├── .gitignore ├── requirements.txt ├── data └── atlantis.csv ├── .devcontainer ├── devcontainer.json └── icon.svg ├── README.md ├── LICENSE └── notebooks ├── population.ipynb └── matplotlib.ipynb /.gitignore: -------------------------------------------------------------------------------- 1 | notebooks/data 2 | notebooks/cifar_net.pth 3 | .ipynb_checkpoints/ 4 | -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | ipywidgets==8.1.2 2 | matplotlib==3.8.4 3 | pandas==2.2.2 4 | torch==2.6.0 5 | torchvision==0.21.0 6 | tqdm==4.66.4 7 | -------------------------------------------------------------------------------- /data/atlantis.csv: -------------------------------------------------------------------------------- 1 | year,population 2 | 2000,12400 3 | 2001,12800 4 | 2002,13800 5 | 2003,13600 6 | 2004,14200 7 | 2005,15600 8 | 2006,17600 9 | 2007,19200 10 | 2008,20300 11 | 2009,20800 12 | 2010,21200 13 | 2011,22400 14 | 2012,23400 15 | 2013,24500 16 | 2014,25800 17 | 2015,26100 18 | 2016,28300 19 | 2017,29600 20 | 2018,32100 21 | 2019,32500 22 | 2020,33200 23 | 2021,33800 24 | -------------------------------------------------------------------------------- /.devcontainer/devcontainer.json: -------------------------------------------------------------------------------- 1 | { 2 | "image": "mcr.microsoft.com/devcontainers/universal:2", 3 | "hostRequirements": { 4 | "cpus": 2 5 | }, 6 | "waitFor": "onCreateCommand", 7 | "updateContentCommand": "python3 -m pip install -r requirements.txt", 8 | "postCreateCommand": "", 9 | "customizations": { 10 | "codespaces": { 11 | "openFiles": [] 12 | }, 13 | "vscode": { 14 | "extensions": [ 15 | "ms-toolsai.jupyter", 16 | "ms-python.python" 17 | ] 18 | } 19 | } 20 | } 21 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # GitHub Codespaces ♥️ Jupyter Notebooks 2 | 3 | Welcome to your shiny new codespace! We've got everything fired up and running for you to explore Python and Jupyter notebooks. 4 | 5 | You've got a blank canvas to work on from a git perspective as well. There's a single initial commit with what you're seeing right now - where you go from here is up to you! 6 | 7 | Everything you do here is contained within this one codespace. There is no repository on GitHub yet. If and when you’re ready you can click "Publish Branch" and we’ll create your repository and push up your project. If you were just exploring then and have no further need for this code then you can simply delete your codespace and it's gone forever. 8 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2022 GitHub 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /.devcontainer/icon.svg: -------------------------------------------------------------------------------- 1 | 2 | Group.svg 3 | Created using Figma 0.90 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 | -------------------------------------------------------------------------------- /notebooks/population.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "# Population Data from CSV\n", 8 | "\n", 9 | "This notebooks reads sample population data from `data/atlantis.csv` and plots it using Matplotlib. Edit `data/atlantis.csv` and re-run this cell to see how the plots change!" 10 | ] 11 | }, 12 | { 13 | "cell_type": "code", 14 | "execution_count": 1, 15 | "metadata": {}, 16 | "outputs": [ 17 | { 18 | "data": { 19 | "image/png": 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", 20 | "text/plain": [ 21 | "
" 22 | ] 23 | }, 24 | "metadata": {}, 25 | "output_type": "display_data" 26 | } 27 | ], 28 | "source": [ 29 | "import matplotlib.pyplot as plt\n", 30 | "import pandas\n", 31 | "\n", 32 | "df = pandas.read_csv('../data/atlantis.csv')\n", 33 | "x = df['year']\n", 34 | "y = df['population']\n", 35 | "\n", 36 | "plt.plot(x,y)\n", 37 | "plt.title(\"Population of Atlantis\")\n", 38 | "plt.xlabel('Year')\n", 39 | "plt.ylabel('Population')\n", 40 | "plt.show()" 41 | ] 42 | } 43 | ], 44 | "metadata": { 45 | "kernelspec": { 46 | "display_name": "Python 3.10.4 64-bit", 47 | "language": "python", 48 | "name": "python3" 49 | }, 50 | "language_info": { 51 | "codemirror_mode": { 52 | "name": "ipython", 53 | "version": 3 54 | }, 55 | "file_extension": ".py", 56 | "mimetype": "text/x-python", 57 | "name": "python", 58 | "nbconvert_exporter": "python", 59 | "pygments_lexer": "ipython3", 60 | "version": "3.10.4" 61 | }, 62 | "orig_nbformat": 4, 63 | "vscode": { 64 | "interpreter": { 65 | "hash": "3ad933181bd8a04b432d3370b9dc3b0662ad032c4dfaa4e4f1596c548f763858" 66 | } 67 | } 68 | }, 69 | "nbformat": 4, 70 | "nbformat_minor": 2 71 | } 72 | -------------------------------------------------------------------------------- /notebooks/matplotlib.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "# Pyplot tutorial\n", 8 | "\n", 9 | "An introduction to the pyplot interface." 10 | ] 11 | }, 12 | { 13 | "cell_type": "markdown", 14 | "metadata": {}, 15 | "source": [ 16 | "## Intro to pyplot\n", 17 | "\n", 18 | "`matplotlib.pyplot` is a collection of functions that make matplotlib work like MATLAB. Each `pyplot` function makes some change to a figure: e.g., creates a figure, creates a plotting area in a figure, plots some lines in a plotting area, decorates the plot with labels, etc.\n", 19 | "\n", 20 | "In `matplotlib.pyplot` various states are preserved across function calls, so that it keeps track of things like the current figure and plotting area, and the plotting functions are directed to the current axes (please note that \"axes\" here and in most places in the documentation refers to the axes part of a figure and not the strict mathematical term for more than one axis).\n", 21 | "\n", 22 | "> **Note**\n", 23 | ">\n", 24 | ">the pyplot API is generally less-flexible than the object-oriented API. Most of the function calls you see here can also be called as methods from an Axes object. We recommend browsing the tutorials and examples to see how this works.\n", 25 | ">\n", 26 | "\n", 27 | "Generating visualizations with pyplot is very quick:" 28 | ] 29 | }, 30 | { 31 | "cell_type": "code", 32 | "execution_count": 1, 33 | "metadata": {}, 34 | "outputs": [ 35 | { 36 | "data": { 37 | "image/png": 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", 38 | "text/plain": [ 39 | "
" 40 | ] 41 | }, 42 | "metadata": {}, 43 | "output_type": "display_data" 44 | } 45 | ], 46 | "source": [ 47 | "import matplotlib.pyplot as plt\n", 48 | "plt.plot([1, 2, 3, 4, 5])\n", 49 | "plt.ylabel('Numbers')\n", 50 | "plt.show()" 51 | ] 52 | }, 53 | { 54 | "cell_type": "markdown", 55 | "metadata": {}, 56 | "source": [ 57 | "You may be wondering why the x-axis ranges from 0-3 and the y-axis from 1-4. If you provide a single list or array to plot, matplotlib assumes it is a sequence of y values, and automatically generates the x values for you. Since python ranges start with 0, the default x vector has the same length as y but starts with 0. Hence the x data are `[0, 1, 2, 3]`.\n", 58 | "\n", 59 | "`plot` is a versatile function, and will take an arbitrary number of arguments. For example, to plot x versus y, you can write:" 60 | ] 61 | }, 62 | { 63 | "cell_type": "code", 64 | "execution_count": 2, 65 | "metadata": {}, 66 | "outputs": [ 67 | { 68 | "data": { 69 | "image/png": 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", 70 | "text/plain": [ 71 | "
" 72 | ] 73 | }, 74 | "metadata": {}, 75 | "output_type": "display_data" 76 | } 77 | ], 78 | "source": [ 79 | "plt.plot([1, 2, 3, 4], [1, 4, 9, 16])\n", 80 | "plt.show()" 81 | ] 82 | }, 83 | { 84 | "cell_type": "markdown", 85 | "metadata": {}, 86 | "source": [ 87 | "## Formatting the style of your plot\n", 88 | "\n", 89 | "For every x, y pair of arguments, there is an optional third argument which is the format string that indicates the color and line type of the plot. The letters and symbols of the format string are from MATLAB, and you concatenate a color string with a line style string. The default format string is 'b-', which is a solid blue line. For example, to plot the above with red circles, you would issue" 90 | ] 91 | }, 92 | { 93 | "cell_type": "code", 94 | "execution_count": 3, 95 | "metadata": {}, 96 | "outputs": [ 97 | { 98 | "data": { 99 | "image/png": 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", 100 | "text/plain": [ 101 | "
" 102 | ] 103 | }, 104 | "metadata": {}, 105 | "output_type": "display_data" 106 | } 107 | ], 108 | "source": [ 109 | "plt.plot([1, 2, 3, 4], [1, 4, 9, 16], 'ro')\n", 110 | "plt.axis([0, 6, 0, 20])\n", 111 | "plt.show()" 112 | ] 113 | }, 114 | { 115 | "cell_type": "markdown", 116 | "metadata": {}, 117 | "source": [ 118 | "See the `plot` documentation for a complete list of line styles and format strings. The `axis` function in the example above takes a list of `[xmin, xmax, ymin, ymax]` and specifies the viewport of the axes.\n", 119 | "\n", 120 | "If matplotlib were limited to working with lists, it would be fairly useless for numeric processing. Generally, you will use numpy arrays. In fact, all sequences are converted to numpy arrays internally. The example below illustrates plotting several lines with different format styles in one function call using arrays." 121 | ] 122 | }, 123 | { 124 | "cell_type": "code", 125 | "execution_count": 4, 126 | "metadata": {}, 127 | "outputs": [ 128 | { 129 | "data": { 130 | "image/png": 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", 131 | "text/plain": [ 132 | "
" 133 | ] 134 | }, 135 | "metadata": {}, 136 | "output_type": "display_data" 137 | } 138 | ], 139 | "source": [ 140 | "import numpy as np\n", 141 | "\n", 142 | "# evenly sampled time at 200ms intervals\n", 143 | "t = np.arange(0., 5., 0.2)\n", 144 | "\n", 145 | "# red dashes, blue squares and green triangles\n", 146 | "plt.plot(t, t, 'r--', t, t**2, 'bs', t, t**3, 'g^')\n", 147 | "plt.show()" 148 | ] 149 | }, 150 | { 151 | "cell_type": "markdown", 152 | "metadata": {}, 153 | "source": [ 154 | "## Plotting with keyword strings\n", 155 | "\n", 156 | "There are some instances where you have data in a format that lets you access particular variables with strings. For example, with `numpy.recarray` or `pandas.DataFrame`.\n", 157 | "\n", 158 | "Matplotlib allows you provide such an object with the `data` keyword argument. If provided, then you may generate plots with the strings corresponding to these variables." 159 | ] 160 | }, 161 | { 162 | "cell_type": "code", 163 | "execution_count": 5, 164 | "metadata": {}, 165 | "outputs": [ 166 | { 167 | "data": { 168 | "image/png": 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", 169 | "text/plain": [ 170 | "
" 171 | ] 172 | }, 173 | "metadata": {}, 174 | "output_type": "display_data" 175 | } 176 | ], 177 | "source": [ 178 | "data = {'a': np.arange(50),\n", 179 | " 'c': np.random.randint(0, 50, 50),\n", 180 | " 'd': np.random.randn(50)}\n", 181 | "data['b'] = data['a'] + 10 * np.random.randn(50)\n", 182 | "data['d'] = np.abs(data['d']) * 100\n", 183 | "\n", 184 | "plt.scatter('a', 'b', c='c', s='d', data=data)\n", 185 | "plt.xlabel('entry a')\n", 186 | "plt.ylabel('entry b')\n", 187 | "plt.show()" 188 | ] 189 | }, 190 | { 191 | "cell_type": "markdown", 192 | "metadata": {}, 193 | "source": [ 194 | "## Plotting with categorical variables\n", 195 | "\n", 196 | "It is also possible to create a plot using categorical variables. Matplotlib allows you to pass categorical variables directly to many plotting functions. For example:" 197 | ] 198 | }, 199 | { 200 | "cell_type": "code", 201 | "execution_count": 6, 202 | "metadata": {}, 203 | "outputs": [ 204 | { 205 | "data": { 206 | "image/png": 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AAAAABCmaAQAAACBI0QwAAAAAQSrM6gBqw+l0au/evYqLi5PNZrM6HKDOM8YoPz9fqampCgmpm78BUNdAzVDXQGDydW37ZTOwd+9epaWlWR0G4Hd27dqlpk2bWh1GlahroHaoayAw+aq2/bIZiIuLk1SepPj4eIujAeq+vLw8paWluWqnLqKugZqhroHA5Ova9stmoGKoMT4+ng8XoAbq8jA9dQ3UDnUNBCZf1XbdPMkQAAAAgNfRDAAAAABBimYAAAAACFJ+ec0AAACBqsxptGLbYe3PL1RiXJS6ZiQoNKTuXhcAwL/VeGRgyZIlGjhwoFJTU2Wz2TR37ly39cYY3XfffUpJSVF0dLT69eunTZs2uW1z+PBhDR06VPHx8apfv75GjBihI0eOnNEbAVB71DVQN8xfs08XPrxQf33+W93+1o/66/Pf6sKHF2r+mn01PhZ1DaA6atwMFBQUqGPHjpo5c2aV6x955BE9/fTTmj17tpYvX66YmBj1799fhYWFrm2GDh2qX375RZ9//rk+/PBDLVmyRCNHjqz9uwBwRqhrwHrz1+zTqNe+1z5HodvybEehRr32fY0bAuoaQLWYMyDJvPfee67nTqfTJCcnm0cffdS1LDc310RGRpo333zTGGPM2rVrjSSzcuVK1zaffPKJsdlsZs+ePdV6XYfDYSQZh8NxJuEDQaMmNUNdA75XWuY0Fzz4hWn+jw+rfKT/40NzwYNfmNIyp2sf6hrwLwvX5ZhXvtlmjhWXnnI7X9eNRy8g3rZtm7Kzs9WvXz/XMrvdrm7dumnZsmWSpGXLlql+/frq0qWLa5t+/fopJCREy5cvr/K4RUVFysvLc3sA8A3qGvC+FdsOVxoROJ6RtM9RqBXbDnvk9ahrwLecTqOH56/Xve//ojlLt1sdjhuPXkCcnZ0tSUpKSnJbnpSU5FqXnZ2txMRE9yDCwpSQkODa5kRTp07VpEmTPBkqglj63R9ZHYLHbX/oCq8dm7oGvG9//skbgdpsdzrUNeBbn6/L0frsfMVGhunars2sDseNX0wtOmHCBDkcDtdj165dVocE4AxR18DvEuOiPLqdVahroDJjjKYvLL84f1j35rLXC7c4IncebQaSk5MlSTk5OW7Lc3JyXOuSk5O1f/9+t/WlpaU6fPiwa5sTRUZGum5lzi3NAd+irgHv65qRoBR7lE42gahNUoq9fJpRT6CuAd/5csMBrdmTp3oRoRpxYQurw6nEo81ARkaGkpOTtWDBAteyvLw8LV++XFlZWZKkrKws5ebmatWqVa5tFi5cKKfTqW7dunkyHAAeQF0D3hcaYtP9A9tKUqWGoOL5/QPbeux+A9Q14BvGGD3926jAdRc0V0JMhMURVVbjawaOHDmizZs3u55v27ZNP/74oxISEtSsWTONGzdOU6ZMUatWrZSRkaF7771XqampGjx4sCSpTZs2uuyyy3TTTTdp9uzZKikp0ZgxY3TNNdcoNTXVY28MQPVR14D1LmufolnXnadJ89a6XUycbI/S/QPb6rL2KTU6HnUNWG/p5kP6YWeuIsNCdGPPDKvDqVKNm4HvvvtOffr0cT0fP368JGnYsGF66aWXdNddd6mgoEAjR45Ubm6uLrzwQs2fP19RUb+f5/j6669rzJgxuvjiixUSEqIhQ4bo6aef9sDbAVAb1DVQN1zWPkWXtE32yB2IqWvAehWjAn/t2qzOXvNjM8YYq4Ooqby8PNntdjkcDs5HRI0F42xC/lAz/hAjUJf4Q834Q4yAt3y79ZCuee5bRYSGaMldfZRsr14z4Ou68YvZhAAAAAB/UjGD0J+6NK12I2AFmgEAAADAg1bt+FVLNx9SWIhNo3q3tDqcU6IZAAAAADyoYlTg6vOaqGmDehZHc2o0AwAAAICH/LQ7V19uOKAQm3Rr70yrwzktmgEAAADAQ6YvLJ/Sd1CnJkpvFGNxNKdHMwAAAAB4wLp9efp8bY5sNml0n7o/KiDRDAAAAAAeMeO3UYE/nJOizMRYi6OpHpoBAAAA4Axt3p+vj9fskySN7esfowISzQAAAABwxmYs3CxjpEvbJql1sv/cZI9mAAAAADgD2w4W6IPVeyVJY/u2sjiamqEZAAAAAM7AM4s2y2mkPmc31jlN7VaHUyM0AwAAAEAt7Tp8VO/9sEeSNPZi/xoVkGgGAAAAgFqbtXiLSp1GF2Y20nnNGlgdTo3RDAAAAAC1sM9xTO9+t1uSf80gdDyaAQAAAKAWnl28VcVlTnXNSFC3Fg2tDqdWaAYAAACAGtqfX6g3V+yUJN3mZzMIHY9mAAAAAKih55dsVVGpU+c2q68emf45KiDRDAAAAAA1cuhIkV779vdRAZvNZnFEtUczAAAAANTAC19v07GSMp3TxK7eZze2OpwzQjMAAAAAVFPu0WK9smyHpPIZhPx5VECiGQAAAACqbc7S7TpSVKrWyXG6pG2S1eGcMZoBAAAAoBryC0s0Z+k2SdJYP79WoALNAAAAAFANryzbobzCUmUmxury9slWh+MRNAMAAADAaRQUlepfX22VJI3pk6mQEP8fFZBoBgAAAIDTeu3bHfr1aInSG9bTgA4pVofjMTQDAAAAwCkcKy7T87+NCtzaJ1NhoYHzFTpw3gkAAADgBW+u2KmDR4rVtEG0rjq3idXheBTNAAAAAHAShSVlenbJFknSqN4tFR5AowISzQAAAABwUu+s2q2cvCKl2KP0x85NrQ7H42gGAAAAgCoUlzo1+8vyUYGbL2qhyLBQiyPyPJoBAAAAoArv/bBbe3KPqVFspK7p2szqcLyCZgDAaZWVlenee+9VRkaGoqOj1bJlSz3wwAMyxri2McbovvvuU0pKiqKjo9WvXz9t2rTJwqgBnAp1DZxaaZlTMxf9PioQFR54owISzQCAanj44Yc1a9YszZgxQ+vWrdPDDz+sRx55RNOnT3dt88gjj+jpp5/W7NmztXz5csXExKh///4qLCy0MHIAJ0NdA6f2weq92nn4qBJiIjT0gsAcFZC80AzwSwMQeL755hsNGjRIV1xxhdLT0/XHP/5Rl156qVasWCGpvKanTZume+65R4MGDVKHDh30yiuvaO/evZo7d661wQOoEnUNnFyZ02jGos2SpBEXZqheRJjFEXmPx5sBfmkAAk/37t21YMECbdy4UZK0evVqff3117r88sslSdu2bVN2drb69evn2sdut6tbt25atmxZlccsKipSXl6e2wOA71DXwMl9/PM+bT1QIHt0uK7Pam51OF7l8Tbn+F8aJCk9PV1vvvnmSX9pkKRXXnlFSUlJmjt3rq655hpPhwTgDN19993Ky8tT69atFRoaqrKyMv3f//2fhg4dKknKzs6WJCUlJbntl5SU5Fp3oqlTp2rSpEneDRzASVHXQNWcTqMZC8tHBf7WI11xUeEWR+RdHh8Z4JcGIPD8+9//1uuvv6433nhD33//vV5++WU99thjevnll2t9zAkTJsjhcLgeu3bt8mDEAE6Hugaq9tnaHG3IyVdsZJj+1j3D6nC8zuMjA/zSAASeO++8U3fffbdr5O6cc87Rjh07NHXqVA0bNkzJycmSpJycHKWkpLj2y8nJUadOnao8ZmRkpCIjI70eO4CqUddAZcYYTV9Yfh3rsO7NZa8X2KMCkhdGBvilAQg8R48eVUiI+8dFaGionE6nJCkjI0PJyclasGCBa31eXp6WL1+urKwsn8YKoHqoa6CyRRv265e9eaoXEaoRF7awOhyf8PjIAL80AIFn4MCB+r//+z81a9ZM7dq10w8//KAnnnhCN9xwgyTJZrNp3LhxmjJlilq1aqWMjAzde++9Sk1N1eDBg60NHkCVqGvAnTFGTy8ov1bguguaKyEmwuKIfMPjzUBNfmmo+PJf8UvDqFGjPB0OAA+YPn267r33Xt16663av3+/UlNTdfPNN+u+++5zbXPXXXepoKBAI0eOVG5uri688ELNnz9fUVFRFkYO4GSoa8Dd15sP6sdduYoMC9GNPQP/WoEKNnP8DQA8YPjw4friiy/07LPPun5pGDlypG644QY9/PDDksqnH33ooYf08ssvu35p+Omnn7R27dpqfcDk5eXJbrfL4XAoPj7ek+EjCKTf/ZHVIXjc9oeuOOV6f6gZf4gRqEv8oWb8IUagwp9nL9OK7Yc1vHu6Jl7ZzrI4fF03Hh8Z4JcGAAAA+JNvtx7Siu2HFREaolt6tbQ6HJ/yeDMQFxenadOmadq0aSfdxmazafLkyZo8ebKnXx4AAACokYoZhP58flMl24Prx2mPzyYEAAAA+ItVO37V0s2HFBZiC7pRAYlmAAAAAEGsYlRgyHlN1bRBPYuj8T2aAQAAAASln3bn6ssNBxQaYtOtfYJvVECiGQAAAECQmr6w/L4CgzqmqnnDGIujsQbNAAAAAILO2r15+nxtjmw26dY+mVaHYxmaAQAAAASdGYvKrxW44pwUZSbGWhyNdWgGAAAAEFQ25eTrkzXZkqQxfYN3VECiGQAAAECQmbFos4yR+rdLUuvk4L47Ns0AAAAAgsa2gwWat3qvJGls31YWR2M9mgEAAAAEjZmLNstppL6tE9W+id3qcCxHMwAAAICgsOvwUb33wx5J0tggv1agAs0AAAAAgsIzX25RmdOoZ6tGOrdZA6vDqRNoBgAAABDw9uYe07urdkniWoHj0QwAAAAg4D27eItKyoy6ZSSoa0aC1eHUGTQDAAAACGj78wr15sryUYHbLmZU4Hg0AwAAAAhozy3ZquJSp85rVl/dWza0Opw6hWYAAAAAAevQkSK9vnynJGnsxa1ks9ksjqhuoRkAAABAwPrX19t0rKRMHZra1fusxlaHU+fQDAAAACAg5R4t1ivfbJckjemTyahAFWgGAAAAEJBeXLpdBcVlap0cp0vaJlkdTp1EMwAAAICAk1dYojlLt0kqv68AowJVoxkAAABAwHnlm+3KLyxVZmKsLm+fbHU4dRbNAAAAAAJKQVGpXvi6fFRgTJ9MhYQwKnAyNAMAAAAIKK99u0O/Hi1ResN6GtAhxepw6jSaAQAAAASMY8Vlev6rrZKkW/tkKiyUr7unQnYAAAAQMN5csVMHjxSraYNoXXVuE6vDqfNoBgAAABAQCkvK9OySLZKkW3tnKpxRgdMiQwAAAAgI76zarZy8IqXYozSkM6MC1UEzAAAAAL9XXOrU7C/LRwVu6dVSkWGhFkfkH2gGAAAA4Pfe+2G39uQeU+O4SP3l/DSrw/EbNAMAqmXPnj267rrr1LBhQ0VHR+ucc87Rd99951pvjNF9992nlJQURUdHq1+/ftq0aZOFEQM4HeoagaK0zKmZi8pHBW6+qIWiwhkVqC6aAQCn9euvv6pHjx4KDw/XJ598orVr1+rxxx9XgwYNXNs88sgjevrppzV79mwtX75cMTEx6t+/vwoLCy2MHMDJUNcIJO//uFc7Dx9VQkyEru3WzOpw/EqY1QEAqPsefvhhpaWlac6cOa5lGRkZrn8bYzRt2jTdc889GjRokCTplVdeUVJSkubOnatrrrmm0jGLiopUVFTkep6Xl+fFdwDgRNQ1AkWZ02jmos2SpBt7ZqheBF9va8IrIwMMOwKB5YMPPlCXLl30pz/9SYmJiTr33HP1/PPPu9Zv27ZN2dnZ6tevn2uZ3W5Xt27dtGzZsiqPOXXqVNntdtcjLY3zOwFfoq4RKD76eZ+2HiyQPTpc12elWx2O3/F4M8CwIxB4tm7dqlmzZqlVq1b69NNPNWrUKN122216+eWXJUnZ2dmSpKSkJLf9kpKSXOtONGHCBDkcDtdj165d3n0TANxQ1wgETqfRjIXlPyjf0CNDsZGMCtSUxzPmjWFHANZyOp3q0qWLHnzwQUnSueeeqzVr1mj27NkaNmxYrY4ZGRmpyMhIT4YJoAaoawSCz9Zma2POEcVFhml4j3Srw/FLHh8Z8MawY1FRkfLy8tweAHwnJSVFbdu2dVvWpk0b7dy5U5KUnJwsScrJyXHbJicnx7UOQN1CXcPfGWM0fWH5tQLDuqfLHh1ucUT+yePNgDeGHTkHEbBWjx49tGHDBrdlGzduVPPmzSWVj/4lJydrwYIFrvV5eXlavny5srKyfBorgOqhruHvFq7fr1/25qleRKhuuDDj9DugSh4/Tcgbw44TJkzQ+PHjXc/z8vJoCAAfuuOOO9S9e3c9+OCD+vOf/6wVK1boueee03PPPSdJstlsGjdunKZMmaJWrVopIyND9957r1JTUzV48GBrgwdQJeoa/swYo6d/GxX4nwuaKyEmwuKI/JfHm4GTDTv+5z//keQ+7JiSkuLaJicnR506darymJyDCFjr/PPP13vvvacJEyZo8uTJysjI0LRp0zR06FDXNnfddZcKCgo0cuRI5ebm6sILL9T8+fMVFRVlYeQAToa6hj/7atNBrd6Vq6jwEN3Ys4XV4fg1jzcDNRl2rPjyXzHsOGrUKE+HA8BDBgwYoAEDBpx0vc1m0+TJkzV58mQfRgXgTFDX8Efl1wqUzyD0167N1DiOH4zPhMebAYYdAQAA4C3fbj2sldt/VURoiG6+qKXV4fg9jzcDDDsCAADAWypGBf58flMl2/nueKa8cmcGhh0BAADgaat2HNY3Ww4pLMSmW3oxKuAJHp9aFAAAAPCGpxeUzyA05LymatqgnsXRBAaaAQAAANR5q3flavHGAwoNsenWPowKeArNAAAAAOq8irsND+qYquYNYyyOJnDQDAAAAKBOW7s3T1+sy5HNJt3aJ9PqcAIKzQAAAADqtBmLymcQuuKcFGUmxlocTWChGQAAAECdtSknX5+syZYkjenLqICn0QwAAACgzpqxaLOMkS5rl6zWyfFWhxNwaAYAAABQJ207WKB5q/dKYlTAW2gGAAAAUCfNXLRZTiNd3DpR7ZvYrQ4nINEMAAAAoM7Zdfio3vthjyRp7MWtLI4mcNEMAAAAoM555sstKnMa9WzVSJ3S6lsdTsCiGQAAAECdsif3mN5dtUuSdBujAl5FMwAAAIA65dnFW1RSZnRBiwSdn55gdTgBjWYAAAAAdcb+vEK9tfK3UYG+jAp4G80AAAAA6oxnl2xVcalTnZs3UFbLhlaHE/BoBgAAAFAnHDxSpNeX75Akje2bKZvNZnFEgY9mAAAAAHXCv77apsISpzo0tavXWY2tDico0AwAAADAcr8WFOvVZdslSWP7tmJUwEdoBgAAAGC5OUu3qaC4TG1S4tWvTaLV4QQNmgEAAABYKq+wRHO+2S6JawV8jWYAAAAAlnp56XblF5aqVWKsLmuXbHU4QYVmAAAAAJY5UlSqF5ZukySN6ZupkBBGBXyJZgAAAACWee3bHco9WqKMRjEa0CHV6nCCDs0AAAAALHGsuEz/+mqrJOnW3i0VyqiAz9EMAAAAwBJvrNipg0eK1bRBtAaf28TqcIISzQAAAAB8rrCkTM8u3iJJurV3psJD+VpqBbIOAAAAn3vnu13an1+kFHuUhnRmVMAqNAMAAADwqeJSp2Z9WT4qcEuvlooMC7U4ouBFMwCgxh566CHZbDaNGzfOtaywsFCjR49Ww4YNFRsbqyFDhignJ8e6IAHUCHUNX/rv97u111GoxnGR+sv5aVaHE9RoBgDUyMqVK/Xss8+qQ4cObsvvuOMOzZs3T++8844WL16svXv36uqrr7YoSgA1QV3Dl0rLnHrmt1GBmy9qoahwRgWsRDMAoNqOHDmioUOH6vnnn1eDBg1cyx0Oh1544QU98cQT6tu3rzp37qw5c+bom2++0bfffmthxABOh7qGr73/417tPHxUCTERurZbM6vDCXo0AwCqbfTo0briiivUr18/t+WrVq1SSUmJ2/LWrVurWbNmWrZsWZXHKioqUl5entsDgO9R1/ClMqfRzEWbJUk39sxQvYgwiyMCfwEA1fLWW2/p+++/18qVKyuty87OVkREhOrXr++2PCkpSdnZ2VUeb+rUqZo0aZI3QgVQTdQ1fO2jn/dp68EC1a8Xruuz0q0OB/LByAAXJAH+b9euXbr99tv1+uuvKyoqyiPHnDBhghwOh+uxa9cujxwXQPVQ1/A1p9NoxsJNkqQbemQoNpLfpOsCrzYDXJAEBIZVq1Zp//79Ou+88xQWFqawsDAtXrxYTz/9tMLCwpSUlKTi4mLl5ua67ZeTk6Pk5OQqjxkZGan4+Hi3BwDfoa7ha5+tzdbGnCOKiwzTsO7pVoeD33itGeCCJCBwXHzxxfr555/1448/uh5dunTR0KFDXf8ODw/XggULXPts2LBBO3fuVFZWloWRAzgZ6hq+ZIzR0wvKrxUY3iNd9uhwiyNCBa+Nzxx/QdKUKVNcy093QdIFF1xQ6VhFRUUqKipyPeeCJMC34uLi1L59e7dlMTExatiwoWv5iBEjNH78eCUkJCg+Pl5jx45VVlZWlTUNwHrUNXxpwbr9WrsvTzERobqhR4bV4eA4XmkGuCAJCD5PPvmkQkJCNGTIEBUVFal///565plnrA4LwBmgruEJxhhN/+1ageuymqtBTITFEeF4Hm8GKi5I+vzzzz16QdL48eNdz/Py8pSWxt3qACt9+eWXbs+joqI0c+ZMzZw505qAAJwx6hresGTTQa3e7VBUeIhu6tnC6nBwAo9fM8AFSQAAAJB+GxVYUD4qcG3X5moUG2lxRDiRx0cGKi5IOt7f/vY3tW7dWv/4xz+UlpbmuiBpyJAhkrggCQAAIBAt23pI3+34VRFhIbq5F6MCdZHHmwEuSAIAAIAkTf9tBqG/dElTUrxnTh+HZ1lytwcuSAIAAAhs320/rGVbDyk81KZbere0OhychE+aAS5IAgAACC5PLywfFRhyXlM1qR9tcTQ4Ga/egRgAAADB58dduVqy8YBCQ2y6tXem1eHgFGgGAAAA4FEzfruvwKBOqWrWsJ7F0eBUaAYAAADgMb/sdeiLdftls0mj+zAqUNfRDAAAAMBjZvx2rcCADqlq2TjW4mhwOjQDAAAA8IiNOfn6ZE22JGkMowJ+gWYAAAAAHlExKnBZu2SdnRxncTSoDpoBAAAAnLGtB47ow5/2SpLG9GVUwF/QDAAAAOCMzVy0RU4jXdw6Ue2b2K0OB9VEMwAAAIAzsvPQUc39cY8kaezFrSyOBjVBMwAAAIAzMmvxZpU5jXq2aqROafWtDgc1QDMAAACAWtuTe0zvrtotSbqNUQG/QzMAAACAWnt28RaVlBld0CJB56cnWB0OaohmAAAAALWyP69Qb63cJUm6rS+jAv6IZgAAAAC18uySrSoudapL8wbKatnQ6nBQCzQDAAAAqLGDR4r0+vIdkspnELLZbBZHhNqgGQAAAECN/eurbSoscapjU7suatXI6nBQSzQDAAAAqJFfC4r1yrLtkqSxfRkV8Gc0AwAAAKiRF5du09HiMrVNidfFbRKtDgdngGYAAAAA1eY4VqKXlm6XJI3tm8mogJ+jGQAAAEC1vfzNduUXleqspFj1b5dsdTg4QzQDAAAAqJYjRaV6cek2SdLoPpkKCWFUwN/RDAAAAKBaXl22Q7lHS9SiUYwGdEi1Ohx4AM0AAAAATutocan+9dVWSdKtfTIVyqhAQKAZAAAAwGm9sXynDhUUKy0hWoM6MSoQKGgGAAAAcEqFJWV6bslvowK9MxUeylfIQMFfEgAAAKf07+92aX9+kVLtURpyXlOrw4EH0QwAAADgpIpLnZr95RZJ0i29WyoijK+PgYS/JoDTmjp1qs4//3zFxcUpMTFRgwcP1oYNG9y2KSws1OjRo9WwYUPFxsZqyJAhysnJsShiAKdDXaO6/vP9bu11FCoxLlJ/7pJmdTjwMJoBAKe1ePFijR49Wt9++60+//xzlZSU6NJLL1VBQYFrmzvuuEPz5s3TO++8o8WLF2vv3r26+uqrLYwawKlQ16iOkjKnnvlysyRp5EUtFBUeanFE8LQwqwMAUPfNnz/f7flLL72kxMRErVq1ShdddJEcDodeeOEFvfHGG+rbt68kac6cOWrTpo2+/fZbXXDBBZWOWVRUpKKiItfzvLw8774JAG6oa1TH+z/u1a7Dx9QwJkJDuzW3Ohx4ASMDAGrM4XBIkhISEiRJq1atUklJifr16+fapnXr1mrWrJmWLVtW5TGmTp0qu93ueqSlMfQMWIm6xonKnEbPLCofFbixZwtFRzAqEIhoBgDUiNPp1Lhx49SjRw+1b99ekpSdna2IiAjVr1/fbdukpCRlZ2dXeZwJEybI4XC4Hrt27fJ26ABOgrpGVT78aa+2HixQ/Xrh+p8sRgUCFacJAaiR0aNHa82aNfr666/P6DiRkZGKjIz0UFQAzgR1jRM5nUYzfxsVuKFHhmIj+coYqDw+MsDsBEDgGjNmjD788EMtWrRITZv+Ps90cnKyiouLlZub67Z9Tk6OkpOTfRwlgJqgrlGVT3/J1sacI4qLDNOw7ulWhwMv8ngzwOwEQOAxxmjMmDF67733tHDhQmVkZLit79y5s8LDw7VgwQLXsg0bNmjnzp3KysrydbgAqoG6xskYYzR9YfmowPAe6bJHh1scEbzJ42M+3pidAIC1Ro8erTfeeEPvv/++4uLiXOcL2+12RUdHy263a8SIERo/frwSEhIUHx+vsWPHKisri5oG6ijqGiezYN1+rd2Xp5iIUN3QI+P0O8Cvef0EsJrOTsBUZUDdM2vWLElS79693ZbPmTNHw4cPlyQ9+eSTCgkJ0ZAhQ1RUVKT+/fvrmWee8XGkAKqLukZVykcFNkmSrstqrgYxERZHBG/zajPgqdkJpk6dqkmTJnkzVACnYIw57TZRUVGaOXOmZs6c6YOIAJwp6hpVWbLpoFbvdigqPEQ39WxhdTjwAa9OLVoxO8Fbb711RsdhqjIAAADvMsZo+oLyUYGh3ZqrUSwzQwUDr40MVMxOsGTJkpPOTnD86MCpZidgqjIAAADvWrb1kL7b8asiwkJ080WMCgQLj48MMDsBAACA/5m+oHwGoWvOT1NifJTF0cBXPD4ywOwEAAAA/mXl9sNatvWQwkNtuqVXS6vDgQ95vBlgdgIAAAD/8vRv1wr8sXNTpdaPtjga+JLHmwFmJwAAAPAfP+7K1VebDio0xKZRvTKtDgc+5tXZhAAAAFC3VcwgNLhTEzVrWM/iaOBrNAMAAABBas0ehxas368QmzS6D9cKBCOaAQAAgCA1Y2H5DEIDOqSqReNYi6OBFbx6B2IAgH8rcxqt2HZY+/MLlRgXpa4ZCQoNsVkdFgAP2JCdr/m/lM/6OKYv1woEK5oBAECV5q/Zp0nz1mqfo9C1LMUepfsHttVl7VMsjAyAJ8xYVD4qcHn7ZJ2VFGdxNLAKpwkBACqZv2afRr32vVsjIEnZjkKNeu17zV+zz6LIAHjClgNH9OFPeyUxKhDsaAYAAG7KnEaT5q1VVRNFVyybNG+typynn0oaQN1SVFqm1bty9eBH62SM1K9Notql2q0OCxbiNCEAgJsV2w5XGhE4npG0z1GoFdsOK6tlQ98FBqBGSsqc2pRzRD/tztVPexz6ebdD67PzVFL2eyM/tm8rCyNEXUAzAABwsz//5I1AbbYD4H1lTqNtB49o9S6Hft7j0E+7c/XL3jwVlTorbdugXrg6NK2vq89roo5p9X0fLOoUmgEAgJvEuCiPbgfAs4wx2nn4qFbvdujn3bn6abdDa/Y4VFBcVmnbuMgwndPUrnOa2tWxaX2d08Supg2iZbMxKxjK0QwAANx0zUhQij1K2Y7CKq8bsElKtpdPMwrAu4wx2ucoLD/VZ3fFr/4OOY6VVNo2OjxU7ZvE65wm9dUxza5zmtiV3jBGIUwHjFOgGQAAuAkNsen+gW016rXvZZPcGoKKrxT3D2zL/QYALziQX6Sf9+S6ne5z8Ehxpe0iQkPUJjVeHZrY1aGpXR2a1lfLxjEKC2VuGNQMzQAAoJLL2qdo1nXnVbrPQDL3GQA8JvdoseuX/opf/qu6eD80xKazk+LU4bjTfc5KilNEGF/8ceZoBgAAVbqsfYouaZvMHYgBDzhSVKo1e37/0v/Tbod2Hj5aaTubTWrZOLb81/4mdnVIq6+2KfGKCg+1IGoEA5oBAMBJhYbYmD4UqKFjxWVauy9PP+3O1c+7HVq9O1dbDxbIVHERTnrDejqnaX3X6T7tmtgVG8nXM/gO/7UFifS7P7I6BI/b/tAVVocAAAhyxaVObcjO1+rjvvhv2n+kypvypdqj1KFpfbeZfez1wi2IGvgdzQAAAEA1lJY5tfnAEf20y6Gf9pR/+V+3L1/FZZXn8m8UG6mOx53j376JXY3jIi2IGjg1mgEAAIATOJ1G2w4VuJ3j/8tehwpLKn/xt0eH/zajT/msPh2a2pUcH8Vc/vALNAMAACCoGWO0+9djrlN9Km7ilV9UWmnb2MgwtW8SX366T5PyX/3TEriJF/wXzQAAAAgaxhjl5BX9/sV/T/ldfH89WvkmXlHhIWqXWn7zropf/Vs04iZeCCw0AwAAIGAdOlLkOs3n5z25Wr3boQP5RZW2Cw+1qU1KvNsX/1aJsdzECwGPZgAAAAQEx7ESrdnjcDvdZ0/usUrbhdiks1w38aqvjk3tOjs5TpFhzOWP4EMzAMBvlDkNN8ACIEkqKCrVL3vzjrvAN1fbD1W+iZcktWgc45rKs2OaXW1T7IqO4Is/INEMAPAT89fs06R5a7XPUehalmKP0v0D2+qy9ikWRgbA2wpLyrRuX57rdJ+fdudq84EjVd7EKy0hunxGnyblp/q0bxKvuCjm8gdOhmYAQJ03f80+jXrte534//1sR6FGvfa9Zl13Hg0BECBKyspv4uU6x3+XQxtz8lVaxU28kuOjXFN6VtzFt0FMhAVRA/6LZgCAR82cOVOPPvqosrOz1bFjR02fPl1du3at9fHKnEaT5q2t1AhIkpFkkzRp3lpd0jaZU4YAL/F0XR8vJ69QSzYe0M97HFq926F1+/JUXFp5Lv+GMRFuX/o7NLUrMT7KIzEAwYxmAIDHvP322xo/frxmz56tbt26adq0aerfv782bNigxMTEWh1zxbbDbqcGnchI2uco1Ipth5XVsmEtIwdwMt6o6+P9sDNXd777k9uyuKiw32/g1cSuDmn1lWrnJl6AN9AMAPCYJ554QjfddJP+9re/SZJmz56tjz76SC+++KLuvvvuWh1zf/7JG4HabAegZrxR18frmGZX1/SE3371L7+JV/OG9fjiD/gIzQAAjyguLtaqVas0YcIE17KQkBD169dPy5Ytq7R9UVGRiop+n+s7Ly+vyuMmxlXvNIDqbgeg+rxV18dLsUfr37dkeSZgADXGnTQAeMTBgwdVVlampKQkt+VJSUnKzs6utP3UqVNlt9tdj7S0tCqP2zUjQSn2KJ3sN0KbymcV6pqRcIbvAMCJvFXXAOoOmgEAlpgwYYIcDofrsWvXriq3Cw2x6f6BbSWpUkNQ8fz+gW25eBioA6pb1wDqDk4TAuARjRo1UmhoqHJyctyW5+TkKDk5udL2kZGRioyMrNaxL2ufolnXnVfpPgPJ3GcA8Cpv1jWAuoFmAIBHREREqHPnzlqwYIEGDx4sSXI6nVqwYIHGjBlzxse/rH2KLmmbzB2IAR/ydl0DsJ6lpwnNnDlT6enpioqKUrdu3bRixQorwwFwhsaPH6/nn39eL7/8statW6dRo0apoKDANQvJmQoNsSmrZUMN6tREWS0b0ggAPuDtugZgLctGBrw9bzEA3/vLX/6iAwcO6L777lN2drY6deqk+fPnV7r4EID/oK6BwGYzxlR1Y0+v69atm84//3zNmDFDUvmwY1pamsaOHXvaeYvz8vJkt9vlcDgUHx9/0u3S7/7IozHXFdsfuqLG+wRiLmqTByk4c1HdmrGSP8QI1CX+UDP+ECNQ1/i6biwZGTjTeYsdDoek089f7Cw66qGI65bqzNt8okDMRW3yIAVnLirWW9T7V0tFbLX9uwLBhroGApOva9uSZuBU8xavX7++0vZTp07VpEmTKi0P1vmL7dOsjqBuIA+/q24u8vPzZbfbvRpLbeXn50sK3roGaou6BgKTr2rbL2YTmjBhgsaPH+967nQ6dfjwYTVs2LBO3K48Ly9PaWlp2rVrV9APg5KLcnUtD8YY5efnKzU11epQTio1NVW7du1SXFzcKeu6ruU2GJBz36tOzqlrnAly7nvVzbmva9uSZsAT8xbXr1/fmyHWSnx8PAX1G3JRri7loa7+clghJCRETZs2rfb2dSm3wYKc+97pck5d40yRc9+rTs59WduWTC16/LzFFSrmLc7KyrIiJAAAACDoWHaa0Pjx4zVs2DB16dJFXbt21bRp05i3GAAAAPAhy5qBQJq3ODIyUvfffz+3YBe5qEAevIfc+h45971gy3mwvd+6gJz7Xl3NuWX3GQAAAABgLUuuGQAAAABgPZoBAAAAIEjRDAAAAABBimYAAAAACFI0A6jTvvzyS9lsNuXm5lodCgAPoa6BwERt+yeaAQAAACBI0QycoLi42OoQ6gTy8Dty4R3k1ffIuTWCKe/B9F7rEvLue4GU84BvBvLz8zV06FDFxMQoJSVFTz75pHr37q1x48ZJktLT0/XAAw/o+uuvV3x8vEaOHClJ+s9//qN27dopMjJS6enpevzxx92Oa7PZNHfuXLdl9evX10svvSRJ2r59u2w2m9566y11795dUVFRat++vRYvXlytuMvKyjRixAhlZGQoOjpaZ599tp566qmgy0OFpUuXqkOHDoqKitIFF1ygNWvW1CoPkn/n4pdfftGAAQMUHx+vuLg49ezZU1u2bKl1LjzJX/Pq6VrzJX/NeQVP1rUv+XPea/oZ4q/v1Z/rWvLfvFfwx9r255yf8XcDE+BuvPFG07x5c/PFF1+Yn3/+2Vx11VUmLi7O3H777cYYY5o3b27i4+PNY489ZjZv3mw2b95svvvuOxMSEmImT55sNmzYYObMmWOio6PNnDlzXMeVZN577z2317Lb7a5ttm3bZiSZpk2bmnfffdesXbvW3HjjjSYuLs4cPHjwtHEXFxeb++67z6xcudJs3brVvPbaa6ZevXrm7bffDqo8LFq0yEgybdq0MZ999pn56aefzIABA0x6eropLi4Oqlzs3r3bJCQkmKuvvtqsXLnSbNiwwbz44otm/fr1tcqDp/lrXj1da77krzn3Rl37kr/mvTafIf76Xv25ro3x37z7c237a8498d0goJuBvLw8Ex4ebt555x3XstzcXFOvXj23P+7gwYPd9rv22mvNJZdc4rbszjvvNG3btnU9r+4f96GHHnKtLykpMU2bNjUPP/xwrd7P6NGjzZAhQ2q8nz/noeKD5a233nItO3TokImOjq7Vh7o/52LChAkmIyOjTn6g+nNeq1LbWvMlf865p+val/w57zX9DPHn91oVf6hrY/w77/5a2/6cc098Nwjo04S2bt2qkpISde3a1bXMbrfr7LPPdtuuS5cubs/XrVunHj16uC3r0aOHNm3apLKyshrFkJWV5fp3WFiYunTponXr1lVr35kzZ6pz585q3LixYmNj9dxzz2nnzp01en3J//Nw4v4JCQk6++yza7R/BX/OxY8//qiePXsqPDy8Rq/nC/6cV8lzteZL/p7zE/c/k7r2JX/Oe00/Q/z5vUr+WdeS/+f9xP39obb9Oeee+G4Q0M1AdcXExNR4H5vNpvKG73clJSWeCklvvfWW/vd//1cjRozQZ599ph9//FF/+9vfvHrBSl3Mg1XqYi6io6M9diyr1MW8WlFrvlQXcx4M6mLevfUZUhffa6DXtVQ38x7o6mLOPVHXAd0MtGjRQuHh4Vq5cqVrmcPh0MaNG0+5X5s2bbR06VK3ZUuXLtVZZ52l0NBQSVLjxo21b98+1/pNmzbp6NGjlY717bffuv5dWlqqVatWqU2bNqeNfenSperevbtuvfVWnXvuucrMzKz1haL+nIeq9v/111+1cePGGu1fwZ9z0aFDB3311Vd18oPbn/PqyVrzJX/OeVX7n0ld+5I/572mnyH+/F79ta4l/857Vfv7Q237c8498t2g1icY+Ykbb7zRZGRkmIULF5o1a9aYIUOGmLi4ODNu3DhjTPk5YE8++aTbPqtWrXK7IOSll16qdEHINddcY9q0aWO+//57s3LlStO3b18THh5e6RywZs2amf/+979m3bp1ZuTIkSY2NtYcOHDgtHE/9dRTJj4+3syfP99s2LDB3HPPPSY+Pt507NgxqPJQcf5hu3btXBf1XHnllaZZs2amqKgoqHJx8OBB07BhQ9dFQhs3bjSvvPJKnbqA2B/z6ula8yV/zbk36tqX/DXvtfkM8df36s91bYz/5t2fa9tfc+6J7wYB3wzk5eWZa6+91tSrV88kJyebJ554wnTt2tXcfffdxpiq/7jGGPPuu++atm3bmvDwcNOsWTPz6KOPuq3fs2ePufTSS01MTIxp1aqV+fjjj6u8IOSNN94wXbt2NREREaZt27Zm4cKF1Yq7sLDQDB8+3NjtdlO/fn0zatQoc/fdd9f6g8xf81DxwTJv3jzTrl07ExERYbp27WpWr15dqzz4cy6MMWb16tXm0ksvNfXq1TNxcXGmZ8+eZsuWLbXOhSf5a149XWu+5K8590Zd+5K/5t2Ymn+G+Ot79ee6NsZ/8+7Pte2vOTfmzL8bBHwzcKIjR44Yu91u/vWvf3n1dSr+uD/88INXX6e2yMPvyIV3kFffI+fWCKa8B9N7rUvIu+8FU87Dan+CkX/44YcftH79enXt2lUOh0OTJ0+WJA0aNMjiyHyLPPyOXHgHefU9cm6NYMp7ML3XuoS8+14w5zygLyCu8Nhjj6ljx47q16+fCgoK9NVXX6lRo0aWxnTLLbcoNja2ysctt9zildckD78jF95BXn2PnFsjmPIeTO+1LiHvvhesObcZc8J8R/CJ/fv3Ky8vr8p18fHxSkxM9HFE1iAPvyMX3kFefY+cWyOY8h5M77UuIe++54uc0wwAAAAAQSooThMCAAAAUBnNAAAAABCkaAYAAACAIEUzAAAAAAQpmgEAAAAgSNEMAAAAAEGKZgAAAAAIUv8fTAQopLJoxbgAAAAASUVORK5CYII=", 207 | "text/plain": [ 208 | "
" 209 | ] 210 | }, 211 | "metadata": {}, 212 | "output_type": "display_data" 213 | } 214 | ], 215 | "source": [ 216 | "names = ['group_a', 'group_b', 'group_c']\n", 217 | "values = [1, 10, 100]\n", 218 | "\n", 219 | "plt.figure(figsize=(9, 3))\n", 220 | "\n", 221 | "plt.subplot(131)\n", 222 | "plt.bar(names, values)\n", 223 | "plt.subplot(132)\n", 224 | "plt.scatter(names, values)\n", 225 | "plt.subplot(133)\n", 226 | "plt.plot(names, values)\n", 227 | "plt.suptitle('Categorical Plotting')\n", 228 | "plt.show()" 229 | ] 230 | } 231 | ], 232 | "metadata": { 233 | "kernelspec": { 234 | "display_name": "Python 3.10.4 64-bit", 235 | "language": "python", 236 | "name": "python3" 237 | }, 238 | "language_info": { 239 | "codemirror_mode": { 240 | "name": "ipython", 241 | "version": 3 242 | }, 243 | "file_extension": ".py", 244 | "mimetype": "text/x-python", 245 | "name": "python", 246 | "nbconvert_exporter": "python", 247 | "pygments_lexer": "ipython3", 248 | "version": "3.10.13" 249 | }, 250 | "orig_nbformat": 4, 251 | "vscode": { 252 | "interpreter": { 253 | "hash": "3ad933181bd8a04b432d3370b9dc3b0662ad032c4dfaa4e4f1596c548f763858" 254 | } 255 | } 256 | }, 257 | "nbformat": 4, 258 | "nbformat_minor": 2 259 | } 260 | --------------------------------------------------------------------------------