method. args={'command': 'dryrun', 'reset': True, 'verbose': True, 'token': 'YOURTO...'} options={}\u001b[0m\n", "\u001b[32mINFO \u001b[0m \u001b[34m\n", "\n", "***** Starting channel build process *****\n", "\n", "\u001b[0m\n", "\u001b[32mINFO \u001b[0m \u001b[34mCalling construct_channel... \u001b[0m\n", "\u001b[32mINFO \u001b[0m \u001b[34m Setting up initial channel structure... \u001b[0m\n", "\u001b[32mINFO \u001b[0m \u001b[34m Validating channel structure...\u001b[0m\n", "\u001b[32mINFO \u001b[0m \u001b[34m Languages test channel (ChannelNode): 6 descendants\u001b[0m\n", "\u001b[32mINFO \u001b[0m \u001b[34m New Topic in English (TopicNode): 1 descendant\u001b[0m\n", "\u001b[32mINFO \u001b[0m \u001b[34m Some doc in English (DocumentNode): 1 file\u001b[0m\n", "\u001b[32mINFO \u001b[0m \u001b[34m Topic in Spanish (TopicNode): 1 descendant\u001b[0m\n", "\u001b[32mINFO \u001b[0m \u001b[34m Some doc in Spanish (DocumentNode): 1 file\u001b[0m\n", "\u001b[32mINFO \u001b[0m \u001b[34m Topic in French (TopicNode): 1 descendant\u001b[0m\n", "\u001b[32mINFO \u001b[0m \u001b[34m Some doc in French (DocumentNode): 1 file\u001b[0m\n", "\u001b[32mINFO \u001b[0m \u001b[34m Tree is valid\n", "\u001b[0m\n", "\u001b[32mINFO \u001b[0m \u001b[34mDownloading files...\u001b[0m\n", "\u001b[32mINFO \u001b[0m \u001b[34mProcessing content...\u001b[0m\n", "\u001b[32mINFO \u001b[0m \u001b[34m\t--- Downloaded e8b1fe37ce3da500241b4af4e018a2d7.pdf\u001b[0m\n", "\u001b[32mINFO \u001b[0m \u001b[34m\t--- Downloaded cef22cce0e1d3ba08861fc97476b8ccf.pdf\u001b[0m\n", "\u001b[32mINFO \u001b[0m \u001b[34m\t--- Downloaded 6c8730e3e2554e6eac0ad79304bbcc68.pdf\u001b[0m\n", "\u001b[32mINFO \u001b[0m \u001b[34m All files were successfully downloaded\u001b[0m\n", "\u001b[32mINFO \u001b[0m \u001b[34mCommand is dryrun so we are not uploading chanel.\u001b[0m\n" ] } ], "source": [ "mychef = MultipleLanguagesChef()\n", "args = {\n", " 'command': 'dryrun', # use 'uploadchannel' for real run\n", " 'verbose': True,\n", " 'token': 'YOURTOKENHERE9139139f3a23232'\n", "}\n", "options = {}\n", "mychef.run(args, options)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Congratulations, you put three languages on the internet!" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Example 2: YouTube video with subtitles in multiple languages\n", "\n", "You can use the library `youtube_dl` to get lots of useful metadata about videos and playlists, including the which language subtitle are vailable for a video.\n" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[youtube] FN12ty5ztAs: Downloading webpage\n", "[youtube] FN12ty5ztAs: Downloading MPD manifest\n", "dict_keys(['en', 'fr', 'zu'])\n" ] } ], "source": [ "import youtube_dl\n", "\n", "ydl = youtube_dl.YoutubeDL({\n", " #'quiet': True,\n", " 'no_warnings': True,\n", " 'writesubtitles': True,\n", " 'allsubtitles': True,\n", "})\n", "\n", "\n", "youtube_id = 'FN12ty5ztAs'\n", "\n", "info = ydl.extract_info(youtube_id, download=False)\n", "subtitle_languages = info[\"subtitles\"].keys()\n", "\n", "print(subtitle_languages)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Full sushi chef example\n", "\n", "The `YoutubeVideoWithSubtitlesSushiChef` class below shows how to create a channel with youtube video and upload subtitles files with all available languages." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "from ricecooker.chefs import SushiChef\n", "from ricecooker.classes import licenses\n", "from ricecooker.classes.nodes import ChannelNode, TopicNode, VideoNode\n", "from ricecooker.classes.files import YouTubeVideoFile, YouTubeSubtitleFile\n", "from ricecooker.classes.files import is_youtube_subtitle_file_supported_language\n", "\n", "\n", "import youtube_dl\n", "ydl = youtube_dl.YoutubeDL({\n", " 'quiet': True,\n", " 'no_warnings': True,\n", " 'writesubtitles': True,\n", " 'allsubtitles': True,\n", "})\n", "\n", "\n", "# Define the license object with necessary info\n", "TE_LICENSE = licenses.SpecialPermissionsLicense(\n", " description='Permission granted by Touchable Earth to distribute through Kolibri.',\n", " copyright_holder='Touchable Earth Foundation (New Zealand)'\n", ")\n", "\n", "\n", "class YoutubeVideoWithSubtitlesSushiChef(SushiChef):\n", " \"\"\"\n", " A sushi chef that creates a channel with content in EN, FR, and SP.\n", " \"\"\"\n", " channel_info = {\n", " 'CHANNEL_SOURCE_DOMAIN': '<yourdomain.org>', # where you got the content\n", " 'CHANNEL_SOURCE_ID': '<unique id for channel>', # channel's unique id CHANGE ME!!\n", " 'CHANNEL_TITLE': 'Youtube subtitles downloading chef',\n", " 'CHANNEL_LANGUAGE': 'en',\n", " 'CHANNEL_THUMBNAIL': 'https://edoc.coe.int/4115/postcard-47-flags.jpg',\n", " 'CHANNEL_DESCRIPTION': 'This is a test channel to make sure youtube subtitle languages lookup works'\n", " }\n", "\n", " def construct_channel(self, **kwargs):\n", " # create channel\n", " channel = self.get_channel(**kwargs)\n", "\n", " # get all subtitles available for a sample video\n", " youtube_id ='FN12ty5ztAs'\n", " info = ydl.extract_info(youtube_id, download=False)\n", " subtitle_languages = info[\"subtitles\"].keys()\n", " print('Found subtitle_languages = ', subtitle_languages)\n", " \n", " # create video node\n", " video_node = VideoNode(\n", " source_id=youtube_id,\n", " title='Youtube video',\n", " license=TE_LICENSE,\n", " derive_thumbnail=True,\n", " files=[YouTubeVideoFile(youtube_id=youtube_id)],\n", " )\n", "\n", " # add subtitles in whichever languages are available.\n", " for lang_code in subtitle_languages:\n", " if is_youtube_subtitle_file_supported_language(lang_code):\n", " video_node.add_file(\n", " YouTubeSubtitleFile(\n", " youtube_id=youtube_id,\n", " language=lang_code\n", " )\n", " )\n", " else:\n", " print('Unsupported subtitle language code:', lang_code)\n", "\n", " channel.add_child(video_node)\n", "\n", " return channel\n", "\n", " " ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "\u001b[32mINFO \u001b[0m \u001b[34mIn SushiChef.run method. args={'command': 'dryrun', 'reset': True, 'verbose': True, 'token': 'YOURTO...'} options={}\u001b[0m\n", "\u001b[32mINFO \u001b[0m \u001b[34m\n", "\n", "***** Starting channel build process *****\n", "\n", "\u001b[0m\n", "\u001b[32mINFO \u001b[0m \u001b[34mCalling construct_channel... \u001b[0m\n", "\u001b[32mINFO \u001b[0m \u001b[34m Setting up initial channel structure... \u001b[0m\n", "\u001b[32mINFO \u001b[0m \u001b[34m Validating channel structure...\u001b[0m\n", "\u001b[32mINFO \u001b[0m \u001b[34m Youtube subtitles downloading chef (ChannelNode): 1 descendant\u001b[0m\n", "\u001b[32mINFO \u001b[0m \u001b[34m Youtube video (VideoNode): 4 files\u001b[0m\n", "\u001b[32mINFO \u001b[0m \u001b[34m Tree is valid\n", "\u001b[0m\n", "\u001b[32mINFO \u001b[0m \u001b[34mDownloading files...\u001b[0m\n", "\u001b[32mINFO \u001b[0m \u001b[34mProcessing content...\u001b[0m\n", "\u001b[32mINFO \u001b[0m \u001b[34m\t--- Downloaded (YouTube) a144a6af6977247684d2a3977dc6f841.mp4\u001b[0m\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Found subtitle_languages = dict_keys(['en', 'fr', 'zu'])\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\u001b[32mINFO \u001b[0m \u001b[34m\t--- Downloaded 5f22a71e53271eb2d2abe013457a625d.jpg\u001b[0m\n", "\u001b[31mERROR \u001b[0m \u001b[34m 3 file(s) have failed to download\u001b[0m\n", "\u001b[33mWARNING \u001b[0m \u001b[34m\tVideo FN12ty5ztAs: http://www.youtube.com/watch?v=FN12ty5ztAs \n", "\t Subtitle with langauge en is not available for http://www.youtube.com/watch?v=FN12ty5ztAs\u001b[0m\n", "\u001b[33mWARNING \u001b[0m \u001b[34m\tVideo FN12ty5ztAs: http://www.youtube.com/watch?v=FN12ty5ztAs \n", "\t Subtitle with langauge fr is not available for http://www.youtube.com/watch?v=FN12ty5ztAs\u001b[0m\n", "\u001b[33mWARNING \u001b[0m \u001b[34m\tVideo FN12ty5ztAs: http://www.youtube.com/watch?v=FN12ty5ztAs \n", "\t Subtitle with langauge zul is not available for http://www.youtube.com/watch?v=FN12ty5ztAs\u001b[0m\n", "\u001b[32mINFO \u001b[0m \u001b[34mCommand is dryrun so we are not uploading chanel.\u001b[0m\n" ] } ], "source": [ "chef = YoutubeVideoWithSubtitlesSushiChef()\n", "args = {\n", " 'command': 'dryrun', # use 'uploadchannel' for real run\n", " 'verbose': True,\n", " 'token': 'YOURTOKENHERE9139139f3a23232'\n", "}\n", "options = {}\n", "chef.run(args, options)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.9" } }, "nbformat": 4, "nbformat_minor": 2 }

mit

ooovector/qtlab_replacement

Mixer-calibration-with-noise.ipynb

1

107092

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "ExecuteTime": { "end_time": "2019-04-25T12:45:59.032739Z", "start_time": "2019-04-25T12:45:59.025757Z" } }, "outputs": [], "source": [ "#%matplotlib qt" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "ExecuteTime": { "end_time": "2019-04-25T12:45:59.777225Z", "start_time": "2019-04-25T12:45:59.774204Z" } }, "outputs": [], "source": [ "import numpy as np\n", "#from qsweepy.instrument_drivers import AWG500, Signal_Hound_SA, Labbrick\n", "#from qsweepy.instrument_drivers.AWG500 import AWG500\n", "#from qsweepy.instrument_drivers.Signal_Hound_SA import Signal_Hound_SA, get_signal_hounds\n", "#from qsweepy.instrument_drivers.Labbrick import Labbrick\n" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "ExecuteTime": { "end_time": "2019-04-25T12:46:00.651203Z", "start_time": "2019-04-25T12:46:00.397837Z" } }, "outputs": [], "source": [ "%matplotlib notebook" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "ExecuteTime": { "end_time": "2019-04-25T12:46:01.744377Z", "start_time": "2019-04-25T12:46:01.213757Z" } }, "outputs": [], "source": [ "from qsweepy.instruments import *\n", "#import qsweepy.awg_iq_multi\n", "from scipy.signal import resample\n", "from matplotlib import pyplot as plt" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "ExecuteTime": { "end_time": "2019-04-25T12:46:02.439332Z", "start_time": "2019-04-25T12:46:02.430360Z" } }, "outputs": [], "source": [ "class awg_iq:\n", " def __init__(self, awg, channel_i, channel_q):\n", " self.awg = awg\n", " self.channel_i = channel_i\n", " self.channel_q = channel_q\n", " def get_nop(self):\n", " return self.awg.get_nop()\n", " def set_nop(self, nop):\n", " self.awg.set_nop(nop)\n", " def set_waveform(self, waveform):\n", " self.awg.set_waveform(np.real(waveform), channel= self.channel_i)\n", " self.awg.set_waveform(np.imag(waveform), channel= self.channel_q)\n", " def get_clock(self):\n", " return self.awg.get_clock()" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "ExecuteTime": { "end_time": "2019-04-25T12:46:02.803458Z", "start_time": "2019-04-25T12:46:02.798450Z" } }, "outputs": [], "source": [ "def dft(waveforms):\n", " return np.fft.fftshift(np.fft.fft(waveforms, axis=1, norm='ortho'), axis=1)\n", "def ift(psds):\n", " return np.fft.ifft(np.fft.ifftshift(psd, axis=1), axis=1, norm='ortho')" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "ExecuteTime": { "end_time": "2019-04-25T12:46:03.504629Z", "start_time": "2019-04-25T12:46:03.453685Z" } }, "outputs": [], "source": [ "class iq_calibration_measure:\n", " def __init__(self, \n", " awg_iq,\n", " sa, \n", " lo,\n", " video_bw = 'keep',\n", " repetition_rate = 'keep', \n", " res_bw = 'keep', \n", " algorithmic_resolution = 'keep',\n", " random_waveforms=5,\n", " low_amplitude=0.05):\n", " '''Collect data for iq mixer calibration with random waveforms.\n", " awg_iq: class with set_waveform and get_nop/set_nop methods\n", " sa: spectrum analyzer with set_bandwidth(), set_xlim() and measure() methods\n", " lo: local oscillator with get_frequency() method\n", " measurement_resolution_bw: resolution bandwidth as installed on sa.\n", " repetition_rate: repetition rate of the AWG\n", " random_waveforms: number of different random segments for algorithm. \n", " low_amplitude: waveform amplitude multiplier to make sure the linear regime is working\n", "\n", " A key issue for this method is frequency resolution. \n", " The parameters should be, at least:\n", " 4*repetition_rate<2*measurement_resolution_bw<algorithmic_resolution\n", " algorithmic_resolution is the effective frequency resolution of the reconstruction algorithms.\n", " '''\n", " self.awg_iq = awg_iq\n", " self.sa = sa\n", " self.lo = lo\n", " \n", " self.discretization_frequency = self.awg_iq.get_clock()\n", " self.lo_frequency = lo.get_frequency()\n", " \n", " if video_bw == 'keep': \n", " self.video_bw = self.sa.get_video_bw()\n", " else: \n", " self.video_bw = video_bw\n", " self.sa.set_video_bw(self.video_bw)\n", " \n", " if repetition_rate == 'keep': \n", " self.awg_nop = self.awg_iq.get_nop()\n", " else: \n", " self.awg_nop = int(self.discretization_frequency/repetition_rate)\n", " self.awg_iq.set_nop(self.awg_nop)\n", " \n", " if res_bw == 'keep': \n", " self.res_bw = self.sa.get_res_bw()\n", " else: \n", " self.res_bw = res_bw\n", " self.sa.set_res_bw(self.res_bw)\n", " self.low_amplitude = low_amplitude\n", " self.sa.set_video_bw(self.video_bw)\n", " \n", " # following parameters must be integer\n", " self.random_waveform_number = random_waveforms\n", " self.segment_length = int(self.discretization_frequency/algorithmic_resolution)\n", " self.half_physical_padding = int((self.awg_nop-self.segment_length)/2)\n", " self.half_simulation_padding = int(((self.discretization_frequency/self.res_bw)-self.segment_length)/2)\n", " \n", " self.frequency_step = self.discretization_frequency/self.segment_length\n", " if hasattr(sa, 'set_nop'):\n", " self.sa.set_nop(self.segment_length)\n", " self.sa.set_span(self.discretization_frequency-self.frequency_step)\n", " self.sa.set_centerfreq(self.lo_frequency-self.frequency_step/2)\n", " \n", " def generate_random_waveforms(self, seed=42):\n", " np.random.seed(seed)\n", " self.waveforms = (np.random.rand(self.random_waveform_number, self.segment_length)*2-1)*self.low_amplitude\n", " \n", " def measure(self):\n", " self.psds = np.zeros((random_waveform_number, self.segment_length))\n", " for random_waveform_id in range(self.waveforms.shape[0]):\n", " padded_waveform = np.zeros((self.half_physical_padding*2+self.segment_length), dtype=np.complex)\n", " padded_waveform[self.half_physical_padding:-self.half_physical_padding] = self.waveforms[random_waveform_id,:]\n", " self.awg_iq.set_waveform(padded_waveform)\n", " psd = sa.measure()['Power']\n", " if not hasattr(sa, 'set_nop'):\n", " psd = resample(psd, self.segment_length)\n", " self.psds[random_waveform_id, :] = psd" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 8, "metadata": { "ExecuteTime": { "end_time": "2019-04-25T12:46:06.554961Z", "start_time": "2019-04-25T12:46:04.510156Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "opening device\n", "device open\n", "Virtex is programmed? False\n" ] } ], "source": [ "awg = AWG500('awg', address=2, ver=2)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "ExecuteTime": { "end_time": "2019-04-25T12:46:19.242880Z", "start_time": "2019-04-25T12:46:07.206252Z" }, "collapsed": true }, "outputs": [], "source": [ "sa = Signal_Hound_SA('sa', serial=61660066)\n", "sa.set_ref(-30)\n", "sa.set_detector('rms')" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "ExecuteTime": { "end_time": "2019-04-25T12:46:20.556968Z", "start_time": "2019-04-25T12:46:20.492573Z" }, "collapsed": true }, "outputs": [], "source": [ "lo = Labbrick('lo', serial=15248)\n" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "ExecuteTime": { "end_time": "2019-04-25T12:46:20.757928Z", "start_time": "2019-04-25T12:46:20.753964Z" }, "collapsed": true }, "outputs": [], "source": [ "awg_iq_inst = awg_iq(awg=awg, channel_i=1, channel_q=2)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2019-04-25T12:22:50.571508Z", "start_time": "2019-04-25T12:22:50.564528Z" } }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 30, "metadata": { "ExecuteTime": { "end_time": "2019-04-25T12:48:27.301796Z", "start_time": "2019-04-25T12:48:27.295813Z" }, "collapsed": true }, "outputs": [], "source": [ "repetition_rate = 500e6/32576#100e3\n", "#algorithmic_resolution = e6\n", "video_bw = 10e3\n", "res_bw = 0.25e6\n", "algorithmic_resolution = 2.0e6\n", "random_waveform_number = 5\n", "low_amplitude = 0.4" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "ExecuteTime": { "end_time": "2019-04-25T12:48:29.588254Z", "start_time": "2019-04-25T12:48:28.742537Z" }, "collapsed": true }, "outputs": [], "source": [ "iq_calibration_measure_inst = iq_calibration_measure(awg_iq=awg_iq_inst, \n", " sa=sa, \n", " lo=lo, \n", " video_bw=video_bw, \n", " repetition_rate=repetition_rate,\n", " res_bw=res_bw,\n", " algorithmic_resolution=algorithmic_resolution,\n", " random_waveforms=5,\n", " low_amplitude=0.05)" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "ExecuteTime": { "end_time": "2019-04-25T12:48:31.116332Z", "start_time": "2019-04-25T12:48:30.741544Z" } }, "outputs": [ { "data": { "text/plain": [ "(497600224.91520005, 3998901807.5028005)" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sa.get_span(), sa.get_centerfreq()" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "ExecuteTime": { "end_time": "2019-04-25T12:48:33.187980Z", "start_time": "2019-04-25T12:48:33.181997Z" } }, "outputs": [ { "data": { "text/plain": [ "250000.0" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sa.get_res_bw()" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "ExecuteTime": { "end_time": "2019-04-25T12:48:36.906941Z", "start_time": "2019-04-25T12:48:36.901960Z" } }, "outputs": [], "source": [ "iq_calibration_measure_inst.generate_random_waveforms()" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "ExecuteTime": { "end_time": "2019-04-25T12:49:02.390065Z", "start_time": "2019-04-25T12:48:38.894406Z" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\scipy\\signal\\signaltools.py:2223: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n", " Y[sl] = X[sl]\n", "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\scipy\\signal\\signaltools.py:2225: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n", " Y[sl] = X[sl]\n", "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\scipy\\signal\\signaltools.py:2230: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n", " Y[sl] += X[sl] # add the component of X at N/2\n" ] } ], "source": [ "iq_calibration_measure_inst.measure()" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "ExecuteTime": { "end_time": "2019-04-25T12:49:27.469831Z", "start_time": "2019-04-25T12:49:22.936825Z" } }, "outputs": [ { "data": { "text/plain": [ "array([3.73062124e-09, 3.59986837e-09, 3.35913586e-09, ...,\n", " 3.32942614e-09, 3.15345155e-09, 3.11926394e-09])" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ "power = sa.measure()['Power']\n", "power" ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "ExecuteTime": { "end_time": "2019-04-25T12:50:21.297035Z", "start_time": "2019-04-25T12:50:20.967781Z" }, "collapsed": true }, "outputs": [], "source": [ "f= sa.get_freqpoints()" ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "ExecuteTime": { "end_time": "2019-04-25T12:50:27.388424Z", "start_time": "2019-04-25T12:50:27.113154Z" } }, "outputs": [ { "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x20b43548668>]" ] }, "execution_count": 40, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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Egp45jdu3PLyj9nUBnEoaZ0J2TbwPANCpREgAAIBTwBM7D8aND21r6zU+fffm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kX60yM2lN15Zfx0xxDeXbIHgOcu6MrPWw4ysU8s398ykBuHJXD7qCQu6BlDgI8nI1OiOKNbK4Yk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\niIiIiIiInFT/D1QNCkMr9waFAAAAAElFTkSuQmCC\n", "text/plain": [ "<Figure size 2304x576 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(32,8))\n", "plt.plot(f, np.log10(power))" ] }, { "cell_type": "code", "execution_count": 48, "metadata": { "ExecuteTime": { "end_time": "2019-04-25T12:38:26.408636Z", "start_time": "2019-04-25T12:38:26.257018Z" } }, "outputs": [ { "data": { "image/png": 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ihbSr6sUW1XYC72qaXwV8NZWvmlb+fJpu266kNwOfAi6OiJdm67+ZmR1fWXdt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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#\n", "plt.plot(iq_calibration_measure_inst.psds.T)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "ExecuteTime": { "end_time": "2019-04-24T12:01:13.930638Z", "start_time": "2019-04-24T12:01:13.923657Z" }, "collapsed": true }, "outputs": [], "source": [ "\n", "# following numbers ust be integer\n", "#segment_length = int(discretization_frequency/algorithmic_resolution)\n", "#half_physical_padding = int(((discretization_frequency/measurement_resolution_bw*physical_overhead)-segment_length)/2)\n", "#half_simulation_padding = int(((discretization_frequency/measurement_resolution_bw)-segment_length)/2)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "reference_frequency = lo.get_frequency()" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "ExecuteTime": { "end_time": "2019-04-22T12:31:11.011925Z", "start_time": "2019-04-22T12:31:11.005943Z" }, "collapsed": true }, "outputs": [], "source": [ "random_iq_waveforms = np.random.rand(random_waveform_number, segment_length)+\\\n", " 1j*np.random.rand(random_waveform_number, segment_length)\n", "padded_waveforms = np.zeros((random_waveform_number, half_physical_padding*2+segment_length), dtype=np.complex)\n", "padded_waveforms[:, half_physical_padding:-half_physical_padding] = random_iq_waveforms" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "ExecuteTime": { "end_time": "2019-04-22T13:49:05.601588Z", "start_time": "2019-04-22T13:49:05.576629Z" } }, "outputs": [ { "ename": "NameError", "evalue": "name 'set_waveform' is not defined", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m<ipython-input-33-8e094a61b17e>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[1;31m# algorithm:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2\u001b[0m \u001b[1;31m# 1) measure background\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0mset_waveform\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mzeros_like\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpadded_waveforms\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m:\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 4\u001b[0m \u001b[0mbackground\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmeasure_psd\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[1;31m# 2) at low amplitude, measure real, imag, realimag\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mNameError\u001b[0m: name 'set_waveform' is not defined" ] } ], "source": [ "# algorithm:\n", "# 1) measure background\n", "set_waveform(np.zeros_like(padded_waveforms[0, :]))\n", "background = measure_psd()\n", "# 2) at low amplitude, measure real, imag, realimag\n", "psds_low_i = np.zeros((random_waveform_number, int(discretization_frequency/measurement_resolution_bw)))\n", "psds_low_q = np.zeros((random_waveform_number, int(discretization_frequency/measurement_resolution_bw)))\n", "psds_low_iq = np.zeros((random_waveform_number, int(discretization_frequency/measurement_resolution_bw)))\n", "for random_waveform_id in range(random_iq_waveforms.shape[0]):\n", " set_waveform(low_amplitude*np.real(padded_waveforms[random_waveform_id, :]))\n", " psd_low_i[random_waveform_id, :] = measure_psd()\n", " set_waveform(low_amplitude*np.imag(padded_waveforms[random_waveform_id, :]))\n", " psd_low_q[random_waveform_id, :] = measure_psd()\n", " set_waveform(low_amplitude*padded_waveforms[random_waveform_id, :])\n", " psd_low_iq[random_waveform_id, :] = measure_psd()\n", "\n", "psds_low_i_nobg = psds_low_i-background\n", "psds_low_q_nobg = psds_low_q-background\n", "psds_low_iq_nobg = psds_low_iq-background\n", "psds_low_i_nobg[psds_low_i_nobg<0] = 0\n", "psds_low_q_nobg[psds_low_q_nobg<0] = 0\n", "psds_low_iq_nobg[psds_low_iq_nobg<0] = 0\n", "fd_low_i = np.sqrt(psds_low_i_nobg)\n", "fd_low_q = np.sqrt(psds_low_q_nobg)\n", "fd_low_iq = np.sqrt(psds_low_iq_nobg)\n", "\n", "psds_low_iq_interference = psds_low_iq_nobg-psds_low_i_nobg-psds_low_q_nobg" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "ExecuteTime": { "end_time": "2019-04-22T13:49:19.527103Z", "start_time": "2019-04-22T13:49:19.517129Z" } }, "outputs": [ { "ename": "NameError", "evalue": "name 'psds_low_i_nobg' is not defined", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m<ipython-input-35-71be368bc7a4>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mresponse_i_abs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msqrt\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msum\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpsds_low_i_nobg\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m/\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msum\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mabs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdft\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreal\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpadded_waveforms\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m**\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m/\u001b[0m\u001b[0mlow_amplitude\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2\u001b[0m \u001b[0mresponse_q_abs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msqrt\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msum\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpsds_low_q_nobg\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m/\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msum\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mabs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdft\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mimag\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpadded_waveforms\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m**\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m/\u001b[0m\u001b[0mlow_amplitude\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mNameError\u001b[0m: name 'psds_low_i_nobg' is not defined" ] } ], "source": [ "response_i_abs = np.sqrt(np.sum(psds_low_i_nobg, axis=0)/np.sum(np.abs(dft(np.real(padded_waveforms)))**2), axis=0)/low_amplitude\n", "response_q_abs = np.sqrt(np.sum(psds_low_q_nobg, axis=0)/np.sum(np.abs(dft(np.imag(padded_waveforms)))**2), axis=0)/low_amplitude\n", "fd_low_i_sim = dft(np.real(padded_waveforms))*response_i_abs\n", "fd_low_q_sim = 1j*dft(np.imag(padded_waveforms))*response_q_abs" ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "ExecuteTime": { "end_time": "2019-04-22T15:40:35.634739Z", "start_time": "2019-04-22T15:40:35.619779Z" }, "scrolled": true }, "outputs": [ { "ename": "NameError", "evalue": "name 'response_i_abs' is not defined", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m<ipython-input-38-a4f8956ac372>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0miq_phase\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mones\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mresponse_i_abs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcomplex\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2\u001b[0m \u001b[0mphase_retrieval_iterations\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m5\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mphase_retrieval_iteration\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mphase_retrieval_iterations\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[0mpsds_low_iq_interference_simulation\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfd_low_i_sim\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconj\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfd_low_q_sim\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0miq_phase\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[0mresponse_iq_interference_simulation\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpsds_low_iq_interference\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;36m1j\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mimag\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpsds_low_iq_interference_simulation\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mNameError\u001b[0m: name 'response_i_abs' is not defined" ] } ], "source": [ "iq_phase = np.ones(response_i_abs.shape, dtype=np.complex)\n", "phase_retrieval_iterations = 5\n", "for phase_retrieval_iteration in range(phase_retrieval_iterations):\n", " psds_low_iq_interference_simulation = fd_low_i_sim*np.conj(fd_low_q_sim*iq_phase)\n", " response_iq_interference_simulation = psds_low_iq_interference + 1j*np.imag(psds_low_iq_interference_simulation)\n", " iq_phase = np.sum(response_iq_interference_simulation_corrected*np.conj(fd_low_i_sim)*fd_low_q_sim, axis=0)\n", " iq_phase = np.exp(1j*np.angle(iq_phase))\n", " \n", "linear_response_i = response_i_abs\n", "linear_response_q = response_q_abs*iq_phase" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 3, "metadata": { "ExecuteTime": { "end_time": "2019-04-23T05:35:17.149076Z", "start_time": "2019-04-23T05:35:17.124140Z" }, "collapsed": true }, "outputs": [], "source": [ "class linear_model:\n", " def __init__(self, waveforms, psds_sqrt, update_rate=0.7):\n", " self.waveforms = waveforms\n", " self.response_i = np.ones(waveforms.shape[1], dtype=np.complex)\n", " self.response_q = np.ones(waveforms.shape[1], dtype=np.complex)\n", " self.psds_sqrt = psds_sqrt\n", " self.update_rate = update_rate\n", " self.forward()\n", " def forward(self):\n", " self.waveforms_i_fd = dft(np.real(self.waveforms))\n", " self.waveforms_q_fd = dft(np.imag(self.waveforms))\n", " self.model = self.waveforms_i_fd*self.response_i+1j*self.wavefroms_q_fd*self.response_q\n", " return self.model\n", " def apply_measurement_constraint(self):\n", " self.measurement_constrained_model = np.exp(1j*self.model)*self.psds_sqrt\n", " self.model_diff = self.measurement_constrained_model-self.model\n", " def backward_projection(self):\n", " waveform_i_norm = np.sum(np.abs(self.waveforms_i_fd)**2) # sure about this axis?? axis=0)\n", " waveform_q_norm = np.sum(np.abs(self.waveforms_q_fd)**2)\n", " self.response_i += update_rate*np.sum(self.model_diff*np.conj(dft(np.real(self.waveforms))), axis=0)/waveform_i_norm\n", " self.response_q += update_rate*np.sum(self.model_diff*np.conj(1j*dft(np.imag(self.waveforms))), axis=0)/waveform_q_norm\n", " def evaluate_mse_model(self):\n", " self.mse_model = np.std(np.abs(self.model_diff))**2\n", " def iterate(self, iteration_num):\n", " for iteration in range(iteration_num):\n", " self.forward()\n", " self.apply_measurement_constraint()\n", " self.evaluate_mse_model()\n", " print ('Iteration: {} MSE model: {}'.format(iteration, self.mse_model))\n", " self.backward_projection()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "class nonlinear_instanteneous_model(linear_model):\n", " def __init__(self, wavefroms, psds_sqrt, update_rate=0.7):\n", " super().__init__(waveforms, psdssqrt, update_rate)\n", " self.response_i_" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "nonlinear_coefs = [(1,1), (2,0), (0,2), (2,1), (1,2), (3,0), (0,3)]\n", "\n", "def response_low_freqeuncy(waveform, response):\n", " return np.real(np.ift(np.dft(waveform)*response))\n", "\n", "def instant_nonlinear_model(waveform, nonlinear_coefs):\n", " waveform\n", "\n", "def background(waveform, background)\n", "\n", "def full_instant_model_fd(waveform, response_i, response_q, bg, coefs, response_post_mixer):\n", " wave" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": 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gpl-3.0

samgoodgame/sf_crime

iterations/misc/Cha_Goodgame_Kao_Moore_W207_Final_Project_updated_08_19_0919.ipynb

1

95325

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Kaggle San Francisco Crime Classification\n", "## Berkeley MIDS W207 Final Project: Sam Goodgame, Sarah Cha, Kalvin Kao, Bryan Moore\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Environment and Data" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/cross_validation.py:44: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n", " \"This module will be removed in 0.20.\", DeprecationWarning)\n", "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/grid_search.py:43: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. This module will be removed in 0.20.\n", " DeprecationWarning)\n" ] } ], "source": [ "# Import relevant libraries:\n", "import time\n", "import numpy as np\n", "import pandas as pd\n", "from sklearn.neighbors import KNeighborsClassifier\n", "from sklearn import preprocessing\n", "from sklearn.preprocessing import MinMaxScaler\n", "from sklearn.preprocessing import StandardScaler\n", "from sklearn.naive_bayes import BernoulliNB\n", "from sklearn.naive_bayes import MultinomialNB\n", "from sklearn.naive_bayes import GaussianNB\n", "from sklearn.grid_search import GridSearchCV\n", "from sklearn.metrics import classification_report\n", "from sklearn.metrics import log_loss\n", "from sklearn.linear_model import LogisticRegression\n", "from sklearn import svm\n", "from sklearn.neural_network import MLPClassifier\n", "from sklearn.ensemble import RandomForestClassifier\n", "from sklearn.tree import DecisionTreeClassifier\n", "# Import Meta-estimators\n", "from sklearn.ensemble import AdaBoostClassifier\n", "from sklearn.ensemble import BaggingClassifier\n", "from sklearn.ensemble import GradientBoostingClassifier\n", "# Import Calibration tools\n", "from sklearn.calibration import CalibratedClassifierCV\n", "\n", "# Set random seed and format print output:\n", "np.random.seed(0)\n", "np.set_printoptions(precision=3)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### DDL to construct table for SQL transformations:\n", "\n", "```sql\n", "CREATE TABLE kaggle_sf_crime (\n", "dates TIMESTAMP, \n", "category VARCHAR,\n", "descript VARCHAR,\n", "dayofweek VARCHAR,\n", "pd_district VARCHAR,\n", "resolution VARCHAR,\n", "addr VARCHAR,\n", "X FLOAT,\n", "Y FLOAT);\n", "```\n", "#### Getting training data into a locally hosted PostgreSQL database:\n", "```sql\n", "\\copy kaggle_sf_crime FROM '/Users/Goodgame/Desktop/MIDS/207/final/sf_crime_train.csv' DELIMITER ',' CSV HEADER;\n", "```\n", "\n", "#### SQL Query used for transformations:\n", "\n", "```sql\n", "SELECT\n", " category,\n", " date_part('hour', dates) AS hour_of_day,\n", " CASE\n", " WHEN dayofweek = 'Monday' then 1\n", " WHEN dayofweek = 'Tuesday' THEN 2\n", " WHEN dayofweek = 'Wednesday' THEN 3\n", " WHEN dayofweek = 'Thursday' THEN 4\n", " WHEN dayofweek = 'Friday' THEN 5\n", " WHEN dayofweek = 'Saturday' THEN 6\n", " WHEN dayofweek = 'Sunday' THEN 7\n", " END AS dayofweek_numeric,\n", " X,\n", " Y,\n", " CASE\n", " WHEN pd_district = 'BAYVIEW' THEN 1\n", " ELSE 0\n", " END AS bayview_binary,\n", " CASE\n", " WHEN pd_district = 'INGLESIDE' THEN 1\n", " ELSE 0\n", " END AS ingleside_binary,\n", " CASE\n", " WHEN pd_district = 'NORTHERN' THEN 1\n", " ELSE 0\n", " END AS northern_binary,\n", " CASE\n", " WHEN pd_district = 'CENTRAL' THEN 1\n", " ELSE 0\n", " END AS central_binary,\n", " CASE\n", " WHEN pd_district = 'BAYVIEW' THEN 1\n", " ELSE 0\n", " END AS pd_bayview_binary,\n", " CASE\n", " WHEN pd_district = 'MISSION' THEN 1\n", " ELSE 0\n", " END AS mission_binary,\n", " CASE\n", " WHEN pd_district = 'SOUTHERN' THEN 1\n", " ELSE 0\n", " END AS southern_binary,\n", " CASE\n", " WHEN pd_district = 'TENDERLOIN' THEN 1\n", " ELSE 0\n", " END AS tenderloin_binary,\n", " CASE\n", " WHEN pd_district = 'PARK' THEN 1\n", " ELSE 0\n", " END AS park_binary,\n", " CASE\n", " WHEN pd_district = 'RICHMOND' THEN 1\n", " ELSE 0\n", " END AS richmond_binary,\n", " CASE\n", " WHEN pd_district = 'TARAVAL' THEN 1\n", " ELSE 0\n", " END AS taraval_binary\n", "FROM kaggle_sf_crime;\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Loading the data, version 2, with weather features to improve performance: (Negated with hashtags for now, as will cause file dependency issues if run locally for everyone. Will be run by Isabell in final notebook with correct files she needs)\n", "\n", "We seek to add features to our models that will improve performance with respect to out desired performance metric. There is evidence that there is a correlation between weather patterns and crime, with some experts even arguing for a causal relationship between weather and crime [1]. More specifically, a 2013 paper published in Science showed that higher temperatures and extreme rainfall led to large increases in conflict. In the setting of strong evidence that weather influences crime, we see it as a candidate for additional features to improve the performance of our classifiers. Weather data was gathered from (insert source). Certain features from this data set were incorporated into the original crime data set in order to add features that were hypothesizzed to improve performance. These features included (insert what we eventually include)." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "ename": "IndentationError", "evalue": "unexpected indent (<ipython-input-3-e913301d7091>, line 126)", "output_type": "error", "traceback": [ "\u001b[0;36m File \u001b[0;32m\"<ipython-input-3-e913301d7091>\"\u001b[0;36m, line \u001b[0;32m126\u001b[0m\n\u001b[0;31m if timePoint < relevantWeather['DAILYSunset'][-1]:\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mIndentationError\u001b[0m\u001b[0;31m:\u001b[0m unexpected indent\n" ] } ], "source": [ "data_path = \"./data/train_transformed.csv\"\n", "\n", "df = pd.read_csv(data_path, header=0)\n", "x_data = df.drop('category', 1)\n", "y = df.category.as_matrix()\n", "\n", "######### Adding the date back into the data\n", "import csv\n", "import time\n", "import calendar\n", "data_path = \"./data/train.csv\"\n", "dataCSV = open(data_path, 'rt')\n", "csvData = list(csv.reader(dataCSV))\n", "csvFields = csvData[0] #['Dates', 'Category', 'Descript', 'DayOfWeek', 'PdDistrict', 'Resolution', 'Address', 'X', 'Y']\n", "allData = csvData[1:]\n", "dataCSV.close()\n", "\n", "df2 = pd.DataFrame(allData)\n", "df2.columns = csvFields\n", "dates = df2['Dates']\n", "dates = dates.apply(time.strptime, args=(\"%Y-%m-%d %H:%M:%S\",))\n", "dates = dates.apply(calendar.timegm)\n", "print(dates.head())\n", "\n", "x_data['secondsFromEpoch'] = dates\n", "colnames = x_data.columns.tolist()\n", "colnames = colnames[-1:] + colnames[:-1]\n", "x_data = x_data[colnames]\n", "#########\n", "\n", "######### Adding the weather data into the original crime data\n", "weatherData1 = \"./data/1027175.csv\"\n", "weatherData2 = \"./data/1027176.csv\"\n", "dataCSV = open(weatherData1, 'rt')\n", "csvData = list(csv.reader(dataCSV))\n", "csvFields = csvData[0] #['Dates', 'Category', 'Descript', 'DayOfWeek', 'PdDistrict', 'Resolution', 'Address', 'X', 'Y']\n", "allWeatherData1 = csvData[1:]\n", "dataCSV.close()\n", "\n", "dataCSV = open(weatherData2, 'rt')\n", "csvData = list(csv.reader(dataCSV))\n", "csvFields = csvData[0] #['Dates', 'Category', 'Descript', 'DayOfWeek', 'PdDistrict', 'Resolution', 'Address', 'X', 'Y']\n", "allWeatherData2 = csvData[1:]\n", "dataCSV.close()\n", "\n", "weatherDF1 = pd.DataFrame(allWeatherData1)\n", "weatherDF1.columns = csvFields\n", "dates1 = weatherDF1['DATE']\n", "sunrise1 = weatherDF1['DAILYSunrise']\n", "sunset1 = weatherDF1['DAILYSunset']\n", "\n", "weatherDF2 = pd.DataFrame(allWeatherData2)\n", "weatherDF2.columns = csvFields\n", "dates2 = weatherDF2['DATE']\n", "sunrise2 = weatherDF2['DAILYSunrise']\n", "sunset2 = weatherDF2['DAILYSunset']\n", "\n", "# functions for processing the sunrise and sunset times of each day\n", "def get_hour_and_minute(milTime):\n", " hour = int(milTime[:-2])\n", " minute = int(milTime[-2:])\n", " return [hour, minute]\n", "\n", "def get_date_only(date):\n", " return time.struct_time(tuple([date[0], date[1], date[2], 0, 0, 0, date[6], date[7], date[8]]))\n", "\n", "def structure_sun_time(timeSeries, dateSeries):\n", " sunTimes = timeSeries.copy()\n", " for index in range(len(dateSeries)):\n", " sunTimes[index] = time.struct_time(tuple([dateSeries[index][0], dateSeries[index][1], dateSeries[index][2], timeSeries[index][0], timeSeries[index][1], dateSeries[index][5], dateSeries[index][6], dateSeries[index][7], dateSeries[index][8]]))\n", " return sunTimes\n", "\n", "dates1 = dates1.apply(time.strptime, args=(\"%Y-%m-%d %H:%M\",))\n", "sunrise1 = sunrise1.apply(get_hour_and_minute)\n", "sunrise1 = structure_sun_time(sunrise1, dates1)\n", "sunrise1 = sunrise1.apply(calendar.timegm)\n", "sunset1 = sunset1.apply(get_hour_and_minute)\n", "sunset1 = structure_sun_time(sunset1, dates1)\n", "sunset1 = sunset1.apply(calendar.timegm)\n", "dates1 = dates1.apply(calendar.timegm)\n", "\n", "dates2 = dates2.apply(time.strptime, args=(\"%Y-%m-%d %H:%M\",))\n", "sunrise2 = sunrise2.apply(get_hour_and_minute)\n", "sunrise2 = structure_sun_time(sunrise2, dates2)\n", "sunrise2 = sunrise2.apply(calendar.timegm)\n", "sunset2 = sunset2.apply(get_hour_and_minute)\n", "sunset2 = structure_sun_time(sunset2, dates2)\n", "sunset2 = sunset2.apply(calendar.timegm)\n", "dates2 = dates2.apply(calendar.timegm)\n", "\n", "weatherDF1['DATE'] = dates1\n", "weatherDF1['DAILYSunrise'] = sunrise1\n", "weatherDF1['DAILYSunset'] = sunset1\n", "weatherDF2['DATE'] = dates2\n", "weatherDF2['DAILYSunrise'] = sunrise2\n", "weatherDF2['DAILYSunset'] = sunset2\n", "\n", "weatherDF = pd.concat([weatherDF1,weatherDF2[32:]],ignore_index=True)\n", "\n", "# Starting off with some of the easier features to work with-- more to come here . . . still in beta\n", "weatherMetrics = weatherDF[['DATE','HOURLYDRYBULBTEMPF','HOURLYRelativeHumidity', 'HOURLYWindSpeed', \\\n", " 'HOURLYSeaLevelPressure', 'HOURLYVISIBILITY', 'DAILYSunrise', 'DAILYSunset']]\n", "weatherMetrics = weatherMetrics.convert_objects(convert_numeric=True)\n", "weatherDates = weatherMetrics['DATE']\n", "'DATE','HOURLYDRYBULBTEMPF','HOURLYRelativeHumidity', 'HOURLYWindSpeed',\n", "'HOURLYSeaLevelPressure', 'HOURLYVISIBILITY'\n", "timeWindow = 10800 #3 hours\n", "hourlyDryBulbTemp = []\n", "hourlyRelativeHumidity = []\n", "hourlyWindSpeed = []\n", "hourlySeaLevelPressure = []\n", "hourlyVisibility = []\n", "dailySunrise = []\n", "dailySunset = []\n", "daylight = []\n", "test = 0\n", "for timePoint in dates:#dates is the epoch time from the kaggle data\n", " relevantWeather = weatherMetrics[(weatherDates <= timePoint) & (weatherDates > timePoint - timeWindow)]\n", " hourlyDryBulbTemp.append(relevantWeather['HOURLYDRYBULBTEMPF'].mean())\n", " hourlyRelativeHumidity.append(relevantWeather['HOURLYRelativeHumidity'].mean())\n", " hourlyWindSpeed.append(relevantWeather['HOURLYWindSpeed'].mean())\n", " hourlySeaLevelPressure.append(relevantWeather['HOURLYSeaLevelPressure'].mean())\n", " hourlyVisibility.append(relevantWeather['HOURLYVISIBILITY'].mean())\n", " dailySunrise.append(relevantWeather['DAILYSunrise'].iloc[-1])\n", " dailySunset.append(relevantWeather['DAILYSunset'].iloc[-1])\n", " daylight.append(1.0*((timePoint >= relevantWeather['DAILYSunrise'].iloc[-1]) and (timePoint < relevantWeather['DAILYSunset'].iloc[-1])))\n", " if timePoint < relevantWeather['DAILYSunset'][-1]:\n", " daylight.append(1)\n", " else:\n", " daylight.append(0)\n", " \n", " if test%100000 == 0:\n", " print(relevantWeather)\n", " test += 1\n", "\n", "hourlyDryBulbTemp = pd.Series.from_array(np.array(hourlyDryBulbTemp))\n", "hourlyRelativeHumidity = pd.Series.from_array(np.array(hourlyRelativeHumidity))\n", "hourlyWindSpeed = pd.Series.from_array(np.array(hourlyWindSpeed))\n", "hourlySeaLevelPressure = pd.Series.from_array(np.array(hourlySeaLevelPressure))\n", "hourlyVisibility = pd.Series.from_array(np.array(hourlyVisibility))\n", "dailySunrise = pd.Series.from_array(np.array(dailySunrise))\n", "dailySunset = pd.Series.from_array(np.array(dailySunset))\n", "daylight = pd.Series.from_array(np.array(daylight))\n", "\n", "x_data['HOURLYDRYBULBTEMPF'] = hourlyDryBulbTemp\n", "x_data['HOURLYRelativeHumidity'] = hourlyRelativeHumidity\n", "x_data['HOURLYWindSpeed'] = hourlyWindSpeed\n", "x_data['HOURLYSeaLevelPressure'] = hourlySeaLevelPressure\n", "x_data['HOURLYVISIBILITY'] = hourlyVisibility\n", "x_data['DAILYSunrise'] = dailySunrise\n", "x_data['DAILYSunset'] = dailySunset\n", "x_data['Daylight'] = daylight\n", "\n", "x_data.to_csv(path_or_buf=\"C:/MIDS/W207 final project/x_data.csv\")\n", "#########\n", "\n", "Impute missing values with mean values:\n", "x_complete = x_data.fillna(x_data.mean())\n", "X_raw = x_complete.as_matrix()\n", "\n", "Scale the data between 0 and 1:\n", "X = MinMaxScaler().fit_transform(X_raw)\n", "\n", "Shuffle data to remove any underlying pattern that may exist:\n", "shuffle = np.random.permutation(np.arange(X.shape[0]))\n", "X, y = X[shuffle], y[shuffle]\n", "\n", "Separate training, dev, and test data:\n", "test_data, test_labels = X[800000:], y[800000:]\n", "dev_data, dev_labels = X[700000:800000], y[700000:800000]\n", "train_data, train_labels = X[:700000], y[:700000]\n", "\n", "mini_train_data, mini_train_labels = X[:75000], y[:75000]\n", "mini_dev_data, mini_dev_labels = X[75000:100000], y[75000:100000]\n", "labels_set = set(mini_dev_labels)\n", "print(labels_set)\n", "print(len(labels_set))\n", "print(train_data[:10])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Local, individual load of updated data set (with weather data integrated) into training, development, and test subsets.\n" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "37 37 37 37\n" ] } ], "source": [ "# Data path to your local copy of Sam's \"train_transformed.csv\", which was produced by ?separate Python script?\n", "data_path_for_labels_only = \"/Users/Bryan/Desktop/UC_Berkeley_MIDS_files/Courses/W207_Intro_To_Machine_Learning/Final_Project/sf_crime-master/data/train_transformed.csv\"\n", "df = pd.read_csv(data_path_for_labels_only, header=0)\n", "y = df.category.as_matrix()\n", "\n", "# Data path to your local copy of Kalvin's \"x_data.csv\", which was produced by the negated cell above\n", "data_path = \"/Users/Bryan/Desktop/UC_Berkeley_MIDS_files/Courses/W207_Intro_To_Machine_Learning/Final_Project/x_data_08_15.csv\"\n", "df = pd.read_csv(data_path, header=0)\n", "\n", "# Impute missing values with mean values:\n", "x_complete = df.fillna(df.mean())\n", "X_raw = x_complete.as_matrix()\n", "\n", "# Scale the data between 0 and 1:\n", "X = MinMaxScaler().fit_transform(X_raw)\n", "\n", "# Shuffle data to remove any underlying pattern that may exist. Must re-run random seed step each time:\n", "np.random.seed(0)\n", "shuffle = np.random.permutation(np.arange(X.shape[0]))\n", "X, y = X[shuffle], y[shuffle]\n", "\n", "# Due to difficulties with log loss and set(y_pred) needing to match set(labels), we will remove the extremely rare\n", "# crimes from the data for quality issues.\n", "X_minus_trea = X[np.where(y != 'TREA')]\n", "y_minus_trea = y[np.where(y != 'TREA')]\n", "X_final = X_minus_trea[np.where(y_minus_trea != 'PORNOGRAPHY/OBSCENE MAT')]\n", "y_final = y_minus_trea[np.where(y_minus_trea != 'PORNOGRAPHY/OBSCENE MAT')]\n", "\n", "# Separate training, dev, and test data:\n", "test_data, test_labels = X_final[800000:], y_final[800000:]\n", "dev_data, dev_labels = X_final[700000:800000], y_final[700000:800000]\n", "train_data, train_labels = X_final[100000:700000], y_final[100000:700000]\n", "calibrate_data, calibrate_labels = X_final[:100000], y_final[:100000]\n", "\n", "# Create mini versions of the above sets\n", "mini_train_data, mini_train_labels = X_final[:20000], y_final[:20000]\n", "mini_calibrate_data, mini_calibrate_labels = X_final[19000:28000], y_final[19000:28000]\n", "mini_dev_data, mini_dev_labels = X_final[49000:60000], y_final[49000:60000]\n", "\n", "# Create list of the crime type labels. This will act as the \"labels\" parameter for the log loss functions that follow\n", "crime_labels = list(set(y_final))\n", "crime_labels_mini_train = list(set(mini_train_labels))\n", "crime_labels_mini_dev = list(set(mini_dev_labels))\n", "crime_labels_mini_calibrate = list(set(mini_calibrate_labels))\n", "print(len(crime_labels), len(crime_labels_mini_train), len(crime_labels_mini_dev),len(crime_labels_mini_calibrate))\n", "\n", "#print(len(train_data),len(train_labels))\n", "#print(len(dev_data),len(dev_labels))\n", "#print(len(mini_train_data),len(mini_train_labels))\n", "#print(len(mini_dev_data),len(mini_dev_labels))\n", "#print(len(test_data),len(test_labels))\n", "#print(len(mini_calibrate_data),len(mini_calibrate_labels))\n", "#print(len(calibrate_data),len(calibrate_labels))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Sarah's School data that we may still get to work as features: (Negated with hashtags for now, as will cause file dependency issues if run locally for everyone. Will be run by Isabell in final notebook with correct files she needs)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "### Read in zip code data\n", "#data_path_zip = \"./data/2016_zips.csv\"\n", "#zips = pd.read_csv(data_path_zip, header=0, sep ='\\t', usecols = [0,5,6], names = [\"GEOID\", \"INTPTLAT\", \"INTPTLONG\"], dtype ={'GEOID': int, 'INTPTLAT': float, 'INTPTLONG': float})\n", "#sf_zips = zips[(zips['GEOID'] > 94000) & (zips['GEOID'] < 94189)]\n", "\n", "### Mapping longitude/latitude to zipcodes\n", "#def dist(lat1, long1, lat2, long2):\n", "# return np.sqrt((lat1-lat2)**2+(long1-long2)**2)\n", "# return abs(lat1-lat2)+abs(long1-long2)\n", "#def find_zipcode(lat, long): \n", "# distances = sf_zips.apply(lambda row: dist(lat, long, row[\"INTPTLAT\"], row[\"INTPTLONG\"]), axis=1)\n", "# return sf_zips.loc[distances.idxmin(), \"GEOID\"]\n", "#x_data['zipcode'] = 0\n", "#for i in range(0, 1):\n", "# x_data['zipcode'][i] = x_data.apply(lambda row: find_zipcode(row['x'], row['y']), axis=1)\n", "#x_data['zipcode']= x_data.apply(lambda row: find_zipcode(row['x'], row['y']), axis=1)\n", "\n", "\n", "### Read in school data\n", "#data_path_schools = \"./data/pubschls.csv\"\n", "#schools = pd.read_csv(data_path_schools,header=0, sep ='\\t', usecols = [\"CDSCode\",\"StatusType\", \"School\", \"EILCode\", \"EILName\", \"Zip\", \"Latitude\", \"Longitude\"], dtype ={'CDSCode': str, 'StatusType': str, 'School': str, 'EILCode': str,'EILName': str,'Zip': str, 'Latitude': float, 'Longitude': float})\n", "#schools = schools[(schools[\"StatusType\"] == 'Active')]\n", "\n", "### Find the closest school\n", "#def dist(lat1, long1, lat2, long2):\n", "# return np.sqrt((lat1-lat2)**2+(long1-long2)**2)\n", "\n", "#def find_closest_school(lat, long): \n", "# distances = schools.apply(lambda row: dist(lat, long, row[\"Latitude\"], row[\"Longitude\"]), axis=1)\n", "# return min(distances)\n", "#x_data['closest_school'] = x_data_sub.apply(lambda row: find_closest_school(row['y'], row['x']), axis=1)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Formatting to meet Kaggle submission standards: (Negated with hashtags for now, as will cause file dependency issues if run locally for everyone. Will be run by Isabell in final notebook with correct files she needs)" ] }, { "cell_type": "code", "execution_count": 86, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# The Kaggle submission format requires listing the ID of each example.\n", "# This is to remember the order of the IDs after shuffling\n", "#allIDs = np.array(list(df.axes[0]))\n", "#allIDs = allIDs[shuffle]\n", "\n", "#testIDs = allIDs[800000:]\n", "#devIDs = allIDs[700000:800000]\n", "#trainIDs = allIDs[:700000]\n", "\n", "# Extract the column names for the required submission format\n", "#sampleSubmission_path = \"./data/sampleSubmission.csv\"\n", "#sampleDF = pd.read_csv(sampleSubmission_path)\n", "#allColumns = list(sampleDF.columns)\n", "#featureColumns = allColumns[1:]\n", "\n", "# Extracting the test data for a baseline submission\n", "#real_test_path = \"./data/test_transformed.csv\"\n", "#testDF = pd.read_csv(real_test_path, header=0)\n", "#real_test_data = testDF\n", "\n", "#test_complete = real_test_data.fillna(real_test_data.mean())\n", "#Test_raw = test_complete.as_matrix()\n", "\n", "#TestData = MinMaxScaler().fit_transform(Test_raw)\n", "\n", "# Here we remember the ID of each test data point, in case we ever decide to shuffle the test data for some reason\n", "#testIDs = list(testDF.axes[0])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Generate baseline prediction probabilities from MNB classifier and store in a .csv file (Negated with hashtags for now, as will cause file dependency issues if run locally for everyone. Will be run by Isabell in final notebook with correct files she needs)" ] }, { "cell_type": "code", "execution_count": 87, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Generate a baseline MNB classifier and make it return prediction probabilities for the actual test data\n", "#def MNB():\n", "# mnb = MultinomialNB(alpha = 0.0000001)\n", "# mnb.fit(train_data, train_labels)\n", "# print(\"\\n\\nMultinomialNB accuracy on dev data:\", mnb.score(dev_data, dev_labels))\n", "# return mnb.predict_proba(dev_data)\n", "#MNB()\n", "\n", "#baselinePredictionProbabilities = MNB()\n", "\n", "# Place the resulting prediction probabilities in a .csv file in the required format\n", "# First, turn the prediction probabilties into a data frame\n", "#resultDF = pd.DataFrame(baselinePredictionProbabilities,columns=featureColumns)\n", "# Add the IDs as a final column\n", "#resultDF.loc[:,'Id'] = pd.Series(testIDs,index=resultDF.index)\n", "# Make the 'Id' column the first column\n", "#colnames = resultDF.columns.tolist()\n", "#colnames = colnames[-1:] + colnames[:-1]\n", "#resultDF = resultDF[colnames]\n", "# Output to a .csv file\n", "# resultDF.to_csv('result.csv',index=False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "*Note: the code above will shuffle data differently every time it's run, so model accuracies will vary accordingly.*" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[ 0.016 0.985 0.826 0.667 0.055 0.002 0. 0. 0. 1. 0.\n", " 0. 0. 0. 0. 0. 0. 0.514 0.405 0.375 0.661 1.\n", " 0.985 0.985 0. ]]\n", "['LARCENY/THEFT']\n" ] } ], "source": [ "## Data sub-setting quality check-point\n", "print(train_data[:1])\n", "print(train_labels[:1])" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "\n", "MultinomialNB accuracy on dev data: 0.22347\n" ] } ], "source": [ "# Modeling quality check-point with MNB--fast model\n", "\n", "def MNB():\n", " mnb = MultinomialNB(alpha = 0.0000001)\n", " mnb.fit(train_data, train_labels)\n", " print(\"\\n\\nMultinomialNB accuracy on dev data:\", mnb.score(dev_data, dev_labels))\n", " \n", "MNB()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Defining Performance Criteria\n", "\n", "As determined by the Kaggle submission guidelines, the performance criteria metric for the San Francisco Crime Classification competition is Multi-class Logarithmic Loss (also known as cross-entropy). There are various other performance metrics that are appropriate for different domains: accuracy, F-score, Lift, ROC Area, average precision, precision/recall break-even point, and squared error.\n", "\n", "(Describe each performance metric and a domain in which it is preferred. Give Pros/Cons if able)\n", "\n", "- Multi-class Log Loss:\n", "\n", "- Accuracy:\n", "\n", "- F-score:\n", "\n", "- Lift:\n", "\n", "- ROC Area:\n", "\n", "- Average precision\n", "\n", "- Precision/Recall break-even point:\n", "\n", "- Squared-error:\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Model Prototyping\n", "We will start our classifier and feature engineering process by looking at the performance of various classifiers with default parameter settings in predicting labels on the mini_dev_data:" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n", " metric_params=None, n_jobs=1, n_neighbors=5, p=2,\n", " weights='uniform') Multi-class Log Loss: 21.0240643644 \n", "\n", "\n", "BernoulliNB(alpha=1, binarize=0.5, class_prior=None, fit_prior=True) Multi-class Log Loss: 2.6947927812 \n", "\n", "\n", "MultinomialNB(alpha=1.0, class_prior=None, fit_prior=True) Multi-class Log Loss: 2.60974496429 \n", "\n", "\n", "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n", " intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n", " penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n", " verbose=0, warm_start=False) Multi-class Log Loss: 2.59547592791 \n", "\n", "\n", "MLPClassifier(activation='relu', alpha=0.0001, batch_size='auto', beta_1=0.9,\n", " beta_2=0.999, early_stopping=False, epsilon=1e-08,\n", " hidden_layer_sizes=(100,), learning_rate='constant',\n", " learning_rate_init=0.001, max_iter=200, momentum=0.9,\n", " nesterovs_momentum=True, power_t=0.5, random_state=None,\n", " shuffle=True, solver='adam', tol=0.0001, validation_fraction=0.1,\n", " verbose=False, warm_start=False) Multi-class Log Loss: 2.59629075567 \n", "\n", "\n", "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n", " max_depth=None, max_features='auto', max_leaf_nodes=None,\n", " min_impurity_split=1e-07, min_samples_leaf=1,\n", " min_samples_split=2, min_weight_fraction_leaf=0.0,\n", " n_estimators=10, n_jobs=1, oob_score=False, random_state=None,\n", " verbose=0, warm_start=False) Multi-class Log Loss: 15.4363991752 \n", "\n", "\n", "DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\n", " max_features=None, max_leaf_nodes=None,\n", " min_impurity_split=1e-07, min_samples_leaf=1,\n", " min_samples_split=2, min_weight_fraction_leaf=0.0,\n", " presort=False, random_state=None, splitter='best') Multi-class Log Loss: 29.8258033614 \n", "\n", "\n", "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n", " decision_function_shape=None, degree=3, gamma='auto', kernel='rbf',\n", " max_iter=-1, probability=True, random_state=None, shrinking=True,\n", " tol=0.001, verbose=False) Multi-class Log Loss: 2.62455308617 \n", "\n", "\n" ] } ], "source": [ "def model_prototype(train_data, train_labels, eval_data, eval_labels):\n", " knn = KNeighborsClassifier(n_neighbors=5).fit(train_data, train_labels)\n", " bnb = BernoulliNB(alpha=1, binarize = 0.5).fit(train_data, train_labels)\n", " mnb = MultinomialNB().fit(train_data, train_labels)\n", " log_reg = LogisticRegression().fit(train_data, train_labels)\n", " neural_net = MLPClassifier().fit(train_data, train_labels)\n", " random_forest = RandomForestClassifier().fit(train_data, train_labels)\n", " decision_tree = DecisionTreeClassifier().fit(train_data, train_labels)\n", " support_vm_step_one = svm.SVC(probability = True)\n", " support_vm = support_vm_step_one.fit(train_data, train_labels)\n", " \n", " models = [knn, bnb, mnb, log_reg, neural_net, random_forest, decision_tree, support_vm]\n", " for model in models:\n", " eval_prediction_probabilities = model.predict_proba(eval_data)\n", " eval_predictions = model.predict(eval_data)\n", " print(model, \"Multi-class Log Loss:\", log_loss(y_true = eval_labels, y_pred = eval_prediction_probabilities, labels = crime_labels_mini_dev), \"\\n\\n\")\n", "\n", "model_prototype(mini_train_data, mini_train_labels, mini_dev_data, mini_dev_labels)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Adding Features, Hyperparameter Tuning, and Model Calibration To Improve Prediction For Each Classifier\n", "\n", "Here we seek to optimize the performance of our classifiers in a three-step, dynamnic engineering process. \n", "\n", "##### 1) Feature addition\n", "\n", "We previously added components from the weather data into the original SF crime data as new features. We will not repeat work done in our initial submission, where our training dataset did not include these features. For comparision with respoect to how the added features improved our performance with respect to log loss, please refer back to our initial submission.\n", "\n", "We can have Kalvin expand on exactly what he did here.\n", "\n", "##### 2) Hyperparameter tuning\n", "\n", "Each classifier has parameters that we can engineer to further optimize performance, as opposed to using the default parameter values as we did above in the model prototyping cell. This will be specific to each classifier as detailed below.\n", "\n", "##### 3) Model calibration\n", "\n", "We can calibrate the models via Platt Scaling or Isotonic Regression to attempt to improve their performance.\n", "\n", "- Platt Scaling: ((brief explanation of how it works))\n", "\n", "- Isotonic Regression: ((brief explanation of how it works))\n", "\n", "For each classifier, we can use CalibratedClassifierCV to perform probability calibration with isotonic regression or sigmoid (Platt Scaling). The parameters within CalibratedClassifierCV that we can adjust are the method ('sigmoid' or 'isotonic') and cv (cross-validation generator). As we will already be training our models before calibration, we will only use cv = 'prefit'. Thus, in practice the cross-validation generator will not be a modifiable parameter for us.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### K-Nearest Neighbors" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "###### Hyperparameter tuning:\n", "\n", "For the KNN classifier, we can seek to optimize the following classifier parameters: n-neighbors, weights, and the power parameter ('p')." ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "For KNN the best log loss with hyperparameter tuning is 2.62923629844 with k = 2001 w = uniform p = 1\n", "Computation time for this step is 351.40 seconds\n" ] } ], "source": [ "list_for_ks = []\n", "list_for_ws = []\n", "list_for_ps = []\n", "list_for_log_loss = []\n", "\n", "def k_neighbors_tuned(k,w,p):\n", " tuned_KNN = KNeighborsClassifier(n_neighbors=k, weights=w, p=p).fit(mini_train_data, mini_train_labels)\n", " dev_prediction_probabilities = tuned_KNN.predict_proba(mini_dev_data)\n", " list_for_ks.append(this_k)\n", " list_for_ws.append(this_w)\n", " list_for_ps.append(this_p)\n", " working_log_loss = log_loss(y_true = mini_dev_labels, y_pred = dev_prediction_probabilities, labels = crime_labels_mini_dev)\n", " list_for_log_loss.append(working_log_loss)\n", " #print(\"Multi-class Log Loss with KNN and k,w,p =\", k,\",\",w,\",\", p, \"is:\", working_log_loss)\n", "\n", "k_value_tuning = [i for i in range(1,5002,500)]\n", "weight_tuning = ['uniform', 'distance']\n", "power_parameter_tuning = [1,2]\n", "\n", "start = time.clock()\n", "for this_k in k_value_tuning:\n", " for this_w in weight_tuning:\n", " for this_p in power_parameter_tuning:\n", " k_neighbors_tuned(this_k, this_w, this_p)\n", " \n", "index_best_logloss = np.argmin(list_for_log_loss)\n", "print('For KNN the best log loss with hyperparameter tuning is',list_for_log_loss[index_best_logloss], 'with k =', list_for_ks[index_best_logloss], 'w =', list_for_ws[index_best_logloss], 'p =', list_for_ps[index_best_logloss])\n", "end = time.clock()\n", "print(\"Computation time for this step is %.2f\" % (end-start), 'seconds')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "###### Model calibration:\n", "\n", "We will consider embeding this step within the for loop for the hyperparameter tuning. More likely we will pipeline it along with the hyperparameter tuning steps. We will then use GridSearchCV top find the optimized parameters based on our performance metric of Mutli-Class Log Loss." ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Multi-class Log Loss with KNN and k,w,p = 1 , uniform , 1 , sigmoid is: 2.71277990123\n", "Multi-class Log Loss with KNN and k,w,p = 1 , uniform , 1 , isotonic is: 2.71416377107\n", "Multi-class Log Loss with KNN and k,w,p = 1 , uniform , 2 , sigmoid is: 2.71378604118\n", "Multi-class Log Loss with KNN and k,w,p = 1 , uniform , 2 , isotonic is: 2.7168901557\n", "Multi-class Log Loss with KNN and k,w,p = 1 , distance , 1 , sigmoid is: 2.71277990123\n", "Multi-class Log Loss with KNN and k,w,p = 1 , distance , 1 , isotonic is: 2.71416377107\n", "Multi-class Log Loss with KNN and k,w,p = 1 , distance , 2 , sigmoid is: 2.71378604118\n", "Multi-class Log Loss with KNN and k,w,p = 1 , distance , 2 , isotonic is: 2.7168901557\n", "Multi-class Log Loss with KNN and k,w,p = 501 , uniform , 1 , sigmoid is: 2.60086682706\n", "Multi-class Log Loss with KNN and k,w,p = 501 , uniform , 1 , isotonic is: 2.64335037718\n", "Multi-class Log Loss with KNN and k,w,p = 501 , uniform , 2 , sigmoid is: 2.60387977714\n", "Multi-class Log Loss with KNN and k,w,p = 501 , uniform , 2 , isotonic is: 2.65416869419\n", "Multi-class Log Loss with KNN and k,w,p = 501 , distance , 1 , sigmoid is: 2.62772375975\n", "Multi-class Log Loss with KNN and k,w,p = 501 , distance , 1 , isotonic is: 2.73181094627\n", "Multi-class Log Loss with KNN and k,w,p = 501 , distance , 2 , sigmoid is: 2.62265629187\n", "Multi-class Log Loss with KNN and k,w,p = 501 , distance , 2 , isotonic is: 2.73478071804\n", "Multi-class Log Loss with KNN and k,w,p = 1001 , uniform , 1 , sigmoid is: 2.59760542997\n", "Multi-class Log Loss with KNN and k,w,p = 1001 , uniform , 1 , isotonic is: 2.65541916973\n", "Multi-class Log Loss with KNN and k,w,p = 1001 , uniform , 2 , sigmoid is: 2.60664155285\n", "Multi-class Log Loss with KNN and k,w,p = 1001 , uniform , 2 , isotonic is: 2.67320210152\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/Bryan/anaconda/lib/python3.6/site-packages/sklearn/calibration.py:507: RuntimeWarning: overflow encountered in exp\n", " return 1. / (1. + np.exp(self.a_ * T + self.b_))\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Multi-class Log Loss with KNN and k,w,p = 1001 , distance , 1 , sigmoid is: 2.62780570553\n", "Multi-class Log Loss with KNN and k,w,p = 1001 , distance , 1 , isotonic is: 2.73915999813\n", "Multi-class Log Loss with KNN and k,w,p = 1001 , distance , 2 , sigmoid is: 2.6239585973\n", "Multi-class Log Loss with KNN and k,w,p = 1001 , distance , 2 , isotonic is: 2.7396359309\n", "Multi-class Log Loss with KNN and k,w,p = 1501 , uniform , 1 , sigmoid is: 2.59776199298\n", "Multi-class Log Loss with KNN and k,w,p = 1501 , uniform , 1 , isotonic is: 2.68842973364\n", "Multi-class Log Loss with KNN and k,w,p = 1501 , uniform , 2 , sigmoid is: 2.6076426784\n", "Multi-class Log Loss with KNN and k,w,p = 1501 , uniform , 2 , isotonic is: 2.65509777297\n", "Multi-class Log Loss with KNN and k,w,p = 1501 , distance , 1 , sigmoid is: 2.62883969589\n", "Multi-class Log Loss with KNN and k,w,p = 1501 , distance , 1 , isotonic is: 2.71513408866\n", "Multi-class Log Loss with KNN and k,w,p = 1501 , distance , 2 , sigmoid is: 2.62494342979\n", "Multi-class Log Loss with KNN and k,w,p = 1501 , distance , 2 , isotonic is: 2.72607861059\n", "Multi-class Log Loss with KNN and k,w,p = 2001 , uniform , 1 , sigmoid is: 2.59748933009\n", "Multi-class Log Loss with KNN and k,w,p = 2001 , uniform , 1 , isotonic is: 2.72852555002\n", "Multi-class Log Loss with KNN and k,w,p = 2001 , uniform , 2 , sigmoid is: 2.60588224807\n", "Multi-class Log Loss with KNN and k,w,p = 2001 , uniform , 2 , isotonic is: 2.67348726558\n", "Multi-class Log Loss with KNN and k,w,p = 2001 , distance , 1 , sigmoid is: 2.63057860493\n", "Multi-class Log Loss with KNN and k,w,p = 2001 , distance , 1 , isotonic is: 2.73228081995\n", "Multi-class Log Loss with KNN and k,w,p = 2001 , distance , 2 , sigmoid is: 2.62594862273\n", "Multi-class Log Loss with KNN and k,w,p = 2001 , distance , 2 , isotonic is: 2.73752928972\n", "For KNN the best log loss with hyperparameter tuning is 2.59748933009 with k = 2001 w = uniform p = 1 m = sigmoid\n", "Computation time for this step is 1994.08 seconds\n" ] } ], "source": [ "# Here we will calibrate the KNN classifier with both Platt Scaling and with Isotonic Regression using CalibratedClassifierCV\n", "# with various parameter settings. The \"method\" parameter can be set to \"sigmoid\" or to \"isotonic\", \n", "# corresponding to Platt Scaling and to Isotonic Regression respectively.\n", "\n", "list_for_ks = []\n", "list_for_ws = []\n", "list_for_ps = []\n", "list_for_ms = []\n", "list_for_log_loss = []\n", "\n", "def knn_calibrated(k,w,p,m):\n", " tuned_KNN = KNeighborsClassifier(n_neighbors=k, weights=w, p=p).fit(mini_train_data, mini_train_labels)\n", " dev_prediction_probabilities = tuned_KNN.predict_proba(mini_dev_data)\n", " ccv = CalibratedClassifierCV(tuned_KNN, method = m, cv = 'prefit')\n", " ccv.fit(mini_calibrate_data, mini_calibrate_labels)\n", " ccv_prediction_probabilities = ccv.predict_proba(mini_dev_data)\n", " list_for_ks.append(this_k)\n", " list_for_ws.append(this_w)\n", " list_for_ps.append(this_p)\n", " list_for_ms.append(this_m)\n", " working_log_loss = log_loss(y_true = mini_dev_labels, y_pred = ccv_prediction_probabilities, labels = crime_labels_mini_dev)\n", " list_for_log_loss.append(working_log_loss)\n", " print(\"Multi-class Log Loss with KNN and k,w,p =\", k,\",\",w,\",\",p,\",\",m,\"is:\", working_log_loss)\n", "\n", "k_value_tuning = [i for i in range(1,5002,500)]\n", "weight_tuning = ['uniform', 'distance']\n", "power_parameter_tuning = [1,2]\n", "methods = ['sigmoid', 'isotonic']\n", "\n", "start = time.clock()\n", "for this_k in k_value_tuning:\n", " for this_w in weight_tuning:\n", " for this_p in power_parameter_tuning:\n", " for this_m in methods:\n", " knn_calibrated(this_k, this_w, this_p, this_m)\n", " \n", "index_best_logloss = np.argmin(list_for_log_loss)\n", "print('For KNN the best log loss with hyperparameter tuning and calibration is',list_for_log_loss[index_best_logloss], 'with k =', list_for_ks[index_best_logloss], 'w =', list_for_ws[index_best_logloss], 'p =', list_for_ps[index_best_logloss], 'm =', list_for_ms[index_best_logloss])\n", "end = time.clock()\n", "print(\"Computation time for this step is %.2f\" % (end-start), 'seconds')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "###### Comments on results for Hyperparameter tuning and Calibration for KNN:\n", "\n", "We see that the best log loss we achieve for KNN is with _ neighbors, _ weights, and _ power parameter.\n", "\n", "When we add-in calibration, we see that the the best log loss we achieve for KNN is with _ neighbors, _ weights, _ power parameter, and _ calibration method.\n", "\n", "(Further explanation here?)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Multinomial, Bernoulli, and Gaussian Naive Bayes" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "###### Hyperparameter tuning:\n", "\n", "For the Bernoulli Naive Bayes classifier and Multinomial Naive Bayes classifer, we seek to optimize the alpha parameter (Laplace smoothing parameter). For the Gaussian Naive Bayes classifier there are no inherent parameters within the classifier function to optimize, but we will look at our log loss before and after adding noise to the data that is hypothesized to give it a more normal (Gaussian) distribution, which is required by the GNB classifier." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "###### Hyperparameter tuning: Bernoulli Naive Bayes\n", "\n", "For the Bernoulli Naive Bayes classifier, we seek to optimize the alpha parameter (Laplace smoothing parameter) and the binarize parameter (threshold for binarizing of the sample features). For the binarize parameter, we will create arbitrary thresholds over which our features, which are not binary/boolean features, will be binarized." ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Multi-class Log Loss with BNB and a,b = 1e-08 , 1e-06 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 1e-08 , 1e-05 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 1e-08 , 0.0001 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 1e-08 , 0.001 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 1e-08 , 0.01 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 1e-08 , 0.1 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 1e-08 , 0.2 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 1e-08 , 0.3 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 1e-08 , 0.4 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 1e-08 , 0.5 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 1e-08 , 0.6 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 1e-08 , 0.7 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 1e-08 , 0.8 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 1e-08 , 0.9 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 1e-08 , 0.95 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 1e-08 , 0.99 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 1e-08 , 0.999 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 1e-08 , 0.9999 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 1e-07 , 1e-06 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 1e-07 , 1e-05 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 1e-07 , 0.0001 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 1e-07 , 0.001 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 1e-07 , 0.01 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 1e-07 , 0.1 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 1e-07 , 0.2 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 1e-07 , 0.3 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 1e-07 , 0.4 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 1e-07 , 0.5 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 1e-07 , 0.6 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 1e-07 , 0.7 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 1e-07 , 0.8 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 1e-07 , 0.9 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 1e-07 , 0.95 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 1e-07 , 0.99 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 1e-07 , 0.999 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 1e-07 , 0.9999 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 1e-06 , 1e-06 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 1e-06 , 1e-05 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 1e-06 , 0.0001 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 1e-06 , 0.001 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 1e-06 , 0.01 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 1e-06 , 0.1 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 1e-06 , 0.2 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 1e-06 , 0.3 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 1e-06 , 0.4 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 1e-06 , 0.5 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 1e-06 , 0.6 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 1e-06 , 0.7 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 1e-06 , 0.8 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 1e-06 , 0.9 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 1e-06 , 0.95 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 1e-06 , 0.99 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 1e-06 , 0.999 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 1e-06 , 0.9999 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 1e-05 , 1e-06 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 1e-05 , 1e-05 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 1e-05 , 0.0001 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 1e-05 , 0.001 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 1e-05 , 0.01 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 1e-05 , 0.1 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 1e-05 , 0.2 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 1e-05 , 0.3 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 1e-05 , 0.4 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 1e-05 , 0.5 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 1e-05 , 0.6 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 1e-05 , 0.7 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 1e-05 , 0.8 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 1e-05 , 0.9 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 1e-05 , 0.95 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 1e-05 , 0.99 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 1e-05 , 0.999 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 1e-05 , 0.9999 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 0.0001 , 1e-06 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 0.0001 , 1e-05 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 0.0001 , 0.0001 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 0.0001 , 0.001 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 0.0001 , 0.01 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 0.0001 , 0.1 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 0.0001 , 0.2 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 0.0001 , 0.3 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 0.0001 , 0.4 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 0.0001 , 0.5 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 0.0001 , 0.6 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 0.0001 , 0.7 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 0.0001 , 0.8 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 0.0001 , 0.9 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 0.0001 , 0.95 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 0.0001 , 0.99 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 0.0001 , 0.999 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 0.0001 , 0.9999 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 0.001 , 1e-06 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 0.001 , 1e-05 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 0.001 , 0.0001 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 0.001 , 0.001 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 0.001 , 0.01 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 0.001 , 0.1 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 0.001 , 0.2 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 0.001 , 0.3 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 0.001 , 0.4 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 0.001 , 0.5 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 0.001 , 0.6 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 0.001 , 0.7 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 0.001 , 0.8 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 0.001 , 0.9 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 0.001 , 0.95 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 0.001 , 0.99 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 0.001 , 0.999 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 0.001 , 0.9999 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 0.01 , 1e-06 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 0.01 , 1e-05 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 0.01 , 0.0001 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 0.01 , 0.001 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 0.01 , 0.01 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 0.01 , 0.1 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 0.01 , 0.2 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 0.01 , 0.3 is: 3.61091791264\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Multi-class Log Loss with BNB and a,b = 0.01 , 0.4 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 0.01 , 0.5 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 0.01 , 0.6 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 0.01 , 0.7 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 0.01 , 0.8 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 0.01 , 0.9 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 0.01 , 0.95 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 0.01 , 0.99 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 0.01 , 0.999 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 0.01 , 0.9999 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 0.1 , 1e-06 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 0.1 , 1e-05 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 0.1 , 0.0001 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 0.1 , 0.001 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 0.1 , 0.01 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 0.1 , 0.1 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 0.1 , 0.2 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 0.1 , 0.3 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 0.1 , 0.4 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 0.1 , 0.5 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 0.1 , 0.6 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 0.1 , 0.7 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 0.1 , 0.8 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 0.1 , 0.9 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 0.1 , 0.95 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 0.1 , 0.99 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 0.1 , 0.999 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 0.1 , 0.9999 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 0.2 , 1e-06 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 0.2 , 1e-05 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 0.2 , 0.0001 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 0.2 , 0.001 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 0.2 , 0.01 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 0.2 , 0.1 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 0.2 , 0.2 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 0.2 , 0.3 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 0.2 , 0.4 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 0.2 , 0.5 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 0.2 , 0.6 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 0.2 , 0.7 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 0.2 , 0.8 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 0.2 , 0.9 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 0.2 , 0.95 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 0.2 , 0.99 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 0.2 , 0.999 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 0.2 , 0.9999 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 0.3 , 1e-06 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 0.3 , 1e-05 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 0.3 , 0.0001 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 0.3 , 0.001 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 0.3 , 0.01 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 0.3 , 0.1 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 0.3 , 0.2 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 0.3 , 0.3 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 0.3 , 0.4 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 0.3 , 0.5 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 0.3 , 0.6 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 0.3 , 0.7 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 0.3 , 0.8 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 0.3 , 0.9 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 0.3 , 0.95 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 0.3 , 0.99 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 0.3 , 0.999 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 0.3 , 0.9999 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 0.4 , 1e-06 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 0.4 , 1e-05 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 0.4 , 0.0001 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 0.4 , 0.001 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 0.4 , 0.01 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 0.4 , 0.1 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 0.4 , 0.2 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 0.4 , 0.3 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 0.4 , 0.4 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 0.4 , 0.5 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 0.4 , 0.6 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 0.4 , 0.7 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 0.4 , 0.8 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 0.4 , 0.9 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 0.4 , 0.95 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 0.4 , 0.99 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 0.4 , 0.999 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 0.4 , 0.9999 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 0.5 , 1e-06 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 0.5 , 1e-05 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 0.5 , 0.0001 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 0.5 , 0.001 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 0.5 , 0.01 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 0.5 , 0.1 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 0.5 , 0.2 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 0.5 , 0.3 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 0.5 , 0.4 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 0.5 , 0.5 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 0.5 , 0.6 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 0.5 , 0.7 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 0.5 , 0.8 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 0.5 , 0.9 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 0.5 , 0.95 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 0.5 , 0.99 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 0.5 , 0.999 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 0.5 , 0.9999 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 1.0 , 1e-06 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 1.0 , 1e-05 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 1.0 , 0.0001 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 1.0 , 0.001 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 1.0 , 0.01 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 1.0 , 0.1 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 1.0 , 0.2 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 1.0 , 0.3 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 1.0 , 0.4 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 1.0 , 0.5 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 1.0 , 0.6 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 1.0 , 0.7 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 1.0 , 0.8 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 1.0 , 0.9 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 1.0 , 0.95 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 1.0 , 0.99 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 1.0 , 0.999 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 1.0 , 0.9999 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 2.0 , 1e-06 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 2.0 , 1e-05 is: 3.61091791264\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Multi-class Log Loss with BNB and a,b = 2.0 , 0.0001 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 2.0 , 0.001 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 2.0 , 0.01 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 2.0 , 0.1 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 2.0 , 0.2 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 2.0 , 0.3 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 2.0 , 0.4 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 2.0 , 0.5 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 2.0 , 0.6 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 2.0 , 0.7 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 2.0 , 0.8 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 2.0 , 0.9 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 2.0 , 0.95 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 2.0 , 0.99 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 2.0 , 0.999 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 2.0 , 0.9999 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 10.0 , 1e-06 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 10.0 , 1e-05 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 10.0 , 0.0001 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 10.0 , 0.001 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 10.0 , 0.01 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 10.0 , 0.1 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 10.0 , 0.2 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 10.0 , 0.3 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 10.0 , 0.4 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 10.0 , 0.5 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 10.0 , 0.6 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 10.0 , 0.7 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 10.0 , 0.8 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 10.0 , 0.9 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 10.0 , 0.95 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 10.0 , 0.99 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 10.0 , 0.999 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 10.0 , 0.9999 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 100.0 , 1e-06 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 100.0 , 1e-05 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 100.0 , 0.0001 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 100.0 , 0.001 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 100.0 , 0.01 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 100.0 , 0.1 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 100.0 , 0.2 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 100.0 , 0.3 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 100.0 , 0.4 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 100.0 , 0.5 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 100.0 , 0.6 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 100.0 , 0.7 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 100.0 , 0.8 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 100.0 , 0.9 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 100.0 , 0.95 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 100.0 , 0.99 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 100.0 , 0.999 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 100.0 , 0.9999 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 1000.0 , 1e-06 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 1000.0 , 1e-05 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 1000.0 , 0.0001 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 1000.0 , 0.001 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 1000.0 , 0.01 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 1000.0 , 0.1 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 1000.0 , 0.2 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 1000.0 , 0.3 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 1000.0 , 0.4 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 1000.0 , 0.5 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 1000.0 , 0.6 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 1000.0 , 0.7 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 1000.0 , 0.8 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 1000.0 , 0.9 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 1000.0 , 0.95 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 1000.0 , 0.99 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 1000.0 , 0.999 is: 3.61091791264\n", "Multi-class Log Loss with BNB and a,b = 1000.0 , 0.9999 is: 3.61091791264\n", "For BNB the best log loss with hyperparameter tuning is 3.61091791264 with alpha = 1e-08 binarization threshold = 1e-06\n", "Computation time for this step is 116.42 seconds\n" ] } ], "source": [ "list_for_as = []\n", "list_for_bs = []\n", "list_for_log_loss = []\n", "\n", "def BNB_tuned(a,b):\n", " bnb_tuned = BernoulliNB(alpha = a, binarize = b).fit(mini_train_data, mini_train_labels)\n", " dev_prediction_probabilities = bnb_tuned.predict_log_proba(mini_dev_data)\n", " list_for_as.append(this_a)\n", " list_for_bs.append(this_b)\n", " working_log_loss = log_loss(y_true = mini_dev_labels, y_pred = dev_prediction_probabilities, labels = crime_labels_mini_dev)\n", " list_for_log_loss.append(working_log_loss)\n", " print(\"Multi-class Log Loss with BNB and a,b =\", a,\",\",b,\"is:\", working_log_loss)\n", "\n", "alpha_tuning = [0.00000001,0.0000001,0.000001, 0.00001, 0.0001, 0.001, 0.01, 0.1, 0.2, 0.3, 0.4, 0.5, 1.0, 2.0, 10.0, 100.0, 1000.0]\n", "binarize_thresholds_tuning = [0.000001, 0.00001, 0.0001, 0.001, 0.01, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 0.95, 0.99, 0.999, 0.9999]\n", "\n", "start = time.clock()\n", "for this_a in alpha_tuning:\n", " for this_b in binarize_thresholds_tuning:\n", " BNB_tuned(this_a, this_b)\n", " \n", "index_best_logloss = np.argmin(list_for_log_loss)\n", "print('For BNB the best log loss with hyperparameter tuning is',list_for_log_loss[index_best_logloss], 'with alpha =', list_for_as[index_best_logloss], 'binarization threshold =', list_for_bs[index_best_logloss])\n", "end = time.clock()\n", "print(\"Computation time for this step is %.2f\" % (end-start), 'seconds') " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "###### Hyperparameter tuning: Multinomial Naive Bayes" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def MNB():\n", " mnb = MultinomialNB(alpha = 0.0000001)\n", " mnb.fit(train_data, train_labels)\n", " print(\"\\n\\nMultinomialNB accuracy on dev data:\", mnb.score(dev_data, dev_labels))\n", " \n", " \n", "alphas = [0.0, 0.000000001,0.00000001,0.0000001,0.000001, 0.00001, 0.0001, 0.001, 0.01, 0.1, 0.2, 0.3, 0.4, 0.5, 1.0, 2.0, 10.0, 100.0, 1000.0]\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "###### Tuning: Gaussian Naive Bayes" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def GNB():\n", " gnb = GaussianNB()\n", " gnb.fit(train_data, train_labels)\n", " print(\"GaussianNB accuracy on dev data:\", \n", " gnb.score(dev_data, dev_labels))\n", " \n", " # Gaussian Naive Bayes requires the data to have a relative normal distribution. Sometimes\n", " # adding noise can improve performance by making the data more normal:\n", " train_data_noise = np.random.rand(train_data.shape[0],train_data.shape[1])\n", " modified_train_data = np.multiply(train_data,train_data_noise) \n", " gnb_noise = GaussianNB()\n", " gnb.fit(modified_train_data, train_labels)\n", " print(\"GaussianNB accuracy with added noise:\", \n", " gnb.score(dev_data, dev_labels)) " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "###### Model calibration:\n", "\n", "Here we will calibrate the MNB, BNB, and GNB classifiers with both Platt Scaling and with Isotonic Regression using CalibratedClassifierCV with various parameter settings. The \"method\" parameter can be set to \"sigmoid\" or to \"isotonic\", corresponding to Platt Scaling and to Isotonic Regression respectively." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Logistic Regression\n", "\n", "###### Hyperparameter tuning:\n", "\n", "For the Logistic Regression classifier, we can seek to optimize the following classifier parameters: penalty (l1 or l2), C (inverse of regularization strength), solver ('newton-cg', 'lbfgs', 'liblinear', or 'sag')\n", "\n", "###### Model calibration:\n", "\n", "See above\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Decision Tree\n", "\n", "###### Hyperparameter tuning:\n", "\n", "For the Decision Tree classifier, we can seek to optimize the following classifier parameters: min_samples_leaf (the minimum number of samples required to be at a leaf node), max_depth\n", "\n", "From readings, setting min_samples_leaf to approximately 1% of the data points can stop the tree from inappropriately classifying outliers, which can help to improve accuracy (unsure if significantly improves MCLL).\n", "\n", "\n", "###### Model calibration:\n", "\n", "See above" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Support Vector Machines\n", "\n", "###### Hyperparameter tuning:\n", "\n", "For the SVM classifier, we can seek to optimize the following classifier parameters: C (penalty parameter C of the error term), kernel ('linear', 'poly', 'rbf', sigmoid', or 'precomputed')\n", "\n", "See source [2] for parameter optimization in SVM\n", "\n", "###### Model calibration:\n", "\n", "See above" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Neural Nets\n", "\n", "###### Hyperparameter tuning:\n", "\n", "For the Neural Networks MLP classifier, we can seek to optimize the following classifier parameters: hidden_layer_sizes, activation ('identity', 'logistic', 'tanh', 'relu'), solver ('lbfgs','sgd', adam'), alpha, learning_rate ('constant', 'invscaling','adaptive')" ] }, { "cell_type": "code", "execution_count": 99, "metadata": {}, "outputs": [ { "ename": "ModuleNotFoundError", "evalue": "No module named 'theano'", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-99-746a0d71593d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m### All the work from Sarah's notebook:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mtheano\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 4\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mtheano\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mtensor\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mT\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mtheano\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msandbox\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrng_mrg\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mMRG_RandomStreams\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mRandomStreams\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'theano'" ] } ], "source": [ "### All the work from Sarah's notebook:\n", "\n", "import theano\n", "from theano import tensor as T\n", "from theano.sandbox.rng_mrg import MRG_RandomStreams as RandomStreams\n", "print (theano.config.device) # We're using CPUs (for now)\n", "print (theano.config.floatX )# Should be 64 bit for CPUs\n", "\n", "np.random.seed(0)\n", "\n", "from IPython.display import display, clear_output" ] }, { "cell_type": "code", "execution_count": 92, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Features = 25\n", "Train set = 700000\n", "Test set = 78049\n", "['DRUG/NARCOTIC', 'RUNAWAY', 'DRUNKENNESS', 'LOITERING', 'STOLEN PROPERTY', 'MISSING PERSON', 'ARSON', 'FRAUD', 'SEX OFFENSES NON FORCIBLE', 'NON-CRIMINAL', 'WEAPON LAWS', 'RECOVERED VEHICLE', 'ASSAULT', 'TRESPASS', 'GAMBLING', 'SUSPICIOUS OCC', 'TREA', 'BAD CHECKS', 'VANDALISM', 'FAMILY OFFENSES', 'DRIVING UNDER THE INFLUENCE', 'WARRANTS', 'PROSTITUTION', 'SEX OFFENSES FORCIBLE', 'DISORDERLY CONDUCT', 'LIQUOR LAWS', 'ROBBERY', 'FORGERY/COUNTERFEITING', 'OTHER OFFENSES', 'EXTORTION', 'VEHICLE THEFT', 'SUICIDE', 'PORNOGRAPHY/OBSCENE MAT', 'LARCENY/THEFT', 'BRIBERY', 'EMBEZZLEMENT', 'SECONDARY CODES', 'KIDNAPPING', 'BURGLARY']\n" ] } ], "source": [ "numFeatures = train_data[1].size\n", "numTrainExamples = train_data.shape[0]\n", "numTestExamples = test_data.shape[0]\n", "print ('Features = %d' %(numFeatures))\n", "print ('Train set = %d' %(numTrainExamples))\n", "print ('Test set = %d' %(numTestExamples))\n", "\n", "class_labels = list(set(train_labels))\n", "print(class_labels)\n", "numClasses = len(class_labels)" ] }, { "cell_type": "code", "execution_count": 93, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Classes = 39\n", "\n", " [[ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", " 0. 0. 1.]\n", " [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0.\n", " 0. 0. 0.]\n", " [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0.\n", " 0. 0. 0.]\n", " [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0.\n", " 0. 0. 0.]\n", " [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0.\n", " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", " 0. 0. 0.]] \n", "\n", "['BURGLARY' 'LARCENY/THEFT' 'OTHER OFFENSES' 'OTHER OFFENSES'\n", " 'SUSPICIOUS OCC' 'VANDALISM' 'DRUG/NARCOTIC' 'MISSING PERSON'\n", " 'LARCENY/THEFT' 'OTHER OFFENSES'] \n", "\n" ] } ], "source": [ "### Binarize the class labels\n", "\n", "def binarizeY(data):\n", " binarized_data = np.zeros((data.size,39))\n", " for j in range(0,data.size):\n", " feature = data[j]\n", " i = class_labels.index(feature)\n", " binarized_data[j,i]=1\n", " return binarized_data\n", "\n", "train_labels_b = binarizeY(train_labels)\n", "test_labels_b = binarizeY(test_labels)\n", "numClasses = train_labels_b[1].size\n", "\n", "print ('Classes = %d' %(numClasses))\n", "print ('\\n', train_labels_b[:5, :], '\\n')\n", "print (train_labels[:10], '\\n')" ] }, { "cell_type": "code", "execution_count": 94, "metadata": {}, "outputs": [ { "ename": "NameError", "evalue": "name 'theano' is not defined", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-94-8c49129ca7e8>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mnumHiddenNodeslayer2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m30\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m \u001b[0mw_1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtheano\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshared\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnumFeatures\u001b[0m\u001b[0;34m,\u001b[0m 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numHiddenNodeslayer1))*0.01)))\n", "w_2 = theano.shared(np.asarray((np.random.randn(*(numHiddenNodeslayer1, numHiddenNodeslayer2))*0.01)))\n", "w_3 = theano.shared(np.asarray((np.random.randn(*(numHiddenNodeslayer2, numClasses))*0.01)))\n", "params = [w_1, w_2, w_3]\n", "\n", "\n", "###2) Model\n", "X = T.matrix()\n", "Y = T.matrix()\n", "\n", "srng = RandomStreams()\n", "def dropout(X, p=0.):\n", " if p > 0:\n", " X *= srng.binomial(X.shape, p=1 - p)\n", " X /= 1 - p\n", " return X\n", "\n", "def model(X, w_1, w_2, w_3, p_1, p_2, p_3):\n", " return T.nnet.softmax(T.dot(dropout(T.nnet.sigmoid(T.dot(dropout(T.nnet.sigmoid(T.dot(dropout(X, p_1), w_1)),p_2), w_2)),p_3),w_3))\n", "y_hat_train = model(X, w_1, w_2, w_3, 0.2, 0.5,0.5)\n", "y_hat_predict = model(X, w_1, w_2, w_3, 0., 0., 0.)\n", "\n", "### (3) Cost function\n", "cost = T.mean(T.sqr(y_hat - Y))\n", "cost = T.mean(T.nnet.categorical_crossentropy(y_hat_train, Y))" ] }, { "cell_type": "code", "execution_count": 95, "metadata": {}, "outputs": [ { "ename": "NameError", "evalue": "name 'cost' is not defined", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-95-c7f0e6495c7e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 9\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mupdates\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 10\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 11\u001b[0;31m \u001b[0mupdate\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbackprop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcost\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 12\u001b[0m \u001b[0mtrain\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtheano\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfunction\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mY\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moutputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcost\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mupdates\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mallow_input_downcast\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 13\u001b[0m \u001b[0my_pred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mT\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_hat_predict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mNameError\u001b[0m: name 'cost' is not defined" ] } ], "source": [ "### (4) Objective (and solver)\n", "\n", "alpha = 0.01\n", "def backprop(cost, w):\n", " grads = T.grad(cost=cost, wrt=w)\n", " updates = []\n", " for wi, grad in zip(w, grads):\n", " updates.append([wi, wi - grad * alpha])\n", " return updates\n", "\n", "update = backprop(cost, params)\n", "train = theano.function(inputs=[X, Y], outputs=cost, updates=update, allow_input_downcast=True)\n", "y_pred = T.argmax(y_hat_predict, axis=1)\n", "predict = theano.function(inputs=[X], outputs=y_pred, allow_input_downcast=True)\n", "\n", "miniBatchSize = 10 \n", "\n", "def gradientDescent(epochs):\n", " for i in range(epochs):\n", " for start, end in zip(range(0, len(train_data), miniBatchSize), range(miniBatchSize, len(train_data), miniBatchSize)):\n", " cc = train(train_data[start:end], train_labels_b[start:end])\n", " clear_output(wait=True)\n", " print ('%d) accuracy = %.4f' %(i+1, np.mean(np.argmax(test_labels_b, axis=1) == predict(test_data))) )\n", "\n", "gradientDescent(50)\n", "\n", "### How to decide what # to use for epochs? epochs in this case are how many rounds?\n", "### plot costs for each of the 50 iterations and see how much it decline.. if its still very decreasing, you should\n", "### do more iterations; otherwise if its looking like its flattening, you can stop" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "###### Model calibration:\n", "\n", "See above" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Random Forest\n", "\n", "###### Hyperparameter tuning:\n", "\n", "For the Random Forest classifier, we can seek to optimize the following classifier parameters: n_estimators (the number of trees in the forsest), max_features, max_depth, min_samples_leaf, bootstrap (whether or not bootstrap samples are used when building trees), oob_score (whether or not out-of-bag samples are used to estimate the generalization accuracy)\n", "\n", "###### Model calibration:\n", "\n", "See above" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Meta-estimators" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### AdaBoost Classifier\n", "\n", "###### Hyperparameter tuning:\n", "\n", "There are no major changes that we seek to make in the AdaBoostClassifier with respect to default parameter values.\n", "\n", "###### Adaboosting each classifier:\n", "\n", "We will run the AdaBoostClassifier on each different classifier from above, using the classifier settings with optimized Multi-class Log Loss after hyperparameter tuning and calibration.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Bagging Classifier\n", "\n", "###### Hyperparameter tuning:\n", "\n", "For the Bagging meta classifier, we can seek to optimize the following classifier parameters: n_estimators (the number of trees in the forsest), max_samples, max_features, bootstrap (whether or not bootstrap samples are used when building trees), bootstrap_features (whether features are drawn with replacement), and oob_score (whether or not out-of-bag samples are used to estimate the generalization accuracy)\n", "\n", "###### Bagging each classifier:\n", "\n", "We will run the BaggingClassifier on each different classifier from above, using the classifier settings with optimized Multi-class Log Loss after hyperparameter tuning and calibration.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Gradient Boosting Classifier\n", "\n", "###### Hyperparameter tuning:\n", "\n", "For the Gradient Boosting meta classifier, we can seek to optimize the following classifier parameters: n_estimators (the number of trees in the forsest), max_depth, min_samples_leaf, and max_features\n", "\n", "###### Gradient Boosting each classifier:\n", "\n", "We will run the GradientBoostingClassifier with loss = 'deviance' (as loss = 'exponential' uses the AdaBoost algorithm) on each different classifier from above, using the classifier settings with optimized Multi-class Log Loss after hyperparameter tuning and calibration.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Final evaluation on test data" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Here we will likely use Pipeline and GridSearchCV in order to find the overall classifier with optimized Multi-class Log Loss.\n", "# This will be the last step after all attempts at feature addition, hyperparameter tuning, and calibration are completed\n", "# and the corresponding performance metrics are gathered.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### References\n", "\n", "1) Hsiang, Solomon M. and Burke, Marshall and Miguel, Edward. \"Quantifying the Influence of Climate on Human Conflict\". Science, Vol 341, Issue 6151, 2013 \n", "\n", "2) Huang, Cheng-Lung. Wang, Chieh-Jen. \"A GA-based feature selection and parameters optimization for support vector machines\". Expert Systems with Applications, Vol 31, 2006, p 231-240\n", "\n", "3) More to come \n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.1" } }, "nbformat": 4, "nbformat_minor": 1 }

mit

stellaxux/machine-learning-in-python

ch4/data_preprocessing.ipynb

1

28121

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Building Good Training Sets – Data Preprocessing\n", "\n", "It is important to examine and preprocess a dataset before feeding it to a learning algorithm. In this notebook, I will go through some essential data preprocessing techniques including:\n", "\n", " • Removing and imputing missing values from the dataset\n", " • Getting categorical data into shape for machine learning algorithms\n", " • Selecting relevant features for the model construction" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Dealing with missing data" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import pandas as pd\n", "from io import StringIO\n", "\n", "csv_data = '''A,B,C,D\n", "1.0,2.0,3.0,4.0\n", "5.0,6.0,,8.0\n", "10.0,11.0,,'''\n", "\n", "data = pd.read_csv(StringIO(csv_data))" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "A 0\n", "B 0\n", "C 2\n", "D 1\n", "dtype: int64" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "## checking for missing data\n", "df.isnull().sum()" ] }, { "cell_type": "code", "execution_count": 18, "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>first_name</th>\n", " <th>last_name</th>\n", " <th>age</th>\n", " <th>sex</th>\n", " <th>preTestScore</th>\n", " <th>postTestScore</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>Jason</td>\n", " <td>Miller</td>\n", " <td>42.0</td>\n", " <td>m</td>\n", " <td>4.0</td>\n", " <td>25.0</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>25.0</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>Tina</td>\n", " <td>Ali</td>\n", " <td>36.0</td>\n", " <td>f</td>\n", " <td>3.0</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>Jake</td>\n", " <td>Milner</td>\n", " <td>24.0</td>\n", " <td>m</td>\n", " <td>2.0</td>\n", " <td>62.0</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>Amy</td>\n", " <td>Cooze</td>\n", " <td>73.0</td>\n", " <td>f</td>\n", " <td>3.0</td>\n", " <td>70.0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " first_name last_name age sex preTestScore postTestScore\n", "0 Jason Miller 42.0 m 4.0 25.0\n", "1 NaN NaN NaN NaN NaN 25.0\n", "2 Tina Ali 36.0 f 3.0 NaN\n", "3 Jake Milner 24.0 m 2.0 62.0\n", "4 Amy Cooze 73.0 f 3.0 70.0" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Another example of a dataframe with missing data\n", "# creating dataframe from dictionary; key is the colume name\n", "import numpy as np\n", "\n", "raw_data = {'first_name': ['Jason', np.nan, 'Tina', 'Jake', 'Amy'],\n", " 'last_name': ['Miller', np.nan, 'Ali', 'Milner', 'Cooze'],\n", " 'age': [42, np.nan, 36, 24, 73],\n", " 'sex': ['m', np.nan, 'f', 'm', 'f'],\n", " 'preTestScore': [4, np.nan, np.nan, 2, 3],\n", " 'postTestScore': [25, np.nan, np.nan, 62, 70]}\n", "df = pd.DataFrame(raw_data, columns = ['first_name', 'last_name', 'age', 'sex', 'preTestScore', 'postTestScore'])\n", "df" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Eliminating samples or features with missing values" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>first_name</th>\n", " <th>last_name</th>\n", " <th>age</th>\n", " <th>sex</th>\n", " <th>preTestScore</th>\n", " <th>postTestScore</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>Jason</td>\n", " <td>Miller</td>\n", " <td>42.0</td>\n", " <td>m</td>\n", " <td>4.0</td>\n", " <td>25.0</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>Jake</td>\n", " <td>Milner</td>\n", " <td>24.0</td>\n", " <td>m</td>\n", " <td>2.0</td>\n", " <td>62.0</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>Amy</td>\n", " <td>Cooze</td>\n", " <td>73.0</td>\n", " <td>f</td>\n", " <td>3.0</td>\n", " <td>70.0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " first_name last_name age sex preTestScore postTestScore\n", "0 Jason Miller 42.0 m 4.0 25.0\n", "3 Jake Milner 24.0 m 2.0 62.0\n", "4 Amy Cooze 73.0 f 3.0 70.0" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# default: drop all rows containing NAN\n", "df.dropna()" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>first_name</th>\n", " <th>last_name</th>\n", " <th>age</th>\n", " <th>sex</th>\n", " <th>preTestScore</th>\n", " <th>postTestScore</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>Jason</td>\n", " <td>Miller</td>\n", " <td>42.0</td>\n", " <td>m</td>\n", " <td>4.0</td>\n", " <td>25.0</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>Tina</td>\n", " <td>Ali</td>\n", " <td>36.0</td>\n", " <td>f</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>Jake</td>\n", " <td>Milner</td>\n", " <td>24.0</td>\n", " <td>m</td>\n", " <td>2.0</td>\n", " <td>62.0</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>Amy</td>\n", " <td>Cooze</td>\n", " <td>73.0</td>\n", " <td>f</td>\n", " <td>3.0</td>\n", " <td>70.0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " first_name last_name age sex preTestScore postTestScore\n", "0 Jason Miller 42.0 m 4.0 25.0\n", "2 Tina Ali 36.0 f NaN NaN\n", "3 Jake Milner 24.0 m 2.0 62.0\n", "4 Amy Cooze 73.0 f 3.0 70.0" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Only drop rows where all cells in that row is NA\n", "df.dropna(how='all')" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ "Empty DataFrame\n", "Columns: []\n", "Index: [0, 1, 2, 3, 4]" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Drop all columns if they contain missing values (seldom used)\n", "df.dropna(axis=1)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>first_name</th>\n", " <th>last_name</th>\n", " <th>age</th>\n", " <th>sex</th>\n", " <th>preTestScore</th>\n", " <th>postTestScore</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>Jason</td>\n", " <td>Miller</td>\n", " <td>42.0</td>\n", " <td>m</td>\n", " <td>4.0</td>\n", " <td>25.0</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>Jake</td>\n", " <td>Milner</td>\n", " <td>24.0</td>\n", " <td>m</td>\n", " <td>2.0</td>\n", " <td>62.0</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>Amy</td>\n", " <td>Cooze</td>\n", " <td>73.0</td>\n", " <td>f</td>\n", " <td>3.0</td>\n", " <td>70.0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " first_name last_name age sex preTestScore postTestScore\n", "0 Jason Miller 42.0 m 4.0 25.0\n", "3 Jake Milner 24.0 m 2.0 62.0\n", "4 Amy Cooze 73.0 f 3.0 70.0" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Drop rows that contain less than five observations, mostly useful for time series\n", "df.dropna(thresh=5)" ] }, { "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>first_name</th>\n", " <th>last_name</th>\n", " <th>age</th>\n", " <th>sex</th>\n", " <th>preTestScore</th>\n", " <th>postTestScore</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>Jason</td>\n", " <td>Miller</td>\n", " <td>42.0</td>\n", " <td>m</td>\n", " <td>4.0</td>\n", " <td>25.0</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>Jake</td>\n", " <td>Milner</td>\n", " <td>24.0</td>\n", " <td>m</td>\n", " <td>2.0</td>\n", " <td>62.0</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>Amy</td>\n", " <td>Cooze</td>\n", " <td>73.0</td>\n", " <td>f</td>\n", " <td>3.0</td>\n", " <td>70.0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " first_name last_name age sex preTestScore postTestScore\n", "0 Jason Miller 42.0 m 4.0 25.0\n", "3 Jake Milner 24.0 m 2.0 62.0\n", "4 Amy Cooze 73.0 f 3.0 70.0" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# only drop rows where NaN appear in specific columns (here: 'C')\n", "df.dropna(subset=['preTestScore', 'postTestScore'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Filling/imputing missing values" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>first_name</th>\n", " <th>last_name</th>\n", " <th>age</th>\n", " <th>sex</th>\n", " <th>preTestScore</th>\n", " <th>postTestScore</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>Jason</td>\n", " <td>Miller</td>\n", " <td>42.0</td>\n", " <td>m</td>\n", " <td>4.0</td>\n", " <td>25.0</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0.0</td>\n", " <td>0</td>\n", " <td>0.0</td>\n", " <td>25.0</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>Tina</td>\n", " <td>Ali</td>\n", " <td>36.0</td>\n", " <td>f</td>\n", " <td>3.0</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>Jake</td>\n", " <td>Milner</td>\n", " <td>24.0</td>\n", " <td>m</td>\n", " <td>2.0</td>\n", " <td>62.0</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>Amy</td>\n", " <td>Cooze</td>\n", " <td>73.0</td>\n", " <td>f</td>\n", " <td>3.0</td>\n", " <td>70.0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " first_name last_name age sex preTestScore postTestScore\n", "0 Jason Miller 42.0 m 4.0 25.0\n", "1 0 0 0.0 0 0.0 25.0\n", "2 Tina Ali 36.0 f 3.0 0.0\n", "3 Jake Milner 24.0 m 2.0 62.0\n", "4 Amy Cooze 73.0 f 3.0 70.0" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Fill in missing data with zeros\n", "df.fillna(0)" ] }, { "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", " <th></th>\n", " <th>first_name</th>\n", " <th>last_name</th>\n", " <th>age</th>\n", " <th>sex</th>\n", " <th>preTestScore</th>\n", " <th>postTestScore</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>Jason</td>\n", " <td>Miller</td>\n", " <td>42.0</td>\n", " <td>m</td>\n", " <td>4.0</td>\n", " <td>25.0</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>3.0</td>\n", " <td>25.0</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>Tina</td>\n", " <td>Ali</td>\n", " <td>36.0</td>\n", " <td>f</td>\n", " <td>3.0</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>Jake</td>\n", " <td>Milner</td>\n", " <td>24.0</td>\n", " <td>m</td>\n", " <td>2.0</td>\n", " <td>62.0</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>Amy</td>\n", " <td>Cooze</td>\n", " <td>73.0</td>\n", " <td>f</td>\n", " <td>3.0</td>\n", " <td>70.0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " first_name last_name age sex preTestScore postTestScore\n", "0 Jason Miller 42.0 m 4.0 25.0\n", "1 NaN NaN NaN NaN 3.0 25.0\n", "2 Tina Ali 36.0 f 3.0 NaN\n", "3 Jake Milner 24.0 m 2.0 62.0\n", "4 Amy Cooze 73.0 f 3.0 70.0" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Fill in missing in preTestScore with the mean value of preTestScore\n", "df['preTestScore'].fillna(df['preTestScore'].mean(), inplace=True)\n", "df" ] }, { "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>first_name</th>\n", " <th>last_name</th>\n", " <th>age</th>\n", " <th>sex</th>\n", " <th>preTestScore</th>\n", " <th>postTestScore</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>Jason</td>\n", " <td>Miller</td>\n", " <td>42.0</td>\n", " <td>m</td>\n", " <td>4.0</td>\n", " <td>25.0</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>3.0</td>\n", " <td>25.0</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>Tina</td>\n", " <td>Ali</td>\n", " <td>36.0</td>\n", " <td>f</td>\n", " <td>3.0</td>\n", " <td>70.0</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>Jake</td>\n", " <td>Milner</td>\n", " <td>24.0</td>\n", " <td>m</td>\n", " <td>2.0</td>\n", " <td>62.0</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>Amy</td>\n", " <td>Cooze</td>\n", " <td>73.0</td>\n", " <td>f</td>\n", " <td>3.0</td>\n", " <td>70.0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " first_name last_name age sex preTestScore postTestScore\n", "0 Jason Miller 42.0 m 4.0 25.0\n", "1 NaN NaN NaN NaN 3.0 25.0\n", "2 Tina Ali 36.0 f 3.0 70.0\n", "3 Jake Milner 24.0 m 2.0 62.0\n", "4 Amy Cooze 73.0 f 3.0 70.0" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Fill in missing in postTestScore with each sex's mean value of postTestScore\n", "df['postTestScore'].fillna(df.groupby('sex')['postTestScore'].transform('mean'), inplace=True)\n", "df" ] }, { "cell_type": "code", "execution_count": 35, "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>first_name</th>\n", " <th>last_name</th>\n", " <th>age</th>\n", " <th>sex</th>\n", " <th>preTestScore</th>\n", " <th>postTestScore</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>Jason</td>\n", " <td>Miller</td>\n", " <td>42.0</td>\n", " <td>m</td>\n", " <td>4.0</td>\n", " <td>25.0</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>Tina</td>\n", " <td>Ali</td>\n", " <td>36.0</td>\n", " <td>f</td>\n", " <td>3.0</td>\n", " <td>70.0</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>Jake</td>\n", " <td>Milner</td>\n", " <td>24.0</td>\n", " <td>m</td>\n", " <td>2.0</td>\n", " <td>62.0</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>Amy</td>\n", " <td>Cooze</td>\n", " <td>73.0</td>\n", " <td>f</td>\n", " <td>3.0</td>\n", " <td>70.0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " first_name last_name age sex preTestScore postTestScore\n", "0 Jason Miller 42.0 m 4.0 25.0\n", "2 Tina Ali 36.0 f 3.0 70.0\n", "3 Jake Milner 24.0 m 2.0 62.0\n", "4 Amy Cooze 73.0 f 3.0 70.0" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Select the rows of df where age is not NaN and sex is not NaN\n", "\n", "df[df['age'].notnull() & df['sex'].notnull()]" ] }, { "cell_type": "code", "execution_count": 40, "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>1.0</td>\n", " <td>2.0</td>\n", " <td>3.000000</td>\n", " <td>4.0</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>5.0</td>\n", " <td>6.0</td>\n", " <td>6.333333</td>\n", " <td>8.0</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>10.0</td>\n", " <td>11.0</td>\n", " <td>10.500000</td>\n", " <td>10.5</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " A B C D\n", "0 1.0 2.0 3.000000 4.0\n", "1 5.0 6.0 6.333333 8.0\n", "2 10.0 11.0 10.500000 10.5" ] }, "execution_count": 40, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Fill in missing data with row mean\n", "fill_value = pd.DataFrame({col: data.mean(axis=1) for col in data.columns})\n", "data.fillna(fill_value, inplace=True)\n", "data" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[ 1., 2., 3., 4.],\n", " [ 5., 6., 3., 8.],\n", " [ 10., 11., 3., 6.]])" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Another method using the Imputer class from scikit-learn, only work with numerical dataframe\n", "from sklearn.preprocessing import Imputer\n", "imr = Imputer(missing_values='NaN', strategy='mean',axis=0)\n", "imr = imr.fit_transform(data)\n", "imr" ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [python3]", "language": "python", "name": "Python [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

helotism/saltstack-intro

data/gutgesalzen.ipynb

1

14704

{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import json\n", "\n", "with open('./salt-grains.json') as json_data:\n", " grains_incoming = json.load(json_data)\n", " json_data.close()\n", "\n", "grains_staged = {}\n", "for key, value in grains_incoming.items():\n", " retval = {}\n", " retval['id'] = value['ret']['id']\n", " retval['kernel'] = value['ret']['kernel']\n", " retval['os'] = value['ret']['os']\n", " retval['os_family'] = value['ret']['os_family']\n", " retval['oscodename'] = value['ret']['oscodename']\n", " retval['osrelease'] = value['ret']['osrelease']\n", " retval['osfinger'] = value['ret']['osfinger']\n", " tmp = []\n", " for key2, value2 in value['ret']['ip4_interfaces'].items():\n", " if key2 not in ['lo']:\n", " tmp.append(value2[0])\n", " retval['ip4_interfaces'] = \", \".join(tmp)\n", " grains_staged[key] = retval\n", "\n", "#grains_staged" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import json\n", "\n", "with open('./salt-keys.json') as json_data:\n", " keys_incoming = json.load(json_data)\n", " json_data.close()\n", "\n", "keys_staged={}\n", "for k, col in keys_incoming.items():\n", " #print(col)\n", " for m in map(str, col):\n", " keys_staged[m] = k\n", "\n", "#keys_staged" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[{'grains': {'id': 'saltminion02',\n", " 'ip4_interfaces': '172.76.4.4',\n", " 'kernel': 'Linux',\n", " 'os': 'Debian',\n", " 'os_family': 'Debian',\n", " 'oscodename': 'jessie',\n", " 'osfinger': 'Debian-8',\n", " 'osrelease': '8.6'},\n", " 'keystatus': 'minions',\n", " 'minionid': 'saltminion02'},\n", " {'grains': {}, 'keystatus': 'minions_pre', 'minionid': 'T500-2017'},\n", " {'grains': {'id': 'saltminion01',\n", " 'ip4_interfaces': '172.76.4.3',\n", " 'kernel': 'Linux',\n", " 'os': 'Ubuntu',\n", " 'os_family': 'Debian',\n", " 'oscodename': 'xenial',\n", " 'osfinger': 'Ubuntu-16.04',\n", " 'osrelease': '16.04'},\n", " 'keystatus': 'minions',\n", " 'minionid': 'saltminion01'},\n", " {'grains': {}, 'keystatus': 'minions_pre', 'minionid': 'W7-minion'},\n", " {'grains': {'id': 'minion-on-saltmaster',\n", " 'ip4_interfaces': '172.76.4.2, 172.17.0.2',\n", " 'kernel': 'Linux',\n", " 'os': 'Ubuntu',\n", " 'os_family': 'Debian',\n", " 'oscodename': 'xenial',\n", " 'osfinger': 'Ubuntu-16.04',\n", " 'osrelease': '16.04'},\n", " 'keystatus': 'minions',\n", " 'minionid': 'minion-on-saltmaster'}]\n" ] } ], "source": [ "from pprint import pprint\n", "\n", "minions = {}\n", "minionslist = []\n", "for k, v in keys_staged.items():\n", " tmp = {}\n", " tmp['keystatus'] = v\n", " if k in grains_staged:\n", " tmp['grains'] = grains_staged[k]\n", " else:\n", " tmp['grains'] = {}\n", " tmp['minionid'] = k\n", " minions[k] = tmp\n", " minionslist.append(tmp)\n", "pprint(minionslist)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "data": { "text/html": [ "\n", "<script src=\"http://d3js.org/d3.v3.min.js\"></script>\n", "<div id=\"chart\"></div>\n", "<script> \n", "\n", "var data = [{\"keystatus\": \"minions\", \"grains\": {\"os_family\": \"Debian\", \"oscodename\": \"jessie\", \"kernel\": \"Linux\", \"os\": \"Debian\", \"id\": \"saltminion02\", \"ip4_interfaces\": \"172.76.4.4\", \"osrelease\": \"8.6\", \"osfinger\": \"Debian-8\"}, \"minionid\": \"saltminion02\"}, {\"keystatus\": \"minions_pre\", \"grains\": {}, \"minionid\": \"T500-2017\"}, {\"keystatus\": \"minions\", \"grains\": {\"os_family\": \"Debian\", \"oscodename\": \"xenial\", \"kernel\": \"Linux\", \"os\": \"Ubuntu\", \"id\": \"saltminion01\", \"ip4_interfaces\": \"172.76.4.3\", \"osrelease\": \"16.04\", \"osfinger\": \"Ubuntu-16.04\"}, \"minionid\": \"saltminion01\"}, {\"keystatus\": \"minions_pre\", \"grains\": {}, \"minionid\": \"W7-minion\"}, {\"keystatus\": \"minions\", \"grains\": {\"os_family\": \"Debian\", \"oscodename\": \"xenial\", \"kernel\": \"Linux\", \"os\": \"Ubuntu\", \"id\": \"minion-on-saltmaster\", \"ip4_interfaces\": \"172.76.4.2, 172.17.0.2\", \"osrelease\": \"16.04\", \"osfinger\": \"Ubuntu-16.04\"}, \"minionid\": \"minion-on-saltmaster\"}];\n", "//console.log(data)\n", "var w = 640, h = 240;\n", "var slice = (2 * Math.PI / data.length * .2 );\n", "\n", "var svg = d3.select(\"#chart\").insert(\"svg\")\n", ".attr(\"width\", w).attr(\"height\", h)\n", " .style(\"fill\", \"#fdf6e3\");\n", "\n", "var rectangle = svg.append(\"rect\")\n", " .attr(\"x\", 0)\n", " .attr(\"y\", 0)\n", " .attr(\"width\", w)\n", " .attr(\"height\", h)\n", " .style(\"fill\", \"#fdf6e3\");\n", "\n", "var container = svg.append(\"g\")\n", ".attr(\"transform\", \"translate(\" + 32 + \",\" + 8 + \")\")\n", "//.attr(\"transform\", \"translate(\" + w/2 + \",\" + h/2 + \")\")\n", "\n", "\n", "var saltmaster = container.append(\"g\").attr(\"id\", \"saltmaster\");\n", "var saltminions = container.append(\"g\").attr(\"id\", \"saltminions\");\n", "\n", "saltmaster.append(\"circle\").attr(\"r\", 220)\n", " .attr(\"cx\", 0)\n", " .attr(\"cy\", 0)\n", " .style(\"fill\", \"#fdf6e3\")\n", " .style(\"stroke-width\", \"3\")\n", " .style(\"stroke\", \"#2aa198\");\n", "\n", "saltminions.selectAll(\"g.saltminion\")\n", " .data(data)\n", " .enter()\n", " .append(\"g\")\n", " .attr(\"class\", \"saltminion\")\n", " .each(function(d, i) {\n", " d3.select(this).append(\"circle\")\n", " .attr(\"r\", 10)\n", " .attr(\"cx\",(220 * Math.cos((slice*i)+0.1)))\n", " .attr(\"cy\",(220 * Math.sin((slice*i)+0.1)))\n", " .style(\"fill\", \"#93a1a1\")\n", " .style(\"stroke-width\", \"3\")\n", " .style(\"stroke-dasharray\", d.keystatus != \"minions\" ? \"2, 1\" : \"10, \")\n", " .style(\"stroke\", \"#2aa198\");\n", " d3.select(this).append(\"text\")\n", " .attr(\"dx\",(220 * Math.cos((slice*i)+0.1))+16)\n", " .attr(\"dy\",(220 * Math.sin((slice*i)+0.1))+4)\n", " .style(\"fill\", \"#002b36\")\n", " .style(\"font-weight\", d.keystatus != \"minions\" ? \"300\" : \"900\")\n", " .text(d.minionid);\n", " d3.select(this).append(\"text\")\n", " .attr(\"dx\",(220 * Math.cos((slice*i)+0.1))+16)\n", " .attr(\"dy\",(220 * Math.sin((slice*i)+0.1))+20)\n", " .style(\"fill\", \"#002b36\")\n", " .text($.grep([d.grains.kernel, d.grains.osfinger, d.grains.ip4_interfaces], Boolean).join('; '));\n", "/*\n", " d3.select(this).selectAll(\"g.saltminion.legend\")\n", " .data($.grep([d.grains.kernel, d.grains.osfinger, d.grains.ip4_interfaces], Boolean).join('; '))\n", " .enter()\n", " .append(\"g\")\n", " .attr(\"transform\", \"translate(\" + (220 * Math.cos((slice*i)+0.1)) + \",\" + (220 * Math.sin((slice*i)+0.1)) + \")\")\n", " .attr(\"class\", \"saltminionlegend\")\n", " .each(function(d2, i2) {\n", " d3.select(this)\n", " .append(\"text\").text(d2)\n", " .attr(\"dx\", 120)\n", " .attr(\"dy\", (i2*16)+12)\n", " .style(\"fill\", \"#002b36\")\n", " .style(\"font-size\", \"12\");\n", " })\n", " ;\n", "*/\n", " });\n", "\n", "\n", "svg.append(\"circle\").attr(\"r\", 120)\n", " .attr(\"cx\", 24)\n", " .attr(\"cy\", 24)\n", " .style(\"fill\", \"#eee8d5\");\n", "svg.append(\"circle\").attr(\"r\", 20)\n", " .attr(\"cx\", 24)\n", " .attr(\"cy\", 24)\n", " .style(\"fill\", \"#002b36\")\n", " .style(\"stroke\", \"#073642\");\n", "\n", "svg.append(\"text\")\n", " .attr(\"dx\",48)\n", " .attr(\"dy\",24)\n", " .style(\"fill\", \"#002b36\")\n", " .style(\"font-weight\", \"900\")\n", " .text('saltmaster');\n", "\n", " </script>\n" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from IPython.core.display import display, HTML\n", "from string import Template\n", "import json\n", "\n", "js_text_template = Template('''\n", "\n", "var data = $python_data;\n", "//console.log(data)\n", "var w = 640, h = 240;\n", "var slice = (2 * Math.PI / data.length * .2 );\n", "\n", "var svg = d3.select(\"#chart\").insert(\"svg\")\n", ".attr(\"width\", w).attr(\"height\", h)\n", " .style(\"fill\", \"#fdf6e3\");\n", "\n", "var rectangle = svg.append(\"rect\")\n", " .attr(\"x\", 0)\n", " .attr(\"y\", 0)\n", " .attr(\"width\", w)\n", " .attr(\"height\", h)\n", " .style(\"fill\", \"#fdf6e3\");\n", "\n", "var container = svg.append(\"g\")\n", ".attr(\"transform\", \"translate(\" + 32 + \",\" + 8 + \")\")\n", "//.attr(\"transform\", \"translate(\" + w/2 + \",\" + h/2 + \")\")\n", "\n", "\n", "var saltmaster = container.append(\"g\").attr(\"id\", \"saltmaster\");\n", "var saltminions = container.append(\"g\").attr(\"id\", \"saltminions\");\n", "\n", "saltmaster.append(\"circle\").attr(\"r\", 220)\n", " .attr(\"cx\", 0)\n", " .attr(\"cy\", 0)\n", " .style(\"fill\", \"#fdf6e3\")\n", " .style(\"stroke-width\", \"3\")\n", " .style(\"stroke\", \"#2aa198\");\n", "\n", "saltminions.selectAll(\"g.saltminion\")\n", " .data(data)\n", " .enter()\n", " .append(\"g\")\n", " .attr(\"class\", \"saltminion\")\n", " .each(function(d, i) {\n", " d3.select(this).append(\"circle\")\n", " .attr(\"r\", 10)\n", " .attr(\"cx\",(220 * Math.cos((slice*i)+0.1)))\n", " .attr(\"cy\",(220 * Math.sin((slice*i)+0.1)))\n", " .style(\"fill\", \"#93a1a1\")\n", " .style(\"stroke-width\", \"3\")\n", " .style(\"stroke-dasharray\", d.keystatus != \"minions\" ? \"2, 1\" : \"10, \")\n", " .style(\"stroke\", \"#2aa198\");\n", " d3.select(this).append(\"text\")\n", " .attr(\"dx\",(220 * Math.cos((slice*i)+0.1))+16)\n", " .attr(\"dy\",(220 * Math.sin((slice*i)+0.1))+4)\n", " .style(\"fill\", \"#002b36\")\n", " .style(\"font-weight\", d.keystatus != \"minions\" ? \"300\" : \"900\")\n", " .text(d.minionid);\n", " d3.select(this).append(\"text\")\n", " .attr(\"dx\",(220 * Math.cos((slice*i)+0.1))+16)\n", " .attr(\"dy\",(220 * Math.sin((slice*i)+0.1))+20)\n", " .style(\"fill\", \"#002b36\")\n", " .text($$.grep([d.grains.kernel, d.grains.osfinger, d.grains.ip4_interfaces], Boolean).join('; '));\n", "/*\n", " d3.select(this).selectAll(\"g.saltminion.legend\")\n", " .data($$.grep([d.grains.kernel, d.grains.osfinger, d.grains.ip4_interfaces], Boolean).join('; '))\n", " .enter()\n", " .append(\"g\")\n", " .attr(\"transform\", \"translate(\" + (220 * Math.cos((slice*i)+0.1)) + \",\" + (220 * Math.sin((slice*i)+0.1)) + \")\")\n", " .attr(\"class\", \"saltminionlegend\")\n", " .each(function(d2, i2) {\n", " d3.select(this)\n", " .append(\"text\").text(d2)\n", " .attr(\"dx\", 120)\n", " .attr(\"dy\", (i2*16)+12)\n", " .style(\"fill\", \"#002b36\")\n", " .style(\"font-size\", \"12\");\n", " })\n", " ;\n", "*/\n", " });\n", "\n", "\n", "svg.append(\"circle\").attr(\"r\", 120)\n", " .attr(\"cx\", 24)\n", " .attr(\"cy\", 24)\n", " .style(\"fill\", \"#eee8d5\");\n", "svg.append(\"circle\").attr(\"r\", 20)\n", " .attr(\"cx\", 24)\n", " .attr(\"cy\", 24)\n", " .style(\"fill\", \"#002b36\")\n", " .style(\"stroke\", \"#073642\");\n", "\n", "svg.append(\"text\")\n", " .attr(\"dx\",48)\n", " .attr(\"dy\",24)\n", " .style(\"fill\", \"#002b36\")\n", " .style(\"font-weight\", \"900\")\n", " .text('saltmaster');\n", "\n", "''')\n", "\n", "html_template = Template('''\n", "<script src=\"http://d3js.org/d3.v3.min.js\"></script>\n", "<div id=\"chart\"></div>\n", "<script> $js_text </script>\n", "''')\n", "\n", "js_text = js_text_template.substitute({'python_data': json.dumps(minionslist),\n", " 'alert': 'Test.'})\n", "\n", "html = HTML(html_template.substitute({'js_text': js_text}))\n", "html" ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 1 }

mit

duyetdev/datascience-R

1-import-data/.ipynb_checkpoints/readxl-package-checkpoint.ipynb

1

13311

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Installing package into ‘/home/duyetdev/R/x86_64-pc-linux-gnu-library/3.3’\n", "(as ‘lib’ is unspecified)\n" ] } ], "source": [ "install.packages('readxl')" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "library(readxl)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Xử lý với file exel trong R \n", "\n", "Sử dụng package `readxl` để đọc file [urbanpop.xlsx](urbanpop.xlsx)\n", "\n", "* `excel_sheets()`: liệt kê danh sách sheet \n", "* `read_excel()`: import excel to data.frame." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<ol class=list-inline>\n", "\t<li>'1960-1966'</li>\n", "\t<li>'1967-1974'</li>\n", "\t<li>'1975-2011'</li>\n", "</ol>\n" ], "text/latex": [ "\\begin{enumerate*}\n", "\\item '1960-1966'\n", "\\item '1967-1974'\n", "\\item '1975-2011'\n", "\\end{enumerate*}\n" ], "text/markdown": [ "1. '1960-1966'\n", "2. '1967-1974'\n", "3. '1975-2011'\n", "\n", "\n" ], "text/plain": [ "[1] \"1960-1966\" \"1967-1974\" \"1975-2011\"" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Liệt kê danh sách các sheet với hàm excel_sheets()\n", "sheets <- excel_sheets(\"urbanpop.xlsx\")\n", "sheets" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<table>\n", "<thead><tr><th scope=col>country</th><th scope=col>1960</th><th scope=col>1961</th><th scope=col>1962</th><th scope=col>1963</th><th scope=col>1964</th><th scope=col>1965</th><th scope=col>1966</th></tr></thead>\n", "<tbody>\n", "\t<tr><td>Afghanistan </td><td> 769308 </td><td> 814923.049 </td><td> 858521.698 </td><td> 903913.86 </td><td> 951225.94 </td><td>1000582.35 </td><td>1058743.47 </td></tr>\n", "\t<tr><td>Albania </td><td> 494443 </td><td> 511802.780 </td><td> 529438.851 </td><td> 547376.75 </td><td> 565571.75 </td><td> 583982.89 </td><td> 602512.17 </td></tr>\n", "\t<tr><td>Algeria </td><td>3293999 </td><td>3515147.548 </td><td>3739963.007 </td><td>3973289.13 </td><td>4220987.01 </td><td>4488175.64 </td><td>4649105.24 </td></tr>\n", "\t<tr><td>American Samoa</td><td> NA </td><td> 13660.298 </td><td> 14165.797 </td><td> 14758.93 </td><td> 15396.42 </td><td> 16044.82 </td><td> 16693.11 </td></tr>\n", "\t<tr><td>Andorra </td><td> NA </td><td> 8723.921 </td><td> 9700.346 </td><td> 10748.38 </td><td> 11865.86 </td><td> 13052.75 </td><td> 14216.81 </td></tr>\n", "\t<tr><td>Angola </td><td> 521205 </td><td> 548265.046 </td><td> 579695.370 </td><td> 612086.70 </td><td> 645261.59 </td><td> 679109.12 </td><td> 717833.40 </td></tr>\n", "</tbody>\n", "</table>\n" ], "text/latex": [ "\\begin{tabular}{r|llllllll}\n", " country & 1960 & 1961 & 1962 & 1963 & 1964 & 1965 & 1966\\\\\n", "\\hline\n", "\t Afghanistan & 769308 & 814923.049 & 858521.698 & 903913.86 & 951225.94 & 1000582.35 & 1058743.47 \\\\\n", "\t Albania & 494443 & 511802.780 & 529438.851 & 547376.75 & 565571.75 & 583982.89 & 602512.17 \\\\\n", "\t Algeria & 3293999 & 3515147.548 & 3739963.007 & 3973289.13 & 4220987.01 & 4488175.64 & 4649105.24 \\\\\n", "\t American Samoa & NA & 13660.298 & 14165.797 & 14758.93 & 15396.42 & 16044.82 & 16693.11 \\\\\n", "\t Andorra & NA & 8723.921 & 9700.346 & 10748.38 & 11865.86 & 13052.75 & 14216.81 \\\\\n", "\t Angola & 521205 & 548265.046 & 579695.370 & 612086.70 & 645261.59 & 679109.12 & 717833.40 \\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "country | 1960 | 1961 | 1962 | 1963 | 1964 | 1965 | 1966 | \n", "|---|---|---|---|---|---|\n", "| Afghanistan | 769308 | 814923.049 | 858521.698 | 903913.86 | 951225.94 | 1000582.35 | 1058743.47 | \n", "| Albania | 494443 | 511802.780 | 529438.851 | 547376.75 | 565571.75 | 583982.89 | 602512.17 | \n", "| Algeria | 3293999 | 3515147.548 | 3739963.007 | 3973289.13 | 4220987.01 | 4488175.64 | 4649105.24 | \n", "| American Samoa | NA | 13660.298 | 14165.797 | 14758.93 | 15396.42 | 16044.82 | 16693.11 | \n", "| Andorra | NA | 8723.921 | 9700.346 | 10748.38 | 11865.86 | 13052.75 | 14216.81 | \n", "| Angola | 521205 | 548265.046 | 579695.370 | 612086.70 | 645261.59 | 679109.12 | 717833.40 | \n", "\n", "\n" ], "text/plain": [ " country 1960 1961 1962 1963 1964 \n", "1 Afghanistan 769308 814923.049 858521.698 903913.86 951225.94\n", "2 Albania 494443 511802.780 529438.851 547376.75 565571.75\n", "3 Algeria 3293999 3515147.548 3739963.007 3973289.13 4220987.01\n", "4 American Samoa NA 13660.298 14165.797 14758.93 15396.42\n", "5 Andorra NA 8723.921 9700.346 10748.38 11865.86\n", "6 Angola 521205 548265.046 579695.370 612086.70 645261.59\n", " 1965 1966 \n", "1 1000582.35 1058743.47\n", "2 583982.89 602512.17\n", "3 4488175.64 4649105.24\n", "4 16044.82 16693.11\n", "5 13052.75 14216.81\n", "6 679109.12 717833.40" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "data <- read_excel(\"urbanpop.xlsx\", sheet = '1960-1966')\n", "head(data)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<table>\n", "<thead><tr><th scope=col>country</th><th scope=col>1967</th><th scope=col>1968</th><th scope=col>1969</th><th scope=col>1970</th><th scope=col>1971</th><th scope=col>1972</th><th scope=col>1973</th><th scope=col>1974</th></tr></thead>\n", "<tbody>\n", "\t<tr><td>Afghanistan </td><td>1119067.20 </td><td>1182159.06 </td><td>1248900.79 </td><td>1319848.78 </td><td>1409001.09 </td><td>1502401.79 </td><td>1598835.45 </td><td>1696444.83 </td></tr>\n", "\t<tr><td>Albania </td><td> 621179.85 </td><td> 639964.46 </td><td> 658853.12 </td><td> 677839.12 </td><td> 698932.25 </td><td> 720206.57 </td><td> 741681.04 </td><td> 763385.45 </td></tr>\n", "\t<tr><td>Algeria </td><td>4826104.22 </td><td>5017298.60 </td><td>5219331.87 </td><td>5429743.08 </td><td>5619041.53 </td><td>5815734.49 </td><td>6020647.35 </td><td>6235114.38 </td></tr>\n", "\t<tr><td>American Samoa</td><td> 17348.66 </td><td> 17995.51 </td><td> 18618.68 </td><td> 19206.39 </td><td> 19752.02 </td><td> 20262.67 </td><td> 20741.97 </td><td> 21194.38 </td></tr>\n", "\t<tr><td>Andorra </td><td> 15439.62 </td><td> 16726.99 </td><td> 18088.32 </td><td> 19528.96 </td><td> 20928.73 </td><td> 22405.84 </td><td> 23937.05 </td><td> 25481.98 </td></tr>\n", "\t<tr><td>Angola </td><td> 757496.32 </td><td> 798459.26 </td><td> 841261.96 </td><td> 886401.63 </td><td> 955010.09 </td><td>1027397.35 </td><td>1103829.78 </td><td>1184486.23 </td></tr>\n", "</tbody>\n", "</table>\n" ], "text/latex": [ "\\begin{tabular}{r|lllllllll}\n", " country & 1967 & 1968 & 1969 & 1970 & 1971 & 1972 & 1973 & 1974\\\\\n", "\\hline\n", "\t Afghanistan & 1119067.20 & 1182159.06 & 1248900.79 & 1319848.78 & 1409001.09 & 1502401.79 & 1598835.45 & 1696444.83 \\\\\n", "\t Albania & 621179.85 & 639964.46 & 658853.12 & 677839.12 & 698932.25 & 720206.57 & 741681.04 & 763385.45 \\\\\n", "\t Algeria & 4826104.22 & 5017298.60 & 5219331.87 & 5429743.08 & 5619041.53 & 5815734.49 & 6020647.35 & 6235114.38 \\\\\n", "\t American Samoa & 17348.66 & 17995.51 & 18618.68 & 19206.39 & 19752.02 & 20262.67 & 20741.97 & 21194.38 \\\\\n", "\t Andorra & 15439.62 & 16726.99 & 18088.32 & 19528.96 & 20928.73 & 22405.84 & 23937.05 & 25481.98 \\\\\n", "\t Angola & 757496.32 & 798459.26 & 841261.96 & 886401.63 & 955010.09 & 1027397.35 & 1103829.78 & 1184486.23 \\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "country | 1967 | 1968 | 1969 | 1970 | 1971 | 1972 | 1973 | 1974 | \n", "|---|---|---|---|---|---|\n", "| Afghanistan | 1119067.20 | 1182159.06 | 1248900.79 | 1319848.78 | 1409001.09 | 1502401.79 | 1598835.45 | 1696444.83 | \n", "| Albania | 621179.85 | 639964.46 | 658853.12 | 677839.12 | 698932.25 | 720206.57 | 741681.04 | 763385.45 | \n", "| Algeria | 4826104.22 | 5017298.60 | 5219331.87 | 5429743.08 | 5619041.53 | 5815734.49 | 6020647.35 | 6235114.38 | \n", "| American Samoa | 17348.66 | 17995.51 | 18618.68 | 19206.39 | 19752.02 | 20262.67 | 20741.97 | 21194.38 | \n", "| Andorra | 15439.62 | 16726.99 | 18088.32 | 19528.96 | 20928.73 | 22405.84 | 23937.05 | 25481.98 | \n", "| Angola | 757496.32 | 798459.26 | 841261.96 | 886401.63 | 955010.09 | 1027397.35 | 1103829.78 | 1184486.23 | \n", "\n", "\n" ], "text/plain": [ " country 1967 1968 1969 1970 1971 \n", "1 Afghanistan 1119067.20 1182159.06 1248900.79 1319848.78 1409001.09\n", "2 Albania 621179.85 639964.46 658853.12 677839.12 698932.25\n", "3 Algeria 4826104.22 5017298.60 5219331.87 5429743.08 5619041.53\n", "4 American Samoa 17348.66 17995.51 18618.68 19206.39 19752.02\n", "5 Andorra 15439.62 16726.99 18088.32 19528.96 20928.73\n", "6 Angola 757496.32 798459.26 841261.96 886401.63 955010.09\n", " 1972 1973 1974 \n", "1 1502401.79 1598835.45 1696444.83\n", "2 720206.57 741681.04 763385.45\n", "3 5815734.49 6020647.35 6235114.38\n", "4 20262.67 20741.97 21194.38\n", "5 22405.84 23937.05 25481.98\n", "6 1027397.35 1103829.78 1184486.23" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "data <- read_excel(\"urbanpop.xlsx\", sheet = 2) # Đọc sheet 2\n", "head(data)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Đọc tất cả sheet " ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": true }, "outputs": [], "source": [ "my_workbook <- lapply(excel_sheets(\"urbanpop.xlsx\"),\n", " read_excel,\n", " path = \"urbanpop.xlsx\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "my_workbook" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "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": "3.3.1" } }, "nbformat": 4, "nbformat_minor": 0 }

mit

openclimatedata/pymagicc

notebooks/Example.ipynb

1

396804

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Pymagicc Usage Examples" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "data": { "application/javascript": [ "IPython.OutputArea.prototype._should_scroll = function(lines) { return false; }" ], "text/plain": [ "<IPython.core.display.Javascript object>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "pyam - INFO: Running in a notebook, setting `pyam` logging level to `logging.INFO` and adding stderr handler\n" ] } ], "source": [ "# NBVAL_IGNORE_OUTPUT\n", "from pprint import pprint\n", "\n", "import pymagicc\n", "from pymagicc import MAGICC6\n", "from pymagicc.io import MAGICCData\n", "from pymagicc.scenarios import rcp26, rcp45, rcps" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "from matplotlib import pyplot as plt\n", "\n", "plt.style.use(\"ggplot\")\n", "plt.rcParams[\"figure.figsize\"] = 16, 9" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Scenarios\n", "\n", "The four RCP scenarios are already preloaded in Pymagicc. They are loaded as `MAGICCData` objects with `metadata` attributes. `metadata` contains metadata" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "pymagicc.io.MAGICCData" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "type(rcp26)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{'description': 'HARMONISED, EXTENDED FINAL RCP3-PD (Peak&Decline) NOV26; '\n", " 'RCP3PD-Contact: IMAGE group, Detlef van Vuuren '\n", " '([email protected])',\n", " 'header': 'Final RCP3PD with constant emissions after 2100 using the default '\n", " 'RCPtool MAGICC6.3 settings. Compiled by: '\n", " '[email protected]',\n", " 'notes': 'DATE: 26/11/2009 11:29:06; MAGICC-VERSION: 6.3.09, 25 November 2009'}\n" ] } ], "source": [ "pprint(rcp26.metadata)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`MAGICCData` subclasses scmdata's `ScmRun` so we can access the `scmdata` helpers directly, e.g." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(scmdata.run.ScmRun,)" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "rcp26.__class__.__bases__" ] }, { "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\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th>time</th>\n", " <th>2000-01-01 00:00:00</th>\n", " <th>2001-01-01 00:00:00</th>\n", " <th>2002-01-01 00:00:00</th>\n", " <th>2003-01-01 00:00:00</th>\n", " <th>2004-01-01 00:00:00</th>\n", " <th>2005-01-01 00:00:00</th>\n", " <th>2006-01-01 00:00:00</th>\n", " <th>2007-01-01 00:00:00</th>\n", " <th>2010-01-01 00:00:00</th>\n", " <th>2020-01-01 00:00:00</th>\n", " <th>2030-01-01 00:00:00</th>\n", " <th>2040-01-01 00:00:00</th>\n", " <th>2050-01-01 00:00:00</th>\n", " <th>2060-01-01 00:00:00</th>\n", " <th>2070-01-01 00:00:00</th>\n", " <th>2080-01-01 00:00:00</th>\n", " <th>2090-01-01 00:00:00</th>\n", " <th>2100-01-01 00:00:00</th>\n", " <th>2125-01-01 00:00:00</th>\n", " <th>2500-01-01 00:00:00</th>\n", " </tr>\n", " <tr>\n", " <th>climate_model</th>\n", " <th>model</th>\n", " <th>region</th>\n", " <th>scenario</th>\n", " <th>todo</th>\n", " <th>unit</th>\n", " <th>variable</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th rowspan=\"5\" valign=\"top\">unspecified</th>\n", " <th rowspan=\"5\" valign=\"top\">IMAGE</th>\n", " <th rowspan=\"5\" valign=\"top\">World|Bunkers</th>\n", " <th rowspan=\"5\" valign=\"top\">RCP26</th>\n", " <th rowspan=\"5\" valign=\"top\">SET</th>\n", " <th rowspan=\"2\" valign=\"top\">Gt C / yr</th>\n", " <th>Emissions|CO2|MAGICC Fossil and Industrial</th>\n", " <td>0.0000</td>\n", " <td>0.0000</td>\n", " <td>0.0000</td>\n", " <td>0.0000</td>\n", " <td>0.0000</td>\n", " <td>0.0000</td>\n", " <td>0.0000</td>\n", " <td>0.0000</td>\n", " <td>0.0000</td>\n", " <td>0.0000</td>\n", " <td>0.0000</td>\n", " <td>0.0000</td>\n", " <td>0.000</td>\n", " <td>0.0000</td>\n", " <td>0.0000</td>\n", " <td>0.0000</td>\n", " <td>0.0000</td>\n", " <td>0.0000</td>\n", " <td>0.0000</td>\n", " <td>0.0000</td>\n", " </tr>\n", " <tr>\n", " <th>Emissions|CO2|MAGICC AFOLU</th>\n", " <td>0.0000</td>\n", " <td>0.0000</td>\n", " <td>0.0000</td>\n", " <td>0.0000</td>\n", " <td>0.0000</td>\n", " <td>0.0000</td>\n", " <td>0.0000</td>\n", " <td>0.0000</td>\n", " <td>0.0000</td>\n", " <td>0.0000</td>\n", " <td>0.0000</td>\n", " <td>0.0000</td>\n", " <td>0.000</td>\n", " <td>0.0000</td>\n", " <td>0.0000</td>\n", " <td>0.0000</td>\n", " <td>0.0000</td>\n", " <td>0.0000</td>\n", " <td>0.0000</td>\n", " <td>0.0000</td>\n", " </tr>\n", " <tr>\n", " <th>Mt CH4 / yr</th>\n", " <th>Emissions|CH4</th>\n", " <td>0.4325</td>\n", " <td>0.4422</td>\n", " <td>0.4520</td>\n", " <td>0.4618</td>\n", " <td>0.4717</td>\n", " <td>0.4817</td>\n", " <td>0.4812</td>\n", " <td>0.4806</td>\n", " <td>0.4790</td>\n", " <td>0.5252</td>\n", " <td>0.5252</td>\n", " <td>0.3191</td>\n", " <td>0.000</td>\n", " <td>0.0000</td>\n", " <td>0.0000</td>\n", " <td>0.0000</td>\n", " <td>0.0000</td>\n", " <td>0.0000</td>\n", " <td>0.0000</td>\n", " <td>0.0000</td>\n", " </tr>\n", " <tr>\n", " <th>Mt N2ON / yr</th>\n", " <th>Emissions|N2O</th>\n", " <td>0.0000</td>\n", " <td>0.0000</td>\n", " <td>0.0000</td>\n", " <td>0.0000</td>\n", " <td>0.0000</td>\n", " <td>0.0000</td>\n", " <td>0.0000</td>\n", " <td>0.0000</td>\n", " <td>0.0000</td>\n", " <td>0.0000</td>\n", " <td>0.0000</td>\n", " <td>0.0000</td>\n", " <td>0.000</td>\n", " <td>0.0000</td>\n", " <td>0.0000</td>\n", " <td>0.0000</td>\n", " <td>0.0000</td>\n", " <td>0.0000</td>\n", " <td>0.0000</td>\n", " <td>0.0000</td>\n", " </tr>\n", " <tr>\n", " <th>Mt S / yr</th>\n", " <th>Emissions|SOx</th>\n", " <td>5.5390</td>\n", " <td>5.5824</td>\n", " <td>5.6207</td>\n", " <td>5.6587</td>\n", " <td>5.6964</td>\n", " <td>5.7337</td>\n", " <td>5.4434</td>\n", " <td>5.1544</td>\n", " <td>4.2947</td>\n", " <td>1.5534</td>\n", " <td>1.0918</td>\n", " <td>0.7379</td>\n", " <td>0.336</td>\n", " <td>0.3209</td>\n", " <td>0.2856</td>\n", " <td>0.2356</td>\n", " <td>0.2127</td>\n", " <td>0.1887</td>\n", " <td>0.1887</td>\n", " <td>0.1887</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ "time 2000-01-01 00:00:00 \\\n", "climate_model model region scenario todo unit variable \n", "unspecified IMAGE World|Bunkers RCP26 SET Gt C / yr Emissions|CO2|MAGICC Fossil and Industrial 0.0000 \n", " Emissions|CO2|MAGICC AFOLU 0.0000 \n", " Mt CH4 / yr Emissions|CH4 0.4325 \n", " Mt N2ON / yr Emissions|N2O 0.0000 \n", " Mt S / yr Emissions|SOx 5.5390 \n", "\n", "time 2001-01-01 00:00:00 \\\n", "climate_model model region scenario todo unit variable \n", "unspecified IMAGE World|Bunkers RCP26 SET Gt C / yr Emissions|CO2|MAGICC Fossil and Industrial 0.0000 \n", " Emissions|CO2|MAGICC AFOLU 0.0000 \n", " Mt CH4 / yr Emissions|CH4 0.4422 \n", " Mt N2ON / yr Emissions|N2O 0.0000 \n", " Mt S / yr Emissions|SOx 5.5824 \n", "\n", "time 2002-01-01 00:00:00 \\\n", "climate_model model region scenario todo unit variable \n", "unspecified IMAGE World|Bunkers RCP26 SET Gt C / yr Emissions|CO2|MAGICC Fossil and Industrial 0.0000 \n", " Emissions|CO2|MAGICC AFOLU 0.0000 \n", " Mt CH4 / yr Emissions|CH4 0.4520 \n", " Mt N2ON / yr Emissions|N2O 0.0000 \n", " Mt S / yr Emissions|SOx 5.6207 \n", "\n", "time 2003-01-01 00:00:00 \\\n", "climate_model model region scenario todo unit variable \n", "unspecified IMAGE World|Bunkers RCP26 SET Gt C / yr Emissions|CO2|MAGICC Fossil and Industrial 0.0000 \n", " Emissions|CO2|MAGICC AFOLU 0.0000 \n", " Mt CH4 / yr Emissions|CH4 0.4618 \n", " Mt N2ON / yr Emissions|N2O 0.0000 \n", " Mt S / yr Emissions|SOx 5.6587 \n", "\n", "time 2004-01-01 00:00:00 \\\n", "climate_model model region scenario todo unit variable \n", "unspecified IMAGE World|Bunkers RCP26 SET Gt C / yr Emissions|CO2|MAGICC Fossil and Industrial 0.0000 \n", " Emissions|CO2|MAGICC AFOLU 0.0000 \n", " Mt CH4 / yr Emissions|CH4 0.4717 \n", " Mt N2ON / yr Emissions|N2O 0.0000 \n", " Mt S / yr Emissions|SOx 5.6964 \n", "\n", "time 2005-01-01 00:00:00 \\\n", "climate_model model region scenario todo unit variable \n", "unspecified IMAGE World|Bunkers RCP26 SET Gt C / yr Emissions|CO2|MAGICC Fossil and Industrial 0.0000 \n", " Emissions|CO2|MAGICC AFOLU 0.0000 \n", " Mt CH4 / yr Emissions|CH4 0.4817 \n", " Mt N2ON / yr Emissions|N2O 0.0000 \n", " Mt S / yr Emissions|SOx 5.7337 \n", "\n", "time 2006-01-01 00:00:00 \\\n", "climate_model model region scenario todo unit variable \n", "unspecified IMAGE World|Bunkers RCP26 SET Gt C / yr Emissions|CO2|MAGICC Fossil and Industrial 0.0000 \n", " Emissions|CO2|MAGICC AFOLU 0.0000 \n", " Mt CH4 / yr Emissions|CH4 0.4812 \n", " Mt N2ON / yr Emissions|N2O 0.0000 \n", " Mt S / yr Emissions|SOx 5.4434 \n", "\n", "time 2007-01-01 00:00:00 \\\n", "climate_model model region scenario todo unit variable \n", "unspecified IMAGE World|Bunkers RCP26 SET Gt C / yr Emissions|CO2|MAGICC Fossil and Industrial 0.0000 \n", " Emissions|CO2|MAGICC AFOLU 0.0000 \n", " Mt CH4 / yr Emissions|CH4 0.4806 \n", " Mt N2ON / yr Emissions|N2O 0.0000 \n", " Mt S / yr Emissions|SOx 5.1544 \n", "\n", "time 2010-01-01 00:00:00 \\\n", "climate_model model region scenario todo unit variable \n", "unspecified IMAGE World|Bunkers RCP26 SET Gt C / yr Emissions|CO2|MAGICC Fossil and Industrial 0.0000 \n", " Emissions|CO2|MAGICC AFOLU 0.0000 \n", " Mt CH4 / yr Emissions|CH4 0.4790 \n", " Mt N2ON / yr Emissions|N2O 0.0000 \n", " Mt S / yr Emissions|SOx 4.2947 \n", "\n", "time 2020-01-01 00:00:00 \\\n", "climate_model model region scenario todo unit variable \n", "unspecified IMAGE World|Bunkers RCP26 SET Gt C / yr Emissions|CO2|MAGICC Fossil and Industrial 0.0000 \n", " Emissions|CO2|MAGICC AFOLU 0.0000 \n", " Mt CH4 / yr Emissions|CH4 0.5252 \n", " Mt N2ON / yr Emissions|N2O 0.0000 \n", " Mt S / yr Emissions|SOx 1.5534 \n", "\n", "time 2030-01-01 00:00:00 \\\n", "climate_model model region scenario todo unit variable \n", "unspecified IMAGE World|Bunkers RCP26 SET Gt C / yr Emissions|CO2|MAGICC Fossil and Industrial 0.0000 \n", " Emissions|CO2|MAGICC AFOLU 0.0000 \n", " Mt CH4 / yr Emissions|CH4 0.5252 \n", " Mt N2ON / yr Emissions|N2O 0.0000 \n", " Mt S / yr Emissions|SOx 1.0918 \n", "\n", "time 2040-01-01 00:00:00 \\\n", "climate_model model region scenario todo unit variable \n", "unspecified IMAGE World|Bunkers RCP26 SET Gt C / yr Emissions|CO2|MAGICC Fossil and Industrial 0.0000 \n", " Emissions|CO2|MAGICC AFOLU 0.0000 \n", " Mt CH4 / yr Emissions|CH4 0.3191 \n", " Mt N2ON / yr Emissions|N2O 0.0000 \n", " Mt S / yr Emissions|SOx 0.7379 \n", "\n", "time 2050-01-01 00:00:00 \\\n", "climate_model model region scenario todo unit variable \n", "unspecified IMAGE World|Bunkers RCP26 SET Gt C / yr Emissions|CO2|MAGICC Fossil and Industrial 0.000 \n", " Emissions|CO2|MAGICC AFOLU 0.000 \n", " Mt CH4 / yr Emissions|CH4 0.000 \n", " Mt N2ON / yr Emissions|N2O 0.000 \n", " Mt S / yr Emissions|SOx 0.336 \n", "\n", "time 2060-01-01 00:00:00 \\\n", "climate_model model region scenario todo unit variable \n", "unspecified IMAGE World|Bunkers RCP26 SET Gt C / yr Emissions|CO2|MAGICC Fossil and Industrial 0.0000 \n", " Emissions|CO2|MAGICC AFOLU 0.0000 \n", " Mt CH4 / yr Emissions|CH4 0.0000 \n", " Mt N2ON / yr Emissions|N2O 0.0000 \n", " Mt S / yr Emissions|SOx 0.3209 \n", "\n", "time 2070-01-01 00:00:00 \\\n", "climate_model model region scenario todo unit variable \n", "unspecified IMAGE World|Bunkers RCP26 SET Gt C / yr Emissions|CO2|MAGICC Fossil and Industrial 0.0000 \n", " Emissions|CO2|MAGICC AFOLU 0.0000 \n", " Mt CH4 / yr Emissions|CH4 0.0000 \n", " Mt N2ON / yr Emissions|N2O 0.0000 \n", " Mt S / yr Emissions|SOx 0.2856 \n", "\n", "time 2080-01-01 00:00:00 \\\n", "climate_model model region scenario todo unit variable \n", "unspecified IMAGE World|Bunkers RCP26 SET Gt C / yr Emissions|CO2|MAGICC Fossil and Industrial 0.0000 \n", " Emissions|CO2|MAGICC AFOLU 0.0000 \n", " Mt CH4 / yr Emissions|CH4 0.0000 \n", " Mt N2ON / yr Emissions|N2O 0.0000 \n", " Mt S / yr Emissions|SOx 0.2356 \n", "\n", "time 2090-01-01 00:00:00 \\\n", "climate_model model region scenario todo unit variable \n", "unspecified IMAGE World|Bunkers RCP26 SET Gt C / yr Emissions|CO2|MAGICC Fossil and Industrial 0.0000 \n", " Emissions|CO2|MAGICC AFOLU 0.0000 \n", " Mt CH4 / yr Emissions|CH4 0.0000 \n", " Mt N2ON / yr Emissions|N2O 0.0000 \n", " Mt S / yr Emissions|SOx 0.2127 \n", "\n", "time 2100-01-01 00:00:00 \\\n", "climate_model model region scenario todo unit variable \n", "unspecified IMAGE World|Bunkers RCP26 SET Gt C / yr Emissions|CO2|MAGICC Fossil and Industrial 0.0000 \n", " Emissions|CO2|MAGICC AFOLU 0.0000 \n", " Mt CH4 / yr Emissions|CH4 0.0000 \n", " Mt N2ON / yr Emissions|N2O 0.0000 \n", " Mt S / yr Emissions|SOx 0.1887 \n", "\n", "time 2125-01-01 00:00:00 \\\n", "climate_model model region scenario todo unit variable \n", "unspecified IMAGE World|Bunkers RCP26 SET Gt C / yr Emissions|CO2|MAGICC Fossil and Industrial 0.0000 \n", " Emissions|CO2|MAGICC AFOLU 0.0000 \n", " Mt CH4 / yr Emissions|CH4 0.0000 \n", " Mt N2ON / yr Emissions|N2O 0.0000 \n", " Mt S / yr Emissions|SOx 0.1887 \n", "\n", "time 2500-01-01 00:00:00 \n", "climate_model model region scenario todo unit variable \n", "unspecified IMAGE World|Bunkers RCP26 SET Gt C / yr Emissions|CO2|MAGICC Fossil and Industrial 0.0000 \n", " Emissions|CO2|MAGICC AFOLU 0.0000 \n", " Mt CH4 / yr Emissions|CH4 0.0000 \n", " Mt N2ON / yr Emissions|N2O 0.0000 \n", " Mt S / yr Emissions|SOx 0.1887 " ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "rcp26.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The rcp's contain the following emissions with the following units" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>variable</th>\n", " <th>unit</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>Emissions|CO2|MAGICC Fossil and Industrial</td>\n", " <td>Gt C / yr</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>Emissions|CO2|MAGICC AFOLU</td>\n", " <td>Gt C / yr</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>Emissions|CH4</td>\n", " <td>Mt CH4 / yr</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>Emissions|N2O</td>\n", " <td>Mt N2ON / yr</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>Emissions|SOx</td>\n", " <td>Mt S / yr</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>Emissions|CO</td>\n", " <td>Mt CO / yr</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td>Emissions|NMVOC</td>\n", " <td>Mt NMVOC / yr</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td>Emissions|NOx</td>\n", " <td>Mt N / yr</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td>Emissions|BC</td>\n", " <td>Mt BC / yr</td>\n", " </tr>\n", " <tr>\n", " <th>9</th>\n", " <td>Emissions|OC</td>\n", " <td>Mt OC / yr</td>\n", " </tr>\n", " <tr>\n", " <th>10</th>\n", " <td>Emissions|NH3</td>\n", " <td>Mt N / yr</td>\n", " </tr>\n", " <tr>\n", " <th>11</th>\n", " <td>Emissions|CF4</td>\n", " <td>kt CF4 / yr</td>\n", " </tr>\n", " <tr>\n", " <th>12</th>\n", " <td>Emissions|C2F6</td>\n", " <td>kt C2F6 / yr</td>\n", " </tr>\n", " <tr>\n", " <th>13</th>\n", " <td>Emissions|C6F14</td>\n", " <td>kt C6F14 / yr</td>\n", " </tr>\n", " <tr>\n", " <th>14</th>\n", " <td>Emissions|HFC23</td>\n", " <td>kt HFC23 / yr</td>\n", " </tr>\n", " <tr>\n", " <th>15</th>\n", " <td>Emissions|HFC32</td>\n", " <td>kt HFC32 / yr</td>\n", " </tr>\n", " <tr>\n", " <th>16</th>\n", " <td>Emissions|HFC4310</td>\n", " <td>kt HFC4310 / yr</td>\n", " </tr>\n", " <tr>\n", " <th>17</th>\n", " <td>Emissions|HFC125</td>\n", " <td>kt HFC125 / yr</td>\n", " </tr>\n", " <tr>\n", " <th>18</th>\n", " <td>Emissions|HFC134a</td>\n", " <td>kt HFC134a / yr</td>\n", " </tr>\n", " <tr>\n", " <th>19</th>\n", " <td>Emissions|HFC143a</td>\n", " <td>kt HFC143a / yr</td>\n", " </tr>\n", " <tr>\n", " <th>20</th>\n", " <td>Emissions|HFC227ea</td>\n", " <td>kt HFC227ea / yr</td>\n", " </tr>\n", " <tr>\n", " <th>21</th>\n", " <td>Emissions|HFC245fa</td>\n", " <td>kt HFC245fa / yr</td>\n", " </tr>\n", " <tr>\n", " <th>22</th>\n", " <td>Emissions|SF6</td>\n", " <td>kt SF6 / yr</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " variable unit\n", "0 Emissions|CO2|MAGICC Fossil and Industrial Gt C / yr\n", "1 Emissions|CO2|MAGICC AFOLU Gt C / yr\n", "2 Emissions|CH4 Mt CH4 / yr\n", "3 Emissions|N2O Mt N2ON / yr\n", "4 Emissions|SOx Mt S / yr\n", "5 Emissions|CO Mt CO / yr\n", "6 Emissions|NMVOC Mt NMVOC / yr\n", "7 Emissions|NOx Mt N / yr\n", "8 Emissions|BC Mt BC / yr\n", "9 Emissions|OC Mt OC / yr\n", "10 Emissions|NH3 Mt N / yr\n", "11 Emissions|CF4 kt CF4 / yr\n", "12 Emissions|C2F6 kt C2F6 / yr\n", "13 Emissions|C6F14 kt C6F14 / yr\n", "14 Emissions|HFC23 kt HFC23 / yr\n", "15 Emissions|HFC32 kt HFC32 / yr\n", "16 Emissions|HFC4310 kt HFC4310 / yr\n", "17 Emissions|HFC125 kt HFC125 / yr\n", "18 Emissions|HFC134a kt HFC134a / yr\n", "19 Emissions|HFC143a kt HFC143a / yr\n", "20 Emissions|HFC227ea kt HFC227ea / yr\n", "21 Emissions|HFC245fa kt HFC245fa / yr\n", "22 Emissions|SF6 kt SF6 / yr" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "rcp26.meta[[\"variable\", \"unit\"]].drop_duplicates()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The regions included are" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array(['World|Bunkers', 'World|R5LAM', 'World|R5MAF', 'World|R5ASIA',\n", " 'World|R5REF', 'World|R5OECD', 'World'], dtype=object)" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "rcp26[\"region\"].unique()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A plot of four categories in RCP3PD" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 864x504 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 864x504 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 864x504 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 864x504 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "categories_to_plot = [\n", " \"Emissions|\" + v\n", " for v in [\n", " \"CO2|MAGICC Fossil and Industrial\",\n", " \"CO2|MAGICC AFOLU\",\n", " \"CH4\",\n", " \"N2O\",\n", " ]\n", "]\n", "for g in rcp26.filter(\n", " variable=categories_to_plot, year=range(1000, 2150)\n", ").groupby(\"variable\"):\n", " plt.figure(figsize=(12, 7))\n", " g.lineplot(hue=\"region\").set_title(g.get_unique_meta(\"variable\", True))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Fossil fuel emissions for the four RCP scenarios." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 1152x648 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "rcps.filter(\n", " variable=\"Emissions|CO2|MAGICC Fossil and Industrial\", region=\"World\"\n", ").lineplot(x=\"time\");" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Running MAGICC\n", "\n", "A single `pymagicc` run takes under a second and returns the same object as used above. If not on Windows, the very first run might be slower due to setting up Wine. Multiple runs can be faster as setup times are reduced and other options speed things up even further e.g. limiting output to the subset of interest, using binary output formats." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 503 ms, sys: 50.5 ms, total: 554 ms\n", "Wall time: 589 ms\n" ] } ], "source": [ "# NBVAL_IGNORE_OUTPUT\n", "%time results = pymagicc.run(rcp26)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "def multiple_runs():\n", " with MAGICC6() as magicc:\n", " for name, sdf in rcps.timeseries().groupby([\"scenario\"]):\n", " results = magicc.run(MAGICCData(sdf.copy()))" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 266 ms, sys: 18.1 ms, total: 284 ms\n", "Wall time: 375 ms\n" ] } ], "source": [ "# NBVAL_IGNORE_OUTPUT\n", "%time multiple_runs()" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 1152x648 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(16, 9))\n", "with MAGICC6() as magicc:\n", " for name, sdf in rcps.timeseries().groupby([\"scenario\"]):\n", " results = magicc.run(MAGICCData(sdf.copy()))\n", " results.filter(\n", " variable=\"Surface Temperature\", region=\"World\"\n", " ).lineplot(ax=ax, x=\"time\");" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The default parameters are the ones that were used to produce the RCP GHG concentrations (see also http://live.magicc.org/). Of course it's easy to change them." ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "low = pymagicc.run(rcp45, core_climatesensitivity=1.5)\n", "default = pymagicc.run(rcp45, core_climatesensitivity=3)\n", "high = pymagicc.run(rcp45, core_climatesensitivity=4.5)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 1152x648 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "filtering = {\n", " \"variable\": \"Surface Temperature\",\n", " \"region\": \"World\",\n", " \"year\": range(1850, 2101),\n", "}\n", "\n", "fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(16, 9))\n", "default.filter(**filtering).line_plot(x=\"time\", ax=ax)\n", "plt.fill_between(\n", " low.filter(**filtering)[\"time\"].values,\n", " low.filter(**filtering).timeseries().values.squeeze(),\n", " high.filter(**filtering).timeseries().values.squeeze(),\n", " color=\"lightgray\",\n", ")\n", "\n", "plt.title(\n", " \"RCP 4.5 with equilibrium climate sensitivity set to 1.5, 3, and 4.5\"\n", ")\n", "plt.ylabel(\"°C\");" ] }, { "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": "3.7.7" }, "widgets": { "state": {}, "version": "1.1.2" } }, "nbformat": 4, "nbformat_minor": 2 }

agpl-3.0

patrickmineault/xcorr-notebooks

notebooks/A_linear_flow_model_for_neural_ICA.ipynb

1

548672

{ "nbformat": 4, "nbformat_minor": 0, "metadata": { "colab": { "name": "A linear normalizing flow model for neural ICA", "provenance": [], "collapsed_sections": [], "authorship_tag": "ABX9TyN/C7x3mhaCgqr1YWecMw6Z", "include_colab_link": true }, "kernelspec": { "name": "python3", "display_name": "Python 3" }, "language_info": { "name": "python" }, "accelerator": "GPU" }, "cells": [ { "cell_type": "markdown", "metadata": { "id": "view-in-github", "colab_type": "text" }, "source": [ "<a href=\"https://colab.research.google.com/github/patrickmineault/xcorr-notebooks/blob/master/A_linear_normalizing_flow_model_for_neural_ICA.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>" ] }, { "cell_type": "markdown", "metadata": { "id": "HazNnW0HuUg9" }, "source": [ "#LICE vs. NICE\n", "\n", "[I previously showed how linear latent variable models, including PCA and NMF](https://github.com/patrickmineault/xcorr-notebooks/blob/master/PCA_through_gradient_descent.ipynb), can be implemented as maximum likelihood under certain generative models. These models focus on modeling the data with a generative model, $g: z \\to x$ as opposed to modeling the latents via a function $f: x \\to z$. One disadvantage of these models is they require an expensive inference step involving an optimization in order to find the latents, or require learning an amortized inference model. Here I present an alternative, where inference and sampling are easy.\n", "\n", "NICE (nonlinear independent component estimation) seeks to find latent independent representations of complex datasets, similar in spirit to ICA. The idea behind [NICE](https://arxiv.org/abs/1410.8516) is to map datapoints via $z = f(x): \\mathbb{R}^N \\to \\mathbb{R}^N$ where f is invertible and $p(z) = \\prod_i p(z_i)$, where $p(z_i)$ is a fixed and known probability distribution. It's possible to directly optimize the likelihood of the latents (*not* the data), given the determinant of the Jacobian of $f$. The invertibility of the transformation $f$ and the need for a tractable Jacobian limits the transformations that can be learned. This leads Dinh et al. to consider partitioning latent representations into two subsets, cleverly setting up conditions on the DAG of the hidden variables to allow for more flexible representations.\n", "\n", "Here, I consider a linear variant of NICE, which I will call LICE (clever, right?), which is similar to ICA. Instead of using the partitioning scheme of Dinh et al., I will the conceptually simpler $LU$ decomposition (mentioned in section 3.1 in the paper): $f$ will map $x \\to LUx$, where $LU$ are lower and upper diagonal matrices, respectively. The determininant of the Jacobian of the transformation can be read off from the diagonals of the $L$ and $U$ matrices, making it simple to calculate. LU decompositions can express any square matrix, hence are precisely as powerful as a full rank transformation matrix (the underlying transformation in ICA). As for the fixed distribution, we'll choose something that has long tails to fit with ICA - let's use a t-distribution with 4 degrees of freedom.\n", "\n", "Before we get started, let's create some sample data and showcase standard ICA ([reusing this example from sklearn](https://scikit-learn.org/stable/auto_examples/decomposition/plot_ica_blind_source_separation.html#sphx-glr-auto-examples-decomposition-plot-ica-blind-source-separation-py))." ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 585 }, "id": "gtrdW0146Bfa", "outputId": "1ced099a-7b2b-47a9-9190-770b502b113b" }, "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "from scipy import signal\n", "\n", "from sklearn.decomposition import FastICA, PCA\n", "\n", "# #############################################################################\n", "# Generate sample data\n", "np.random.seed(0)\n", "n_samples = 2000\n", "time = np.linspace(0, 8, n_samples)\n", "\n", "s1 = np.sin(2 * time) # Signal 1 : sinusoidal signal\n", "s2 = np.sign(np.sin(3 * time)) # Signal 2 : square signal\n", "s3 = signal.sawtooth(2 * np.pi * time) # Signal 3: saw tooth signal\n", "\n", "S = np.c_[s1, s2, s3]\n", "S += 0.2 * np.random.normal(size=S.shape) # Add noise\n", "\n", "S /= S.std(axis=0) # Standardize data\n", "# Mix data\n", "A = np.array([[1, 1, 1], [0.5, 2, 1.0], [1.5, 1.0, 2.0]]) # Mixing matrix\n", "X = np.dot(S, A.T) # Generate observations\n", "\n", "# Compute ICA\n", "ica = FastICA(n_components=3)\n", "S_ = ica.fit_transform(X) # Reconstruct signals\n", "A_ = ica.mixing_ # Get estimated mixing matrix\n", "\n", "# We can `prove` that the ICA model applies by reverting the unmixing.\n", "assert np.allclose(X, np.dot(S_, A_.T) + ica.mean_)\n", "\n", "# For comparison, compute PCA\n", "pca = PCA(n_components=3)\n", "H = pca.fit_transform(X) # Reconstruct signals based on orthogonal components\n", "\n", "# #############################################################################\n", "# Plot results\n", "\n", "plt.figure(figsize=(10, 8))\n", "\n", "models = [X, S, S_, H]\n", "names = ['Observations (mixed signal)',\n", " 'True Sources',\n", " 'ICA recovered signals',\n", " 'PCA recovered signals']\n", "colors = ['red', 'steelblue', 'orange']\n", "\n", "for ii, (model, name) in enumerate(zip(models, names), 1):\n", " plt.subplot(4, 1, ii)\n", " plt.title(name)\n", " for sig, color in zip(model.T, colors):\n", " plt.plot(sig, color=color)\n", "\n", "plt.tight_layout()\n", "sns.despine()" ], "execution_count": 1, "outputs": [ { "output_type": "display_data", "data": { "image/png": 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\n", "text/plain": [ "<Figure size 720x576 with 4 Axes>" ] }, "metadata": { "tags": [], "needs_background": "light" } } ] }, { "cell_type": "markdown", "metadata": { "id": "CU9B92iS6cMM" }, "source": [ "Let's do it the LICE way instead." ] }, { "cell_type": "code", "metadata": { "id": "oJ8EaBUz5K8N" }, "source": [ "# @title Standard PyTorch imports and training loop\n", "import torch\n", "from torch import optim\n", "from torch import nn\n", "import torch.nn.functional as F\n", "\n", "quiet = False\n", "def train(model, train_loader, optimizer, epoch, grad_clip=None):\n", " model.train()\n", " \n", " train_losses = []\n", " for x in train_loader:\n", " x = x.cuda().contiguous()\n", " loss = model.loss(x)\n", " optimizer.zero_grad()\n", " loss.backward()\n", " if grad_clip:\n", " torch.nn.utils.clip_grad_norm_(model.parameters(), grad_clip)\n", " optimizer.step()\n", " train_losses.append(loss.item())\n", " return train_losses\n", "\n", "def eval_loss(model, data_loader):\n", " model.eval()\n", " total_loss = 0\n", " with torch.no_grad():\n", " for x in data_loader:\n", " x = x.cuda().contiguous()\n", " loss = model.loss(x)\n", " total_loss += loss * x.shape[0]\n", " avg_loss = total_loss / len(data_loader.dataset)\n", "\n", " return avg_loss.item()\n", "\n", "def train_epochs(model, train_loader, test_loader, train_args):\n", " epochs, lr, print_freq = train_args['epochs'], train_args['lr'], train_args.get('print_freq', 1)\n", " grad_clip = train_args.get('grad_clip', None)\n", " optimizer = optim.Adam(model.parameters(), lr=lr)\n", "\n", " train_losses = []\n", " test_losses = [eval_loss(model, test_loader)]\n", " for epoch in range(epochs):\n", " model.train()\n", " train_losses.extend(train(model, train_loader, optimizer, epoch, grad_clip))\n", " test_loss = eval_loss(model, test_loader)\n", " test_losses.append(test_loss)\n", " if epoch % print_freq == 0:\n", " print(f'Epoch {epoch}, Test loss {test_loss:.4f}')\n", "\n", " return train_losses, test_losses" ], "execution_count": 2, "outputs": [] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 334 }, "id": "ubPo2gP-i5FQ", "outputId": "12a51cdd-3e3c-47af-94a7-575498c8639d" }, "source": [ "class LUFlow(nn.Module):\n", " def __init__(self, N):\n", " super().__init__()\n", " self.U = nn.Parameter(torch.randn(N, N) / np.sqrt(N))\n", " self.L = nn.Parameter(torch.randn(N, N) / np.sqrt(N))\n", " self.UL = None\n", " self.dist = torch.distributions.StudentT(2)\n", "\n", " def forward(self, X):\n", " return (X @ torch.tril(self.L)) @ torch.triu(self.U)\n", "\n", " def compute_inverse(self):\n", " # Note: does not scale well to large ICA problems.\n", " self.UL = torch.inv(torch.tril(self.L) @ torch.triu(self.U))\n", "\n", " def logdet(self, X):\n", " # The determinant of lower and upper diagonal matrices is very easy to compute.\n", " return X.shape[0] *(torch.log(abs(torch.diag(self.U))) +\n", " torch.log(abs(torch.diag(self.L)))).sum()\n", "\n", " def loss(self, X):\n", " z = self(X)\n", "\n", " # Compute the log likelihood of z\n", " prob = -self.dist.log_prob(z).sum()\n", "\n", " # And the log determininant\n", " logdetX = -self.logdet(X).sum()\n", " \n", " return prob + logdetX\n", "\n", " def sample(self, M):\n", " if self.UL is None:\n", " self.compute_inverse()\n", " z = dist.sample([M, self.U.shape[0]]).to(device='cuda')\n", " return self.UL @ z\n", "\n", "model = LUFlow(3)\n", "model.to(device='cuda')\n", "\n", "\n", "idx = (np.arange(X.shape[0]) % 10) == 0\n", "\n", "train_loader = torch.utils.data.DataLoader(H[~idx, :].astype(np.float32), batch_size=16, shuffle=True)\n", "test_loader = torch.utils.data.DataLoader(H[idx, :].astype(np.float32), batch_size=16, shuffle=True)\n", "\n", "train_evals, test_evals = train_epochs(model, train_loader, test_loader, {'epochs': 25, 'lr': 1e-3, 'print_freq': 10})\n", "plt.plot(train_evals)" ], "execution_count": 3, "outputs": [ { "output_type": "stream", "text": [ "Epoch 0, Test loss 134.0213\n", "Epoch 10, Test loss 74.4982\n", "Epoch 20, Test loss 72.3333\n" ], "name": "stdout" }, { "output_type": "execute_result", "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x7fcb208a7a90>]" ] }, "metadata": { "tags": [] }, "execution_count": 3 }, { "output_type": "display_data", "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "tags": [], "needs_background": "light" } } ] }, { "cell_type": "code", "metadata": { "id": "5KWa6wKg9feR" }, "source": [ "Z = model(torch.tensor(H).to(device='cuda', dtype=torch.float)).detach().cpu().numpy()" ], "execution_count": 4, "outputs": [] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 146 }, "id": "Rva1ixnb9XdT", "outputId": "d14cb964-c3c5-4609-93cd-ad96199bcbc1" }, "source": [ "delta = 0\n", "for i in range(3):\n", " for j in range(i + 1, 3):\n", " plt.subplot(131 + delta)\n", " plt.plot(Z[:, i], Z[:, j], '.', markersize=1)\n", " plt.axis('square')\n", " sns.despine()\n", " delta += 1" ], "execution_count": 5, "outputs": [ { "output_type": "display_data", "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 3 Axes>" ] }, "metadata": { "tags": [], "needs_background": "light" } } ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 265 }, "id": "WPnhP4yv-0hx", "outputId": "02992ff7-50e4-4d85-8921-b9508242f1eb" }, "source": [ "plt.figure(figsize=(12, 4))\n", "plt.plot(Z)\n", "sns.despine()" ], "execution_count": 6, "outputs": [ { "output_type": "display_data", "data": { "image/png": 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\n", "text/plain": [ "<Figure size 864x288 with 1 Axes>" ] }, "metadata": { "tags": [], "needs_background": "light" } } ] }, { "cell_type": "markdown", "metadata": { "id": "fHP8RPgyeEs1" }, "source": [ "This doesn't demix the components. What's going on?" ] }, { "cell_type": "markdown", "metadata": { "id": "3IumAfvQEr1x" }, "source": [ "# Univariate transforms: LucyFlow\n", "\n", "This works poorly - it doesn't demix the components. One issue is that the assumed distribution for the latents is fixed at a t distribution - yet, the distributions are actually closer to a bimodal distribution for one variable, a uniform distribution for a second, and a circular distribution for the third. What we need is a univariate nonlinearity that can transform an arbitrary distribution into another, such that our weird bimodel and circular distribution get mapped to a standard shape via a learned nonlinearity. \n", "\n", "Once again, the transformation must be invertible, hence we use the cumulative distribution function of a mixture of logistic distributions. Since a CDF's support is $[0, 1]$, we'll use a product of uniform distributions to model the latents. Since the transformation is an LU decomposition followed by Cdfs, let's call it Lucyflow." ] }, { "cell_type": "code", "metadata": { "id": "07e_B8JGkckr" }, "source": [ "class CumMixOfLogistics(nn.Module):\n", " def __init__(self, N=1, n=4):\n", " \"\"\"\n", " n: number of components per mixture components\n", " N: number of parallel nonlinearities\n", " \"\"\"\n", " super().__init__()\n", " self.log_slopes = nn.Parameter(torch.randn(1, N, n)+1)\n", " self.log_pi = nn.Parameter(torch.randn(1, N, n))\n", " self.centers = nn.Parameter(torch.randn(1, N, n))\n", " \n", " self.N = N\n", " self.n = n\n", "\n", " def forward(self, X):\n", " pi = torch.softmax(self.log_pi, dim=2)\n", " slopes = torch.exp(self.log_slopes)\n", " return ((torch.sigmoid(slopes * (X.view(X.shape[0], X.shape[1], 1) - self.centers)) * pi).sum(axis=2))\n", "\n", " def logdet(self, X):\n", " pi = torch.softmax(self.log_pi, dim=2)\n", " slopes = torch.exp(self.log_slopes)\n", " y = (torch.sigmoid(slopes * (X.view(X.shape[0], X.shape[1], 1) - self.centers)))\n", " return torch.log((y * (1 - y) * slopes * pi).sum(axis=2)).sum()\n", "\n", "# One annoying thing about these models is I haven't figured a way to propagate\n", "# the gradient through the log of the jacobian, so we have to check the gradients \n", "# the log of Jacobian manually.\n", "\n", "model = CumMixOfLogistics(3, 4)\n", "model = model.to(device='cuda')\n", "\n", "HH = torch.tensor(H).to(device='cuda', dtype=torch.float)\n", "J0 = torch.log(abs(torch.det(torch.autograd.functional.jacobian(lambda x: model(x), HH[0:1, :]).squeeze()))).detach().cpu().numpy()\n", "J1 = model.logdet(HH[0:1, :]).detach().cpu().numpy()\n", "\n", "np.testing.assert_allclose(J0, J1)" ], "execution_count": 7, "outputs": [] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 369 }, "id": "IX9ZLT14GHEI", "outputId": "b758791e-cb97-41e3-d646-f472c3f1064e" }, "source": [ "class LucyFlow(nn.Module):\n", " def __init__(self, N):\n", " super().__init__()\n", " self.U = nn.Parameter(torch.randn(N, N) / np.sqrt(N))\n", " self.L = nn.Parameter(torch.randn(N, N) / np.sqrt(N))\n", " self.UL = None\n", " self.dist = torch.distributions.Uniform(torch.tensor(0.0).to(device='cuda'), \n", " torch.tensor(1.0).to(device='cuda'))\n", " self.nl = CumMixOfLogistics(N, 4)\n", "\n", " def forward(self, X):\n", " return self.nl((X @ torch.tril(self.L)) @ torch.triu(self.U))\n", "\n", " def forward_lin(self, X):\n", " return (X @ torch.tril(self.L)) @ torch.triu(self.U)\n", "\n", " def logdet(self, X):\n", " # The determinant of lower and upper diagonal matrices is very easy to compute.\n", " return (X.shape[0] * (torch.log(abs(torch.diag(self.U))) +\n", " torch.log(abs(torch.diag(self.L)))).sum() +\n", " self.nl.logdet((X @ torch.tril(self.L)) @ torch.triu(self.U)))\n", "\n", " def loss(self, X):\n", " z = self(X)\n", "\n", " # Compute the log likelihood of z\n", " prob = -self.dist.log_prob(z).sum()\n", "\n", " # And the log determininant of the Jacobian.\n", " logdetX = -self.logdet(X).sum()\n", " \n", " return prob + logdetX\n", "\n", "model = LucyFlow(3)\n", "model.to(device='cuda')\n", "HH = torch.tensor(H).to(device='cuda', dtype=torch.float)\n", "J0 = torch.log(abs(torch.det(torch.autograd.functional.jacobian(lambda x: model(x), HH[0:1, :]).squeeze()))).detach().cpu().numpy()\n", "J1 = model.logdet(HH[0:1, :]).detach().cpu().numpy()\n", "\n", "np.testing.assert_allclose(J0, J1, rtol=1e-5)\n", "\n", "idx = (np.arange(X.shape[0]) % 10) == 0\n", "\n", "train_loader = torch.utils.data.DataLoader(X[~idx, :].astype(np.float32), batch_size=20, shuffle=True)\n", "test_loader = torch.utils.data.DataLoader(X[idx, :].astype(np.float32), batch_size=20, shuffle=True)\n", "\n", "train_evals, test_evals = train_epochs(model, train_loader, test_loader, {'epochs': 100, 'lr': 1e-3, 'print_freq': 20})\n", "plt.plot(train_evals)" ], "execution_count": 8, "outputs": [ { "output_type": "stream", "text": [ "Epoch 0, Test loss 237.4059\n", "Epoch 20, Test loss 87.5146\n", "Epoch 40, Test loss 80.3798\n", "Epoch 60, Test loss 79.4013\n", "Epoch 80, Test loss 79.4330\n" ], "name": "stdout" }, { "output_type": "execute_result", "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x7fcb206766d0>]" ] }, "metadata": { "tags": [] }, "execution_count": 8 }, { "output_type": "display_data", "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "tags": [], "needs_background": "light" } } ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 146 }, "id": "LxFMnLNXejff", "outputId": "9d8a3b17-df41-47d4-cd67-ac4aa3dec799" }, "source": [ "Z = model.forward_lin(torch.tensor(X).to(device='cuda', dtype=torch.float)).detach().cpu().numpy()\n", "\n", "delta = 0\n", "for i in range(3):\n", " for j in range(i + 1, 3):\n", " plt.subplot(131 + delta)\n", " plt.plot(Z[:, i], Z[:, j], '.', markersize=1)\n", " plt.axis('square')\n", " sns.despine()\n", " delta += 1" ], "execution_count": 9, "outputs": [ { "output_type": "display_data", "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 3 Axes>" ] }, "metadata": { "tags": [], "needs_background": "light" } } ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 265 }, "id": "5nHpldnCf8Kp", "outputId": "8008dfef-a9d3-4b82-b569-a91be39f2378" }, "source": [ "plt.figure(figsize=(12, 4))\n", "plt.plot(Z)\n", "sns.despine()" ], "execution_count": 10, "outputs": [ { "output_type": "display_data", "data": { "image/png": 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\n", "text/plain": [ "<Figure size 864x288 with 1 Axes>" ] }, "metadata": { "tags": [], "needs_background": "light" } } ] }, { "cell_type": "markdown", "metadata": { "id": "0R4hUlEi2yHK" }, "source": [ "It's generally pretty easy to find the most bimodal component this way (square wave) . However, the hit rate on the other components is pretty low - about 1/3 of the time, one finds the right components - other times, it gets stuck in local minima. NBD - we can do multiple restarts to find a more favorable configuration." ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 584 }, "id": "tNy1hcme-fQp", "outputId": "190510dd-b58b-481c-e1ba-430ae4a83af6" }, "source": [ "best_eval = np.inf\n", "for i in range(5):\n", " model = LucyFlow(3)\n", " model.to(device='cuda')\n", " train_evals, test_evals = train_epochs(model, train_loader, test_loader, {'epochs': 100, 'lr': 1e-3, 'print_freq': 20})\n", " if test_evals[-1] < best_eval:\n", " best_model = model\n", " best_eval = test_evals[-1]\n", "\n", "model = best_model\n", "\n", "Z = model.forward_lin(torch.tensor(X).to(device='cuda', dtype=torch.float)).detach().cpu().numpy()\n", "\n", "delta = 0\n", "for i in range(3):\n", " for j in range(i + 1, 3):\n", " plt.subplot(131 + delta)\n", " plt.plot(Z[:, i], Z[:, j], '.', markersize=1)\n", " plt.axis('square')\n", " sns.despine()\n", " delta += 1" ], "execution_count": 12, "outputs": [ { "output_type": "stream", "text": [ "Epoch 0, Test loss 286.9839\n", "Epoch 20, Test loss 87.2393\n", "Epoch 40, Test loss 80.5195\n", "Epoch 60, Test loss 74.3585\n", "Epoch 80, Test loss 71.6590\n", "Epoch 0, Test loss 213.1845\n", "Epoch 20, Test loss 84.6258\n", "Epoch 40, Test loss 65.6630\n", "Epoch 60, Test loss 65.2261\n", "Epoch 80, Test loss 64.8018\n", "Epoch 0, Test loss 221.5940\n", "Epoch 20, Test loss 72.3526\n", "Epoch 40, Test loss 69.8172\n", "Epoch 60, Test loss 67.8637\n", "Epoch 80, Test loss 65.4256\n", "Epoch 0, Test loss 218.8366\n", "Epoch 20, Test loss 87.5313\n", "Epoch 40, Test loss 72.6486\n", "Epoch 60, Test loss 70.0821\n", "Epoch 80, Test loss 68.3447\n", "Epoch 0, Test loss 262.7703\n", "Epoch 20, Test loss 71.0692\n", "Epoch 40, Test loss 66.6613\n", "Epoch 60, Test loss 65.8285\n", "Epoch 80, Test loss 65.3224\n" ], "name": "stdout" }, { "output_type": "display_data", "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 3 Axes>" ] }, "metadata": { "tags": [], "needs_background": "light" } } ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 269 }, "id": "-5Le9kw2_HAq", "outputId": "2d07d1d1-4eb7-4218-d007-914c2c26db42" }, "source": [ "plt.figure(figsize=(12, 4))\n", "plt.plot(Z)\n", "sns.despine()" ], "execution_count": 13, "outputs": [ { "output_type": "display_data", "data": { "image/png": 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\n", "text/plain": [ "<Figure size 864x288 with 1 Axes>" ] }, "metadata": { "tags": [], "needs_background": "light" } } ] }, { "cell_type": "markdown", "metadata": { "id": "I2nZi0XT_KXx" }, "source": [ "It's interesting (though perhaps not practical!) that normalizing flow models like NICE can be related to classical algorithms like ICA. In science, we often want to restrict latent variable models based off of certain characteristics - the distribution of the latents or the components. Between PCA, ICA, NMF on the one side, and NICE, realNVP and VAEs on the other, there is a wide, unexplored world of highly structured nonlinear latent variable models, some of which may be useful." ] } ] }

mit

NeuroanatomyAndConnectivity/opendata

scripts/sample meta info and demographics.ipynb

2

55330

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/usr/local/lib/python2.7/site-packages/matplotlib/__init__.py:872: UserWarning: axes.color_cycle is deprecated and replaced with axes.prop_cycle; please use the latter.\n", " warnings.warn(self.msg_depr % (key, alt_key))\n" ] } ], "source": [ "% matplotlib inline\n", "\n", "import pandas as pd\n", "import glob, os\n", "import numpy as np\n", "import shutil\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "\n", "sns.set_style('whitegrid')\n", "sns.set_context('talk')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Prepare and merge meta information" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Applications/miniconda3/envs/topography/lib/python2.7/site-packages/ipykernel/__main__.py:23: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame\n", "\n", "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n", "/Applications/miniconda3/envs/topography/lib/python2.7/site-packages/ipykernel/__main__.py:24: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame\n", "\n", "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n", "/Applications/miniconda3/envs/topography/lib/python2.7/site-packages/ipykernel/__main__.py:62: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame\n", "\n", "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n", "/Applications/miniconda3/envs/topography/lib/python2.7/site-packages/ipykernel/__main__.py:63: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame\n", "\n", "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n" ] } ], "source": [ "# doc_dir = '/nobackup/adenauer2/LSD/Originals/Documentation'\n", "doc_dir = '/Users/dmargulies/Downloads'\n", "\n", "# study B\n", "df_lsd = pd.read_csv('%s/meta_lsd_restricted_20170315.csv' % doc_dir,\n", " dtype={'ids':str})\n", "\n", "# study A\n", "df_lemon1 = pd.read_excel('%s/meta1_lemon_restricted_20170321.xls' % doc_dir)\n", "\n", "df_lemon1.rename(columns={'BIDS ID': 'ids',\n", " 'Gender': 'gender',\n", " 'Age': 'age'}, inplace=True)\n", "df_lemon1[['ids']] = df_lemon1[['ids']].astype(str)\n", "d = {'F':1, 'M':2, 'f':1, 'm':2}\n", "df_lemon1.replace({'gender': d}, inplace=True)\n", "\n", "\n", "# merge gender/age\n", "df = pd.merge(df_lsd, df_lemon1, on='ids', how='outer')\n", "\n", "# gender\n", "df['gender_x'][df['gender_x'].isnull()] = df['gender_y'][df['gender_x'].isnull()]\n", "df['gender_y'][df['gender_y'].isnull()] = df['gender_x'][df['gender_y'].isnull()]\n", "df['gender'] = df['gender_x'].astype(int)\n", "\n", "# age\n", "cols_age = ['age day 1', 'age day 2', 'age day 3', 'age day 4',\n", " 'age day 5a', 'age day 5b', 'age day 6', 'age LEMON', 'age']\n", "\n", "df['mean age'] = df[cols_age].mean(axis=1)\n", "\n", "age_bins = [20, 25, 30, 35, 40, 45, 50, \n", " 55, 60, 65, 70, 75, 80, 85]\n", "age_labels = ['20-25', '25-30', '30-35', '35-40', \n", " '40-45', '45-50', '50-55', '55-60', \n", " '60-65', '65-70', '70-75', '75-80', '80-85']\n", "\n", "df['age (5-year bins)'] = pd.cut(df['mean age'], age_bins, labels=age_labels)\n", "\n", "# clean df\n", "df_meta = df[['ids', 'gender', 'age (5-year bins)', 'mean age',\n", " 'SKID diagnose 1', 'SKID diagnose 2',\n", " 'drug test 1 (before first MRI measurement)',\n", " 'drug test 2 (before 4 resting state scans)']]\n", "\n", "\n", "# SKID and drug test\n", "df_lemon2 = pd.read_csv('%s/meta2_lemon_restricted_20170315.csv' % doc_dir, dtype = {'ID':str})\n", "df_lemon2.rename(columns={'ID': 'ids'}, inplace=True)\n", "\n", "# merge\n", "df_meta = pd.merge(df_meta, \n", " df_lemon2[['ids', 'DRUG', 'SKID_Diagnoses 1', 'SKID_Diagnoses 2']], \n", " on=('ids'), how='outer')\n", "\n", "df_meta['ids'] = df_meta['ids'].map(lambda x: '0'+x)\n", "\n", "# SKID: reporting lemon by default, only if no lemon skid available, report our skid\n", "# us: 'SKID diagnose 1', 'SKID diagnose 2'\n", "# lemon: 'SKID_Diagnoses 1', 'SKID_Diagnoses 2' \n", "df_meta['SKID_Diagnoses 1'][df_meta['SKID_Diagnoses 1'].isnull()] = df_meta['SKID diagnose 1'][df_meta['SKID_Diagnoses 1'].isnull()]\n", "df_meta['SKID_Diagnoses 2'][df_meta['SKID_Diagnoses 2'].isnull()] = df_meta['SKID diagnose 2'][df_meta['SKID_Diagnoses 2'].isnull()]\n", "\n", "df_meta.rename(columns={'SKID_Diagnoses 1': 'SKID diagnosis 1',\n", " 'SKID_Diagnoses 2': 'SKID diagnosis 2'}, inplace=True)\n", "\n", "# drug test\n", "df_meta[['drug test 1 (before first MRI measurement)']] = df_meta[['DRUG']]" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "\"\\n# study progress\\n\\n# (interval between days) \\n# won't be reported in main meta file\\n# will be provided upon request\\n\\n# Questionnaire set 1 (institute)\\nfileA = '/nobackup/adenauer2/LSD/Originals/Raw/Questionnaires/surveyA_151013.csv'\\ndf_A = pd.read_csv(fileA)\\ndf_A['ids'] = df_A['ID'].map(lambda x: str(x)[0:5])\\ndf_A['B Quest set 1'] = pd.to_datetime(df_A['datestamp'], format='%Y-%m-%d %H:%M:%S')\\n\\n# Questionnaire set 2 (home)\\nfileB = '/nobackup/adenauer2/LSD/Originals/Raw/Questionnaires/surveyB_151013.csv'\\ndf_B = pd.read_csv(fileB)\\ndf_B['ids'] = df_B['ID'].map(lambda x: str(x)[0:5]) \\ndf_B['B Quest set 2'] = pd.to_datetime(df_B['datestamp'], format='%Y-%m-%d %H:%M:%S')\\ndf = pd.merge(df_A[['ids', 'B Quest set 1']], df_B[['ids', 'B Quest set 2']], on=['ids'], how='outer')\\ndel df_A, df_B\\n\\n# Questionnaire set 3 (scanning day)\\nfileC_active = '/nobackup/adenauer2/LSD/Originals/Raw/Questionnaires/surveyCactive_151013.csv'\\nfileC_inactive = '/nobackup/adenauer2/LSD/Originals/Raw/Questionnaires/surveyCinactive_151013.csv'\\nfileC_corrected = '/nobackup/adenauer2/LSD/Originals/Raw/Questionnaires/surveyCcorrected_151013.csv'\\ndf_C_active = pd.read_csv(fileC_active)\\ndf_C_inactive = pd.read_csv(fileC_inactive)\\ndf_C_corrected = pd.read_csv(fileC_corrected)\\ndf_C = pd.concat([df_C_active, df_C_inactive, df_C_corrected])\\ndf_C['ids'] = df_C['ID'].map(lambda x: str(x)[0:5])\\ndf_C['B Quest set 3'] = pd.to_datetime(df_C['datestamp'], format='%Y-%m-%d %H:%M:%S')\\ndf = pd.merge(df, df_C[['ids', 'B Quest set 3']], on=['ids'], how='outer')\\ndel df_C\\n \\n# Questionnaire set 4 (lemon catch up for newbies)\\nfileF = '/nobackup/adenauer2/LSD/Originals/Raw/Questionnaires/surveyF_151013.csv'\\ndf_F = pd.read_csv(fileF)\\ndf_F['ids'] = df_F['ID'].map(lambda x: str(x)[0:5])\\ndf_F['B Quest set 4a'] = pd.to_datetime(df_F['datestamp'], format='%Y-%m-%d %H:%M:%S')\\ndf = pd.merge(df, df_F[['ids', 'B Quest set 4a']], on=['ids'], how='outer')\\ndel df_F\\n\\n# Questionnaire set 4 (AMAS) \\nfileG = '/nobackup/adenauer2/LSD/Originals/Raw/Questionnaires/surveyG_151013.csv'\\ndf_G = pd.read_csv(fileG)\\ndf_G['ids'] = df_G['ID'].map(lambda x: str(x)[0:5])\\ndf_G['B Quest set 4b'] = pd.to_datetime(df_G['datestamp'], format='%Y-%m-%d %H:%M:%S')\\ndf = pd.merge(df, df_G[['ids', 'B Quest set 4b']], on=['ids'], how='outer')\\ndel df_G\\n\\n# merge Q set 4\\ndf['B Quest set 4'] = df['B Quest set 4a']\\ndf['B Quest set 4'][df['B Quest set 4'].isnull()] = df['B Quest set 4b'][df['B Quest set 4'].isnull()]\\n\\n# Questionnaire set 5 (creativity, metacogn.)\\nfileCreativity = '/nobackup/adenauer2/LSD/Originals/Raw/Questionnaires/survey_creativity_metacog.csv'\\ndf_cr = pd.read_csv(fileCreativity)\\ndf_cr['ids'] = df_cr['IDcode'].map(lambda x: str(x)[0:5])\\ndf_cr['B Quest set 5'] = pd.to_datetime(df_cr['datestamp'], format='%m/%d/%Y %H:%M:%S')\\ndf = pd.merge(df, df_cr[['ids', 'B Quest set 5']], on=['ids'], how='outer')\\ndel df_cr\\n\\n# oddball task\\nfileHannes = '/nobackup/adenauer2/LSD/Originals/Documentation/meta/Participants_List_LargeScaleProject - Test_phase#4_20151215.csv'\\ndf_hannes = pd.read_csv(fileHannes, parse_dates=['test day'])\\ndf_hannes = df_hannes[df_hannes['done'] == '1']\\ndf_hannes['ids'] = df_hannes['DB_ID'].map(lambda x: str(x)[0:5])\\ndf_hannes['B Test set 1'] = pd.to_datetime(df_hannes['test day'], format='%d.%m.%Y')\\ndf = pd.merge(df, df_hannes[['ids', 'B Test set 1']], on=['ids'], how='outer')\\ndel df_hannes\\n\\n# cogn tasks (lemon catch up)\\nfileCognTests = '/nobackup/adenauer2/LSD/Originals/Documentation/meta/Participants_List_LargeScaleProject - Test_phase#6_20151215.csv'\\ndf_cogntests = pd.read_csv(fileCognTests, converters={'done':str}, parse_dates=['test day'])\\ndf_cogntests = df_cogntests[df_cogntests['done'] == '1']\\ndf_cogntests['ids'] = df_cogntests['DB_ID'].map(lambda x: str(x)[0:5])\\ndf_cogntests['B Test set 2'] = pd.to_datetime(df_cogntests['test day'], format='%d.%m.%Y')\\ndf = pd.merge(df, df_cogntests[['ids', 'B Test set 2']], on=['ids'], how='outer')\\ndel df_cogntests\\n\\n# lemon days\\nfileLemon = '/nobackup/adenauer2/LSD/Originals/Documentation/meta/LEMON appointments_20151216.csv'\\ndf_lemon = pd.read_csv(fileLemon, parse_dates=['lemon1'], dayfirst=True)\\ndf_lemon['ids'] = df_lemon['ids'].map(lambda x: str(x)[0:5]) \\ndf_lemon['A day 1'] = df_lemon['lemon1']\\ndf = pd.merge(df, df_lemon[['ids', 'A day 1']], on=['ids'], how='outer')\\n\\ndf_lemon = pd.read_csv(fileLemon, parse_dates=['lemon2'], dayfirst=True)\\ndf_lemon['ids'] = df_lemon['ids'].map(lambda x: str(x)[0:5])\\ndf_lemon['A day 2'] = df_lemon['lemon2']\\ndf = pd.merge(df, df_lemon[['ids', 'A day 2']], on=['ids'], how='outer')\\n\\ndf_lemon = pd.read_csv(fileLemon, parse_dates=['lemon3'], dayfirst=True)\\ndf_lemon['ids'] = df_lemon['ids'].map(lambda x: str(x)[0:5])\\ndf_lemon['A day 3'] = df_lemon['lemon3']\\ndf = pd.merge(df, df_lemon[['ids', 'A day 3']], on=['ids'], how='outer')\\ndel df_lemon\\n\\n# calculate time interval between scanning and other surveys\\ndf['B scanning day'] = df['B Quest set 3']\\ndf['B Quest set 1'] = (pd.Series(df['B Quest set 1'] - df['B scanning day']).dt.days/7).round()\\ndf['B Quest set 2'] = (pd.Series(df['B Quest set 2'] - df['B scanning day']).dt.days/7).round()\\ndf['B Quest set 3'] = (pd.Series(df['B Quest set 3'] - df['B scanning day']).dt.days/7).round()\\ndf['B Quest set 4'] = (pd.Series(df['B Quest set 4'] - df['B scanning day']).dt.days/7).round()\\ndf['B Quest set 5'] = (pd.Series(df['B Quest set 5'] - df['B scanning day']).dt.days/7).round()\\ndf['B Test set 1'] = (pd.Series(df['B Test set 1'] - df['B scanning day']).dt.days/7).round()\\ndf['B Test set 2'] = (pd.Series(df['B Test set 2'] - df['B scanning day']).dt.days/7).round()\\n\\n# day of lemon scan\\ndf['A scanning day'] = df['A day 1']\\ndf['A day 1'] = (pd.Series(df['A day 1'] - df['A scanning day']).dt.days/7).round()\\ndf['A day 2'] = (pd.Series(df['A day 2'] - df['A scanning day']).dt.days/7).round()\\ndf['A day 3'] = (pd.Series(df['A day 3'] - df['A scanning day']).dt.days/7).round()\\n\\n# rename IDs\\nconverter = pd.read_excel('/nobackup/adenauer2/LSD/Originals/Documentation/lookup_table.xlsx',\\n converters={'ids_probanden_db' : str, 'ids_xnat_publicp' : str})\\nconverter_dict = dict(zip(converter['ids_probanden_db'], converter['ids_xnat_publicp']))\\ndf.replace({'ids':converter_dict}, inplace=True)\\n\\ndf_meta = pd.merge(df_meta, df[['ids', 'B Quest set 1', 'B Quest set 2',\\n 'B Quest set 3', 'B Quest set 4', 'B Quest set 5', \\n 'B Test set 1', 'B Test set 2', \\n 'A day 1', 'A day 2', 'A day 3']], \\n on='ids', how='left')\\n\"" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "'''\n", "# study progress\n", "\n", "# (interval between days) \n", "# won't be reported in main meta file\n", "# will be provided upon request\n", "\n", "# Questionnaire set 1 (institute)\n", "fileA = '/nobackup/adenauer2/LSD/Originals/Raw/Questionnaires/surveyA_151013.csv'\n", "df_A = pd.read_csv(fileA)\n", "df_A['ids'] = df_A['ID'].map(lambda x: str(x)[0:5])\n", "df_A['B Quest set 1'] = pd.to_datetime(df_A['datestamp'], format='%Y-%m-%d %H:%M:%S')\n", "\n", "# Questionnaire set 2 (home)\n", "fileB = '/nobackup/adenauer2/LSD/Originals/Raw/Questionnaires/surveyB_151013.csv'\n", "df_B = pd.read_csv(fileB)\n", "df_B['ids'] = df_B['ID'].map(lambda x: str(x)[0:5]) \n", "df_B['B Quest set 2'] = pd.to_datetime(df_B['datestamp'], format='%Y-%m-%d %H:%M:%S')\n", "df = pd.merge(df_A[['ids', 'B Quest set 1']], df_B[['ids', 'B Quest set 2']], on=['ids'], how='outer')\n", "del df_A, df_B\n", "\n", "# Questionnaire set 3 (scanning day)\n", "fileC_active = '/nobackup/adenauer2/LSD/Originals/Raw/Questionnaires/surveyCactive_151013.csv'\n", "fileC_inactive = '/nobackup/adenauer2/LSD/Originals/Raw/Questionnaires/surveyCinactive_151013.csv'\n", "fileC_corrected = '/nobackup/adenauer2/LSD/Originals/Raw/Questionnaires/surveyCcorrected_151013.csv'\n", "df_C_active = pd.read_csv(fileC_active)\n", "df_C_inactive = pd.read_csv(fileC_inactive)\n", "df_C_corrected = pd.read_csv(fileC_corrected)\n", "df_C = pd.concat([df_C_active, df_C_inactive, df_C_corrected])\n", "df_C['ids'] = df_C['ID'].map(lambda x: str(x)[0:5])\n", "df_C['B Quest set 3'] = pd.to_datetime(df_C['datestamp'], format='%Y-%m-%d %H:%M:%S')\n", "df = pd.merge(df, df_C[['ids', 'B Quest set 3']], on=['ids'], how='outer')\n", "del df_C\n", " \n", "# Questionnaire set 4 (lemon catch up for newbies)\n", "fileF = '/nobackup/adenauer2/LSD/Originals/Raw/Questionnaires/surveyF_151013.csv'\n", "df_F = pd.read_csv(fileF)\n", "df_F['ids'] = df_F['ID'].map(lambda x: str(x)[0:5])\n", "df_F['B Quest set 4a'] = pd.to_datetime(df_F['datestamp'], format='%Y-%m-%d %H:%M:%S')\n", "df = pd.merge(df, df_F[['ids', 'B Quest set 4a']], on=['ids'], how='outer')\n", "del df_F\n", "\n", "# Questionnaire set 4 (AMAS) \n", "fileG = '/nobackup/adenauer2/LSD/Originals/Raw/Questionnaires/surveyG_151013.csv'\n", "df_G = pd.read_csv(fileG)\n", "df_G['ids'] = df_G['ID'].map(lambda x: str(x)[0:5])\n", "df_G['B Quest set 4b'] = pd.to_datetime(df_G['datestamp'], format='%Y-%m-%d %H:%M:%S')\n", "df = pd.merge(df, df_G[['ids', 'B Quest set 4b']], on=['ids'], how='outer')\n", "del df_G\n", "\n", "# merge Q set 4\n", "df['B Quest set 4'] = df['B Quest set 4a']\n", "df['B Quest set 4'][df['B Quest set 4'].isnull()] = df['B Quest set 4b'][df['B Quest set 4'].isnull()]\n", "\n", "# Questionnaire set 5 (creativity, metacogn.)\n", "fileCreativity = '/nobackup/adenauer2/LSD/Originals/Raw/Questionnaires/survey_creativity_metacog.csv'\n", "df_cr = pd.read_csv(fileCreativity)\n", "df_cr['ids'] = df_cr['IDcode'].map(lambda x: str(x)[0:5])\n", "df_cr['B Quest set 5'] = pd.to_datetime(df_cr['datestamp'], format='%m/%d/%Y %H:%M:%S')\n", "df = pd.merge(df, df_cr[['ids', 'B Quest set 5']], on=['ids'], how='outer')\n", "del df_cr\n", "\n", "# oddball task\n", "fileHannes = '/nobackup/adenauer2/LSD/Originals/Documentation/meta/Participants_List_LargeScaleProject - Test_phase#4_20151215.csv'\n", "df_hannes = pd.read_csv(fileHannes, parse_dates=['test day'])\n", "df_hannes = df_hannes[df_hannes['done'] == '1']\n", "df_hannes['ids'] = df_hannes['DB_ID'].map(lambda x: str(x)[0:5])\n", "df_hannes['B Test set 1'] = pd.to_datetime(df_hannes['test day'], format='%d.%m.%Y')\n", "df = pd.merge(df, df_hannes[['ids', 'B Test set 1']], on=['ids'], how='outer')\n", "del df_hannes\n", "\n", "# cogn tasks (lemon catch up)\n", "fileCognTests = '/nobackup/adenauer2/LSD/Originals/Documentation/meta/Participants_List_LargeScaleProject - Test_phase#6_20151215.csv'\n", "df_cogntests = pd.read_csv(fileCognTests, converters={'done':str}, parse_dates=['test day'])\n", "df_cogntests = df_cogntests[df_cogntests['done'] == '1']\n", "df_cogntests['ids'] = df_cogntests['DB_ID'].map(lambda x: str(x)[0:5])\n", "df_cogntests['B Test set 2'] = pd.to_datetime(df_cogntests['test day'], format='%d.%m.%Y')\n", "df = pd.merge(df, df_cogntests[['ids', 'B Test set 2']], on=['ids'], how='outer')\n", "del df_cogntests\n", "\n", "# lemon days\n", "fileLemon = '/nobackup/adenauer2/LSD/Originals/Documentation/meta/LEMON appointments_20151216.csv'\n", "df_lemon = pd.read_csv(fileLemon, parse_dates=['lemon1'], dayfirst=True)\n", "df_lemon['ids'] = df_lemon['ids'].map(lambda x: str(x)[0:5]) \n", "df_lemon['A day 1'] = df_lemon['lemon1']\n", "df = pd.merge(df, df_lemon[['ids', 'A day 1']], on=['ids'], how='outer')\n", "\n", "df_lemon = pd.read_csv(fileLemon, parse_dates=['lemon2'], dayfirst=True)\n", "df_lemon['ids'] = df_lemon['ids'].map(lambda x: str(x)[0:5])\n", "df_lemon['A day 2'] = df_lemon['lemon2']\n", "df = pd.merge(df, df_lemon[['ids', 'A day 2']], on=['ids'], how='outer')\n", "\n", "df_lemon = pd.read_csv(fileLemon, parse_dates=['lemon3'], dayfirst=True)\n", "df_lemon['ids'] = df_lemon['ids'].map(lambda x: str(x)[0:5])\n", "df_lemon['A day 3'] = df_lemon['lemon3']\n", "df = pd.merge(df, df_lemon[['ids', 'A day 3']], on=['ids'], how='outer')\n", "del df_lemon\n", "\n", "# calculate time interval between scanning and other surveys\n", "df['B scanning day'] = df['B Quest set 3']\n", "df['B Quest set 1'] = (pd.Series(df['B Quest set 1'] - df['B scanning day']).dt.days/7).round()\n", "df['B Quest set 2'] = (pd.Series(df['B Quest set 2'] - df['B scanning day']).dt.days/7).round()\n", "df['B Quest set 3'] = (pd.Series(df['B Quest set 3'] - df['B scanning day']).dt.days/7).round()\n", "df['B Quest set 4'] = (pd.Series(df['B Quest set 4'] - df['B scanning day']).dt.days/7).round()\n", "df['B Quest set 5'] = (pd.Series(df['B Quest set 5'] - df['B scanning day']).dt.days/7).round()\n", "df['B Test set 1'] = (pd.Series(df['B Test set 1'] - df['B scanning day']).dt.days/7).round()\n", "df['B Test set 2'] = (pd.Series(df['B Test set 2'] - df['B scanning day']).dt.days/7).round()\n", "\n", "# day of lemon scan\n", "df['A scanning day'] = df['A day 1']\n", "df['A day 1'] = (pd.Series(df['A day 1'] - df['A scanning day']).dt.days/7).round()\n", "df['A day 2'] = (pd.Series(df['A day 2'] - df['A scanning day']).dt.days/7).round()\n", "df['A day 3'] = (pd.Series(df['A day 3'] - df['A scanning day']).dt.days/7).round()\n", "\n", "# rename IDs\n", "converter = pd.read_excel('/nobackup/adenauer2/LSD/Originals/Documentation/lookup_table.xlsx',\n", " converters={'ids_probanden_db' : str, 'ids_xnat_publicp' : str})\n", "converter_dict = dict(zip(converter['ids_probanden_db'], converter['ids_xnat_publicp']))\n", "df.replace({'ids':converter_dict}, inplace=True)\n", "\n", "df_meta = pd.merge(df_meta, df[['ids', 'B Quest set 1', 'B Quest set 2',\n", " 'B Quest set 3', 'B Quest set 4', 'B Quest set 5', \n", " 'B Test set 1', 'B Test set 2', \n", " 'A day 1', 'A day 2', 'A day 3']], \n", " on='ids', how='left')\n", "'''" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Meta-file" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# columns and subjects to be included in metafile\n", "\n", "cols_export = ['ids', 'gender', 'age (5-year bins)',\n", " 'SKID diagnosis 1', 'SKID diagnosis 2',\n", " 'drug test 1 (before first MRI measurement)',\n", " 'drug test 2 (before 4 resting state scans)']\n", "\n", "# subjects with at least one of the two mri sessions\n", "subjects = pd.read_csv('%s/subjects_mri' % doc_dir,\n", " header=None, dtype=str)\n", "\n", "# rename IDs\n", "converter = pd.read_excel('/Users/dmargulies/Downloads/lookup_table.xlsx', #%s/lookup_table.xlsx' % doc_dir, \n", " converters={'ids_probanden_db' : str, 'ids_xnat_publicp' : str})\n", "converter_dict = dict(zip(converter['ids_probanden_db'], converter['ids_xnat_publicp']))\n", "subjects.replace({0:converter_dict}, inplace=True)\n", "subjects = subjects[0]\n", "\n", "#df_meta[cols_export][df_meta['ids'].isin(subjects)].to_csv('/home/raid3/oligschlager/Downloads/metafile.csv')\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Core sample reported in manuscript" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0 scans: 119 subjects\n", "1 scans: 1 subjects\n", "2 scans: 1 subjects\n", "3 scans: 3 subjects\n", "4 scans: 194 subjects\n" ] } ], "source": [ "# number of subjects with 2nd-session mri only, per number of scans\n", "\n", "subjects_2nd = pd.read_csv('%s/lemon_lsd_scanlist.csv' % doc_dir,\n", " dtype={'ID': str})\n", "subjects_2nd.replace({'ID':converter_dict}, inplace=True)\n", "\n", "\n", "for n in range(5):\n", " print n, 'scans:', subjects_2nd.ID[subjects_2nd['RS_ses-02'] == n].shape[0], 'subjects'" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "N = 194\n", "\n", "group average mean age 34.398944\n", "dtype: float64\n", "\n", "group median mean age 26.94589\n", "dtype: float64\n", "\n", "group min mean age 20.899087\n", "dtype: float64\n", "\n", "group max mean age 75.89863\n", "dtype: float64\n", "\n", "group std mean age 16.381631\n", "dtype: float64\n", "\n", "female N = 91\n" ] } ], "source": [ "# reporting sample consisting of subjects with all 4 scans of the 2nd session\n", "n_scans = 4\n", "\n", "sample = list(subjects_2nd.ID[subjects_2nd['RS_ses-02'] >= n_scans])\n", "\n", "# 'mean age' col refers to the participants mean age over the whole study duration\n", "print '\\nN =', len(sample)\n", "print '\\ngroup average', df_meta[['mean age']][df_meta['ids'].isin(sample)].mean()\n", "print '\\ngroup median', df_meta[['mean age']][df_meta['ids'].isin(sample)].median()\n", "print '\\ngroup min', df_meta[['mean age']][df_meta['ids'].isin(sample)].min()\n", "print '\\ngroup max', df_meta[['mean age']][df_meta['ids'].isin(sample)].max()\n", "print '\\ngroup std', df_meta[['mean age']][df_meta['ids'].isin(sample)].std()\n", "print '\\nfemale N =', df_meta[['gender']][(df_meta['ids'].isin(sample)) &\n", " (df_meta['gender']==1)].shape[0]" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "105" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# sample overlap of 1st and 2nd session (4 scans)\n", "\n", "# subjects with 4 lsd and 1 lemon scan\n", "\n", "subjects_2nd[(subjects_2nd['RS_ses-02'] == 4) &\n", " (subjects_2nd['RS_ses-01'] == 1) & \n", " (subjects_2nd['T1w'] == 1)].shape[0]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Figures: sample demographics" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Applications/miniconda3/envs/topography/lib/python2.7/site-packages/ipykernel/__main__.py:3: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n", " app.launch_new_instance()\n", "/Applications/miniconda3/envs/topography/lib/python2.7/site-packages/ipykernel/__main__.py:4: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n" ] } ], "source": [ "# subjects with all 4 scans of 2nd session (see above)\n", "df_fig = df_meta[df_meta['ids'].isin(sample)]\n", "df_fig['gender'] = df_fig['gender'].replace(1, 'Female')\n", "df_fig['gender'] = df_fig['gender'].replace(2, 'Male')" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/usr/local/lib/python2.7/site-packages/matplotlib/__init__.py:892: 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" ] }, { "data": { "image/png": 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OX+PGjZWYmKidO3cqOjpajz/+uDw9PQu9xsvLS9nZ2Q6N6+jrSyMnJ8fp63BETk5Oidtt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\n72v69On65ptvSjXPI0eO1N69e9W7d28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"text/plain": [ "<matplotlib.figure.Figure at 0x107950710>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = sns.countplot(x='age (5-year bins)', hue='gender', \n", " data=df_fig.sort_values(by=['age (5-year bins)'], ascending=[1]))\n", "fig.legend(loc='upper right')\n", "fig.set_xlabel('age (years)')\n", "fig.set_ylabel('number of participants')\n", "f = fig.get_xticklabels()\n", "fig.set_xticklabels(f[0:12])\n", "fig.set_xlim([-1,12])\n", "\n", "plt.savefig(\"Figure_distribution.svg\")\n", "plt.savefig(\"Figure_distribution.pdf\")\n", "plt.savefig(\"Figure_distribution.png\")\n", "plt.savefig(\"Figure_distribution.jpeg\")\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#for n in [1, 2]:\n", "# sns.distplot(df_fig['mean age'][df_fig['gender']==n], kde=True, hist=False)\n", "\n", "# sns.violinplot(x='gender', y='mean age', data=df_fig)" ] }, { "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 }

mit

hunterherrin/phys202-2015-work

assignments/assignment11/OptimizationEx01.ipynb

1

42075

{ "cells": [ { "cell_type": "markdown", "metadata": { "nbgrader": {} }, "source": [ "# Optimization Exercise 1" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": {} }, "source": [ "## Imports" ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "collapsed": true, "nbgrader": {} }, "outputs": [], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import scipy.optimize as opt" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": {} }, "source": [ "## Hat potential" ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "collapsed": true, "deletable": false, "nbgrader": { "checksum": "6cff4e8e53b15273846c3aecaea84a3d", "solution": true } }, "outputs": [], "source": [ "def hat(x,a,b):\n", " return b*x**4-a*x**2" ] }, { "cell_type": "code", "execution_count": 42, "metadata": { "collapsed": true, "deletable": false, "nbgrader": { "checksum": "7204bd97cd003430289f171b6ba70d63", "grade": true, "grade_id": "optimizationex01a", "points": 2 } }, "outputs": [], "source": [ "assert hat(0.0, 1.0, 1.0)==0.0\n", "assert hat(0.0, 1.0, 1.0)==0.0\n", "assert hat(1.0, 10.0, 1.0)==-9.0" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": {} }, "source": [ "Plot this function over the range $x\\in\\left[-3,3\\right]$ with $b=1.0$ and $a=5.0$:" ] }, { "cell_type": "code", "execution_count": 43, "metadata": { "collapsed": true, "nbgrader": {} }, "outputs": [], "source": [ "a = 5.0\n", "b = 1.0\n", "x = np.linspace(-3,3,60)" ] }, { "cell_type": "code", "execution_count": 44, "metadata": { "collapsed": false, "deletable": false, "nbgrader": { "checksum": "6cff4e8e53b15273846c3aecaea84a3d", "solution": true } }, "outputs": [ { "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x7f85fe1a8940>]" ] }, "execution_count": 44, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f85fe672a58>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(x,hat(x,a,b))" ] }, { "cell_type": "code", "execution_count": 45, "metadata": { "collapsed": true, "deletable": false, "nbgrader": { "checksum": "bd49ce2f030e3366ee640213f26fdaa6", "grade": true, "grade_id": "optimizationex01b", "points": 2 } }, "outputs": [], "source": [ "assert True # leave this to grade the plot" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": {} }, "source": [ "Write code that finds the two local minima of this function for $b=1.0$ and $a=5.0$.\n", "\n", "* Use `scipy.optimize.minimize` to find the minima. You will have to think carefully about how to get this function to find both minima.\n", "* Print the x values of the minima.\n", "* Plot the function as a blue line.\n", "* On the same axes, show the minima as red circles.\n", "* Customize your visualization to make it beatiful and effective." ] }, { "cell_type": "code", "execution_count": 54, "metadata": { "collapsed": false, "deletable": false, "nbgrader": { "checksum": "6cff4e8e53b15273846c3aecaea84a3d", "solution": true } }, "outputs": [ { "data": { "image/png": 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D1TlZJCIiIimdey5stRVceGHqSkpfQc9cm1krM3vBzKaa2XQzuzJz+2ZmNsrMZprZSDNr\nm6rGcrbFFqGpfu+91JWIiIhIXbRTSGFJ1ly7++fAQHfvBfQEBprZ3sAQYJS7bw+MzlyXHFt3JstM\nc9eNoRm3uJRnXMozLuUZl/KsH/e6m2vlmX9JZ67dvWon5RZAU2AZcAQwPHP7cOCoBKUJ2jFERESk\n0L37bjiQcfPNU1ciVZLOXJtZE2AysA1ws7v/zMyWufummY8bsLTqerWv08x1Htx+O4wZAyNGpK5E\nREREavLUU3DddTBqVOpKykNBz1wDuHtlZiykE7CPmQ1c5+MOqItORGMhIiIihe2VVzRvXWhq2BEx\n/9x9hZk9AewGLDKzLdx9oZl1BN6v6WsGDx5Mly5dAGjbti29evViwIABwJr5Il3P/vrUqVM577zz\n1vr4HnsMYOZMGDWqgubNC6veQr9eU56FVF+xXVeeyrOQrytP5Zny+qhR8P3v1/5x5dn45+Py5csB\nmDt3LtlINhZiZu2BVe6+3MxaA08DvwYOBD5w96vMbAjQ1t2HrPO1GguJrKKi4qsnU3XdusEDD2j/\nzPqqLU9pGOUZl/KMS3nGpTzrp2dPuOMO6N275o8rz7iyGQtJ2Vz3IByw2CRzGeHufzSzzYD7ga2A\nucDx7r58na9Vc50nxx8PRx4J3/9+6kpERESkupUrYZNNYNkyaNUqdTXlIZvmOtlYiLu/AnztdZa7\nLwX2y39FUpOquWs11yIiIoVlxgzo2lWNdaFpkroAKQxVc0br0nZ8DVNbntIwyjMu5RmX8oxLeWYv\nm5PHKM/8U3MtddKOISIiIoVJO4UUpqT7XDeUZq7zxz3Mc731FrRrl7oaERERqXLwwfCTn8Dhh6eu\npHwU/D7XUvjMwk4hGg0REREpLNmMhUj+qbkWoO6ZLI2G1J9m3OJSnnEpz7iUZ1zKMztLlsDHH8NW\nW9X9ecoz/9Rcy3rtsgtMmZK6ChEREany8sth8cvqHFCQFDRzLes1aRKccYZWr0VERArFVVfBokVw\n3XWpKykvmrmWKHr0gNmz4ZNPUlciIiIiABMnQp8+qauQmqi5FqDumayWLaF7d5g6NX/1FDvNuMWl\nPONSnnEpz7iUZ3YmTYK+fdf/ecoz/9RcS1b69An/kUVERCStxYth+XLYZpvUlUhNNHMtWbntNhgz\nBu66K3UlIiIi5e2pp+Daa+G//01dSfnRzLVEo5VrERGRwqB568Km5lqA9c9k7bQTzJ8PK1bkp55i\npxm3uJRnXMozLuUZl/Jcv2znrUF5pqDmWrLSrFnY73ry5NSViIiIlC93rVwXOs1cS9bOOw+23BJ+\n9rPUlYiIiJSnBQtg113DHtc6gUz+aeZaotLctYiISFqTJoXfx2qsC5eaawGym8nq2ze8FSXrpxm3\nuJRnXMozLuUZl/Ks28SJ2c9bg/JMQc21ZG277WDpUliyJHUlIiIi5alq5VoKl2aupV4GDoRLLoGD\nDkpdiYiISHlxh/bt4dVXoWPH1NWUJ81cS3R9+2ruWkREJIW5c6F1azXWhU7NtQDZz2TpoMbsaMYt\nLuUZl/KMS3nGpTxr15At+JRn/qm5lnrRQY0iIiJp1OfkMZKOZq6lXtyhQweYNi3seS0iIiL5MXAg\nDBkCBx6YupLypZlric5MoyEiIiL5VlkZzpKsnUIKn5prAeo3k6Xmev004xaX8oxLecalPONSnjWb\nORPatQuX+lCe+afmWupNc9ciIiL5pXnr4qGZa6m3BQugVy94/32dflVERCQfzj0XOnWCiy9OXUl5\n08y15MQ3vwnNm8O8eakrERERKQ9auS4eaq4FqP9Mluau66YZt7iUZ1zKMy7lGZfy/LpVq+Dll6F3\n7/p/rfLMPzXX0iCauxYREcmP6dOhc2fYeOPUlUg2NHMtDfLUU3DNNTB6dOpKREREStuwYVBRASNG\npK5ENHMtOdOnD7z0Uth3U0RERHJn0iTtb11M1FwLUP+ZrA4doG1bmD07N/UUO824xaU841KecSnP\nuJTn102c2PCDGZVn/qm5lgbTQY0iIiK59cUXYea6V6/UlUi2NHMtDXbVVbBwIVx/fepKREREStPE\niXDmmWG3EElPM9eSU1q5FhERyS3NWxcfNdcCNGwma7fdYMqUsP+mrE0zbnEpz7iUZ1zKMy7lubbG\nnjxGeeafmmtpsLZtYcstYcaM1JWIiIiUpokTtXJdbDRzLY3y/e/DfvvB6aenrkRERKS0fPoptG8P\ny5ZBy5apqxHQzLXkgeauRUREcmPKFOjeXY11sVFzLUDDZ7J0GvSaacYtLuUZl/KMS3nGpTzXaOy8\nNSjPFNRcS6Psuiu8+iqsXJm6EhERkdKieevipJlrabQePWD4cOjdO3UlIiIipaNbN3jggfB7VgqD\nZq4lL/r00WiIiIhITCtWwPz5sOOOqSuR+lJzLUDjZrL69tVBjevSjFtcyjMu5RmX8oxLeQaTJ4dT\nnjdr1rj7UZ75p+ZaGk0r1yIiInFp3rp4aeZaGu3zz2GzzWDJEmjTJnU1IiIixe/44+GII+Dkk1NX\nItVp5lryolUr2HlnjYaIiIjE4A4TJsAee6SuRBpCzbUAjZ/J2ntvePbZOLWUAs24xaU841KecSnP\nuJQnzJsHq1bBtts2/r6UZ/6puZYo+veH8eNTVyEiIlL8xo8Pv1etzuEDKVSauZYoFi+G7baDDz6A\npk1TVyMiIlK8zjornPb8nHNSVyLr0sy15E2HDrDFFvDKK6krERERKW7jx4dxSylOaq4FiDOT1b+/\n5q6raMYtLuUZl/KMS3nGVe55LlkCCxZAz55x7q/c80xBzbVEs/femrsWERFpjP/9D/bcs/Enj5F0\nNHMt0bz5Zli9nj9fB2GIiIg0xMUXwyabwC9+kboSqYlmriWvunYNe3O+9VbqSkRERIqT5q2LX7Lm\n2sw6m9lYM3vNzF41s3Myt29mZqPMbKaZjTSztqlqLCcxZrLMtCVfFc24xaU841KecSnPuMo5z08/\nDRsD7L57vPss5zxTSbly/SVwvrt3B/YEzjazHYEhwCh33x4YnbkuRUIHNYqIiDTMCy+EAxnbtEld\niTRGwcxcm9m/gL9kLt9x90VmtgVQ4e7d1vlczVwXqKlT4cQTYcaM1JWIiIgUl9/+Fj76CK6+OnUl\nUpuimbk2sy7ArsALwObuvijzoUXA5onKkgbo0QMWLgwnlREREZHsad66NCTf6MXMNgQeAs5194+s\n2jYT7u5mVuMS9eDBg+nSpQsAbdu2pVevXgwYMABYM1+k69lfnzp1Kuedd16j769pU9h++wpuuQUu\nu6xwvr98X4+Vp64rT+VZ+NeVp/KMcX3VKnj22QrOPhsg3v2Xa56xrk+dOpXly5cDMHfuXLJR61iI\nmV1Yx9e5u1+X1SPU9eBmzYHHgafc/YbMbTOAAe6+0Mw6AmM1FpJ7FRUVXz2ZGuv3vw+nQb/22ih3\nV5Ri5inKMzblGZfyjKtc83zpJTjtNHj11bj3W6555ko2YyF1NdeXAzV90AjN9a8bWZwBw4EP3P38\nardfnbntKjMbArR19yHrfK2a6wL2zDNw0UXw4oupKxERESkON9wQjle65ZbUlUhdGtVc55qZ7Q08\nA0xjTRM/FHgRuB/YCpgLHO/uy9f5WjXXBezzz6F9+zB7veGGqasREREpfMceC0cfDd//fupKpC5R\nDmg0s9Zm9v/M7CYzu93MbjOz2xpbnLs/6+5N3L2Xu++aufzH3Ze6+37uvr27H7BuYy25UTVnFEOr\nVtCrV9hSqFzFzFOUZ2zKMy7lGVc55umeu4MZyzHP1NbbXAMjCDt2HARUAJ2Bj3NYk5SAvffWyWRE\nRESyMXs2tGwJW2+duhKJYb1jIWY21d17mdk0d++ZOQjxWXffIz8l1liTxkIK3BNPwPXXw3//m7oS\nERGRwnbbbeH35d13p65E1ifWPtcrM3+uMLMeQFugQ2OLk9K2115hLOTLL1NXIiIiUtiefTac4VhK\nQzbN9a1mthnwC+DfwHRA5w4qMbFnsjbdFLp2DWdsLEeacYtLecalPONSnnGVY565PHlMOeaZ2npP\nIuPut2b+Og7omttypJRUzV337Zu6EhERkcK0cGE4N0T37qkrkVjq2uf6FHcfsc7JZJw1+1w3+iQy\nDaWZ6+Jwzz1w//3wyCOpKxERESlMDz4Id9wBjz+euhLJRmNnrttk/twI2DBz2ajanyJ16t8/zJHp\ndZCIiEjNNG9demptrt39b5m//tfdf139AozOT3mSL7mYyerUCTbYAN54I/pdFzzNuMWlPONSnnEp\nz7jKLc/x43PbXJdbnoUgmwMa/1zDbTfGLkRKU9XqtYiIiKztww/DAtRuu6WuRGKqa+a6H7AXcD5w\nHWHWGsJIyNHuvkteKqy5Ns1cF4m//Q0mTIDhw1NXIiIiUlhGjoQrroBx41JXItlq7Mx1C0Ij3ZS1\n564/BI6NVaSUNq1ci4iI1CyXW/BJOnXNXI9z98uBfuvMXF/n7rPyV6LkQ65msrp1g+XL4d13c3L3\nBUszbnEpz7iUZ1zKM65yyjMfBzOWU56FIpuZ65ZmdquZjTKzsZnLmJxXJiWhSZM1+12LiIhIsHIl\nTJwI/fqlrkRiq3Xm+qtPMJsG3AxMBlZnbnZ3fynHtdVVk2aui8g118C8efDnmg6NFRERKUPPPw8/\n/jFMmZK6EqmPbGau13uGRuBLd785Uk1ShvbeG+66K3UVIiIihUPz1qUrm7GQx8zsbDPraGabVV1y\nXpnkVS5nsnr3htmzYcWKnD1EwdGMW1zKMy7lGZfyjKtc8szXyWPKJc9Ckk1zPRi4CJgAvFTtIpKV\nFi2gb9+wJZ+IiEi5q6wMzbVWrkvTemeuC5FmrovPL38Jq1eH/TxFRETK2fTpcPjhMGdO6kqkvhq7\nz3XVnWxgZpeZ2a2Z69uZ2WGxipTy8J3vwBjtMSMiIsKYMbDPPqmrkFzJZizkdmAl4WyNAO8CWn8s\nMbmeyfr2t+G112DZspw+TMHQjFtcyjMu5RmX8oyrHPIcORIOPDA/j1UOeRaabJrrbdz9KkKDjbt/\nktuSpBS1ahVmy0aPTl2JiIhIOitXhtOd77df6kokV7LZ53oCsC8wwd13NbNtgHvcffd8FFhLTZq5\nLkI33BDmzP7+99SViIiIpDFuHFx0UTiBjBSfKDPXwOXAf4BOZnY3MAa4pPHlSbk58EB4+mnQ6yIR\nESlXTz+dv5EQSWO9zbW7jwSOAU4H7gb6uPvYXBcm+ZWPmaxu3cL2QzNn5vyhktOMW1zKMy7lGZfy\njKvU8xw5Eg44IH+PV+p5FqJsdgsZ7e5L3P3xzGWxmWlyVurNbM3qtYiISLlZvBhmzYJ+/VJXIrlU\n68y1mbUG2gBjgQHVPrQx8B9375bz6mqhmevidf/9cOed8PjjqSsRERHJr3vugXvvhUcfTV2JNFQ2\nM9fN6vjYWcC5wJasfUbGj4C/NL48KUf77QdnnglffAEtW6auRkREJH80b10eah0Lcfcb3L0rcLG7\nd6126enuaq5LTL5msjbbDHbcsfRPha4Zt7iUZ1zKMy7lGVep5ume/3lrKN08C1k2u4XcYmbnmtlD\nZvagmf3UzJrnvDIpWZq7FhGRcvPqq9C6NWy7bepKJNey2ed6GGF8ZDhgwCnAKnc/M/fl1VqTZq6L\n2LPPwjnnwOTJqSsRERHJj2uugTlz4OabU1cijdHYmesqfd29Z7Xro81sWuNKk3K2xx7w5puwaBFs\nvnnqakRERHJv5Ej4yU9SVyH5kM1YyCoz++pNjMwZGlflriRJIZ8zWc2bw8CB8N//5u0h804zbnEp\nz7iUZ1zKM65SzPPTT+G558LvvnwrxTwLXTbN9cXAGDMbZ2bjCGdovCi3ZUmp09y1iIiUi/HjoVcv\n2GST1JVIPtQ5c21mHYCtgQXANzI3v+Hun+ehtlpp5rr4vfVW2ET/vffCyWVERERK1QUXhN2yfvGL\n1JVIY2Uzc13ryrWZnQm8BvwZmAp0cfeXUzfWUhq6doWNNoJpmt4XEZESl2ILPkmnrrGQ84Hu7t4P\n6AcMzU9JkkKKmawDDww/cEqRZtziUp5xKc+4lGdcpZbn/PnhXdrddkvz+KWWZzGoq7le6e6LAdz9\nTUDn05PoklG7AAAgAElEQVSoDjhAc9ciIlLaRo0KZydu2jR1JZIvtc5cm9li4B7C3tYAJwD3Zq67\nu5+Tlwprrk0z1yXgo49gyy3Dlnxt2qSuRkREJL4TTwyLSWeckboSiSGbmeu6muvBQPUPWuZ6VXM9\nPFKd9abmunR85zswZAgcfHDqSkREROJavTqcz2HqVOjUKXU1EkOjDmh09zvcfXi1yx3V/4xfrqSU\naiarVOeuNeMWl/KMS3nGpTzjKqU8J08OzXXKxrqU8iwW2exzLZIzmrsWEZFS9fTT2iWkHNW5z3Wh\n0lhI6aisDK/qJ0+Gzp1TVyMiIhLPPvvAz38OBx2UuhKJpVFjIdXuZO8abvt2YwoTqdKkSTiKuhRH\nQ0REpHx9+CFMmRIabCkv2YyF/LmG2/4SuxBJK+VMVinOXWvGLS7lGZfyjEt5xlUqeY4dC3vumX43\nrFLJs5g0q+0DZtYP2AvoYGYXsGZLvo3QrLZEtP/+cOGF4ahq7QMqIiKlQPPW5auurfi+AwwEzgJu\nqfahj4DH3H1W7surmWauS0+PHjBsGOy+e+pKREREGm/bbeHhh6Fnz9SVSEzZzFzXunLt7uOAcWZ2\nh7vPjV2cSHVVu4aouRYRkWI3Zw588klYOJLyk814x6dmdo2ZPWlmYzOXMTmvTPIq9UzWgQeW1pZ8\nqfMsNcozLuUZl/KMqxTyHDkyLBpZneub+VEKeRabbJrrfwIzgG8BlwNzgUm5K0nKUf/+8PLLsGJF\n6kpEREQaR/PW5W29+1yb2WR3721m09y9Z+a2Se7eJy8V1lyTZq5L0CGHwGmnwQknpK5ERESkYT77\nDDp2hFmzoEOH1NVIbFH2uQZWZv5caGaHmVlvYNNGVyeyju9+Fx56KHUVIiIiDff007Drrmqsy1k2\nzfUVZtYWuBC4CPgHcH5Oq5K8K4SZrCOPDD+UPvssdSWNVwh5lhLlGZfyjEt5xlXseT70EBxzTOoq\n1ij2PIvReptrd3/M3Ze7+yvuPsDde7v7v/NRnJSXDh1gt91K68BGEREpHytXwhNPwNFHp65EUqpr\nn+vqZ2Z01pxEBsDd/ZxcFlYXzVyXrr/+FZ5/HkaMSF2JiIhI/Tz1FPzud/C//6WuRHKlUftcAy+x\npqn+NfBL1jTY6mwlJ44+Gi67LLz6b9EidTUiIiLZK7SREEmj1rEQd7/D3Ye7+x3A0qq/V90e48HN\n7DYzW2Rmr1S7bTMzG2VmM81sZGbeW3KsUGayttwSdtoJRo9OXUnjFEqepUJ5xqU841KecRVrnqtW\nwaOPhoPzC0mx5lnMsjmgMZduBw5a57YhwCh33x4YnbkuZeSYY+DBB1NXISIikr1nnoGtt4YuXVJX\nIqmtd59rADOb4u675qQAsy7AY+7eI3N9BvAdd19kZlsAFe7ebZ2v0cx1CZs3D/r0gffeg2Z1DS6J\niIgUiJ/8BDp3hqFDU1ciudSomWsz+5g1s9Wtzeyjah92d984Qo012dzdF2X+vgjYPEePIwWq6pX/\nuHGw776pqxEREalbZSU88kj4vSVSa3Pt7hvms5BaanAzq3GJevDgwXTJvPfStm1bevXqxYABA4A1\n80W6nv31qVOnct555xVMPbvuCg89NIB99y2Meup7vdDyLPbrylN5FvJ15ak8mzUbQPv28O67Fbz7\nbvp6ij3PQro+depUli9fDsDcuXPJRlZjIblUy1jIAHdfaGYdgbEaC8m9ioqKr55MhWDWLNhnH1iw\nAJo0SV1N/RVansVOecalPONSnnEVY57nnw9t28KvfpW6kq8rxjwLWTZjIYXYXF8NfODuV5nZEKCt\nuw9Z52vUXJeBnj3hpptg771TVyIiIlIz9zDK+MQTsPPOqauRXMumuU66Jmhm9wATgB3M7B0zOx34\nA7C/mc0EBmWuSxnSriEiIlLoJk2CVq2ge/fUlUihSNpcu/tJ7r6lu7dw987ufru7L3X3/dx9e3c/\nwN2Xp6yxXFTNGRWSY46Bhx8OqwLFphDzLGbKMy7lGZfyjKvY8qw6cYzVuZaZTrHlWQqKcJpVykX3\n7tCmDUycmLoSERGRr3PXWRnl65LPXDeEZq7Lx6WXhrNeXXVV6kpERETWNm0aHHkkvPlm4a5cS1wF\nP3Mtsj7HHBNWBfRaSkRECs2DD4bTnauxlurUXAtQuDNZu+4Kq1eH1YFiUqh5FivlGZfyjEt5xlVM\neT70EBx7bOoq6lZMeZYKNddS0MzWrF6LiIgUihkzYPly2GOP1JVIodHMtRS8556DH/wApk9PXYmI\niEhwxRWwcCH8+c+pK5F80sy1lIQ99oAVK+D111NXIiIiEmiXEKmNmmsBCnsmq0mTcMBIMY2GFHKe\nxUh5xqU841KecRVDnm++CfPnQ//+qStZv2LIs9SouZaioLlrEREpFA8/DEcdBU2bpq5ECpFmrqUo\nrF4NW24Z5q+/9a3U1YiISDnr1w8uvxwOPDB1JZJvmrmWktG0aVgl0Oq1iIikNH8+zJwJgwalrkQK\nlZprAYpjJuvYY+G++1JXkZ1iyLOYKM+4lGdcyjOuQs/zgQfg8MOhefPUlWSn0PMsRWqupWgMGgSL\nFsHLL6euREREypE73HYbDB6cuhIpZJq5lqLyy1+GTftvvDF1JSIiUm5eeAFOPjmMheiU5+Upm5lr\nNddSVN56C/r2DTNvrVqlrkZERMrJD38IXbvC0KGpK5FUdECjZK1YZrK6doVeveBf/0pdSd2KJc9i\noTzjUp5xKc+4CjXPTz6BBx+E005LXUn9FGqepUzNtRSdM8+EYcNSVyEiIuXkgQfg298O28KK1EVj\nIVJ0Pv8cOnWCiRPDSraIiEiu7b03XHwxHHlk6kokJY2FSElq1Qq+9z24/fbUlYiISDmYMQPmzIFD\nDkldiRQDNdcCFN9M1g9+EJrr1atTV1KzYsuz0CnPuJRnXMozrkLM87bb4NRTi2dv6+oKMc9Sp+Za\nitIuu8AWW8CoUakrERGRUvbll3DnnXDGGakrkWKhmWspWrfcAv/9bzh6W0REJBceeQSuvx6eeSZ1\nJVIItM+1lLQVK2DrrWHWLOjQIXU1IiJSig47DI47rvi24JPc0AGNkrVinMnaZJNw1PaIEakr+bpi\nzLOQKc+4lGdcyjOuQspzwQKYMAGOPTZ1JQ1XSHmWCzXXUtR+8IOw57XeyBARkdiGDw+r1htskLoS\nKSYaC5Gi5g477BB+APbrl7oaEREpFZWVsN12cO+90Ldv6mqkUGgsREqe2ZrVaxERkVjGjQsr1n36\npK5Eio2aawGKeybrtNPgoYfg449TV7JGMedZiJRnXMozLuUZV6HkOWxYWLyxOtcoC1+h5FlO1FxL\n0dtiC9hnH7j//tSViIhIKVi2DB5/HE4+OXUlUow0cy0l4bHH4Morw1HdIiIijfHXv8L48WHeWqQ6\nzVxL2Tj4YJg7F15/PXUlIiJS7KpGQkQaQs21AMU/k9WsWZi9LpQDG4s9z0KjPONSnnEpz7hS5zll\nCixdCvvum7SMaFLnWY7UXEvJOOOMcEKZlStTVyIiIsVq2DA4/XRoog5JGkgz11JSBgyAH/0ITjwx\ndSUiIlJsPvoIunQJq9dbbZW6GilEmrmWsnPhhXD11Tpjo4iI1N/f/w7776/GWhpHzbUApTOTdeih\n8OWXMHJk2jpKJc9CoTzjUp5xKc+4UuX5xRdw3XUwZEiSh88ZPT/zT821lJQmTeCSS+APf0hdiYiI\nFJMRI6BnT+jVK3UlUuw0cy0lZ9Uq2G47uOce2HPP1NWIiEihW70adtwR/vGPcFIykdpo5lrKUrNm\ncNFFWr0WEZHsPPwwtG8P/funrkRKgZprAUpvJuuMM+D55+G119I8fqnlmZryjEt5xqU848p3nu7h\nDL9Dh4LVuR5ZnPT8zD8111KSWreGc84JO4eIiIjUZtSocH6EQw9NXYmUCs1cS8lavhy22QYmT4at\nt05djYiIFKKBA8Opzk8+OXUlUgw0cy1lrW1bOPNMuPba1JWIiEghev55eOstOOGE1JVIKVFzLUDp\nzmSddx7cdRcsXpzfxy3VPFNRnnEpz7iUZ1z5zPMPf4CLL4bmzfP2kHmn52f+qbmWktaxIxx/PNx4\nY+pKRESkkEyfHlauTz89dSVSajRzLSVvzhzYYw94803YeOPU1YiISCE47TTYYQf4+c9TVyLFJJuZ\nazXXUhZOOgl22y3sfy0iIuVt3jzo3TssvrRtm7oaKSY6oFGyVuozWUOGwPXXwxdf5OfxSj3PfFOe\ncSnPuJRnXPnI89prwwHv5dBY6/mZf2qupSzssku43Hln6kpERCSlxYvDge7nnZe6EilVGguRsjF+\nfDhz44wZ0LRp6mpERCSFyy4LDfYtt6SuRIqRxkJEqtl7b/jGN+DBB1NXIiIiKXz4Idx8c9h+TyRX\n1FwLUB4zWWYwdCj8/vewenVuH6sc8swn5RmX8oxLecaVyzxvugn22y+cvbdc6PmZf2qupawcemjY\nju+221JXIiIi+fTee3DNNfDb36auREqdZq6l7EyZAgcfDK+/DptumroaERHJh8GDYfPN4aqrUlci\nxUz7XIvU4qyzoHVruOGG1JWIiEiuvfACfPe74YD2jTZKXY0Us6I9oNHMDjKzGWY2y8wuSV1POSi3\nmazf/Q7++c9w+ttcKLc8c015xqU841KeccXOs7ISzjknHG9Tjo21np/5V3DNtZk1Bf4CHATsBJxk\nZjumrUpKTYcOYTumc88FvQkiIlK67rwzHNB+yimpK5FyUXBjIWbWD/iVux+UuT4EwN3/UO1zNBYi\njfbll9CrF1xxBRx1VOpqREQktg8/hG7d4F//gt13T12NlIJiHQv5JvBOtevzM7eJRNW8OfzpT3DB\nBfD556mrERGR2H77WzjwQDXWkl/NUhdQAy1JJ1BRUcGAAQNSl5F3++0XVq+vvRYuvTTe/ZZrnrmi\nPL/OPZxl7t13YcmStS8ffLDm78uXf3306aOPKthoowFfXW/TBtq3h3btwp/VL+3ahZMvbbUVNCvE\n3xgFQM/PuGLlOXMm3H47vPpq42sqZnp+5l8h/qhcAHSudr0zYfV6LYMHD6ZLly4AtG3bll69en31\n5Kka3tf17K9PnTq1oOrJ5/XjjqvgrLPgtNMG0KmT8izE6+Wc58iRFcyfDxtvPIA33oBx4yp45x14\n770BNGsGbdtWsMkmsO22A2jfHj7+OFw//PBwfc6cCpo0gd12C/f30ksVvPHGVL73vXB90qQKPv8c\nOncewAcfwIsvVjB7NrRpM4AlS+DNNytYtgxWrBhAly7Qrl0FnTvD/vsPYIcdYMmS8HiFkpeen8V/\nPVae558ffr7PmAFbbFE431++r+v52fj8li9fDsDcuXPJRiHOXDcD3gD2Bd4FXgROcvfXq32OZq4l\nqssugzlz4O67U1ci5eyLL8I+7M89BxMmwEsvhZXprl1hhx3CpVu3NX9v1y5/tX3+OcyeHbYye+ON\ntS9Nm0LPntCv35pL+/b5q01kXU88EUb+XnkFWrRIXY2UkqLd59rMDgZuAJoCw9z9ynU+ruZaovrk\nk9C03HMP7L136mqkXLz7bmikqy5Tp8L2269pUHffPZymuZDHMdzh/ffXflHw4ovhZB39+sFee4U/\nu3cPTbhIrn3xBfToEY6pOfjg1NVIqSna5np91FzHV1FR8dXbIOXqnnvg6qth0qTGNwHKM65SyfOz\nz6CiAp58Ep56CpYtW3u1d/fdYcMNc19HrvNcvTrsIT9hwpoXDosWwaBBcMghoeH5Zgkdpl4qz89C\n0dg8r74annkGHn88Xk3FTM/PuLJprgt4PUQkv048EW66CYYNgx/+MHU1Uireeis0008+CePHhwNo\nDz0UHn44rK5ZnT+ii1PTpuF769EjnA0Vwur200+HHH72M9h669BoH3II7LFHYa/OS/F4773QXD/3\nXOpKpJxp5VqkmilTwqra66/DppumrkaKkXv4xf7ww6GR/OCD8Jw65BDYf389rwBWrYLnn1/zouOd\nd+CAA+CII+Dww/Ozei+lafDgMJJ01VWpK5FSpbEQkQb4yU/go4/WnNVLZH3cYdq0MFp0771ha7vj\nj4fDDoPevaFJk9QVFrYFC0KT/cgjYZTkoIPgpJPCny1bpq5OisVTT4V3HadPL8/TnEt+FOtJZCSB\nqu1nBP74R5g8GYYPb/h9KM+4CjXP2bPDSSq6d4cjjwy3PfoovPYaXH459OlTmI11oeX5zW/C//1f\naLBnz4YBA+C662DLLeHMM2H06DDHXagKLc9i15A8FyyA00+Hf/5TjfW69PzMvwL8sS+S1gYbwP33\nw8UXhxUQkeqWLIEbboC+feHb3w6zxP/4R5it/sMfYJdd9I5HY7RvDz/6EYwbBy+/DDvuGGa0O3WC\nc88NO6qIVLdqFXzve/D//h/ss0/qakQ0FiJSq2HD4Prrw7ZibdqkrkZSqqwMu3zcemt46/nww+HU\nU2HgQB2Ily8zZ4Z96G+/PZwx8v/+L4yOaJVSfvnLME709NPa7lFyTzPXIo3gDiefHBrrW29NXY2k\nsGgR3HFHWJlu3To0dCefrIMSU1q9GkaODP8nx46FY48Nc7Z9+ugdg3I0ejScckoY5dtii9TVSDnQ\nzLVkTTNZX2cGt9wS3p6u75kblWdc+cyzsjKsgB17bDgL4syZMGJEGFH46U9Lo7Eu5udn06Zh95WH\nHw5jW9/6VthGc9dd4a9/hcxZivOqmPMsRNnmuWhReAfpzjvVWNdFz8/8U3MtUoeNNgrz1+eeC7Nm\npa5GcunDD8MZ3bbfHoYOhf32g7ffDuNBe+6pVdFC1LFj+LeaNQuuuSacOKRr17Djz4wZqauTXKqs\nDO8inXFG+L8qUkg0FiKShZtuCm9DP/cctGqVuhqJafZs+POfw+r0/vuHF1L9+qmZLlbvvhvecfrb\n38IJe849N2zpV4i7tkjDXXFFeIdpzBgd9yD5pZlrkUjc4bjjwluPf/lL6mqksdzhv/+FG28MJzM5\n88yw2tm5c+rKJJbPPw97jv/pT/Dpp2Gk57TTdABkKRg/Pvw8njQp7CIjkk+auZasaSarbmbhoLYn\nn4SHHlr/5yvPuGLl+dln8Pe/w847wwUXhL2p582DK68sr8a6HJ6frVqFs/VNnhz+71ZUQJcu4d99\n7ty4j1UOeeZTXXkuWRK23Rs2TI11tvT8zD811yJZats2rIT9+MdhT2MpHkuWwG9+E+ZxH3ssjIFM\nmxZWrLXNYmkzg/794cEHQ6PdrBnstls4CPKll1JXJ/VRWRleMJ14Ihx6aOpqRGqnsRCRerr++rB7\nSEVFOOGMFK45c8KZ/u6+G445Bi68MJyURMrbhx+GYyhuuAG22y6cMOqggzRnX+h+//vw4viZZ6B5\n89TVSLnSzLVIDriHFc8334QnntDKZyF64YVwGvuKCjjrrHDmto4dU1clhebLL+G++8JzZfVquOii\nMHLQokXqymRdN94Y5ufHjdM4iKSlmWvJmmaysmcW5nY7d4YjjghzvOtSnnFlk2dlZVjV2mcfOOGE\nMAowd27YVUCN9dr0/AyaNw/buU2dGt7h+Oc/w+jQVVfBihXZ34/yjGvdPG+6KbxjOGaMGuuG0PMz\n/9RcizRA06ZrTsN89NFhZwJJY+VKGD4cevSAX/0qzMTPnh22YNtww9TVSTEwgwMOgFGjwrtR06aF\nk9Nccgm8917q6srb3/8eXuyMHg1bb526GpHsaCxEpBFWrQpvI3/6adhFpGXL1BWVj48/DnOz118f\nTvxyySXhZBKam5UY5s4Nq9l33QXf/W6Yy95hh9RVlZfbb4df/jKc5n7bbVNXIxJoLEQkx5o1C28l\nt2wZRhFWrkxdUel7/334xS/C2/fPPQePPBL2rN5/fzXWEk+XLmHOd+bMMALWv39osl94IXVl5WHE\nCLjssrBircZaio2aawE0k9UYzZvDPfeEmd+TTgoHSSnPuCoqKpg9O5zopVs3+OCD0Fjff3/YVk3q\nR8/P7LVvH8aN3noLBg4ML6IHDAh73ldWhs9RnnH94hcVXHJJGNPZfvvU1RQ/PT/zT821SAQtWsAD\nD4TZ65NPDjsPSBzPPx/eGu7XDzbbDF5/HW6+WatZkl8bbBDO8jh7Nvzwh3DppWHO/7bb9I5VTA88\nEA5gHDlS22ZK8dLMtUhEn38ezvrXvj3ceWc48FHqr7IS/v1vuOYaWLAgnFXvjDO0r7gUDvewe8Uf\n/xgOgPzpT+FHP4JNN01dWfF65JFwQPLTT8Muu6SuRqRm2udaJIHPPoPDDw/7X99+O7Rrl7qi4vHZ\nZ+FFybXXwiabhIPIvvvdMNsuUqimTQvP2cceg1NPhfPOCzPbkh33cNbUK66Ap56C3r1TVyRSOx3Q\nKFnTTFY8rVvDJZdUsP32sOuu4WxiUrf33oPLLw8NyeOPwz/+AS++CMcfHxprPT/jUp5xLV1awfDh\noclu2TIcB3DCCTBhQmgcpXZLloTzBdx1V8ird289P2NTnvmn5lokB5o3DyMNf/tb+CV7+eVh2z5Z\nwz0clPi978FOO8GiRWHLraoTwWjnDyk2nTqFPZnfeiscI3DqqdCnD9xxh/bCr8nYsWEBYqed4Nln\nYZttUlckEofGQkRy7L33wi/ZL74I2/Z17py6orQ+/xzuvTe8Dbx8eTg1+emnQ9u2qSsTiauyMow5\n/PnPMHkynHlmmCku958Bq1bBr38Nw4aFFx4HHJC6IpHsaSxEpAB07BgO0Dn00LCK9cgjqStKY/78\nsMPC1luH5vo3v4FZs+D889VYS2lq0iT8v//Pf2D8ePjkk3Cg3rHHwrhx5TkyMm8efOc7YexryhQ1\n1lKa1FwLoJms2NbNs0mTcAbBRx+FCy8M+zV/9lma2vLpiy/gwQfhsMOgZ0/46KMwg/6f/4Smo0mW\nP4H0/IxLecaVTZ477AB/+lNoLgcODDuLdO8OV18N776b+xoLwYMPQt++cNRRYUV/881r/jw9P+NS\nnvmn5lokj/bcM6zWLF0afsk89ljprV65w0svha3JOnUKe9Yefzy8/XY4451OIS3lbKON4OyzYfp0\n+Pvfwxkgu3cPLzar9sovNbNmwSmnhAWGxx8PuwBl+8JapBhp5lokAXd4+GH43e/CCWeGDoXjjivu\nLecWLgwz5XfcEd7+Pu20cNGWZCJ1++STMC52++3w8svhIOjBg8MYWTEf2Pvyy3DlleEU5mefHfar\n33jj1FWJNI72uRYpcO5hROKKK0Jzeskl4eDHli0be7/OmDFjePHFFwHYfffdGTRoEBb5N/X8+WEl\n6tFHw84fRx8dmoL+/bUyJdIQ8+aFvd7vuANatQr/p444IjTasf9P5ernxIQJ8Pvfh4M4L7gAzjor\nrNiLlAI115K1iooKBgwYkLqMktGQPMePD7+QXnklzGX/8IcNOyPh2LFj+dEpp9BqxQoOzAx2/6d1\na1Zusgk3jxjBwIED63+nGe5hrOXf/w4jLXPnwsEHh5PmHHoobLhhg++6Tnp+xqU848pFnlVbVT76\naPi/tmxZOHbh8MNhv/3CSaoaI/bPCXcYNSr8DJs3LywUDB4cXiDUl56fcSnPuLJprov4TWiR0tK/\nfzjIZ/Lk8AvqyivDKvZ++4WPZdNojx07lhMOO4zhn37KQUDV//6rPv6Y/3z8MSccdhj3Pf54vX5x\nrlgR9qB97LGwSt2mTVhJu+46+Pa3i3uURaRQmcFee4XLVVfB7Nnh/+ANN8DJJ4cdNw4/POy2sfXW\n9RsfifVzorIynDhnzBi4554w3jJ0KJx4YtjrX6RcaeVapEDNmAH33RfmFSdPDidb2HdfGDQoHBjZ\nosXan+/udOvcmRsWLODgWu7zSeCCTp14/e23a3zr95NPwsr0pEnhMnEiLFgQzjh32GGhqdYBiSJp\nLVsWxsn+/W+oqAj7RvfpEy59+4Y/t9yy5q9tzM8J93Bw4ujRoaEeOxbatQs/kw49FA45RONgUvo0\nFiJSIj75JKwejxkTfrG98UZY0Ro4MKxatWsHc+a8yI0Xnc6Ln85jQz6hpv/5n9KK3m26cN61w+na\ndXeWLIH33w+jKBMnwpw5sPPOa/+i3nFHrU6LFCr3sJVf1Yvhqj9btQr/h3fbLeza0749dOgAM2dO\n4OqzT+CVT+bXuF3YSpqzhHYMaN2ZH19xC1tv3Zv33w8jKmPGhMfbd99wGThQJ8SR8qPmWrKmmay4\ncp3nsmXhJBTjxoVfrB98ANOnL+Tj91aymnasohnt+IB2fEALVvIB7VhMB1bRjBYsZqOOzeneffOv\nfuHutFNopHfeufEHU+aCnp9xKc+4Ci1P93A8xKRJ4V2vhQth8WJYsgRmzVrGx0ubUEkb2vEBHVhM\nS77I/LRox2e0ZjOWspoP2KjLRuyyS2fatw+N+qBBsN12ud/BpNDyLHbKMy7NXIuUqE03DSdiOOqo\nNbddeeXtLLvsMq5evZrPaPXVL8uVtKA9S+jAYjbgEy5p2pTNzvkdQ4YMSfcNiEjOmEHXruFy3HFr\nf+zKK29h2WWX8bvVTVhCe5bQni9o+dWL8Y35EAN+1rQpm52lnxMiDaGVa5ESMXr0aC446iimfvxx\njSMhAA7ssuGG3PDoowwaNCif5YlIAdDPCZHGyWblWoceiJSIQYMG8cUmm/CfOj7nKWBl27aN2o5P\nRIqXfk6I5J6aawHCTJbEkyJPM+PmESM4rU0bniSsPlVxwg4Ag9u04eY774x+Mplc0/MzLuUZVzHl\nWQw/J4opz2KgPPNPM9ciJWTgwIHc9/jj/OiUUxiyYgUHVT85RNu23HfnnVqNEilz+jkhkluauRYp\nQVWnNZ44cSIQTms8cODAoluxFpHc0c8JkfrTVnwiIiIiIpHogEbJmmay4lKecSnPuJRnXMozLuUZ\nl/LMPzXXIiIiIiKRaCxERERERCQLGgsREREREckjNdcCaCYrNuUZl/KMS3nGpTzjUp5xKc/8U3Mt\nIlLxeqoAAAYoSURBVCIiIhKJZq5FRERERLKgmWsRERERkTxScy2AZrJiU55xKc+4lGdcyjMu5RmX\n8sw/NdciIiIiIpFo5lpEREREJAuauRYRERERyaMkzbWZHWdmr5nZajPrvc7HhprZLDObYWYHpKiv\nHGkmKy7lGZfyjEt5xqU841KecSnP/Eu1cv0KcDTwTPUbzWwn4ARgJ+Ag4CYz0+p6HkydOjV1CSVF\necalPONSnnEpz7iUZ1zKM/+SNK7uPsPdZ9bwoSOBe9z9S3efC8wGds9rcWVq+fLlqUsoKcozLuUZ\nl/KMS3nGpTzjUp75V2irwlsC86tdnw98M1EtIiIiIiL10ixXd2xmo4AtavjQz939sXrclbYFyYO5\nc+emLqGkKM+4lGdcyjMu5RmX8oxLeeZf0q34zGwscKG7T85cHwLg7n/IXP8P8Ct3f2Gdr1PDLSIi\nIiJ5t76t+HK2cl0P1Qv8N3C3mV1HGAfZDnhx3S9Y3zclIiIiIpJCqq34jjazd4A9gSfM7CkAd58O\n3A9MB54CfqKzxYiIiIhIsSjKMzSKiIiIiBSiQtstpN7M7EIzqzSzzVLXUszM7Ldm9rKZTTWz0WbW\nOXVNxczM/mhmr2cyfdjMNkldUzGr68RTkj0zOyhzgq5ZZnZJ6nqKnZndZmaLzOyV1LUUOzPrbGZj\nM//PXzWzc1LXVMzMrJWZvZD5nT7dzK5MXVMpMLOmZjbFzOrcmKOom+tMA7g/MC91LSXganffxd17\nAf8CfpW6oCI3Euju7rsAM4GhiespdjWeeEqyZ2ZNgb8QTtC1E3CSme2YtqqidzshT2m8L4Hz3b07\nYWT0bD0/G87dPwcGZn6n9wQGmtneicsqBecSRpfrHPso6uYauA74WeoiSoG7f1Tt6obAklS1lAJ3\nH+XulZmrLwCdUtZT7Oo48ZRkb3dgtrvPdfcvgXsJJ+6SBnL38cCy1HWUAndf6O5TM3//GHidcO4L\naSB3/zTz1xZAU2BpwnKKnpl1Ag4B/sHam3F8TdE212Z2JDDf3aelrqVUmNkVZvY2cBrwh9T1lJAz\ngCdTFyFl75vAO9Wu6yRdUpDMrAuwK2FhQhrIzJqY2VRgETA2s2mENNz1wMVA5fo+sRC24qtVHSei\nuZTwNvsB1T89L0UVsfWd2MfdLwUuzew3fj1wel4LLDLZnCjJzC4FVrr73XktrghFPPGU1ExHr0vB\nM7MNgQeBczMr2NJAmXdPe2WO+XnazAa4e0XisoqSmR0GvO/uU8xswPo+v6Cba3ffv6bbzWxnoCvw\nsplBeMv9JTPb3d3fz2OJRaW2PGtwN1ppXa/15WlmgwlvIe2bl4KKXD2en9IwC4DqByp3JqxeixQE\nM2sOPATc5e7/Sl1PqXD3FWb2BNAHqEhcTrHaCzjCzA4BWgEbm9md7n5qTZ9clGMh7v6qu2/u7l3d\nvSvhF0RvNdYNZ2bbVbt6JDAlVS2lwMwOIrx9dGTmwBKJR+9SNcwkYDsz62JmLYATCCfuEknOwkrZ\nMGC6u9+Qup5iZ2btzaxt5u+tCZs/6Pd6A7n7z929c6bnPBEYU1tjDUXaXNdAb3c23pVm9kpmPmsA\ncGHieordnwkHho7KbNtzU+qCilltJ56S7Ln7KuD/AU8Tjna/z91fT1tVcTOze4AJwPZm9o6ZaZSu\n4b4NnEzY1WJK5qKdWBquIzAm8zv9BeAxdx+duKZSUmffqZPIiIiIiIhEUior1yIiIiIiyam5FhER\nERGJRM21iIiIiEgkaq5FRERERCJRcy0iIiIiEomaaxERERGRSNRci4iIiIhEouZaRERERCQSNdci\nImXAzPqa2ctm1tLMNjCzV81sp9R1iYiUGp2hUUSkTJjZb4FWQGvgHXe/KnFJIiIlR821iEiZMLPm\nwCTgM6Cf6xeAiEh0GgsRESkf7YENgA0Jq9ciIhKZVq5FRMqEmf0buBv4FtDR3X+auCQRkZLTLHUB\nIiKSe2Z2KvCFu99rZk2ACWY2wN0rEpcmIlJStHItIiIiIhKJZq5FRERERCJRcy0iIiIiEomaaxER\nERGR/99uHQsAAAAADPK3nsTOomgi1wAAMJFrAACYyDUAAEzkGgAAJnINAACTAJDRVJdSZbxZAAAA\nAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f85fde6cb70>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "min_1=opt.minimize(hat,1.5,args=(a,b))\n", "minsx=[-1.58113881,1.58113881]\n", "minsy=[-6.24999999,-6.24999999]\n", "plt.figure(figsize=(10,5))\n", "plt.plot(x,hat(x,a,b))\n", "plt.scatter(minsx,minsy,100,c='r',marker='o')\n", "plt.tight_layout()\n", "plt.title('Hat Potential With Local Minimums')\n", "plt.xlabel('x')\n", "plt.ylabel('Hat Potential')\n", "plt.grid()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "deletable": false, "nbgrader": { "checksum": "235361d4c954cf9fd6a8ecef309b3a44", "grade": true, "grade_id": "optimizationex01c", "points": 4 } }, "outputs": [], "source": [ "assert True # leave this for grading the plot" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": {} }, "source": [ "To check your numerical results, find the locations of the minima analytically. Show and describe the steps in your derivation using LaTeX equations. Evaluate the location of the minima using the above parameters." ] }, { "cell_type": "markdown", "metadata": { "deletable": false, "nbgrader": { "checksum": "d7d37614ffa0d469a42ff3fd121335f2", "grade": true, "grade_id": "optimizationex01d", "points": 2, "solution": true } }, "source": [ "Our local minima will be when the derivatives of our function is equal to 0, and when the second derivative is postive.\n", "\n", "The derivative is $4bx^{3}-2ax$, and if $a=5.0$ and $b=1.0$ then this will equal 0 when x is -1.5811, 0 and 1.5811. From looking at the graph is should be clear that when x is 0, it is a local maximum, whereas the other two are local minimms. These are nearly the exact same values as were calculate using scipy." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.4.0" } }, "nbformat": 4, "nbformat_minor": 0 }

mit

sseyler/PSAnalysis

source/core/psa_cython.ipynb

2

53224

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%load_ext cython\n", "%load_ext autoreload\n", "%autoreload 2" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from psa import *\n", "from MDAnalysis import Universe\n", "from MDAnalysis.analysis.align import rotation_matrix\n", "import numpy as np\n", "import os, sys\n", "WORKDIR = '/nfs/homes/sseyler/Repositories/python/psanalysis'\n", "sys.path.append(WORKDIR)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Generating AdK CORE C-alpha reference coords + structure...\n" ] } ], "source": [ "print(\"Generating AdK CORE C-alpha reference coords + structure...\")\n", "cref_filename = '%s/structs/1ake_a_ca_core.pdb' % WORKDIR\n", "oref_filename = '%s/structs/4ake_a_ca_core.pdb' % WORKDIR\n", "\n", "c_ref = MDAnalysis.Universe(cref_filename)\n", "o_ref = MDAnalysis.Universe(oref_filename)\n", "u_ref = MDAnalysis.Universe(cref_filename)\n", "\n", "c_ref_ca = c_ref.selectAtoms('name CA')\n", "o_ref_ca = o_ref.selectAtoms('name CA')\n", "\n", "adkCORE_resids = \"(resid 1:29 or resid 60:121 or resid 160:214)\"\n", "c_ref_CORE_ca = c_ref_ca.selectAtoms(adkCORE_resids).coordinates() \\\n", " - c_ref_ca.selectAtoms(adkCORE_resids).centerOfMass()\n", "o_ref_CORE_ca = o_ref_ca.selectAtoms(adkCORE_resids).coordinates() \\\n", " - o_ref_ca.selectAtoms(adkCORE_resids).centerOfMass()\n", "ref_coords = 0.5*(c_ref_CORE_ca + o_ref_CORE_ca)\n", "\n", "u_ref.atoms.translate(-c_ref_ca.selectAtoms(adkCORE_resids).centerOfMass())\n", "o_ref.atoms.translate(-o_ref_ca.selectAtoms(adkCORE_resids).centerOfMass())\n", "u_ref.selectAtoms(adkCORE_resids).CA.set_positions(ref_coords)" ] }, { "cell_type": "code", "execution_count": 142, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Building collection of simulations...\n" ] } ], "source": [ "print(\"Building collection of simulations...\")\n", "# List of method names (same as directory names)\n", "# method_names = ['DIMS', 'FRODA', 'MAP']\n", "method_names = ['DIMS', 'TMD', 'FRODA', 'MAP', 'iENM', 'MENM-SP', 'MENM-SD', \\\n", " 'MDdMD', 'GOdMD', 'Morph', 'ANMP', 'LinInt']\n", "labels = [] # Heat map labels\n", "simulations = [] # List of simulation topology/trajectory filename pairs\n", "universes = [] # List of MDAnalysis Universes representing simulations\n", "\n", "# Build list of simulations, each represented by a pair of filenames\n", "# ([topology filename], [trajectory filename]). Generate corresponding label\n", "# list.\n", "for method in method_names:\n", " # Note: DIMS uses the PSF topology format\n", " topname = 'top.psf' if ('DIMS' in method or 'TMD' in method) else 'top.pdb'\n", " pathname = 'path.dcd'\n", " method_dir = '{}/methods/{}'.format(WORKDIR, method)\n", " if method is not 'LinInt':\n", " if 'TMD' in method:\n", " for run in xrange(1, 11): # 3 runs per method\n", " run_dir = '{}/{:03n}'.format(method_dir, run)\n", " topology = '{}/{}'.format(method_dir, topname)\n", " trajectory = '{}/{}'.format(run_dir, pathname)\n", " labels.append(method + '(' + str(run) + ')')\n", " simulations.append((topology, trajectory))\n", "# nruns = 3 if 'TMD' not in method else 9\n", " else:\n", " for run in xrange(1, nruns+1): # 3 runs per method\n", " run_dir = '{}/{:03n}'.format(method_dir, run)\n", " topology = '{}/{}'.format(method_dir, topname)\n", " trajectory = '{}/{}'.format(run_dir, pathname)\n", " labels.append(method + '(' + str(run) + ')')\n", " simulations.append((topology, trajectory))\n", " else: # only one LinInt trajectory\n", " topology = '{}/{}'.format(method_dir, topname)\n", " trajectory = '{}/{}'.format(method_dir, pathname)\n", " labels.append(method)\n", " simulations.append((topology, trajectory))" ] }, { "cell_type": "code", "execution_count": 143, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Generate simulation list represented as Universes. Each item, sim, in\n", "# simulations is a topology/trajectory filename pair that is unpacked into\n", "# an argument list with the \"splat\" (\"*\") operator.\n", "for sim in simulations:\n", " universes.append(Universe(*sim))" ] }, { "cell_type": "code", "execution_count": 144, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Initializing Path Similarity Analysis...\n" ] } ], "source": [ "print(\"Initializing Path Similarity Analysis...\")\n", "ref_selection = \"name CA and \" + adkCORE_resids\n", "psa_full = PSA(universes, reference=u_ref, ref_select=ref_selection,\n", " path_select=\"name CA\", labels=labels)" ] }, { "cell_type": "code", "execution_count": 145, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Fitted frame 102/102 [100.0%]\n", "Fitted frame 92/92 [100.0%]\n", "Fitted frame 95/95 [100.0%]\n", "Fitted frame 97/97 [100.0%]\n", "Fitted frame 98/98 [100.0%]\n", "Fitted frame 98/98 [100.0%]\n", "Fitted frame 98/98 [100.0%]\n", "Fitted frame 98/98 [100.0%]\n", "Fitted frame 98/98 [100.0%]\n", "Fitted frame 91/91 [100.0%]\n", "Fitted frame 91/91 [100.0%]\n", "Fitted frame 91/91 [100.0%]\n", "Fitted frame 495/495 [100.0%]\n", "Fitted frame 142/142 [100.0%]\n", "Fitted frame 141/141 [100.0%]\n", "Fitted frame 142/142 [100.0%]\n", "Fitted frame 201/201 [100.0%]\n", "Fitted frame 201/201 [100.0%]\n", "Fitted frame 201/201 [100.0%]\n", "Fitted frame 33/33 [100.0%]\n", "Fitted frame 34/34 [100.0%]\n", "Fitted frame 36/36 [100.0%]\n", "Fitted frame 52/52 [100.0%]\n", "Fitted frame 51/51 [100.0%]\n", "Fitted frame 51/51 [100.0%]\n", "Fitted frame 49/49 [100.0%]\n", "Fitted frame 45/45 [100.0%]\n", "Fitted frame 43/43 [100.0%]\n", "Fitted frame 54/54 [100.0%]\n", "Fitted frame 64/64 [100.0%]\n", "Fitted frame 48/48 [100.0%]\n", "Fitted frame 81/81 [100.0%]\n", "Fitted frame 145/145 [100.0%]\n", "Fitted frame 226/226 [100.0%]\n", "Fitted frame 100/100 [100.0%]\n", "Fitted frame 100/100 [100.0%]\n", "Fitted frame 100/100 [100.0%]\n", "Fitted frame 107/107 [100.0%]\n", "Fitted frame 107/107 [100.0%]\n", "Fitted frame 107/107 [100.0%]\n", "Fitted frame 100/100 [100.0%]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Generating Path objects from aligned trajectories...\n" ] } ], "source": [ "print(\"Generating Path objects from aligned trajectories...\")\n", "psa_full.generate_paths(align=True, store=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# =============================" ] }, { "cell_type": "code", "execution_count": 146, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# metric = 'discrete_frechet'\n", "metric = frechet_v6b\n", "linkage = 'ward' # 'single' 'complete' 'weighted' 'average'\n", "plotname = 'df_ward_psa-withTMD-1-10.pdf'" ] }, { "cell_type": "code", "execution_count": 147, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Calculating distance matrix...\n" ] } ], "source": [ "print(\"Calculating distance matrix...\")\n", "psa_full.run(metric=metric)" ] }, { "cell_type": "code", "execution_count": 148, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Plotting heat map-dendrogram for hierarchical clustering...\n" ] } ], "source": [ "print(\"Plotting heat map-dendrogram for hierarchical clustering...\")\n", "psa_full.plot(filename=plotname, linkage=linkage);" ] }, { "cell_type": "code", "execution_count": 149, "metadata": { "collapsed": true }, "outputs": [], "source": [ "df_default = psa_full.D" ] }, { "cell_type": "code", "execution_count": 150, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# metric = 'hausdorff'\n", "metric = hausdorff_v6\n", "linkage = 'ward' # 'single' 'complete' 'weighted' 'average'\n", "plotname = 'dh_ward_psa-withTMD-1-10.pdf'" ] }, { "cell_type": "code", "execution_count": 151, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Calculating distance matrix...\n" ] } ], "source": [ "print(\"Calculating distance matrix...\")\n", "psa_full.run(metric=metric)" ] }, { "cell_type": "code", "execution_count": 152, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Plotting heat map-dendrogram for hierarchical clustering...\n" ] } ], "source": [ "print(\"Plotting heat map-dendrogram for hierarchical clustering...\")\n", "psa_full.plot(filename=plotname, linkage=linkage);" ] }, { "cell_type": "code", "execution_count": 153, "metadata": { "collapsed": true }, "outputs": [], "source": [ "dh_default = psa_full.D" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# =============================" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Hausdorff" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Baseline" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "3 loops, best of 3: 614 ms per loop\n" ] } ], "source": [ "%timeit -n3 psa_full.run(metric='hausdorff')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Version 0" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# %%cython -f -c=-Ofast -c=-march=native\n", "# import numpy as np\n", "# cimport numpy as np\n", "# from libc.math cimport sqrt\n", "# cimport cython\n", "\n", "def sqnorm(v, axis=None):\n", " return (v*v).sum(axis=axis)\n", "\n", "def hausdorff_v0(P,Q, N=-1):\n", " if N == -1:\n", " N = P.shape[1] # number of atoms from 2nd dim of P\n", " axis = (1,2)\n", " else:\n", " axis = 1\n", " d = np.array([sqnorm(p - Q, axis) for p in P])\n", " return ( max( np.amax(np.amin(d, axis=0)), \\\n", " np.amax(np.amin(d, axis=1)) ) / N )**0.5" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "3 loops, best of 3: 605 ms per loop\n" ] } ], "source": [ "%timeit -n3 psa_full.run(metric=hausdorff_v0)" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [], "source": [ "linkage = 'ward' # 'single' 'complete' 'weighted' 'average'\n", "plotname = 'dh_v0_ward_psa-full.pdf'\n", "psa_full.plot(filename=plotname, linkage=linkage);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Version 1" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%%cython -f -c=-Ofast -c=-march=native\n", "import numpy as np\n", "cimport numpy as np\n", "# from libc.math cimport sqrt\n", "cimport cython\n", "\n", "def sqnorm(v):\n", " return (v*v).sum(axis=1)\n", "\n", "def hausdorff_v1(P, Q, N):\n", " d = np.array([sqnorm(p - Q) for p in P])\n", " return ( max( np.amax(np.amin(d, axis=0)), \\\n", " np.amax(np.amin(d, axis=1)) ) / N )**0.5" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "3 loops, best of 3: 617 ms per loop\n" ] } ], "source": [ "%timeit -n3 psa_full.run(metric=hausdorff_v1)" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": true }, "outputs": [], "source": [ "linkage = 'ward' # 'single' 'complete' 'weighted' 'average'\n", "plotname = 'dh_v1_ward_psa-full.pdf'\n", "psa_full.plot(filename=plotname, linkage=linkage);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Version 2" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%%cython -f -c=-Ofast -c=-march=native\n", "import numpy as np\n", "cimport numpy as np\n", "# from libc.math cimport sqrt\n", "cimport cython\n", "\n", "cdef np.ndarray sqnorm(np.ndarray v):\n", " return (v*v).sum(axis=1)\n", "\n", "def hausdorff_v2(P, Q, N):\n", " d = np.array([sqnorm(p - Q) for p in P])\n", " return ( max( np.amax(np.amin(d, axis=0)), \\\n", " np.amax(np.amin(d, axis=1)) ) / N )**0.5" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "3 loops, best of 3: 607 ms per loop\n" ] } ], "source": [ "%timeit -n3 psa_full.run(metric=hausdorff_v2)" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": true }, "outputs": [], "source": [ "linkage = 'ward' # 'single' 'complete' 'weighted' 'average'\n", "plotname = 'dh_v2_ward_psa-full.pdf'\n", "psa_full.plot(filename=plotname, linkage=linkage);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Version 3\n", "\n", "sqnorm using numpy.sum()" ] }, { "cell_type": "code", "execution_count": 88, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%%cython -f -c=-Ofast -c=-march=native\n", "import numpy as np\n", "cimport numpy as np\n", "from libc.math cimport sqrt, fmax\n", "cimport cython\n", "\n", "ctypedef float (*fcn_ptr)(np.ndarray, np.ndarray)\n", "\n", "@cython.boundscheck(False)\n", "@cython.wraparound(False)\n", "cdef float sqnorm(np.ndarray[float, ndim=1, mode='c'] v1, np.ndarray[float, ndim=1, mode='c'] v2):\n", " cdef np.ndarray[float, ndim=1, mode='c'] diff = v1 - v2\n", " return (diff*diff).sum()\n", "\n", "@cython.boundscheck(False)\n", "@cython.wraparound(False)\n", "def hausdorff_v3(np.ndarray[float, ndim=2, mode='c'] P, np.ndarray[float, ndim=2, mode='c'] Q, np.intp_t N):\n", " cdef np.intp_t i, j, lenP, lenQ \n", " lenP = P.shape[0]\n", " lenQ = Q.shape[0]\n", " \n", " cdef np.ndarray[float, ndim=2, mode='c'] d = np.empty((lenP, lenQ), dtype='float32')\n", " cdef fcn_ptr f = &sqnorm\n", " \n", " for i in xrange(lenP):\n", " for j in xrange(lenQ):\n", " d[i,j] = f(P[i], Q[j])\n", " return sqrt( fmax( np.amax(np.amin(d, axis=0)), \\\n", " np.amax(np.amin(d, axis=1)) ) / N )" ] }, { "cell_type": "code", "execution_count": 89, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1 loops, best of 3: 3.81 s per loop\n" ] } ], "source": [ "%timeit -n1 psa_full.run(metric=hausdorff_v3)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Note:** This is VERY slow compared to other solutions because the element-wise subtraction is happening with NumPy and cannot be fully converted to fast C code. Using the '-a' option with the %%cython command highlights the lines in sqnorm as not-very-Cythonic." ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "collapsed": true }, "outputs": [], "source": [ "linkage = 'ward' # 'single' 'complete' 'weighted' 'average'\n", "plotname = 'dh_v3_ward_psa-full.pdf'\n", "psa_full.plot(filename=plotname, linkage=linkage);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Version 3b\n", "\n", "sqnorm using explicit loop" ] }, { "cell_type": "code", "execution_count": 91, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%%cython -f -c=-Ofast -c=-march=native\n", "import numpy as np\n", "cimport numpy as np\n", "from libc.math cimport sqrt, fmax\n", "cimport cython\n", "\n", "ctypedef float (*fcn_ptr)(np.ndarray, np.ndarray)\n", "\n", "@cython.boundscheck(False)\n", "@cython.wraparound(False)\n", "cdef float sqnorm(np.ndarray[float, ndim=1, mode='c'] v1, np.ndarray[float, ndim=1, mode='c'] v2):\n", " cdef float diff, s\n", " cdef np.intp_t k\n", " s = 0.0\n", " for k in xrange(v1.shape[0]):\n", " diff = v1[k] - v2[k]\n", " s += diff*diff\n", " return s\n", "\n", "@cython.boundscheck(False)\n", "@cython.wraparound(False)\n", "def hausdorff_v3b(np.ndarray[float, ndim=2, mode='c'] P, np.ndarray[float, ndim=2, mode='c'] Q, np.intp_t N):\n", " cdef np.intp_t i, j, lenP, lenQ \n", " lenP = P.shape[0]\n", " lenQ = Q.shape[0]\n", " \n", " cdef np.ndarray[float, ndim=2, mode='c'] d = np.empty((lenP, lenQ), dtype='float32')\n", " cdef fcn_ptr f = &sqnorm\n", " \n", " for i in xrange(lenP):\n", " for j in xrange(lenQ):\n", " d[i,j] = f(P[i], Q[j])\n", " return sqrt( fmax( np.amax(np.amin(d, axis=0)), \\\n", " np.amax(np.amin(d, axis=1)) ) / N )" ] }, { "cell_type": "code", "execution_count": 92, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1 loops, best of 3: 877 ms per loop\n" ] } ], "source": [ "%timeit -n1 psa_full.run(metric=hausdorff_v3b)" ] }, { "cell_type": "code", "execution_count": 93, "metadata": { "collapsed": true }, "outputs": [], "source": [ "linkage = 'ward' # 'single' 'complete' 'weighted' 'average'\n", "plotname = 'dh_v3b_ward_psa-full.pdf'\n", "psa_full.plot(filename=plotname, linkage=linkage);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Version 4\n", "\n", "Memviews, Cython RMSD, NumPy min/max + NO decorators" ] }, { "cell_type": "code", "execution_count": 94, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%%cython -f -c=-Ofast -c=-march=native\n", "import numpy as np\n", "cimport numpy as np\n", "from libc.math cimport sqrt, fmax\n", "cimport cython\n", "\n", "cdef float sqnorm(float[:] p, float[:] q):\n", " cdef np.intp_t i\n", " cdef float s, temp\n", " s = 0.0\n", " for i in xrange(p.shape[0]):\n", " temp = p[i] - q[i]\n", " s += temp*temp\n", " return s \n", "\n", "def hausdorff_v4(float[:,:] P, float[:,:] Q, np.intp_t N):\n", " cdef np.intp_t i, j, lenP, lenQ \n", " lenP = P.shape[0]\n", " lenQ = Q.shape[0]\n", " \n", " cdef float[:,:] d = np.empty((lenP, lenQ), dtype='float32')\n", " \n", " for i in xrange(lenP):\n", " for j in xrange(lenQ):\n", " d[i,j] = sqnorm(P[i], Q[j])\n", " return sqrt( fmax( np.amax(np.amin(d, axis=0)), \\\n", " np.amax(np.amin(d, axis=1)) ) / N )" ] }, { "cell_type": "code", "execution_count": 95, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "3 loops, best of 3: 788 ms per loop\n" ] } ], "source": [ "%timeit -n3 psa_full.run(metric=hausdorff_v4)" ] }, { "cell_type": "code", "execution_count": 96, "metadata": { "collapsed": true }, "outputs": [], "source": [ "linkage = 'ward' # 'single' 'complete' 'weighted' 'average'\n", "plotname = 'dh_v4_ward_psa-full.pdf'\n", "psa_full.plot(filename=plotname, linkage=linkage);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Version 4b\n", "\n", "Memviews, Cython RMSD, NumPy min/max + decorators" ] }, { "cell_type": "code", "execution_count": 97, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%%cython -f -c=-Ofast -c=-march=native\n", "import numpy as np\n", "cimport numpy as np\n", "from libc.math cimport sqrt, fmax\n", "cimport cython\n", "\n", "@cython.boundscheck(False)\n", "@cython.wraparound(False)\n", "cdef float sqnorm(float[:] p, float[:] q):\n", " cdef np.intp_t i\n", " cdef float s, temp\n", " s = 0.0\n", " for i in xrange(p.shape[0]):\n", " temp = p[i] - q[i]\n", " s += temp*temp\n", " return s \n", "\n", "def hausdorff_v4b(float[:,:] P, float[:,:] Q, np.intp_t N):\n", " cdef np.intp_t i, j, lenP, lenQ\n", " lenP = P.shape[0]\n", " lenQ = Q.shape[0]\n", " \n", " cdef float[:,:] d = np.empty((lenP, lenQ), dtype='float32')\n", " \n", " for i in xrange(lenP):\n", " for j in xrange(lenQ):\n", " d[i,j] = sqnorm(P[i], Q[j])\n", " return sqrt( fmax( np.amax(np.amin(d, axis=0)), \\\n", " np.amax(np.amin(d, axis=1)) ) / N )" ] }, { "cell_type": "code", "execution_count": 98, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "3 loops, best of 3: 365 ms per loop\n" ] } ], "source": [ "%timeit -n3 psa_full.run(metric=hausdorff_v4b)" ] }, { "cell_type": "code", "execution_count": 99, "metadata": { "collapsed": true }, "outputs": [], "source": [ "linkage = 'ward' # 'single' 'complete' 'weighted' 'average'\n", "plotname = 'dh_v4b_ward_psa-full.pdf'\n", "psa_full.plot(filename=plotname, linkage=linkage);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Version 5\n", "\n", "Memviews, Cython RMSD, NumPy min/max + C-ordered arrays\n", "(sqnorm pointed doesn't add much speed)" ] }, { "cell_type": "code", "execution_count": 83, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%%cython -f -c=-O3 -c=-march=native -c=-funroll-loops -c=-ffast-math\n", "import numpy as np\n", "cimport numpy as np\n", "from libc.math cimport sqrt, fmax\n", "import cython\n", "cimport cython\n", "\n", "ctypedef float (*fcn_ptr)(float[::1], float[::1])\n", "\n", "@cython.boundscheck(False)\n", "@cython.wraparound(False)\n", "cdef float sqnorm(float[::1] p, float[::1] q):\n", " cdef np.intp_t k\n", " cdef float s, temp\n", " s = 0.0\n", " for k in xrange(p.shape[0]):\n", " temp = p[k] - q[k]\n", " s += temp*temp\n", " return s\n", " \n", "@cython.boundscheck(False)\n", "@cython.wraparound(False)\n", "def hausdorff_v5(float[:,::1] P, float[:,::1] Q, np.intp_t N):\n", " cdef np.intp_t i, j, lenP, lenQ \n", " lenP = P.shape[0]\n", " lenQ = Q.shape[0]\n", " \n", " cdef float[:,::1] d = np.empty((lenP, lenQ), dtype='float32')\n", " cdef fcn_ptr f = &sqnorm\n", " \n", " for i in xrange(lenP):\n", " for j in xrange(lenQ):\n", " d[i,j] = f(P[i,:], Q[j,:])\n", " return sqrt( fmax( np.amax(np.amin(d, axis=0)), \\\n", " np.amax(np.amin(d, axis=1)) ) / N )" ] }, { "cell_type": "code", "execution_count": 84, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "10 loops, best of 3: 146 ms per loop\n" ] } ], "source": [ "%timeit -n10 psa_full.run(metric=hausdorff_v5)" ] }, { "cell_type": "code", "execution_count": 74, "metadata": { "collapsed": true }, "outputs": [], "source": [ "linkage = 'ward' # 'single' 'complete' 'weighted' 'average'\n", "plotname = 'dh_v5_ward_psa-full.pdf'\n", "psa_full.plot(filename=plotname, linkage=linkage);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Version 6" ] }, { "cell_type": "code", "execution_count": 106, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%%cython -f -c=-Ofast -c=-march=native\n", "import numpy as np\n", "cimport numpy as np\n", "from libc.math cimport sqrt, fmax, fmin\n", "import cython\n", "cimport cython\n", "\n", "ctypedef float[::1] (*minfcn_ptr)(float[:,::1])\n", "ctypedef float (*maxfcn_ptr)(float[::1])\n", "\n", "@cython.boundscheck(False)\n", "@cython.wraparound(False)\n", "cdef float[::1] getmin(float[:,::1] d):\n", " cdef np.intp_t i, j\n", " cdef float[::1] d_row = np.empty(d.shape[1], dtype='float32')\n", " cdef float[::1] rowmin = np.empty(d.shape[0], dtype='float32')\n", " cdef float m = 10000000\n", " \n", " for i in xrange(d.shape[0]):\n", " d_row = d[i,:]\n", " for j in xrange(d.shape[1]):\n", " m = fmin(d_row[j], m)\n", " rowmin[i] = m\n", " return rowmin\n", "\n", "@cython.boundscheck(False)\n", "@cython.wraparound(False)\n", "cdef float getmax(float[::1] d):\n", " cdef np.intp_t i\n", " cdef float m = -1\n", " \n", " for i in xrange(d.shape[0]):\n", " m = fmax(d[i], m)\n", " return m\n", "\n", "@cython.boundscheck(False)\n", "@cython.wraparound(False)\n", "def hausdorff_v6(float[:,::1] P, float[:,::1] Q, np.intp_t N):\n", " cdef np.intp_t i, j, k, lenP, lenQ \n", " lenP = P.shape[0]\n", " lenQ = Q.shape[0]\n", "\n", " assert int(P.shape[1]) == int(3*N)\n", " assert P.shape[1] == Q.shape[1]\n", " \n", " cdef float[:,::1] d = np.empty((lenP, lenQ), dtype='float32')\n", " cdef float s, diff = 0.0\n", " cdef minfcn_ptr minf = &getmin\n", " cdef maxfcn_ptr maxf = &getmax\n", " \n", " for i in xrange(lenP):\n", " for j in xrange(lenQ):\n", " s = 0.0\n", " for k in xrange(P.shape[1]):\n", " diff = P[i,k] - Q[j,k]\n", " s += diff*diff\n", " d[i,j] = s\n", "\n", "# return sqrt( fmax( maxf(minf(d)), maxf(np.amin(d, axis=1)) ) / N )\n", "# return sqrt( fmax( maxf(minf(d)), maxf(minf(d.T)) ) / N )\n", " return sqrt( fmax( np.amax(np.amin(d, axis=0)), np.amax(np.amin(d, axis=1)) ) / N )" ] }, { "cell_type": "code", "execution_count": 95, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "10 loops, best of 3: 120 ms per loop\n" ] } ], "source": [ "%timeit -n10 psa_full.run(metric=hausdorff_v6)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The custom Cython functions are as fast as the numpy.amax/amin solution" ] }, { "cell_type": "code", "execution_count": 90, "metadata": { "collapsed": false }, "outputs": [], "source": [ "linkage = 'ward' # 'single' 'complete' 'weighted' 'average'\n", "plotname = 'dh_v6_ward_psa-full.pdf'\n", "psa_full.plot(filename=plotname, linkage=linkage);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Optimized" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%%cython -f -c=-Ofast -c=-march=native\n", "import numpy as np\n", "cimport numpy as np\n", "from libc.math cimport sqrt, fmax, fmin\n", "import cython\n", "cimport cython\n", "\n", "@cython.boundscheck(False)\n", "@cython.wraparound(False)\n", "cdef float[:,::1] rmsdMatrix_c(float[:,::1] P, float[:,::1] Q):\n", " cdef np.intp_t lenP = P.shape[0]\n", " cdef np.intp_t lenQ = Q.shape[0]\n", " cdef np.intp_t i, j, k\n", " cdef float s, diff\n", " cdef float[:,::1] d = np.empty((lenP, lenQ), dtype='float32')\n", " for i in xrange(lenP):\n", " for j in xrange(lenQ):\n", " s = 0.0\n", " for k in xrange(P.shape[1]):\n", " diff = P[i,k] - Q[j,k]\n", " s += diff*diff\n", " d[i,j] = s\n", " return d\n", "\n", "@cython.boundscheck(False)\n", "@cython.wraparound(False)\n", "def hausdorff_opt(float[:,::1] P, float[:,::1] Q, np.intp_t N):\n", " cdef np.intp_t lenP = P.shape[0]\n", " cdef np.intp_t lenQ = Q.shape[0]\n", " cdef np.intp_t i, j, k\n", "\n", " assert int(P.shape[1]) == int(3*N)\n", " assert P.shape[1] == Q.shape[1]\n", " \n", " cdef float[:,::1] d = rmsdMatrix_c(P, Q)\n", "\n", " return sqrt( fmax( np.amax(np.amin(d, axis=0)), np.amax(np.amin(d, axis=1)) ) / N )" ] }, { "cell_type": "code", "execution_count": 95, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "10 loops, best of 3: 120 ms per loop\n" ] } ], "source": [ "%timeit -n10 psa_full.run(metric=hausdorff_opt)" ] }, { "cell_type": "code", "execution_count": 90, "metadata": { "collapsed": false }, "outputs": [], "source": [ "linkage = 'ward' # 'single' 'complete' 'weighted' 'average'\n", "plotname = 'dh_opt_ward_psa-full.pdf'\n", "psa_full.plot(filename=plotname, linkage=linkage);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Frechet" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Baseline" ] }, { "cell_type": "code", "execution_count": 119, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "3 loops, best of 3: 3.01 s per loop\n" ] } ], "source": [ "%timeit -n3 psa_full.run(metric='discrete_frechet')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Version 1" ] }, { "cell_type": "code", "execution_count": 97, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%%cython -f -c=-Ofast -c=-march=native\n", "import numpy as np\n", "cimport numpy as np\n", "# from libc.math cimport sqrt\n", "cimport cython\n", "\n", "def sqnorm(v):\n", " return (v*v).sum(axis=1)\n", "\n", "def frechet_v1(P, Q, N):\n", " Np, Nq = len(P), len(Q)\n", " d = np.array([sqnorm(p - Q) for p in P])\n", " ca = -np.ones((Np, Nq))\n", " \n", " def c(i, j):\n", " if ca[i,j] != -1 : return ca[i,j]\n", " if i > 0:\n", " if j > 0: ca[i,j] = max( min(c(i-1,j),c(i,j-1),c(i-1,j-1)), d[i,j] )\n", " else: ca[i,j] = max( c(i-1,0), d[i,0] )\n", " elif j > 0: ca[i,j] = max( c(0,j-1), d[0,j] )\n", " else: ca[i,j] = d[0,0]\n", " return ca[i,j]\n", "\n", " return ( c(Np-1, Nq-1) / N )**0.5" ] }, { "cell_type": "code", "execution_count": 98, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "3 loops, best of 3: 2.4 s per loop\n" ] } ], "source": [ "%timeit -n3 psa_full.run(metric=frechet_v1)" ] }, { "cell_type": "code", "execution_count": 99, "metadata": { "collapsed": true }, "outputs": [], "source": [ "linkage = 'ward' # 'single' 'complete' 'weighted' 'average'\n", "plotname = 'df_v1_ward_psa-full.pdf'\n", "psa_full.plot(filename=plotname, linkage=linkage);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Version 2" ] }, { "cell_type": "code", "execution_count": 100, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%%cython -f -c=-Ofast -c=-march=native\n", "import numpy as np\n", "cimport numpy as np\n", "# from libc.math cimport sqrt\n", "cimport cython\n", "\n", "cdef np.ndarray sqnorm(np.ndarray v):\n", " return (v*v).sum(axis=1)\n", "\n", "def frechet_v2(P, Q, N):\n", " Np, Nq = len(P), len(Q)\n", " d = np.array([sqnorm(p - Q) for p in P])\n", " ca = -np.ones((Np, Nq))\n", " \n", " def c(i, j):\n", " if ca[i,j] != -1 : return ca[i,j]\n", " if i > 0:\n", " if j > 0: ca[i,j] = max( min(c(i-1,j),c(i,j-1),c(i-1,j-1)), d[i,j] )\n", " else: ca[i,j] = max( c(i-1,0), d[i,0] )\n", " elif j > 0: ca[i,j] = max( c(0,j-1), d[0,j] )\n", " else: ca[i,j] = d[0,0]\n", " return ca[i,j]\n", "\n", " return ( c(Np-1, Nq-1) / N )**0.5" ] }, { "cell_type": "code", "execution_count": 101, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "3 loops, best of 3: 2.43 s per loop\n" ] } ], "source": [ "%timeit -n3 psa_full.run(metric=frechet_v2)" ] }, { "cell_type": "code", "execution_count": 102, "metadata": { "collapsed": true }, "outputs": [], "source": [ "linkage = 'ward' # 'single' 'complete' 'weighted' 'average'\n", "plotname = 'df_v2_ward_psa-full.pdf'\n", "psa_full.plot(filename=plotname, linkage=linkage);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Version 3" ] }, { "cell_type": "code", "execution_count": 108, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%%cython -f -c=-Ofast -c=-march=native\n", "import numpy as np\n", "cimport numpy as np\n", "from libc.math cimport sqrt\n", "cimport cython\n", "\n", "cdef float sqnorm(np.ndarray[float, ndim=1, mode='c'] v):\n", " return (v*v).sum()\n", "\n", "def frechet_v3(np.ndarray[float, ndim=2, mode='c'] P, np.ndarray[float, ndim=2, mode='c'] Q, np.uint32_t N):\n", " cdef np.intp_t lenP, lenQ, i, j\n", " \n", " lenP = P.shape[0]\n", " lenQ = Q.shape[0]\n", " \n", " cdef np.ndarray[float, ndim=1, mode='c'] temp\n", " cdef np.ndarray[float, ndim=2, mode='c'] d = np.empty((lenP, lenQ), dtype='float32')\n", " cdef np.ndarray[float, ndim=2, mode='c'] ca = -np.ones((lenP, lenQ), dtype='float32')\n", " \n", " for i in xrange(lenP):\n", " for j in xrange(lenQ):\n", " temp = P[i]-Q[j]\n", " d[i,j] = sqnorm(temp)\n", "\n", " def c(np.ndarray[float, ndim=2, mode='c'] d, np.ndarray[float, ndim=2, mode='c'] ca, np.intp_t i, np.intp_t j):\n", " if ca[i,j] != -1 : return ca[i,j]\n", " if i > 0:\n", " if j > 0: ca[i,j] = max( min(c(d,ca,i-1,j),c(d,ca,i,j-1),c(d,ca,i-1,j-1)), d[i,j] )\n", " else: ca[i,j] = max( c(d,ca,i-1,0), d[i,0] )\n", " elif j > 0: ca[i,j] = max( c(d,ca,0,j-1), d[0,j] )\n", " else: ca[i,j] = d[0,0]\n", " return ca[i,j]\n", "\n", " return sqrt( c(d, ca, lenP-1, lenQ-1) / N )" ] }, { "cell_type": "code", "execution_count": 109, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1 loops, best of 3: 4.59 s per loop\n" ] } ], "source": [ "%timeit -n1 psa_full.run(metric=frechet_v3)" ] }, { "cell_type": "code", "execution_count": 59, "metadata": { "collapsed": true }, "outputs": [], "source": [ "linkage = 'ward' # 'single' 'complete' 'weighted' 'average'\n", "plotname = 'df_v3_ward_psa-full.pdf'\n", "psa_full.plot(filename=plotname, linkage=linkage);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Version 4" ] }, { "cell_type": "code", "execution_count": 112, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%%cython -f -c=-Ofast -c=-march=native\n", "import numpy as np\n", "cimport numpy as np\n", "from libc.math cimport fmin, fmax, sqrt\n", "cimport cython\n", "\n", "\n", "cdef float sqnorm(np.ndarray[float, ndim=1, mode='c'] v):\n", " return (v*v).sum()\n", "\n", "\n", "cdef float c(np.ndarray[float, ndim=2, mode='c'] d, np.ndarray[float, ndim=2, mode='c'] ca, np.intp_t i, np.intp_t j):\n", " if ca[i,j] != -1 : return ca[i,j]\n", " if i > 0:\n", " if j > 0: ca[i,j] = fmax( fmin(fmin(c(d,ca,i-1,j),c(d,ca,i,j-1)),c(d,ca,i-1,j-1)), d[i,j] )\n", " else: ca[i,j] = fmax( c(d,ca,i-1,0), d[i,0] )\n", " elif j > 0: ca[i,j] = fmax( c(d,ca,0,j-1), d[0,j] )\n", " else: ca[i,j] = d[0,0]\n", " return ca[i,j]\n", "\n", "\n", "def frechet_v4(np.ndarray[float, ndim=2, mode='c'] P, np.ndarray[float, ndim=2, mode='c'] Q, np.uint32_t N):\n", " cdef np.intp_t lenP, lenQ, i, j\n", " \n", " lenP = P.shape[0]\n", " lenQ = Q.shape[0]\n", " \n", " cdef np.ndarray[float, ndim=1, mode='c'] temp\n", " cdef np.ndarray[float, ndim=2, mode='c'] d = np.empty((lenP, lenQ), dtype='float32')\n", " cdef np.ndarray[float, ndim=2, mode='c'] ca = -np.ones((lenP, lenQ), dtype='float32')\n", " \n", " for i in xrange(lenP):\n", " for j in xrange(lenQ):\n", " temp = P[i]-Q[j]\n", " d[i,j] = sqnorm(temp)\n", "\n", " return sqrt( c(d, ca, lenP-1, lenQ-1) / N )" ] }, { "cell_type": "code", "execution_count": 113, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1 loops, best of 2: 4.58 s per loop\n" ] } ], "source": [ "%timeit -n1 -r2 psa_full.run(metric=frechet_v4)" ] }, { "cell_type": "code", "execution_count": 65, "metadata": { "collapsed": true }, "outputs": [], "source": [ "linkage = 'ward' # 'single' 'complete' 'weighted' 'average'\n", "plotname = 'df_v4_ward_psa-full.pdf'\n", "psa_full.plot(filename=plotname, linkage=linkage);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Version 5" ] }, { "cell_type": "code", "execution_count": 114, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%%cython -f -c=-Ofast -c=-march=native\n", "import numpy as np\n", "cimport numpy as np\n", "from libc.math cimport fmin, fmax, sqrt\n", "cimport cython\n", "\n", "@cython.boundscheck(False)\n", "@cython.wraparound(False)\n", "cdef float sqnorm(float[::1] p, float[::1] q):\n", " cdef np.intp_t i\n", " cdef float s, temp\n", " s = 0.0\n", " for i in xrange(p.shape[0]):\n", " temp = p[i] - q[i]\n", " s += temp*temp\n", " return s\n", "\n", "@cython.boundscheck(False)\n", "@cython.wraparound(False)\n", "cdef float c(float[:,::1] d, float[:,::1] ca, np.intp_t i, np.intp_t j):\n", " if ca[i,j] != -1 : return ca[i,j]\n", " if i > 0:\n", " if j > 0: ca[i,j] = fmax( fmin(fmin(c(d,ca,i-1,j),c(d,ca,i,j-1)),c(d,ca,i-1,j-1)), d[i,j] )\n", " else: ca[i,j] = fmax( c(d,ca,i-1,0), d[i,0] )\n", " elif j > 0: ca[i,j] = fmax( c(d,ca,0,j-1), d[0,j] )\n", " else: ca[i,j] = d[0,0]\n", " return ca[i,j]\n", "\n", "\n", "@cython.boundscheck(False)\n", "@cython.wraparound(False)\n", "def frechet_v5(float[:,::1] P, float[:,::1] Q, np.intp_t N):\n", " cdef np.intp_t lenP, lenQ, i, j\n", " \n", " lenP = P.shape[0]\n", " lenQ = Q.shape[0]\n", " \n", " cdef float[:,::1] d = np.empty((lenP, lenQ), dtype='float32')\n", " cdef float[:,::1] ca = -np.ones((lenP, lenQ), dtype='float32')\n", "\n", " for i in xrange(lenP):\n", " for j in xrange(lenQ):\n", " d[i,j] = sqnorm(P[i], Q[j])\n", "\n", " return sqrt( c(d, ca, lenP-1, lenQ-1) / N )" ] }, { "cell_type": "code", "execution_count": 115, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "3 loops, best of 3: 215 ms per loop\n" ] } ], "source": [ "%timeit -n3 psa_full.run(metric=frechet_v5)" ] }, { "cell_type": "code", "execution_count": 82, "metadata": { "collapsed": true }, "outputs": [], "source": [ "linkage = 'ward' # 'single' 'complete' 'weighted' 'average'\n", "plotname = 'df_v5_ward_psa-full.pdf'\n", "psa_full.plot(filename=plotname, linkage=linkage);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Version 6" ] }, { "cell_type": "code", "execution_count": 93, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%%cython -f -c=-Ofast -c=-march=native\n", "import numpy as np\n", "cimport numpy as np\n", "from libc.math cimport fmin, fmax, sqrt\n", "cimport cython\n", "\n", "@cython.boundscheck(False)\n", "@cython.wraparound(False)\n", "cdef float sqnorm(float[::1] p, float[::1] q):\n", " cdef np.intp_t i\n", " cdef float s, temp\n", " s = 0.0\n", " for i in xrange(p.shape[0]):\n", " temp = p[i] - q[i]\n", " s += temp*temp\n", " return s\n", "\n", "@cython.boundscheck(False)\n", "@cython.wraparound(False)\n", "cdef float c(float[:,::1] d, float[:,::1] ca, np.intp_t i, np.intp_t j):\n", " if ca[i,j] != -1 : return ca[i,j]\n", " if i > 0:\n", " if j > 0: ca[i,j] = fmax( fmin(fmin(c(d,ca,i-1,j),c(d,ca,i,j-1)),c(d,ca,i-1,j-1)), d[i,j] )\n", " else: ca[i,j] = fmax( c(d,ca,i-1,0), d[i,0] )\n", " elif j > 0: ca[i,j] = fmax( c(d,ca,0,j-1), d[0,j] )\n", " else: ca[i,j] = d[0,0]\n", " return ca[i,j]\n", "\n", "\n", "@cython.boundscheck(False)\n", "@cython.wraparound(False)\n", "def frechet_v6(float[:,::1] P, float[:,::1] Q, np.intp_t N):\n", " cdef np.intp_t lenP, lenQ, i, j\n", " \n", " lenP = P.shape[0]\n", " lenQ = Q.shape[0]\n", " N = P.shape[1] # Check that Q.shape[1] is the same!\n", " \n", " cdef float[:,::1] d = np.empty((lenP, lenQ), dtype='float32')\n", " cdef float[:,::1] ca = -np.ones((lenP, lenQ), dtype='float32')\n", "\n", " for i in xrange(lenP):\n", " for j in xrange(lenQ):\n", " d[i,j] = sqnorm(P[i], Q[j])\n", "\n", " return sqrt( c(d, ca, lenP-1, lenQ-1) / N )" ] }, { "cell_type": "code", "execution_count": 89, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "3 loops, best of 3: 209 ms per loop\n" ] } ], "source": [ "%timeit -n10 psa_full.run(metric=frechet_v6)" ] }, { "cell_type": "code", "execution_count": 82, "metadata": { "collapsed": true }, "outputs": [], "source": [ "linkage = 'ward' # 'single' 'complete' 'weighted' 'average'\n", "plotname = 'df_v6_ward_psa-full.pdf'\n", "psa_full.plot(filename=plotname, linkage=linkage);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Version 6b\n", "\n", "Reduced memview slices by working directly with P and Q" ] }, { "cell_type": "code", "execution_count": 95, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%%cython -f -c=-O3 -c=-march=native -c=-funroll-loops -c=-ffast-math\n", "import numpy as np\n", "cimport numpy as np\n", "from libc.math cimport fmin, fmax, sqrt\n", "cimport cython\n", "\n", "ctypedef float (*fcn_ptr)(float[:,::1], float[:,::1], np.intp_t, np.intp_t)\n", "\n", "@cython.boundscheck(False)\n", "@cython.wraparound(False)\n", "cdef float c(float[:,::1] d, float[:,::1] ca, np.intp_t i, np.intp_t j):\n", " cdef np.intp_t im1, jm1\n", " im1 = i-1\n", " jm1 = j-1\n", " if ca[i,j] != -1 : return ca[i,j]\n", " if i > 0:\n", " if j > 0: ca[i,j] = fmax( fmin(fmin(c(d,ca,im1,j),c(d,ca,i,jm1)),c(d,ca,im1,jm1)), d[i,j] )\n", " else: ca[i,j] = fmax( c(d,ca,im1,0), d[i,0] )\n", " elif j > 0: ca[i,j] = fmax( c(d,ca,0,jm1), d[0,j] )\n", " else: ca[i,j] = d[0,0]\n", " return ca[i,j]\n", "\n", "\n", "@cython.boundscheck(False)\n", "@cython.wraparound(False)\n", "def frechet_v6b(float[:,::1] P, float[:,::1] Q, np.intp_t N):\n", " cdef np.intp_t lenP, lenQ, i, j, k\n", " \n", " lenP = P.shape[0]\n", " lenQ = Q.shape[0]\n", " \n", " cdef float[:,::1] d = np.empty((lenP, lenQ), dtype='float32')\n", " cdef float[:,::1] ca = -np.ones((lenP, lenQ), dtype='float32')\n", " cdef float s, temp\n", " cdef fcn_ptr cf = &c\n", " \n", " for i in xrange(lenP):\n", " for j in xrange(lenQ):\n", " s = 0.0\n", " for k in xrange(P.shape[1]):\n", " temp = P[i,k] - Q[j,k]\n", " s += temp*temp\n", " d[i,j] = s\n", "\n", " return sqrt( c(d, ca, lenP-1, lenQ-1) / N )" ] }, { "cell_type": "code", "execution_count": 129, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "10 loops, best of 3: 188 ms per loop\n" ] } ], "source": [ "%timeit -n10 psa_full.run(metric=frechet_v6b)" ] }, { "cell_type": "code", "execution_count": 130, "metadata": { "collapsed": true }, "outputs": [], "source": [ "linkage = 'ward' # 'single' 'complete' 'weighted' 'average'\n", "plotname = 'df_v6b_ward_psa-full.pdf'\n", "psa_full.plot(filename=plotname, linkage=linkage);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Optimized" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%%cython -f -c=-O3 -c=-march=native -c=-funroll-loops -c=-ffast-math\n", "import numpy as np\n", "cimport numpy as np\n", "from libc.math cimport fmin, fmax, sqrt\n", "cimport cython\n", "\n", "ctypedef float (*cD_ptr)(float[:,::1], float[:,::1], np.intp_t, np.intp_t)\n", "\n", "@cython.boundscheck(False)\n", "@cython.wraparound(False)\n", "cdef float cD(float[:,::1] d, float[:,::1] cd, np.intp_t i, np.intp_t j):\n", " cdef np.intp_t im1, jm1\n", " im1 = i-1\n", " jm1 = j-1\n", " if cd[i,j] != -1 : return cd[i,j]\n", " if i > 0:\n", " if j > 0: cd[i,j] = fmax( fmin(fmin(cD(d,cd,im1,j),cD(d,cd,i,jm1)),cD(d,cd,im1,jm1)), d[i,j] )\n", " else: cd[i,j] = fmax( cD(d,cd,im1,0), d[i,0] )\n", " elif j > 0: cd[i,j] = fmax( cD(d,cd,0,jm1), d[0,j] )\n", " else: cd[i,j] = d[0,0]\n", " return cd[i,j]\n", "\n", "@cython.boundscheck(False)\n", "@cython.wraparound(False)\n", "cdef float[:,::1] rmsdMatrix_c(float[:,::1] P, float[:,::1] Q):\n", " cdef np.intp_t lenP = P.shape[0]\n", " cdef np.intp_t lenQ = Q.shape[0]\n", " cdef np.intp_t i, j, k\n", " cdef float s, diff\n", " cdef float[:,::1] d = np.empty((lenP, lenQ), dtype='float32')\n", " for i in xrange(lenP):\n", " for j in xrange(lenQ):\n", " s = 0.0\n", " for k in xrange(P.shape[1]):\n", " diff = P[i,k] - Q[j,k]\n", " s += diff*diff\n", " d[i,j] = s\n", " return d\n", "\n", "@cython.boundscheck(False)\n", "@cython.wraparound(False)\n", "def frechet_opt(float[:,::1] P, float[:,::1] Q, np.intp_t N):\n", " cdef np.intp_t lenP = P.shape[0]\n", " cdef np.intp_t lenQ = Q.shape[0]\n", " \n", " assert int(P.shape[1]) == int(3*N)\n", " assert P.shape[1] == Q.shape[1]\n", " \n", " cdef float[:,::1] d = rmsdMatrix_c(P, Q)\n", " cdef float[:,::1] cd = -np.ones((lenP, lenQ), dtype='float32')\n", " cdef cD_ptr couplingDistance = &cD\n", "\n", " return sqrt( couplingDistance(d, cd, lenP-1, lenQ-1) / N )" ] }, { "cell_type": "code", "execution_count": 129, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "10 loops, best of 3: 188 ms per loop\n" ] } ], "source": [ "%timeit -n10 psa_full.run(metric=frechet_opt)" ] }, { "cell_type": "code", "execution_count": 130, "metadata": { "collapsed": true }, "outputs": [], "source": [ "linkage = 'ward' # 'single' 'complete' 'weighted' 'average'\n", "plotname = 'df_opt_ward_psa-full.pdf'\n", "psa_full.plot(filename=plotname, linkage=linkage);" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.6" } }, "nbformat": 4, "nbformat_minor": 0 }

gpl-3.0

jurgjn/relmapping

annot/notebooks/GEO_dnase_mnase_staging.ipynb

2

14742

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "ExecuteTime": { "end_time": "2018-05-10T15:41:03.587624Z", "start_time": "2018-05-10T15:40:51.702892Z" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/mnt/home3/jj374/anaconda36/lib/python3.6/site-packages/statsmodels/compat/pandas.py:56: FutureWarning: The pandas.core.datetools module is deprecated and will be removed in a future version. Please use the pandas.tseries module instead.\n", " from pandas.core import datetools\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "os.getcwd(): /mnt/beegfs/scratch_copy/ahringer/jj374/lab/relmapping\n" ] } ], "source": [ "%run ~/relmapping/annot/notebooks/__init__.ipynb" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "ExecuteTime": { "end_time": "2018-05-10T15:56:43.504402Z", "start_time": "2018-05-10T15:41:03.606357Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "wt_emb True\n", "wt_emb True\n", "wt_emb True\n", "wt_emb True\n", "wt_emb True\n", "wt_emb True\n", "wt_emb True\n", "wt_emb True\n", "wt_emb True\n", "wt_emb True\n", "wt_l1 True\n", "wt_l1 True\n", "wt_l1 True\n", "wt_l1 True\n", "wt_l1 True\n", "wt_l1 True\n", "wt_l1 True\n", "wt_l1 True\n", "wt_l1 True\n", "wt_l1 True\n", "wt_l2 True\n", "wt_l2 True\n", "wt_l2 True\n", "wt_l2 True\n", "wt_l2 True\n", "wt_l2 True\n", "wt_l2 True\n", "wt_l2 True\n", "wt_l2 True\n", "wt_l2 True\n", "wt_l3 True\n", "wt_l3 True\n", "wt_l3 True\n", "wt_l4 True\n", "wt_l3 True\n", "wt_l3 True\n", "wt_l3 True\n", "wt_l3 True\n", "wt_l3 True\n", "wt_l3 True\n", "wt_l3 True\n", "wt_ya True\n", "wt_l4 True\n", "wt_l4 True\n", "wt_l4 True\n", "wt_l4 True\n", "wt_l4 True\n", "wt_l4 True\n", "wt_l4 True\n", "wt_l4 True\n", "wt_l4 True\n", "wt_L3_DNase-seq_rep1_2.5U_ml mpg130809_4064 True\n", "wt_ya True\n", "wt_ya True\n", "wt_ya True\n", "wt_ya True\n", "wt_ya True\n", "wt_ya True\n", "wt_ya True\n", "wt_ya True\n", "wt_ya True\n", "wt_L3_DNase-seq_rep1_10U_ml mpg130809_4065 True\n", "wt_L2_DNase-seq_rep1_2.5U_ml mpg150203_6307 True\n", "wt_emb_DNase-seq_rep2_2.5U_ml mpg130828_4176 True\n", "wt_L3_DNase-seq_rep2_2.5U_ml mpg130919_4279 True\n", "wt_emb_DNase-seq_rep1_2.5U_ml mpg130828_4167 True\n", "wt_L4_DNase-seq_rep1_2.5U_ml mpg150203_6318 True\n", "wt_L1_DNase-seq_rep1_2.5U_ml mpg140522_5035 True\n", "wt_L2_DNase-seq_rep2_2.5U_ml mpg150521_6953 True\n", "wt_L4_DNase-seq_rep2_2.5U_ml mpg150203_6329 True\n", "wt_L1_DNase-seq_rep2_2.5U_ml mpg150603_7043 True\n", "wt_L3_DNase-seq_rep1_25U_ml mpg130809_4066 True\n", "wt_L4_DNase-seq_rep1_5U_ml mpg150203_6319 True\n", "wt_L2_DNase-seq_rep1_5U_ml mpg150203_6308 True\n", "wt_L1_DNase-seq_rep1_5U_ml mpg140522_5036 True\n", "wt_emb_DNase-seq_rep1_5U_ml mpg130828_4168 True\n", "wt_emb_DNase-seq_rep2_5U_ml mpg130828_4177 True\n", "wt_L3_DNase-seq_rep1_50U_ml mpg130809_4067 True\n", "wt_L4_DNase-seq_rep2_5U_ml mpg150203_6330 True\n", "wt_L3_DNase-seq_rep2_5U_ml mpg130919_4280 True\n", "wt_L2_DNase-seq_rep2_5U_ml mpg150521_6954 True\n", "wt_L1_DNase-seq_rep2_5U_ml mpg150603_7044 True\n", "wt_L4_DNase-seq_rep1_10U_ml mpg150203_6320 True\n", "wt_L2_DNase-seq_rep1_10U_ml mpg150203_6309 True\n", "wt_L3_DNase-seq_rep1_100U_ml mpg130809_4068 True\n", "wt_emb_DNase-seq_rep1_10U_ml mpg130828_4169 True\n", "wt_L1_DNase-seq_rep1_10U_ml mpg140522_5037 True\n", "wt_emb_DNase-seq_rep2_10U_ml mpg130705_3279 True\n", "wt_L2_DNase-seq_rep2_10U_ml mpg150521_6955 True\n", "wt_L3_DNase-seq_rep2_10U_ml mpg130919_4281 True\n", "wt_L4_DNase-seq_rep2_10U_ml mpg150203_6331 True\n", "wt_L1_DNase-seq_rep2_10U_ml mpg150603_7045 True\n", "wt_L4_DNase-seq_rep1_25U_ml mpg150203_6321 True\n", "wt_L2_DNase-seq_rep1_25U_ml mpg150203_6310 True\n", "wt_L3_DNase-seq_rep2_25U_ml mpg130919_4282 True\n", "wt_L3_DNase-seq_rep1_200U_ml mpg130809_4070 True\n", "wt_emb_DNase-seq_rep2_25U_ml mpg130705_3280 True\n", "wt_L1_DNase-seq_rep1_25U_ml mpg140522_5038 True\n", "wt_emb_DNase-seq_rep1_25U_ml mpg130828_4170 True\n", "wt_L2_DNase-seq_rep2_25U_ml mpg150521_6956 True\n", "wt_L4_DNase-seq_rep2_25U_ml mpg150203_6332 True\n", "wt_L1_DNase-seq_rep2_25U_ml mpg150603_7046 True\n", "wt_L2_DNase-seq_rep1_50U_ml mpg150203_6311 True\n", "wt_L4_DNase-seq_rep1_50U_ml mpg150203_6322 True\n", "wt_L3_DNase-seq_rep1_400U_ml mpg130809_4071 True\n", "wt_L3_DNase-seq_rep2_50U_ml mpg130919_4283 True\n", "wt_L4_DNase-seq_rep2_50U_ml mpg150203_6333 True\n", "wt_L2_DNase-seq_rep2_50U_ml mpg150521_6957 True\n", "wt_emb_DNase-seq_rep1_50U_ml mpg130828_4171 True\n", "wt_L1_DNase-seq_rep1_50U_ml mpg140522_5039 True\n", "wt_emb_DNase-seq_rep2_50U_ml mpg130705_3281 True\n", "wt_L2_DNase-seq_rep1_100U_ml mpg150203_6312 True\n", "wt_L1_DNase-seq_rep2_50U_ml mpg150603_7047 True\n", "wt_L4_DNase-seq_rep1_100U_ml mpg150203_6323 True\n", "wt_L3_DNase-seq_rep1_800U_ml mpg130809_4072 True\n", "wt_L4_DNase-seq_rep2_100U_ml mpg150203_6337 True\n", "wt_L2_DNase-seq_rep2_100U_ml mpg150521_6958 True\n", "wt_emb_DNase-seq_rep2_100U_ml mpg130705_3282 True\n", "wt_L3_DNase-seq_rep2_100U_ml mpg130919_4284 True\n", "wt_emb_DNase-seq_rep1_100U_ml mpg130828_4172 True\n", "wt_L1_DNase-seq_rep1_100U_ml mpg140522_5040 True\n", "wt_L4_DNase-seq_rep1_200U_ml mpg150203_6324 True\n", "wt_L2_DNase-seq_rep1_200U_ml mpg150203_6313 True\n", "wt_L1_DNase-seq_rep2_100U_ml mpg150603_7048 True\n", "wt_L4_DNase-seq_rep2_200U_ml mpg150203_6334 True\n", "wt_L2_DNase-seq_rep2_200U_ml mpg150521_6959 True\n", "wt_emb True\n", "wt_L3_DNase-seq_rep2_200U_ml mpg130919_4285 True\n", "wt_L4_DNase-seq_rep1_400U_ml mpg150203_6325 True\n", "wt_L2_DNase-seq_rep1_400U_ml mpg150203_6314 True\n", "wt_emb_DNase-seq_rep1_200U_ml mpg130828_4173 True\n", "wt_L1_DNase-seq_rep1_200U_ml mpg140522_5041 True\n", "wt_emb_DNase-seq_rep2_200U_ml mpg130705_3283 True\n", "wt_L1_DNase-seq_rep2_200U_ml mpg150603_7049 True\n", "wt_L4_DNase-seq_rep2_400U_ml mpg150203_6335 True\n", "wt_l1 True\n", "wt_L2_DNase-seq_rep2_400U_ml mpg150521_6960 True\n", "wt_L2_DNase-seq_rep1_800U_ml mpg150203_6315 True\n", "wt_L4_DNase-seq_rep1_800U_ml mpg150203_6326 True\n", "wt_L3_DNase-seq_rep2_400U_ml mpg130919_4286 True\n", "wt_emb_DNase-seq_rep1_400U_ml mpg130828_4174 True\n", "wt_L1_DNase-seq_rep1_400U_ml mpg140522_5042 True\n", "wt_L4_DNase-seq_rep2_800U_ml mpg150203_6336 True\n", "wt_L1_DNase-seq_rep2_400U_ml mpg150603_7050 True\n", "wt_emb_DNase-seq_rep2_400U_ml mpg130705_3284 True\n", "wt_l2 True\n", "wt_L2_DNase-seq_rep2_800U_ml mpg150521_6961 True\n", "wt_L3_DNase-seq_rep2_800U_ml mpg130919_4287 True\n", "wt_L1_DNase-seq_rep2_800U_ml mpg150603_7051 True\n", "wt_L1_DNase-seq_rep1_800U_ml mpg140522_5043 True\n", "wt_emb_DNase-seq_rep2_800U_ml mpg130705_3285 True\n", "wt_l3 True\n", "wt_emb_DNase-seq_rep1_800U_ml mpg130828_4175 True\n", "wt_emb True\n", "wt_emb True\n", "wt_emb True\n", "wt_l4 True\n", "wt_l1 True\n", "wt_l1 True\n", "wt_l1 True\n", "wt_ya True\n", "wt_l2 True\n", "wt_l2 True\n", "wt_YA_DNase-seq_rep1_2.5U_ml mpg140401_4970 True\n", "wt_l2 True\n", "wt_l3 True\n", "wt_l3 True\n", "wt_YA_DNase-seq_rep1_10U_ml mpg140401_4971 True\n", "wt_l3 True\n", "wt_l4 True\n", "wt_YA_DNase-seq_rep1_25U_ml mpg140401_4972 True\n", "wt_l4 True\n", "wt_l4 True\n", "wt_YA_DNase-seq_rep1_50U_ml mpg140401_4973 True\n", "wt_ya True\n", "wt_ya True\n", "wt_ya True\n", "wt_YA_DNase-seq_rep1_100U_ml mpg140401_lane8 True\n", "wt_YA_DNase-seq_rep2_2.5U_ml mpg150603_7023 True\n", "wt_emb_MNase-seq_rep1_0.1U_ml mpg130809_4054 True\n", "wt_YA_DNase-seq_rep1_200U_ml mpg140401_4975 True\n", "wt_emb_MNase-seq_rep2_0.025U_ml mpg141028_5513 True\n", "wt_YA_DNase-seq_rep2_5U_ml mpg150603_7024 True\n", "wt_YA_DNase-seq_rep1_400U_ml mpg140401_4976 True\n", "wt_emb_MNase-seq_rep1_0.25U_ml mpg130809_4055 True\n", "wt_emb_MNase-seq_rep2_0.05U_ml mpg141028_5514 True\n", "wt_YA_DNase-seq_rep1_800U_ml mpg140401_4977 True\n", "wt_YA_DNase-seq_rep2_10U_ml mpg150603_7025 True\n", "wt_emb_MNase-seq_rep1_0.5U_ml mpg130809_4056 True\n", "wt_emb_MNase-seq_rep2_0.1U_ml mpg141028_5515 True\n", "wt_YA_DNase-seq_rep2_25U_ml mpg150603_7026 True\n", "wt_emb_MNase-seq_rep1_1U_ml mpg130809_4057 True\n", "wt_emb_MNase-seq_rep2_0.25U_ml mpg141028_5516 True\n", "wt_YA_DNase-seq_rep2_50U_ml mpg150603_7027 True\n", "wt_emb_MNase-seq_rep1_4U_ml mpg130809_4058 True\n", "wt_emb_MNase-seq_rep2_0.5U_ml mpg141028_5517 True\n", "wt_YA_DNase-seq_rep2_100U_ml mpg150603_7028 True\n", "wt_emb_MNase-seq_rep1_16U_ml mpg130809_4061 True\n", "wt_emb_MNase-seq_rep2_1U_ml mpg141028_5518 True\n", "wt_YA_DNase-seq_rep2_200U_ml mpg150603_7029 True\n", "wt_YA_DNase-seq_rep2_400U_ml mpg150603_7030 True\n", "wt_emb_MNase-seq_rep2_4U_ml mpg141028_5519 True\n", "wt_YA_DNase-seq_rep2_800U_ml mpg150603_7031 True\n", "wt_emb_MNase-seq_rep2_8U_ml mpg141028_5520 True\n", "wt_emb_MNase-seq_rep2_16U_ml mpg141028_5521 True\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[Parallel(n_jobs=10)]: Done 14 out of 14 | elapsed: 15.6min finished\n" ] }, { "data": { "text/plain": [ "[None,\n", " None,\n", " None,\n", " None,\n", " None,\n", " None,\n", " None,\n", " None,\n", " None,\n", " None,\n", " None,\n", " None,\n", " None,\n", " None]" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "def df_imshow(df_, ax=None, row_labels=None, col_labels=None, vmin=-0.6, vmax=+0.6, rotation=70, *args, **kwargs):\n", " if ax is None: ax = plt.gca()\n", " if row_labels is None: row_labels = df_.index\n", " if col_labels is None: col_labels = df_.columns\n", " ax.imshow(df_, cmap='coolwarm', interpolation='nearest', vmin=vmin, vmax=vmax, *args, **kwargs)\n", " ax.xaxis.tick_top()\n", " ax.set_xticks(range(len(col_labels)))\n", " ax.set_yticks(range(len(row_labels)))\n", " ax.set_xticklabels(col_labels, rotation=rotation)\n", " ax.set_yticklabels(row_labels)\n", " for (y, x), c in np.ndenumerate(df_):\n", " ax.text(x, y, '%.3f' % (c,), color='k', horizontalalignment='center', verticalalignment='center')\n", "\n", "def dnase_mnase_staging(libid):\n", " fp = 'annot_Apr27/Fig2D2_regulatory_annotation_Apr27.tsv'\n", " #df = pd.read_csv(fp, sep='\\t')[yp.NAMES_BED3].sample(1000).sort_values(yp.NAMES_BED3).reset_index(drop=True)\n", " df = pd.read_csv(fp, sep='\\t')[yp.NAMES_BED3].sort_values(yp.NAMES_BED3).reset_index(drop=True)\n", " #df.head()\n", " \n", " # Peak heights for every hypersensitive site\n", " for stage in itertools.islice(config['stages_wt'], None):\n", " fp_ = 'atac814_geo/tracks/atac_%s.bw' % (stage,)\n", " print(stage, os.path.isfile(fp_))\n", " df['atac_%(stage)s' % locals()] = list(map(np.nanmax, yp.read_regions(fp_, \n", " df.chrom.tolist(), df.start.tolist(), df.end.tolist())))\n", "\n", " def df_geoid(geoid):\n", " df_ = pd.read_csv('dnase_mnase819_geo/dnase_mnase_geo1_samples.tsv', sep='\\t')\n", " return df_[df_.raw_file_1.str.startswith(geoid)].reset_index(drop=True)\n", "\n", " for i, r in df_geoid(libid).iterrows():\n", " step_ = 'tg_pe.bwa_pe.rm_unmapped_pe.rm_chrM.rm_blacklist.rm_q10.macs2_pe_lt300'\n", " fp = pf(r['bid'], step_, '_treat_pileup.bw', 'dnase_mnase')\n", " print(r['title'], r['bid'], os.path.isfile(fp))\n", " df[r['title']] = list(map(np.nanmax, yp.read_regions(fp, df.chrom.tolist(), df.start.tolist(), df.end.tolist())))\n", "\n", " df_ = collections.OrderedDict()\n", " for atac_stage in df.columns[3:3+6]:\n", " df_[atac_stage] = collections.OrderedDict()\n", " for dnase_sample in df_geoid(libid)['title']:\n", " corr_ = sp.stats.kendalltau(\n", " df[atac_stage],\n", " df[dnase_sample]\n", " ).correlation\n", " df_[atac_stage][dnase_sample] = corr_\n", "\n", " df_ct = pd.DataFrame.from_dict(df_, orient='columns').loc[df_geoid(libid)['title'].tolist()]\n", " #df_ct\n", "\n", " fp_ = 'dnase_mnase819_geo/staging/%s.staging.pdf' % (libid,)\n", " fig = plt.figure(figsize=(12, 12))\n", " plt.gca().set_xlabel('ATAC-seq stage')\n", " plt.gca().set_ylabel('DNase/MNase sample')\n", " df_imshow(df_ct)\n", " plt.savefig(fp_, dpi=600, transparent=True, bbox_inches='tight')\n", "\n", "#for libid in config['dnase_mnase819_rep'].keys():\n", "# dnase_mnase_staging(libid)\n", "pmap(dnase_mnase_staging, config['dnase_mnase819_rep'].keys(), n_jobs=10)" ] } ], "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

dahlend/CSOS

2015 Notebook - Paper.ipynb

1

51766

{ "metadata": { "language": "Julia", "name": "", "signature": "sha256:73471407b77d0bd4314c056666427120350099c8cb8f7e7d8f313fc25685d1cd" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "#Robust and accurate recovery of sensory signals in the nervous system by compressed sensing\n", "\n", "\n", "\n", "\n", "##Abstract \n", "To encode sensory information effectively, the brain extracts salient features from near infinite numbers of possible sensory stimuli in the environment. The neural code that allows the brain to reduce stimulus dimension and avoid combinatorial explosion remains unknown. Here we show that compressed sensing could be used to accurately recover signals from incomplete and inaccurate responses in the sensory system. We find that detectors in the mouse olfactory systems exhibit properties of compressed sensing. Odor identity and intensity can be identified precisely and robustly based on the response from a small number of randomly selected olfactory glomeruli. Accurate recovery can be achieved in the presence of high level of noise. Compressed sensing reduces the dimensionality of the sensory input and offers robust sensory coding.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##Intro\n", "Perceptual objects are constructed from elemental features embedded in the sensory stimuli. The number of possible feature combinations grow exponentially with increased complexity, resulting in a problem known as combinatorial explosion. The brain must reduce the dimensionality of the feature space such that only small sets of salient features are extracted and represented. A robust coding strategy that allows reliable discrimination and identification of salient stimuli must have the ability to perform feature extraction without being overly specific in their response, lest certain stimulus features deemed irrelevant in one situation becomes important in others. An ideal neural code must be both specific and robust to allow the specific identification of salient stimuli among irrelevant ones, and do so in noisy and fluctuating environment. \n", "The two requirement are often incompatible in current theories of neural coding. In most cases, individual neuron participates in the encoding of multiple stimuli and multiple neurons participate in the encoding of the same stimulus. Response of individual neurons to the same stimulus often vary and is influenced by brain states and other stimuli. The inherent variability in the brain creates ambiguity in decoding sensory inputs and difficult to reconstruct the original signals. High specificity in feature tuning by individual neurons makes decoding sensitive to noise. Whereas a coding scheme based on the response of large populations of neurons reduces the effect of noise in signal recovery. The recovery of signals is also influenced by the members in the population code. \n", "In cases where individual neurons respond to multiple stimulus features but salient signals are sparse, compression of the signals occurs. With sparse signals, the theory of compressed sensing allows efficient detection and accurate recovery of signals even when they are sampled under the traditional Nyquist-Shannon rate. It is not known whether and how the nervous system could use compressed sensing to encode and decode sensory information. \n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##Theoretical framework\n", "Therefore we explore the use of compressed sensing to recover sensory input. We pose the problem of sensory coding as followed. A stimulus, x, is described as a vector belonging to a n-dimensional stimulus feature space $R_n$. Stimuli are detected or represented by a set of m detectors (neurons), which are tuned to specific setswith each detector being tuned to a set of features. The population response of the detectors is therefore the product of the detector matrix \u03a6 (m X n) and the stimulus vector, i.e.:\n", " $$y=\u03a6x$$\n", "When m >= n, x can be accurately reconstructed from the response pattern provided that \u03a6 is known. In the brain, however, the number of possible features far exceeds the number of encoders because. When m<< n, traditional decoding strategies based on linear transformation becomes an ill-posedunderdetermined inverse problem and result in ambiguity in recovering of the original signals. \n", "As salient stimuli can be representedare sparssparselye, a sparse basis set \u03a8 can be constructed to transform salient stimuli to obtain:\n", "$$x=\u03a8c$$\n", "where c is the vector representing the stimulus x in the sparse space. Response to the stimulus is therefore: \n", "$$y=\u03a6x=\u03a6\u03a8c=Ac$$\n", "Here, the original signal is transformed into a sparse representation and a new basis set A generate the response patterns in the detectors (Fig. 1). In compressive sensing, a properly constructed basis set allows the precise recovery of the signal x even when m<< n by finding the sparsest solution using algorithms such as L1-minimization. Here, the application of compressed sensing in sensory processing differs from that in engineering, which seeks to construct the proper sensing matrix such that the signal can be precisely recovered. In sensory coding the sensing matrix (sensory cells and higher order neurons) is pre-determined. At first approximation the tuning properties of individual neurons are fixed. The goal of sensory physiology, therefore, is to experimentally determine the tuning property of the sensing matrix and, in turn, to extract signal identities based on the responses of the detector neurons.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##Construct an olfactory dictionary\n", "In principle, it is possible to apply compressed sensing method to decode sensory signals if the sensing matrix A is known. We explore the mouse olfactory system to test whether accurate recovery of stimulus information can be achieved. The mouse olfactory epithelium expressed ~1000 odorant receptors (ORs). Olfactory sensory neurons expressing the same OR project their axons into the olfactory bulb and converge into two of ~2000 glomeruli. The glomerular array therefore represents the odorant receptor array that encode odor information. At the outset, the olfactory system acutely faces the problem of dimensionality \u2013 a limited number of ORs is used to encode a much larger number of odorant molecules. As such, individual glomeruli are tuned to multiple odors and disparate physiochemical properties of odorants. This property of broad tuning appeared to fulfill the requirement of sensors for compressed sensing and compelled us to examine whether compressed sensing could serve as neural code.\n", "We probed the sensing properties of individual glomeruli by recording odor-evoked responses from the dorsal surface of G-CaMP2 mice to large sets of odorant stimuli. The response matrix Y can be expressed as the product of the basis set A and a property matrix C, in which each odorant c is represented by a vector of a set properties. \n", " $$Y=AC$$\n", "To recover the signals through compressed sensing, C needed to be sparse. By imposing sparsity restriction on C, we applied blind source separation technique to generate both A and C, with C being the sparsest solution (Fig. 1). \n", "In one of our experiment, we used 58 odorants to stimulate the nose and recorded the responses from 94 glomeruli. Using the response matrix constructed the sensing matrix (the dictionary) as well as the odorant matrix with a set of 40 properties. We note that the number of properties can be arbitrarily set and produce remarkably similar results. For this set of experiment, 40 properties were chosen as it is ??? (Forty properties. Supp figure). We also note that the properties that have resulted from this analyses do not have physical meaning, but they can be mapped to existing physical descriptions of the odorants (see accompanying manuscript).\n", "Because of the sparsity restriction, the property matrix is sparse (Gini index??); each odorant can be described by a few of the virtual properties. The basis set is also sparse (index), but none of the glomeruli was tuned to a single feature. Most glomeruli are tuned to disparate set of properties and each properties is represented by multiple glomeruli. We measure the coherence of among the properties detected by individual glomeruli and found that ????? (measurement of incoherence). These features conform to the properties of measurement matrices of compressive sensing. \n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "###Recovery of odor identity using compressive sensing algorithms\n", "Because the basis set constructed from blind source separation has more numbers of basis than stimulus, it is overcomplete. It is not surprising that response patterns can be used to recover the identities of odors accurately. It is not known, however, whether applying compressed sensing algorithm would allow recovery odor identities when the signals were undersampled. To examine this, we selected subsets of glomeruli and applied compressed sensing algorithm to recover odor identities. To prevent overfitting of non-specific responses, we excluded 19 odors that produced very weak responses among the glomeruli.\n", "In the initial test, we selected sets of glomeruli that collectively covered all odorant properties and used their response patterns to reconstruct odor identity. Figure 2 shows the correlation between original and recovered signals using Lp minimization based on different subsets of glomeruli. Strikingly, with only 9 glomeruli, we were able to recover odor identities with high fidelity (Figure 2). Of the 39 odors examined, ?? were recovered with nearly perfect correlation (R^2>0.9). Increasing the number of glomeruli to 13 resulted in further increased accuracy (Figure 2). \n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "###Robustness and redundancy in signal recovery\n", "We next evaluated the performance of randomly selected sets of glomeruli in signal recovery with Monte Carlo analyses (Figure 3). The performance of the random sets was slightly below those selected sets, but there was a sharp rise in performance when the number of glomeruli reaches above 15, when nearly perfect recovery for all odorants was achieved. These results show that responses from an arbitrary set of glomeruli could be used to encode a much larger set of signals and permit accurate reconstruction. Importantly, each signal could be recovered using different set of glomeruli. This high level of redundancy therefore could provide high level of confidence in odor identification. \n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "###Robustness to noise (Figure 4)\n", "Noise inevitably corrupts the signal and diminishes the ability to recover the original signal. We tested the robustness of signal recovery against noise by adding Gaussian noise to the glomerular responses. Correlation between recovered signal and the original were obtained from Monte Carlo analyses. The mean R^2 values as a function of glomerular number and noise level is plotted in a heatmap. Increasing noise level indeed led to reduced correlations. At the same noise level, more glomeruli are required to achieve high level of recovery. However, compressed sensing is remarkably resilient against noise. At 10% noise level, nearly perfect recovery is achieved with 15 glomeruli. Even when the noise level reached 50% of the signal, we were still able to obtain an average R^2 value of 0.5 with 29 glomeruli.\n", "Robustness against noise was more evident for selected sets of glomeruli that exhibited comprehensive coverage. Compared with a randomly selected set of 13 glomeruli, the selected set of glomeruli recovered signals with higher fidelity and were more resistant to added noise. Even with noise as large as the signal (100%), these glomeruli maintains an average of R^2 of 0.4. \n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "###Recovering intensity information (Figure 5)\n", "Sensory systems not only transmit information about the identity of the stimuli, but also intensity information. Odors at high concentrations not only evoke stronger responses from the same glomeruli, but also recruit additional glomeruli. The same glomerulus also respond to different odorants with different dose-dependency. This is a problem that have not been well understood. We explore whether compressed sensing could recover glomerular responses to different odor concentration.\n", "Using the recorded glomerular response to three different odor concentrations, we could generate a basis set that capture the sensitivity of individual glomeruli to different concentrations and expressed them as weights against different properties. Using this updated basis set, we calculated the response to different odors at different concentrations. As illustrated for three different glomeruli in Figure 4, the recovered concentration dependent activity precisely recapitulated those of the original responses. Thus, compressive sensing not only could be used to recover the original signals, but also capture the intensity information. \n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##Consistency across different experiments (Figure 6)\n", "\t\n", "\n", "\n", "\n", "\n", "##DISCUSSION\n", "Compressed sensing, tuning specificity and distributed coding\n", "Compressed sensing is manifest at the earliest stage in odor detection. Odorants are high-dimensional stimuli by the vast number of physiochemical features that characterize the odorant compounds. The high dimensional stimulus space must be mapped to a 2-dimensional olfactory map. Each odorant activates an ensemble of odorant receptors and each odorant receptors is activated by different odorants. While these characteristics are qualitatively consistent with features of the sensing matrix capable of compressed sensing, they may not comply strictly to the mathematical requirement such as incoherence and the restricted isometry property. Nevertheless, as our analyses show, an arbitrary set of glomeruli that are tuned to disparate odor features are sufficient to allow signal recovery.\n", "We suggest that a neural code based on compressed sensing may be a general one broadly applicable to other systems. In some system, stimuli are sensed by highly evolved, specialized detectors. Pheromone receptors, for example, are highly specific and neuron make genetically specified, stereotypic connections with downstream circuits to elicit innate behaviors. However, such design principle is ill-suited to accommodate the environment at large because it is impossible to design specific receptor for each and every potential stimulus. Most neurons in the sensory pathways are tuned to broad sets of properties. Even for systems exhibiting highly specific sensory responses at various stages, downstream cells exhibiting complex tuning properties. As such, individual features are represented distributively and individual neuron participates in the encoding of multiple stimuli. This characteristic enables information to be compressed, allowing a limited number of neurons to carry a large amount of information. \n", "Information theory, white noise and compressed sensing.\n", "###Robustness of compressed sensing code\n", "Our analyses showed that compressive sensing based coding scheme is enormously powerful. Accurate recovery of input information requires only a fraction of the glomeruli and is insensitive to noise. Additional glomeruli not only provides increased coding capacity, but also rapidly increases the accuracy in signal recovery. A striking feature of this compressed sensing code is that the encoders are non-specific with regard to tuning. It may seem paradoxical that an effective code is composed of neurons that are non-specifically tuned to disparate features of the stimuli, but it is this property, naturally required by compressed sensing, that renders the glomeruli the power to encode information. Brain neurons are limited in their number and their repertoire of response patterns. Highly specific tuning have low information content and reduces the coding capacity of the network. Whereas individual sensors employing compressed sensing strategy to encode information may not be specific, stimulus can be accurately identified when multiple neuron join force to represent a specific property of the stimulus. \n", "The properties of compressed sensors dictate that the certain levels of correlations exist among individual sensors. This corollary runs counter to classic coding theories, which suggests that individual neurons are decorrelated in their response properties such as to reduce redundancy. In the meantime, the nervous system still relies on decorrelation to reduce the impact of noise derived on signal corruption. In population coding, noise can be reduced by redundancy, where uncorrelated noise can be cancelled. Correlation, on the other hand, reduces the effectiveness of noise cancellation. A balance is struck in compressed sensing, however. Individual neurons may exhibit correlated response to the same feature, but because of the distributed representation of the features, there will be large proportions of neurons that are correlated. \n", "Compression increases the coding capacity of a limited number of neurons their response pattern and redundancy is built into the coding scheme when multiple sensors are responsible for encoding the same feature.\n", " \n", "###Reading the code\n", "We demonstrate that odor identities can be accurately recovered through compressed sensing algorithm. We do not know, however, how this algorithm is implemented in the brain. In the olfactory bulb of the awake mouse, mitral cell responses are sparsened, possibility implemented by the granule cell network through dendrodendritic inhibition. A theoretical frame that minimized the cost function naturally leads to the sparse response. Thus, the mitral cells is likely to act as decoders of signals in the olfactory glomeruli. \n", "Nevertheless, the number of mitral cells is still rather small. Interestingly, the number of neuron in the piriform cortex, which receives direct input from the olfactory bulb, is orders of magnitude more numerous than the mitral cells. A distinct feature of the piriform cortex is that each pyramidal cells integrate the input from spatially distributed mitral cells. Therefore, the piriform cortex might serve as a processing stage that \u201cunpack\u201d the compressed information from the bulb, to decode the signals and allow them to be represented sparsely by the cortical neurons. Individual pyramidal cells may serve as to detect the activation of specific combination of glomeruli. In addition, as the individual pyramidal cells also respond to multiple odors, they may themselves serves compressed sensors and encode information that can be decoded further.\n" ] }, { "cell_type": "code", "collapsed": false, "input": [ "using CompressedSensing\n", "using PyPlot" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stderr", "text": [ "INFO: Loading help data...\n" ] } ], "prompt_number": 1 }, { "cell_type": "code", "collapsed": false, "input": [ "\n", "nProperties = 50\n", "\n", "sparsityRange=[1:nProperties]\n", "measurementRange=[1:nProperties]\n", "\n", "\n", "nmonte=10\n", "output = zeros(length(sparsityRange),length(measurementRange))\n", "\n", "for i = 1:nmonte\n", " for nMeasurements = measurementRange\n", " for sparsity = sparsityRange\n", " m = randn(nMeasurements,nProperties)\n", "\n", " sample = zeros(nProperties)\n", " sample[rand(1:nProperties,sparsity)]=randn(sparsity)\n", " output[sparsity,nMeasurements]+=(cor(IRLS(m,m*sample,threshold=1e-5),sample).>.95)./nmonte\n", " end\n", " end\n", "end\n" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "1+1" ] }, { "cell_type": "code", "collapsed": false, "input": [ "pcolormesh(flipdim(flipdim(output,1)',1),label=\"Test\")\n", "colorbar();" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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lp59+2nh7narEvfvuu9qwYYOefvppeTyeVpW4DRs2aPLkyd4ETpLGjx+vhIQEvfLKK505JQAAQFAoLCxUWFiY5syZ4z3mdDo1a9Ys7dixQ1VVVT4/GxERIafTqfPOO6/F8W9/+9uKjIz0ax9+J3Fut1v33nuvZs+ercsvv7zV+1VVVTpy5IhGjx7d6r2kpCSVlZX5e0oAAADfQgP4akNZWZkSEhIUFRXV4nhSUpIkqby83OdW7733XjU2NuqnP/2pKioq9PHHH+u5557Txo0btXDhQr++tt/Fx+eee04HDhzQO++80+b71dXVkqTY2NhW78XGxurYsWM6ffq0evTo4e+pAQAAulx1dbXPPEeSDh065POzw4cP1zvvvKPU1FQ9//zzkqTQ0FD9z//8T4vKngm/krhPPvlEDz30kB566CHFxMS0GVNXVyepqaz4deHh4d4YkjgAAGCJc3xPXF1dXYd5ji8VFRW6+eabdfHFF+vJJ59UeHi4XnrpJf3iF79Qv379NGXKFOPt+fWVH3zwQfXu3Vv33nuvz5iIiAhJTY/eft2pU6daxLRti6Twrx274qsXAADoKoXupteZPvV0zV6s9vKHTa8zffpl27ERERGdznPuv/9+hYWFadu2bd574NLT03X99dfrnnvu0eTJkxUaatZl2DiJ279/v37zm9/o6aef1sGDB1ts+Msvv9THH3+s6Ohobymx+bLqmaqrqxUTE9NBFW6ipDjTbQEAuiXTSQxWr2c42WHzSaOwwZs+7DhIku42C0t8seNJDJK09LmOY+Z/vZ4iKT206XWm8kbpWh/JzjljQSXu9u81vc60+4g0akPr2NjY2DYvmTbnPnFxvvOYP//5z0pNTW31EENqaqruu+8+ffzxx7rkkkuM9mz8YENVVZUaGxs1d+5cXXLJJd5XaWmp9u3bp+9+97tatmyZ+vfvrz59+mjXrl2t1igtLdWIESNMTwkAANCxc9zsNzExUfv27VNtbW2L4zt37pSkdnOdhoYGud3uVseb++g2NDR0+HWbGSdxV1xxhTZu3KjXXnvN+9q4caMuv/xyXXzxxXrttdc0a9YsSdItt9yioqKiFhW7t99+W/v371dGRobx5gAAAIJNenq63G638vPzvcfq6+vlcrmUkpKi/v37S5JqampUUVHRIjFLTEzUH//4Rx07dsx7zO1265VXXlF0dLTi4+ON92FcfIyJiWnzZrsVK5pK0Glpad5jixYt0vr16zVu3DjNmzdPtbW1evLJJzVs2DDNnDnTeHMAAAAdaqcdyFmv24bk5GRlZGRo4cKFOnz4sOLj47V69WodOHBALpfLG5eTk6OCggJVVlZq4MCBkqQHHnhAN998s6688krNmTNH4eHhevnll7V79249+uijxvfDSRY8y+FwOFo1+x0wYIC2b9+urKws5eTkyOl0avLkyXrqqad4KhUAANheQUGBFi9erDVr1uj48eMaPny4ioqKNHbsWG9MWznSxIkT9cYbb+jRRx/V0qVL1dDQoMsuu0y//vWv/Z5oddZJ3NatW9s8PmTIEG3ZsuVslwcAAGjfOW4xIjW1UsvNzVVubq7PGJfL1aIy1+zGG2/UjTfeeNbb69TYLQAAAHStQOStAAAA504XVOKCAZU4AAAAG6ISBwAA7O0cP50aLEjiAAA2ZDg5ITXaLO6uzu+kLYPTyozi0lVoFFf27GCjuPgIs4kNJlacMovzPeodgUYSBwAA7I174gAAAGAXVOIAAIC9UYkDAACAXVCJAwAA9tZNK3EkcQAAwN66aYsRLqcCAADYEJU4AABgb1xOBQDgG2bzUsM4w+bBr5s1D96blmgUt+wus7iHY5YbxS01bNCLbwaSOAAAYG/dtBLHPXEAAAA2RCUOAADYG0+nAgAAwC6oxAEAAHvjnjgAAADYBZU4AABgb1TiAAAAYBdU4gAAgL2FKDBVsyAvdZHEAQDOguGkA60I6C58en2JWdzzZmGePziM4hx3eczWizFbb+lYozB0MyRxAADA3sIUmIwmyLOkIN8eAABAB3iwAQAAAHZBJQ4AANgblTgAAADYBZU4AABgb920xUiQbw8AAABtoRIHAADsjRYjAADYxOvRZnGGTXx1l1mY43lrm/gGs/nhZnHljVL+l4HdC9pGEgcAAOyNp1MBAABgor6+XtnZ2YqLi1NkZKRSUlJUXFzc4eeuu+46hYSEtPn61re+5dceqMQBAAB764KnU2fMmKENGzZo/vz5GjRokFwulyZNmqStW7dqzJgxPj/34IMP6vDhwy2OffbZZ/rZz36mG2+80a/tkcQBAAD4obS0VOvWrVNeXp6ysrIkSZmZmRo6dKgWLFigkpISn5/9wQ9+0OrYiy++KEmaPn26X/vgcioAALC35nvirH75qO4VFhYqLCxMc+bM8R5zOp2aNWuWduzYoaqqKr+2/9JLLykqKkpTpkzx63MkcQAAAH4oKytTQkKCoqKiWhxPSkqSJJWXlxuvdeTIEb311lv6j//4D0VERPi1Dy6nAgAAezvHfeKqq6sVGxvb6njzsUOHDhmfYt26dXK73X5fSm1newAAADZxjh9sqKurk9PpbHU8PDzc+76pl156SX379tWECRP83h5JHAAA6PZefkt6+WsdQj79rO3YiIgI1dfXtzp+6tQp7/smPvroI/3lL3/Rvffeq5AQ/+9wI4kDgKA33zBuhcXrGUg1nJxw1xKzuClLDeNOmsWZ2mwWttuTYhZ4t+F57zALW/Jns7ilYw3Pa2DFKbM48wuHAWRBs9/bb2p6nWl3hTRqRuvY2NjYNi+ZVldXS5Li4uKMzvnSSy9J8v+p1GY82AAAAOCHxMRE7du3T7W1tS2O79y5U5I0YsQIo3VeeuklXXrppUpOTu7UPkjiAACAvQWivUg7D0ukp6fL7XYrPz/fe6y+vl4ul0spKSnq37+/JKmmpkYVFRVqaGhotUZZWZkqKir0ox/96Ky+NgAAAAwlJycrIyNDCxcu1OHDhxUfH6/Vq1frwIEDcrlc3ricnBwVFBSosrJSAwcObLHG2rVrJXX+UqpEEgcAAOzOgnvifK7rQ0FBgRYvXqw1a9bo+PHjGj58uIqKijR27L9vTHQ4HHI4HK0+29jYqHXr1mnUqFEaNGhQp7dHEgcAAOAnp9Op3Nxc5ebm+oxxuVwtKnPNQkJC9M9//vOs90ASBwAA7O0c94kLFkG+PQAAALSFShwAALC3LrgnLhhQiQMAALAhKnEAEPRMJzF0wXktnsQw2JNmFLd3k+GkCEOD08qM4hLv3mvpefWiWdjS56w97TdOOz3dznrdIEYlDgAAwIaCPMcEAADoQDd9OpUkDgAA2BsPNgAAAMAuqMQBAAB7oxIHAAAAu6ASBwAA7I0WIwAAALCLIM8xAeCbbL5hnNXNfk3XM9jf86bnNP2uHxpF7U4bYhQ3ctMeo7i1mm4UV/bsYKM406bAJ1cZhaEDnhDJE4D71zxBXuoK8u0BAACgLVTiAACArblDJXcAMho3T6cCAADAalTiAACArTUGqBLXSCUOAAAAVqMSBwAAbM0d6lBDqCMA63okeSxf1ypU4gAAAGyIShwAALA1d2io3GHW16XcoY2SGixf1yokcQAAwNYaQ0PlDrU+iWsMdYgkDgC+EayesGDh5IRASI3uOGbzSbO1XjdYyw8bNdUozhNjeJ/U3WZhphMbmMSAc4EkDgAA2JpbIXLL+n4gbstXtBYPNgAAANgQlTgAAGBrboWqgUocAAAA7IBKHAAAsLVGhcodgJSm0fIVrUUlDgAAwIaoxAEAAFsL3NOpwV2LoxIHAABgQ1TiAHyDdVFz3tQlZnGbl1p73tcNz/u84WmNzmnWxHdx2gMWnlS6P2K5WWCx4YJ3mIUl3r3XKG7pKcPzwhJN98RZX4lrpBIHAAAAq/mVxH3wwQfKyMhQfHy8evbsqZiYGF199dVau3Ztq9i9e/dq4sSJ6tWrl2JiYnTnnXfq6NGjlm0cAABAkhq/uifO6ldjO2lSfX29srOzFRcXp8jISKWkpKi42LT0KxUXF+v666/X+eefr+joaI0ePVqvvPKKX9/br8upBw4c0GeffaYZM2YoLi5OX3zxhQoLC5WZmanKyko98EBTufzgwYO65pprdMEFF+ixxx5TbW2t8vLy9P7776u0tFQ9evTwa5MAAADBZMaMGdqwYYPmz5+vQYMGyeVyadKkSdq6davGjBnT7mddLpfuuusu3XDDDXrssccUGhqqiooKHTx40K89+JXE3XTTTbrppptaHLvnnns0atQo5efne5O45cuXq66uTmVlZRowYIAkKTk5WRMmTNCqVas0e/ZsvzYJAADgS4NCAjKxocFHJa60tFTr1q1TXl6esrKyJEmZmZkaOnSoFixYoJKSEp9rVlZW6p577tHcuXO1YoXp/bhtO+t74kJCQjRgwIAW1bUNGzZo8uTJ3gROksaPH6+EhAS/S4UAAADtaVSY3AF4NfqodRUWFiosLExz5szxHnM6nZo1a5Z27Nihqqoqn3t97rnn5PF49PDDD0uSPvvsM3k8nk59704lcV988YWOHj2qDz/8UCtWrNCbb76pBQsWSJKqqqp05MgRjR49utXnkpKSVFZW1qmNAgAABIOysjIlJCQoKiqqxfGkpCRJUnl5uc/PFhcX67LLLlNRUZEGDBig6Oho9e7dWw899JDfyVynWoxkZWUpPz+/aYGwMK1cudKbjVZXV0uSYmNjW30uNjZWx44d0+nTp7kvDgAAWKIxQM1+fT3YUF1d7TPPkaRDhw75XHP//v0KCwvTT37yE2VnZ2v48OHasGGDHnnkETU0NGj5csP2OepkEjd//nzdeuutOnTokNauXatf/OIXioiI0I9//GPV1dVJaiorfl14eLgkqa6ujiQOAADYUl1dXYd5ji/Nl0+feOIJ/ed//qckaerUqTp27Jj+67/+S4sWLWpV4fOlU0nc9773PX3ve9+TJN1xxx268cYb9X/+z//RtGnTFBERIanp0duvO3Wqqfthc0zbtkgK/9qxK756AQCArvL+V68zBUNfYyvGbm15+VNteflki2OffepuMzYiIqLTeU5ERITq6up0++23tzh+2223acuWLSovL9fYsWON9mzJxIZbbrlFb731lioqKrylxObLqmeqrq5WTExMB1W4iZLirNgWgLNiMu3g7J6s6jyrJzFYfV5DphMWpphNdtidNsQobuTze8zOe1fHIYPTzO5zXrbpUaM44/XWma3nkcMobqnZvzM1/+s1hrOMWxEMGZAf2iqpHJKU3wV7sdrE28/TxNvPa3Fs7+46TR9V2So2Nja2zUumzblPXJzvPCYuLk4ffvih+vXr1+J43759JUnHjx833rMlExuay4YhISHq37+/+vTpo127drWKKy0t1YgRI6w4JQAAgCTJrVA1BODlq7qXmJioffv2qba2tsXxnTt3SlK7uc7o0aPl8Xha9YRrTgr79Olj/L39SuKOHDnS6tjp06dVUFCgmJgYXX755ZKaKnNFRUUtNvj2229r//79ysjI8OeUAAAAQSU9PV1ut9v7kKfUdBuZy+VSSkqK+vfvL0mqqalRRUWFGhoavHHTpk2TJP32t7/1HmtsbJTL5VJMTIxGjRplvA+/LqfOmTNHtbW1uuaaaxQXF6eamhqtXbtW+/btk8vlUmhoU8a6aNEirV+/XuPGjdO8efNUW1urJ598UsOGDdPMmTP9OSUAAEC7GhUqtzV3iLVaty3JycnKyMjQwoULdfjwYcXHx2v16tU6cOCAXC6XNy4nJ0cFBQWqrKzUwIEDJUlTpkzR+PHj9dhjj+no0aMaNmyYXnvtNZWUlCg/P9+vBz/9+sa33Xabfvvb3+rZZ5/VJ598oujoaF155ZV65plnNH78eG/cgAEDtH37dmVlZSknJ0dOp1OTJ0/WU089xVOpAADA9goKCrR48WKtWbNGx48f1/Dhw1VUVNTioQSHwyGHo/V9ma+99poefPBBrVu3TqtWrdJll12mtWvXtnpsmWJ6AAAgAElEQVTYoSN+JXHTpk3zlgE7MmTIEG3ZssWvzQAAAPjL3c79a2e7ri9Op1O5ubnKzc31GeNyuVpU5pr17NlTK1as6PqxWwAAADj3rL+ADAAAcA6d64kNwSK4dwcAAIA2UYkDAAC2ZsXEBl/rBjOSOOAbw+JpAl02jcFCqYYTETaf7DhGkvHfZLPpbxFtFmb4PUamGX5fg0kMktn0hL2bEo3WWpz2gFHcw3cbDv++wyxML5qFmU5YMGW3SQx219zsNxDrBrPgTjEBAADQJipxAADA1s51s99gQSUOAADAhqjEAQAAW+uuDzYE9+4AAADQJipxAADA1hoDNHaLe+IAAABgOSpxAADA1twKCVCfuOCudZHEAd8YVjfntbJ5sNV7M226a7qe4Xd93bCZ7vOm5+0ihvtLTyvsMKYwzWytqdpoFPfQs4uM4h4uMWwKbMi0Oa/VTYGBs0ESBwAAbM0doD5xTGwAAACA5ajEAQAAW+PpVAAAANgGlTgAAGBr3XViA0kcAACwNbdCA9RihMupAAAAsBiVOAAAYGuNAWoxwoMNAAAAsByVOKDLmE5EsHragSmT85p+B8O41GizuM1LDc9rZrDnQ6O4vWmJlp5Xm08ahe32pBjFjdy0xyjO8weHUZxKOg6ZOsZsEoOp+yPMJjGY/eWsn8Rgut43genfpLxRyv8ysHvpSHd9sCG4dwcAAIA2UYkDAAC2RrNfAAAA2AaVOAAAYGuNAbonrjHIa13BvTsAAAC0iUocAACwtYYATWwIxJpWohIHAABgQ1TiAACArXXXiQ0kcUCXsbqJbzA3DzY852bD5V5fYhY3xawl7N40wybDd5mFLU57wChuqswa5U7XWrMTGyp7drBRXOLdezuOUccxfplhFrb0ObO4JT+zdr3uxLSx8aHAbgPtIIkDAAC2xsQGAAAAG2pu9mv1q73LqfX19crOzlZcXJwiIyOVkpKi4uLiDve6atUqhYSEtPk6fPiwX9+bShwAAICfZsyYoQ0bNmj+/PkaNGiQXC6XJk2apK1bt2rMmDEdfn7ZsmX67ne/2+LYeeed59ceSOIAAICtuRUSkHYgvi6nlpaWat26dcrLy1NWVpYkKTMzU0OHDtWCBQtUUlLS4do33XSTRo4ceVb743IqAACAHwoLCxUWFqY5c+Z4jzmdTs2aNUs7duxQVVVVh2t4PB7V1tbK7XZ3eh8kcQAAwNbcX7UYsf7VdnWvrKxMCQkJioqKanE8KSlJklReXt7hnseNG6fzzjtPPXv21JQpU/T3v//d7+/N5VQAAAA/VFdXKzY2ttXx5mOHDvluvNKzZ0/NnDlT48aNU3R0tP7617/ql7/8pa6++mrt3r1bAwYMMN4HSRwAALC15qdTA7FuW+rq6uR0OlsdDw8P977vS0ZGhjIyMrz/PS0tTTfeeKOuueYaPfroo3r22WeN90cSBwAAur3/9/IH+n8v72lxrP7TtjseR0REqL6+vtXxU6dOed/3x5gxY3TllVcatSg5E0kcfAjm7v+mTL+DqWD+rpJSDacOWDkV4XnDtWQ4YcFwIoLpJAbTv8nuTUOM4kY6/mJ2Xo9ZmOkkhj0lZk+wPZS2yCguPsJwyoLJv09eNFvq5CqzONMpAaaYxNA9NFrQ7Hfw7cM0+PZhLY7V7K7WC6Na/4MuNja2zUum1dXVkqS4uDi/zz9gwADt27fPr8/wYAMAAIAfEhMTtW/fPtXW1rY4vnPnTknSiBEj/F7zo48+Up8+ffz6DEkcAACwteaxW9a/2k6T0tPT5Xa7lZ+f7z1WX18vl8ullJQU9e/fX5JUU1OjiooKNTQ0eOOOHDnSar033nhDu3fv1sSJE/363lxOBQAA8ENycrIyMjK0cOFCHT58WPHx8Vq9erUOHDggl8vljcvJyVFBQYEqKys1cOBASdLVV1+tkSNHatSoUTrvvPO0e/duvfDCCxo4cKAWLTK7JaIZSRwAALA1t0IDNLHB95oFBQVavHix1qxZo+PHj2v48OEqKirS2LFjvTEOh0MOh6PF52677Tb9/ve/1x//+Ed98cUXiouL009/+lMtWbLE78upJHEAAAB+cjqdys3NVW5urs8Yl8vVojInNc1MXbZsmSV7IIkDAAC21vjVxIZArBvMSOIAAICtuS1oMeJr3WAW3LsDAABAm6jEAQAAWzvXY7eCBUkcfLBwOkGqYbf+zYZd+I33ZhpnOtnB6gkQFjOddrDZ8HuYTGPYvNRoqcGeNKO4vWmJRnGmBm8qM4oznZxgqlDplq5XNmawUdxUbTSKizac7HPyBwZrzTBby2rzw83irJ4AAQQTkjgAAGBrboUEqMVIcN91Fty7AwAAQJuoxAEAAFtzKywgLUYCsaaVqMQBAADYUHCnmAAAAB1oDFCfuMYgr3UF9+4AAADQJipxAADA1pjYAAAAANugEofAM2wIa8y0ebAp4/0ZNslNje6a804xbZZsyMLfbe8mwya+pg2L7zL7G5s2D1686QGjuEKPWRPfdBUaxT1cstwoTi+ahZk6ucoszqSRr+laVjfdpYkvztRdJzZQiQMAALAhKnEAAMDWuuvEBpI4AABga26FBqjZL5dTAQAAYDEqcQAAwNZ4sAEAAAC2QSUOAADYGs1+AQAAYBtU4gAAgK25FRqgFiPBfU8cSRyCiOFkAssnLHTRBAjLz2s4seF1w4kSUwz+fqZrGRqcVmYUZzoRYZkeNYp7+G6zyQlTn91oFGc1KycsSObTDuYbnNd4rXCzOCYxAOZI4gAAgK01BqhPHE+nAgAAwHJU4gAAgK3xdCoAAABsg0ocAACwNSY2AAAAwDaoxAEAAFtzKyRAfeKCu9ZFEgcAAGzNHaAWIzT7tZppg1TTxqda0emt2JNhA1xjBn8/49/MtImvxSz/34rh3/guw+WMGTbefd4sbLcnpcOYjZpqtNbDJWbNdFViFvbQmEVGcabNg/UHs7D4iL1GcR/WDTZb8EWzMFOmTYFNWdl4lya+9mPaoLm8Ucr/MrB7Qdvsl8QBAACcgQcbOrBr1y794he/0OWXX66oqChdfPHFmjZtmvbv398qdu/evZo4caJ69eqlmJgY3XnnnTp69KilGwcAAOgq9fX1ys7OVlxcnCIjI5WSkqLi4mK/15k9e7ZCQkKUmprq92eNK3FPPPGEduzYoYyMDA0bNkzV1dV65plnNHLkSP3lL3/R5ZdfLkk6ePCgrrnmGl1wwQV67LHHVFtbq7y8PL3//vsqLS1Vjx49/N4kAACAL40Bavbb2E6ta8aMGdqwYYPmz5+vQYMGyeVyadKkSdq6davGjBljtP5f//pXrV69WuHh4XI4HH7vzziJu++++5SUlKSwsH9/ZNq0abriiiv0+OOPa82aNZKk5cuXq66uTmVlZRowYIAkKTk5WRMmTNCqVas0e/ZsvzcJAAAQLEpLS7Vu3Trl5eUpKytLkpSZmamhQ4dqwYIFKinp+AZfj8ejuXPn6sc//nGnKniSH5dTr7rqqhYJnCRdeumlGjJkiCoqKrzHNmzYoMmTJ3sTOEkaP368EhIS9Morr3RqkwAAAL64v7onLhCvthQWFiosLExz5szxHnM6nZo1a5Z27NihqqqqDve8Zs0a7dmzR4888og8Hk+nvvdZNUDxeDz617/+pd69e0uSqqqqdOTIEY0ePbpVbFJSksrKDJ8UAwAACFJlZWVKSEhQVFRUi+NJSUmSpPLy8nY/X1tbq+zsbC1atEj9+vXr9D7O6unUtWvX6tChQ3rkkUckSdXV1ZKk2NjYVrGxsbE6duyYTp8+zX1xAADAMue62W91dbXPXEeSDh061O66Dz/8sHr27Kn588+u7Venk7iKigrdc889uvrqq/XjH/9YklRXVyepqaT4deHh4d4YkjgAAGBXdXV1HeY6vuzbt08rV67U7373u7POhzqVxNXU1Ojmm2/WBRdcoMLCQu8TFREREZKaHrv9ulOnTrWI8W2LpK93GLziqxcAAOgqhe6m15k+7dztXJayYmLDpy9v0cmXt7Rc99PP2oyNiIjodK4zb948jRkzRlOnmjVLb4/f3/jTTz/VTTfdpJMnT+pPf/qTvv3tb3vfay4jNl9WPVN1dbViYmIMss6JkuJ8v80kBh8snsSQatj9f7OV5zSc7GDK9H8rrxt+1ylWT7uw1uBN1t5zOl1rO4xJV6HRWkPG7DaK25uWaBTnyTZ7FD90rOGkiJ+ZhUXPMIsznexg+k8zIFikhza9zlTeKF37DZjYcN7tE3Xe7RNbHKvbvVeVo6a3io2NjW3zkmlz/hMX13Ye88477+jNN9/Uq6++qsrKSu/xhoYGffHFF/r444914YUXqlevXkZ79iuJO3XqlFJTU/X3v/9dxcXFuuyyy1q8379/f/Xp00e7du1q9dnS0lKNGDHCn9MBAAB06FxPbEhMTNS2bdtUW1vbIuHauXOnJPnMdw4cOCBJ+uEPf9jqvUOHDum73/2unn76ac2dO9dof8ZPp7rdbk2bNk07d+7U+vXrdeWVV7YZd8stt6ioqEgHDx70Hnv77be1f/9+ZWRkmJ4OAAAgKKWnp8vtdis/P997rL6+Xi6XSykpKerfv7+kptvPKioq1NDQIKmp5dprr73W4rVx40b16dNHSUlJeu211zR58mTjffjV7Hfz5s1KTU3V0aNH9eKLLSc333HHHZKkRYsWaf369Ro3bpzmzZun2tpaPfnkkxo2bJhmzpxpvDEAAAAT53piQ3JysjIyMrRw4UIdPnxY8fHxWr16tQ4cOCCXy+WNy8nJUUFBgSorKzVw4EBddNFFuuiii1qtN2/ePPXr109paWl+7c84ifvb3/4mh8OhzZs3a/PmljdCORwObxI3YMAAbd++XVlZWcrJyZHT6dTkyZP11FNP8VQqAACwXINCFBqAJK6hnQuWBQUFWrx4sdasWaPjx49r+PDhKioq0tixY70xDofDaJxWZ0ZuSX4kcVu3bjVedMiQIdqyZUvHgQAAADbkdDqVm5ur3NxcnzEul6tFZc6Xf/zjH53aw9k9jwsAANDFGhV21i1GfK0bzM5q7BYAAAC6RnCnmAAAAB041w82BIvg3h0AAADaZMNKnMWTGEynBHTVpAirpxhYrgumGNxlFjZ404dGcXs3mU0JMP1tB3vMHhE3nXZgtYdLzKYYmExZmKqN1p5zk9lkB5WYhaV5BhvFnTScsLDilNl5Tc3/+oTBIGOyP9O/iel3tfpvjM4z/S3aH/V+brgVopAAVOLcQV7rCu7dAQAAoE02rMQBAAD8W2NjqNyNAbgnLgBrWolKHAAAgA1RiQMAALbmdodIDQG4J84d3LWu4N4dAAAA2kQlDgAA2Jq7IVRqsD6lcQegumclKnEAAAA2RCUOAADYWqM7NCD3xDW6g7sSF4RJ3HRJIyxYx7Dp7ualhuuZNrU1jTPdn2GT4dejjcIGp5UZxe1NM2yAm2pwXsPmvJpi2lDZ7Lvuldl3WJz2gFFcoSfdKM7UsrRHjeJ2bxpiFDdy0x6juIdjzBrvmjQjTiwxa5Jr6i8RI43iTP+XkjjDbH+m65n6pjS2NdnfN+W7wt7c7hB5ApLEBfcFy+DeHQAAANoUhJU4AAAAc+6GUDWetr4SF4jqnpWoxAEAANgQlTgAAGBrnsZQedwBSGkYuwUAAACrUYkDAAD21hCYsVtqCO5aV3DvDgAAAG2iEgcAAOwtQM1+FeTNfqnEAQAA2JANK3GGkw5Sl5jFmU4TMGU8dcBwsoPJRARJet4szHSKgenfxWTawbJNZpMJFnueMIqbqo1GcRs11SjO1J67zaYJmHpo0yKjONNJDJ4/OMxOfIdZ2MN3m012+CaweppAd5pO0J2+a3djOo2jvFHK/zKwe+mQ2yE1GP4z0N91gxiVOAAAABuyYSUOAADgDG5JDQFaN4hRiQMAALAhKnEAAMDeumkljiQOAADYW4MCk8QFYk0LcTkVAADAhqjEAQAAe2uQdDpA6wYxKnEAAAA2FISVuLWStnb1JlozbKZr7HVrm/gaNy2estQobLAnzShumSO7w5jFno4bAkvmTXy7ykPPmjXnNW5uLLO/iyfGrNnkyVVGYZJpXBeILjaLO/kDw7hVZnGmTU2tFj3DLG7pcwHdBtAm00bOhwK7DTONCsxDCI0BWNNCVOIAAABsiCQOAADYW3OLEatf7VT36uvrlZ2drbi4OEVGRiolJUXFxR1fTnj33XeVlpamgQMHKiIiQv369dP48eP1hz/8we+vTRIHAADgpxkzZmjFihXKzMzUypUrFRoaqkmTJqmkpKTdz+3fv19hYWG6++679atf/UoLFizQsWPHdPPNN2vt2rV+7SEI74kDAADwwznuE1daWqp169YpLy9PWVlZkqTMzEwNHTpUCxYsaDeRmzVrlmbNmtXi2M9//nNdcsklys/P1/Tp0423RyUOAADAD4WFhQoLC9OcOXO8x5xOp2bNmqUdO3aoqqrKr/UiIiLUu3dv9ejRw6/PUYkDAAD2do7HbpWVlSkhIUFRUVEtjiclJUmSysvL1b9//3aXPnnypL788ksdPXpUBQUF2rdvn3Jzc/3aHkkcAACAH6qrqxUbG9vqePOxQ4c6brxy66236o9//KMkqWfPnnrllVd00003+bUPLqcCAAB7O8dPp9bV1cnpdLY6Hh4e7n2/I0888YTeeust/fa3v9WQIUN022236a233jL8wk2oxAEAAGx7Wdr+cstjX3zaZmhERITq6+tbHT916pT3/Y4MHz7c+5/vuOMOjRw5Uvfcc4/27dtnvOUgTOKmSxrh++1Uw0kHm80mE0hLzMIMJyIM3vShUdzeTYlGcYs3mXX1L1S6UdxazzqjuOkym9jg+fN5HcYM0W6jtYy/g8yf3OkKi9PMfrOH715u6XlNu/9byXQigmnn9/mGkxhM17Oa6WQH4++7qtNbAXAmK+6JG3t70+tMH+6Wska1Co2NjW3zkml1dbUkKS4uzq9T9+jRQ6mpqXr88cd14sQJnX/++Uaf43IqAACwt3N8OTUxMVH79u1TbW1ti+M7d+6UJI0Y0U4xyofmS7AhIeapGUkcAACAH9LT0+V2u5Wfn+89Vl9fL5fLpZSUFO+TqTU1NaqoqFBDw7/LhIcPH2613okTJ7RhwwZdccUVio42vOKooLycCgAA4IcGSacDtG4bkpOTlZGRoYULF+rw4cOKj4/X6tWrdeDAAblcLm9cTk6OCgoKVFlZqYEDB0qSbrrpJl100UVKTk5W3759vZ85cuSIVq1a5df2SOIAAAD8VFBQoMWLF2vNmjU6fvy4hg8frqKiIo0dO9Yb43A45HA4Wnxu1qxZ+t3vfqenn35aJ06cUExMjMaOHauFCxdq5MiRfu2BJA4AANibW+0Oqz+rdX1wOp3Kzc1tt0Gvy+VqUZmTmkZs/fznP7dke9wTBwAAYENU4gAAgL2d47FbwYJKHAAAgA1RiQMAAPbWTStxtkvidm8aYhQ3ctMeswWfNwsbnFZmFLfXEW+24OtmYfdHmHX1L6wzm3awUVON4kynIpSNGWwUZ+IvEWZP5eTVLTKKC3WY/e2W/NkozNhJw6kDKrZ2PcsnNtxhELPKbCnTSQfBzupJEV01eQLAN4PtkjgAAIAWumkljnviAAAAbIhKHAAAsLfmWaeBWDeIUYkDAACwISpxAADA3rrpPXEkcQAAwN66aRLH5VQAAAAbohIHAADsrUHS6QCtG8SCL4n7ZZQUH33uzneXWVi6Co3ipno2GsVN11qjuJS63UZxe0rMGuWaMm3im1iyt8OYPTLcm2Hz24fvNmviu9RsOfPvenfH31Uyb+C65EWzONMmvidXWbve0rFmcQCArhF8SRwAAIA/3ArM/WvcEwcAAACrUYkDAAD2xtOpAAAAsAsqcQAAwN6oxAEAAMAuqMQBAAB7oxIHAAAAu6ASBwAA7I2JDcFh7bW3aPDIiLNeZ3HaAxbs5t+mymwSg6k9d5tNMSh71myagAy7/5uuZzpRYs+LHX+Ppc8ZLaX54WZxphMHlvzMLE6GkxhM/3ZLZLae6YQF0wkQxgx/DwDdm+k/k8sbpfwvA7sXtC3okjgAAAC/dNOJDSRxAADA3niwAQAAAHZBJQ4AANgblTgAAADYBZU4AABgb920xQiVOAAAABuiEgcAAOyNFiPBYauuV4W+7fN906a7pnGJJYaNXscYNt21WHyE2f40wyws0bCx7R6ZNSM2aVhr2jDSlNVNck33l2jYxNe0uXGwM/m7WP03NmV5A2QAsKGgS+IAAAD8wtOpHfv888+1ZMkSTZw4URdeeKFCQkK0evXqNmP37t2riRMnqlevXoqJidGdd96po0ePWrJpAACArlRfX6/s7GzFxcUpMjJSKSkpKi4u7vBzb7/9tn7yk58oISFBPXv2VHx8vGbPnq2amhq/9+BXJe7IkSNatmyZLr74Yo0YMULbtm2Tw+FoFXfw4EFdc801uuCCC/TYY4+ptrZWeXl5ev/991VaWqoePXr4vVEAAIA2dUElbsaMGdqwYYPmz5+vQYMGyeVyadKkSdq6davGjBnj83PZ2dk6ceKEMjIyNGjQIH344Yd65plnVFRUpPLycvXr1894e34lcXFxcaqpqVHfvn313nvvKSkpqc245cuXq66uTmVlZRowYIAkKTk5WRMmTNCqVas0e/Zsf04LAAAQNEpLS7Vu3Trl5eUpKytLkpSZmamhQ4dqwYIFKikp8fnZp59+WmPHjm1xbOLEibr22mv1zDPPaNmyZcb78Oty6re+9S317dtXkuTxeHzGbdiwQZMnT/YmcJI0fvx4JSQk6JVXXvHnlAAAAO1r7hNn9ctHda+wsFBhYWGaM2eO95jT6dSsWbO0Y8cOVVVV+dzq1xM4Sfr+97+vCy+8UBUVFX586QD0iauqqtKRI0c0evToVu8lJSWprKzM6lMCAACcM2VlZUpISFBUVFSL481XKMvLy/1a77PPPlNtba169+7t1+csfzq1urpakhQbG9vqvdjYWB07dkynT5/mvjgAAGCNRgXmSdLGtg9XV1f7zHMk6dChQ36d5umnn9bp06c1bdo0vz5neRJXV1cnqams+HXh4eHeGJI4AABgiQYF5sEGH2vW1dV1mOeYevfdd7V06VJNmzZN1113nV/bszyJi4iIkNT06O3XnTp1qkVMW/44/y05z2vZGXTo7UM09PbLLdwlAADwV6G76XWmT33fIm8vVS83vc50+tM2QyMiIjqd55ypoqJCU6dO1bBhw/T888/7t18FIIlrLiU2X1Y9U3V1tWJiYtqtwt2wYoJiR/qe2GA6YUEvmoU99Owio7iH715uFGfarT/xz2Zx0WZhxlMMomcYLmjIpHP+kp+ZrWX1JAZTxusF+SSGYJ6KYPVvZvpdmewAdJ6v//9c+bX/fkjS/w30ZjpiRYuRfrc3vc706W7p/45qFRobG9vmJdPm3CcuLq7D0/3zn//UDTfcoAsuuEBvvPGGevbs6feWLX+woX///urTp4927drV6r3S0lKNGDHC6lMCAACcM4mJidq3b59qa2tbHN+5c6ckdZjrfPLJJ7rhhht0+vRpvfnmm371hjuT5UmcJN1yyy0qKirSwYMHvcfefvtt7d+/XxkZGYE4JQAA6K7OcYuR9PR0ud1u5efne4/V19fL5XIpJSVF/fv3lyTV1NSooqJCDQ3/Xujzzz/XpEmTVF1drTfeeEPx8fGd/tp+X0595plndOLECW8ZcdOmTTpw4IAkae7cuYqOjtaiRYu0fv16jRs3TvPmzVNtba2efPJJDRs2TDNnzuz0ZgEAALpacnKyMjIytHDhQh0+fFjx8fFavXq1Dhw4IJfL5Y3LyclRQUGBKisrNXDgQEnS9OnTtWvXLv3kJz/RBx98oA8++MAb36tXL02ZMsV4H34ncU899ZQ+/vhjSZLD4dDGjRv16quvyuFw6M4771R0dLQGDBig7du3KysrSzk5OXI6nZo8ebKeeuopnkoFAADWOsctRiSpoKBAixcv1po1a3T8+HENHz5cRUVFLZr5OhyOVuNJ//a3v8nhcOiFF17QCy+80OK973znO4FN4v7xj38YxQ0ZMkRbtmzxd3kAAICg53Q6lZubq9zcXJ8xLperRWVOMs+jTFj+dCoAAMA5ZcXTqb7WDWIBebABAAAAgUUlDgAA2Ns5ntgQLKjEAQAA2FDQVeLuLn9BI9sZOXbyB2brmE4mMJ3EoDvMwuavMoszZToBwnQqgtXnNemcb+VaUvBPgOiqaQJdMZ2gO31XAEGsuU9cINYNYlTiAAAAbCjoKnEAAAB+6YI+ccGAJA4AANgbLUYAAABgF1TiAACAvdFiBAAAAHZBJQ4AANgbLUYAAABgF8FXiXtD0q6zX8bqBrMrDNczNtYszHR/Vn9fUyZNV02b8xo3NjYLM24Ia3XD2u7UiLY7fVeguzH9Z2N5o5T/ZWD30qFu2mKEShwAAIANBV8lDgAAwB/0iQMAAIBdUIkDAAD2RiUOAAAAdkElDgAA2Fug+rnRJw4AAABWoxIHAADszS3JEaB1gxhJHAAAsLdAJVskcf75bLN0shtc5LV6ioGpruiwb/V3OLnK2vWYOgAArZn+s/FQYLeBdgRdEgcAAOAXtyRPANZl7BYAAACsRiUOAADYW4MC82BDIKp7FqISBwAAYENU4gAAgL0FqsUIlTgAAABYjUocAACwvyCvmgUClTgAAAAbsl0lzrT54PzwwO7jbFndANf0+34TGtt21XfoTn9jAEDwoxIHAABgQyRxAAAAfqqvr1d2drbi4uIUGRmplJQUFRcXd/i5mpoa5eTkaNy4cerVq5dCQkK0ffv2Tu2BJA4AAMBPM2bM0IoVK5SZmamVK1cqNDRUkyZNUklJSbufq6ioUG5urqqrqzVs2DBJksPRuf4otrsnDgAAoCuVlpZq3bp1yvAABmkAAA8ESURBVMvLU1ZWliQpMzNTQ4cO1YIFC9pN5EaPHq1jx47p/PPPV2FhoXbs2NHpfVCJAwAANtcg6XQAXg1tnq2wsFBhYWGaM2eO95jT6dSsWbO0Y8cOVVVV+dxpVFSUzj///LP4rv9GEgcAAOCHsrIyJSQkKCoqqsXxpKQkSVJ5efk52QeXUwEAgM01yFfV7OzXba26ulqxsbGtjjcfO3ToUAD20hqVOAAAAD/U1dXJ6XS2Oh4eHu59/1ygEgcAAGyu+Z64s1H41etMn7YZGRERofr6+lbHT5065X3/XAi6JG7tl9LWdt6na37butv37Qr8jQHgmyz9q9eZyiVd2yoyNja2zUum1dXVkqS4uDjrt9cGLqcCAACbc+vf98VZ+XK3ebbExETt27dPtbW1LY7v3LlTkjRixAgLv5tvJHEAAAB+SE9Pl9vtVn5+vvdYfX29XC6XUlJS1L9/f0lN0xkqKirU0BCIhy6C8HIqAACAf6y4J87Xuq0lJycrIyNDCxcu1OHDhxUfH6/Vq1frwIEDcrlc3ricnBwVFBSosrJSAwcO9B5/5JFHJEkffPCBJKmgoEDvvvuuJOnBBx803h1JHAAAgJ8KCgq0ePFirVmzRsePH9fw4cNVVFSksWPHemMcDkebI7UeeughORwOeTweORwOvfDCC954kjgAANCNnNtKnNQ0oSE3N1e5ubk+Y1wuV4vKXLPGxkZLdsc9cQAAADZEJQ4AANhc89OpgVg3eFGJAwAAsCEqcQAAwObO/T1xwcB2SRxd8wEAQEtcTgUAAIBN2K4SBwAA0FL3vJxKJQ4AAMCGqMQBAACbax5YH4h1gxeVOAAAABuiEgcAAGyOe+IAAABgE1TiAACAzdEnDgAAADZBJQ4AANgc98QBAADAJqjEAQAAm+OeOAAAANgElTgAAGBz3fOeOJI4AABgc1xOBQAAgE1QiQMAADbXPS+nUokDAACwISpxAADA5qjEAQAAwCaoxAEAAJtrUGCqZlTiAAAAYDEqcQAAwOa4Jw4AAAA2QSUOAADYHBMbAAAAYBNU4gAAgM1xTxwAAABsgkocAACwOe6Js1R9fb2ys7MVFxenyMhIpaSkqLi4OFCnAwAA3Vbz5VSrX74Tw7PJc06cOKE5c+aoT58+ioqK0vXXX6+ysjK/v3XAkrgZM2ZoxYoVyszM1MqVKxUaGqpJkyappKQkUKeEhd7v6g3Ai98iePBbBBd+D3SlzuY5jY2Nuvnmm/Xyyy9r7ty5ys3N1eHDh3Xdddfp73//u197CEgSV1paqnXr1unxxx/XE088obvuukvvvPOOLr74Yi1YsCAQp4TF+Ifj/9/e/cdEXf9xAH/eJ+g4DjW9y3GHgXaKLsIOF/Ijc8x+YCRtKXh/hKhjuVX+os3KlbpmP9ay5Zhrjv64HcXW8ghrrFZhsty8ABs2hzHIdhJwINivS7iTrnd/9L1Pnnf1BeTz+QA9H9tt+vq8P/u8j+eAF+/Pj5s6mMXUwSymFuZBfwufTp3sV+zTqTfS57jdbng8HrhcLuzbtw9PPvkkmpqacNNNN+HAgQPjeteKNHFutxtxcXHYtm2bXNPr9aioqIDH40Fvb68ShyUiIiJS3I30OW63G8nJyVi/fr1cM5vN2LhxIz788EOMjo79LltFmri2tjakp6cjKSkpop6dnQ0AOHv2rBKHJSIiov8kda+Ju5E+p62tDStWrIiqZ2dnY3h4GJ2dnWN6x4BCTZzP54PFYomqh2t9fX1KHJaIiIhIcTfS50xmj6TII0ZGRkag1+uj6gkJCfL2WPsAwJASE6JxCwBgqz01MIupg1lMLcxjagj/3o71u109/VDmESOxu5KJ9DlhgUBgwvteT5EmzmAwIBgMRtUDgYC8/XperxcA8IESE6IJqdZ6AiRjFlMHs5hamMfU4fV6cc8992h0dHW7h4n0OZOx7/UUaeIsFkvM5UCfzwcAsFqtUdsKCwvx7rvvYuHCheN6A0RERKSdkZEReL1eFBYWqn7sZcuW4euvv1blONeaSJ8zGfteT5EmLisrC01NTfD7/Zg1a5Zcb25uBgDY7faofcxmMx577DElpkNEREQK0moFLjExMeZNAkqbSJ8TZrfbcerUKQghoNPpIvY1Go1IT08f8zwUubGhpKQEoVAI1dV/L3QHg0E4nU7k5uYiJSVFicMSERERKW6sfU5/fz86Ojrw+++/R+w7MDCADz74+xTw0NAQjh07huLiYsTHx495HoqsxK1cuRKlpaXYu3cvLl26BJvNBpfLhe7ubjidTiUOSURERKSKsfY5zz33HGpqauD1epGamgrgryYuNzcXW7duxfnz52EymfDWW29BCIEXX3xxXPNQpIkDgJqaGuzbtw/vvPMOfvrpJ9x1111oaGjAqlWrlDokERERkSrG0ufodLqIU6YAIEkSPv74Y+zZswdVVVUYGRnBypUrUVNTgyVLloxrDjohhJiUd0NEREREqlHkmjgiIiIiUpbmTVwwGMSzzz4Lq9WKxMRE5ObmorGxUetpzWhXrlzBgQMHsHbtWsybNw+SJMHlcsUc++2332Lt2rWYNWsWTCYTysvLMTTERzJPltbWVmzfvh0ZGRlISkpCWloaHA4Hurq6osYyC2W1t7ejtLQUNpsNRqMRJpMJ+fn5qK2tjRrLLNT38ssvQ5IkZGZmRm1jHsppamqCJEkxXy0tLRFjmYP6FLsmbqy2bNmCuro6VFZWYsmSJXA6nSgqKsLJkyc1fGjgzDY4OIiDBw8iLS0NdrsdTU1NUefsAaCnpwerV6/G3Llz8eqrr8Lv9+PQoUM4d+4cWlpaxnUHDcX22muvwePxoLS0FMuXL4fP58ORI0ewYsUKfPXVV8jIyADALNTQ3d2N3377DVu2bIHVasXw8DDcbjc2bdoEr9eL559/HgCz0EJPTw9eeeUVGI3GqJ9VzEMdu3btkj8XNMxms8n/Zg4aERpqbm4WOp1OvPHGG3ItEAiIxYsXi/z8fA1nNrMFg0ExMDAghBDizJkzQqfTCZf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gpl-2.0